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9th International Conference on Digital Home, ICDH 2022

作者： Zhou, Jingsong Lan, Rushi Wang, Xiaoqing Pang, Cheng Luo, Xiaonan Guilin University of Electronic Technology School of Computer Science and Information Security Guilin541004 China Guilin University of Electronic Technology Nanning Research Institute Nanning China Guilin Huigu Institute of Artificial Intelligence Industrial Technology Guilin China National Local Joint Engineering Research Center of Satellite Navigation and Location Service

ISBN: (数字)9781665454780

ISBN: (纸本)9781665454780

Video super-resolution are drawing increasing attention in the computer vision community. Temporal modeling is crucial for video super-resolution. A challenge for video super-resolution to fully mining temporal-spatial information in video sequence. In this work, we propose 3D deformable kernels for video super-resolution (DK3Dnet). Specifically, we introduce 3D deformable kernels (DK3D) to integrate deformable convolution with 3D convolution to enhance spatio-temporal modeling capability. To enhance the quality of subsequent restoration. we use a Temporal and Spatial Attention fusion module (TSA fusion), in which attention is applied both temporally and spatially. Finally, we use channel-wise attention residual block (CARB) to enhance the quality of video frame in DK3Dnet reconstruction module. Experimental results show that DK3Dnet can exploiting spatio-temporal information to improve the performance of video super-resolution. © 2022 IEEE.

关键词： Convolution

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Integrating Design Thinking into Software Process Improvement 4

Integrating Design Thinking into Software Process Improvemen...

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4th International Conference on Emerging Trends in Networks and computer Communications, ETNCC 2024

作者： Khalid, Anum Hashmi, Amber Sarwar Kiani, Azaz Ahmed Muzammal, Syeda Mariam Jhanjhi, N.Z. Bibi, Ruqia Kumar, Sandeep Foundation University Department of Software Engineering Islamabad Pakistan Rawalpindi Women University Department of Software Engineering Rawalpindi Pakistan National University of Modern Languages Rawalpindi Pakistan School of Computer Science Taylor's University Faculty of Innovation and Technology Subang Jaya Malaysia University Institute of Information Technology Pir Mehr Ali Shah Arid Agriculture University Rawalpindi Pakistan Lovely Professional University Division of Research and Development Phagwara144411 India KKR and KSR institute of technology and sciences Andhra Pradesh Guntur India

ISBN: (纸本)9798350353266

The need for software improvement is growing widely due to the demand for technology development. The issues that arise by expanding the software processes in the workplace are expense, lack of understanding, time constrained, and lack user experience. This research paper focuses on proposing a framework for integrating design thinking into software process improvement. The secret to project success may lie in fusing methodological and reusable approaches with human-centric techniques. A mixed-methods research strategy is proposed to compile thorough information and reach conclusions. In addition, it incorporates components of both qualitative and quantitative research (secondary research and descriptive analysis). The assumption is that the existing model significantly affects the user satisfaction variable. This indicates a statistically significant association between the individual's experience (number of years) and design thinking integration into the software process improvement. The variables in the proposed model and the dependent variable user satisfaction accounts for 6.1 © 2024 IEEE.

关键词： Software design

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CETIS: Retrofitting Intel CET for Generic and Efficient Intra-process Memory Isolation 22

CETIS: Retrofitting Intel CET for Generic and Efficient Intr...

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28th ACM SIGSAC Conference on computer and Communications Security, CCS 2022

作者： Xie, Mengyao Wu, Chenggang Zhang, Yinqian Xu, Jiali Lai, Yuanming Kang, Yan Wang, Wei Wang, Zhe Sklp Institute of Computing Technology Cas University of Chinese Academy of Sciences Beijing China Zhongguancun Laboratory Beijing China Department of Computer Science and Engineering SUSTech Research Institute of Trustworthy Autonomous Systems SUSTech Shenzhen China Sklp Institute of Computing Technology Cas Beijing China

ISBN: (纸本)9781450394505

Intel control-flow enforcement technology (CET) is a new hardware feature available in recent Intel processors. It supports the coarse-grained control-flow integrity for software to defeat memory corruption attacks. In this paper, we retrofit CET, particularly the write-protected shadow pages of CET used for implementing shadow stacks, to develop a generic and efficient intra-process memory isolation mechanism, dubbed CETIS. To provide user-friendly interfaces, a CETIS framework was developed, which provides memory file abstraction for the isolated memory regions and a set of APIs to access said regions. CETIS also comes with a compiler-assisted tool chain for users to build secure applications easily. The practicality of using CETIS to protect CPI, CFIXX, and JIT-compilers was demonstrated, and the evaluation reveals that CETIS is performed better than state-of-the-art intra-memory isolation mechanisms, such as MPK. © 2022 Owner/Author.

