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Smart automated highway lighting system using IoT: a survey

Energy Informatics 2024年第1期7卷 76页

作者： Achar, Tejaswini Eshwar Rekha, C. Shreyas, J. Department of Computer Science and Engineering Government Engineering College Koppal Talakal India Department of Information Science and Engineering Bapuji Institute of Engineering and Technology Davanagere India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India

Efficient highway lighting is crucial for ensuring road safety and reducing energy consumption and costs. Traditional highway lighting systems rely on timers or simple photosensors, leading to inefficient operation by illuminating lights when not needed or failing to adjust to changing conditions. The emergence of the Internet of Things (IoT) and related technologies has enabled the development of smart automated highway lighting systems that can dynamically control illumination levels based on real-time data. This paper provides a comprehensive review of the current state-of-the-art in smart automated highway lighting systems employing IoT technologies. Key components, communication protocols, data processing techniques, and lighting control strategies are discussed. The integration of renewable energy sources and energy storage systems is explored for environmentally sustainable operations. Practical implementation case studies are analyzed to highlight benefits and challenges. Open research issues and future directions for further enhancements are identified. © The Author(s) 2024.

关键词： Street lighting

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Workout Action Recognition in Video Streams Using an Attention Driven Residual DC-GRU Network

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computers, Materials & Continua 2024年第5期79卷 3067-3087页

作者： Arnab Dey Samit Biswas Dac-Nhuong Le Department of Computer Science and Technology Indian Institute of Engineering Science and TechnologyShibpurHowrah711103India Faculty of Information Technology Haiphong UniversityHaiphong180000Vietnam

Regular exercise is a crucial aspect of daily life, as it enables individuals to stay physically active, lowers thelikelihood of developing illnesses, and enhances life expectancy. The recognition of workout actions in videostreams holds significant importance in computer vision research, as it aims to enhance exercise adherence, enableinstant recognition, advance fitness tracking technologies, and optimize fitness routines. However, existing actiondatasets often lack diversity and specificity for workout actions, hindering the development of accurate recognitionmodels. To address this gap, the Workout Action Video dataset (WAVd) has been introduced as a significantcontribution. WAVd comprises a diverse collection of labeled workout action videos, meticulously curated toencompass various exercises performed by numerous individuals in different settings. This research proposes aninnovative framework based on the Attention driven Residual Deep Convolutional-Gated Recurrent Unit (ResDCGRU)network for workout action recognition in video streams. Unlike image-based action recognition, videoscontain spatio-temporal information, making the task more complex and challenging. While substantial progresshas been made in this area, challenges persist in detecting subtle and complex actions, handling occlusions,and managing the computational demands of deep learning approaches. The proposed ResDC-GRU Attentionmodel demonstrated exceptional classification performance with 95.81% accuracy in classifying workout actionvideos and also outperformed various state-of-the-art models. The method also yielded 81.6%, 97.2%, 95.6%, and93.2% accuracy on established benchmark datasets, namely HMDB51, Youtube Actions, UCF50, and UCF101,respectively, showcasing its superiority and robustness in action recognition. The findings suggest practicalimplications in real-world scenarios where precise video action recognition is paramount, addressing the persistingchallenges in the field. TheWAVd datas

关键词： Workout action recognition video stream action recognition residual network GRU attention

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Pre-trained SAM as data augmentation for image segmentation

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CAAI Transactions on Intelligence technology 2025年第1期10卷 268-282页

作者： Junjun Wu Yunbo Rao Shaoning Zeng Bob Zhang Yangtze Delta Region Institute(Huzhou) University of Electronic Science and Technology of ChinaHuzhouChina School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengduChina Pattern Analysis and Machine Intelligence Research Group Department of Computer and Information ScienceUniversity of MacaoMacaoChina

Data augmentation plays an important role in training deep neural model by expanding the size and diversity of the ***,data augmentation mainly involved some simple transformations of ***,in order to increase the diversity and complexity of data,more advanced methods appeared and evolved to sophisticated generative ***,these methods required a mass of computation of training or *** this paper,a novel training-free method that utilises the Pre-Trained Segment Anything Model(SAM)model as a data augmentation tool(PTSAM-DA)is proposed to generate the augmented annotations for *** the need for training,it obtains prompt boxes from the original annotations and then feeds the boxes to the pre-trained SAM to generate diverse and improved *** this way,annotations are augmented more ingenious than simple manipulations without incurring huge computation for training a data augmentation *** comparative experiments on three datasets are conducted,including an in-house dataset,ADE20K and *** this in-house dataset,namely Agricultural Plot Segmentation Dataset,maximum improvements of 3.77%and 8.92%are gained in two mainstream metrics,mIoU and mAcc,***,large vision models like SAM are proven to be promising not only in image segmentation but also in data augmentation.

