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30th Annual Network and Distributed System Security Symposium, NDSS 2023

作者： Chen, Tianyang Xu, Peng Picek, Stjepan Luo, Bo Susilo, Willy Jin, Hai Liang, Kaitai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab China Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering China Cluster and Grid Computing Lab School of Computer Science and Technology China Huazhong University of Science and Technology Wuhan430074 China Digital Security Group Radboud University Nijmegen Netherlands Department of EECS Institute of Information Sciences The University of Kansas LawrenceKS United States Institute of Cybersecurity and Cryptology School of Computing and Information Technology University of Wollongong WollongongNSW2522 Australia Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft2628 CD Netherlands

ISBN: (纸本)1891562835

Dynamic searchable symmetric encryption (DSSE) enables users to delegate the keyword search over dynamically updated encrypted databases to an honest-but-curious server without losing keyword privacy. This paper studies a new and practical security risk to DSSE, namely, secret key compromise (e.g., a user’s secret key is leaked or stolen), which threatens all the security guarantees offered by existing DSSE schemes. To address this open problem, we introduce the notion of searchable encryption with key-update (SEKU) that provides users with the option of non-interactive key updates. We further define the notion of post-compromise secure with respect to leakage functions to study whether DSSE schemes can still provide data security after the client’s secret key is compromised. We demonstrate that post-compromise security is achievable with a proposed protocol called "Bamboo". Interestingly, the leakage functions of Bamboo satisfy the requirements for both forward and backward security. We conduct a performance evaluation of Bamboo using a real-world dataset and compare its runtime efficiency with the existing forward-and-backward secure DSSE schemes. The result shows that Bamboo provides strong security with better or comparable performance. © 2023 30th Annual Network and Distributed System Security Symposium, NDSS 2023. All Rights Reserved.

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Class Incremental Learning for Knowledge Acquisition Based on Prototype Network Gating Mechanism

Class Incremental Learning for Knowledge Acquisition Based o...

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Medical Artificial Intelligence (MedAI), IEEE International Conference on

作者： Yuming Feng Xiaolong Jin Haozhe Liang Jiafeng Guo School of Computer Science and Technology University of Chinese Academy of Sciences CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Institute of Systems Engineering Academy of Military Science

ISBN: (数字)9798350377613

ISBN: (纸本)9798350377620

With the development of information technology, the medical field has accumulated a large amount of medical data, which continues to grow. Efficient extraction and effective utilization of knowledge from this vast amount of medical data are crucial for realizing its immense value. Traditional knowledge acquisition algorithms face challenges in effectively identifying and acquiring new types of knowledge in practical applications. To address this, this paper proposes a class-incremental learning method based on a prototype network gating mechanism, called PGMoE. This method integrates multiple experts through a prototype network-based gating mechanism, dynamically adjusting the model's response to new and old knowledge, thereby effectively mitigating the issue of catastrophic forgetting in class-incremental learning. PGMoE not only improves the stability and accuracy of the model in continuous learning processes but also ensures the model's ability to remember historical knowledge. Comparative experiments on multiple datasets with existing class-incremental learning methods demonstrate that PGMoE has significant performance advantages in handling continuous learning tasks.

关键词： Learning systems Accuracy Incremental learning Knowledge acquisition Scalability Heuristic algorithms Prototypes Logic gates Stability analysis Information technology

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A Preliminary Investigation into Theory-Practice Barriers in Sino-New Zealand Undergraduate computing Education

arXiv

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arXiv 2025年

作者： Dai, Fei Robins, Anthony Peng, Zhihao Huang, Wanni Tan, Chiu-Pih Chen, Tianzhen School of Computing Eastern Institute of Technology Napier New Zealand School of Computing University of Otago Dunedin New Zealand EIT Data Science and Communication College Zhejiang Yuexiu University Zhejiang Shaoxing China

