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Paper recommendation with personalized methods helps researchers to track the latest academic trends and master cutting-edge academic trends efficiently. Meanwhile, the methods of previous paper recommendation suffer from three problems: data sparsity of content-based and collaborative filtering methods; Graph-based recommendations do not fully consider the personalized information of authors and their articles; Cold start based on deep learning. To overcome those difficulties, we propose a personalized paper recommendation method based on a knowledge graph and Transformer encoder (KGTE) with a self-attention mechanism. Firstly, we add auxiliary information (article title, publication year, citation times, and abstract) as attributes to the nodes of knowledge graph(KG), which contain author, digital object unique identifier(DOI) and keywords. Secondly, BERT is used to represent the semantic information features of the article and Transformer is introduced to fully integrate the feature context. After that, by using RippleNet, we traverse the knowledge graph, filter the user preference distribution and form a set of pre recommended nodes with multi_hop nodes. Finally, the prediction layer sorts the set and gets a Top_n paper recommendation. In the experiments on the DBLP and Aminer datasets, the precision value of KGTE improved by an average of 2.59% over the existing baseline methods DER and 4.23% improvement in NDCG. [ABSTRACT FROM AUTHOR]

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]

During the past decades, several academic paper recommendation systems were introduced in literature aiming to assist users in finding relevant papers close to their needs. In particular, those models have used users' past preferences combined with papers' side information to personalize their recommendations. Unfortunately, the majority of those models fail to utilize the implicit relationship between the crucial elements along with the semantic relations of the nodes in a Heterogeneous Information Network (HIN), which can further improve the accuracy of the models. In this paper, we propose a research article recommendation model that aims to tackle those issues by exploiting both textual and graph representations, simultaneously. The model employs SPECTER document embedding to learn context-preserving research article representations. In particular, it learns the features from a HIN, which is a knowledge graph of the node entities and their relationships. Then, an attention module strengthens the semantic feature extraction process and learns enhanced representations. The combined semantic and structural features are then provided as input to a Deep Neural Network (DNN) in order to learn high-level representations of query and candidate papers. We have evaluated our model against state-of-the-art models over two popular datasets. The results indicate a significant improvement in terms of MAP, recall, and MRR. [ABSTRACT FROM AUTHOR]

Real-world information is often characterized by uncertainty and partial reliability, which led Zadeh to introduce the concept of Z-numbers as a more appropriate formal structure for describing such information. However, the computation of Z-numbers requires solving highly complex optimization problems, limiting their practical application. Although linguistic Z-numbers have been explored for their computational straightforwardness, they lack theoretical support from Z-number theory and exhibit certain limitations. To address these issues and provide theoretical support from Z-numbers, we propose a Z-number linguistic term set to facilitate more efficient processing of Z-number-based information. Specifically, we redefine linguistic Z-numbers as Z-number linguistic terms. By analyzing the hidden probability density functions of these terms, we identify patterns for ranking them. These patterns are used to define the Z-number linguistic term set, which includes all Z-number linguistic terms sorted in order. We also discuss the basic operators between these terms. Furthermore, we develop a multi-criteria group decision-making (MCGDM) model based on the Z-number linguistic term set. Applying our method to predict the acceptance of academic papers, we demonstrate its effectiveness and superiority. We compare the performance of our MCGDM method with five existing Z-number-based MCGDM methods and eight traditional machine learning clustering algorithms. Our results show that the proposed method outperforms others in terms of accuracy and time consumption, highlighting the potential of Z-number linguistic terms for enhancing Z-number computation and extending the application of Z-number-based information to real-world problems. [ABSTRACT FROM AUTHOR]

In the field of deep matching, a large amount of noisy data in Chinese long texts affects the matching effect. Most long-form text matching models use all text data indiscriminately, which results in a large amount of noisy data, and thus the PageRank algorithm is combined with Transformer to filter noise. For sentence-level noise detection, after calculating the overlap rate of words to evaluate the similarity, a sentence-level relationship graph is constructed and filtered by using the PageRank algorithm; for word-level noise detection, based on the attention score in Transformer, a word graph is established, then the PageRank algorithm is executed on graph, combined with self-attention weights, to select keywords to highlight topic relevance, the noisy words are filtered sequentially at different layers in the module, layer by layer. In addition, during the model training, PolyLoss is applied to replace the traditional binary Cross-Entropy loss function, thus reducing the difficulty of hyperparameter tuning. Finally, a better filtering strategy is proposed and experiments are conducted to verify it on two Chinese long-form text matching datasets. The result shows that the matching model based on the noise filtering strategy of this paper can better filter the noise and capture the matching signal more accurately. [ABSTRACT FROM AUTHOR]

Recommender systems have been increasingly popular in entertainment and consumption, and they are evident in academics, especially for applications that suggest submitting scientific articles to scientists. However, due to the various acceptance rates, impact factors, and rankings in different publishers, searching for a proper venue or journal to submit a scientific work usually takes a lot of time and effort. In this paper, we aim to present two new approaches extended from our paper (Huynh et al. 2021) presented at IAE/AIE 2021. In the first approach, we employ RNN structures besides using Conv1D. In the second approach, we introduce a new method, using DistillBert for two cases of uppercase and lowercase words. It can help vectorize features (such as Title, Abstract, and Keywords) and then use Conv1d to perform feature extraction. Furthermore, we propose a new calculation method for similarity score for Aim & Scope with other features (we name this approach is DistilBertAims). It can help keep the weights of similarity score calculation continuously updated and then continue to fit more data. The experimental results show that the second approach could obtain a better performance, which are 62.46%, 90.32%, 94.89%, 97.96% while the best performance in previous studies barely gained 50.02%, 78.89%, 86.27%, 93.23% in terms of Top K Accuracy (K = 1, 3, 5, 10). Interestingly, our best approach in this paper is higher than 12.44% the best of the previous study (Huynh et al. 2021) in terms of the Top 1 accuracy, which was presented in the conference IEA/AIE 2021. [ABSTRACT FROM AUTHOR]

The process of retrieving suitable papers which are related to an interesting topic while doing research normally takes a lot of time and effort. Citation recommendation is frequently used to solve this problem by automatically suggesting a list of candidate papers that should match with the user's references or topics of interest. Applying the recent research results of deep learning in multiple disciplines, the performance of citation recommendation systems has been significantly improved with the facilitation of powerful deep neural data analysis and representation learning techniques. However, most of the recent Neural Citation Network (NCN) model-based techniques still encounter limitations related to the capability of integrating auxiliary information to assist the citation contextual learning process. Thus, in this paper, we propose a novel context-aware NCN-based model with the extra textual data integration and Bidirectional Encoder Representations from Transformers (BERT) model to improve the performance of the citation recommendation task. To do this, an extensive deep neural auto-encoding mechanism with a self-attention-based mechanism is utilized in our proposed model to flexibly learn both associated textual and citation contextual data in the given dataset. These enriched citation contextual information representations are then utilized to improve the performance of the citation recommendation task as the end-to-end neural learning process. Extensive experiments in the standard arXiv dataset show the effectiveness and good performance of the proposed model. [ABSTRACT FROM AUTHOR]

