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5th IEEE International Symposium on Joint Communications and Sensing, JC and S 2025

作者： Mukherjee, Anirban Susarla, Praneeth Katragunta, Pravallika Bulusu, S. S. Krishna Chaitanya Silven, Olli Juntti, Markku Jayagopi, Dinesh Babu Lopez, Miguel Bordallo International Institute of Information Technology Bangalore Multimodal Perception Lab Bangalore560100 India University of Oulu Center for Machine Vision and Signal Analysis Oulu90570 Finland Carleton University Department of Computer Science Ottawa Canada University of Oulu Centre for Wireless Communications Oulu90570 Finland

ISBN: (纸本)9798331531652

Indoor localization is essential for future 6G systems that combine communication and sensing. Accurate positioning is vital for applications like augmented reality and autonomous robotics. In this context, we propose a multi-domain channel state information (CSI)-based localization approach using deep attention networks (DAN) in a massive multiple input multiple output-based (maMIMO) system. We introduce the extraction of features from CSI information in multiple domains, including time, frequency, and Doppler, and design uni-domain and multidomain feature sets. We implement the proposed DAN approach leveraging attention mechanisms to integrate and effectively process the multi-domain CSI data. We evaluate the performance of our model using a publicly available maMIMO dataset and compare it with baseline convolutional neural network (CNN) models. Our results indicate that the DAN-based approach enhances localization performance more than uni-domain, multidomain CNN models and also existing multi-domain-based CNN benchmarks. These findings highlight the benefits of using multidomain features, especially from the Doppler domain along with attention mechanisms for reliable indoor localization. © 2025 IEEE.

关键词： 5G mobile communication systems

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A Deep Learning-Based Approach to Detect Correct Suryanamaskara Pose

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SN computer science 2022年第5期3卷 337页

作者： Bhaumik, Ujjayanta Singh, Koushlendra Kumar Akbari, Akbar Sheikh Bajpai, Manish Kumar University College London United Kingdom Machine Vision and Intelligence Lab Department of Computer Science and Engineering National Institute of Technology Jamshedpur India School of Computing Creative Technologies and Engineering at Leeds Beckett University Leeds United Kingdom IIITDM Jabalpur India

We present a technique to analyse Suryanamaskar poses using keypoint estimation and statistical analysis. The proposed approach uses a trained model based on COCO keypoint detection dataset and uses it to determine keypoints in yoga poses. Our work uses the keypoint detection to suggest a self yoga correction system. A novel dataset, Surya-yoga, containing 10000 Suryanamaskara poses has been generated and made publicly available. The model presented in this paper performed better on the COCO dataset and combined COCO and Surya-yoga dataset when tested using part affinity fields. The work also presents an analytical method of distinguishing different Suryanamaskar poses alongside deep learning methods. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Keypoint detection Pose detection Suryanamaskar Yoga

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A Novel Twitter Spam Detection Technique by Integrating Inception Network with Attention based LSTM 5

A Novel Twitter Spam Detection Technique by Integrating Ince...

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5th International Conference on Trends in Electronics and Informatics, ICOEI 2021

作者： Neha, M.V. Nair, Madhu S. Artificial Intelligence Computer Vision Lab Cochin University of Science and Technology Department of Computer Science Kerala Kochi682022 India

ISBN: (纸本)9781665415712

Online Social Networking sites have become a well-known way for web surfers to connect and meet. Twitter got to be a well-known micro blogging site that clients post and associate with messages known as tweets. As this networking site gains its popularity, spammers target Twitter to spread spam posts. Hence, several spam detection techniques have been proposed by analysts to create Twitter a spam-free stage. Be that as it may, the accessible machine learning algorithms cannot effectively distin- guish spammers on Twitter because of reasonable information controls by unsolicited clients to elude spam discovery. As a result, here, we present an incipient approach predicated on a deep learning technique that leverages a text-predicated feature to detect spammers. A novel architecture that contains a one-dimensional dimension reduction inception module stacked with LSTM along with an attention layer is introduced here. Within the proposed model, the inception module extricates the features from the vectors after GloVe word embedding, and then LSTM is utilized to get the context representations. An Attention layer is also used in this model to give attention to the data outputted from LSTM module. At long last, the sigmoid classifier is utilized to classify the labels as spam or ham. Here, the execution of our proposed model is compared with four machine learning-based and two deep learning-based approaches, exhibiting our approach acquired the best results with an F1-score of 95.74, accuracy of 95.75, and precision of 95.58. © 2021 IEEE.

关键词： Feature extraction

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Transformer-Enhanced Iterative Feedback Mechanism For Polyp Segmentation

Transformer-Enhanced Iterative Feedback Mechanism For Polyp ...

