检索结果-内蒙古大学图书馆

An Efficient and Fast Area Optimization Approach for Mixed Polarity Reed-Muller Logic Circuits

Chinese Journal of Electronics 2024年第5期33卷 1165-1180页

作者： Yuhao ZHOU Zhenxue HE Jianhui JIANG Xiaojun ZHAO Fan ZHANG Limin XIAO Xiang WANG School of Software Engineering Tongji University Key Laboratory of Agricultural Big Data of Hebei Province Hebei Agricultural University School of Computer Science and Engineering Beihang University School of Electronic and Information Engineering Beihang University

Area has become one of the main bottlenecks restricting the development of integrated circuits. The area optimization approaches of existing XNOR/OR-based mixed polarity Reed-Muller(MPRM) circuits have poor optimization effect and efficiency. Given that the area optimization of MPRM logic circuits is a combinatorial optimization problem, we propose a whole annealing adaptive bacterial foraging algorithm(WAA-BFA), which includes individual evolution based on Markov chain and Metropolis acceptance criteria, and individual mutation based on adaptive probability. To address the issue of low conversion efficiency in existing polarity conversion approaches, we introduce a fast polarity conversion algorithm(FPCA). Moreover, we present an MPRM circuits area optimization approach that uses the FPCA and WAA-BFA to search for the best polarity corresponding to the minimum circuits area. Experimental results demonstrate that the proposed MPRM circuits area optimization approach is effective and can be used as a promising EDA tool.

关键词： Integrated circuits Microorganisms Annealing Logic circuits Circuits Benchmark testing Delays

来源：评论

学校读者我要写书评

暂无评论

MedLogic-AQA: Enhancing Medical Question Answering with Abstractive Models Focusing on Logical Structures

MedLogic-AQA: Enhancing Medical Question Answering with Abst...

引用

2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Zafar, Aizan Mishra, Kshitij Ekbal, Asif Department of Computer Science and Engineering Indian Institute of Technology Patna India School of AI and Data Science Indian Institute of Technology Jodhpur India

ISBN: (纸本)9798891761681

In Medical question-answering (QA) tasks, the need for effective systems is pivotal in delivering accurate responses to intricate medical queries. However, existing approaches often struggle to grasp the intricate logical structures and relationships inherent in medical contexts, thus limiting their capacity to furnish precise and nuanced answers. In this work, we address this gap by proposing a novel Abstractive QA system MEDLOGIC-AQA that harnesses First Order Logic (FOL) based rules extracted from both context and questions to generate well-grounded answers. Through initial experimentation, we identified six pertinent first-order logical rules, which were then used to train a Logic-Understanding (LU) model capable of generating logical triples for a given context, question, and answer. These logic triples are then integrated into the training of MEDLOGIC-AQA, enabling effective and coherent reasoning during answer generation. This distinctive fusion of logical reasoning with abstractive QA equips our system to produce answers that are logically sound, relevant, and engaging. Evaluation with respect to both automated and human-based demonstrates the robustness of MEDLOGIC-AQA against strong baselines. Through empirical assessments and case studies, we validate the efficacy of MEDLOGIC-AQA in elevating the quality and comprehensiveness of answers in terms of reasoning as well as informativeness. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

来源：评论

学校读者我要写书评

暂无评论

Enhancing Document-Level Relation Extraction through Entity-Pair-Level Interaction Modeling

Enhancing Document-Level Relation Extraction through Entity-...

