Reviewerly/exHarmony huggingface dataset:
Adversarial Bootstrapped Question Representation Learning for Knowledge Tracing

Knowledge tracing (KT), which estimates and traces the degree of learners' mastery of concepts based on students' responses to learning resources, has become an increasingly relevant problem in intelligent education. The accuracy of predictions greatly depends on the quality of question representations. While contrastive learning has been commonly used to generate high-quality representations, the selection of positive and negative samples for knowledge tracing remains a challenge. To address this issue, we propose an adversarial bootstrapped question representation (ABQR) model, which can generate robust and high-quality question representations without requiring negative samples. Specifically, ABQR introduces the bootstrap self-supervised learning framework, which learns question representations from different views of the skill-informed question interaction graph and facilitates question representations between each view to predict one another, thereby circumventing the need for negative sample selection. Moreover, we propose a multi-objective multi-round feature adversarial graph augmentation method to obtain a higher-quality target view, while preserving the structural information of the original graph. ABQR is versatile and can be easily integrated with any base KT model as a plug-in to enhance the quality of question representation. Extensive experiments demonstrate that ABQR significantly improves the performance of the base KT model and outperforms state-of-the-art models. Ablation experiments confirm the effectiveness of each module of ABQR. The code is available at https://github.com/lilstrawberry/ABQR.
Feature Decoupling-Recycling Network for Fast Interactive Segmentation

Recent interactive segmentation methods iteratively take source image, user guidance and previously predicted mask as the input without considering the invariant nature of the source image. As a result, the process of extracting features from the source image is repeated in each interaction, resulting in substantial computational redundancy. In this work, we propose the Feature Decoupling-Recycling Network (FDRN), which decouples the modeling components based on their intrinsic discrepancies and then recycles components that can be reused for each user interaction. Thus, the efficiency of the whole interactive process can be significantly improved. To be specific, we apply the Decoupling-Recycling strategy from three perspectives to address three types of discrepancies, respectively. First, our model decouples the learning of source image semantics from the encoding of user guidance to process two types of input domains separately. Second, FDRN decouples high-level and low-level features from stratified semantic representations to enhance feature learning. Third, during the encoding of user guidance, current user guidance is decoupled from historical guidance to highlight the effect of current user guidance. We conduct extensive experiments on 6 datasets from different domains and modalities, which demonstrate the following merits of our model: 1) superior efficiency than other methods, particularly advantageous in the challenging scenarios requiring long-term interactions (up to 4.25x faster), while achieving favorable segmentation performance; 2) strong applicability to various methods serving as a universal enhancement technique; 3) well cross-task generalizability, e.g., to medical image segmentation, and robustness against misleading user guidance.
On Regularizing Multiple Clusterings for Ensemble Clustering by Graph Tensor Learning

Ensemble clustering has shown its promising ability in fusing multiple base clusterings into a probably better and more robust clustering result. Typically, the co-association matrix based ensemble clustering methods attempt to integrate multiple connective matrices from base clusterings by weighted fusion to acquire a common graph representation. However, few of them are aware of the potential noise or corruption from the common representation by direct integration of different connective matrices with distinct cluster structures, and further consider the mutual information propagation between the input observations. In this paper, we propose a Graph Tensor Learning based Ensemble Clustering (GTLEC) method to refine multiple connective matrices by the substantial rank recovery and graph tensor learning. Within this framework, each input connective matrix is dexterously refined to approximate a graph structure by obeying the theoretical rank constraint with an adaptive weight coefficient. Further, we stack multiple refined connective matrices into a three-order tensor to extract their higher-order similarities via graph tensor learning, where the mutual information propagation across different graph matrices will also be promoted. Extensive experiments on several challenging datasets have confirmed the superiority of GTLEC compared with the state-of-the-art.
Hierarchical Visual Attribute Learning in the Wild

Observing objects' attributes at different levels of detail is a fundamental aspect of how humans perceive and understand the world around them. Existing studies focused on attribute prediction in a flat way, but they overlook the underlying attribute hierarchy, e.g., navy blue is a subcategory of blue. In recent years, large language models, e.g., ChatGPT, have emerged with the ability to perform an extensive range of natural language processing tasks like text generation and classification. The factual knowledge learned by LLM can assist us build the hierarchical relations of visual attributes in the wild. Based on that, we propose a model called the object-specific attribute relation net, which takes advantage of three types of relations among attributes - positive, negative, and hierarchical - to better facilitate attribute recognition in images. Guided by the extracted hierarchical relations, our model can predict attributes from coarse to fine. Additionally, we introduce several evaluation metrics for attribute hierarchy to comprehensively assess the model's ability to comprehend hierarchical relations. Our extensive experiments demonstrate that our proposed hierarchical annotation brings improvements to the model's understanding of hierarchical relations of attributes, and the object-specific attribute relation net can recognize visual attributes more accurately.
Principal component analysis on twenty years (2000–2020) of geochemical and geophysical observations at Campi Flegrei active caldera

