Reviewerly/exHarmony huggingface dataset:
User Multi-interest Collaboration for Service Recommendations

Traditional recommendation methods solve the user data sparsity problem by collaborative learning and using nearest neighbor information. However, most methods do not specifically analyze multiple interests of users. In fact, it is more likely that there exists a certain common interest preference among users, but their interests in other aspects are completely contradictory. This paper proposes a service recommendation model based on user multi-interest collaboration (UMIC), which takes both user multiple interest preferences and nearest neighbor user information into account during the recommendation. UMIC obtains multiple interest preferences of users through dynamic routing mechanism, considers the features under user’s current interest in nearest neighbor construction, and dynamically filters user’s nearest neighbors according to the items to be tested, to solve the user data sparsity problem and improve the quality of recommendation. The experimental results show that the evaluation metrics of the proposed model in this paper outperform various advanced sequential models on widely used datasets.
Retracted: Construction Method of Industrial College in Vocational Colleges Based on Cluster Analysis Algorithm

[This retracts the article DOI: 10.1155/2022/3278395.].
Retrain AI Systems Responsibly! Use Sustainable Concept Drift Adaptation Techniques

Deployed machine learning systems often suffer from accuracy degradation over time generated by constant data shifts, also known as concept drift. Therefore, these systems require regular maintenance, in which the machine learning model needs to be adapted to concept drift. The literature presents plenty of model adaptation techniques. The most common technique is periodically executing the whole training pipeline with all the data gathered until a particular point in time, yielding a massive energy footprint. In this paper, we propose a research path that uses concept drift detection and adaptation to enable sustainable AI systems.
Hierarchical Contextual Embeddings for Context-Aware Recommendations (Extended Abstract)

Recommender systems (RSs) have become one of the major applications that aim to tailor items to the user’s preferences. Traditional recommendation algorithms capture users’ interests and their interactions with items without taking into account contextual information, such as time and location. However, user interests may change depending on the context [1] . In real-life applications, there is plenty of information regarding user’s circumstances and surroundings (e.g., the activity of the user, time, location, weather, etc.). Such contextual information can be high-dimensional and is gathered from multiple sources, such as web pages, mobile devices, and more. RSs taking context information into account are called context-aware recommender systems (CARSs) [1] .
Who Would be Interested in Services? An Entity Graph Learning System for User Targeting

With the growing popularity of various mobile devices, user targeting has received a growing amount of attention, which aims at effectively and efficiently locating target users that are interested in specific services. Most pioneering works for user targeting tasks commonly perform similarity-based expansion with a few active users as seeds, suffering from the following major issues: the unavailability of seed users for new-coming services and the unfriendliness of black-box procedures towards marketers. In this paper, we design an Entity Graph Learning (EGL) system to provide explainable user targeting ability meanwhile applicable to addressing the cold-start issue. EGL System follows the hybrid online-offline architecture to satisfy the requirements of scalability and timeliness. Specifically, in the offline stage, the system focuses on the heavyweight entity graph construction and user entity preference learning, in which we propose a Three-stage Relation Mining Procedure (TRMP), breaking loose from the expensive seed users. At the online stage, the system offers the ability of user targeting in real-time based on the entity graph from the offline stage. Since the user targeting process is based on graph reasoning, the whole process is transparent and operation-friendly to marketers. Finally, extensive offline experiments and online A/B testing demonstrate the superior performance of the proposed EGL System.
Path planning of mobile robot based on improved artificial potential field method

Artificial potential field method is widely used in robot path planning because of its simplicity, efficiency and smooth path generation. In this paper, based on the introduction of the basic principle of the artificial potential field method, the limitations of the algorithm are analysed in depth, and improvement methods are summarized for these problems. Aiming at the problem that the target near the obstacle is unreachable in the traditional artificial potential field method, an improved repulsive force potential field function is used to introduce the distance between the robot and the target point into the potential field function, so that the potential field of the target position is minimized in the global potential field, so that the robot can successfully reach the target. Using the obstacle connection method, the robot can quickly get rid of the local minimum point, go out of the local minimum area, and complete the path planning. The simulation results show that the method is effective.
THE EFFECT OF BLENDED LEARNING TOWARDS PUPILS’ VOCABULARY DEVELOPMENT AND MOTIVATION IN AN ESL CLASSROOM

