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SSRN 2022年

作者： Win, Hsu Myat Tan, Shin Hwei Sui, Yulei Faculty of Engineering and Information Technology University of Technology SydneyNSW2007 Australia Department of Computer Science and Engineering Southern University of Science and Technology Guangdong Shenzhen518055 China

Dynamic slicing aims to find all program statements that directly or indirectly a ff ect the value of a variable occurrence under agiven program input. It is an enabling technique for many software engineering tasks (e.g., program understanding and debugging). Due to Android’s event-driven nature, dynamic slicing for Android is more challenging than that for traditional Java programs. Its asynchronous events drive the execution of an app through inter-component communications. These non-deterministic user events often yield a large search space when applying existing dynamic slicing techniques, which introduce redundant statements into the resulting slice. We present ESDroid, an Event-aware dynamic Slicing technique for AnDroid applications. The novelty of our approach lies in the combination of segment-based delta debugging and dynamic backward slicing to narrow the search space to produce precise slices for Android. Our experiment across 38 apps shows that ESDroid can help with slicing buggy code from exception program points. We compare the e ff ectiveness of ESDroid with the state-of-the-art dynamic slicing tools (AndroidSlicer and Mandoline). ESDroid outperforms both tools in precision by reporting up to 72% fewer spurious statements than AndroidSlicer, and 50% fewer than Mandoline in the resulting slice (number of instructions to be examined). © 2022, The Authors. All rights reserved.

关键词： Android (operating system)

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An Integrated Online-Offline Hybrid Particle Swarm Optimization Framework for Medium Scale Expensive Problems*

An Integrated Online-Offline Hybrid Particle Swarm Optimizat...

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Data-driven Optimization of Complex Systems (DOCS), International Conference on

作者： Hanyu Hu Jian Wang Xiaodong Huang Sergey V. Ablameyko College of Computer Science and Technology China University of Petroleum (East China) Qingdao China College of science) China University of Petroleum (East China) Qingdao China College of Control Science and Engineering China University of Petroleum (East China) Qingdao China Faculty of Applied Mathematics and Computer Science Belarusian State University Minsk Belarus

ISBN: (数字)9798350377842

ISBN: (纸本)9798350377859

Real-world optimization problems often involve computationally expensive evaluations, making efficient optimization crucial. To address medium-scale expensive optimization problems, this paper proposes and investigates an Integrated Online-Offline Hybrid Particle Swarm Optimization Framework (IOOPSOF). The framework integrates concepts from active learning and ensemble learning, iteratively querying the most uncertain and highest-precision solutions to achieve efficient utilization of evaluation resources. IOOPSOF employs two distinct sample selection criteria, with offline optimization for exploration and online optimization for exploitation. The effectiveness of the proposed method is validated through experiments on a series of low to medium-dimensional benchmark functions and the operational parameter optimization of Enhanced Geothermal Systems (EGS). Results demonstrate that IOOPSOF significantly outperforms existing methods, achieving global optimum (0) on most benchmark functions and showing superior performance over single surrogate-assisted evolutionary algorithms (SAEAs) on real-world problems, thereby showcasing its exceptional potential for solving expensive optimization problems.

关键词： Decision making Benchmark testing Computational efficiency Ensemble learning Particle swarm optimization Complex systems Optimization Standards

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Energy Efficient WSN Sink-Cloud Server Authentication Protocol 5

Energy Efficient WSN Sink-Cloud Server Authentication Protoc...

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5th International Conference on Information Systems and computer Networks, ISCON 2021

作者： Nyangaresi, Vincent Omollo Abood, Enas Wahab Abduljabbar, Zaid Ameen Al Sibahe, Mustafa A. Tom Mboya University College Faculty of Biological Physical Sciences Homabay Kenya University of Basrah College of Science Department of Mathemaitics Basrah Iraq University of Basrah College of Education for Pure Science Computer Science Department Basrah Iraq Shenzhen Insitute of Huazhong University of Science & Technology Shenzhen China Iraq University College Department of Computer Technology Engineering Basrah Iraq College of Big Data & Internet Shenzhen Technology University Shenzhen China

ISBN: (纸本)9781665403412

Wireless sensor networks (WSNs) find applications in critical domains where they are deployed to collect mission critical data. The transmission of this private and sensitive data over untrusted communication channels exposes it to several security and privacy attacks. To curb these threats, security schemes have been presented that employ machine learning, bilinear pairing, elliptic curve cryptosystems and biometrics among other approaches. However, machine learning algorithms and bilinear pairing operations have extremely high computation overheads, which is unsuitable for sensor devices. Schemes based on biometrics are susceptible to privileged insider attackers who may collect and misuse user data, while most of the elliptic curve cryptosystems are based on asymmetric cryptography which is inefficient. In light of this, we present an energy efficient and secure authentication scheme. Security evaluation demonstrates that this protocol offers strong mutual authentication and is resilient against impersonation, node capture, stolen verifier untraceability, offline password guessing and anonymity attacks. In terms of performance, it exhibits the lowest computation overheads, energy consumption and average communication overheads. © 2021 IEEE.