关键词： data augmentation image segmentation large model segment anything model

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A software Defect Prediction Method Using a Multivariate Heterogeneous Hybrid Deep Learning Algorithm

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computers, Materials & Continua 2025年第2期82卷 3251-3279页

作者： Qi Fei Haojun Hu Guisheng Yin Zhian Sun College of Computer Science and Technology Harbin Engineering UniversityHarbin150001China Information Technology Research Department Jiangsu Automation Research InstituteLianyungang222062China School of Computer Science and Technology Harbin Institute of TechnologyShenzhen518055China

software defect prediction plays a critical role in software development and quality assurance processes. Effective defect prediction enables testers to accurately prioritize testing efforts and enhance defect detection efficiency. Additionally, this technology provides developers with a means to quickly identify errors, thereby improving software robustness and overall quality. However, current research in software defect prediction often faces challenges, such as relying on a single data source or failing to adequately account for the characteristics of multiple coexisting data sources. This approach may overlook the differences and potential value of various data sources, affecting the accuracy and generalization performance of prediction results. To address this issue, this study proposes a multivariate heterogeneous hybrid deep learning algorithm for defect prediction (DP-MHHDL). Initially, Abstract Syntax Tree (AST), Code Dependency Network (CDN), and code static quality metrics are extracted from source code files and used as inputs to ensure data diversity. Subsequently, for the three types of heterogeneous data, the study employs a graph convolutional network optimization model based on adjacency and spatial topologies, a Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) hybrid neural network model, and a TabNet model to extract data features. These features are then concatenated and processed through a fully connected neural network for defect prediction. Finally, the proposed framework is evaluated using ten promise defect repository projects, and performance is assessed with three metrics: F1, Area under the curve (AUC), and Matthews correlation coefficient (MCC). The experimental results demonstrate that the proposed algorithm outperforms existing methods, offering a novel solution for software defect prediction.

关键词： software defect prediction multiple heterogeneous data graph convolutional network models based on adjacency and spatial topologies CNN-BiLSTM TabNet

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Committed-programming reductions: formalizations,implications and relations

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science China(information sciences) 2024年第10期67卷 151-171页

作者： Jiang ZHANG Yu YU Dengguo FENG Shuqin FAN Zhenfeng ZHANG State Key Laboratory of Cryptology Department of Computer Science and Engineering Shanghai Jiao Tong University Trusted Computing and Information Assurance Laboratory Institute of SoftwareChinese Academy of Sciences

In this work, we introduce a class of black-box(BB) reductions called committed-programming reduction(CPRed) in the random oracle model(ROM) and obtain the following interesting results:(1) we demonstrate that some well-known schemes, including the full-domain hash(FDH) signature(Eurocrypt1996) and the Boneh-Franklin identity-based encryption(IBE) scheme(Crypto 2001), are provably secure under CPReds;(2) we prove that a CPRed associated with an instance-extraction algorithm implies a reduction in the quantum ROM(QROM). This unifies several recent results, including the security of the Gentry-Peikert-Vaikuntanathan IBE scheme by Zhandry(Crypto 2012) and the key encapsulation mechanism(KEM) variants using the Fujisaki-Okamoto transform by Jiang et al.(Crypto 2018) in the ***, we show that CPReds are incomparable to non-programming reductions(NPReds) and randomly-programming reductions(RPReds) formalized by Fischlin et al.(Asiacrypt 2010).

关键词： provable security random oracle model quantum random oracle model black-box reduction/separation programmability

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Drug-Target Interactions Prediction Based on Signed Heterogeneous Graph Neural Networks

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Chinese Journal of Electronics 2024年第1期33卷 231-244页

作者： Ming CHEN Yajian JIANG Xiujuan LEI Yi PAN Chunyan JI Wei JIANG College of Information Science and Engineering Hunan Normal University School of Computer Science Shaanxi Normal University Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences Computer Science Department BNU-HKBU United International College

Drug-target interactions(DTIs) prediction plays an important role in the process of drug *** computational methods treat it as a binary prediction problem, determining whether there are connections between drugs and targets while ignoring relational types information. Considering the positive or negative effects of DTIs will facilitate the study on comprehensive mechanisms of multiple drugs on a common target, in this work, we model DTIs on signed heterogeneous networks, through categorizing interaction patterns of DTIs and additionally extracting interactions within drug pairs and target protein pairs. We propose signed heterogeneous graph neural networks(SHGNNs), further put forward an end-to-end framework for signed DTIs prediction, called SHGNN-DTI,which not only adapts to signed bipartite networks, but also could naturally incorporate auxiliary information from drug-drug interactions(DDIs) and protein-protein interactions(PPIs). For the framework, we solve the message passing and aggregation problem on signed DTI networks, and consider different training modes on the whole networks consisting of DTIs, DDIs and PPIs. Experiments are conducted on two datasets extracted from Drug Bank and related databases, under different settings of initial inputs, embedding dimensions and training modes. The prediction results show excellent performance in terms of metric indicators, and the feasibility is further verified by the case study with two drugs on breast cancer.

