Reviewerly/exHarmony huggingface dataset:
Web Server Load Balancing Mechanism with Least Connection Algorithm and Multi-Agent System

Demands for information over the Internet massively increase through the continuous expansion of website applications. Therefore, generating powerful and efficient server architecture for web servers is a must to satisfy Internet users and avoid the overloaded system. The research focuses on developing a new mechanism for load balancing to distribute incoming HTTP requests in website applications by combining the Least Connection algorithm and Multi-Agent System (LC-MAS). The proposed mechanism distributes the request based on load condition and the fewest number of active connections. The research applies virtualization technology to build servers on this proposed mechanism. The architecture is built inside a physical server with Proxmox as virtualization management and Linux Debian 7.11 as an operating system. Then, the research is tested in two scenarios (LCMAS and LC) using 500, 1,000, and 1,500 requests. The performance of this proposed mechanism is measured through the values of average response time, throughput, and error percentage. The results show that the proposed mechanism (LC-MAS) distributes the workload more equally than LC, with an average response time for 1,500 requests of 1338.8 milliseconds, 20.07% error, and 125 transactions per second. The LC-MAS makes the website application performance much better when the request increases. The LC-MAS helps in the utilization of system resources and improves system robustness.
The Use of Video as a Learning Medium to Increase Interest in Learning English for Students of SDN 01 Wonorejo

Currently, there are many learning media that are very innovative, one of which is video learning media, either videos obtained from Youtube or self-downloaded.The purpose of this study was to determinethe use of video learning media in increasing students' interest in learning at SDN 01 Wonorejo inEnglish lessons.The instruments in this study are in the form of observations and interviews and the analysis techniques are in the form of verbal data, such as notes, suggestions, and comments from students.The result of this study is that the interest in learning and learning motivation of SDN 01 Wonorejo students emerged with the existence of video learning media, because according to them, learning by video seems more interesting and they easily understand the lessons delivered
A Perifacial EMG Acquisition System for Facial-Muscle-Movement Recognition

This paper proposes a portable wireless transmission system for the multi-channel acquisition of surface electromyography (EMG) signals. Because EMG signals have great application value in psychotherapy and human–computer interaction, this system is designed to acquire reliable, real-time facial-muscle-movement signals. Electrodes placed on the surface of a facial-muscle source can inhibit facial-muscle movement due to weight, size, etc., and we propose to solve this problem by placing the electrodes at the periphery of the face to acquire the signals. The multi-channel approach allows this system to detect muscle activity in 16 regions simultaneously. Wireless transmission (Wi-Fi) technology is employed to increase the flexibility of portable applications. The sampling rate is 1 KHz and the resolution is 24 bit. To verify the reliability and practicality of this system, we carried out a comparison with a commercial device and achieved a correlation coefficient of more than 70% on the comparison metrics. Next, to test the system’s utility, we placed 16 electrodes around the face for the recognition of five facial movements. Three classifiers, random forest, support vector machine (SVM) and backpropagation neural network (BPNN), were used for the recognition of the five facial movements, in which random forest proved to be practical by achieving a classification accuracy of 91.79%. It is also demonstrated that electrodes placed around the face can still achieve good recognition of facial movements, making the landing of wearable EMG signal-acquisition devices more feasible.
Zero-shot Micro-video Classification with Neural Variational Inference in Graph Prototype Network

Micro-video classification plays a central role in online content recommendation platforms, such as Kwai and Tik-Tok. Existing works on video classification largely exploit the interactions between users and items as well as the item labels to provide quality recommendation services. However, scarce or even no labeled data of emerging videos is a great challenge for existing classification methods. In this paper, we propose a zero-shot micro-video classification model (NVIGPN) by exploiting the hidden topics behind items to guide the representation learning in user-item interactions. Specifically, we study this zero-shot classification in two stages: (1) exploiting a generalized semantic hidden topic descriptions for transferable knowledge learning, and (2) designing a graph-based learning model for guiding the minor seen class information to the unseen ones. Through mining the transferable knowledge between the hidden topics and the small number of the seen classes, NVIGPN can achieves state-of-the-art performances in predicting the unseen classes of micro-videos. We conduct extensive experiments to demonstrate the effectiveness of our method.
Empowering Deaf-Hearing Communication: Exploring Synergies between Predictive and Generative AI-Based Strategies towards (Portuguese) Sign Language Interpretation

