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Advancement in Data Science, E-learning and Information Systems (ICADEIS), International Conference on

作者： Novian Anggis Suwastika Paulus Berliz Sitohang Rahmat Yasirandi Maslin Masrom Qori Qonita Razak Faculty of Technology and Informatics Universiti Teknologi Malaysia Kuala Lumpur Malaysia School of Computing Telkom University Bandung Indonesia School of Information Technology King Mongkut's Institute of Technology Ladkrabang Ladkrabang Thailand Broadcast and Film Area of Expertise Public Vocational High School 10 Bandung Bandung Indonesia

Children can identify body parts either on dolls or on their bodies. The inability of toddlers (aged 2-3 years) to demonstrate body parts can indicate potential delays in language, cognitive, and body scheme development. The integration of teaching aids for body part identification with the Internet of Things (IoT) offers several advantages, including automation and customization of activities, digital storage of activity results for easy accessibility, real-time and accurate evaluation and feedback, as well as the freedom to repeat activities without limitations. This study focuses on implementing the integration of body part identification trainers with IoT, following the five phases of IoT development: initialization, analysis, design, implementation, and evaluation. The initialization phase involves problem identification, and the position of this research is based on a literature study. The analysis phase establishes parameters for measuring the functionality and performance of the system, as well as requirements for software and hardware components. The design phase produces the system architecture and system flowchart. The implementation phase results in a prototype integrating hardware and software components. Evaluation results based on the predefined functionality and performance parameters set during the analysis phase indicate that all system functionalities have been successfully achieved. The system demonstrates a response time below 1 second for sensor buttons and LED lights, and the feedback system shows 100% accuracy. This prototype highlights the potential and opportunities of implementing IoT to support learning at various levels, specifically at the pre-school level, as shown in this study.

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Toward efficient resource utilization at edge nodes in federated learning

arXiv

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arXiv 2023年

作者： Alawadi, Sadi Ait-Mlouk, Addi Toor, Salman Hellander, Andreas Department of Computer Science Blekinge Institute of Technology Karlskrona Sweden School of Informatics University of Skövde Skövde Sweden Department of Information Technology Division of Scientific Computing Uppsala University Sweden Scaleout Systems Sweden

Federated learning (FL) enables edge nodes to collaboratively contribute to constructing a global model without sharing their data. This is accomplished by devices computing local, private model updates that are then aggregated by a server. However, computational resource constraints and network communication can become a severe bottleneck for larger model sizes typical for deep learning (DL) applications. Edge nodes tend to have limited hardware resources (RAM, CPU), and the network bandwidth and reliability at the edge is a concern for scaling federated fleet applications. In this paper, we propose and evaluate a FL strategy inspired by transfer learning in order to reduce resource utilization on devices, as well as the load on the server and network in each global training round. For each local model update, we randomly select layers to train, freezing the remaining part of the model. In doing so, we can reduce both server load and communication costs per round by excluding all untrained layer weights from being transferred to the server. The goal of this study is to empirically explore the potential trade-off between resource utilization on devices and global model convergence under the proposed strategy. We implement the approach using the FL framework FEDn. A number of experiments were carried out over different datasets (CIFAR-10, CASA, and IMDB), performing different tasks using different DL model architectures. Our results show that training the model partially can accelerate the training process, efficiently utilizes resources on-device, and reduce the data transmission by around 75% and 53% when we train 25%, and 50% of the model layers, respectively, without harming the resulting global model accuracy. Furthermore, our results demonstrate a negative correlation between the number of participating clients in the training process and the number of layers that need to be trained on each client’s side. As the number of clients increases, there is a decrease

关键词： Data privacy

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DETERMINISTIC COUNTING FROM COUPLING INDEPENDENCE

arXiv

引用

arXiv 2024年

作者： Chen, Xiaoyu Feng, Weiming Guo, Heng Zhang, Xinyuan Zou, Zongrui State Key Laboratory for Novel Software Technology New Cornerstone Science Laboratory Nanjing University 163 Xianlin Avenue Jiangsu Province Nanjing China School of Computing and Data Science The University of Hong Kong Pokfulam Road Hong Kong School of Informatics University of Edinburgh Informatics Forum EdinburghEH8 9AB United Kingdom

