Reviewerly/exHarmony huggingface dataset:
Effect of Shallow and Ensemble Learning Models for Human Activity Recognition in Uncontrolled Environment

Human activity is one of the most exploited areas in the field of artificial intelligence and wearable sensor technology. Various users create different datasets in distinct environments to classify daily living activities using a suitable learning algorithm. Data collected in the simulated or controlled environment suffers in real-time activity recognition. We created a new HAR dataset in an uncontrolled environment and compared its result with different learning models. Various algorithms based on shallow and ensemble approaches are incorporated for the experimental study, and their effect is analyzed in detail. The importance of different data pre-processing steps are laid out with empirical evidence. We managed to get an average accuracy of 98.6% and 98.85% with random forest and eXtreme gradient boosting algorithms, respectively. Further, we concluded that the ensemble approach is more suitable for activity recognition and has a potential advantage over performance evaluation.
Learning Based Joint Coding-Modulation for Digital Semantic Communication Systems

In learning-based semantic communications, neural networks have replaced different building blocks in traditional communication systems. However, the digital modulation still remains a challenge for neural networks. The intrinsic mech-anism of neural network based digital modulation is mapping continuous output of the neural network encoder into discrete constellation symbols, which is a non-differentiable function that cannot be trained with existing gradient descend algorithms. To overcome this challenge, in this paper we develop a joint coding-modulation scheme for digital semantic communications with BPSK modulation. In our method, the neural network outputs the likelihood of each constellation point, instead of having a concrete mapping. A random code rather than a deterministic code is hence used, which preserves more information for the symbols with a close likelihood on each constellation point. The joint coding-modulation design can match the modulation process with channel states, and hence improve the performance of digital semantic communications. Experiment results show that our method outperforms existing digital modulation methods in semantic communications over a wide range of SNR, and outperforms neural network based analog modulation method in low SNR regime.
Hate Speech Text Classification Using Long Short-Term Memory (LSTM)

This paper aims to build hate speech text classification model by applying a combination of LSTM and FastText. The features of hate speech & non-hate speech, target hate speech, and categories of the hate speech. Dataset of those features taken from previous research by Okky Ibrohim. FastText word embeddings is used for formation of text vectors that will be used as input of the LSTM training model. The evaluation results obtained by getting the level of accuracy using confusion matrix. The accuracy value of text classification in this study is 83.52% on the classification of hate speech, 78.44% on the classification of target labels for hate speech, 82.75% on the classification of the label for category of hate speech.
Web Browser Extension Development of Structured Query Language Injection Vulnerability Detection Using Long Short-Term Memory Algorithm

Structured Query Language Injection (SQLi) is a client-side application vulnerability that allows attackers to inject malicious SQL queries with harmful intents, including stealing sensitive information, bypassing authentication, and even executing illegal operations to cause more catastrophic damage to users on the web application. According to OWASP, the top 10 harmful attacks against web applications are SQL Injection attacks. Moreover, based on data reports from the UK's National Fraud Authority, SQL Injection is responsible for 97% of data exposures. Therefore, in order to prevent the SQL Injection attack, detection SQLi system is essential. The contribution of this research is securing web applications by developing a browser extension for Google Chrome using Long Short-Term Memory (LSTM), which is a unique kind of RNN algorithm capable of learning long-term dependencies like SQL Injection attacks. The results of the model will be deployed in static analysis in a browser extension, and the LSTM algorithm will learn to identify the URL that has to be injected into Damn Vulnerable Web Application (DVWA) as a sample-tested web application. Experimental results show that the proposed SQLi detection model based on the LSTM algorithm achieves an accuracy rate of 99.97%, which means that a reliable client-side can effectively detect whether the URL being accessed contains a SQLi attack or not.
Superiority combination learning distributed particle swarm optimization for large-scale optimization

