Reviewerly/exHarmony huggingface dataset:
Learning of Educational Decentralization: Capability and Adaptation of Educational Service Management

This study aims to identify the impacts of education decentralization on the management capability and adaptation of education service management. This is qualitative research with a case study. Focus group discussions and interviews were conducted with three Branch Heads of the East Java Provincial Education Office and nine School Principals. This study finds that the intention of decentralization in the education sector is positive. This study concludes that decentralization must occur through a proper coordination process based on careful planning. There must be a balance between central, provincial, and local/district-level education management authorities in education management and decision-making.
LLM-Japanese-Dataset v0: Construction of Japanese Chat Dataset for Large Language Models and its Methodology

This study constructed a Japanese chat dataset for tuning large language models (LLMs), which consist of about 8.4 million records. Recently, LLMs have been developed and gaining popularity. However, high-performing LLMs are usually mainly for English. There are two ways to support languages other than English by those LLMs: constructing LLMs from scratch or tuning existing models. However, in both ways, datasets are necessary parts. In this study, we focused on supporting Japanese in those LLMs and making a dataset for training or tuning LLMs in Japanese.The dataset we constructed consisted of various tasks, such as translation and knowledge tasks. In our experiment, we tuned an existing LLM using our dataset and evaluated the performance qualitatively. The results suggest that our dataset is possibly beneficial for LLMs. However, we also revealed some difficulties in constructing LLMs in languages other than English.
CLERA: A Unified Model for Joint Cognitive Load and Eye Region Analysis in the Wild

Non-intrusive, real-time analysis of the dynamics of the eye region allows us to monitor humans' visual attention allocation and estimate their mental state during the performance of real-world tasks, which can potentially benefit a wide range of human-computer interaction (HCI) applications. While commercial eye-tracking devices have been frequently employed, the difficulty of customizing these devices places unnecessary constraints on the exploration of more efficient, end-to-end models of eye dynamics. In this work, we propose CLERA, a unified model for Cognitive Load and Eye Region Analysis, which achieves precise keypoint detection and spatiotemporal tracking in a joint-learning framework. Our method demonstrates significant efficiency and outperforms prior work on tasks including cognitive load estimation, eye landmark detection, and blink estimation. We also introduce a large-scale dataset of 30k human faces with joint pupil, eye-openness, and landmark annotation, which aims to support future HCI research on human factors and eye-related analysis.
FORMATION OF PROFESSIONAL-ORIENTED TRANSITIVITY AMONG TECHNICAL UNIVERSITY STUDENTS WITH THE HELP OF PEDAGOGICAL TECHNOLOGY OF WORKSHOPS

Social and economic relations at the present stage are undergoing deep changes associated with the active formation of the information society and the digital economy. Within the framework of one professional field, this is manifested in the fact that many labor functions are automated, new professions come to replace outdated professions that require a specialist to update and update professional knowledge and skills. In many ways, professional employment becomes discrete in the framework of the implementation of a limited-time project. A competitive specialist in such conditions should be able not only to constantly study, but also to be able to integrate into a new team, taking into account the personal and cultural differences of the participants. The article considers the issues of changing the educational process in higher educational institutions in terms of compliance with the needs of society and an individual. The authors paid attention to the important personal quality that ensures the competitiveness of the specialist - professionaloriented transitivity. It is indicated that the manufacturability of the construction of the pedagogical process will make it possible to modernize students' education with the lowest time and material costs. Competencies that will ensure the manifestation of the necessary personal qualities become diagnostic indicators of performance. Information and communication competence is determined by the integrative quality of the person that needs to be purposefully formed. Modular learning technology creates a conceptual framework for the educational process. The teaching technology of the second level on the basis of compliance with the selected criteria of biogenicity, psychogenicity and sociogenicity determines the technology of workshops. The results of analytical processing of empirical data collected during the experiment are presented, confirming the effectiveness of the use of the selected pedagogical technology and the technological construction of training in general for formation professional-oriented transitivity.
A dynamic displacement map based on deep Q-network to assist the rendering of stylised 3D models

