Reviewerly/exHarmony huggingface dataset:
Development and maturity of co-word thematic clusters: the field of linked data

Purpose This research aimed to visualize and analyze the co-word network and thematic clusters of the intellectual structure in the field of linked data during 1900–2021. Design/methodology/approach This applied research employed a descriptive and analytical method, scientometric indicators, co-word techniques, and social network analysis. VOSviewer, SPSS, Python programming, and UCINet software were used for data analysis and network structure visualization. Findings The top ranks of the Web of Science (WOS) subject categorization belonged to various fields of computer science. Besides, the USA was the most prolific country. The keyword ontology had the highest frequency of co-occurrence. Ontology and semantic were the most frequent co-word pairs. In terms of the network structure, nine major topic clusters were identified based on co-occurrence, and 29 thematic clusters were identified based on hierarchical clustering. Comparisons between the two clustering techniques indicated that three clusters, namely semantic bioinformatics, knowledge representation, and semantic tools were in common. The most mature and mainstream thematic clusters were natural language processing techniques to boost modeling and visualization, context-aware knowledge discovery, probabilistic latent semantic analysis (PLSA), semantic tools, latent semantic indexing, web ontology language (OWL) syntax, and ontology-based deep learning. Originality/value This study adopted various techniques such as co-word analysis, social network analysis network structure visualization, and hierarchical clustering to represent a suitable, visual, methodical, and comprehensive perspective into linked data.
Matching Point of Interests and Travel Blog with Multi-view Information Fusion

The past few years have witnessed an explosive growth of user-generated POI-centric travel blogs, which can provide a comprehensive understanding of a POI for people. However, evaluating the quality of the POI-centric travel blogs and ranking the blogs is not a simple task without domain knowledge or actual travel experience on the target POI. Nevertheless, our insight is that the user search behavior related to the target POI on the online map service can partly valid the rationality of the POIs appearing in the travel blogs, which helps for travel blogs ranking. To this end, in this paper, we propose a novel end-to-end framework for travel blogs ranking, coined Matching POI and Travel Blogs with Multi-view InFormation (MOTIF). Concretely, we first construct two POI graphs as multi-view information: (1) the search-level POI graph which reflects the user behaviors on the online map service; and (2) the document-level POI graph which shows the POI co-occurrence frequency in travel blogs. Then, to better model the intrinsic correlation of the two graphs, we adopt Mutual Information Maximization to align the search-level and document-level semantic spaces. Moreover, we leverage a pair-wise ranking loss for POI-document relevance scoring. Extensive experiments on two real-world datasets demonstrate the superiority of our method.
Next Basket Recommendation with Intent-aware Hypergraph Adversarial Network

Next Basket Recommendation (NBR) that recommends a basket of items to users has become a promising promotion artifice for online businesses. The key challenge of NBR is rooted in the complicated relations of items that are dependent on one another in a same basket with users' diverse purchasing intentions, which goes far beyond the pairwise item relations in traditional recommendation tasks, and yet has not been well addressed by existing NBR methods that mostly model the inter-basket item relations only. To that end, in this paper, we construct a hypergraph from basket-wise purchasing records and probe the inter-basket and intra-basket item relations behind the hyperedges. In particular, we combine the strength of HyperGraph Neural Network with disentangled representation learning to derive the intent-aware representations of hyperedges for characterizing the nuances of user purchasing patterns. Moreover, considering the information loss in traditional item-wise optimization, we propose a novel basket-wise optimization scheme via an adversarial network to generate high-quality negative baskets. Extensive experiments conducted on four different data sets demonstrate the superior performances over the state-of-the-art NBR methods. Notably, our method is shown to strike a good balance in recommending both repeated and explorative items as a basket.
PersonalTM: Transformer Memory for Personalized Retrieval

The Transformer Memory as a Differentiable Search Index (DSI) has been proposed as a new information retrieval paradigm, which aims to address the limitations of dual-encoder retrieval framework based on the similarity score. The DSI framework outperforms strong baselines by directly generating relevant document identifiers from queries without relying on an explicit index. The memorization power of DSI framework makes it suitable for personalized retrieval tasks. Therefore, we propose a Personal Transformer Memory (PersonalTM) architecture for personalized text retrieval. PersonalTM incorporates user-specific profiles and contextual user click behaviors, and introduces hierarchical loss in the decoding process to align with the hierarchical assignment of document identifier. Additionally, PersonalTM also employs an adapter architecture to improve the scalability for index updates and reduce computation costs, compared to the vanilla DSI. Experiments show that PersonalTM outperforms the DSI baseline, BM25, fine-tuned dual-encoder, and other personalized models in terms of precision at top 1st and 10th positions and Mean Reciprocal Rank (MRR). Specifically, PersonalTM improves p@1 by 58%, 49%, and 12% compared to BM25, Dual-encoder, and DSI, respectively.
ExaRanker: Synthetic Explanations Improve Neural Rankers

