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Are you wondering how you can elevate your business and keep up with all the advancements in the housing market? You’re not alone, industry leaders are integrating property technology to evolve in the 21st century. Property technology (Proptech) has the ability to elevate the real estate industry in all avenues, from purchase, maintenance, management, and even investment. It is now an industry that can assist with everything from property listing and rental services to mortgage applications, virtual and augmented reality modeling, Internet of Things (IoT) home devices, and property management software. $18 billion The Proptech industry is booming, and for good reason. In a survey of more than 100 real estate industry leaders, said that adopting new technologies was a strategic priority. two-thirds Reasons for this include effectively utilizing the environment, social, and governance (ESG) data, reducing costs, and the ability to access new revenue sources. Most industry leaders see property technology as an important tool, and the housing market is no exception. But what exactly is it? And how can these technologies change the market as we know it today? What is Proptech? Proptech takes information on platform economics and applies it to real estate markets. The goal of these technological innovations is to speed up transactions for those working in the housing market while making these transactions more secure and reducing the amount of paperwork required. The earliest form of Proptech is eCommerce; before the invention of the internet, it would be unheard of to rent or sell your property to complete strangers hundreds of miles away, but after decades of refining online marketplaces, the housing market is booming. Innovations in property technology have evolved into interlinked systems; they all have different functions but work together with like artificial intelligence (AI), virtual reality (VR), contract automation, real estate listings, data, the Internet of Things, construction technologies, and even chatbots! technologies The Rise of Proptech After the Pandemic The pandemic disrupted life and ushered in a new wave of online real estate tours that limit close contact, with VR home viewing and site visits skyrocketing in popularity and becoming a convenient solution for house-hunting during a modern-day plague. found that in 2020, walkthrough views where prospective buyers viewed the property online through a 3D scan increased a whopping 560%! Redfin.com Remote property appraisals have also become more normalized in the last few years, saving time, money, and cutting contact with people and their germs. There is also potential for VR to lead to an increase in sales of prefab buildings. It may be hard for a potential buyer to imagine the potential of a site, but with the use of VR, they would be able to see and experience of layouts, floor plans, and finishes. 3D representations Many companies Compass, Zillow, and Sotheby’s International Realty have integrated this technology into their home viewing models. including Where Do Start-ups Fit In? Start-ups are currently flooding the real estate market with innovative ideas and creative solutions. With this new wave, the industry is seeing an influx of changes when it comes to management experience and purchasing properties. Some of these include operating buildings in a more sustainable manner, eliminating the middleman from property purchasing, and giving tenants data and insights on how they use their space and shortcuts they can take to more efficiently save power and energy. changes Proptech is implemented by all kinds of start-ups in the real estate industry, property owner analytics, building efficiency data, and researching market analytics. including One such company is CompStak, which just received in funding to crowdsource and compile detailed data on office, industrial, and retail lease and sales terms. Currently, they have 1.7 million properties in their database. And they are constantly expanding. $50 million is a Proptech app that allows building managers to quickly and efficiently notify tenants of issues. The app also collects and analyzes occupancy data that give property owners a cohesive understanding of how the space is used. This can help optimize the space, as well as inform renovation decisions. Spaceti Another example of a Proptech-based start-up is . Perfect for Airbnb hosts, as well as anyone else who rents out their home or office building, the igloohome technology works to add security and ease to the experience. igloohome Users can their Airbnb calendar with their igloohome lock, which will automatically generate custom PIN codes only valid for the duration of the stay and send them to the guest(s). sync The real estate industry is one of the largest in the world but is lagging behind when it comes to technological implementation. The goal is to add to the existing formula and to make cumbersome transactions easier, one way or another. Implementing new strategies, models, and technologies can be intimidating, but it's pertinent for the housing industry to evolve and adapt. , Co-founder of Chris Pchelintsev Architecturalist

Proptech: The Evolution of the Housing Market

Authors:
(1) Ángel Merino, Department of Telematic Engineering Universidad Carlos III de Madrid {angel.merino@uc3m.es};
(2) José González-Cabañas, UC3M-Santander Big Data Institute {jose.gonzalez.cabanas@uc3m.es}
(3) Ángel Cuevas, Department of Telematic Engineering Universidad Carlos III de Madrid & UC3M-Santander Big Data Institute {acrumin@it.uc3m.es};
(4) Rubén Cuevas, Department of Telematic Engineering Universidad Carlos III de Madrid & UC3M-Santander Big Data Institute {rcuevas@it.uc3m.es}. Table of Links Abstract and Introduction LinkedIn Advertising Platform Background Dataset Methodology User’s Uniqueness on LinkedIn Nanotargeting proof of concept Discussion Related work Ethics and legal considerations Conclusions, Acknowledgments, and References Appendix 4 Methodology 4.1 Location Dimension Using the location may substantially reduce the number of skills that make a user unique on LinkedIn. Because 99.47% of the profiles in our data sample released their location, it does not make sense not to use the location in a nanotargeting campaign in case it is available. Still, some users do not report their location. We aim to analyze both cases and estimate the number of skills that make a user unique when they report their location and when they do not report their location. The initial conditions for the audience size in each of these cases differ. For instance, if we only consider skills, the starting point is the worldwide audience size reported by the LinkedIn Ads manager when carrying out our research, which included 780M users. In contrast, if we also consider the locations, the starting point for a given user is the audience size of the reported location. For instance, at the time of writing this paper, the audience size for the US, the state of New York, and New York City was: 200M, 12M, and 7M, respectively. 4.2 Methodology to compute Np For each user, ui(i ∈ [1, 1699]), we leverage the LinkedIn Campaign Manager to obtain the audience size from a combination of N skills with N ranging from 1 to 50. We limit the number of skills to 50 because it is the highest number of skills a user can report on LinkedIn. For N=1, we obtain a vector with 1699 different audience sizes by selecting one skill per user from the skills they have reported in their profile. For N=2, we get another vector with 1699 different audience sizes by selecting two skills per user from the skills they have reported in their profile. We repeat the same operation for N = 3, 4, ..., 50. At the end of the process, we have 50 vectors that show the audience size distribution for each value of N. We note the number of users reporting N skills in their profile is lower as N increases. Therefore for a given value of N, the vector will include as many samples as the number of users in our dataset that have reported N or more skills in their profile. Figure 3 shows the vector length for each value of N and the two cases considered in our analysis: skills and location+skills. We acknowledge that the number of samples for N ≥ 30 may be small (< 500 users), but as we will show in the next section, our methodology will not rely on those vectors to compute the number of skills that make a user unique on LinkedIn. 4.3 Skills Selection and Scenarios In this work, we will compare two different strategies. In the first strategy, we randomly select the skills from the skill set. In contrast, the second strategy sequentially adds skills from the least to the most popular. We refer to the former strategy as Random Selection and the latter as Least Popular Selection. Overall, we will apply the described methodology in 4 different scenarios: • Sk_R : In this scenario, we only use the user’s skills, selected following the random strategy. • Sk_LP : In this scenario, we only use the user’s skills, selected following the least popular strategy. • Lo_R : In this scenario, we use the reported location and skills of the user, selected following the random strategy. • Lo_LP : In this scenario, we use the reported location and skills of the user, selected following the least popular strategy. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors: (1) Ángel Merino, Department of Telematic Engineering Universidad Carlos III de Madrid {angel.merino@uc3m.es}; (2) José González-Cabañas, UC3M-Santander Big Data Institute {jose.gonzalez.cabanas@uc3m.es} (3) Ángel Cuevas, Department of Telematic Engineering Universidad Carlos III de Madrid & UC3M-Santander Big Data Institute {acrumin@it.uc3m.es}; (4) Rubén Cuevas, Department of Telematic Engineering Universidad Carlos III de Madrid & UC3M-Santander Big Data Institute {rcuevas@it.uc3m.es}. Authors: Authors: (1) Ángel Merino, Department of Telematic Engineering Universidad Carlos III de Madrid {angel.merino@uc3m.es}; (2) José González-Cabañas, UC3M-Santander Big Data Institute {jose.gonzalez.cabanas@uc3m.es} (3) Ángel Cuevas, Department of Telematic Engineering Universidad Carlos III de Madrid & UC3M-Santander Big Data Institute {acrumin@it.uc3m.es}; (4) Rubén Cuevas, Department of Telematic Engineering Universidad Carlos III de Madrid & UC3M-Santander Big Data Institute {rcuevas@it.uc3m.es}. Table of Links Abstract and Introduction Abstract and Introduction LinkedIn Advertising Platform Background LinkedIn Advertising Platform Background Dataset Dataset Methodology Methodology User’s Uniqueness on LinkedIn User’s Uniqueness on LinkedIn Nanotargeting proof of concept Nanotargeting proof of concept Discussion Discussion Related work Related work Ethics and legal considerations Ethics and legal considerations Conclusions, Acknowledgments, and References Conclusions, Acknowledgments, and References Appendix Appendix 4 Methodology 4.1 Location Dimension Using the location may substantially reduce the number of skills that make a user unique on LinkedIn. Because 99.47% of the profiles in our data sample released their location, it does not make sense not to use the location in a nanotargeting campaign in case it is available. Still, some users do not report their location. We aim to analyze both cases and estimate the number of skills that make a user unique when they report their location and when they do not report their location. The initial conditions for the audience size in each of these cases differ. For instance, if we only consider skills, the starting point is the worldwide audience size reported by the LinkedIn Ads manager when carrying out our research, which included 780M users. In contrast, if we also consider the locations, the starting point for a given user is the audience size of the reported location. For instance, at the time of writing this paper, the audience size for the US, the state of New York, and New York City was: 200M, 12M, and 7M, respectively. 4.2 Methodology to compute Np For each user, ui(i ∈ [1, 1699]), we leverage the LinkedIn Campaign Manager to obtain the audience size from a combination of N skills with N ranging from 1 to 50. We limit the number of skills to 50 because it is the highest number of skills a user can report on LinkedIn. For N=1, we obtain a vector with 1699 different audience sizes by selecting one skill per user from the skills they have reported in their profile. For N=2, we get another vector with 1699 different audience sizes by selecting two skills per user from the skills they have reported in their profile. We repeat the same operation for N = 3, 4, ..., 50. At the end of the process, we have 50 vectors that show the audience size distribution for each value of N. We note the number of users reporting N skills in their profile is lower as N increases. Therefore for a given value of N, the vector will include as many samples as the number of users in our dataset that have reported N or more skills in their profile. Figure 3 shows the vector length for each value of N and the two cases considered in our analysis: skills and location+skills. We acknowledge that the number of samples for N ≥ 30 may be small (< 500 users), but as we will show in the next section, our methodology will not rely on those vectors to compute the number of skills that make a user unique on LinkedIn. 4.3 Skills Selection and Scenarios In this work, we will compare two different strategies. In the first strategy, we randomly select the skills from the skill set. In contrast, the second strategy sequentially adds skills from the least to the most popular. We refer to the former strategy as Random Selection and the latter as Least Popular Selection. Least Popular Selection. Overall, we will apply the described methodology in 4 different scenarios: • Sk_R : In this scenario, we only use the user’s skills, selected following the random strategy. • Sk_R : • Sk_R : • Sk_LP : In this scenario, we only use the user’s skills, selected following the least popular strategy. • Sk_LP • Sk_LP • Lo_R : In this scenario, we use the reported location and skills of the user, selected following the random strategy. • Lo_R : • Lo_R : • Lo_LP : In this scenario, we use the reported location and skills of the user, selected following the least popular strategy. • Lo_LP : • Lo_LP : This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. available on arxiv

