SPREAD

spread

Authors:
(1) Andrew Draganov, Aarhus University and All authors contributed equally to this research;
(2) David Saulpic, Université Paris Cité & CNRS;
(3) Chris Schwiegelshohn, Aarhus University. Table of Links Abstract and 1 Introduction 2 Preliminaries and Related Work 2.1 On Sampling Strategies 2.2 Other Coreset Strategies 2.3 Coresets for Database Applications 2.4 Quadtree Embeddings 3 Fast-Coresets 4 Reducing the Impact of the Spread 4.1 Computing a crude upper-bound 4.2 From Approximate Solution to Reduced Spread 5 Fast Compression in Practice 5.1 Goal and Scope of the Empirical Analysis 5.2 Experimental Setup 5.3 Evaluating Sampling Strategies 5.4 Streaming Setting and 5.5 Takeaways 6 Conclusion 7 Acknowledgements 8 Proofs, Pseudo-Code, and Extensions and 8.1 Proof of Corollary 3.2 8.2 Reduction of k-means to k-median 8.3 Estimation of the Optimal Cost in a Tree 8.4 Extensions to Algorithm 1 References 4 Reducing the Impact of the Spread We now show how one can replace the linear time-dependency on log ∆ with a logarithmic one (i.e., log ∆ → log(log ∆)). Overview of the Approach Without loss of generality, we assume that the smallest pairwise distance is at least 1, and ∆ is a (known) upper-bound on the diameter of the input. To remove the log ∆ dependency, we proceed by producing a substitute dataset P ′ that has a lower-bound on its minimum distance and an upper-bound on its maximum distance. We then show that, with overwhelming likelihood, reasonable solutions on P ′ have the same cost as solutions on P up to an additive error. In order to produce the substitute dataset P ′ , we first find a crude upper-bound on the cost of the optimal solution to the clustering task on P. We then create a grid such that, for every cluster in the optimal solution, the cluster is entirely contained in a grid cell with overwhelming likelihood – in some sense, points in different grid cell do not interact. We can then freely move these grid cells around without affecting the cost of the solution, as long as they stay far enough away so that points in different cells still do not interact. Thus, we can constrain the diameter of P ′ to be small with respect to the quality of the approximate solution. We show later how to constrain the minimum distance to be comparable to the quality of this approximate solution as well. We will focus this section on the simpler k-median problem but show how to reduce k-means to this case in Section 8.2. 4.1 Computing a crude upper-bound As described, we start by computing an approximate solution U such that OPT ≤ U ≤ poly(n)· OPT. For this, the first step is to embed the input into a quadtree. This embedding has two key properties. First, distances are preserved up to a multiplicative factor O(d log ∆), and therefore the k-median cost is preserved up to this factor as well. Second, the metric is a hierarchically separated tree: it can be represented with a tree, where points of P are the leafs. The distance between two points is then given by the depth of their lowest common ancestor – if it is at depth ℓ, their distance is √ d∆2−ℓ . Our first lemma shows that finding the first level of the tree for which the input lies in k + 1 disjoint subtrees provides us with the desired approximation. We prove this in Section 8.3. A direct consequence is that the first level of the tree for which at least k + 1 cells are non empty allows us to compute an O(n)-approximation for k-median on the tree metric. Since the tree metric approximates the original Euclidean metric up to O(d log ∆), this is therefore an O(nd log ∆)-approximation to k-median in the Euclidean space. To turn this observation into an algorithm, one needs to count the number of non-empty cells at a given level ℓ: for each point, we identify the cell that contains it using modulo operations. Furthermore, we count the number of distinct non-empty cells using a standard dictionary data structure. This is done in time O˜(nd), and pseudo-code is given Algorithm 2. Using a binary search on the O(log ∆) many levels then gives the following result: Given this crude approximate solution, it remains to create a substitute dataset P ′ that satisfies two requirements: 1 First, P ′ must have spread linear in the quality of the approximate solution. If this holds, Algorithm 1 on P ′ will take O˜(nd log log ∆) time. 2 Second, any reasonable solution on P ′ should be roughly equivalent to a corresponding solution on P. This would mean that running Algorithm 1 on P ′ gives us a valid coreset for P. 4.2 From Approximate Solution to Reduced Spread Proposition 4.4. Let P ′ be the dataset produced by Algorithm 3. It holds that: Combining the algorithm of Lemma 4.2, which gives a bound on U, with Lemma 4.5 brings us to the following theorem: To summarize, we have shown that one can, in O˜(nd) time, find a modified dataset P ′ that preserves the cost of corresponding k-means and k-median solutions from P. Importantly, this P ′ has spread that is logarithmic in the spread of P. As a result, one can apply Algorithm 1 onto P ′ in O˜(nd) time without compromising the compression quality with respect to P. Lastly, this compression on P ′ can be re-formatted onto P in O˜(nd) time. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Andrew Draganov, Aarhus University and All authors contributed equally to this research; (2) David Saulpic, Université Paris Cité & CNRS; (3) Chris Schwiegelshohn, Aarhus University. Authors: Authors: (1) Andrew Draganov, Aarhus University and All authors contributed equally to this research; (2) David Saulpic, Université Paris Cité & CNRS; (3) Chris Schwiegelshohn, Aarhus University. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Preliminaries and Related Work 2.1 On Sampling Strategies 2.1 On Sampling Strategies 2.2 Other Coreset Strategies 2.2 Other Coreset Strategies 2.3 Coresets for Database Applications 2.3 Coresets for Database Applications 2.4 Quadtree Embeddings 2.4 Quadtree Embeddings 3 Fast-Coresets 3 Fast-Coresets 4 Reducing the Impact of the Spread 4 Reducing the Impact of the Spread 4.1 Computing a crude upper-bound 4.1 Computing a crude upper-bound 4.2 From Approximate Solution to Reduced Spread 4.2 From Approximate Solution to Reduced Spread 5 Fast Compression in Practice 5 Fast Compression in Practice 5.1 Goal and Scope of the Empirical Analysis 5.1 Goal and Scope of the Empirical Analysis 5.2 Experimental Setup 5.2 Experimental Setup 5.3 Evaluating Sampling Strategies 5.3 Evaluating Sampling Strategies 5.4 Streaming Setting and 5.5 Takeaways 5.4 Streaming Setting and 5.5 Takeaways 6 Conclusion 6 Conclusion 7 Acknowledgements 7 Acknowledgements 8 Proofs, Pseudo-Code, and Extensions and 8.1 Proof of Corollary 3.2 8 Proofs, Pseudo-Code, and Extensions and 8.1 Proof of Corollary 3.2 8.2 Reduction of k-means to k-median 8.2 Reduction of k-means to k-median 8.3 Estimation of the Optimal Cost in a Tree 8.3 Estimation of the Optimal Cost in a Tree 8.4 Extensions to Algorithm 1 8.4 Extensions to Algorithm 1 References References 4 Reducing the Impact of the Spread We now show how one can replace the linear time-dependency on log ∆ with a logarithmic one (i.e., log ∆ → log(log ∆)). Overview of the Approach Without loss of generality, we assume that the smallest pairwise distance is at least 1, and ∆ is a (known) upper-bound on the diameter of the input. To remove the log ∆ dependency, we proceed by producing a substitute dataset P ′ that has a lower-bound on its minimum distance and an upper-bound on its maximum distance. We then show that, with overwhelming likelihood, reasonable solutions on P ′ have the same cost as solutions on P up to an additive error. Overview of the Approach In order to produce the substitute dataset P ′ , we first find a crude upper-bound on the cost of the optimal solution to the clustering task on P. We then create a grid such that, for every cluster in the optimal solution, the cluster is entirely contained in a grid cell with overwhelming likelihood – in some sense, points in different grid cell do not interact. We can then freely move these grid cells around without affecting the cost of the solution, as long as they stay far enough away so that points in different cells still do not interact. Thus, we can constrain the diameter of P ′ to be small with respect to the quality of the approximate solution. We show later how to constrain the minimum distance to be comparable to the quality of this approximate solution as well. We will focus this section on the simpler k-median problem but show how to reduce k-means to this case in Section 8.2. 4.1 Computing a crude upper-bound As described, we start by computing an approximate solution U such that OPT ≤ U ≤ poly(n)· OPT. For this, the first step is to embed the input into a quadtree. This embedding has two key properties. First, distances are preserved up to a multiplicative factor O(d log ∆), and therefore the k-median cost is preserved up to this factor as well. Second, the metric is a hierarchically separated tree: it can be represented with a tree, where points of P are the leafs. The distance between two points is then given by the depth of their lowest common ancestor – if it is at depth ℓ, their distance is √ d∆2−ℓ . Our first lemma shows that finding the first level of the tree for which the input lies in k + 1 disjoint subtrees provides us with the desired approximation. We prove this in Section 8.3. A direct consequence is that the first level of the tree for which at least k + 1 cells are non empty allows us to compute an O(n)-approximation for k-median on the tree metric. Since the tree metric approximates the original Euclidean metric up to O(d log ∆), this is therefore an O(nd log ∆)-approximation to k-median in the Euclidean space. To turn this observation into an algorithm, one needs to count the number of non-empty cells at a given level ℓ: for each point, we identify the cell that contains it using modulo operations. Furthermore, we count the number of distinct non-empty cells using a standard dictionary data structure. This is done in time O˜(nd), and pseudo-code is given Algorithm 2. Using a binary search on the O(log ∆) many levels then gives the following result: Given this crude approximate solution, it remains to create a substitute dataset P ′ that satisfies two requirements: 1 First, P ′ must have spread linear in the quality of the approximate solution. If this holds, Algorithm 1 on P ′ will take O˜(nd log log ∆) time. 2 Second, any reasonable solution on P ′ should be roughly equivalent to a corresponding solution on P. This would mean that running Algorithm 1 on P ′ gives us a valid coreset for P. 4.2 From Approximate Solution to Reduced Spread Proposition 4.4. Let P ′ be the dataset produced by Algorithm 3. It holds that: Proposition 4.4. Let P ′ be the dataset produced by Algorithm 3. It holds that: Combining the algorithm of Lemma 4.2, which gives a bound on U, with Lemma 4.5 brings us to the following theorem: To summarize, we have shown that one can, in O˜(nd) time, find a modified dataset P ′ that preserves the cost of corresponding k-means and k-median solutions from P. Importantly, this P ′ has spread that is logarithmic in the spread of P. As a result, one can apply Algorithm 1 onto P ′ in O˜(nd) time without compromising the compression quality with respect to P. Lastly, this compression on P ′ can be re-formatted onto P in O˜(nd) time. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

