检索结果-内蒙古大学图书馆

7th International Conference on Information and computer Technologies, ICICT 2024

作者： Dilworth, Robert Gudla, Charan Rana, Md Shohel Ahmed, Feroz Mississippi State University Department Of Computer Science And Engineering StarkvilleMS United States Florida Gulf Coast University Department Of Computing And Software Engineering Fort MyersFL United States Prediction 3D

ISBN: (纸本)9798350385625

This paper reviews security testing, covering the concepts and notions essential to the discipline. The following analysis uses an inverted funnel approach to reduce the broad topic of software testing into its constituent parts. With this framework in mind, the paper will attempt to answer the following questions. Why is software testing significant? What are the roles of users and developers as they relate to software? What techniques are commonly used when testing software? Is the discipline of software testing unified;if not, what controversies exist, and what do they concern? What is security testing? What is the purpose of security testing? What metrics are used to guide security testing? What are common threats that hamper or impede security testing? How can developers mitigate the risk posed by security threats;what defensive options are available? What improvements can be made to software testing? What can occur in the absence of software/security testing? © 2024 IEEE.

关键词： software testing

来源：评论

学校读者我要写书评

暂无评论

Improving PEM Hydrogen Electrolyser Efficiency using AC Excitation

Improving PEM Hydrogen Electrolyser Efficiency using AC Exci...

引用

2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Larsen, Thea Kim, Seho Kennedy, John Liu, Jingjing University of Auckland Department of Electrical Computer and Software Engineering Auckland New Zealand Earth Resources and Materials Geological and Nuclear Science Wellington New Zealand The University of Auckland Department of Chemical and Materials Engineering Auckland New Zealand

ISBN: (纸本)9798350376067

Hydrogen is a potential clean energy source that minimises environmental impacts but is currently economically prohibitive due to the low energy conversion efficiency. In order to improve the economic viability, this paper investigates improving the efficiency of hydrogen electrolysis. The prototype power electronics presented in this paper provide low-frequency AC or pulsed current to a proton exchange membrane (PEM) electrolyser to improve the operational efficiency up to 4.2% in contrast to the typical constant current or voltage excitation. © 2024 IEEE.

关键词： Clean energy

来源：评论

学校读者我要写书评

暂无评论

A Self-play and Sentiment-Emphasized Comment Integration Framework Based on Deep Q-Learning in a Crowdsourcing Scenario : Extended Abstract 39

A Self-play and Sentiment-Emphasized Comment Integration Fra...

引用

39th IEEE International Conference on Data engineering, ICDE 2023

作者： Rong, Huan Sheng, Victor S. Ma, Tinghuai Zhou, Yang Al-Rodhaan, Mznah Nanjing University of Information Science & Technology School of Artificial Intelligence Nanjing China Texas Tech University Department of Computer Science Lubbock United States Nanjing University of Information Science & Technology School of Computer & Software Nanjing China Auburn University Department of Computer Science & Software Engineering Auburn United States King Saud University College of Computer & Information Sciences Riyadh Saudi Arabia

ISBN: (纸本)9798350322279

Crowdsourcing is a sourcing model where individuals or organizations obtain goods and services from a large, relatively open and often rapidly evolving group of internet users. The most common way that crowdsourcing can facilitate machine learning is to annotate instances with labels [1]. However, the same instance may have inconsistent class labels, in the eyes of various annotators. Therefore, current efforts in crowdsourcing mainly focus on the truth inference or label integration, to remove inconsistent labels or to alleviate biased labeling. In turn, instances with the integrated labels could facilitate the training on machine learning models. The future direction of crowdsourcing is to apply more fine-grained truth inference methods to different application domains [2]. Consequently, we evolve toward another challenging problem of comment integration. That is, how can we integrate or summarize the core opinions of multiple product comments obtained from users, rather than the discrete labels. © 2023 IEEE.