关键词： Retrofitting

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Multi-Partner Project: Safe, Secure and Dependable Multi-UAV Systems for Search and Rescue Operations

Multi-Partner Project: Safe, Secure and Dependable Multi-UAV...

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Design, Automation and Test in Europe Conference and Exhibition

作者： Panagiota Nikolaou Antonis Savva Ioannis Sorokos Koorosh Aslansefat Sondess Missaoui Akram Mohammed Naveed Daniel Hillen Marc Lorenz Martin D. Walker Manos Papoutsakis Simos Gerasimou Panayiotis Kolios Yiannis Papadopoulos Jan Reich Sotiris Ioannidis Maria K. Michael University of Central Lancashire Cyprus KIOS Research and Innovation Center of Excellence University of Cyprus Fraunhofer IESE Germany University of Hull UK University of York UK Institute of Computer Science Foundation for Research and Technology Greece Computer-Science Department University of Cyprus Electrical and Computer Engineering Department University of Cyprus

ISBN: (数字)9783982674100

ISBN: (纸本)9798331534646

Unmanned Aerial Vehicles (UAVs) have become essential in search and rescue operations, especially in disaster management scenarios. Their effective navigation and the integration of a plethora of sensors assist in efficient person detection, making them an essential technological tool to first responders. Multi-UAV systems extend these benefits by using coordinated strategies to cover large areas efficiently, reducing overall mission response time and enhancing its success. Despite these advantages, challenges remain in ensuring the safety, security, and dependability of (mutli-)UAV missions. Issues such as navigation risks, potential cyber threats, and hardware-/software-related reliability issues can impact the mission results. Additionally, UAVs are highly constrained devices with limited battery capacity, requiring the use of lightweight technologies. In this paper, we present part of the results of the SESAME project, an EU multi-partner project that aims to develop safe and secure multi-robot Systems. In particular, we present some of the developed SESAME Executable Digital Dependability Identities (EDDI) technologies based on Markov models, statistical distance measures, and other advanced approaches for enhancing safety, security and dependability of the UAV platform and underlying models. These EDDI technologies are seamlessly integrated using the ConSerts framework in a multi-UAV platform and tested using search and rescue scenarios. The results demonstrate significant improvements in multi-UAV safety, with an availability rate of 91% and a search and rescue algorithmic accuracy of 99.8%. Additionally, the system achieves precise detection of spoofing attacks, using collaborative localization as a mitigation technique to guide the UAV to a safe landing, even in the absence of GPS signals,

关键词： Location awareness Navigation System performance Collaboration Autonomous aerial vehicles Safety Sensors Security Reliability Time factors

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Empowering Adaptive Endogenous Security Trend Prediction Detection for IoT Sensor Nodes

Empowering Adaptive Endogenous Security Trend Prediction Det...

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Advances in Electrical, Electronics and Computing technology (EECT), International Conference on

作者： Lihu Zhou Xiuying Dong Enli Zhang Ting Wang Xiao Zhang Chong Zhang School of Computer Science and Software Engineering Southwest Petroleum University Chengdu China Exploration and Development Research Institute of PetroChina Southwest Oil & Gasfield Company Chengdu China School of Computer Science Chengdu University of Information Technology Chengdu China

ISBN: (数字)9798331541545

ISBN: (纸本)9798331541552

Massively deployed IoT devices require a lightweight design to reduce costs. However, this architecture inherently limits their security, increasing the risk of data breaches and tampering on a large scale. In this paper, we propose CGAD, a model that leverages one-dimensional convolution (Conv1D), gated recurrent units (GRU), and the attention mechanism to analyze multi-sensor time series data at the data gateway from sensing terminals. To achieve this, we generate time series predictions and detect anomalies by comparing predicted values with real data using a threshold-based approach, thereby enhancing data security. To address the labeling problem of massive data, we employ unsupervised learning and design a dynamic sliding window threshold to improve the judgment of time series with varying characteristics. We also used a number of datasets, such as the KW51 railroad bridge dataset and the self-picked temperature and humidity dataset, to evaluate our system. The results demonstrate that our CGAD model achieves recall rates of 98 % and 99 % for anomaly detection on the two datasets, respectively. Additionally, the precision and F1 scores surpass other methods, ensuring cost-effectiveness for massive terminals and addressing the security issues of sensitive data. These advantages contribute to realizing the Internet of Everything.

