Reviewerly/exHarmony huggingface dataset:
Application Research of Computer Big Data Technology in Monitoring Intelligent Terminal of Urban Power Distribution System

Aiming at the problems existing in the operation of the existing distribution network related information system, a set of distribution network integrated monitoring platform is constructed. The platform collects data from the existing distribution automation system, GIS system, and load control system, and integrates the scattered data of each system into a unified distribution network model after data processing. Based on the micro-service architecture, a set of upper-layer applications with various business functions can be flexibly expanded. Users can view real-time data and key indicators of distribution network operation on the platform and perform statistical analysis to further improve the level of distribution network operation and maintenance management.
Study on the Calculation of Attenuation Quality Factor Q-Value in Microseismic Monitoring: Results from Two Rock Engineering Cases

The quality factor Q-value is one of the important parameters for calculating microseismic source parameters, and it is also an important reference for reflecting geological information. However, there are few studies on the Q-value of rock engineering at present, and its understanding needs to be further improved. For the above reasons, we adopted the widely used Atkinson method to calculate Q in two rock engineering. The results show that the Q in the microseismic monitoring is mainly on the order of 101∼102, which is generally smaller than the Q in earthquake seismology. When the frequency is less than 500 Hz, the Q is a constant, and when the frequency is greater than 500 Hz, it increases with the frequency. The research results also show that the Q in the engineering with high in-situ stress and complete rock mass structure is larger than that in the low in-situ stress and broken rock mass is relatively broken, indicating that the Q-value can reflect the geological information to a certain extent.
Research on Construction Method for Automatic Classification of Group Enterprise R&amp;D Resources Space

In order to improve the organization and management ability of R&D resources in the group enterprise, and realize the efficient utilization of R&D resources, this paper puts forward the design resource space automatic construction classification method based on the analysis of the space construction requirements of R&D resources in the group enterprise.Machine learning is used to extract keywords from R&D resources, and semantic maps are built based on the semantic similarity of keywords.Hierarchical clustering is implemented through a community detection algorithm.Then, based on the BRET-LEAM model, the mapping of R&D resources to resource space is achieved, and the construction of multidimensional R&D resource space automatic classification is finally completed.Finally, an instance of group enterprise R&D resources automatic classification is built.
LDA Topic Mining of Customer Text Sentiment Analysis on the E-Commerce Platform

Collect the reviews left by users on the e-commerce platform for processing and analysis to understand users' needs, opinions, and product strengths and weaknesses.The text review data on the Jingdong platform was collected through a Python crawler, which was pre-processed by word separation, lexical annotation, and deactivation, and the key information of the reviews was extracted using the LDA topic model for research and analysis.The analysis of the integrated theme and its high-frequency feature words will help to obtain the valuable content and emotional orientation of the text review data and derive the advantages and disadvantages of the product and the corresponding needs of the users, providing consumers, merchants, and regulators with references and suggestions for collection.
Hey! You Photographed My Screen without Approved: A Practical Screen Content Protection Scheme Based on Visual Cryptography

Nowadays , with the continuous development of digital media, electronic screens have become the most direct tool for people to interact with information. Various styles of electronic screens provide vivid information exchange methods for people's lives, and at the same time make information leakage more likely to happen. Electronic screens are susceptible to stealing photographs when sharing information, which leads to information leakage and security issues. Under the unavoidable situation of such leaks, various protection schemes have been proposed before, but these schemes cannot effectively protect the screen content. Therefore, in this paper, we propose an effective a screen content protection scheme, based on Visual Cryptography(VC) for image encryption, combined with Scale-Invariant Feature Transform(SIFT) to deal with screen stealing scenes. In addition, our experiments show that our scheme is superior to existing schemes in terms of convenience and security.
Composition of UML Class Diagrams Using Category Theory and External Constraints

In large software development projects, there is always a need for refactoring and optimization of the design.Usually software designs are represented using UML diagrams (e.g., class diagrams).A software engineering team may create multiple versions of class diagrams satisfying some external constraints.In some cases, subdiagrams of the developed diagrams can be selected and combined into one diagram.It is difficult to perform this task manually since the manual process is very time consuming, is prone to human errors, and is not manageable for large projects.In this paper, we present algorithmic support for automating the generation of composing diagrams, where the composed diagram satisfies a given collection of external constraints and is optimal with respect to a given objective function.The composition of diagrams is based on the colimit operation from category theory.The developed approach was verified experimentally by generating random external constraints (expressed in SPARQL and OWL), generating random class diagrams using these external constraints, generating composed diagrams that satisfy these external constraints and computing class diagram metrics for each composed diagram.
Recent Language Teaching Activities Created by English Language Instructors Using Smartphones