关键词： Crowdsourcing

来源：评论

学校读者我要写书评

暂无评论

By artificial intelligence algorithms and machine learning models to diagnosis cancer

引用

Materials Today: Proceedings 2023年 80卷 2969-2975页

作者： Agarwal, Seema Yadav, Ajay Singh Dinesh, Vennapoosa Vatsav, Kolluru Sai Sri Prakash, Kolluru Sai Surya Jaiswal, Sushma Department of Mathematics SRM Institute of Science and Technology Delhi-NCR Campus U.P. Ghaziabad India Software Engineer Mechanical Engineering National Institute Of Technology Mizoram Aizwal Mizoram India Computer Science Engineering Gitam university Andhra Pradesh Visakhapatnam India Bilaspur India

Cancer is one of the world's most critical health issues. Many in the past decade More accurately, diagnostic tests and methodologies have been enhanced. Tests are categorised into imaging tests, endoscopic procedures Includes testing for biopsy and cytology. These diagnostic tests yield huge numbers. volumes of data that must be evaluated and differentiated by specialists Between tumours that are benign and malignant. Artificial intelligence now (AI) Large quantities of diagnostic imaging may be examined using applications Improved accuracy to enhance health system efficiency. New AI algorithms technical advances and computer enhancements Hardware may be used to train diagnostic neural artificial networks Experience with a wide range of scans. Already a thorough understanding and Machines can educate computers to compare and assess models Huge quantity of cancer scanning data. Highly diagnostic skills software has been evaluated and compared to the conventional Cancer specialists' diagnostic tools. Their precision is much increased and regarded highly effective in early diagnosis and extended forecasting Different cancers. Scientists have become systems of artificial intelligence (AI). that may exceed human specialists in the prognosis of breast cancer and A great deal earlier diagnosis. Similarly, informatics developed an AI algorithm. and profound learning algorithms that may forecast to which persons Develop lung cancer using low dose CT analysis (computerized tomography) Lung scans. Lung scans. Use of the convolutionary neural network recently (CNNs) was employed in the invasion depth diagnosis of Gastric endoscopy-based gastric cancer. Studies have also been shown AI techniques paired with imagery may have a significant influence early diagnosis with algorithm-guided identification of oral cancer results Heterogeneity of the oral lesion. This review gathered some pretty interesting information Research publications on the topic. © 2021

关键词： Cytology

来源：评论

学校读者我要写书评

暂无评论

DNBP-CCA:A Novel Approach to Enhancing Heterogeneous Data Traffic and Reliable Data Transmission for Body Area Network

引用

computers, Materials & Continua 2024年第5期79卷 2851-2878页

作者： Abdulwadood Alawadhi Mohd.Hasbullah Omar Abdullah Almogahed Noradila Nordin Salman A.Alqahtani Atif M.Alamri InterNetWorks Research Laboratory School of ComputingUniversiti Utara MalaysiaSintok06010Malaysia Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn MalaysiaParit RajaJohor86400Malaysia School of Games&Creative Technology University for the Creative ArtsFarnhamGU97DSUK Computer Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia

The increased adoption of Internet of Medical Things (IoMT) technologies has resulted in the widespread use ofBody Area Networks (BANs) in medical and non-medical domains. However, the performance of IEEE 802.15.4-based BANs is impacted by challenges related to heterogeneous data traffic requirements among nodes, includingcontention during finite backoff periods, association delays, and traffic channel access through clear channelassessment (CCA) algorithms. These challenges lead to increased packet collisions, queuing delays, retransmissions,and the neglect of critical traffic, thereby hindering performance indicators such as throughput, packet deliveryratio, packet drop rate, and packet delay. Therefore, we propose Dynamic Next Backoff Period and Clear ChannelAssessment (DNBP-CCA) schemes to address these issues. The DNBP-CCA schemes leverage a combination ofthe Dynamic Next Backoff Period (DNBP) scheme and the Dynamic Next Clear Channel Assessment (DNCCA)scheme. The DNBP scheme employs a fuzzy Takagi, Sugeno, and Kang (TSK) model’s inference system toquantitatively analyze backoff exponent, channel clearance, collision ratio, and data rate as input parameters. Onthe other hand, the DNCCA scheme dynamically adapts the CCA process based on requested data transmission tothe coordinator, considering input parameters such as buffer status ratio and acknowledgement ratio. As a result,simulations demonstrate that our proposed schemes are better than some existing representative approaches andenhance data transmission, reduce node collisions, improve average throughput, and packet delivery ratio, anddecrease average packet drop rate and packet delay.

关键词： Internet of Medical Things body area networks backoff period tsk fuzzy model clear channel assessment media access control

来源：评论

学校读者我要写书评

暂无评论

A Framework Design of ML Classifier Algorithm for Retrieve the Information about the Drugs and its Quality 4

A Framework Design of ML Classifier Algorithm for Retrieve t...

