Reviewerly/exHarmony huggingface dataset:
A Systematic Literature Review on Requirements Engineering Practices and Challenges in Open-Source Projects

Open-source software (OSS) development has become increasingly influential in the software industry, promoting collaboration and knowledge sharing among developers and users. Along with rapidly evolving OSS projects, this paper explores requirements engineering (RE) practices and challenges through a systematic literature review (SLR). Synthesizing data from 43 selected papers, the study reports practices, techniques, and methods that assist RE activities in OSS projects, and also addresses challenges faced by practitioners and the potential solutions. The results of the literature review indicate a growing interest in using machine learning and statistical methods to assist RE activities, focusing on automated requirements identification and analysis using information from project discussion forums, issue reports, and other online resources. The findings also highlight the importance of community involvement, with many studies examining developers’ interaction patterns, expertise levels, and influence on projects. These findings provide valuable insights for OSS project managers and researchers, offering guidance on effectively handling requirements in OSS projects.
Convergence Improvement by Parameters Exchange in Asynchronous Decentralized Federated Learning for Non-IID Data

Asynchronous decentralized federated learning is a promising distributed machine learning framework from the viewpoint of the server cost saving and communication bottleneck mitigation as well as privacy and security protection. Although several methods have been proposed for asynchronous decentralized federated learning, most existing works have a problem of performance degradation for non-independent and identically distributed (non-IID) data. Even the methods aimed for non-IID data do not achieve high accuracy or fast convergence for highly non-IID data in sparse network topologies. To address this issue, we propose a new parameters exchange approach "Skip" and employ it in combination with an existing approach "Swap" to let the distributed models efficiently learn non-local data. Then, we propose two novel asynchronous decentralized federated learning methods, Greedy-Skip & Swap SGD (GSS SGD) and Topology-aware-Skip & Swap SGD (TSS SGD), by combining Skip and Swap in a topology-agnostic and topology-aware fashion, respectively. Our evaluation demonstrated that our TSS SGD outperforms existing methods for highly non-IID data, in terms of the inference accuracy and convergence speed, regardless of the sparsity of topologies.
Availability-Aware Dedicated Path Protection Schemes for Key Service in Quantum-Key-Distribution Optical Networks

Two availability-aware dedicated path protection schemes for key service were proposed to improve the availability in quantum-key-distribution (QKD) optical network. Simulation results show that the proposed schemes performs well in terms of success ratio, timeslot occupancy, and average unavailability of key services.
A Chaotic Encryption Algorithm with Dynamic Key Enabled by Power-Division Multiplexed Transmission

Using power-division multiplexing, a chaotic encryption method is proposed based on dynamic key generated from the instantaneously transmitted information. The influence of power allocation is analyzed and the security with different key mismatches is demonstrated.
Experimental Demonstration Advantage of Photonic Finite Automata

Finite automata have abundant applications such as finding reachability and so on. This work presents the first experimental implementation of the two-way quantum finite automaton with the optical platform and demonstrates its potential quantum advantages.
Analyzing DNA Pattern Matching through String Similarity Measurements in Cancer Sequence Data

Cancer is a deadly disease with actual cause still being an unknown mystery. Classifying the different kinds of tumors is rather essential for diagnosing cancer and discovering new treatments. The majority of prior cancer classification studies, however, are clinically oriented, which limits their diagnostic utility. Classifying cancer using gene expression data is crucial to cancer diagnosis and medication discovery. Genes of DNA convey information about cause of cancer and hence search for patterns associated with cancer in human DNA sequence is prominent. Some small stretches of human DNA include cancer-causing gene sequences. This proposed work applies various similarity measures used in string similarity into bioinformatics and evaluates the best method that can used in cancer pattern searching. Ten distance measures are considered and tested on 15 human DNA sequence dataset searching eight cancer patterns. To ascertain whether a cancer pattern is found exactly or approximately, distance measures build a number of mathematical equations. Therefore, out of ten distance, only seven of them yield exact match and amidst them Levenshtein is very computationally intensive. Closest matches are found using Jaro Winkler, Jaccard, and Cosine distances out of the total of ten distance metrics. Obtaining a precise match using the Levenshtein distance requires a lot of processing power. Six distance measures such as Euclidean, Manhattan, Minkowski, Canberra, Hamming and Jaro distance have been identified as the best metrics in terms of accuracy, time and space complexity, are applicable metrics for pattern search in biological sequences.
Reservoir Computing in Rehabilitation Video Analyses