This study specifically attempts to investigate how English language instructors use smart phones in their classes. The major focus of investigation deals with details of the activities the English instructors created with smartphones. For this purpose, ten instructors from a school of foreign languages at a university in the southeast of Turkey who reported to have used smartphones in their classes were included in the study. To collect data, the instructors were interviewed with semi-structured questions on the details of using smartphones while teaching English as a foreign language. The interviews which were transcribed were analyzed with content analysis. Each instructor described an activity that he or she created and found the most useful one while teaching a foreign language. The interpretation of the language activities have showed that language teachers create their own activities successfully with smartphones. The instructor use smartphones in every step of the teaching a foreign language such as ice-breaking, introducing a new pronunciation topic, assisting speaking skill, revising the previous topic and practicing the language out of the classroom. The instructor have become creative into tailoring the use of smartphones for their specific needs such as changing the mood of the class with a piece of song from their phone, taking the photos of the board for revision the next day, recording the voice of the learner for a valid assessment or motiving the learners to share their video of practicing a topic from the class time. In consideration of the results of the study, some suggestions were proposed like preparing interactive teaching materials resources where the instructors can share further activities as they come along.
Development of Teaching-Learning Indicators of Creative Confluence for Early Childhood Teacher's toCultivate Instructional Expertise

This study developed a teaching-learning indicator for creativity confluence as a guideline for early childhood teachers to perform creative confluence classes with instructional expertise. This study came about in three stages. In the first stage, creative confluence class and FGI were conducted on domestic and foreign literature analysis; and the concept and component of teaching-learning of creative confluence for early childhood. In the second stage of indicator development, the first and second Delphi surveys confirmed the content validity (CVI) and consensus of the survey items and developed the provisional indicator. In the third stage of the indicator completion, there was confirmation of content validity (CVI) and agreement on the components and indicators of teaching-learning of creative confluence for early childhood were confirmed through the expert content validity verification. as results of this study was confirmed that a creative confluence teaching-learning indicator for cultivating instructional expertise of early childhood teachers, the components required for performance in professional creative confluence classes were 'teacher's creative confluence class ability, creative confluence class design, creative confluence class execution, creative confluence class evaluation, creative confluence class environment.
Mobile Augmented Reality Applications in Higher Education

Visual technologies such as AR (Augmented Reality), VR (Virtual Reality), or MR (Mixed Reality) show an increase in popularity in the educational field. In addition to the fact that they can significantly improve the transfer of information in education, they can turn lessons into more interesting and interactive activities. Among these technologies, AR has become the most accessible, due to the rise in the popularity of mobile devices. The increasing popularity of distance teaching and the increasing performance of mobile devices and applications will make this topic an important one in the future in educational psychology. The current study aims to evaluate the effectiveness of using AR in learning and compare it with classical 3D models. Following the calculations, we discovered a minor influence. This implies that the group of students who used simple 3D outperformed the group of students who used Assemblr AR, but the gap between the 2 learning methods was not very wide. The results indicate that the use of 3D models is effective in learning but there is no significant difference between the AR and normal 3D models. The limited effects and the findings of other studies on AR in education imply that additional research is required.
A Coarse-to-Fine Text Matching Framework for Customer Service Question Answering

Customer service question answering have recently seen increased interest in NLP due to their potential commercial values. However, existing methods are largely based on Deep Neural Networks (DNNs) that are computationally expensive and memory intensive, which hinder their deployment in many real-world scenarios. In addition, the customer service dialogue data is very domain-specific, and it is difficult to achieve a high matching accuracy without specific model optimization. In this paper, we propose CFTM, A Coarse-to-Fine Text Matching Framework, which consists of Fasttext coarse-grained classification, and Roformer-sim fine-grained sentence vector matching. This Coarse-to-Fine structure can effectively reduce the amount of model parameters and speed up system inference. We also use the CoSENT loss function to optimize the Roformer-sim model according to the characteristics of customer service dialogue data, which effectively improves the matching accuracy of the framework. We conduct extensive experiments on CHUZHOU and EIP customer service questioning datasets from KONKA. The result shows that CFTM outperforms baselines across all metrics, achieving a 2.5 improvement in F1-Score and a 30% improvement in inference time, which demonstrates that our CFTM gets higher response accuracy and faster interaction speed in customer service question answering.
Management of Virtual Environments with Emphasis on Security