Reducing Spread Impact in Clustering Algorithms

Authors:
(1) Andrew Draganov, Aarhus University and All authors contributed equally to this research;
(2) David Saulpic, Université Paris Cité & CNRS;
(3) Chris Schwiegelshohn, Aarhus University. Table of Links Abstract and 1 Introduction 2 Preliminaries and Related Work 2.1 On Sampling Strategies 2.2 Other Coreset Strategies 2.3 Coresets for Database Applications 2.4 Quadtree Embeddings 3 Fast-Coresets 4 Reducing the Impact of the Spread 4.1 Computing a crude upper-bound 4.2 From Approximate Solution to Reduced Spread 5 Fast Compression in Practice 5.1 Goal and Scope of the Empirical Analysis 5.2 Experimental Setup 5.3 Evaluating Sampling Strategies 5.4 Streaming Setting and 5.5 Takeaways 6 Conclusion 7 Acknowledgements 8 Proofs, Pseudo-Code, and Extensions and 8.1 Proof of Corollary 3.2 8.2 Reduction of k-means to k-median 8.3 Estimation of the Optimal Cost in a Tree 8.4 Extensions to Algorithm 1 References Abstract We study the theoretical and practical runtime limits of k-means and k-median clustering on large datasets. Since effectively all clustering methods are slower than the time it takes to read the dataset, the fastest approach is to quickly compress the data and perform the clustering on the compressed representation. Unfortunately, there is no universal best choice for compressing the number of points – while random sampling runs in sublinear time and coresets provide theoretical guarantees, the former does not enforce accuracy while the latter is too slow as the numbers of points and clusters grow. Indeed, it has been conjectured that any sensitivity-based coreset construction requires super-linear time in the dataset size. We examine this relationship by first showing that there does exist an algorithm that obtains coresets via sensitivity sampling in effectively linear time – within log-factors of the time it takes to read the data. Any approach that significantly improves on this must then resort to practical heuristics, leading us to consider the spectrum of sampling strategies across both real and artificial datasets in the static and streaming settings. Through this, we show the conditions in which coresets are necessary for preserving cluster validity as well as the settings in which faster, cruder sampling strategies are sufficient. As a result, we provide a comprehensive theoretical and practical blueprint for effective clustering regardless of data size. Our code is publicly available at source and has scripts to recreate the experiments. 1 Introduction The modern data analyst has no shortage of clustering algorithms to choose from but, given the ever-increasing size of relevant datasets, many are often too slow to be practically useful. This is particularly relevant for big-data pipelines, where clustering algorithms are commonly used for compression. The goal is to replace a very large dataset by a smaller, more manageable one for downstream tasks, with the hope it represents the original input well. Lloyd’s algorithm [49] was introduced for precisely this reason and minimizes the quantization error – the sum of square distance from each input point to its representative in the compressed dataset. Arguably the most popular clustering algorithm, Lloyd’s runs for multiple iterations until convergence with every iteration requiring O(ndk) time, where n is the number of points, d is the number of features and k is the number of clusters – or the size of the targeted compression. For such applications, the number of points can easily be hundreds of millions and, since the quality of compression increases with k, standard objectives can have k in the thousands [41, 4]. In such settings, any O(ndk) algorithm is prohibitively slow. Examples like these have prompted the rise of big data algorithms that provide both theoretical and practical runtime improvements. The perspectives of theoretical soundness and practical efficacy are, however, often at odds with one another. On the one hand, theoretical guarantees provide assurance that the algorithm will work regardless of whatever unlucky inputs it receives. On the other hand, it may be difficult to convince oneself to implement the theoretically optimal algorithm when there are cruder methods that are faster to get running and perform well in practice. Since datasets can be large in the number of points n and/or the number of features d, big-data methods must mitigate the effects of both. With respect to the feature space, the question is effectively closed as random projections are fast (running in effectively linear time), practical to implement [50], and provide tight guarantees on the embedding’s size and quality. The outlook is less clear when reducing the number of points n, and there are two separate paradigms that each provide distinct advantages. On the one hand, we have uniform sampling, which runs in sublinear time but may miss important subsets of the data and therefore can only guarantee accuracy under certain strong assumptions on the data [45]. On the other hand, the most accurate sampling strategies provide the strong coreset guarantee, wherein the cost of any solution on the compressed data is within an ε-factor of that solution’s cost on the original dataset [25]. Our contributions We study both paradigms (uniform sampling and strong coresets) with respect to a classic problem – compression for the k-means and k-median objectives. Whereas uniform sampling provides optimal speed but no worst-case accuracy guarantee, all available coreset constructions have a running time of at least Ω˜(nd + nk) when yielding tight bounds on the minimum number of samples required for accurate compression. It is easy to show that any algorithm that achieves a compression guarantee must read the entire dataset[1]. Thus a clear open question is what guarantees are achievable in linear or nearly-linear time. Indeed, currently available fast sampling algorithms for clustering [6, 5] cannot achieve strong coreset guarantees. Recently, [31] proposed a method for strong coresets that runs in time O˜(nd + nk) and conjectured this to be optimal for k-median and k-means. While this bound is effectively optimal for small values of k, there are many applications such as computer vision [34] or algorithmic fairness [18] where the number of clusters can be larger than the number of features by several orders of magnitude. In such settings, the question of time-optimal coresets remains open. Since the issue of determining a coreset of optimal size has recently been closed [25, 28, 44], this is arguably the main open problem in coreset research for center-based clustering. We resolve this by showing that there exists an easy-to-implement algorithm that constructs coresets in O˜(nd) time – only logarithmic factors away from the time it takes to read in the dataset. Nonetheless, this does not fully illuminate the landscape among sampling algorithms for clustering in practice. Although our algorithm achieves both an optimal runtime and an optimal compression, it is certainly possible that other, cruder methods may be just as viable for all practical purposes. We state this formally in the following question: When are optimal k-means and k-median coresets necessary and what is the practical tradeoff between coreset speed and accuracy? o answer this, we perform a thorough comparison across the full span of sampling algorithms that are faster than our proposed method. Through this we verify that, while these faster methods are sufficiently accurate on many real-world datasets, there exist data distributions that cause catastrophic failure for each of them. Indeed, these cases can only be avoided with a strong-coreset method. Thus, while many practical settings do not require the full coreset guarantee, one cannot cut corners if one wants to be confident in their compression. We verify that this extends to the streaming paradigm and applies to downstream clustering approaches. In summary, our contributions are as follows: • We show that one can obtain strong coresets for k-means and k-median in O˜(nd) time. This resolves a conjecture on the necessary runtime for k-means coresets [31] and is theoretically optimal up to log-factors. • Through a comprehensive analysis across datasets, tasks, and streaming/non-streaming paradigms, we verify that there is a necessary tradeoff between speed and accuracy among the linear- and sublinear-time sampling methods. This gives the clustering practitioner a blueprint on when to use each compression algorithm for effective clustering results in the fastest possible time. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Andrew Draganov, Aarhus University and All authors contributed equally to this research; (2) David Saulpic, Université Paris Cité & CNRS; (3) Chris Schwiegelshohn, Aarhus University. Authors: Authors: (1) Andrew Draganov, Aarhus University and All authors contributed equally to this research; (2) David Saulpic, Université Paris Cité & CNRS; (3) Chris Schwiegelshohn, Aarhus University. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Preliminaries and Related Work 2.1 On Sampling Strategies 2.1 On Sampling Strategies 2.2 Other Coreset Strategies 2.2 Other Coreset Strategies 2.3 Coresets for Database Applications 2.3 Coresets for Database Applications 2.4 Quadtree Embeddings 2.4 Quadtree Embeddings 3 Fast-Coresets 3 Fast-Coresets 4 Reducing the Impact of the Spread 4 Reducing the Impact of the Spread 4.1 Computing a crude upper-bound 4.1 Computing a crude upper-bound 4.2 From Approximate Solution to Reduced Spread 4.2 From Approximate Solution to Reduced Spread 5 Fast Compression in Practice 5 Fast Compression in Practice 5.1 Goal and Scope of the Empirical Analysis 5.1 Goal and Scope of the Empirical Analysis 5.2 Experimental Setup 5.2 Experimental Setup 5.3 Evaluating Sampling Strategies 5.3 Evaluating Sampling Strategies 5.4 Streaming Setting and 5.5 Takeaways 5.4 Streaming Setting and 5.5 Takeaways 6 Conclusion 6 Conclusion 7 Acknowledgements 7 Acknowledgements 8 Proofs, Pseudo-Code, and Extensions and 8.1 Proof of Corollary 3.2 8 Proofs, Pseudo-Code, and Extensions and 8.1 Proof of Corollary 3.2 8.2 Reduction of k-means to k-median 8.2 Reduction of k-means to k-median 8.3 Estimation of the Optimal Cost in a Tree 8.3 Estimation of the Optimal Cost in a Tree 8.4 Extensions to Algorithm 1 8.4 Extensions to Algorithm 1 References References Abstract We study the theoretical and practical runtime limits of k-means and k-median clustering on large datasets. Since effectively all clustering methods are slower than the time it takes to read the dataset, the fastest approach is to quickly compress the data and perform the clustering on the compressed representation. Unfortunately, there is no universal best choice for compressing the number of points – while random sampling runs in sublinear time and coresets provide theoretical guarantees, the former does not enforce accuracy while the latter is too slow as the numbers of points and clusters grow. Indeed, it has been conjectured that any sensitivity-based coreset construction requires super-linear time in the dataset size. We examine this relationship by first showing that there does exist an algorithm that obtains coresets via sensitivity sampling in effectively linear time – within log-factors of the time it takes to read the data. Any approach that significantly improves on this must then resort to practical heuristics, leading us to consider the spectrum of sampling strategies across both real and artificial datasets in the static and streaming settings. Through this, we show the conditions in which coresets are necessary for preserving cluster validity as well as the settings in which faster, cruder sampling strategies are sufficient. As a result, we provide a comprehensive theoretical and practical blueprint for effective clustering regardless of data size. Our code is publicly available at source and has scripts to recreate the experiments. 1 Introduction The modern data analyst has no shortage of clustering algorithms to choose from but, given the ever-increasing size of relevant datasets, many are often too slow to be practically useful. This is particularly relevant for big-data pipelines, where clustering algorithms are commonly used for compression. The goal is to replace a very large dataset by a smaller, more manageable one for downstream tasks, with the hope it represents the original input well. Lloyd’s algorithm [49] was introduced for precisely this reason and minimizes the quantization error – the sum of square distance from each input point to its representative in the compressed dataset. Arguably the most popular clustering algorithm, Lloyd’s runs for multiple iterations until convergence with every iteration requiring O(ndk) time, where n is the number of points, d is the number of features and k is the number of clusters – or the size of the targeted compression. For such applications, the number of points can easily be hundreds of millions and, since the quality of compression increases with k, standard objectives can have k in the thousands [41, 4]. In such settings, any O(ndk) algorithm is prohibitively slow. Examples like these have prompted the rise of big data algorithms that provide both theoretical and practical runtime improvements. The perspectives of theoretical soundness and practical efficacy are, however, often at odds with one another. On the one hand, theoretical guarantees provide assurance that the algorithm will work regardless of whatever unlucky inputs it receives. On the other hand, it may be difficult to convince oneself to implement the theoretically optimal algorithm when there are cruder methods that are faster to get running and perform well in practice. Since datasets can be large in the number of points n and/or the number of features d, big-data methods must mitigate the effects of both. With respect to the feature space, the question is effectively closed as random projections are fast (running in effectively linear time), practical to implement [50], and provide tight guarantees on the embedding’s size and quality. The outlook is less clear when reducing the number of points n, and there are two separate paradigms that each provide distinct advantages. On the one hand, we have uniform sampling, which runs in sublinear time but may miss important subsets of the data and therefore can only guarantee accuracy under certain strong assumptions on the data [45]. On the other hand, the most accurate sampling strategies provide the strong coreset guarantee, wherein the cost of any solution on the compressed data is within an ε-factor of that solution’s cost on the original dataset [25]. Our contributions We study both paradigms (uniform sampling and strong coresets) with respect to a classic problem – compression for the k-means and k-median objectives. Whereas uniform sampling provides optimal speed but no worst-case accuracy guarantee, all available coreset constructions have a running time of at least Ω˜(nd + nk) when yielding tight bounds on the minimum number of samples required for accurate compression. It is easy to show that any algorithm that achieves a compression guarantee must read the entire dataset[1]. Thus a clear open question is what guarantees are achievable in linear or nearly-linear time. Indeed, currently available fast sampling algorithms for clustering [6, 5] cannot achieve strong coreset guarantees. Recently, [31] proposed a method for strong coresets that runs in time O˜(nd + nk) and conjectured this to be optimal for k-median and k-means. While this bound is effectively optimal for small values of k, there are many applications such as computer vision [34] or algorithmic fairness [18] where the number of clusters can be larger than the number of features by several orders of magnitude. In such settings, the question of time-optimal coresets remains open. Since the issue of determining a coreset of optimal size has recently been closed [25, 28, 44], this is arguably the main open problem in coreset research for center-based clustering. We resolve this by showing that there exists an easy-to-implement algorithm that constructs coresets in O˜(nd) time – only logarithmic factors away from the time it takes to read in the dataset. Nonetheless, this does not fully illuminate the landscape among sampling algorithms for clustering in practice. Although our algorithm achieves both an optimal runtime and an optimal compression, it is certainly possible that other, cruder methods may be just as viable for all practical purposes. We state this formally in the following question: When are optimal k-means and k-median coresets necessary and what is the practical tradeoff between coreset speed and accuracy? o answer this, we perform a thorough comparison across the full span of sampling algorithms that are faster than our proposed method. Through this we verify that, while these faster methods are sufficiently accurate on many real-world datasets, there exist data distributions that cause catastrophic failure for each of them. Indeed, these cases can only be avoided with a strong-coreset method. Thus, while many practical settings do not require the full coreset guarantee, one cannot cut corners if one wants to be confident in their compression. We verify that this extends to the streaming paradigm and applies to downstream clustering approaches. In summary, our contributions are as follows: • We show that one can obtain strong coresets for k-means and k-median in O˜(nd) time. This resolves a conjecture on the necessary runtime for k-means coresets [31] and is theoretically optimal up to log-factors. • Through a comprehensive analysis across datasets, tasks, and streaming/non-streaming paradigms, we verify that there is a necessary tradeoff between speed and accuracy among the linear- and sublinear-time sampling methods. This gives the clustering practitioner a blueprint on when to use each compression algorithm for effective clustering results in the fastest possible time. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