Spatio-temporal information processing is fundamental in rehabilitation video analyses. Current strategies for spatio-temporal pattern recognition usually involve explicit feature extraction followed by feature aggregation, which requires a large amount of labeled data. There are two major challenges for recognizing complex spatio-temporal patterns. One is on extracting the representative features; and the other is on extracting the temporal structure, in particular, the temporal order, of image sequences. Reservoir Computing (RC) offers a promising tool by enabling automatic and accurate analyses of complex movement patterns in rehabilitation videos. Unlike traditional machine learning techniques, RC does not require explicit training of the reservoir connections, which is less data- demanding, more resistant to overfilling and easier to implement. The unique properties make it an attractive option for analyzing rehabilitation video data, where patients may experience changes in their movement patterns over time with a large data flow for processing. By analyzing new video data, the reservoir can adapt and refine its output to reflect the patient’s current conditions.
Prediction of Loan Pricing on the basis of Area Location using K-Nearest Neighbour and Support Vector Machine Learning Algorithms

-Estimating the Loan amount settlement depends on the area of location and issuance of Loan for the customer of the bank is the risk-taking process. This research aims to employ a novel K-Nearest Neighbour (KNN) approach, in contrast to the Support Vector Machine (SVM) algorithm, for estimating loan prices based on geographical factors. The objective is to enhance the accuracy of loan price predictions within the banking system. Datasets were downloaded from kaggle.com and used to train utilising Novel KNN algorithms on 346 records with 10 attributes, with a sample size of 10. The model will utilize advanced K-Nearest Neighbour and Support Vector Machines to analyze and forecast loan defaulters across diverse geographical locations. KNN and SVM have a significance level less than 0.05 with two tailed independent sample tests with value (p<0.001), and the 95 percent confidence interval, KNN obtains an accuracy level of 71 percent vs 52 percent for SVM. In the comparison between the Support Vector Machine method and the K-Nearest Neighbour technique for predicting loan pricing based on dataset locations, the K-Nearest Neighbour algorithm demonstrates superior accuracy.
Enhancing Document Digitization: Image Denoising with a Cycle Generative Adversarial Network

In the era of big data, we now live on, there is an increasing demand to convert large amounts of scanned documents, such as texts, medical records, and images into digital formats. However, often when scanning introduces imperfections such as salt-and-pepper or background noise, blurring caused by camera motion, watermarking, coffee stains, wrinkles, or faded text. These imperfections carry significant challenges to current algorithms of text recognition, leading to a decline in their performance. To date, a wide range of methods are aimed at reducing noise. This work compares the performance of a CycleGAN model concerning median filter, Wiener filter, adaptive threshold, morphological filtering, and a CNN-based autoencoder. While the CNN-based autoencoder technique gave us the best results, the CycleGAN model approach provided us with comparable results with only 50 training epochs in contrast to the 700 epochs of the CNN-based autoencoder and was superior to the rest of the other contrasted methods. Likewise, data preparation for the training is much simpler in the CycleGAN model due to its property of requiring only unpaired data for training.
How Are Digital Immigrants Adapting to Online Learning?

Changes in the learning method at the Faculty of Social and Political Sciences, the University of Bengkulu, are drastic, and available options are to organize lectures virtually. This article wants to identify how digital immigrants adapt to online learning and the constraints and solutions given in online learning at the University of Bengkulu. The research method uses a mixed method. Quantitative methods are used to answer measurement ability literacy technology in learning. Qualitative methods are used to dig for completeness of information related to constraints and solutions in online learning. Data collection was carried out with questionnaires, interviews, and observations. Research results show that 91.77% of the use of social media applications and learning online is 91.77%. Constraints include limited time in using the application's usual Zoom meetings, difficulties in understanding the features of the application learning online, and how difficult it is to arrange effective use when the number of participants is too many. The solution with increased feature application is for premium Zoom meetings to overcome time and availability limitations. Work the same good and announce between lecturers and students in the study and teach activity process in a manner online.
Aplikasi Pemetaan Pertambangan Berbasis Web GIS (Geographic Information System) di Sulawesi Utara

This research aims to apply GIS (geographic information system) to a website-based mining mapping application in North Sulawesi Province as a source of information that can be accessed to facilitate the monitoring of mining activities. The public can find out the location of mining areas and mining information. The method used to identify stages in the system is the waterfall method. The system was created using Sublime Text 3, PHP as a programming language, and a MySQL database. The research results show that the implementation and testing of the application are smooth, with every feature functioning optimally. Users get information from the website regarding mining coordinates (locations) and their distribution, as well as information from each mining location.
PENGARUH BELAJAR MANDIRI DAN MOTIVASI BELAJAR TERHADAP PRESTASI BELAJAR SISWA DI MTS NEGERI 3 KOTA BEKASI

