With algorithms and AI we configure buildings based on input parameters from the users

Parametric Solutions

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Edson Pindza, Tshwane University of Technology; Department of Mathematics and Statistics; 175 Nelson Mandela Drive OR Private Bag X680 and Pretoria 0001; South Africa [edsonpindza@gmail.com];
(2) Jules Clement Mba, University of Johannesburg; School of Economics, College of Business and Economics and P. O. Box 524, Auckland Park 2006; South Africa [jmba@uj.ac.za];
(3) Sutene Mwambi, University of Johannesburg; School of Economics, College of Business and Economics and P. O. Box 524, Auckland Park 2006; South Africa [sutenem@uj.ac.za];
(4) Nneka Umeorah, Cardiff University; School of Mathematics; Cardiff CF24 4AG; United Kingdom [umeorahn@cardiff.ac.uk]. Table of Links Abstract and Introduction
Methodology
Neural Network Methodology
Numerical results, implementation and discussion
Conclusion, Acknowledgments, and Funding
Availability of data, code and materials, Contributions and Declarations
References 3. Neural Network Methodology The neural network (NN) algorithm is used for solving problems relating to dimensionality reduction [42, 49], visualization of data [31], clustering [11, 52] and classification issues [37, 40]. It is also used in regression [2, 21, 45] in solving regression problems since they do not require prior knowledge about the distribution of the data. The NN algorithm often has the tendency to predict with higher accuracy than other techniques due to the NN’s capability to fit any continuous functions [18]. Mathematically, the NN can be referred to as a directed graph having neurons as vertices and links as edges. Different forms of NN depend on the connectivity of their neurons, and the simplest one is the Multi-layer perceptron, also known as the feedforward NN. Basically, a NN can have only one input and one output layer in its simplest form, and this can be written as: where m is the number of input variables, Φ is the activation function, wi,k is the weight of the input layer i with respect to the output k, b is the bias, and the input vector x is connected to the output k (denoting the kth neuron in the output layer) through a biased weighted sum. In the presence of hidden layers positioned between the input layers and the output layers, the output can be written as: where m1 is the number of input variables, m2 is the number of nodes in the hidden layer, Φ : R → R refers to non-linear activation functions for each of the layers in the brackets, and Φout is the possibly new output function. Recently, the NN has become an indispensable tool for learning the solutions of differential equations, and this section will utilize the potential of the NN in solving PDE-related problems [1, 10, 20, 50]. Also, the NN has been implemented in solving equations without analytical solutions. For instance, Nwankwo et al. [38] considered the solution of the American options PDE by incorporating the Landau transformation and solving the corresponding transformed function via a neural network approach. In many scientific and industrial contexts, there is a need to solve parametric PDEs using NN techniques and Khoo et al. [24] developed such a technique which solves PDE with inhomogeneous coefficient fields. For high-dimensional parametric PDE, [14] analyzed the deep parametric PDE method to solve high-dimensional parametric PDEs with much emphasis on financial applications. In the following, we describe the Bitcoin options with the corresponding Black-Scholes pricing PDE: The above equation which is a non-homogeneous PDE can be re-written as Standard theorems guarantee a classical smooth solution exists for the pricing PDE (3.13) given the assumed dynamics. The continuity and linear growth conditions on the coefficient functions ensure they satisfy Lipschitz continuity. Contingent claims theory shows that Lipschitz continuity is sufficient for existence and uniqueness under the stochastic process specifications. In particular, the smooth past price dependence in the diffusion coefficient allows the application results for delayed Black-Scholes equations. Therefore, the Cauchy problem is well-posed under the model assumptions, and a smooth pricing solution is guaranteed to exist based on the theorems. This provides the theoretical foundation for using a neural network to approximate the price function numerically. Thus, employing the ANN to solve the PDE, we introduce an approximating function ζ(t, S : θ) with parameter set θ. The loss or cost function associated with this training is measured by how well the approximation function satisfies the differential operator, boundary conditions and the terminal condition of the option pricing PDE. These are given respectively as Suppose L(ζ) = 0, then ζ(t, S : θ) is a solution of the PDE in equation (3.13). The major aim is to obtain a set of parameters θ such that the function ζ(t, S : θ) minimizes the observed error of L(ζ). The procedure for seeking a good parameter set by minimizing this loss function using the stochastic gradient descent (SGD) optimizer is called “training”. Thus, using a Machine Learning approach, and in this case, the artificial NN, it will be feasible to minimize the function L(ζ) by applying the SGD approach on the sequence of asset and time points which are drawn at random. A part of the input samples, fully dependent on the mini-batch size, is drawn within each iteration to estimate the gradient of the given objective function. The gradient is then estimated over these mini-batches due to the limitations of the computer memory [30]. The training of NN is classified into first identifying the network’s hyperparameters, that is, the architectural structure and the loss function of the network. Next, use the SGD to optimize or estimate the loss minimizer, and then the derivatives of the loss function are computed using the backpropagation techniques. Essentially, the process of searching and identifying the best θ parameter by minimizing the loss function using the gradient descent-based optimizers is referred to as “training”. The whole procedure is well illustrated in the algorithm below [46], and can be implemented in solving the PDE in equation (3.13): The learning rate α is employed to scale the intensity of the parameter updates during the gradient descent. Choosing the learning rate, as the descent parameter α, in the network training is crucial since this factor plays a significant role in aiding the algorithm’s convergence to the true solution in the gradient descent. It equally affects the pace at which the algorithm learns and whether or not the cost function is minimized. When α implies divergence, and thus, the optimal point can be missed. Also, a fixed value of α can most likely make the local optima overshoot after some iterations, leading to divergence. Defining the learning rate as a dynamic decreasing function is preferable since it allows the algorithm to identify the needed point rapidly. Another limitation in employing the gradient descent method is obtaining the value of the initial parameters of the loss function. If the value is significantly close to the local optimum, then the slope of the loss function reduces, thereby leading to the optimal convergence. Otherwise, there will be no convergence as the solution explodes abnormally. 