This article is part of the Topical Collection on Computer Science This special issue mainly aims to provide a platform for the related researchers and practitioners to communicate the new advancement and successful applications of multi-view learning. The paper entitled "Multi-View Attention-Convolution Pooling Network for 3D Point Cloud Classification" by Wenju Wang, Tao Wang and Yu Cai propose a multi-view attention-convolution pooling network on multiple 2D views for 3D point cloud classification task. The paper entitled "Semi-Supervised Multi-View Binary Learning for Large-Scale Image Clustering" by Mingyang Liu, Zuyuan Yang, Wei Han, Junhang Chen and Weijun Sun design a multi-view binary learning method to deal with large-scale clustering problem in semi-supervised way. [Extracted from the article]

Around a third of the total population of Europe suffers from mental disorders. The use of electroencephalography (EEG) together with Machine Learning (ML) algorithms to diagnose mental disorders has recently been shown to be a prominent research area, as exposed by several reviews focused on the field. Nevertheless, previous to the application of ML algorithms, EEG data should be correctly preprocessed and prepared via Feature Engineering (FE). In fact, the choice of FE techniques can make the difference between an unusable ML model and a simple, effective model. In other words, it can be said that FE is crucial, especially when using complex, non-stationary data such as EEG. To this aim, in this paper we present a Systematic Mapping Study (SMS) focused on FE from EEG data used to identify mental disorders. Our SMS covers more than 900 papers, making it one of the most comprehensive to date, to the best of our knowledge. We gathered the mental disorder addressed, all the FE techniques used, and the Artificial Intelligence (AI) algorithm applied for classification from each paper. Our main contributions are: (i) we offer a starting point for new researchers on these topics, (ii) we extract the most used FE techniques to classify mental disorders, (iii) we show several graphical distributions of all used techniques, and (iv) we provide critical conclusions for detecting mental disorders. To provide a better overview of existing techniques, the FE process is divided into three parts: (i) signal transformation, (ii) feature extraction, and (iii) feature selection. Moreover, we classify and analyze the distribution of existing papers according to the mental disorder they treat, the FE processes used, and the ML techniques applied. As a result, we provide a valuable reference for the scientific community to identify which techniques have been proven and tested and where the gaps are located in the current state of the art. [ABSTRACT FROM AUTHOR]

Large Language Models (LLMs) are a type of machine learning that handles a wide range of Natural Language Processing (NLP) scenarios. Recently, in December 2022, a company called OpenAI released ChatGPT, a tool that, within a few months, became the most representative example of LLMs, automatically generating unique and coherent text on many topics, summarising and rewriting it, or even translating it to other languages. ChatGPT originated some controversy in academia since students can generate unique text for writing assessments being sometimes extremely difficult to distinguish whether it comes from ChatGPT or a person. In research, some journals specifically banned ChatGPT in scientific papers. However, when used correctly, it becomes a powerful tool to rewrite, for instance, scientific papers and, thus, deliver researchers' messages in a better way. In this paper, we conduct an empirical study of the impact of ChatGPT in research. We downloaded the abstract of over 45,000 papers from over 300 journals from Dec 2022 and Feb 2023 belonging to different research editorials. We use four of the most known ChatGPT detection tools and conclude that ChatGPT played a role in around 10% of the papers published in every editorial, showing that authors from different fields have rapidly adopted such a tool in their research. [ABSTRACT FROM AUTHOR]

After emergencies occur, decision-makers can reference historical cases with similar causes to take similar emergency response measures. However, information about emergencies is usually recorded and stored in textual form, and it is difficult for decision-makers to obtain effective information from large amounts of textual data and make decisions that balance various factors. To address these issues, this paper presents an emergency assisted decision-making model based on a knowledge graph and many-objective optimization. First, we preprocess the information by extracting entities to construct a knowledge graph of emergencies to make the event information more structured and easier to use. A knowledge graph is also used to narrow the range of matching historical events. Second, we construct a many-objective model with four objectives: similarity, diversity, processing time, and resource cost. Finally, combining the model characteristics, we design genetic operations for duplicate location matching and substitution removal strategies to obtain the nonrepetitive VaEA algorithm. This algorithm is used to optimize the model and generate a list of reference cases in the reduced matching range to provide rescue strategy suggestions for the current situation. The experimental results show that the algorithm outperforms the comparison algorithms under all four evaluation metrics. This indicates that the method in this paper can match the higher-quality historical emergency solutions applicable to the current decision-making situation in the case base and provide support for decision-makers to respond reasonably in emergencies. [ABSTRACT FROM AUTHOR]

The prediction of current scientific impact of papers and authors has been extensively studied to help researchers find valuable papers and recent research directions, also help policymakers make recruitment decisions or funding allocation. However, how to accurately evaluate the future impact of them, especially for new papers and young researchers, is the focus of scientific impact prediction research, and is less explored. Existing graph-based methods heavily depend on the global structure information of heterogeneous academic network and ignore the local structure information and text information, which may provide important clues to identify influential papers and authors with novel perspective. In this paper, we propose a hybrid model called ESMR to predict the future influence of papers and authors by mainly exploiting these information mentioned above. Specifically, we first put forward a novel network embedding-based model, which can capture not only the local structure information, but also the text information of papers into a unified embedding representation. Then, the future impact of papers and authors is mutually ranked by integrating the learned embedding representations into a multivariate random-walk model. Empirical results on two real datasets demonstrate that the proposed method significantly outperforms the existing state-of-the-art ranking methods. [ABSTRACT FROM AUTHOR]

This paper proposes an innovative image encryption algorithm that leverages partial face recognition and DNA diffusion, building upon advancements in chaotic image encryption and face recognition technologies. The key is generated using the secure SHA-512 algorithm, while the chaotic sequence for the encryption algorithm is derived from the Chen system. This paper begins by employing facial recognition technology to identify and focus on specific facial regions within an image. Subsequently, DNA diffusion is applied to encrypt the recognized face segment via a diffusion-scrambling encryption process. Following this, the entire image undergoes diffusion utilizing the devised diffusion rules. Finally, a uniform scrambling operation based on Fisher-Yeats is performed on the entire image. The experimental results and safety analysis show that the information entropy can exceed 7.9973, the average correlation can be less than 0.0023, the pixel number change rate can reach 99.6052%, and the unified average change intensity can reach 33.4644%, all of which are very close to the ideal values. [ABSTRACT FROM AUTHOR]

In this work, an improved moth-flame optimization algorithm is proposed to alleviate the problems of premature convergence and convergence to local minima. From the perspective of diversity, an inertia weight of diversity feedback control is introduced in the moth-flame optimization to balance the algorithm's exploitation and global search abilities. Furthermore, a small probability mutation after the position update stage is added to improve the optimization performance. The performance of the proposed algorithm is extensively evaluated on a suite of CEC'2014 series benchmark functions and four constrained engineering optimization problems. The results of the proposed algorithm are compared with the ones of other improved algorithms presented in literatures. It is observed that the proposed method has a superior performance to improve the convergence ability of the algorithm. In addition, the proposed algorithm assists in escaping the local minima. [ABSTRACT FROM AUTHOR]