Background and Purpose: Vocabulary is the fundamental aspect of a language. However, second language (L2) learners often perceive vocabulary learning as boring and difficult. As such, various efforts must be made by teachers to empower pupils’ vocabulary base to be able to be competent in the target language. Hence, this study attempts to address the use of blended learning on pupils’ vocabulary development and motivation.&#x0D; &#x0D; Methodology: This research adopted the quantitative research design. The research participants consisted of two classes of primary school pupils chosen through purposive sampling. The pupils were divided into the experimental group and the control group. The instruments used were pre-test, post-test, and questionnaires. An independent t-test was used to compare the mean score to see any significant difference in terms of pupils’ pre-test and post-test scores whereas mean interpretation was used to determine pupils’ level of motivation.&#x0D; &#x0D; Findings: The results revealed that pupils who learned through blended learning performed better in terms of vocabulary development compared to the pupils who learned through traditional learning. Similarly, pupils have high motivation towards blended learning instructions. It is apparent that pupils become motivated learners when their need for autonomy, competence, and relatedness are fulfilled.&#x0D; &#x0D; Contributions: This research aids teachers in constructing suitable blended learning environments for content delivery as an alternative way for students to interact with information in preparation for classroom work.&#x0D; Keywords: Blended learning, vocabulary development, motivation, ESL classroom, purposive sampling.&#x0D; &#x0D; Cite as: Teik Hong, A. L., &amp; Stapa, M. (2023). The effect of blended learning towards pupils’ vocabulary development and motivation in an ESL classroom. Journal of Nusantara Studies, 8(1), 406-428. http://dx.doi.org/10.24200/jonus.vol8iss1pp406-428
A Spatio-Temporal Heterogeneity Data Accuracy Detection Method Fused by GCN and TCN

Spatio-temporal heterogeneous data is the database for decision-making in many fields, and checking its accuracy can provide data support for making decisions. Due to the randomness, complexity, global and local correlation of spatiotemporal heterogeneous data in the temporal and spatial dimensions, traditional detection methods can not guarantee both detection speed and accuracy. Therefore, this article proposes a method for detecting the accuracy of spatiotemporal heterogeneous data by fusing graph convolution and temporal convolution networks. Firstly, the geographic weighting function is introduced and improved to quantify the degree of association between nodes and calculate the weighted adjacency value to simplify the complex topology. Secondly, design spatiotemporal convolutional units based on graph convolutional neural networks and temporal convolutional networks to improve detection speed and accuracy. Finally, the proposed method is compared with three methods, ARIMA, T-GCN, and STGCN, in real scenarios to verify its effectiveness in terms of detection speed, detection accuracy and stability. The experimental results show that the RMSE, MAE, and MAPE of this method are the smallest in the cases of simple connectivity and complex connectivity degree, which are 13.82/12.08, 2.77/2.41, and 16.70/14.73, respectively. Also, it detects the shortest time of 672.31/887.36, respectively. In addition, the evaluation results are the same under different time periods of processing and complex topology environment, which indicates that the detection accuracy of this method is the highest and has good research value and application prospects.
Application of the STAD model assisted by media share board to improve the numerical literacy of grade II students at SD Negeri 3 Jambangan Malang

The purpose of this study was to determine the application of the STAD model assisted by Shard Board media to improve students' numeracy literacy in class III fraction material at SD Negeri 3 Jambangan, Dampit District. The research used was classroom action research, the research was carried out through two cycles with steps namely Planning, Implementation, Observation, Reflection. Collecting data using research instruments required includes interviews, pretest, test, posttest, observation sheets, documentation. The results showed that the application of the STAD model assisted by Shard Board media was proven to increase students' numeracy literacy. This can be seen through cycle I getting an average of 67.5 and cycle II getting an average of 81.78. Therefore it is necessary to apply the STAD model assisted by Shard Board media when learning in class, so that it can provide variations in the learning process and can improve students' numeracy literacy.
ASSESSMENT OF QUALITY AND TECHNIQUE IN RECEPTIVE LANGUAGE SKILL IN RETELLING NEWS IN JAVANESE: SYSTEMIC FUNCTIONAL LINGUISTICS APPROACH

&lt;p&gt;Finding solution in society in ‘Banjaragung Village’ in East Java seems that the people mostly use Indonesian in official meeting in the event of PKK and even many people prefer using Indonesian than Javanese. This research is conducted to see how good the society in retelling news by using Javanese as well as the technique to see the level of receptive language analyzed with systemic functional linguistics. It can also see how the society maintains Javanese as character principle. The research is done in some steps, namely: giving short materials or pre-knowledge about receptive skill and theory of complex thinking in systemic functional linguistics theory; giving retelling news test; and analyzing as well as reporting the findings to find out the patterns which are used. The findings show that the receptive language skill in society in ‘Banjaragung Village’ in East Java tend to be high as much as 77,85% that can retell the news well in complex clauses by using Javanese.&lt;/p&gt;&lt;p&gt;Keywords: Assessment, Receptive Skill, Retelling News, Javanese&lt;/p&gt;
EFL Teacher’s Strategies and Challenges in Developing Digital Competency: A Narrative Inquiry

