Reviewerly/exHarmony huggingface dataset:
Deep features fusion for KCF-based moving object tracking

Abstract Real-time object tracking and occlusion handling are critical research areas in computer vision and machine learning. Developing an efficient and accurate object-tracking method that can operate in real-time while handling occlusion is essential for various applications, including surveillance, autonomous driving, and robotics. However, relying solely on a single hand-crafted feature results in less robust tracking. As a hand-crafted feature extraction technique, HOG effectively detects edges and contours, which is essential in localizing objects in images. However, it does not capture fine details in object appearance and is sensitive to changes in lighting conditions. On the other hand, the grayscale feature has computational efficiency and robustness to changes in lighting conditions. The deep feature can extract features that express the image in more detail and discriminate between different objects. By fusing different features, the tracking method can overcome the limitations of individual features and capture a complete representation of the object. The deep features can be generated with transfer learning networks. However, selecting the right network is difficult, even in real-time applications. This study integrated the deep feature architecture and hand-crafted features HOG and grayscale in the KCF method to solve this problem. The object images were obtained through at least three convolution blocks of transfer learning architecture, such as Xception, DenseNet, VGG16, and MobileNet. Once the deep feature was extracted, the HOG and grayscale features were computed and combined into a single stack. In the KCF method, the stacked features acquired the actual object location by conveying a maximum response. The result shows that this proposed method, especially in the combination of Xception, grayscale, and HOG features, can be implemented in real-time applications with a small center location error.
An empirical study on English preservice teachers’ digital competence regarding ICT self-efficacy, collegial collaboration and infrastructural support

This study aims to investigate English preservice teachers' digital competence regarding their self-efficacy in information and communication technologies (ICT), their collaboration with colleagues, and the support they received from the infrastructure. A questionnaire based on the "Digital competence of educators (DigCompEdu)" was used in this research. Structural equation modeling (SEM) was applied to verify the hypothesized model using data obtained from 425 graduate students majoring in English pedagogy. This study produced significant findings: (1) English preservice teachers' ICT self-efficacy has strong or moderate positive associations with their perceptions of collegial collaboration, infrastructural support and digital competence; (2) the association between participants' perspectives on collegial collaboration and digital competence is statistically equivalent to the association between their ICT self-efficacy and digital competence; (3) although English preservice teachers' perceptions of infrastructural support have a positive association with their views on digital competence, it is not as significant as the former ones; and (4) further study is needed, as the dependent variables in this study explained only 66% of the variation in collegial collaboration, 44% in infrastructural support, and 78% in digital competence.
Space-invariant projection in streaming network embedding

The introduction of new nodes in dynamic networks gradually leads to drift in the node embedding space, and the retraining of node embeddings and downstream models is indispensable. The exact threshold of these new nodes, below which the node embedding space will be maintained, however, is rarely considered from theoretical or experimental perspectives. When considering the matrix perturbation theory, the threshold for the maximum number of new nodes to keep the node embedding space approximately equivalent is analytically provided and empirically validated. It is therefore theoretically guaranteed that when the number of new nodes is below this threshold, the embeddings of these new nodes can be quickly derived from the embeddings of the original nodes. A generation framework, space-invariant projection (SIP), is accordingly proposed to enable arbitrary static matrix factorization-based embedding schemes to quickly embed new nodes in dynamic networks. The time complexity of the SIP framework is linear with the network size. By combining SIP with four state-of-the-art MF (matrix factorization) -based schemes, we show that SIP exhibits not only wide adaptability but also strong empirical performance in terms of efficiency and efficacy in both the node classification and link prediction tasks with different real-world datasets.
QuickETC2-HQ: Improved ETC2 encoding techniques for real-time, high-quality texture compression

ETC2 is a widely-used texture compression format in Android devices and applications, so efficient ETC2 encoding can reduce application development time. We present QuickETC2-HQ, a set of improved ETC2 encoding techniques for real-time, high-quality texture compression. Our modifications to the luma-based approximations used in etcpak 1.0, the state-of-the-art encoder which integrated the QuickETC2 method, allow the execution of additional compression modes and more accurate error comparisons. As a result, the image quality of QuickETC2-HQ is improved compared to that of etcpak 1.0 and is comparable to that of the reference encoder, ETCPACK 2.74, with the fast mode. In terms of performance, QuickETC2-HQ is orders of magnitude faster than ETCPACK, making it practical for real-time application execution and offline production builds of applications.
Method and models for sentiment analysis and hidden propaganda finding