In the process of rendering with NPR, there will often be a problem of reduced perception of the NPR effect caused by the fixed spatial structure of the static mesh. Therefore, this research first establishes a convolutional neural network to conduct supervised training on numerous hand-drawn target stylised images, and perform continuous angle recognition through stylised images then output its angle vector. Secondly, the displacement map is generated in real-time by inputting the observation angle through the fully connected neural network model. After that, the real-time displacement map is sampled to dynamically generate the deformation of the model. In the end, the goal of breaking the model's sense of space and enhancing the NPR rendering effect can be achieved. Besides, the experimental results of this study verify the effectiveness of the method.
Android-based Leadership Learning Assessment: Teacher Performance Support Applications in Indonesia

The development of information technology has an impact on school principals in carrying out their duties in Indonesia. The aim of this research is to produce an android-based learning leadership assessment model for school principals as an effort to improve teacher performance. The research subjects used were school principals, teachers and school supervisors. Data obtained with the help of focus group discussions, questionnaires, observation, documentation, and interviews. The data analysis technique used is descriptive analysis. The research findings show that the assessment model developed is an application based on android technology. Through this product, the assessment is carried out with the help of an android smartphone. Th results of the head of the education office above, the average score shows that the android-based learning leadership assessment model for principals as an effort to improve the performance of high school teachers as a whole and in terms of aspects has been considered important or very important with an average score of 4.30 or a good category between value &gt; 3.5 - 4.2. the average score shows that the android-based learning leadership assessment model for school principals as an effort to improve the performance of high school teachers as a whole and in terms of aspects has been assessed as very important with an average score of 4.29. The study findings conclude that the android-based learning leadership assessment model for principals to improve the performance of high school teachers was very necessary and very important to be developed.
A Study on English teacher's ESP Teaching Strategies for Cruise Crew major students

This research paper explores the effective teaching strategies for English for Specific Purposes (ESP) for Cruise Crew major students. The aim of the research is to investigate the strategies and methods that English teachers can use to help Cruise Crew major students develop the necessary language skills needed to perform their job roles effectively. The study draws on existing literature on ESP teaching strategies and interviews with English teachers who have experience teaching Cruise Crew major students. The results of the study indicate that effective teaching strategies for Cruise Crew major students should include needs analysis, authentic materials, role plays and simulations, vocabulary building, grammar instruction, group work, technology, and assessment.
Identifying and Removing the Ghosts of Reproducibility in Service Recommendation Research

A service recommendation system is an information system that helps build mashups quickly to implement new features in response to environmental changes. With the development of deep learning (DL) in recent years, more researchers have started using DL-based methods to solve service recommendation problem and have achieved remarkable results. However, these works have some common deficiencies on non-unified dataset, pre-trained model, evaluation protocol, and experiment environment. These issues will disrupt evaluating the performance of models accurately and make reproducing them difficult. To solve these problems, we propose a service mashup recommendation benchmark (SMRB) that provides a standard environment to enhance comparability between models and credibility of results. We implement eight models (five from top service computing conferences and journals and three created by ourselves) based on SMRB and compare their performance, which proves the effectiveness of SMRB. After analyzing these results, we found that most DL-based models do not perform as well as they promise; instead, the simplest Multilayer perceptron (MLP) models perform better after tuning the parameters, which inspires us to re-examine whether the particular structure of the model can be helpful for the intended purpose and whether it can really improve the performance of the recommendation.
Research on automatic hole making technology of industrial robot based on Hough circle detection algorithm

With the improvement of hole-making quality requirements in the modern mechanical manufacturing industry, the traditional manual detection method is no longer applicable. The mechanical system of real-time quality control and automatic hole-making is necessary. Given this, this research combines Hough circle detection with an industrial robot automatic hole-making detection system. In the research process, by analysing the overall architecture of the automatic hole-making detection system, confirm the important position and requirements of the detection link, then apply Hough circle detection, and test its application effect through experiments. The average inaccuracy of the detection results of four types of defects is 98.0%. It shows that the application of Hough circle detection to industrial robot automatic hole-making system has high effectiveness and feasibility, and provides a feasible idea for related fields.
KEPEMIMPINAN GURU WANITA DALAM PENGELOLAAN KELAS

