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During graduate education, postgraduates have to spend considerable time finding papers to explore the development branches of their field. However, existing paper recommendation methods focus on several attributes (title, author, keyword, venue, etc.). The network schema constructed by these attributes is extremely sparse, which easily causes the loss of important semantic paths between attributes. This results in a lack of correlations among relevant papers, which affects paper recommendation efficiency. Moreover, the relationships between multiple semantic paths can be found through common homogeneous and heterogeneous attributes. These relationships can establish many correlations among relevant papers. To address the above problems, this paper proposes a new approach to fuse multi-semantic paths into a heterogeneous educational network (HEN) for personalized paper recommendation. After data processing, a new HEN schema is built by enriching nodes and edges in heterogeneous networks. Then, different semantic meta-paths are generated by projection sub-nets. Next, a new HEN embedding method is proposed by multi-semantic path fusion to generate rich HEN node sequences. Finally, personalized paper recommendation for postgraduates by targeted path similarity. The proposed method was performed on two paper datasets in the fields of educational intergenerational mobility from 1987 to 2021 and data mining and intelligent media from 1997 to 2021. Substantial experiments demonstrate that the proposed approach is effective. [ABSTRACT FROM AUTHOR]

Highly cited papers are influenced by external factors that are not directly related to the document's intrinsic quality. In this study, 50 characteristics for measuring the performance of 68 highly cited papers, from the Journal of The American Medical Informatics Association indexed in Web of Science (WOS), from 2009 to 2019 were investigated. In the first step, a Pearson correlation analysis is performed to eliminate variables with zero or weak correlation with the target ("dependent") variable (number of citations in WOS). Consequently, 32 variables are selected for the next step. By applying the Ridge technique, 13 features show a positive effect on the number of citations. Using three different algorithms, i.e., Ridge, Lasso, and Boruta, 6 factors appear to be the most relevant ones. The "Number of citations by international researchers", "Journal self-citations in citing documents", and "Authors' self-citations in citing documents", are recognized as the most important features by all three methods here used. The "First author's scientific age", "Open-access paper", and "Number of first author's citations in WOS" are identified as the important features of highly cited papers by only two methods, Ridge and Lasso. Notice that we use specific machine learning algorithms as feature selection methods (Ridge, Lasso, and Boruta) to identify the most important features of highly cited papers, tools that had not previously been used for this purpose. In conclusion, we re-emphasize the performance resulting from such algorithms. Moreover, we do not advise authors to seek to increase the citations of their articles by manipulating the identified performance features. Indeed, ethical rules regarding these characteristics must be strictly obeyed. Highlights: Comparing 3 feature selection methods: Ridge, Lasso, Boruta Analyzed data set relies on a representative sample from a health informatics journal Highly cited papers are studied "External" features which affect a high citation level are addressed [ABSTRACT FROM AUTHOR]

A database of historic paper properties was developed using 729 samples of European origin (1350–1990), analysed for acidity, degree or polymerisation (DP), molecular weight of cellulose, grammage, tensile strength, as well as contents of ash, aluminium, carbonyl groups, rosin, protein, lignin and fibre furnish. Using Spearman's rank correlation coefficient and principal component analysis, the data were examined with respect to methods of manufacture, as well as chemical stability of paper. Novel patterns emerged related to loss of DP and accumulation of carbonyl groups and acidity with time and the role of lignin and rosin, as well as rate of degradation (k = 10−5 year−1) at room conditions. In-depth understanding of long-term degradation of lignin and rosin is needed to better understand the relationships between composition and degradation of historic paper. This study highlights the importance of mining significant volumes of analytical data, and its variability, obtained from real historic objects. [ABSTRACT FROM AUTHOR]

This paper introduces CORE, a widely used scholarly service, which provides access to the world's largest collection of open access research publications, acquired from a global network of repositories and journals. CORE was created with the goal of enabling text and data mining of scientific literature and thus supporting scientific discovery, but it is now used in a wide range of use cases within higher education, industry, not-for-profit organisations, as well as by the general public. Through the provided services, CORE powers innovative use cases, such as plagiarism detection, in market-leading third-party organisations. CORE has played a pivotal role in the global move towards universal open access by making scientific knowledge more easily and freely discoverable. In this paper, we describe CORE's continuously growing dataset and the motivation behind its creation, present the challenges associated with systematically gathering research papers from thousands of data providers worldwide at scale, and introduce the novel solutions that were developed to overcome these challenges. The paper then provides an in-depth discussion of the services and tools built on top of the aggregated data and finally examines several use cases that have leveraged the CORE dataset and services. [ABSTRACT FROM AUTHOR]

Research classification is an important aspect of conducting research projects because it allows researchers to efficiently identify papers that are in line with the latest research in each field and relevant to projects. There are different approaches to the classification of research papers, such as subject-based, methodology-based, text-based, and machine learning-based. Each approach has its advantages and disadvantages, and the choice of classification method depends on the specific research question and available data. The classification of scientific literature helps to better organize and structure the vast amount of information and knowledge generated in scientific research. It enables researchers and other interested parties to access relevant information in a fast and efficient manner. Classification methods allow easier and more accurate extraction of scientific knowledge to be used as a basis for scientific research in each subject area. In this regard, this paper aims to propose a research classification model using data mining methods and techniques. To test the model, we selected scientific articles on digital workplace accessibility for the disabled retrieved from Scopus and Web of Science repositories. We believe that the classification model is universal and can be applied in other scientific fields. [ABSTRACT FROM AUTHOR]

Scientific retrieval systems need to be given domain search terms for searching publications, however, as natural language, search terms provided by users are often fuzzy and limited and some relevant terms are always overlooked in searching. Meanwhile, users always desire to be given domain related keywords to enlighten themselves what other terms can be used for their searching. This paper presents a concepts recommendation model in scientific paper retrieval, in which concepts are extracted from keyword in scientific papers, and some data mining algorithms are used to calculate the similarity between search terms and concepts and do recommendation for users. This model is simple and can be used with small dataset, in which all training data used is from meta data of papers that is easy to acquired. Experimental result hold good precision, which shows that this research not only simplifies searching step and improves the searching quality for users, but also lays the foundation for semantic search. [ABSTRACT FROM AUTHOR]

