Reviewerly/exHarmony huggingface dataset:
A Fast Distributed Accelerated Gradient Algorithm for Big Data Classification

With the development of mobile Internet technology, various applications generate a huge amount of data in Cyber-Physical-Social systems. The exponential growth of data brings great difficulties to big data classification, especially time efficiency. The Alternating Direction Method of Multipliers (ADMM) is widely used for distributed machine learning tasks. However, it usually suffers from a slow convergence speed, and thus communication still is a significant bottleneck of distributed algorithms. To this end, in this paper, we pay attention to subproblem optimization in distributed algorithms, and propose a novel Distributed Accelerated Stochastic Variance Reduced Gradient algorithm (DAcSVRG+) for big data classification. Specially, we study the alternating direction method of multipliers for distributed learning framework, and transform the global classification problem into several small subproblems which can be solved in parallel. For the subproblem optimization, we adopt a variance reduction algorithm with Nesterov acceleration strategy, accelerated stochastic variance reduced gradient algorithm, to solve subproblems, and thus further improve the time efficiency. The experimental results on four public and benchmark datasets show that our proposed distributed algorithm can converge faster and achieve the competitive accuracy performance compared with other distributed classification methods with variance reduction.
Outlier Detection Using Machine Learning Algorithms Integrated with Bayesian Optimization

This study aimed to discuss the performance of machine learning algorithms on the outlier detection problem and to investigate the effect of Bayesian Optimization Algorithm (BOA) on improving the performance of machine learning mod-els.Firstly, Classification And Regression Tree (CART), Extreme Gradient Boosting (XGB), and Light Gradient Boosting Machine (LGBM) models were constructed. The models were trained using default parameters, and it was found that XGB and LSTM performed much better than CART. secondly, the best hyperparameters were searched using grid search, random search and Bayesian optimization, respectively, and the comparison revealed that BOA could search for the best hyperparameters in the shortest time, resulting in the maximum model performance. Finally, by analyzing the ROC and PRC curves of the three ML models tuned to the optimal hyperparameters using BOA, it is found that the combination of LSTM+BOA has the best detection effect on outlier points with an average precision of 0.911. Through the results of this study, it is confirmed that the machine learning model tuned with BOA can have a robust outlier detection capability, and the method can be applied to other method can be applied to other outlier detection problems, such as network intrusion detection.
The Mismatch between Graph Neural Networks’ Expressivity and Propagation Graphs in Fake News Detection

We investigate works under the propagation-based fake news detection domain, which recently seeks to improve performance through the use of Graph Neural Networks (GNNs). Generally, existing works argue that using GNNs can give results superior to what was obtained using classic graph-based methods. We agree with this argument given that GNNs are capable of gaining superior performance by leveraging node features. But we argue that existing works haven’t identified the fact that the expressivity of GNNs is limited and bounded by node features. Existing works do not acknowledge that, by utilizing GNNs, they implicitly assume node features are strongly correlated to node labels. There are evidence that node features that have been employed do not necessarily correlate to node label. Instead of having a profound theoretical motivation, they have empirically observed that focusing on nodes features with strong feature-label correlation can increase predictive capability. This is a sub-optimal approach to view this problem, in fact, we argue that finding node features based on correlation is not practical or effective. Our first contribution is shifting readers from a node-level view i.e correlating node features with labels, to a graph-level view. In the graph-level view, we exploit the relationship between graph isomorphism and GNNs’ expressivity which can be utilized to well understand and interpret the relation between node features and GNNs’ expressivity. We conduct a wide range of experiments on basis of both node-level view and graph-level view and found graph-level view is more interpretable and strongly matches with results. Further, we gained insights on node features that wouldn’t be obtainable by a node-level view. In order to have a fair and comprehensive analysis of node features, we built a unified dataset that includes a wide range of node features. Our results indicate, as we improve model accuracy on basis of the graph level view, models’ generalizability decreases. We provide our hypothesis for this performance trade-off on the basis of the graph-level view. Our results and insights call for a much broader discussion on whether any sort of filtering method is effective. So, we conclude our work by providing readers with possible solutions that can be helpful to find harmony between node features and GNNs’ expressivity.
The Comparison of Classification of Stress Level Related to Student's Menstrual Cycle Irregularities with Support Vector Machine and Decision Tree Algorithm

Stress is a major problem that affects both physical and mental health. The menstrual cycle is one of the stress effects. Some of the studies only focused on their relationship. Menstrual cycle irregularity becomes more severe when the stress level is high. This study proposed the stress classification related to menstrual cycle by comparing SVM and Decision Tree. Start with data collection, preprocessing data (SMOTE), splitting dataset, classifying with SVM (kernel RBF) and Decision Tree (Gini index) and analyzing the result (confusion matrix). The results show that the accuracy was increased after adding the menstrual variable. This study found that the performance of SVM is better than the Decision Tree. The accuracy for the data without menstrual was 95.88% for SVM and 90.72% for the Decision Tree. But, when the menstrual variable was added the accuracy was 96.91% for SVM and 93.81% for the Decision Tree. The highest level was experienced by the final year with medium stress level.
A Combination Reduction Algorithm and Its Application