We show that spin systems with bounded degrees and coupling independence admit fully polynomial time approximation schemes (FPTAS). We design a new recursive deterministic counting algorithm to achieve this. As applications, we give the first FPTASes for q-colourings on graphs of bounded maximum degree ∆ ≥ 3, when q ≥ (11/6 − Ε0)∆ for some small Ε0 ≈ 10−5, or when ∆ ≥ 125 and q ≥ 1.809∆, and on graphs with sufficiently large (but constant) girth, when q ≥ ∆ + 3. These bounds match the current best randomised approximate counting algorithms by Chen, Delcourt, Moitra, Perarnau, and Postle (2019), Carlson and Vigoda (2024), and Chen, Liu, Mani, and Moitra (2023), respectively. © 2024, CC BY.

关键词： Polynomial approximation

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A Closer Look at Branch Classifiers of Multi-exit Architectures

arXiv

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arXiv 2022年

作者： Lin, Shaohui Ji, Bo Ji, Rongrong Yao, Angela The School of Computer Science and Technology East China Normal University 200062 China The School of Computing National University of Singapore Singapore117417 Singapore The Lab of Media Analytics and Computing School of Informatics Xiamen University 361005 China

Multi-exit architectures consist of a backbone and branch classifiers that offer shortened inference pathways to reduce the run-time of deep neural networks. In this paper, we analyze different branching patterns that vary in their allocation of computational complexity for the branch classifiers. Constant-complexity branching keeps all branches the same, while complexity-increasing and complexity-decreasing branching place more complex branches later or earlier in the backbone respectively. Through extensive experimentation on multiple backbones and datasets, we find that complexity-decreasing branches are more effective than constant-complexity or complexity-increasing branches, which achieve the best accuracy-cost trade-off. We investigate a cause by using knowledge consistency to probe the effect of adding branches onto a backbone. Our findings show that complexity-decreasing branching yields the least disruption to the feature abstraction hierarchy of the backbone, which explains the effectiveness of the branching patterns. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep neural networks

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Hyperledger Fabric Blockchain: Secure and Efficient Solution for Electronic Health Records

引用

Computers, Materials & Continua 2021年第8期68卷 2377-2397页

作者： Mueen Uddin M.S.Memon Irfana Memon Imtiaz Ali Jamshed Memon Maha Abdelhaq Raed Alsaqour Faculty of Science Universiti Brunei DarusslamGadongBE1410Negara Brunei Darussalam CSE Department QUESTNawabshahPakistan School of Computing and Mathematics Ulster UniversityJordanstown CampusNewtownabbeyBT370QBUK Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia Department of Information Technology College of Computing and InformaticsSaudi Electronic UniversityRiyadh93499Saudi Arabia

Background:Electronic Health Record(EHR)systems are used as an efficient and effective technique for sharing patient’s health records among different hospitals and various other key stakeholders of the healthcare industry to achieve better diagnosis and treatment of patients ***,the existing EHR systems mostly lack in providing appropriate security,entrusted access control and handling privacy and secrecy issues and challenges in current hospital ***:To solve this delicate problem,we propose a Blockchain-enabled Hyperledger Fabric Architecture for different EHR ***:In our EHR blockchain system,Peer nodes from various organizations(stakeholders)create a ledger network,where channels are created to enable secure and private communication between different stakeholders on the ledger *** patients and other stakeholders are identified and registered on the network by unique digital certificates issued by membership service provider(MSP)component of the fabric ***:We created and implemented different Chaincodes to handle the business logic for executing separate EHR transactions on the *** proposed fabric architecture provides a secure,transparent and immutable mechanism to store,share and exchange EHRs in a peer-to-peer network of different healthcare *** ensures interoperability,scalability and availability in adapting the existing EHRs for strengthening and providing an effective and secure method to integrate and manage patient records among medical institutions in the healthcare ecosystem.

关键词： Electronic health records blockchain hyperledger fabric patient data privacy private permissioned blockchain healthcare ecosystem

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LIBALCHEMY: A Two-Layer Persistent Summary Design for Taming Third-Party Libraries in Static Bug-Finding Systems

LIBALCHEMY: A Two-Layer Persistent Summary Design for Taming...