The student changing of teaching and learning systems from face-to-face classes to online platforms because of the Covid-19 pandemic urged EFL teachers to develop digital competency. This study aims to investigate the ways teachers develop strategies and cope with challenges, as well as to identify the level of teachers’ digital competency (TDC). The narrative inquiry method was applied by conducting a semi-structured interview with three EFL teachers of secondary levels. The questions were developed based on the UNESCO ICT Framework for Teachers (ICT-CFT) which include the components regarding curriculum and assessment, teaching and learning process, collaborative learning, and professional development. The interview results were then transcribed into written texts verbatim and excerpts were selected for inclusion in the narrative stories, specifically dealing with strategies and challenges. To identify the TDC, thematic analysis was done by coding and categorizing components that were also taken from the indicators of the UNESCO ICT-CFT which include the competencies of technological ability, communicative ability, pedagogical ability, management ability, and research ability. The findings revealed that the three EFL teachers had various but similar strategies by learning themselves through various digital platforms, joining webinars and training, as well as asking to and sharing with colleagues. Challenges were found mostly from the students’ inability and reluctance of using digital tools. The TDC covered most competencies but only reached the exploration and integration level, not the innovation level yet. Recommendations for future actions and studies are mentioned in this study.&#x0D;
A PORTRAYAL OF MAIN CHARACTER’S INTERPERSONAL BEHAVIOR IN NINA LACOUR’S WE ARE OKAY

The purpose of this study was to find out interpersonal behavior portrayed by the main character in We Are Okay by Nina LaCour. The analysis of this novel uses the theory of psychoanalysis that focuses on interpersonal behavior by William Schutz. It also explains the impact of interpersonal behavior in Marin and Mabel as the main character. The research of this study used qualitative research methods. The source of data of this study is the novel We Are Okay. The data of this study are the character’s utterances and author’s narration. Analysis of the data used five processes by Yin (2016); Compiling, Disassembling, Reassembling (and Arraying), Interpreting, and Concluding. The results showed the findings of inclusion, control, and affection in Marin and Mabel’s friendship. Then, the impact of interpersonal behavior are; Marin included Expressed behavior which became an open and loyal person, while Mabel included Wanted behavior which became a pleasant person and made herself available to others.
Ethical issues associated with the use of virtual reality in medicine

Due to improvements in the medical techniques used, virtual reality technologies are increasingly used to treat various diseases. Simultaneously, various ethical issues emerged. Knowing exactly what problems the treating physician and the patient may be facing is important to increase the effectiveness of therapy. This will help minimize or at least prevent negative consequences. A qualitative secondary study was conducted on the ethical issues arising from the application of virtual reality technologies in treatment. Papers suitable for analysis were searched using the keywords virtual reality, ethical issues, and medicine. The works of the last 10 years were selected, most of the sources being at most 5 years old. After analyzing papers and studies which describe the experience of applying virtual reality technologies in medicine, the range of ethical problems that are currently relevant was determined most accurately. Firstly, the vulnerability of patients is evident, which arises from the realism of the avatars and the deep immersion in the virtual world. Without the physicians supervision, the patient may experience a high degree of psychological pressure, which has a negative effect. Secondly, strong emotional involvement is possible, which may develop attachment to the artificial intelligence and cause derealization, depersonalization, and other mental disorders. Thirdly, active interaction with the virtual world may be accompanied by long-lasting effects, such as gaming disorder. Finally, patients may have difficulties in distinguishing between reality and simulation, which affects autonomous choice and compromises informed consent. Virtual reality technologies may potentially change the lives of trauma patients and improve their quality of life. However, these changes are accompanied by ethical issues that must be addressed with respect to the vulnerability and nature of trauma.
IMPLEMENTATION OF INFORMATION TECHNOLOGY IN FINANCIAL SYSTEM AT UNIVERSITY X

&lt;em&gt;&lt;span lang="EN-US"&gt;The rapid development of technology requires organizations to keep up with innovation in order to achieve competitive advantages.&lt;/span&gt;&lt;/em&gt;&lt;em&gt;&lt;span lang="EN-US"&gt; University X is a university that is beginning to prepare for this wave of change by implementing a system in the financial transaction process. This study aims to analyze the application of information technology in the financial system at University X. &lt;/span&gt;&lt;/em&gt;&lt;em&gt;&lt;span lang="EN-US"&gt;The data collection approach used in this study is the interview method. It is a qualitative descriptive study that uses primary data&lt;em&gt;. The respondents in this study were those who were directly involved in the transition process from manual work before 2011 to using the new IFS system in 2022, including the Deputy Chancellor for Finance, Head of Finance, Staff of the Salary Section of the Finance Section, Staff of the Bookkeeping Section of the Finance Section, and System Staff in the HR Section. The results of the analysis can be concluded: before 2011, a manual system was implemented, then from 2011 to 2016, an intranet system was implemented; from 2016 to 2018, the development was intranet system A; from 2019 to 2020, the K-system; and from 2022 until now, IFS. &lt;/em&gt;&lt;/span&gt;&lt;/em&gt;&lt;span lang="EN-US"&gt;However, before 2019, the payroll division was still worked out manually using simple Excel and printed payslips. From 2019 to 2020, a new system was implemented that could provide notifications of expired allowances. The findings in this study can be used to assist universities dealing with technological changes, especially in the process of implementing a new financial system so that it can be accepted by its users and, moreover, the system is actually implemented successfully in an organization.&lt;/span&gt;
NEnv: Neural Environment Maps for Global Illumination