This paper investigates the barriers hindering the effective transition from theoretical knowledge to practical application in a Sino-New Zealand double-degree undergraduate computing programme. In this unique educational setting, students study at a campus in China but complete both Chinese and New Zealand courses taught jointly by lecturers from both countries. Many enrolled students come from non-technical backgrounds, compounding the challenges they face when bridging theoretical concepts and hands-on computing tasks. Through a questionnaire administered to these students, we identify critical obstacles such as insufficient foundational knowledge, language barriers, cultural and pedagogical differences, and difficulties adapting to distinct educational systems. Our analysis reveals that these barriers significantly affect students’ academic performance, engagement, and skill development. Additionally, we examine how factors such as student interest, motivation, and career aspirations relate to these challenges. Based on the findings, we propose targeted interventions, including specialized bridging courses, enhanced language support, refined teaching methods, and improved resource allocation. These recommendations aim to foster a more supportive learning environment that helps students overcome specific theory-practice gaps. Ultimately, this study’s insights contribute to improving educational practices in Sino-New Zealand cooperative programmes and other cross-cultural computing education contexts. © 2025, CC BY-NC-ND.

关键词： Students

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Combat data shift in few-shot learning with knowledge graph

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Frontiers of Computer science 2023年第1期17卷 101-111页

作者： Yongchun ZHU Fuzhen ZHUANG Xiangliang ZHANG Zhiyuan QI Zhiping SHI Juan CAO Qing HE Key Lab of Intelligent Information Processing of Chinese Academy of Sciences(CAS) Institute of Computing TechnologyCASBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China Institute of Artificial Intelligence Beihang UniversityBeijing 100191China Xiamen Institute of Data Intelligence Xiamen 361021China Computer Science and Engineering University of Notre DameIN 46556USA University of California San Diego La JollaCA 92093USA College of Information Engineering&Academy for Multidisciplinary Studies Capital Normal UniversityBeijing 100089China

Many few-shot learning approaches have been designed under the meta-learning framework, which learns from a variety of learning tasks and generalizes to new tasks. These meta-learning approaches achieve the expected performance in the scenario where all samples are drawn from the same distributions (i.i.d. observations). However, in real-world applications, few-shot learning paradigm often suffers from data shift, i.e., samples in different tasks, even in the same task, could be drawn from various data distributions. Most existing few-shot learning approaches are not designed with the consideration of data shift, and thus show downgraded performance when data distribution shifts. However, it is non-trivial to address the data shift problem in few-shot learning, due to the limited number of labeled samples in each task. Targeting at addressing this problem, we propose a novel metric-based meta-learning framework to extract task-specific representations and task-shared representations with the help of knowledge graph. The data shift within/between tasks can thus be combated by the combination of task-shared and task-specific representations. The proposed model is evaluated on popular benchmarks and two constructed new challenging datasets. The evaluation results demonstrate its remarkable performance.

关键词： few-shot data shift knowledge graph

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Copy-Move Detection in Optical Microscopy: A Segmentation Network and A dataset

arXiv

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arXiv 2024年

作者： Shao, Hao-Chiang Liao, Yuan-Rong Tseng, Tse-Yu Chuo, Yen-Liang Lin, Fong-Yi Institute of Data Science and Information Computing National Chung Hsing University Taiwan SOE Technology Inc. Taiwan Ltd. Taiwan

With increasing revelations of academic fraud, detecting forged experimental images in the biomedical field has become a public concern. The challenge lies in the fact that copy-move targets can include background tissue, small foreground objects, or both, which may be out of the training domain and subject to unseen attacks, rendering standard object-detection-based approaches less effective. To address this, we reformulate the problem of detecting biomedical copy-move forgery regions as an intra-image co-saliency detection task and propose CMSeg-Net, a copy-move forgery segmentation network capable of identifying unseen duplicated areas. Built on a multi-resolution encoder-decoder architecture, CMSeg-Net incorporates self-correlation and correlation-assisted spatial-attention modules to detect intra-image regional similarities within feature tensors at each observation scale. This design helps distinguish even small copy-move targets in complex microscopic images from other similar objects. Furthermore, we created a copy-move forgery dataset of optical microscopic images, named FakeParaEgg, using open data from the ICIP 2022 Challenge to support CMSeg-Net’s development and verify its performance. Extensive experiments demonstrate that our approach outperforms previous state-of-the-art methods on the FakeParaEgg dataset and other open copy-move detection datasets, including CASIA-CMFD, CoMoFoD, and CMF. The FakeParaEgg dataset, our source code, and the CMF dataset with our manually defined segmentation ground truths are available at https://***/YoursEver/FakeParaEgg. © 2024, CC BY.