3.1. Solution of bitcoin option pricing PDE While the pricing PDE has a proven smooth solution under the model dynamics, obtaining the analytical form is intractable. Numerical methods must be used to approximate the solution. Neural networks provide a flexible parametric approach based on their universal approximation theoretical results. Given sufficient network capacity, neural networks can represent a wide class of functions, including solutions to PDEs like the pricing equation. However, challenges remain in finding the optimal network parameters to recover the true solution robustly. While existence is guaranteed, the non-convex optimization of neural networks does not assure convergence to global optimality. Care must be taken in specifying the network architecture, loss function, regularization, and training methodology to promote the learning of the pricing function. Subject to these caveats, neural networks present a promising computational technique for approximating smooth pricing solutions, circumventing discretization of the domain. We note the limitations of using neural networks as function approximators. However, their generalization capabilities provide a methodology for data-driven extraction of the pricing mapping across the entire domain. This avoids the constraints of methods like grid-based techniques that rely on local consistency. Future work should explore neural network training enhancements and theoretical guarantees to ensure robust solutions. This paper is available on arxiv under CC 4.0 license. Authors: (1) Edson Pindza, Tshwane University of Technology; Department of Mathematics and Statistics; 175 Nelson Mandela Drive OR Private Bag X680 and Pretoria 0001; South Africa [edsonpindza@gmail.com]; (2) Jules Clement Mba, University of Johannesburg; School of Economics, College of Business and Economics and P. O. Box 524, Auckland Park 2006; South Africa [jmba@uj.ac.za]; (3) Sutene Mwambi, University of Johannesburg; School of Economics, College of Business and Economics and P. O. Box 524, Auckland Park 2006; South Africa [sutenem@uj.ac.za]; (4) Nneka Umeorah, Cardiff University; School of Mathematics; Cardiff CF24 4AG; United Kingdom [umeorahn@cardiff.ac.uk]. This paper is available on arxiv under CC 4.0 license. Authors: Authors: (1) Edson Pindza, Tshwane University of Technology; Department of Mathematics and Statistics; 175 Nelson Mandela Drive OR Private Bag X680 and Pretoria 0001; South Africa [edsonpindza@gmail.com]; (2) Jules Clement Mba, University of Johannesburg; School of Economics, College of Business and Economics and P. O. Box 524, Auckland Park 2006; South Africa [jmba@uj.ac.za]; (3) Sutene Mwambi, University of Johannesburg; School of Economics, College of Business and Economics and P. O. Box 524, Auckland Park 2006; South Africa [sutenem@uj.ac.za]; (4) Nneka Umeorah, Cardiff University; School of Mathematics; Cardiff CF24 4AG; United Kingdom [umeorahn@cardiff.ac.uk]. Table of Links Abstract and Introduction Methodology Neural Network Methodology Numerical results, implementation and discussion Conclusion, Acknowledgments, and Funding Availability of data, code and materials, Contributions and Declarations References Abstract and Introduction Abstract and Introduction Methodology Methodology Neural Network Methodology Neural Network Methodology Numerical results, implementation and discussion Numerical results, implementation and discussion Conclusion, Acknowledgments, and Funding Conclusion, Acknowledgments, and Funding Availability of data, code and materials, Contributions and Declarations Availability of data, code and materials, Contributions and Declarations References References 3. Neural Network Methodology The neural network (NN) algorithm is used for solving problems relating to dimensionality reduction [42, 49], visualization of data [31], clustering [11, 52] and classification issues [37, 40]. It is also used in regression [2, 21, 45] in solving regression problems since they do not require prior knowledge about the distribution of the data. The NN algorithm often has the tendency to predict with higher accuracy than other techniques due to the NN’s capability to fit any continuous functions [18]. Mathematically, the NN can be referred to as a directed graph having neurons as vertices and links as edges. Different forms of NN depend on the connectivity of their neurons, and the simplest one is the Multi-layer perceptron, also known as the feedforward NN. Basically, a NN can have only one input and one output layer in its simplest form, and this can be written as: where m is the number of input variables, Φ is the activation function, wi,k is the weight of the input layer i with respect to the output k, b is the bias, and the input vector x is connected to the output k (denoting the kth neuron in the output layer) through a biased weighted sum. In the presence of hidden layers positioned between the input layers and the output layers, the output can be written as: where m1 is the number of input variables, m2 is the number of nodes in the hidden layer, Φ : R → R refers to non-linear activation functions for each of the layers in the brackets, and Φout is the possibly new output function. Recently, the NN has become an indispensable tool for learning the solutions of differential equations, and this section will utilize the potential of the NN in solving PDE-related problems [1, 10, 20, 50]. Also, the NN has been implemented in solving equations without analytical solutions. For instance, Nwankwo et al. [38] considered the solution of the American options PDE by incorporating the Landau transformation and solving the corresponding transformed function via a neural network approach. In many scientific and industrial contexts, there is a need to solve parametric PDEs using NN techniques and Khoo et al. [24] developed such a technique which solves PDE with inhomogeneous coefficient fields. For high-dimensional parametric PDE, [14] analyzed the deep parametric PDE method to solve high-dimensional parametric PDEs with much emphasis on financial applications. In the following, we describe the Bitcoin options with the corresponding Black-Scholes pricing PDE: The above equation which is a non-homogeneous PDE can be re-written as Standard theorems guarantee a classical smooth solution exists for the pricing PDE (3.13) given the assumed dynamics. The continuity and linear growth conditions on the coefficient functions ensure they satisfy Lipschitz continuity. Contingent claims theory shows that Lipschitz continuity is sufficient for existence and uniqueness under the stochastic process specifications. In particular, the smooth past price dependence in the diffusion coefficient allows the application results for delayed Black-Scholes equations. Therefore, the Cauchy problem is well-posed under the model assumptions, and a smooth pricing solution is guaranteed to exist based on the theorems. This provides the theoretical foundation for using a neural network to approximate the price function numerically. Thus, employing the ANN to solve the PDE, we introduce an approximating function ζ(t, S : θ) with parameter set θ. The loss or cost function associated with this training is measured by how well the approximation function satisfies the differential operator, boundary conditions and the terminal condition of the option pricing PDE. These are given respectively as Suppose L(ζ) = 0, then ζ(t, S : θ) is a solution of the PDE in equation (3.13). The major aim is to obtain a set of parameters θ such that the function ζ(t, S : θ) minimizes the observed error of L(ζ). The procedure for seeking a good parameter set by minimizing this loss function using the stochastic gradient descent (SGD) optimizer is called “training”. Thus, using a Machine Learning approach, and in this case, the artificial NN, it will be feasible to minimize the function L(ζ) by applying the SGD approach on the sequence of asset and time points which are drawn at random. A part of the input samples, fully dependent on the mini-batch size, is drawn within each iteration to estimate the gradient of the given objective function. The gradient is then estimated over these mini-batches due to the limitations of the computer memory [30]. The training of NN is classified into first identifying the network’s hyperparameters, that is, the architectural structure and the loss function of the network. Next, use the SGD to optimize or estimate the loss minimizer, and then the derivatives of the loss function are computed using the backpropagation techniques. Essentially, the process of searching and identifying the best θ parameter by minimizing the loss function using the gradient descent-based optimizers is referred to as “training”. The whole procedure is well illustrated in the algorithm below [46], and can be implemented in solving the PDE in equation (3.13): The learning rate α is employed to scale the intensity of the parameter updates during the gradient descent. Choosing the learning rate, as the descent parameter α, in the network training is crucial since this factor plays a significant role in aiding the algorithm’s convergence to the true solution in the gradient descent. It equally affects the pace at which the algorithm learns and whether or not the cost function is minimized. When α implies divergence, and thus, the optimal point can be missed. Also, a fixed value of α can most likely make the local optima overshoot after some iterations, leading to divergence. Defining the learning rate as a dynamic decreasing function is preferable since it allows the algorithm to identify the needed point rapidly. Another limitation in employing the gradient descent method is obtaining the value of the initial parameters of the loss function. If the value is significantly close to the local optimum, then the slope of the loss function reduces, thereby leading to the optimal convergence. Otherwise, there will be no convergence as the solution explodes abnormally. 