How to Fit an Elephant in a Spreadsheet

Πώς να τοποθετήσετε έναν ελέφαντα σε ένα υπολογιστικό φύλλο

Cómo colocar un elefante en una hoja de cálculo

כיצד להתאים פיל בגיליון אלקטרוני

Hogyan illeszthetünk el egy elefántot egy táblázatba

スプレッドシートに象を収める方法

Электрондук таблицага пилди кантип батырса болот

Зааныг хүснэгтэнд хэрхэн оруулах вэ

Nigute Uhuza Inzovu murupapuro rusesuye

Kuisa Nzou muSpreadsheet

Како уклопити слона у табелу

اسپریڈشیٹ میں ہاتھی کو کیسے فٹ کریں۔

This is Part 7 of a 12-part series based on the research paper “Human-Machine Social Systems.” Use the table of links below to navigate to the next part. Table of Links Abstract and Introduction Human-machine interactions Collective outcomes Box 1: Competition in high-frequency trading markets Box 2: Contagion on Twitter Box 3: Cooperation and coordination on Wikipedia Box 4: Cooperation and contagion on Reddit Discussion Implications for research Implications for design Implications for policy Conclusion, Acknowledgments, References, and Competing interests Box 4: Cooperation and Contagion on Reddit Reddit is a popular news aggregation, content rating, and discussion website founded in 2005. Bots on Reddit provide internal moderation and communication, augment functionality (e.g., for mobile users), or post content, ranging from comic and playful posts by evident automated accounts such as haiku robot and ObamaRobot, to trolling and provocative comments by undercover social bots[214]. Similarly to Wikipedia, Reddit has developed norms and protocols for deploying bots [215], but similarly to Twitter, it has no effective service limitations to prevent covert and malicious automated accounts. Nevertheless, Reddit differs from Twitter in several crucial ways – content on the site is posted within communities, heavily moderated, up-/downvoted, and extensively discussed. Due to these structural differences, political misinformation, polarization, and conflict are less pronounced on the platform. Reddit offers evidence for collaboration and contagion between humans and bots. Content moderators have widely adopted Automod – a bot that is flexible to updates, adaptable to community rules, and interpretable. The bot aids with menial tasks but does not necessarily decrease workloads as it requires continuous updates in response to changes in user behavior and language and involves high volumes of correspondence with incorrectly banned users [216]. On the other side, regular users engage with evident entertainment bots, and their direct replies imitate the sentiment and the words of the bot posts [217]. Thus, emotional contagion and lexical entrainment can occur between humans and bots, even when humans are aware of the simple automated script behind the bot. Authors:
(1) Milena Tsvetkova, Department of Methodology, London School of Economics and Political Science, London, United Kingdom;
(2) Taha Yasseri, School of Sociology, University College Dublin, Dublin, Ireland and Geary Institute for Public Policy, University College Dublin, Dublin, Ireland;
(3) Niccolo Pescetelli, Collective Intelligence Lab, New Jersey Institute of Technology, Newark, New Jersey, USA;
(4) Tobias Werner, Center for Humans and Machines, Max Planck Institute for Human Development, Berlin, Germany. This paper is available on arxiv under CC BY 4.0 DEED license. This is Part 7 of a 12-part series based on the research paper “Human-Machine Social Systems.” Use the table of links below to navigate to the next part. This is Part 7 of a 12-part series based on the research paper “Human-Machine Social Systems.” Use the table of links below to navigate to the next part. “ Human-Machine Social Systems .” Human-Machine Social Systems Table of Links Abstract and Introduction Abstract and Introduction Human-machine interactions Human-machine interactions Collective outcomes Collective outcomes Box 1: Competition in high-frequency trading markets Box 1: Competition in high-frequency trading markets Box 2: Contagion on Twitter Box 2: Contagion on Twitter Box 3: Cooperation and coordination on Wikipedia Box 3: Cooperation and coordination on Wikipedia Box 4: Cooperation and contagion on Reddit Box 4: Cooperation and contagion on Reddit Discussion Discussion Implications for research Implications for research Implications for design Implications for design Implications for policy Implications for policy Conclusion, Acknowledgments, References, and Competing interests Conclusion, Acknowledgments, References, and Competing interests Box 4: Cooperation and Contagion on Reddit Reddit is a popular news aggregation, content rating, and discussion website founded in 2005. Bots on Reddit provide internal moderation and communication, augment functionality (e.g., for mobile users), or post content, ranging from comic and playful posts by evident automated accounts such as haiku robot and ObamaRobot, to trolling and provocative comments by undercover social bots[214]. Similarly to Wikipedia, Reddit has developed norms and protocols for deploying bots [215], but similarly to Twitter, it has no effective service limitations to prevent covert and malicious automated accounts. Nevertheless, Reddit differs from Twitter in several crucial ways – content on the site is posted within communities, heavily moderated, up-/downvoted, and extensively discussed. Due to these structural differences, political misinformation, polarization, and conflict are less pronounced on the platform. Reddit offers evidence for collaboration and contagion between humans and bots. Content moderators have widely adopted Automod – a bot that is flexible to updates, adaptable to community rules, and interpretable. The bot aids with menial tasks but does not necessarily decrease workloads as it requires continuous updates in response to changes in user behavior and language and involves high volumes of correspondence with incorrectly banned users [216]. On the other side, regular users engage with evident entertainment bots, and their direct replies imitate the sentiment and the words of the bot posts [217]. Thus, emotional contagion and lexical entrainment can occur between humans and bots, even when humans are aware of the simple automated script behind the bot. Authors: (1) Milena Tsvetkova, Department of Methodology, London School of Economics and Political Science, London, United Kingdom; (2) Taha Yasseri, School of Sociology, University College Dublin, Dublin, Ireland and Geary Institute for Public Policy, University College Dublin, Dublin, Ireland; (3) Niccolo Pescetelli, Collective Intelligence Lab, New Jersey Institute of Technology, Newark, New Jersey, USA; (4) Tobias Werner, Center for Humans and Machines, Max Planck Institute for Human Development, Berlin, Germany. Authors: Authors: (1) Milena Tsvetkova, Department of Methodology, London School of Economics and Political Science, London, United Kingdom; (2) Taha Yasseri, School of Sociology, University College Dublin, Dublin, Ireland and Geary Institute for Public Policy, University College Dublin, Dublin, Ireland; (3) Niccolo Pescetelli, Collective Intelligence Lab, New Jersey Institute of Technology, Newark, New Jersey, USA; (4) Tobias Werner, Center for Humans and Machines, Max Planck Institute for Human Development, Berlin, Germany. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

Reddit Bots Improve Moderation, Encourage Cooperation, and Spread Emotions

This is Part 5 of a 12-part series based on the research paper “Human-Machine Social Systems.” Use the table of links below to navigate to the next part. Table of Links Abstract and Introduction Human-machine interactions Collective outcomes Box 1: Competition in high-frequency trading markets Box 2: Contagion on Twitter Box 3: Cooperation and coordination on Wikipedia Box 4: Cooperation and contagion on Reddit Discussion Implications for research Implications for design Implications for policy Conclusion, Acknowledgments, References, and Competing interests Box 2: Contagion on Twitter Social bots on the micro-blogging platform Twitter (re-branded as X in 2022) are covert automated accounts designed to impersonate humans to boost followers, disseminate information, and promote products. Bots and bot detection methods have co-evolved, resulting in increasingly more sophisticated imitation or detection strategies [177,178,179,180,39,181,182], but detection is inherently limited due to the overlap between covert autonomous bots, managed user accounts, hacked accounts, cyborgs, sock-puppets, and coordinated botnets [24,183,22,184]. Estimates suggest that 9-15% of Twitter users are bots185,182, with bot activity typically increasing around controversial political events [186]. Twitter social bots, who do not follow social instincts but neither succumb to fatigue, engage less in social interactions via replies and mentions than humans but produce more content [187]. The bots mainly retweet – a passive strategy to indicate support and gain followers – but are less successful in attracting friends and followers than humans [182]. Overall, they are less connected and bot-bot (2%), bot-human (19%), and human-bot (3%) interactions are considerably less common than human-human interactions (76%) 1[86]. Despite their rudimentary social behavior and weak network integration, Twitter bots significantly influence political communication, public opinion, elections, and markets. They play an important role in misinformation dissemination in relation to political events [188,189,190,191,192,193,194], COVID-19[195,196], and stock market investment [197]. Bots can affect human interaction networks by encouraging followings and conversations [198] and amplify low-credibility content early on by targeting influential humans through replies and mentions [192]. Bots’ large numbers enhance their visibility and influence to trigger deep information cascades [199]. Bots equally link to true and false news from low-credibility sites, but people prefer false content, making humans ultimately responsible for the spread of false news [200]. Twitter bots significantly contribute to negative sentiment and conflict escalation. Acting from the periphery, they target central human users to exert indirect influence. They amplify existing negative sentiment and selectively promote inflammatory content, often targeting only one of the factions[186]. Their success stems from exploiting human tendencies to connect with similar others and engage with messages that reinforce their beliefs [201]. Consequently, bots increase ideological polarization and negatively affect democratic discourse on social media, as seen in the 2016 US presidential election[202], the 2016 UK Brexit Referendum[201], and the 2017 Catalan independence referendum[186]. In sum, Twitter’s covert social bots are considered harmful, prompting the platform to cull them[203,204]. Their strength lies in indirect action: they skew the platform’s recommendation system to bias content popularity [137] and exploit human behavioral weaknesses like attention seeking, confirmation bias, moral outrage, and ideological homophily. Authors:
(1) Milena Tsvetkova, Department of Methodology, London School of Economics and Political Science, London, United Kingdom;
(2) Taha Yasseri, School of Sociology, University College Dublin, Dublin, Ireland and Geary Institute for Public Policy, University College Dublin, Dublin, Ireland;
(3) Niccolo Pescetelli, Collective Intelligence Lab, New Jersey Institute of Technology, Newark, New Jersey, USA;
(4) Tobias Werner, Center for Humans and Machines, Max Planck Institute for Human Development, Berlin, Germany. This paper is available on arxiv under CC BY 4.0 DEED license. This is Part 5 of a 12-part series based on the research paper “Human-Machine Social Systems.” Use the table of links below to navigate to the next part. This is Part 5 of a 12-part series based on the research paper “Human-Machine Social Systems.” Use the table of links below to navigate to the next part. “ Human-Machine Social Systems .” Human-Machine Social Systems Table of Links Abstract and Introduction Abstract and Introduction Human-machine interactions Human-machine interactions Collective outcomes Collective outcomes Box 1: Competition in high-frequency trading markets Box 1: Competition in high-frequency trading markets Box 2: Contagion on Twitter Box 2: Contagion on Twitter Box 3: Cooperation and coordination on Wikipedia Box 3: Cooperation and coordination on Wikipedia Box 4: Cooperation and contagion on Reddit Box 4: Cooperation and contagion on Reddit Discussion Discussion Implications for research Implications for research Implications for design Implications for design Implications for policy Implications for policy Conclusion, Acknowledgments, References, and Competing interests Conclusion, Acknowledgments, References, and Competing interests Box 2: Contagion on Twitter Social bots on the micro-blogging platform Twitter (re-branded as X in 2022) are covert automated accounts designed to impersonate humans to boost followers, disseminate information, and promote products. Bots and bot detection methods have co-evolved, resulting in increasingly more sophisticated imitation or detection strategies [177,178,179,180,39,181,182], but detection is inherently limited due to the overlap between covert autonomous bots, managed user accounts, hacked accounts, cyborgs, sock-puppets, and coordinated botnets [24,183,22,184]. Estimates suggest that 9-15% of Twitter users are bots185,182, with bot activity typically increasing around controversial political events [186]. Twitter social bots, who do not follow social instincts but neither succumb to fatigue, engage less in social interactions via replies and mentions than humans but produce more content [187]. The bots mainly retweet – a passive strategy to indicate support and gain followers – but are less successful in attracting friends and followers than humans [182]. Overall, they are less connected and bot-bot (2%), bot-human (19%), and human-bot (3%) interactions are considerably less common than human-human interactions (76%) 1[86]. Despite their rudimentary social behavior and weak network integration, Twitter bots significantly influence political communication, public opinion, elections, and markets. They play an important role in misinformation dissemination in relation to political events [188,189,190,191,192,193,194], COVID-19[195,196], and stock market investment [197]. Bots can affect human interaction networks by encouraging followings and conversations [198] and amplify low-credibility content early on by targeting influential humans through replies and mentions [192]. Bots’ large numbers enhance their visibility and influence to trigger deep information cascades [199]. Bots equally link to true and false news from low-credibility sites, but people prefer false content, making humans ultimately responsible for the spread of false news [200]. Twitter bots significantly contribute to negative sentiment and conflict escalation. Acting from the periphery, they target central human users to exert indirect influence. They amplify existing negative sentiment and selectively promote inflammatory content, often targeting only one of the factions[186]. Their success stems from exploiting human tendencies to connect with similar others and engage with messages that reinforce their beliefs [201]. Consequently, bots increase ideological polarization and negatively affect democratic discourse on social media, as seen in the 2016 US presidential election[202], the 2016 UK Brexit Referendum[201], and the 2017 Catalan independence referendum[186]. In sum, Twitter’s covert social bots are considered harmful, prompting the platform to cull them[203,204]. Their strength lies in indirect action: they skew the platform’s recommendation system to bias content popularity [137] and exploit human behavioral weaknesses like attention seeking, confirmation bias, moral outrage, and ideological homophily. Authors: (1) Milena Tsvetkova, Department of Methodology, London School of Economics and Political Science, London, United Kingdom; (2) Taha Yasseri, School of Sociology, University College Dublin, Dublin, Ireland and Geary Institute for Public Policy, University College Dublin, Dublin, Ireland; (3) Niccolo Pescetelli, Collective Intelligence Lab, New Jersey Institute of Technology, Newark, New Jersey, USA; (4) Tobias Werner, Center for Humans and Machines, Max Planck Institute for Human Development, Berlin, Germany. Authors: Authors: (1) Milena Tsvetkova, Department of Methodology, London School of Economics and Political Science, London, United Kingdom; (2) Taha Yasseri, School of Sociology, University College Dublin, Dublin, Ireland and Geary Institute for Public Policy, University College Dublin, Dublin, Ireland; (3) Niccolo Pescetelli, Collective Intelligence Lab, New Jersey Institute of Technology, Newark, New Jersey, USA; (4) Tobias Werner, Center for Humans and Machines, Max Planck Institute for Human Development, Berlin, Germany. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

