Reviewerly/exHarmony huggingface dataset:
Enhanced Off-Policy Reinforcement Learning With Focused Experience Replay

Utilizing the collected experience tuples in the replay buffer (RB) is the primary way of exploiting the experiences in the off-policy reinforcement learning (RL) algorithms, and, therefore, the sampling scheme for the experience tuples in the RB can be critical for experience utilization.In this paper, it is found that a widely used sampling scheme in the off-policy RL suffers from inefficiency due to the inadequate uneven sampling of experience tuples from the RB.In fact, the conventional uniform sampling of the experience tuples in the RB causes a severely unbalanced experience utilization, since experiences stored earlier in the RB is sampled with much higher frequency especially in the early stage of learning.We mitigate this fundamental problem by employing a half-normal sampling probability window that allocates a higher sampling probability to newer experiences in the RB.In addition, we propose general and local size adjustment schemes that determine the standard deviation of the half-normal sampling window to enhance the learning speed and performance and to mitigate the temporary performance degradation during training, respectively.For performance demonstration, we apply the proposed sampling technique to the state-of-the-art off-policy RL algorithms and test for various RL benchmark tasks such as MuJoCo gym and CARLA simulator.As a result, the proposed technique shows considerable learning speed and final performance improvement, especially on the tasks with large state and action space.Furthermore, the proposed sampling technique increases the stability of the considered RL algorithms, verified with less variance of the performance results across different random seeds of network initialization.
A directional margin paradigm for noise suppression in face recognition

Convolutional neural networks (CNN) have achieved outstanding face recognition (FR) performance with increasing large-scale face datasets. With face dataset size grown, noisy data will inevitably increase, undoubtedly bringing difficulties to data cleaning. In this paper, the probability that the sample belongs to noise can be determined based on the cosine distance (cosθ) of normalized angle center and face feature vector in the margin-based loss functions. According to this finding, we propose a two-step learning method integrated into the loss function. The new proposed directional margin loss function combines the noise probability with the label as the supervision information. Experiments show that our method can tolerate noisy data and get high FR accuracy when the training datasets mix with more than 30% noise. Our approach can also achieve a great result of 79.33% in MegaFace challenge one using a noisy training dataset.
The Role of Digital Transformation in Increasing the Efficiency of Banks’ Performance to Enhance Competitive Advantage

The research aims to identify the role of digital transformation in increasing the efficiency of the performance of banks listed on the Palestine Stock Exchange, identifying the digital transformation in banks and the role of digital transformation in increasing the efficiency of bank performance to enhance competitive advantage and achieve a stable financial situation. The study recommended that banks review the quality mechanisms of digital services constantly and take action to improve these services, use technology to monitor the quality of electronic services, develop innovative and innovative models to employ emerging electronic technologies to monitor performance, commit to tasks and responsibilities in accordance with the governance framework and predict deviations before they occur, and to report proactively to the management of the system. Senior departments and departments concerned with the employment of emerging electronic technologies in monitoring performance indicators in order to enhance competitive advantage.
Adaptive Bayesian State Estimation Integrating Non-stationary DGNSS Inter-Agent Distances

Bayesian navigation filters are broadly exploited in precise state estimation for kinematic applications such as vehicular positioning and navigation. Among these, Particle Filter (PF) has been shown as a valuable solution to support hybrid positioning algorithms such as sensor fusion to Global Navigation Satellite System (GNSS) and Cooperative Positioning (CP). Despite of an increased computational complexity w.r.t. conventional Kalman Filters (KFs), an effective weighting of the input measurements generally provides an improved accuracy of the output estimate. In the framework of the Differential GNSS (DGNSS) CP, this work presents an algorithm for the automated selection of the most appropriate error models for the tight-integration of non-stationary Differential GNSS (DGNSS) collaborative inter-agent distances. A model switching technique named Automated Adaptive Likelihood Switch (AALS) is proposed for a Cognitive Particle Filter (C-PF) architecture, based on the real-time approximation of the statistics of the inter-agent distances errors. The results achieved through realistic simulations demonstrated the effectiveness of the proposed solution in terms of error model selection. Therefore, an improvement of the position estimation accuracy was observed, since the cases in which DGNSS-CP would degrade performance due to possible mismodelling of the selected likelihood function are avoided.
Achieve Better Endorsement Balance on Blockchain Systems

