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computers and Artificial Intelligence Technologies (CAIT), International Conference on

作者： Ying Xiao Yu Chen Wenting Li Xiaoyan Yang Yuhang Shi Yongjun Zhang College of Computer Science Guizhou University Guiyang China School of Information Engineering Guizhou University of Commerce Guiyang China School of Computer and Big Data Minjiang University Fuzhou China School of Computer Science and Technology Guangdong University of Technology Guangzhou China

ISBN: (数字)9798331530891

ISBN: (纸本)9798331530907

The emergence of Large Language Models (LLMs) has had a significant impact on fields such as question answering (Q&A) and machine understanding. However, due to the specificity of government documents, there are several LLMs exclusively focused on government document writing. In this survey, we thoroughly examine the application of LLMs in the creation and refinement of government documents. A systematic comparison of various models, i.e., ChatGPT-4 and Kimi, is provided here. Our findings reveal that, although LLMs can significantly streamline the document preparation process, there is considerable variation in their effectiveness. ChatGPT-4 stands out as the top-performing model, excelling in context understanding and grammatical accuracy, followed closely by QWen2.5. In contrast, TinyLlama performs limited comprehension of Chinese and exhibits a propensity for English output. This survey has two main contributions: (1) emphasizes the importance of the task of polishing government documents; (2) fills the gap in the market for a comprehensive comparison of LLMs in the task of polishing government documents. Through comparison analysis, this paper aims at giving a reference for future research and practical application of Large Language Models (LLMs) in the government sector. At the same time, it can assure the maximum of potential and lower the risks of it.

关键词： Surveys Systematics Large language models Government Refining Training data Writing Question answering (information retrieval) Multilingual Text processing

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A power optimization approach for mixed polarity Reed–Muller logic circuits based on multi-strategy fusion memetic algorithm

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Frontiers of Information Technology & Electronic engineering 2025年第3期26卷 415-426页

作者： Mengyu ZHANG Zhenxue HE Yijin WANG Xiaojun ZHAO Xiaodan ZHANG Limin XIAO Xiang WANG Intelligent Agricultural Equipment Research Institute Hebei Agricultural UniversityBaoding 071001China Key Laboratory of Agricultural Big Data of Hebei Province Hebei Agricultural UniversityBaoding 071001China School of Computer Science and Engineering Beihang UniversityBeijing 100191China School of Electronic Information Engineering Beihang UniversityBeijing 100191China

The power optimization of mixed polarity Reed–Muller(MPRM)logic circuits is a classic combinatorial optimization *** optimization approaches often suffer from slow convergence and a propensity to converge to local optima,limiting their effectiveness in achieving optimal power ***,we propose a novel multi-strategy fusion memetic algorithm(MFMA).MFMA integrates global exploration via the chimp optimization algorithm with local exploration using the coati optimization algorithm based on the optimal position learning and adaptive weight factor(COA-OLA),complemented by population management through truncation ***,leveraging MFMA,we propose a power optimization approach for MPRM logic circuits that searches for the best polarity configuration to minimize circuit *** results based on Microelectronics Center of North Carolina(MCNC)benchmark circuits demonstrate significant improvements over existing power optimization *** achieves a maximum power saving rate of 72.30%and an average optimization rate of 43.37%;it searches for solutions faster and with higher quality,validating its effectiveness and superiority in power optimization.

关键词： Power optimization Multi-strategy fusion memetic algorithm(MFMA) Mixed polarity Reed-Muller(MPRM) Combinatorial optimization problem

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Image Analysis of MRI-based Brain Tumor Classification and Segmentation using BSA and RELM Networks 2

Image Analysis of MRI-based Brain Tumor Classification and S...

