Reviewerly/exHarmony huggingface dataset:
Salient Object Detection Based on Improved Pyramid Pooling Network

Salient object detection is an important task in computer vision, which is to segment visually prominent objects from images accurately. However, image segmentation for small objects and complex background objects is still a challenging task. In order to improve the segmentation effect, a new pyramid-pool network method based on PoolNet network is proposed in this paper. The network, called CDSPNet, integrates Convolutional Block Attention Module (CBAM) and Deep Supervision (DS), wherein the convolutional block attention module can effectively improve the representation ability of feature maps. Depth supervision adds side output to better blend rich deep features. The experimental results show that CDSPNet achieves better salient object detection performance by comparing with other 8 models in F-measure and MAE on 6 public datasets.
Research on Deep Collaborative Recommendation Model based on Attention Mechanism

Neural Collaborative Filtering (NCF) is widely used in recommendation systems, but it ignores the implicit information of users and project description documents and has the problem of data sparsity. In this paper, the explicit data scoring matrix is fused with the implicit feedback information of user and item description documents as input to alleviate the data sparsity problem; meanwhile, because the attention mechanism has better feature learning ability, which is beneficial to the neural network to better extract the nonlinear features of the input vector, this paper proposes a deep collaborative recommendation model based on the attention mechanism. Finally, comparative experimental analysis is conducted on Movielens and LastFM datasets, and the results show that the proposed model improves on two evaluation indexes, Hit Ratio (HR) and Normalized Discounted Cumulative Gain (NDCG), and effectively improves the recommendation accuracy.
Optimizing Transaction Performance in Crossing Heterogeneous Blockchain Network

As the cryptocurrency network evolves, encompassing platforms like Bitcoin, Etherrurm, and other heterogeneous networks, performance has emerged as a significant limitation. This study introduces an enhanced iteration of the rollup process to address cross-chain transactions between the heterogeneous blockchain network. The refined transaction procedure is segregated into two phases: handling at the crossing-chain gateway and subsequent relay-chain processing. Users are given the option to include their transactions in the rollup batch transaction pool. Empirical tests underscore the substantial performance enhancement afforded by this transactional approach.
Using Elf Strategies in Teaching Students Who Speak Bilingual/Multilingual in the Classroom

This study is aimed to show the implementation of English as a lingua franca in elementary school students. The researchers would like to present the strategies used by the teachers in teaching some subjects to students who barely speak Indonesian. This study has not been discussed by many researchers. The use of the ELF strategy by teachers in teaching subjects in a classroom is to keep students engaged in the topic that is being discussed. The researchers employed qualitative research involving five teachers who taught English, Indonesian, and also class teachers in the elementary school. In collecting the data, the researchers participatory sampling and open-ended questions for the participants. In this research, 4 classes have several students who can speak more than one language but are not too fluent in Indonesian. The result of the study showed that teachers used some techniques to teach the students such as using repetition and paraphrasing, code-switching, using pictures, and help-seeking. Moreover, the researchers also found that through the strategies implied by the teacher, students could grasp the material taught by the teacher easily.
Reviewing the Recurrent Restrictions of e-Learning among Palestinian Students: A case study of PAU

This study explores the real challenges of e-learning the Palestinian college learners have already encountered while they are having their classes online during and post COVID-19 pandemic. It aims to quantify these pedagogical emerging challenges, acknowledge the strategic plans the students have already developed to face these challenges, and finally qualify the consequences of e-learning on both autonomy and mastery of learning. The study fits into an interdisciplinary field of studies integrating human, social, educational, and technical aspects of learning. It also deals with the interface between theories and modes of learning and methods of teaching. The study applies a qualitative research method through which the fully structured interview is utilized as an instrument. Data is collected from different categories of learners doing their under- and graduate studies at different faculties and programs at Palestine Ahliya University. The data collected is approached critically and analytically from an insider’s perspective in order to unearth the real challenges, prospects, and conclusions of applying an electronic mode of learning on Palestine advanced students during emergencies. It has been found that the real challenges of e-learning are categorized into pedagogical, technical, and ethical ones. Pedagogically, techniques of meaning negotiation, tactics of collaboration, mechanisms of teamwork, and steps of problem solving are all among the shortcomings of e-learning. They are quite poor in the electronic mood -if compared to the outcomings of the traditional one. It has also been found that the e-learning mode is superior in achieving both notions of autonomous and mastery learning. Consequently, it has been suggested that the electronic platform used at PAU should be exploited properly to add certain activities and resources that can scaffold learners’ self-sufficiency and proficiency.&#x0D; Actually, both researchers hope that their study contributes to the literature of e-Learning, in general and that of e-Learning and blended learning during and post international crises, in particular.
Operation Effectiveness Evaluation of a New Engineering Vehicle Based on Grey-AHP