Large-scale optimization problems (LSOPs) have become increasingly significant and challenging in the evolutionary computation (EC) community. This article proposes a superiority combination learning distributed particle swarm optimization (SCLDPSO) for LSOPs. In algorithm design, a master–slave multi-subpopulation distributed model is adopted, which can obtain the full communication and information exchange among different subpopulations, further achieving the diversity enhancement. Moreover, a superiority combination learning (SCL) strategy is proposed, where each worse particle in the poor-performance subpopulation randomly selects two well-performance subpopulations with better particles for learning. In the learning process, each well-performance subpopulation generates a learning particle by merging different dimensions of different particles, which can fully combine the superiorities of all the particles in the current well-performance subpopulation. The worse particle can significantly improve itself by learning these two superiority combination particles from the well-performance subpopulations, leading to a successful search. Experimental results show that SCLDPSO performs better than or at least comparable with other state-of-the-art large-scale optimization algorithms on both CEC2010 and CEC2013 large-scale optimization test suites, including the winner of the competition on large-scale optimization. Besides, the extended experiments with increasing dimensions to 2000 show the scalability of SCLDPSO. At last, an application in large-scale portfolio optimization problems further illustrates the applicability of SCLDPSO.
Formal convergence analysis on deterministic <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si198.svg"><mml:mrow><mml:msub><mml:mrow><mml:mi>ℓ</mml:mi></mml:mrow><mml:mrow><mml:mn>1</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:math>-regularization based mini-batch learning for RBF networks

Conventional convergence analysis on mini-batch learning is usually based on the stochastic gradient concept, in which we assume that the training data are presented in a random order. Also, some convergence results require that the learning rate should decrease with the number of training cycles, and that the objective function is a smooth function. Practically speaking, a deterministic presentation scheme with a fixed learning rate is more preferable. Hence, there is a gap between theoretical results and actual implementation. This paper aims at filling the gap. We use the radial basis function (RBF) model for nonlinear regression problems as an example to analyze the convergence properties of mini-batch learning. This paper considers a nonsmooth objective function, which consists of three terms. The coexistence of these three terms is able to handle a number of situations. The first term is a conventional training set error. The second term is a quadratic term which is used to suppress the effect of imperfections in the implementation. The last term is an ℓ1-norm term which is used to select important RBF nodes for the resultant network. Note that the ℓ1-norm term is a nonsmooth function. Although a nonsmooth ℓ1-norm is included and the mini-batch algorithm is deterministic, we are still able to derive the convergence properties, including the sufficient conditions for convergence and range of learning rate. With our results, we have a better theoretical understanding on the behaviour of mini-batch learning and obtain some guidelines on choosing the learning rate. The analysis results can be extended to other flat structural neural network models and other objective functions, which are with quadratic terms and ℓ1-norm.
KC-GCN: A Semi-Supervised Detection Model against Various Group Shilling Attacks in Recommender Systems

Various detection methods have been proposed for defense against group shilling attacks in recommender systems; however, these methods cannot effectively detect attack groups generated based on adversarial attacks (e.g., GOAT) or mixed attack groups. In this study, we propose a two-stage method, called KC-GCN, which is based on <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>k</mi> </math> -cliques and graph convolutional networks. First, we construct a user relationship graph, generate suspicious candidate groups, and extract influential users by calculating the user nearest-neighbor similarity. We construct the user relationship graph by calculating the edge weight between any two users through analyzing their similarity over suspicious time intervals on each item. Second, we combine the extracted user initial embeddings and the structural features hidden in the user relationship graph to detect attackers. On the Netflix and sampled Amazon datasets, the detection results of KC-GCN surpass those of the state-of-the-art methods under different types of group shilling attacks. The F1-measure of KC-GCN can reach above 93% and 87% on these two datasets, respectively.
Active Temporal Knowledge Graph Alignment

Entity alignment aims to identify equivalent entity pairs from different knowledge graphs (KGs). Recently, aligning temporal knowledge graphs (TKGs) that contain time information has aroused increasingly more interest, as the time dimension is widely used in real-life applications. The matching between TKGs requires seed entity pairs, which are lacking in practice. Hence, it is of great significance to study TKG alignment under scarce supervision. In this work, the authors formally formulate the problem of TKG alignment with limited labeled data and propose to solve it under the active learning framework. As the core of active learning is to devise query strategies to select the most informative instances to label, the authors propose to make full use of time information and put forward novel time-aware strategies to meet the requirement of weakly supervised temporal entity alignment. Extensive experimental results on multiple real-world datasets show that it is important to study TKG alignment with scarce supervision, and the proposed time-aware strategy is effective.
Learning Disentangled Features for Person Re-identification under Clothes Changing

Clothes changing is one of the challenges in person re-identification (ReID), since clothes provide remarkable and reliable information for decision, especially when the resolution of an image is low. Variation of clothes significantly downgrades standard ReID models, since the clothes information dominates the decisions. The performance of the existing methods considering clothes changing is still not satisfying, since they fail to extract sufficient identity information that excludes clothes information. This study aims to disentangle identity, clothes, and unrelated features with a Generative Adversarial Network (GAN). A GAN model with three encoders, one generator, and three discriminators, and its training procedure are proposed to learn these kinds of features separately and exclusively. Experimental results indicate that our model generally achieves the best performance among state-of-the-art methods in both ReID tasks with and without clothes changing, which confirms that the identity, clothes, and unrelated features are extracted by our model more precisely and effectively.
Modeling the Barriers of Blockchain Technology implementation in Supply Chain