引用

2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Liu, Wanlong Zeng, Dingyi Zhou, Li Xiao, Yichen Zhang, Malu Chen, Wenyu School of Computer Science and Engineering University of Electronic Science and Technology of China China School of Data Science The Chinese University of Hong Kong Shenzhen China

ISBN: (纸本)9798350368741

Document-level relation extraction aims at extracting relational facts between two entities in a document. Existing approaches mainly focus on target entities, utilizing techniques such as graph neural networks to enhance their representations. However, they ignore the rich semantic correlations among entity pairs which provide wider and multifaceted information at a higher level. In this paper, we propose the Relation-based Entity-pair-level Inference (REI) model, which facilitates information interaction at the entity-pair level, enhancing logical reasoning among entities and capturing semantic correlations among entity pairs. Our REI model comprises two modules: Relation-based Information Aggregation (RIA) and Entity-pair-level Information Interaction (EII). The RIA module builds and integrates relation representations to filter out distractions from unrelated entity pairs, while the EII module models entity-pair-level information interaction through multi-head attentions. Extensive experiments on the DocRED, DWIE, CDR, and GDA datasets demonstrate the superiority of the proposed REI model, outperforming previous state-of-the-art approaches. Furthermore, we provide detailed experimental analyses based on the performance gains and illustrate the interpretability. © 2025 IEEE.

关键词： Information Extraction Linguistic Inference Natural Language Processing Relation Extraction

来源：评论

学校读者我要写书评

暂无评论

Forensic Investigation of Malicious Activities in Digital Environments 4

Forensic Investigation of Malicious Activities in Digital En...

引用

4th IEEE International Conference on data engineering and Communication Systems, ICDECS 2024

作者： Pasumpon Pandian, A. Anakath, A.S. Kannadasan, R. Ravikumar, K. Abdul Kareem, D. Care College of Engineering Department of Computer Science and Engineering Trichy India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India School of Computer Science and Engineering Department of Software Systems Tamil Nadu Vellore India Rrase College of Engineering Padappai Department of Computer Science & Engineering Chennai India Grt Institute of Engineering and Technology Department of Artificial Intelligence and Data Science Tamil Nadu Tiruttani India

ISBN: (纸本)9798350393354

Several digital dangers were investigated. Malware dominated analysis with 45 attacks. We found 30 phishing attacks. 22 data breaches, 15 cyber espionage, 18 identity theft. This indicates the kind and frequency of harassment digital forensic specialists endure. Digital evidence was crucial to the case. 65 pieces of evidence were collected on computers. 42 incidents showed smartphones and tablets' digital investigative potential. There were 55 examples of communication patterns requiring network logs. In 28 and 12, cloud services and removable media provided proof. A wide range of evidence sources stresses the need for extensive collecting. Digital forensics were essential to the inquiry. In 70% of cases, disk imaging software created forensic copies of metadata-and timestamp-containing storage media. Network packet analyzers examined 48 traffic logs and communication patterns. To analyze volatile memory, 35 cases used memory forensics. In 25 cases, malware analysis platforms decrypted malware. In 60 situations, file recovery software recovered lost or hidden files. Digital forensic specialists' diverse arsenal for thorough investigations is shown here. Legal and ethical considerations dominated the study. The law was important, and all inquiries followed norms. A strong chain of custody protects evidence integrity and admissibility. The data privacy rules were respected. Legal evidence is admissible in court. Ethics ensured objective investigations. This underlines digital forensic investigations' legal and ethical foundation. © 2024 IEEE.

关键词： Cybersecurity

来源：评论

学校读者我要写书评

暂无评论

Monkeypox Disease Classification from Skin Lesion Images using Deep Convolution Neural Network 3

Monkeypox Disease Classification from Skin Lesion Images usi...

引用

3rd International Conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Thangaraj, Rajasekaran Vadivel, Balasubramaniam Kumar, S. Manoj Sadesh, S. Karunakaran, P. Prakash, P. Nandha Engineering College Department of Computer Science and Engineering Erode India Sri Ramakrishna Institute of Technology Department of Computer Science and Engineering Coimbatore India KPR Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Velalar College of Engineering and Technology Department of Artificial Intelligence and Data Science Erode India Nandha Engineering College Department of Artificial Intelligence and Data Science Erode India Velalar College of Engineering and Technology Department of Information Technology Erode India

