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International Conference on Process Mining (ICPM)

作者： Zihang Su Tianshi Yu Nir Lipovetzky Alireza Mohammadi Denny Oetomo Artem Polyvyanyy Sebastian Sardiña Ying Tan Nick van Beest School of Computing and Information Systems The University of Melbourne Australia Department of Mechanical Engineering The University of Melbourne Australia School of Computing Technologies RMIT University Australia Software Systems Group Data61 | CSIRO Australia

A transhumeral prosthesis restores missing anatomical segments below the shoulder, including the hand. Active prostheses utilize real-valued, continuous sensor data to recognize patient target poses, or goals, and proactively move the artificial limb. Previous studies have examined how well the data collected in stationary poses, without considering the time steps, can help discriminate the goals. In this case study paper, we focus on using time series data from surface electromyography electrodes and kinematic sensors to sequentially recognize patients' goals. Our approach involves transforming the data into discrete events and training an existing process mining-based goal recognition system. Results from data collected in a virtual reality setting with ten subjects demonstrate the effectiveness of our proposed goal recognition approach, which achieves significantly better precision and recall than the state-of-the-art machine learning techniques and is less confident when wrong, which is beneficial when approximating smoother movements of prostheses.

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Verilua: An Open Source Versatile Framework for Efficient Hardware Verification and Analysis Using LuaJIT

Verilua: An Open Source Versatile Framework for Efficient Ha...

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Design, Automation and Test in Europe Conference and Exhibition

作者： Ye Cai Chuyu Zheng Wei He Dan Tang College of Computer Science and Software Engineering Shenzhen University Shenzhen China Beijing Institute of Open Source Chip Beijing China Institute of Computing Technology Chinese Academy of Sciences (lCT) Beijing China

ISBN: (数字)9783982674100

ISBN: (纸本)9798331534646

The growing complexity of hardware verification highlights limitations in existing frameworks, particularly regarding flexibility and reusability. Current methodologies often require multiple specialized environments for functional verification, waveform analysis, and simulation, leading to toolchain fragmentation and inefficient code reuse. This paper presents Verilua, a unified framework leveraging LuaJIT and the Verilog Procedural Interface (VPI), which integrates three core functional-ities: Lua-based functional verification, a scripting engine for RTL simulation, and waveform analysis. By enabling complete code reuse through a unified Lua codebase, the framework achieves a 12x speedup in RTL simulation compared to cocotb and a 70x improvement in waveform analysis over state-of-the-art solutions. Through consolidating verification tasks into a single platform, Verilua enhances efficiency while reducing tool fragmentation and learning overhead, addressing critical challenges in modern hardware design.

关键词： Codes Europe Hardware Complexity theory Signal analysis Hardware design languages Engines

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Quantization and Deployment Study of Classification Models for Embedded Platforms

Quantization and Deployment Study of Classification Models f...

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International Conference on Automation and computing (ICAC)

作者： Zihan Huang Jin Jin Chaolong Zhang Zhijie Xu Yuanping Xu Chao Kong Qin Wen Dan Tang School of Software Engineering Chengdu University of Information Technology Chengdu China School of Computing & Engineering University of Hudderseld Queensgate Hudderseld UK College of artificial intelligence Neijiang Normal University

Deep learning models find extensive applications across various domains. However, their large number of parameters, high storage requirements, and computational overhead pose challenges for deploying these models on resource-constrained embedded devices. This study focuses on addressing this issue by exploring techniques to optimize and deploy lightweight models on embedded devices. The approach involves optimization and adjustment of the model, followed by model conversion, quantization, and quantization calibration, aimed at reducing model size and improving inference speed. Notably, improvements are made to the quantization calibration algorithm to mitigate accuracy loss caused by model quantization. The experimental results demonstrate that light quantization significantly reduces model size, facilitating storage on embedded devices. Although there is a slight reduction in accuracy, the inference speed is substantially improved, enabling real-time human face recognition in video scenarios.

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Quantum Machine Learning for Security Data Analysis

Quantum Machine Learning for Security Data Analysis

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AI IoT Congress (AIIoT), World

作者： Dhanunjai Bandi Yong Shi Hossain Shahriar Dan Lo Kun Suo Hongmei Chi Kai Qian College of Computing and Software Engineering Kennesaw State University USA Department of Computer and Information Sciences Florida A&M University Tallahassee FL USA

In this paper, we apply Quantum Machine Learning to analyze security datasets. We compare cross-models, Quantum Machine Learning (QML) against Classical Machine Learning (CML), performance with increasing data size, and performance with increasing iteration numbers using commonly used machine learning techniques such as Neural Networks (NN), Support Vector Machines (SVM), and Logistic Regression (LR). Our study focuses on assessing the accuracy of QML and CML approaches on real-world security datasets. The results provide light on the advantages and disadvantages of both QML and CML methodologies, with implications for their use in security data analysis. The experimental findings provide useful information on the applicability of QML and CML for security-related applications. The study contributes to the growing field of quantum machine learning research, particularly in the context of security data analysis, and offers helpful advice for academics and practitioners working in this area.