Recent work has shown that incorporating explanations into the output generated by large language models (LLMs) can significantly enhance performance on a broad spectrum of reasoning tasks. Our study extends these findings by demonstrating the benefits of explanations for neural rankers. By utilizing LLMs such as GPT-3.5 to enrich retrieval datasets with explanations, we trained a sequence-to-sequence ranking model, dubbed ExaRanker, to generate relevance labels and explanations for query-document pairs. The ExaRanker model, finetuned on a limited number of examples and synthetic explanations, exhibits performance comparable to models finetuned on three times more examples, but without explanations. Moreover, incorporating explanations imposes no additional computational overhead into the reranking step and allows for on-demand explanation generation. The codebase and datasets used in this study will be available at https://github.com/unicamp-dl/ExaRanker
Intelligent multifunctional wheelchair controlled by MTPA and improved A-star algorithm

The purpose of this paper is to design an intelligent wheelchair that can follow the patient's walk independently. Besides, it can help rehabilitation patients recover better through the intelligent automatic control system. On the one hand, the cost compensation function is introduced to improve the traditional Astar algorithm. It can make the intelligent wheelchair more adaptable to the environment. The underground environment is simulated to verify it. Simulation shows that the surface intelligent wheelchair can identify the path more accurately. On the other hand, the wheels of the intelligent wheelchair and the driving motor of the mechanical arm both use IPMSM. To control the motor more efficiently and accurately, the MTPA control method is adopted in this paper. The process of an intelligent wheelchair starting, moving at a constant speed, and slowing down and stopping on flat ground is simulated and verified. The simulation shows that the intelligent wheelchair can realize the start and stop acceleration which makes the patient feel comfortable. The results show that the intelligent wheelchair has a good effect on the recovery of patients.
The effect of morality and Machiavellianism on government fraud: The moderating role of education

The high number of fraud cases in governments has increasingly become a concern among academics and practitioners. Many approaches can be used to minimize fraud cases. However, the role of education in the influence of personality on fraud in the Indonesian government has not been considered. Thus, this study aims to analyze the influence of morality and Machiavellianism as personality factors leading to fraud. Education was tested as a moderating variable. This quantitative study involved a sample of Pekanbaru City government officials. To collect data, questionnaires were sent directly to the respondents. Through the purposive sampling technique, 251 samples were collected. A structural equation with partial least squares was used to test the hypotheses. The test results show that all the proposed hypotheses are accepted. Morality affects fraud with a p-value &amp;amp;lt; 0.001, less than 0.05. Machiavellianism affects fraud with a p-value &amp;amp;lt; 0.001. Education moderates the relationship between morality and fraud with a p-value of 0.048, less than 0.05. Moreover, education moderates the relationship between Machiavellianism and fraud with a p-value &amp;amp;lt; 0.001, less than 0.05. In conclusion, this study contributes to shaping government officials’ behavior to prevent fraud. Character building and high education standards for officials must be integrated to reduce the risk of abuse in Indonesian government.
Analysis of Reading Ability in Terms of Parenting Patterns in Grade I Students (Case Study in Elementary School Gugus V Seririt District 2022/2023 Academic Year)

The problem in this study is that the reading ability experienced by grade I students is still low. Teachers at school have provided maximum guidance at school, but students not only learn at school, but students can also learn at home with their parents. Students have more time studying at home with parental guidance than when they study at school. The form of parenting applied by parents greatly affects the development of children, especially in terms of reading. This study aims to determine the factors that influence the reading ability of grade I students and the form of parenting applied by parents at home case study in SD Gugus V, Seririt District, Buleleng Regency. This type of research is descriptive qualitative research using the case study method. The sample determination used purposive sampling technique with a total sample size of 7 student subjects and 7 student parent subjects. Data collection used non-test methods in the form of observation, interviews, questionnaires, and documentation. The data analysis technique uses 2 techniques, namely qualitative descriptive analysis and quantitative description analysis. The results of the data analysis of this study found that the percentage score on the factors that influence reading ability in both aspects is described as follows: 1) internal factors with a percentage score of 69%, and 2) external factors with a percentage score of 72%. Meanwhile, the score of parenting patterns in the three aspects is different. Each aspect is described as follows: 1) authoritarian parenting has a percentage score of 65%, 2) democratic parenting has a percentage score of 78%, and 3) permissive parenting has a percentage score of 66%. From these results, it can be concluded that most parents have applied democratic parenting in educating their children, but the factors that greatly affect the reading ability of most students are external factors.&#x0D;
Modern ways of learning as a means of enhancing the cognitive activity of students