ISBN: (纸本)9798331523893

Recently monkeypox outbreak has raised concerns due to its increasing number of cases and diverse dermatological symptoms in 2024, which can complicate early diagnosis due to similarities with other viral infections such as measles and chickenpox. To enhance diagnostic accuracy, transfer learning models and artificial intelligence (AI) have been explored as effective tools. Pre-trained models such as VGG16, VGG19, ResNet50V2, and MobileNetV2 have been employed for monkeypox detection, each with distinct trade-offs in terms of computational efficiency and diagnostic precision. MobileNetV2 offers superior efficiency, but this comes at the cost of reduced accuracy. VGG models provide higher accuracy but require much more computational *** these models, DenseNet121 stands out by achieving 99% accuracy while requiring 37% less computational power, making it the model optimized choice for efficient and accurate monkeypox classification. © 2025 IEEE.

关键词： Transfer learning

来源：评论

学校读者我要写书评

暂无评论

Automated Waste Classification using Convolutional Neural Network 14

Automated Waste Classification using Convolutional Neural Ne...

引用

14th International Conference on Software Technology and engineering, ICSTE 2024

作者： Thinakaran, Rajermani Somasekar, J. Neerugatti, Vikram Guru Saran, P. INTI International University Faculty of Data Science and Information Technology Negeri Sembilan Malaysia Department of Computer Science and Engineering Karnataka Banglore India

ISBN: (纸本)9798350378955

Efficient waste management relies on precise waste classification, which facilitates recycling, composting, and appropriate disposal. Conventional waste classification methods often involve manual sorting or simple rule-based systems, which are labor intensive and prone to errors. This study investigates the application of Convolutional Neural Networks (CNNs) for automated waste classification using image data. CNNs have shown significant success in various image classification tasks due to their ability to learn hierarchical features directly from pixel values. The methodology outlined covers data collection, preprocessing, model architecture design, and training procedures for the CNN model. Experimental results demonstrate the effectiveness of the CNN approach in accurately categorizing various waste types. Additionally, we discuss the practical implications of CNN-based waste classification systems for real-world waste management applications. By leveraging CNNs, waste management processes can be optimized, leading to better resource utilization and environmental sustainability. The authors also highlight potential avenues for future research, including exploring advanced CNN architectures and integrating emerging technologies like Internet of Things (IoT) sensors for improved waste management solutions. Overall, this study emphasizes the potential of CNNs to transform waste classification practices and enhance the efficiency of waste management systems. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Feature Selection for GPSR Based on Maximal Information Coefficient and Shapley Values 13

Feature Selection for GPSR Based on Maximal Information Coef...

引用

13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Rimas, Mohamad Anfar, Mohamad Chen, Qi Zhang, Mengjie School of Engineering and Computer Science Victoria University of Wellington Centre for Data Science and Artificial Intelligence PO Box 600 Wellington New Zealand

ISBN: (纸本)9798350308365

Feature selection is a critical aspect of improving the interpretability of machine learning models. Genetic Programming (GP) has a built-in feature selection mechanism that explores the search space to include informative features in models. However, this built-in mechanism is insufficient for identifying important features, when dealing with high-dimensional feature spaces. To overcome this limitation, the paper introduces a novel feature importance measurement based on the Maximal Infor-mation Coefficient and Shapley Values. The proposed algorithm operates in two stages. In the first stage, it identifies the best individuals from different populations. In the second stage, the best individuals from the first stage are utilized for the calculation of the novel individual feature importance measurement. The new feature importance measurement offers valuable insights into the significance and relevance of the selected features. Regression experiments were conducted on six datasets to assess the effectiveness of the proposed method. Furthermore, comparisons were made with two other algorithms to evaluate its performance. The results indicate that the proposed approach enhances GP performance for high dimensional datasets while maintaining GP trees of similar size compared to standard GP. © 2024 IEEE.

关键词： Genetic programming

来源：评论

学校读者我要写书评

暂无评论

ABLE: Personalized Disability Support with Politeness and Empathy Integration

ABLE: Personalized Disability Support with Politeness and Em...