LinkedIn User Uniqueness Study: Computation Methods and Selection Strategies

Benchmarking the Computational Performance of Poseidon2 in Plonky3

Authors:
(1) Renan F. F. da Silva, Institute of Computing, University of Campinas;
(2) Yulle G. F. Borges, Institute of Computing, University of Campinas;
(3) Rafael C. S. Schouery, Institute of Computing, University of Campinas. 8 Computational Results Next, we present the instances used in our experiments [1] . Sequentially, we show some results comparing our algorithms in two-stage. We compare our initial heuristics using a performance profile and statistic tests in the first stage. In the second stage, we make a statistical comparison between the VNS and the MH. 8.1 Instances The instances used in experiments have four classes. The BPP instances AI and FalkenauerT are available in the BPP Lib created by Delorme et al [15], and their optimal solutions are known. In the BPP, AI instances are very hard instances to find an optimal solution, but they are easy to find a solution with an extra bin. The instances FalkenauerT  and Random25to50 are similar, but we create the Random25to50 because FalkenauerT does not  have instances with a large number of items or a large bin capacity. 8.2 Comparison between Initial Heuristics Next, we compare our initial heuristics using a performance profile and statistic tests. 8.2.1 Performance Profile In Figure 1, we have the performance profile for the combinations of AI instances with the coloring Q2 and Half Q2. As we can see, the heuristic Two-by-Two is the best, being up to two times better than the BFD in some instances in coloring Half Q2. The graph for the other coloring is similar to Q2, but the difference decreases as we increase the number of colors. This behavior also happens in other instances, so we omit the graphs for these coloring. Figure 2 presents the results for Random25to50 instances with coloring Q2 and Half Q2. Again, the heuristic Two-by-Two is the best one. This time, it has a reasonable superiority to Hard-BFD, which use at least 10% more bins. See that the two MBS’ versions are curiously better than the Hard-BFD in 90% of the instances with Q2. The results for the FalkenauerT instances are similar to Random25to50. Next, in Figure 3, we have the performance profile for Random10to80 with coloring Q2 and Q5. Observe that with the coloring Q2, we do not have a clear dominance between the heuristics Twoby-Two and Hard-BFD. Finally, note that the good-ordering often improves the solution quality, but exceptions exist. For example, the Good Ordering heuristic is worse than the BFD heuristic in AI instances with Q2. 8.2.2 Statistic Tests The comparison we made is based on non-parametric analysis, as was made by Demˇsar [16]. As we can compare multiple strategies on a set of different instances, we use the adaptation of the Friedman test (Friedman [20]) made by Iman and Davenport [24], rejecting the null hypothesis when the value of the test is greater than the critical one according to the F-distribution. In such a case, we proceed with a post hoc analysis using the Nemenyi test (Nemenyi [29]) to identify the group of statistically equivalent strategies. This situation is identified when the average ranks of two samples differ at most by a given critical difference (CD). Our test has k = 6 algorithms and N = 1200 instances. For these parameters, the critical value of the F-distribution is 2.22. Next, we can compute the test value FF to the Iman-Davenport, where our data has FF = 484.02. As FF is greater than the critical value, the null hypothesis H0 was rejected, and we accepted the alternative hypothesis H1. Therefore, we can apply the Nemenyi test to determine which heuristics have statistical differences. Figure 4 presents a graph illustrating the Nemenyi test. For our data, we have the critical difference CD = 0.22. The heuristic average ranks are classified from left to right, where the better heuristic is the most on the left. If two heuristics have average ranks with a difference less than the CD, then they are connected by a horizontal line, which means that we did not find a statistical difference between them. As we can see in the figure, the Two-by-Two is the best heuristic. Note that the “good-ordering” is beneficial in the general case since MBS’-GO and GO heuristics perform better than their original versions MBS’ and BFD. Finally, we do not find a statistical difference between MBS’ and GO heuristics. 8.3 Comparison between VNS and MH In the Tables 1 and 2, the columns “Initial Avg. Diff.” e “Final Avg. Diff.” indicate the average differences between the lower bound L1 and the initial and final solution values, respectively. The column “Final Max. Diff.” indicate the maximum difference of a final solution about lower bound L1. Moreover, the column “Time” gives the average time in seconds. Finally, we highlight in bold the best “Final Avg. Diff.” of each line. As we can see, our approaches obtained good results since they optimally solve all Random25to50 instances with 102 and 501 items and find solutions with at most one extra bin than the optimal solution with 2001 items. Moreover, we can emphasize this class with 10002 items and coloring Half Q2, where the average initial difference is 82.1 bins, and our approaches decrease this difference to at most 1 bin. Besides, our approaches also solve all FalkenauerT (FK) instances. Note that there is no clear relation between difficulty and the number of items since instances with 120 items and Half Q2 are the most costly in execution time. Moreover, see that the coloring Half Q2 has the greater initial average difference in all instance classes. However, it is not necessarily reflected in more hard instances since in some cases, the final maximum difference is slightly smaller in this coloring. Finally, our MH is superior to VNS since the MH optimally solves 124, 70, and 9 of the 150 AI instances with 202, 403, and 601 items, respectively, while the VNS does not solve any of them. Moreover, in some cases, the average final difference in Random10to80 instances is several times smaller in MH than in VNS. Note that the average difference in the final solutions is less than 3 in the MH. This difference is significantly greater in Random10to80 instances, where L1 is probably not a tight lower bound. [1] All instances and source codes are available at https://gitlab.com/renanfernandofranco/ fast-neighborhood-search-heuristics-for-the-colorful-bin-packing-problem. This paper is available on arxiv under CC 4.0 license. Authors: (1) Renan F. F. da Silva, Institute of Computing, University of Campinas; (2) Yulle G. F. Borges, Institute of Computing, University of Campinas; (3) Rafael C. S. Schouery, Institute of Computing, University of Campinas. Authors: (1) Renan F. F. da Silva, Institute of Computing, University of Campinas; (2) Yulle G. F. Borges, Institute of Computing, University of Campinas; (3) Rafael C. S. Schouery, Institute of Computing, University of Campinas. 8 Computational Results Next, we present the instances used in our experiments [1] . Sequentially, we show some results comparing our algorithms in two-stage. We compare our initial heuristics using a performance profile and statistic tests in the first stage. In the second stage, we make a statistical comparison between the VNS and the MH. 8.1 Instances The instances used in experiments have four classes. The BPP instances AI and FalkenauerT are available in the BPP Lib created by Delorme et al [15], and their optimal solutions are known. In the BPP, AI instances are very hard instances to find an optimal solution, but they are easy to find a solution with an extra bin. The instances FalkenauerT  and Random25to50 are similar, but we create the Random25to50 because FalkenauerT does not  have instances with a large number of items or a large bin capacity. 8.2 Comparison between Initial Heuristics Next, we compare our initial heuristics using a performance profile and statistic tests. 8.2.1 Performance Profile In Figure 1, we have the performance profile for the combinations of AI instances with the coloring Q2 and Half Q2. As we can see, the heuristic Two-by-Two is the best, being up to two times better than the BFD in some instances in coloring Half Q2. The graph for the other coloring is similar to Q2, but the difference decreases as we increase the number of colors. This behavior also happens in other instances, so we omit the graphs for these coloring. Figure 2 presents the results for Random25to50 instances with coloring Q2 and Half Q2. Again, the heuristic Two-by-Two is the best one. This time, it has a reasonable superiority to Hard-BFD, which use at least 10% more bins. See that the two MBS’ versions are curiously better than the Hard-BFD in 90% of the instances with Q2. The results for the FalkenauerT instances are similar to Random25to50. Next, in Figure 3, we have the performance profile for Random10to80 with coloring Q2 and Q5. Observe that with the coloring Q2, we do not have a clear dominance between the heuristics Twoby-Two and Hard-BFD. Finally, note that the good-ordering often improves the solution quality, but exceptions exist. For example, the Good Ordering heuristic is worse than the BFD heuristic in AI instances with Q2. 8.2.2 Statistic Tests The comparison we made is based on non-parametric analysis, as was made by Demˇsar [16]. As we can compare multiple strategies on a set of different instances, we use the adaptation of the Friedman test (Friedman [20]) made by Iman and Davenport [24], rejecting the null hypothesis when the value of the test is greater than the critical one according to the F-distribution. In such a case, we proceed with a post hoc analysis using the Nemenyi test (Nemenyi [29]) to identify the group of statistically equivalent strategies. This situation is identified when the average ranks of two samples differ at most by a given critical difference (CD). Our test has k = 6 algorithms and N = 1200 instances. For these parameters, the critical value of the F-distribution is 2.22. Next, we can compute the test value FF to the Iman-Davenport, where our data has FF = 484.02. As FF is greater than the critical value, the null hypothesis H0 was rejected, and we accepted the alternative hypothesis H1. Therefore, we can apply the Nemenyi test to determine which heuristics have statistical differences. Figure 4 presents a graph illustrating the Nemenyi test. For our data, we have the critical difference CD = 0.22. The heuristic average ranks are classified from left to right, where the better heuristic is the most on the left. If two heuristics have average ranks with a difference less than the CD, then they are connected by a horizontal line, which means that we did not find a statistical difference between them. As we can see in the figure, the Two-by-Two is the best heuristic. Note that the “good-ordering” is beneficial in the general case since MBS’-GO and GO heuristics perform better than their original versions MBS’ and BFD. Finally, we do not find a statistical difference between MBS’ and GO heuristics. 8.3 Comparison between VNS and MH In the Tables 1 and 2, the columns “Initial Avg. Diff.” e “Final Avg. Diff.” indicate the average differences between the lower bound L1 and the initial and final solution values, respectively. The column “Final Max. Diff.” indicate the maximum difference of a final solution about lower bound L1. Moreover, the column “Time” gives the average time in seconds. Finally, we highlight in bold the best “Final Avg. Diff.” of each line. As we can see, our approaches obtained good results since they optimally solve all Random25to50 instances with 102 and 501 items and find solutions with at most one extra bin than the optimal solution with 2001 items. Moreover, we can emphasize this class with 10002 items and coloring Half Q2, where the average initial difference is 82.1 bins, and our approaches decrease this difference to at most 1 bin. Besides, our approaches also solve all FalkenauerT (FK) instances. Note that there is no clear relation between difficulty and the number of items since instances with 120 items and Half Q2 are the most costly in execution time. Moreover, see that the coloring Half Q2 has the greater initial average difference in all instance classes. However, it is not necessarily reflected in more hard instances since in some cases, the final maximum difference is slightly smaller in this coloring. Finally, our MH is superior to VNS since the MH optimally solves 124, 70, and 9 of the 150 AI instances with 202, 403, and 601 items, respectively, while the VNS does not solve any of them. Moreover, in some cases, the average final difference in Random10to80 instances is several times smaller in MH than in VNS. Note that the average difference in the final solutions is less than 3 in the MH. This difference is significantly greater in Random10to80 instances, where L1 is probably not a tight lower bound. [1] All instances and source codes are available at https://gitlab.com/renanfernandofranco/ fast-neighborhood-search-heuristics-for-the-colorful-bin-packing-problem. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Analyzing Computational Results: Insights into Bin Packing Heuristic Performance

How Useful are Computational Models to Traders in Their Decision Making?