The paper describes the method and system architecture for the intellectual analysis of text and emotions to support decision-making in the field of national security and defense. Considering the latest events in the world, mass media are becoming a powerful tool for manipulating public consciousness and promoting the interests of one country over another. The article describes the methodology of collecting historical articles from a website, analyzing peak news outbreaks, and analyzing each article's text. The morphological tagging and named-entity recognition as the core of natural language processing was described. A hybrid method based on learning rules and an ensemble of machine learning methods has been developed for sentiment analysis and covert propaganda. The proposed rule-based model allows choosing the class-based lexical approach or on collected dictionaries. The combination of the methods based on dictionaries and rules with the ensemble of machine learning models are developed. The developed stacking model combines weak classifiers and deformed meta-attributes based on the results of pairwise multiplication. Finally, the distorted features are used together with the training dataset in the meta-model. This combination avoids the correlation of the results of weak classifiers and increases the generalizability of the model. The proposed approach demonstrates high accuracy and usage for Russian and Ukrainian languages. The developed method is built on Chambers's proposal. As a result of the analysis, the manipulation of public consciousness and the number of negative articles about the two countries are determined. The results of the check give us reason to consider the information spread by the media to be manipulative.
A comprehensive framework for hand gesture recognition using hybrid-metaheuristic algorithms and deep learning models

This paper presents a novel methodology that utilizes gesture recognition data, which are collected with a Leap Motion Controller (LMC), in tandem with the Spotted Hyena-based Chimp Optimization Algorithm (SSC) for feature selection and training of deep neural networks (DNNs). An expansive tabular database was created using the LMC for eight distinct gestures and the SSC algorithm was used for discerning and selecting salient features. This refined feature subset is then utilized in the subsequent training of a DNN model. A comprehensive comparative analysis is conducted to evaluate the performance of the SSC algorithm in comparison with established optimization techniques, such as Particle Swarm Optimization(PSO), Grey Wolf Optimizer(GWO), and Sine Cosine Algorithm(SCA), specifically in the context of feature selection. The empirical findings decisively establish the efficacy of the SSC algorithm, consistently achieving a high accuracy rate of 98% in the domain of gesture recognition tasks. The feature selection approach proposed emphasizes its intrinsic capacity to enhance not only the accuracy of gesture recognition systems and its wider suitability across diverse domains that require sophisticated feature extraction techniques.
Gradient boosting Bayesian neural networks via Langevin MCMC

Bayesian neural networks harness the power of Bayesian inference which provides an approach to neural learning that not only focuses on accuracy, but also uncertainty quantification. Markov Chain Monte Carlo (MCMC) methods implement Bayesian inference by sampling from the posterior distribution of the model parameters. In the case of Bayesian neural networks, the model parameters refer to weights and biases. MCMC methods suffer from scalability issues in large models, such as deep neural networks with thousands to millions of parameters. In this paper, we present a Bayesian ensemble learning framework that utilises gradient boosting by combining multiple shallow neural networks (base learners) that are trained by MCMC sampling. We present two Bayesian gradient boosting strategies that employ simple neural networks as base learners with Langevin MCMC sampling. We evaluate the performance of these methods on various classification and time-series prediction problems. We demonstrate that the proposed framework improves prediction accuracy of canonical gradient boosting, while providing uncertainty quantification via Bayesian inference. Furthermore, we demonstrate that the respective methods scale well when the size of the dataset and model increases.
Application of UAVs and Image Processing for Riverbank Inspection

Many rivers are polluted by trash and garbage that can affect the environment. Riverbank inspection usually relies on workers of the environmental protection office, but sometimes the places are unreachable. This study applies unmanned aerial vehicles (UAVs) to perform the inspection task, which can significantly relieve labor work. Two UAVs are used to cover a wide area of riverside and capture riverbank images. The images from different UAVs are stitched using the scale-invariant feature transform (SIFT) algorithm. Static and dynamic image stitching are tested. Different you only look once (YOLO) algorithms are applied to identify riverbank garbage. Modified YOLO algorithms improve the accuracy of riverine waste identification, while the SIFT algorithm stitches the images obtained from the UAV cameras. Then, the stitching results and garbage data are sent to a video streaming server, allowing government officials to check waste information from the real-time multi-camera stitching images. The UAVs utilize 4G communication to transmit the video stream to the server. The transmission distance is long enough for this study, and the reliability is excellent in the test fields that are covered by the 4G communication network. In the automatic reconnection mechanism, we set the timeout to 1.8 s. The UAVs will automatically reconnect to the video streaming server if the disconnection time exceeds the timeout. Based on the energy provided by the onboard battery, the UAV can be operated for 20 min in a mission. The UAV inspection distance along a preplanned path is about 1 km at a speed of 1 m/s. The proposed UAV system can replace inspection labor, successfully identify riverside garbage, and transmit the related information and location on the map to the ground control center in real time.
Training of Feed-Forward Neural Networks by Using Optimization Algorithms Based on Swarm-Intelligent for Maximum Power Point Tracking