With the development of image feature matching technology, feature matching algorithms based on deep learning have achieved excellent results, but in scenarios with low texture or extreme perspective changes, the matching accuracy is still difficult to guarantee. In this paper, a superresolution reconstruction method based on a Residual-ESPCN (efficient subpixel convolutional neural network) approach is proposed based on LoFTR (local feature matching with transformers). The superresolution method is used to improve the interpolation method used in ASFF (adaptive spatial feature fusion) to increase the image resolution, enhance the detailed information of the image, and make the extracted features richer. Then, ASFF is introduced into the local feature extraction module of LoFTR, which can alleviate the inconsistency problem of information transmission between different scale features of the feature pyramid and lessen the amount of information lost during transmission from low- to high-resolution levels. Moreover, to improve the adaptability of the algorithm to different scenarios, OTSU is introduced to adaptively calculate the threshold of feature matching. The experimental results show that in different indoor or outdoor scenarios, our proposed algorithm for matching features can effectively improve the adaptability of feature matching and can achieve good results in terms of the area under the curve (AUC), accuracy and recall. [ABSTRACT FROM AUTHOR]

The paper presents a novel active learning algorithm (ALSCN) consisting of two networks, a convolutional neural network and a self-correcting neural network (SCN). The paper titled " B One-class ensemble classifier for data imbalance problems b " by Hayashi et al. presents the One-class Classification (OCC) and One-class ensemble (OCCE) for data imbalance classification. [Extracted from the article]

In the field of food safety, milk has become an indispensable beverage in people's lives. Therefore, it is of great significance to detect milk freshness. Starting from the milk freshness detection, the perspective can be extended to liquid detection. Liquid detection has attracted much attention and has been applied in many fields. However, current liquid detection methods are either contact detection methods that can lead to sample damage, or require specialized instruments, expertise for operation or cumbersome hardware deployment. This paper introduces a non-contact milk freshness detection method based on image features and tensor construction. Unlike existing liquid detection methods, our method relies on ubiquitous WiFi signals to achieve non-contact and non-invasive milk freshness detection. The design intuition is that the WiFi signals will lead to different multipath propagation when passing through milk with different freshness, which can be used to detect milk freshness. We use existing commercial devices to collect WiFi signal data of milk with different freshness, denoise the collected data and transform the denoised data into multiple types of time-frequency images and spatial-temporal images, and input the images into the deep learning network to extract image features that contain richer and more comprehensive information, and then utilize the extracted image features to perform tensor construction to better retain the original time, frequency and spatial feature information, and apply 3D convolutional layer and fully connected layers for milk freshness detection. The experimental results show that our method achieves a high accuracy of 93.25% in detecting milk freshness. [ABSTRACT FROM AUTHOR]

The lubrication system supplies lubrication and cleans the rotating parts and contacting machinery during the operation of an aero-engine. It is crucial to maintain an adequate amount of lubricant by predicting and analyzing the consumption rate to ensure endurance and maintenance programs are effective. This paper examines the combination of temporal and non-temporal data that impact the characteristic parameters of lubricant consumption rate in aero-engines. Our study focuses on the merging of LSTM (Long Short-Term Memory) + LightGBM (Light Gradient Boosting Machine) + CatBoost, and uses KPCA dimensionality reduction optimization, along with Stacking for the fusion of a multi-feature regression prediction algorithm. On the one hand, this study utilizes integrated learning to fuse feature extractions from LSTM for temporal information and non-temporal information by GDBT (Gradient Boosting Decision Tree). This approach considers the trend and distribution of feature samples to develop a more robust feature extraction method. On the other hand, the integrated learning framework incorporates multi-decision making and feature importance extraction to strengthen the mapping relationship with the predicted output of lubrication oil consumption rate, enabling regression prediction. The algorithm for regression prediction has been executed and the results indicate a final regression prediction MAPE (Mean Absolute Percentage Error) of less than 3%. MSE and RMSE reached 1.28% and 1.33%, the results are in an ideal state. The algorithms used in this paper will be applied in the future to aero-engine lubricant systems and eventually to engines in general. [ABSTRACT FROM AUTHOR]

With the acceleration of urbanization, there is an urgent need to build more transportation facilities to alleviate travel pressure. However, during the construction of a subway station, a large amount of muck is generated and must be transported to a treatment center. In response to transportation policies, this paper establishes a regional and time-limited transportation model for muck trucks based on their departure time points. The model aims to dispatch the least number of vehicles and complete all transportation tasks as quickly as possible, taking into account constraints such as restricted travel time. This paper uses the NSGA-II algorithm with multi-segment encoding to solve this problem, and numerical experiments are conducted to analyze the performance of the proposed method. The results indicate that the improved algorithm has better convergence and distribution than the standard NSGA-II. The study also validates the effectiveness of the proposed method through a real-world example of muck transportation at subway stations in a specific city. The collaborative scheduling schemes developed through the methods proposed in this paper have effectively avoided the travel restriction period, providing managers with multiple decision-making options. [ABSTRACT FROM AUTHOR]

Schizophrenia (SZ) is a prevalent mental disorder characterized by cognitive, emotional, and behavioral changes. Symptoms of SZ include hallucinations, illusions, delusions, lack of motivation, and difficulties in concentration. While the exact causes of SZ remain unproven, factors such as brain injuries, stress, and psychotropic drugs have been implicated in its development. SZ can be classified into different types, including paranoid, disorganized, catatonic, undifferentiated, and residual. Diagnosing SZ involves employing various tools, including clinical interviews, physical examinations, psychological evaluations, the Diagnostic and Statistical Manual of Mental Disorders (DSM), and neuroimaging techniques. Electroencephalography (EEG) recording is a significant functional neuroimaging modality that provides valuable insights into brain function during SZ. However, EEG signal analysis poses challenges for neurologists and scientists due to the presence of artifacts, long-term recordings, and the utilization of multiple channels. To address these challenges, researchers have introduced artificial intelligence (AI) techniques, encompassing conventional machine learning (ML) and deep learning (DL) methods, to aid in SZ diagnosis. This study reviews papers focused on SZ diagnosis utilizing EEG signals and AI methods. The introduction section provides a comprehensive explanation of SZ diagnosis methods and intervention techniques. Subsequently, review papers in this field are discussed, followed by an introduction to the AI methods employed for SZ diagnosis and a summary of relevant papers presented in tabular form. Additionally, this study reports on the most significant challenges encountered in SZ diagnosis, as identified through a review of papers in this field. Future directions to overcome these challenges are also addressed. The discussion section examines the specific details of each paper, culminating in the presentation of conclusions and findings. [ABSTRACT FROM AUTHOR]