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Clustering With Adaptive Graph Learning and Spectral Rotation 7

Clustering With Adaptive Graph Learning and Spectral Rotatio...

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7th International Conference on Vision, Image and Signal Processing, ICVISP 2023

作者： Yuan, Ge Zhou, Jie Huang, Chucheng Gao, Can Wang, Yangbo Shen, Xinrui National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Guangdong Shenzhen518060 China College of Computer Science and Software Engineering Shenzhen University Guangdong Shenzhen518060 China College of Mathematics and Statistics Shenzhen University Guangdong Shenzhen518060 China SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Shenzhen518060 China

ISBN: (纸本)9781839539848

Prototype-based clustering algorithms have garnered considerable attention in the field of machine learning due to their efficiency and interpretability. Nonetheless, these algorithms often face performance degradation when confronted with high-dimensional or non-ellipsoidal data distributions. To surmount these challenges, this study introduces a novel clustering approach, dubbed Clustering with Adaptive Graph learning and Spectral Rotation (CAGSR). In CAGSR, the imposed spectral rotation operation mitigates the discrepancy between the membership matrix, which adheres to the notion of fuzzy clustering, and the spectral representations derived from an adaptive graph rather than a predefined one. This enables the generation of a comprehensive representation of the data across multiple spaces. Furthermore, the clustering and graph learning tasks are jointly optimized in a projected subspace, which can effectively reduce the adverse impact caused by irrelevant features in the original space. The proposed method seamlessly integrates fuzzy clustering, graph structure learning, and spectral rotation into a unified model, facilitating the detection of intrinsic structures. Experimental evaluations conducted on benchmark data sets substantiate the effectiveness of CAGSR when compared to related clustering approaches. © The Institution of engineering & Technology 2023.

关键词： Fuzzy clustering

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Assessing the alignment between the information needs of developers and the documentation of programming languages: A case study on Rust

arXiv

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arXiv 2022年

作者： Cogo, Filipe R. Xia, Xin Hassan, Ahmed E. Centre for Software Excellence HI Canada Software Engineering Application Technology Lab Huawei China School of Computing Queen's University Canada

Programming language documentation refers to the set of technical documents that provide application developers with a description of the high-level concepts of a language. Such documentation is essential to support application developers in the effective use of a programming language. One of the challenges faced by documenters (i.e., personnel that produce documentation) is to ensure that documentation has relevant information that aligns with the concrete needs of developers. In this paper, we present an automated approach to support documenters in evaluating the differences and similarities between the concrete information need of developers and the current state of documentation (a problem that we refer to as the topical alignment of a programming language documentation). Our approach leverages semi-supervised topic modelling to assess the similarities and differences between the topics of Q&A posts and the official documentation. To demonstrate the application of our approach, we perform a case study on the documentation of Rust. Our results show that there is a relatively high level of topical alignment in Rust documentation. Still, information about specific topics is scarce in both the Q&A websites and the documentation, particularly related topics with programming niches such as network, game, and database development. For other topics (e.g., related topics with language features such as structs, patterns and matchings, and foreign function interface), information is only available on Q&A websites while lacking in the official documentation. Finally, we discuss implications for programming language documenters, particularly how to leverage our approach to prioritize topics that should be added to the documentation. © 2022, CC BY-NC-SA.

关键词： Websites

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A Speckle Noise Suppression Method Based on Quality Map

A Speckle Noise Suppression Method Based on Quality Map

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International Conference on Automation and computing (ICAC)

作者： Chengxin Zhong Chao Kong Chaolong Zhang Yuanping Xu Zhijie Xu Tukun Li Jin Jin Qin Wen School of Software Engineering Chengdu University of Information Technology Chengdu China School of Computing & Engineering University of Hudderseld Queensgate Hudderseld UK College of artificial intelligence Neijiang Normal University

Digital holography is a technique for measuring the shape of an object in 3D. It uses interference patterns generated by a laser or other light source, which record both the phase and intensity information of the light reflected from the surface of the object. This information is then encoded into digital holograms and reconstructed using computational methods to produce a 3D shape of the object. In the process of digital holography measurement, due to the high coherence of the laser, a large amount of speckle noise is generated when measuring rough surfaces due to diffuse reflection, leading to errors in the reconstruction results. Although traditional phase filtering has a good effect on noise filtering, it may cause distortion of normal phase. In order to suppress speckle noise, this study compared the common quality maps, proposed a local threshold adaptive speckle noise extraction method based on quality maps, and finally obtained the correct phase map by restoring the speckle noise points.