引用

4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Meenakshi, M. Ruban, S. Nandhini, T.J. Loganayagi, S. Sayed, Raad Muhammed Hassan, Enas Al-Saidi, Imad Al-Din Hussein Saveetha University Saveetha School of Engineering Chennai Thandalam602117 India St Aloysius Deemed to Be University Department of Software Technology India Saveetha University Saveetha School of Engineering-SIMATS Department of Computer Science and Engineering chennai India Department of Computer Science and Engineering chennai tamil nadu thandalam602105 India Collage of Nursing National University of Science and Tqechnology dhi qar64001 Iraq Al-Farahidi University Baghdad Iraq Al-kunooze University College Computer Engineer Department basra Iraq

ISBN: (纸本)9798350360165

Every day, pharmaceutical firms have to do extensive product analyses. The same product is frequently registered more than once in several systems, each using unique properties. For these businesses, precise and high-quality information is crucial, especially considering the nature of the pharmaceuticals these goods contain. The hypothesis of this research project is that machine learning provides a workable way to effectively combine different data sources and match records pertaining to the same product. It is no longer feasible to match records by hand because of the extraordinarily large volume that has to be processed. In a large data context, this paper presents a framework for matching pharmacological records using machine learning approaches. The framework trains machine learning by utilising specified criteria for record matching. These models are then tested by forecasting matches between records that deviate from these preset guidelines. Lastly, by creating a huge variety of record combinations and forecasting matches based on them, the system mimics the production environment. The findings show that although the training datasets produce good results - the top model has an average accuracy of about 85% - the production environment has its own set of difficulties. In spite of this, matches that depart from established norms are effectively predicted by the framework. These results highlight how machine learning can be used to perform record matching tasks, as manual processing at this size is impossible. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

GoT: Effective Graph-of-Thought Reasoning in Language Models

GoT: Effective Graph-of-Thought Reasoning in Language Models

引用

2024 Findings of the Association for Computational Linguistics: NAACL 2024

作者： Yao, Yao Li, Zuchao Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University China National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan430072 China

ISBN: (纸本)9798891761193

With the widespread use of language models (LMs) in NLP tasks, researchers have discovered the potential of Chain-of-thought (CoT) to assist LMs in accomplishing complex reasoning tasks by generating intermediate steps. However, human thought processes are often non-linear, rather than simply sequential chains of thoughts. Therefore, we propose Graph-of-Thought (GoT) reasoning, which models human thought processes not only as a chain but also as a graph. By representing thought units as nodes and connections between them as edges, our approach captures the non-sequential nature of human thinking and allows for a more realistic modeling of thought processes. GoT adopts a two-stage framework with an additional GoT encoder for thought graph representation and fuses the graph representation with the original input representation through a gated fusion mechanism. We evaluate GoT's performance on a text-only reasoning task (AQUA-RAT) and a multimodal reasoning task (scienceQA). Our model achieves significant improvement over the strong CoT baseline on the AQUA-RAT test set and boosts accuracy from 85.19% to 87.59% using the T5-base model over the state-of-the-art Multimodal-CoT (Zhang et al., 2023) on the scienceQA test set. Our code is publicly available at https://***/Zoeyyao27/Graphof-Thought. © 2024 Association for Computational Linguistics.

关键词： Rats

来源：评论

学校读者我要写书评

暂无评论

HiPoser: 3D Human Pose Estimation with Hierarchical Shared Learning at Parts-Level Using Inertial Measurement Units 39

HiPoser: 3D Human Pose Estimation with Hierarchical Shared L...

引用

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Liao, Guorui Zheng, Chunyuan Cheng, Li Xie, Haoyu Huang, Shanshan Liao, Jun Li, Haoxuan Liu, Li School of Big Data & Software Engineering Chongqing University China School of Mathematical Sciences Peking University China Department of Electrical and Computer Engineering University of Alberta Canada Center for Data Science Peking University China

ISBN: (纸本)157735897X

This paper considers the challenging problem of 3D Human Pose Estimation (HPE) from a sparse set of Inertial Measurement Units (IMUs). Existing efforts typically reconstruct a pose sequence by either directly tackling whole-body motions or focusing on distinctive spatio-temporal features of local body parts. Unfortunately, these methods ignore existing interdependent motor synergies amongst body parts, which may lead to pose estimation with ambiguous local parts. This observation motivates us to propose a hierarchical learning-based approach, HiPoser, which utilizes a hierarchical shared structure using Mamba blocks as the backbone to focus on the following estimation tasks, involving: 1) torso pose, 2) lower limbs pose, 3) upper limbs pose, and finally 4) global translation. These tasks selectively incorporate body motion states and are to be carried out sequentially in reconstructing part-based poses, which are amalgamated to estimate the final full-body pose with the global translation that satisfies inter-part consistencies. Our hierarchical structure allows HiPoser the flexibility in prioritizing different aspects of pose estimation, to emphasize more on detail or stability. Empirical evaluations over three benchmark datasets demonstrate the superiority of HiPoser over existing state-of-the-art models, suggesting that analyzing the synergistic movement of body parts is indeed important for advancing IMU-based 3D HPE. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Metric system

来源：评论

学校读者我要写书评

暂无评论

AI-Based Discourse Analysis System (ADAS) for Improved STEM Education 14

AI-Based Discourse Analysis System (ADAS) for Improved STEM ...

