检索结果-内蒙古大学图书馆

25th International Arab Conference on information technology, ACIT 2024

作者： Samhan, Ahmad Alhajhassan, Suhair Dabaa't, Sara Abu Elrashidi, Ali Zarqa University Faculty of Information Technology Software Engineering Department Zarqa Jordan Applied Science Private University Faculty of Information Technology Software Engineering Department Amman Jordan University of Business and Technology Faculty of Engineering Electrical Engineering Department Jeddah Saudi Arabia

ISBN: (纸本)9798331540012

This paper presents a review study that explores the landscape and challenges of AI-assisted impact analysis in software requirements engineering. The growth of complex and change-prone software systems has highlighted the need for AI techniques - especially machine learning (ML) and natural language processing (NLP) - to be harnessed to improve impact analysis scalability and efficiency. Key challenges identified include data quality issues, scalability constraints, and ethical problems related to bias and accountability. To investigate these challenges and provide insights, a review was conducted across IEEE Xplore, ACM Digital Library, and Google Scholar, cross referencing between keywords that include concepts from the domains of "software requirements", "impact analysis", and "AI-Assisted approaches". Inclusion criteria focused on studies that presents empirical data on AI-assisted impact analysis. This study highlights research gaps and presents insights for overcoming obstacles using emerging technologies such as edge AI in order to enhance post-impact analysis. Findings show that while AI improves traditional impact analysis, further work is necessary to deliver effective, ethical and practical implementations within evolving software contexts. © 2024 IEEE.

关键词： AI Challenges Artificial Intelligence Edge AI Impact Analysis Natural Language Processing software engineering software Requirements

来源：评论

学校读者我要写书评

暂无评论

Adaptive design and implementation of automatic modulation recognition accelerator

引用

Journal of Ambient Intelligence and Humanized Computing 2024年第1期15卷 779-795页

作者： Wang, Bin Wei, Xianglin Wang, Chao Li, Junnan Jiao, Xiang Fan, Jianhua Li, Peng School of Electronics and Information Engineering Nanjing University of Information Science and Technology Nanjing210044 China School of Computer and Software Nanjing University of Information Science and Technology Nanjing210044 China 63rd Research Institute National University of Defense Technology Nanjing210007 China

Automatic modulation recognition-oriented Deep Neural Networks (ADNNs) have achieved higher recognition accuracy than traditional methods with less labor overhead. However, their high computation complexity usually far exceeds the computation capacity of communication devices built on Field Programmable Gate Array (FPGA) platform. When solving the problem of insufficient resources, the complete operation of FPGA-based accelerator can be achieved by dividing the calculation into several parts and calculating them separately, but this will cause unaccepTable latency. In this backdrop, we develop a new ADNN model, named VT-CNN2+, to promote the recognition accuracy. Then, after stating the resources and latency problems for implementing VT-CNN2+ on the FPGA platform, an adaptive hardware accelerator is put forward. To implement the accelerator, Area folding is introduced to optimize resources consumption. Moreover, Literacy Optimization, Parallelism Optimization, Inter-layer Cascading, Temporary Cache and Data Loading Optimization are adopted to reduce latency. Afterwords, the two components in our accelerator are detailed, i.e., Once-designed module and Re-designed module. Finally, to evaluate the performance and adaptivity of our accelerator, a series of experiments are conducted on two different FPGA platforms, i.e., AX7350 and ZedBoard. Results show that our accelerator can successfully adapt to different FPGA platforms and it can remarkably reduce the processing latency. Moreover, our accelerator’s processing speed of 0.066249s per single data sample with much lower energy consumption is one order of magnitude faster than desktop-level Central Processing Units (CPUs), two orders of magnitude faster than embedded CPUs. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

COVID-19 Prediction Model Empowered with Fused Computational Intelligence Technique

