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Development of a Model for Discriminating Electroencephalograms of Patients with Mild Cognitive Impairment and Healthy Individuals Using a Support Vector Machine 13

Development of a Model for Discriminating Electroencephalogr...

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Joint 13th International Conference on Soft Computing and Intelligent Systems and 25th International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2024

作者： Tanabe, Akifumi Nakatani, Kyosuke Harachi, Kento Yamamoto, Yusuke Muramatsu, Ayumi Nagahara, Hajime Takemura, Noriko Shimojo, Shinji Furihata, Daisuke Mizuno-Matsumoto, Yuko Kamae-Shigenobu, Kazue Shinosaki, Kazuhiro Graduate School of Information Science University of Hyogo Kobe Japan School of Life Science and Technology Tokyo Institute of Technology Yokohama Japan Aino University Department of Medical Engineering Ibaraki Japan Institute for Datability Science Osaka University Suita Japan Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Iizuka Japan Aomori University Faculty of Software and Information Technology Aomori Japan Osaka University Cyber Media Center Suita Japan Asakayama General Hospital Sakai Japan

ISBN: (纸本)9798350373332

We performed machine learning using spectrum analysis and power spectra of electroencephalograms (EEGs) of patients with mild cognitive impairment (MCI). Spectrum analysis was performed on the EEGs of MCI patients and healthy individuals, and the power spectrum values of the two groups were calculated and compared. Based on the power spectrum values of the theta and alpha bands, models for discriminating MCI patients were created using a support vector machine (SVM), and their accuracies were compared. In the spectrum analysis, the theta band showed significantly higher power spectrum values in the MCI group. In the comparison of the accuracies of SVM models, the model using the theta band showed higher values in parameters, such as accuracy rate. These results suggested that the power spectrum values of the theta band are useful for discriminating MCI patients. © 2024 IEEE.

关键词： Electroencephalography

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Adaptive and Guided Attention in Transformer for Visual Question Answering

Adaptive and Guided Attention in Transformer for Visual Ques...

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International Conference on Virtual Reality and Visualization (ICVRV)

作者： Shan Zhong Shiyang He Shengrong Gong Gengsheng Xie Yangtao Xue Xiaoyang Tang Silin Zhou School of Computer Science and Engineering Changshu Institute of Technology State Key Lab. for Novel Software Technology Nanjing University Changshu China School of Computer Science and Technology Soochow University Suzhou China School of Computer Science and Engineering Changshu Institute of Technology Changshu China School of Software Jiangxi Normal University Nanchang China

ISBN: (数字)9798331515447

ISBN: (纸本)9798331515454

Transformer-based techniques for Visual Question Answering (VQA) have shown commendable prowess in modeling global relationships. However, they often encounter a decrement in accuracy due to their limitations in capturing local dependencies. In response to this challenge, we propose a novel solution, the Adaptive and Guided Attention in Transformer (AGAT), tailored for the VQA task. AGAT constructs an adaptive selfattention method, where a neighborhood prediction module is adopted to dynamically fine-tune attention span and weight by sampling the query token from a default neighborhood. This innovative approach enables the comprehensive modeling of long-term dependency relationships and facilitates extracting contextual information from diverse image regions. The guided attention, which leverages information from the question encoder, is further integrated. It interplays between different modalities multiple times to align the text and image. Experimental results demonstrate that AGAT outperforms the representative methods significantly in terms of accuracy.

关键词： Visualization Adaptation models Solid modeling Accuracy Noise Virtual reality Transformers Question answering (information retrieval) Data mining Context modeling

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Open Source software Supply Chain Recommendation Based on Heterogeneous Information Network 14th

Open Source Software Supply Chain Recommendation Based on H...

