Reviewerly/exHarmony huggingface dataset:
ADAPTING TO CYBERSECURITY CHALLENGES: ASSESSING THE EFFECTIVENESS OF INTERNATIONAL LAW AGAINST CYBER TERRORISM

The study critically examines current condition of global rule in resolving the developing hazard of cyber terrorism. The research dives right into the complexities and problems posed by cyber terrorism as well as reviews the effectiveness of existing lawful platforms in responding to this progressing threat. Employing a diverse series of academic jobs, the study investigates the requirements for brand-new global lawful frameworks, developments in cybercrime inspection as well as digital forensics, as well as the function of surfacing modern technologies in rule administration. Also, the report stresses the significance of enhancing global cyber resilience and response abilities by means of worldwide teamwork and collaboration. The study's searching highlights varied attributes of cyber violence and demand for a complete, collaborated, and international reaction to fight this threat. The analysis uses valuable knowledge right into advancement of brand-new lawful platforms, technologies in cybercrime examination &amp; enlargement of global cyber resilience to deal with problems positioned through cyber terrorism.
Isolation and screening of crude oil-degrading microbes from contaminated sites in Koko Community, Delta State, Nigeria

The potential use of these isolated microbes for bioremediation of contaminated sites in Koko Community, Delta State, Nigeria. Bioremediation is an attractive option for reducing the environmental damage caused by crude oil contamination. This study aimed at isolating and screening a consortium of indigenous oil microbes with biodegradation capabilities from an oil-polluted spilled site situated in Koko community Delta State, South-Southern, Nigeria. The samples were collected from the farm soil and the river water in the area. The hydrocarbon utilizing bacteria (HUC), The heterotrophic place count (HPC), Total fungi count (TFC)and hydrocarbon-utilizing fungi (HFC were identified and enumerated using standard methods. The mean HPC, HUC and TFC and HFC for the crude polluted soil was (8.70 × 104 CFU/g, 8.20 × 104 CFU/g, 2.00 × 104 CFU/g and 1.90 × 104 CFU/g respectively), farm soil was (1.18 × 107 CFU/g, 5.90 × 104 CFU/g, 1.80 × 103 CFU/g and 1.80 × 103 CFU/g respectively), crude oil polluted river was (1.77 × 104 CFU/g, 1.15 × 104 CFU/g, 1.01 × 102 CFU/g and 6.60 × 101 CFU/g respectively) and for water was (4.50 × 103 CFU/g, 1.80 × 101 CFU/g, 3.00 × 101 CFU/g and 3.00 × 101 CFU/g respectively). The bacteria and fungi isolated were Bacillus sp., Citrobacter sp., Pseudomonas, Escherichia coli, Saccharomyces sp., Penicillium sp., Aspergillus sp. and Cladosporium sp. The results of this study may also be used to inform the management of crude oil-contaminated sites in similar environments as well as be employed to design and incorporate suitable strategies for effective monitoring and management of crude oil contamination in the environment. This study concludes that that the organisms isolated have biodegradable abilities and thus ideal candidates for bioremediation in oil spill polluted environment.
3D Sports Field Registration via Parametric Learning

This paper addresses the challenge of registering a 3D sports field from a baseball pitcher scene image. Some recent works have proposed calibrating a 2D homography matrix and using it to project keypoints onto the court field. However, this approach has limitations in 2D registration scenarios. By using 3D registration instead, these systems can provide more precise data analysis and better visual effects. Furthermore, we introduce parametric model regression to predict the 3D spatial information of the sports field. Based on parametric model regression, domain generalization is employed to improve generalizability. Experiments show that our approach significantly outperforms other 2D registration methods.
On Some Characteristics of the Joint Distribution of Sample Variances

The joint distribution of correlated sample variances and their product moments have been derived. Finite expressions have been derived for product moments of sample variances of integer orders. Marginal and conditional distributions, conditional moments, coefficient of skewness and kurtosis of conditional distribution of a sample variance given the other variance have also been discussed. Shannon entropy of the distribution is also derived. When the variables are uncorrelated, the resulting characteristics match with the independent case of sample variances.
DEVELOPMENT OF EMOTIONAL INTELLIGENCE OF THE FUTURE EDUCATIONALPSYCHOLOGIST FOR EDUCATION IN A COMPLEX SOCIETY