3.1. Solution of bitcoin option pricing PDE 3.1. Solution of bitcoin option pricing PDE While the pricing PDE has a proven smooth solution under the model dynamics, obtaining the analytical form is intractable. Numerical methods must be used to approximate the solution. Neural networks provide a flexible parametric approach based on their universal approximation theoretical results. Given sufficient network capacity, neural networks can represent a wide class of functions, including solutions to PDEs like the pricing equation. However, challenges remain in finding the optimal network parameters to recover the true solution robustly. While existence is guaranteed, the non-convex optimization of neural networks does not assure convergence to global optimality. Care must be taken in specifying the network architecture, loss function, regularization, and training methodology to promote the learning of the pricing function. Subject to these caveats, neural networks present a promising computational technique for approximating smooth pricing solutions, circumventing discretization of the domain. We note the limitations of using neural networks as function approximators. However, their generalization capabilities provide a methodology for data-driven extraction of the pricing mapping across the entire domain. This avoids the constraints of methods like grid-based techniques that rely on local consistency. Future work should explore neural network training enhancements and theoretical guarantees to ensure robust solutions.

Neural Network for Valuing Bitcoin: Neural Network Methodology

Navigation with Large Language Models: Discussion and References

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

Let's learn about via these 46 free stories. They are ordered by most time reading created on HackerNoon. Visit the to find the most read stories about any technology. Insurance /Learn Repo 1. Allstate Accused of Overcharging Loyal California Customers $1 Billion Allstate, the insurance company, has been accused of overcharging customers in California. 2. If You Do Not Report Your Gun Stolen or Missing You Can Held Liable For Crimes the Gun is Used For Crimes committed with stolen guns that were not reported remain liable for property damage etc. 3. The Insurers' Dilemma: Balancing Consumer Demands and Legacy Conventions In an era of rapidly changing technology, the greatest challenge facing the insurance industry is its ability to keep up with consumer demand. 4. Top 3 Benefits of Insurance Data Analytics The Importance of data analytics and data-driven decisions across the board and in this case insurance data. 5. "Insurers Miss Out On Millions Of Dollars In Business", Manuel San Miguel, Ignatica CEO Ignatica CEO and Co-Founder Manuel San Miguel discusses why he founded the company, AI and Machine Learning and why he should have gone to Japan sooner. 6. The Social Impact of Mixing Business and Medicine COVID-19 has been hitting the healthcare system pretty hard these last few weeks. In U.S. hospitals they are now facing critical shortages of equipment. As of the end of March, nearly 1 in 4 hospitals have fewer than 100 N95 masks on hand and 1 in 5 reports immediate need for more ventilators. Already back in February, the FDA reported shortages in drugs related to coronavirus. 7. A Guide to HIPAA Compliance in 2021 & Its Benefits Following HIPAA laws provides a higher level of safety for your corporation and its leaders. 8. The Perks and Obstacles of AI Adoption in Insurance Imagine that you are a leader at an insurance company. You know that artificial intelligence (AI) will give you a competitive edge and have decided to invest. 9. The Glorious Return of the Conference: Highlights from My First Tech Conference in 2 Years As the world reopens, it's becoming evident that the new Insuretechs don’t see what they are doing as disruption but as an evolution of their strategies. 10. What Are The Advantages Of Usage-Based Car Insurance? In recent years, Usage-Based Insurance (UBI) has become one of the most talked-about technologies in the automotive insurance industry - and for good reason. 11. A Digital-First Approach Could Take the Headaches Away from Buying Insurance A deep dive into major tech-driven changes within the legacy insurance industry. 12. Startup Interview with Ato Kasymov, CEO & Co-founder of Zentist 13. How To Be a Responsible Employer and a Leader in Times of Workplace Crisis Based on the type of work you are doing and the capacity in which your workers operate, any or more of the following occupational injuries can occur to your employees. 14. "All DeFi Applications Are Shooting For The Sky But no One is Wearing a Parachute" - Allan Henders There is a lot to take away from the price performance of the digital asset market in 2020. One of such is that the concept of bitcoin halving, as expected, is a major contributing factor to the success of the bitcoin market and has eventually delivered the level of price swings projected during the pre-halving days. Another is that DeFi is perhaps the most potent application of cryptocurrency technology, which makes it the most plausible instigator of the next wave of growth. In the months following the global market crash, the DeFi landscape has attracted around $14 billion worth of capital. Why is this so? 15. Can Robots Manage Health Benefits? In an attempt to manage soaring health care costs, some government officials and health care companies are turning to algorithms to determine how to allocate limited benefits, who to provide care to first, or whether a person should receive care at all. 16. Insurance for Digital Assets Can Help to Protect Users in the Crypto Space Cryptocurrency insurance can be instrumental in reducing the losses that come with engaging in the crypto market. 17. Fintech Is Dead: All Hail Insurtech Wall Street. Tokyo. London. Shanghai. Hong Kong. 18. How I can Save my Insurance Firm During Disaster? Natural disasters are unpredictable that has the ability to destruct the
globe entirely. It has so much force that even giant construction can
get into the ground in seconds, imagine where humans would go. In
such scenarios, one should stay full secure but preparedness is the
