Reviewerly/exHarmony huggingface dataset:
Granular Intents Learning Via Mutual Information Maximization for Knowledge-Aware Recommendation

Knowledge-aware recommender systems, which utilize knowledge graphs (KGs) for enriching item information, have been shown to be effective in improving recommendation. Besides, KGs are explored to figure the intents, the reason why users select items of interest. The conventional method represents intents with a set of relations in a KG. However, such relation-level representation of intents hinders identifying a specific reason for selecting an item. In the movie domain, for instance, a user chooses a movie with specific actors rather than just attending to “actors”. To address this issue, we propose a new KG-based user Intent Extraction Framework (KIEF) for recommendation to capture user intents at a more fine-grained level. Specifically, we propose a novel intent representation constructed with relation-aware entity representation, encouraging finer granularity for user intents. Furthermore, since a KG may contain noisy information, we consider which factors in a KG are important to represent a user’s intents. Thus, we introduce global intent which are comprehensive features for the entire interactions and local intent which are empirical features of individual users. By maximizing mutual information between global and local intents, KIEF captures user preference for items. Through extensive experiments, we analyze interpretable explanations for understanding user intents.
Radial distortion correction method based on depth separable network

Fisheye camera has been widely used in our daily life with its wide field of vision, and at the same time it also has serious distortion. Radial distortion is the most common form of distortion, and Zhang Zhengyou calibration correction rule is the most common radial distortion correction method. However, it has some problems, such as complicated calibration process and easily influenced by calibration target quality. Therefore, we propose a distortion parameter prediction model based on depth separable network, and directly establish the mapping relationship between fisheye image and distortion parameter to solve the above problems. Firstly, the loss function and learning rate of the model are determined through a series of experiments, and compared with Zhang Zhengyou calibration method and double longitude method in three index values SSIM, PSNR, NRMSE and subjective vision. The results show that our proposed method has the best performance.
Research on the Inhomogeneous Latency of Block Propagation in the Blockchain-enabled Internet of Ships

With the global maritime industry promoting the development of smart ships, the security and authenticity of data in ship networking must be guaranteed in the future, and blockchain technology can better solve a series of problems in ship networking, so it is applied in ship networking. However, due to the complex technical composition and topology of ship networking, it will cause certain obstacles to the consensus of blockchain. This thesis will analyze and study the impact of block propagation delay on the operation of DPOS consensus mechanism under network heterogeneity. In this paper, we simulate the witness election and the heterogeneous propagation of blocks under the heterogeneity of the network by using MATLAB software to explore the effect of network heterogeneity on the witness election and what degree of network delay will lead to the heterogeneous propagation of blocks. The simulation results are used to explore how network heterogeneity leads to heterogeneous propagation of blocks and the probability of generating a fork in the blockchain, and to summarize the trigger conditions and probabilities of various fork phenomena generated in the blockchain. Finally, this paper briefly looks at the feasible means to suppress the heterogeneous propagation problem of blocks.
Optimization of ship command and dispatch in the waters between the three gorges dams based on GP algorithm

At present, the backlog of ships passing through the Three Gorges has become the norm, and the difference between the passing capacity of the Three Gorges-Gezhouba locks and the passing capacity of the waters between the two dams has led to a reduction in the efficiency of ship navigation through the locks and an increase in the difficulty of control. In view of the above problems, this paper constructs an optimization model for the control and command of formation ship groups under the existing ship navigation rules, taking into account the safety distance to be maintained between ship groups, the continuity of navigation and other realistic constraints, with the objective of minimizing the imbalance between navigation time and delay time. Based on the characteristics that the ship navigation process is similar to the processing operation in the production plant, a heuristic scheduling rule set and Genetic Programming (GP) algorithm are established for the solution. The research results show that the method of determining the navigation order of vessel groups by generating composite dispatching rules (CDR) with GP algorithm can reduce the navigation time of vessels crossing the gate in the waters between two dams and effectively improve the fairness of scheduling, while meeting the requirements of vessel navigation rules and safety.
An Improved Detection Method of Traffic Prohibition Sign for Intelligent Vehicles based on YOLOV5s