With the popularity of using virtual environments, it urges important measures that increase its security, as well as maintain a good user experience. A widespread attack is a denial of service which proposes to break the availability of service through a large number of illegitimate requests employing all computing resources of the target and degrading the user experience. In order to be effective in this particular type of attack, usually powerful equipment or a combination of them is required. This article proposes a new approach to this attack through a language-based Erlang application, which uses the processing power of a low-cost device. Its use would open the possibility of effective attacks coming from devices with less processing power, or from IoT devices, but capable of at least degrading the experience of a legitimate user, anonymously.
A Kernel Clustering Algorithm Based on Diameters

This paper analyzes an iterative kernel partitioning clustering algorithm that dynamically merges, removes and adds clusters using some characteristics, like the radii and diameters of the clusters, and distance between centers. The clustering is carried out in feature space in terms of a kernel function so that non-linearly separable clusters are identified. The preliminary experiments with seven datasets show that the proposed algorithm is able to successfully converge to the expected clustering. It is also shown that the algorithm performance is sensitive to the parameter $$\sigma $$ of the Gaussian kernel.
A New Adaptive TV-Based BM3D Algorithm for Image Denoising

Block matching 3D filtering (BM3D) algorithm is more effective than traditional denoising methods especially for Gaussian noise. However, the traditional hard-threshold used in BM3D algorithm can not recognize the noise intensity in the process of removing additive noise with BM3D, some image details will be lost. Aiming at this problem, an improved BM3D algorithm is proposed. Firstly, the traditional hard-thresholding of the BM3D method is substituted by an adaptive filtering technique. This technique has a high capacity to acclimate and can change according to the noise intensity. Secondly, when the noise intensity is less than the threshold, the new TV model is used to replace the hard-threshold filtering, when the noise intensity exceeds the threshold, the hard and soft thresholding algorithm is used to replace the hard-threshold filtering. Through the adaptive threshold, the high noise and low noise image feature areas are screened out, targeted denoising is carried out, and the edge details are preserved. Experimental results show that the performance of the improved BM3D method is better than traditional methods.
Multi-Relational Cognitive Diagnosis for Intelligent Education

In intelligent education, cognitive diagnosis is a fundamental but important task, which aims to discover students’ mastery of different knowledge concepts. Plenty of methods have been proposed to exploit student-exercise interactions, especially graph-based methods. However, most of them treat student behaviors to exercises as a binary interaction (i.e., interacted or not), neglecting diverse behavior patterns (i.e., correct and incorrect interactions). Moreover, the number of concepts is much smaller than exercises, presenting a challenge for measuring student proficiency. Therefore, in this paper, we propose a novel Multi-Relational Cognitive Diagnosis (MRCD) framework. Specifically, we first divide students’ answer behaviors into correct and incorrect interactions with exercises, and form the corresponding two student-exercise relation graphs. We then leverage Graph Convolutional Network to learn exercise-level representations of students and exercises based on different relation graphs. Since dividing operation exacerbate the data sparsity problem, we employ graph contrastive learning to enhance MRCD on representation learning. Moreover, considering the relatively small number of concepts, we directly employ attention mechanism to generate student and exercise representations based on relevant concepts. After that, we fuse exercise-level and concept-level representations, and send them to a cognitive diagnosis model to predict student performance. Extensive experiments over two real-world datasets demonstrate the effectiveness of our proposed model.
Purchase Pattern Based Anti-Fraud Framework in Online E-Commerce Platform Using Graph Neural Network