Twitter Bots Spread Misinformation, Shape Opinions, & Amplify Polarization by Exploiting Human Bias

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Microsoft Excel has become the standard for traditional software that provides spreadsheet capabilities. As such, many organizations are looking to offer similar in-demand capabilities for Angular web applications. In fact, these solutions may even go a step further by providing developers with customized spreadsheet components especially suited to address the unique needs of their businesses and the web applications critical to their operations. Why Would Someone Need a Spreadsheet Component? A spreadsheet component is the best option for developers who want to equip web applications with spreadsheet functionality. Solutions like MESCIUS’ SpreadJS, for example, provide web applications with the same ability to use spreadsheet functions without sacrificing the user experience in the application. Companies in many industries need to ensure that their web applications have functional and easy-to-use spreadsheets. The use of spreadsheets, in many cases, has become a fundamental part of how companies operate. Consider companies in the finance industry. These companies routinely rely on Excel spreadsheets and models to provide snapshots and data analysis to enhance every aspect of business, including modeling, budgeting, reporting, etc. Integrating spreadsheet components in their web applications ensures they have the same degree of spreadsheet functionality where business data is increasingly consumed, i.e., in the cloud. Another reason a spreadsheet component is necessary is that Excel carries privacy and security risks with it. Excel files are susceptible to malware and other cybersecurity threats. Spreadsheet components are specially designed for web applications, allowing you to implement your own required security and permissions checks to ensure your data is kept safe and secure. In this post, we will examine some of the top Angular spreadsheet components available so that you can choose the best spreadsheet component for your JavaScript web applications. MESCIUS – SpreadJS SpreadJS is an Angular spreadsheet solution that enables you to secure, manage, and control your spreadsheet data in JavaScript applications while helping to eliminate risk and maintaining functionality and ease of use. It offers features to analyze and visualize data effortlessly for almost any web application use case. With SpreadJS, users can: Import from and export to Excel quickly
Use a complete function library, including statistical and financial functions, dynamic arrays, and custom user-defined functions
Leverage total table, charts, validation, and conditional formatting support With SpreadJS, users can accomplish the same tasks as with Excel by navigating the familiar spreadsheet UI with its content menus, row and column headers, worksheets, and workbooks. There are advanced functionalities, including uploading and editing images, adding comments, cell formatting with a host of options, and conditional formatting. Users can also import XLSX, CSV, JSON, and SJS files and export XLSX, CSV, HTML, JSON, SJS, and PDF files. SpreadJS has a built-in calculation engine that provides over 500 functions. Users can create custom formulas and use a formula panel to debug and optimize complex formulas. There is also support for dynamic formula arrays. There is no shortage of visualization options that help users better interpret data. SpreadJS provides built-in visualization tools for data that contain a wide range of charts, including sparklines. There are essential optional add-ons, such as Pivot Table, GanttSheet for Gantt charts, ReportSheet, and a customizable Designer Ribbon. SpreadJS offers full support for other environments, including React, Vue, and TypeScript. It is a spreadsheet solution that efficiently provides any spreadsheet usability JavaScript developers need to integrate into web applications. Handsontable HandsonTable, which bills itself as a data grid with a spreadsheet UI, is a serviceable platform that provides several spreadsheet capabilities for web applications. Its capabilities may not be as plentiful as other spreadsheet solutions, but some users will consider it a decent option. It features a user interface that feels very familiar to regular Excel users and can be localized according to the user's locale. The layout includes the customary column and row headers and context menus, making navigation easy. You can import and export Excel files. Also, its HyperFormula calculation engine is a plug-in with almost 400 functions to satisfy your company’s business needs. There is a wide range of functionality, such as comments, built-in cell types, numerous cell formatting options, and conditional formatting. Users can also upload files to custom cells. However, it has some limitations that can affect how well you’re able to create your desired spreadsheet. For example, HandsonTable doesn’t provide chart support. Developers must resort to using custom plugins to achieve some desired functionalities. Syncfusion – Angular Spreadsheet Syncfusion’s Angular Spreadsheet includes an Excel-inspired interface with a built-in, interactive designer ribbon where you can find tabs organizing the platform’s features. Users can import Excel and CSV files from JSON and other data sources. They can also export to XLSX, XLS, CSV, and PDF. Syncfusion’s wide range of functionality is probably its top selling point. Users can perform data analysis and create visualizations similar to Excel. Users can view, insert, and edit images. Features also include data binding, cell formatting, conditional formatting, localization, wrap or merge text, data validation, and more. You can create and customize various charts and use the built-in calculation engine with more than 400 formulas. Additional useful components for integration include Gantt charts and pivot tables. The platform supports other frameworks, including React, JavaScript, Vue, ASP.NET Core, and ASP.NET MVC. With Syncfusion’s feature set and similar Excel-like interface, this tool is a good option for integrating spreadsheet capabilities into web applications. Jspreadsheet Jspreadsheet’s component provides spreadsheet capabilities for not just Angular but also React, Vue, and Typescript. It offers a viable solution for JavaScript developers looking for a no-frills, straightforward option for adding spreadsheets to their web applications. Jspreadsheet’s interface resembles that of Excel and is user-friendly and interactive. Users can format cells, insert basic comments, and conduct other rudimentary operations. Other, more common, advanced functionalities that incorporate further usability and control over spreadsheets in an application are only available as extensions. For example, importing and exporting XSLX documents, creating charts, advanced comments, data validation, and exporting to PDF require extensions. The formula extension provides over 400 Excel functions in JavaScript. Infragistics – Ignite UI Spreadsheet From the Excel-like interface, you can access a range of essential spreadsheet features. Just make sure to install the Core and Excel packages in addition to installing the Angular spreadsheet component. The spreadsheet features data validation, hyperlinks, conditional formatting, cell formatting, a clipboard, localization, and much more to manipulate data. To help with data visualization, there are over 65 chart and series types, including sparklines. Over 300 formulas are also available to users. Users can easily import, export, and edit Excel and CSV files. The platform also supports Blazor, React, and web components. Telerik – Kendo UI for Angular Spreadsheet The Kendo UI provides a user experience similar to that of Excel. There is a wide range of cell formatting options, styles, and themes for customization.  Keyboard-only navigation is included to improve accessibility.  A sheet bar is available to conveniently manage worksheets. There is also a context menu, a sheets bar to handle sheets, and the capability to modify header, row, and column dimensions. Users will find built-in tools for exporting and importing Excel and JSON formatted files and exporting content to a PDF document. The calculation functionality supports over 250 functions and array formulas. You can use events to control how the spreadsheet responds to certain user behavior. Data visualization options include a variety of customizable charts. Conclusion Which Angular spreadsheet is the top choice depends on your specific business needs. However, SpreadJS provides a vast range of functionality and easy usability to suit any spreadsheet use-case for your web application projects. This is possible with key features like: Familiar Excel-like spreadsheet experience
Support for over 500 functions to meet nearly any calculation need
Plenty of data visualization options, including charts and sparklines Users can format spreadsheet elements, create custom visualizations, execute formulas, and much more with this reliable component. Microsoft Excel has become the standard for traditional software that provides spreadsheet capabilities. As such, many organizations are looking to offer similar in-demand capabilities for Angular web applications. In fact, these solutions may even go a step further by providing developers with customized spreadsheet components especially suited to address the unique needs of their businesses and the web applications critical to their operations. Why Would Someone Need a Spreadsheet Component? A spreadsheet component is the best option for developers who want to equip web applications with spreadsheet functionality. Solutions like MESCIUS’ SpreadJS, for example, provide web applications with the same ability to use spreadsheet functions without sacrificing the user experience in the application. Companies in many industries need to ensure that their web applications have functional and easy-to-use spreadsheets. The use of spreadsheets, in many cases, has become a fundamental part of how companies operate. Consider companies in the finance industry . These companies routinely rely on Excel spreadsheets and models to provide snapshots and data analysis to enhance every aspect of business, including modeling, budgeting, reporting, etc. Integrating spreadsheet components in their web applications ensures they have the same degree of spreadsheet functionality where business data is increasingly consumed, i.e., in the cloud. finance industry Another reason a spreadsheet component is necessary is that Excel carries privacy and security risks with it. Excel files are susceptible to malware and other cybersecurity threats. Spreadsheet components are specially designed for web applications, allowing you to implement your own required security and permissions checks to ensure your data is kept safe and secure. In this post, we will examine some of the top Angular spreadsheet components available so that you can choose the best spreadsheet component for your JavaScript web applications. MESCIUS – SpreadJS SpreadJS is an Angular spreadsheet solution that enables you to secure, manage, and control your spreadsheet data in JavaScript applications while helping to eliminate risk and maintaining functionality and ease of use. It offers features to analyze and visualize data effortlessly for almost any web application use case. With SpreadJS, users can: SpreadJS Import from and export to Excel quickly Use a complete function library, including statistical and financial functions, dynamic arrays, and custom user-defined functions Leverage total table, charts, validation, and conditional formatting support Import from and export to Excel quickly Use a complete function library, including statistical and financial functions, dynamic arrays, and custom user-defined functions Leverage total table, charts, validation, and conditional formatting support With SpreadJS, users can accomplish the same tasks as with Excel by navigating the familiar spreadsheet UI with its content menus, row and column headers, worksheets, and workbooks. There are advanced functionalities, including uploading and editing images, adding comments, cell formatting with a host of options, and conditional formatting. Users can also import XLSX, CSV, JSON, and SJS files and export XLSX, CSV, HTML, JSON, SJS, and PDF files. SpreadJS has a built-in calculation engine that provides over 500 functions. Users can create custom formulas and use a formula panel to debug and optimize complex formulas. There is also support for dynamic formula arrays. There is no shortage of visualization options that help users better interpret data. SpreadJS provides built-in visualization tools for data that contain a wide range of charts, including sparklines. There are essential optional add-ons, such as Pivot Table, GanttSheet for Gantt charts, ReportSheet, and a customizable Designer Ribbon. SpreadJS offers full support for other environments, including React, Vue, and TypeScript. It is a spreadsheet solution that efficiently provides any spreadsheet usability JavaScript developers need to integrate into web applications. Handsontable HandsonTable , which bills itself as a data grid with a spreadsheet UI, is a serviceable platform that provides several spreadsheet capabilities for web applications. Its capabilities may not be as plentiful as other spreadsheet solutions, but some users will consider it a decent option. HandsonTable It features a user interface that feels very familiar to regular Excel users and can be localized according to the user's locale. The layout includes the customary column and row headers and context menus, making navigation easy. You can import and export Excel files. Also, its HyperFormula calculation engine is a plug-in with almost 400 functions to satisfy your company’s business needs. There is a wide range of functionality, such as comments, built-in cell types, numerous cell formatting options, and conditional formatting. Users can also upload files to custom cells. However, it has some limitations that can affect how well you’re able to create your desired spreadsheet. For example, HandsonTable doesn’t provide chart support . Developers must resort to using custom plugins to achieve some desired functionalities. doesn’t provide chart support Syncfusion – Angular Spreadsheet Syncfusion’s Angular Spreadsheet includes an Excel-inspired interface with a built-in, interactive designer ribbon where you can find tabs organizing the platform’s features. Syncfusion’s Angular Spreadsheet Users can import Excel and CSV files from JSON and other data sources. They can also export to XLSX, XLS, CSV, and PDF. Syncfusion’s wide range of functionality is probably its top selling point. Users can perform data analysis and create visualizations similar to Excel. Users can view, insert, and edit images. Features also include data binding, cell formatting, conditional formatting, localization, wrap or merge text, data validation, and more. You can create and customize various charts and use the built-in calculation engine with more than 400 formulas. Additional useful components for integration include Gantt charts and pivot tables. The platform supports other frameworks, including React, JavaScript, Vue, ASP.NET Core, and ASP.NET MVC. With Syncfusion’s feature set and similar Excel-like interface, this tool is a good option for integrating spreadsheet capabilities into web applications. Jspreadsheet Jspreadsheet’s component provides spreadsheet capabilities for not just Angular but also React, Vue, and Typescript. It offers a viable solution for JavaScript developers looking for a no-frills, straightforward option for adding spreadsheets to their web applications. Jspreadsheet’s component Jspreadsheet’s interface resembles that of Excel and is user-friendly and interactive. Users can format cells, insert basic comments, and conduct other rudimentary operations. Other, more common, advanced functionalities that incorporate further usability and control over spreadsheets in an application are only available as extensions. For example, importing and exporting XSLX documents, creating charts, advanced comments, data validation, and exporting to PDF require extensions. The formula extension provides over 400 Excel functions in JavaScript. Infragistics – Ignite UI Spreadsheet From the Excel-like interface, you can access a range of essential spreadsheet features. Just make sure to install the Core and Excel packages in addition to installing the Angular spreadsheet component. The spreadsheet features data validation, hyperlinks, conditional formatting, cell formatting, a clipboard, localization, and much more to manipulate data. To help with data visualization, there are over 65 chart and series types, including sparklines. Over 300 formulas are also available to users. spreadsheet Users can easily import, export, and edit Excel and CSV files. The platform also supports Blazor, React, and web components. Telerik – Kendo UI for Angular Spreadsheet The Kendo UI provides a user experience similar to that of Excel. There is a wide range of cell formatting options, styles, and themes for customization.  Keyboard-only navigation is included to improve accessibility.  A sheet bar is available to conveniently manage worksheets. There is also a context menu, a sheets bar to handle sheets, and the capability to modify header, row, and column dimensions. Users will find built-in tools for exporting and importing Excel and JSON formatted files and exporting content to a PDF document. The calculation functionality supports over 250 functions and array formulas. You can use events to control how the spreadsheet responds to certain user behavior. Data visualization options include a variety of customizable charts. spreadsheet Conclusion Which Angular spreadsheet is the top choice depends on your specific business needs. However, SpreadJS provides a vast range of functionality and easy usability to suit any spreadsheet use-case for your web application projects. This is possible with key features like: SpreadJS Familiar Excel-like spreadsheet experience Support for over 500 functions to meet nearly any calculation need Plenty of data visualization options, including charts and sparklines Familiar Excel-like spreadsheet experience Support for over 500 functions to meet nearly any calculation need Plenty of data visualization options, including charts and sparklines Users can format spreadsheet elements, create custom visualizations, execute formulas, and much more with this reliable component.

