Reviewerly/exHarmony huggingface dataset:
Depth-guided Hole-filling Algorithm for View Synthesis

The significance of precise image inpainting for image view synthesis has grown in recent years due to the rapid development of autonomous driving or XR (eXtended Reality) devices or robotics. Numerous studies have been conducted on image inpainting during this time, but they have involved intricate procedures and neglected depth information. This paper introduces a novel and straightforward hole-filling algorithm that leverages local depth information. We generate direction vector map based on the local depth information for each hole pixel and utilize the map for the image inpainting. We perform experiments using two distinct simple inpainting methods and compare them with the method augmented by depth information, evaluating both qualitative and quantitative metrics. The results of these experiments demonstrate improvements in numerical accuracy and plausibility.
The Utilisation of Synchronous and Asynchronous Online Learning Media in Distance Learning in Madrasah Aliyah

The use of online learning media, both synchronous and asynchronous, has become part of an interesting phenomenon and deserves attention in today's era of technological development. This study aims to analyse the use of online media in distance learning in Madrasah Aliyah in terms of media utilisation, namely, the aspect of determining media and the aspect of media implementation. This research used descriptive qualitative methods in Madrasah Aliyah Negeri in Ponorogo. Data collection techniques consist of interviews, observation and documentation. The data is analysed using the Miles Huberman model of data reduction, display, and verification. The results of this study are as follows. First, the determination of synchronous and asynchronous online media in the implementation of distance learning in Madrasah Aliyah is carried out through three main points sequentially, namely setting criteria, determining alternative media, and performance in the classroom. Second, the implementation of synchronous and asynchronous online media in the implementation of distance learning in Madrasah Aliyah is carried out in two forms, individually and in combination. Synchronous online media is implemented in virtual face-to-face activities and material deepening. In contrast, asynchronous online media is implemented in practical exercises and assignments. As for presentations, discussions and exams of synchronous and asynchronous online media are implemented combatively.
Implementation of Feature Enginering and K-Mean Clustering Model in Predictive Analysis of Improving The Quality of Junior High School Education in Bogor Regency

In order to improve the quality of education. The government of the Republic of Indonesia has established the National Education Standard (SNP) which is the minimum criterion set by the government in the education system. SNP is a standard that must be met by every school and all stakeholders in managing and delivering education. Through data analysis using feature enginering and K-meann methods to predict the quality of junior high school education in Bogor Regency shows that the junior high school education standards in Bogor Regency are significantly influenced by the achievement value of 3 education quality standards, namely educator and education staff standards, facilities and infrastructure standards, and management standards. So that the education office of Bogor Regency needs to focus the direction of policies to improve the quality of junior high school education in indicators that affect these 3 standards. The results of the k-men clustering of school quality showed that junior high schools in Bogor Regency were divided into 2 major groups of education quality level, namely below the value of the 5.07 education quality level as many as 57.12% of junior high schools and the rest above the value of the 5.07 education quality level of 42.88% of junior high schools. Further development of the model needs to be further developed to the level of sub-indicators so that school quality can be more specifically measured, and the data used as analysis material is expanded and enriched by other educational data from various sectors so that the model developed can be more comprehensive in measuring the achievement of education quality.
Testing Paraphrase Models on Recognising Sentence Pairs at Different Degrees of Semantic Overlap

Paraphrase detection is useful in many natural language understanding applications.Current works typically formulate this problem as a sentence pair binary classification task.However, this setup is not a good fit for many of the intended applications of paraphrase models.In particular, such applications often involve finding the closest paraphrases of the target sentence from a group of candidate sentences where they exhibit different degrees of semantic overlap with the target sentence.To apply models to this paraphrase retrieval scenario, the model must be sensitive to the degree to which two sentences are paraphrases of one another.However, many existing datasets ignore and fail to test models in this setup.In response, we propose adversarial paradigms to create evaluation datasets, which could examine the sensitivity to different degrees of semantic overlap.Empirical results show that, while paraphrase models and different sentence encoders appear successful on standard evaluations, measuring the degree of semantic overlap still remains a big challenge for them.
Converge at WASSA 2023 Empathy, Emotion and Personality Shared Task: A Transformer-based Approach for Multi-Label Emotion Classification

