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10th International Symposium on Dependable software engineering: Theories, Tools and Applications, SETTA 2024

作者： Deng, Shaojun Liu, Wanwei Zhang, Miaomiao College of Computer Science and Technology National University of Defense Technology Changsha China School of Software Engineering Tongji Uniersity Shanghai China

ISBN: (纸本)9789819606016

Neural-symbolic systems (NSSs), which are typically cyber-physical systems integrated with artificial intelligence modules, have received much attention in both academic and industrial fields. However, thorough verification (such as model checking) of such systems in general leads to prohibitively high costs due to the scale of the state space, and this makes it barely accomplishable within desired time-span. Consequently, light-weight verification techniques are introduced to deal with such cases, e.g., runtime verification (RV, for short). In this paper, we investigate an RV framework for NSSs against signal temporal logic (STL) properties. To guarantee that the on-line monitoring could be accomplished in time, we utilize Euler-prediction to sample the system under scrutiny at discrete time-steps. Our framework supports both qualitative and quantitative on-line monitoring of STL specifications, and thus it terminates whenever a property is satisfied and/or violated by a finite prefix of the trajectory, and it can also provide the bounds of the quantitative satisfaction/violation w.r.t. the remaining execution. Notably, for the so-called piece-wise linear NSSs against affine STL formulas, our approach is exact. On top of it, a prototype tool is implemented, and is experimentally evaluated. The results demonstrate the feasibility of the presented runtime verification approach. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Model checking

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VisualRWKV: Exploring Recurrent Neural Networks for Visual Language Models 31

VisualRWKV: Exploring Recurrent Neural Networks for Visual L...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Hou, Haowen Zeng, Peigen Ma, Fei Yu, Fei Richard Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shool of Information Technology Carleton University Canada

ISBN: (纸本)9798891761964

Visual Language Models (VLMs) have rapidly progressed with the recent success of large language models. However, there have been few attempts to incorporate efficient linear Recurrent Neural Networks (RNNs) architectures into VLMs. In this study, we introduce VisualRWKV, the first application of a linear RNN model to multimodal learning tasks, leveraging the pre-trained RWKV language model. We propose a data-dependent recurrence and sandwich prompts to enhance our modeling capabilities, along with a 2D image scanning mechanism to enrich the processing of visual sequences. Extensive experiments demonstrate that VisualRWKV achieves competitive performance compared to Transformer-based models like LLaVA-1.5 on various benchmarks. Compared to LLaVA-1.5, VisualRWKV has a speed advantage of 3.98 times and can save 54% of GPU memory when reaching an inference length of 24K tokens. To facilitate further research and analysis, we have made the checkpoints and the associated code publicly accessible at the following GitHub repository: https://***/howard-hou/VisualRWKV. © 2025 Association for Computational Linguistics.

关键词： Recurrent neural networks

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Deep-USIpred: identifying substrates of ubiquitin protein ligases E3 and deubiquitinases with pretrained protein embedding and bayesian neural network

Deep-USIpred: identifying substrates of ubiquitin protein li...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Wang, Jia Pan, Gui-Qing Li, Jian-Qiang Shang, Xue-Qun You, Zhu-Hong Huang, Yu-An Shenzhen University College of Computer Science and Software Engineering Shenzhen China Northwesterm Polytechnical University School of Computer Science Shaanxi China

ISBN: (纸本)9798350337488

Identifying the substrates of ubiquitin protein ligase (E3) and deubiquitinases (DUB) contributes to the discovery of potential therapeutic targets for diseases. However, experimental identification of E3/DUB-substrate interactions is costly and time-consuming. Current computational methods for predicting E3/DUB-substrate interactions rely heavily on specific domain knowledge and involve complex and diverse biological data processing. To address this challenge, we proposed a deep learning prediction model, named Deep-USIpred, which predicts E3/DUB-substrate interactions using protein sequences. The proposed Deep-USIpred model encodes protein sequences with a pretrained model and utilizes 1DCNN-BNN deep learning algorithm to make a robust prediction model. We evaluated the performance of the proposed model on real datasets, and our experimental results show that it can achieve excellent prediction performance on the tasks of ESI and DSI. Our proposed method provides a promising alternative for the prediction of E3/DUB-substrate interactions, which has the potential to accelerate drug discovery for various diseases. The source code and dataset are available at https://***/PGTSING/Deep-USIpred. © 2023 IEEE.