 The Top Angular Spreadsheet Tools

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In the dynamic political environment of the United States, the rapidly approaching 2024 presidential election is becoming the arena for a new kind of battle—one against the insidious rise of AI-generated deepfakes. These sophisticated synthetic constructs, capable of distorting reality, have started a legislative marathon across the nation, as state leaders press forward with decisive measures to uphold the integrity of voter information. There is no federal law that deals with deepfakes, leaving it up to individual states to legislate. The top official at the Federal Election Commission (FEC) has signaled expectations for the issue to reach a resolution later this year. This forecast hints at a significant stretch of the 2024 election period potentially unfolding with this brand of misinformation remaining unchecked. Recently, bridging the partisan divide with a shared vision for election integrity, Sens. Amy Klobuchar of Minnesota and Lisa Murkowski of Alaska joined forces. Their collaborative legislative endeavor presents a forward-thinking bill aimed squarely at the digital frontier of political advertising. This proposed legislation would bring much-needed clarity to the murky waters of AI-generated political ads by enforcing disclaimers. But it doesn't stop there; the bill also empowers the Federal Election Commission (FEC) to step up as the digital watchdog, ensuring that any infractions don't go unnoticed or unaddressed. It's a bold stride towards safeguarding transparency as we sail into the next wave of electoral campaigns. What is going to happen during this election year if, as expected, there will be a slew of politically-motivated deepfakes? How can people be protected from being influenced? There is no easy answer. Deepfakes Decoded: Understanding the Synthetic Threat Deepfakes—a term blending 'deep learning' with 'fake'—refer to artificially generated audio, images, or videos designed to replicate a person's likeness with unnerving accuracy. February's New Hampshire incident serves as a harrowing example: an AI-generated voice, eerily mimicking President Joe Biden, misled thousands with false voting information. This event underscores the pressing need for informed and proactive legislation. "In a surprising turn of events this Tuesday, a voice on the airwaves was heard declaring, 'Your vote today is a step towards bringing Donald Trump back into office,'" recounted an associate from Rep. Dean Phillips' team, D-Minn., during an interview with NBC News. This individual, working for an underdog in the race against Biden, revealed that he had orchestrated the ad on his own accord, without the campaign's knowledge or approval. Shifting to the campaign trail on the Republican side, a striking audio piecesurfaced from the Trump camp. The clip was artfully edited to give the illusion that Florida's Gov. Ron DeSantis was in dialogue with none other than Adolf Hitler. In a retaliatory move, the DeSantis campaign then distributed a doctored photopresenting Trump in a warm embrace with Anthony Fauci, sending ripples through social media platforms. The Legislative Surge: Over 100 Bills to Protect Voters As guardians of electoral integrity, the Voting Rights Lab vigilantly surveys an ever-growing wave of legislative initiatives. In a landscape where over 100 bills have sprung up across 40 states, the commitment to combating AI-generated election disinformation appears as unwavering. With each bill introduced or passed, there's an unmistakable sense of urgency among legislators to reinforce the foundations of democracy. It's a legislative crusade that's rapidly gaining traction, showcasing the collective resolve to shield our democratic processes from the deceptive shadows cast by digital manipulations. Emerging Legislative Frameworks In the wake of the robocall incident, a flurry of legislative activity ensued. States like Arizona, Florida, and Wisconsin are now at the forefront, each showcasing a different regulatory trend that epitomizes the varied approaches being adopted to combat these technological ploys. Wisconsin recently enacted a law requiring political affiliates to disclose AI-generated content, while Florida awaits the governor's signature on a similar bill. Arizona, on the other hand, is threading the needle between regulation and constitutional rights, crafting bills that could classify the omission of labels on AI political media as a felony under certain conditions. Another bill in Arizona under consideration opens the door for individuals who have been wronged to initiate civil litigation against the creators of the content, providing them with the potential to obtain monetary compensation. Since 2019, both California and Texas have established laws in place. In Indiana, a bill that has garnered bipartisan backing is making its way to the governor's office. Should it pass, it would mandate that advertisements employing deepfake technology must clearly state, "Parts of this media have been digitally modified or synthetically produced." This directive would extend across images, videos, and audio content. Candidates at the state and federal level who find themselves impacted by such content would have the legal standing to pursue compensation through civil litigation. Drawing inspiration from the deceptive robocall incident mimicking President Biden, a bipartisan effort happened in Kansas. Spearheaded by Republican State Representative Pat Proctor alongside a Democratic leader, they pushed forward a legislative proposal that's tackling the deepfake dilemma head-on. This bill not only called for mandatory disclosures on deepfake advertisements but also proposed stringent penalties for those impersonating election officials, like secretaries of state, with the malicious aim of dissuading voters from casting their ballots. Currently, the bill is dead, but according to Proctor, can be brought back to life. Tackling the Disinformation Threat Beyond Legislation Legislation is just one facet of the multi-pronged strategy needed to address AI-generated deepfakes. Entities like NewDeal are spearheading initiatives that equip election administrators with tools to counter disinformation campaigns effectively. Incident response preparation and public awareness campaigns, such as those launched by secretaries of state in New Mexico and Michigan, are vital components in educating voters and reinforcing the democratic infrastructure. Big Tech Transparency Moves Major tech companies are proactively setting new standards for authenticity in online content, with a sharp focus on advertisements influenced by AI. Meta, the umbrella company for social platforms like Facebook, Instagram, and Threads, is diligently removing any advertisements that are misleading or lack context, particularly those that could potentially undermine the electoral process or jump the gun on election results. All AI-generated ads are subject to this rigorous scrutiny. In sync with these efforts, Google has made a declaration that transparency must accompany any AI-enhanced images, videos, or audio used in election advertising. As of November, such content will require explicit disclosures. YouTube, under Google's wing, is committing to transparency by ensuring its users can recognize when content has been digitally manipulated. These strategic moves by Meta and Google signal a pivotal shift toward clarity and honesty in the digital content we consume, especially in the political sphere. As we navigate the complexities of an election year increasingly influenced by AI technology, the question remains: Are state laws sufficiently robust to control the spread of deepfakes? With the FEC indicating that national resolutions may not arrive until later in the year, the responsibility falls on state legislatures, tech companies, and the public to build a bulwark against these digital deceptions. The legislative efforts, both proposed and enacted across the states, represent a patchwork of defenses, with some offering a glimpse of the rigorous measures needed to ensure fair play in our political arenas. The commitment to clarity and integrity from tech companies, too, adds a layer of much-needed oversight. Yet, as sophisticated as these measures may be, they are not impermeable. There will always be new challenges and loopholes to close, especially as technology advances at a breakneck pace. In this environment, voter education and awareness become as crucial as the laws themselves. The dissemination of information on how to discern real from synthetic media, alongside a discerning eye for what we consume and share, is paramount. It's a collective effort—a blend of legal, corporate, and individual actions—that shapes our resilience against the rising tide of AI-generated misinformation. As the 2024 election approaches, it's evident that there is no panacea for the deepfake phenomenon. Yet, the burgeoning legislative efforts, combined with proactive measures from tech companies and the vigilant participation of the public, set the stage for a robust defense. These are the foundations upon which we can hope to preserve the sanctity of our elections, fostering a well-informed electorate that is equipped to navigate the murky waters of digital propaganda. In conclusion, while the legislative landscape is still forming and the digital battleground is ever-changing, our collective vigilance and adaptability are key. The actions taken today by lawmakers, technologists, and every voter will determine the efficacy of our defense against deepfakes. In this election year and beyond, the safeguarding of truth remains a shared responsibility—one that we must continuously strive to uphold. In the dynamic political environment of the United States, the rapidly approaching 2024 presidential election is becoming the arena for a new kind of battle—one against the insidious rise of AI-generated deepfakes. These sophisticated synthetic constructs, capable of distorting reality, have started a legislative marathon across the nation, as state leaders press forward with decisive measures to uphold the integrity of voter information. There is no federal law that deals with deepfakes, leaving it up to individual states to legislate. The top official at the Federal Election Commission (FEC) has signaled expectations for the issue to reach a resolution later this year. This forecast hints at a significant stretch of the 2024 election period potentially unfolding with this brand of misinformation remaining unchecked. Recently, bridging the partisan divide with a shared vision for election integrity, Sens. Amy Klobuchar of Minnesota and Lisa Murkowski of Alaska joined forces. Their collaborative legislative endeavor presents a forward-thinking bill aimed squarely at the digital frontier of political advertising. This proposed legislation would bring much-needed clarity to the murky waters of AI-generated political ads by enforcing disclaimers. But it doesn't stop there; the bill also empowers the Federal Election Commission (FEC) to step up as the digital watchdog, ensuring that any infractions don't go unnoticed or unaddressed. It's a bold stride towards safeguarding transparency as we sail into the next wave of electoral campaigns. What is going to happen during this election year if, as expected, there will be a slew of politically-motivated deepfakes? How can people be protected from being influenced? There is no easy answer. Deepfakes Decoded: Understanding the Synthetic Threat Deepfakes—a term blending 'deep learning' with 'fake'—refer to artificially generated audio, images, or videos designed to replicate a person's likeness with unnerving accuracy. February's New Hampshire incident serves as a harrowing example: an AI-generated voice, eerily mimicking President Joe Biden, misled thousands with false voting information. This event underscores the pressing need for informed and proactive legislation. "In a surprising turn of events this Tuesday, a voice on the airwaves was heard declaring, 'Your vote today is a step towards bringing Donald Trump back into office,'" recounted an associate from Rep. Dean Phillips' team, D-Minn., during an interview with NBC News. This individual, working for an underdog in the race against Biden, revealed that he had orchestrated the ad on his own accord, without the campaign's knowledge or approval. Shifting to the campaign trail on the Republican side, a striking audio piecesurfaced from the Trump camp. The clip was artfully edited to give the illusion that Florida's Gov. Ron DeSantis was in dialogue with none other than Adolf Hitler. In a retaliatory move, the DeSantis campaign then distributed a doctored photopresenting Trump in a warm embrace with Anthony Fauci, sending ripples through social media platforms. The Legislative Surge: Over 100 Bills to Protect Voters As guardians of electoral integrity, the Voting Rights Lab vigilantly surveys an ever-growing wave of legislative initiatives. In a landscape where over 100 bills have sprung up across 40 states, the commitment to combating AI-generated election disinformation appears as unwavering. With each bill introduced or passed, there's an unmistakable sense of urgency among legislators to reinforce the foundations of democracy. It's a legislative crusade that's rapidly gaining traction, showcasing the collective resolve to shield our democratic processes from the deceptive shadows cast by digital manipulations. Emerging Legislative Frameworks In the wake of the robocall incident, a flurry of legislative activity ensued. States like Arizona, Florida, and Wisconsin are now at the forefront, each showcasing a different regulatory trend that epitomizes the varied approaches being adopted to combat these technological ploys. Wisconsin recently enacted a law requiring political affiliates to disclose AI-generated content, while Florida awaits the governor's signature on a similar bill. Arizona, on the other hand, is threading the needle between regulation and constitutional rights, crafting bills that could classify the omission of labels on AI political media as a felony under certain conditions. Another bill in Arizona under consideration opens the door for individuals who have been wronged to initiate civil litigation against the creators of the content, providing them with the potential to obtain monetary compensation. Since 2019, both California and Texas have established laws in place. In Indiana, a bill that has garnered bipartisan backing is making its way to the governor's office. Should it pass, it would mandate that advertisements employing deepfake technology must clearly state, "Parts of this media have been digitally modified or synthetically produced." This directive would extend across images, videos, and audio content. Candidates at the state and federal level who find themselves impacted by such content would have the legal standing to pursue compensation through civil litigation. Drawing inspiration from the deceptive robocall incident mimicking President Biden, a bipartisan effort happened in Kansas. Spearheaded by Republican State Representative Pat Proctor alongside a Democratic leader, they pushed forward a legislative proposal that's tackling the deepfake dilemma head-on. This bill not only called for mandatory disclosures on deepfake advertisements but also proposed stringent penalties for those impersonating election officials, like secretaries of state, with the malicious aim of dissuading voters from casting their ballots. Currently, the bill is dead, but according to Proctor, can be brought back to life. Tackling the Disinformation Threat Beyond Legislation Legislation is just one facet of the multi-pronged strategy needed to address AI-generated deepfakes. Entities like NewDeal are spearheading initiatives that equip election administrators with tools to counter disinformation campaigns effectively. Incident response preparation and public awareness campaigns, such as those launched by secretaries of state in New Mexico and Michigan, are vital components in educating voters and reinforcing the democratic infrastructure. Big Tech Transparency Moves Major tech companies are proactively setting new standards for authenticity in online content, with a sharp focus on advertisements influenced by AI. Meta, the umbrella company for social platforms like Facebook, Instagram, and Threads, is diligently removing any advertisements that are misleading or lack context, particularly those that could potentially undermine the electoral process or jump the gun on election results. All AI-generated ads are subject to this rigorous scrutiny. In sync with these efforts, Google has made a declaration that transparency must accompany any AI-enhanced images, videos, or audio used in election advertising. As of November, such content will require explicit disclosures. YouTube, under Google's wing, is committing to transparency by ensuring its users can recognize when content has been digitally manipulated. These strategic moves by Meta and Google signal a pivotal shift toward clarity and honesty in the digital content we consume, especially in the political sphere. As we navigate the complexities of an election year increasingly influenced by AI technology, the question remains: Are state laws sufficiently robust to control the spread of deepfakes? With the FEC indicating that national resolutions may not arrive until later in the year, the responsibility falls on state legislatures, tech companies, and the public to build a bulwark against these digital deceptions. The legislative efforts, both proposed and enacted across the states, represent a patchwork of defenses, with some offering a glimpse of the rigorous measures needed to ensure fair play in our political arenas. The commitment to clarity and integrity from tech companies, too, adds a layer of much-needed oversight. Yet, as sophisticated as these measures may be, they are not impermeable. There will always be new challenges and loopholes to close, especially as technology advances at a breakneck pace. In this environment, voter education and awareness become as crucial as the laws themselves. The dissemination of information on how to discern real from synthetic media, alongside a discerning eye for what we consume and share, is paramount. It's a collective effort—a blend of legal, corporate, and individual actions—that shapes our resilience against the rising tide of AI-generated misinformation. As the 2024 election approaches, it's evident that there is no panacea for the deepfake phenomenon. Yet, the burgeoning legislative efforts, combined with proactive measures from tech companies and the vigilant participation of the public, set the stage for a robust defense. These are the foundations upon which we can hope to preserve the sanctity of our elections, fostering a well-informed electorate that is equipped to navigate the murky waters of digital propaganda. In conclusion, while the legislative landscape is still forming and the digital battleground is ever-changing, our collective vigilance and adaptability are key. The actions taken today by lawmakers, technologists, and every voter will determine the efficacy of our defense against deepfakes. In this election year and beyond, the safeguarding of truth remains a shared responsibility—one that we must continuously strive to uphold.

