Reviewerly/exHarmony huggingface dataset:
Test to Code Traceability

This paper presents a novel approach to establishing test-to-code traceability, using requirements as a mediator between functional tests and source code. Our approach consists of two steps: first, we link functional tests to requirements, and then we use these links to establish connections between requirements and code. We implement our approach using a combination of traditional and advanced machine learning models, including cosine similarity and GPT-LLAMA 2. By leveraging these models, we are able to automate the traceability process and ensure that tests are accurately linked to the relevant code components. Our implementation demonstrates the feasibility and effectiveness of our approach in establishing and maintaining traceability between functional tests, requirements, and source code.
Enhancing Similarity and Distance Measurements in Fermatean Fuzzy Sets: Tanimoto-Inspired Measures and Decision-Making Applications

Fermatean fuzzy sets (FFSs) serve as a nascent yet potent approach for coping with fuzziness, with their efficacy recently being demonstrated across a spectrum of practical contexts. Nevertheless, the scholarly literature remains limited in exploring the similarity and distance measures tailored for FFSs. The limited existing measures on FFSs sometimes yield counter-intuitive outcomes, which can obfuscate the accurate quantification of similarity and difference among FFSs. This paper introduces a suite of similarity and distance measures tailored for FFSs, drawing inspiration from the Tanimoto measure. We delve into the characteristics of these novel measures and offer some comparative studies with existing FFSs measures, highlighting their superior efficacy in the processing of fuzzy data from FFSs. Our proposed measures effectively rectify the counter-intuitive situations encountered with many existing measures and demonstrate a significant enhancement in differentiating between diverse FFSs. Moreover, we showcase the real-world applicability of our proposed measures through case studies in pattern recognition, medical diagnostics, and multi-attribute decision-making.
Occurrence of Polycyclic Aromatic Hydrocarbons and Polychlorinated Biphenyls in Fogwater at Urban, Suburban, and Rural Sites in Northeast France between 2015 and 2021

Polycyclic aromatic hydrocarbons (PAHs) and polychlorinated biphenyls (PCBs) exist in the atmosphere in the vapor and particulate phases, as well as in solubilized form in fog/rain/cloud waters. In the current paper, fogwater samples are collected during 42 events between 2015 and 2021 at four different sites (Strasbourg, Geispolsheim, Erstein, Cronenbourg) in the Alsace region. Organics are extracted using liquid–liquid extraction (LLE) supported on a solid cartridge (XTR Chromabond), and then analyzed by gas chromatography–tandem mass spectrometry (GC-MS/MS). The total PAHs and PCBs concentrations in fog samples vary between 0.58 and 6.7 µg L−1 (average of 2.70 µg L−1), and 0.14 and 15.5 µg L−1 (average of 2.75 µg L−1). Low-molecular-weight (LMW) PAHs are predominant and highly detectable compared to high-molecular-weight (HMW) PAHs, while pentachloro-biphenyls are the dominant PCB congener. The PAHs and PCBs concentrations have increased over the sampling years at all sites, except for a slight decrease in PCBs level at Geispolsheim. A diagnostic ratio (DR) and principal component analysis (PCA) are applied to suggest potential contamination sources at Strasbourg metropolitan. Their results reveal that PAHs derive from a mixture of pyrogenic activities, while PCBs mainly come from industrial activities. The results also reveal, in some cases, inter-site variability for simultaneous and successive fog events.
Building Organizational Defense: A Comprehensive Approach to Implementing IT Controls for Sox Compliance

The security and resilience of organizational IT systems are of paramount importance in today's fast-paced digital environment. With the proliferation of “cyber threats”, organizations must adopt proactive measures to protect their assets and maintain operational continuity. The purpose of this article is to provide insight into the nuances of the implementation of a robust framework for “IT controls”, so that organizations seeking to strengthen their defenses can better understand the importance of each component. The article begins by emphasizing the foundational role of governance and oversight in establishing clear policies, procedures, and accountability structures. Strong governance sets the stage for cultivating a culture of compliance and accountability throughout the organization. Next, the article delves into the critical aspect of “risk management”, highlighting the importance of identifying and mitigating IT-related risks to safeguard organizational security and resilience. Through comprehensive “risk assessments”, organizations can prioritize mitigation efforts and allocate resources effectively. In order to prevent unauthorized access to critical IT systems and resources, access controls are discussed, emphasizing the importance of robust authentication mechanisms and role-based access controls. Change management processes are explored as essential components in managing IT changes effectively, minimizing the risk of disruptions and vulnerabilities associated with system changes. “Data security” emerges as a key focus area, with an emphasis on protecting the confidentiality, integrity, and availability of organizational data assets through robust encryption, access controls, and data loss prevention technologies. Incident management processes are highlighted as crucial for responding swiftly and effectively to security incidents, minimizing their impact and restoring normal operations promptly. Compliance and audit processes are discussed in the context of ensuring adherence to regulatory requirements and industry standards, mitigating the risk of non-compliance penalties and reputational damage. The importance of security awareness training in cultivating a culture of security among employees is emphasized, along with the need for continuous monitoring and improvement to adapt to evolving “threats” effectively. Overall, the article underscores the importance of implementing a comprehensive framework for IT controls to enhance “organizational security”, “compliance” and resilience in today's digital landscape. Through collaborative efforts across all levels of the organization, organizations can effectively “mitigate risks”, safeguard their assets and achieve their business objectives amidst evolving “cyber threats”.
Research and Development of a Modern Deep Learning Model for Emotional Analysis Management of Text Data