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ESR: Optimizing Gene Feature Selection for scRNA-seq Data

ESR: Optimizing Gene Feature Selection for scRNA-seq Data

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IEEE International Conference on Cyber Security and Cloud computing (CSCloud)

作者： Tao Huang Xiang Chen Li Peng School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan Hunan China Hunan Key Laboratory for Service computing and Novel Software Technology

The rapid development of single-cell RNA sequencing (scRNA-seq) technology has enabled researchers to explore gene expression differences at the level of individual cells, revealing more refined cell types and states. However, due to the low expression and high noise of scRNA-seq data, feature selection has become particularly important in the analysis of single-cell data. Here, we introduce the Entropy Stepwise Regression (ESR) method for feature selection. This method utilizes the correlation between genes and the entropy values of each feature to filter out genes that are conducive to downstream analysis. In mouse kidney samples, we compared the performance of three methods in terms of Adjusted Rand Index and achieved good results. This indicates that the method can improve the accuracy of downstream analysis.

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Natural Language Processing for Analyzing Electronic Health Records and Clinical Notes in Cancer Research: A Review

arXiv

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arXiv 2024年

作者： Bilal, Muhammad Hamza, Ameer Malik, Nadia Department of Pharmaceutical Outcomes and Policy University of Florida GainesvilleFL32611 United States Department of Computer Science Faculty of Computing and IT University of Sargodha Punjab Sargodha40100 Pakistan Department of Software Engineering Faculty of Computing and IT University of Sargodha Punjab Sargodha40100 Pakistan

Objective: This review aims to analyze the application of natural language processing (NLP) techniques in cancer research using electronic health records (EHRs) and clinical notes. This review addresses gaps in the existing literature by providing a broader perspective than previous studies focused on specific cancer types or applications. Methods: A comprehensive literature search was conducted using the Scopus database, identifying 94 relevant studies published between 2019 and 2024. Data extraction included study characteristics, cancer types, NLP methodologies, dataset information, performance metrics, challenges, and future directions. Studies were categorized based on cancer types and NLP applications. Results: The results showed a growing trend in NLP applications for cancer research, with breast, lung, and colorectal cancers being the most studied. Information extraction and text classification emerged as predominant NLP tasks. A shift from rule-based to advanced machine learning techniques, particularly transformer-based models, was observed. The Dataset sizes used in existing studies varied widely. Key challenges included the limited generalizability of proposed solutions and the need for improved integration into clinical workflows. Conclusion: NLP techniques show significant potential in analyzing EHRs and clinical notes for cancer research. However, future work should focus on improving model generalizability, enhancing robustness in handling complex clinical language, and expanding applications to understudied cancer types. Integration of NLP tools into clinical practice and addressing ethical considerations remain crucial for utilizing the full potential of NLP in enhancing cancer diagnosis, treatment, and patient outcomes. © 2024, CC BY.

关键词： Electronic health record

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Solving the Two-Criteria Maximum Coverage Location Problem of Continuous Area Monitored by Sensor Network 12

Solving the Two-Criteria Maximum Coverage Location Problem o...

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12th IEEE International Conference on Intelligent Data Acquisition and Advanced computing Systems: Technology and Applications, IDAACS 2023

作者： Yakovlev, Sergiy Wojciechowski, Adam Podzeha, Dmytro Illiashenko, Oleg Yakovleva, Iryna National Aerospace University 'KhAI' Kharkiv61070 Ukraine Lodz University of Technology Lodz90-924 Poland Institute of Information Science and Technologies 'Alessandro Faedo' - Isti Cnr Software Engineering & Dependable Computing Laboratory Pisa56124 Italy National University of Urban Economy in Kharkiv Kharkiv61002 Ukraine

ISBN: (纸本)9798350358056

The paper investigates the problem of optimizing a sensor network for monitoring a continuous area, considering the bounded coverage areas of sensors. This task is formulated in terms of the maximum coverage location problem. A mathematical model is proposed as a two-criteria optimization problem. The objective functions are the maximum area of the covered part of the region and the minimum total overlapping of sensor coverage areas. This model is transformed into an elastic (quasi-physical quasi-human) model, which differs from the known one in forming the extrusion potential energy function. To solve the problem, an original approach was implemented, combining local and global optimization stages. At the stage of local optimization, the Broyden-Fletcher-Goldfarb-Shanno method was used, in which the gradients were calculated analytically or from first-order differences depending on the shape of sensor coverage areas. At the stage of local optimization, the multistart method was used. The implementation of the approach has been tested for the polygonal shape of the region and elliptical shapes of the sensor coverage areas. © 2023 IEEE.

关键词： elastic model mathematical model maximum coverage location problem optimization sensor network testing

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