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检索条件"任意字段=2023 International Conference on Computer Science and Automation Technology, CSAT 2023"

共 68642 条记录，以下是461-470 订阅

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Exploration of Software Field Engineer Training Based on AI+Capability Model 24

Exploration of Software Field Engineer Training Based on AI+...

引用

2nd international conference on Advances in Artificial Intelligence and Applications, AAIA 2024

作者： Zhou, Junjie Wu, Qian School of Information Wuhan Software Engineering Vocational College Wuhan Open University Hubei Wuhan China School of Computer Science Wuhan University Hubei Wuhan China Hubei Engineering Research Center for Intelligent Detection and Identification of Complex Parts Wuhan China

ISBN: (纸本)9798400712883

With the rapid development of artificial intelligence technology, its application in the field of education and teaching is becoming increasingly widespread, bringing revolutionary changes to traditional education models. To cultivate high-quality software vocational skills talents, a digital management platform based on AI+capability model is constructed to explore the innovative application of artificial intelligence in education and teaching, realizing intelligent auxiliary learning functions such as personalized resource recommendation, student growth monitoring, and achievement label portrait. At the same time, a series of intelligent teaching tools such as"AI+Code Generation, AI+Inspection Assistant, AI+Process Map" will be developed to explore and promote the vertical application of big models in talent cultivation. Through innovative models such as"introducing enterprises into education, cultivating students through real-life situations, AI+empowerment, teaching according to students’ aptitude, trinity, innovative training, strengthening processes, and diverse evaluations", it has been fully applied to the talent training scene of software technology field engineers, and its teaching effectiveness is significant. © 2024 Copyright held by the owner/author(s).

关键词： Students

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SANS: Spatial-Aware Neural Solver for Plane Geometry Problem 27th

SANS: Spatial-Aware Neural Solver for Plane Geometry Proble...

引用

27th international conference on Pattern Recognition, ICPR 2024

作者： Lin, Zi-Hao Xiao, Shun-Xin Chen, Zi-Rong Li, Jian-Min Wang, Da-Han Zhang, Xu-Yao School of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen361024 China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation of Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9783031781186

Geometry problem solving (GPS) is an important research direction in artificial intelligence. Previous studies have demonstrated the effectiveness of neural solvers in GPS. However, they are deficiencies in accurately representing spatial relationships of geometric primitives within visually rich geometric diagrams. This paper presents a novel neural solver termed spatial-aware neural solver (SANS) that can perceive spatial relationships between geometric primitives. SANS includes two new modules: multimodal dual-branch spatial awareness pre-trained language module and point-primitive spatial-aware attention module. The pre-training module employs a dual-branch visual-textual point-matching strategy to align visual and textual points, and utilizes semantic structure pre-training to model global relationships. Additionally, the point-primitive spatial awareness attention module enhances the model’s ability to perceive spatial relationships between geometric primitives by accounting for the relative positions of points. Experiments show that SANS achieves 81.5 and 74.1 of accuracy on the Geometry3K and PGPS9K datasets. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Semantics

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Double Domain Converter Transformer For Improving EEG-Based Emotion Recognition from Video to Game Scenarios

Double Domain Converter Transformer For Improving EEG-Based ...

引用

2025 IEEE international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Pan, Jun-Yu Yin, Hao-Long Zheng, Wei-Long SJTU Paris Elite Institute of Technology Shanghai Jiao Tong University Shanghai China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9798350368741

Emotion recognition (ER) plays an important role in the field of modern technology and human-computer interaction. Traditional emotion recognition approaches usually utilize videos as stimuli. However, watching video lacks interaction. Recently, more and more game-related stimuli have been used. To explore emotion recognition in a more natural state, this study pioneers the use of Genshin Impact game and gameplay-related videos to evoke positive and neutral emotions. As Electroencephalogram (EEG) feature distributions of video and game scenarios are very different, we propose the Double Domain Converter Transformer network (DDCT) to enhance EEG-based cross-scenario emotion recognition, making full use of information from different domains. Our model has two converters that can convert input data into the source domain and the target domain, diminishing distribution difference in two feature spaces. Experimental results demonstrate that our model achieves a remarkable prediction accuracy of 84.95% in video-game mix-scenario emotion recognition and 74.76% in video to game cross-scenario emotion recognition. Our code is available at https://***/AlbertMentat/DDCT. © 2025 IEEE.