The article presents materials of research conducted at the M. Kozybayev North Kazakhstan University. The research objective is to foster the emotional intelligence of prospective educational psychologists. The study tasks involve establishing organizational and pedagogical conditions for students' emotional intelligence development and assessing their influence on professional growth. Using the methodology of scientific and pedagogical research, such methods as review, analysis of world practice and benchmarking, pedagogical observation, questionnaire survey and experiment, control, and measurement, enabled to obtain a number of results: 1) The established impact of the proposed program on the coordinated and constructive development of emotional intelligence parameters in students, taking into account the peculiarities of their professional and pedagogical becoming. 2) The clarification of emotional intelligence resources required for academic achievements and psychological well-being of students. 3) The identification of the most effective strategies for emotional self-regulation in the professional communication environment. The scientific novelty of the research is defined by specifying the profile of emotional intelligence in university students and future school psychologists, the manifestations of this profile in the academic environment of social and pedagogical collaboration, as well as in the school community. Based on the research findings, we came to believe that the development of emotional intelligence parameters is constructively aligned with the formation of a teacher's professional competence. It implies the well-informed and systematic application of self-regulation strategies for emotional regulation by self-directed learners to enhance pedagogical thinking and build personal and professional qualities.
A Novel Hybrid Method for Effective Identification and Extraction of Digital Evidence Masked by Steganographic Techniques in WAV and MP3 Files

Anti-forensics techniques, particularly steganography and cryptography, have become increasingly pressing issues affecting current digital forensics practices. This paper advances the automation of hidden evidence extraction in audio files by proposing a novel multi-approach method. This method facilitates the correlation between unprocessed artefacts, indexed and live forensics analysis, and traditional steganographic and cryptographic detection techniques. In this work, we opted for experimental research methodology in the form of a quantitative analysis of the efficiency of the proposed automation in detecting and extracting hidden artefacts in WAV and MP3 audio files. This comparison is made against standard industry systems. This work advances the current automation in extracting evidence hidden by cryptographic and steganographic techniques during forensic investigations. The proposed multi-approach demonstrates a clear enhancement in terms of coverage and accuracy, notably on large audio files (MP3 and WAV), where manual forensic analysis is complex, time-consuming and requires significant expertise. Nonetheless, the proposed multi-approach automation may occasionally produce false positives (detecting steganography where none exists) or false negatives (failing to detect steganography that is present). However, it strikes a good balance between efficiently and effectively detecting hidden evidence, minimising false negatives and validating its reliability.
Designing a Media Literacy Training Curriculum Framework for Junior High School Teachers

Being capable of media literacy is an important competence in the contemporary era characterized by widespread digitalization. Teachers play a vital role in integrating learning and imparting it in schools. However, due to the absence of media literacy training, their level of media literacy remains low. It necessitates a solution involving the creation of a training program by designing its curriculum beforehand. This study attempts to design a curriculum framework for media literacy training for teachers. The participants were junior high school teachers in West Java, with a total sample size of 381 teachers who used purposive sampling techniques. A design research method has been used to produce a prototype for research purposes. Research procedure refers to the three decisions that determine the design outcome, covering procedure design, problem analysis and solution design Data collected with questionnaires given to teachers and it has been analyzed using descriptive statistics with SPSS software 26. Results of this study provide a curriculum framework for media literacy training for junior high school teachers in West Java, including objectives, content, methods, and assessment. Its current training curriculum framework serves as a valuable reference point for enhancing the media literacy competence of teachers.
Sentiment Classification of Post-Earthquake Consumer Brand Hate on Social Media Using Machine Learning Techniques

The widespread use of social media allows consumers to evaluate brands and to get into a direct interaction with brands and other followers of the same brands. After the devastating earthquake on February 6th, 2023, in ten provinces in Turkey a social media brand hatred was observed on two global brands Netflix and Starbucks. Brands were accused of not showing the necessary sensitivity and empathy towards the affected and the brand devotees. The objective of this study is to examine and classify brand hatred in online consumer-generated content using supervised machine learning methods. While the construct of brand hate has been extensively investigated in the discipline of marketing using different data collection methodologies, this is one of the first attempts to use machine learning methods for the analysis of the phenomenon. Unlike classic polarization, the labeling process was associated with the size of brand hatred; 0 denotes neutral reactions, -1 negative emotional reactions, and -2 negative relationship reactions. Support Vector Machines (SVM) was identified as the most successful algorithm for the explanation of the phenomenon.
Adapting Object Detection to Fisheye Cameras: A Knowledge Distillation with Semi-Pseudo-Label Approach