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14th International Symposium on Benchmarking, Measuring, and Optimization, Bench 2022

作者： Lin, HaiMing Liang, Guanyu Wu, Yanjun Wu, Bin Tian, Chunqi Wang, Wei Nanjing Institute of Software Technology Nanjing211135 China School of Computer and Science Tongji University Shanghai201804 China Institude of Software Chinese Academy of Science Beijing100190 China School of Data Science and Engineering East China Normal University Shanghai200062 China

ISBN: (纸本)9783031311796

In the GitHub open-source collaborative development scenario, each entity type and the link relationship between them have natural heterogeneous attributes. In order to improve the accuracy of project recommendation, it is necessary to effectively integrate this multi-source information. Therefore, for the project recommendation scenario, this paper defines an open source weighted heterogeneous information network to represent the different entity types and link relationships in the GitHub open source collaborative development scenario, and effectively model the complex interaction among developers, projects and other entities. Using the weighted heterogeneous information network embedding method, extract and use the rich structural and semantic information in the weighted heterogeneous open source information network to learn the node representation of developers and projects, and fuse the personalized nonlinear fusion function into the matrix decomposition model for open source project recommendation. Finally, this paper makes a large number of comparative experiments based on the real GitHub open data set, and compares it with other project recommendation methods to verify the effectiveness of our proposed open source project recommendation model. At the same time, it also explores the impact of different metapaths on the effect of project recommendation. The experimental results show that the recommendation method based on heterogeneous information network can effectively improve the recommendation quality. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Open source software

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Image Emotion Classification Network Based on Multilayer Attentional Interaction,Adaptive Feature Aggregation

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computers, Materials & Continua 2023年第5期75卷 4273-4291页

作者： Xiaorui Zhang Chunlin Yuan Wei Sun Sunil Kumar Jha Engineering Research Center of Digital Forensics Ministry of EducationJiangsu Engineering Center of Network MonitoringSchool of Computer and SoftwareNanjing University of Information Science&TechnologyNanjing210044China Wuxi Research Institute Nanjing University of Information Science&TechnologyWuxi214100China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Nanjing University of Information Science&TechnologyNanjing210044China School of Automation Nanjing University of Information Science&TechnologyNanjing210044China Adani University AhmedabadGujaratIndia

The image emotion classification task aims to use the model to automatically predict the emotional response of people when they see the *** have shown that certain local regions are more likely to inspire an emotional response than the whole ***,existing methods perform poorly in predicting the details of emotional regions and are prone to overfitting during training due to the small size of the ***,this study proposes an image emotion classification network based on multilayer attentional interaction and adaptive feature *** perform more accurate emotional region prediction,this study designs a multilayer attentional interaction *** module calculates spatial attention maps for higher-layer semantic features and fusion features through amultilayer shuffle attention *** layer-by-layer up-sampling and gating operations,the higher-layer features guide the lower-layer features to learn,eventually achieving sentiment region prediction at the optimal *** complement the important information lost by layer-by-layer fusion,this study not only adds an intra-layer fusion to the multilayer attention interaction module but also designs an adaptive feature aggregation *** module uses global average pooling to compress spatial information and connect channel information from all ***,the module adaptively generates a set of aggregated weights through two fully connected layers to augment the original features of each ***,the semantics and details of the different layers are aggregated through gating operations and residual connectivity to complement the lost *** reduce overfitting on small datasets,the network is pre-trained on the FI dataset,and further weight fine-tuning is performed on the small *** experimental results on the FI,Twitter I and Emotion ROI(Region of Interest)datasets show that the proposed network exceeds existing image emotion classification methods

关键词： Attentionmechanism emotional region prediction image emotion classification transfer learning

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Primary User-Awareness-Based Energy-Efficient Duty-Cycle Scheme in Cognitive Radio Networks

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computers, Materials & Continua 2022年第3期70卷 5991-6005页

作者： Zilong Jin Chengbo Zhang Kan Yao Dun Cao Seokhoon Kim Yuanfeng Jin School of Computer Science Nanjing University of Information Science and TechnologyNanjing210044China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET)Nanjing University of Information Science and Technology Nanjing210044China Jiangsu Institute of Economic and Information Technology Nanjing210003China School of Computer and Communications Engineering Changsha University of Science and TechnologyChangsha410114China Department of Computer Software Engineering Soonchunhyang UniversityAsan31538Rep.of Korea Department of Mathematics Yanbian UniversityYanji133002China

Cognitive radio devices can utilize the licensed channels in an opportunistic manner to solve the spectrum scarcity issue occurring in the unlicensed ***,these cognitive radio devices(secondary users)are greatly affected by the original users(primary users)of licensed *** users have to adjust operation parameters frequently to adapt to the dynamic network environment,which causes extra energy *** consumption can be reduced by predicting the future activity of primary ***,the traditional prediction-based algorithms require large historical data to achieve a satisfying precision accuracy which will consume a lot of time and memory ***,many of these schemes lack methods to deal with the very busy network *** this paper,one semi-supervised learning algorithm,i.e.,tri-training,has been employed to investigate the prediction of primary *** on the prediction results of tri-training,a duty-cycle mechanism and an intermediate node selection approach are proposed to improve the energy *** results show the effectiveness of the proposed algorithm.