引用

2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Mishra, Kshitij Burja, Manisha Ekbal, Asif Department of Computer Science and Engineering Indian Institute of Technology Patna India School of AI and Data Science Indian Institute of Technology Jodhpur India

ISBN: (纸本)9798891761643

In today's dynamic world, providing inclusive and personalized support for individuals with physical disabilities is imperative. With diverse needs and preferences, tailored assistance according to user personas is crucial. In this paper, we introduce ABLE (Adaptive, Bespoke, Listen and Empathetic), a Conversational Support System for Physical Disabilities. By tracking user personas, including gender, age, and personality traits based on the OCEAN model, ABLE ensures that support interactions are uniquely tailored to each user's characteristics and preferences. Moreover, integrating politeness and empathy levels in responses enhances user satisfaction and engagement, fostering a supportive and respectful environment. The development of ABLE involves compiling a comprehensive conversational dataset enriched with user profile annotations. Leveraging reinforcement learning techniques and diverse reward mechanisms, ABLE trains a model to generate responses aligned with individual user profiles while maintaining appropriate levels of politeness and empathy. Based on rigorous empirical analysis encompassing automatic and human evaluation metrics based on persona-consistency, politeness accuracy, empathy accuracy, perplexity, and conversation coherence, the efficacy of ABLE is assessed. Our findings underscore ABLE's success in delivering tailored support to individuals grappling with physical disabilities. To the best of our knowledge, this is the very first attempt towards building a user's persona-oriented physical disability support system. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

来源：评论

学校读者我要写书评

暂无评论

LearnSC: An Efficient and Unified Learning-Based Framework for Subgraph Counting Problem 40

LearnSC: An Efficient and Unified Learning-Based Framework f...

引用

40th IEEE International Conference on data engineering, ICDE 2024

作者： Hou, Wenzhe Zhao, Xiang Tang, Bo National University of Defense Technology Laboratory for Big Data and Decision China Southern University of Science and Technology Department of Computer Science and Engineering China

ISBN: (纸本)9798350317152

Graphs are valuable data structures used to represent complex relationships between entities in a wide range of applications, such as social networks and chemical reactions. Subgraph counting problem is a well-known hard problem, as its core subroutine, the subgraph matching, is NP-complete. In this work, we propose an efficient and unified deep learning-based solution framework LearnSC, which solves the subgraph counting problem approximately. This framework offers two key advantages: (i) it is a generic solution that is orthogonal to the existing techniques of learning-based solutions;and (ii) it is equipped with a suite of optimizations to significantly improve the accuracy of the estimated results. Our experimental results on 7 datasets demonstrate that our proposal is highly accurate, robust, and scalable, making it an excellent solution for subgraph counting problem among all statistics-based and learning-based competitors. © 2024 IEEE.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Assessing the efficiency of Gene Expression data for cancer diagnosis with Artificial Intelligence Techniques 10

Assessing the efficiency of Gene Expression data for cancer ...

引用

10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Chandrakanth, G. Mathu, T. Thanka, M. Roshni Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Data Science and Cybersecurity Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (纸本)9798350353068

Sparse multi-dimensional gene expression data refers to datasets that has a vast number of features and observations, where a substantial portion of the entries are zero or missing values. In such datasets, the number of observations is typically fewer than the number of features. Analyzing such data poses significant challenges, particularly in the context of making accurate diagnoses. In this study, we conducted a comparative evaluation of a Deep Learning(DL) model (BiLSTM) and five well-known Machine Learning(ML) models, including Logistic Regression, Ensemble Learning, Random Forest, and Bagging models. Our comparison led to the conclusion that the DL model (Bi-LSTM) outperformed the ML models, achieving an average accuracy of 98.13% which asserts that DL models are superior to ML in terms of diagnosis. © 2024 IEEE.

关键词： Long short-term memory

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：