In order to accurately evaluate the operational effectiveness of a new engineering vehicle, the main factors affecting the operational effectiveness of the new engineering vehicle in the corps were analyzed, from engineering work capability, motor ability, communication ability, protective ability and system adaptability five aspects constructing evaluation index system, the gray analytic hierarchy process (Grey-AHP) is adopted to establish the evaluation model, combined with the analytic hierarchy process(AHP) and expert evaluation method to determine index weight at all levels, the use of grey number and the albino function, automatically calculating results by the C language and the Python programming, The evaluation results are analyzed and fed back, and suggestions are put forward for the operational application and equipment improvement of engineering reconnaissance vehicle.
Feasibility Analysis of Items for End of Semester Assesment Questions Class VII Indonesian Subjects of SMPN 1 Ngaglik

This research is to determine the feasibility of the test items on the Indonesian language PAS questions for class VII at SMP Negeri 1 Ngaglik for the 2021/2022 academic year usingdocument analysis research with a quantitative descriptive approach. Sources of research data, namely the test questions grid, PAS question sheets in the form of student responses. The research subjects were 32 class VII B students at Ngaglik 1 Public Middle School for the 2021/2022 academic year. The research variable is the feasibility of the Indonesian PAS questions which consist of 40 questions. The research instruments were in the form of documentation guidelines, documentation collection techniques, and data analysis techniques in this study using descriptive statistical techniques. The research results show(1) validity level of 40 questions100% valid, (2) the reliability of the test items was 0.807 and declared reliable, (3) ITK, namely there were 6 easy questions, 30 moderate questions, 3 difficult questions, and 1 very difficult question; IDB there are 27 questions that are appropriate and 13 questions that are not feasible. (4) distractor analysis, there are 30 items (92.5%) that have very good distractor quality and 3 items (7.5%) that have poor distractor quality.
Kebutuhan Air Irigasi pada D.I Krueng Pasee Aceh Utara

Water demand is the amount of water volume that must be met for irrigation, in the Krueng Pasee irrigation area of North Aceh has an irrigation network area of 5579 Ha. The purpose ofthis research is to obtain the maximum and minimum irrigation water demand discharge in the study area. This research can be an additional source of knowledge for writers in particular and for readers in general. As well as the results of this study can be known the value of rice planting water needs. The data used in the research is rainfall data sourced from BMKG (Meteorology, Climatology, and Geophysics Agency) Malikussaleh, used to calculate effective rainfall and climate data sourced from BMKGMalikussaleh in the form of temperature data, solar irradiation data, wind speed data and humidity or temperature data used to calculate evapotranspiration (ETo). ETo is a combination of evaporation and transpiration which is defined as the event of water loss from plant tissue and the surface of the soil usedas a place to grow plants. In analyzing ETo using the Penman Modification Methodology. The Penman method by adjusting the wind function according to local conditions produces the best approach to the evapotranspiration rate of the lysimeter results. The consumptive use of plants (ETc) during the land preparation period uses the results of the calculation of water requirements for land preparation (IR). Crop consumptive use (ETc) after land preparation is a calculation between evapotranspiration and cropcoefficient. The calculated irrigation water requirement (NFR) results in water requirement in the form of mm/day. Irrigation water requirements are converted to l/dt/ha with a conversion rate of 1/8.64. The smallest water demand calculation discharge was obtained in November-1 at Q = 4.32 m3/det and thelargest water demand in February -1 at Q = 13.40 m3/det. At the research location, rice planting begins in January-I, with a rice-soybean planting pattern. From the calculation of water demand, Krueng Pasee Irrigation Area has the smallest water demand in November-1 and the largest water demand in February-1.
Research on the innovative teaching concept and strategy of university teachers