Click Farming is fraudulent behaviors sponsored by malicious merchants to increase exposure by hiring fraudulent teams to place fraudulent orders, posing a serious threat to the operation of platforms. Traditional anti-fraud strategies are no longer applicable as they analyzed fraudulent behaviors individually and only rely on static statistical characteristics. In this paper, we propose a novel graph-based fraud detection framework deployed on JD.com composed of Dynamic Purchase Pattern learning (DPP) and Graph Neural Network with Similarities and Relations (GSR). Specifically, the DPP module is a feature extractor based on user click location sequences collected from websites. And the GSR module is a neighborhood sampling and aggregation algorithm for locating more accurate fraud groups and aggregating various information encoded by different types of subgroups. We conduct graph node classification experiments on a large-scale real-world dataset to verify the effectiveness of our framework, and the experimental results show that the DPP is able to capture more discriminative user patterns. Furthermore, GSR achieves the best performance compared to several state-of-the-art methods. Our method can be easily extended to other domains with the same problems as our task.
Taking Virtual Reality and Augmented Reality to The Next Level: Artificial Intelligence with Mixed Reality

Virtual reality (VR) and augmented reality (AR) have caused significant excitement, but Mixed reality (MR) offers an authentic experience beyond what they have are imagined and it takes this exciting experience to the next level. MR collides virtual data with natural objects, creating realistic physical and emotional interactions so that digital objects are positioned like natural objects in real environments and positioning takes place in real-time by aligning with real things. With the advent of Metaverse, the reality concepts are being redefined and highlighted by both businesses and academia. Apart from business value and high-tech aspects, Metaverse can include mystical, spiritual, and religious perspectives, as it is defined as a virtual and transitive structure in which we can exist by creating a duplicate of our existence in the physical world and its alternative. Through multidisciplinary analyzes and evaluations in this study, the possibility to provide not only the transition from the physical to the virtual, but also the transition from the virtual to the physical through transferring the data to the physical location, objects, or sensors is questioned.
On the existence of \(D(-3)\)-quadruples over \(\mathbb{Z}\)

In this paper we prove that there does not exist a set of four non-zero polynomials from \(\mathbb{Z}[X]\), not all constant, such that the product of any two of its distinct elements decreased by \(3\) is a square of a polynomial from \(\mathbb{Z}[X]\).
Comparison Analysis of Usability Using Controllers and Hand Tracking in Virtual Reality Gamelan (Sharon) Based On User Experience

Gamelan is one of the traditional musical instruments in Indonesia. Gamelan is also one of the cultures that must be preserved. To preserve gamelan, one way is by combining gamelan with technology that is currently developing. The technology that is currently trending is virtual reality. Virtual reality is often used for games in three-dimensional form. Gamelan can be something interesting in VR. In VR interactions, there are two interactions that are often used, namely controllers and hand tracking. Hand gestures are usually tracked using the built-in camera of the VR. Hand tracking can be used in VR applications to control the virtual object just like a controllers. This study aims to compare the use of controllers and hand tracking that involve interactions, namely collision, pressing, grabbing, and release. Thirty two Atma Jaya Yogyakarta University students who had tried virtual reality and played gamelan or had seen gamelan performances explore the differences between VR hand tracking and VR controllers in VR gamelan sharon. SUS (System Usability Scale) and USEQ (Use Questionnaire) were used to measure user usability and satisfaction. The result of the SUS score showed that users were more interested in using controllers than hand tracking. But the USEQ results showed that there were no difference in usefulness, ease of use, and satisfication. Based on the results of the interaction, using controllers looked more comfortable, but using hand tracking, playing gamelan looked more realistic.
Reply to: Modern graph neural networks do worse than classical greedy algorithms in solving combinatorial optimization problems like maximum independent set

We provide a comprehensive reply to the comment written by Chiara Angelini and Federico Ricci-Tersenghi [arXiv:2206.13211] and argue that the comment singles out one particular non-representative example problem, entirely focusing on the maximum independent set (MIS) on sparse graphs, for which greedy algorithms are expected to perform well. Conversely, we highlight the broader algorithmic development underlying our original work, and (within our original framework) provide additional numerical results showing sizable improvements over our original results, thereby refuting the comment's performance statements. We also provide results showing run-time scaling superior to the results provided by Angelini and Ricci-Tersenghi. Furthermore, we show that the proposed set of random d-regular graphs does not provide a universal set of benchmark instances, nor do greedy heuristics provide a universal algorithmic baseline. Finally, we argue that the internal (parallel) anatomy of graph neural networks is very different from the (sequential) nature of greedy algorithms and emphasize that graph neural networks have demonstrated their potential for superior scalability compared to existing heuristics such as parallel tempering. We conclude by discussing the conceptual novelty of our work and outline some potential extensions.