Cybersecurity professionals need hands-on training to prepare for managing the current advanced cyber threats. To practice cybersecurity skills, training participants use numerous software tools in computer-supported interactive learning environments to perform offensive or defensive actions. The interaction involves typing commands, communicating over the network, and engaging with the training environment. The training artifacts (data resulting from this interaction) can be highly beneficial in educational research. For example, in cybersecurity education, they provide insights into the trainees' learning processes and support effective learning interventions. However, this research area is not yet well-understood. Therefore, this paper surveys publications that enhance cybersecurity education by leveraging trainee-generated data from interactive learning environments. We identified and examined 3021 papers, ultimately selecting 35 articles for a detailed review. First, we investigated which data are employed in which areas of cybersecurity training, how, and why. Second, we examined the applications and impact of research in this area, and third, we explored the community of researchers. Our contribution is a systematic literature review of relevant papers and their categorization according to the collected data, analysis methods, and application contexts. These results provide researchers, developers, and educators with an original perspective on this emerging topic. To motivate further research, we identify trends and gaps, propose ideas for future work, and present practical recommendations. Overall, this paper provides in-depth insight into the recently growing research on collecting and analyzing data from hands-on training in security contexts. [ABSTRACT FROM AUTHOR]

With the rapid evolution of scientific research, there are a huge volume of papers published every year and the number of scholars is also growing fast. How to effectively predict the scientific impact has become an important research problem, attracting the attention of researchers in various fields, and it is of great significance in improving research efficiency and assisting in decision-making and scientific evaluation. In this paper, we propose a new framework to perform a systematical survey of scientific impact prediction research. Specifically, we take the four common academic entities into account: papers, scholars, venues and institutions. We reviewed all the prediction tasks reported in the literature in detail; the input features are divided into six groups: paper-related, author-related, venue-related, institution-related, network-related and altmetrics-related. Moreover, we classify the forecasting methods into mathematical statistics-based, traditional machine learning-based, deep learning-based and graph-based, and subdivide each category according to the characteristics. Finally, we discuss open issues and existing challenges, and provide potential research directions. [ABSTRACT FROM AUTHOR]

In recent years, with the development of 3D digitization of cultural relics, most cultural sites contain a large number of fine 3D data of cultural relics, especially complex geometric objects such as painted cultural relics. At present, how to automatically extract surface damage information from the fine 3D color model of painted cultural relics and avoid the loss of accuracy caused by reducing the dimension using conventional methods is an urgentproblem. In view of the above issues, this paper proposes an automatic and high-precision extraction method for cultural relics surface shedding diseases based on 3D fine data. First, this paper designs a 2D and 3D integrated data conversion model based on OpenSceneGraph, a 3D engine, which performs mutual conversion between 3D color model textures and 2D images. Second, this paper proposes a simple linear iterative clustering segmentation algorithm with an adaptive k value, which solves the problem of setting the superpixel k value and improves the accuracy of image segmentation. Finally, through the 2D and 3D integrated models, the disease is statistically analyzed and labeled on the 3D model. Experiments show that for painted plastic objects with complex surfaces, the disease extraction method based on the 3D fine model proposed in this paper has improved geometric accuracy compared with the current popular orthophoto extraction method, and the disease investigation is more comprehensive. Compared with the current 3D manual extraction method in commercial software, this method greatly improves the efficiency of disease extraction while ensuring extraction accuracy. The research method of this paper activates many existing 3D fine data of cultural protection units and converts conventional 2D data mining and analysis into 3D, which is more in line with the scientific utilization of data in terms of accuracy and efficiency and has certain scientific research value, leading value and practical significance. [ABSTRACT FROM AUTHOR]

Exploring massive mobile data for location-based services becomes one of the key challenges in mobile data mining. In this paper, we investigate a problem of finding a correlation between the collective behavior of mobile users and the distribution of points of interest (POIs) in a city. Specifically, we use large-scale cell tower data dumps collected from cell towers and POIs extracted from a popular social network service, Weibo. Our objective is to make use of the data from these two different types of sources to build a model for predicting the POI densities of different regions in the covered area. An application domain that may benefit from our research is a business recommendation application, where a prediction result can be used as a recommendation for opening a new store/branch. The crux of our contribution is the method of representing the collective behavior of mobile users as a histogram of connection counts over a period of time in each region. This representation ultimately enables us to apply a supervised learning algorithm to our problem in order to train a POI prediction model using the POI data set as the ground truth. We studied 12 state-of-the-art classification and regression algorithms; experimental results demonstrate the feasibility and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper comprehends the global research scenario about incorporating cultural components in contemporary urban planning practice. Various urban cultural elements have been analysed under the umbrella of 'Urban Cultural Dynamics' (UCD). This study aims to identify hot spots and explore influential sources through a bibliometric investigation of the evolutionary patterns of cultural dynamics in urban planning research. Data was collected from the Web of Science core collection (1965-2021). The data mining tools VOSviewer, Biblioshiny, KNIME, and WordStat were used to analyse hot topics, keywords, influential journals, core papers, top authors, and cooperation networks of countries and institutions. The Analytical Hierarchy Process (AHP) is employed in journal analysis. The analysis showed that publications on UCD increased exponentially between 2016 and 2022. Italy (32), China (26), and Spain (18) are the top countries in total publications. Technical universities of Dresden and Munich have the most significant influence on UCD's research, with 103 and 101 total citations, respectively. Landscape and Urban Planning, Journal of Cultural Heritage, and Tourism Geographies are the top three consistently influential journals. The top ten core papers were identified. The keywords and topic analysis charted out the evolution of multidisciplinary UCD research topics. The hotspot analysis revealed the significance of cultural dynamics in sustainable urban development. The results highlighted the need for intercontinental cooperation between developed countries and the third world. The future scope of research on UCD topics is discussed in the conclusion. [ABSTRACT FROM AUTHOR]

Reservoir dispatching regulations are a crucial basis for reservoir operation, and using information extraction technology to extract entities and relationships from heterogeneous texts to form triples can provide structured knowledge support for professionals in making dispatch decisions and intelligent recommendations. Current information extraction technologies require manual data labeling, consuming a significant amount of time. As the number of dispatch rules increases, this method cannot meet the need for timely generation of dispatch plans during emergency flood control periods. Furthermore, utilizing natural language prompts to guide large language models in completing reservoir dispatch extraction tasks also presents challenges of cognitive load and instability in model output. Therefore, this paper proposes an entity and relationship extraction method for reservoir dispatch based on structured prompt language. Initially, a variety of labels are refined according to the extraction tasks, then organized and defined using the Backus–Naur Form (BNF) to create a structured format, thus better guiding large language models in the extraction work. Moreover, an AI agent based on this method has been developed to facilitate operation by dispatch professionals, allowing for the quick acquisition of structured data. Experimental verification has shown that, in the task of extracting entities and relationships for reservoir dispatch, this AI agent not only effectively reduces cognitive burden and the impact of instability in model output but also demonstrates high extraction performance (with F1 scores for extracting entities and relationships both above 80%), offering a new solution approach for knowledge extraction tasks in other water resource fields. [ABSTRACT FROM AUTHOR]