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2nd International Conference on Automation, Computing and Renewable Systems, ICACRS 2023

作者： Saranya, R. Pushpakumar, R. Srinivasan, P. Aparna, N. Karthika, K. Chauhan, Amit Department of Computer science with Data Analytics PSG College of Arts and Science Coimbatore India Department of Information Technology Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai India Department of ECE Sona College of Technology Tamilnadu Salem India Department of Biomedical Engineering Dhaanish Ahmed Institute of Technology Tamil Nadu Veerappanur India Civil Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Karnataka Bengaluru India

ISBN: (纸本)9798350340235

Brain tumor segmentation plays a crucial role in medical image analysis. Brain tumor patients considerably benefit from early discovery due to the increased likelihood of a successful outcome from therapy. Due to the sheer volume of MRI images generated in everyday clinical practice, manually isolating brain tumors for cancer diagnosis is a challenging task. Automatic segmentation of images of brain tumors is essential. This system aimed to synthesize previous methods for BSA-RELM-based brain tumor segmentation. The proposed methodology rests on four fundamental pillars: preprocessing, segmentation, feature extraction, and model training. Filtering, scaling, boosting contrast, and sharpening are all examples of preprocessing techniques. When doing segmentation, a clustering technique based on Fuzzy Clustering Means (FCM) is used to breakdown the overall dataset into numerous subsets. The proposed approach used the region of filling for feature extraction. After that, a BSA-RELM is used to train the models with the input features. The proposed technique outperforms BSA and RELM, two of the most common alternatives. There was a 98.61 percent success rate with the recommended method. © 2023 IEEE.

关键词： Brain Tumor Segmentation Central Nervous System (CNS) FuzzyClustering Method (FCM)

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Efficient Chroma Intra Prediction via Exemplar Colorization Network for Versatile Video Coding

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IEEE Transactions on Multimedia 2025年

作者： Pan, Zhaoqing Chen, Jixing Peng, Bo Lei, Jianjun Wang, Fu Lee Ling, Nam Kwong, Sam Tianjin University School of Electrical and Information Engineering Tianjin300072 China Hong Kong Metropolitan University School of Science and Technology Hong Kong Hong Kong Santa Clara University Department of Computer Science and Engineering Santa ClaraCA95053 United States Lingnan University School of Data Science Hong Kong Hong Kong

Chroma intra prediction aims to reduce chroma redundancies within a frame, which plays an important role in improving the coding efficiency of intra coding. Existing chroma intra prediction methods typically utilize the spatial relationship between the current luma block and its neighboring reference luma blocks to predict its chroma samples. However, the spatial properties of luma components differ from those of chroma components, which limits the accuracy of chroma intra prediction. To tackle this issue, an efficient Exemplar Colorization Network (ECNet)-based chroma intra prediction method is proposed in this paper, in which the colorization relationship between reference luma and chroma components is exploited to predict the chroma components for the current luma component. Inspired by the principle that semantic information in an image exhibits short-range continuity, a Spatial-consistency-based Colorization Transfer Network (SCTNet) is proposed, which builds and transfers colorization representations of neighboring reference blocks for chroma prediction. To improve the chroma prediction capability of SCTNet, a colorization learning module is developed to learn the robust mapping relationship from the luma component to the chroma component in a region-to-pixel manner, and a weight-adaptive reconstruction module is designed to adaptively utilize reference information from neighboring blocks to generate an initial prediction result. In addition, to further improve the accuracy of chroma intra prediction, a multi-reference-based chroma refinement network is proposed, which simultaneously uses the spatial information of neighboring reference chroma blocks and the current luma block to eliminate blocking and color-bleeding artifacts in the initial prediction result. Experimental results demonstrate that our proposed ECNet outperforms the state-of-the-art chroma intra prediction methods in terms of coding performance. © 1999-2012 IEEE.