In this paper, we introduce a lightweight object detection system, custom-designed for fisheye cameras and optimized for quick deployment on embedded systems. Given the constraints of training solely on standard images, our methodology centers on the effective knowledge transfer to accentuate object detection in fisheye scenarios. The integration of the Parallel Residual Bi-Fusion (PRB) Feature Pyramid Network (FPN) into the state-of-the-art YOLOv7 backbone specifically addresses the challenges of detecting tiny objects often present in fisheye images.
A High-Accuracy Progressive Training Scheme to Combat the Recognition Error of MZI-ONN

This paper proposes a progressive training scheme to configure the phase shift in Mach-Zehnder interferometer (MZI)-based feedforward optical neural networks (ONNs). The optimal phase of the MZI is obtained column-by-column through a certain number of iterations to solve the problem of degradation of recognition accuracy in MZI-ONNs due to the phase error and beam splitter error. We use the Iris and the MNIST datasets to verify the effectiveness of the proposed training scheme, and the simulation results show that the proposed scheme can improve the recognition accuracy of the ONN average by 39%, which greatly improves the error-resistant performance of ONN.
Normalization in R2-Based Hypervolume and Hypervolume Contribution Approximation

In this paper, we examine the effect of normalization in R2-based hypervolume and hypervolume contribution approximation. The fact is that the region with different scales on objective space brings approximation bias. The basic idea of normalization is to perform a coordinate transformation to make the shape of the approximated region more regular, and then transform it to obtain the final value according to the property of hypervolume and hypervolume contribution. The performance of normalization is evaluated on different datasets by comparing it with the original R2-based method. We use two different metrics to evaluate hypervolume and hypervolume contribution separately, and the results indicate that normalization does exactly improve the approximation accuracy and outperforms the original R2-based method.
Video Anomaly Latent Training GAN (VALT GAN): Enhancing Anomaly Detection Through Latent Space Mining

Anomaly detection in video data plays a crucial role in numerous applications, such as industrial monitoring and automated surveillance. This paper presents a novel method for video anomaly detection (VAD) using Generative Adversarial Networks (GANs). The proposed method called VALT-GAN combines two separate branches, one for spatial information and the other for temporal information, to capture relevant features from video data. The framework is utilized to learn the normal features from the training video dataset, enabling the generator to produce realistic samples. However, existing GAN-based methods face challenges in detecting subtle or unseen anomalies. To address this, we introduce latent mining for adversarial training which allows us to train a robust GAN model with high anomaly detection (AD) capability. We exploit the latent space following the continuous nature of the generator using the Iterative Fast Gradient Signed Method (IFGSM) which improves the quality of the generated images. Experimental evaluations show the effectiveness of VALT-GAN as compared to traditional methods on UCSD (University of California, San Diego) Peds2, CUHK (Chinese University of Hong Kong) Avenue, and ShanghaiTech datasets.
Context-Adaptive Deep Learning for Efficient Image Parsing in Remote Sensing: An Automated Parameter Selection Approach

Image parsing is among the core tasks in the field of image processing and computer vision having wide-ranging applications in the areas of autonomous driving, image interpretation, medical analysis, and remote sensing. The modern techniques despite performing the labeling tasks accurately face several challenges. Among these challenges, the computation of contextual information and the selection of optimized parameters are of prime importance in pixel-wise segmentation tasks. We propose a novel context adaptive image parsing framework that utilizes a unique parameter selection strategy to produce final pixel labels. The automatic parameter selection minimizes the computational overhead, reduces the time complexity, and improves upon the segmentation labels produced. The proposed framework is evaluated on Wuhan Dense Labelling Dataset (WHDLD). In addition, a comprehensive comparison with state- of-the-art image segmentation techniques is presented. Finally, the analysis supporting the dominance of proposed architecture is presented in collation with existing techniques.
Spoken Digit Classification through Neural Networks with Combined Regularization

