Reviewerly/exHarmony huggingface dataset:
Precise Wireless Camera Localization Leveraging Traffic-aided Spatial Analysis

Wireless cameras nowadays commonly employ motion sensors to identify that something is occurring in their fields of vision before starting to record and notifying the property owner of the activity. In this paper, we discover that the motion sensing action can disclose the location of the camera through a novel wireless camera localization technique we call MotionCompass. By creating motion stimuli and sniffing wireless traffic for a response to that stimuli, a user can obtain the motion trajectories within the motion detection zone and then use them to calculate the camera's location. We also extend the camera localization algorithm to pinpoint cameras in always-active mode. We develop an Android app to implement MotionCompass. Our extensive experiments using the developed app and 18 popular wireless cameras demonstrate that for cameras with one motion sensor, MotionCompass can attain a mean localization error of around 5 cm with less than 140 seconds. We also discuss defenses against MotionCompass. Our localization technique builds upon existing work that detects the existence of hidden cameras, to pinpoint their exact location.
A Hybrid Moth Flame Optimization and Golden Jackal Optimization Algorithm Based Opposition for Global Optimization Problems

The Golden Jackal Optimization (GJO) algorithm has found applications in various fields such as feature selection, image segmentation, mechanical engineering, and Internet of Things intrusion detection. However, the original GJO algorithm suffers from several limitations, including premature convergence, slow convergence speed, and low calculation accuracy. To address these shortcomings, we propose a hybrid Moth Flame Optimization (MFO) and GJO algorithm based on opposition learning (OMGJO), which combines opposition learning and spiral path search technology of MFO to enhance the algorithm’s performance and convergence speed. To evaluate the effectiveness of the proposed algorithm, we compare it with 10 other meta-heuristic algorithms using 30 benchmark functions, and perform statistical tests on the results. The experimental outcomes demonstrate that the OMGJO algorithm offers highly competitive results. Compared to the traditional GJO algorithm and other optimization methods, the improved algorithm exhibits superior performance in terms of convergence speed and search efficiency. Additionally, when tested on various engineering problems, the algorithm demonstrates its competitiveness and adaptability in practical applications.
Systematic Information Management in Quantum Computing: Progress and Challenges

Interdisciplinary research has a significant impact on invention. Quantum computing, a cutting-edge emerging technology in this area, has shown remarkable growth and development in the current time. Therefore, an inclusive analysis of the academic literature is needed to understand the research advancement in the domain. The present study fulfills the need by analyzing published academic work from the Scopus database using the CiteSpace visualization tool. The research findings classify the quantum domain into five stages for research exploration, viz. quantum state preparation, quantum entanglement, decoherence, quantum error correction, and quantum optimization. Keyword co-occurrence, document cocitation, and burst analysis of the network pinpoint nascent research topics, inclinations, and advancements in each stage. Inclusively, the systematized and comprehensive study presents an evolutionary pathway for future researchers to gain in-depth knowledge, identify essential research subjects, and identify challenging issues.
A Hyperparameter Self-Evolving SHADE-Based Dendritic Neuron Model for Classification

In recent years, artificial neural networks (ANNs), which are based on the foundational model established by McCulloch and Pitts in 1943, have been at the forefront of computational research. Despite their prominence, ANNs have encountered a number of challenges, including hyperparameter tuning and the need for vast datasets. It is because many strategies have predominantly focused on enhancing the depth and intricacy of these networks that the essence of the processing capabilities of individual neurons is occasionally overlooked. Consequently, a model emphasizing a biologically accurate dendritic neuron model (DNM) that mirrors the spatio-temporal features of real neurons was introduced. However, while the DNM shows outstanding performance in classification tasks, it struggles with complexities in parameter adjustments. In this study, we introduced the hyperparameters of the DNM into an evolutionary algorithm, thereby transforming the method of setting DNM’s hyperparameters from the previous manual adjustments to adaptive adjustments as the algorithm iterates. The newly proposed framework, represents a neuron that evolves alongside the iterations, thus simplifying the parameter-tuning process. Comparative evaluation on benchmark classification datasets from the UCI Machine Learning Repository indicates that our minor enhancements lead to significant improvements in the performance of DNM, surpassing other leading-edge algorithms in terms of both accuracy and efficiency. In addition, we also analyzed the iterative process using complex networks, and the results indicated that the information interaction during the iteration and evolution of the DNM follows a power-law distribution. With this finding, some insights could be provided for the study of neuron model training.
Multimedia teaching resource allocation method for distance online education based on packet cluster mapping