引用

International Conference on Software Engineering (ICSE)

作者： Rongxin Wu Yuxuan He Jiafeng Huang Chengpeng Wang Wensheng Tang Qingkai Shi Xiao Xiao Charles Zhang Xiamen Key Laboratory of Intelligent Storage and Computing School of Informatics Xiamen University Xiamen China The Hong Kong University of Science and Technology Hong Kong China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Person Hang Zhou China

ISBN: (数字)9798400702174

ISBN: (纸本)9798350382143

Despite the benefits of using third-party libraries (TPLs), the misuse of TPL functions raises quality and security concerns. Using traditional static analysis to detect bugs caused by TPL function is nontrivial. One promising solution would be to automatically generate and persist the summaries of TPL functions offline and then reuse these summaries in compositional static analysis online. However, when dealing with millions of lines of TPL code, the summaries designed by existing studies suffer from an unresolved paradox. That is, a highly precise form of summary leads to an unaffordable space and time overhead, while an imprecise one seriously hurts its precision or recall. To address the paradox, we propose a novel two-layer summary design. The first layer utilizes a line-sized program representation known as the program dependence graph to compactly encode path conditions, while the second layer encodes bug-type-specific properties. We implemented our idea as a tool called Libalchemy and evaluated it on fifteen mature and extensively checked open-source projects. Experimental results show that Libalchemy can check over ten million lines of code within ten hours. Libalchemy has detected 55 true bugs with a high precision of 90.16%, eleven of which have been assigned CVE IDs. Compared to whole-program analysis and the conventional design of path-sensitively precise summaries, Libalchemy achieves an 18.56× and 12.77× speedup and saves 91.49% and 90.51% of memory usage, respectively.

关键词： Codes Computer bugs Static analysis Libraries Security Software engineering

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FROM IDEA TO IMPLEMENTATION: EVALUATING THE INFLUENCE OF LARGE LANGUAGE MODELS IN SOFTWARE DEVELOPMENT - AN OPINION PAPER

arXiv

引用

arXiv 2025年

作者： Yadav, Sargam Qureshi, Asifa Mehmood Kaushik, Abhishek Sharma, Shubham Loughran, Roisin Kazhuparambil, Subramaniam Shaw, Andrew Sabry, Mohammed Lynch, Niamh St John Singh, Nikhil O’Hara, Padraic Jaiswal, Pranay Chandru, Roshan Lillis, David School of Informatics and Creative Arts Dundalk Institute of Technology Dundalk Ireland TU Dublin Dublin Ireland Zendesk Dublin Ireland Department of Computing Science and Mathematics Dundalk Institute of Technology Dundalk Ireland ADAPT Centre Dublin Ireland National College of Ireland Dublin Ireland Dublin Ireland

The introduction of transformer architecture was a turning point in Natural Language Processing (NLP). Models based on the transformer architecture such as Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre-Trained Transformer (GPT) have gained widespread popularity in various applications such as software development and education. The availability of Large Language Models (LLMs) such as ChatGPT and Bard to the general public has showcased the tremendous potential of these models and encouraged their integration into various domains such as software development for tasks such as code generation, debugging, and documentation generation. In this study, opinions from 11 experts regarding their experience with LLMs for software development have been gathered and analysed to draw insights that can guide successful and responsible integration. The overall opinion of the experts is positive, with the experts identifying advantages such as increase in productivity and reduced coding time. Potential concerns and challenges such as risk of over-dependence and ethical considerations have also been highlighted. © 2025, CC BY.