关键词： Drug-target interactions Signed heterogeneous network Link sign prediction Graph neural networks

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Stock Indexes Community Identification Using BAT-Modified Optimization Algorithm

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SN computer science 2024年第8期5卷 1-14页

作者： Aggarwal, Kirti Arora, Anuja Department of Computer Science and Engineering and Information Technology Jaypee Institute of Information Technology Noida India

Stock price accelerates interest and preference of the young generation to explore the stock market with elicit interest. An autopilot system is needed where users choose beneficial stocks of their choice without paying attention to the manual inspection or implication. This study aims to form highly correlated stock communities based on fluctuations in stock price which will be helpful in the prediction and decision-making of users about their expenditure in the stock market. For the enormous volume of stock price time series data, the opted data representation method is the network which is generated with the help of a data statistical correlation test. Another research challenge was to detect the community in an optimized manner due to complex stock networks. For the same, BAT optimization algorithm and a proposed BAT-modified (BAT-M) algorithm are applied for complex stock network community formation. The better community formation results showcase that BAT-M gives better performance in comparison to the standard BAT algorithm on various performance evaluation factors. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： BAT Community detection Correlation Modularity Nature-inspired Optimization Stock indexes Stock network

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A Novel Approach for Optimal Cluster Identification and N-Order Hesitation Based Time Series Forecasting

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SN computer science 2024年第7期5卷 872页

作者： Dixit, Ankit Jain, Shikha Department of Computer Science and Engineering and Information Technology Jaypee Institute of Information Technology Noida India

Time series forecasting is an important field of research, especially when the series is completely random, known as a strictly non-stationary time series (NS-TS). To handle the randomness efficiently, the paper presents a novel approach to identify the optimal number of clusters in NS-TS followed by a hesitation-based N-order IFS forecasting algorithm (called IFSH_new). The performance of the proposed approach for determining the optimal number of clusters is demonstrated using four variants of the proposed algorithm, IFSH_new, across five datasets. Furthermore, a comparison of the best variant of the proposed approach with existing state-of-art work based on mean absolute percentage error (MAPE) and root mean square error (RMSE) confirms the superiority of the proposed model over others. Additionally, the graphical representation of actual versus forecasted data points for different datasets shows good improvement at high change points compared to other approaches. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Degree of hesitation Intuitionistic fuzzy set (IFS) N-order forecasting model Non-stationary time series (NS-TS) Time series forecasting model

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Exploring Personalized Internet of Things(PIoT),social connectivity,and Artificial Social Intelligence(ASI):A survey

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High-Confidence Computing 2024年第3期4卷 136-162页

作者： Bisma Gulzar Shabir Ahmad Sofi Sahil Sholla Department of Information Technology National Institute of TechnologySrinagar 190001India Department of Computer Science and Engineering Islamic University of Science and TechnologyGazipur 1700India

Pervasive Computing has become more personal with the widespread adoption of the Internet of Things(IoT)in our day-to-day *** emerging domain that encompasses devices,sensors,storage,and computing of personal use and surroundings leads to Personal IoT(PIoT).PIoT offers users high levels of personalization,automation,and *** proliferation of PIoT technology has extended into society,social engagement,and the interconnectivity of PIoT objects,resulting in the emergence of the Social Internet of Things(SIoT).The combination of PIoT and SIoT has spurred the need for autonomous learning,comprehension,and understanding of both the physical and social *** research on PIoT is dedicated to enabling seamless communication among devices,striking a balance between observation,sensing,and perceiving the extended physical and social environment,and facilitating information ***,the virtualization of independent learning from the social environment has given rise to Artificial Social Intelligence(ASI)in PIoT ***,autonomous data communication between different nodes within a social setup presents various resource management challenges that require careful *** paper provides a comprehensive review of the evolving domains of PIoT,SIoT,and ***,the paper offers insightful modeling and a case study exploring the role of PIoT in post-COVID *** study contributes to a deeper understanding of the intricacies of PIoT and its various dimensions,paving the way for further advancements in this transformative field.

关键词： Personal Internet of Things(PIoT) Artificial Social Intelligence(ASI) Hyperpersonalization

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Sensitive information leakage analysis of database code by abstract interpretation

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International Journal of Security and Networks 2023年第2期18卷 91-105页

作者： Jana, Angshuman Department of Computer Science and Engineering Indian Institute of Information Technology Guwahati India

An information system stores outside data in the backend database to process them efficiently and protects sensitive data from illegitimate flow or unauthorised users. However, most information systems are made in such a way that the sensitive information stored in a database may be leaked explicitly or implicitly during data processing along with the control structure of the program to the output channels. Therefore, sensitive data leakage is one of the crucial security threat. In this paper, the main objective is to detect the illegitimate flow of confidential information in an information system. We propose a framework to detect sensitive information leakage through the data-flow paths of an information system. In particular, to compute the precise set of data-flow paths, we use the non-relational abstract property of the interval domain and the relational abstract property of the polyhedra domain that enables the framework to produce efficient security analysis results. © 2023 Inderscience Enterprises Ltd.

关键词： Formal methods

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