This study aims to determine the leadership of female teachers in managing classes in Muhammadiyah schools in the Pleret region, Bantul, Yogyakarta (DIY). This research uses a quantitative descriptive approach to classify the leadership abilities of female teachers in managing the class. The research was carried out in Muhammadiyah schools in the Pleret area, Bantul, DIY. Respondents in this study were 60 teachers who teach at Muhammadiyah schools in Pleret, Bantul. The data collection technique in this study was to use a questionnaire that was distributed to all research samples who were randomly selected. Based on the results of the research and discussion that has been carried out, it can be concluded that most of the female teachers in the Muhamamdiyah Pleret school, Bantul, have fairly good leadership abilities (83%) and 15% of female teachers have excellent leadership in managing learning. This is a natural potential possessed by Muhammmadiyah in Pleret Bantul, to continue to develop their abilities so that they become better in the future.
36.5/Bay of Bengal

36.5/Bay of Bengal Sarah Cameron Sunde and Saiful Wadud Helal Click for larger view View full resolution 36.5/Bay of Bengal, Bangladesh, 2017. The passing of the hour is marked with music and dance. Screenshot of video work by Sarah Cameron Sunde and Saiful Wadud Helal. [End Page 252] Copyright © 2020 University of Nebraska Press
Remote Expert Assistance System for Mixed-HMD Clients over 5G Infrastructure

When operating under adverse conditions or at distant locations, it is not always feasible to obtain the assistance of an expert on site. In such cases, remote expert assistance may provide a solution. Current remote assistance systems employ immersive data visualization or video-based communication. By introducing XR-based multi-user communication system to remote expert assistance solutions, we expect to improve the effectiveness of both the operator and the supporting experts. Our multi-user XR collaboration demo integrates XR and cloud/network technologies. It enables three users to collaborate in a way that makes them feel that they are solving a challenging task together.
Securing ResNet50 against Adversarial Attacks: Evasion and Defense using BIM Algorithm

Deep neural networks, such as ResNet50, have shown remarkable performance in image classification tasks. However, susceptibility to adversarial attacks, where small perturbations to input images can result in misclassifications, is a concern. The BIM algorithm is a popular technique for generating adversarial examples. The objective of this research is to explore the vulnerability of ResNet50 DNNs trained on the ImageNet Stubs dataset to evasion attacks using the BIM algorithm. Additionally, the effectiveness of various defense strategies, including adversarial training, defensive distillation, and input transformations, is examined. The results reveal that ResNet50 DNNs are vulnerable to BIM-based evasion attacks and that these defenses can enhance their robustness. Overall, this work underscores the importance of defending against BIM-based evasion attacks to ensure the security and reliability of ResNet50 DNNs.
Label-wise Deep Semantic-Alignment Hashing for Cross-Modal Retrieval

Hashing plays an important role in the content-based retrieval of multimedia data. Existing methods focus on designing various joint-optimization strategies to preserve the similarity relationships between different modalities and improve the performance of cross-modal retrieval. However, when the intrinsic information of any modality is significantly insufficient compared with others, the final hash space will collapse to the trivial space due to the joint training. Compared with inter-modal semantic alignment, directly aligning the semantics of each modality with the label-wise semantics can obtain higher-quality common semantic spaces, and only a pair-wise alignment between each space can obtain a unified representation. Following this idea, we design a general hash generation framework for uni-modal embedding and directly align the hashing code from different modalities using a pair-wise loss, further improving the retrieval performance within the less-semantic modality. Within this framework, we introduce two optimizations to penalize dissimilar items. First, with a Gaussian distribution to describe the hashing semantic distribution, JS-divergence is introduced to keep the consistency of label-wise semantics and hashing similarity. Then the attention mechanism is used for hard-sample re-weighting to learn fine-grained distribution alignment further. We conduct extensive experiments on three public datasets to validate the enhancements of our work.
A Unified Model for Video Understanding and Knowledge Embedding with Heterogeneous Knowledge Graph Dataset

Video understanding is an important task in short video business platforms and it has a wide application in video recommendation and classification. Most of the existing video understanding works only focus on the information that appeared within the video content, including the video frames, audio and text. However, introducing common sense knowledge from the external Knowledge Graph (KG) dataset is essential for video understanding when referring to the content which is less relevant to the video. Owing to the lack of video knowledge graph dataset, the work which integrates video understanding and KG is rare. In this paper, we propose a heterogeneous dataset that contains the multi-modal video entity and fruitful common sense relations. This dataset also provides multiple novel video inference tasks like the Video-Relation-Tag (VRT) and Video-Relation-Video (VRV) tasks. Furthermore, based on this dataset, we propose an end-to-end model that jointly optimizes the video understanding objective with knowledge graph embedding, which can not only better inject factual knowledge into video understanding but also generate effective multi-modal entity embedding for KG. Comprehensive experiments indicate that combining video understanding embedding with factual knowledge benefits the content-based video retrieval performance. Moreover, it also helps the model generate better knowledge graph embedding which outperforms traditional KGE-based methods on VRT and VRV tasks with at least 42.36% and 17.73% improvement in [email protected].
Restructuring hospital payment systems using middle platform technology

