Reviewerly/exHarmony huggingface dataset:
The Influence of Online Attendance System and Work Discipline on The Performance of Educational Staff in Universitas Negeri Semarang

This research is aimed at understanding the influence of the online attendance system and work discipline on the performance of educational staff at Universitas Negeri Semarang (UNNES). The sampling technique employed in this research is random sampling, where the researcher randomly took samples without considering the variety of the population’s background. There were 140 (20%) Educational Staff in UNNES taken as the samples of this research. This research features a quantitative approach where the primary data were obtained from surveys through questionnaires distributed to the respondents. The data analysis and processing utilised the regression equation in the SPSS, a statistics software.&#x0D; The problems underlying this research are formulated as follows: Do the online attendance system and work discipline significantly influence the performance of educational staff in Universitas Negeri Semarang?&#x0D; The result shows that the main factor influencing employees' work performance in UNNES is work discipline. The implementation of an online attendance system has minimum influence on employees’ performance. Online attendance systems, followed by work discipline, directly had a more significant impact than merely the online attendance systems.&#x0D;
Graph Sampling based Fairness-aware Recommendation over Sensitive Attribute Removal

Discrimination against different user groups has received growing attention in the recommendation field. To address this problem, existing works typically remove sensitive attributes that may cause discrimination through adversary learning to achieve fair recommendations. However, these approaches leverage all available interactions for learning user representations and overlook the fact that different interactions have varying relevance to users’ sensitive attributes. Ignoring this issue may weaken the effectiveness of adversary learning in removing sensitive attributes. To tackle this challenge, we propose a novel model called GS-FairRec, which distinguishes between user interactions to achieve better removal of sensitive attributes. The model consists of three modules: graph sampling-based representation learning, pseudo-user representation learning, and adversarial learning. Firstly, the graph sampling-based representation learning module removes some irrelevant neighbors from a user-item bipartite graph and employs a graph convolutional network (GCN) to learn user/item representations. Next, items that are relevant to a user’s sensitive information but do not match their preferences are defined as the user’s pseudo-interest items, which are leveraged to learn the pseudo-user representation. In the adversarial learning module, the user’s two kinds of representations are fused for adversarial learning to remove sensitive information. Additionally, we design a new metric to measure the model’s ability to remove sensitive attributes based on how a generated recommendation list discloses the user’s sensitive attributes. Finally, we conduct experiments on two real-world datasets, and our results demonstrate the superiority of our proposed model in fairness tasks.
The Demonstration of a Low-Cost Tabletop Microscale Hydroforming System With a Self-Aligning Sealing System

Abstract A microscale/multiscale tube hydroforming tabletop test bed that includes the tabletop testbed, PLC controller, and high-pressure hydraulic pump has been developed. Currently, feedback and controls are being implemented along with adapting previously built open-die tooling as part of a collaborative initiative between researchers in Louisiana and at Michigan Technological University. The tooling utilizes the floating die concept and was designed and built based upon conventional macroscale manufacturing processes to produce microscale components. At this point in the system development, the objective is to implement controlled expansion of AISI 304 tubing with an OD of 2-mm using an existing floating rigid die. Later modifications to the system will allow the production of tubing with complex geometry such as tee-sections that can be used in microfluidic devices such as fuel cells, microscale heat exchangers, and MEMS. Preliminary results for formed sample tubes are discussed.
Fuzzy Graph Modeling and Clustering Analysis of Nonlinear Dynamical Systems

Analyzing stability and designing control strategies for interconnected nonlinear dynamical systems poses mathematical challenges due to combinatorial growth in complexity. This paper develops a methodology integrating fuzzy graph theory with spectral clustering techniques to enable tractable certification of stability properties. Fuzzy similarity relations between state variables model imprecise couplings within the nonlinear dynamics. Representing these relations through graph adjacency matrices facilitates partitioning strongly connected states using spectral algorithms. Stability of the overall fuzzy state graph is inferred from the spectral radii of decoupled fuzzy subgraphs. The graph-theoretic abstraction provides a coarse-grained lens into nonlinear stability properties while circumventing computational barriers. The fuzzy graph modeling and spectral partitioning pipeline ultimately streamlines control synthesis targeting local clustered subdynamics. Case studies on power network stabilization, swarm navigation, and process operations showcase scalable applications to high-dimensional complex systems. The integrated fuzzy graph and spectral clustering approach provides a systematic toolkit for analysis and control of heavily interconnected nonlinear dynamical systems across engineering domains.
Scaled Down Lean BERT-like Language Models for Anaphora Resolution and Beyond