In order to overcome the problems of low resource utilisation and long allocation time existing in traditional teaching resource allocation methods, this paper proposes a new multimedia teaching resource allocation method based on packet cluster mapping. This paper uses the mining algorithm of time series to mine the multimedia teaching resources of distance online education, extracts the characteristics of multimedia teaching resources of distance online education, and uses the Naive Bayesian classification algorithm to classify the types of multimedia teaching resources of distance online education. According to the results of resource partition, the objective function of resource allocation is constructed by using the packet cluster mapping technology to complete the resource allocation of multimedia teaching in distance online education. The simulation results show that this method has higher resource utilisation and shorter allocation time.
Robust Online Multi-robot Simultaneous Exploration and Coverage Path Planning

Abstract In this paper, a robust online Multi-robot Simultaneous exploration and coveragepath planning problem is presented. The entire workspace is initially partitionedusing the Manhattan Voronoi partitioning method, and the robots execute Multi-Robot Simultaneous Exploration and Coverage (MRSimExCoverage) using theSpanning Tree Coverage (STC) algorithm and cover the workspace. Once the robot(s) failure is detected the uncovered portions of the Voronoi cell of the failedrobot will be shared between other eligible robots or a replacement strategy,ifavailable, is performed. Simulation experiments within the V-rep environment isused to demonstrate and validate the performance of the proposed algorithm.Though the authors used the STC algorithm for path planning for the purposeof demonstration, any suitable coverage algorithm may be used.
Key aspects of designing information infrastructure for a medical enterprise

The provision of necessary information for the operations of any medical institution is a com-plex and labor-intensive process aimed at addressing professional, organizational, and social challenges. Effective information support in the healthcare sector is crucial for the quality of medical services at all stages and levels, ranging from primary healthcare to online consulta-tions. This article explores the key aspects of designing an information infrastructure, including the selection of necessary technologies, system architecture development, and integration with existing medical processes. The paper delves into the process of analyzing the information ex-change and processing needs of a medical enterprise. Key requirements and functional capabili-ties of a medical information system are identified. The importance of efficient information support in medical facilities to enhance the quality of healthcare services is justified, with par-ticular emphasis on the protection of confidential medical information. Security measures such as data encryption, access control, and backup systems are considered. Additionally, practical aspects of implementing a medical information system, including requirement analysis, data protection, and staff training, are examined. The significance of developing and implementing access control systems to restrict access to medical data only to authorized personnel is high-lighted. Based on the analysis of the domain and initial customer requirements, functional and non-functional requirements for the proposed information system are formulated, and system behavior is modeled through the construction of UML diagrams and class diagrams with ac-companying specifications. Special attention is given to the development of an additional fea-ture for labeling critical patient syndromes.
A Comprehensive Review of Software Development Life Cycle methodologies: Pros, Cons, and Future Directions

The software development process needs specific and studied steps within a reliable plan to achieve the requirements for the success of any project. Software development life cycle (SDLC) methodologies have provided several models that meet the needs of the various proposed projects. These methodologies present various scenarios that can be applied in the process of developing systems to make them more efficient and predictive. The paper aims to illuminate the paramount Software Development Life Cycle (SDLC) methodologies by conducting a comprehensive review of the pros and cons of the various models widely used for software design. Furthermore, the paper discusses fundamental trajectories that are shaping the future landscape of SDLC methodologies. This review included two main types of software development life cycle approaches such as traditional SDLC (heavy-weight) and agile SDLC (light-weight) approaches. The traditional approach included several models such as the Waterfall model, Iterative model, Spiral model, V-Model, and Big Bang Model. Whereas, the agile approach included various models such as the eXtreme Programming (XP) Model, scrum Model, Feature Driven Development (FDD) Model, and Kanban Model. A comprehensive analytical study of all software development life cycle models was achieved and highlighted their most prominent strengths and weaknesses of them. SDLC methodologies wield substantial ramifications across a multitude of sectors, contingent upon several models tailored to individual developmental and research contexts. In culmination, the paper furnishes an all-encompassing perspective on paramount SDLC models, encompassing two principal paradigms: the traditional and the agile approaches. These encompass fundamental sub-models that encapsulate pioneering models poised for application in system development, thus facilitating their refinement more efficiently and predictably.
A review of deep learning approaches for video-based crowd anomaly detection