关键词： Authentication

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A Parallel and Asynchronous Blob Shape Descriptor Implemented in CMOS technology

A Parallel and Asynchronous Blob Shape Descriptor Implemente...

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International Conference on Microelectronics, MIEL

作者： P. Bogacki M. Długosz T. Talaśka R. Długosz Aptiv Services Poland Kraków Poland Institute of Telecommunications Faculty of Computer Science Electronics and Telecommunications AGH University of Science and Technology Kraków Poland Faculty of Control Robotics and Electrical Engineering Institute of Automation and Robotics Division of Signal Processing and Electronic Systems Poznan University of Technology Poznan Poland Faculty of Telecommunication Computer Science and Electrical Engineering Bydgoszcz University of Science and Technology Bydgoszcz Poland

The paper presents a family of novel light blob shape descriptors for use in selected active safety algorithms used in advanced Driver Assistance Systems (ADAS). One of the motivations was to obtain a descriptor that would ensure low computational complexity. This makes it easy to implement both in software and hardware. One assumption is that the location of the center of a given light spot is approximately known. The principle of its operation is then to count white pixels in selected directions, starting from this central point. A key issue here is an efficient way of determining indexes of particular pixels belonging to the image patch, as well as the location of points representing places where the white area turns into black. In the case of a hardware implementation, this can be done using a parallel circuit operating in asynchronous mode, without the need for a control clock.

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Unlocking the Global Synergies in Low-Rank Adapters

arXiv

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arXiv 2024年

作者： Zhang, Zixi Zhang, Cheng Gao, Xitong Mullins, Robert D. Constantinides, George A. Zhao, Yiren Department of Computer Science University of Cambridge Cambridge United Kingdom Department of Electrical and Eletronic Engineering Imperial College London London United Kingdom Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong China

Low-rank Adaption (LoRA) has been the de-facto parameter-efficient fine-tuning technique for large language models. We present HeteroLoRA, a light-weight search algorithm that leverages zero-cost proxies to allocate the limited LoRA trainable parameters across the model for better fine-tuned performance. In addition to the allocation for the standard LoRA-adapted models, we also demonstrate the efficacy of HeteroLoRA by performing the allocation in a more challenging search space that includes LoRA modules and LoRA-adapted shortcut connections. Experiments show that HeteroLoRA enables improvements in model performance given the same parameter budge. For example, on MRPC, we see an improvement of 1.6% in accuracy with similar training parameter budget. We will open-source our algorithm once the paper is accepted. © 2024, CC BY.

关键词： Budget control

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A Benchmark and Frequency Compression Method for Infrared Few-Shot Object Detection

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IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： Zhang, Ruiheng Yang, Biwen Xu, Lixin Huang, Yan Xu, Xiaofeng Zhang, Qi Jiang, Zhizhuo Liu, Yu Beijing Institute of Technology State Key Laboratory of Electromechanical Dynamic Control Beijing100081 China University of Technology Sydney School of Electrical and Data Engineering UltimoNSW2007 Australia Anhui Polytechnic University School of Computer and Information Wuhan241000 China Technical University of Munich Data Science in Earth Observation Munich85521 Germany Tsinghua University Shenzhen International Graduate School Shenzhen518055 China Tsinghua University Department of Electronic Engineering Beijing100084 China