关键词： cross-scenario emotion recognition domain adaptation EEG Transformer video games

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Drug Review Classification by Using Sentiment Analysis 3rd

Drug Review Classification by Using Sentiment Analysis

引用

3rd international conference on Intelligent Systems and Sustainable Computing, ICISSC 2023

作者： Dasari, Krishna Kumar Bhaskar, Nuthanakanti Bagam, Laxmaiah Madhusudhan, Mudimela Santhuja, Pandu Avala, Raji Reddy Department of Computer Science and Engineering CMR Technical Campus Hyderabad India Department of Mechanical Engineering CMR Technical Campus Hyderabad India

ISBN: (纸本)9789819783540

The drugs sentiment analysis has grown significantly in importance in the modern day since categorizing medications based on their efficacy by examining user evaluations can help possible future customers in learning more and making better selections about certain drugs. Using social media, blogs, and other sources, sentiment analysis is a popular approach for obtaining opinions from a large number of people. In addition to a lack of accessibility, many people started using medications on their own without the necessary consultations it increased the severity of the health issue. Numerous fields have found applications for machine learning, and creative work for automation is increasing. The information intends to show a medication recommendation method that could significantly decrease the need for specialists. Implementing machine learning methods, such as light gradient boosting machine (LGBM) K-nearest neighbours (KNN), CAT (computer-aided translation) boost, decision tree (DT), and, in order to assess the information and create a model that conducts sentiment classification. Measurements for precision, accuracy, recall, and F1 score were utilized to evaluate the attitudes that were shown. As a consequence, the outcomes demonstrate that the classifier outperforms other approaches. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Nearest neighbor search

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Glare-SNet: Unsupervised Glare Suppression Balance Network 27th

Glare-SNet: Unsupervised Glare Suppression Balance Network

引用

27th international conference on Pattern Recognition, ICPR 2024

作者： Li, Pei Zuo, Chengyu Wei, Wangjuan Pan, Xiaoying Wang, Zhanhao School of Computer Science and Technology Xi’an University of Posts and Telecommunications Xi’an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’an China Shaanxi Key Laboratory of Intelligent Media Computing and interaction Xi’an China Xi’an Key Laboratory of Big Data and Intelligent Computing Xi’an China

ISBN: (纸本)9783031781681

In light of the problems associated with glare and halo effects in low-light images, as well as the inadequacy of existing processing algorithms in handling details, a glare suppression balance network based on unsupervised learning has been designed and implemented in this study. A network that perceives brightness through light decomposition has been proposed, and an illumination learning network has been designed. The idea of integration has been employed to form an unsupervised glare suppression balance network, aiming to tackle the issues of glare and the accompanying halo effects. Through objective and subjective evaluations of real images, it has been proven that the method introduced in this paper surpasses other methods in suppressing glare and enhancing the overall quality of low-light images. Furthermore, it has been shown to exhibit good performance and effectiveness in target detection. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image enhancement

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Learning Explicit Radical Representations for Zero-Shot Chinese Character Recognition 27th

Learning Explicit Radical Representations for Zero-Shot Chin...

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27th international conference on Pattern Recognition, ICPR 2024

作者： Pan, Song-Liang Wang, Da-Han Jiang, Nanfeng Zhang, Xu-Yao Zhu, Shunzhi School of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen361024 China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation of Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9783031781186

Zero-shot Chinese character recognition (ZSCCR) aims to recognize unseen Chinese characters by learning the semantic knowledge of seen characters. Radical-based methods treat Chinese characters as combinations of radicals, recognizing characters by predicting the radicals in the images. Existing radical-based methods have a closed radical parsing process that cannot be intervened in mid-course, relying only on semantic labels for constraints. However, semantic embedding vectors are usually manually designed and lack alignment with visual features, making it extremely difficult for the model to learn and locate discriminative radical representations from visual features. This paper proposes a ZSCCR network called Learning Explicit Radical Representations (LERRNet). LERRNet introduces learnable attribute hint vectors to guide the model in locating discriminative radicals and learning explicit representations of images. Specifically, we introduce a Radical Relevance Enhanced Encoder (RREE) to enhance the correlation of local radicals by augmenting the relationships between grid regions in visual features. Guided by attribute hint vectors, LERRNet employs a Radical Representation Decoder (RRD) to locate the most relevant regions of each radical in the given image and learn explicit radical representations. Extensive experiments demonstrate that LERRNet outperforms state-of-the-art radical/stroke-based methods across three ZSCCR benchmarks. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Zero-shot learning

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Layout-level Hardware Trojan Prevention in the Context of Physical Design 24

Layout-level Hardware Trojan Prevention in the Context of Ph...