There are many ways people express their reactions in the media. Text data is one of them, for example, comments, reviews, blog posts, messages, etc. Analysis of emotions expressed there is in high demand nowadays for various purposes. This research provides a method of performing sentiment analysis of text information using machine learning. The authors trained a classifier based on the BERT encoder, which recognizes emotions in text messages in English written in chat style. To handle raw chat-style messages, authors developed an enhanced text standardization layer. The list of emotions identified includes admiration, amusement, anger, annoyance, approval, caring, confusion, curiosity, desire, disappointment, disapproval, disgust, embarrassment, excitement, fear, gratitude, grief, joy, love, nervousness, optimism, pride, realization, relief, remorse, sadness, and surprise. The model solves the problem of multiclass multilabel text classification, which means that more than one class can be predicted from one piece of text. The authors trained the model on the GoEmotions dataset, which consists of 54,263 text comments from Reddit. The model reached a macro-averaged F1-Score of 0.50704 in emotions prediction and 0.7349 in sentiments prediction on the testing dataset. The presented model increased the quality of emotions prediction by 10.2% and sentiments prediction by 6.5% in comparison to the baseline approach.
Pelatihan Literasi dan Numerasi Mahasantri Melalui Program Kepenulisan Karya Ilmiah

The importance of producing scientific papers in an academic environment is a necessity. However, it is a challenge for students who have just entered the world of writing. Therefore, this writing training activity aims to increase students' writing motivation and literacy and numeracy skills in compiling a scientific paper. The method of implementing activities uses the forum group discussion (FGD) method and the simulation method. The results of the activity showed that the writing training activities for students at Ma'had Aly Asslafiyyah went well. Participants have been able to prepare a draft of a scientific paper. Apart from that, 95% of participants had a strong desire to learn to improve their literacy and numeracy skills, as seen from the very positive response from participants participating in the training until the end of the activity. Through the Scientific Writing training activities at Ma'had Aly Assalafiyyah, students' enthusiasm and enthusiasm for improving their literacy and numeracy skills can be seen increasing, with follow-up plans aimed at strengthening the quality of learning and preparing students to contribute to the scientific world.
An Online Inquiry and Information Management System in Bicol Garments

Information management plays a very significant role in an organization. It helps the organization manage files and documents in an organized way. The main objective of this study is to propose a system that would help improve the process of handling and recording customer information as well as enhance communication between the customer and the business organization. In order to ensure the improvement of the process in Bicol Garments, the proponents have identified the following problems: manual listing and processing of customers' information and job orders; as well as communication problems. With this, business process management was used for modeling and reengineering the workflow process. The study utilizes business process management to show the existing process and the proposed new process for Bicol Garments. By analyzing the As-Is process, the proponents noticed several problems in the current process and proposed a To-Be BPMN in order to resolve the issue. The experimentation in the study also shows that the use of the proposed system will improve the current process at Bicol Garments and provide ease of access to the customer. The study has proposed several protocols to help improve the process. These protocols were simulated in several instances and have shown improvements. However, the result of this study cannot be completely generalized because it has not yet been implemented in Bicol Garments.
A Study on Electromyography Signal as a Controller