Communication between Deaf and hearing individuals remains a persistent challenge requiring attention to foster inclusivity. Despite notable efforts in the development of digital solutions for sign language recognition (SLR), several issues persist, such as cross-platform interoperability and strategies for tokenizing signs to enable continuous conversations and coherent sentence construction. To address such issues, this paper proposes a non-invasive Portuguese Sign Language (Língua Gestual Portuguesa or LGP) interpretation system-as-a-service, leveraging skeletal posture sequence inference powered by long-short term memory (LSTM) architectures. To address the scarcity of examples during machine learning (ML) model training, dataset augmentation strategies are explored. Additionally, a buffer-based interaction technique is introduced to facilitate LGP terms tokenization. This technique provides real-time feedback to users, allowing them to gauge the time remaining to complete a sign, which aids in the construction of grammatically coherent sentences based on inferred terms/words. To support human-like conditioning rules for interpretation, a large language model (LLM) service is integrated. Experiments reveal that LSTM-based neural networks, trained with 50 LGP terms and subjected to data augmentation, achieved accuracy levels ranging from 80% to 95.6%. Users unanimously reported a high level of intuition when using the buffer-based interaction strategy for terms/words tokenization. Furthermore, tests with an LLM-specifically ChatGPT-demonstrated promising semantic correlation rates in generated sentences, comparable to expected sentences.
Security in Cloud-Native Services: A Survey

Cloud-native services face unique cybersecurity challenges due to their distributed infrastructure. They are susceptible to various threats like malware, DDoS attacks, and Man-in-the-Middle (MITM) attacks. Additionally, these services often process sensitive data that must be protected from unauthorized access. On top of that, the dynamic and scalable nature of cloud-native services makes it difficult to maintain consistent security, as deploying new instances and infrastructure introduces new vulnerabilities. To address these challenges, efficient security solutions are needed to mitigate potential threats while aligning with the characteristics of cloud-native services. Despite the abundance of works focusing on security aspects in the cloud, there has been a notable lack of research that is focused on the security of cloud-native services. To address this gap, this work is the first survey that is dedicated to exploring security in cloud-native services. This work aims to provide a comprehensive investigation of the aspects, features, and solutions that are associated with security in cloud-native services. It serves as a uniquely structured mapping study that maps the key aspects to the corresponding features, and these features to numerous contemporary solutions. Furthermore, it includes the identification of various candidate open-source technologies that are capable of supporting the realization of each explored solution. Finally, it showcases how these solutions can work together in order to establish each corresponding feature. The insights and findings of this work can be used by cybersecurity professionals, such as developers and researchers, to enhance the security of cloud-native services.
Identifying Root-Cause Changes for User-Reported Incidents in Online Service Systems

In online service systems, a majority of incidents are caused by changes, which can influence user experience and cause huge economic loss. Experiences with a real-world, large-scale online service system show that more than half of the change-induced incidents are reported by users. Identifying root-cause changes for these incidents is challenging due to the inherent gap between user-perceived functional-level incident information and component-level change details. Inadequate causal knowledge also brings challenges. In this paper, we propose a novel causal knowledge mining based approach aiming at root-cause change identification for user-reported incidents named Raccoon. To bridge the gap between incidents and changes, it utilizes the fault tree and software product line to represent incidents and changes at the user-perceived functional level. They are also used as the backbone of causal knowledge. To overcome the lack of causal knowledge, Raccoon adopts efficient knowledge extraction and inference methods. Moreover, Raccoon provides recommendations at the software product line and change granularity to meet diverse demands of incident triage and root-cause change identification scenarios in incident management. We evaluate Raccoon on a real-world dataset collected in a large-scale online service system. The result shows that Raccoon significantly outperforms the state-of-the-art baseline approaches, which proves its effectiveness.
fKPISelect: Fault-Injection Based Automated KPI Selection for Practical Multivariate Anomaly Detection

IT services are now popularly hosted in cloud systems. In order to enhance the availability of cloud services, an emerging approach for detecting failures of cloud components is to monitor Key Performance Indicators (KPIs) of the components and apply Neural Network based AI technologies to detect KPI anomalies. Multivariate Time Series Anomaly Detection (TSAD) models have been designed for this purpose. However, when applying such models directly to real-world cloud systems the anomaly detection performance is not as good. This is because the number of KPIs in real cloud systems is typically much more than the number of KPIs in the datasets used for model evaluation, and the larger number of KPIs bring about a performance loss of the models’ anomaly detection. Therefore, selecting KPIs properly is essential for applying multivariant KPI data for any practical anomaly detection. This paper studies this performance loss issue when TSAD models are applied onto real-world cloud systems, and proposes fKPISelect, a mechanism of automated KPI selection based on fault injection. We implemented fKPISelect, deployed it to a real cloud system, and created a real-world KPI dataset. We conducted extensive experiments, and the experimental results show the effectiveness and practicality of fKPISelect: it improves the F1 score of anomaly detection from 0.68 to 0.91 for real-world KPI data.
A Demand-Aware Adaptive Streaming Strategy for High-Quality WebRTC Videoconferencing