Subgroup discovery (SD) is an exploratory pattern mining paradigm that comes into its own when dealing with large real-world data, which typically involves many attributes, of a mixture of data types. Essential is the ability to deal with numeric attributes, whether they concern the target (a regression setting) or the description attributes (by which subgroups are identified). Various specific algorithms have been proposed in the literature for both cases, but a systematic review of the available options is missing. This paper presents a generic framework that can be instantiated in various ways in order to create different strategies for dealing with numeric data. The bulk of the work in this paper describes an experimental comparison of a considerable range of numeric strategies in SD, where these strategies are organised according to four central dimensions. These experiments are furthermore repeated for both the classification task (target is nominal) and regression task (target is numeric), and the strategies are compared based on the quality of the top subgroup, and the quality and redundancy of the top-k result set. Results of three search strategies are compared: traditional beam search, complete search, and a variant of diverse subgroup set discovery called cover-based subgroup selection. Although there are various subtleties in the outcome of the experiments, the following general conclusions can be drawn: it is often best to determine numeric thresholds dynamically (locally), in a fine-grained manner, with binary splits, while considering multiple candidate thresholds per attribute. [ABSTRACT FROM AUTHOR]

Graph theory is a well-established theory with many methods used in mathematics to study graph structures. In the field of medicine, electronic health records (EHR) are commonly used to store and analyze patient data. Consequently, it seems straight-forward to perform research on modeling EHR data as graphs. This systematic literature review aims to investigate the frontiers of the current research in the field of graphs representing and processing patient data. We want to show, which areas of research in this context need further investigation. The databases MEDLINE, Web of Science, IEEE Xplore and ACM digital library were queried by using the search terms health record, graph and related terms. Based on the "Preferred Reporting Items for Systematic Reviews and Meta-Analysis" (PRISMA) statement guidelines the articles were screened and evaluated using full-text analysis. Eleven out of 383 articles found in systematic literature review were finally included for analysis in this literature review. Most of them use graphs to represent temporal relations, often representing the connection among laboratory data points. Only two papers report that the graph data were further processed by comparing the patient graphs using similarity measurements. Graphs representing individual patients are hardly used in research context, only eleven papers considered such kind of graphs in their investigations. The potential of graph theoretical algorithms, which are already well established, could help increasing this research field, but currently there are too few papers to estimate how this area of research will develop. Altogether, the use of such patient graphs could be a promising technique to develop decision support systems for diagnosis, medication or therapy of patients using similarity measurements or different kinds of analysis. [ABSTRACT FROM AUTHOR]

Ensemble methods overcome the limitations of single machine learning techniques by combining different techniques, and are employed in the quest to achieve a high level of accuracy. This approach has been investigated in various fields, one of them being that of bioinformatics. One of the most frequent applications of ensemble techniques involves research into cardiovascular diseases, which are considered the leading cause of death worldwide. The purpose of this research work is to identify the papers that investigate ensemble classification techniques applied to cardiology diseases, and to analyse them according to nine aspects: their publication venues, the medical tasks tackled, the empirical and research types adopted, the types of ensembles proposed, the single techniques used to construct the ensembles, the validation frameworks adopted to evaluate the proposed ensembles, the tools used to build the ensembles, and the optimization methods employed for the single techniques. This paper reports the carrying out of a systematic mapping study. An extensive automatic search in four digital libraries: IEEE Xplore, ACM Digital Library, PubMed, and Scopus, followed by a study selection process, resulted in the identification of 351 papers that were used to address our mapping questions. This study found that the papers selected had been published in a large number of different resources. The medical task addressed most frequently by the selected studies was diagnosis. In addition, the experiment-based empirical type and evaluation-based research type were the most dominant approaches adopted by the selected studies. Homogeneous ensembles were the ensemble type that was developed most often in literature, while decision trees, artificial neural networks and Bayesian classifiers were the single techniques used most frequently to develop ensemble classification methods. The weighted majority and majority voting rules were adopted to obtain the final decision of the ensembles developed. With regard to evaluation frameworks, the datasets obtained from the UCI and PhysioBank repositories were those used most often to evaluate the ensemble methods, while the k-fold cross-validation method was the most frequently-employed validation technique. Several tools with which to build ensemble classifiers were identified, and the type of software adopted with the greatest frequency was open source. Finally, only a few researchers took into account the optimization of the parameter settings of either single or meta ensemble classifiers. This mapping study attempts to provide a greater insight into the application of ensemble classification methods in cardiovascular diseases. The majority of the selected papers reported positive feedback as regards the ability of ensemble methods to perform better than single methods. Further analysis is required to aggregate the evidence reported in literature. [ABSTRACT FROM AUTHOR]

This work presents a new approach for analysing the ability of existing research metrics to identify research which has strongly influenced future developments. More specifically, we focus on the ability of citation counts and Mendeley reader counts to distinguish between publications regarded as seminal and publications regarded as literature reviews by field experts. The main motivation behind our research is to gain a better understanding of whether and how well the existing research metrics relate to research quality. For this experiment we have created a new dataset which we call TrueImpactDataset and which contains two types of publications, seminal papers and literature reviews. Using the dataset, we conduct a set of experiments to study how citation and reader counts perform in distinguishing these publication types, following the intuition that causing a change in a field signifies research quality. Our research shows that citation counts work better than a random baseline (by a margin of 10%) in distinguishing important seminal research papers from literature reviews while Mendeley reader counts do not work better than the baseline. [ABSTRACT FROM AUTHOR]

Emergent news is characterized by few labels, and news detection methods that rely on a large number of labels are difficult to apply to learned features for emerging events and are ineffective in coping with less labeled emergent news detection. To address the challenge of limited labeled data, this study first establishes a scenario for detecting breaking news, ensuring that the domain of detecting events is distinct from the domain of historical events. Secondly, we propose the Clip-GCN multimodal fake news detection model. The model utilizes the Clip pre-training model to perform joint semantic feature extraction of image-text information, with text information as the supervisory signal, which solves the problem of semantic interaction between modalities. Meanwhile, considering the domain attributes of news, the model is trained to extract inter-domain invariant features through Adversarial Neural Network ideation, and intra-domain knowledge information is utilized through graph convolutional networks (GCN) to detect emergent news. Through an extensive number of experiments on Chinese and English datasets from two major social media platforms, Weibo and Twitter, it is demonstrated that the model proposed in this paper can accurately screen multimodal emergent news on social media with an average accuracy of 88.7%. The contribution of this study lies not only in the improvement of model performance but also in the proposal of a solution for the challenges posed by limited labels and multimodal breaking news. This provides robust support for research in related fields. [ABSTRACT FROM AUTHOR]