only way to combat a natural disaster. 19. What Does 2021 Hold For Insurance on The Blockchain? Blockchain has seen multiple use cases over the years, from simple payment coins to tokenizing art. But there is a class of projects aiming to build solutions to last over the years, with a level of complexity rivaling traditional business and financial structures. 20. Insure your Bitcoin with Options [A How-To Guide] 21. Technology Is Transforming The Auto Insurance Industry Faster Than We Know It Car insurance is about as dull as things can get, right? WRONG. In the dawn of artificial intelligence, machine learning and robotics, just about every industry is undergoing huge transformation - and the insurance industry is no different. For instance, auto-reminders are now in place so clients can have a peace of mind - one of the many ways technology affords us more convenience. 22. Guide to Online Liability Insurance Application Thinking of applying for liability insurance for your business online? Congratulations, you have come to the right place! We will take you through each step of the process, so you know exactly what to expect when you apply online. 23. Cryptocurrency Insurance Can Protect You Against Hacks [Infographic] Crypto insurance is worth checking out in order to protect your cryptocurrency gains in the market. 24. Unlocking the Power of Artificial Intelligence in the Insurance Industry Today, artificial intelligence (AI) has barely begun to scratch the surface of the insurance sector. Here are a few of the most AI-friendly applications. 25. How Can Insurance Companies Pivot To A Digital-First Future With Technology? The future of the insurance industry looks bright with insurance technology trends such as AI, ML, cloud computing, hyper-automation and APIs on the rise. 26. A Buyer's Guide To Cyber Insurance, Cyber Liability, and Fraud Protection Cyber insurance can be a great way to protect your company against cybersecurity risks like fraud. But what are you liable for and what does insurance cover? 27. RPA in Insurance: Your Complete Guide 28. Unemployment Insurance Fraud Rose During the Pandemic A ProPublica investigation reveals that much of the fraud has been organized — both in the U.S. and abroad. 29. Did Insurance Companies Benefit From COVID-19? Lewis Carroll – author of Alice's Adventures in Wonderland – once wrote, “every story has a moral you just need to be clever enough to find it.” The Covid-19 pandemic, for example, has taught us that things can always take an unexpected turn. The question then emerges, how can one protect against this kind of event? The answer is very simple – insurance. 30. Providing Decentralized Insurance Services Worldwide with Hybrid Smart Contracts In this piece, the CTO of Etherisc discusses the central role that hybrid smart contracts and oracles play in enabling parametric insurance products on-chain 31. "Be ready to fire youself," advised Markus Wunsch, Executive Chairman at Mount Wish Mount Wish was nominated as one of the best startups in New York City in Startups of the Year hosted by HackerNoon. 32. 4 Challenges Of Implementing AI In The Insurance Industry and How To Overcome Them AI in insurance comes with its set of challenges that can be pivoted into opportunities. Read all about it here. 33. Implementing a Bridge Between Next-Gen Insurance Claims and Exceptional Customer Service In the insurance sector, customer service takes on multiple roles and features at various touchpoints. 34. Hacking Auto Insurance: Are Givebacks a Good Deal? COVID-19 has changed the way many of us live our lives as stores, restaurants, and more have begun closing up shop, causing many to lose their jobs or start working from home. With millions now stuck in their homes, people are driving much less. Less driving means fewer low-speed accidents, and fewer incidents of aggressive acceleration. Insurance companies have started to give back to their customers to help them financially through the pandemic. Insurance companies are offering financial assistance and more flexible payment programs, penalty-free grace period for late payments, to pause cancellation due to non-payment, and waive deductibles for commuting healthcare workers. In response to the sharp decrease in Americans’ driving mileage, some of the nation’s largest insurers have begun to refund auto insurance premiums. Companies such as Allstate, Geico, Progressive, and StateFarm have returned a total of $14 billion to policyholders. 35. Drones Will Change The Insurance Industry It is never too early to be thinking about the future. And if you are thinking about the future, think about drones. 36. How Self-Driving Cars Will Change the Auto Insurance Industry Welcome to the dawn of the self-driving car era, and a subsequent emerging challenge: how to insure them. 37. Startup Largo Coin Found a Solution to Reduce Investment Risks Largo Coin Limited has launched a blockchain-enabled warranty service targeted at securing private investors’ funds. This project looks to partner with startups and established firms by providing a trustless investment relations solution. 38. Founder Interviews: Lingke Wang of Ethos Lingke Wang and the Ethos team have recently raised over $46 million in order to simplify and modernize the archaic life insurance industry. 39. The Defi Ecosystem: Risk Reduction by Defi Insurance Everything you need to know about DeFi insurance 40. Hacking Better Solutions With AI Powered Insurance Verification A look at how improving the healthcare insurance data capture system will improve through OCR and AI card scan. 41. Research: Organizational AI Adoption in the Insurance Industry Executive Summary 42. Insurance and Blockchain: An A-Z Guide The insurance industry is a huge market. In the United States, in 2019, the volume of paid insurance premiums amounted to a trillion dollars. However, despite such volumes, the industry is experiencing great difficulties, because of which insurance companies are losing more than $100 billion every year. 43. 12 Years of Blockchain: Where Are The Applications? There were a lot of talking about where and how blockchain is suitable. Attempting to stick the blockchain everywhere and to everything is a parody. 44. Is Cyber Insurance Worthwhile? A cyber attack can cost a business everything. Cyber insurance isn’t a bulletproof shield, so is it worth it? 45. Allstate's Car Insurance Algorithm: How Insurance Algorithm Squeezes Big Spenders Seven years ago, Allstate Corporation told Maryland regulators it was time to update its auto insurance rates. The insurer said its new, sophisticated risk analysis showed it was charging nearly all of its 93,000 Maryland customers outdated premiums. Some of the old rates were off by miles. One 36-year-old man from Prince George’s County, Md., who Allstate said in public records should have been paying $3,750 every six months, was instead being charged twice that, more than $7,500. Other customers were paying hundreds or thousands of dollars less than they should have been, based on Allstate’s new calculation of the risk that they would file a claim. 46. Allstate's Car Insurance Algorithm: How Insurance Algorithm Was Analyzed State regulators and consumer advocacy groups have scrutinized Allstate Corporation’s use of big data and personalized pricing in the way it calculates how much the company charges its private auto insurance customers. Thank you for checking out the 46 most read stories about Insurance on HackerNoon. to find the most read stories about any technology. Visit the /Learn Repo

46 Stories To Learn About Insurance

309 Stories To Learn About Smart Contracts

Let's learn about via these 57 free stories. They are ordered by most time reading created on HackerNoon. Visit the to find the most read stories about any technology. Decentralized /Learn Repo 1. Is DeSci the Future of Research? The DeSci movement advocates for real decentralization, rejecting institutional influence, and supporting citizen research in the quest for truth. 2. Writing Decentralized Applications in JavaScript - Libp2p Basics Libp2p is the base of the Interplanetary Filesystem and it will be the base of Ethereum 2.0. 3. My First Impressions on BitClout I have been following alternative social networks for a while, infact I made a list. While I was browsing Twitter, I stumbled upon a newly emerging social network called BitClout. I decided to create a profile, and for those curious my handle is AMKN. You can actually follow my posts here. That said, I have some initial thoughts. 4. Hot Business Models: Revenue in the Gaming Industry No longer an arcade machine you’d drop some pennies in, video-game revenue models have evolved. 5. What is a Dapp? And What it Most Definitely is Not. Definition 6. Web 3.0 and the Metaverse Walk Into a Bar... Do they have anything to do with all of this intergalactic hype around blockchain and decentralization? 7. Why are Decentralized Messengers Better than Centralized Messengers? We are using centralized applications and losing our privacy. If you care about privacy and web3, try to consider switching to decentralized messengers. 