This article analyzes the effectiveness of interactive teaching methods and identifies the most popular practices among students. The study explores various interactive approaches, such as small group work, case studies, business games, training exercises, and self-assessment. The research was conducted using theoretical and empirical procedures, and an investigation was applied to a sample of 150 students from different disciplines. The results indicate that small group work and case studies are the methods most employed by teachers, while students prefer business games. Debate processes, case studies, and group work are also widely favored. The article emphasizes the relevance of incorporating interactive approaches to promote active learning, critical thinking, and collaboration. These findings offer valuable insights for teachers and postgraduate students interested in implementing interactive teaching methods in higher education.
Local Culture-Based Comic Strip: Improving Elementary Students' Social Skills

The aim of the research is to develop comic strips based on local culture that are suitable for use by students; develop local culture-based comic strips that are practically used by students; and develop local culture-based comic strips that are effectively used to improve students' social skills.The 4D development model includes the define, design, develop, and disseminate stages.The research was conducted at SD Negeri Babah Krueng Even Semester for the 2022-2023 academic year in Chapter 6, My Indonesia is Rich in Culture.At the development stage, it consists of validation tests and development tests based on validation assessments from media experts, material experts, and users (teachers).At this stage, trials and dissemination are carried out.The results of the validity test from three experts, namely linguists, presentation experts, and content feasibility experts, obtained results in the "Good" category.The results of the Practicality Test showed that the number of trial scores in percent was 90%.Thus, the results of the pilot study fall into the "very good/very practical" category.The effectiveness test results obtained Gain-Score or increased learning outcomes: seen from the pretest and protest, there is an increase in the classical average score of 0.6; when referring to the gain-score interpretation table, these results indicate an increase that is "Good".The observation results of student activities obtained a preset value of 75%, which is in the range of 61% to 80% with good achievement criteria.
Time-slicing high dynamic range 3D imaging

Fringe projection profilometry (FPP) has been widely used in 3D measurement due to its high precision and non-contact properties. Nevertheless, it still faces great challenges in measuring scenes with complex reflectivity, in which the dynamic range of the reflected light field of the scene is significantly higher than that of the image detector. In this paper, we propose a time-slicing strategy for high dynamics range 3D imaging by projecting a series of sinusoidal fringe patterns with short and equal length exposure time and performing the fusion of different numbers of short exposure images according to the local gray-value distribution of the images. Moreover, to further improve the measurement efficiency, we realize phase unwrapping using complementary Gray code patterns, which are binary and insensitive to the image sensor’s nonlinear response to the reflected light from the scene under test. Experiments are conducted to demonstrate the feasibility and efficiency of the proposed method.
Solving trade secret disputes in Chinese courts: some empirical evidence

Many developed countries are concerned about the strength of trade secret protection in China. This study empirically reviews all the 68 public judicial decisions regarding the misappropriation of technical secrets in China between January 2013 and December 2021. It explores both the judicial efforts on strengthening trade secret protection and the defects of enforcing the protection in civil judicial proceedings by inspecting the process of adjudication and legal analyses shown in judicial decisions. This study shows the limitations of Chinese trade secret statutes and exposes some problems in the application of the statutes in court. It also shows the strong deference to expert opinions and criminal investigations in civil adjudication proceedings regarding technical secrets, which may create more defects beyond the limitations of legislation and enforcement.
Blockchain-enabled markets: a literature review with a focus on decentralised energy markets

More and more industries are being disrupted by the creation of electronic market platforms, which allow individual users to not only act as consumers, but to take a more active role as producers of goods or providers of services as well. Many of these marketplaces are enabled by digital platforms, which aggregate and manage supply and demand. Blockchain technology makes it possible to take this development one step further and to remove the central intermediary, thus enabling direct peer-to-peer trading. One particularly prominent use case for blockchain-based peer-to-peer trading is a market for distributed energy resources (DER). While DER are a potential solution for meeting future electricity demand, their integration into centralised power markets on the large scale is challenging. This chapter first investigates whether blockchain-enabled markets create an added benefit compared to centrally mediated platforms from a general perspective. To that end, we derive a research framework and conduct a theoretical literature review of the existing information systems literature on blockchain-enabled markets. Hence, we generate a structured overview of the characteristics of blockchain-enabled markets through an economic lens. In a second step, we discuss specifics of the energy domain in this context. Our findings identify challenges which blockchain systems need to overcome in general to provide an added value to electronic markets, and discuss domain-specific aspects for peer-to-peer energy trading.
Query Interface for Smart City Internet of Things Data Marketplaces: A Case Study

