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5th Joint International Conference on AI, Big Data and Blockchain, ABB 2024

作者： Kassaymeh, Sofian Rjoub, Gaith Dssouli, Rachida Bentahar, Jamal Almobydeen, Shahed Bassam Faculty of Information Technology Aqaba University of Technology Aqaba Jordan Concordia Institute for Information Systems Engineering Concordia University Montreal Canada Department of Computer Science 6G Research Center Khalifa University Abu Dhabi United Arab Emirates

ISBN: (纸本)9783031731501

In order to design and develop secure and reliable software, accurate prediction of software errors is crucial. The complex, nonlinear relationship between software features and bugs that occur during development despite precautions and measures taken to prevent them makes it difficult for traditional empirical models to predict these bugs with any degree of accuracy. An integration between the Decision Tree (DT) model and the SHAP (Shapley Additive exPlanations) technique was developed in this work with the aim of predicting software faults and providing informative explanations of the expected results. The synergistic advantages of integrating DT and SHAP allow the creation of an accurate, efficient, and fully interpretable technique. In order to make the process visible and reliable, SHAP provides a global explanation of how features of the developed software affect quality and a local explanation of how features contribute to each prediction. The tendency of software features to influence software quality was revealed by feature dependence analysis. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Adversarial machine learning

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A Hierarchal Clustered Based Proactive Caching in NDN-Based Vehicular network

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computer Systems science & engineering 2023年第10期47卷 1185-1208页

作者： Muhammad Yasir Khan Muhammad Adnan Jawaid Iqbal Noor ul Amin Byeong-Hee Roh Jehad Ali Department of Computer Science&Information Technology Hazara University Mansehra21120Pakistan Department of Electronics Engineering Kookmin University Seoul Korea Faculty of Computing Riphah International University Islamabad Pakistan Department of AI Convergence Network Ajou UniversitySuwon16499Korea

An information-Centric network(ICN)provides a promising paradigm for the upcoming internet architecture,which will struggle with steady growth in data and changes in *** ICN architectures have been designed,including Named Data networking(NDN),which is designed around content delivery instead of *** data is the central part of the ***,NDN was developed to get rid of the dependency on IP addresses and provide content *** is one of the major research dimensions for this upcoming internet *** research has been carried out to solve the mobility issues,but it still has problems like handover delay and packet loss ratio during real-time video streaming in the case of consumer and producer *** solve this issue,an efficient hierarchical Cluster Base Proactive Caching for Device Mobility Management(CB-PC-DMM)in NDN Vehicular networks(NDN-VN)is proposed,through which the consumer receives the contents proactively after handover during the mobility of the *** a consumer moves to the next destination,a handover interest is sent to the connected router,then the router multicasts the consumer’s desired data packet to the next hop of neighboring ***,once the handover process is completed,consumers can easily get the content to the newly connected router.A CB-PCDMM in NDN-VN is proposed that improves the packet delivery ratio and reduces the handover delay aswell as cluster ***,the intra and inter-domain handover handling procedures in CB-PC-DMM for NDN-VN have been *** the validation of our proposed scheme,MATLAB simulations are *** simulation results show that our proposed scheme reduces the handover delay and increases the consumer’s interest satisfaction *** proposed scheme is compared with the existing stateof-the-art schemes,and the total percentage of handover delays is decreased by up to 0.1632%,0.3267%,2.3437%,2.3255%,and 3.7313%at the mobil

关键词： Vehicular network named data networking caching hierarchical architecture

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Sampled-Data Safe Stabilization of Nonlinear Time-Delay Systems

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IFAC-PapersOnLine 2024年第27期58卷 78-83页

作者： M. Di Ferdinando A. Borri S. Di Gennaro P. Pepe Department of Information Engineering Computer Science and Mathematics Center of Excellence DEWS University of L'Aquila L'Aquila Italy Institute of Systems Analysis and Computer Science National Research Council Rome Italy

In this paper, the sampled-data stabilization problem under safety constraints is studied for nonlinear time-delay systems. In particular, a methodology for the design of sampled-data safe stabilizers is provided for nonlinear systems affected by state delays. As a first step, the new notion of Safe Steepest Descent Feedback (SSDF), continuous or not, is introduced by suitably combining the well-known notions of Steepest Descent Feedbacks and Barrier functions considered to cope with safety requirements. Then, the stabilization in the sample-and-hold sense theory is used as a tool to show the existence of a suitably fast sampling such that: the digital implementation of SSDFs, continuous or not, ensures the semi–global practical safe stability property of the related closed-loop system with arbitrarily small final target ball of the origin. In the theory developed here, time-varying sampling periods are allowed. The proposed theoretical results are validated through an application concerning the plasma glucose regulation problem in Type-2 diabetic patients via artificial pancreas.