Authors: (1) JunJie Wee, Department of Mathematics, Michigan State University; (2) Jiahui Chen, Department of Mathematical Sciences, University of Arkansas; (3) Kelin Xia, Division of Mathematical Sciences, School of Physical and Mathematical Sciences Nanyang Technological University & xiakelin@ntu.edu.sg; (4)Guo-Wei Wei, 1Department of Mathematics, Michigan State University, Department of Biochemistry and Molecular Biology, Michigan State University, Department of Electrical and Computer Engineering, Michigan State University & xiakelin@ntu.edu.sg. Table of Links Abstract & Introduction Results Discussion Conclusion Materials and Methods Software and resources, Code and Data Availability Supporting Information, Acknowledgments & References 5 Materials and Methods In this section, we endeavor to elucidate key mathematical and computational foundations that are instrumental for the work presented in this study. Specifically, we delve into spectral graph theory, simplicial complex, and persistent Laplacian methods, highlighting their significance in capturing topological and spectral properties essential for the characterization of proteins. Additionally, we discuss machine learning and deep learning paradigms, focusing on their role in processing, analyzing, and interpreting these complex features, especially within the confines of test datasets and validation settings. 5.1 Persistent Laplacian characterization of proteins A simplicial complex is made up of a set of simplices and generalises beyond graph networks at higher dimensions [44, 45, 46, 47]. Every simplex is a finite set of vertices which can be interpreted as the atoms in a protein structure. Essentially, simplices can be a point (0-simplex), an edge (1-simplex), a triangle (2-simplex), a tetrahedron (3-simplex), or in higher dimensions, a p-simplex. In other words, a k-simplex σ k = {v0, v1, · · · , vk} is the convex hull formed by k + 1 affinely independent points v0, v1, · · · , vk as follows, Simplicial complex A geometric simplicial complex K is a finite set of geometric simplexes that satisfy two essential conditions. First, any face of a simplex from K is also in K. Second, the intersection of any two simplexes in K is either empty or shares faces. Commonly used methods to construct simplicial complexes are Cech complex, Vietoris-Rips complex, Alpha complex, Clique ˇ complex, Cubic complex, and Morse complex [44, 45, 46, 47]. In the case k = 0, then L0 = B1B⊤ 1 since ∂0 is a zero map Persistent Laplacian (PL) were first introduced by integrating graph Laplacian and multiscale filtration [25]. Analyzing the spectra of k-combinatorial Laplacian matrix allows both topological and geometric information (i.e. connectivity and robustness of simple graphs) to be obtained. However, this method is genuinely free of metrics or coordinates, which induced too little topological and geometric information that can be used to describe a single configuration. Persistent Laplacian Therefore, PL was extended to simplicial complexes. This allows a sequence of simplicial complexes from a filtration process to generate persistent Laplacian which is largely inspired by persistent homology and in earlier works in multiscale graphs. For the rest of this section, we introduce mainly on the construction of PL. First, a k-combinatorial Laplacian matrix is symmetric and positive semi-definite. Therefore, its eigenvalues are all real and non-negative. The multiplicity of zero spectra (also called harmonic spectra) reveals the topological information, and the geometric information will be preserved in the non-harmonic spectra. This nested sequence of simplicial complexes induces a family of chain complexes In order to illustrate the difference between PL and PH, Figure 5 describes a point cloud, basic simplices, a filtration process and the comparison between persistent Laplacian and persistent homology barcodes of 13 points. The filtration process in Figure 5(c) shows the different stages of a Rips filtration process for the 13 points. Figure 5(d) shows the persistent homology barcodes (in blue) and persistent non-harmonic spectra (in red). It can be seen that the nonharmonic spectra provides the additional homotopic shape evolution that is missing in persistent homology in the later part of the filtration process. 5.2 Persistent Laplacian descriptors where DE(·, ·) is the Euclidean distance between the two atoms and Loc(·) refers to the atom’s location which is either in the mutation site or in the rest of the protein. Here, we construct two types of simplicial complexes in our PL computation, such as Vietoris-Rips complex (VC) and Alpha complex (AC). Both complexes are used to characterize the first order interactions and higher order patterns respectively. To capture and characterize different types of atomatom interactions, we generate the PL based on different atom subsets by selecting one type of atom in the mutation site and one other atom type in the rest of the protein. Different types of atom-atom interactions characterize the different interactions in proteins. For example, interactions generated from carbon atoms are associated with hydrophobic interactions. Similarly, interactions between nitrogen and oxygen atoms correlate to hydrophilic interactions and/or hydrogen bonds. Both types of interactions are illustrated in Figure 6. Interactive PLs have the capability to unveil additional details about bonding interactions and offer a fresh and distinct representation of molecular interactions in proteins. For zero dimensions, we consider both the harmonic spectra and non-harmonic spectra information for each persistent Laplacian. Filtration using Rips complex with DI distance is used. The 0-dimensional PL features are generated from 0A to 6 ˚ A with 0.5 ˚ A gridsize. For the ˚ non-harmonic spectra information, we count the number of non-harmonic spectra and calculate seven statistical values of non-harmonic spectra such as sum, minimum, maximum, mean, standard deviation, variance and the sum of eigenvalues squared. This generates eight statistical values for each of the nine atomic pairs. Therefore, the dimension of 0-dimensional PL features for a protein is 72. In total, the 0-dimensional PL-based feature size after concatenating features at different dimensions for wild type and mutant is 1872. For one or two dimensions, we perform the filtration using Alpha complex with the DE distance. The limited number of atoms in the local protein structure can create only a few high-dimensional simplexes, resulting in minimal alterations in shape. As a result, it suffice to consider features from only harmonic spectra of persistent Laplacians by coding the topological invariants for the high-dimensional interactions. Using GUDHI[51], the persistence of the harmonic spectra can be represented by persistent barcodes. The topological feature vectors are generated by computing the statistics of bar lengths, births and deaths. Bars shorter than 0.1A are excluded as they do not exhibit any clear physical meaning. The remaining bars are ˚ then used for computing the statistics: (1) sum, maximum and mean for lengths of bars; (2) minimum and maximum for the birth values of bars; (3) minimum and maximum for the death values of bars. Each set of point clouds leads to a seven-dimensional vector. These features are calculated on nine single atomic pairs and one heavy atom pair. The dimension of oneand two-dimensional PL feature vectors for a protein is 140. In total, the higher-dimensional PL-based feature size after concatenating features at different dimensions for wild type, mutant and their difference is 420. 5.3 Persistent Homology Persistent homology is part of the harmonic spectra of PL. The homology groups in PH illustrate the persistence of topological invariants, hence providing the harmonic spectral information in PL. The site- and element-specific PH features are generated in a similar way as compared to PL. Similar to PL, filtration construction is also employed to PH. For the zero dimension, the filtration parameter can be discretized into several equally spaced bins, namely [0, 0.5], (0.5, 1], · · · , (5.5, 6]A. The death value of the bars are summed in each bin resulting in 12 ˚ ×18 features. For each bin, we count the numbers of persistent bars, resulting in a nine-dimensional vector for each point cloud. Similarly, this is performed for each of the nine single atomic pairs. Hence, the dimension of PH features for a protein is 216. For one or two dimensions, the identical featurization from the statistics of persistent bars in PH is used. The PH embedding combines features at different dimensions as described above and concatenated for wild type, mutant and their difference, resulting in a 648-dimensional vector 5.4 Transformer Features Recently, we have seen significant advancements in modelling protein properties using largescale protein transformer models trained on hundreds of millions of sequences. These models, like ESM [33] (evolutionary scale modeling) and ProtTrans[34, 35], have demonstrated impressive performance. Moreover, hybrid fine-tuning approaches that leverage both local and global evolutionary data have proven to enhance these models even further. For instance, eUniRep is an improved LSTM-based UniRep model achieved through fine-tuning with knowledge extracted from local multiple sequence alignments (MSAs). Additionally, the ESM model can be fine-tuned using either downstream task data or local MSAs. In our research, we employed the ESM-1b transformer, a model that falls under the transformer architecture. This particular variant was trained on a dataset of 250 million sequences using a masked filling procedure and boasts an architecture comprising 34 layers with a whopping 650 million parameters. The ESM transformer’s primary role in our work was to generate sequence embeddings. At each layer of the ESM model, it encoded a sequence of length L into a matrix sized at 1,280×L, excluding the start and terminal tokens. For our study, we utilized the sequence representation derived from the final (34th) layer and computed the average along the sequence length axis, resulting in a 1,280-component vector. 5.5 Performance Metrics PPV and NPV assesses the true positive and true negative proportion of the predicted results for each solubility class. PPV and NPV are computed based on TP, TN, FP and FN which represents the true positive, true negative, false positive and false negative values for each solubility class. For each solubility class, PPV and NPV can be computed by: This paper is under CC 4.0 license. available on arxiv