Human computer interaction (HCI) is the study of interfaces between human and computer. When an input keyboard is pressed the output is displayed in the monitor is a simple example of human and computer interaction. World Wide Web is yet another example of HCI. HCI is everywhere and has become an important aspect in human life. HCI have many subfields and one among them is the study of biosignals. Signals that are generated from living body during muscle contraction, eye movement, brain signal are biosignals and these signals have potential for developing an interface for human computer interaction. There are many such bio electric signals which can be made to use for developing interface and that can be done by acquiring these signals which will form a linkage with the computer technique. These types of signals are brain signal called Electroencephalogram (EEG), heart signal Electrocardiogram (ECG), eye movement signal Electrooculogram (EOG) and muscle signalElectromyogram (EMG). The paper focuses on the study of muscle signal controller as HCI, EMG signals are captured during contraction of a skeletal muscle. The signal is then amplified and converted into usable signals that will be fed as an input to computer and can be used for controlling certain devices.
A Novel Method for Self-Driving Solar-Powered Drones

This project presented the transformative potential of integrating solar panels into drones. This innovative approach addresses the long-standing issue of limited battery life, enabling drones to operate continuously, adapt to changing mission demands, and contribute to sustainability efforts in the field of unmanned aerial vehicles. This development represents a significant step forward in the evolution of drone technology, promising a more versatile and self-sustaining future for drones across various sectors. Factors like high-speed flight, aggressive maneuvers, heavy payloads, and adverse weather can dramatically reduce battery life. Surveillance drones, for instance, are confined to covering limited areas before returning for battery changes or recharging. A groundbreaking solution lies in the incorporation of solar panels directly into the drones, allowing them to self-charge when required. This innovation ensures uninterrupted drone operation, regardless of the prospects of energy demands, thus marking a significant step forward in drone technology. With this integration of solar power, drones are poised to become not only versatile but also autonomous, promising a transformative development in the world of unmanned aerial vehicles.
Real-time recognition of Indonesian sign language using recurrent neural network

Hand gestures serve as a vital means of communication for deaf individuals. They often face communication challenges in their daily interactions due to the language barrier. This underscores the necessity of sign language interpreters. However, prevailing methods primarily rely on the Indonesian Sign Language System (SIBI), despite the widespread use of Indonesian Sign Language (BISINDO) for communication. Additionally, the effectiveness of these methods hinges greatly on the accuracy of feature extraction. To address this limitation, this study introduces a Recurrent Neural Network (RNN) approach for BISINDO interpretation. Data acquisition involved the use of a webcam to capture video data, subsequently transformed into frames andarrays. Collected from three respondents, the dataset comprises 3,240 videos and 97,200 array data points, encompassing letters and numbers. Among the tested parameters, training results indicate that utilizing the Adam optimizer with a learning rate of 0.0001 and 500 epochs yields the highest accuracy and minimal loss compared to other configurations. Subsequently, this modelunderwent real-time testing, conducted five times for 36 classes, achieving an accuracy of 81.67%. It is important to note that errors may arise due to similarities within hand signal language, particularly involving characters such as I, J, D, P, M, and N.
Monetary Reform and Central Bank Digital Currencies: The Impact on Retail Banking

This research aims to clarify the impact of monetary reform on retail banking, with the introduction of private crypto-currencies (CCs) and retail central bank digital currencies (CBDCs). Given the exponential increase in private de-centralized CCs, involving stablecoins and non-backed digital assets, total crypto assets have now developed into a USD 1.04 trillion market capitalization. Governments and monetary authorities were initially reticent, but now a number of central banks are developing their own wholesale and retail CBDCs. By conducting library research, document and content analysis involving secondary data, this research confirms, involving central bank admission, that the current centralized monetary system allows commercial banks to lend the vast majority of deposit money into existence. However, the impact of private CCs and introduction of retail CBDCs will eliminate credit creation, thus rendering the commercial banking model obsolete. This will inevitably involve a structural change in the global financial system, with the separation of the public issuance of money and the private issuance of equity finance and investment. Ultimately, retail CBDCs will have to develop into public stablecoins that are pegged to intrinsic value to satisfy the store of value function of money and provide stability as the common denominator for all economic transactions.
Deep-Feature-Based Asymmetrical Background-Aware Correlation Filter for Object Tracking

