Reviewerly/exHarmony huggingface dataset:
Dropout Graph Product for Improved Relationship Discovery Across Multiple Heterogeneous Graphs

Relationship discovery across multiple heterogeneous graphs has recently attracted considerable interest. A major challenge is how to fuse and utilize the structure and properties of multiple heterogeneous graphs complementarily to improve relationship discovery between graph pairs where there are only a few observed relationships. In this paper, we seek to solve the problem through label propagation on dropout graph product. We first map multiple heterogeneous graphs onto a single homogeneous graph through graph product. The internal structure and properties of each individual graphs, as well as the observed inter-graph relationships across multiple graphs are fused losslessly via assigning edge weights and encoding node vectors of the graph product. As a result, the complex problem of relationship discovery across multiple heterogeneous graphs is transformed into a simple problem of node classification on homogeneous graph product. However, the size of graph product increases quickly with the size and the number of factor graphs, which leads to low accuracy and high computational cost. Dropout is therefore introduced to address this problem, which is applied to label propagation on graph product. Finally, we combine the results of a set of label propagations on different dropout graph products using a product of experts model. The proposed approach is characterized by flexibility in defining graph product, good generalization ability, high accuracy, and efficient computation. The experiments on real-world datasets show that the proposed approach significantly improves relationship discovery across multiple heterogeneous graphs, obtaining better results on the benchmark datasets than the baseline approaches.
A convolution neural network with encoder-decoder applied to the study of Bengali letters classification

<abstract> Handwritten grapheme recognition is popular research in computer vision and now widespread in the commercial industry due to its large number of applications in document analysis and recognition. Bengali grapheme classification is a complex task as it has 49 letters and 18 potential diacritics with almost 13,000 possible variations. Bengali is now the fifth most spoken native language and the seventh most spoken language by the total number of speakers in the world. Having set a bigger scope, this paper deals with the recognition of Bengali handwritten script letters. A class of deep Convolutional Neural Networks (CNNs) with encoder-decoder is used to classify handwritten letters. We use several serial non-linear layers as the encoders and a corresponding set of decoders that work as a pixel-wise classifier for letter recognition. The key idea is to encode images by convolution and decode them by deconvolution so that the max-pooling and up-sampling layers can correctly identify grapheme pixel-by-pixel. In this study, almost 200,840 grapheme images were analyzed, including root, vowels, and consonants. A large number of variations make additional complexity in recognition and may lead the model into over-fitting or under-fitting. We introduce regularization techniques to reduce the over-fitting in the fully connected layers. The results suggest that CNN with encoder-decoder can recognize complex grapheme characters with higher precision than traditional CNN. Experimental results show that the images' augmentation helps the model train better and improves its accuracy and loss. </abstract>
Visual Analytics Methods for Interactively Exploring the Campus Lifestyle

Exploring campus lifestyle is conducive to innovating education management, optimizing campus resources allocation, and providing personalized services, but little attention had been paid to the exploration campus lifestyle. A novel interactive system based on behavioral data of campus cards is presented in this paper to provide new ideas and technical support for campus management. Interactive visualization techniques are utilized to help users analyze campus lifestyle via intelligible diagrams. The system contains three functional modules: providing a decision-making reference to educators on students' poverty subsidies, predicting students' academic performance by quantitative analysis, and scheduling cafeteria repast based on the scheduling model during the outbreak of COVID-19. Finally, three exploratory case studies are presented to demonstrate the effectiveness of the system.
The identification of preservice biology teacher’s misconceptions on the concept of mangrove ecology using the certainty of response index (CRI)

Abstract The integration of mangrove ecological concepts and contexts in biology learning is expected to support maritime-based education. The concept of mangrove ecology is important to understand by preservice biology teachers, especially in coastal areas. The real knowledge and experience of preservice biology teachers in coastal areas related to mangroves is predicted to be different from the concept of mangrove ecology that has been studied by experts. This causes misconceptions that can affect the integration of the concept of mangroves ecology in biology learning. To analyzed the misconceptions of preservice biology teachers, the Certainty of Response Index (CRI) method was used. The instrument used was a multiple choice test equipped with a confidence level in answering it. Identification of misconceptions using CRI was carried out to determine the level of understanding and misconceptions of preservice biology teachers, then also to identify what sub-concepts these misconceptions occurred. The research was conducted on 12 preservice biology teacher who took mangrove ecology courses in the 2018/2019 school year. The results of the average distribution preservice biology teacher’ understanding levels are 45% understanding of the concept, 7% guessing, 8% not understanding the concept, and 40% misconception. The sub-concept with the most misconceptions was the mangrove research method as much as 47.92%, while the least occurred in the sub-concept of mangrove resilience 33.33%. Given the high level of misconceptions, it is necessary to increase the understanding of the concept of preservice biology teacher related to the concept of mangrove ecology. The use of the CRI method can be used to distinguish groups between students who experience misconceptions and students who lack of knowledge but are unable to explain the reasoning process and the specific causes of misconceptions that occur in students. In order for misconceptions to be identified more specifically and accurately, a combination of other techniques is needed in addition to the use of tests to carry out further misconception analysis.
A Study of Chinese English Language Characteristics and Its Influence on English Teaching Based on Needs Analysis