Abstract Environment maps are commonly used to represent and compute far‐field illumination in virtual scenes. However, they are expensive to evaluate and sample from, limiting their applicability to real‐time rendering. Previous methods have focused on compression through spherical‐domain approximations, or on learning priors for natural, day‐light illumination. These hinder both accuracy and generality, and do not provide the probability information required for importance‐sampling Monte Carlo integration. We propose NEnv, a deep‐learning fully‐differentiable method, capable of compressing and learning to sample from a single environment map. NEnv is composed of two different neural networks: A normalizing flow, able to map samples from uniform distributions to the probability density of the illumination, also providing their corresponding probabilities; and an implicit neural representation which compresses the environment map into an efficient differentiable function. The computation time of environment samples with NEnv is two orders of magnitude less than with traditional methods. NEnv makes no assumptions regarding the content (i.e. natural illumination), thus achieving higher generality than previous learning‐based approaches. We share our implementation and a diverse dataset of trained neural environment maps, which can be easily integrated into existing rendering engines.
Modelling an Efficient URL Phishing Detection Approach Based on a Dense Network Model

The social engineering cyber-attack is where culprits mislead the users by getting the login details which provides the information to the evil server called phishing. The deep learning approaches and the machine learning are compared in the proposed system for presenting the methodology that can detect phishing websites via Uniform Resource Locator (URLs) analysis. The legal class is composed of the home pages with no inclusion of login forms in most of the present modern solutions, which deals with the detection of phishing. Contrarily, the URLs in both classes from the login page due, considering the representation of a real case scenario and the demonstration for obtaining the rate of false-positive with the existing approaches during the legal login pages provides the test having URLs. In addition, some model reduces the accuracy rather than training the base model and testing the latest URLs. In addition, a feature analysis is performed on the present phishing domains to identify various approaches to using the phishers in the campaign. A new dataset called the MUPD dataset is used for evaluation. Lastly, a prediction model, the Dense forward-backwards Long Short Term Memory (LSTM) model (), is presented for combining the forward and backward propagation of LSMT to obtain the accuracy of 98.5% on the initiated login URL dataset.
Language Learning Strategies Employed by Urban Primary School Female Students in Enhancing Listening and Speaking Skills

The art of listening and speaking is a valuable skill set that effectively enables the exchange of information through oral communication.Effective listening and speaking are key components of language learning.Numerous factors may contribute to primary school students in Malaysia having difficulty in choosing suitable methods to develop their speaking and listening skills.Therefore, this research was conducted to identify the language learning strategies that are most used for learning listening and speaking skills among female primary school students in urban areas.A quantitative investigation was conducted using a threepoint Likert scale questionnaire adopted from Cohen and Oxford's Young Learners Language Strategy Use Survey.The questionnaire was distributed to the respondents in order to identify the learning strategies used by year 4, 5, and 6 students at an urban primary school in a district in Johor, Malaysia, for listening and speaking.Through purposive sampling, 30 samples were selected.Descriptive statistics were used to analyse the data.The results shown will be further discussed in this paper.
Language Learning Strategy Employed for Improving Reading Skills Among Form 5 Suburban Secondary School Students

Reading is a productive skill, and it is one of the essential skills in the English Language subject.Therefore, language learning strategies are fundamental to assist learners towards the learning goals.However, students in suburban secondary schools could face difficulties choosing which reading strategy suits them.Thus, this study is employed among Form 5 students from a suburban secondary school to investigate the learning strategy used to improve their reading skills.This quantitative research collects data from a questionnaire done with 30 Form 5 students.The questionnaire consists of 20 questions adapted from Language Strategy Survey focusing on reading strategy.Descriptive analysis of data consists of frequency and percentage of each item.The results showed that the most preferred reading strategy chosen by the students is using a dictionary to look for meanings and the least preferred reading strategy is summarising while reading.As for the literal comprehension, students preferred reviewing to find gist while finding supporting examples is the least favoured strategy for them.The findings are able to assist teachers to develop a reading environment for their students to become efficient readers with goals and strategies suitable and preferable for them.