After the Snowden incident, cryptographic subversion attack has attracted widespread attentions. Subversion attack is an unconventional attack inside machines, which has strong concealment. It will threaten the security of existing cryptography systems and seriously damage the confidentiality and integrity of communication. In this paper, we construct a subversion attack scheme on the multi-bit version of the learning with errors (LWE) encryption scheme proposed by Peikert, Vaikuntanathan and Waters, which is similar to the construction over the single-bit LWE encryption scheme. During the construction, the NTRU encryption scheme proposed by Zhang et al., is used to encrypt and decrypt the underlying message. In addition, the process of embedding underlying message into LWE ciphertext can be transformed into solving the ISIS problem. Therefore, a subversion attack scheme on the multi-bit version of LWE encryption scheme can be constructed by solving the ISIS problem successfully. With proper parameters selection, we use BKZ algorithm, BKZ algorithm and segment-LLL combined reduction algorithm to solve the ISIS problem, respectively. Finally, our experiments show that the combination reduction algorithm can improve the success rate of solving ISIS problem, and then promote the attack effect of subversion attack.
Regularization and Momentum Coefficient-Free Non-negative Latent Factor Analysis via PSO

A big-data-related application [1–11] commonly involves numerous nodes with the inherent non-negativity interaction relationships, i.e., user-item interactions in a recommender system [12–14]. Due to the exponential growth of involved nodes, it is impossible to obtain their whole interaction relationships (e.g., a user touches a tiny subset of items only). Hence, a high-dimensional and sparse (HiDS) matrix [15–19] can described such inherent non-negativity interaction relationships, which has only a few known entries (describing the known interactions) while the most others are unknown rather than zeroes (describing the unknown ones).
Bagging and random forest classification methods for unbalanced data school dropout cases in Lampung province

Classification modeling is currently growing and its use is often found in various fields of work. A lot of researches have been conducted to determine the best classification method in predicting a class of an observation. Most of it says that bagging and random forest methods are the best in predicting a class of observation. However, most of the classification methods will encounter a problem when it is used to modeling an unbalanced data. It is also known that the number of school dropout is relatively less than the number of students who are currently active, so this can be a case study of unbalanced data. The purpose of this research is to compare the performance of bagging and random forest method before handling unbalanced data and the bagging and random forest method after handling unbalanced data with Synthetic Minority Oversampling Technique (SMOTE). The comparison of performance can be seen from the sensitivity score, balanced accuracy, and F1 score of each classification method. The comparison results show that the random forest method has better performance than the bagging method, both before and after handling unbalanced data.
Reduced precision discretization based on information theory

In recent years, new technological areas have emerged and proliferated, such as the Internet of Things or embedded systems in drones, which are usually characterized by making use of devices with strict requirements of weight, size, cost and power consumption. As a consequence, there has been a growing interest in the implementation of machine learning algorithms with reduced precision that can be embedded in these constrained devices. These algorithms cover not only learning, but they can also be applied to other stages such as feature selection or data discretization. In this work we study the behavior of the Minimum Description Length Principle (MDLP) discretizer, proposed by Fayyad and Irani, when reduced precision is used, and how much it affects to a typical machine learning pipeline. Experimental results show that the use of fixed-point format is sufficient to achieve performances similar to those obtained when using double-precision format, which opens the door to the use of reduced-precision discretizers in embedded systems, minimizing energy consumption and carbon emissions.
COMPARISON OF WAVELET TRANSFORM FOR IMAGE RECOGNITION SYSTEM USING LEARNING VECTOR QUANTIZATION

Image is a spatial dimension contains information, color, and not time-dependent. Nowadays image is very important for recognition system as source/data. In order to obtain certain information (features), image transformed or extracted. Wavelet is mathematical function that is able to classify the image energy concentrated on a small group of coefficients (approximation), while other coefficient group (detail) contains very small energy which can be eliminated. This study purpose to compare wavelet family include Haar, Daubechies and Coiflet, then applied to the fingerprint recognition system and face recognition system.There are three major steps in this paper, preprocessing that includes resize normalization, histogram equalization and thresholding, feature extraction using three different wavelet family, learning using Learning Vector Quantization (LVQ) and matching using Euclidean Distance. The experimental result show Haar is the best among the others. In Fingerprint recognition system the accuracy is 85% and 90% for face recognition system.Keywords; Daubichies, Haar, Image Recognition, Learning Vector Quantization, Wavelet Comparison
IEMAM: Image-Feature Extraction Based on Multi-Scale Attention Mechanism

