Reviewerly/exHarmony huggingface dataset:
Computer Laboratory Management In Improving Students' Hard Skills In The Field Of Multimedia

The computer laboratory is a support for learning, skills and school infrastructure. Then required planning, organizing, implementation and evaluation. To make the computer laboratory directed and managerial so that the goal of procuring a computer laboratory is achieved effectively and efficiently. As for improving the hard skills themselves, it requires teaching that is repeated consistently and always evaluates so that the hard skills are even better. This research is included in qualitative research with a descriptive approach, the data collection methods used are interviews, observation and documentation. The key informant in this research is the head of the computer laboratory. While the supporting informants were the Head of School, Deputy Head of School for Facilities and Infrastructure, Deputy Head of School for Curriculum, and students. This research was conducted at MAN 1 Jombang. Based on the results of research on computer laboratory management including planning activities or goals for managing computer laboratories as well as organizing which includes the selection of good human resources and the preparation of organizational structures, the implementation of evaluation which is a managerial activity here also describes how to increase hard skills in the multimedia field. It also describes the supporting and inhibiting factors in managing computer laboratories in improving students' hard skills in the field of multimedia at MAN 1 Jombang.
Real-time 3-D reconstruction from multiple simultaneous ultrasound scans for enhanced imaging

Ultrasound imaging offers numerous benefits compared to other modalities, including portability, affordability, and real-time volumetric imaging capabilities. However, its limited field of view hinders image interpretation, significantly limiting its potential clinical applications. This study focuses on creating tomographic ultrasound imaging by automatically registering partially overlapping 3-D ultrasound volumes (coarsely localized in space). This approach can be used to gather a comprehensive field of view of the anatomical area imaged and to enhance the acquired image information. Potential applications involve both tomographic/wide-view imaging using conventional ultrasound probes and future technologies, where multiple ultrasound probes will be able to scan simultaneously the anatomy of interest. This work describes the development of advanced artificial intelligence and image analysis methods to create real-time 3-D anatomical reconstructions of a designated area of interest. Simultaneously (or quasi simultaneously) acquired ultrasound volumes with partial overlap can be spatially aligned using rigid registration, because they present geometrically similar information. We used novel supervised and unsupervised transformers and compared the results with more traditional methods such as mutual information and cross-correlation. This application contributes to creating new imaging processing techniques for ultrasound imaging and making this complex modality more user-friendly and clinically usable.
Assessing Students’ Writing Skill in Summary Writing Narrative Text

The purpose of this study was to find out the results of assessing students' writing skill in summary writing narrative text. In this study, researchers used a quantitative method with a quantitative descriptive design. The population in this study were students of class XII MA Cisaat Filial MAN 1 Serang. Researchers used simple random sampling technique. The research instrument is a written test. The researcher used an assessment rubric to determine students' ability to write summaries. In the rubric assessment there are five indicators, namely: content, organization, grammar, mechanics, and vocabulary. The result of this study is that students' summary writing skills are included in the "fair" category, with an average of 66.6. This can be seen from the assessment carried out on the results of writing student summaries. In the assessment of the five indicators, most students also get the "fair" category.
Utilizing Reading Progress Feature In Microsoft Teams To Improve Speaking and Listening Competence Of English Foreign Language Learners

This study investigated whether the Reading Progress Feature substantially improves speaking abilities. This study used a quasi-experimental design using non-equivalent pretest-posttest control groups or comparison groups, and the population of this study comprised all undergraduate students of Universitas Prof Dr Moestopo Beragama. Purposive sampling was used to select 78 English Foreign Learners as the sample. The findings revealed a significant improvement in the EFL learners speaking comprehension after being evaluated using Reading Progress Feature to those who were not. There was also a significant mean difference in the EFL learners speaking comprehension after being evaluated using Reading Progress Feature than those not. On the other hand, technological Knowledge in Microsoft Team influences EFL learners speaking ability, and the evaluation from the Reading Progress Feature contributes to the speaking comprehension. Fluency contributed to speaking skills (aspects) to overall speaking accomplishment. As a result, it is possible to infer that employing Reading Progress Feature considerably improves EFL Learners' speaking abilities. The research's pedagogical conclusion is that instructors should consider Microsoft Team, including academic training in the English language online classroom since this might increase EFL learners' desire to speak and write by exposing them to real intriguing materials
HongTu: Scalable Full-Graph GNN Training on Multiple GPUs