To solve the tracking accuracy degradation problem in scenarios with large displacements or nonrigid motion during target tracking, this paper proposes an optical flow estimation method based on multiscale anisotropic convolution. The network structure is improved in a step-by-step manner by extracting the data flow from the network according to the observed features. For the low-level neural network, a layered multiscale structure is used to build a cascade network by using hybrid dilated convolution to obtain feature information at different scales while ensuring the tracking accuracy. For the upper-layer neural network, hybrid inflated deformable convolution is used by learning the contextual long-range correlations and multidirectional adaptive offsets of features. Experiments are conducted on the Flying Chairs, KITTI, and MPI datasets. The results show that compared with various popular algorithm methods, the model in this paper reduces endpoint errors while retaining edge information in regions with large displacements or nonrigid motion. Code is available at https://github.com/yifanna/MACFlow-pytorch. [ABSTRACT FROM AUTHOR]

Simultaneous Localization and Mapping (SLAM) is one of the research hotspots in the field of robotics, and it is also a prerequisite for autonomous robot navigation. The localization accuracy and stability of traditional SLAM based on static scene assumption declines due to the interference of dynamic objects. To solve the problem, this paper proposes a semantic SLAM system for dynamic environments based on object detection named DO-SLAM. Firstly, DO-SLAM uses YOLOv5 to identify objects in dynamic environments and obtains the semantic information; Then, combined with the outlier detection mechanism proposed in this paper, the dynamic objects are effectively determined and the feature points on the dynamic objects are eliminated; The combination method can reduce the interference of dynamic objects to SLAM, and improve the stability and localization accuracy of the system. At the same time, a static dense point cloud map is constructed for high-level tasks. Finally, the effectiveness of DO-SLAM is verified on the TUM RGB-D dataset. The results show that the Absolute Trajectory Error (ATE) and Relative Pose Error (RPE) are reduced, indicating that DO-SLAM can reduce the interference of dynamic objects. [ABSTRACT FROM AUTHOR]

Fault diagnosis of industrial processes plays an important role in avoiding heavy losses and ensuring production safety. Complex industrial processes often have many working conditions, and the actual industrial process often concentrates on certain working conditions. As a result, the running time of some working conditions is shorter, so the data of these conditions are difficult to obtain. However, almost all fault diagnosis methods based on Deep Learning (DL) requires a large amount of data. Therefore, it is a big challenge to realize the fault diagnosis of few-shot industrial processes. In order to solve these problems, this paper proposes a Deep Feature Transfer Fusion (DFTF) framework based on hybrid domain transfer learning. The purpose is to take few-shot working conditions as the target domain and carry out fault diagnosis for them. As the features of industrial process images are more complex, this paper introduces Pyramid Hybrid Vision Transformer (PHVT) model, which have stronger feature extraction capabilities and spatial perception, as feature extraction module. In order to improve the transferability of the model, this paper introduces the In-Cross Domain Hybrid Transfer Learning (ICTL) method. By fusing the general object features from ResNet50 which pre-trained under public dataset of common object and the features extracted from PHVT which pre-trained under industrial dataset of multiple working conditions, the adaptability of the model to different scenes is enhanced. The experimental results based on Pronto dataset of Process System Engineering lab of Cranfield University show that the proposed transfer learning method has excellent performance. [ABSTRACT FROM AUTHOR]

Privacy preserving utility mining (PPUM) aims to solve the problem of sensitive information leakage in utility pattern mining. In recent years, researchers have proposed algorithms to solve the privacy-preserving problem. However, these algorithms have high side effects, long sanitization time, and computational complexity. Although the FPUTT algorithm reduces the number of database scans, tree construction and traversal still take much time. The paper proposes a fast utility-list dictionary algorithm (FULD). The utility-list dictionary consists of all sensitive items. Through dictionary lookup, sensitive items can be found and modified. In addition, the novel concepts of SINS and tns are proposed to reduce the side effects of the algorithm. In this paper, the experiments show that the FULD algorithm has good performance, such as running time and side effects. The running time of the FULD is 15–20 times shorter than the FPUTT algorithm. It performs well both on sparse and dense datasets. [ABSTRACT FROM AUTHOR]

Significant research has been carried out in the field of User Behavior Analysis, focused on understanding, modeling and predicting past, present and future behaviors of users. However, the heterogeneity of the approaches makes their comprehension very complicated. Thus, domain and Machine Learning experts have to work together to achieve their objectives. The main motivation for this work is to obtain an understanding of this field by providing a categorization of state-of-the-art works grouping them based on specific features. This paper presents a comprehensive survey of the existing literature in the areas of Cybersecurity, Networks, Safety and Health, and Service Delivery Improvement. The survey is organized based on four different topic-based features which categorize existing works: keywords, application domain, Machine Learning algorithm, and data type. This paper aims to thoroughly analyze the existing references, to promote the dissemination of state-of-the-art approaches discussing their strong and weak points, and to identify open challenges and prospective future research directions. In addition, 127 discussed papers have been scored and ranked according to relevance-based features: paper reputation, maximum author reputation, novelty, innovation and data quality. Both types of features, topic-based and relevance-based have been combined to build a similarity metric enabling a rich visualization of all considered publications. The obtained graphic representation provides a guide of recent advancements in User Behavior Analysis by topic, highlighting the most relevant ones. [ABSTRACT FROM AUTHOR]

Information exchange and dissemination happen among individuals in a group or social network, which is an inevitable process before the formation of the group and individual opinions. Opinion dynamics is a multidisciplinary research field that models information/opinion exchange processes to simulate the dynamic of opinions in networks with different scales and features. The research on the dynamic of opinions is complex because the involved interactions with individuals are complicated. Many pieces of research and reviews have been conducted on relative models focusing on diverse aspects. Unlike the existing studies, this paper develops a bibliometric analysis and brief introduction of opinion dynamics by science mapping from a relatively macroscopical perspective. Bibliometrics tools such as VOS Viewer and Cite Space are applied to figure out the collaborative relationship networks of countries/regions, organizations, and authors, the bibliographic coupling analysis of documents, and the detection and clusters of keywords. A basic and brief introduction of the Ising model, Voter model, Majority model, Sznajd model, and Bounded confidence model are also given. This paper is propaedeutic material for readers who want to grasp the fundamental knowledge of opinion dynamics on what it is, how it develops, what the hotspots are, which articles to read, and what the cooperative relationship looks like. [ABSTRACT FROM AUTHOR]

Currently, 2D anomaly detection has demonstrated outstanding performance. However, 2D images limit the improvement of anomaly detection accuracy without utilizing depth information. Therefore, this paper proposes a Dual Reconstruction viAInpainting Network for 3D industrial anomaly detection (DRAIN). Firstly, we design a 3D reconstruction network using an encoder-decoder-based U-shaped network for processing RGB images and depth images. Subsequently, accurate anomaly segmentation is implemented through a 3D segmentation network. We introduce a lightweight MLP module to enhance segmentation performance to capture long-range dependencies in the reconstructed images. Furthermore, we propose a dual attention-based information entropy fusion module to expedite feature fusion in the inference process, aiming for enhanced deployment in the industry. Extensive experiments demonstrate that DRAIN achieves a 94.3% AUROC on the 3D anomaly detection dataset MVTec 3D-AD, surpassing other research methods. Overall architecture for 3D industrial anomaly detection via dual reconstruction network [ABSTRACT FROM AUTHOR]