Large variations in the scale of visual entities make it a challenge to reduce the computational complexity of image-feature extraction. To address this challenge, this paper presents an image-feature extraction method based on multi-scale attention mechanism(IEMAM). The method first introduces a hierarchical image shrinkage strategy, which uses linear projection to control the size of image features at each layer. Next, a multi-head attention module based on dimensional trimming is proposed to reduce computational complexity. Finally, IEMAM constructs an attention aggregation structure to aggregate each local feature of multi-scale images. These qualities of IEMAM make it compatible with a better performance of object detection tasks: on the MS COCO dataset, the average accuracy of AP, AP50, and AP75 is improved by 1.98%, 5.77%, and 3.45% compared to Pyramid Vision Transformer. On the dam defect image datasets, IEMAM improves 1.21% in mAP while significantly reducing computational overhead for the same input conditions.
Vehicle Tracking and Speed Estimation Using Deep Sort

In the developing countries like India, the vehicle riders are increasing day by day, where it also constitutes to increase in the number of accidents across the country. The current system which lacks of proper surveillance is the main cause for growth of accident rate each year. Proper surveillance in heavy traffic zones such as junctions might decrease the death rate caused by the most of the accidents by tracking the vehicles in real time. To overcome this problem, we need a system that analyze a footage of camera in a selected junction calculating motion and dimensions of vehicle using You Only Look Once (YOLO v4 which is implemented using Convolution Neural Network (CNN)) and tracking vehicles using Simple Online and Realtime Tracking (SORT) with deep correlation metric and finding their speeds frame by frame accurately with efficient computations. The system is an improvised version of current tracking architectures which is capable of classifying the vehicles and calculating their speeds.
Mutation Rate Analysis Using a Self-Adaptive Genetic Algorithm on the OneMax Problem

In this paper, a variation of a genetic algorithm for optimization problems is presented, focusing on the adjustment of the mutation rate parameter by fuzzifying the diversity of the population and the value of the individual's adaptation. Here, it is important to remember that this parameter directly interferes with the convergence and quality of the solution found by the genetic algorithm. To evaluate the performance of the proposed solution, experiments were conducted on the OneMax problem, analyzing aspects such as: convergence, quality of the solution, the diversity of the population, and the number of individuals evaluated. Obtained results and their impacts are presented in this paper.
An Improved Weighted QMIX Based on Weight Function with Q-Value

Multi-agent reinforcement learning has developed rapidly in recent years. WQMIX, which bases on the Q-value decomposition is one of the most prominent algorithms recently. However, the weight function in WQMIX is a little bit simple. This function divides the actions into two parts by one condition. And because of the binary weighting of action space, the convergence speed of the mixing network is slower than QMIX’s convergence speed. To solve these problems, this paper proposes a new weight function. With this new weight function, the actions of the agents can be divided into four different categories by three conditions. The leveled actions produce different loss and the mixing network can optimize the loss function faster than the regular WQMIX. We have also given the proof that the weighted actions can divide the result of the loss function. We conducted experiments on two maps in the SMAC multi-agent reinforcement learning framework. The experiments show that in the 5m_vs_6m map, MWQMIX got an average reward of 11.6 and the maximum reward of 26. The algorism got the maximum win rate of 56%. In the more complex map, 3s5z_vs_3s5z, MWQMIX got mean reward of 12.37 and the maximum reward of 18. In this map the algorism got the maximum of 9.4%. The results show that our algorism performs better than VDN, QMIX and WQMIX in both two maps.
Channels Through Which Financial Inclusion Reduces Poverty, What is the Role of 4IR, Digital Technologies in This Process?

The chapter's goal is to demonstrate how financial inclusion can contribute to the eradication of poverty. To determine the many ways financial inclusion can help fight poverty, a thorough assessment of the literature was conducted. The study also examines how innovations from the Fourth Industrial Revolution relate to the eradication of poverty. The chapter emphasizes how the relationship between the growth of the financial markets, economic growth, and the alleviation of poverty has been a major issue in the study of economics and interest in the connection between financial development and economic growth has increased since the start of the 2007 financial crisis. Again, the chapter demonstrates that while there are theories explaining the relationship between financial inclusion and poverty, they are not always accurate and exact. The chapter concludes by demonstrating how digital financial inclusion affects poverty.
Fast situation-based correction of AI systems