8. The Main Challenges of Decentralization Decentralization stands in opposition to centralization and is currently rapidly breaking into the world order of the modern world. 9. Using Blockchain to Replace Commonly used Internet Services Tired of depending on centralized HTTP services, we found Ethereum can do the same but much more efficiently. 10. What Makes Facebook’s Libra More Centralized? 11. VPNs are No More Sufficient to Protect Your Privacy on the Web As criminals and central entities spoil our internet experience, people search for solutions to bypass the hurdles and enjoy the internet with ultimate freedom. 12. Damned if you do, damned if you don’t: Censorship, Decentralized Networks and Moderation The current landscape has given newfound attention to the concept of deplatforming and led many to wonder what the right solution is. 13. The Simple Rationality of Token Bonding Curves Via using mathematical functions, assets can have a reduction in volatility along with a more forward price guidance. Token Bonding Curves are exactly that. 14. Decentralizing Software Ownership with Blockchain Blockchain Startup building a decentralized code repository for community-owned software products and self-governing organizations 15. How We Built a Smart Inventory on a Blockchain Smart Inventory is a decentralized application that tracks the history and life path of valuable items. 16. The Economy of Ethereum: A Simple Explanation for Beginners A simple view at Ethereum blocksize limit and its gas fee structure in regard to being a world decentralized computer that power the new internet application. 17. 10 DeFi marketing agencies to follow: who can you trust to promote your financial protocol The decentralized finance (DeFi) has so far been one of the best-performing arts of the entire cryptocurrency industry. Its growth has powered several projects into the billion-dollar status, and it almost seems like the only place for it to go is up. 18. True Meaning Of DAOs — Understanding A New, Emerging Phenomenon DAOs set out to replace organizations as we know them today with an approach of openness and decentralization. But what are DAOs really? 19. Contests and Hackathons in Blockchain: An Overview Free TON, Solana, Near, Cosmos - we compare contests and prizes and
choose the most “generous” and favorable ecosystem for users and developers. 20. #ForTheLastTime Blockchain is More Than Just Crypto In this story, we will explain the true potential of blockchain and why it's a lot more interesting to use this technology outside of cryptocurrency. 21. Why YouTube’s biggest competition will come from decentralized video platforms YouTube creators and users are fleeing to decentralized, blockchain-based platforms, which offer greater choice and less exploitation. 22. 10 Shared Beliefs of a Bitcoin Maximalist While some maximalists assert that altcoins are just unregistered securities, most consider them technically and morally questionable, if not outright scams. 23. Overview of the Innovative Features Brought to Financial Markets by Level01 It has become common knowledge that blockchain has immense disruptive power that allows it to push a multifaceted narrative that seems to encapsulate the entirety of the global market, especially the financial markets. While this is a given, very few startups have successfully implemented blockchain solutions targeted at a large demographic and are poised to take on a financial sector that is traditionally known to thrive on intermediate services. 24. As The Bitcoin Blockchain Becomes More Centralized, We Need To Look At zk-SNARKS During the creation of the Bitcoin blockchain, Satoshi Nakamoto may don't have the proper technology/resources or vision that's why he created a blockchain that grows with every transaction and now it is 285.06 gigabytes! 25. Stablecoin Models: Evaluating Centralized Vs. Decentralized Architectures Stablecoin adoption has exploded over the last year, seeing a collective market cap rise from around $10 billion to over $100 billion. This was at least partly amplified by yield generating opportunities from the DeFi market boom and a growing interest in digital assets, providing the tokenized and frictionless benefits of moving between crypto and fiat over permissionless blockchains. 26. Understand Bitcoin in just 835 Words Caption: Chaintimes 27. Zora: What It Is and Why It's Essential for NFT Creators Zora is a universal media registry protocol on Ethereum. This protocol makes it easy to buy, create, or sell NFTs in a decentralized and permissionless fashion. 28. Providing Decentralized Insurance Services Worldwide with Hybrid Smart Contracts In this piece, the CTO of Etherisc discusses the central role that hybrid smart contracts and oracles play in enabling parametric insurance products on-chain 29. Decentralized Learning: Debunking Myths About Education Blockchain will help revolutionize the education system. The concept of "Ongoing Learning Journeys" will change the way have been seeing learning and education. 30. Build a DAO in Minutes with Zora's New Nouns Builder🧱 Nouns Builder is a new Zora tool that can help you form a DAO in minutes and then sustain it over time through Nouns DAO-like auctions. 31. Improving the Cultural Value of NFTs With TCRs 🪙 JPG just launched a token-curated registry (TCR) system that’s on track to become the first community-sourced public good of its kind. 32. Streaming just got weird. Can blockchain fix it? Tracking royalties and other IP rights across the growing cohort of streaming platforms can be very tedious. While blockchain can’t be one cure-all fix, it can still deliver feasible benefits to production, publishing and distribution of creative assets. 33. Blockchain-Based On-Chain Solutions: An Overview With more than a decade having past since the creation of Bitcoin, blockchains have come a long way. There is no doubt that the Bitcoin blockchain has its specific uses, and as developers figured out what else blockchains could be used for new types of chains had to be created. 34. A Post Pandemic Environment Will Be Ripe for The Adoption of Decentralized Stablecoins The global crisis is causing an explosive increase in demand for stablecoins. Some even say that it's stablecoins that will drive the mass adoption of crypto, not hypervolatile coins like Bitcoin. It's time to ask the question: which stablecoins will benefit more from the recession – regular ones, such as USDT, or decentralized ones, like DAI and others? 35. Blockchain Innovation to Revive the Tourism Industry in the Post-pandemic World The coronavirus pandemic affected most global industries and, probably, the tourism industry the most. One of the biggest hit was taken by airlines, with an estimate of 44% fall below 2019’s revenue Actually, in the US, “The impact on travel is six or seven times greater than the 9/11 attacks,” according to Roger Dow, president and CEO of the US Travel Association. Stepping into the post-pandemic days, the world is desperate for solutions that could revive the tourism industry. 36. Cryptographic Magic in Decentralized Storage On Web2 or the traditional web, storage systems were designed to suit