引用

IEIE Transactions on Smart Processing and Computing 2025年第1期14卷 109-117页

作者： Khan, Muhammad Adnan Naseer, Iftikhar Ali, Muhammad Nadeem Kim, Byung-Seo Department of Software Faculty of Artificial Intelligence & Software Gachon University Seongnam13120 Korea Republic of Faculty of Computer Science & Information Technology The Superior University Lahore54600 Pakistan Department of Software and Communications Engineering Hongik University Sejong Campus Korea Republic of

The novel Coronavirus (COVID-19) spread rapidly around the world and caused overwhelming effects on the health and economy of the world. It first appeared in Wuhan city of China and was declared a pandemic by the World Health Organization (WHO). Many researchers, as well as experts in clinical and artificial intelligence experts, are working together to control the rapid spread of COVID-19 with early detection. This study focused on intelligent prediction for coronavirus using computational intelligence approaches (IPC-FCIA) like convolutional neural networks, support vector machines, and fuzzy logic techniques. The proposed IPC-FCIA model is based on two sections namely the training section and the validation section. Features fusion and decision-level fusion are used in this study to enhance the performance of the recommended IPC-FCIA model. The proposed model predicts the early detection of COVID-19 in two types COVID-negative and COVID-positive. The benchmark results of the model show an accuracy of 97.66 % on decision-level fusion in the detection of COVID-19. The proposed model can be helpful for medical experts as well as COVID-19-affected patients. © 2025 Institute of Electronics Engineers of Korea. All rights reserved.

关键词： COVID-19

来源：评论

学校读者我要写书评

暂无评论

SET-MRTS:An Empirical Experiment Tool for Real-Time Scheduling and Synchronization

引用

Journal of Electronic Science and technology 2022年第1期20卷 30-42页

作者： Ze-Wei Chen Hang Lei Mao-Lin Yang Yong Liao School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu 610054

In the real-time scheduling theory,schedulability and synchronization analyses are used to evaluate scheduling algorithms and real-time locking protocols,respectively,and the empirical synthesis experiment is one of the major methods to compare the performance of such ***,since many sophisticated techniques have been adopted to improve the analytical accuracy,the implementation of such analyses and experiments is often *** paper proposes a schedulability experiment toolkit for multiprocessor real-time systems(SET-MRTS),which provides a framework with infrastructures to implement the schedulability and synchronization analyses and the deployment of empirical synthesis ***,with well-designed peripheral components for the input and output,experiments can be conducted easily and flexibly on *** demonstration further proves the effectiveness of SET-MRTS in both functionality and availability.

关键词： Experiment real-time system schedulability software tools synchronization

来源：评论

学校读者我要写书评

暂无评论

Knowledge and Task-Driven Multimodal Adaptive Transfer Through LLMs with Limited Data

Knowledge and Task-Driven Multimodal Adaptive Transfer Throu...

引用

2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zhang, Xu Chen, Zhikui Ren, Hao Tian, Yang The Dalian University of Technology School of Software Technology International School of Information Science and Engineering Dalian China

ISBN: (纸本)9798350386226

The scarcity of annotated data and the challenge of generative models that meet the authenticity requirements collectively constrain the modeling of highly specialized deep medical models, making data remain a central issue in model training. The emergence of large-scale models has facilitated access to knowledge and data references for various tasks, even those with high specialization. Therefore, in the absence of data sources, latent knowledge and model structure can be considered for reuse instead of directly utilizing samples, and generalizing specific medical models based on large-scale models is expected to be competitive in the future. However, due to significant differences between fine-grained medical features and tasks, generalization based on large language models (LLMs) is not entirely satisfactory, it requires a more targeted inheritance of knowledge and important structures to achieve effective generalization. This paper presents a novel multi-modal medical model training approach designed to circumvent the need for extensive real data preparation, leveraging the capabilities of LLMs to generalize downstream tasks. Initially, knowledge threads are extracted to facilitate multi-modal diagnostic knowledge retrieval from LLMs without relying on massive datasets. These threads are then utilized to generate multimodal task instructions, guiding LLMs in providing feedback and further fine-tuning them for direct adaptation to downstream tasks by encoding samples into a unified feature representation, thereby simplifying the search and traversal process. Finally, an adaptive knowledge transfer strategy is proposed, implementing collaborative modeling with key patterns anchored under the joint influence of knowledge threads and task instructions, leading to the generation of an equivalent and concise initialization model guided by LLMs. The experimental design validates the efficacy of the approach in rapid generalizing specific models, demonstrating high accu

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Incorporating Contextual Cues for Image Recognition: A Multi-Modal Semantic Fusion Model Sensitive to Key information

Incorporating Contextual Cues for Image Recognition: A Multi...