Camouflaged object detection (COD) is to distinguish the target objects with varied sizes and shapes from the low-contrast real-world scenarios. Although deep learning-based methods have made great progress, it is still challenging to accurately detect and segment the complete and edge-preserving camouflaged objects. In this paper, we propose a novel cross-level context supplement network (CCSNet) to effectively utilize cross-level features to provide additional information, which can compensate for the deficiencies of the current level potentials. Specifically, we develop a selective cross-level aggregation (SCA) module to fully explore the cross-level different but complementary cues to detect camouflaged objects with different scales. It makes each level of the network adaptively focus on the informative features with the assist of the channel dependencies and spatial relationship provided by the adjacent levels. Furthermore, considering that the camouflaged objects are hardly distinguishable from the backgrounds, we design a location and boundary supplement (LBS) module to directly incorporate the global and edge information to different levels, thus enhancing the spatial coherence of interior regions and reducing the uncertainty of boundary regions. Comprehensive experiments are conducted on four public datasets to demonstrate the effectiveness of our CCSNet. In addition, we apply our model to two other dense pixel prediction tasks to further demonstrate the effectiveness and generalization ability of our network. The code, trained model and predicted maps will be available upon acceptance of this paper for publication. [ABSTRACT FROM AUTHOR]

Skeleton-based action recognition methods have become a research hotspot due to their robustness against variations in lighting, complex backgrounds, and viewpoint changes. Addressing the issues of long-distance joint associations and time-varying joint correlations in skeleton data, this paper proposes a Multi-Stream Attention-Enhanced Recursive Graph Convolution method for action recognition. This method extracts four types of features from the skeleton data: joints, bones, joint movements, and bone movements. It models the potential relationships between non-adjacent nodes through adaptive graph convolution and utilizes a long short-term memory network for recursive learning of the graph structure to capture the temporal correlations of joints. Additionally, a spatio-temporal channel attention module is introduced to enable the model to focus more on important joints, frames, and channel features, further improving performance. Finally, the recognition results of the four branches are fused at the decision level to complete the action recognition. Experimental results on public datasets (UTD-MHAD, CZU-MHAD) and a self-constructed dataset (KTH-Skeleton) demonstrate that the proposed method achieves accuracies of 94.65%, 95.01%, and 97.50%, respectively, fully proving the good performance of the method. [ABSTRACT FROM AUTHOR]

Heterogeneous Graph Neural Networks represent a powerful approach to understand and utilize the intricate structures and semantics within complex graphs. When it comes to semi-supervised learning on graphs, the challenge lies in effectively leveraging labeled data to generalize predictions to unlabeled nodes. Traditional methods often fall short in fully utilizing labeled information, limiting their performance to the number of available labels. To overcome these limitations, in this paper, we propose a Semi-Supervised Heterogeneous Graph Contrastive Learning with Label-Guided (SSGCL-LG) model. SSGCL-LG tackles this challenge by fully integrating label information into the learning process through contrastive learning. Specifically, it constructs a label graph that incorporates both node and label representations, enhancing the supervised signal. Moreover, we propose a novel strategy for selecting positive and negative samples based on labels and meta-paths, effectively pulling positive samples closer together in the embedding space. To optimize node representations, SSGCL-LG combines contrastive loss with semi-supervised loss, enabling the model to learn from both labeled and unlabeled data. Extensive experiments on real-world datasets validate the effectiveness of our framework, demonstrating its superiority over existing methods. The code for this work is publicly available in the https://github.com/sun281210/SSGCL-LG. [ABSTRACT FROM AUTHOR]

In order to solve the problem that the Proximal Policy Optimization (PPO) algorithm is difficult to converge in the air-sea battle scenarios with high dynamics, strong interference, and complex state space, the Ray-LAPPO algorithm based on Long Short-Term Memory (LSTM) and Attention mechanism is proposed in this paper under the distributed training framework Ray. Firstly, the idea of Centralized Training Distributed Execution (CTDE) is adopted to extend the PPO algorithm to the field of multi-agent and the policy entropy is added to the loss function to encourage the exploration of agents; Secondly, the LSTM network is added to the actor and critic networks to explore the timing relationship between non-independent and identically distributed samples and improve the learning performance of the UAV; In addition, the Attention mechanism is introduced to obtain the states at different time steps and establish a weighted differentiation model of the final value function; Finally, the simulation experiments on the self-developed heterogeneous UAV collaborative decision-making environment show that Ray-LAPPO can get the most advanced performance in different scenarios, and also possesses potential value for large-scale real-world applications. [ABSTRACT FROM AUTHOR]

Identifying discriminative patterns, known as shapelets, within time series is a critical step in many time series classification tasks. A major limitation of shapelet learning is that often hindered by their unsupervised methods, treating shapelet learning as an unsupervised subsequence clustering process and discovery based on pre-defined metric, which performed sequentially. This sequential procedure presents challenges, as it fails to establish a direct connection between shapelets and samples, and lacks the capacity to explicitly incorporate label information. In this paper, we proposed a novel shapelet learning algorithm called Discriminative Shapelet Learning via Temporal Clustering and Matrix Factorization (DSLMF). DSLMF introduced a joint framework that combines matrix factorization and coherent temporal clustering to discovery salient and coherent feature subsets. To further enhance discriminability and prevent arbitrary shapelet shapes, DSLMF integrates a label-specific shapelet regularization as a guiding mechanism enabling the learning of shapelets optimized for higher classification performance. The proposed algorithm has shown to be effective for capturing the temporal cluster structure and interpretability of shapelet-based method. The results of experiments showcased in this paper highlight DSLMF's effectiveness in capturing temporal cluster structures and learning meaningful shapelets, ultimately leading to promising performance on benchmark datasets. [ABSTRACT FROM AUTHOR]

Temporal action detection is an important and fundamental video understanding task that aims to locate the temporal regions where human actions or events may occur and to identify the classes of actions in untrimmed videos. The main challenge of temporal action detection is that videos are usually of different durations and untrimmed. Although existing methods have achieved better results in recent years, there are still some challenges, such as a lack of full utilisation of video context features, insufficient accuracy of generated action boundaries and failure to consider the relationship between proposals. To address the above issues, this paper proposes a Multi-branch Boundary Generation Network (MBGNet) with temporal context aggregation. It improves the performance of temporal action proposal generation by exploiting rich temporal context features and complementary boundary generators.First, we propose a multi-path temporal context feature aggregation (MTCA) module to exploit "local and global" contextual temporal features for the generation of temporal action proposals. Second, in order to generate accurate action boundaries, we design a multi-branch temporal boundary detector (MBG) to optimise the prediction results by exploiting the complementary relationship between the two boundary detectors.In addition, to accurately predict the confidence of densely distributed proposals, we design a proposal relation-aware module (PRAM) that exploits global correlation for proposal relationship modelling. Experiments on the popular datasets ActivityNet1.3, THUMOS14, and HACS demonstrate the effectiveness of the method proposed in this paper on the task of temporal action proposal generation, which can generate action proposals with high precision and recall. Moreover, combining with existing action classifiers can also achieve better performance in temporal action detection.These results demonstrate the effectiveness of the method in this paper in improving the accuracy of temporal action proposal generation and detection. [ABSTRACT FROM AUTHOR]