Can The Government, The Technology Platforms, or Anyone Else Control the Spread of Deepfakes?

Können die Regierung, die Technologieplattformen oder sonst jemand die Verbreitung von Deepfakes kontrollieren?

¿Pueden el gobierno, las plataformas tecnológicas o cualquier otra persona controlar la propagación de deepfakes?

آیا دولت، پلتفرم‌های فناوری یا هر کس دیگری می‌توانند گسترش دیپ‌فیک را کنترل کنند؟

Poden o goberno, as plataformas tecnolóxicas ou calquera outra persoa controlar a propagación dos Deepfakes?

क्या सरकार, प्रौद्योगिकी प्लेटफॉर्म या कोई अन्य डीपफेक के प्रसार को नियंत्रित कर सकता है?

政府、テクノロジープラットフォーム、あるいは他の誰かがディープフェイクの拡散を制御できるのでしょうか?

Afaka mifehy ny fiparitahan'ny Deepfakes ve ny governemanta, ny sehatra teknolojia, na olon-kafa?

Дали владата, технолошките платформи или некој друг може да го контролира ширењето на Deepfakes?

के सरकार, टेक्नोलोजी प्लेटफर्म, वा अरू कसैले Deepfakes को फैलावट नियन्त्रण गर्न सक्छ?

Czy rząd, platformy technologiczne lub ktokolwiek inny może kontrolować rozprzestrzenianie się deepfake'ów?

Dawladdu, Platform-yada Tignoolajiyada, ama cid kale ma xakamayn kartaa fiditaanka been-abuurka?

Чи може уряд, технологічні платформи чи будь-хто інший контролювати поширення Deepfakes?

