Reviewerly/exHarmony huggingface dataset:
Prediction and Correction of Software Defects in Message-Passing Interfaces Using a Static Analysis Tool and Machine Learning

The Software Defect Prediction (SDP) method forecasts the occurrence of defects at the beginning of the software development process. Early fault detection will decrease the overall cost of software and improve its dependability. However, no effort has been made in high-performance software to address it. The contribution of this paper is predicting and correcting software defects in the Message Passing Interface (MPI) based on machine learning (ML). This system predicts defects including deadlock, race conditions, and mismatch, by dividing the model into three stages: training, testing, and prediction. The training phase extracts and combines the features as well as the label and then trains on classification. During the testing phase, these features are extracted and classified. The prediction phase inputs the MPI code and determines whether it includes defects. If it discovers a defect, the correction subsystem corrects it. We collected 40 MPI codes in C++, including all MPI communication. Results show the NB classifiers have high accuracy, precision, and recall, which are about 1.
Arabic News Classification Based on the Country of Origin Using Machine Learning and Deep Learning Techniques

With the rise of Arabic news articles published daily, people are becoming increasingly concerned about following the news from reliable sources, especially regarding events that impact their country. To assess a news article’s significance to the user, it is essential to identify the article’s country of origin. This paper proposes several classification models that categorize Arabic news articles based on their country of origin. The models were developed using comprehensive machine learning and deep learning techniques with several feature training methods. The results show the ability of our model to classify news articles based on their country of origin, with close accuracy between machine learning and deep learning techniques of up to 94%.
Anomaly Detection in Pedestrian Walkways for Intelligent Transportation System Using Federated Learning and Harris Hawks Optimizer on Remote Sensing Images

Anomaly detection in pedestrian walkways is a vital research area that uses remote sensing, which helps to optimize pedestrian traffic and enhance flow to improve pedestrian safety in intelligent transportation systems (ITS). Engineers and researchers can formulate more potential techniques and tools with the power of computer vision (CV) and machine learning (ML) for mitigating potential safety hazards and identifying anomalies (i.e., vehicles) in pedestrian walkways. The real-world challenges of scenes and dynamics of environmental complexity cannot be handled by the conventional offline learning-based vehicle detection method and shallow approach. With recent advances in deep learning (DL) and ML areas, authors have found that the image detection issue ought to be devised as a two-class classification problem. Therefore, this study presents an Anomaly Detection in Pedestrian Walkways for Intelligent Transportation Systems using Federated Learning and Harris Hawks Optimizer (ADPW-FLHHO) algorithm on remote sensing images. The presented ADPW-FLHHO technique focuses on the identification and classification of anomalies, i.e., vehicles in the pedestrian walkways. To accomplish this, the ADPW-FLHHO technique uses the HybridNet model for feature vector generation. In addition, the HHO approach is implemented for the optimal hyperparameter tuning process. For anomaly detection, the ADPW-FLHHO technique uses a multi deep belief network (MDBN) model. The experimental results illustrated the promising performance of the ADPW-FLHHO technique over existing models with a maximum AUC score of 99.36%, 99.19%, and 98.90% on the University of California San Diego (UCSD) Ped1, UCSD Ped2, and avenue datasets, respectively. Therefore, the proposed model can be employed for accurate and automated anomaly detection in the ITS environment.
Emotion recognition of social media users based on deep learning

Issues with sentiment analysis in social media include neglecting the long-distance semantic link of emotional features, failing to capture the feature words with emotional hue effectively, and depending excessively on manual annotation. This research provides a user emotion recognition model to achieve the emotional analysis of microblog public opinion events. Three types of inspiring text, "joy," "anger," and "sadness," are obtained by the data collecting and data preprocessing of micro-blog public opinion event comment text. Then, an algorithm using the linear discriminant analysis (LDA) model, emotion dictionary, and manual annotation is created to extract emotional feature words. The captured motivational text is converted into a word vector using Word2vec. After gathering the long-distance semantic data with bidirectional long short-term memories (BiLSTM) and convolutional neural networks (CNN) extract the text's key characteristics to finish the emotion categorization. The test results demonstrate an average increase in F1 value of 3.66 percent for six machine learning models and an average increase in F1 value of 1.84 percent for seven deep learning models. The suggested model performs better at identifying the emotions of social media users than the current machine learning and deep learning methods.
Adaptive Weighted Low-Rank Sparse Representation for Multi-View Clustering