This study aims to determine the effect of independent learning variables and student motivation on learning achievement in PAI subjects. The research was conducted at MTsN 3 Bekasi City from November to December 2022. This type of research is quantitative with a correlational descriptive approach, the nature of the data is ex post facto. The sample in this study were 50 students who were taken 20% of the total population of 128 students spread across grades IX A and IX B using a purposive sampling technique. previously validated questionnaires were used to collect data. Data analysis used multiple regression, by carrying out previous classical assumption tests including normality tests, heteroscedasticity tests, autocorrelation tests, and multicollinearity tests. The data analysis carried out yielded the following results: 1) independent learning did not have a significant effect on PAI learning achievement, and 2) learning motivation had a significant effect on PAI learning achievement, 3) independent learning and student motivation together had a significant effect on PAI learning achievement. Based on the results of the F test, the calculated F value is 8.160. At a significance level of 5%, an F table value of 3.231 is obtained. So, F count 8.160 &gt; 3.231 F table Thus, H1 is approved and H0 is rejected. This means that there is a significant influence between independent learning and Learning Motivation on Student Achievement MTsN 3 Bekasi City.
Computer Aided Breast Cancer Diagnosis using Arithmetic Optimization Algorithm with Fuzzy Logic on Histopathological Images

Computer-aided breast cancer (BC) detection is a medical application that utilizes machine learning approaches and computer algorithms to support doctors in the diagnosis and detection of BC. The primary target is to enhance the efficiency and accuracy of BC diagnosis and screening. BC detection on histopathological images (HPI) is the process of analyzing tissue samples collected during surgery or biopsy to identify cancer cells. By examining these tissue samples under a microscope HPI is acquired and used for the BC grading and diagnosis. This study presents a new Computer Aided Breast Cancer Diagnosis by employing an Arithmetic Optimization Algorithm with Machine Learning (CABCD-AOAML) approach on Histopathological Images. The presented CABCD-AOAML approach intends to detect and classify the occurrence of BC. In the initial phase, the CABCD-AOAML technique applies the CLAHE model for the contrast enhancement process. Besides, the CABCD-AOAML technique uses an ensemble feature fusion process, comprising residual network (ResNet) and DenseNet model. To adjust the hyperparameters of the DL models, the AOA is used to improve the performance. Lastly, the adaptive neuro-fuzzy inference system (ANFIS) approach is employed for the recognition and classification of BC. The investigational output of the CABCD-AOAML technique is assessed on the BreakHis database. The attained outputs portray the better achievement of the CABCD-AOAML technique over recent methods.
Design and Implementation of an Intelligent Shopping Trolley Integrated with a Mobile Application

The Automatic Billing Trolley system provides an answer to the problem of excessive time spent in grocery stores. Customers have to wait until all products have been scanned to generate a bill, which is a time-consuming process that requires additional staff. This research study has considered using an Automatic Billing Trolley to solve this issue. Radio-frequency identification (RFID) tags will be placed on every merchandise in the mall and shown on shoppers' mobile devices. When a customer places an item in their shopping cart that requires RFID scanning, the name of the item and its price will appear on their mobile device. Paytm, the Unified Payments Interface (UPI), and Phone Pay are just few of the many online payment methods available to the client.
Grammar Error Correction Using Natural Language Processing

In the digital era, mos t of the communication is happening through online platforms. English plays a vital role for better understanding. Good grammar helps in framing a better sentence. This demands for the development of an automatic Grammar Error Correction (GEC) tool. Many research works have been initiated to make Grammar Error Correction possible by using various machine learning algorithms. Some of the algorithms are Naive Bayes, BERT, Support Vector Machine, and ULMFIT. Among these algorithms BERT gives a comparatively best accuracy rate. The fundamental goal of this research work is to compare these algorithms with respect to accuracy Here, NLP is used for analyzing the grammatical corrections in the sentence. Further, this study can be extended to overcome the drawbacks faced by the BERT algorithm such as increase in time cons umption for training and cos t.
Anomaly Detection in Automated Surveillance using Semi-Supervised Autoencoders

