Reviewerly/exHarmony huggingface dataset:
Behavior sequence aggregation and attention mechanism-based interest recommendation

Deep Interest Networks (DINs) capture potential user interests by mining long-term and short-term behavioral sequences of users, thereby predicting the information that users are interested in. However, vanilla DINs are unable to handle user behavior sequences exceeding a predetermined length, resulting in the inability to effectively capture potential long-term needs of users. To address this issue, an Attention-based Interest Network(AIN) is proposed which utilizes aggregated user behavior sequences. AIN aggregates users' historical behavior sequences and adopts attention mechanism to enhance expression capabilities. Experimental results demonstrate that AIN can significantly improve recommendation performance. Compared to vanilla DIN models, AIN achieves the best AUC of 0.6429 and ReIaImpr of 7.44.
Computer Systems and Processes as an Object of Modeling

The chapter is devoted to the main objects that will be discussed in this book (structures, systems, processes) in the field of computing. A brief review has been made, starting from specifying the role of “information” and “information processing,” in order to specify the place of the technological environment for the implementation of information processes and to define its main functionalities. Given that the technological environment is a computer implementation, the two basic levels are presented – high user level (macro-level) and low internal machine level (micro-level) with an initial formalization of basic system resources. The main object of analysis is the performance with its factors (performance indices) which are defined and which have an impact on the workload of the computing environment based on input workflow. In this respect, a formal description of workload and specifying the role of the process profile are presented. In addition, a formalization of information technology is presented.
Research on neural reinforcement learning-based non-inverse kinematics robotic arm control system

Existing robotic arm control systems usually rely on artificial inverse kinematics modeling to solve, but due to the complexity of the robotic arm, this method is often expensive, particularly when the structure of the robotic arm changes, requiring it to be remodeled and solved again, making it too complex. This paper proposes a neural reinforcement learning-based inverse kinematics robotic arm control system, which only needs to model the forward kinematics of the robotic arm in the virtual simulation environment to JIT generate self-supervised training data to train the neural network, and the network can be used to control any size of hardware robotic arm after the network training converges, as long as its joint arm length ratio is the same as the ratio of training. Experiments show that we can complete this task with a small neural network, and low latency inference servo angle parameters under $150 Jetson Nano, in virtual simulation testing, our control accuracy reaches 97%, while in hardware testing, the accuracy of the camera and servo is reduced, but the control is still effective in the general range. Therefore, this inspires us to study the future research on servo angle nonlinear error correction and the end position control technology under the controlled environment.
Research on optimization of convolution back propagation on domestic heterogeneous platforms

Convolution neural network is widely used in various fields. The convolution layer is the core layer of the convolution neural network. The back propagation speed of the convolution layer will directly affect the training speed of the whole network, thus affecting the whole performance. For the convolution layer with stride ≥ 2, the error transmission phase of back propagation will carry out a large amount of padding in the feature graph, resulting in a large amount of additional overhead in access and calculation. In this case, we propose a new optimization method, which can reduce the overhead caused by padding to almost zero, and implement it by implicit convolution on domestic heterogeneous platforms. The experiment shows that the performance of the operator optimized by this method is nearly 50% higher than that of the original operator of the platform, and the average performance reaches 90% of that of NVIDIA V100 operator.
RoPE-BAM: nested entity recognition based on rotary position embedding and biaffine attention mechanism

Named entity recognition (NER) involves two main types: nested NER and flat NER. The span-based approach classifies entity types by head-tail pair span representations and can handle nested and flat entities uniformly. However, the span-based approach uses a single feature and ignores the relative position information between head-tail pairs, which affects the precision of entity recognition. Therefore, we propose a nested entity recognition model that combines rotary position embedding and biaffine attention mechanism (RoPE-BAM) to improve the model performance by adding relative position features to the span representations. Concretely, we first obtain the head sequence and tail sequence representations through two feedforward networks. Then, to incorporate the relative position features, rotary position embedding is applied to both head and tail sequences. Finally, we use a biaffine attention mechanism to capture the span representations while generating the relative position information in the span. Extensive experiments were conducted on five widely-used benchmark datasets to demonstrate the effectiveness of our proposed RoPE-BAM model.
Hyper Markov law in undirected graphical models with its applications