This report focuses on spoken digit classification using neural networks and explores regularization techniques to improve model generalization. Initially, we increased neural network complexity, leading to overfitting. To mitigate this, we applied noise injection, batch normalization, and dropout individually for regularization. We introduced ‘combined regularization’ by combining these methods, and I selected the three best performing models from both individual regularization and combined regularization. These models were ensembled via voting, and the training dataset was split into three folds, generating nine models. This analysis was conducted on a Chinese spoken digit dataset, and knowledge transfer to an English dataset showed competitive accuracy. In summary, our study enhances spoken digit classification through regularization and ensemble voting.
Resource Aware Weighted Least Connection Load Balancing Technique in Cloud Computing

Cloud computing became a pivotal for the most of the real time applications. In cloud computing, the customer demands the services with the best performance even when the application is expanding rapidly. Therefore, it is essential to manage the resources effectively because the number of users and services growing proportionately. The main aim of the load balancing technique is to allocate the customers' requests with the large pool of resources efficiently. The problem is how to evenly distribute the load of requests among the compute nodes according to their capacity. Therefore, there is a need for an effective load balancing technique for smooth continuity of operations in a distributed environment with a heterogeneous server configuration. This paper presents a novel load balancing technique, namely, Resource aware weighted least connection load balancing which addresses the above said problem efficiently. The essence of this work is to assign the requests across multiple servers based on the requested resource and the status of the number of connections presently served by each server. This work used standard score technique to enumerate the weight of each node. Experiments were conducted using Cloud Analyst, a famous cloud simulator breed from CloudSim. Appropriate performance parameters were analysed to measure the effectiveness of the proposed technique. Future directions for the extension of the implemented technique also identified.
Improving Efficiency of RetinaFace Models by Controlled Branch Pruning

The goal of this paper is to improve the efficiency of a RetinaFace model for face recognition in a specific situation where the detection of faces that are large in relation to the image resolution is required. The improvement is made in a way that will not adversely affect the performance of the model and does not require the model retraining. The method that will be considered is pruning the model used for detection, which means removing the branches of the model that contribute to the detection of smaller faces (in relation to the image resolution).
Real-Time Neural Control for Discrete Nonlinear Systems Under Unknown Input and State Disturbances

This paper presents the design of an intelligent controller for uncertain discrete-time nonlinear systems. The proposed controller is resilient to external unknown disturbances as well as state and input uncertainties, even though the model of the system is considered unknown. An intelligent controller is designed for an unknown discrete-time nonlinear system, this controller is model-free and it is based on sensor measurements and therefore including unknown system dynamics, actuator nonlinearities, measurement errors, noise, uncertainties, external disturbances and other phenomena associated to real-world applications. Then, using the neural model, a backstepping controller is designed to ensure a resilient performance. Finally, realtime results are included to demonstrate the effectiveness of the proposed approach using a three-phase induction motor.
Parameter Optimisation for Context-Adaptive Deep Layered Network for Semantic Segmentation

Evolutionary optimization methods have been utilized to optimize a wide range of models, including many complex neural network models. Manual parameter selection requires substantial trial and error and specialist domain knowledge of the inherent structure, which does not guarantee the best outcomes. We propose a three-layered novel architecture for semantic segmentation and optimize it using two distinct evolutionary algorithm-based optimization processes namely genetic algorithm and particle swarm optimization. To fully optimize an end-to-end image segmentation framework, the network design is tested using various combinations of a few parameters. An automatic search is conducted for the optimal parameter values to maximize the performance of the image segmentation framework. Evolutionary Algorithm (EA)-based optimization of the three-layered semantic segmentation network optimizes parameter values holistically, which produces the best performance. We evaluated our proposed architecture and optimization on two benchmark datasets. The evaluation results show that the proposed optimization can achieve better accuracy than the existing approaches.
Interpreting Restricted Boltzmann Machines from Optics Theory Perspectives

Currently, lack of interpretability (or explainability) is one of the major drawbacks for artificial intelligence (AI) models. When we intend to build a physical artificial intelligence (PAI) systems, the model interpretability (MI) becomes a crucial problem. To tackle MI problem, we give the explanation of restricted Boltzmann machines (RBM) from optics theory perspectives in this work. Furthermore, we present a discussion about how to implement optical learning neural network with our developed Optics Theory and Design Methods - OTDMs. With OTDMs, we can better understand the principle behind the good performance of deep neural networks. OTDMs not only give us an alternative explanation of RBM with optics theories, but also provide the guidance on designing a reliable PAI system also. Consequently, OTDMs pave the road to PAI systems, and make it to become possible for realizing all-optical learning neural network.