关键词： Image segmentation

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A Medical Chatbot for Diabetic Patients Using Artificial Intelligence

A Medical Chatbot for Diabetic Patients Using Artificial Int...

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International Conference on Biosignals, Images and Instrumentation (ICBSII)

作者： S. Mangai A. Supriya V. Kavitha S. Viveka S. Maheswari S. Govindaraj Department of Biomedical Engineering Velalar College of Engineering and Technology Erode India Department of Data Science and Business systems SRM Institute of Science and Technology Chennai India Department of Computing Technologies SRM Institute of Science and Technology Chennai India

ISBN: (数字)9798331513863

ISBN: (纸本)9798331513870

The quick rise in diabetes cases globally underscores the essential need for technology-driven, innovative management solutions. The project gives the introduction about the cutting-edge in AI-based chatbot tailored for diabetic patients. The combination of Support Vector Machine (SVM), K-Nearest Neighbors (KNN), results the chatbot to provide highly accurate, personalized recommendations to improve self-management. The added advantage is the inclusion of voice-based interaction which offers enhanced accessibility, especially for the elders and visually impaired persons. This study demonstrates about the ability of the chatbot to interpret the complex health data, delivers the real-time insights, and promote adherence to the treatment plans. The advancement in the fusion of machine learning algorithms and voice interaction secures a dynamic and user-friendly experience for diabetes management.

关键词： Support vector machines Accuracy Medical services Machine learning Transforms Nearest neighbor methods Chatbots Diabetes Diseases Biomedical imaging

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EVDNET: Towards Explainable Multi Scale, Anchor Free Vehicle Detection Network in High Resolution Aerial Imagery

EVDNET: Towards Explainable Multi Scale, Anchor Free Vehicle...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Nandini Saini Shubham Gupta Chiranjoy Chattopadhyay Debasis Das Suman Kundu Computer Science & Engineering Indian Institute of Technology Jodhpur Rajasthan India School of Computing and Data Sciences FLAME University Pune Maharashtra India

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

The rapid advancement in deep learning-based object detection methods has made them a prevalent choice for real-time applications. Families of object detectors, including one-stage detectors, two-stage detectors, and region-based CNN networks, offer superior performance in accurately detecting objects. Despite their high accuracy, the complex design and black-box functionality of these models are not directly transferable in aerial imagery. Also, raise questions among users regarding the transparency of the algorithm in locating objects. Consequently, to demystify the decision process of these models, there is a need for Explainable AI (XAI) tools. XAI enables an understanding of the significance of each pixel in an image, shedding light on the contributions that lead to the model’s final output. In this context, this work will present an efficient, explainable, multi-scale vehicle detection network from high resolution aerial imagery, named as EVDNet. The EVDNet model has trained with two publicly available aerial image benchmark dataset DOTA and VEDAI. To enhance interpretability, we leverage XAI method using GradCam. The experimental results not only showcase the effectiveness and performance of the EVDNet model but also provide valuable insights into the object detection process. This research contributes to bridging the gap between complex object detection models and user understanding, offering a more transparent and interpretable approach to high-resolution aerial imagery analysis.