In recent years, the video surveillance system has gained huge demand in public and private places to provide security and safety. Video-based crowd anomaly detection (VCAD) is one of the crucial applications of a surveillance system whose timely detection and localization can prevent massive loss of public or private properties and the lives of many people. Crowd anomalies or abnormal activities can be defined as irregular activities that deviate from normal crowd behavior patterns. Some abnormal activities in crowd scenes are panic, fights, stampedes, congestion, riots, and abandoned luggage, whose real-time detection is paramount. The crowd anomaly detection (CAD) becomes a more challenging task due to the dynamic nature of the crowd, the effect of the cluttered background, daylight changes, shape variation due to perspective distortion, and lack of large-scale ground-truth crowd datasets. Both conventional machine learning and deep learning approaches have been explored to provide different solutions for CAD. The current research trend shows the vast development of deep-learning approaches for CAD. However, state-of-the-art reviews still need to address the comprehensive analysis of deep models, performance evaluation methodologies, open issues, and challenges for VCAD. Therefore, the main objective of this review is to provide an insightful analysis of several deep models for VCAD, their comparative analysis on different datasets based on various performance metrics, and to discuss future research scope for VCAD.
A Study of Competency Mapping and Its Significance for Faculties

Skills are components of a job that are reflected in the behavior observed in a workplace. The most commonly mentioned common element is knowledge, skills, abilities, abilities, personal physical behavior and the impact on work performance. The main focus of organizations began to believe in excelling and not competing. Skill mapping affects employees' existing skills. As a result, the paper attempts to measure the role of skills mapping in the effective performance of faculties and to ascertain several functional areas where mapping of skills can be beneficial. The research of the study was based on primary data where data has been collected through a questionnaire that circulated to different faculties’ member, located in different colleges. In the same way, the result of the study shows that skills mapping is necessary for the performance and future growth of the faculties and colleges, since the growth and development of each colleges is based on an efficient and satisfied faculties. I have try to connote the significance of competence-mapping in the professional up-gradation of the teaching staff’s existing in numerous educational institutions with the help of eight selected competencies and find the mean score and standard deviation to identify the need of improvement and area for changes for the faculties and students
Secure One-time Password Generation using Shamir's Secret Sharing

- The process of authenticating users is critical for ensuring security. Static passwords are a popular and traditional authentication method due to their simplicity, but they are vulnerable to attacks such as password leakage, replay, guessing, eavesdropping, phishing, and spoofing, among others. One-time passwords (OTP) were introduced to address these limitations, but research has found that they are not immune to middle man attacks. To enhance the security of OTP, a new technique called Visual Cryptography Schema is proposed in this paper. In this method, we use Shamir’s Secret Sharing theory in which the main OTP is parted into three shares. Out of these shares, one is sent to the user and the other two shares are stored in different locations in the server. To retrieve
Revisiting the Impact of Fragility Indices on Economic Growth: New Insights From Sub-Saharan Africa

This research examines fragility impact on Sub-Saharan Africa's economic growth from 2006 to 2019. The variables of interest for this study include economic decline, economic inequality, human flight, brain drain, external intervention, and economic growth. The research objectives are to empirically investigate the connection between fragility indices and economic growth and determine the impact of fragility indices on economic growth. The existence of cointegration was ascertained in our variable using the Westerlund cointegration test. A fully modified ordinary least squares estimation technique was adopted for the first objective, and the findings showed that only external intervention indices positively impacted economic growth. In achieving our second objective, we adopted mean group, augmented mean group, and common correlated effect mean group estimation technique and found that the coefficients of economic decline, economic inequality, and human flight and brain drain had negative impacts on economic growth. In contrast, the external intervention had a positive impact. The research study recommends that fragile countries' governments develop corrective measures to remove these indicators to ensure progressive growth. Also, state administrations must ensure that suitable policies are implemented to encourage research and development. This will limit the problem of human flight and brain drain, which are significant problems in fragile countries.
Perancangan Aplikasi E-Modul Berbasis Android untuk Pelajaran Animasi 2D dan 3D

This study has the aim of designing an Android-based 2D and 3D animation e-module application which will later become a source of learning for students and also determining the feasibility level of the 2D and 3D animation e-module application to improve student learning outcomes. This study uses research and development (R &amp; D) research methods and uses the Luther development model in developing software engineering applications using MIT App Inventor. This type of research is in the form of quantitative research in which all data is obtained through questionnaires from media experts, material experts, and students, as well as the results of pre-test and post-test students. The respondents in this study were 11 students in Class XI multimedia animation lessons in 2D and 3D. The hypothesis was tested using a paired t-test. The study's findings revealed that the use of 2D and 3D animation e-module applications had significant an impact on improving student learning outcomes.
Prevention of Fraud in the Banking Sector