引用

43rd international conference on computer-Aided Design, ICCAD 2024

作者： Tong, Xingyu Chen, Guohao Wei, Min Cai, Zhijie Zou, Peng Lin, Zhifeng Chen, Jianli State Key Laboratory of Integrated Chips and Systems Fudan University Shanghai China School of Microelectronics Fudan University Shanghai China Shanghai LEDA Technology Co. Ltd. Shanghai China School of Mathematics and Statistics Fuzhou University Fuzhou China

ISBN: (纸本)9798400710773

A growing recognition of potential vulnerabilities to layout-level Hardware Trojan (HT) attacks has spurred significant research efforts aimed at enhancing the resilience of ICs against such threats. However, traditional hardware security has been predominantly concerned with defensive measures, often overlooking the original key metrics in physical design evaluation: power, performance, and area (PPA). This study introduces an automated methodology incorporating HT considerations into the practical physical design process. Utilizing a Bayesian optimization framework, it effectively navigates the operation of commercial physical implementation tools in the solution space of hyper-parameter settings. Innovative strategies inspired by mosaic techniques, such as cell shifting and buffer insertion, realize additional improvements in layout-level trojan prevention. Comparative evaluations have shown that our approach outperforms leading entries from the ISPD 2023 Contest in terms of PPA and HT prevention metrics, thereby providing significant insights into the synergy between these critical factors. © 2024 Copyright is held by the owner/author(s).

关键词： computer viruses

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Role of AI in Healthcare 5th

Role of AI in Healthcare

引用

5th international conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Aparna, M. G. Narayanamma Institute of Technology and Science Shaikpet Telangana Hyderabad India

ISBN: (纸本)9789819780426

Artificial intelligence (AI) is a field of computer science and technology that focuses on developing software and hardware that are intelligent enough to carry out tasks that ordinarily require human intelligence. Problem-solving, education, reasoning, perception, understanding of natural language and decision-making are some of these tasks [6]. Artificial intelligence mimics, simulates or improves human cognitive abilities using computer algorithms and data. In this fast-paced automated digital world, people’s feelings about Artificial Intelligence applications in healthcare depend on the diverse attitudes of the patients. It also depends on trustworthy sources and concerns over AI and healthcare. However, healthcare has a lot of obstacles to overcome in order to succeed. Improvements in population health, patient and carer experiences and cost containment are the four basic objectives of healthcare. Artificial intelligence has advanced significantly in the medical field, revolutionizing how doctors identify, treat, and oversee patients. Healthcare services might be made more accurate, effective, and accessible thanks to AI technologies, which would eventually enhance patient outcomes [1]. Though people are worried about the impact of AI in the medical field despite having a greater understanding of healthcare conditions and being urged to think more broadly about prospective computerized technology. Therefore, utilizing AI and technology in healthcare can benefit hospitals, particularly when viruses like COVID-19 reduce the workforce. As a result, if AI is properly deployed, the hospital experience might change quickly, healthcare staff could be protected from viruses or diseases that could be deadly and diagnosis would be efficient, effective, fast, and accurate. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Drug discovery

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

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

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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

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Nanophotonic Edge Computing System For Ultra-Low Latency Human-Machine Interface 38

Nanophotonic Edge Computing System For Ultra-Low Latency Hum...

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38th IEEE international conference on Micro Electro Mechanical Systems, MEMS 2025

作者： Ren, Zhihao Zhang, Zixuan Zhuge, Yangyang Xiao, Zian Xu, Siyu Zhou, Jingkai Lee, Chengkuo National University of Singapore Department of Electrical & Computer Engineering Singapore Singapore

ISBN: (纸本)9798331508890

This paper introduces a nanophotonic edge computing (NEC) system built on a bilayer AlN/Si waveguide platform, optimized for AI processing in wearable sensors. Utilizing AlN electro-optic micro-ring resonators (EOMRRs) for feature extraction and Si thermo-optic Mach-Zehnder interferometers (TOMZIs) for neural network computations, the NEC chip demonstrates high classification accuracy for gesture (96.77%) and gait (98.31%) analysis, ultra-low latency ( © 2025 IEEE.

关键词： Mach-Zehnder interferometers

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