Traffic flow prediction plays a critical role in urban traffic management and planning. Accurate prediction of traffic flow can enhance traffic efficiency, improve traffic safety, conserve traffic resources, and promote sustainable urban development. Graph convolutional networks (GCN) have great potential in traffic flow prediction due to their ability to handle complex data with topological structures while considering spatial relationships between nodes in data. However, it is difficult for the traditional GCN-based models to capture dynamic spatiotemporal dependencies between the stations since these models usually use static relationship graph in GCN. This paper presents an enhanced approach called the coupled generative graph convolution model (CGGCN) for capturing dynamic regional traffic relationships. The CGGCN learns the adjacency matrix of graph convolution in a generative manner, thereby improving the ability of previous graph convolution models to effectively capture the dynamic relationships of regional traffic within multi-layer structures. Additionally, the paper optimizes the fusion method of coupling graph convolution in multi-level matrix feature flow information aggregation, leading to the efficient extraction of spatiotemporal information. To validate the effectiveness of the proposed model, extensive experiments were conducted on two real traffic datasets obtained from New York, namely NYCBike and NYCTaxi. The results demonstrate the superior performance of the CGGCN model when compared to other eight baseline models. [ABSTRACT FROM AUTHOR]

With a significant number of mechanical products approaching the retirement phase, the batch recycling of discarded mechanical parts necessitates a preliminary assessment of their surface condition. However, the presence of surface rust poses a challenge to defect identification. Therefore, this paper proposes a method for detecting heavily rusted surface defects based on an improved YOLOv8n network. In the Backbone, the C2f-DBB module of re-parameterized deep feature extraction was introduced, and the attention module was designed to improve the accuracy of information extraction. In the Neck part, a Bi-Afpn multiscale feature fusion strategy is designed to facilitate information exchange between features at different scales. Finally, Focal-CIoU is employed as the bounding box loss function to enhance the network's localization performance and accuracy for defects. Experimentally, it is proved that the improved network in this paper improves the Recall, Precision, and mAP0.5 by 1.2%, 2.1%, and 1.9%, respectively, on the original basis, which is better than other network models. [ABSTRACT FROM AUTHOR]

We are pleased to present a special issue of Data Science and Engineering (DSE), which contains a collection of eight extended papers from the ICDM 2019 conference. The eight extended papers in this special issue cover a variety of topics related to data science and engineering. The seventh paper "Contextual Sentiment Neural Network for Document Sentiment Analysis" proposes a novel neural network model that can explain the process of its sentiment analysis prediction in a way that humans find natural and agreeable and can catch-up the summary of the contents. [Extracted from the article]

The incursion of social media in our lives has been much accentuated in the last decade. This has led to a multiplication of data mining tools aimed at obtaining knowledge from these data sources. One of the greatest challenges in this area is to be able to obtain this knowledge without the need for training processes, which requires structured information and pre-labelled datasets. This is where unsupervised data mining techniques come in. These techniques can obtain value from these unstructured and unlabelled data, providing very interesting solutions to enhance the decision-making process. In this paper, we first address the problem of social media mining, as well as the need for unsupervised techniques, in particular association rules, for its treatment. We follow with a broad overview of the applications of association rules in the domain of social media mining, specifically, their application to the problems of mining textual entities, such as tweets. We also focus on the strengths and weaknesses of using association rules for solving different tasks in textual social media. Finally, the paper provides a perspective overview of the challenges that association rules must face in the next decade within the field of social media mining. [ABSTRACT FROM AUTHOR]

At present, there is little research on the application of wireless sensor networks in the agricultural field. In order to improve the operating performance and practical effects of the intelligent agricultural system, based on data mining technology, this paper uses ZigBee wireless sensor network as the networking technology to cover all aspects of crops under the guidance of the concept of sustainable agricultural development, and realizes the data collection and remote-control process of the agricultural production process, and conducts data analysis and processing through data mining. Moreover, in order to improve the performance of the model, this paper proposes to use the single-point crossover multiple-generation genetic algorithm to optimize the weights and thresholds of the BP neural network to establish the Multi-Generation Genetic Algorithm Back Propagation MGABP model. In addition, in application, the analytic hierarchy process is introduced as the guidance mechanism of neural networks. Finally, this paper designs experiments to analyze the performance of the system constructed in this paper, and uses mathematical statistics to perform statistics on experimental results. The experimental analysis and statistical diagrams of various parameters shows the outcome of this study. The research results show that the intelligent agricultural system model constructed in this paper has certain practical effects. [ABSTRACT FROM AUTHOR]

Textile defect detection is a key link in measuring textile quality. An accurate and efficient textile detection method is essential for textile production processes. In the textile production process, the problem of large variation in textile defect shape scale and edge uncertainty makes textile defect detection very difficult. We propose a textile defect detection method based on context receptive field and adaptive feature fusion. Firstly, we introduce an improved context receptive field block (CRFB) in the backbone network CSPDarknet53. It makes full use of local and global context information to enhance the extraction capability of the backbone network for textile defect features. Secondly, a deconvolution-based adaptive feature fusion network (DAFF) is designed to improve the transfer efficiency of shallow localization information and feature scale invariance. Finally, exponential distance IoU (EDIoU) is proposed to optimize the calculation of bounding box loss and adaptively weighting the bounding box gradient to improve the detection accuracy of the model. The experimental results on ZJU-Leaper and Tianchi datasets show that the mAP of this paper reaches 42.5% and 61.5%, which are 2.9% and 3% higher than the baseline YOLOv5s, respectively. Meanwhile, the model in this paper outperforms most existing networks in terms of detection accuracy and speed. [ABSTRACT FROM AUTHOR]

Resource Description Framework (RDF) graphs have become an important data source for many knowledge discovery algorithms and data mining tasks. However, most complex analyses that use knowledge discovery algorithms require data in a vector representation format. As a result, several RDF entity embedding techniques have emerged in which entities in the RDF graph are represented as low-dimensional vectors. These techniques generate sequences of entities using graph walk and use language modeling techniques to extract the feature from the sequences used to learn the embedding. However, sequences produced by graph walks only capture structural context; they are unable to capture latent context, such as semantically related information, which is an important property of RDF data. In this paper, we present a novel method that consists of a series of steps that generate sequences. These sequences not only capture structural context but also semantic property. The method for structural context includes (1) a new concept of similar entities in which tradeoffs are made between similar outgoing edges and outgoing nodes and (2) a new structural similarity, which calculates the similarity between two entities in each sequence. We can generate sequences based on structural similarity so that similar entities contain sequences with similar structures. The method for the semantic property combines sequences with the same semantic to generate latent sequences that cannot be generated by traversing the graph. This paper presents experimental results and a case study using real graphs to show that the proposed method outperforms existing methods in terms of quality and efficiency. [ABSTRACT FROM AUTHOR]

Highlights from the article: In a very short time (from 1992 to date), the data warehouse technology has gone through all the phases of a technological product's life: research, introduction on the market, growth, maturity and paving way for new extensions. Big Data is one of the challenges of companies owning data warehousing technology, since they are obliged to align their business solution to Big Data requirements.