This paper devises a neural-network-based data-driven (NN-DD) algorithm to address the trajectory tracking control (TC) of discrete non-affine MIMO systems with unknown models and repetitive operation patterns. Data-driven control no longer relies on the precise model of the controlled system, thereby breaking free from the limitations of model-based control strategies. Inspired by this, the primary objective of the algorithm is to ensure that the tracking error of the system is uniformly ultimately bounded through a data-driven approach. The algorithm is comprised of a DD modeling approach based on an enhanced stochastic configuration network (ESCN), and a control input solving approach based on radial basis function neural networks (RBFNNs). The numerical simulations indicate that the proposed algorithm achieves a decrease in the modeling error to 3.29 e - 7 and the tracking error to 1 e - 8 after just 20 iterations. In addition, the numerical simulations also demonstrate that the modeling algorithm based on an ESCN reduces the modeling errors by 48.11 % and 99.95 % respectively compared to the modeling algorithms using only stochastic configuration networks (SCNs) or extreme learning machines (ELMs). Regarding tracking errors, the proposed RBFNN-based controller reduces the tracking error by 100 % compared to the backpropagation NN (BPNN)-based controller. Furthermore, the robustness of the algorithm against time-varying interference is tested via the unmanned vehicle simulations. This paper covers several contributions: 1) The proposed algorithm is entirely DD and can directly establish the relationship between inputs and outputs. 2) The designed ESCN fully integrates the advantages of SCNs and ELMs, in contrast to simply combining basic algorithms. 3) The RBFNN-based controller is independent of the actual system structure and exhibits excellent generalization capabilities. [ABSTRACT FROM AUTHOR]

Furnace temperature and oxygen content are important parameters reflecting the combustion inside a circulating fluidized bed (CFB) boiler. Accurately predicting boiler output is a complex task due to the high noise and nonsmoothness of actual boiler input and output data. In this paper, a new hybrid convolutional neural network (CNN), bidirectional long short-term memory (biLSTM) network, and squeezing and excitation (SE) network prediction model is proposed to significantly improve the prediction accuracy of oxygen content and furnace temperature by combining the advantages of multiple deep learning networks. This network can extract spatiotemporal characteristics of input parameters such as coal feed to effectively predict boiler furnace temperature and oxygen content. CNNs can extract complex features such as dynamic and static nonlinearities between multiple variables affecting the furnace temperature and oxygen content, as well as high noise. The biLSTM network layer can efficiently handle the temporal information of irregular trends in modeling time series components; SE can extract the important information between channels through the feature relationships between channels for better overfeature extraction. The CNN-biLSTM-SE model can effectively solve the problem of nonlinear mapping complexity between inputs and outputs. Experiments show that the proposed CNN-biLSTM-SE model outperforms existing methods. The experimental results showed that the average MAPE errors for oxygen content prediction were CNN-biLSTM-SE (0.038), CNN-biLSTM with attention mechanism (AM) (0.043), CNN-biLSTM (0.051), CNN-LSTM (0.051), biLSTM (0.051), RNN (0.051), LSTM(0.0052), and CNN(0.0054). Extensive experiments in CFB boilers with oxygen content and furnace temperature show that the proposed CNN-biLSTM-SE model achieves better results in terms of goodness-of-fit, generalization ability and accuracy. [ABSTRACT FROM AUTHOR]

Communication networks are crucial components of the underlying digital infrastructure in any smart city setup. The increasing usage of computer networks brings additional cyber security concerns, and every organization has to implement preventive measures to protect valuable data and business processes. Due to the inherent distributed nature of the city infrastructures as well as the critical nature of its resources and data, any solution to the attack detection calls for distributed, efficient and privacy preserving solutions. In this paper, we extend the evaluation of our federated learning framework for network attacks detection and classification based on random forest. Previously the framework was evaluated only for attack detection using four well-known intrusion detection datasets (KDD, NSL-KDD, UNSW-NB15, and CIC-IDS-2017). In this paper, we extend the evaluation for attack classification. We also evaluate how adding differential privacy into random forest, as an additional protective mechanism, affects the framework performances. The results show that the framework outperforms the average performance of independent random forests on clients for both attack detection and classification. Adding differential privacy penalizes the performance of random forest, as expected, but the use of the proposed framework still brings benefits in comparison to the use of independent local models. The code used in this paper is publicly available, to enable transparency and facilitate reproducibility within the research community. [ABSTRACT FROM AUTHOR]

Deep Cross-Modal Hashing (DCMH) has garnered significant attention in the field of cross-modal retrieval due to its advantages such as high computational efficiency and small storage space. However, existing DCMH methods still face certain limitations: (1) they neglect the correlation between labels, while label features exhibit high sparsity; (2) they lack fine-grained semantic alignment; (3) they fail to effectively address data imbalance. In order to tackle these issues, this paper introduces a framework named Semantic-Alignment Transformer and Adversary Hashing for Cross-modal Retrieval (SATAH). To the best of our knowledge, this is the first attempt at the Semantic-Alignment Transformer algorithm. Specifically, this paper first designs a label learning network that utilizes a crafted transformer module to extract label information, guiding adversarial learning and hash function learning accordingly. Subsequently, a Balanced Conditional Generative Adversarial Network (BCGAN) is constructed, marking the first instance of adversarial training guided by label information. Furthermore, a Weighted Semi-Hard Cosine Triplet Constraint is proposed to better ensure high-ranking similarity relationships among all items. Lastly, considering the correlation between labels, a semantic-alignment constraint is introduced to handle label correlation from a fine-grained perspective, capturing similarity on a global scale more effectively. Extensive experiments are conducted on multiple representative cross-modal datasets. In experiments with 64-bit hash code length, SATAH achieves average mAP values of 84.75%, 68.87%, and 68.73% on MIR Flickr, NUS-WIDE, and MS COCO datasets, respectively, outperforming state-of-the-art methods. The code is available at https://github.com/Daydaylight/SATAH. [ABSTRACT FROM AUTHOR]

Healthcare Industry 4.0 refers to intelligent operation processes in the medical industry. With the development of information technology, large-scale group decision making (GDM), which allows a larger number of decision makers (DMs) from different places or sectors to participate in decision making, has been rapidly developed and applied in Healthcare Industry 4.0 to help to make decisions efficiently and smartly. To make full use of GDM methods to promote the developments of the medical industry, it is necessary to review the existing relevant achievements. Therefore, this paper conducts an overview to generate a comprehensive understanding of GDM in Healthcare Industry 4.0 and to identify future development directions. Bibliometric analyses are conducted in order to learn the development trends from published papers. The implementations of GDM methods in Healthcare Industry 4.0 are reviewed in accordance with the paradigm of the general GDM process, which includes information representation, dimension reduction, consensus reaching, and result elicitation. We also provide current research challenges and future directions regarding medical GDM. It is hoped that our study will be helpful for researchers in the field of GDM in Healthcare Industry 4.0. [ABSTRACT FROM AUTHOR]