关键词： Sampled-Data Controllers Safe Stabilization Nonlinear Time-Delay Systems Stabilization in the Sample-and-Hold sense Barrier Functions Lyapunov-Krasovskii functionals Glucose-Insulin model

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MSPA-DLA++: A Multi-Scale Phase Attention Deep Layer Aggregation for Lesion Detection in Multi-Phase CT Images

MSPA-DLA++: A Multi-Scale Phase Attention Deep Layer Aggrega...

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19th World Congress on Medical and Health Informatics, MedInfo 2023

作者： Kitrungrotsakul, Titinunt Xu, Yingying Chen, Qingqing Liu, Jing Li, Yinhao Lin, Lanfen Hu, Hongjie Tong, Ruofeng Li, Jingsong Chen, Yen-Wei Research Center for Healthcare Data Science Zhejiang Lab China Department of Radiology Sir Run Run Shaw Hospital China Graduate School of Information Science and Engineering Ritsumeikan Univ. Japan College of Computer Science and Technology Zhejiang Univ. China

ISBN: (纸本)9781643684567

Object detection using convolutional neural networks (CNNs) has achieved high performance and achieved state-of-the-art results with natural images. Compared to natural images, medical images present several challenges for lesion detection. First, the sizes of lesions vary tremendously, from several millimeters to several centimeters. Scale variations significantly affect lesion detection accuracy, especially for the detection of small lesions. Moreover, the effective extraction of temporal and spatial features from multi-phase CT images is also an important issue. In this paper, we propose a group-based deep layer aggregation method with multiphase attention for liver lesion detection in multi-phase CT images. The method, which is called MSPA-DLA++, is a backbone feature extraction network for anchor-free liver lesion detection in multi-phase CT images that addresses scale variations and extracts hidden features from such images. The effectiveness of the proposed method is demonstrated on public datasets (LiTS2017) and our private multiphase dataset. The results of the experiments show that MSPA-DLA++ can improve upon the performance of state-of-the-art networks by approximately 3.7%. © 2024 International Medical Informatics Association (IMIA) and IOS Press.

关键词： Feature extraction

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Band Sampling of Hyperspectral Anomaly Detection in Effective Anomaly Space

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IEEE Transactions on Geoscience and Remote Sensing 2024年 62卷 1-29页

作者： Chang, Chein-I Lin, Chien-Yu Hu, Peter Fuming Information and Technology College Dalian116026 China University of Maryland Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering BaltimoreMD21250 United States National Cheng Kung University Department of Electrical Engineering Tainan70101 Taiwan University of Maryland School of Medicine R Adams Cowley Shock Trauma Center Shock Trauma Anesthesia Organized Research Center Department of Anesthesia BaltimoreMD21201 United States

This article investigates four issues, background (BKG) suppression (BS), anomaly detectability, noise effect, and interband correlation reduction (IBCR), which have significant impacts on its performance. Despite that a recently developed effective anomaly space (EAS) was designed to use data sphering (DS) to remove the second-order data statistics characterized by BKG, enhance anomaly detectability, and reduce noise effect, it does not address the IBCR issue. To cope with this issue, this article introduces band sampling (BSam) into EAS to reduce IBCR and further suppress BKG more effectively. By implementing EAS in conjunction with BSam (EAS-BSam), these four issues can be resolved altogether for any arbitrary anomaly detector. It first modifies iterative spectral-spatial hyperspectral anomaly detection (ISSHAD) to develop a new variant of ISSHAD, called iterative spectral-spatial maximal map (ISSMax), and then generalizes ISSMax to EAS-ISSMax, which further enhances anomaly detectability and noise removal. Finally, EAS-BSam is implemented to reduce IBCR. As a result, combining EAS, BSam, and ISSMax yields four versions: EAS-BSam, EAS-ISSMax, BSam-SSMAX, and EAS-BSam-SSMax. Such integration presents a great challenge because all these four versions are derived from different aspects, iterative spectral-spatial feedback process, compressive sensing, and low-rank and sparse matrix decomposition. Experiments demonstrate that EAS-BSam and EAS-BSam-SSMax show their superiority to ISSHAD and many current existing hyperspectral anomaly detection (HAD) methods. © 1980-2012 IEEE.