The recent large-scale deployment of cameras has paved way for a great demand to produce and use systems capable of capturing anomalous behaviors and events when provided with surveillance feeds. A great deal of tradeoff faced here is between managing the complexity of the algorithm and attaining high levels of accuracy and efficiency. This paper aims to achieve relatively high level of accuracy with less complexity on anomalous data by formulating a spatiotemporal autoencoder model and using ‘Semi-supervised’ learning approach. An autoencoder is a deep machine learning algorithm which encodes the input feature to a lower dimension and decodes the original input after reconstruction. It is usually unsupervised in nature. However, here semi-supervised learning technique has been used which aids in better clusterization of un-labelled data, increasing the accuracy of output. The proposed model has been tested with the benchmark i.e., UCF crime dataset. The proposed method has also been tested on other datasets, such as subway entrance, avenue and subway exit datasets to perform one-on-one comparison between different methods. Results obtained using proposed model have mostly improved compared to related research. The model was analyzed in both batch-wise and frame-wise modes. An anomalous event detection rate of 94% and 90% has been achieved for batch-wise and frame-wise, respectively. Computation time has been observed in batch mode. Though it has reduced mostly, however, it varies with length of video and type of crime.
On Exploring Audio Anomaly in Speech

Existing anomaly detection works mainly focus on abnormal activities in image and video settings, while assessing audio manipulation, namely the presence of anomalous audio in speech, has not yet been explored. To overcome this limitation, we propose a setup in the context of Active Speaker Detection (ASD) by defining a methodology to perceive audio anomaly, assessing the performance of anomaly models, and establishing setup variations. This way, we evaluate models performance in identifying the presence of anomalies (detection) and localizing the timeframe where they occur (localization). To complement anomaly detection, we propose Anomaly Score (AS), a metric to assess anomaly localization that balances precision and mis-localization. Given the sequential nature of audio, we explore the performance of a density-based approach for video anomaly (CPD) and recurrent models (LSTM and RNN) on detecting and localizing audio anomalies. The results show that: 1) anomaly inclusion in talking portions increases models resilience toward anomaly localization; 2) CPD is superior in anomaly detection, while recurrent models perform better in anomaly localization; 3) anomaly with distinctive audio benefits precise anomaly localization; and 4) using original ASD audio is overall the best approach, relative to other processing approaches. The setup and experiments of this work serve as a baseline for future works on speech anomaly detection.
Hyperchaos-Based Secure Communication Using Lyapunov Theory

This study presents an approach for secure communication based on the underactuated synchronization of a 4D-hyperchaotic system recently introduced in the literature. The presence of disturbances is considered in all states of the master and slave systems for a more robust synchronizer. The Lyapunov theory is used to demonstrate the effectiveness of the proposed system, ensuring that the synchronization error is bounded and achieves convergence in a finite time. Also, this work includes computer simulations based on Matlab/Simulink to validate the proposed system, including the master-slave synchronization and the implementation of a secure communication scheme.
Metaverse and Accounting Education: Potentials and Opportunities in the Accounting Internship Courses

Higher education has gone through significant changes since 2020 due to the social distancing regulations related to the COVID-19 outbreak and backed up by the existence of accelerated innovations in information and communication technologies (ICT). As a result, many universities were obliged and smoothly went online for a certain period of time and exploited new information and communication technologies to overcome the obstacles of conducting face-to-face classes and meetings. In this case, lectures and meetings were conducted online using communication platforms (for example, MS teams and zoom) and exams took place using online proctoring software. Nevertheless, it seems that stay-at-home protocols was an opportunity to experiment new methods of work as well as study. Keeping in mind these changes in the delivery mode of study, one might raise the question of what next? Is this the best so far that ICT can offer for online learning? There is no doubt that what have been exploited so far by universities regarding online learning has only scratched the surface of the potentials offered by information and communication technologies regarding higher education. This paper discusses the opportunity of virtual learning as an extension and enhancement of online learning. The potentials of the Metaverse provides opportunity for a more interactive platform that imitates face-to-face learning. The world of metaverse provides an excellent opportunity to, at least for the time being, experiment the possibility of virtual university, virtual classroom, virtual labs, and virtual internship programs.
Spatial Remapped Network for Transformer Tracking

Recently, the transformer-based trackers have achieved significant performance improvements due to the powerful global modeling capabilities of attention. However, the existing trackers still suffer from the problem of effective discrimination between targets and similar interference when face with complex scenarios such as similar target distractor, occlusion, and significant target changes in the target neighborhood. In this work, we propose a spatial remapped network for transformer tracking, namely TrSRM. The spatial remapped (SRM)network establishes more significant discrepancy between targets and similar interference by remapping features, enhancing discriminative location ability to achieve more accurate target tracking. In addition, we design a training method based on Model-Agnostic Meta-Learning (MAML) for helping the SRM network to obtain a target tasksensitive initialization parameter. Experiments on benchmarks show that the proposed tracker outperforms several state-of-the-art methods, demonstrating the effectiveness of our method.