Traffic sign detection and recognition is an important task in the perception of intelligent vehicles (IVs). Among various categories of traffic signs, prohibition signs are of paramount importance. However, there is relatively little research on the detection and recognition of prohibition signs at present. In this study, a modified lightweight model based on YOLOV5s is proposed for detecting the 12 most common types of Chinese prohibition signs. A new dataset is established by collecting images of Chinese prohibition traffic signs in real-world scenarios. The improved model replaces the normal convolution with ghost convolution in the feature fusion network, which greatly reduces the number of parameters and computational complexity. The introduction of the coordinate attention mechanism in the feature extraction network helps the network to better learn and leverage the position information of the target on the feature map. The Mosaic data augmentation strategy at the input end is refined by increasing the number of concatenated images from 4 to 9, allowing the network to learn a richer representation of the scene and target features. The experimental results on the self-built dataset demonstrate that the proposed algorithm, compared to YOLOV5s, achieves a significant reduction in model parameters of 28.4%, model size of 27.5%, and computational cost of 27% while only experiencing a slight decrease in accuracy of 0.3%. Further comparative experiment results show that compared with current mainstream lightweight algorithms, our proposed model achieves a better balance between lightweight and accuracy.
Constrained Environment Optimization for Prioritized Multi-Agent Navigation

Traditional approaches for multi-agent navigation consider the environment as a fixed constraint, despite the obvious influence of spatial constraints on agents' performance. Yet hand-designing conducive environments is inefficient and potentially expensive. The goal of this paper is to consider the obstacle layout of the environment as a decision variable in a system-level optimization problem. In other words, we aim to find an automated solution that optimizes the obstacle layout to improve the performance of multi-agent navigation, under a variety of realistic constraints. Towards this end, we propose novel problems of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">unprioritized</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">prioritized environment optimization</i> , where the former considers agents unbiasedly and the latter incorporates agent priorities into optimization. We show, through formal proofs, under which conditions the environment can change to guarantee completeness (i.e., all agents reach goals), and analyze the role of agent priorities in the environment optimization. We proceed to impose constraints on the environment optimization that correspond to real-world restrictions on obstacle changes, and formulate it mathematically as a constrained stochastic optimization problem. Since the relationship between agents, environment and performance is challenging to model, we leverage reinforcement learning to develop a model-free solution and a primal-dual mechanism to handle constraints. Distinct information processing architectures are integrated for various implementation scenarios, including online/offline optimization and discrete/continuous environment. Numerical results corroborate the theory and demonstrate the validity and adaptability of our approach.
Trends in Mathematics Research in Early Childhood: Bibliometric Review

Early childhood education is a critical stage in individual development. At an early age, children have the ability to absorb knowledge quickly, including in mathematics. The aim of this study was to identify trends and patterns of mathematics research in early childhood using bibliometric analysis. The data was obtained from the Scopus database which has been refined with 4 stages (identification, screening, eligibility, and inclusion). The results showed that the peak of publications related to mathematics research in early childhood occurred in 2022. 2017 was the year with the most influential publications. Journal “Early Childhood Education Indexed Journal Scopus with the highest quartile score (Q1) is the most popular place for publication of previous research related to this field. The United States is the most influential country in this field because both the number of publications and the number of citations are the highest than any other country. As for three focus study related study mathematics in children age early on the Scopus database from 1988-2023 namely, 1) mathematics, human, learning and gender; 2) early childhood, professional development, and teachers; 3) mathematics learning, early childhood, curriculum, and achievement.
Performance and Economical Analysis of Solar Roof Plant with and without Reflectors

Abstract: This thesis aims to explore the feasibility and potential benefits of implementing a solar roof plant in Gwalior, Madhya Pradesh. The escalating demand for energy, coupled with the urgent need to reduce greenhouse gas emissions, calls for innovative and sustainable solutions. Solar energy has emerged as a viable alternative to conventional power sources, offering significant environmental and economic advantages. This research focuses on Gwalior, a city with abundant sunlight and ample roof space, making it an ideal candidate for solar energy harnessing. The study begins with a comprehensive assessment of the current energy scenario in Gwalior, analyzing the energy consumption patterns and the existing power infrastructure. This analysis establishes a baseline for evaluating the impact and potential of a solar roof plant in the region. Key areas of investigation include the technical feasibility of installing solar panels on rooftops, the economic viability of the project, and the environmental benefits it would bring. The thesis also addresses the regulatory framework and policy support necessary to encourage solar power adoption in Gwalior. Key stakeholders, such as government bodies, utility companies, and local communities, will be involved in the research process to ensure a holistic approach. By utilizing advanced simulation tools and data analysis techniques, this thesis will quantify the energy generation potential of a solar roof plant in Gwalior. Factors such as solar irradiance, panel efficiency, and rooftop suitability will be considered to determine the optimal configuration and capacity of the plant. Furthermore, financial models will be developed to assess the return on investment and the long-term cost-effectiveness of the project. The findings of this study are expected to demonstrate the significant advantages of a solar roof plant in Gwalior. These advantages include reduced reliance on fossil fuels, decreased carbon emissions, and improved energy security. The research outcomes will contribute to the body of knowledge on solar energy implementation and serve as a valuable reference for policymakers, urban planners, and renewable energy enthusiasts.
The Influence of Online Games towards Students' Learning Interests at Grade X SMK Dara Jingga Star Up Vocational School