关键词： Cognitive radio tri-training duty-cycle intermediate node energy efficiency

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Triple Diseases Prediction System using Deep Learning Techniques

Triple Diseases Prediction System using Deep Learning Techni...

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Emerging Trends in Information technology and engineering (ic-ETITE), Conference on

作者： R. Vijayan V. Mareeswari A. B. Jaswanth Department of Information Technology School of Computer Science Engineering and Information Systems (SCORE) Vellore Institute of Technology (VIT) Vellore Tamilnadu Department of Software and Systems School of Computer Science Engineering and Information Systems (SCORE) Vellore Institute of Technology (VIT) Vellore Tamilnadu MTech (Software Engineering) School of Computer Science Engineering and Information Systems (SCORE) Vellore Institute of Technology (VIT) Vellore Tamilnadu

With the existing deep learning models in predicting multiple diseases primarily focus on analyzing individual diseases in isolation, lacking a unified system for multi-disease prediction. This project presents an approach to predict multiple diseases using Flask API, with a specific focus on brain tumors, COVID-19 and pneumonia. The proposed work represents a significant contribution to the field of disease prediction, harnessing the power of deep learning algorithms and modern web application development. The primary focus is on disease prediction, with a particular emphasis on ensuring accuracy and accessibility for end-users. The initial phase of this research involves data collection, where relevant datasets of various diseases are gathered. These datasets serve as the foundation for training and validating the deep learning models. Two prominent deep learning algorithms, Sequential CNN and VGG16, are employed for this purpose. These algorithms are chosen for their ability to handle complex data and recognize patterns within medical images and other health-related data. The core of the research involves training the deep learning models using the collected datasets. This step is crucial in enabling the models to learn and generalize from the provided data, ultimately enhancing their predictive capabilities. The models are modified to elevate their performance and accuracy. Following the training phase, the models are rigorously tested to evaluate their predictive accuracy. This assessment is vital in gauging the real-world applicability of the models in medical diagnosis. To make these powerful disease prediction models accessible to a wider audience, a front-end web application is developed.

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Noise-Aware Speech Separation with Contrastive Learning

Noise-Aware Speech Separation with Contrastive Learning

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zizheng Zhang Chen Chen Hsin-Hung Chen Xiang Liu Yuchen Hu Eng Siong Chng School of Software and Microelectronics Peking University China School of Computer Science and Engineering Nanyang Technological University Singapore School of Electrical and Computer Engineering Georgia Institute of Technology USA

Recently, speech separation (SS) task has achieved remarkable progress driven by deep learning technique. However, it is still challenging to separate target speech from noisy mixture, as the neural model is vulnerable to assign background noise to each speaker. In this paper, we propose a noise-aware SS (NASS) method, which aims to improve the speech quality for separated signals under noisy conditions. Specifically, NASS views background noise as an additional output and predicts it along with other speakers in a mask-based manner. To effectively denoise, we introduce patch-wise contrastive learning (PCL) between noise and speaker representations from the decoder input and encoder output. PCL loss aims to minimize the mutual information between predicted noise and other speakers at multiple-patch level to suppress the noise information in separated signals. Experimental results show that NASS achieves 1 to 2dB SI-SNRi or SDRi over DPRNN and Sepformer on WHAM! and LibriMix noisy datasets, with less than 0.1M parameter increase.

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Automated Spelling Checker And Grammatical Error Detection And Correction Model for Sinhala Language

Automated Spelling Checker And Grammatical Error Detection A...