引用

14th IEEE Integrated STEM Education Conference, ISEC 2024

作者： Gottam, Varun Dehbozorgi, Nasrin Lee, Soon Kennesaw State University Department of Computer Science United States Kennesaw State University Department of Software Engineering United States Kennesaw State University Department of Elementary and Early Childhood Education United States

ISBN: (纸本)9798350352801

Recent advancements in Artificial Intelligence and Natural Language Processing play a key role in building systems that facilitate machine comprehension of human language. The integration of Natural Language Processing offers myriad educational benefits, including automated assessments, interactive tutorials, and sophisticated feedback systems, thereby enhancing the efficiency of teaching and learning endeavors. Particularly in science education, the analysis of dialogues between educators and students holds significant importance. Researchers have developed the Classroom Discourse Analysis Tool, a framework for categorizing discourse patterns in science classrooms, thereby enriching educational interactions. However, the manual application of this tool poses challenges due to its timeconsuming and resource-intensive nature. To address this challenge, we propose developing an AI-based Discourse Analysis System tailored for educational settings. The primary objective is to create a user-friendly web application accessible to educators nationwide, enabling them to upload classroom discussion transcripts and receive immediate, comprehensive feedback on discourse types. By harnessing the capabilities of AI, this system aims to provide insightful analyses of classroom dialogues, scientific discourse patterns, and student engagement levels. The incorporation of AI into the education sector marks a significant milestone in the advancement of educational technology, promising to revolutionize teaching methodologies and enhance learning outcomes. © 2024 IEEE.

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

GPT-4 enhanced multimodal grounding for autonomous driving:Leveraging cross-modal attention with large language models

引用

Communications in Transportation Research 2024年第1期4卷 5-23页

作者： Haicheng Liao Huanming Shen Zhenning Li Chengyue Wang Guofa Li Yiming Bie Chengzhong Xu State Key Laboratory of Internet of Things for Smart City and Department of Computer and Information Science University of MacaoMacao SAR999078China Department of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu610000China State Key Laboratory of Internet of Things for Smart City and Departments of Civil and Environmental Engineering and Computer and Information Science University of MacaoMacao SAR999078China State Key Laboratory of Internet of Things for Smart City and Departments of Civil and Environmental Engineering University of MacaoMacao SAR999078China College of Mechanical and Vehicle Engineering Chongqing UniversityChongqing400030China School of Transportation Jilin UniversityChangchun130000China

In the field of autonomous vehicles(AVs),accurately discerning commander intent and executing linguistic commands within a visual context presents a significant *** paper introduces a sophisticated encoder-decoder framework,developed to address visual grounding in *** Context-Aware Visual Grounding(CAVG)model is an advanced system that integrates five core encoders—Text,Emotion,Image,Context,and Cross-Modal—with a multimodal *** integration enables the CAVG model to adeptly capture contextual semantics and to learn human emotional features,augmented by state-of-the-art Large Language Models(LLMs)including *** architecture of CAVG is reinforced by the implementation of multi-head cross-modal attention mechanisms and a Region-Specific Dynamic(RSD)layer for attention *** architectural design enables the model to efficiently process and interpret a range of cross-modal inputs,yielding a comprehensive understanding of the correlation between verbal commands and corresponding visual *** evaluations on the Talk2Car dataset,a real-world benchmark,demonstrate that CAVG establishes new standards in prediction accuracy and operational ***,the model exhibits exceptional performance even with limited training data,ranging from 50%to 75%of the full *** feature highlights its effectiveness and potential for deployment in practical AV ***,CAVG has shown remarkable robustness and adaptability in challenging scenarios,including long-text command interpretation,low-light conditions,ambiguous command contexts,inclement weather conditions,and densely populated urban environments.

关键词： Autonomous driving Visual grounding Cross-modal attention Large language models Human-machine interaction

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：