引用

2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zhang, Xu Chen, Zhikui Ren, Hao Jin, Shan Tian, Yang The Dalian University of Technology School of Software Technology International School of Information Science and Engineering Dalian China

ISBN: (纸本)9798350386226

Multi-modal data feature fusion can effectively improve the accuracy of primary modal pattern recognition and address the issue of missing data through multi-modal collaboration. To some extent, supplementing multi-view information can alleviate the conflict between medical images' reliance on professional annotations and the limited number of annotations. However, there are still several challenges in engineering multimodal feature perception and fusion for medical images, including imbalanced patterns that make it challenging to establish a weighted allocation criterion suitable for different tasks, misleading factors not positively correlated with knowledge richness, and complexities associated with integrating modalities having diverse formats for text and time series data into overall feature engineering and similarity. This paper introduces a multi-modal semantic fusion model that is sensitive to key information represented by contextual cues. Furthermore, it incorporates a dynamic feature engineering scheme capable of acquiring and integrating important insights from three perspectives with an optimized structure. Additionally, it presents a novel similarity evaluation criteria and fusion mode that adaptively adjust weights based on their contributions, facilitating the measurement of target object category likelihoods across various modalities for weighted analyses. The system combines LSTM and CNN models to builda sequence and local information sensitive structure designed to integrate heterogeneous features while embedding the multimodal similarity fusion. Emphasizing small yet compelling pieces of information such as contextual cues that prompt medical representation assignment to the target category, our system places significant importance on specificity removal and accuracy enhancement, exemplified by introducing contextual information demonstrating our sensitivity to contextual cues despite our non-specifically structured approach. © 2024 IEEE.

关键词： Image annotation

来源：评论

学校读者我要写书评

暂无评论

NNC: A Nearest Neighbors Constraint-based Metric Learning for Multivariate Time Series Classification 2

NNC: A Nearest Neighbors Constraint-based Metric Learning fo...

引用

2nd IEEE International Conference on Signal, information and Data Processing, ICSIDP 2024

作者： Chen, Zhi Zhang, Fengli Zhu, Jiajing Zhang, Yun Li, Qiaoqin Liu, Yongguo University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu China

ISBN: (纸本)9798331515669

Multivariate time series (MTS) classification has gained significant attention as a prominent research area in recent years. Studies using Mahalanobis distance-based dynamic time warping along with metric learning algorithms have reported significant results. Existing metric learning approaches, such as large margin nearest neighbor, seek to optimize a loss function with two components: one that reduces the distance between samples with the same label and another that increases the distance between samples with different labels. However, these two terms play an opposite role in some situations and it is difficult to explore a way to balance such two competitive terms. In this paper, we introduce a nearest neighbors constraint (NNC) model designed for MTS classification. In NNC, a data-adaptive Mahalanobis distance-based dynamic time warping distance metric is designed to compute the distance between two MTS. To learn the Mahalanobis distance, a concise and effective loss function is developed. Instead of scanning all triplets, NNC only employs the triplets which violate the rule that an MTS's k nearest neighbors should share the same label with it to learn the Mahalanobis distance. Experiments on 9 public MTS datasets demonstrate that the proposed model achieves competitive performance. © 2024 IEEE.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Multi-user reinforcement learning based task migration in mobile edge computing