At the beginning of the transaction flow in blockchain system Hyperedger Fabric, client have to select endorsement peer nodes to send proposals. Fabric provides two ways for client: static configuration and random selection using service discovery. However, when a large number of transaction requests arrive, the assignment of tasks to endorsement peer nodes may become unfair. The result of the above situation may lead to the high utilization of computing resources and the working pressure of Fabric nodes, which will eventually affect the transaction performance and throughput. In this paper, we propose a calculation method to accurately measure peer node endorsement load and a dynamic stochastic load minimization algorithm, DSLM. Using DSLM to dynamically determine endorsed peer nodes based on real-time work load will make the distribute of endorsement tasks more balanced. In addition, we implement our approach on Hyperledger Fabric v1.4 LTS and carry out a series of evaluation experiments. By observing the experimental results, the proposed method can effectively balance and reduce the overall workload, and improve the performance of the blockchain system in the case of high concurrency.
Designing and Developing Graphical User Interface for the MultiChain Blockchain: Towards Incorporating HCI in Blockchain

Blockchain is a revolutionary technology that is gradually changing the transaction structure, database system, and even communication system. Among many, MultiChain is one of the most prominent blockchains for deploying private blockchains. As the MultiChain platform is a script-based tool, it has a very challenging user experience. Using a research point of view, we have conducted some experiments to find a solution to make the MultiChain platform usable for people with non-technical backgrounds and came up with a graphical user interface (GUI) that is going to increase the usability of MultiChain. A within-subject evaluation study showed that the developed GUI system significantly increased the usability of MultiChain. With this new interface, MultiChain can be used in financial, educational, medical, and various other sectors with great ease.
Erasure decoding of convolutional codes using first-order representations

It is well known that there is a correspondence between convolutional codes and discrete-time linear systems over finite fields. In this paper, we employ the linear systems representation of a convolutional code to develop a decoding algorithm for convolutional codes over the erasure channel. In this kind of channel, which is important due to its use for data transmission over the Internet, the receiver knows if a received symbol is correct. We study the decoding problem using the state space description of a convolutional code, and this provides in a natural way additional information. With respect to previously known decoding algorithms, our new algorithm has the advantage that it is able to reduce the decoding delay as well as the computational effort in the erasure recovery process. We describe which properties a convolutional code should have in order to obtain a good decoding performance and illustrate it with an example.
On the Sample Complexity of solving LWE using BKW-Style Algorithms

The Learning with Errors (LWE) problem receives much attention in cryptography, mainly due to its fundamental significance in post-quantum cryptography. Among its solving algorithms, the Blum-Kalai-Wasserman (BKW) algorithm, originally proposed for solving the Learning Parity with Noise (LPN) problem, performs well, especially for certain parameter settings with cryptographic importance. The BKW algorithm consists of two phases, the reduction phase and the solving phase. In this work, we study the performance of distinguishers used in the solving phase. We show that the Fast Fourier Transform (FFT) distinguisher from Eurocrypt'15 has the same sample complexity as the optimal distinguisher, when making the same number of hypotheses. We also show that it performs much better than theory predicts and introduce an improvement of it called the pruned FFT distinguisher. Finally, we indicate, via extensive experiments, that the sample dependency due to both LF2 and sample amplification is limited.
Implications of Data Anonymization on the Statistical Evidence of Disparity

Research and practical development of data-anonymization techniques have proliferated in recent years. Yet, limited attention has been paid to examine the potentially disparate impact of privacy protection on underprivileged subpopulations. This study is one of the first attempts to examine the extent to which data anonymization could mask the gross statistical disparities between subpopulations in the data. We first describe two common mechanisms of data anonymization and two prevalent types of statistical evidence for disparity. Then, we develop conceptual foundation and mathematical formalism demonstrating that the two data-anonymization mechanisms have distinctive impacts on the identifiability of disparity, which also varies based on its statistical operationalization. After validating our findings with empirical evidence, we discuss the business and policy implications, highlighting the need for firms and policy makers to balance between the protection of privacy and the recognition/rectification of disparate impact. This paper was accepted by Chris Forman, information systems.
Determining Chess Game State from an Image

Identifying the configuration of chess pieces from an image of a chessboard is a problem in computer vision that has not yet been solved accurately. However, it is important for helping amateur chess players improve their games by facilitating automatic computer analysis without the overhead of manually entering the pieces. Current approaches are limited by the lack of large datasets and are not designed to adapt to unseen chess sets. This paper puts forth a new dataset synthesised from a 3D model that is an order of magnitude larger than existing ones. Trained on this dataset, a novel end-to-end chess recognition system is presented that combines traditional computer vision techniques with deep learning. It localises the chessboard using a RANSAC-based algorithm that computes a projective transformation of the board onto a regular grid. Using two convolutional neural networks, it then predicts an occupancy mask for the squares in the warped image and finally classifies the pieces. The described system achieves an error rate of 0.23% per square on the test set, 28 times better than the current state of the art. Further, a few-shot transfer learning approach is developed that is able to adapt the inference system to a previously unseen chess set using just two photos of the starting position, obtaining a per-square accuracy of 99.83% on images of that new chess set. The code, dataset, and trained models are made available online.
Crafting Adversarial Email Content against Machine Learning Based Spam Email Detection

