Reviewerly/exHarmony huggingface dataset:
Effect of Using Interactive Electronic Module with Contextual Approach on Students Learning Motivation on Conductor and Insulator Materials

This study aims to develop an interactive electronic module product with a contextual approach and determine the effect of its use on students' learning motivation on conductor and insulator material. This study uses the R&amp;D method with a 4D model. The research instrument is a questionnaire. The product was tested for feasibility, material validity, readability test, and measurement of student's learning motivation. The research subjects were seventh-grade students of MTsN 8 Banyuwangi and 5 validators. Data were analyzed qualitatively. The results of the study are the average product score is 3.9 with very good criteria and the percentage of 98% is very feasible, the validity of the material with a score of 3.8 with very good criteria and the percentage of 95% is very valid, the readability test obtained a score of 3 with very good criteria and percentage of 75% and learning motivation of students obtained a score of 3 and a percentage of 100% with high criteria. It can be concluded that the contextual approach interactive electronic module affects students' learning motivation on conductor and insulator material
Monocular catadioptric panoramic depth estimation via improved end-to-end neural network model

In this paper, we propose a monocular catadioptric panoramic depth estimation algorithm based on an improved end-to-end neural network model. First, we use an enhanced concentric circle approximation unfolding algorithm to unfold the panoramic images captured by the catadioptric panoramic camera and then extract the effective regions. In addition, the integration of the Non-local attention mechanism is exploited to improve image understanding. Finally, a depth smoothness loss strategy is implemented to further enhance the reliability and precision of the estimated depths. Experimental results confirm that this refined algorithm is capable of providing highly accurate estimates of object depth.
Modeling the Workflow of Bug Prioritization Tasks Descriptively Using the Past Events

Prioritizing bugs is one of the critical decision-related tasks in managing the maintenance phase whereas it is exposed as a key challenge in handling bug reports. On the other hand, the bug triager is a prominent role to observe influencing factors for handling the bug prioritization tasks effectively. Analysis of previous bug reports shows that it is essential to handle bug prioritization tasks with the appropriate workflow. However, it is revealed that there is a research gap in modeling the workflow of prioritization tasks. The paper aims to characterize the workflow model of prioritization tasks. This research is based on a document analysis design using qualitative data from previous bug reports and other artefacts. Over 100 bug reports from large software corporations are accessed and filtered, while 20 bug reports are used for obtaining empirical data. In this study, a descriptive workflow model for prioritizing bugs is proposed by analyzing past events. This model characterizes the states of bug prioritization tasks, their statuses, and the transitions between them. Additionally, this research analyzes the industrial aspect of the proposed model and demonstrates its usefulness in providing valuable insights to the bug triager into ongoing prioritization tasks that will assist him in decision-making in prioritizing bugs retrospectively and prospectively. The finding of this research also reveals that bug reports are a valuable resource that contains significant prioritization features which is useful for illustrating the workflow of bug prioritization tasks descriptively. Thus, the implications of the model for theory and practice are discussed.
Deep Convolutional Generative Adversarial Networks for Imbalance Medical Image Classification.

Medical image classification is an essential task in clinical practice and research. It enables medical professionals to be assisted in diagnosing medical conditions accurately and efficiently, leading to improved patient outcomes and survival rates. However, traditional manual interpretation methods for diagnosing medical images have some drawbacks. Firstly, imbalanced medical images often exhibit a significant disparity in the number of samples across different classes, posing challenges in training accurate and robust models that can effectively learn from limited data in the minority class while avoiding biases towards the majority class. Secondly, the limited availability of labelled data will put a further load on the healthcare system, as labelling medical images is a time-consuming and resource-intensive task, often requiring expert knowledge. This paper proposed a generative adversarial network (GAN) with the purpose of improving the limitations associated with the imbalanced distribution of medical images. Based on the experiments conducted, it shows that the proposed model exhibits a high level of accuracy for two-class labelled dataset, with a low performance for the skin cancer dataset due to number of the labelled dataset is more than two
Real‐time vehicle detection using segmentation‐based detection network and trajectory prediction