Currently, cognitive services provide a more interactive method for understanding users' requirements via human-machine conversations. In other words, it has to capture users' requirements from their utterances and respond to them with relevant and suitable service resources. To this end, two phases must be applied: I.sequence planning and real-time detection of user requirements, and II.service resource selection and response generation. The existing works ignore the potential connection between these two phases. To model their connection, the two-phase requirement elicitation method is proposed. In Phase I, this paper proposes a user requirement elicitation framework (URef) to plan a potential requirement sequence grounded on the user profile and personal knowledge base before the conversation. It can also predict user's true requirement and judge whether the requirement is completed based on the user's utterance during the conversation. In Phase II, this paper proposes a response generation model based on attention, SaRSNet. It can select the appropriate resource (i.e., knowledge triple) in line with the requirement predicted by URef, and then generates a suitable response for recommendation. The experimental results on the open dataset DuRecDial have been significantly improved compared to the baseline, which proves the effectiveness of the proposed methods. (This Manuscript based partly on work [1] presented at conference (ICWS 2021).) [ABSTRACT FROM AUTHOR]

The decision-making of generator start-up sequence plays a pivotal role in the power system restoration process following the blackout. In this paper, an optimized deep Q-learning network (DQN) algorithm is proposed to address this challenge. The generator start-up process is modeled as a Markov Decision Process (MDP) based on its characteristics. The DQN is tasked with deciding both the generator start-up sequence and the corresponding restoration path. To address the limitations of DQN, such as low exploration efficiency and slow convergence, the study incorporates the Artificial Potential Field (APF) algorithm to refine the reward function of it. This integration results in the development of the F-DQN (APF-DQN) algorithm, which enhances training efficiency. The effectiveness of this proposed method is demonstrated through the IEEE 39-bus test system. The results reveal that the DQN algorithm is capable of efficiently solving the model of the generator start-up sequence after the blackout. Moreover, the F-DQN algorithm exhibits superior learning efficiency, faster convergence, and higher-quality optimal solutions compared to the DQN. This paper also discusses the applicability of this method under partial blackouts. When compared to other decision-making algorithms, the proposed method offers a restoration scheme that is both time-efficient and results in increased electricity generation. [ABSTRACT FROM AUTHOR]

Accurate cell segmentation is a pivotal step throughout the cervical cancer treatment continuum, encompassing early screening, guiding treatment decisions, and assessing long-term prognosis. Currently, in clinical practice, pathologists rely on microscopic examination of cell characteristics followed by manual annotation, leveraging their expert knowledge. Nonetheless, this approach is labor-intensive, time-consuming, and subject to subjectivity. While existing segmentation methods successfully delineate cell clusters, they struggle with single-cell segmentation in overlapping scenarios, often relying on pixel classification methods. This approach tends to produce discontinuous boundaries and incomplete contours. In this paper, we introduce a two-phase framework, Solo_GAN, capable of generating complete boundaries for single cells in overlapping cell clusters and complex backgrounds. In the first phase of our framework, we propose a target detection model based on YOLOv3 for identifying single-cell regions of interest (ROI) in Pap cervical images. In the second stage, the ROIs are fed into a novel network called the dual-domain mapping segmentation network, which is used to generate complete single-cell boundary maps. The process involves conversion between cervical cell images in different states, achieving the generation of single-cell boundaries while retaining the original features of the image. Our method has been extensively evaluated on a Hybrid cervical cell dataset (public and private sets) for its effectiveness. The results demonstrate that our approach consistently outperforms the state-of-the-art methods and proves highly effective. We provide visualizations of the output at each processing stage and compare them with mainstream methodologies. The cell segmentation method proposed in this paper holds considerable significance for clinical research on cells in different stages of cervical cancer. [ABSTRACT FROM AUTHOR]

Marine mammals are an important part of marine ecosystems, and human intervention seriously threatens their living environments. Few studies exist on the marine mammal call recognition task, and the accuracy of current research needs to improve. In this paper, a novel MG-ResFormer two-channel fusion network architecture is proposed, which can extract local features and global timing information from sound signals almost perfectly. Second, in the input stage of the model, we propose an improved acoustic feature energy fingerprint, which is different from the traditional single feature approach. This feature also contains frequency, energy, time sequence and other speech information and has a strong identity. Additionally, to achieve more reliable accuracy in the multiclass call recognition task, we propose a multigranular joint layer to capture the family and genus relationships between classes. In the experimental section, the proposed method is compared with the existing feature extraction methods and recognition methods. In addition, this paper also compares with the latest research, and the proposed method is the most advanced algorithm thus far. Ultimately, our proposed method achieves an accuracy of 99.39% in the marine mammal call recognition task. [ABSTRACT FROM AUTHOR]

Knowledge graph embedding converts knowledge graphs based on symbolic representations into low-dimensional vectors. Effective knowledge graph embedding methods are key to ensuring downstream tasks. Some studies have shown significant performance differences among various knowledge graph embedding models on different datasets. They attribute this issue to the insufficient representation ability of the models. However, what representation ability knowledge graph embedding models possess is still unknown. Therefore, this paper first selects three representative models for analysis: translation and rotation models in distance models, and the Bert model in neural network models. Based on the analysis results, it can be concluded that the translation model focuses on clustering features, the rotation model focuses on hierarchy features, and the Bert model focuses on word co-occurrence features. This paper categorize clustering and hierarchy as structure features, and word co-occurrence as semantic features. Furthermore, a model that solely focuses on a single feature will lead to a lack of accuracy and generality, making it challenging for the model to be applicable to modern large-scale knowledge graphs. Therefore, this paper proposes an ensemble model with structure and semantic features for knowledge graph embedding. Specifically, the ensemble model includes a structure part and a semantic part. The structure part consists of three models: translation, rotation and cross. Translation and rotation models serve as basic feature extraction, while the cross model enhances the interaction between them. The semantic part is built based on Bert and integrated with the structure part after fine-tuning. In addition, this paper also introduces a frequency model to mitigate the training imbalance caused by differences in entity frequencies. Finally, we verify the effectiveness of the model through link prediction. Experiments show that the ensemble model has achieved improvement on FB15k-237 and YAGO3-10, and also has good performance on WN18RR, proving the effectiveness of the model. [ABSTRACT FROM AUTHOR]