People's thinking activities must be reflected in language, and the language of each nation has a profound influence on the thinking mode of each nation. Influenced by Chinese social culture and Chinese thinking mode, China English has strong Chinese characteristics, which is relatively unique in discourse, sentence and pronunciation. When things are expressed properly, China English will play a great role in application. We should be familiar with the variant theory of Chinese English and its linguistic features to guide English teaching. According to the needs analysis theory, this paper investigates students' English learning needs, aiming at improving the effect and quality of foreign language education, seeking effective practical English teaching methods and improving foreign language teaching level faster.
A Reversible Data Hiding Method by Compressing Overlapping Pixels

Due to some of the data hiding schemes requiring additional key to restore image and complex in implement, a scheme deleting the overlapping pixels information and embedding the watermark was proposed, because the information of overlapping pixels is redundant which can be compressed produce storage space. At the same time, an embedding data algorithm and an extracting embedded data algorithm was designed. The algorithm is simple. The experiments show that the embedding image have good peak signal to noise ratio (PSNR) and big embedding capacity, the larger redundant space of image pixel information, the larger the capacity of data hiding.
New Parameter Sets for SPHINCS&lt;sup&gt;+&lt;/sup&gt;

SPHINCS+ is a state-of-the-art post-quantum hash-based signature that is a candidate for the NIST post-quantum cryptography standard. For a target bit security, SPHINCS+ supports many different tradeoffs between the signature size and the signing speed. SPHINCS+ provides 6 parameter sets: 3 parameter sets for size optimization and 3 parameter sets for speed optimization. We propose new parameter sets with better performance. Specifically, SPHINCS+ implementations with our parameter sets are up to 26.5% faster with slightly shorter signature sizes.
Effect of dissolved oxygen deficiency on 210Pb content in mussel's tissues at the Johor strait, Malaysia

Aim: To investigate the effect of dissolved oxygen deficiency on the level of 210Pb in mussel's tissues, Perna viridis which has been reported to be highly tolerant to hypoxia. Methodology: Mussels (Perna viridis), seawater and sediment samples were collected from nine stations along the Johor Straits during North-east monsoon (November 2017), inter-monsoon (March 2018), and South-west monsoon (August 2018). The 210Pb contents were extracted and estimated by Gross Alpha-beta Counter. Results: The lowest dissolved oxygen was recorded at Station 4 which decreased from 2.14 mg l-1 to 2.10 mg l-1 and subsequently to 1.63 mg l-1 during first to third samplings. In sediment, 210Pb activities increased with the increase in organic matter. Meanwhile, in mussels, the 210Pb activities showed almost similar trend with sediment where 210Pb activities were much higher adjacent to the causeway structure, especially at the East part of Johor Strait. The level of 210Pb in mussel's tissue and sediment also increased with decreasing level of dissolved oxygen. Interpretation: A causeway structure disrupted the water quality of Johor Straits and caused almost hypoxic conditions nearby. The coverage of oxygen depletion expanded during inter-monsoon. An inverse relationship between 210Pb activities and dissolved oxygen prove that deficiency of dissolved oxygen potentially influenced the bioaccumulation of 210Pb in mussels to correspond to the build-up of concentration factor in their tissues which exceeded the recommended value.
Social Media Ethics Section 3: Digital Citizenship

The effortlessly available online knowledgebase found in the Internet has essentially succeeded other media in terms of acquiring knowledge for daily life. In this digital age, persons in Western industrialized countries are progressively exhibiting themselves online and communicating with others in disembodied contexts that span time and space.
Design and Development of an ICT Intervention for Early Childhood Development in Minority Ethnic Communities in Bangladesh