Abstract The position of vehicles is determined using an algorithm that includes two stages of detection and prediction. The more the number of frames in which the detection network is used, the more accurate the detector is, and the more the prediction network is used, the algorithm is faster. Therefore, the algorithm is very flexible to achieve the required accuracy and speed. YOLO's base detection network is designed to be robust against vehicle scale changes. Also, feature maps are produced in the detector network, which contribute greatly to increasing the accuracy of the detector. In these maps, using differential images and a u‐net‐based module, image segmentation has been done into two classes: vehicle and background. To increase the accuracy of the recursive predictive network, vehicle manoeuvres are classified. For this purpose, the spatial and temporal information of the vehicles are considered simultaneously. This classifier is much more effective than classifiers that consider spatial and temporal information separately. The Highway and UA‐DETRAC datasets demonstrate the performance of the proposed algorithm in urban traffic monitoring systems.
Quantum secret sharing protocol through noisy channel with application in visual cryptography

Summary This study presents a quantum secret sharing (QSS) protocol designed using Grover's search algorithm in a noisy environment. The proposed protocol utilizes Grover's three‐particle quantum state. The proposed scheme is divided into secret information sharing and eavesdropping checking. The dealer prepares an encoded state by encoding the classical information as a marked state and shares the states' qubits between three participants. Using the amplitude‐damping noise and the phase‐damping noise as conventional noisy channels, it can be demonstrated that secret information can be conveyed between participants with some information lost. The security analysis shows the scheme is stringent against malicious participants or eavesdroppers. The simulation analysis is done on the cloud platform IBM‐QE thereby showing the practical feasibility of the scheme. Finally, an application of the proposed scheme is demonstrated in visual cryptography using the GNEQR representation of images.
Оценка методов скелетизации двумерных бинарных изображений

In the realm of modern image processing, the emphasis often lies on engineering-based approaches rather than scientific solutions to address diverse practical problems. One prevalent task within this domain involves the skeletonization of binary images. Skeletonization is a powerful process for extracting the skeleton of objects located in digital binary images. This process is widely employed for automating many tasks in numerous fields such as pattern recognition, robot vision, animation, and image analysis. The existing skeletonization techniques are mainly based on three approaches: boundary erosion, distance coding, and Voronoi diagram for identifying an approximate skeleton. In this work, we present an empirical evaluation of a set of well-known techniques and report our findings. We specifically deal with computing skeletons in 2d binary images by selecting different approaches and evaluating their effectiveness. Visual evaluation is the primary method used to showcase the performance of selected skeletonization algorithms. Due to the absence of a definitive definition for the "true" skeleton of a digital object, accurately assessing the effectiveness of skeletonization algorithms poses a significant research challenge. Although researchers have attempted quantitative assessments, these measures are typically customized for specific domains and may not be suitable for our current work. The experimental results shown in this work illustrate the performance of the three main approaches in applying skeletonization with respect to different perspectives.
Анализ рисков информационной безопасности в пищевой промышленности с использованием системы нечеткого вывода

Recently, different attempts have been made to characterize information security threats, particularly in the industrial sector. Yet, there have been a number of mysterious threats that could jeopardize the safety of food processing industry data, information, and resources. This research paper aims to increase the efficiency of information security risk analysis in food processing industrial information systems, and the participants in this study were experts in executive management, regular staff, technical and asset operators, third-party consultancy companies, and risk management professionals from the food processing sector in Sub-Saharan Africa. A questionnaire and interview with a variety of questions using qualitative and quantitative risk analysis approaches were used to gather the risk identifications, and the fuzzy inference system method was also applied to analyze the risk factor in this paper. The findings revealed that among information security concerns, electronic data in a data theft threat has a high-risk outcome of 75.67%, and human resource management (HRM) in a social engineering threat has a low-risk impact of 26.67%. Thus, the high-probability risk factors need quick action, and the risk components with a high probability call for rapid corrective action. Finally, the root causes of such threats should be identified and controlled before experiencing detrimental effects. It's also important to note that primary interests and worldwide policies must be taken into consideration while examining information security in food processing industrial information systems.
Цветовая кодировка кубитных состояний

Difficulties in algorithmic simulation of natural thinking point to the inadequacy of information encodings used to this end. The promising approach to this problem represents information by the qubit states of quantum theory, structurally aligned with major theories of cognitive semantics. The paper develops this idea by linking qubit states with color as fundamental carrier of affective meaning. The approach builds on geometric affinity of Hilbert space of qubit states and color solids, used to establish precise one-to-one mapping between them. This is enabled by original decomposition of qubit in three non-orthogonal basis vectors corresponding to red, green, and blue colors. Real-valued coefficients of such decomposition are identical to the tomograms of the qubit state in the corresponding directions, related to ordinary Stokes parameters by rotational transform. Classical compositions of black, white and six main colors (red, green, blue, yellow, magenta and cyan) are then mapped to analogous superposition of the qubit states. Pure and mixed colors intuitively map to pure and mixed qubit states on the surface and in the volume of the Bloch ball, while grayscale is mapped to the diameter of the Bloch sphere. Herewith, the lightness of color corresponds to the probability of the qubit’s basis state «1», while saturation and hue encode coherence and phase of the qubit, respectively. The developed code identifies color as a bridge between quantum-theoretic formalism and qualitative regularities of the natural mind. This opens prospects for deeper integration of quantum informatics in semantic analysis of data, image processing, and the development of nature-like computational architectures.
Combination optimization of green energy supply in data center based on simulated annealing particle swarm optimization algorithm