Cities are increasingly becoming augmented with sensors through public, private, and academic sector initiatives. Most of the time, these sensors are deployed with a primary purpose (objective) in mind (e.g., deploy sensors to understand noise pollution) by a sensor owner (i.e., the organization that invests in sensing hardware, e.g., a city council). Over the past few years, communities undertaking smart city development projects have understood the importance of making the sensor data available to a wider community—beyond their primary usage. Different business models have been proposed to achieve this, including creating data marketplaces. The vision is to encourage new startups and small and medium-scale businesses to create novel products and services using sensor data to generate additional economic value. Currently, data are sold as pre-defined independent datasets (e.g., noise level and parking status data may be sold separately). This approach creates several challenges, such as (i) difficulties in pricing, which leads to higher prices (per dataset); (ii) higher network communication and bandwidth requirements; and (iii) information overload for data consumers (i.e., those who purchase data). We investigate the benefit of semantic representation and its reasoning capabilities toward creating a business model that offers data on demand within smart city Internet of Things data marketplaces. The objective is to help data consumers (i.e., small and medium enterprises) acquire the most relevant data they need. We demonstrate the utility of our approach by integrating it into a real-world IoT data marketplace (developed by the synchronicity-iot.eu project). We discuss design decisions and their consequences (i.e., tradeoffs) on the choice and selection of datasets. Subsequently, we present a series of data modeling principles and recommendations for implementing IoT data marketplaces.
The Impact of Self-efficacy Towards Speaking Performance

This present study is an associational study that employed simple linear regression model. There were two variables in this study such as self-efficacy as the independent variable while speaking performance as the dependent one. This study was conducted at a public university in Indonesia. The subjects recruited were 75 students. The data were collected by administering a self-efficacy questionnaire which contains 28 items. The data were analyzed quantitatively using SPSS program. The results show that mostly, the students had average and high category of SE as shown by 22.7% and 38.7% respectively. Moreover, it was found that F (1, 72) = 17.950, p &lt; 0.001. It means that statistically there was an impact of students’ self-efficacy toward students’ speaking performance. Based on the aforementioned results, it can be concluded that the students had good potential to enhance their speaking skills and their SE had significant contribution to their speaking performance.
Development Of Video Learning Based On The Indonesian Language Signing System (SIBI) Method For Class II Deaf Chill At SDN 2 Bengkala Academic Year 2022/2023

This research is development research (research and development). The product developed is Learning Video Media Based on the SIBI Method for Deaf Children, Grade II. This study aims to (1) determine the design of learning video media based on the SIBI method for grade II deaf students; (2) determine the validity of learning video media based on the SIBI method for grade II deaf children; and (3) determine the response of deaf students to learning video media based on the SIBI method for grade II deaf children. The 4D (four-D) model is a method used in the process of developing learning videos based on the SIBI method. The data collection method used is a questionnaire, which is then filled out by the respondents. Data analysis methods and techniques used in this research are qualitative and quantitative descriptive statistical analysis. In this SIBI method-based learning video media obtained a validation score by learning media experts of 94.5% (very good), then the validity test score by learning material experts was 97.85% (very good), then the results of media assessment through practitioner/teacher responses were 93.5% (very practical), and through student responses, a score of 86% (good). It is concluded that this development research obtained results with very good and very practical qualification levels, so that the products produced are feasible to be applied and used in the learning process for deaf children in grade II SDN 2 Bengkala
Improved RRT algorithm based on adaptive hyperspherical sampling space and local motion policy for robot path planning

The Rapidly-exploring Random Tree (RRT) algorithm is widely used in robot path planning, but the slow convergence rate and poor quality of planned paths have always been its problems. To overcome these limitations, this paper proposes an RRT algorithm based on the improvement of the sampling space and local motion planner. Firstly, an adaptive hyperspherical sampling space is proposed, of which the radius is adjusted according to the minimum distance of the search tree nodes from the goal, which can effectively reduce the number of low-quality random sampling states; secondly, a local motion planner combining optimal motion policy and random motion policy is proposed, which improves the smoothness of the paths and accelerates the convergence. Simulations are conducted with a seven-degree-of-freedom space manipulator, and the results show that compared with the RRT* algorithm, the average search time of the improved RRT algorithm is reduced by 34%, the average path length is shortened by 2.5%, and the average path smoothness is improved by 59%, which verifies the effectiveness and practicality of the improved RRT algorithm proposed in this paper.