关键词： Software design

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DQFORMER: Dynamic Query Transformer for Lane Detection

DQFORMER: Dynamic Query Transformer for Lane Detection

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Hao Yang Shuyuan Lin Runqing Jiang Yang Lu Hanzi Wang Fujian Key Laboratory of Sensing and Computing for Smart City School of Informatics Xiamen University Xiamen China College of Information Science and Technology / College of Cyber Security Jinan University Guangzhou China

Lane detection is one of the most important tasks in self-driving. The critical purpose of lane detection is the prediction of lane shapes. Meanwhile, it is challenging and difficult to determine lane instance positions before predicting lane shapes in an image. In this paper, we propose a top-down method called Dynamic Query Transformer (DQFormer), which uses a Dynamic Lane Queries (DLQs) module to predict lane shapes. Specifically, to accurately predict lane shapes, we propose a new framework for generating dynamic weights based on DLQs, which can focus on the context of lane shapes dynamically. Unlike existing transformer-based methods, the proposed DQFormer does not require setting a fixed number of lane queries, so it is suitable for various scenes. In addition, we further propose a Line Voting Module (LVM) which collects votes from other lanes to enhance lane features, to determine lane instance positions. Extensive experiments demonstrate that DQFormer outperforms several state-of-the-art methods on two popular lane detection benchmarks (i.e., CULane and TuSimple).

关键词： Shape Lane detection Signal processing Benchmark testing Transformers Acoustics Task analysis

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Cross-Modality Perturbation Synergy Attack for Person Re-identification

arXiv

引用

arXiv 2024年

作者： Gong, Yunpeng Zhong, Zhun Qu, Yansong Luo, Zhiming Ji, Rongrong Jiang, Min School of Informatics Xiamen University China School of Computer Science and Information Engineering Hefei University of Technology China Department of Artificial Intelligence Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China School of Informatics Key Laboratory of Digital Protection and Intelligent Processing of Intangible CulturalHeritage of Fujian and Taiwan Ministry of Culture and Tourism Xiamen University Fujian Xiamen361005 China

In recent years, there has been significant research focusing on addressing security concerns in single-modal person re-identification (ReID) systems that are based on RGB images. However, the safety of cross-modality scenarios, which are more commonly encountered in practical applications involving images captured by infrared cameras, has not received adequate attention. The main challenge in cross-modality ReID lies in effectively dealing with visual differences between different modalities. For instance, infrared images are typically grayscale, unlike visible images that contain color information. Existing attack methods have primarily focused on the characteristics of the visible image modality, overlooking the features of other modalities and the variations in data distribution among different modalities. This oversight can potentially undermine the effectiveness of these methods in image retrieval across diverse modalities. This study represents the first exploration into the security of cross-modality ReID models and proposes a universal perturbation attack specifically designed for cross-modality ReID. This attack optimizes perturbations by leveraging gradients from diverse modality data, thereby disrupting the discriminator and reinforcing the differences between modalities. We conducted experiments on three widely used cross-modality datasets, namely RegDB, SYSU, and LLCM. The results not only demonstrate the effectiveness of our method but also provide insights for future improvements in the robustness of cross-modality ReID systems. © 2024, CC BY-NC-ND.

关键词： Infrared imaging

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DPLUT: Unsupervised Low-light Image Enhancement with Lookup Tables and Diffusion Priors 39

DPLUT: Unsupervised Low-light Image Enhancement with Lookup ...

引用

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Lin, Yunlong Fu, Zhenqi Wen, Kairun Ye, Tian Chen, Sixiang Meng, Ge Wang, Yingying Kong, Chui Huang, Yue Tu, Xiaotong Ding, Xinghao Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China Xiamen University China School of Informatics Xiamen University China Tsinghua University China The Hong Kong University of Science and Technology Guangzhou China Fudan University China

ISBN: (纸本)157735897X

Low-light image enhancement (LIE) aims at precisely and efficiently recovering an image degraded in poor illumination environments. Recent advanced LIE techniques are using deep neural networks, which require lots of low-normal light image pairs, network parameters, and computational resources. As a result, their practicality is limited. In this work, we devise a novel unsupervised LIE framework based on diffusion priors and lookup tables (DPLUT) to achieve efficient low-light image recovery. The proposed approach comprises two critical components: a light adjustment lookup table (LLUT) and a noise suppression lookup table (NLUT). LLUT is optimized with a set of unsupervised losses. It aims at predicting pixel-wise curve parameters for the dynamic range adjustment of a specific image. NLUT is designed to remove the amplified noise after the light brightens. As diffusion models are sensitive to noise, diffusion priors are introduced to achieve high-performance noise suppression. Extensive experiments demonstrate that our approach outperforms state-of-the-art methods in terms of visual quality and efficiency. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Table lookup

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