fast-growing, collaborative environments. These storage systems boasted different degrees of data availability and data security and protection from loss. As Web2 grew in adoption so did the generation of Web2 data volume and density in many formats. This data could be large files, non-transactional, often created by a single user, and may be shared over some geographical distance. This design led to the reliance on central authorities to store the world's data. 37. Middleware Is Where The Next Tens of Billions Market Is In The Polkadot Ecosystem Polkadot is a highly scalable project that can accommodate a large number of ecological niches. 38. Should Online Platforms Have Unions? In October 2019, two DoorDash drivers — Dave Levy and Nikos Kanelopoulos — launched the #DeclineNow Facebook group. 39. Examining Decentralized Exchange Token Sales Decentralized exchanges are truly having their year in crypto. We are starting to see more decentralization for capital raises facilitated by trustless, non-custodial platforms. 40. How To Decentralize Virtual Communities? The widespread use of social media has changed the way we communicate, however, not everyone thinks these changes are positive. For example, the lack of strict moderation of social media content has contributed to the spread of misinformation during the COVID-19 outbreak.Even before COVID-19, many users were unsatisfied of how social media platforms were addressing disinformation, censorship, privacy, political neutrality, user control, and malicious activity. Decentralized social networks and communities are more flexible than centralized ones, but they provide more opportunities for the disadvantages that are suppressed in centralized communities. This is the small paradox of decentralized communities. 41. How Blockchain Impacts and Benefits App Development Blockchain is driving future on its shoulder or at least contributing a great deal. Since blockchain became popular in 2017, it’s been a critical factor in several emerging technology trends. 42. Government Adoption of Decentralized Technology Can decentralization exist in the governmental sector? Let's explore whether or not that's within the realm of possibility. 43. WaykiChain CEO Gordon Gao Explores the Potential of Synths If you let me choose the best concept of DeFi, I vote for synths. The power of such protocols starts with TVL. 44. An Intro to the Decentralized Organization Chain Decentralized Organization Chain (DOC) allows companies to start their own company blockchain and receive funding by selling tokens to investors. 45. How to Evaluate Security of Different Oracle Designs The first challenge of oracle design starts with a philosophical question: What is the truth? Is the truth subjective or objective? 46. Finding Solutions to Business Woes: Will Blockchain Play A Key Role? The beauty of decentralization is in its key feature, no central authority, meaning no need for a third party in any transaction 47. Taking a Sneak Peak at NFT Aggregator Blur 🚅 Blur is an NFT marketplace built in an effort to be the “best NFT Marketplace for pro traders” 48. The Future of Organizations is Decentralized and DAO Alpha Venture DAO aims to be a community of Web3 users, thought leaders, investors, and operators who build and innovate Web3 solutions. 49. "Technology is much more advanced than that of 10-year-old blockchains," says Mr. Grygoriy Sytenko Grygoriy Sytenko, the CEO of crypto wallet OWNR Wallet, gives an insider’s view on what’s going on in the niche of Decentralized Finance. 50. Distributed Ledgers: The Next Logical Step How modern blockchain approaches the problem of data storage in decentralized systems and how a distributed ledger can be organized. 51. How to Build a DAO - the Story of One Solution How one DAO solution is one step ahead of most competitors because it automates the execution process for many smart contracts in decentralised governance. 52. Kickoff Your Application With js-libp2p I want to welcome you to libp2p! This guide will walk you through setting up a fully functional libp2p node, so that you can take control of the spacecraft 🚀 from that point on and build your application. 53. AI Startup Financing: 6 Best Seed Funding Options in 2022 So you have a new cutting-edge AI product/service idea. 54. Collabolancer is a Decentralized Freelancing Marketplace Collabolancer is an open source proof-of-concept decentralized freelancing marketplace, that enables seamless collaboration between employers, worker(s), and so 55. How The Capital Markets Industry is Being Innovated and Why You Should Be Happy About It The adaption of digital assets, along with blockchain infrastructures, has placed tremendous pressure on stock exchanges, marketplaces, and trading vehicles. 56. Introducing the True NFT Solution On-chain storage, configuration, distributed execution and GraphQL queries - presentation of a True NFT solution based on Free TON blockchain. 57. The Global App: It's a Future Thing Scroll down for call to action & source code. Thank you for checking out the 57 most read stories about Decentralized on HackerNoon. to find the most read stories about any technology. Visit the /Learn Repo

57 Stories To Learn About Decentralized

In the presentation below, Tomás Ruiz, technical lead of Witnet, presented on the importance of standardization for oracle networks, the challenges of implementing them, as well as his proposed solutions to these issues. https://youtu.be/whVIyGjf7PA?embedable=true Below are some important points to note from his presentation. What is Witnet? Witnet is a multi-chain decentralized oracle network (DON) that focuses on connecting smart contracts to online data sources in a secure and trustless manner. It is a protocol, which means that it operates as its own blockchain. The protocol features a network of called “witnesses” that are incentivized to provide data to smart contracts with . Wrongdoers or dishonest reporting is punished by having a percentage of their staked tokens “slashed” – taken away – in order to maintain a high quality of data reporting. Witnet is also a “parametric oracle”, which means that users are able to customize their data requests using a domain specific language called RADON, and consume data from any public of their choosing. For more information on how to request public APIs on Witnet check out in their documentation. layer-1 nodes Wit tokens API this section When the team started building Witnet, they intended for it to be blockchain agnostic. Blockchain agnostic solutions are protocols that are compatible with any blockchain network. Witnet focused on building a chain agnostic solution because they believed that the world was moving towards becoming more decentralized and “multi-chain”. Witnet is currently available on Ethereum, Celo, Metis ! However, back in 2017 when Witnet was being built, and , which are open source frameworks for building custom, interoperable blockchains, did not exist. Consequently, the Witnet team had to spend 2 years developing their entire platform from scratch. and more cosmos SDK substrate Why Create A Standardized Interface for Oracles? In order to streamline the user experience, the Witnet team wanted to develop an oracle solution that would be easy for smart contract developers to use. In order to achieve this, the team wanted to build out a standardized interface that allowed developers to implement Witnet in the exact same way that other oracles were being used. However, the team found that there was no common method of consuming oracles. Instead each oracle had vastly different ways of operating. According to Tomás, implementing a standardized interface for oracles would bring about many benefits. Other than improving the overall developer experience, implementing common standards for oracles will facilitate greater , which allows developers to reuse existing software modules to create new systems. Developing a standardized oracle interface would also provide the opportunity for industry players to discuss and address inconveniences related to oracle use, this strengthening one of the most important layers of the Web3 ecosystem. composability Alliance of Decentralized Oracles (ADO) The Alliance of Decentralized Oracles (ADO) was formed by a group of oracle protocols with the aim of creating standardized plug-and-play oracle interfaces in order to streamline oracle use for developers. The group, which is open to anyone interested in decentralized oracles, includes representatives from Witnet, , and among others. In 2020, ADO decided to implement a new standard, called . Unlike previous standards that were not successfully implemented, the EIP-2362 standard did not focus on how oracles should behave. Instead, it focused on how the interface would be used by developers and, according to Tomás, is extremely easy to implement and follow. Tellor BAND protocol iExec EIP-2362 For more information on the EIP-2362 standard, check out of the standard, featuring Witnet founder Adan Sanchez, Tellor co-founder, Nicholas