Abstract Campi Flegrei (CF) is an active and densely populated caldera in Southern Italy, which has manifested signs of significant unrest in the last 50 years. Due to the high volcanic risk, monitoring networks of the most sensitive unrest indicators have been implemented and improved over time. Precious database constituted by geophysical and geochemical data allowed the study of the caldera unrest phases. In this paper we retrace the caldera history in the time span 2000–2020 by analyzing displacement, seismicity and geochemical time series in a unified framework. To this end, Principal Component Analysis (PCA) was firstly applied only on geochemical data because of their compositional nature. The retrieved first three components were successively analyzed via PCA together with the geophysical and thermodynamical variables. Our results suggest that three independent processes relay on geochemical observations: a heating/pressurizing of the hydrothermal system, a process related to magmatic fluids injection at the hydrothermal system roots, and third process probably connected with a deeper magmatic dynamic. The actual volcano alert state seems mainly linked to the variation of the hydrothermal system activity. Our approach made it possible to explore the interrelation among observations of different nature highlighting the importance of the relative driving processes over time.
Innovative tools for developing professional culture of future technical specialists

The article addresses the pressing issue of fostering the professional culture of students enrolled in technical higher education institutions. The task of cultivating a high level of professional culture is pertinent because modern technical specialists are required not only to possess technical knowledge but also to develop interdisciplinary and intercultural skills. The article analyzes key definitions, including “professional thinking”, “professional culture”, and “professional competence.” It also characterizes innovative approaches and tools that can be employed to successfully develop the professional culture of future technical specialists.&#x0D; The authors of the article have analyzed innovative approaches and tools that can be employed in the educational process to effectively foster the professional culture of future technical specialists. They highlight the role of modern methodologies, such as active learning, project work, and individualized approaches, in creating a conducive environment for the development of professional culture. The emphasis is placed on the importance of utilizing contemporary technologies, methodologies, and pedagogical approaches that contribute to the developm ent of students' creativity, critical thinking, and practical skills.&#x0D; The prospects for further research encompass several aspects that will contribute to improving the preparation of technical specialists and taking into account contemporary trends in higher technical education. These include: developing a system for fostering and enhancing the motivation of technical specialists towards professional self-improvement, considering individual characteristics and students' needs; investigating the impact of the organization of training for future technical specialists within the context of the Bologna Process on the formation of their professional culture; analyzing the challenges related to organizing independent and scientific activities among students with the aim of purposefully developing their professional culture. Exploring these areas can provide valuable insights and innovative approaches to further enhance the education and professional development of technical specialists.
Pedagogical competence as a necessary component of professional competence of future technical specialists

The article addresses the pressing issue of developing pedagogical competence as an essential component of the professional competence of future professionals in technical specialties. Various aspects of the process of forming the investigated competence are considered, including its structure, components, and formation methods. It emphasizes that success in technical fields extends beyond technical skills and also requires professionals to effectively communicate, teach, and collaborate with others. The research underscores the importance of developing pedagogical competence in students of technical specialties to prepare qualified professionals who can successfully apply their knowledge and skills in pedagogical and professional activities.&#x0D; The authors provide a rationale for the methods and approaches used to develop pedagogical competence in students of technical specialties. They offer practical recommendations for educational institutions on integrating pedagogical training into curricula and teaching methodologies for students in technical fields. The importance of motivation for professional growth, the development of integrative skills, and the connection between educational content and practical activities is established. The role of methodological support and modern information technology in the learning process is emphasized. The significance of pedagogical competence as a key element in the successful preparation of future professionals in technical specialties is highlighted, underscoring the necessity for continuous development of this competence throughout one's lifetime to achieve high professional effectiveness.&#x0D; The prospects for further research will focus on conducting a pedagogical experiment to determine the effectiveness of the proposed pedagogical conditions.
SUMMER DAY CAMP AS A MULTIFUNCTIONAL TOOL OF HIGHER EDUCATION INSTITUTIONS WORKING WITH ADOLESCENTS