Ongoing researches on multiple view data are showing competitive behavior in the machine learning field.Multi-view clustering has gained widespread acceptance for managing multi-view data and improves clustering efficiency.Large dimensionality in data from various views has recently drawn a lot of interest from researchers.How to efficiently learns the appropriate lower dimensional subspace which can manage the valuable information from the diverse views is challenging and considerable issue.To concentrate on the mentioned issue, we asserted a novel clustering approach for multiple view data through low-rank representation.We consider the importance of each view by assigning the weight control factor.We combine consensus representation with the degree of disagreement among lower rank matrices.The single objective function unifies all factors.Furthermore, we give the efficient solution to update the variable and to optimized the objective function through the Augmented Lagrange's Multiplier strategy.Real-world datasets are utilized in this study to exemplify the efficiency of the introduced technique, and it is contemplated to preceding algorithms to demonstrate its superiority.
An Improved Unscented Kalman Filtering Combined with Feature Triangle for Head Position Tracking

Aiming at the problem of feature point tracking loss caused by large head rotation and facial occlusion in doctors, this paper designs a head-position-tracking system based on geometric triangles and unscented Kalman filtering. By interconnecting the three feature points of the left and right pupil centers and the tip of the nose, they form a coplanar triangle. When the posture of the doctor’s head changes due to rotation, the shape of the corresponding geometric triangle will also deform. Using the inherent laws therein, the head posture can be estimated based on changes in the geometric model. Due to the inaccurate positioning of feature points caused by the deflection of the human head wearing a mask, traditional linear Kalman filtering algorithms are difficult to accurately track feature points. This paper combines geometric triangles with an unscented Kalman Filter (UKF) to obtain head posture, which has been fully tested in different environments, such as different faces, wearing/not wearing masks, and dark/bright light via public and measured datasets. The final experimental results show that compared to the linear Kalman filtering algorithm with a single feature point, the traceless Kalman filtering algorithm combined with geometric triangles in this paper not only improves the robustness of nonlinear angle of view tracking but also can provide more accurate estimates than traditional Kalman filters.
The implications of national blockchain infrastructure for financial crime

Purpose This paper aims to explain the implications of the impending establishment of national blockchain infrastructure by governments around the world, and how these structures can be integrated with existing legislation and assist in the prevention of financial crime. Design/methodology/approach The methodology used is a literature review and analysis of progress being made to establish national blockchain infrastructure. It provides a discussion of the connection between blockchain and financial crime, and how this infrastructure will interact with existing regulatory frameworks, and particularly, financial crime legislation. Findings This paper documents financial crime risks posed by digital currencies and smart contracts and the role that national blockchain infrastructure can potentially play in mitigating these risks. It highlights the need for governments to devote resources to developing this infrastructure and associated regulatory frameworks. Originality/value There are few, if any, academic papers in the financial crime, or wider literature, that have examined the potential for national blockchain infrastructures prevent financial crime, including the implications for existing regulation in the field.
Exploring the quantum frontier in school informatics: a pedagogical journey

This study delves into the theoretical examination, creation, and experimental testing of diverse instructional approaches aimed at introducing lyceum students to the fundamental principles of quantum informatics. Our research encompasses the following accomplishments: 1) A comprehensive analysis of relevant literature on the teaching of quantum informatics, both within Ukraine and internationally; 2) Theoretical grounding and development of the structure and content of competencies in the fundamentals of quantum informatics for lyceum students, incorporating insights from expert surveys and the European competence framework in quantum technologies; 3) Development of a robust structural and functional model for cultivating competencies in the fundamentals of quantum informatics; 4) Proposal of a methodological framework for teaching the fundamentals of quantum informatics to lyceum students through an optional course; and 5) Experimental validation of the efficacy of the developed methodology in enhancing students' competency in the fundamentals of quantum informatics. Furthermore, we outline future scientific investigations pertaining to the quantum transformation of informatics education in schools.
Windowed variation kernel Wiener filter model for image denoising with edge preservation

Due to the inherent characteristics of redundancy in natural images, sparsity and low-rank approximation can be applied to image processing tasks. However, most existing denoising algorithms based on low-rank approximation are pron to arise over-smoothing problem, resulting in a loss of details and structures. In this paper, a windowed variation kernel Wiener filter (WV-KWF) image denoising algorithm based on low-rank approximation is proposed. We first estimate the reference image of kernel Wiener filtering by using a low-rank approximation method. Then a windowed inherent variation is used to describe image local gradient information, and a shape-aware kernel function is introduced to describe image local complex structures. Finally the optimal kernel Wiener filter approach can be obtained for image denoising while preserving edges. The experimental results show that compared with the existing state-of-the-art methods, our proposed method is more competitive in maintaining image structures and removing noises both subjectively and objectively.
SL-Swin: A Transformer-Based Deep Learning Approach for Macro- and Micro-Expression Spotting on Small-Size Expression Datasets