Interrogating TopLabGBT's Computational Framework

This paper is under CC 4.0 license. available on arxiv Authors: (1) Tariq Bontekoe, University of Groningen; (2) Dimka Karastoyanova, University of Groningen; (3) Fatih Turkmen, University of Groningen. Table of Links Abstract & Introduction Preliminaries Zero-knowledge proofs and verifiable computing Privacy-preserving computations Requirements: an application’s perspective Verifiable, privacy-preserving computing Open challenges and future directions Conclusion & References 4. Privacy-preserving computations There are different PETs that can be used to construct solutions for privacy-preserving computations. Two of them are specifically suitable for our setting: MPC and HE. Both are well-established PETs, providing flexibility and offering strong privacy guarantees, which make them excellent building blocks for verifiable privacy-preserving computations. Below we discuss both building blocks, and provide an intuitive answer to . RQ1 4.1. Secure multi-party computation MPC is a collection of techniques that allows for performing joint computations over distributed data, while keeping all input data private and revealing nothing but the computation output. Generally, there are n parties that participate in an MPC computation, and any participating party contributes to the collaboration with their respective private inputs. All n parties execute an interactive protocol to compute the joint function output from their private inputs. An MPC protocol must therefore be resistant to the fact that some participating parties may be corrupted by an adversary. As discussed in Section 2.2, there exist different security models. However, any secure MPC protocol should at least satisfy the following (informal) requirements [14], given the constraints of the used adversarial model: • Input privacy: No party should learn anything other than the function output it is assigned. Specifically, no party should learn anything about other parties’ inputs, other than what can be derived from its assigned output. • Correctness: Every party that receives a (partial) output, obtains a value that is correct with respect to the protocol specification and input data. • Independence of inputs: Corrupted parties must pick their inputs independently of the honest parties’ private inputs. There are several ways to construct an MPC protocol or framework. Below, we present the two most common constructions: secret sharing and garbled circuits. In a secret sharing approach, private data is split over multiple parties. Information is split in such a way that only predefined sets of parties can reconstruct the full information, while other (sub)sets cannot even deduce partial information. Most secret sharing schemes divide a secret s among n parties in such a way that only subsets with t or more parties can reconstruct s. Such a scheme is called a t-out-of-n-threshold scheme. Secret Sharing. Finally, we note that shares from an LSSS can be multiplied efficiently using so called Beaver’s multiplication triplets [30]. These triplets are input/function-independent and can thus be generated in a so called offline preprocessing phase. Using this approach, the remaining computations can be performed in a very efficient online phase. Many popular, modern secret sharing schemes make use of linear shares in combination with Beaver triplets or oblivious transfer. Example schemes using secret sharing are: Shamir’s secret sharing (SSS) [31] or, in the online phase, SPDZ [32] and MASCOT [33]. For an up-to-date overview on existing schemes with implementations we refer to the MP-SPDZ library [34]. Yao’s garbled circuits (GCs) [35] are a way to enable two distrusting parties to securely evaluate a function on their private input data. It requires that the underlying function is expressed as a boolean circuit consisting of 1-out-2-in gates. Garbled Circuits. One party, the garbler, generates a garbled version of the entire circuit, by garbling the individual gates. In the original implementation, a gate is garbled by assigning a unique, random label to the possible values (true, false) of each input wire, and doubly encrypting the four possible output values, under the corresponding input labels. The garbler randomly permutes the encrypted outputs and shares these with the evaluator accompanied by the random labels corresponding to the private inputs of the garbler. The evaluator then participates in an oblivious transfer (OT) protocol [36] to obtain the labels corresponding to their private inputs, without revealing those. Having received both input labels, the evaluator can correctly decrypt exactly one of the garbled outputs, thereby obtaining the true output bit(s). When a gate output is used as input to another gate, the true output will not be a bit, but rather a new random label that is used in the decryption of this other gate. An alternative construction to Yao’s GCs is the BMR framework [37]. Implementations of both and other frameworks can be found in, e.g., MP-SPDZ [34], ABY [38], or ABY 3 [39]. Most MPC frameworks are applied in a fully decentralized setting, where all parties communicate with one another directly. Many MPC schemes guarantee privacy for a given threat model, under the assumption of a certain number of honest parties, ranging from a strict majority to only one. Therefore, one might say that correctness is a largely solved problem. RQ1a: Why should we verify MPC? However, none of the above takes into account the use case in which parties that did not participate in the computation want to verify the results. In this situation, the results could be incorrect when all computing parties are colluding. The schemes are thus not publicly verifiable or publicly auditable [40], making the results untrustworthy for external parties. Another issue regarding (public) verifiability, that is often overlooked, is verification of data authenticity. Correctness of results is often only guaranteed given the assumption that input data is provided honestly. This especially plays a role when input data of parties should be the same over multiple computations, e.g., for reproducibility, or when input data should be authenticated, e.g., signed by an expert authority. In practice, both external and computation parties that want verifiably correct results, will require verification of computation and data authenticity. 4.2. Homomorphic encryption HE is a term used to denote a collection of (asymmetric) encryption schemes that allow users to perform operations on encrypted data without decrypting it first. More formally, and following [41], an HE scheme is described by four (probabilistic) polynomial-time algorithms (KeyGen, Enc, Dec, Eval): • KeyGen(1κ ) → (sk, pk, evk) is the key generation algorithm. It takes a unary representation of the security parameter κ as input and outputs a secret decryption key sk, public encryption key pk, and public evaluation key evk. While there are different ways to represent the function f in HE schemes, most constructions prefer a translation to an arithmetic circuit Cf over M. Notably, not all homomorphic encryption schemes can be used to evaluate arbitrary arithmetic circuits. Therefore, we distinguish, following [42], several types of homomorphic schemes in increasing order of evaluation capabilities. PHE is a type of homomorphic encryption that is only additively or only multiplicatively homomorphic, implying that it can only evaluate arithmetic circuits consisting solely of addition, respectively multiplication gates. In general, partially homomorphic schemes have the lowest computational cost and stable ciphertext size. Partially homomorphic encryption. SWHE constructions can evaluate arithmetic circuits consisting of both addition and multiplication gates. However, the circuits that can be evaluated may be limited in the number of operations (of either type), for example a scheme that can only evaluate circuits with a maximum multiplication depth of 2. Moreover, for SWHE there is no requirement that prevents exponential blowup of the ciphertext size.. Somewhat homomorphic encryption. LHE resembles SWHE in that it can evaluate circuits that are limited in depth d. However, since d is provided as input to KeyGen, the scheme parameters may be a function of d. Next to this, the length of the ciphertext output of the Eval algorithm may not depend on the depth d, meaning that ciphertexts do not blow up exponentially. Notice that the key sizes may still depend on the depth d and can get impractically large. Leveled homomorphic encryption. FHE schemes can evaluate arbitrary arithmetic circuits, without the schemes parameters depending upon any circuit property. This generality often comes at the cost of expensive bootstrapping algorithms after a certain number of multiplications [43]. Fully homomorphic encryption. HE is often used in a setting where one (cloud) server or central entity performs the majority of computations, for example in an outsourcing scenario. A frequently observed scenario is where all data owners use a distributed, or threshold, key generation protocol to obtain a public-private key pair where the private key is shared over all data owners [44], [45]. To perform a computation, all data owners encrypt their data under the same public key and let the central server perform the actual computation on the received ciphertexts. Due to the high computational cost of operations on homomorphically encrypted data, it is often preferred to have a single, central computing server with sufficient computational resources. RQ1b: Why should we verify HE? In most cases all parties are assumed to behave semihonestly. This assumption, however, is unrealistic in most real-world scenarios and raises significant integrity and verifiability issues. Integrity issues arise from the possibility of key-recovery attacks [46], [47], [48], whereas verifiability issues stem from the fact that the server can deviate from the requested computation. Another issue regarding verifiability, is whether external parties can verify the results of a computation that uses HE. These guarantees may be warranted in cases where external auditing is required or when results are of interest to a larger community. Therefore, verifiable solutions should provide guarantees even in the case where all data owners and the central computation server(s) are colluding. Next to this, external parties may also require guarantees concerning the integrity and authenticity of input data, which is in general not considered in existing work.