In order to reshape the medical payment service system, we have designed a component based on the middle platform architecture. Our purpose is to standardized settlement and enhance work efficiency. Firstly, we Analyzed the application scenario of middle platform in hospital medical payment. Secondly, striped off the common functional modules, encapsulated the independent business interface. Thirdly, provided the ability to provide shared services, and expanded the application scenario of intelligent payment. After all this very fruitful work, the operation efficiency of the hospital payment system is improved, the coupling degree of the system is reduced, the cohesion is improved, and good results are obtained in practice. We think that the middle platform architecture can be combined according to the application requirements of the hospital, and it can also be extended for individual modules, which can meet the needs of hospital construction.
THE INTERPLAY OF TECHNOLOGY AND CULTURE: A STUDY ON MOBILE PHONE PRACTICES AMONG MUKKUVA COMMUNITY

The widespread use of digital technologies has expanded globally and penetrated even the most remote regions of the global south, including people living in underprivileged and marginalised conditions. Despite the increasing influence of mobile phones in all aspects of social life, there is limited knowledge about how mobile phone practices impact the local culture of the Mukkuva community in Kerala. Though Kerala has made remarkable socioeconomic progress, the Mukkuva fishing community remains marginalised and neglected. The fishers' exclusion from mainstream society, resulting from the caste system, has historically limited their interaction with others. This study employs in-depth interviews and non-participant observation to explore the mobile phone practices of the fishing community in two selected coastal villages of Kerala and how it affects the community's language and culture. Using the cultural identity framework of Stuart Hall and a thematic method of data analysis, the paper examines the changing dynamics of the relationship between the church and the community, the evolving self-concept of community members, and their economic activities. The research demonstrates the significance of mobile phones in the changing cultural dynamics of the Mukkuva community, highlighting its role in enabling increased communication and interaction with mainstream society. The study's findings suggest that mobile phones have enabled the community to create a new identity, distinct from their traditional practices, thereby allowing them to engage more actively in social, economic, and political activities. The paper concludes that the increasing use of mobile phones in marginalised communities provides an opportunity to bridge the gap between mainstream and marginalised societies, and encourages the empowerment of these groups.
Privacy-Preserving Techniques for Secure Cloud Computing : A Survey of Recent Advances

Cloud computing has gained immense popularity in recent years due to its on-demand and scalable computing resources. However, with the growth of cloud computing, privacy and security concerns have also increased. The primary concern is how to ensure the confidentiality and integrity of data in the cloud, as the data is stored on third-party servers. To address these concerns, various privacy-preserving techniques have been proposed, which allow users to store and process their data in the cloud without compromising privacy and security. We provide a thorough overview of current developments in privacy-preserving methods for safe cloud computing in this study. We start by giving a general review of cloud computing and the security issues it presents. Then, we go over a variety of privacy-preserving methods, such as differential privacy, homomorphic encryption, secure outsourcing, and secure multi-party computation. We also highlight their advantages and limitations. Finally, we conclude with some future research directions in privacy-preserving cloud computing.
Predicting Cyberbullying in Tweets Using Machine Learning Techniques

The proposed project aims to address the escalating concern of cyberbullying on social media platforms by utilizing machine learning classification algorithms, such as Support Vector Machine, to mitigate its effects.To improve the accuracy of the solution, the project will leverage the Natural Language Toolkit to extract features including bigrams, trigrams, n-grams, and unigrams.To evaluate the efficacy of this approach in detecting cyberbullying in tweets, a comparison will be conducted against baseline features and alternative machine learning algorithms.However, it is crucial to consider ethical implications when employing machine learning algorithms to minimize false positives and safeguard innocent individuals from potential harm.In essence, this project holds promise in advancing the development of robust tools for identifying and preventing cyberbullying on social media platforms, a pressing issue in our modern digital era.