In recent years, the analysis of macro- and micro-expression has drawn the attention of researchers. These expressions provide visual cues to an individual’s emotions, which can be used in a broad range of potential applications such as lie detection and policing. In this paper, we address the challenge of spotting facial macro- and micro-expression from videos and present compelling results by using a deep learning approach to analyze the optical flow features. Unlike other deep learning approaches that are mainly based on Convolutional Neural Networks (CNNs), we propose a Transformer-based deep learning approach that predicts a score indicating the probability of a frame being within an expression interval. In contrast to other Transformer-based models that achieve high performance by being pre-trained on large datasets, our deep learning model, called SL-Swin, which incorporates Shifted Patch Tokenization and Locality Self-Attention into the backbone Swin Transformer network, effectively spots macro- and micro-expressions by being trained from scratch on small-size expression datasets. Our evaluation outcomes surpass the MEGC 2022 spotting baseline result, obtaining an overall F1-score of 0.1366. Additionally, our approach performs well on the MEGC 2021 spotting task, with an overall F1-score of 0.1824 and 0.1357 on the CAS(ME)2 and SAMM Long Videos, respectively. The code is publicly available on GitHub.
Study on a Low-Illumination Enhancement Method for Online Monitoring Images Considering Multiple-Exposure Image Sequence Fusion

In order to improve the problem of low image quality caused by insufficient illumination, a low-light image enhancement method with robustness is proposed, which can effectively handle extremely dark images while achieving good results for scenes with insufficient local illumination. First, we expose the images to different degrees to form a multi-exposure image sequence; then, we introduce global-based luminance weights and contrast-based gradient weights to fuse the multi-exposure image sequence; finally, we use a bootstrap filter to suppress the noise that may occur during the image processing. We employ pertinent assessment criteria, such as the Peak Signal to Noise Ratio (PSNR), Structural Similarity (SSIM), the Average Gradient (AG), and the Figure Definition (FD), to assess how well the method enhances. Experimental results show that PSNR (31.32) and SSIM (0.74) are the highest in pretty dark scenes compared to most conventional algorithms such as MF, BIMEF, LECARM, etc. Similarly, in processing uneven illumination such as “moonlit night” images, the AG (10.21) and the FD (14.54) are at maximum. In addition, other evaluation metrics such as Shannon (SH) are optimal in the above scenarios. In addition, we apply the algorithm in this paper to the online monitoring images of electric power equipment, which can improve the image lightness while recovering the detail information. The algorithm has strong robustness in extremely dark images and natural low-light images, and the enhanced images have minimal distortion and best appearance in different low-light scenes.
MBAB-YOLO: A Modified Lightweight Architecture for Real-Time Small Target Detection

Current target detection methods have achieved high accuracy for detecting large and medium-sized targets.However, due to factors such as the small number of pixels and features available for targets in images, the detection performance for small targets is generally unsatisfactory.In addition, the real-time performance of target detection is also critical.In conclusion, a modified lightweight architecture for real-time small target detection, i.e., MBAB-YOLO, is proposed based on You Only Look Once (YOLO) model by combining channel-wise attention block, space-attention block and multi-branch-ConvNet (Convolutional Neural network) structure.Specifically, our method is more suitable for the rich scale information of small targets through proposed adaptive multi-receptive-field focusing, and then combines proposed blended attention block (BAB) to re-calibrate small target information to make it more prominent and improve the discriminability of small target features.Finally, extensive experiments have been conducted on the open source data set for the proposed real-time small target detection method, i.e., MBAB-YOLO.The results of ablation experiment and contrast experiment show that our method has excellent performance, not only with high detection accuracy, but also with fast detection speed.Compared with the various benchmark methods, it achieves a good trade-off between the two aspects mentioned above.In addition, this paper gives a comprehensive and detailed review of the current work about small target detection from different several perspectives, which can be used as a reference for future researchers.
Social media governance and strategies to combat online hatespeech in Germany

Abstract Concerns over online hatespeech have prompted governments to strengthen social media governance. However, claims by policy‐makers and political activists regarding the effectiveness and likely consequences of legal regulations remain largely untested. We rely on qualitative interviews and two expert surveys to examine the behavior of public relations professionals in response to online hatespeech when having the option of using the new user‐complaint mechanism under the German Network Enforcement Act (NetzDG). Our findings reveal that strategies depend on whether professionals work at public sector institutions, business, or civil society organizations and political parties. Public sector institutions are likely to report to the platform, but not under NetzDG. Civil society organizations are likely to choose content moderation, counterspeech, and other forms of intervention. Businesses deploy a wide range of strategies. In practice, Germany's procedural approach relying on user‐complaint mechanisms to deal with online hatespeech is not used by experts as a means to combat online harassment.
Impact of transmitter imbalances on the security of continuous variables quantum key distribution