In this paper we present algorithms for continuous maintenance and improvement in a broad class Artificial Intelligence (AI) system whose primary function is to detect and report various multiple co-occurring objects or events in each data frame. The data frame combines features of various objects of interests as well as their relevant environments and as such represents a “situation”. Examples of such data frames and “situations” are feature vectors of deep neural networks with YOLO backbone. A distinct property of these data is that important operational information, such as input data corresponding to errors and misclassifications, does not always have fixed features associated with it. Instead, and depending on the context, this information can move from one feature to the other making the task of learning errors difficult. Here we present a solution to this problem by exploring clustered structure of data in high-dimensional spaces and analyse the effectiveness of these algorithms when presented with training samples of full input spaces. In addition to correcting errors, we test the outlined algorithms in the task of detecting adversarial attacks in a full input space. To illustrate the concepts in use, a case study is given which demonstrates the adaptive removal of false positives in an object-detection AI census system being developed for use in industry.
Interaction and Alignment for Visible-Infrared Person Re-Identification

Visible-Infrared Person Re-Identification (VI-ReID) is a challenging person matching problem and is also a practical solution for intelligent surveillance systems at night. Due to the heterogeneity between visible and infrared modalities, the retrieval performance is seriously damaged. To address the issue of the discrepancy of the information between visible and infrared modalities, many works have been proposed. However, the relationship between cross-modality samples has rarely been mined. In this paper, we propose a Cross-modality Interaction and Alignment (CIA) module to solve the discrepancy problem. Through transforming the information between different modalities, the module guides the network to capture the modality-shared feature, which is beneficial to address the cross-modality discrepancy. Meanwhile, to better supervise the network, an enhanced contrastive loss is introduced. Contributed by the further optimization in the distance between intra-class samples, the network gains more effective supervision. Extensive experiments on two benchmark datasets show that our method achieves an excellent performance in VI-ReID.
Space-Efficient Storage Structure of Blockchain Transactions Supporting Secure Verification

The rapid growth of the blockchain size is a major bottleneck hindering its implementations in data-heavy applications. Current efforts improve the distributed storage ways and transactions' storage mechanisms of blockchain, however, the blockchain distribution and integrity are destroyed. Simplified Payment Verification (SPV) is closely related to blockchain storage, but the current solutions did not explore the privacy-preserving SPV. In this paper, we propose a new storage structure for blockchain transactions, called Coloring Index (CI), to reduce the blockchain's space occupation. Specifically, we devise an index building algorithm to simply calculate the indices of transactions for the sake of information concealing. By improving the Coloring Embedder for multi-sets query, we can store the indices into the Coloring Embedder to achieve the structured storage of transactions with small space occupation. Using CI, SPV query proceeds without revealing the user's address, thereby achieving secure data sharing in applications such as the intelligent vehicles' communications and distributed IoT. We prove CI's security against malicious full nodes when establishing possible connections between the address and the user. The experiments show that blockchain systems using our CI store one time more transactions than Merkle tree and half more than Bloom filter.
MicroBlind: Flexible and Secure File System Middleware for Application Sandboxes

Virtual machine (VM) based application sandboxes leverage strong isolation guarantees of virtualization techniques to address several security issues through effective containment of malware. Specifically, in end-user physical hosts, potentially vulnerable applications can be isolated from each other (and the host) using VM based sandboxes. However, sharing data across applications executing within different sandboxes is a non-trivial requirement for end-user systems because at the end of the day, all applications are used by the end-user owning the device. Existing file sharing techniques compromise the security or efficiency, especially considering lack of technical expertise of many end-users in the contemporary times. In this paper, we propose MicroBlind, a security hardened file sharing framework for virtualized sandboxes to support efficient data sharing across different application sandboxes. MicroBlind enables a simple file sharing management API for end users where the end user can orchestrate file sharing across different VM sandboxes in a secure manner. To demonstrate the efficacy of MicroBlind, we perform comprehensive empirical analysis against existing data sharing techniques (augmented for the sandboxing setup) and show that MicroBlind provides improved security and efficiency.
Single Deterministic Neural Network with Hierarchical Gaussian Mixture Model for Uncertainty Quantification

We propose a method to train a deterministic deep network for uncertainty quantification (UQ) with a single forward pass. Traditional Monte Carlo or ensemble based UQ methods largely leverage the variation of neural network weights to introduce uncertainty. We propose a hierarchical Gaussian mixture model (GMM) based nonlinear classifier to shape the extracted feature more flexibly and express the uncertainty by the entropy of the predicted posterior distribution. We perform large-scale training with this hierarchical GMM based loss function and introduce a natural gradient descent algorithm to update the parameters of the hierarchical GMM. With a single deterministic neural network, our uncertainty quantification approach performs well when training and testing on large datasets. We show competitive performance scores on several benchmark datasets and the out-of-distribution detection task on notable challenging dataset pairs such as CIFAR-10 vs. STL10/SVHN, and CIFAR100 vs. STL10/SVHN.