Understanding Privacy-preserving Computations

This paper is under CC BY 4.0 DEED license. available on arxiv Authors: (1) Ehsan Toreini, University of Surrey, UK; (2) Maryam Mehrnezhad, Royal Holloway University of London; (3) Aad Van Moorsel, Birmingham University. Table of Links Abstract & Introduction Background and Related Work FaaS Architecture Implementation and Performance Analysis Conclusion Acknowledgment and References Acknowledgment The authors in this project have been funded by UK EPSRC grant “FinTrust: Trust Engineering for the Financial Industry” under grant number EP/R033595/1, and UK EPSRC grant “AGENCY: Assuring Citizen Agency in a World with Complex Online Harms” under grant EP/W032481/1 and PETRAS National Centre of Excellence for IoT Systems Cybersecurity, which has been funded by the UK EPSRC under grant number EP/S035362/1. References [1] Philip Adler, Casey Falk, Sorelle A Friedler, Tionney Nix, Gabriel Rybeck, Carlos Scheidegger, Brandon Smith, and Suresh Venkatasubramanian. Auditing black-box models for indirect influence. Knowledge and Information Systems, 54(1):95–122, 2018. [2] Elaine Angelino, Nicholas Larus-Stone, Daniel Alabi, Margo Seltzer, and Cynthia Rudin. Learning certifiably optimal rule lists for categorical data. arXiv preprint arXiv:1704.01701, 2017. [3] Muhammad Ajmal Azad, Samiran Bag, Simon Parkinson, and Feng Hao. Trustvote: Privacy-preserving node ranking in vehicular networks. IEEE Internet of Things Journal, 6(4):5878–5891, 2018. [4] Olivier Baudron, Pierre-Alain Fouque, David Pointcheval, Jacques Stern, and Guillaume Poupard. Practical multi-candidate election system. In Proceedings of the twentieth annual ACM symposium on Principles of distributed computing, pages 274–283, 2001. [5] Alexandra Chouldechova. Fair prediction with disparate impact: A study of bias in recidivism prediction instruments. Big data, 5(2):153–163, 2017. [6] Sam Corbett-Davies, Emma Pierson, Avi Feller, Sharad Goel, and Aziz Huq. Algorithmic decision making and the cost of fairness. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 797–806. ACM, 2017. [7] Ronald Cramer, Ivan Damg˚ard, and Berry Schoenmakers. Proofs of partial knowledge and simplified design of witness hiding protocols. In Annual International Cryptology Conference, pages 174–187. Springer, 1994. [8] Craig E. Carroll and Rowena Olegario. Pathways to corporate accountability: Corporate reputation and its alternatives. Journal of Business Ethics, 163(2):173–181, 2020. [9] Michael Feldman, Sorelle A Friedler, John Moeller, Carlos Scheidegger, and Suresh Venkatasubramanian. Certifying and removing disparate impact. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 259–268. ACM, 2015. [10] Amos Fiat and Adi Shamir. How to prove yourself: Practical solutions to identification and signature problems. In Conference on the theory and application of cryptographic techniques, pages 186–194. Springer, 1986. [11] Sorelle A Friedler, Carlos Scheidegger, and Suresh Venkatasubramanian. On the (im) possibility of fairness. arXiv preprint arXiv:1609.07236, 2016. [12] Riccardo Guidotti, Anna Monreale, Salvatore Ruggieri, Franco Turini, Fosca Giannotti, and Dino Pedreschi. A survey of methods for explaining black box models. ACM computing surveys (CSUR), 51(5):1–42, 2018. [13] Feng Hao, Matthew N Kreeger, Brian Randell, Dylan Clarke, Siamak F Shahandashti, and Peter Hyun-Jeen Lee. Every vote counts: Ensuring integrity in large-scale electronic voting. In 2014 Electronic Voting Technology Workshop/Workshop on Trustworthy Elections (EVT/WOTE 14), 2014. [14] Feng Hao, Peter YA Ryan, and Piotr Zieli´nski. Anonymous voting by two-round public discussion. IET Information Security, 4(2):62–67, 2010. [15] Moritz Hardt, Eric Price, Nati Srebro, and others. Equality of opportunity in supervised learning. In Advances in neural information processing systems, pages 3315–3323, 2016. [16] Hui Hu, Yijun Liu, Zhen Wang, and Chao Lan. A distributed fair machine learning framework with private demographic data protection. In 2019 IEEE International Conference on Data Mining (ICDM), pages 1102–1107. IEEE, 2019. [17] Matthew Jagielski, Michael Kearns, Jieming Mao, Alina Oprea, Aaron Roth, Saeed Sharifi-Malvajerdi, and Jonathan Ullman. Differentially private fair learning. In International Conference on Machine Learning, pages 3000–3008. PMLR, 2019. [18] Jonathan Katz and Yehuda Lindell. Introduction to modern cryptography. CRC press, 2014. [19] Niki Kilbertus, Adria Gascon, Matt Kusner, Michael Veale, Krishna P Gummadi, and Adrian Weller. Blind justice: Fairness with encrypted sensitive attributes. In 35th International Conference on Machine Learning, pages 2630–2639. PMLR, 2018. [20] Jeff Larson, Surya Mattu, Lauren Kirchner, and Julia Angwin. How we analyzed the COMPAS recidivism algorithm. ProPublica (5 2016), 9(1), 2016. [21] Jiacheng Liu, Fei Yu, and Lixin Song. A systematic investigation on the research publications that have used the medical expenditure panel survey (MEPS) data through a bibliometrics approach. Library Hi Tech, 2020. [22] Scott M Lundberg and Su-In Lee. A unified approach to interpreting model predictions. Advances in neural information processing systems, 30, 2017. [23] Arwa Mahdawi. It’s not just A-levels – algorithms have a nightmarish new power over our lives. The Guardian, 2020. [24] Arvind Narayanan. Translation tutorial: 21 fairness definitions and their politics. In Proc. Conf. Fairness Accountability Transp., New York, USA, 2018. [25] Cecilia Panigutti, Alan Perotti, Andr´e Panisson, Paolo Bajardi, and Dino Pedreschi. Fairlens: Auditing black-box clinical decision support systems. Information Processing & Management, 58(5):102657, 2021. [26] Cecilia Panigutti, Alan Perotti, and Dino Pedreschi. Doctor xai: an ontology-based approach to black-box sequential data classification explanations. In Proceedings of the 2020 conference on fairness, accountability, and transparency, pages 629–639, 2020. [27] Saerom Park, Seongmin Kim, and Yeon-sup Lim. Fairness audit of machine learning models with confidential computing. In Proceedings of the ACM Web Conference 2022, pages 3488–3499, 2022. [28] Reuters. Amazon ditched AI recruiting tool that favored men for technical jobs. The Guardian, 2018. [29] Shahar Segal, Yossi Adi, Benny Pinkas, Carsten Baum, Chaya Ganesh, and Joseph Keshet. Fairness in the eyes of the data: Certifying machinelearning models. In Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society, pages 926–935, 2021. [30] Keng Siau and Weiyu Wang. Building trust in artificial intelligence, machine learning, and robotics. Cutter business technology journal, 31(2):47– 53, 2018. [31] Douglas Robert Stinson and Maura Paterson. Cryptography: theory and practice. CRC press, 2018. [32] Ehsan Toreini, Mhairi Aitken, Kovila Coopamootoo, Karen Elliott, Carlos Gonzalez Zelaya, and Aad van Moorsel. The relationship between trust in AI and trustworthy machine learning technologies. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, pages 272–283, 2020. [33] Michael Veale and Reuben Binns. Fairer machine learning in the real world: Mitigating discrimination without collecting sensitive data. Big Data & Society, 4(2):2053951717743530, 2017. [34] Tong Wang, Cynthia Rudin, Finale Doshi-Velez, Yimin Liu, Erica Klampfl, and Perry MacNeille. A bayesian framework for learning rule sets for interpretable classification. The Journal of Machine Learning Research, 18(1):2357–2393, 2017. [35] Qiang Yang, Yang Liu, Tianjian Chen, and Yongxin Tong. Federated machine learning: Concept and applications. ACM Transactions on Intelligent Systems and Technology (TIST), 10(2):1–19, 2019.

Privacy-preserving Computation of Fairness for ML Systems: Acknowledgement & References

This paper is under CC BY 4.0 DEED license. available on arxiv Authors: (1) Ehsan Toreini, University of Surrey, UK; (2) Maryam Mehrnezhad, Royal Holloway University of London; (3) Aad Van Moorsel, Birmingham University. Table of Links Abstract & Introduction Background and Related Work FaaS Architecture Implementation and Performance Analysis Conclusion Acknowledgment and References 5 Conclusion This paper proposes Fairness as a Service (FaaS), a trustworthy service architecture and secure protocol for the calculation of algorithmic fairness. FaaS is designed as a service that calculates fairness without asking the ML system to share the original dataset or model information. Instead, it requires an encrypted representation of the values of the data features delivered by the ML system in the shape of cryptograms. We used non-interactive Zero Knowledge Proofs within the cryptogram to assure that the protocol is executed as it should. These cryptograms are posted on a public fairness board for everyone to inspect the correctness of the computations for the fairness of the ML system. This is a new approach in privacy–preserving computation of fairness since unlike other similar proposals that use federated learning approach, our FaaS architecture does not rely on a specific machine learning model or a fairness metric definition for its operation. Instead, one have the freedom of deploying their desired model and the fairness metric of choice. In this paper we proved that the security protocol guarantees the privacy of data and does not leak any model information. Compared to earlier designs, trust in our design is in the correct construction of the cryptogram by the ML system. Arguably, this is more realistic as a solution than providing full access to data to the trusted third party, taking into account the many legal, business and ethical requirements of ML systems. At the same time, this provides a new challenge in increasing the trust one has in the ML system. Increasing trust in the construction of the cryptograms remains an interesting research challenge following from the presented protocol. We implemented a proof-of-concept of FaaS and conducted performance experiments on commodity hardware. The protocol takes seconds per data point to complete, thus demonstrating in performance challenges if the number of data points is large (tens of thousands). To mitigate the performance challenge, the security protocol is staged such that the construction of the cryptogram can be done off-line. The performance of the calculation of fairness from the cryptogram is a challenge to address in future work. All together, we believe FaaS and the presented underlying security protocol provide a new and promising approach to calculating and verifying fairness of AI algorithms.

Verifiable Fairness: Privacy-preserving Computation of Fairness for ML Systems: Conclusion

যাচাইযোগ্য ন্যায্যতা: এমএল সিস্টেমের জন্য ন্যায্যতার গোপনীয়তা-সংরক্ষণ গণনা: উপসংহার

Überprüfbare Fairness: Datenschutzbewahrende Fairnessberechnung für ML-Systeme: Fazit

Equidad verificable: Cálculo de equidad que preserva la privacidad para sistemas de aprendizaje automático: Conclusión

Équité vérifiable : calcul de l'équité préservant la confidentialité pour les systèmes ML : conclusion

सत्यापन योग्य निष्पक्षता: गोपनीयता-संरक्षण एमएल सिस्टम के लिए निष्पक्षता की गणना: निष्कर्ष

検証可能な公平性: ML システムのプライバシーを保護した公平性の計算: 結論

검증 가능한 공정성: ML 시스템의 개인정보 보호 공정성 계산: 결론

Justiça verificável: cálculo de justiça que preserva a privacidade para sistemas de ML: conclusão

Поддающаяся проверке честность: расчет справедливости для систем машинного обучения с сохранением конфиденциальности: заключение

Doğrulanabilir Adillik: ML Sistemleri için Gizliliği Koruyan Adil Hesaplama: Sonuç

Tính công bằng có thể kiểm chứng: Tính toán đảm bảo quyền riêng tư về tính công bằng cho các hệ thống ML: Kết luận