WebRTC videoconferencing has risen to prominence in recent years with a sharp increase in remote work and communication. Conferences with large numbers of participants and/or high-quality video streams can overload network and CPU resources and degrade performance, limiting the use of the technology. Several partial solutions exist to mitigate these issues, such as simulcast and Scalable Video Coding (SVC), but these solutions have shortcomings and require trade-offs that make them inadequate for some scenarios. We propose Demand-Aware Adaptive Streaming (DAAS), a novel method of increasing network and CPU efficiency that reduces stream quality—and thus resource consumption—at the source when possible. The method entails using WebRTC data channels to track the presented resolution of each video stream and adapting stream quality to match presentation requirements. The video publisher maintains full quality when required and adaptively reduces quality when possible. We show that the proposed method has advantages over existing methods in that it reduces network bandwidth and CPU requirements for all participants—senders and receivers—in a WebRTC videoconference.
Classification and analysis for Focused Crawled Textual Dataset for retrieving Indian origin scientists

Text classification also called (text categorization or text tagging) is a crucial and extensively used approach in Natural Language Processing (NLP), to predict unseen content documents into prearranged categories. In this paper, we evaluate the dataset construction and evaluation process as a component of text classification. To begin with, we produced a newly created dataset for Indian Origin Scientists for text classification, which was collected by applying focused crawling and web scraping techniques. We then demonstrate an extensive evaluation of numerous models on this recently constructed dataset. Our evaluations display that the Random forest model outperforms the rest of the supervised models. Our results produce a fine beginning for additional research in Indian Origin Scientists' classification of text. Investigational outcome with K Nearest Neighbor, Logistic Regression, and Support Vector Machine for Indian-origin scientists produced much better performances for Random Forest when combined with SMOTE and K fold cross-validation techniques. We apply the Area under the ROC Curve to compute the effectiveness of the chosen models. Overall, the Random Forest classifier exhibited the best output along with 90% micro-average AUC.
Performance Analysis of Image Caption Generation Techniques Using CNN-Based Encoder–Decoder Architecture

Image captioning is the method of generating textual descriptions for an image using deep neural networks. Its objective is to produce accurate results to specify the hidden features and to satisfy its wide applications. There are various Convolutional Neural Network (CNN)-based encoder architectures available in the literature for image caption generation and there is a need to empirically evaluate the best-performing architecture on multiple and diverse datasets to check their efficacy and generalization capability. To address this, we performed the experiments using the Flickr30K dataset containing 31,783 images along with the commonly used Flickr8K dataset consisting of 8091 images. In this study, we aim to discover the best-suited CNN architecture models for caption generation. The study evaluated various encoder architectures, including VGG16, VGG19, InceptionV3, and InceptionResNetV2, for extracting image features and Long Short-Term Memory (LSTM) as a decoder for generating accurate captions. The models are analyzed based on accuracy variation and value loss metrics on both datasets. The results depict that all models perform better on the larger dataset, i.e., Flickr30K achieves better accuracy with minimum loss values and VGG19 shows the best results among all.
Acute Lymphoblastic Leukemia Detection Using DenseNet Model from Microscopic Blood Smear Images

Acute lymphoblastic leukemia (ALL) is a cancerous condition that affects the bone marrow and blood. It is a fast developing illness that, if not identified and treated as soon as possible, could be fatal. ALL is often identified by looking at blood and bone marrow smears under a microscope. Leukemia can be detected and classified using detailed cytochemical tests. However, these procedures are expensive, time-consuming, and dependent on the knowledge and skills of the specialists involved. Using image processing techniques that examine microscopic blood smear images to search for the leukemic cells, leukemia can be detected. These methods are simple, quick, cost-effective, and unaffected by the judgments of experts. The suggested study describes a computer-aided diagnosis method that uses deep convolutional neural networks (CNNs) that have already been trained to compare leukemia images to normal images. The public dataset ALL-IDB 2 was used for the proposed research. The study uses the pre-trained model DenseNet-201 for performing the classification. With the DenseNet201 pre-trained networks employed in the study for the ALL_IDB2 dataset, a classification accuracy of 94.6% is achieved. In all of the classifications carried out, optimization strategies such as cross-validation, fine-tuning, and real-time augmentation are also compared. Also use Pre-trained series models like ResNet-50, VGG-19, Inceptionv3, MobileNet-v2, Xceptionv3, and VGG-16 for performing the comparison. The experimental result gives an improvement in accuracy (17.76, 10.6, and 13.2%) in comparison to the other approaches namely, residual neural network, customized combined CNN, and conVNet neural network, respectively.
iCOIL: Scenario Aware Autonomous Parking Via Integrated Constrained Optimization and Imitation Learning