The author uses the conducted research to formulate suggestions on preventing fraud in the banking sector. The goal of the study is to develop practical recommendations on improving the existing system of preventing fraud and enhancing its effectiveness. Within organizational and managerial measures, it is recommended to improve the inter-agency cooperation between law enforcement bodies and banks and other credit organizations; to carry out a thorough selection, training and adaptation of employees for the financial-credit supervision bodies, as the usefulness of a well-trained bank employee is difficult to overestimate. Special attention is paid to the necessity of counteracting “leaks” of bank clients’ personal data. As technical measures make it possible to solve most problems of counteracting bank fraud, the systems capable of adaptation to the new fraud schemes should be designed and improved. Within criminal law measures, it is suggested that special crimes of Art. 159.1 and 159.3 of the Criminal Code of the Russian Federation should be excluded from criminal law, and liability for them should be transferred to the general crime of fraud. To improve the protection level for victims of bank fraud, it is recommended to conduct preventive work with persons characterized by age and role victimity; to raise the level of digital, financial and technical literacy of potential and actual victims in order to prevent repeat victimity; to provide information about new ways of committing crimes. It is stressed that both physical and juridical persons could be victims of bank fraud, so the specific features of the victim should be considered in the development of prevention measures. It is pointed out that active work in these directions is, according to the author, an important step towards effective identification and prevention of fraud in the banking sector.
PENGENALAN KOSAKATA BAHASA INGGRIS UNTUK ANAK-ANAK USIA DINI DENGAN GERAK DAN LAGU DI PANTI ASUHAN RUMAH SHALOM SEMARANG

On this occasion USM community service team had the opportunity to carry out community service activities at Rumah Shalom Orphanage Semarang. We found most children there difficult to memorize new vocabularies in English. To help our partners solve the problems, the team applies Total Physical Responses (TPR) method with a face to face lecture and discussion (question and answer) approach as well as training or practice methods. Students are asked to follow and answer the vocabulary that has been taught using the music video. After the activity conducted, it can be concluded that the objectives of the service activities at Rumah Shalom Orphanage can be achieved. The material we deliver is very encouraging, informative and new understanding to students about new vocabulary in English. The participants are also able to understand and memorize new vocabulary in English easily.
Enhancing BERT-Based Chinese Address Recognition Model with Tag Revision Module

Address recognition refers to segmenting the address text and identifying the corresponding types. However, rule-based and conditional probability-based methods are limited in applicability and scalability, and deep learning-based methods may overlook features at the word level and exhibit lower accuracy than at the character level. To address these issues, this paper utilizes sequence labeling method and proposes a tag revision module to enhance Chinese address recognition based on the BERT pre-training model, which utilizes the high accuracy rate at the character level and features of address text. The experimental results on the Chinese address recognition dataset from the China Knowledge Graph and Semantic Computing Conference demonstrate the effectiveness of the proposed method, achieving an F1-score of 0.9402. Moreover, the proposed scheme in this paper improves the BERT model by 0.73 percentage points.
Novel Autonomous Software for Enhanced Data Center Operational Efficiency and Botnet Detection

This work presents original research, validated by experimental results on the efficiency and security potential of an optimized and novel approach to an Autonomous Management Distributed System (AMDS) running in a Cloud Computing environment. The AMDS reduces hardware power consumption by autonomously moving virtual servers around a network to balance out hardware loads, as well as being easily configurable and scalable; these are advantages provided through its bespoke software architecture. The novel software module design for the AMDS uses a heuristic detection algorithm. The system also enhances data center security by detecting Botnet activity and preventing the disruption of day-to-day operations. The results demonstrate the AMDS’ potential as an industrial application to be used in modern data centers, both in terms of its ability to reconfigure itself on the fly, resulting in a 14 percent increase in efficiency over its lifetime, and in demonstrating an overall malicious (Botnet) data packet detection rate of over 52 percent (significant for the 5,000 network data samples analyzed by the Botnet software module integrated into the AMDS). Both experiments used for validation were performed in a VMWare run cloud environment; however due to the AMDS’ abstract architecture, this has the potential to interface with any existing cloud management system that exposes an API.
Metric Learning Approach for End-to-End Multilingual Automatic Speech Recognition Model

This study explores the application of metric learning in an end-to-end multilingual automatic speech recognition (ASR) model, employing the wav2vec 2.0 framework. In the proposed method, the E2E ASR model implements metric learning by obtaining acoustic features corresponding to character labels through forced alignment. When metric learning was applied to a six-language E2E ASR model during training, the model incorporating metric learning demonstrated a 0.7-point improvement in the character error rate (from 8.4% to 7.7%) over the baseline model, which was trained without metric learning. Additionally, the visualization of feature vectors indicated a decrease in both the variation of acoustic feature vectors for individual characters and inter-character interference, further underscoring the effectiveness of our approach.