We study performance of BERT-like distributive semantic language models on anaphora resolution and related tasks with the purpose of selecting a model for on-device inference. We have found that lean (narrow and deep) language models provide the best balance of speed and quality for word-level tasks, and opensource1 RuLUKE-tiny and RuLUKE-slim models we have trained. Both are significantly (over 27%) faster than models with comparable accuracy. We hypothesise that the model depth may play a critical role for performance as, according to recent findings each layer behaves as a gradient descent step in autoregressive setting.
Analytical Review of Data Lakes and Perspectives of Application in the Field of Education

An analytical review of the development of Data Lakes and its application in various industries, as part of Big data concept solutions, was conducted. The available standard architectural solutions for the Data Lake organization are considered. Also, specialized areas that require different or additional aspects to solve the tasks, depending on the field of Data Lake use, are taken into account. For the proper organization of Data Lake, various data processing tools are used, including distributed data storage systems, semantic networks, and especially metadata. Metadata plays a huge role in recognizing the purpose of data and possible relationships between it and entities. An overview of the prospects for the use of Data Lake, in particular as context of Smart City, distance education and the education industry in general, was conducted.
Dual trustworthy mechanism for illness classification with multi-modality data

Multi-modality classification has flourished in recent years. Traditional methods mainly focus on advancing deep neural networks (DNN) to meet high performance. However, the interpretability of these methods remains blind due to the complexity and ambiguity of DNN, which also causes distrust. This problem is enlarged in sensitive areas, such as biomedical computing. Hence, we propose a novel dual trustworthy mechanism for multi-modality classification (DTMC), which can make the process and results of DNN more credible and interpretable while increasing performance. Specifically, a confidence attention mechanism is performed from local and global views to improve the process’ confidence by evaluating the attention scores and distinguishing the abnormal information. A confidence probability mechanism from local and global perspectives is conducted in the prediction stage to enhance the results’ confidence. Extensive experiments on multi-modality medical classification datasets show superior performance with the interpretability of the proposed method compared to the state-of-the-art (SOTA) methods. Our resources are open at https://github.com/ghh1125/data.
Outcome-Based Education Scoring System Utilizing Modular Object-Oriented Dynamic Learning Environment

The dynamic nature of education necessitates inventive approaches to assessment and evaluation. This study concentrates on formulating a Scoring System aligned with Outcome-Based Education (OBE) principles, utilizing the Modular Object-Oriented Dynamic Learning Environment (MOODLE) platform. OBE prioritizes showcasing specific learning outcomes, cultivating a student-centric approach. The proposed system seeks to improve assessments through a flexible framework accommodating diverse learning objectives, employing a modular and object-oriented design. Integration with MOODLE, a widely-used e-learning platform, explores seamless implementation and user-friendly interaction. The Scoring System aids educators in efficiently evaluating student performance against predefined outcomes, fostering transparency and accountability.Key features include customizable assessments, progress tracking, and timely feedback. The study also examines the system's impact on student engagement, motivation, and overall learning outcomes, contributing valuable insights to innovative assessment methodologies in contemporary education. In conclusion, the research introduces a Scoring System harmonizing OBE principles with MOODLE's flexibility, benefiting educators, students, and institutions. The study's outcomes provide valuable implications for educators and technologists aiming to enhance assessments in the evolving education landscape.
Utilizing Machine Learning to Analyze and Implement Task Scheduling Algorithms in Cloud

Today many online users may access a number of facilities that utilizes cloud computing. Task scheduling, which involves assigning work to virtual computers hosted in data centers, is one of these services. Typically, there are more tasks than virtual machines, thus algorithms must be used to guarantee two important goals: (i) the swift completion of all tasks, and/or (ii) the fair allocation of the burden. In order to assess their performance across various tasks, we have tested both conventional algorithms (such as First Come First Serve, Round Robin, and Shortest Job First) and evolutionary algorithms (such as Particle Swarm Optimization and Ant Colony Optimization) within the CloudSim framework. The job completion durations of these algorithms are then contrasted. Result shows that analysis varies depends on number of data centers and the number of virtual machines for conventional algorithms. Among the evolutionary algorithms, Ant Colony Optimization is better choice.
Automatic Synthesis of Realistic Images From Text using DC-Generative Adversarial Network (DCGAN)