Correlation filter-based video object-tracking algorithms have gained widespread attention due to their efficiency and excellent tracking performance. However, traditional correlation filtering tracking algorithms possess several limitations. (1) They extract image features only by using rule sampling, which ignores the shape information of the target, resulting in insufficient discriminative power of the features. (2) They focus only on the difference between the background and the object, ignoring the effects of more challenging intra-class interference. (3) They do not further evaluate the reliability of the optimal candidate samples, resulting in easy failure in occlusion scenarios. This paper proposes an asymmetric background-aware correlation filter method for object tracking with deep features to solve the above limitations. First, an asymmetrical background-aware sampling method based on the shape information of the object is proposed. This sampling method significantly differs from the symmetrical sampling method in the traditional correlation filter framework. By exploring the shape information of the object, improving the otherness between the background and object samples is easy, thus suppressing the intra-class interferences. Second, deep neural networks are introduced in the correlation filter framework to extract the deep object features and a spatio-temporal regularization factor is adaptively assigned to suppress the boundary effects, intra-class distractors and aberrance between frames. Finally, a multi-modal object pool is constructed to evaluate the optimal candidate sample in each frame. This template pool fully exploits object diversity and solves the tracking drift and failure caused by invalid appearance changes in scenes such as occlusion and vigorous motion scenarios. To validate the effectiveness of the proposed method, it was compared with the state-of-the-art methods on public datasets. The experimental results show that the tracking performance of the proposed method is competitive.
Analysis of the Content of Carbon Neutrality Education Presented in Middle and High School Science and Social Studies Curriculum

This study analyzed the content of carbon neutrality education in the science and social studies curricula of middle and high schools. Through the analysis, it was confirmed that the curriculum contains some content related to carbon neutrality. Specifically, the science curriculum predominantly addresses responses to climate change, while the social studies curriculum focuses on the impacts of climate change. However, there was a highlighted need for both the science and social studies curricula to comprehensively and evenly incorporate content on the causes and effects of climate change. Furthermore, the study confirmed a strong linkage between the carbon neutrality learning elements in middle school and those in high school. The significance of this research lies in its analysis of the current status and necessities of carbon neutrality education in the curriculum, proposing effective strategies for its implementation.
Influence of Learning Experience by Academic Year on College Students’ Major Satisfaction and Perceived Competencies: A Study Focused on I University

This study analyzes the impact of college students’ characteristics (demographic background, learning amotivation) and learning experiences (experience, collaboration, and inquiry-based learning) on major satisfaction and perceived competency development, associated with academic year. Data from 839 students across 1st, 2nd, and 3rd academic years at I University were collected and analyzed using hierarchical multiple regression. The results show that collaboration and inquiry-based learning, as well as faculty-student interaction have significantly influence on major satisfaction and perceived competencies, regardless of academic years. In addition, experiential learning shows significance only in the perceived competencies of 2nd, and 3rd academic years. The most influential learning experience varied by year: inquiry-based learning for 1st year students in both satisfaction and competency development, while for 2nd and 3rd year students, collaborative learning influenced major satisfaction, and inquiry-based learning did competency development. The study provides insights for educational strategies for teaching and learning, tailored to each academic year.
A Case Study on the Establishment of SMEs FOM–MES Interworking System for Multidimensional Analysis of 4M Data in Manufacturing Sites

Most small- and medium-sized manufacturing companies have poor utilization of MES systems and manufacturing data owing to financial conditions and labor shortages. For corporate growth by improving the productivity and securing competitiveness of small- and medium-sized manufacturing companies, improving the use of 4M-based manufacturing data, analyzing data, managing the change, establishing decision-making, and applying FOM solutions to companies are essential. However, applying the FOM solution is difficult owing to the difficulty of securing MES system data and FOM solution analysis data file set. Through this study, the effect applied to actual companies was verified by establishing an FOM–MES linkage system and simplifying the process of producing FOM analysis data.
An Overview on Intuitionistic Fuzzy Multi-Hypergroup

Abstract: The major goal of this work is to introduce the Intuitionistic Fuzzy Multi-Hypergroup and to introduce non membership on multihyper group to provide new path way in the field of hypergroup.
Review on Customer Experience with Personalized Recommendation and Targeted Ads

Abstract: The application for malls or e commerce platform that divide the customers into profitable and non-profitable customers plays a vital role in the marketing sector. The administrator within the shopping centers chooses to promote methods and client division points to form a relationship with the first profitable clients by arranging the foremost appropriate marketing procedure. Numerous methods are applied to separate the advertising, but outstandingly tremendous data is uncommonly effective in decreasing their adequacy. Many works used association rule learning is used to establish a relationship between variables. With the use of an appropriate algorithm in this, we find what fitems customers frequently buy together by generating the set of rules and can be used those rules for various market strategies. By using that rules, we also develop a recommender framework that will offer assistance the mall managers or e commerce managers to empowering the market strategies. This not only helps customers have a better choice but also gives advice to businesses selling products with reasonable prices. Customer segmentation is done based on their interest using association learning algorithms like ECLAT algorithm or SFIT algorithm.