The article discusses the operation of a day camp as a multifunctional tool that serves several important functions not only for children and teenagers but also for other participants in the educational process. Beneficiary groups of the campʼs activities are identified. The experience of conducting a summer camp for school and college graduates, PUET Summer Camp-2022, is analyzed. It has been established that the camp has a powerful multifaceted effect that contributes to addressing the tasks of several groups simultaneously: the higher education institution, children, their parents, and so on. The role of camps in overcoming social isolation caused by online learning due to the coronavirus epidemic and the war in Ukraine is highlighted.&#x0D; Keywords: summer camp; childrenʼs camp; pre-school camp; institution of higher education; social isolation; vacations; teenagers; applicants.
ANALYSIS OF VERBAL LINGUISTIC INTELLIGENCE THROUGH THE READING HABITS OF CLASS 2 MI HIDAYATUL MUTA'ALLIMIN STUDENTS

This research aims to know 1) to know the level of child verbal linguistic intelligence in mi hidayatul muta 'alimin medallion senori tuban, 2) to know the retard factor and support of the child's verbal linguistic intelligence in mi hidayatul muta 'alimin medallion senori tuban. In this study a descriptive qualitative method. As for the data-gathering technique in this study, observation, interview and documentation. The results the researchers found in this study are 1) the level of verbal liguistic intelligence analysis of children through the second class of reading habits in mi hidayatul muta 'allimin is seen in terms of the interests, attention, and involvement of participants in the reading. The strategy employed by homeroom teachers a and b is to read aloud, reading by playing and reading by connecting with others. observations related to interest in reading in class II A students: BB = 0 children, MB = 2 children, BSH = 15 children, BSB = 4 children, while interest in reading in class II B students: BB = 0 children, MB = 2 children, BSH = 10 Children, BSB = 4 Children. Interest in reading should also be supported with some access to continue increasing student interest in reading. And to enhance reading of the student is done by fulfilling the means and infrastructure that is by providing convenient libraries, and the library is a form of efforts by the principal and the educator to increase the interest, interest and involvement of the student. Reading dispenses many positive effects on students, so students are able and brave to be actively active in public Spaces in their activities. 2) a contributing factor in developing a child's intellect is the way a teacher teaches a teacher is a major factor in supporting the habit of reading in a teacher's child, encouragement, and guidance for the student to be diligent in reading, and reading must also be supported by the availability of good tools and infrastructure, where each school is required to have a library anda comfortable place to read students. While inhibitors in some students are still at the process stage beginning to develop in improving their reading ability. So there are certain tasks for teachers to direct. 4 children are still hampering in their ability to read, with a contributing factor in the development of verbal linguistic intelligence that is difficult to recognize letters, lack of confidence in children, lack of interest in reading, and oft-wrong pronunciation. This impediment stems from students' full mastery of the few vocabulary in reading, the acidity of the books in the library is of primary concern as it appeals to the students' hearts to study conveniently. Therefore, the library's clean-trimming efforts must be done well in order for students to be optimally educated.
Enhancing Data Security in Healthcare IoT: An Innovative Blockchain-based Solution

The Internet of Things (IoT) has revolutionized the healthcare industry by enabling the seamless integration of medical devices, sensors, and data-driven applications. However, the large influx of sensitive healthcare data and the proliferation of linked devices have caused grave worries about data security and privacy. Traditional centralized security systems are unable to handle the changing threats and problems in the IoT healthcare setting. This study suggests a novel strategy for boosting data security in the healthcare industry that makes use of blockchain technology. The main goal of this research is to develop and deploy a trustworthy framework that safeguards private healthcare information in IoT networks. Blockchain, as a distributed and decentralized ledger, offers inherent security features such as immutability, transparency, and cryptographic mechanisms. In this research, it is suggested that healthcare data be gathered via the IoT and stored in the Interplanetary File System (IPFS) using Ethereum-based blockchain technology for data security. The suggested method creates a reliable environment for managing healthcare data by exploiting the special features of blockchain. The json and jpeg files are utilized five times on a distributed database housed on IPFS and a centralized database hosted on Firebase, and the upload and download times are recorded. For IoT-based healthcare systems, we have also investigated the cost and length of time required to implement smart contracts on blockchain platforms like Rinkeby, Binance, and Matic. This research suggests implementing the Blockchain platform in an IoT-based healthcare system to provide data confidentiality, integrity, and accessibility.
Clustering Content Types and User Motivation Using DBSCAN on Twitter