Automatic Conversion of text to realistic images is an interesting, amazing and useful technique. There are several methodologies proposed by many researchers in achieving this but none of them have addressed this effectively. They lack efficiency in terms of images generated in terms of quality or precision. This work intends to generate realistic and semantically correct images from the given input text. Generative Adversarial Networks (GAN) are used to convert human written description text into its corresponding image pixels. The model is designed to initially learn the features hidden in text to capture the unique visual details. These factors are further used to synthesize semantically correct images which humans might mistake for real images. The data set, the Oxford 102 flower dataset is used which has at least five text descriptions describing the image. The specific Deep Convolutional - Generative Adversarial Network consisting of a Generator and a Discriminator is used to implement the proposed model. DC-GAN is a direct extension of the GAN which explicitly uses convolutional and convolutional-transpose layers in the discriminator and generator. The aim is to train the DC-GAN model to produce compelling and accurate images for the corresponding input description. The clarity of the image obtained is directly proportional to the number of epochs the model was trained for. The greater the number of epochs the model is trained, the better the efficiency/clarity of the image. The model is evaluated for the various performance metrics. The model is evaluated based on the image clarity obtained and the context of image generation. The performance of the model is measured based on the clarity of the images generated and the context for which the image was generated.
Object Detection in Adverse Weather Conditions using Machine Learning

Object detection in challenging weather conditions is a formidable hurdle in the realm of computer vision. Unfavorable weather circumstances, encompassing rain, snow, fog, and low light conditions, substantially impede the effectiveness of conventional computer vision algorithms. This research paper introduces an innovative approach to image-to-image translation by harnessing the power of CycleGAN, a deep learning architecture renowned for its proficiency in transmuting images from one domain to another. This study is dedicated to tailoring the CycleGAN framework to the specific task of enhancing images captured in adverse weather conditions, such as rain, fog, and snow. The proposed methodology capitalizes on the deployment of two distinctive generators, trained cyclically, for the transformation of images between a source domain (representing adverse weather) and a target domain (signifying clear weather). These generators are bolstered by discriminator networks, enabling adversarial training to enhance the quality of image translation.
An Empirical study of Gradient Compression Techniques for Federated Learning

Federated Learning (FL) is a promising technique for decentralizing machine learning on multiple devices. In a FL environment, clients train an initial global ML model using their local data. They transfer the weights or gradients of the trained model to the server which, in turn, aggregates the received weights or gradients to obtain an improved global model. Thus, data is not shared with the server and this enhances user privacy as well as reduces the required communication bandwidth. However, resource-constrained devices and bandwidth-limited networks can fully leverage FL if the size of the weights or gradients can be reduced too. This paper performs an empirical investigation of a few existing gradient compression techniques for FL. The techniques of sparsification and Wyner-Ziv coding are studied and evaluated with respect to global model accuracy and communication overhead in various Federated and centralized scenarios. It is observed that the accuracy of the model is compromised if communication overhead is reduced. However, it is possible to achieve a balance between the two by selecting a compression gradient scheme according to the system requirements.
Leveraging Semantic Representations via Knowledge Graph Embeddings

Abstract The representation and exploitation of semantics has been gaining popularity in recent research, as exemplified by the uptake of large language models in the field of Natural Language Processing (NLP) and knowledge graphs (KGs) in the Semantic Web. Although KGs are already employed in manufacturing to integrate and standardize domain knowledge, the generation and application of corresponding KG embeddings as lean feature representations of graph elements have yet to be extensively explored in this domain. Existing KGs in manufacturing often focus on top-level domain knowledge and thus ignore domain dynamics, or they lack interconnectedness, i.e., nodes primarily represent non-contextual data values with single adjacent edges, such as sensor measurements. Consequently, context-dependent KG embedding algorithms are either restricted to non-dynamic use cases or cannot be applied at all due to the given KG characteristics. Therefore, this work provides an overview of state-of-the-art KG embedding methods and their functionalities, identifying the lack of dynamic embedding formalisms and application scenarios as the key obstacles that hinder their implementation in manufacturing. Accordingly, we introduce an approach for dynamizing existing KG embeddings based on local embedding reconstructions. Furthermore, we address the utilization of KG embeddings in the Horizon2020 project Teaming.AI ( www.teamingai-project.eu .) focusing on their respective benefits.
Exploring the Role of Automated Video Inspection and Recognition in Security Enhancement

Security becomes the highest priority for people, organisations, and governments in a world that is getting more interconnected and complicated. The use of automated video inspection and recognition systems is becoming a very popular and effective method for strengthening security measures. This is a technology-driven system that integrates image processing, Artificial Intelligence, Machine Learning to identify specific objects, analyse their behaviour and movement and also recognize a specific trend to detect unusual activities. This comprehensive review paper explores and compares different technologies that have been used in automated video inspection and recognition systems. This survey paper aims to comprehensively examine several approaches used for automated video inspection. It explores the real-world applications and challenges of automated video inspection systems, providing a comprehensive grasp of this vital part of security and monitoring systems.
Dynamic Residual Illuminating Network for Low-Light Image Enhancement