While machine learning based spam detectors have proven useful, spammers are learning to bypass the detectors by modifying their email content. Adversarial attacks on machine learning models have been observed in domains such as image classification. Applying such adversarial attack algorithms to craft spam emails to evade spam email detectors, however, has limitations. Such algorithms generate adversarial perturbations in the feature space. Different from image data, translating the adversarial perturbations from the feature space to text formats, as in emails, changes the effectiveness of the adversarial perturbations. It can reduce the attack success rate in the case of spam email detection. In this paper, we study the feasibility of adversarial attacks on machine learning based spam detectors and propose two novel text crafting methods leveraging adversarial perturbations generated by the adversarial example generation algorithms to improve the attack effectiveness. One method tries to approximate the feature values and the other adds special words to original emails. In experimentation, we use PGD as an example to demonstrate and compare the effectiveness of our attack methods on spam email detectors. We also examine the transferability of the proposed attack methods on different machine learning models.
Auction-Based Consensus of Autonomous Vehicles for Multi-Target Dynamic Task Allocation and Path Planning in an Unknown Obstacle Environment

The autonomous vehicle technology has recently been developed rapidly in a wide variety of applications. However, coordinating a team of autonomous vehicles to complete missions in an unknown and changing environment has been a challenging and complicated task. We modify the consensus-based auction algorithm (CBAA) so that it can dynamically reallocate tasks among autonomous vehicles that can flexibly find a path to reach multiple dynamic targets while avoiding unexpected obstacles and staying close as a group as possible simultaneously. We propose the core algorithms and simulate with many scenarios empirically to illustrate how the proposed framework works. Specifically, we show that how autonomous vehicles could reallocate the tasks among each other in finding dynamically changing paths while certain targets may appear and disappear during the movement mission. We also discuss some challenging problems as a future work.
Separating the Effects of Batch Normalization on CNN Training Speed and Stability Using Classical Adaptive Filter Theory

Batch Normalization (BatchNorm) is commonly used in Convolutional Neural Networks (CNNs) to improve training speed and stability. However, there is still limited consensus on why this technique is effective. This paper uses concepts from the traditional adaptive filter domain to provide insight into the dynamics and inner workings of BatchNorm. First, we show that the convolution weight updates have natural modes whose stability and convergence speed are tied to the eigenvalues of the input autocorrelation matrices, which are controlled by BatchNorm through the convolution layers’ channel-wise structure. Furthermore, our experiments demonstrate that the speed and stability benefits are distinct effects. At low learning rates, it is BatchNorm’s amplification of the smallest eigenvalues that improves convergence speed, while at high learning rates, it is BatchNorm’s suppression of the largest eigenvalues that ensures stability. Lastly, we prove that in the first training step, when normalization is needed most, BatchNorm satisfies the same optimization as Normalized Least Mean Square (NLMS), while it continues to approximate this condition in subsequent steps. The analyses provided in this paper lay the groundwork for gaining further insight into the operation of modern neural network structures using adaptive filter theory.
Exploration of Classification Algorithms for Divorce Prediction

Marital life is very important for any individual. Some marriages are successful, but nowadays many are unsuccessful. Divorce petition is filed in the family court due to various reasons. The couple faces lots of emotional and mental stress during the process. In addition, divorce gives wrong impact on the couple. The married couple ends up in divorce. Prediction is better than reaction. To avoid this situation, the comfort that will prevail among the bride and the groom is analyzed, and the success is predicted before the actual marriage. Using this prediction, the unnecessary formalities, expenses, stress can be devoid of. To analyze, the dataset is collected about the pre-marital status of the couple, which enables to predict if a marriage would be successful or otherwise before getting married. In this paper, the conclusion is drawn based on the performance of multiple classification algorithms for divorce prediction dataset. Evaluation was based on several criteria like k-fold cross-validation, mapping accuracy, sensitivity to dataset size and noise. The classification algorithms considered for the study are random forest, decision tree, XGBoost, bagging, and voting classifier.
FAIR Linked Data - Towards a Linked Data Backbone for Users and Machines