Entity-relationship extraction is a fine-grained task for constructing a knowledge graph of food public opinion in the field of food public opinion, and it is also an important research topic in the field of current information extraction. This paper aims at the multi-entity-to-relationship problem that often occurs in food public opinion, the entity-relationship types are extracted from the BERT (Bidirectional Encoder Representation from Transformers) network model; In the bidirectional long short-term memory network (BLSTM), the entity-relationship types extracted by BERT model are integrated, and the semantic role attention mechanism based on position awareness is introduced to construct a model BERT-BLSTM-based entity-relationship extraction model for food public opinion at the same time. In this paper, comparative experiments were conducted on the food sentiment data set. The experimental results show that the accuracy of the BERT-BLSTM-based food sentiment entity-relationship extraction model proposed in this paper is 8.7 ~ 13.94% higher than several commonly used deep neural network models on the food sentiment data set, which verifies the rationality and effectiveness of the model proposed in this paper. [ABSTRACT FROM AUTHOR]

The intelligent appearance quality classification method for Auricularia auricula is of great significance to promote this industry. This paper proposes an appearance quality classification method for Auricularia auricula based on the improved Faster Region-based Convolutional Neural Networks (improved Faster RCNN) framework. The original Faster RCNN is improved by establishing a multiscale feature fusion detection model to improve the accuracy and real-time performance of the model. The multiscale feature fusion detection model makes full use of shallow feature information to complete target detection. It fuses shallow features with rich detailed information with deep features rich in strong semantic information. Since the fusion algorithm directly uses the existing information of the feature extraction network, there is no additional calculation. The fused features contain more original detailed feature information. Therefore, the improved Faster RCNN can improve the final detection rate without sacrificing speed. By comparing with the original Faster RCNN model, the mean average precision (mAP) of the improved Faster RCNN is increased by 2.13%. The average precision (AP) of the first-level Auricularia auricula is almost unchanged at a high level. The AP of the second-level Auricularia auricula is increased by nearly 5%. And the third-level Auricularia auricula AP is increased by 1%. The improved Faster RCNN improves the frames per second from 6.81 of the original Faster RCNN to 13.5. Meanwhile, the influence of complex environment and image resolution on the Auricularia auricula detection is explored. [ABSTRACT FROM AUTHOR]

The construction of information management system is the basis to consider the management level of an enterprise, so the construction of enterprise management information system is an important problem to be solved by enterprises. Using advanced software technology is an important way to improve the level of enterprise management. At the same time, innovative management form is also an important embodiment of enterprise system innovation. The development of cloud platform and Internet of things technology has brought revolutionary impact on enterprise management mode, methods and means. Based on information reconstruction model and Internet of things technology, this paper constructs an enterprise integrated management system, in order to provide reference for the development of enterprises. Novelty of the paper is: (1) Infrastructure level. The comprehensive management informatization is very important to the enterprise management decision-making, which is the key to improve the management level of the enterprise, hence, we use the novel MIS model to make the system efficient. We improve the traditional MIS model to make it fit for the business analytic process. (2) Algorithm design level. The traditional genetic algorithm will converge to a point in the iterative solution, resulting in inbreeding and destroying the diversity of the population. Therefore, the algorithm can only get the internal optimal value and cannot get the global optimal solution. Hence, we consider the novel data analytic model to make the system efficient. We optimize the traditional GA to make it robust for the complex data scenarios. (3) Application level. The business intelligence scenario is considered as the applications. The performance of the proposed pipeline is verified through the experimental analysis, we compare the proposed model with the latest ones and test the performance on regression performance, average response time of the system, number of hits per second of the system and the overall comparison analysis. [ABSTRACT FROM AUTHOR]

To reveal complex causes of aircraft events, this paper aims to mine association rules between the trigger probability and relative strength via a modified Apriori algorithm. Clustering is adopted for data preprocessing and TF–IDF value calculation. Causative item sets of aircraft events are obtained based on the accident causation 2–4 model and are coded to establish code indicators. By avoiding the use of statistical methodologies to resolve not-a-number (NaN) values for altering the interrelations among causes, an enhancement in the Apriori algorithm is proposed by considering frequent items. By extracting frequent patterns, in this paper, all the association rules that satisfy three perspectives (support, confidence and lift) are determined by constantly generating and pruning candidate item sets. A network graph is used to visualize the association rules between different unsafe events and all types of causes. Finally, 9835 representative pieces of data, including general unsafe events, general incidents and serious incidents from the Southwest Air Traffic Management Bureau, are selected for analysis. The results show that improper energy allocation, poor conflict resolution ability, inadequate onsite management duties, adoption of a luck mentality, and occurrence of controller oversight are highly correlated with general unsafe events, and failure to rectify incorrect recitation is notably correlated with general incidents, while inadequate manual promotion, lack of conflict judgement and insufficient safety management are strongly correlated with serious incidents. This study quantitatively reveals the potential patterns and characteristics of mutual interactions among various types of historical aircraft events and highlights directions for controllable prevention and prediction of aircraft events. [ABSTRACT FROM AUTHOR]

Neighborhood rough sets, as an effective tool for processing numerical data, is widely used in many fields, such as data mining, machine learning and decision-making system. However, most of the existing neighborhood rough set-based attribute reduction algorithms have low efficiency. To address the limitation, this paper has proposed an efficient positive region search algorithm based on multiple hash buckets and multiple granularity mechanisms. This algorithm achieves a more accurate neighborhood extent by superimposing the effects of multiple hash buckets, and accelerates positive region searching through the idea of multiple granularity. In addition, on the foundation the positive region search algorithm, we improved the existing algorithm and proposed an attribute reduction algorithm based on multi-hash bucket and multi-granularity. To further remove the redundant attributes, the two algorithms mentioned above are applied into a novel attribute reduction approach based on random walk. Experiments conducted on UCI datasets show that our attribute reduction algorithm has high efficiency. Moreover, attribute reduction approach we proposed can further compress the reduced attribute set, and the results maintain similar or even better classification accuracy. [ABSTRACT FROM AUTHOR]

In recent years, the field of bionics has attracted the attention of numerous scholars. Some models combined with biological vision have achieved excellent performance in computer vision and image processing tasks. In this paper, we propose a new bio-inspired lightweight contour detection network (BLCDNet) by combining parallel processing mechanisms of bio-visual information with convolutional neural networks. The backbone network of BLCDNet simulates the parallel pathways of ganglion cell–lateral geniculate nucleus and primary visual cortex (V1) area, realizing parallel processing and step-by-step extraction of input information, effectively extracting local features and detailed features in images, and thus improving the overall performance of the model. In addition, we design a depth feature extraction module combining depth separable convolution and residual connection in the decoding network to integrate the output of the backbone network, which further improves the performance of the model. We conducted a large number of experiments on BSDS500 and NYUD datasets, and the experimental results show that the BLCDNet proposed in this paper achieves the best performance compared with traditional methods and previous biologically inspired contour detection methods. In addition, BLCDNet still outperforms some VGG-based contour detection methods without pre-training and with fewer parameters, and it is competitive among all of them. The research in this paper also provides a new idea for the combination of biological vision and convolutional neural networks. [ABSTRACT FROM AUTHOR]