关键词： Iterative methods

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QoS Improvement using Federated Learning-based Position Prediction for mobile IoT 10

QoS Improvement using Federated Learning-based Position Pred...

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10th IEEE World Forum on Internet of Things, WF-IoT 2024

作者： Zafar, Saniya Alshehri, Mohammed S. Shah, Syed Aziz Ahmad, Jawad Zafar, Adnan Khattak, Aizaz Ahmad Institute of Space Technology Department of Electrical Engineering Islamabad Pakistan Najran University College of Computer Science and Information Systems Department of Computer Science Saudi Arabia Coventry University Research Centre for Intelligent Healthcare United Kingdom Prince Mohammad Bin Fahd University Cybersecurity Center Alkhobar Saudi Arabia Edinburgh Napier University School of Computing Engineering and the Built Environment United Kingdom

ISBN: (纸本)9798350373011

The rapid advancement in mobile broadband technology has evolved wireless infrastructure influencing internet-of-things (IoTs). This evolution has made mobile IoTs (mIoTs) as centre of attention due to its potential of providing seamless connectivity to enable intelligent transportation systems with huge data demands of vehicular users. To address the ever-escalating data demands of vehicular mobile users, vehicular edge computing (VEC) and moving small cells (mSCs) play a pivotal role. mSCs together with VEC are capable to provide improved service quality for mIoTs. However, incorporating the mSCs in vehicles for mIoTs presents challenges while assigning the resources in mIoT-mSC network. This is due to the existence of time varying interference among mSCs. This work explores the distributed resource assignment mechanism for RSU assisted mIoT-mSC network formed in vehicular environment. In the proposed work, location prediction of city buses deployed with mSCs is done using federated learning (FL) which is subsequently utilized for efficient assignment of resources in mIoT-mSC network. Numerical results are provided to validate the proposed resource assignment mechanism. © 2024 IEEE.

关键词： Intelligent systems

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Planning Data Poisoning Attacks on Heterogeneous Recommender Systems in a Multiplayer Setting

Planning Data Poisoning Attacks on Heterogeneous Recommender...

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39th IEEE International Conference on Data engineering, ICDE 2023

作者： Yeh, Chin-Yuan Chen, Hsi-Wen Yang, De-Nian Lee, Wang-Chien Yu, Philip S. Chen, Ming-Syan National Taiwan University Graduate Institute of Communication Engineering Taiwan National Taiwan University Department of Electrical Engineering Taiwan Academia Sinica Institute of Information Science Taiwan Academia Sinica Research Center for Information Technology Innovation Taiwan The Pennsylvania State University Department of Computer Science and Engineering United States University of Illinois Chicago Department of Computer Science United States

ISBN: (纸本)9798350322279

Data poisoning attacks against recommender systems (RecSys) often assume a single seller as the adversary. However, in reality, there are usually multiple sellers attempting to promote their items through RecSys manipulation. To obtain the best data poisoning plan, it is important for an attacker to anticipate and withstand the actions of his opponents. This work studies the problem of Multiplayer Comprehensive Attack (MCA) from the perspective of the attacker, considering the subsequent attacks by his opponents. In MCA, we target the Heterogeneous RecSys, where user-item interaction records, user social network, and item correlation graph are used for recommendations. To tackle MCA, we present the Multilevel Stackelberg Optimization over Progressive Differentiable Surrogate (MSOPDS). The Multilevel Stackelberg Optimization (MSO) method is used to form the optimum strategies by solving the Stackelberg game equilibrium between the attacker and his opponents, while the Progressive Differentiable Surrogate (PDS) addresses technical challenges in deriving gradients for candidate poisoning actions. Experiments on Heterogeneous RecSys trained with public datasets show that MSOPDS outperforms all examined prior works by up to 10.6% in average predicted ratings and up to 11.4% in HitRate@3 for an item targeted by an attacker facing one opponent. Source code provided in https://***/jimmy-academia/MSOPDS. © 2023 IEEE.