AbstractBy exploring the prime decomposition of undirected graphs, this work investigates the hyper Markov property within the framework of arbitrary undirected graph, which can be seen as the generalization of that for decomposable graphical models proposed by Dawid and Laurizten. The proposed hyper Markov properties of this article can be used to characterize the conditional independence of a distribution or a statistical quantity and helpful to simplify the likelihood ratio functions for statistical test for two different graphs obtained by removing or adding one edge. As an application of these properties, the G-Wishart law is introduced as a prior law for graphical Gaussian models for Bayesian posterior updating, and a hypothesis test for precision matrix is designed to determine the model structures. Our simulation experiments are implemented by using the Gibbs Sampler algorithm and the results show that it performs better in convergence speed.Keywords: Graphical modelsprime decompositionhyper Markov lawslikelihood ratio testthe G-Wishart lawMathematics Subject Classification: 62D9962H86 Disclosure statementNo potential conflict of interest was reported by the authors.Additional informationFunding This work was partially supported by the Natural Science Foundation of Xinjiang Uygur Autonomous Region (Grant No. 2022D01C406), the Open Research Fund of KLAS, Northeast Normal University and the National Natural Science Foundation of China (Grant Nos. 11726629, 11726630) and the National Key Research and Development Program of China (Grant No. 2020YFA0714100).
How a Deep Contextualized Representation and Attention Mechanism Justifies Explainable Cross-Lingual Sentiment Analysis

The number of applications in sentiment analysis is growing daily, and research in this field is increasing. Despite the rapid growth of data sources in English, low-resource languages suffer from a lack of data for accurate training models. Moreover, users cannot trust such systems without explaining the output. In this study, we propose a cross-lingual deep neural model to improve the accuracy of sentiment analysis for low-resource languages while providing an explainable description of the predictions. The proposed model contains a word representation model where we use XLM-RoBERTa, a pre-trained contextualized transformer-based cross-lingual language model, and a long short-term memory network together with an attention mechanism that helps improve the explainability of the model and detect the informative words that impact text polarity. Our experiments show the superiority of the proposed model compared to the state-of-the-art mono-lingual techniques and cross-lingual models. The results show 0.55% improvement compared to the cross-lingual sentiment analysis proposed by Ghasemi et al. and 15.08% improvement compared to the mono-lingual contextualized sentiment analysis. Moreover, we achieve 0.54% further improvement when using attention mechanisms for enhancing the model with explainability.
LEADERSHIP STYLES AND SUSTAINABLE COMPETITIVE PERFORMANCE IN PAKISTANI SMES: AN INDUSTRY 4.0 PERSPECTIVE

Previous research has primarily concentrated on the technological and operational aspects of Industry 4.0 in SMEs, leaving a significant gap in our understanding of the leadership's role in this context. This article aforesaid gap by empirically investigating impact of entrepreneurial leadership, transformational leadership, transactional leadership, and ethical leadership on the sustainable competitive performance of Small and Medium-sized Enterprises (SMEs) in Pakistan within the context of Industry 4.0. The study adopts a quantitative approach and collects data from Pakistani SMEs sector, including manufacturing sports, and textiles, using purposive sampling. A total of 500 questionnaires were distributed. The study's findings reveal that entrepreneurial leadership and ethical leadership significantly influence organizational learning. Furthermore, a significant relationship is observed between organizational learning and sustainable competitive performance. However, the study does not find a significant relationship between transactional leadership and organizational learning. Moreover, the research highlights a significant mediating relationship between organizational learning and the impact of entrepreneurial leadership, ethical leadership, and transformational leadership on sustainable competitive performance. However, transactional leadership does not exhibit a significant mediating effect. The study's contributions to the existing literature on Industry 4.0 are valuable as they shed light on the role of leadership styles in the context of SMEs in Pakistan. In the era of Industry 4.0, where technological advancements are transforming industries, understanding the impact of leadership on sustainable competitive performance is crucial. This research offers insights into which leadership styles are most effective in this rapidly changing landscape.
Kalman filter based on intermediate transition state for radar target tracking with nonlinear measurements