Background: Emotion is a complex phenomenon that greatly affects human behavior and thinking in daily life. Electroencephalography (EEG), one of the human physiological signals, has been emphasized by most researchers in emotion recognition as its specific properties are closely associated with human emotion. However, the number of human emotion recognition studies using computer games as stimuli is still insufficient as there were no relevant publicly available datasets provided in the past decades. Most of the recent studies using the Gameemo public dataset have not clarified the relationship between the EEG signal's changes and the emotion elicited using computer games. Thus, this paper is proposed to introduce the use of data mining techniques in investigating the relationships between the frequency changes of EEG signals and the human emotion elicited when playing different kinds of computer games. The data acquisition stage, data pre-processing, data annotation and feature extraction stage were designed and conducted in this paper to obtain and extract the EEG features from the Gameemo dataset. The cross-subject and subject-based experiments were conducted to evaluate the classifiers' performance. The top 10 association rules generated by the RCAR classifier will be examined to determine the possible relationship between the EEG signal's frequency changes and game-induced emotions. Results: The RCAR classifier constructed for cross-subject experiment achieved highest accuracy, precision, recall and F1-score evaluated with over 90% in classifying the HAPV, HANV and LANV game-induced emotions. The 20 experiment cases' results from subject-based experiments supported that the SVM classifier could accurately classify the 4 emotion states with a kappa value over 0.62, demonstrating the SVM-based algorithm's capabilities in precisely determining the emotion label for each participant's EEG features' instance. Conclusion: The findings in this study fill the existing gap of game-induced emotion recognition field by providing an in-depth evaluation on the ruleset algorithm's performance and feasibility of applying the generated rules on the game-induced EEG data for justifying the emotional state prediction result. [ABSTRACT FROM AUTHOR]

The paper reports findings from a study that examining how cross-platform social media analysis can help to map the digital discourse on sustainable mobility and sustainable transport, and enhance the understanding of sociotechnical low-carbon transport transitions. Using the hashtag search queries #sustainabletransport and #sustainablemobility, 33,121 Tweets (2013–2021) and 8,089 Instagram images including captions (2017/2018–2021) were scraped using the Python modules Twint and Instaloader. Quantitative text and sentiment analyses were applied to the Tweets and image captions. Additionally, an automated machine learning-based image analysis of the Instagram images was conducted using object detection via OpenCV. Synthesized results formed the base for a cross-platform analysis inspired by Rogers' method comprising hot topics/key themes, user mentions, sentiment polarity, and co-hashtags. Notably, electromobility emerged as a prominent theme, particularly on Instagram, while #sustainabletransport was closely associated with active travel, notably bicycling, and #sustainablemobility showcased a dominance of electromobility discourse. The study demonstrates the investigative potentials of cross-platform social media analysis studies to enhance the understanding of sociotechnical low-carbon transport transitions. Drawing on key results, the paper suggests an adapted version of the Geelsean Multi-Level Perspective on Sociotechnical Transitions. [ABSTRACT FROM AUTHOR]

Mangroves are ecosystems that grow in the intertidal areas of coastal zones, playing crucial ecological roles and possessing unique economic and social values. They have garnered significant attention and research interest. Semantic segmentation of mangroves is a fundamental step for further investigations. However, mangrove remote sensing images often have large dimensions, with a substantial portion of the image containing mangrove features. Deep learning convolutional kernels may lead to inadequate receptive fields for accurate mangrove recognition. In mangrove remote sensing images, various challenges arise, including the presence of small and intricate details aside from the mangrove regions, which intensify the segmentation complexity. To address these issues, this paper primarily focuses on two key aspects: first, the exploration of methods to achieve a large receptive field, and second, the fusion of multi-scale information. To this end, we propose the Multi-Scale Fusion Attention Network (MSFANet), which incorporates a multi-scale network structure with a large receptive field for feature fusion. We emphasize preserving spatial information by integrating spatial data across different scales, employing separable convolutions to reduce computational complexity. Additionally, we introduce an Attention Fusion Module (AFM). This module helps mitigate the influence of irrelevant information and enhances segmentation quality. To retain more semantic information, this paper introduces a dual channel approach for information extraction through the deep structure of ResNet. We fuse features using the Feature Fusion Module (FFM) to combine both semantic and spatial information for the final output, further enhancing segmentation accuracy. In this study, a total of 230 images with dimensions of 768 pixels in width and height were selected for this experiment, with 184 images used for training and 46 images for validation. Experimental results demonstrate that our proposed method achieves excellent segmentation results on a small sample dataset of remote-sensing images, with significant practical value. This paper primarily focuses on three key aspects: the generation of mangrove datasets, the preprocessing of mangrove data, and the design and training of models. The primary contribution of this paper lies in the development of an effective approach for multi-scale information fusion and advanced feature preservation, providing a novel solution for mangrove remote sensing image segmentation tasks. The best Mean Intersection over Union (MIoU) achieved on the mangrove dataset is 86%, surpassing other existing models by a significant margin. [ABSTRACT FROM AUTHOR]

Micro-expressions (MEs) are natural facial mechanisms with short duration and subtle changes. It has attracted much attention in the real world due to its accuracy and uncontrollability of mental expression. With the development of computer vision, micro-expression Recognition (MER) methods have been continuously proposed and improved by scholars. However, the existing MER methods still have some deficiencies in processing Spatio-temporal redundant information and feature extraction. This paper proposes an MER network based on Differential Feature Fusion (DFF) method to solve this problem. First, inputs the onset frame and apex frame of the face, divide each image into small blocks, and uses part of the SE-ResNet50 model for feature extraction. Second, the Spatio-Temporal information of the features is extracted by using a DFF module composed of a differential feature module, CapsuleNet, and a Fully Connected (FC) layer. Finally, inputs the feature vector to the FC module for classification. This study is based on the Leave One Subject Out (LOSO) cross-validation protocol and uses the CASMEII dataset. Experiments and comparisons show the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

Terrorist Network Analysis (TNA) is the field of analyzing and defining the scope of terrorism and researching the countermeasures in order to handle exponentially increasing threats due to ever growing terrorist based activities. This field constitutes several sub-domains such as crawling the data about terrorist attacks/groups, classification, behavioral, and predictive analysis. In this paper we present a systematic review of TNA which includes study of different terrorist groups and attack characteristics, use of online social networks, machine learning techniques and data mining tools in order to counter terrorism. Our survey is divided into three sections of TNA: Data Collection, Analysis Approaches and Future Directions. Each section highlights the major research achievements in order to present active use of research methodology to counter terrorism. Furthermore, the metrics used for TNA analysis have been thoroughly studied and identified. The paper has been written with an intent of providing all the necessary background to the researchers who plan to carry out similar studies in this emerging field of TNA. Our contributions to TNA field are with respect to effective utilization of computational techniques of data mining, machine learning, online social networks, and highlighting the research gaps and challenges in various sub domains. [ABSTRACT FROM AUTHOR]