In this paper, we highlight our approach for the "WASSA 2023 Empathy and Emotion Shared Task".We present our approach for track 3 of the shared task which aims to identify emotions from text.Each sample in the dataset has one or more labels making it a multi-label classification task.We compared multiple transformerbased models by fine-tuning them for multilabel classification.Oversampling was used to overcome the class imbalance in the dataset.Ensembling techniques were used to improve the performance of the system.We obtained a macro F1-score of 0.5649 using XLNet on the test dataset in the official phase and secured rank 6 on the official leaderboard.During the post-competition phase, a threshold-based voting mechanism was performed on three models (Longformer, BERT, BigBird) that yielded the highest overall macro F1-score of 0.6605.
The history of the formation of mentoring in arts education

The article is devoted to the phenomenon of mentoring in art education. The essence of the concept of mentoring in foreign and domestic scientific literature is comprehensively studied. The mentoring role as a special form of interaction between a teacher and a pupil is substantiated Through the prism of philosophical views. The outstanding mentors' activity in art education is analyzed.
The effects of the legal environment on cybersecurity

Cybersecurity failure poses a risk to all firms. Cyber attackers exploit vulnerabilities to steal assets and information, jeopardising the victim's ability to deliver products or services. Understanding the ways to strengthen cybersecurity can help mitigate the risk of cybersecurity failures. Examining the effects of a country's legal environment on cybersecurity, this study finds that the strength of cybersecurity is positively associated with the strength of laws in place and the extent of legal enforcement. In addition, we observe stronger cybersecurity in countries with more resources and higher percentages of internet users. This study helps regulators understand the factors affecting cybersecurity. Analysing factors determining cybersecurity allows investors to predict and assess international differences in cybersecurity risks.
Service fault diagnosis method of information operation and maintenance platform based on Gaussian kernel function

In order to solve the problems of low diagnosis accuracy and long diagnosis time of existing information operation and maintenance platform service fault diagnosis methods, this paper proposes a service fault diagnosis method for information operation and maintenance platforms based on the Gaussian kernel function. Through the analysis of the service fault data density of the information operation and maintenance platform, the service fault feature extraction of information operation and maintenance platform is completed. The key noise is extracted, and the fault data is denoised. Using the kernel density of the Gaussian kernel function to estimate the kernel density of fault data, the feature data of the nearest distance of fault data is obtained, and the service fault diagnosis of information operation and maintenance platform is completed. The experimental results show that the proposed method has the highest accuracy of 97%, and the diagnosis time is less than 0.2 s.
THE INFLUENCE OF ENTREPRENEURIAL SELF EFFICACY ON BUSINESS PERFORMANCE MEDIATED BY INNOVATION AND COMPETITIVE ADVANTAGE VARIABLES: A STUDY OF COFFEE SHOPS IN MALANG RAYA

This research analyzes the influence of (1) entrepreneurial self-efficacy (X1) on business performance (Y1); (2) entrepreneurial self-efficacy (X1) on innovation (Z1); ( 3) innovation (Z1) on business performance (Y1); (4) entrepreneurial self-efficacy (X1) on competitive advantage (Z2); and (5) competitive advantage (Z2) on performance (Y1).This study adopts an explanatory research design with a quantitative approach, and it uses four variables: entrepreneurial self-efficacy (X), innovation (Z1), competitive advantage (Z2), and business performance (Y).Samples consist of 104 individuals.The results of the study indicate that (1) entrepreneurial self-efficacy does not have a significant influence on business performance;(2) entrepreneurial self-efficacy has a significant influence on innovation; (3) innovation has a significant influence on business performance; (4) entrepreneurial self-efficacy does not have a significant influence on competitive advantage, competitive advantage does not have a significant influence on business performance; (5) entrepreneurial self-efficacy significantly influences business performance when mediated by innovation; and (6) entrepreneurial selfefficacy does not have a significant impact on business performance when mediated by competitive advantage.
Adaptive intelligent particle filter for visual localization of mobile robot

In the field of unmanned ships, which has attracted widly attention in recent years, the small terminals distributed on the huge ship need to independently judge the collision risks. However, the performance of terminal devices is significantly limited to ambient noise which affect the docking guidance. In this paper, Adaptive Intelligent Particle Filter (AIPF), an assisted tracking method based on Intelligent Particle Filter (IPF) is proposed to smooth and continuous state observation, thus effectively eliminating abnormal and leaping data. Specifically, this method regards the coordinates of the target in a period of time as a process with stationary independent increment, and the possible motion model is explored from an engineering perspective, so as to optimize low weight particles and adjust the number of particles dynamically. The experimental results on the ARMv7 platform demonstrate that even under the harsh conditions of computational power, the suggested method can still reduce particle degeneracy, improve the performance of particle swarm to better achieve auxiliary tracking.
Research of sliding mode control based on novel reaching law for PMSM