引用

Frontiers of Computer Science 2024年第4期18卷 161-173页

作者： Yuya CUI Degan ZHANG Jie ZHANG Ting ZHANG Lixiang CAO Lu CHEN Tianjin Key Lab of Intelligent Computing and Novel Software Technology Tianjin University of TechnologyTianjin 300384China School of Internet of Things Engineering Jiangsu Vocational College of Information TechnologyWuxi 214153China School of Electronic and Information Engineering Beijing Jiaotong UniversityBeijing 100044China School of Sports Economics and Management Tianjin University of SportTianjin 301617China

Mobile Edge Computing(MEC)is a promising *** service migration is a key technology in *** order to maintain the continuity of services in a dynamic environment,mobile users need to migrate tasks between multiple servers in real *** to the uncertainty of movement,frequent migration will increase delays and costs and non-migration will lead to service ***,it is very challenging to design an optimal migration *** this paper,we investigate the multi-user task migration problem in a dynamic environment and minimizes the average service delay while meeting the migration *** order to optimize the service delay and migration cost,we propose an adaptive weight deep deterministic policy gradient(AWDDPG)*** distributed execution and centralized training are adopted to solve the high-dimensional *** show that the proposed algorithm can greatly reduce the migration cost and service delay compared with the other related algorithms.

关键词： mobile edge computing mobility service migration deep reinforcement learning deep deterministic policy gradient

来源：评论

学校读者我要写书评

暂无评论

MRLKG: Multimodal Representation Learning with Keyword-Based Graph for Hashtag Recommendation 7

MRLKG: Multimodal Representation Learning with Keyword-Based...

引用

7th International Conference on Machine Learning and Natural Language Processing, MLNLP 2024

作者： Yasir Khalil, Mian Muhammad Wang, Qingxian Chen, Bo Rauf, Muhammad Arslan Wang, Weidong School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798350354973

The increasing volume of social media content has made hashtag recommendation a critical task for enhancing content discoverability and user engagement. This paper presents MRLKG (Multimodal Representation Learning with Keywordbased Graph) for hashtag recommendation. Our approach integrates cross-modality joint representation with a keyword-guided graph. The joint embedding provides a contextually rich representation of the content. In parallel, a keyword-guided Graph Convolutional Network (GCN) generates a refined representation of each microblog post content by emphasizing key concepts and their relational context with other microblog posts, essential for understanding the broader context and trends that hashtags often represent. By integrating the contextually rich joint embedding provided by transformers with the KeyBERT-GCN's focus on key concepts and their relational structure, the model can recommend hashtags that are both contextually relevant and aligned with the key trends represented by other microblog posts. Experimental results demonstrate the effectiveness of our method, showing improved performance over existing approaches. This method offers a robust solution for hashtag recommendation by leveraging both contextual understanding and graph-based reasoning. © 2024 IEEE.

关键词： Graph embeddings

来源：评论

学校读者我要写书评

暂无评论

AOA Based Localization and orientation estimation of UWB via Weighted Least Squares 24

AOA Based Localization and orientation estimation of UWB via...

引用

24th IEEE International Conference on Communication technology, ICCT 2024

作者： Zhang, Di Li, Ye Yin, Guangqiang University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu China

ISBN: (纸本)9798350363760

In the Internet of Things (IoT), robot localization using ultra-wideband (UWB) technology requires estimating both the position and orientation of the robot. However, current localization and orientation estimation methods often rely on rigid body models, which require multiple anchors, leading to high system costs. To address this limitation, this paper focuses a self-localization approach based on the angle of arrival (AOA), which reduces complexity with only a single anchor. We propose an enhanced algorithm that employs weighted least squares (WLS) to improve estimation accuracy. Additionally, we derive the closed-form Cramér-Rao lower bound (CRLB) as a theoretical benchmark to evaluate the performance of the proposed algorithm. Both simulation results and real-world experiments demonstrate that the proposed algorithm achieves high localization accuracy, providing a cost-effective and efficient solution for IoT applications. © 2024 IEEE.

关键词： Ultra-wideband (UWB)

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：