关键词： Visualization Accuracy Image resolution Explainable AI Error analysis Vehicle detection Object detection

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Synergizing Vision and Language in Remote Sensing: A Multimodal Approach for Enhanced Disaster Classification in Emergency Response Systems

Synergizing Vision and Language in Remote Sensing: A Multimo...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Shubham Gupta Nandini Saini Suman Kundu Chiranjoy Chattopadhyay Debasis Das Computer Science & Engineering Indian Institute of Technology Jodhpur Rajasthan India School of Computing and Data Sciences FLAME University Pune Maharashtra India

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

As remote sensing capabilities continue to advance, there is a growing interest in leveraging computer vision and natural language processing for enhanced interpretation of remote sensing scenes. This paper explores the integration of textual information with images to augment traditional disaster classification methods. Our approach utilizes a predefined vision-language model to generate descriptive captions for images, fostering a more nuanced understanding of the remote sensing data. Next, we seamlessly integrate the generated textual information with image data through multimodal training, employing a multimodal deep learning method for disaster classification. The system categorizes input data into predefined disaster categories, presenting a comprehensive and accurate approach to emergency response system development. Experimental evaluations conducted on the AIDER dataset (Aerial Image database for Emergency Response applications) showcase the efficacy of our approach, demonstrating improved accuracy compare to unimodal approach and reliability in disaster classification. This research contributes to the advancement of intelligent emergency response systems by harnessing the synergy between vision and language in the context of remote sensing.

关键词： Training Accuracy Social networking (online) Image databases Disasters Emergency services Real-time systems

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TPIA: Towards Target-specific Prompt Injection Attack against Code-oriented Large Language Models

arXiv

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arXiv 2024年

作者： Yang, Yuchen Yao, Hongwei Yang, Bingrun He, Yiling Li, Yiming Zhang, Tianwei Qin, Zhan State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou310007 China Institute of Blockchain and Data Security Hangzhou310053 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore

Recently, code-oriented large language models (Code LLMs) have been widely and successfully exploited to simplify and facilitate programming. Unfortunately, a few pioneering works revealed that these Code LLMs are vulnerable to backdoor and adversarial attacks. The former poisons the training data or model parameters, hijacking the LLMs to generate malicious code snippets when encountering the trigger. The latter crafts malicious adversarial input codes to reduce the quality of the generated codes. In this paper, we reveal that both attacks have some inherent limitations: backdoor attacks rely on the adversary’s capability of controlling the model training process, which may not be practical;adversarial attacks struggle with fulfilling specific malicious purposes. To alleviate these problems, this paper presents a novel attack paradigm against Code LLMs, namely target-specific prompt injection attack (TPIA). TPIA generates non-functional perturbations containing the information of malicious instructions and inserts them into the victim’s code context by spreading them into potentially used dependencies (e.g., packages or RAG’s knowledge base). It induces the Code LLMs to generate attacker-specified malicious code snippets at the target location. In general, we compress the attacker-specified malicious objective into the perturbation by adversarial optimization based on greedy token search. We collect 13 representative malicious objectives to design 31 threat cases for three popular programming languages. We show that our TPIA can successfully attack three representative open-source Code LLMs (with an attack success rate of up to 97.9%) and two mainstream commercial Code LLM-integrated applications (with an attack success rate of over 90%) in all threat cases, using only a 12-token non-functional perturbation. Copyright © 2024, The Authors. All rights reserved.

关键词： Open source software

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RIS-Assisted Localization: A Novel Conditional Sample Mean Approach without CSI

arXiv

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arXiv 2025年

作者： Yao, Jiawei Mao, Yijie Chen, Mingzhe School of Information Science and Technology ShanghaiTech University Shanghai China Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL United States

Reconfigurable intelligent surface (RIS) has been recognized as a promising solution for enhancing localization accuracy. Traditional RIS-based localization methods typically rely on prior channel knowledge, beam scanning, and pilot-based assistance. These approaches often result in substantial energy and computational overhead, and require real-time coordination between the base station (BS) and the RIS. In this work, we propose a novel multiple RISs aided localization approach to address these challenges. The proposed method first estimates the angle-of-directions (AoDs) between the RISs and the user using the conditional sample mean approach, and then uses the estimated multiple AoD pairs to determine the user’s position. This approach only requires measuring the received signal strength at the BS for a set of randomly generated phase shifts across all RISs, thereby eliminating the need for real-time RIS phase shift design or user-to-BS pilot transmissions. Numerical results show that the proposed localization approach improves localization accuracy while significantly reducing energy and signaling overhead compared to conventional methods. Copyright © 2025, The Authors. All rights reserved.

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