At present, the high energy consumption of data centers based on grid power supply not only brings huge direct cost of electricity, but also indirectly produces a lot of greenhouse gases, which affects the natural environment. Academia and industry are beginning to introduce clean renewable energy sources such as wind and solar power into data centers to reduce operating costs and environmental damage by building new green data centers. To solve this problem, this study considers the use of waste heat for refrigeration while taking natural gas power generation into account, and introduces wind energy as a green energy source. On the premise of considering the response level of data centers, the two resources are combined and deployed to improve resource utilization and reduce energy consumption costs. Aiming at the instability of wind power generation, a particle swarm energy scheduling optimization algorithm based on simulated annealing algorithm was proposed by combining simulated annealing algorithm and particle swarm optimization algorithm. The research shows that, considering the response level of data centers, the use of natural gas and wind energy as the main energy supply can effectively reduce the overall energy consumption of data centers.
Learning from West African storytellers

Several research works propose non-textual alternatives to textual documentation and similar forms of representing information in software development. This is because of the problems that stem from writing these documents, which range from incomprehensible requirements to ambiguous user stories. The various proposals of researchers often contain some trace of oral or visual communication. In this paper, we study the implications of eliminating textual communication and substituting unnecessary writing by extracting the values of West African oral storytellers. Traditional West African communities did not make use of writing for thousands of years and yet their legends, customs, beliefs, and knowledge were effectively transmitted across several generations. How did they manage to accomplish this? What can we learn from their storytellers? How can these lessons be applied to software products? These are all questions that this paper attempts to answer. Perhaps if we fully understand how they operated, then we can target our written communication to the activities where it is needed instead of spreading writing across plenty of tasks as it is currently. To achieve this, we performed an analysis of the two domains: West African oral storytelling and software development and found similarities, then selected some key elements from oral storytelling and explained how they can have relevance in software development. The theme directly encompasses diversity and inclusion by bringing into software engineering a perspective of a region where its literacy research is scarcely being explored. The study found that traditional oral storytelling can provide insights into effective communication and audience engagement, and identified four ways in which software development can be compared to oral storytelling. The study also found that certain elements of storytelling, such as audience relationship, story structure, parables and proverbs, and community relaxation and support, can be applied to writing tasks in software development.
HCM-LMB Filter: Pedestrian Number Estimation with Millimeter-Wave Radar in Closed Spaces

The electromagnetic wave transmitted by the millimeter-wave radar can penetrate flames, smoke, and the high-temperature field, and is the main sensor for detecting disaster victims in closed spaces. However, a moving target in the closed space will produce a considerable number of false detections in the point cloud data collected by the radar due to multipath scattering. The false detections lead to false trajectories generated by multi-target tracking filters, such as the labeled multi-Bernoulli (LMB) filter, which, therefore, leads to inaccurate estimation of the number of pedestrians. Addressing this problem, in this paper, a three-class combination of the clutter point clouds model is proposed: static clutter, non-continuous dynamic clutter (NCDC), and continuous dynamic clutter (CDC). The model is based on the spatial and temporal distribution characteristics of the CDC sequence captured by a two-dimensional (2D) millimeter-wave (MMW) radar. However, in open space, CDC appears infrequently in radar tracking applications, and thus has not been considered in multi-target tracking filters such as the LMB filter. This leads to confusion between the CDC point cloud collected by the high-resolution radar in closed spaces and the real-target point cloud. To solve this problem, the impact mechanism of the LMB filter on prediction, update, and state estimation is modeled in this paper in different stages based on the temporal and spatial distribution characteristics of CDC. Finally, a hybrid clutter model-based LMB filter (HCM-LMB) is proposed, which focuses on scenes where NCDC and CDC are mixed. The filter introduces the temporal and spatial distribution characteristics of NCDC based on the original LMB filter, and improves the prediction, update, and state estimation of the original filter by combining the impact mechanism model and the new CDC prediction, CDC estimation, and false trajectory label management algorithm. Experiments were conducted on pedestrians in building corridors using 2D MMW radar perception. The experimental results show that under the influence of CDC, the total number of pedestrians estimated by the HCM-LMB filter was reduced by 22.5% compared with that estimated by the LMB filter.
Software quality models: Exploratory review