关键词： bayesian neural network deep learning deubiquitinases sequence-based prediction ubiquitin protein ligases E3

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TunSLR-25: A New Static Tunisian Sign Language Recognition System 41st

TunSLR-25: A New Static Tunisian Sign Language Recognition S...

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41st computer Graphics International Conference, CGI 2024

作者： Aloui, Sarra Wang, Yu Fan-fan, Zhou College of Computer Science and Software Engineering Hohai University Nanjing China First Affiliated Hospital of Kunming Medical University Kunming China

ISBN: (纸本)9783031818059

There are 466 M deaf across the world according to World Health Organisation. They are deprived of their basic human right which is communicating with others. Many solutions have been developed to help hard-of-hearing people overcome communication barriers. The existing sign language recognition research focuses on sign languages other than Tunisian Sign Language. Consequently, a Tunisian Sign Language dataset, TunSL-D, was developed for this research. The dataset contains static images that are more complex and can generalize the classification tasks. The images represent the most used words and alphabets in Tunisian Sign language. This paper proposes a novel model TunSLR-25 that recognizes and classifies static Tunisian sign language. The model is first trained on the words data, achieving an accuracy of 95.49%, then trained on both words and alphabets, achieving an accuracy of 88%, with a low parameter number of 1,098,901. The TunSL-D has been trained on SLRNet-8 CNN, a 4-layer CNN model, pre-trained models Resnet50, Resnet101, and InceptionV3. The paper compares the performance of these CNN models and the proposed CNN model TunSLR-25. The system will be integrated into a web application designed to be accessible and user-friendly for this community. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Classification SLR Tunisian Sign Language

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Internet of Things (IoT) applications security trends and challenges

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Discover Internet of Things 2024年第1期4卷 1-22页

作者： Laghari, Asif Ali Li, Hang Khan, Abdullah Ayub Shoulin, Yin Karim, Shahid Khani, Muhammad Adnan Kaim Software College Shenyang Normal University Shenyang China Department of Computer Science Bahria University Karachi Campus Karachi75000 Pakistan School of Information Engineering Xi’an Eurasia University Shaanxi Xian710065 China Department of Computer Science Ilma University Karachi Pakistan

The idea behind layered design is the foundation of the Internet of Things. Each tier uses a variety of technologies for capacity, preparation, and information transmission. With regard to the risks and vulnerabilities related to IoT-enabled devices, as well as potential assurance mechanisms in light of equipment constraints and novel information transfer techniques, this study attempts to analyze the current architecture of the Internet of Things. After that, we talk about IoT architecture and applications. The following is a list of effective real-time IoT applications that are now in use: Self-driving cars, traffic management systems, smart grids, logistic management hierarchies, environment monitoring, building safety applications, and many more are examples of emerging technologies. Specifically, we highlight the latest improvements and challenges associated with IoT security. Finally, we discuss the state-of-the-art research and its conclusions, as well as the outstanding problems for future IoT security research. © The Author(s) 2024.

关键词： Cloud security

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Deep Learning-Based APT Malware and Variants Detection with Attribution Analysis 6

Deep Learning-Based APT Malware and Variants Detection with ...