关键词： Adaptation models Data security Time series analysis Humidity Predictive models Logic gates Data models Sensors Internet of Things Anomaly detection

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Harnessing Blockchain and Deep Learning for Enhanced Energy Analytics in Photovoltaic Systems

Harnessing Blockchain and Deep Learning for Enhanced Energy ...

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IEEE International Conference on System, Computation, Automation and Networking (ICSCAN)

作者： Subbulakshmi G Sujatha S Saraswathi V Ranganathan Rani Hemamlini Department of Artificial Intelligence and Data Science Engineering St Joseph's College of Engineering and Technology Chennai India Department of Computer Appication BIT Campus Anna University Tiruchirappalli India Department of Computer Science Engineering Chennai Institute of Technology Chennai India Department of Electrical And Elctronics Engineering St. Peter's Institute of Higher Education And Research Chennai India

ISBN: (数字)9798331510022

ISBN: (纸本)9798331510039

The rapid evolution of solar energy in industrial sectors has sparked a growing interest in renewable energy, particularly within the context of smart grids and systems. The effective detection and management of energies within photovoltaic (PV) systems stand as crucial tasks in this landscape. This abstract introduces a proposed model aimed at exploring the integration of blockchain networks within large-scale solar energy infrastructures. The primary objective is to establish a framework that enables the distribution of solar energy in a decentralized manner to end-users while ensuring secure energy transactions between producers and consumers. Blockchain technology serves as the cornerstone of this model, encompassing intelligent techniques to address energy mitigation challenges within the solar energy network. By leveraging blockchain, the detection of energies within solar power plants becomes shared knowledge accessible to all users connected to the solar power network. The research within this framework emphasizes the exploration and application of diverse Deep Learning (DL) architectures, including autoencoders, Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, Deep Stacking Networks (DSNs), and Deep Belief Networks (DBNs). Furthermore, this research extends beyond conventional machine learning approaches, incorporating knowledge-driven methodologies. It incorporates generative models such as adversarial networks and transfer learning, elevating the scope of machine learning in optimizing energy detection, distribution, and consumption within the block chain-based solar energy infrastructure. This interdisciplinary approach signifies a significant stride in fortifying renewable energy systems. By amalgamating blockchain's decentralized nature with sophisticated DL architectures and knowledge-driven models, it establishes a resilient and efficient renewable energy infrastructure capable of meeting escalating demands for sustainabl

关键词： Deep learning Photovoltaic systems Renewable energy sources Heuristic algorithms Solar energy Sensor systems Blockchains Sensors Peer-to-peer computing Long short term memory

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Multimodal Depression Recognition Based on Self-Supervised Learning

Multimodal Depression Recognition Based on Self-Supervised L...

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computer science, Electronic Information engineering and Intelligent Control technology (CEI), IEEE International Conference on

作者： Guangkai Wang Rui Wang Qin Yang Yong Wu School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Wuhan Second Ship Design and Research Institute Wuhan China Ordnance NCO Academy Army Engineering University of PLA Wuhan China

ISBN: (数字)9798350378542

ISBN: (纸本)9798350378559

Depression is a widespread and severe mental health disorder, characterized by a significant prevalence and a high incidence rate, which presents numerous challenges in diagnosis and treatment. We introduce an innovative multimodal approach for depression recognition utilizing self-supervised learning. The method analyzes audio and video data through three steps: extracting image features, extracting audio features, and performing multimodal temporal fusion, enabling an assessment of depression severity. Extensive experiments conducted on the AVEC2014 dataset demonstrate that the proposed model achieves a Mean Absolute Error (MAE) of 7.013 and a Root Mean Square Error (RMSE) of 8.616 in depression prediction tasks, showcasing strong performance.

关键词： Image recognition Computational modeling Mental health Contrastive learning Predictive models Feature extraction Depression Data mining Root mean square Intelligent control

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A Survey on Web Application Testing: A Decade of Evolution

arXiv

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arXiv 2024年

作者： Li, Tao Huang, Rubing Cui, Chenhui Towey, Dave Ma, Lei Li, Yuan-Fang Xia, Wen School of Computer Science and Engineering Macau University of Science and Technology Macau Taipa999078 China Macau University of Science and Technology Zhuhai MUST Science and Technology Research Institute Guangdong Province Zhuhai519099 China School of Computer Science University of Nottingham Ningbo China Zhejiang Ningbo999078 China School of Science University of Tokyo Tokyo113-0033 Japan Faculty of Information Technology Monash University MelbourneVIC Australia School of Computer Science and Technology Harbin Institute of Technology Shenzhen Shenzhen518055 China