Mining user data and constructing web user profiles of older adults from the perspective of elderly services is conducive to understanding their behavioral habits, needs, and usage preferences on the web, which provides more targeted elderly care services. In this paper, IGA-SOMK + + , which is a novel clustering method for constructing web user profiles of older adults, is proposed based on the China Family Panel Studies (CFPS) survey data, which include 6596 older adults aged greater than 60 years. The selected data aspects include basic information, work situation, health situation, living habits, and web use services. To describe the web user profiles of older adults, a hybrid method based on improved genetic algorithm (IGA) feature selection, self-organizing feature maps (SOM), and K-means + + is proposed. Data on older adults' web use behaviors are first processed, and IGA is used for feature selection based on the adaptive crossover and mutation probabilities. SOM is then used to determine the initial center vectors of K-means + + for further clustering, which is referred to as SOMK + + (SOM-K-means + +). The results of IGA-SOMK + + are compared with those of the state-of-the-art methods, including the K-means, mini batch K-means, Agnes, K-modes, FCM, K-means + + , SOMK + + , and IHPSO-KM. In addition, the significance and robustness of IGA-SOMK + + are analyzed. The experimental results show that the IGA feature selection reduces the influence of the redundant feature factors and improves the performance of the clustering algorithm. SOMK + + overcomes the sensitivity of K-means to initial cluster centers. Moreover, IGA-SOMK + + has the best clustering effect among the compared algorithms in terms of silhouette coefficient (SC), calinski-harabaz (CH) index, and davies-bouldin (DB) metrics. For example, it increases the SC from 0.280 to 0.629. Finally, by analyzing the results, the user group of older adults is segmented to perform the deep mining of CFPS data, which verifies the feasibility of the user profile model. This paper summarizes the basic situation of the current web access of older adults in China in terms of web use services, as well as the importance of the web in their lives and in the information channels. It also provides suggestions for the current problems of older adults in accessing the web. [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]

The authors suggest to trade-off diagnosis quality versus runtime performance, exploiting learning for constraint ordering and where matrix factorization is used to estimate diagnoses. The final manuscript, "Applying matrix factorization to consistency-based direct diagnosis", authored by Seda Polat Erdeniz et al., deals with diagnosis as a basis for configuration problems when efficient solving is required in real-world applications. Screening all the accepted papers, the top 7 authors of the accepted papers were encouraged to submit their papers for this special issue, asking for substantial extensions to their submissions for creating manuscripts that are appropriate for being published in Applied Intelligence. [Extracted from the article]

As malicious code attacks continue to evolve, attackers leverage techniques like packing and code obfuscation to generate numerous variants, challenging traditional detection methods. Addressing the limitations of current deep learning-based malicious code classification approaches in feature extraction and accuracy, this paper introduces an innovative RGB visualization detection method based on a hybrid multi-head attention mechanism. Initially, a feature representation method utilizing RGB images is introduced. This approach focuses on semantic relationships between a malware's binary information, assembly details, and API data, generating images with richer textural information. This technique effectively uncovers the deep dependencies between the original and variant versions of malicious code, providing stronger support for subsequent classification tasks. Furthermore, to tackle the issues of malware encryption and obfuscation, a deep neural network framework is adopted, incorporating a modular design philosophy and integrating a multi-head attention mechanism. This design not only enhances the expressiveness of critical features but also helps the model better focus on key aspects of the malicious code, thereby improving classification accuracy. Through comparative experiments and in-depth analysis, the effectiveness and superiority of the proposed RGB visualization method and MSA-ResNet model in the field of malicious code variant classification are validated. The accuracy rates achieved on the Kaggle and DataCon datasets are 99.49% and 97.70%, respectively, representing significant improvements over other methods. This approach demonstrates strong generalization capabilities and resistance to obfuscation, offering a new and effective tool for malicious code detection. [ABSTRACT FROM AUTHOR]

Recent years have witnessed the rapid development of the China's stock market, but investment risks have also emerged. Stock price is always unstable and non-linear, affected not only by historical transaction data but also by national policies, news, and other data. Stock price and textual data are beginning to be employed in the prediction process. However, the challenge lies in effectively integrating feature information derived from stock price and textual information. To address the problem, in this paper, this paper proposes a Hybrid Data-driven Multi-task Learning(HDML) framework to predict stock price. HDML adopts hybrid data as model input, mining the transaction and capital flow data information in the stock market and considering the impact of investors' emotions on the stock market. In addition, we incorporate multi-task learning, which predicts the closing price range of stock based on structured data and then corrects the prediction results through investors' comment text data. HDML effectively captures the relationship between different modal data through multi-task learning and achieve improvements on both tasks. The experimental results show that compared with previous work, HDML reduces the RMSE of the evaluation set by 12.14% and improves the F1 score by an average of 13.64% at the same time. Moreover, value at risk (VaR), together with the HDML model, can help investors weigh the potential gains against the associated risks. [ABSTRACT FROM AUTHOR]

In the cloud computing domain, significant strides have been made in performance prediction for cloud workflows, yet link prediction for cloud workflows remains largely unexplored. This paper introduces a novel challenge: joint node and link prediction in cloud workflows, with the aim of increasing the efficiency and overall performance of cloud computing resources. GNN-based methods have gained traction in handling graph-related tasks. The unique format of the DAG presents an underexplored area for GNNs effectiveness. To enhance comprehension of intricate graph structures and interrelationships, this paper introduces two novel models under the DAGCN framework: DAG-ConvGCN and DAG-AttGCN. The former synergizes the local receptive fields of the CNN with the global interpretive power of the GCN, whereas the latter integrates an attention mechanism to dynamically weigh the significance of node adjacencies. Through rigorous experimentation on a meticulously crafted joint node and link prediction task utilizing the Cluster-trace-v2018 dataset, both DAG-ConvGCN and DAG-AttGCN demonstrate superior performance over a spectrum of established machine learning and deep learning benchmarks. Moreover, the application of similarity measures such as the propagation kernel and the innovative GRBF kernel-which merges the graphlet kernel with the radial basis function kernel to accentuate graph topology and node features-reinforces the superiority of DAGCN models over graph-level prediction accuracy conventional baselines. This paper offers a fresh vantage point for advancing predictive methodologies within graph theory. [ABSTRACT FROM AUTHOR]

In recent years, factorization combined with attribute information has played an important role in attribute community detection. However, previous studies focused more on connecting the original shallow topological and attribute data. They ignored the potential representation structure of the network. To solve the problem that shallow information cannot fully represent the network structure, this paper proposes an attribute node classification method based on Attribute Edges weights Fusion and graph Embedding Factorization, called AEFEF. First, AEFEF converts topological information and attribute information into corresponding matrices representing node associations. Then, AEFEF constructs a new adjacency matrix by increasing the weights of shared edges between the topology structure and attribute structure. This operation can strengthen the tightness between nodes. Second, to explore the potential community structure, feature embedding is obtained by factorizing the attribute similarity matrix. Meanwhile, the new adjacency matrix is designed as a weight matrix to make the feature embedding between related nodes more similar. Finally, semi Non-negative Matrix Factorization (NMF) is introduced to modify the feature embedding by converting the negative values into positive values. Then the embedding is factorized to generate the membership matrix. At the same time, the network with a rich structure is decomposed with topological data as the main component. Otherwise, attribute information is the main component of NMF used to increase the accuracy of node classification. AEFEF is compared with 10 state-of-the-art algorithms on 7 real network datasets. The results reveal that AEFEF can improve the precision of attribute community detection. [ABSTRACT FROM AUTHOR]