关键词： Video signal processing

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XC-NAS: A New Cellular Encoding Approach for Neural Architecture Search of Multi-path Convolutional Neural Networks

arXiv

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arXiv 2023年

作者： Londt, Trevor Gao, Xiaoying Andreae, Peter Mei, Yi Centre for Data Science and Artificial Intelligence School of Engineering and Computer Science Victoria University of Wellington PO Box 600 Wellington6140 New Zealand

Convolutional Neural Networks (CNNs) continue to achieve great success in classification tasks as innovative techniques and complex multi-path architecture topologies are introduced. Neural Architecture Search (NAS) aims to automate the design of these complex architectures, reducing the need for costly manual design work by human experts. Cellular Encoding (CE) is an evolutionary computation technique which excels in constructing novel multi-path topologies of varying complexity and has recently been applied with NAS to evolve CNN architectures for various classification tasks. However, existing CE approaches have severe limitations. They are restricted to only one domain, only partially implement the theme of CE, or only focus on the micro-architecture search space. This paper introduces a new CE representation and algorithm capable of evolving novel multi-path CNN architectures of varying depth, width, and complexity for image and text classification tasks. The algorithm explicitly focuses on the macro-architecture search space. Furthermore, by using a surrogate model approach, we show that the algorithm can evolve a performant CNN architecture in less than one GPU day, thereby allowing a sufficient number of experiment runs to be conducted to achieve scientific robustness. Experiment results show that the approach is highly competitive, defeating several state-of-the-art methods, and is generalisable to both the image and text domains. Copyright © 2023, The Authors. All rights reserved.

关键词： Convolutional neural networks

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SADBA: Self-Adaptive Distributed Backdoor Attack Against Federated Learning 39

SADBA: Self-Adaptive Distributed Backdoor Attack Against Fed...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Feng, Jun Lai, Yuzhe Sun, Hong Ren, Bocheng Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Economics Wuhan Textile University China School of Computer Science and Technology Hainan University China

ISBN: (纸本)157735897X

Backdoor attacks in federated learning (FL) face challenges such as lower attack success rates and compromised main task accuracy (MA) compared to local training. Existing methods like distributed backdoor attack (DBA) mitigate these issues by modifying malicious clients’ updates and partitioning global triggers to enhance backdoor persistence and stealth. The recent full combination backdoor attack (FCBA) further improves backdoor efficiency with a full combination strategy. However, these methods are mainly applicable in small-scale FL. In large-scale FL, small trigger patterns weaken impact, and scaling them requires controlling exponentially more clients, which poses significant challenges, while simply reverting to DBA may decrease backdoor performance. To overcome these challenges, we propose the self-adaptive distributed backdoor attack (SADBA), which achieves similar performance to FCBA with a lower percentage of malicious clients (PMC). It also adapts more flexibly through an optimized model poisoning strategy and a self-adaptive data poisoning strategy. Experiments demonstrate SADBA outperforms state-of-the-art methods, achieving higher or comparable backdoor performance and MA across various datasets with limited PMC. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Federated learning

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Binary Code Similarity Detection

arXiv

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arXiv 2023年

作者： Liu, Zian Department of Computer Science and Software Engineering Swinburne University of Technology Data61 CSIRO Melbourne Australia

Binary code similarity detection is to detect the similarity of code at binary (assembly) level without source code. Existing works have their limitations when dealing with mutated binary code generated by different compiling options. In this paper, we propose a novel approach to addressing this problem. By inspecting the binary code, we found that generally, within a function, some instructions aim to calculate (prepare) values for other instructions. The latter instructions are defined by us as key instructions. Currently, we define four categories of key instructions: calling subfunctions, comparing instruction, returning instruction, and memory-store instruction. Thus if we symbolically execute similar binary codes, symbolic values at these key instructions are expected to be similar. As such, we implement a prototype tool, which has three steps. First, it symbolically executes binary code;Second, it extracts symbolic values at defined key instructions into a graph;Last, it compares the symbolic graph similarity. In our implementation, we also address some problems, including path explosion and loop handling. © 2023, CC BY.