Fett, BAND Protocol co-founder Paul Nattapatsiri and Hadrien Croubois, ex-research engineer from iExec. The demonstration details exactly how to integrate EIP-2362 into existing projects and smart contracts. this public demonstration Implications of The Blockchain Trilemma on Oracles The Blockchain Trilemma is a term coined by Ethereum founder, Vitalik Buterin. It describes a common challenge faced by developers, where they are forced to make trade-offs between security, scalability and decentralization when building a blockchain network. It is almost impossible to achieve an optimal level of all three components on one blockchain. Similarly, oracles are also constrained by the same trilemma. For example, an oracle that prioritizes decentralization & scalability, will be less secure than an oracle that is focused on security & scalability. However, it will be less decentralized than the former. In his presentation, Tomás reiterates the importance for protocols implementing oracle solutions to thoroughly research the available oracle solutions and the various features and trade-offs being made. Depending on the different trade-offs made, each oracle would be better suited for different use cases. Therefore, it is imperative that developers are fully aware of the exact trade-offs being made in each oracle solution being used. by Sasa Milic provides some insight into the differences between some of the more well-known oracles like Chainlink, Tellor and UMA. This presentation According to Tomás, it is also important to always assume that your oracle can fail. Being aware of the specific trade offs will help users to make more educated decisions and implement suitable solutions in case of an oracle failure. Check out on their dual oracle design that has sparked lively discussions in the space about possibly introducing new industry standards for oracle use. Liquity’s presentation Looking Forward Oracles are a cornerstone to DeFi, and many other blockchain use cases. Due to their significance to the entire ecosystem, should oracles be made a public good? Should the industry be looking for ways to sponsor or subsidise data requests & oracles? As explained by Tomás, this is a problem that is yet to be solved. A lot more discussion and collaboration is needed in order for the entire industry to move closer towards achieving more robust oracle solutions, and educating end-users on the proper implementations of these protocols. The Blockchain Oracle Summit was the world’s first conference to focus solely on the important role that oracles play in the wider blockchain ecosystem and their limitations. Leading speakers from across the world gathered in Berlin to share their work and experience building and using oracle solutions. Find out more about Witnet: Witnet Documentation

Tomás Ruiz on the Standardization of Oracle Networks, its Challenge, and Call To Action

1952 witnessed the world’s first computer that could learn while it was running. It was a game of checkers developed by Arthur Samuel. It has barely been half a century since then and we’re already having a conversation about whether we should commercialize self-driving cars or not. Machine Learning gave birth to some of these great advancements in technology and we’re going to dive deep into everything it can do to make our lives much easier than ever before. What can Machine Learning do Today? A lot of things, really. Machine Learning is being termed as a bridge which will help us leap into the future, transforming almost every single industry along the way. But before you get overwhelmed, let’s only focus on things that are currently popular among the business owners:- = Analyze Sales Data At present, business organizations can use analytical data so as to figure out the most valuable customers, i.e. the potential consumers that are most likely to convert into actual consumers. These companies know what consumes are browsing on social media platforms and other websites, and they target just the right ads to them. Not only can Machine Learning speed up the process of data collection, but can also analyze the data and present it to you in an understandable manner. There are many tools for sales analytics that are available online, all of which are able to generate insights from sales data, trends, and metrics to set targets and forecast future sales performance. These Machine Learning tools will help you develop strategies for improving your sales performance in both the short- and long-term. You may read more about the sales metrics where you can find the right tools for getting started with the sales analytics. = Real-Time Mobile Personalisation According to CIO Dive , up to 70% of contribution to web traffic comes from mobile devices. A recent suggests that 96% of active Facebook user accounts were accessed via smartphones and tablets. These numbers indicate a great degree of consumer inclination towards any form of mobile devices. Mobile marketers and App Developers are looking for a way to leverage it so that they can develop a highly personalized mobile experience, which is where Machine Learning comes into play. Flybits is one company that helps other companies deliver real-time mobile app personalization. This allows you to have instant cloud access to internal and external data and enables you to develop personalized mobile channels. = Image source web design stat Detect Frauds Did you know that 2017 was a record year for the rise in e-commerce fraud? The suggests that e-commerce frauds in the US rose by 30% in 2017, compared to 2016. Considering that e-commerce sales only rose by 16%, it’s really unfortunate that these frauds rose by almost twice as much as the sales did, which is an alarming statistic. Many e-commerce platforms like Amazon and Paypal have their own Machine Learning tools in order to prevent frauds ranging from abuse of promo codes to other payment-related frauds. You may read more about the kind of frauds that Machine Learning can protect you from. The benefits of Machine Learning outrank the downsides in so many ways that can save your business as well as help you gain more consumer trust. = Image source data released by Experian Personalized Recommendations Who doesn’t like scrolling through the home page of an e-commerce website just to have a look at the product recommendations section for them? Well, nowadays, consumers even want to get notified on their smartphones about the latest offers available on their favourite product. Even if many of these consumers are indifferent, it skyrockets your chances of conversion since they’re being constantly reminded of their favourite products. Amazon and Alibaba were one of the first e-commerce ventures to introduce an algorithm that improves the product recommendation process. John Bates, Senior Product Manager at Adobe, believes that by leveraging the power of Machine Learning and predictive analytics, brands can look beyond what consumers are looking for, and have them connect those dots for themselves about what they’re likely to want. = Differential Pricing Brace yourself, for the negotiation culture has finally reached the internet. I bet most of you didn’t know that the travel and retail industries are the masters of dynamic pricing, i.e. different prices for different consumers. This is another way for companies to make more money by changing the prices in accordance with the level of demand. If you didn’t guess it already, there aren’t any humans sitting behind the scenes and changing these prices in real-time, it is Machine Learning that is changing the game for these industries. Both Uber and Airbnb use Machine Learning to create different prices for each person who is using their services. Uber also uses it for minimizing the waiting time and to optimize the ride-sharing aspect of its services. Applications of Machine Learning on Different Industries , the tech giant, believes that current AI technology can boost your business’ productivity by up to 40%. Even , a popular Research and Advisory firm, predicts that by 2020, 85% of the customer interactions will be handled without a human. Accenture Gartner These