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6th IEEE International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2023

作者： Tang, Binhui Leng, Peiyun Shen, Xuxiang Wei, Yuhang Sichuan University Chengdu School of Computer and Software Chengdu Jincheng College School of Cyber Science and Engineering China Chengdu Jincheng College School of Computer and Software Chengdu China

ISBN: (纸本)9798350325485

Malware has become one of the most severe security threats in cyber security, among which APT malware attacks are more threatening than advanced sustainable threat attacks. In this paper, we perform APT malware and variant detection based on deep learning and analyze the results by detecting and attribution. The variant detection of APT malware can inhibit the spread of malicious code, which is important for network security detection and defense. This paper proposes a detection and classification method for APT malware and its variants based on deep learning. Firstly, the feature representation method of APT malware based on RGB images is proposed to solve the gradient explosion and gradient disappearance of grayscale map feature extraction and generate images with rich texture information, which can mine deeper features of APT malware attacks. Secondly, this paper also improves the convolutional neural network model by combining the Self-Attention mechanism with the spatial pyramid pool SPP-net to solve the problem of image input of different sizes, as the accuracy is above 92.14%. Then, the experiment results prove the predictive analysis of the model to understand the organization to which the APT attack belongs and the way of its specific attack, providing the possibility of tracing the source. Finally, the visual analysis of APT threat characteristics is presented. © 2023 IEEE.

关键词： Convolutional neural networks

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CARE-EMRs: Efficient and Privacy-Preserving Data Sharing Framework for Electronic Medical Records

CARE-EMRs: Efficient and Privacy-Preserving Data Sharing Fra...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Tao, Yu Zhou, Lu Zhu, Yi Li, Yuhang Ge, Chunpeng Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing China Jiangsu University College of Computer Science and Communication Engineering Zhenjiang China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing China and Software School Jinan China

ISBN: (纸本)9798350386226

Cross-institutional Electronic Medical Records (EMRs) sharing significantly improves healthcare quality and reduces costs. Current EMRs are often stored on cloud servers and protected by Attribute-based Encryption (ABE) for access control. Cross-institutional sharing allows doctors to request EMRs access from target Healthcare Institutions (HIs) through attribute-based collaboration, enabling flexible and secure access. However, existing collaboration in practical EMRs sharing scenarios suffers from inefficiency, hindering timely access and treatment. This paper proposes an efficient and privacy-preserving data sharing framework for EMRs, called CARE-EMRs. CARE-EMRs enables efficient EMRs data access for doctors across different HIs through an outsourcing mechanism. Furthermore, we designed an improved cryptographic primitive PPD-CP-ABE to serve as the security foundation for CARE-EMRs framework, ensuring privacy of the outsourced process. We prove the security of CARE-EMRs. Performance analysis indicates that it significantly reduces the computation and communication overhead. © 2024 IEEE.

关键词： Electronic health record

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Traffic Management in Internet of Vehicles Using Improved Ant Colony Optimization

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computers, Materials & Continua 2023年第6期75卷 5379-5393页

作者： Abida Sharif Imran Sharif Muhammad Asim Saleem Muhammad Attique Khan Majed Alhaisoni Marriam Nawaz Abdullah Alqahtani Ye Jin Kim Byoungchol Chang Department of Computer Science COMSATS University IslamabadPakistan Faculty of Computing Riphah International UniversityFaisalabadPakistan Department of Computer Science HITEC UniversityTaxilaPakistan Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Science UET TaxilaTaxilaPakistan Department of Software Engineering UET TaxilaTaxilaPakistan Software Engineering Department College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityP.O.Box 151Al-Kharj11942KSA Department of Computer Science Hanyang UniversitySeoul04763Korea Center for Computational Social Science Hanyang UniversitySeoul04763Korea

The Internet of Vehicles(IoV)is a networking paradigm related to the intercommunication of vehicles using a *** a dynamic network,one of the key challenges in IoV is traffic management under increasing vehicles to avoid ***,optimal path selection to route traffic between the origin and destination is *** research proposed a realistic strategy to reduce traffic management service response time by enabling real-time content distribution in IoV systems using heterogeneous network ***,this work proposed a novel use of the Ant Colony Optimization(ACO)algorithm and formulated the path planning optimization problem as an Integer Linear Program(ILP).This integrates the future estimation metric to predict the future arrivals of the vehicles,searching the optimal *** the mobile nature of IOV,fuzzy logic is used for congestion level estimation along with the ACO to determine the optimal *** model results indicate that the suggested scheme outperforms the existing state-of-the-art methods by identifying the shortest and most cost-effective ***,this work strongly supports its use in applications having stringent Quality of Service(QoS)requirements for the vehicles.