Aiming at the nonlinear measurements problem of target tracking, a Kalman filter algorithm based on intermediate transition state (ITS-KF) is proposed in this paper. The new intermediate transition states are introduced in two- and three-dimension scenarios, and the derivations of state transition between the state in Cartesian coordinate and intermediate transition state in measurement coordinate are given. Based on the intermediate transition state, a new ITS-KF algorithm is proposed and the filtering process is given. For maneuvering target tracking case, ITS-KF is combined with the improved IMM (IIMM) and the ITS-KF-IIMM is given. The simulation results show that the tracking accuracy and consistency of the proposed algorithm is obviously improved compared with existing algorithms especially in the case of large angle measurement errors both in non-maneuvering and maneuvering case.
Factors Affecting Family Caregivers’ Behavioral Intention to Use Socially Assistive AI Robots for Elderly Care Within Their Own Home Environment

The necessity of socially assistive AI robots that can assist the elderly in their daily lives to mitigate the burden of family caregivers in aged-care settings continues to increase. However, there is a lack of empirical research on the drivers of family caregivers’ behavioral intention to use these types of robots within their own home environment. This study investigates how family caregivers’ intention to adopt socially assistive AI robots for their elderly family members can be predicted based on utilitarian and hedonic attitudes, product smartness dimensions, and conscientiousness with demographic characteristics as control variables. The partial least squares structural equation modeling (PLS-SEM) method was used to process data collected from 399 respondents. The results show that utilitarian and hedonic attitudes positively affect family caregivers’ intention to adopt socially assistive AI robots. Moreover, utilitarian attitudes are influenced by ability to cooperate, personality, and conscientiousness, while hedonic attitudes are influenced by autonomy, ability to cooperate, humanlike interaction, and personality. Important implications for developing family caregivers’ adopting intention toward socially assistive AI robots are presented based on the findings.
Herd behavior in cryptocurrency market: evidence of network effect

Purpose This study examines herd behavior in the cryptocurrency market at the aggregate level and the determinants of herd behavior, such as asymmetric market returns, the coronavirus disease 2019 (COVID-19) pandemic, 2021 cryptocurrency's bear market and the network effect. Design/methodology/approach The authors applied the Google Search Volume Index (GSVI) as a proxy for the network effect. Since investors who are interested in a particular issue have a common interest, they tend to perform searches using the same keywords in Google and are on the same network. The authors also investigated the daily returns of cryptocurrencies, which are in the top 100 market capitalizations from 2017 to 2022. The authors also examine the association between return dispersion and portfolio return based on aggregate market herding model and employ interactions between herding determinants such as, market direction, market trend, COVID-19 and network effect. Findings The empirical results indicate that herding behavior in the cryptocurrency market is significantly captured when the market returns of cryptocurrency tend to decline and when the network effect of investors tends to expand (e.g. such as during the COVID-19 pandemic or 2021 Bitcoin crash). However, the results confirm anti-herd behavior in cryptocurrency during the COVID-19 pandemic or 2021 Bitcoin crash, regardless of the network effect. Practical implications These findings help investors in the cryptocurrency market make more rational decisions based on their determinants since cryptocurrency is an alternative investment for investors' asset allocation. As imitating trades lead to return comovement, herd behavior in the cryptocurrency has a direct impact on the effectiveness of portfolio diversification. Hence, market participants or investors should consider herd behavior and its underlying factors to fully maximize the benefits of asset allocation, especially during the period of market uncertainty. Originality/value Most previous studies have focused on herd behavior in the stock market. Although some researchers have recently begun studying herd behavior in the cryptocurrency market, the empirical results are inconclusive due to an incorrectly specified model or unclear determinants.
Road Scene Instance Segmentation Based on Improved SOLOv2

Road instance segmentation is vital for autonomous driving, yet the current algorithms struggle in complex city environments, with issues like poor small object segmentation, low-quality mask edge contours, slow processing, and limited model adaptability. This paper introduces an enhanced instance segmentation method based on SOLOv2. It integrates the Bottleneck Transformer (BoT) module into VoVNetV2, replacing the standard convolutions with ghost convolutions. Additionally, it replaces ResNet with an improved VoVNetV2 backbone to enhance the feature extraction and segmentation speed. Furthermore, the algorithm employs Feature Pyramid Grids (FPGs) instead of Feature Pyramid Networks (FPNs) to introduce multi-directional lateral connections for better feature fusion. Lastly, it incorporates a convolutional Block Attention Module (CBAM) into the detection head for refined features by considering the attention weight coefficients in both the channel and spatial dimensions. The experimental results demonstrate the algorithm’s effectiveness, achieving a 27.6% mAP on Cityscapes, a 4.2% improvement over SOLOv2. It also attains a segmentation speed of 8.9 FPS, a 1.7 FPS increase over SOLOv2, confirming its practicality for real-world engineering applications.
Dynamic Anomaly Detection in Gantry Transactions Using Graph Convolutional Network-Gate Recurrent Unit with Adaptive Attention