In today's digital landscape, many companies seek to leverage powerful spreadsheet capabilities like Microsoft Excel in their own applications without relying on the popular desktop application. Whether it's bringing XLSX files online or providing a familiar MS Excel user interface (UI) experience for their users, the demand for web-based Excel-like spreadsheet functionality is growing. This shift is driven by the need for seamless data management, enhanced collaboration, and secure access control in web applications. As businesses increasingly adopt in-house and cloud-based solutions, integrating JavaScript spreadsheet components into these applications becomes a vital strategy for meeting these needs. The Benefits of Adding Excel-Like Functionality to Your Apps Integrating JavaScript spreadsheet components into your web applications offers numerous advantages that can significantly enhance both user experience and operational efficiency. These components enable users to interact with data in a familiar spreadsheet interface, allowing for easy data analysis, visualization, and manipulation. This familiar environment helps users quickly identify trends and patterns, improving decision-making and engagement. JavaScript spreadsheets also offer powerful functionalities tailored for web applications. These include support for complex formulas, conditional formatting, pivot tables, and extensive charting capabilities. Such features enable developers to create rich, interactive data experiences directly within the browser, eliminating the need for external stand-alone desktop applications. Moreover, JavaScript spreadsheets can help the application provide robust security features, such as role-based permissions, which allow organizations to control access and editing rights at a granular level. This is crucial for maintaining data integrity and compliance with security regulations, especially in industries where data privacy is paramount. Another significant benefit is the flexibility and scalability that JavaScript spreadsheets bring. They can seamlessly integrate with various data sources and APIs, enabling real-time data updates across different platforms and devices. This is particularly beneficial for teams working remotely or in different geographical locations, as it ensures that everyone has access to the latest information. In this guide, we will explore some of the most popular JavaScript spreadsheet and spreadsheet-like components available today, including their unique features and capabilities. By understanding these options, you can choose the best solution that aligns with your business needs, whether it's enhancing data analysis, improving user experience, or ensuring secure data management. What Are the Options? SpreadJS MESCIUS' SpreadJS is considered the most complete implementation of an Excel-like spreadsheet for JavaScript development on the market today. It’s difficult to name a Microsoft Excel feature that it doesn’t support. Plus, it offers full support for React, Angular, Vue, and TypeScript. SpreadJS enables companies to bring their Microsoft Excel files online and provide a familiar UI spreadsheet experience in their web applications. It features a powerful built-in calculation engine with over 500 functions and a comprehensive suite of advanced tools, including optional addons: Pivot Tables, a designer ribbon, Gantt sheets, and report sheets. This allows users to seamlessly transition from Excel to a secure, browser-based environment while retaining the functionality and ease of use they expect. There’s a minimal learning curve, so you can hit the ground running right away. Its familiar spreadsheet interface is optimized to offer fast performance when used to create large, complex spreadsheet models in a browser. It provides a complete function library that includes statistical and financial functions, dynamic arrays, and custom user-defined functions. The platform’s customizable formula auditing feature can also help companies maintain regulatory compliance by providing the ability to track changes. It also has a complete table, validation, and conditional formatting support and includes built-in data visualization tools, such as charts and sparklines. This allows users to show trends and patterns in large data sets to improve data readability. The platform will enable you to quickly and easily import XLSX, CSV, JSON, and SJS files and export and print XLSX, CSV, HTML, JSON, SJS, and PDF files. Handsontable Handsontable combines data grid features with a spreadsheet UI designed for many applications. It offers support for React, Angular, Vue, and JavaScript. It is flexible, customizable, and easy to use and integrate. You can edit the source code and implement custom plugins to adjust it as needed. While its text formatting options and advanced features are somewhat limited compared to other solutions, it offers a selection of some advanced capabilities. This includes multi-column sorting, data summaries, trimming rows, and nested headers. Handsontable PRO also offers real-time collaboration support. Syncfusion Essential JS Syncfusion offers an intuitive JavaScript spreadsheet. It’s optimized for loading large datasets in a table and organizing and analyzing them on demand. One of the benefits of Syncfusion’s spreadsheet is its large selection of UI components. Its rich API means there’s plenty of room for customization. For instance, to make the tables more visually appealing, users can add charts and illustrations. It also has an online Theme Studio tool that can help users to easily customize themes. Its large selection of tools means that this option may appear more complex to use than other options. However, documentation is available to solve these issues. Jspreadsheet Jspreadsheet is a flexible JavaScript spreadsheet that supports several basic spreadsheet functionalities with some advanced features. It’s cost-effective, lightweight, and easy to integrate. If you’re looking for a simple, bare-bones option with a slight learning curve, Jspreadsheet could be a good option. However, its advanced features are limited, and it has a smaller community and fewer resources available compared to other options. Infragistics Ignite UI Spreadsheet The Infragistics spreadsheet also offers many features and customizations. It supports most of the basic spreadsheet features, including formulas, cells, workbooks, worksheets, and more. The spreadsheet includes a resizable formula bar and a configurable view that allows users to choose how to work with and consume the data presented to them. Users can import and export their data to Microsoft Excel files as required. It also enables editing and cell formatting features, such as conditional formatting, sorting, and filtering, without requiring Excel installation. Additionally, it has table creation and management capabilities, along with worksheet-level sorting and filtering capabilities. This feature set means that the Ignite UI spreadsheet can be more complex to set up than some of the other options listed, but several samples and documentation are offered to help guide you. Telerik - UI for jQuery Spreadsheet Telerik offers a jQuery spreadsheet featuring several similar capabilities to Excel, including formulas, filtering, sorting, and charting. It also provides several cell formatting options, styles, and themes to allow users to visualize and edit data. It also includes a suite of tools, which may feel overwhelming if you’re looking for a simpler, more lightweight option. It may also be more costly than some of the other options — especially if you’re interested in an enterprise option. However, it has a strong community, and professional support is readily available. Comprehensive documentation is also available. The Telerik spreadsheet supports Internet Explorer 9 and later versions. It’s important to note that according to their documentation, it’s designed to be used on desktops, which means its mobile device functionality may be limited. Which is the Best Option? Each option has its pros and cons, and the winner depends on what exactly you’re looking for and, of course, your budget. If you’re looking for high performance and an extensive suite of features similar to Excel, SpreadJS is the clear winner. If you’re looking to prioritize ease of use and cost-effectiveness, Handsontable or Jspreadsheet may be a better option. If you’re prioritizing a comprehensive UI suite and support, consider Infragistics, Telerik, or Syncfusion. We encourage you to take the next steps by visiting the respective product pages to review online demos and product features. Most, if not all, offer a free trial you can download to test how they will work for your applications, which will help you make the most informed decision. Good luck and happy spreadsheeting! In today's digital landscape, many companies seek to leverage powerful spreadsheet capabilities like Microsoft Excel in their own applications without relying on the popular desktop application. Whether it's bringing XLSX files online or providing a familiar MS Excel user interface (UI) experience for their users, the demand for web-based Excel-like spreadsheet functionality is growing. This shift is driven by the need for seamless data management, enhanced collaboration, and secure access control in web applications. As businesses increasingly adopt in-house and cloud-based solutions, integrating JavaScript spreadsheet components into these applications becomes a vital strategy for meeting these needs. The Benefits of Adding Excel-Like Functionality to Your Apps Integrating JavaScript spreadsheet components into your web applications offers numerous advantages that can significantly enhance both user experience and operational efficiency. These components enable users to interact with data in a familiar spreadsheet interface, allowing for easy data analysis, visualization, and manipulation. This familiar environment helps users quickly identify trends and patterns, improving decision-making and engagement. JavaScript spreadsheets also offer powerful functionalities tailored for web applications. These include support for complex formulas, conditional formatting, pivot tables, and extensive charting capabilities. Such features enable developers to create rich, interactive data experiences directly within the browser, eliminating the need for external stand-alone desktop applications. Moreover, JavaScript spreadsheets can help the application provide robust security features, such as role-based permissions, which allow organizations to control access and editing rights at a granular level. This is crucial for maintaining data integrity and compliance with security regulations, especially in industries where data privacy is paramount. Another significant benefit is the flexibility and scalability that JavaScript spreadsheets bring. They can seamlessly integrate with various data sources and APIs, enabling real-time data updates across different platforms and devices. This is particularly beneficial for teams working remotely or in different geographical locations, as it ensures that everyone has access to the latest information. In this guide, we will explore some of the most popular JavaScript spreadsheet and spreadsheet-like components available today, including their unique features and capabilities. By understanding these options, you can choose the best solution that aligns with your business needs, whether it's enhancing data analysis, improving user experience, or ensuring secure data management. What Are the Options? SpreadJS SpreadJS MESCIUS' SpreadJS is considered the most complete implementation of an Excel-like spreadsheet for JavaScript development on the market today. It’s difficult to name a Microsoft Excel feature that it doesn’t support. Plus, it offers full support for React, Angular, Vue, and TypeScript. SpreadJS enables companies to bring their Microsoft Excel files online and provide a familiar UI spreadsheet experience in their web applications. It features a powerful built-in calculation engine with over 500 functions and a comprehensive suite of advanced tools, including optional addons: Pivot Tables, a designer ribbon, Gantt sheets, and report sheets. This allows users to seamlessly transition from Excel to a secure, browser-based environment while retaining the functionality and ease of use they expect. There’s a minimal learning curve, so you can hit the ground running right away. Its familiar spreadsheet interface is optimized to offer fast performance when used to create large, complex spreadsheet models in a browser. It provides a complete function library that includes statistical and financial functions, dynamic arrays, and custom user-defined functions. The platform’s customizable formula auditing feature can also help companies maintain regulatory compliance by providing the ability to track changes. It also has a complete table, validation, and conditional formatting support and includes built-in data visualization tools, such as charts and sparklines. This allows users to show trends and patterns in large data sets to improve data readability. The platform will enable you to quickly and easily import XLSX, CSV, JSON, and SJS files and export and print XLSX, CSV, HTML, JSON, SJS, and PDF files. Handsontable Handsontable Handsontable combines data grid features with a spreadsheet UI designed for many applications. It offers support for React, Angular, Vue, and JavaScript. It is flexible, customizable, and easy to use and integrate. You can edit the source code and implement custom plugins to adjust it as needed. While its text formatting options and advanced features are somewhat limited compared to other solutions, it offers a selection of some advanced capabilities. This includes multi-column sorting, data summaries, trimming rows, and nested headers. Handsontable PRO also offers real-time collaboration support. Syncfusion Essential JS Syncfusion Essential JS Syncfusion offers an intuitive JavaScript spreadsheet. It’s optimized for loading large datasets in a table and organizing and analyzing them on demand. One of the benefits of Syncfusion’s spreadsheet is its large selection of UI components. Its rich API means there’s plenty of room for customization. For instance, to make the tables more visually appealing, users can add charts and illustrations. It also has an online Theme Studio tool that can help users to easily customize themes. Its large selection of tools means that this option may appear more complex to use than other options. However, documentation is available to solve these issues. Jspreadsheet Jspreadsheet Jspreadsheet is a flexible JavaScript spreadsheet that supports several basic spreadsheet functionalities with some advanced features. It’s cost-effective, lightweight, and easy to integrate. If you’re looking for a simple, bare-bones option with a slight learning curve, Jspreadsheet could be a good option. However, its advanced features are limited, and it has a smaller community and fewer resources available compared to other options. Infragistics Ignite UI Spreadsheet Infragistics Ignite UI Spreadsheet The Infragistics spreadsheet also offers many features and customizations. It supports most of the basic spreadsheet features, including formulas, cells, workbooks, worksheets, and more. The spreadsheet includes a resizable formula bar and a configurable view that allows users to choose how to work with and consume the data presented to them. Users can import and export their data to Microsoft Excel files as required. It also enables editing and cell formatting features, such as conditional formatting, sorting, and filtering, without requiring Excel installation. Additionally, it has table creation and management capabilities, along with worksheet-level sorting and filtering capabilities. This feature set means that the Ignite UI spreadsheet can be more complex to set up than some of the other options listed, but several samples and documentation are offered to help guide you. Telerik - UI for jQuery Spreadsheet Telerik - UI for jQuery Spreadsheet Telerik offers a jQuery spreadsheet featuring several similar capabilities to Excel, including formulas, filtering, sorting, and charting. It also provides several cell formatting options, styles, and themes to allow users to visualize and edit data. It also includes a suite of tools, which may feel overwhelming if you’re looking for a simpler, more lightweight option. It may also be more costly than some of the other options — especially if you’re interested in an enterprise option. However, it has a strong community, and professional support is readily available. Comprehensive documentation is also available. The Telerik spreadsheet supports Internet Explorer 9 and later versions. It’s important to note that according to their documentation, it’s designed to be used on desktops, which means its mobile device functionality may be limited. Which is the Best Option? Each option has its pros and cons, and the winner depends on what exactly you’re looking for and, of course, your budget. If you’re looking for high performance and an extensive suite of features similar to Excel, SpreadJS is the clear winner. If you’re looking to prioritize ease of use and cost-effectiveness, Handsontable or Jspreadsheet may be a better option. If you’re prioritizing a comprehensive UI suite and support, consider Infragistics, Telerik, or Syncfusion. SpreadJS We encourage you to take the next steps by visiting the respective product pages to review online demos and product features. Most, if not all, offer a free trial you can download to test how they will work for your applications, which will help you make the most informed decision. Good luck and happy spreadsheeting! online demos

The Top JavaScript Spreadsheets

Die top JavaScript-sigblaaie

Ən yaxşı JavaScript Cədvəlləri

Nejlepší tabulky JavaScriptu

Las mejores hojas de cálculo de JavaScript

Populārākās JavaScript izklājlapas

د جاوا سکریپټ غوره سپریډ شیټونه

Лучшие электронные таблицы JavaScript

Најпопуларније ЈаваСцрипт табеле

Ҷадвалҳои беҳтарини JavaScript

እቶም ላዕለዎት ስፕረድሺት ጃቫስክሪፕት።

Amaspredishithi aphezulu e-JavaScript

Combating the deluge of disinformation: Best practices for organizations in the age of AI. By Erich Kron Since the emergence of AI technologies, disinformation has been skyrocketing. As we navigate this new age of content fabrication, manipulation, and deception, it’s crucial that organizations develop a deep understanding of AI’s capabilities, its potential for misuse, and the ways in which its harmful effects can be mitigated. AI Is Responsible for a Deluge of Disinformation Generative AI enables bad actors to quickly, cheaply, and easily synthesize media (a.k.a. deepfakes) and create hyper-realistic images, videos, and voice impersonations of people. Such fake audio and video clips of individuals saying or depicting things they never said or did can be weaponized in social engineering attacks, fuel conspiracy theories, and manipulate public opinion. AI can be used to create highly targeted and personalized disinformation narratives. For example, AI can analyze an individual’s online behavior, their hobbies and interests, their connections, location, work experience, history, and more, for the purposes of making content more plausible. Such microtargeting of people can be highly effective in influencing business decisions or harming reputations. AI can significantly magnify the speed, scale, and spread of false content. Social media bots can be deployed to share and promote disinformation at scale; recommendation engines can be intentionally programmed to create so-called “filter bubbles,” where users are only exposed to specific bits of information (including frauds) based on their existing biases, preferences, and online behaviors. AI can also be operationalized to control and contaminate information more broadly. For example, the algorithms of search engines and other information discovery channels can be manipulated to artificially boost the visibility (or perceived importance) of certain content to alter or influence narratives around trending topics, issues, or events. Search engines like Google are already struggling with a rising tide of low-quality AI-generated content that is polluting and eroding the output quality of search results. AI has made it too easy for scammers and bad actors to create fake profiles, fake reviews, and fake websites. What’s more, generative language models have the ability to produce large volumes of original content, enabling propagandists to sound authoritative on a subject without having to copy/paste the same text across multiple websites. This makes it difficult for everyday users to distinguish between truth and falsehood and can undermine efforts to fact-check or debunk disinformation. Nation-state threat actors are using AI tools to conduct large-scale influence campaigns in order to destabilize democratic institutions and processes. The DOJ recently suspended nearly 1,000 Russian-operated bots and trolls on X [Twitter] that were impersonating Americans using AI-generated profile pictures and biographies. On underground market forums, threat actors are offering disinformation-as-a-service. This commercialization of disinformation is making it easier for amateur threat actors who lack the infrastructure and technical skills to launch their own large-scale and highly coordinated disinformation campaigns. As the market for such services grows, disinformation will become more widespread and commoditized. How Can Businesses Combat AI Disinformation? Disinformation is one of the biggest threats to governments, businesses, and societies. Sadly, humans are more likely to trust AI-generated content than trust human-generated content. If efforts are not made to stop this wild dissemination of false and misleading information, it will ultimately lead to an erosion of societal trust and the potential unraveling of our values. No doubt, regulators around the world have sprung into action, working on mandates and policies to thwart or curtail disinformation. But governments cannot tackle this alone. It’s also incumbent on businesses to foster security awareness around these issues and develop effective strategies for countering these threats. Below are some best practices: Teach employees to fact-check: Encourage the workforce to exercise critical thinking, to fact-check content that is suspect; to rely on independent, diverse, and trusted sources of information; to share responsibly, and to report fake stories or identities they encounter online.