可验证的公平性：机器学习系统的隐私保护公平性计算：结论

This paper is under CC BY 4.0 DEED license. available on arxiv Authors: (1) Ehsan Toreini, University of Surrey, UK; (2) Maryam Mehrnezhad, Royal Holloway University of London; (3) Aad Van Moorsel, Birmingham University. Table of Links Abstract & Introduction Background and Related Work FaaS Architecture Implementation and Performance Analysis Conclusion Acknowledgment and References 2 Background and Related Work One of the benefits of auditing ML-based products relates to trust. Trust and trustworthiness (in socio-technical terms) are complicated matters. Toreini et. al [32] proposed a framework for trustworthiness technologies in AI–solutions based on existing social frameworks on trust (i.e. demonstration of Ability, Benevolence and Integrity, a.k.a. ABI and ABI+ frameworks) and technological trustworthiness [30]. They comprehensively reviewed the policy documents on regulating AI and the existing technical literature and derived any ML–based Table 1: Features of FaaS and comparison with other privacy–oriented fair ML proposals (support: full: ✓, partial: ✙, none: ✗) solution needs to demonstrate fairness, explainability, auditability, and safety and security to establish social trust. When using AI solutions, one cannot be assured of the fairness of such systems without trusting the reputation of the technology provider (e.g., datasets and ML models). It is commonly believed that leading tech companies do not make mistake in their implementation [8]; however, in practice, we often witness that such products indeed suffer from bias in ML [28, 23]. 2.1 Fairness Metrics There exist several fairness definitions in the literature. Designing a fair algorithm requires measuring and assessment of fairness. Researchers have worked on formalising fairness for a long time. Narayanan [24] lists at least 21 different fairness definitions in the literature and this number is growing, e.g., [5, 6]. Fairness is typically expressed as discrimination in relation to data features. These features for which discrimination may happen are known as Protected Attributes (PAs) or sensitive attributes. These include, but are not limited to, ethnicity, gender, age, scholarity, nationality, religion and socio-economic group. The majority of fairness definitions expresses fairness in relation to PAs. In this paper, we consider Group Fairness, which refers to a family of definitions, all of which consider the performance of a model on the population groups level. The fairness definitions in this group are focused on keeping decisions consistent across groups and are relevant to both disparate treatment and disparate impact notions, as defined in [9, 15]. In this paper, we will focus on the computations based on the above three fairness metrics. For this computation, the auditor requires to have access to the three pieces of information for each elements in the dataset: (1) the sensitive group membership (binary value for A demonstrating if a sample does or does not belong to a group with PAs) (2) the actual labelling of the sample (binary value for Y ) (3) the predicted label of the sample (binary value for Yˆ ). The ML system transfers this information for each sample from their test set. Then, the auditor uses this information to compute the above fairness metrics. Note that while we consider the above metrics for our protocol and proof-ofconcept implementation in next sections, our core architecture is independent of metrics, and the metric set can be replaced by other metrics too (Fig. 1). 2.2 Auditing ML Models for Fairness The existing research in fair ML normally assumes the computation of the fairness metric to be done locally by the ML system, with full access to the data, including the private attributes [15, 6, 5]. However, there is a lack of verifiability and independence in these approaches which will not necessarily lead to trustworthiness. To increase trust in the ML products, the providers might make the trained model self–explaining (aka transparent or explainable). There is also the transparent–by–design approach [12, 2, 34]. While this approach has its benefits, it is both model–specific and scenario–specific [25]; thus it cannot be generalised. There is also no trusted authority to verify such claims and explanations. Moreover, in reality, the trained model, datasets and feature extraction mechanisms are company assets. Once exposed, it can make them vulnerable to the competitors. Another approach to provide transparency to the fairness implementation comes through the black–box auditing, also known as adhoc [12, 22, 26]. In this way, the model is trained and audited for different purposes [1]. This solution is similar to tax auditing and financial ledgers where accountants verify and ensure these calculations are legitimate.However, unlike the well–established body of certifications and qualifications for accountants in tax auditing and financial ledgers; there does not exist any established processes and resources for fairness computation in AI and ML. The concept of a service that calculates fairness has been proposed before, e.g., in [33]. The authors introduced an architecture to delegate the computation of fairness to a trusted third party that acts as a guarantor of its algorithmic fairness. In this model, the fairness service is trusted both by the ML system and the other stakeholders (e.g. users and activists). In particular, the ML system must trust the service to maintain the privacy of data and secrecy of its model, whilst revealing to the trusted third party the algorithm outcome, sensitive input data and even inner parameters of the model. This is a big assumption to trust that the third party would not misuse the information and hence the leakage of data and model information is not a threat. To address these limitations, Kilbertdus et al. [19] proposed a system known as ‘blind justice’, which utilises multi–party computation protocols to enforce fairness into the ML model. Their proposal considers three groups of participants: User (data owner), Model (ML model owner) and the Regulator (that enforces a fairness metric). These three groups collaborate with each in order to train a fair ML model using a federated learning approach [35]. The outcome is a fair model that is trained with the participation of these three groups in a privacy-preserving way. They only provide a limited degree of verifiability in which the trained model is cryptographically certified after training and each of the participants can make sure if the algorithm has not been modified. It should be noted that since they operate in the training stage of the ML pipeline, their approach is highly dependent on the implementation details of the ML model itself. Jagielski et al. [17] proposed a differential privacy approach in order to train a fair model. Similarly, Hu et al. [16] used a distributed approach to fair learning with only demographic information. Segal et al. [29] used similar cryptographic primitives but took a more holistic approach towards the computation and verification of fairness. They proposed a data-centric approach in which the verifier challenges a trained model via an encrypted and digitally certified dataset using merkle tree and other cryptographic primitives. Furthermore, the regulator will certify the model is fair based on the data received from the clients and a set of dataset provided to the model. Their approach does not provide universal verifiability as the regulator is the only party involved in the computation of fairness. More recently, Park et al. [27] proposed a Trusted Execution Environment (TEE) for the secure computation of fairness. Their proposal requires special hardware components which are cryptographically secure and provide enough guarantees and verification for the correct execution of the code. The previous research generally has integrated fairness into their ML algorithms; therefore, such algorithms should be redesigned to use another fairness metric set. As it can be seen in Table 1, FaaS is the only work which is independent of the ML model and fairness metric with universal verifiability, and hence, can be used as a service.

Background and Related Work on Privacy-preserving Computation of Fairness for ML Systems 

This paper is under CC BY 4.0 DEED license. available on arxiv Authors: (1) Ehsan Toreini, University of Surrey, UK; (2) Maryam Mehrnezhad, Royal Holloway University of London; (3) Aad Van Moorsel, Birmingham University. Table of Links Abstract & Introduction Background and Related Work FaaS Architecture Implementation and Performance Analysis Conclusion Acknowledgement and References Abstract Fair machine learning is a thriving and vibrant research topic. In this paper, we propose Fairness as a Service (FaaS), a secure, verifiable and privacy-preserving protocol to compute and verify the fairness of any machine learning (ML) model. In the deisgn of FaaS, the data and outcomes are represented through cryptograms to ensure privacy. Also, zero knowledge proofs guarantee the well-formedness of the cryptograms and underlying data. FaaS is model–agnostic and can support various fairness metrics; hence, it can be used as a service to audit the fairness of any ML model. Our solution requires no trusted third party or private channels for the computation of the fairness metric. The security guarantees and commitments are implemented in a way that every step is securely transparent and verifiable from the start to the end of the process. The cryptograms of all input data are publicly available for everyone, e.g., auditors, social activists and experts, to verify the correctness of the process. We implemented FaaS to investigate performance and demonstrate the successful use of FaaS for a publicly available data set with thousands of entries. Introduction Demonstrating the fairness of algorithms is critical to the continued proliferation and acceptance of algorithmic decision-making in general, and AI-based systems in particular. There is no shortage of examples that have diminished trust in algorithms because of unfair discrimination of groups within our population. This includes news stories about the human resource decision-making tools used by large companies, which turn out to discriminate against women [28]. There also are well-understood seminal examples studied widely within the academic community, such as the unfair decisions related to recidivism in different ethnicities [20]. In the UK, most recently the algorithm to determine A-levels substitute scores under COVID-19 was widely found to be unfair across demographics [23]. There has been a surge of research that aims to establish metrics that quantify the fairness of an algorithm. This is an important area of research, and tens of different metrics have been proposed, from individual fairness to group fairness. It has been shown that various expressions for fairness cannot be satisfied or optimised at once, thus establishing impossibility results [11]. Moreover, even if one agrees about a metric, this metric on its own does not provide trust to people. It matters not only what the metrics express, but also who computes the metrics and whether one can verify these computations and possibly appeal against them. At the same time, in situations in which verification by stakeholders is possible, the owner of the data wants to be assured that none of the original, typically sensitive and personal, data is leaked. The system that runs the algorithms (later referred to as Machine Learning system or ML system) may have a valid interest in maintaining the secrecy of the model. In other words, if one wants to establish verifiable fairness, one needs to tackle a number of security, privacy and trust concerns. In FaaS, we take a fundamentally different design approach. We leak no data or model information, but the FaaS is still able to calculate fairness for a variety of fairness metrics and independent of the ML model. Thus, replacing the model in the ML system will not impact functionality of FaaS protocol. Moreover, any other party can verify this calculation since all the necessary encrypted information is posted publicly, on a ‘fairness board’. Summarising, our contributions are: • We propose FaaS, a model–agnostic protocol to compute different fairness metrics without accessing sensitive information about the model and the dataset. • FaaS is universally verifiable so everyone can verify the well–formedness of the cryptograms and the steps of the protocol. • We implement a proof-of-concept of the FaaS architecture and protocol using off-the-shelf hardware, software, and datasets and run experiments to demonstrate the practical feasibility of FaaS.

An Introduction to Verifiable Fairness: Privacy-preserving Computation of Fairness for ML Systems

How to become a Lightning Calculator by Anonymous is part of the HackerNoon Books Series. You can jump to any chapter in this book . Interest Computations. here Interest Computations. Multiply the (amount of money at interest) by the time, reduced to days; then divide this product by the quotient obtained by dividing 360 (the number of days in the interest year) by the per cent. of interest, and the quotient thus obtained will be the required interest. Require the interest of $462.50 for one month and eighteen days at 6 per cent. An interest month is 30 days; one month and 18 days equals 48 days. $462.50 multiplied by .48 gives $222.0000; 360 divided by 6 (the per cent. of interest) gives 60, and $222.0000 divided by 60 will give you the exact interest, which is $3.70. If the rate of interest in the above example were 12 per cent., we would divide the $222.0000 by 30 (because 360 divided by 12 gives 30); if 4 per cent., we would divide by 90; if 8 per cent., by 45; and in like manner for any other per cent. principal About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Anonymous (2021). How to become a lightning calculator. Urbana, Illinois: Project Gutenberg. Retrieved https://www.gutenberg.org/cache/epub/65078/pg65078-images.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at , located at . www.gutenberg.org https://www.gutenberg.org/policy/license.html

Interest Computations.