Early childhood development (ECD) encloses cognitive, physical, and socio-emotional growth which are very much absent in the minority ethnic groups of Bangladesh. In ethnic communities, local beliefs and traditions are practiced in bringing up a child rather than scientific methods. Due to this exercise, lots of indigenous children’s futures are compromised in terms of physical and mental well-being. We have designed a smartphone application for the parents of minority ethnic groups to suggest to them about early childhood development, so that every child can get proper attention. Our main objective was to develop a smartphone application for the indigenous children, so that the parents can monitor the mental and physical development of their children from a very early stage. During the development of the app, the cultural and educational context of the indigenous people were considered. Finally, the User Interface (UI) and User Experience (UX) of this intervention are assessed by the members of several indigenous groups for ensuring the appropriateness and the usability of the application.
Analysis of Residue Number System Corrective Properties for Designing Reliable Cloud Storage

Creation of reliable and secure cloud storage is a complicated task, since this type of data storage and processing is efficient and cost effective. The Residue Number System (RNS) can be used to achieve the required level of data security and reliability. RNS is a non-positional number system, the use of which for data protection is promising. Redundant RNS allows the creation of correction codes. Such properties of error-correction codes as effectiveness, safety and reliability depend on the redundancy of the RNS. This paper evaluates the effectiveness of the RNS correction codes usage in multi-cloud data storage and processing systems by examining various sets of RNS moduli.
A Recognition Algorithm Based on Region Growing

Aiming at the uncertainty of object detection in the field scenic spot cloud data, combine the region growing algorithm and PointNet++. A recognition method based on regional growing is proposed. The method first divides the point cloud data into small regions, and regional growth algorithm was used to cluster the local regions; then the PointNet++ method is used to identify cluster objects. The experiment result shows that this method can achieve the segmentation and recognition of objects in the scenic spot cloud at the same time. The segmentation rate can be achieved 84.3%, and the recognition rate was 90.7%.
Text Classification Method for Analysing Accidents in Power System Operation

In order to ensure the safety of equipment and personnel in power system operation, power utilities make a lot of efforts to prevent the occurrence of accidents. It is very beneficial to investigate and learn from the past accident and near miss narratives. As the number of text records related to accidents keeps increasing every year, it will be difficult and time-consuming to analyse these data by manual. In this paper, an improved BiLSTM neural network based method is proposed to realize the automatic classification of the causes behind the accidents. The proposed model consists of embedding layer, encoder and decoder, which are further improved by introduced the mechanism of attention, dropout, layer normalization and residual connection. Comparison studies are conducted on different models, including textCNN, BiLSTM and BiLSTM-based improved models. The results proved the effectiveness and superiority of the proposed model.
Accurate analysis of big data assists teaching in situational teaching and quality development training of college physical education

The situational teaching of physical education in colleges and universities under the thinking of big data and accurate analysis is a new paradigm for the teaching reform of physical education colleges to move towards public service. Under the background of accurate analysis of big data, college physical education situational teaching and quality development training are worthy of the focus of research on college physical education educators. This can strengthen the application of big data technology in classroom teaching and improve the quality of physical education. With the aid of big data accurate analysis technology, the physical education class of college physical education situational teaching combines sports with an optimized learning environment. Compared with ordinary physical education, physical education that adopts situational teaching lies in the process of optimizing the classroom environment and accurately analysing student dynamics. It can effectively stimulate the emotions of students, so as to better achieve the purpose of college sports quality development training on the basis of sports. Therefore, this research analysed the problems that might arise in the process of college physical education situational teaching and the needs of college students through the research of big data, precision physical education, and summarized its methods of thinking design and practical application. At the same time, taking the virtual situational teaching of college football as an example, this research analysed the teaching process of big data and accurate analysis in assisting college sports situational teaching. And on this basis, research on the countermeasures of introducing quality development training into college physical education situational teaching under the precise analysis technology of big data.
Top-k Graph Similarity Search Based on Hierarchical Inverted Index

Graph similarity search is an important research problem in many applications, such as finding result graphs that have a similar structure to a given entity in biochemistry, data mining, and pattern recognition. Top-k graph similarity search is one of graph similarity search tasks, which aims to find the top-k graphs that are most similar to the query graph in a given graph database. In this paper, the top-k similarity search problem based on the graph edit distance is studied according to the corollary of the partitioned similarity theorem. Firstly, in order to speed up the online search process and avoid scanning each graph in the database one by one, an offline hierarchical inverted index is constructed to satisfy top-k search. Secondly, the offline hierarchical inverted index is used to filter the candidate graphs online and verify them, which reduces the time of searching the graphs. Finally, the good performance of the algorithm in running time and scalability is verified by running the similarity algorithm on real dataset and synthetic dataset.
Innovation and Reform of Accounting Professional Training Model Based on the Artificial Intelligence