This research focuses on the innovative teaching ideas and strategies of university teachers. Firstly, it discusses the theoretical basis of the innovative teaching concepts, including pedagogy, psychology and educational informatization theory. Then, the innovative teaching strategies can be adopted by college teachers in practical teaching, such as student-centered, situational teaching, inquiry teaching, cooperative learning, mixed teaching and curriculum thinking policy. Finally, this paper analyzes the challenges that innovative teaching strategies may face in practice, such as the constraints of traditional educational concepts, the limitations of teachers' own abilities and the limitations of educational resources and facilities, and puts forward corresponding countermeasures to these challenges. This study aims to provide college teachers with an innovative teaching concept and strategy to promote the reform and development of education and teaching.
Development of Educational Management Concepts and Models in the Era of Artificial Intelligence

The development of artificial intelligence (AI) technology has provided new ideas and methods for educational management (EM). Studying the development of EM concepts and models in the era of AI helps to explore innovative paths in EM, promote the modernization and intelligent development of EM, provide guidance for educational managers, and promote innovation in EM concepts and models. This article first analyzes the comparative development of educational concepts, selecting personalization, educational resources, and educational evaluation to describe. Then, it analyzes the development of educational models, selecting management methods, participating in management, and comparing and explaining the construction of educational informatization. It then highlights the application of AI in EM, and finally conducts experimental analysis on different educational concepts and models. The results indicate that compared to traditional EM concepts and models, the EM concepts and models in the era of AI have significantly improved students' academic performance.
Research on the Principles of Responsibility for Environmental Pollution Crimes under the Concept of Ecological Legal Interest

With the rapid development of information technology and the popularization of the Internet, the network has become an important part of people's life. Network moral education is one of the important contents of cultivating the all-round development of college students in the new era. It not only helps to improve the moral quality and network literacy of college students, but also can promote the healthy growth of college students and enhance their sense of social responsibility and citizenship. At the same time, network moral education is also an important guarantee for maintaining network order and social stability. As the main group of Internet users, college students' network moral education is becoming increasingly prominent. In the new era, it has become an important task of higher education to cultivate college students' good network moral accomplishment and improve their network moral awareness and behavior ability.
SYSTEMOGENESIS OF THE IMAGE OF THE WORLD IN THE PROCESS OF STUDYING AT THE UNIVERSITY

The results of empirical testing of the author’s theoretical model of the formation of the image of the world of university students, considered as an integral system of spatial-temporal and value-semantic components, are presented. It is established that in the process of studying at the university, the system organization of most of the characteristics of the image of the world occurs, consisting in an increase in the correlation relationships between them and a reduction in the number of factors in its structure. The general pattern of systemogenesis is the integrating role of the system of life orientations, the creation of the necessary conditions for the productive development of which in the learning process can be considered as a promising direction of psychological and pedagogical support for the formation of the image of the world of university students.
DEVELOPMENT OF THE STUDENT’S INDIVIDUALITY BY INCLUDING HIM IN PROJECT ACTIVITIES

The article is devoted to the problem of developing the individuality of a student in the educational process. It is noted that education, being today a specially organized and standardized process of transferring knowledge from older generations to younger ones, should be focused on the development of the individuality of students. The author shares the point of view according to which the modern learning process should be not so much accumulative and knowledge-based, but rather focused on developing students’ skills and abilities of an independent, active, meaningful search for solutions to real life problems. The use of the project method by teachers, according to the author of the article, will allow students to develop creative abilities and will contribute to the development of their individuality.
Utilization of Artificial Intelligence Technology in Higher Education Management: Teaching Theory and Practical Skills of Landscape Architecture Construction Technology

In traditional university education management, landscape construction technology helps students comprehensively master landscape construction technology by teaching theoretical knowledge such as the basic principles of landscape construction and setting up practical bases for landscape construction. However, this approach has some limitations, such as delayed information transmission, which limits the flexibility and effectiveness of learning. Therefore, artificial intelligence technology can be applied to the teaching theory and practice of landscape construction technology in university education management. The word bag model and SVM (Support Vector Machine) algorithm were used as a case analysis tool for landscape construction technology to analyze construction problems and solutions in real cases, and then virtual reality (VR) and augmented reality (AR) technologies were used to enable students to practice landscape construction in a virtual environment. Finally, a Convolutional Neural Network (CNN) model was used to provide specific learning resources and operational recommendations. This article applied artificial intelligence technology to the theory and practice of landscape construction technology in university education management. The average score of students in the test has increased by 8 points, and over 90% of students can independently complete the experiment. With the help of artificial intelligence technology, university education management can break the limitations of time and space, improve the flexibility and convenience of students' learning, and provide more timely feedback for education managers.
ASA-GNN: Adaptive Sampling and Aggregation-Based Graph Neural Network for Transaction Fraud Detection