Infrared few-shot object detection (IFSOD) aims to detect infrared objects with limited labeled examples. Current infrared datasets, however, suffer from limited diversity in object types and classes, hindering robust evaluation of model generalization on novel classes. To systematically assess dataset quality, we propose metrics for class diversity, instance variability, and object density. By integrating three widely used infrared datasets, we construct the first dataset specifically tailored for IFSOD, increasing instance density to 4.8 (a 1.1 improvement) and expanding the number of classes to 18 (a 5-class increase) compared to the source datasets. Furthermore, frequency analysis of spatial features reveals that sparse annotations introduce spectral bias in the frequency domain. Directly transforming spatial features to the frequency domain, however, mixes background noise with object features, causing spectral leakage and impairing the learning of discriminative features for novel classes. To address these issues, we propose the frequency compression few-shot detection (FC-fsd) method, which incorporates a frequency compression (FC) module. The FC module leverages Discrete Cosine Transform (DCT) within localized windows to reduce spectral leakage and enhance feature clarity. With minimal additional computational overhead, FC-fsd significantly outperforms state-of-the-art methods, achieving nAP50 scores of 28.57 (+13.37) and 35.63 (+2.59) in 1-shot and 2-shot settings, respectively. Our dataset is published at https://***/RuihengZhang/IFSOD-dataset. © 2025 IEEE.

关键词： Zero-shot learning

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MEST: Multi-plane Embedding and Spatial-temporal Transformer for Parkinson’s disease diagnosis

MEST: Multi-plane Embedding and Spatial-temporal Transformer...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jin Liu Hao Du Qian Bi Haiyan Liao Yi Pan School of Computer Science and Engineering Central South University Changsha China School of Mathematics and statistics Hunan Frist Normal University Changsha China Department of Radiology the Second Xiangya Hospital Central South University Changsha China Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Parkinson’s disease (PD) is a common neurodegenerative disorder that impairs the patient’s quality of life. Medical imaging technology has provided a variety of neuroimages for PD diagnosis studies. However, how to effectively integrate the rich representations from multi-modality data is still a challenging task. To address this challenging task, we propose a multiplane embedding and spatial-temporal Transformer (MEST) framework for PD diagnosis. Firstly, we project structural magnetic resonance imaging (sMRI) into 2D images from coronal, sagittal and axial directions, respectively. Then, the multi-plane dynamic images are learned by pre-trained VGG11 and attention mechanism for representation learning. Afterwards, time series information of functional magnetic resonance imaging (fMRI) is used to construct dynamic functional connection images. To capture information changes in brain, spatial-temporal connectivity Transformer (SCTransformer) is utilized to address spatial-temporal redundancy and dependencies. To integrate multimodality data, ensemble learning is designed based on majority voting strategy to perform PD diagnosis. We evaluate the proposed method on 279 subjects from an in-house and Parkinsons Progression Markers Initiative (PPMI) dataset. Experimental results show that the MEST achieves promising performance with accuracies of 0.856 and 0.885, and outperforms some state-of-the-art methods.

关键词： Representation learning Time series analysis Redundancy Functional magnetic resonance imaging Transformers Medical diagnosis Ensemble learning

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A Cross-modal Fusion Method for Multispectral Small Ship Detection

A Cross-modal Fusion Method for Multispectral Small Ship Det...

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International Conference on Information Fusion

作者： Yang Liu Yu Liu Xueqian Wang Linping Zhang Zhizhuo Jiang Yaowen Li Chenggang Yan Ying Fu Tao Zhang Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen China Department of Electronic Engineering Tsinghua University Beijing China Tsinghua Shenzhen International Graduate School Tsinghua University and Research Institute of Tsinghua University in Shenzhen Shenzhen China Institute of Information and Control Hangzhou Dianzi University Hangzhou China School of Computer Science and Technology Beijing Institute of Technology Beijing China Lishui Institute Hangzhou Dianzi University Hangzhou China

ISBN: (数字)9781737749769

ISBN: (纸本)9798350371420

The fusion module of RGB and infrared (IR) remote sensing images is the key of multispectral ship detection. Existing works have shown that the cross-attention-based feature fusion can achieve good performance by extracting the complementary information of RGB and IR modalities. However, the existing commonly used cross-attention mechanisms introduce lots of redundancy parameters and mainly focus on global feature interaction of multispectral images, ignoring local detail information that is also important for small ship detection. In this paper, we propose a novel multispectral ship detection approach named LoGFusion. In LoGFusion, we design the cross stage partial module with partial convolution (CSPMPC) to reduce feature redundancy and utilize the local cross-modal fusion module (LoCFM) and global cross-modal fusion module (GCFM) to capture both local and global cross-modal features. Furthermore, we introduce a Multispectral Small Ship Dataset (MSSD) containing over 5k ship targets for small target detection. Experiments on MSSD validate the effectiveness of our method in terms of small ship detection in multispectral images.