This paper introduces the development of a certain type of law enforcement equipment and analyzes the optimization direction of the control system. Aiming at the random disturbance problem of the law enforcement equipment under special working conditions, an improved approach law is designed to solve the contradiction between the approach time of the sliding surface of the traditional approach law and the system jitter, so as to improve the rapid response of the system. Based on the improved reaching law, the speed sliding mode controller suitable for permanent magnet synchronous motor is designed, and the simulation results verify the effectiveness and feasibility of the proposed control algorithm, which has engineering guiding significance for the algorithm optimization of this type of law enforcement equipment.
Data Mining APP for Preschool Teachers' Information Literacy Based on Computer-Generated Intelligence Test System

In order to meet the needs of smart campus construction and mobile learning, a smart classroom test system based on smart mobile terminals is designed.According to the application characteristics of different user groups of the test system, functional modules for teachers and students are designed.Teachers' information literacy has received more and more attention.As an integral part of educational informatization, preschool education informatization, whether it has good information literacy has become the standard for measuring the group of preschool teachers in the new era.The comprehensive improvement of preschool education teachers' information literacy requires the construction of preschool in the perspective of U-G-S.The long-term improvement mechanism of education teachers' information literacy, from the perspective of data mining, according to the individual's many years of teaching and practical experience, as well as the characteristics of pre-school education teachers' information literacy, the research object is divided into three parts.
The Whole Life Cycle Management Method of Contract Electronic Evidence Based on Blockchain

Electronic contract has been applied in more and more scenarios, but in the actual application scenarios, the authenticity authentication of both parties, how to store the contract data for evidence, the tamper-proof security of the contract data, whether the contract data can be verified, and the traceability of the contract signing process. All these problems are urgent to be solved in electronic data of contract. The blockchain technology, on the one hand, has the characteristics of independence and decentralization, which can well solve the trust problem of third-party institutions; Second, the data is difficult to tamper with the characteristics, can better guarantee the security of electronic contract data; Thirdly, blockchain has the characteristics of traceability, which can provide a reliable legal basis for the judicial process in the case of disputes between contract parties in the future. Therefore, this paper proposes a block chain electronic contract model based on intelligent contracts, model using smart contract to solve the security problem of the trusted third party to achieve the security of electronic contract signature mechanism, at the same time, through the establishment of a set of witness the generated network, curing and electronic evidence for verification, and can realize full traceability in the process of signing.
Threat Target Detection System for UAV Equipment on the End Side of Transmission Lines

Safety inspection of the transmission line is the essential precondition to ensure the power grid’s safety. The current approach collects images with cameras and then conducts object detection on the cloud. However, due to the insufficient transmission bandwidth, some images may lose and lead to potential risk. This paper proposes a threatening object detection system with a UAV device for transmission lines to address this issue. Our system consists of two steps, first using the YOLOX network to train the model on existing data from the cloud and then implementing the quantized model on the end-side UAV device. The proposed method can detect threatening objects in real-time, which can ensure the safety of transmission lines. Experiments show that the proposed method has better performance on threatening object detection than the state-of-the-art object detection models, including Faster-RCNN, YOLOv3, YOLOv5, etc.
The influence of combining professional and educational activities on the stress resistance of adult students

In this article, the purpose of the research was to study the impact of combining professional and educational activities on the indicators of stress resistance of adult students in connection with the growing importance of higher education and a large number of competencies in the labor market in order to be a competitive person. Moreover, modern society is undergoing a number of changes that are reflected in various areas of life, which additionally affects the physical and psychological health of a person. Thus, 334 people took part in the study among the respondents: 187 students in the magistracy and 147 not studying at the moment. Techniques for determining stress resistance were used - a test for stress resistance according to the method of K. Schreiner and a test for stress resistance by Holmes and Rage, as well as a method for determining neuropsychic instability "Forecast". The results showed that students in the magistracy have lower stress resistance than adults who do not study, which indicates high workloads associated with the implementation of various types of employment at the same time. For example, many adult learners have families, minor children, dependent parents, other social activities, etc. Thus, the factor of combining professional and educational activities, indeed, affects the instability to stressful experiences, indicating the urgent need to develop adaptive skills of adult respondents.
Multi-View Diffusion Process for Spectral Clustering and Image Retrieval