We are currently in an era full of information and communication technology. One of the communication media used is Twitter. Twitter is a microblogging service that is used by its users to express their thoughts on a topic called a tweet. Tweets that are posted can be either positive tweets or negative tweets. One of the topics that is currently being discussed by Twitter users is Anies Baswedan as a 2024 Indonesian Presidential Candidate. Many people have tweeted this but it is not known how many users support or reject Anies Baswedan to run as a 2024 Indonesian presidential candidate. To assist the analysis, use the method clustering namely algorithm (Density-Based Spatial Clustering of Application with Noise). DBSCAN has the advantage of being able to detect data that is not included in a cluster and will be considered noise. This can improve the accuracy of the grouping because the data in the cluster will be cleaner. The TF-IDF Vectorizer is used to make it easier for programs to manage data because it can turn sentences into vectors that can be processed by the algorithm. To determine the evaluation of the program, the silhouette score method will be used. The results of calculating the silhouette score show a value of 0.29 with the formation of 3 clusters. Then an analysis is carried out based on the top words from each cluster and it can be identified that cluster 0 has a positive category supporting Anies Baswedan to run for the 2024 Presidential Candidate and cluster 1 has a negative category that does not support Anies Baswedan not advancing for the 2024 Presidential Candidate.
Penerapan Algoritma Advanced Encryption Standard (AES) Untuk Keamanan Data Transaksi Pada Sistem E-Marketplace

Sriwedari Village known as a producer of superior plant seeds and fruits in the Salaman District area. The Sriwedari Seed Shop is a form of business from the village government that sells various kinds of plant seeds and fruits from nurseries by the local community. However, in its application the sales and transaction system still uses a fairly conventional method. By opening an offline store and marketing products through social media networks, the Sriwedari Seed Shop has less regular customers. Not to mention the risk of fraud from customers who order seeds through social media is a concern for sellers Therefore, this research was conducted with the intention of answering the problems above by creating an e-marketplace system in the form of an online store that will be integrated with a payment gateway that has various online payment methods. In addition, data security will also be applied to the payment system using the Advanced Encryption Standard (AES) cryptographic algorithm which functions to encrypt payment transaction data so that data records stored in the database are not easy to read and prevent misuse of data by parties who do not have access rights. The method used in developing this system is Rapid Application Development (RAD). This method is very suitable to be applied in software development with minimal resources so that it can speed up the iteration process at each stage of development while still paying attention to input from potential system users. The results of this research are an integrated e-marketplace system with payment gateway services that have various payment methods with transaction data security using the AES cryptographic algorithm.
Students’ Dominant Motivation In Learning Reading At The Tenth Grade Of SMAN 1 Candung

The purpose of this study was to describe the highest motivation in learning to read in class X SMAN 1 Candung. The research question in this study is what is the dominant motivation in learning to read in grade 10 of SMAN 1 Candung. This study uses quantitative descriptive with class X students as the research population. The research sample amounted to 74 students who were selected through total sampling technique. Data collection is done through a questionnaire. Questionnaires were used to obtain data on students' motivation in learning to read comprehension. The type of questionnaire used is a closed questionnaire. That is a questionnaire presented to students, so students only need to provide a checklist for each question/statement. In the questionnaire there are 18 questions written in English and translated into Indonesian to make it easier for students to understand and answer the questions. The results showed that the highest aspect selected was intrinsic motivation (84%) and the lowest was extrinsic motivation (78%). Based on the data, it can be concluded that intrinsic motivation is the dominant motivation in learning to read in class 10 of SMAN 1 Candung.
Augmented Reality Towards Industry 5.0: Improving Voice and Tap Interaction Based on User Experience Feedback

EXtended Reality (XR) has numerous applications in industrial contexts, including to assist in activities such as preventive maintenance and/or reactive interventions in machinery, wherein time-effectiveness is required to mitigate the costs of inactive production lines or the need to subcontract specialized technicians to perform those operations. However, developing an Augmented Reality (AR) application capable of being user-friendly and truly serviceable for aiding in the referred context requires keeping in mind human factors as key aspects and, as such, interaction must be a concern to address, namely, considering user feedback. Therefore, starting from a previous AR system oriented to industrial interventions that was developed and assessed with users and considering the feedback of the participant, the duly alterations were done and a new test was conducted, with a total of 27 participants, divided into 2 groups: 11 users who participated in the first experience and 16 general users who had never contacted with the solution before. A qualitative improvement in the user experience was noticed from the questionnaires feedback. Points such as fluidity, responsiveness, ease, speed, and intuitiveness were highlighted. Voice commands matched the tap commands in terms of user preferences.