Blockchain is an emerging technology that can transform many sectors in the age of digitization. By adding more transparency to the transitions of information and physical items, blockchain is considered a disruptive innovation that has the power to alter conventional supply chain activities. In these circumstances, blockchain technology must also be implemented in the supply chain. While implementing blockchain technology, supply chain partners face a number of barriers. As a result, the main goal of this research is to explore the prime barriers to the adoption of blockchain technology in the supply chain. In order to achieve these goals, we reviewed the literature and sought the advice of industry experts to identify the 10 main barriers to the adoption of blockchain technology. Further, the DEMATEL approach is used to categorize the finalized barriers into influential and influenced groups. The finding of this study suggests that “influential group” barriers demand more attention from the supply chain partners to minimize these barriers. The top influencing factors are “unwillingness of information sharing”, “lack of trust among SC partners” and “lack of technological advancement” and these barriers demand quick attention from supply chain stakeholders. This study looks into the barriers that stand in the way of effectively implementing blockchain in the supply chain, which will help managers do so.
User Experience of a Digital Fashion Show: Exploring the Effectiveness of Interactivity in Virtual Reality

A virtual reality (VR) environment is different from the generally produced video-based experience, as it creates an interactive user experience (UX) by allowing the users to respond to operations or commands using various input devices. In particular, as existing analog-type fashion shows that have limitations in space and time have evolved into digital fashion shows that include interaction, the importance of the naturalness of interaction and visual expression is being emphasized in UX research. However, study cases that maximize user immersion through the interaction of visual changes in the stage and clothes of a digital fashion show are scarce. As such, this study introduces an interactive VR fashion show to analyze the impact of interaction on the UX in a VR environment. In the design of the interactive VR fashion show, various interaction elements that affect the UX are selected and the scope of UX application is suggested. In addition, by utilizing a commercial game engine, the production process necessary to simulate an existing fashion show is shown step-by-step. The user test in this study is examined in-depth by dividing the evaluation of the user into a two-step survey, and the results are objectively verified through a statistical analysis.
A bidirectional DNN partition mechanism for efficient pipeline parallel training in cloud

Abstract Recently, deep neural networks (DNNs) have shown great promise in many fields while their parameter sizes are rapidly expanding. To break through the computation and memory limitation of a single machine, pipeline model parallelism is proposed for large-scale DNN training by fully utilizing the computation and storage power of the distributed cluster. Cloud data centers can also provide sufficient computing, storage and bandwidth resources. However, most existing approaches apply layer-wise partitioning, which is difficult to obtain an even model partition result because of the large computational overhead discrepancy between DNN layers, resulting in degraded efficiency. To tackle this issue, we propose “Bi-Partition”, a novel partitioning method based on bidirectional partitioning for forward propagation (FP) and backward propagation (BP), which improves the efficiency of the pipeline model parallelism system. By deliberated designing distinct cut positions for FP and BP of DNN training, workers in the pipeline get nearly equal computational loads, and the balanced pipeline fully utilizes the computing resources. Experiments on various DNN models and datasets validate the efficiency of our mechanism, e.g., the training efficiency achieving up to 1.9 $$\times$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>×</mml:mo> </mml:math> faster than the state-of-the-art method PipeDream.
Multiple targets video tracking based on extended kalman filter in combination with particle swarm optimization for intelligent applications

Abstract Multiple targets tracking is a major issue in the intelligent applications. Numerous methods have been presented for the multiple targets tracking to capture the targets trajectory in a video sequence in order to increase intelligence and reduce human error. In this paper, a method is proposed based on combining the Extended Kalman Filter (EKF) and Particle Swarm Optimization (PSO) to construct an intermediate tracker and track targets more accurately. The EKF solves targets collision problem, and PSO reduces the covariance of measured noise. Finally, the Joint Probabilistic Data Association (JPDA) filter is used to reduce the number of multiple hypotheses and create a one-to-one correspondence between targets and measurements. To detect targets, frames subtracting along with background modeling and canny edge detector are used. To reduce running time of the proposed method, number of video frames per second (fps) is reduced from 30 to 10 and the sampling rate is also reduced. Despite of this reduction, simulation reults of the proposed method show the multiple targets tracking with 98% accuracy at an acceptable running time compared to the similar methods. In addition, by using the proposed method, the number of assignment states is reduced in the targets tracking process. Overall, the proposed method not only can be used in the intelligent applications, but also in the video compression applications as well.
Image and video compression of fluid flow data