This paper presents a novel approach to multi-view graph learning that combines weight learning and graph learning in an alternating optimization framework. Multi-view graph learning refers to the problem of constructing a unified affinity graph using heterogeneous sources of data representation, which is a popular technique in many learning systems where no prior knowledge of data distribution is available. Our approach is based on a fusion-and-diffusion strategy, in which multiple affinity graphs are fused together via a weight learning scheme based on the unsupervised graph smoothness and utilised as a consensus prior to the diffusion. We propose a novel multi-view diffusion process that learns a manifold-aware affinity graph by propagating affinities on tensor product graphs, leveraging high-order contextual information to enhance pairwise affinities. In contrast to existing multi-view graph learning approaches, our approach is not limited by the quality of initial graphs or the assumption of a latent common subspace among multiple views. Instead, our approach is able to identify the consistency among views and fuse multiple graphs adaptively. We formulate both weight learning and diffusion-based affinity learning in a unified framework and propose an alternating optimization solver that is guaranteed to converge. The proposed approach is applied to image retrieval and clustering tasks on 16 real-world datasets. Extensive experimental results demonstrate that our approach outperforms state-of-the-art methods for both retrieval and clustering on 13 out of 16 datasets.
On the Underspecification of Situations in Open-domain Conversational Datasets

Advances of open-domain conversational systems have been achieved through the creation of numerous conversation datasets.However, many of the commonly used datasets contain little or no information about the conversational situation, such as relevant objects/people, their properties, and relationships.This absence leads to underspecification of the problem space and typically results in undesired dialogue system behavior.This position paper discusses the current state of the field associated with processing situational information.An analysis of response generation using three datasets shows that explicitly provided situational information can improve the coherence and specificity of generated responses, but further experiments reveal that generation systems can be misled by irrelevant information.Our conclusions from this evaluation provide insights into the problem and directions for future research.
Day-Ahead Forecasting of Solar Irradiance &amp; PV Power Output Through Statistical Machine Learning Methods

Energy production from solar photovoltaic (PV) plants is unpredictable, mainly due to the stochastic formation and movement of clouds or aerosol - dust particles which scatter or disperse solar radiation. Accurate forecasts of PV output are essential to Distribution and Transportation System Operators as they assist efficient solar energy trading and management of electricity grids. This work evaluates an autoregressive, computationally-light KNN-regression scheme (TSFKNN) for hourly, day-ahead forecasts of solar irradiance and energy yield of various PV technologies. The model is being tested and validated using data measured in Thuwal, Saudi Arabia. The available measured records span a 60-month period. The developed forecasting models are designed for online systems and provide increased levels of accuracy and low computational cost. Several parametric and nonparametric specifications, coupled with conventional versus outlier-robust estimation procedures are tested, in order to derive an optimal month-specific daily profile (MDP). Current results demonstrate that including intraday variability to the monthly-based irradiance models achieve improved predictive accuracy between 10% and 25% on average.
Optimization of data packet encryption algorithm in network link transport layer

ABSTRACTWith the rapid development of information technology, huge data information is exchanged every moment in various types of networks, which contain information such as commercial secrets and personal privacy, and data security has a great impact on people’s lives. To address the issues of low efficiency and poor security in network link transmission layer database security encryption systems, a new system is studied and designed to strengthen the protection of data information from both hardware and software aspects. Then, a discrete chaotic system is introduced to ensure the stability of the dynamic grouping encryption algorithm. Finally, trajectory loop rotation is used to obtain address clusters using the discrete chaotic system, and dual address loop rotation is used to achieve the variability of the dynamic grouping algorithm. The experiment shows that for different file sizes, the encryption speed of the Dynamic Packet Encryption Optimization (DPEO) algorithm is stable between 5.84 and 5.98 mb/s. And after minor changes in the key, the NPCR of the ciphertext obtained by the DPEO algorithm exceeds 99.5%. In addition, its operation efficiency is excellent, reaching the stable entropy value in only 0. 52 s. In summary, the DPEO algorithm in the network link transport layer database security encryption system can combine high efficiency and excellent security performance.KEYWORDS: Databasedynamic groupingpairwise addresstrajectory ringchaotic system Disclosure statementNo potential conflict of interest was reported by the author(s).