The purpose of this study was to determine the effect of online games on student interest in learning PJOK lessons at SMK Star Up Dara Jingga. The benefits of this research are to add references to studies related to online game opium and provide input to relevant parties in implementing education, be it schools, teachers, and other researchers. The method used in this research is the Ex-post facto method. The data analysis technique in this research is the product-moment correlation. The population in this study were students of class X SMK Star Up Dara Jingga and the sample in this study were students of class X SMK Star Up Dara Jingga totaling 16 students.Based on the results of the study, it shows that there is a significant influence online games on student learning interest in learning PJOK SMK Star Up Dara Jingga from the data obtained based on the results of hypothesis testing stating that the hypothesis is accepted because the calculated r obtained is 0.8950 while the r table 0.497 with a significant 0.05 so it can be concluded that Ha is accepted.
Enabling interoperable distributed ledger technology with legacy platforms for enterprise digitalization

ABSTRACTPresently to achieve enterprise digitalization technologies such as Distributed Ledger Technologies (DLT) has now been deployed to support digital services provided by enterprises. But several challenges in DLTs remain to be addressed, including the interoperability, standardization, and integration. Therefore, this study provides theoretical and practical understanding of DLT interoperability and identified the factors that influence the interoperability of DLTs. Also, an architecture is designed to shows how interoperability can be achieved in DLTs and legacy systems supported by Application Programming Interface (API). A case study is presented to illustrate the applicability of the architecture to support a digital energy marketplace.KEYWORDS: Enterprise digitalisationdistributed ledger technologiesinteroperabilityarchitecture modellingIOTA Tanglecase scenario AcknowledgementSpecial thanks to +CityxChange and Department of Computer Science, Norwegian University of Science and Technology, NTNU who sponsored the post-doctoral fellowship for the author. This publication is a part of the +CityxChange smart city project (https://cityxchange.eu/) under the Smart Cities and Communities topic that is funded by the European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement No. 824260. The authors gratefully acknowledge the support of the participants in the +CityxChange smart city project who provided feedback for modelling of the case scenario.Disclosure statementNo potential conflict of interest was reported by the author.Additional informationFundingThe work was supported by the European Union’s Horizon 2020 Research and Innovation Programme [824260].
Empirical Configuration Study of a Common Document Clustering Pipeline

Document clustering is frequently used in applications of natural language processing, e.g. to classify news articles or creating topic models. In this paper, we study document clustering with the common clustering pipeline that includes vectorization with BERT or Doc2Vec, dimension reduction with PCA or UMAP, and clustering with K-Means or HDBSCAN. We discuss the inter- actions of the different components in the pipeline, parameter settings, and how to determine an appropriate number of dimensions. The results suggest that BERT embeddings combined with UMAP dimension reduction to no less than 15 dimensions provides a good basis for clustering, regardless of the specific clustering algorithm used. Moreover, while UMAP performed better than PCA in our experiments, tuning the UMAP settings showed little impact on the overall performance. Hence, we recommend configuring UMAP so as to optimize its time efficiency. According to our topic model evaluation, the combination of BERT and UMAP, also used in BERTopic, performs best. A topic model based on this pipeline typically benefits from a large number of clusters.
Exploiting Deep Visual Geometry Group Architecture for Fall Detection in the Elderly People