Full-graph training on graph neural networks (GNN) has emerged as a promising training method for its effectiveness. Full-graph training requires extensive memory and computation resources. To accelerate this training process, researchers have proposed employing multi-GPU processing. However the scalability of existing frameworks is limited as they necessitate maintaining the training data for every layer in GPU memory. To efficiently train on large graphs, we present HongTu, a scalable full-graph GNN training system running on GPU-accelerated platforms. HongTu stores vertex data in CPU memory and offloads training to GPUs. HongTu employs a memory-efficient full-graph training framework that reduces runtime memory consumption by using partition-based training and recomputation-caching-hybrid intermediate data management. To address the issue of increased host-GPU communication caused by duplicated neighbor access among partitions, HongTu employs a deduplicated communication framework that converts the redundant host-GPU communication to efficient inter/intra-GPU data access. Further, HongTu uses a cost model-guided graph reorganization method to minimize communication overhead. Experimental results on a 4XA100 GPU server show that HongTu effectively supports billion-scale full-graph GNN training while reducing host-GPU data communication by 25%-71%. Compared to the full-graph GNN system DistGNN running on 16 CPU nodes, HongTu achieves speedups ranging from 7.8X to 20.2X. For small graphs where the training data fits into the GPUs, HongTu achieves performance comparable to existing GPU-based GNN systems.
Implementation of Random Forest for Vehicle Type Classification using Gamma Correction Algorithm

Traffic control systems can be a valuable tool for monitoring road traffic by counting and tracking vehicles in real time. This research is the initial research into the development of methods to improve the accuracy of vehicle detection and recognition. The purpose of the study was to analyze the performance of gamma correction and random forest performance to improve the accuracy of vehicle detection and recognition. Performance measures used in the study were confusion matrix, accuracy, precision, recall, F1-score. Based on experimental results, random forest with gamma=1.5 got the best accuracy of 85.00%, while random forest with gamma=0.5 got accuracy of 81.30%, random forest with gamma=1.0 got accuracy of 84.00%, random forest with gamma=2.0 got accuracy of 84.00%.
Retracted: Deformation Adjustment with Single Real Signature Image for Biometric Verification Using CNN

[This retracts the article DOI: 10.1155/2022/4406101.].
Pedestrian detection algorithm integrating large kernel attention and YOLOV5 lightweight model

In the context of intelligent driving, pedestrian detection faces challenges related to low accuracy in target recognition and positioning. To address this issue, a pedestrian detection algorithm is proposed that integrates a large kernel attention mechanism with the YOLOV5 lightweight model. The algorithm aims to enhance long-term attention and dependence during image processing by fusing the large kernel attention module with the C3 module. Furthermore, it addresses the lack of long-distance relationship information in channel and spatial feature extraction and representation by introducing the Coordinate Attention mechanism. This mechanism effectively extracts local information and focused location details, thereby improving detection accuracy. To improve the positioning accuracy of obscured targets, the alpha CIOU bounding box regression loss function is employed. It helps mitigate the impact of occlusions and enhances the algorithm's ability to precisely localize pedestrians. To evaluate the effectiveness of trained model, experiments are conducted on the BDD100K pedestrian dataset as well as the Pascal VOC dataset. Experimental results demonstrate that the improved attention fusion YOLOV5 lightweight model achieves an average accuracy of 60.3%. Specifically, the detection accuracy improves by 1.1% compared to the original YOLOV5 algorithm, and the accuracy performance index reaches 73.0%. These findings strongly indicate the proposed algorithm in significantly enhancing the accuracy of pedestrian detection in road scenes.
The Translation Quality of Clinically Oriented Anatomy Book in Bahasa Indonesia

The objective of this study are to identify the accuracy of the medical translation and describe the result of the quality translation of the Clinically Oriented Anatomy book in Bahasa Indonesia. This is a descriptive, qualitative research which focus on single case, objective of the data is the medical terminology that found in the translation process. The data collected by Focus Group Discussion, Interview, and Questioner with the key informants who is a specialist dentist, and two general dentist. The number of the data is 359, the result of the research showed that 36.49 % from the specialist doctor, 34.81% from the general dentist and the last 35.93% that give the scale of the accuracy in the translation. It can be conclude that, the accuracy of the transaltion is less accurate.
ChainApparel: A Trustworthy Blockchain and IoT-Based Traceability Framework for Apparel Industry 4.0

Trustworthiness and product traceability are essential factors in the apparel industry 4.0 for establishing successful business relationships among stakeholders such as customers, manufacturers, suppliers, and consumers. Each stakeholder has implemented different technology-based systems to record and track product transactions. However, these systems work in silos, and there is no intra-system communication, leading to a lack of complete supply chain traceability for all apparel stakeholders. Moreover, apparel stakeholders are reluctant to share their business information with business competitors; thus, they involve third-party auditors to ensure the quality of the final product. Furthermore, the apparel manufacturing industry faces challenges with counterfeit products, making it difficult for consumers to determine the authenticity of the products. Therefore, in this paper, a trustworthy apparel product traceability framework called ChainApparel is developed using the Internet of Things (IoT) and blockchain to address these challenges of authenticity and traceability of apparel products. Specifically, multiple smart contracts are designed and developed for registration, process execution, audit, fault, and product traceability to authorize, validate, and trace every business transaction among the apparel stakeholders. Further, the real-time performance analysis of ChainApparel is carried out regarding transaction throughput and latency by deploying the compute nodes at different geographical locations using Hyperledger Fabric. The results conclude that ChainApparel accomplished significant performance under diverse workloads while ensuring complete traceability along the complex supply chain of the apparel industry. Thus, the ChainApparel framework helps make the apparel product more trustworthy and transparent in the market while safeguarding trust among the industry stakeholders.
Dissolvable and Biodegradable Packaging with Agar Agar Using Design Thinking Approach