One of the most used artificial intelligence techniques for maximum power point tracking is artificial neural networks. In order to achieve successful results in maximum power point tracking, the training process of artificial neural networks is important. Metaheuristic algorithms are used extensively in the literature for neural network training. An important group of metaheuristic algorithms is swarm-intelligent-based optimization algorithms. In this study, feed-forward neural network training is carried out for maximum power point tracking by using 13 swarm-intelligent-based optimization algorithms. These algorithms are artificial bee colony, butterfly optimization, cuckoo search, chicken swarm optimization, dragonfly algorithm, firefly algorithm, grasshopper optimization algorithm, krill herd algorithm, particle swarm optimization, salp swarm algorithm, selfish herd optimizer, tunicate swarm algorithm, and tuna swarm optimization. Mean squared error is used as the error metric, and the performances of the algorithms in different network structures are evaluated. Considering the results, a success ranking score is obtained for each algorithm. The three most successful algorithms in both training and testing processes are the firefly algorithm, selfish herd optimizer, and grasshopper optimization algorithm, respectively. The training error values obtained with these algorithms are 4.5 × 10-4, 1.6 × 10-3, and 2.3 × 10-3, respectively. The test error values are 4.6 × 10-4, 1.6 × 10-3, and 2.4 × 10-3, respectively. With these algorithms, effective results have been achieved in a low number of evaluations. In addition to these three algorithms, other algorithms have also achieved mostly acceptable results. This shows that the related algorithms are generally successful ANFIS training algorithms for maximum power point tracking.
Summarization of scholarly articles using BERT and BiGRU: Deep learning-based extractive approach

Extractive text summarization involves selecting and combining key sentences directly from the original text, rather than generating new content. While various methods, both statistical and graph-based, have been explored for this purpose, accurately capturing the intended meaning remains a challenge. To address this, researchers are investigating innovative techniques that harness deep learning models like BERT (Bidirectional Encoder Representations from Transformers). However, BERT has limitations in summarizing lengthy documents due to input length constraints. To find a more effective solution, we propose a novel approach. This approach combines the power of BERT, a transformer network pre-trained on extensive self-supervised datasets, with BiGRU (Bidirectional Gated Recurrent Units), a recurrent neural network that captures sequential dependencies within the text for extracting salient information. Our method involves using BERT to generate sentence-level embeddings, which are then fed into the BiGRU network. This allows us to achieve a comprehensive understanding of the complete document's context. In experimental analysis conducted on arXiv and PubMed datasets, the proposed approach outperformed several state-of-the-art models. It achieved remarkable ROUGE-F scores of (46.7, 19.4, 35.4) and (47.0, 21.3, 39.7) on these datasets respectively. The proposed fusion of BERT and BiGRU significantly enhances extractive text summarization. It shows promising potential for summarizing lengthy documents and benefiting various domains that require concise and informative summaries.
Rural accessibility to digital libraries: requirements and challenges

Purpose This study aims to determine the requirements and find out the challenges for the use of digital library services for rural areas of Capricorn District Municipality, Limpopo province. The research questions of the study are: What are the requirements for the use of digital library services in rural areas of Capricorn District Municipality? What are the challenges of accessing digital library services in rural areas? Design/methodology/approach Data was analysed thematically and deductively in this study, as researcher required to accomplish the purpose of the study through consistent structure (Zalaghi and Khazaei, 2016). In deductive analysis, the researcher starts with a set of categories, which are then used to categorize and organize data (Bertram and Christiansen, 2020). The researcher got familiar with data from the interviewing process and when transcribing data from audio tape. The transcription process was done for the coding purposes. Coding allows the researcher to simplify and focus on specific characteristics of data. Findings The study findings advocate for the establishment of digital libraries in rural areas. The library authorities are challenged to adapt digital ways of information provision. Since librarians have been providing digital content in libraries for use on users’ laptops and other gadgets, this implies that the Department of Sport, Arts and Culture has been lagging behind in finding innovative ways to provide information, especially in rural areas. To successfully keep libraries as sources of information, transformative measures have to be taken, and where possible, revisit the policies and keep drifting with the societal changes. The library authorities have to delve into new ways of providing LIS to the communities. Unquestionably, information and communication technologies have penetrated our societies and became a way of life. In addition, there are unlimited benefits which can be derived from digital technologies, especially given the lack of physical libraries in rural areas. Originality/value To the best of the authors’ knowledge, the academic research is original and has not been published anywhere before.
Challenges to Implementing the International Digital Law to Protect Digital Rights