Over the last couple of decades, human fall detection has gained considerable popularity, especially for the elderly. Elderly people need more attention as compared to others in their homes, hospitals, and care centers. Various solutions have been proposed to deal with this problem, yet, many aspects of this problem are still unresolved. The current study proposed an approach for human fall detection based on the Visual Geometry Architecture of deep learning. The presented approach was weighed up with state-of-the-art approaches including ResNet-50 and even ResNet-101 by using MCF and URFD datasets, outperforming them with an accuracy of 98%. The proposed approach also outperformed these deep architectures in terms of performance efficiency.
Novel Bayesian Inference-Based Approach for the Uncertainty Characterization of Zhang’s Camera Calibration Method

Camera calibration is necessary for many machine vision applications. The calibration methods are based on linear or non-linear optimization techniques that aim to find the best estimate of the camera parameters. One of the most commonly used methods in computer vision for the calibration of intrinsic camera parameters and lens distortion (interior orientation) is Zhang’s method. Additionally, the uncertainty of the camera parameters is normally estimated by assuming that their variability can be explained by the images of the different poses of a checkerboard. However, the degree of reliability for both the best parameter values and their associated uncertainties has not yet been verified. Inaccurate estimates of intrinsic and extrinsic parameters during camera calibration may introduce additional biases in post-processing. This is why we propose a novel Bayesian inference-based approach that has allowed us to evaluate the degree of certainty of Zhang’s camera calibration procedure. For this purpose, the a prioriprobability was assumed to be the one estimated by Zhang, and the intrinsic parameters were recalibrated by Bayesian inversion. The uncertainty of the intrinsic parameters was found to differ from the ones estimated with Zhang’s method. However, the major source of inaccuracy is caused by the procedure for calculating the extrinsic parameters. The procedure used in the novel Bayesian inference-based approach significantly improves the reliability of the predictions of the image points, as it optimizes the extrinsic parameters.
Mining Github for Factors That Affect Open Source Software Sustainability

Open-Source Software (OSS) is everywhere. The availability of such software enables researchers in software engineering to have a deep insight into the factors that affect the success of the software. Some OSS repos get more popular and evolve over time, while others may only survive for a couple of months. This study aims to help developers identify internal factors that affect the sustainability of their software. Firstly, identify the most demanding application domain; secondly, observe the most popular repositories (for the demanded application domain) for about three years to identify the factors that help such repos to survive (sustainability factors). Dara mining algorithms (classification and regression) are used as a tool to find the GitHub factors that affect the sustainability of OSS.
Affective and Psychomotor Taxonomies

Taxonomies can sensitise teachers to develop their learners’ applications of their knowledge and skills in depth when utilised judiciously in preparing experiences for learning and assessments for evaluation (Tyszko and Sheets, 2019).
A Semiotic Analysis: Exploring the Verbal Signs Found in Fast Food Online Advertisement

The linguistic landscape has changed due to social media platforms which provide real-time interaction and high dynamism. Moreover, the emergence of new media has left a profound impact on the techniques adopted by businesses to maintain a relationship with customers through digital marketing. Online advertisement is a sort of on social media platforms. Every advertisement contains signs. The language cues used in fast food ads will be examined in this study. The two objectives of this study are to identify the verbal signs in fast food commercials and examine the importance of those verbal signs. A descriptive qualitative approach is used in this study which will utilise Leech's theory of meaning and Saussure's theory of semiotics for the data analysis. The study's findings indicate that language cues were presented in the selected fast-food advertisements. Declarative sentences are used as verbal cues. The results will contribute to the current knowledge in the area pertaining to social media advertisement and its significance.
Revolutionary applications of trending block chain innovation in accounting and auditing

The primary objective of this study is to provide a rationale for adopting Blockchain technology in the realm of accounting and auditing. The key findings of the study highlight the potential benefits of utilizing Blockchain technology in the recording of financial transactions and the auditing process in the organizations. The incorporation of Blockchain in transaction accounting can result in decreased penalties linked to accounting mistakes and facilitate quicker identification and rectification of errors without extra financial outlay. Furthermore, Blockchain technology has the capability to practically eliminate the loss and misuse of financial data by malicious individuals. The research findings underscore the importance of integrating Blockchain into the structural and technological frameworks of transaction accounting within organizations, as well as in the broader domains of accounting and auditing. The adoption of this technology can optimize accounting procedures, elevate the precision and dependability of financial data, enhance oversight, and lower risks for economic entities. This highlights the potential advantages of using Blockchain as a comprehensive optimization solution in the accounting field. The findings stress that there is need for further exploration and application of Blockchain to enhance the quality of financial accounting, ensure trustworthiness, and alleviate risks within the field.