The research paper investigates agar agar, a naturally occurring polymer obtained from seaweed, as a viable substitute for creating packaging that degrades biodegradably. With the goal of providing a more environmentally friendly and biodegradable alternative, this study seeks to address the rising environmental problems related to standard plastic packaging materials. Because of its special qualities, agar agar is a viable option for lowering plastic pollution and fostering environmental sustainability.
Integrating Bio-metric Authentication in E-challan Machine

In India, as of my last update in January 2023, it was not compulsory to link the Aadhaar card and driving license. However, the Aadhaar card, a unique identification document issued by the Indian government, and the driving license, a legal document that permits individuals to drive vehicles, can be linked for verification purposes. The proposed objective behind linking these documents was to streamline and authenticate personal information, reduce identity fraud, ensure a more accurate database, and make processes more efficient. It's recommended to verify the current status of linking Aadhaar and driving license with concerned authorities or the latest government regulations, as the specific requirements and status may have changed since my last update.
Motivasi Belajar Terhadap Hasil Belajar Siswa Di SMP Negeri 2 Pancalang

Motivation is one of the most important factors in influencing learning and learning outcomes for students at the kindergarten, elementary and junior high school levels. However, in learning activities, it turns out that there are still students who are less active in participating in learning during the lesson. Students or students still look lazy to learn. Students also do not show their expressions that are happy and not enthusiastic in following ongoing learning. Their attention is more focused on something that is opposite them than their learning tasks such as playing games in class and others. Learning motivation has a positive relationship with the learning outcomes of a subject. Thus, learning motivation contributes greatly to the achievement of student learning outcomes. Therefore, teachers must have the ability: (First) encourage learning motivation, (Second) directing learning motivation to achieve goals in achieving the learning outcomes of a subject, and (Third) strengthen learning motivation in order to ensure the consistency of students learning actions.
Obstacle Avoidance Trajectory Planning for Robotic Arm based on Genetic Algorithm

For the problem of obstacle avoidance trajectory planning of a robot arm, a robot arm obstacle avoidance method based on a genetic algorithm is proposed. It is based on the two problems that the motion process can avoid obstacles and the motion process is more stable and efficient. First, the motion of each joint is planned as a sixth-degree polynomial, and the coefficients of the sixth-degree term are set as the pending parameters, and the motion of each joint is changed by changing the pending parameters. Then, the fitness function is then constructed by calculating the collision detection, angular velocity limit detection, acceleration limit detection, and the total trajectory length and rotation angle for each joint. Finally, the fitness function is optimised using a genetic algorithm to obtain smooth, continuous, and collision-free trajectories. Matlab simulation experiments show that this method can obtain the optimal or suboptimal trajectory without collision.
Applications Of Mobile System

A thorough examination of the latest advancements in mobile systems is given in this review article, emphasizing on three key areas: security, performance, and future technologies. It integrates existing studies and literature to provide a summary of the changing terrain in the design and execution of mobile systems. In addition to exploring modern options like biometric authorization, secured enclave tech highlights the obstacles presented by cyber security threats. The study also discusses how new developments in artificial intelligence, Internet of&#x0D; Things (IOT) unity, and 5G networks are influencing the development of mobile systems. The review emphasizes the challenges posed by cyber security threats and explores contemporary solutions, such as biometric authentication, secure enclave technologies. and block chain integration.&#x0D;
Interpretable Model For Decision Support In Healthcare