Hey there, speedster! Is your heavy-duty web app chugging like a tortoise on a coffee break or you’d love to make the faster web app? Well, you're in the right place because we're about to turn that digital sloth into a digital cheetah on an espresso binge! Welcome to the ultimate guide on making your web app faster than a caffeinated hummingbird on rollerblades. We've got tricks up our sleeves that'll make your code say 'I feel the need... the need for speed!' Due to a slower response from your app and a frustrated user has mistakenly sent his CV to WWE, what would happen 😁? Terrible? Awh, yeah! Buckle up, my friend, because we're about to hit the accelerator on your web app's cloud computation. It's time to leave the digital snail club and join the rocket ship league🚀! This article attempts to offer guidelines in simple language so that even readers with a non-technical background can get what’s going on while scrolling down the page. First, it is still safe to mention that fast cloud computation for a heavy-duty web application consists of optimizing various aspects of your application's infrastructure and architecture to ensure it can handle a large number of users and data processing efficiently. If your business needs web apps that should handle a massive influx of users’ requests then these tricks are for you too because no one loves to lose their valuable time. What Is Cloud Computing? Cloud computing refers to the delivery of computing services over the Internet by providing access to a wide range of resources and capabilities without the need for local infrastructure. In essence, it allows users to utilize and manage computing resources as a utility which is similar to electricity or water supply. Key characteristics and components of cloud computing include: ✅On-Demand Service ✅Broad Network Access ✅Resource Pooling ✅Rapid Elasticity ✅Measured Service Cloud computing services are typically categorized into three primary service models: IaaS provides virtualized computing resources over the internet. Users can rent virtual machines, storage, and networking infrastructure, allowing them to build and manage their own virtualized environments. Infrastructure as a Service (IaaS): PaaS offers a higher level of abstraction than IaaS. It provides a platform that includes not only infrastructure but also development tools and services. Users can focus on developing and deploying applications without managing the underlying infrastructure. Platform as a Service (PaaS): SaaS delivers fully functional software applications over the Internet on a subscription basis. Users can access and use software applications hosted by the service provider without needing to install or maintain them locally. Software as a Service (SaaS): Cloud computing can be deployed in various deployment models: Resources are owned and operated by a cloud service provider and made available to the general public. Examples include . Public Cloud: Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP) Resources are dedicated to a single organization and can be hosted on-premises or by a third-party provider. Private clouds offer more control and security but may involve higher costs. Private Cloud: Combines public and private cloud resources by allowing data and applications to be shared between them. Hybrid clouds offer flexibility and can help organizations balance cost, control, and scalability. Hybrid Cloud: Involves the use of multiple cloud providers to avoid vendor lock-in, enhance resilience, and optimize cost and performance. Multi-Cloud: Cloud Computing alone doesn’t work efficiently if the following speed-boosting techniques are not properly adopted for your web app. Here are some key steps to achieve fast cloud computation for your web app by avoiding recurring mistakes: 1. Lazy Loading: Users don’t love to use web apps that need more internet resources as well as time to load web content so you can implement lazy loading for images and other non-essential resources. Load them only when they come into the user's view so it can effectively reduce initial page load times. ✅Benefits: Faster initial page load. It conserves bandwidth because it avoids unnecessary downloads of assets that users may never see if they don't scroll down the page. Servers experience reduced load because they don't need to process and deliver all assets at once. Users can start interacting with the visible content of the page sooner. The amount of data that needs to be transferred over the network during the initial page load. It can benefit search engine optimization (SEO) by prioritizing the loading of important content first. Even if the overall page load time remains the same, lazy loading can make the perceived latency lower. Essentially, a heavy-duty web app requires loading mandatory features to meet users’ demands and it should be carefully considered otherwise users might get distracted. Here is a well-working example that the component. uses React Lazy Load This technique has been already deployed into some of the popular platforms like , etc. You can just scroll down the pages of the search result to see loaded and unloaded images on the page. Google Image Bing Image Search, Also, it's time to avoid those old tags and embrace the future with tags. Picture tags are like having a conversation with your browser. Let it know about the types of images you have and allow it to choose the best one to download based on the device's screen size and supported formats. You can do it as follows: img picture <picture>
   <source srcset="/media/examples/alternate-image-240-200.jpg"
      media="(min-width: 800px)">
   <img src="/media/examples/alternate-image-298-332.jpg" />
</picture> 2. Right Cloud Provider: There are so many online cloud providers to offer cloud computing for your service but sometimes, it is challenging to pick the best-suited providers for you. You should select a cloud provider that offers the necessary computing power and scalability for your application. Popular options include AWS, , , , and others. Assess your specific requirements, budget, and the provider's services before making a decision. Azure IONOS Cloud Compute Engine Google Cloud 3. Prefetching: You can use browser prefetching to load critical resources in the background while the user interacts with your site and it is also a proven fact that faster load times assist higher conversion rates to enhance the user experience. This anticipates their needs and makes your app appear faster. For your reference, you can use the attribute which is used in HTML to instruct the browser to prefetch a resource (such as a script, stylesheet, image, or other asset) in the background. It helps to improve the perceived performance of your web application by loading resources before they are actually needed. Here's how to use guide from . rel="prefetch" rel="prefetch" a well-organized resource 4. HTTP/2 and HTTP/3: If your server supports it, enable HTTP/2 or HTTP/3 for faster and more efficient data transmission. These protocols reduce latency and improve page load times. The dynamic duo of faster web surfing! Is your server feeling a bit old-fashioned and trudging along at a snail's pace? It's time to switch to the superheroes of data transmission: ! HTTP/2 and HTTP/3 Imagine HTTP/2 and HTTP/3 as the Flash and Quicksilver of the internet, whizzing around at lightning speed to fetch your web content. When your server supports these protocols, it's like giving your website a rocket-powered surfboard. HTTP/2 and HTTP/3 have a secret power: they reduce latency which is a fancy word for " " Nobody likes waiting. These protocols make your website load faster than a squirrel on caffeine😁. waiting around. So, if you want your users to have a web experience smoother, enable HTTP/2 or HTTP/3. It's like upgrading your old bicycle to a turbocharged sports car but for your website. Fasten your seatbelts, folks, because your web pages are about to take off like a rocket to the moon! 5. Content Delivery Network (CDN) Fine-Tuning: Optimize your CDN settings for your specific use case.  Experiment with cache settings and edge locations to maximize content delivery speed like playing with LEGO blocks, but for faster websites! In simple words, imagine your website as a giant LEGO castle. Now, the CDN is your secret stash of LEGO pieces that you use to build that castle even faster! Here's the deal: CDNs are like magical teleporters for your web content. But to make them work like a wizard's spell, you've got to fine-tune those CDN settings. It's like adjusting the turbo boosters on your spaceship😉! It's about ensuring that your CDN works harmoniously with your web app to deliver content efficiently. Here's what you need to do: ✅ Adjust the cache settings to strike the right balance between serving fresh content and reducing server load. Proper caching ensures that frequently accessed content is readily available to users and it enhances page load times. Cache Settings: ✅ Consider the geographical distribution of your users and strategically place edge locations. The goal is to minimize the physical distance between users and content delivery servers to reduce latency and improve the overall speed of content delivery. Edge Locations: In the world of web development, CDN fine-tuning is your superpower—like having the ability to build LEGO castles in seconds and make them teleport wherever you want. 6. Resource Minification and Bundling: You can minify and bundle CSS and JavaScript files to reduce the number of HTTP requests and decrease load times. Think of it as tidying up your digital closet. Just like you bundle and compress clothes to make your wardrobe more efficient, minifying and bundling CSS and JavaScript files assists your web app’s performance. Here's the drill: This is like getting rid of all the extra fluff in your code. Unnecessary spaces, comments, and line breaks are trimmed away to keep only the essential instructions. ✅Minify: Imagine you have a bunch of small packages to send. Instead of mailing them one by one, you gather them into a single, more substantial parcel. Bundling does just that for your CSS and JavaScript files to reduce the number of HTTP requests your website needs to make. ✅Bundling: So, while it might not be as exciting as organizing your closet, resource minification and bundling are the digital techniques that assist your web app to perform faster and more efficiently. Less waiting, more speed! 7. Progressive Web Apps (PWAs): Turn your web app into a PWA to provide an app-like experience, including faster load times, offline capabilities, and smoother performance. Transform your standard web app into a PWA using the mighty library like . Workbox library Why settle for ordinary when you can offer your users an extraordinary experience? Here's what Workbox brings to the table: Say goodbye to slow-loading pages. PWAs with Workbox ensure swift and efficient loading, so your users can dive right into your app without frustrating delays. ✅Faster Load Times: With Workbox, your PWA isn't afraid of going offline. It caches essential resources by allowing your app to function even when there's no internet connection. It's like having a trusty backup generator for your digital world. ✅Offline Capabilities: Workbox optimizes your PWA's performance by making interactions buttery smooth. Users won't even notice the transitions between online and offline modes – it's that seamless. ✅Smoother Performance: 8. Service Workers: Utilize service workers to cache assets and enable offline access. It improves the perceived speed of your web app. You can do it by using a library like Workbox and here is an example for service workers in a JavaScript application. import { registerRoute } from 'workbox-routing';
import { CacheFirst } from 'workbox-strategies';
import { CacheableResponsePlugin } from 'workbox-cacheable-response';

const pageStrategy = new CacheFirst({
  // Put all cached files in a cache named 'pages'
  cacheName: 'pages',
  plugins: [
    // Only requests that return with a 200 status are cached
    new CacheableResponsePlugin({
      statuses: [200],
    }),
  ],
}); Prefer this for more hints and strategies. guide This code configures a caching strategy for pages using the strategy and the . CacheFirst CacheableResponsePlugin 9. Browser Caching: You can use browser caching by setting appropriate cache headers for static assets. The best way is to use browser HTTP caching to improve the performance of your web app by reducing the need for redundant downloads of resources like images, stylesheets, and scripts. Browser caching allows the client's browser to store and reuse these resources so that it assists in achieving faster load times and reduced server load. Here's how to utilize browser HTTP caching: ✅Setting Cache-Control Headers: Cache-Control: public, max-age=3600 ✅Cache-Busting Techniques: <link rel="stylesheet" href="styles.css?v=2023-09-06"> 10. Content Delivery Strategy: Implement intelligent content delivery strategies to serve content from the nearest server location to the user. This approach aims to minimize latency which is the delay between a user's request for a web resource and its delivery. The key elements of content delivery strategy involve: 11. Adaptive Images: To deliver a seamless user experience across a variety of devices, it's crucial to employ adaptive image techniques. These strategies involve using responsive design principles to serve images of the right size and format based on the user's device capabilities. Here are some techniques: Create multiple versions of an image in various resolutions and sizes. Use CSS media queries to determine the user's viewport size and load the appropriate image version. So, it prevents users from downloading unnecessarily large images on smaller screens. ✅Image Scaling: HTML's srcset attribute allows you to specify multiple image sources with different resolutions. Browsers can then choose the most suitable image based on the user's device characteristics. For example: ✅Srcset Attribute: <img src="small.jpg" 
     srcset="small.jpg 300w,
             medium.jpg 600w,
             large.jpg 1200w"
     sizes="(max-width: 600px) 100vw, 50vw"> The element provides more control over adaptive images. It allows you to define multiple sources and media conditions to choose the best image source for different scenarios, including varying screen sizes and resolutions. ✅Picture Element: <picture> <picture>
  <source srcset="small.jpg" media="(max-width: 600px)">
  <source srcset="medium.jpg" media="(max-width: 1200px)">
  <img src="large.jpg" alt="An adaptive image">
</picture> 13. Database Sharding: If your web app involves heavy database usage, then it is better to consider sharding your database to distribute the load across multiple servers for faster query performance and cloud computing as well. Essentially, database sharding is a database architecture technique used to partition data across multiple database servers or shards. It is primarily employed to improve the scalability and performance of web applications. 14. Edge Computing: It is a distributed computing approach where certain processing tasks are executed closer to the data source or user rather than relying solely on centralized servers in a remote data center. This concept brings computation to the of the network which is closer to where it's needed most—your users. Imagine your web app as a grand performance, and your servers as backstage crew members. Edge computing allows some of those crew members to move closer to the stage by allowing the show to run smoothly and without delays. "edge" Here's how it benefits your web app: Reduced Latency Bandwidth Savings Enhanced Reliability Scalability 15. Connection Pooling: Let's have a look into caching. It’s a web optimization technique that's like a secret stash of your web files stored in a lightning-fast vault. Here's the lowdown: 📥What is Caching? Caching is the art of preserving a snapshot of your web files in a special storage haven known as a cache. It's a bit like having a quick-access drawer for your favorite snacks – everything's ready for munching without the hassle of fetching it from the pantry. 📥Why Cache? Enabling browser caching offers various of benefits: Caching reduces the need for repetitive resource downloads, saving precious bandwidth. Bandwidth Bliss: With cached files, your web pages load faster than a ninja on roller skates, delivering a seamless user experience. Speedster's Paradise: Users experience reduced waiting times, thanks to cached content being served swiftly from their local device. Latency Lightened: Less frequent server requests mean a lighter workload for your server, leaving it cool, calm, and collected. Server Stress Relief: 📥A Word of Caution But wait, there's a trade-off: caching means you'll essentially have two versions of your website at any given time. While this isn't usually an issue for static content, it can pose challenges for real-time services reliant on up-to-the-minute data accuracy. ✅The Solution? Fear not! Even for real-time services, cache strategies can be fine-tuned to clear specific cache subsections when new data arrives, keeping your information fresh and accurate. In the world of web optimization, caching is your trusty sidekick, boosting performance and user satisfaction. It's all about delivering the goods faster and smarter!" 16. Data Caching: Implement data caching at the application level to store frequently accessed data in memory by reducing the need for repeated database queries. 17. Content Compression: Compress text-based responses using compression to reduce the amount of data transferred over the network. Also, you might prefer to check this . gzip or Brotli well-instructed guide 18. Distributed Content Delivery: You can use cloud regions for to ensure global users experience low-latency access. multiple distributed content delivery Wrapping Up: Alright, friends, we believe that your web app is now transferred from a digital sloth to a caffeinated cheetah on rollerblades! Users won't be sending CVs to WWE anymore. With lazy loading, rocket-powered servers, prefetching magic, and even squirrels on caffeine (thanks to ), your web app is faster than ever. It's like having a superhero makeover for your code. So, keep sipping that espresso, because your web app should be on a rocket to the moon now! HTTP/2 and HTTP/3