machines have really profound algorithms that are designed to process a huge amount of information and to make decisions based on logic, enabling the machines to learn and to complete the task without any further programming. It goes without saying that Machine Learning has widened its spectrum and is taking over many industries. Let’s talk about some of these industries where Machine Learning has instantaneously grown popular:- = Machine Learning Application in Transportation Industry You know how much we all hate sitting in our vehicles, waiting for the lights to turn green, especially when there aren’t any vehicles coming in from the opposite side, but the traffic lights aren’t that smart, or are they? Well, Artificial Intelligence and Machine Learning algorithms seem to be taking over the streets of many countries and they’re efficiently able to predict, monitor, and manage the traffic. = Traffic Regulation Just in case you didn’t get the memo, Self-Driving Cars are the future of transportation, and companies like Tesla and Google have already started doing their test runs. In fact, Tesla automobiles already have an autopilot mode that gives their cars semi-autonomous abilities. Once activated, the autopilot mode can regulate the car’s speed, change lanes, and park it without the driver’s assistance. = Automated Driving Healthcare Industry Machine Learning Application in The diagnostics running through Artificial Intelligence collects patient’s data to diagnose and suggest possible ways to prevent/cure the disease. Bear in mind that it’s only able to project a path with existing medication and not come up with new solutions. It just has the ability to detect medical problems much faster than a human can, which is pretty huge in itself when it comes to saving lives. = Diagnostics Dr. Ziad Obermeyer, an assistant professor at Harvard Medical School, did an interview with , where he made a bold claim saying that in 20 years, radiologists might look more like cyborgs, supervising algorithms while reading thousands of studies per minute. Don’t know how true that comes out to be but companies like DeepMind and UCLH are currently working on applying Machine Learning to help speed up the segmentation process and increase accuracy in radiotherapy planning. = Radiology and Radiotherapy Stat News Finance Industry Machine Learning Application in If you’re a finance news reader or are into finance industry yourself, then you may have heard of Robo-Advisors. Robo-advisors is an online application that provides automated financial guidance. They provide portfolio management services that use Machine Learning algorithms and statistics to automatically establish and manage the investment portfolio of a client. It not only simplifies the investment process but is also cheaper than consulting a human financial advisor. = High-frequency trading, commonly termed as HFT, is a program trading platform that uses heavy duty computers to perform hundreds of thousands of trades at speeds way beyond human comprehension. According to , during 2009-2010, about 60% to 70% of U.S. trading was attributed to HFT. It means that most investment banks, pension funds, and mutual funds, make use of HFT. It uses complex algorithms that analyze multiple markets and execute orders based on market conditions, which gives an edge to master investors as they are able to benefit from minute price differences that exist only for a fraction of a second. Image Source = Portfolio Management High-Frequency Trading (HFT) Investopedia Also Read: Tips to hire PHP developer for your business Machine Learning Application in Agriculture Industry You know that these state-of-the-art approaches have come so far when we are able to predict crop yields based on historical data, and optimize productivity by multi-parametric approach, i.e. by analyzing the crops, weather conditions and even economic conditions to make the most of the yield. The same technology is being used to understand the crop quality, identify diseases and detect weeds. = Field conditions are important for the quality and quantity of the yield, which further facilitates the need for soil and water management. Machine Learning is able to study soil moisture and temperature to understand the dynamics of ecosystems and other hindrances in agriculture. Machine Learning based apps are also connected with daily, weekly and monthly estimates of evapotranspiration, enabling better use of irrigation systems. = Crop Management Field Conditions Management Machine Learning Application in Education Industry Just like you have personalized recommendations on YouTube, the education industry is bringing about changes that will only show specific content to you based on your previous courses you took. We’re talking about the learning platforms that generally offer a systemized academic lessons like Udemy, Teachable, WizIQ, and many more. These are the platforms that are capitalizing on Machine Learning for the same, with the help of which the sellers, i.e. the online coaches, are able to target the right market. = Personalized Learning Have you ever heard of the saying, “When one door closes, another door opens”? Well, times have changed, and so have the problems. We have way too many doors open for us. The problem of the present day and age is to find the right door, on top of which comes a lot of scheduling and management of related routines. which is time consuming, and that’s exactly where Machine Learning comes into play. It makes educators more efficient by completing tasks such as classroom management and scheduling, meanwhile, humans can go focus on tasks these machines can’t perform. = Increasing Efficiency Additional Information Is Machine Learning Different From Artificial Intelligence? Before you read this article further, you must realize that Artificial Intelligence and Machine Learning are quite interrelated and both of these terms will often be used together, which means, whenever I talk about Machine Learning, Artificial Intelligence will most likely be a part of that conversation. Cool? Awesome. Let’s talk about how they seem to be different:- is a technology that makes machines capable of performing tasks in ways that are "intelligent." These machines are programmed to perform more than just a single, repetitive motion. In fact, they have the ability to adapt to different situations. Artificial Intelligence is technically a branch of Artificial Intelligence, but it's a lot more specific than the overall concept. Machine learning is based on the idea that we can build machines to process data, with the help of which they can learn on their own, without requiring constant supervision. Machine learning Understanding with a Recent Example:- Elon Musk’s company Neuralink has created a chip that people can get transplanted inside their heads, which will offer them the ability to not just think really fast, but to control computer devices with just their thoughts, and even interact with AI directly and way more efficiently. But the chip is so small that no human can possibly perform that operation. Keeping that in mind, they developed a neurosurgical robot that has a job to attach very precise threads into our brain. So, the Robot that’s performing the task autonomously is because of Machine Learning but its ability to learn more and adapt accordingly is because of Artificial Intelligence. Let's Wrap Up There may be numerous applications of this technology, and surprisingly, it hasn’t even begun yet, but having made a leap that huge, that too, in such a short time span opens up so many possibilities in the present business models.  No doubt, the rise of machine learning also brings the demand of mobile applications; therefore most of the businesses and agencies & for integrating machine learning features in them. hire Android developers hire iPhone app developers Please share your feedback about this article in the comment section. If you want to add some more information, you can do this in the comment section. And, don't forget to share the article with your friends and family members.

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