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2022 International Research Conference on Smart Computing and Systems engineering, SCSE 2022

作者： Goonawardena, Mithma Kulatunga, Ashini Wickramasinghe, Raveena Weerasekara, Thisuraka De Silva, Hansi Thelijjagoda, Samantha Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Sri Lanka Sri Lanka Institute of Information Technology Sliit Business School Sri Lanka

ISBN: (数字)9781665473750

ISBN: (纸本)9781665473750

Sinhala is a native language spoken by the Sinhalese people, the largest ethnic group in Sri Lanka. It is a morphologically rich language, which is a derivation of Pali and Sanskrit. The Sinhala language creates a diglossia situation, as the language's written form differs from its spoken form. With this difference, the written form requires more complex rules to be followed when in use. Manually proofreading the content of Sinhala material takes up much time and labor, and it can be a tedious task. Hence, a system is necessary which can be used by different industries such as journalism and even students. At present, there are a handful of systems and research that have automated Sinhala spelling analysis and grammar analysis. In addition, the existing systems are mainly focused on either spelling analysis or grammar analysis. However, the proposed system will cover both aspects and improve upon existing work by either optimizing or re-building the process to provide accurate outputs. The proposed system consists of a suffix list built for verbs and subjects, which helps the system stand out from the current proposed solutions. This research intends to implement a service for spell checking and grammar correctness of formal context in Sinhala. The research follows a rule-based approach with some components adopting a hybrid approach. As per the literature survey, many papers were analyzed, related to different aspects of the proposed system and complete systems. The proposed system would be able to overcome most barriers faced by previous papers whilst it takes a fresh take on providing a solution. © 2022 IEEE.

关键词： Automation

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Runtime Enforcement of CPS against Signal Temporal Logic

arXiv

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arXiv 2025年

作者： Su, Han Shankar, Saumya Pinisetty, Srinivas Roop, Partha S. Zhan, Naijun Institute of Software CAS & University of CAS Beijing China Department of Electrical Computer and Software Engineering The University of Auckland Auckland New Zealand Indian Institute of Technology School of Electrical Sciences Bhubaneswar India The University of Auckland Department of Electrical Computer and Software Engineering Auckland New Zealand Peking University School of Computer Science Institute of Software CAS Beijing China

Cyber-Physical Systems (CPSs), especially those involving autonomy, need guarantees of their safety. Runtime Enforcement (RE) is a lightweight method to formally ensure that some specified properties are satisfied over the executions of the system. Hence, there is recent interest in the RE of CPS. However, existing methods are not designed to tackle specifications suitable for the hybrid dynamics of CPS. With this in mind, we develop runtime enforcement of CPS using properties defined in Signal Temporal Logic (STL). In this work, we aim to construct a runtime enforcer for a given STL formula to minimally modify a signal to satisfy the formula. To achieve this, the STL formula to be enforced is first translated into a timed transducer, while the signal to be corrected is encoded as timed words. We provide timed transducers for the temporal operators until and release noting that other temporal operators can be expressed using these two. Our approach enables effective enforcement of STL properties for CPS. A case study is provided to illustrate the approach and generate empirical evidence of its suitability for CPS. © 2025, CC BY-NC-ND.

关键词： Transducers

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Stargan-based camera style transfer for person retrieval

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International Journal of Information and Communication technology 2020年第1期16卷 1-16页

作者： Wang, Yuanyuan Wang, Zhijian Jiang, Mingxin College of Computer and Software Engineering Huaiyin Institute of Technology Huaian China College of Computer and Information Hohai University Nanjing China College of Electronic Information Engineering Huaiyin Institute of Technology Huaian China

Person retrieval is also known as person re-identification (ReID) aiming to match person among cross cameras. Although the results of the person ReID have performed well in small datasets, the issues of the large number of identities in real scenarios or with more cameras have not been fully investigated. Being an image retrieval task under cross multi-cameras of intelligent video security, person ReID is influenced by the image style change caused by different camera illumination and view angles. The number of cameras in the latest datasets is increasing and more camera transfer models need to be trained. Traditional methods of generative adversarial network (GAN) can only handle transfer of two domains. To facilitate the research towards solving these problems, we use star generative adversarial networks (StarGAN) to transfer the image from one camera to another camera in the latest large benchmark datasets. We train multiple transfer models simultaneously, minimising the bias among different cameras. Label smooth regularisation (LSR) algorithm is utilised to mitigate the effects of noise in the model. We learn part-based descriptors from pedestrian samples to generate robust feature representation. Our work is competitive compared to the state-of-the-art. © 2020 Inderscience Enterprises Ltd.

关键词： Generative adversarial networks

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