Many machine learning methods have been proposed to achieve accurate transaction fraud detection, which is essential to the financial security of individuals and banks. However, most existing methods either leverage original features only or require manual feature engineering so that they show a weak ability to learn discriminative representations from transaction data. Moreover, criminals often commit fraud by imitating cardholders’; behaviors, which causes the poor performance of existing detection models. In this article, we propose an adaptive sampling and aggregation-based graph neural network (ASA-GNN) that learns discriminative representations to improve the performance of transaction fraud detection. A neighbor sampling strategy is performed to filter noisy nodes and supplement information for fraudulent nodes. Specifically, we use cosine similarity and edge weights to adaptively select neighbors with similar behavior patterns for target nodes and then find multihop neighbors for fraudulent nodes. A neighbor diversity metric is designed by calculating the entropy of neighbors to tackle the camouflage issue of fraudsters and explicitly alleviate the oversmoothing phenomena. Extensive experiments on three real financial datasets demonstrate that ASA-GNN outperforms state-of-the-art ones.
Consumer Camera Demosaicking and Denoising With a Collaborative Attention Fusion Network

For the consumer cameras with Bayer filter array, raw color filter array (CFA) data collected in real-world is sampled with signal-dependent noise. Various joint denoising and demosaicking (JDD) methods are utilized to reconstruct full-color and noise-free images. However, some artifacts (e.g., remaining noise, color distortion, and fuzzy details) still exist in the reconstructed images by most JDD models, mainly due to the highly related challenges of low sampling rate and signal-dependent noise. In this paper, a collaborative attention fusion network (CAF-Net), with two key modules, is proposed to solve this issue. Firstly, a multi-weight attention module is proposed to efficiently extract image features by realizing the interaction of spatial, channel, and pixel attention mechanisms. By designing a local feedforward network and mask convolution aggregation of multiple receptive fields, we then propose an effective dual-branch feature fusion module, which enhances image details and spatial correlation. Accordingly, the proposed two modules significantly facilitate our CAF-Net to recover a high-quality image, by accurately inferring the correlations of color, noise, and the spatial distribution of the CFA data. Extensive experiments on demosaicking, synthetic, and real image JDD tasks prove that the proposed CAF-Net can achieve advanced performance in terms of objective evaluation index metrics and visual perception.
Universal Approximation and the Topological Neural Network

A topological neural network (TNN), which takes input data from a Tychonoff topological space instead of the usual finite dimensional space, is introduced. As a consequence, a distributional neural network (DNN) that takes Borel measures as data is also introduced. Combined these new neural networks facilitate things like recognizing long range dependence, heavy tails and other properties in stochastic process paths or like acting on belief states produced by particle filtering or hidden Markov model algorithms. The veracity of the TNN and DNN are then established herein by a strong universal approximation theorem for Tychonoff spaces and its corollary for spaces of measures. These theorems show that neural networks can arbitrarily approximate uniformly continuous functions (with respect to the sup metric) associated with a unique uniformity. We also provide some discussion showing that neural networks on positive-finite measures are a generalization of the recent deep learning notion of deep sets.
A Reputation-based Trustworthiness Concept for Wireless Networking in Vehicular Social Networks

Social features are affecting different application areas, from traditional online social networks to economical networks, as well as the study of epidemical behaviors of pandemics and diseases. From the information point of view, also the Internet paradigm has been revised according to social behaviors of nodes, showing intrinsic properties that have allowed the definition of the Social Internet of Things paradigm. Furthermore, considering vehicular networks, social features have been spreading their influence, and defined the concept of Vehicular Social Networks, where mobile nodes show predictable social behavior. In this context, all the networking concepts should be updated accordingly, taking into account node social aspects and interactions. In this paper, we discuss the open challenge of how we should define a trust vehicle, relying on social networking aspects. We introduce the concept of node trustworthiness in a vehicular environment, based on the reputation degree, computed according to real-time interactions and past data. A node can be defined as trusted if exhibiting an acceptable reputation degree, and being successfully able to transmit data packet in a given environment. The framework of a network architecture for the computation of node trustworthiness degree in a vehicular scenario is also presented.