Abstract In recent years, the continuous upgrading and transformation of high-performance computing and Internet technology have brought revolutionary impact to the development of the economic, financial and financial accounting industries. High-tech represented by cloud computing and big data artificial intelligence has promoted the “first Four Industrial Revolutions”, and the application of artificial intelligence technology has had a profound impact on the traditional accounting work process, rules, and the functional positioning of practitioners. How to adapt to the demand for accounting professionals in the artificial intelligence era, and accelerate the construction of innovating the current talent training model is a problem that should be faced with and analyzed in the future.
The Influence of Competence, Lecturers' Educational Background and Its Relation to the Curriculum of the Mandarin Language Study Program

One of the key competencies of the Mandarin Language Study Program (MLSP) specified in the curriculum is the achievement of graduates as translators. However, due to a number of reasons, this competence was not achieved. The aim of this research is to investigate the relationship between competence, lecturers' educational qualifications, and the curriculum of the Mandarin Language Study Program, Faculty of Humanities, Universitas Sumatera Utara. The data for this qualitative study derives from a questionnaire administered to 59 MLSP students in 2018 and 2017. Using SPSS version 22 tools to assess the validity of the instruments in the questionnaire, it was discovered that only 8% (5 respondents) are qualified translators of Mandarin. Poor vocabulary comprehension and Mandarin language skills are factors driving the failure of students in the Mandarin Language Study Program at Universitas Sumatera Utara (USU). In addition to the completeness of services and infrastructure, the curriculum and lecturers have a close relationship in education.
The Application of Machine Learning in Determining Earthquake Magnitude as an Early Warning

Abstract Since ancient times, earthquake disasters have always been one of the most harmful natural disasters, and it is necessary to reduce the damage caused by earthquakes effectively. The earthquake early warning is a new technology that has been gradually mature in recent years and can effectively reduce earthquake disasters. The ability to accurately and quickly predict the earthquake magnitude has become an important but difficult part of the earthquake early warning technology. Currently, many countries in the world have been engaged in the projects of establishing and improving the system of earthquake early warning, and completed two methods to predict the earthquake magnitude as a kind of earthquake early warning, namely, the method of characteristic frequency and the method of characteristic amplitude. In recent years, with the constant improvement of deep learning, the application of machine learning in determining the earthquake magnitude as an early warning has been showing great development prospects and application possibility.
The Reconstruction of Modern Apprenticeship Talent Cultivating Community Motivated by Enrollment Expansion of Vocational Colleges

Modern apprenticeship is an important means of cultivating talents in vocational colleges, but there are difficulties in cooperating with the enterprises and constructing teaching team. The enrollment expansion policy in 2019 offers a new opportunity for the development of modern apprenticeship. A modern apprenticeship talent cultivating community based on district-college integration is reconstructed, with the guidance of society capital and community theories. This mode combines vocational colleges with industry association, cooperating enterprise and students from enrollment expansion. The practice of modern apprenticeship in Wuxi Vocational College makes its Community Management and Service become Top One Specialty in Jiangsu Province, according to Jinpingguo professional ranking 2020. Its success provides a reference for peer vocational colleges.
Joint Engineering Department Manufacturing Methods and Evaluation Analysis

Polytechnic University of Japan has a practical practice class named “development task practice”. The practice is carried out by students with different specialties to create things by discussing each other. The practice education is a practical learning system including manufacturing methods of business companies and integrated with the three methods of how to tackle practical product challenges, exercise learning method and working group learning method. In this study, an automated walking assistive vehicle system is fabricated and the results of the practice are evaluated and analyzed.
Reflexive-Insensitive Logics, the Boxdot Translation, and the Modal Logic of Generic Absoluteness

In this paper we analyze the connection between reflexive-insensitive modal logics, logics of provability, and the modal logic of forcing. Because of the inter-definability of the ∘-operator that characterizes the reflexive-insensitive logics and Boolos’s ⊡-operator, characterization results for the reflexive-insensitive logics can be used to give a partial solution of the boxdot conjecture, and to illuminate some closely related problems. In turn, this will facilitate the application of reflexive-insensitive logics to the study of the modal logic of forcing and truth and the logic of forcing persistent sentences. We then show that the modal logic of forcing, the modal logic of forcing and truth, and the modal logic of forcing persistency give rise to the same characterizability results. We conclude by arguing that the modal logic of forcing should be better understood as the modal logic of generic absoluteness.