关键词： Internet of vehicles internet of things fuzzy logic optimization path planning

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Classification of Unbalanced Data Based on Bayesian Optimal Neural Network Model

Journal of Network Intelligence

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Journal of Network Intelligence 2024年第3期9卷 1427-1440页

作者： Ye, Li-Zhu Alkawaz, Mohammed Hazim Johar, Md Gapar Md School of Information Technology and Engineering Guangzhou College of Commerce Guangzhou511363 China School of Graduate Studies Management and Science University 40100 Shah Alam Selangor Malaysia Department of Computer Science College of Education for Pure Science University of Mosul Mosul41002 Iraq Software Engineering and Digital Innovation Center Management and Science University Shah Alam40100 Malaysia

Aiming at the unbalanced problem of big data classification, this paper proposes a classification method of unbalanced data on the ground of Bayesian optimal neural network model. The method firstly uses Optimized Graph Convolutional Network (OGCN) network to carry out message passing mechanism in data graph, designs neural network model with self-attention pooling and mean maximization pooling, and then uses Bayes optimization to update the posterior probability of optimization function by constantly updating the probability model and evaluating the objective function. Effectively solve the problem of speed and optimal solution of hyperparameter optimization in the neural network model. Secondly, the model is used for data set training to obtain the weight of the entire network. Secondly, the unbalanced data is mixed sampling to reduce the number of negative samples. Finally, the capability of the model was verified on the CiteSeer dataset. Experimental results showed that the accuracy rate, accuracy rate and recall rate of the proposed algorithm were 0.974, 0.982 and 0.973, respectively, which effectively improved the accuracy rate, accuracy rate and recall rate of unbalanced data classification compared with other algorithms. © 2024, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Neural network models

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Leaching Fraction (LF) of Irrigation Water for Saline Soils Using Machine Learning

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Intelligent Automation & Soft Computing 2023年第5期36卷 1915-1930页

作者： Rab Nawaz Bashir Imran Sarwar Bajwa Muhammad Waseem Iqbal Muhammad Usman Ashraf Ahmed Mohammed Alghamdi Adel ABahaddad Khalid Ali Almarhabi Department of Computer Science COMSATS University IslamabadVehari CampusPakistan Department of Computer Science and IT Islamia University BahawalpurPakistan Department of Software Engineering Superior University Lahore54000Pakistan Department of Computer Science GC Women UniversitySialkot53310Pakistan Department of Software Engineering College of Computer Science and EngineeringUniversity of Jeddah21493Saudi Arabia Department of Information System Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science College of Computing in Al-QunfudahUmm Al-Qura UniversityMakkah24381Saudi Arabia

Soil salinity is a serious land degradation issue in *** is a major threat to agriculture *** irrigation water is applied to leach down the salts from the root zone of the plants in the form of a Leaching fraction(LF)of irrigation *** the leaching process to be effective,the LF of irriga-tion water needs to be adjusted according to the environmental conditions and soil salinity level in the form of Evapotranspiration(ET)*** relationship between environmental conditions and ET rate is hard to be deﬁned by a linear relationship and data-driven Machine learning(ML)based decisions are required to determine the calibrated Evapotranspiration(ETc)***-assisted ETc is pro-posed to adjust the LF according to the ETc and soil salinity level.A regression model is proposed to determine the ETc rate according to the prevailing tempera-ture,humidity,and sunshine,which would be used to determine the smart LF according to the ETc and soil salinity *** proposed model is trained and tested against the Blaney Criddle method of Reference evapotranspiration(ETo)*** validation of the model from the test dataset reveals the accu-racy of the ML model in terms of Root mean squared errors(RMSE)are 0.41,Mean absolute errors(MAE)are 0.34,and Mean squared errors(MSE)are 0.28 mm *** applications of the proposed solution in a real-time environ-ment show that the LF by the proposed solution is more effective in reducing the soil salinity as compared to the traditional process of leaching.

关键词： Leaching fraction saline soil evapotranspiration machine learning calibrated evapotranspiration artiﬁcial intelligence blaney criddle method

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