INTRODUCTION: The emerging industrialization of software promotes the continuous development of new evaluation models that adapt to the product's characteristics and the organizations' needs.&#x0D; OBJECTIVES: This article aims to identify the software quality models proposed between 2016 and 2020.&#x0D; METHODS: We conducted an exploratory systematic review in the SciELO, IEEE Xplore, and Scopus databases, resulting in 14 research papers proposing new models for assessing software quality.&#x0D; RESULTS: 79% of the research papers were extracted from IEEE Xplore, and 50% of them were authored by individuals affiliated with European institutions. We presented various software quality models focused on assessing both product quality and usability for specific purposes. Additionally, we reported that the proposed models are based on common software quality metrics standards such as CMMI, ISO/IEC 9126, and others.&#x0D; CONCLUSION: The review serves to update the state of the art regarding the software quality models proposed in recent years, which will be valuable for researchers and software developers when seeking evaluation models that align with their specific needs.
Analysis of the Need for Canva-Based Electronic Modules to Improve Vocational Learning Outcomes

The goal of education is not just to increase one's head knowledge, but also one's skill set, outlook, and frame of mind. Learning results are affected by the quantity and quality of reading resources available in schools. "e-module" refers to an electronic media-based educational tool. Teachers and students alike can benefit from the enhanced imagination that comes from using Canva media. Descriptive qualitative research was used to investigate these issues; in both the literature review and the field research, the researchers provided precise and detailed descriptions of their observations and findings. The researchers here are concerned with the importance of manufacturing things, and their subjects are teachers in vocational high schools (SMK). Teachers have expressed a desire for electronic modules that serve as learning medium for teaching vocational subjects. Students who need learning media other than textbooks to transfer material to vocational high school students benefit from the usage of e-modules because they increase their understanding and learning efficiency both in school and at home. Students require learning materials that are accessible and engaging. One of these resources is an engaging electronic module with homework questions designed to aid in students' character development and moral upbringing
Prediction of cryptocurrency prices by deep learning models: A case study for Bitcoin and Ethereum

Cryptocurrency prediction is important for a variety of stakeholders, from investors to businesses, as it enables them to make more informed decisions about the future of the digital asset market. This paper delves into the application of deep learning models for two of the most popular cryptocurrencies, Bitcoin and Ethereum, outlining how to effectively implement these methods. Our goal is to perform efficient deep learning structure based on the forecasting models specifically recurrent neural networks, convolutional neural network and long short-term memory to predict the Bitcoin and Ethereum prices. Our results include a comparison of these two cryptocurrencies according to the deep learning methods and their effectiveness in predicting the Bitcoin and Ethereum prices.
A Novel Fuzzy Unsupervised Feature Learning Approach

The effectiveness of machine learning approaches depends on the quality of the data representation. In fact, some representations may mislead such learning approaches upon concealing relevant explanatory variables. Although feature engineering, that utilizes domain knowledge and/or expert supervision, yields typical data representation techniques, generic unsupervised feature learning represents an even more objective alternative to determine relevant attributes and generate optimal feature spaces. In this paper, we propose a new fuzzy unsupervised feature learning approach (FUL) that automatically derives new features by revealing the intrinsic structure of the data. In fact, FUL exploits the clusters and the associated fuzzy memberships generated by a fuzzy C-means algorithm, and devises new basis functions and their corresponding representation. The experiments results showed that FUL overtakes relevant state of the art approaches. It yielded the highest F1-measure with an improvement of 8%, 11%, 3%, and 4% on Parkinson, Epilepsy, Gait, and breast cancer datasets, respectively.
Modeling a GDPR Compliant Data Wallet Application in Prova and AspectOWL

Abstract We present a GDPR-compliant data privacy and access use case of a distributed data wallet and we explore its modeling using two options, AspectOWL and Prova. This use case requires a representation capable of expressing the dynamicity and interaction between parties. While both approaches provide the expressiveness of non-monotonic states and fluent state transitions, their scope and semantics are vastly different. AspectOWL is a monotonic contextualized ontology language, able to represent dynamic state transitions and knowledge retention by wrapping parts of the ontology in isolated contexts, called aspects, while Prova can handle state transitions at runtime using non-monotonic state transition semantics. We present the two implementations and we discuss the similarities, advantages, and differences of the two approaches.