Objective: In the ongoing digital era, digital rights are a major concern and demand measures to address challenges that encompass the management of effective law implementation. The following study aims at the contrivance and administration of international law to address digital rights. Method: Legal aspects of digital technology, also known as information technology law, is a practical field of law that has established a strong position among other legal fields in recent years, both in legal firms and educational institutions. Fresh technological advancements like massive data, the Web of Things, quantum computation, distributed ledger technology, and advanced formulas provoke inquiries concerning the governance of these technologies, such as the entitlements and safeguards that individuals possess or ought to possess. The growing utilization of electronic technologies by corporations and governments prompts various inquiries concerning the management of these technologies, specifically concerning the privileges and lawful safeguards individuals have a claim to. Result: The emphasis is primarily on the utilization and possible alteration of current (basic) entitlements. Nevertheless, the argument and lawful exploration in this domain needs a more extensive conversation regarding the novel entitlements that individuals ought to possess in the digital epoch. Occasionally, novel ideas emerge, like the concept of the 'right to erasure'. Conclusion: This piece of writing discusses the inquiry of what fresh, supplementary entitlements could be envisioned in the age of technology if we were to compose them anew, without being restricted to a predetermined collection of essential liberties. To initiate a more extensive lawful discussion on this matter, several novel entitlements for individuals in the electronic sphere are suggested.
Prediction models using artificial intelligence and longitudinal data from electronic health records: a systematic methodological review

Abstract Objective To describe and appraise the use of artificial intelligence (AI) techniques that can cope with longitudinal data from electronic health records (EHRs) to predict health-related outcomes. Methods This review included studies in any language that: EHR was at least one of the data sources, collected longitudinal data, used an AI technique capable of handling longitudinal data, and predicted any health-related outcomes. We searched MEDLINE, Scopus, Web of Science, and IEEE Xplorer from inception to January 3, 2022. Information on the dataset, prediction task, data preprocessing, feature selection, method, validation, performance, and implementation was extracted and summarized using descriptive statistics. Risk of bias and completeness of reporting were assessed using a short form of PROBAST and TRIPOD, respectively. Results Eighty-one studies were included. Follow-up time and number of registers per patient varied greatly, and most predicted disease development or next event based on diagnoses and drug treatments. Architectures generally were based on Recurrent Neural Networks-like layers, though in recent years combining different layers or transformers has become more popular. About half of the included studies performed hyperparameter tuning and used attention mechanisms. Most performed a single train-test partition and could not correctly assess the variability of the model’s performance. Reporting quality was poor, and a third of the studies were at high risk of bias. Conclusions AI models are increasingly using longitudinal data. However, the heterogeneity in reporting methodology and results, and the lack of public EHR datasets and code sharing, complicate the possibility of replication. Registration PROSPERO database (CRD42022331388).
Assessing human worker performance by pattern mining of Kinect sensor skeleton data

The human worker is an in-disposable factor in manufacturing processes. Traditional observation methods to assess their performance is time-consuming and expert-dependent, while it is still impossible to diagnose the detailed movement trajectory with the naked eye. Industry 4.0 technologies can innovate that process with smart sensors paired with data mining techniques for automated operation and develop a database of frequent movements for corporate reference and improvement. This paper proposes an approach to automatically assess worker performance with skeleton data by applying pattern mining methods and supervised learning algorithms. A use case is performed on an electrical assembly line to validate the approach, with the skeleton data collected by Kinect sensor v2. By using supervised learning, the movements of workers in each workstation can be segmented, and the line performance can be assessed. The work movement motifs can be recognized with pattern mining. The mined results can be used to further improve the production processes in terms of work procedures, movement symmetry, body utilization, and other ergonomics factors for both short and long-term human resource development. The promising result motivates further utilization of easy-to-adopt technology in Industry 5.0, which facilitates human-centric data-driven improvements.