A low-light image is an image that is taken under insufficient lighting conditions or captured by an underexposed camera. Low-light images commonly exhibit characteristics such as reduced contrast, diminished brightness, pronounced noise, and color distortion, which make them difficult to see and process. Low-light Image Enhancement is a task that involves improving the qualities of these images and is dominated by deep learning approaches nowadays. However, most deep learning methods utilize standard convolution with fixed kernels in their CNN architectures, hindering model’s capability to process and recover more information. Besides, noise remains challenging in recovering low-light images. To mitigate these issues, we propose a model based on the Deep Lightening Network (DLN), we modify the Dynamic Region-Aware Convolution (DRConv) and serve them as the encoder and decoder to process more intrinsic features, we modify Residual in Residual Dense Block (RRDB) to a lighter one to reduce noise and alleviate computational burden. Experiments demonstrate the advantages of our proposed model in quantitative evaluation and visual quality.
Website Main Content Extraction Using Template-Based Approach and Naïve-Bayes Classification

Every web page will have the main content. The main content is a section, segment or block that contains text or multimedia on a single web page. Important information about local governance generally lies within the main content, thus the need for web content extractor to extract that information. To solve these problems, this research combines two approaches that already existed, template-based approach and machine learning approach using Naïve-Bayes Classifier. Generally, previous research that has been conducted is using one type of approach; it is either using a template-based approach or using a machine learning approach. The result shows that with combining two types of approaches, the model could identify 95% all nodes that contain the main content.
Biceps Muscle Motor Strength Signal Reading Using AD8226 Sensor

Symptoms of ischemic stroke are motor weakness in the legs and arms, facial drooping, and speech impairment. Manual Muscle Testing (MMT) is the most commonly used method of examining muscle strength in the orthopedic, neurological, dental, homeopathic, and physical therapy fields to determine a patient's progress during therapy. MMT has disadvantages such as the examination results being subjective, less accurate, and only performed by health professionals. To be able to increase the reliability of motor weakness examination, the development of motor weakness detection using AD8226 sensor and surface electromyography (EMG) is carried out. The AD8226 sensor is a non-invasive examination, with measurable results, ease of use, and reliable. The AD8226 can represent the waveform of motor strength measurement so that the diagnosis is accurate. Testing of the AD8226 sensor was carried out by 10 elderly people by measuring motor strength in the biceps muscle. The results of testing the tool show that there is motor weakness in the elderly who have a difference in value of more than 0.40 Volts, this is to the history of the elderly who experience motor weakness. So the development of the AD8226 tool can help the MMT method to be more accurate and efficient.
Evolutionary algorithms, swarm intelligence methods, and their applications in water resources engineering: a state-of-the-art review

Evolutionary algorithms and allied fields are getting more visibility as well as familiarity due to their numerous flexibilities such as handling high-dimensional non-linear problems and more. This book will help budding researchers to formulate their research problems, and comprises 10 chapters: three on optimization, five on machine learning algorithms, one on Internet of Things, and one on remote sensing.In Focus–a book series that showcases the latest accomplishments in water research. Each book focuses on a specialist area with papers from top experts in the field. It aims to be a vehicle for in-depth understanding and inspire further conversations in the sector.
Neural machine translation in AVT teaching in China: An in-depth analysis from the readability perspective

As audiovisual translation (AVT) becomes more complex and diverse, the need for advanced machine learning techniques has been increasing sharply, driving the widespread adoption of neural machine translation (NMT) technology in the field. This study contributes to the literature by evaluating the performance of NMT technology in AVT teaching. Based on readability theory, we constructed an evaluation framework with 12 indicators, built comparable corpora consisting of human and post-edited subtitle translations of corporate videos, and used them to examine the performance of four online NMT systems (Google Translate, Baidu Translate, Bing Translator, and Youdao Translate) in AVT teaching. Our statistical analyses and case studies show that Google Translate outperforms the other three platforms in all the readability tests, and it can enhance the readability of post-edited subtitles at five levels (word, syntax, textbase, situation model, genre and rhetorical). The performance of the other three platforms varies across different tests. Concrete examples are provided to substantiate the statistical analyses. Our study adds value to existing research both by examining the application and performance of NMT in AVT teaching and by suggesting potential directions for the refinement of current NMT systems.