Turbocharge Your Heavy-Duty Web App: Speed Hacks for Lightning-Fast Cloud Computation

Decent Land Labs proudly presents the Molecular Execution Machine (MEM), a revolutionary serverless functions platform engineered for web3 applications. Based on the SmartWeave framework, MEM is a culmination of advanced technologies like , and . By simplifying intricate cryptographic elements, MEM empowers developers to concentrate on crafting groundbreaking applications without the hassle of infrastructure management or specialized development. molecule.sh Arweave 3EM The Genesis of MEM Our journey with MEM began as an evolution of EXM, our previous serverless functions platform that was sunset by Community Labs. Over the past year, Decent Land Labs has deployed numerous projects on EXM. Recognizing the need for a more robust and open-source solution, we invested months in R&D and are thrilled to announce MEM's official launch. MEM's Feature Spectrum Molecular Execution Machine is not just a platform; it's a comprehensive ecosystem offering: : MEM supports an array of programming languages for smart contract development, including but not limited to JavaScript, TypeScript, Rust, C++, and Solidity. Multi-Language Compatibility : Utilizing the library, MEM smart contracts can authenticate callers across multiple blockchain networks like Ethereum, Solana, and Polkadot. Cross-Chain Authentication molecule.sh : MEM eliminates the need for in-depth blockchain knowledge, allowing developers to get started effortlessly with its SDK or CLI. Crypto-Agnostic Environment : MEM ensures a secure runtime for smart contract execution, safeguarding against potential vulnerabilities. Robust Security : MEM offers gasless and walletless interactions, providing a seamless user experience. Flexible Payment Options Technical Architecture At its core, MEM leverages 3EM for smart contract runtime, while Bundlr & Arweave act as the data availability layer, ensuring secure and permanent storage. MEM uses ’s ‘Storage Based Consensus Paradigm’ integration further extends its capabilities, enabling financial dApps with gassless and near-instant payment options. everPay Real-World Applications MEM's versatility makes it ideal for a range of applications: : Enable self-sovereign identities across multiple chains. Decentralized Identity Management : Create a censorship-resistant platform for content creators. Content Publishing : Deploy cost-effective, gasless API applications. Serverless API Gateway : Ensure document integrity and authenticity. Immutable Document Signing What's Next? In 2023, MEM aims to introduce: Full EVM support Data encryption and decryption Integration with fiat payment processors like Stripe and NoRamp Get Started MEM is currently in the private beta testing phase. Connect with us on or for access, or sign up at . Twitter Discord mem.tech Additional Resources Documentation Twitter Discord Stay tuned and to Decent Land Labs' for the latest updates. SUBSCRIBE Hackernoon Happy building! Also published . here

Introducing Flexible, Scalable Computation With Decent Land Labs Molecular Execution Machine

Presentamos la computación flexible y escalable con la máquina de ejecución molecular de Decent Land Labs

Présentation du calcul flexible et évolutif avec la machine d'exécution moléculaire de Decent Land Labs

डिसेंट लैंड लैब्स की आणविक निष्पादन मशीन के साथ लचीली, स्केलेबल गणना का परिचय

Decent Land Labs の分子実行マシンによる柔軟でスケーラブルな計算の導入

Apresentando computação flexível e escalável com a máquina de execução molecular da Decent Land Labs

Giới thiệu khả năng tính toán linh hoạt, có thể mở rộng với Máy thực thi phân tử của Decent Land Labs

通过 Decent Land Labs 的分子执行机引入灵活、可扩展的计算

Both Web3 and AI have taken the world by storm in recent years, disrupting traditional models and processes across industries. They bring immense potential for change, especially from the point of view of building community-oriented, value-driven systems. I recently spoke with Jet Liu, Product Director at Phoenix and VP of Product at Apex Technologies, who is working at the intersection of AI and Web3. With Jet, who has more than ten years of experience building enterprise-grade AI applications and data infrastructures, we discussed how a combination of AI, Web3, and innovations in computation technology can help shape the future. Hi Jet. It’s great to speak with you. Please tell us about your journey and how you got interested in AI. Hey, thanks for having me. Having worked in the marketing technology (Martech) space for many years, I eventually arrived at APEX Technologies, Shanghai, where I'm currently the VP of Product. I've helped them build an end-to-end product suite for enterprises needing AI-powered consumer data solutions. Over 400 large enterprises and multinationals have used the company's technology, including the likes of Starbucks, Maserati, PingAn Group, Walmart, McLaren, Michael Kors, Smart Automotive, and many more. While I've been interested in exploring AI deeply for several years, especially alongside other emerging technologies like blockchain and cryptography, the real chance and scope to do so arrived when I joined APEX. I haven't looked back ever since—I didn't need to. Deep down, I've always wanted to do this, and I am also a part of this exciting project, Phoenix. So it's all going great. What problems are you looking to solve with this latest project you mentioned? When it's about emerging tech, you have two options. One, you can sulk and whine about past glories, missing out on the future, clinging on to nostalgia—think of the 'AI will destroy humans' camp. Two, you can get your hands dirty building the future. That's what we're doing with Phoenix. It's an answer to the currently inaccessible AI computation landscape characterized by restrictively high costs and resource-intensive frameworks that most people (or even enterprises) can't use. AI's development has been highly centralized so far, dominated by capital-rich giants like OpenAI, Microsoft, and so on. High entry barriers are a key enabling factor here—something we aim to fix. So, overall, Phoenix is all about democratizing AI computation and making it widely accessible for innovation-driven enterprises. We're leveraging blockchain technology and cryptography to this end, fostering decentralization for a better future. Phoenix has an interesting organizational structure. It's not a typical DAO. Tell us more about this and why it matters. Sure. The Phoenix DAO is unique because it combines three industry leaders in AI data research and development: APEX Technologies, FLC, and Tensor Investment. Backed by NewMargin Ventures, Tencent, and Anker Investment, APEX Technologies is mainly responsible for building Phoenix's enterprise-grade Layer 1 blockchain. FLC is a decentralized AI research organization based in Hong Kong. Their specialization includes federated learning, multi-party computation, and decentralized edge computing. Both APEX and FLC are working on the Phoenix Computation Layer, which enables multi-party computation and AI on-demand decentralized compute. Besides this, FLC is also helping with Phoenix's blockchain integration and the upcoming AIGC NFT Portal dApp. Tensor, an AI-driven proprietary trading firm specializing in emerging asset classes, is the third pillar of Phoenix DAO. They're focusing on the development, modeling, and partnerships for AlphaNet. Plus, they're supporting the ecosystem with Deep Learning and Deep Reinforcement Learning tech and GPU computation resources. It should be clear why Phoenix DAO's structure matters. Something as powerful and broadly impactful as decentralized AI computation demands collaboration among the best of the best, and that's what we're trying to achieve. And we're widening the scope by involving a globally-distributed, engaged, and driven Web3 community. What are the main products offered by Phoenix, and how do they contribute to supporting Web3 communities and enterprises? Currently, we're building three main products for the Phoenix ecosystem. The first and most important one is the decentralized computation layer, of course. That's like the backbone for everything to come. It's a novel infrastructure combining decentralized AI and privacy-oriented multi-party computation (MPC). This'll help intelligent Web3-native applications scale easily and increase the speed/efficiency of deploying AI models. The two other products include AlphaNet and NYBL. AlphaNet is a robust AI-powered platform for high-frequency crypto traders and an integrated scaling infrastructure. NYBL is a metaverse by AIGC that'll utilize our computation layer's AI Node Network for scaling and GPU-based video/image processing. Besides these, Phoenix also has an L1 enterprise-grade blockchain and Oracle platform for max efficiency and scope. Altogether, we can enable decentralized deep learning, GPU resource scaling, codeless AI deployment, and multi-party computation across industries like health, retail, consumer IoT, financial services, supply chain, and Web3. Zooming out a little, what's the biggest advantage/strength of integrating AI with Web3? Thanks for this question. Naysayers spin up AI vs. Web3 debates, but I feel that's pretty naive. These two are the most complimentary technologies we've ever got to date. We're in the age of data, where everything from consumer behaviors to impressions on social media is quantified/quantifiable. So much so that it's now justified to say—data is power. But this also implies a greater need to break up silos and promote decentralization. While AI is the tool for humanity to leverage massive unstructured data sets, monetizing them in an equitable manner, Web3 unlocks infrastructure for decentralization. Web3 also introduces novel organizational structures—like DAOs—besides new asset classes and revenue streams. By efficiently adopting the AI-Web3 combinations, we can finally build the community-oriented and user-centric world that has been on the cards since the Internet's advent. That'll be a truly global world without arbitrary barriers, hierarchical domination, and resource misutilization. What are the challenges of adopting Web3 and AI? And where do you see them a few years down the line? The biggest challenge is misinformation and FUD. The noise-signal ratio is disturbingly high because Web3 and AI are still in the nascent stages of market development and technical design. People are ready to believe anything the self-proclaimed gurus say on Twitter or other social media platforms. There are technical limitations for us to overcome. No doubt. The scalability of L1 blockchains is one example. Then there's the question of AI's accuracy and overcoming bias. But none of these make the concerned technologies inherently bad or harmful. And surely AI isn't here to destroy us—let's get real. It's a powerful tool instead and can significantly boost our progress. Anyway, all this FUD will evaporate once we start seeing more groundbreaking innovations. It's happening already and will only take so much time. The Internet had its critics alright, and we know how things went for them—they became Internet users themselves because what else would they do? It'll be the same with Web3 and AI. Soon these emerging technologies will disrupt almost every aspect of our lives. And once we realize the benefits on a practical, day-to-day level, there'll be no turning back. It's not a question of if but when. The future is inevitable—how we embrace it is what matters.

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