Modeling and forecasting are impressive and active research areas, which have been widely used in diverse theoretical and practical applications, successfully. Accuracy is the most important and at the same time is the most challenging feature of modeling and forecasting approaches that considerable efforts have been made in the literature for improving it. However, despite all interminable endeavors done in this regard, improving accuracy is yet often a problematic task. Weighting are among the most fundamental and critical parts of modeling and forecasting approaches, which have a significant impact on the performance and accuracy. Nevertheless, despite of the substantial importance of weighting algorithms, they have not been appropriately/comprehensively investigated in the literature. This fact that there are no such comprehensive review papers in this field is the core literature gap of modelling and forecasting that construct the main contribution of this paper. In this way, the main core objective of this paper is to comprehensively review and classify the most frequently applied weighting algorithms in various modeling and forecasting approaches. Motivated by this goal, in this review paper, different weighting algorithms, commonly used in the modelling and forecasting field, are classified based on the type of their function (i.e., Continuous/Discrete and Static/Dynamic) into four main categories. After that, the most related works done in these categorizes, including (1) discrete and static, (2) discrete and dynamic, (3) continuous and static, and (4) continuous and dynamic, are systematically reviewed to offer practical as well as theoretical guides for researchers. For this purpose, in each category, the most popular weighting algorithms, their potential limitations, and some highlighted advantages/disadvantages of them are briefly discussed. The weighting reviewed works, investigated in this paper, totally contain 350 paper that have been published since 2016 up to 2020. This is the first study that provides a comprehensive overview of the most frequently used weighting approaches employed in modeling and forecasting. The deep analysis and classification represented in this review paper revealed that the modeling and forecasting methods come from a unique unit which is named the weighting approach. [ABSTRACT FROM AUTHOR]

Document-level relation extraction is a challenging task in information extraction, as it involves identifying semantic relations between entities that are dispersed throughout a document. Existing graph-based approaches often rely on simplistic methods to construct text graphs, which do not provide enough lexical and semantic information to accurately predict the relations between entity pairs. In this paper, we introduce a document-level relation extraction method called SKAMRR (Sememe Knowledge-enhanced Abstract Meaning Representation and Reasoning). First, we generate document-level abstract meaning representation graphs using rules and acquire entity nodes' features through sufficient information propagation. Next, we construct inference graphs for entity pairs and utilize graph neural networks to obtain their representations for relation classification. Additionally, we propose the global adaptive loss to address the issue of long-tailed data. We conduct extensive experiments on four datasets DocRE, CDR, GDA, and HacRED. Our model achieves competitive results and its performance outperforms previous state-of-the-art methods on four datasets. [ABSTRACT FROM AUTHOR]

Alcohol drinking has long been reported to be common in people living with HIV/AIDS, having biological and behavioral impacts on the transmission, progression, and prevention of HIV/AIDS. A total of 7059 eligible articles and reviews published in English from 1990 to 2019 were extracted from the WOS. Results show an increase in publication volume, while citations peak for papers published in 2006. Content analysis reveals a wide-ranging coverage of topics, with the most popular being effects of alcohol consumption on ART adherence and outcomes, alcohol-related sexual behaviors, TB co-infection, and psycho–socio-cultural considerations in examining and designing measures targeting alcohol use and interventions to reduce alcohol dependence in PLWHA. This calls for more active engagement of governments in research and in designing and implementing interventions, as well as collaborations and knowledge transfer from high-income countries to developing counterparts, to effectively address alcohol use-related issues in PLWHA, moving toward the HIV/AIDS eradication target. [ABSTRACT FROM AUTHOR]

Time series data mining techniques have attracted extensive attention from researchers worldwide. Of these techniques, time series classification is an important part of time series mining. Among the many time series classification algorithms, methods based on the bag-of-patterns algorithm have attracted much attention from researchers because of their high accuracy and execution efficiency. However, when using these methods, only the frequency of different patterns is considered. Features such as the position of patterns in a sequence are not mined. Therefore, the aim of this paper is to determine how to solve the problem that the positional relationships among patterns are ignored when using the bag-of-patterns algorithm. To solve this issue, we introduce the graph embedding technique, and an attempt is made to capture the positional relationships among the patterns of time series from the graph perspective. To verify the performance of the method, we perform extensive experiments with the UCR time series archive, and the experimental results demonstrate that our proposed method generally improves the classification ability of models based on the bag-of-patterns algorithm. [ABSTRACT FROM AUTHOR]

In recent years, graph neural network (GNN) and auto-encoding (AE) have been widely utilized in multiple data mining problems. These architectures have demonstrated powers in data high-dimensional embedding for improving the performance of various task-driven learning tasks, like as clustering. However, most of recent GNN-based cluster techniques still suffered several limitations. These limitations are related to the capability of simultaneously preserving the low-levelled latent feature and global structural representations of the given network/graph. These view-varied graph representations can help to improve the performance of multi-scaled clustering task. Moreover, the achieved multi-viewed structural node embeddings which are learnt by GNN-based architectures might also involve with problems. These problems are related to feature noise and data uncertainty. These feature noise/uncertainties are occurred within representation learning process. These limitations can directly lead to the downgrade in the accuracy performance for clustering tasks. To overcome existing limitations, within this paper, we proposed a novel fuzzy-driven noise-reduced attention-based graph auto-encoding mechanism for network clustering, called as: FAGC. In general, our proposed FAGC model is as an attention-driven multi-layered graph-based AE architecture which is integrated with a custom de-noising fuzzy neural network. In our proposed FAGC model, we integrate the fuzzy neural network with GNN to eliminate problems which are related to the feature uncertainty and noise occurrence during the graph representation learning process. Later, the better quality as well as rich-structural network representations which are generated from our FAGC model are utilized to achieve state-of-the-art performances for the network clustering problem. The extensive experiments within benchmark networked datasets and comparative studies demonstrated the effectiveness and outperformance of our proposed FAGC model in comparing with state-of-the-art graph embedding techniques. [ABSTRACT FROM AUTHOR]