Provide real-world training: Phishing simulation exercises can train employees to identify deepfakes, social engineering and phishing scams, bogus content, and fake websites. Use gamification to boost training participation and engagement. For example, run a fact-check challenge (pick a controversial topic and ask users to fact-check), run a “spot the deepfake” contest (using websites like WhichFaceIsReal.com), run a “find the real image” competition (provide an AI-altered image and ask users to look up the real photo).


Update user policies: Provide clear instructions on how employees must identify and report disinformation to the security team. Promote fact-checking websites such as factcheck.org, Politifact, and Snopes. Instruct users to practice the SIFT method (stop, investigate, find the trusted coverage, trace to the original) before believing, clicking, or sharing content.


Foster collaboration and conversation: Engage in open conversations with employees while supporting research and development efforts, advocate strict adherence to cybersecurity policies, promote the use of responsible AI, and partner with analysts, regulators, institutions, and associations to develop collective strategies. These steps can help safeguard the business and mitigate the spread and ill effects of disinformation.
While the challenges posed by AI-fueled disinformation are truly significant, they are not insurmountable. By staying vigilant and proactive, and committing to truth, training, and collaboration, businesses can help mitigate disinformation, preserve consumer trust, and boost market reputation.About the Author A 25-year veteran information security professional with experience in the medical, aerospace, manufacturing, and defense fields, Erich Kron is a Security Awareness Advocate for KnowBe4. Author, and regular contributor to cybersecurity industry publications, he was a security manager for the U.S. Army's 2nd Regional Cyber Center-Western Hemisphere and holds CISSP, CISSP-ISSAP, SACP, and many other certifications.



Erich has worked with information security professionals around the world to provide the tools, training, and educational opportunities to succeed in information security. LinkedIn: https://www.linkedin.com/in/erichkron/ Combating the deluge of disinformation: Best practices for organizations in the age of AI. Combating the deluge of disinformation: Best practices for organizations in the age of AI. By Erich Kron Since the emergence of AI technologies, disinformation has been skyrocketing. As we navigate this new age of content fabrication, manipulation, and deception, it’s crucial that organizations develop a deep understanding of AI’s capabilities, its potential for misuse, and the ways in which its harmful effects can be mitigated. AI Is Responsible for a Deluge of Disinformation AI Is Responsible for a Deluge of Disinformation Generative AI enables bad actors to quickly, cheaply, and easily synthesize media (a.k.a. deepfakes) and create hyper-realistic images, videos, and voice impersonations of people. Such fake audio and video clips of individuals saying or depicting things they never said or did can be weaponized in social engineering attacks, fuel conspiracy theories, and manipulate public opinion. AI can be used to create highly targeted and personalized disinformation narratives. For example, AI can analyze an individual’s online behavior, their hobbies and interests, their connections, location, work experience, history, and more, for the purposes of making content more plausible. Such microtargeting of people can be highly effective in influencing business decisions or harming reputations. AI can significantly magnify the speed, scale, and spread of false content. Social media bots can be deployed to share and promote disinformation at scale; recommendation engines can be intentionally programmed to create so-called “filter bubbles,” where users are only exposed to specific bits of information (including frauds) based on their existing biases, preferences, and online behaviors. AI can also be operationalized to control and contaminate information more broadly. For example, the algorithms of search engines and other information discovery channels can be manipulated to artificially boost the visibility (or perceived importance) of certain content to alter or influence narratives around trending topics, issues, or events. Search engines like Google are already struggling with a rising tide of low-quality AI-generated content that is polluting and eroding the output quality of search results. AI has made it too easy for scammers and bad actors to create fake profiles, fake reviews, and fake websites. What’s more, generative language models have the ability to produce large volumes of original content, enabling propagandists to sound authoritative on a subject without having to copy/paste the same text across multiple websites. This makes it difficult for everyday users to distinguish between truth and falsehood and can undermine efforts to fact-check or debunk disinformation. Nation-state threat actors are using AI tools to conduct large-scale influence campaigns in order to destabilize democratic institutions and processes. The DOJ recently suspended nearly 1,000 Russian-operated bots and trolls on X [Twitter] that were impersonating Americans using AI-generated profile pictures and biographies. On underground market forums, threat actors are offering disinformation-as-a-service. This commercialization of disinformation is making it easier for amateur threat actors who lack the infrastructure and technical skills to launch their own large-scale and highly coordinated disinformation campaigns. As the market for such services grows, disinformation will become more widespread and commoditized. How Can Businesses Combat AI Disinformation? How Can Businesses Combat AI Disinformation? Disinformation is one of the biggest threats to governments, businesses, and societies. Sadly, humans are more likely to trust AI-generated content than trust human-generated content. If efforts are not made to stop this wild dissemination of false and misleading information, it will ultimately lead to an erosion of societal trust and the potential unraveling of our values. No doubt, regulators around the world have sprung into action, working on mandates and policies to thwart or curtail disinformation. But governments cannot tackle this alone. It’s also incumbent on businesses to foster security awareness around these issues and develop effective strategies for countering these threats. Below are some best practices: Teach employees to fact-check: Encourage the workforce to exercise critical thinking, to fact-check content that is suspect; to rely on independent, diverse, and trusted sources of information; to share responsibly, and to report fake stories or identities they encounter online. Provide real-world training: Phishing simulation exercises can train employees to identify deepfakes, social engineering and phishing scams, bogus content, and fake websites. Use gamification to boost training participation and engagement. For example, run a fact-check challenge (pick a controversial topic and ask users to fact-check), run a “spot the deepfake” contest (using websites like WhichFaceIsReal.com), run a “find the real image” competition (provide an AI-altered image and ask users to look up the real photo). Update user policies: Provide clear instructions on how employees must identify and report disinformation to the security team. Promote fact-checking websites such as factcheck.org, Politifact, and Snopes. Instruct users to practice the SIFT method (stop, investigate, find the trusted coverage, trace to the original) before believing, clicking, or sharing content. Foster collaboration and conversation: Engage in open conversations with employees while supporting research and development efforts, advocate strict adherence to cybersecurity policies, promote the use of responsible AI, and partner with analysts, regulators, institutions, and associations to develop collective strategies. These steps can help safeguard the business and mitigate the spread and ill effects of disinformation.
While the challenges posed by AI-fueled disinformation are truly significant, they are not insurmountable. By staying vigilant and proactive, and committing to truth, training, and collaboration, businesses can help mitigate disinformation, preserve consumer trust, and boost market reputation.About the Author A 25-year veteran information security professional with experience in the medical, aerospace, manufacturing, and defense fields, Erich Kron is a Security Awareness Advocate for KnowBe4. Author, and regular contributor to cybersecurity industry publications, he was a security manager for the U.S. Army's 2nd Regional Cyber Center-Western Hemisphere and holds CISSP, CISSP-ISSAP, SACP, and many other certifications. Erich has worked with information security professionals around the world to provide the tools, training, and educational opportunities to succeed in information security. LinkedIn: https://www.linkedin.com/in/erichkron/ Teach employees to fact-check: Encourage the workforce to exercise critical thinking, to fact-check content that is suspect; to rely on independent, diverse, and trusted sources of information; to share responsibly, and to report fake stories or identities they encounter online. Teach employees to fact-check: Encourage the workforce to exercise critical thinking, to fact-check content that is suspect; to rely on independent, diverse, and trusted sources of information; to share responsibly, and to report fake stories or identities they encounter online. Provide real-world training: Phishing simulation exercises can train employees to identify deepfakes, social engineering and phishing scams, bogus content, and fake websites. Use gamification to boost training participation and engagement. For example, run a fact-check challenge (pick a controversial topic and ask users to fact-check), run a “spot the deepfake” contest (using websites like WhichFaceIsReal.com), run a “find the real image” competition (provide an AI-altered image and ask users to look up the real photo). Provide real-world training: Phishing simulation exercises can train employees to identify deepfakes, social engineering and phishing scams, bogus content, and fake websites. Use gamification to boost training participation and engagement. For example, run a fact-check challenge (pick a controversial topic and ask users to fact-check), run a “spot the deepfake” contest (using websites like WhichFaceIsReal.com ), run a “find the real image” competition (provide an AI-altered image and ask users to look up the real photo). WhichFaceIsReal.com Update user policies: Provide clear instructions on how employees must identify and report disinformation to the security team. Promote fact-checking websites such as factcheck.org, Politifact, and Snopes. Instruct users to practice the SIFT method (stop, investigate, find the trusted coverage, trace to the original) before believing, clicking, or sharing content. Update user policies: Provide clear instructions on how employees must identify and report disinformation to the security team. Promote fact-checking websites such as factcheck.org , Politifact, and Snopes. Instruct users to practice the SIFT method (stop, investigate, find the trusted coverage, trace to the original) before believing, clicking, or sharing content. factcheck.org Foster collaboration and conversation: Engage in open conversations with employees while supporting research and development efforts, advocate strict adherence to cybersecurity policies, promote the use of responsible AI, and partner with analysts, regulators, institutions, and associations to develop collective strategies. These steps can help safeguard the business and mitigate the spread and ill effects of disinformation. While the challenges posed by AI-fueled disinformation are truly significant, they are not insurmountable. By staying vigilant and proactive, and committing to truth, training, and collaboration, businesses can help mitigate disinformation, preserve consumer trust, and boost market reputation. About the Author A 25-year veteran information security professional with experience in the medical, aerospace, manufacturing, and defense fields, Erich Kron is a Security Awareness Advocate for KnowBe4. Author, and regular contributor to cybersecurity industry publications, he was a security manager for the U.S. Army's 2nd Regional Cyber Center-Western Hemisphere and holds CISSP, CISSP-ISSAP, SACP, and many other certifications. Foster collaboration and conversation: Engage in open conversations with employees while supporting research and development efforts, advocate strict adherence to cybersecurity policies, promote the use of responsible AI, and partner with analysts, regulators, institutions, and associations to develop collective strategies. These steps can help safeguard the business and mitigate the spread and ill effects of disinformation. While the challenges posed by AI-fueled disinformation are truly significant, they are not insurmountable. By staying vigilant and proactive, and committing to truth, training, and collaboration, businesses can help mitigate disinformation, preserve consumer trust, and boost market reputation. About the Author A 25-year veteran information security professional with experience in the medical, aerospace, manufacturing, and defense fields, Erich Kron is a Security Awareness Advocate for KnowBe4. Author, and regular contributor to cybersecurity industry publications, he was a security manager for the U.S. Army's 2nd Regional Cyber Center-Western Hemisphere and holds CISSP, CISSP-ISSAP, SACP, and many other certifications. About the Author Erich has worked with information security professionals around the world to provide the tools, training, and educational opportunities to succeed in information security. LinkedIn: https://www.linkedin.com/in/erichkron/ Erich has worked with information security professionals around the world to provide the tools, training, and educational opportunities to succeed in information security. LinkedIn: https://www.linkedin.com/in/erichkron/ https://www.linkedin.com/in/erichkron/

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