Autonomous parking (AP) is an emerging technique to navigate an intelligent vehicle to a parking space without any human intervention. Existing AP methods based on mathematical optimization or machine learning may lead to potential failures due to either excessive execution time or lack of generalization. To fill this gap, this paper proposes an integrated constrained optimization and imitation learning (iCOIL) approach to achieve efficient and reliable AP. The iCOIL method has two candidate working modes, i.e., CO and IL, and adopts a hybrid scenario analysis (HSA) model to determine the better mode under various scenarios. We implement and verify iCOIL on the Macao Car Racing Metaverse (MoCAM) platform. Results show that iCOIL properly adapts to different scenarios during the entire AP procedure, and achieves significantly larger success rates than other benchmarks.
New Knowledge, Better Decisions: Promoting Effective Policymaking Through Cybercrime Analysis

profiling of hackers, the role of the media in shaping public perceptions of cyberterrorism, research trends in cybersecurity and cybercrime, as well as the impacts of cyberbullying.
The Effects of Self-control on the Cyber Victim-Offender Overlap

Increasingly, the overlap between victims and offenders has received empirical attention with regard to traditional forms of deviance.More recently, the growth of cyber offending has led to a need to examine whether traditional criminological theories can be used to explain these crimes in the same manner as traditional offenses.However, limited attention has been given to victim-offender overlap in cyber-offending.The current study uses a sample of American college students to examine the influence of self-control on cyber offending, cyber victimization, and the cyber victim-offender overlap.The results indicate that low selfcontrol significantly predicts participation in cyber offending as well as cyber victim-offending, but has a weak relationship with cyber victimization.Interestingly, associating with deviant cyber peers was a significant predictor across all models.Results are discussed in the context of the existing literature.
A Secure Trajectory Planning Method for Connected Autonomous Vehicles at Mining Site

Recently, with the assistance of 5G networks and the Internet of Things, specialized applications of autonomous driving to mining sites have been explored, with the goal of realizing the unmanned operation of mining systems and enhancing the safety of the mining industry. After receiving the loading task, the autonomous driving system will generate a feasible trajectory for the mining truck. It requires that the trajectory be generated in advanced within a limited-time high-latency network. In addition, the secure trajectory planning for mining sites involves factors in the complex environment and an unstable network. Thus, a secure trajectory planning method for autonomous trucks at mining sites is proposed. It simplifies the planning by decoupling the planning into front-end path searching and back-end trajectory generation. First, the planner enhances the Hybrid A* search algorithm to find the hauling path within the boundary of the mining site, and then, it post-processes the path with a well-designed symmetric optimization-based method. Then, considering the interaction with other autonomous trucks, a topology-guided search method for secure decision making is proposed, considering the possibility of cybersecurity. The proposed method was validated in real scenarios of the mining environment. The results verify that the planner can generate the secure trajectory under network delay 2.0 s conditions.
Flexible Alignment Super-Resolution Network for Multi-Contrast Magnetic Resonance Imaging

Super-resolution is essential in improving the image quality of Magnetic Resonance Imaging (MRI). Existing MRI Super-Resolution methods leverage multi-contrast MRI and achieve satisfied effects. However, these methods perform alignment by calculating the similarity of single-scale semantic features between reference images and low-resolution images, which causes misalignment and limits the performance of MRI Super-Resolution. To tackle this problem, we propose the Flexible Alignment Super-resolution Network (FASR-Net) for multi-contrast MRI Super-resolution, which explores the interaction of multi-scale features. To this end, we first use the feature extractor to generate multi-scale features, including hierarchical features and semantic pyramid features. Subsequently, we introduce the Hierarchical-Feature Alignment (HF) module and the Semantic-Pyramid-Feature Alignment (SF) module to align hierarchical features and semantic pyramid features, respectively. Finally, the Cross-Hierarchical Progressive Fusion (CHPF) module fuses these aligned features at different scales, which further improves the model's performance. Extensive experiments on FastMRI and IXI datasets show that FASR-net achieves the most competitive results over state-of-the-art approaches. Our code will be available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FASR-Net</uri> .