Today's factories are considered as smart ecosystems with humans, machines and devices interacting with each other for efficient manufacturing of products. Industry 4.0 is a suite of enabler technologies for such smart ecosystems that allow transformation of industrial processes. When implemented, Industry 4.0 technologies have a huge impact on efficiency, productivity and profitability of businesses. The adoption and implementation of Industry 4.0, however, require to overcome a number of practical challenges, in most cases, due to the lack of modernisation and automation in place with traditional manufacturers. This paper presents a first of its kind case study for moving a traditional food manufacturer, still using the machinery more than one hundred years old, a common occurrence for small- and medium-sized businesses, to adopt the Industry 4.0 technologies. The paper reports the challenges we have encountered during the transformation process and in the development stage. The paper also presents a smart production control system that we have developed by utilising AI, machine learning, Internet of things, big data analytics, cyber-physical systems and cloud computing technologies. The system provides novel data collection, information extraction and intelligent monitoring services, enabling improved efficiency and consistency as well as reduced operational cost. The platform has been developed in real-world settings offered by an Innovate UK-funded project and has been integrated into the company's existing production facilities. In this way, the company has not been required to replace old machinery outright, but rather adapted the existing machinery to an entirely new way of operating. The proposed approach and the lessons outlined can benefit similar food manufacturing industries and other SME industries. [ABSTRACT FROM AUTHOR]

In the study of the defense capacity of ancient military settlements, most existing studies have evaluated the defense capacity of citadels qualitatively from a historiographical perspective. There are fewer quantitative studies on defense efficiency in a specific regional context. This paper takes the coastal defense citadels system of Zhejiang in the Ming Dynasty of China as the research object. It adopts a combination of digital historical data mining and quantitative research to study the defense efficiency of the military defense area in Zhejiang in the Ming Dynasty. This paper first analyzes the defense pattern of Zhejiang's sea defense citadels system in the Ming Dynasty. It determines the control points of defense groups, quantifies the defense group area with a Voronoi diagram, and classifies and assigns the entropy value to the deployment data of guard forts in historical data. Finally, the assigned forces are superimposed on the defense area to obtain the quantified defense efficiency of each defense group, and the defense results are verified. This paper innovatively proposes an "area-force" perspective. It introduces spatial segmentation and quantitative research methods to study the defense efficiency of a specific regional defense group system in ancient times. It proves that this method is feasible and can be applied to the study of other ancient military defense areas. It also expands the scientific perspective of ancient military fortification systems, which can contribute to systematically preserving ancient military settlement heritage on a larger scale. [ABSTRACT FROM AUTHOR]

Demeanor evidence refers to the authoritative opinions of experts using analytical instruments to collect and interpret the formation of external physiological symptoms of the entire human body. With the development of modern technology, the observation of demeanor has entered a new stage of multimodal quantification. The quantifiable results of demeanor observation with the help of scientific and technological instruments play an important role in judicial work such as search, interrogation, and evidence review. The use of man-made reasoning in the field of legal settling has become increasingly broad. Specialists are giving increasingly more consideration to the securing of modular proof. This paper proposes the development of a component examination model for modular proof examination applications. The technique for this paper is to apply the guileless Bayes strategy, propose a superior information mining calculation, and lay out a model for proof observation and examination. The function of these methods is to systematically explore the basic theoretical issues of demeanor evidence based on the status quo of judicial application of demeanor evidence. Through the prediction of individual demeanor based on data mining algorithm, the evidence analysis model is designed, the neurobiological experiment is carried out, and the demeanor evidence animal stress model is constructed to verify the scientific basis of multidemeanor evidence observation. The experimental results showed that after repeated stimulation of SD rats, the maximum changes in the expression of HSP70 gene and SAA gene were 10.77 and 14.1 respectively, reflecting the high reliability of demeanor evidence biological experiments. The model can improve the accuracy of evidence use, and the correct use of demeanor evidence can realize the true litigation value of intelligent justice and the concept of human rights protection, and promote the construction of intelligent justice. [ABSTRACT FROM AUTHOR]

This brief commentary adds to the recent study by paper by Yokomitsu, Kono and Takada (2022). Their study examined social presence in gambling by experimentally investigating the effects of the presence of other people on risky betting among high-risk gamblers. This commentary argues that the paper by Yokomitsu et al. provided a highly selective review on available studies and omitted many of the key studies in the area of social presence and social facilitation in which their findings could have been compared. The commentary also briefly outlines a number of studies that have I co-authored in this area over the past three decades using a variety of different methodologies (e.g., non-participant observation studies, experiments, data mining of account-based tracking data), none of which were mentioned by Yokomitsu et al. despite their clear relevance to this area. [ABSTRACT FROM AUTHOR]

The publication presents an introductory paper to the Special issue of the international journal Pattern Recognition and Image Analysis: Advances in Mathematical Theory and Applications of the Russian Academy of Sciences. The main scientific results of the 8th International Workshop "Image Mining: Theory and Applications," held on August 21, 2022, Montreal, Canada, are presented. Historical information is given on this series of international workshops, and their significant role in the development of the theory and practice of automation of image analysis, pattern recognition, and artificial intelligence is emphasized. The list of papers of the Special issue of PRIA, prepared based on the invited and regular papers selected and recommended for publication by the Program Committee of the IMTA-VIII-2022, is presented. [ABSTRACT FROM AUTHOR]

The study of topic evolution aims to analyze the behavior of different research fields by utilizing various features such as the relationships between articles. In recent years, many published papers consider more than one field of study which has led to a significant increase in the number of inter-field and interdisciplinary articles. Therefore, we can analyze the similarity/dissimilarity and convergence/divergence of research fields based on topic analysis of the published papers. Our research intends to create a methodology for studying the evolution of the research fields. In this paper, we propose an embedding approach for modeling each research topics as a multidimensional vector. Using this model, we measure the topic's distances over the years and investigate how topics evolve over time. The proposed similarity metric showed many advantages over other alternatives (such as Jaccard similarity) and it resulted in better stability and accuracy. As a case study, we applied the proposed method to subsets of computer science for experimental purposes, and the results were quite comprehensible and coherent. [ABSTRACT FROM AUTHOR]

Performance prediction is an important research facet of educational data mining. Most models extract student behavior features from campus card data for prediction. However, most of these methods have coarse time granularity, difficulty in extracting useful high-order behavior combination features, dependence on 6 historical achievements, etc. To solve these problems, this paper utilizes prediction of grade point average (GPA prediction) and whether a specific student has failing subjects (failing prediction) in a term as the goal of performance prediction and proposes a comprehensive performance prediction model of college students based on behavior features. First, a method for representing campus card data based on behavior flow is introduced to retain higher time accuracy. Second, a method for extracting student behavior features based on multi-head self-attention mechanism is proposed to automatically select more important high-order behavior combination features. Finally, a performance prediction model based on student behavior feature mode difference is proposed to improve the model's prediction accuracy and increases the model's robustness for students with significant changes in performance. The performance of the model is verified on actual data collected by the teaching monitoring big data platform of Xi'an Jiaotong University. The results show that the model's prediction performance is better than the comparison algorithms on both the failing prediction and GPA prediction. [ABSTRACT FROM AUTHOR]