"In thе rapidly еvolving landscapе of hеalthcarе, thе intеgration of machinе lеarning modеls into dеcision-making procеssеs has shown grеat promisе. Howеvеr, thе black-box naturе of complеx algorithms oftеn impеdеs thеir adoption in critical hеalthcarе scеnarios. This rеsеarch dеlvеs into thе rеalm of intеrprеtablе modеls for dеcision support in hеalthcarе, addrеssing thе pivotal nееd for transparеncy and comprеhеnsion in mеdical dеcision-making. By еxamining divеrsе datasеts and еmploying intеrprеtablе machinе lеarning tеchniquеs such as dеcision trееs, logistic rеgrеssion, and rulе-basеd modеls, this study shеds light on modеls' intеrprеtability without compromising thеir prеdictivе accuracy. Thе findings not only dеmonstratе thе viability of intеrprеtablе modеls in hеalthcarе contеxts but also undеrscorе thеir еssеntial rolе in еnhancing trust bеtwееn hеalthcarе providеrs and artificial intеlligеncе systеms. This rеsеarch advocatеs for a paradigm shift towards thе widеsprеad adoption of intеrprеtablе modеls, thеrеby paving thе way for informеd, rеliablе, and accountablе dеcision support in hеalthcarе practicеs."&#x0D; This rеsеarch papеr focusеs on addrеssing this challеngе by еxploring thе rеalm of intеrprеtablе modеls for dеcision support in hеalthcarе. Rеcognizing that mеdical profеssionals and stakеholdеrs rеquirе not only accuratе prеdictions but also clеar еxplanations for thosе prеdictions, intеrprеtablе machinе lеarning modеls such as dеcision trееs, logistic rеgrеssion, and rulе-basеd modеls havе gainеd prominеncе. Thеsе modеls offеr a uniquе advantagе: thеy providе insights into thе factors that influеncе prеdictions, allowing hеalthcarе providеrs to validatе thе rеcommеndations and makе wеll-informеd dеcisions collaborativеly with artificial intеlligеncе systеms.
Learning algorithms for oscillatory neural networks as associative memory for pattern recognition

Alternative paradigms to the von Neumann computing scheme are currently arousing huge interest. Oscillatory neural networks (ONNs) using emerging phase-change materials like VO 2 constitute an energy-efficient, massively parallel, brain-inspired, in-memory computing approach. The encoding of information in the phase pattern of frequency-locked, weakly coupled oscillators makes it possible to exploit their rich non-linear dynamics and their synchronization phenomena for computing. A single fully connected ONN layer can implement an auto-associative memory comparable to that of a Hopfield network, hence Hebbian learning rule is the most widely adopted method for configuring ONNs for such applications, despite its well-known limitations. An extensive amount of literature is available about learning in Hopfield networks, with information regarding many different learning algorithms that perform better than the Hebbian rule. However, not all of these algorithms are useful for ONN training due to the constraints imposed by their physical implementation. This paper evaluates different learning methods with respect to their suitability for ONNs. It proposes a new approach, which is compared against previous works. The proposed method has been shown to produce competitive results in terms of pattern recognition accuracy with reduced precision in synaptic weights, and to be suitable for online learning.
Crypto-Stegano Color Image Based on Rivest Cipher 4 (RC4) and Least Significant Bit (LSB)

Rivest Cipher 4 (RC4) has the main factors that make this algorithm widely used, namely its speed and simplicity, so it is known to be easy for efficient implementation. The nature of the key in the RC4 algorithm is symmetrical and performs a plain per digit or byte per byte encryption process with binary operations (usually XOR) with a semirandom number. To improve the visual image after the encryption process, in this article we use the Least Significant Bit (LSB). In this study, the quality of the stego image and the original image has been calculated using MSE, PSNR and Entropy. Experiments were carried out by images with a size of 128x128 pixels to 2048x2048 pixels. Experiments using imperceptibility prove that the stego image quality is very good. This is evidenced by the image quality which has an average PSNR value above 53 dB, while the lowest PSNR value is 48 dB with a minimum dimension of 128x128 pixels.
Visualization

This chapter explores the interpretive possibilities raised by computational visualizations of digitized literature and literary data. Taking Franco Moretti’s Maps, Graphs and Trees (2007) as a starting point, it considers what new insights these techniques of visualization, seldom employed in the humanities, can convey. Carter speaks with leading practitioners in the field to unpack the ways in which new techniques in digital data visualization are allowing scholars “to perform conventional work in new ways.” Applying these techniques to literary data for which they are not designed, however, also reveals a productive push and pull: as one of Carter’s interview subjects, Alex Christie, puts is, “We’re reading the literature on the technology, but we’re also seeing where the literature we’re trying to model pushes against the edges of the technical frameworks we have in hand.”
Learning Program Semantics for Vulnerability Detection via Vulnerability-Specific Inter-procedural Slicing

Learning-based approaches that learn code representations for software vulnerability detection have been proven to produce inspiring results. However, they still fail to capture complete and precise vulnerability semantics for code representations. To address the limitations, in this work, we propose a learning-based approach namely SnapVuln, which first utilizes multiple vulnerability-specific inter-procedural slicing algorithms to capture vulnerability semantics of various types and then employs a Gated Graph Neural Network (GGNN) with an attention mechanism to learn vulnerability semantics. We compare SnapVuln with state-of-the-art learning-based approaches on two public datasets, and confirm that SnapVuln outperforms them. We further perform an ablation study and demonstrate that the completeness and precision of vulnerability semantics captured by SnapVuln contribute to the performance improvement.