Although many FAIR principles could be fulfilled by 5-star Linked Open Data, the successful realization of FAIR poses a multitude of challenges. FAIR publishing and retrieval of Linked Data is still rather a FAIRytale than reality, for users and machines. In this paper, we give an overview on four major approaches that tackle individual challenges of FAIR data and present our vision of a FAIR Linked Data backbone. We propose 1) DBpedia Databus - a flexible, heavily automatable dataset management and publishing platform based on DataID metadata; that is extended by 2) the novel Databus Mods architecture which allows for flexible, unified, community-specific metadata extensions and (search/annotation) overlay systems; 3) DBpedia Archivo an archiving solution for unified handling and improvement of FAIRness for ontologies on publisher and consumer side; as well as 4) the DBpedia Global ID management and lookup services to cluster and discover equivalent entities and properties
Detection of Telephony Spam and Scams using Recurrent Neural Network (RNN) Algorithm

Malicious phone calls, including spam and scams, caused millions of global financial losses every year and was a difficult problem over many years. This work introduces the solution based on machine learning for telecommunications without underlying the telephone network infrastructure. The major obstacle of this ten years old problem is building efficient functions without access to the telephony network infrastructures. The previous Spam Call data set is collected first. The dataset includes several labelbased features to predict malicious calling. We primarily focus on using Recurrent Neural Network (RNN) algorithm to detect the malicious calls. With the proposed features, we review different state of the art methods of machine-learning, and it is inferred that the most optimal approach can minimize malicious calls to 90% while keeping over 90% of the binary call accuracy. The outcomes also show that without significant overhead latency the models can be implemented effectively with the help of an evaluation analysis.
Foreign Currency Exchange Rate Prediction Using Bidirectional Long Short Term Memory

This chapter aims to predict the foreign currency exchange rate over twenty-two different currencies based on the US dollar. This chapter proposes three machine learning algorithms, such as ridge regression, lasso regression, decision tree, and a deep learning algorithm named Bi-directional Long Short-Term Memory (Bi-LSTM) to predict the foreign currency exchange rate. Technical analysis of foreign currency exchange is also discussed in this chapter. The authors use mean absolute error (MAE), mean square error (MSE), root mean squared error (RMSE), and mean absolute percentage error (MAPE) to measure the performance of the algorithms. Empirical findings indicate that overall performance of all the algorithms is satisfactory, but Bi-LSTM performs better than others. This study is beneficial for the stakeholders in setting a range of strategies for the foreign exchange market.
Vectofocimetry: Dioptric power measurement in manual focimeters using power vectors

Dioptric power measurement is classically achieved using manual focimeters. In this contribution, we present vectofocimetry as a novel methodology to be easily implemented in manual focimeters for improving their capabilities regarding dioptric power measurement. The new procedure is conceptually based on power vector formalism of dioptric power which represents the dioptric power as three values in the form of [M, J0, J45], it uses an iterative process of blur reduction, and it only requires a few modifications in the device that are easy-to-adapt and cost-effective. As a result, dioptric power measurements are easily and quickly obtained with comparable results to the reported ones using the traditional method but with some improvements concerning: i) comfortability/usability (no need to see through the device), ii) elimination of the accommodation user's error (no need to adjust the ocular for every user), and iii) elimination of the measurement error coming from wrong interpretation/annotation of the cylinder axis (the test reticle is focused at once as a whole). The new device is presented, the measurement methodology is rigorously described, and proof-of-principle validation is included showing comparable results between the proposed procedure/device and the conventional ones for dioptric power determination. In addition, definition of the marking methodology has been also included and described. Next steps can be directed towards automatization of the measurement process at both levels (decision for final measurement point and avoiding the manual use of the device) thus opening the gate as a potential technology for a new generation of automatic focimeters.
Load Balancing Design of Web Cluster Based on Nginx under Novel Virtualization Platform

With the increase of net citizen and the degradation of web server performance, application of load balancing technology in web cluster is a common approach to resolve the above conflict. Several popular load balancing technologies and advantages of Nginx is introduced first. Then a new implementation of Nginx load balancing is proposed, which is based on novel Cloud-View Virtual Machine (CVM) virtualization platform. After the optimization of Nginx, the response time of the proposed CVM solution and the traditional KVM solution are compared and tested. Experimental results indicate that new method can choose the rear server more reasonably, improve the performance and stability of the cluster more effectively, in addition, it can reduce system latency, and satisfy the design requirements.