With the wide application of Electronic Toll Collection (ETC) systems, the effectiveness of the operation and maintenance of gantry equipment still need to be improved. This paper proposes a dynamic anomaly detection method for gantry transactions, utilizing the contextual attention mechanism and Graph Convolutional Network-Gate Recurrent Unit (GCN-GRU) dynamic anomaly detection method for gantry transactions. In this paper, four different classes of gantry anomalies are defined and modeled, representing gantries as nodes and the connectivity between gantries as edges. First, the spatial distribution of highway ETC gantries is modeled using the GCN model to extract gantry node features. Then, the contextual attention mechanism is utilized to capture the recent patterns of the dynamic transaction graph of the gantries, and the GRU model is used to extract the time-series characteristics of the gantry nodes to dynamically update the gantry leakage. Our model is evaluated on several experimental datasets and compared with other commonly used anomaly detection methods. The experimental results show that our model outperforms other anomaly detection models in terms of accuracy, precision, and other evaluation values of 99%, proving its effectiveness and robustness. This model has a wide application potential in real gantry detection and management.
Computer Vision Tasks for Ambient Intelligence in Children’s Health

Computer vision is a powerful tool for healthcare applications since it can provide objective diagnosis and assessment of pathologies, not depending on clinicians’ skills and experiences. It can also help speed-up population screening, reducing health care costs and improving the quality of service. Several works summarise applications and systems in medical imaging, whereas less work is devoted to surveying approaches for healthcare goals using ambient intelligence, i.e., observing individuals in natural settings. Even more, there is a lack of papers providing a survey of works exhaustively covering computer vision applications for children’s health, which is a particularly challenging research area considering that most existing computer vision technologies have been trained and tested only on adults. The aim of this paper is then to survey, for the first time in the literature, the papers covering children’s health-related issues by ambient intelligence methods and systems relying on computer vision.
DRGAN: Dense Residual Generative Adversarial Network for Image Enhancement in an Underwater Autonomous Driving Device

Underwater autonomous driving devices, such as autonomous underwater vehicles (AUVs), rely on visual sensors, but visual images tend to produce color aberrations and a high turbidity due to the scattering and absorption of underwater light. To address these issues, we propose the Dense Residual Generative Adversarial Network (DRGAN) for underwater image enhancement. Firstly, we adopt a multi-scale feature extraction module to obtain a range of information and increase the receptive field. Secondly, a dense residual block is proposed, to realize the interaction of image features and ensure stable connections in the feature information. Multiple dense residual modules are connected from beginning to end to form a cyclic dense residual network, producing a clear image. Finally, the stability of the network is improved via adjustment to the training with multiple loss functions. Experiments were conducted using the RUIE and Underwater ImageNet datasets. The experimental results show that our proposed DRGAN can remove high turbidity from underwater images and achieve color equalization better than other methods.
Choosing a programming language for high school students

Currently, special attention is paid to the content of the training of schoolchildren in computer science. Interest in software development and code writing methods is growing every year. This is due to the change in methods and means of training in the field of writing program code associated with the emergence and development of new paradigms and programming languages. The purpose of the study is to substantiate the choice of a programming language for the preparation of high school students in the field of programming and solving the tasks of the Unified State Exam in computer science. Materials and methods of research: in the course of the research, a theoretical approach was used: the study, systematization and analysis of theoretical materials, scientific and methodological resources for teaching programming to schoolchildren, demos, test tasks, manuals, educational websites for preparing for the Unified State Exam in computer science, as well as many years of experience in teaching schoolchildren and students the basics of programming. Research results and their discussion: Programming languages PascalABC.NET and Python are currently the most popular in school education. The article analyzes the possibilities of these programming systems and the prospects of their application in teaching high school students the basics of programming. Examples of solving typical problems from the Unified State Exam in computer science in both languages are given to compare the speed and efficiency of the code. Conclusions. Language PascalABC.NET it has a number of advantages and can be recommended as a language for teaching programming. The language is high-performance, well structured, has strict typing and is adapted to the tasks of the Unified State Exam in computer science. When teaching future programmers, the basics of writing code, it is advisable to combine languages PascalABC.NET and Python.