As one of the most popular software applications, a web application is a program, accessible through the web, to dynamically generate content based on user interactions or contextual data, for example, online shopping platforms, social networking sites, and financial services. Web applications operate in diverse environments and leverage web technologies such as HTML, CSS, JavaScript, and Ajax, often incorporating features like asynchronous operations to enhance user experience. Due to the increasing user and popularity of web applications, approaches to their quality have become increasingly important. Web Application Testing (WAT) plays a vital role in ensuring web applications’ functionality, security, and reliability. Given the speed with which web technologies are evolving, WAT is especially important. Over the last decade, various WAT approaches have been developed. The diversity of approaches reflects the many aspects of web applications, such as dynamic content, asynchronous operations, and diverse user environments. This paper provides a comprehensive overview of the main achievements during the past decade: It examines the main steps involved in WAT, including test-case generation and execution, and evaluation and assessment. The currently available tools for WAT are also examined. The paper also discusses some open research challenges and potential future WAT work. © 2024, CC BY.

关键词： Software testing

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Multi-Level Sequence Denoising with Cross-Signal Contrastive Learning for Sequential Recommendation

arXiv

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arXiv 2024年

作者： Zhu, Xiaofei Li, Liang Liu, Weidong Luo, Xin College of Computer Science and Engineering Chongqing University of Technology Chongqing400054 China China Mobile Research Institute China Mobile Beijing100053 China College of Computer and Information Science Southwest University Chongqing400715 China

Sequential recommender systems (SRSs) aim to suggest next item for a user based on her historical interaction sequences. Recently, many research efforts have been devoted to attenuate the influence of noisy items in sequences by either assigning them with lower attention weights or discarding them directly. The major limitation of these methods is that the former would still prone to overfit noisy items while the latter may overlook informative items. To the end, in this paper, we propose a novel model named Multi-level Sequence Denoising with Cross-signal Contrastive Learning (MSDCCL) for sequential recommendation. To be specific, we first introduce a target-aware user interest extractor to simultaneously capture users’ long and short term interest with the guidance of target items. Then, we develop a multi-level sequence denoising module to alleviate the impact of noisy items by employing both soft and hard signal denoising strategies. Additionally, we extend existing curriculum learning by simulating the learning pattern of human beings. It is worth noting that our proposed model can be seamlessly integrated with a majority of existing recommendation models and significantly boost their effectiveness. Experimental studies on five public datasets are conducted and the results demonstrate that the proposed MSDCCL is superior to the state-of-the-art baselines. The source code is publicly available at https://***/lalunex/MSDCCL/tree/main. Copyright © 2024, The Authors. All rights reserved.

关键词： Curricula

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Segmentation and Classification of Skin Cancer Using K-means Clustering and EfficientNetB0 Model 5th

Segmentation and Classification of Skin Cancer Using K-means...

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5th International Conference on Communication, Device and Networking, ICCDN 2021

作者： Anand, Vatsala Gupta, Sheifali Koundal, Deepika Nayak, Soumya Ranjan Shafi, Jana Bhoi, Akash Kumar Institute of Engineering and Technology Chitkara University Chitkara University Punjab Rajpura India Department of Systemics School of Computer Science University of Petroleum and Energy Studies Uttrakhand Dehradun India Amity School of Engineering and Technology Amity University Uttar Pradesh Noida India Department of Computer Science College of Arts and Science Prince Sattam Bin Abdul Aziz University Wadi Ad-Dwasir11991 Saudi Arabia KIET Group of Institutions Delhi-NCR Ghaziabad201206 India Directorate of Research Sikkim Manipal University Gangtok Sikkim737102 India

ISBN: (纸本)9789811920035

Nowadays, skin cancer is the widely recognized cancer all over the world. As the spreading rate of skin cancer is increasing day by day, so, there is a need to develop a technique that can detect skin cancer at an early stage. These days, deep learning has attained outstanding success for the detection and diagnosis of cancers. In this paper, a transfer learning-based EfficientNetB0 model is improved by adding one average pooling layer, one dropout layer, one batch normalization and one dense layer with softmax activation function. The proposed model has been simulated using the Kaggle database. The training and calculation are done with different hyper parameters such as batch size, optimizer and epochs. The data augmentation technique is applied to solve the problem of less amount of images. The proposed model has attained 87% accuracy on Adam optimizer with 32 batch size and 30 epochs. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： K-means clustering

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