We study the compression of spatial and temporal features in fluid flow data using multimedia compression techniques. The efficacy of spatial compression techniques, including JPEG and JPEG2000 (JP2), and spatio-temporal video compression techniques, namely H.264, H.265, and AV1, in limiting the introduction of compression artifacts and preserving underlying flow physics are considered for laminar periodic wake around a cylinder, two-dimensional turbulence, and turbulent channel flow. These compression techniques significantly compress flow data while maintaining dominant flow features with negligible error. AV1 and H.265 compressions present the best performance across a variety of canonical flow regimes and outperform traditional techniques such as proper orthogonal decomposition in some cases. These image and video compression algorithms are flexible, scalable, and generalizable holding potential for a wide range of applications in fluid dynamics in the context of data storage and transfer.
Acceptability and Effectiveness of COVID-19 Contact Tracing Applications: A Case Study in Saudi Arabia of the Tawakkalna Application

Background Contact tracing applications were introduced during the COVID-19 pandemic to mitigate the spread of the infection in several countries. In Saudi Arabia, the Tawakkalna application was developed. The Tawakkalna application is a mobile health solution aimed to track infection cases, save lives, and reduce the burden on health facilities. This study aims to explore the public's attitude to and acceptance levels of the Tawakkalna application and to evaluate its effectiveness regarding privacy and security. The main objective of this study is to investigate the user acceptability of contact tracing applications and explore the safety and privacy effectiveness of the COVID-19 contact tracing application, the Tawakkalna application. In addition, the study analyzes factors associated with acceptance levels and compares the results obtained to similar studies in other countries using similar applications. Methodology This study used a valid and reliable online survey that was used in similar studies conducted in other countries to assess the acceptability of the application. The survey was conducted from September to November 2021, and the final dataset included 205 participants. To investigate the privacy and security performance of the Tawakkalna application, we followed the investigation method used by similar research that investigated 28 contact tracing applications across Europe. Results Out of the 205 participants, 84.87% were in favor of the opt-in voluntary installation of the Tawakkalna application, and 49.75% of the participants were in favor of the opt-out automatic installation. Individuals' trust in the government had a huge impact on acceptance, with 60.98% of the participants supporting the application because they believed that the Tawakkalna application would help them stay healthy during the COVID-19 pandemic. Overall, 49% of the participants supporting the application also agreed to the de-identification of their collected data and providing it for research. The Tawakkalna application ranked at the top compared to other contact tracing applications regarding privacy and security. Conclusions The Tawakkalna application developed by the Saudi Data and Artificial Intelligence Authority was a response to the COVID-19 pandemic, which is considered the biggest public health crisis in recent times. The Saudi Arabian government gained the population's acceptance through effective endorsement and the spread of educational content through media channels. By complying with privacy policies, the Tawakkalna application is an effective tool to combat public health infectious diseases.
Event-Triggered Kalman Filter and Its Performance Analysis

In estimation of linear systems, an efficient event-triggered Kalman filter algorithm is proposed. Based on the hypothesis test of Gaussian distribution, the significance of the event-triggered threshold is given. Based on the threshold, the actual trigger frequency of the estimated system can be accurately set. Combining the threshold and the proposed event-triggered mechanism, an event-triggered Kalman filter is proposed and the approximate estimation accuracy can also be calculated. Whether it is a steady system or a time-varying system, the proposed algorithm can reasonably set the threshold according to the required accuracy in advance. The proposed event-triggered estimator not only effectively reduces the communication cost, but also has high accuracy. Finally, simulation examples verify the correctness and effectiveness of the proposed algorithm.
A Weighted Deep Ensemble for Indian Sign Language Recognition

This work concentrates on developing an Indian sign language (ISL) recognition system using a forearm-worn wearable device to assist hearing-impaired persons. A novel ensemble of convolution neural networks (CNN) is proposed for robust ISL recognition using multi-sensor data. The accuracy for classification of 50 ISL signs improved from 92.5% obtained using a single CNN to 94.2% with 10 ensemble members created using the bagging approach and soft-voting for decision aggregation. Then, the ensemble of CNNs was optimized using weighted voting, where the weights were determined using a differential evolution algorithm. This further improved the classification accuracy to 96.6% with 10 ensemble members.