关键词： Recommender systems

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Error budget of a parametric resonance entangling gate with a tunable coupler

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Physical Review Applied 2024年第1期22卷 014059-014059页

作者： Eyob A. Sete Vinay Tripathi Joseph A. Valery Daniel Lidar Josh Y. Mutus 775 Heinz Avenue Berkeley California 94710 USA Department of Physics & Astronomy and Center for Quantum Information Science & Technology Departments of Electrical & Computer Engineering Chemistry Physics & Astronomy and Center for Quantum Information Science & Technology

We analyze the experimental error budget of parametric resonance gates in a tunable coupler architecture. We identify and characterize various sources of errors, including incoherent, leakage, amplitude, and phase errors. By varying the two-qubit gate time, we explore the dynamics of these errors and their impact on the gate fidelity. To accurately capture the impact of incoherent errors on gate fidelity, we measure the coherence times of qubits under gate operating conditions. Our findings reveal that the incoherent errors, mainly arising from qubit relaxation and dephasing due to white noise, limit the fidelity of the two-qubit gates. Moreover, we demonstrate that leakage to noncomputational states is the second largest contributor to the two-qubit gates infidelity, as characterized using leakage-randomized benchmarking. The error budgeting methodology we develop here can be effectively applied to other types of gate implementations.

关键词： Quantum computation Quantum control Quantum engineering

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Learning Rich information for Quad Bayer Remosaicing and Denoising 17th

Learning Rich Information for Quad Bayer Remosaicing and D...

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17th European Conference on computer Vision, ECCV 2022

作者： Jia, Jun Sun, Hanchi Liu, Xiaohong Xiao, Longan Xu, Qihang Zhai, Guangtao Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Shanghai China John Hopcroft Center for Computer Science Shanghai Jiao Tong University Shanghai China Shanghai Transsion Information Technology Shanghai China

ISBN: (纸本)9783031250712

In this paper, we propose a DNNs-based solution to jointly remosaic and denoise the camera raw data in Quad Bayer pattern. The traditional remosaic problem can be viewed as an interpolation process that converts the Quad Bayer pattern to a normal CFA pattern, such as the RGGB one. However, this process becomes more challenging when the input Quad Bayer data is noisy. In addition, the limited amount of data available for this task is not sufficient to train neural networks. To address these issues, we view the remosaic problem as a bayer reconstruction problem and use an image restoration model to remove noises while remosaicing the Quad Bayer data implicitly. To make full use of the color information, we propose a two-stage training strategy. The first stage uses the ground-truth RGGB Bayer map to supervise the reconstruction process, and the second stage leverages the provided Image Signal Processor (ISP) to generate the RGB images from our reconstructed bayers. With the use of color information in the second stage, the quality of reconstructed bayers is further improved. Moreover, we propose a data pre-processing method including data augmentation and bayer rearrangement. The experimental results show it can significantly benefit the network training. Our solution achieves the best KLD score with one order of magnitude lead, and overall ranks the second in Quad Joint Remosaic and Denoise @ MIPI-challenge. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Image reconstruction

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Quantum Remote State Preparation Based on Quantum network Coding

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computers, Materials & Continua 2022年第10期73卷 119-132页

作者： Zhen-Zhen Li Zi-Chen Li Yi-Ru Sun Haseeb Ahmad Gang Xu Xiu-Bo Chen School of Information Engineering Beijing Institute of Graphic CommunicationBeijing102600China School of Computer Science and Information Engineering Zhejiang Gongshang UniversityHangzhou310018China Information Security Center State key Laboratory of Networking and Switching TechnologyBeijing University of Posts and TelecommunicationsBeijing100876China Department of Computer Science National Textile UniversityFaisalabad37610Pakistan School of Information Science and Technology North China University of TechnologyBeijing100144China Advanced Cryptography and System Security Key Laboratory of Sichuan Province China

As an innovative theory and technology,quantum network coding has become the research hotspot in quantum network *** this paper,a quantum remote state preparation scheme based on quantum network coding is *** with the general quantum remote state preparation schemes,our proposed scheme brings an arbitrary unknown quantum state finally prepared remotely through the quantum network,by designing the appropriate encoding and decoding steps for quantum network *** is worth mentioning,from the network model,this scheme is built on the quantum k-pair network which is the expansion of the typical bottleneck network—butterfly ***,it can be treated as an efficient quantum network preparation scheme due to the characteristics of network coding,and it also makes the proposed scheme more applicable to the large-scale quantum *** addition,the fact of an arbitrary unknown quantum state remotely prepared means that the senders do not need to know the desired quantum ***,the security of the proposed scheme is ***,this scheme can always achieve the success probability of 1 and 1-max flow of value ***,the communication efficiency of the proposed scheme is ***,the proposed scheme turns out to be practicable,secure and efficient,which helps to effectively enrich the theory of quantum remote state preparation.

关键词： Quantum remote state preparation quantum network coding quantum k-pair network

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