关键词： Binary codes

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Efficient Segment-Level Waveform Anomaly Detection for Memory Devices

Efficient Segment-Level Waveform Anomaly Detection for Memor...

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IEEE International Conference on Communications (ICC)

作者： Ching-Lun Tai Peter Harrington School of Electrical and Computer Engineering Georgia Institute of Technology Technology and Products Group - Data Science Micron Technology

With the emergence of big data and advanced technologies, memory devices play an important role. However, there is a lack of efficient approaches for waveform anomaly detection to validate the reliability of memory devices under various operations. In this paper, we propose an efficient segment-level waveform anomaly detection method for memory devices that includes the stages of waveform segmentation and anomaly analysis. Being compatible with general waveform sequences, the proposed method features lightweight computational complexity. During the waveform segmentation stage, we execute downsampled segment collection and downsampled segment pair matching to obtain downsampled segments that are associated with individual operations. During the anomaly analysis stage, we execute ensemble denoising, entrywise alignment, anomaly presence examination through excess kurtosis, and anomaly detection with density-based spatial clustering of applications with noise (DBSCAN) to predict anomaly locations. Simulation results confirm the outstanding performance of the proposed method in terms of matching accuracy, excess kurtosis measurements, and segment-level anomaly detection.

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Disentangling Tabular data Towards Better One-Class Anomaly Detection 39

Disentangling Tabular Data Towards Better One-Class Anomaly ...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Ye, Jianan Tan, Zhaorui Hu, Yijie Yang, Xi Cheng, Guangliang Huang, Kaizhu School of Advanced Technology Xi'an Jiaotong-Liverpool University China School of Electrical Engineering Electronics and Computer Science University of Liverpool United Kingdom Data Science Research Center Duke Kunshan University China

ISBN: (纸本)157735897X

Tabular anomaly detection under the one-class classification setting poses a significant challenge, as it involves accurately conceptualizing "normal" derived exclusively from a single category to discern anomalies from normal data variations. Capturing the intrinsic correlation among attributes within normal samples presents one promising method for learning the concept. To do so, the most recent effort relies on a learnable mask strategy with a reconstruction task. However, this wisdom may suffer from the risk of producing uniform masks, i.e., essentially nothing is masked, leading to less effective correlation learning. To address this issue, we presume that attributes related to others in normal samples can be divided into two non-overlapping and correlated subsets, defined as CorrSets, to capture the intrinsic correlation effectively. Accordingly, we introduce an innovative method that disentangles CorrSets from normal tabular data. To our knowledge, this is a pioneering effort to apply the concept of disentanglement for one-class anomaly detection on tabular data. Extensive experiments on 20 tabular datasets show that our method substantially outperforms the state-of-the-art methods and leads to an average performance improvement of 6.1% on AUC-PR and 2.1% on AUC-ROC. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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Decision Models for Selecting Patterns in Governance-driven Blockchain Systems

Decision Models for Selecting Patterns in Governance-driven ...

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IEEE International Conference on Blockchain (Blockchain)

作者： Yue Liu Qinghua Lu Hye-Young Paik Guangsheng Yu Liming Zhu Data61 CSIRO Australia School of Computer Science and Engineering University of New South Wales Australia

Blockchain technology can construct a decentralised architecture design for diverse software applications. Nevertheless, the defects of on-chain algorithmic autonomy and subsequent tedious arguments for fixing the bugs raised concerns about whether blockchain can operate in a trustworthy way. Although multiple architectural patterns for blockchain governance have been summarised, concentrating on different governance dimensions (i.e., decision rights, incentives, and accountability), there is a need for guidance on the adoption of these patterns. In this paper, we present a set of decision models that can assist architects and developers in selecting appropriate patterns, to achieve governance in blockchain systems. This study provides a mapping between governance-related problems and each architectural pattern, and clarifies the trade-offs and conditions. The proposed decision models were evaluated based on expert opinions regarding the usability, correctness, and completeness collected via interviews.

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