Abstract Continuous-variable quantum key distribution (CV-QKD) provides a theoretical unconditionally secure solution to distribute symmetric keys among users in a communication network. However, the practical devices used to implement these systems are intrinsically imperfect, and, as a result, open the door to eavesdropper attacks. In this work, we study the impact of transmitter stage imperfections on the performance and security of a Discrete Modulated (DM) CV-QKD system using M-symbol Quadrature Amplitude Modulation (M-QAM) and Amplitude and Phase Shift Keying (M-APSK) coupled with Probabilistic Constellation Shaping (PCS). Assuming two different modulation stage topologies, we first deform the constellations and then evaluate the secure key rate achievable with the deformed constellation. The presented results show that, due to the erroneously estimated channel parameters, non-monitored imbalances greatly reduce the system’s performance, with situations where Bob and Alice estimate that no secure bits can be obtained while the real value of the key rate is still positive. Our results show the importance of monitoring these constellation imbalances and show that the optimal constellation may vary depending on the degree of device imperfection.
A hybrid recommendation algorithm based on user comment sentiment and matrix decomposition

In order to improve recommendation quality of recommendation algorithms, this paper proposes a hybrid recommendation algorithm based on user comments sentiment and matrix decomposition (abbreviate as RACSMD). This algorithm first calculates the sentiment tendency towards the user’s comment through the LSTM algorithm, and then integrates the sentiment value of the user’s rating to increase the accuracy of the user’s actual rating before combining the matrix decomposition recommendation algorithm to improve recommendation quality. This paper theoretically verifies the feasibility of RACSMD through an algorithm example. Moreover, corresponding experimental analysis is conducted on the basis of three data sets of Beeradvocate, Modcloth and Amazon. Experimental results show that the introduction to sentiment tendencies towards user comments can effectively improve recommendation quality of recommendation algorithms.
An image encryption scheme based on elementary and life-liked cellular automatons

Abstract Among the encryption technologies with chaos theory, cellular automatons with feature of discrete dynamical system and easy implementation, have unique advantages. Based on the Elementary and Life-liked cellular automaton, a new image encryption scheme is proposed in this paper. In this scheme, encryption equations are space distributed according to a chaotic map, and pixels in different areas might be encrypted with distinct encryption kernels. This fashion can provide additional security for the whole system. The simulation results and security analysis demonstrate the effectiveness and advantages of the proposed cryptosystem.
Monitors That Learn From Failures: Pairing STL and Genetic Programming

In several domains, systems generate continuous streams of data during their execution, including meaningful telemetry information, that can be used to perform tasks like preemptive failure detection. Deep learning models have been exploited for these tasks with increasing success, but they hardly provide guarantees over their execution, a problem which is exacerbated by their lack of interpretability. In many critical contexts, formal methods, which ensure the correct behavior of a system, are thus necessary. However, specifying in advance all the relevant properties and building a complete model of the system against which to check them is often out of reach in real-world scenarios. To overcome these limitations, we design a framework that resorts to monitoring, a lightweight runtime verification technique that does not require an explicit model specification, and pairs it with machine learning. Its goal is to automatically derive relevant properties, related to a bad behavior of the considered system, encoded by means of formulas of Signal Temporal Logic ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathsf {STL}$ </tex-math></inline-formula> ). Results based on experiments performed on well-known benchmark datasets show that the proposed framework is able to effectively anticipate critical system behaviors in an online setting, providing human-interpretable results.
Learning controllable elements oriented representations for reinforcement learning

Deep Reinforcement Learning (deep RL) has been successfully applied to solve various decision-making problems in recent years. However, the observations in many real-world tasks are often high dimensional and include much task-irrelevant information, limiting the applications of RL algorithms. To tackle this problem, we propose LCER, a representation learning method that aims to provide RL algorithms with compact and sufficient descriptions of the original observations. Specifically, LCER trains representations to retain the controllable elements of the environment, which can reflect the action-related environment dynamics and thus are likely to be task-relevant. We demonstrate the strength of LCER on the DMControl Suite, proving that it can achieve state-of-the-art performance. LCER enables the pixel-based SAC to outperform state-based SAC on the DMControl 100 K benchmark, showing that the obtained representations can match the oracle descriptions (i.e. the physical states) of the environment. We also carry out experiments to show that LCER can efficiently filter out various distractions, especially when those distractions are not controllable.