关键词： Convolution Redundancy Object detection Feature extraction Data mining Marine vehicles Remote sensing

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Differential Privacy-based Avatar Update in Cooperative Intelligent Transportation System Metaverse 10

Differential Privacy-based Avatar Update in Cooperative Inte...

引用

10th International Conference on computer and Communications, ICCC 2024

作者： Khan, Salabat Luo, Fei Khan, Mansoor Ullah, Insaf Amin, Farhan Ullah, Shamsher Rahim, Mussadiq Abdul School of Computer and Enformation Engineering Qilu Institute of Technology Jinan China School of Computing and Information Technology Great Bay University Dongguan China School of Intelligent Manufacturing and Control Engineering Qilu Institute of Technology Jinan China Institute for Analytics and Data Science University of Essex ColchesterCO4 3SQ United Kingdom School of Computer Science and Engineering Yeungnam University Korea Republic of Shenzhen University National Engineering Laboratory for Big Data Shenzhen China Dhahran31261 Saudi Arabia

Metaverse is the next generation and the successor to the Internet. Various sectors allocated capital to adapt the metaverse due to its inherent importance. Cooperative Intelligent Transportation Systems (C-ITS) is one of the important sectors that can benefit from metaverse adaptation and integration. The C-ITS and metaverse integration give rise to new use cases and cyber attacks. However, traditional Pseudonym Certificate (PCert) authentication based on Vehicular Public Key Infrastructure (VPKI) cannot meet the new use cases and thwart cyber attacks. We present a Differential Privacy (DP)-based PCert and avatar update model to ensure the privacy of end-devices (e.g., vehicles) in the C-ITS metaverse. The proposed DP-based model adds noise to avatar attributes (e.g., color, behavior) to prevent avatar linkage during the PCert update process. Our method ensures that PCerts and avatars cannot be linked to end-devices and the previous PCerts and avatars. We simulate the DP-based method to investigate it in terms of privacy, latency, color distortion, and adversarial success rate, and the results show that DP-based is effective and practical for avatar updates. © 2024 IEEE.

关键词： advanced driver assistance systems

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Designing a Trajectory control System for Wheelchairs with a Combination of Automatic and Manual Steering Methods

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Periodica Polytechnica Transportation engineering 2024年第4期52卷 412-422页

作者： Vu, Van Tan Nguyen, Quang Thinh Nakagawa, Chihiro Nguyen, Trung Nguyen Le, Thanh Long Nguyen, Van Vinh Hoang, Thi Lan Anh Do, Trong Tu Mihály, András Gáspár, Péter Department of Automotive Mechanical Engineering Faculty of Mechanical Engineering University of Transport and Communications Building A6 no. 3 Cau Giay Street Lang Thuong Ward Dong Da District Hanoi100000 Viet Nam Japan Technology Solution Joint Stock Company 9 Dau Ma Street Section 2 Thi Cau Ward Bac Ninh Bac Ninh222000 Viet Nam School of Engineering Osaka Metropolitan University 1-1 Gakuen-cho Nakaku Sakai Osaka599-8531 Japan Emergency abdominal surgery Department Viet Duc University Hospital no. 40 Trang Thi Street Hanoi100000 Viet Nam Mechanical and Power Engineering Faculty Electric Power University no. 235 Hoang Quoc Viet Street Co Nhue Ward Bac Tu Liem District Hanoi100000 Viet Nam Systems and Control Laboratory Institute for Computer Science and Control Hungarian Research Network Kende u. 13-17 Budapest1111 Hungary

A wheelchair is a specialized form of Personal Mobility Vehicle (PMV) designed to help people with disabilities move safely to their desired locations. Unlike conventional PMVs, wheelchairs for people with disabilities are challenging to manually control in environments with heavy traffic. This study focuses on designing and feasibility testing of control systems for PMVs to meet safety and efficiency standards, even in complex environments. First, a control structure model for PMVs is proposed. This is followed by the application of a combination of First In First Out (FIFO) and Dijkstra traffic control methods to determine effective trajectories for PMVs. The proposed control system is then tested on the campus of the university of Transport and Communications. Simulation results show that when comparing the travel time of 10 PMVs, the order execution time for each PMV remains stable (within 10%) for up to 20 PMVs. However, when the number of PMVs increases to 25, 30, 35, and 40 vehicles, the order execution time increases by 35%, 55%, 103%, and 119%, respectively. These results demonstrate that the system can control a large number of PMVs, ensuring safety, flexibility, and efficiency. © 2024 Budapest university of technology and Economics. All rights reserved.

关键词： Travel time

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