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

作者： Nikhil Kumar Tomar Debesh Jha Koushik Biswas Ulas Bagci Machine & Hybrid Intelligence Lab Department of Radiology Northwestern University Chicago USA Department of Computer Science University of South Dakota Vermillion USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Colorectal cancer (CRC) is the third most common cause of cancer diagnosed in the United States. Notably, CRC is the leading cause of cancer in younger men less than 50 years old. Colonoscopy is considered the gold standard for the early diagnosis of CRC. Skills vary significantly among endoscopists, and a high miss rate is reported. Automated polyp segmentation can reduce the missed rates, and timely treatment is possible in the early stage. To address this challenge, we introduce Feedback Attention Network-v2 (FANetv2), an advanced encoder-decoder network designed to accurately segment polyps from colonoscopy images. Leveraging an initial input mask generated by Otsu thresholding, FANetv2 iteratively refines its binary segmentation masks through a novel feedback attention mechanism informed by the mask predictions of previous epochs. Additionally, it employs a text-guided approach that integrates essential information about the number (one or many) and size (small, medium, large) of polyps to further enhance its feature representation capabilities. This dual-task approach facilitates accurate polyp segmentation and aids in the auxiliary classification of polyp attributes, significantly boosting the model’s performance. Our comprehensive evaluations on the publicly available BKAI-IGH and CVC-ClinicDB datasets demonstrate the superior performance of FANetv2, evidenced by high dice similarity coefficients (DSC) of 0.9186 and 0.9481, along with low Hausdorff distances of 2.83 and 3.19, respectively. The source code for FANetv2 will be made available at https://***/nikhilroxtomar/FANetv2.

关键词： Image segmentation computer vision Source coding Colonoscopy Signal processing Transformers Iterative methods Speech processing Iterative decoding Testing

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Learning Inverse Dynamics of Multi-segmented Larval Crawling Neuromechanics 10

Learning Inverse Dynamics of Multi-segmented Larval Crawling...

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10th IEEE Smart World Congress, SWC 2024

作者： Tang, Weihao Zhang, Hao Gong, Zhefeng Zheng, Nenggan Zhejiang University Qiushi Academy for Advanced Studies College of Computer Science and Technology Hangzhou China Zhejiang University School of Medicine School of Brain Science and Brain Medicine Department of Neurology of the Fourth Hospital Hangzhou310058 China Zhejiang University Qiushi Academy for Advanced Studies The State Key Lab of Brain-Machine Intelligence College of Computer Science and Technology Hangzhou China Bengbu University Bengbu China

ISBN: (纸本)9798331520861

Neuromechanical modeling that bridges neural activity and locomotion patterns is essential for studying locomotion control. The Drosophila larva, with its diverse softbodied locomotion, serves as an ideal subject for biomimetic research. Constructing a neuromechanical model of Drosophila larvae presents two primary challenges: efficiently simulating the larva's soft, multi-segmented body and controlling locomotion within a large action space by actuating muscle groups. This work presents a three-dimensional neuromechanical model of the larva and introduces an inverse dynamics learning method for locomotion control. Our approach efficiently simulates the larva's multi-segmented, deformable body by integrating computational modules for soft tissues, muscles, and friction. We employ a parameterized controller to reproduce peristaltic waves and use a hybrid artificial neural network architecture to learn inverse dynamics from partially observed states. Balancing biological interpretability with modeling complexity, this model advances the understanding of brain-body-environment interactions in Drosophila larvae, contributing to nature-inspired intelligent systems research. © 2024 IEEE.

关键词： Intelligent systems

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Predicting Stock Market Trends Using Deep Learning: A Case Study of Pakistan Stock Exchange 18

Predicting Stock Market Trends Using Deep Learning: A Case S...

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18th International Conference on Open Source Systems and Technologies, ICOSST 2024

作者： Balouch, Maaz Hamayat, Faizan Abbasi, Humza Fazal Javed, Muhammad Qasim Mehrban, Ali Ahmad, Rana Fayyaz Department of Business Studies Namal University Mianwali Pakistan National Center for Physics Machine Learning and Deep Learning Lab Artificial Intelligence Technology Center Islamabad Pakistan Department of Computer Science Bahria University Islamabad Pakistan School of Electrical and Electronics Engineering Newcastle University Newcastle United Kingdom

ISBN: (纸本)9798331508692

This research investigates the short-term stock price prediction in the Pakistan Stock Exchange (PSX) using Deep Learning (DL) techniques. By integrating historical stock data, economic indicators, and market news, the research aims to address the limitations of traditional stock analysis methods. Data was collected and pre-processed from various sources, including the PSX, economic indicators, and market news. Four techniques were implemented: ARIMA, LSTM, BI-LSTM, and a hybrid CNN-BI-LSTM. Their performance was evaluated using RMSE, MAE, and MAPE metrics. Comparative analysis revealed that BILSTM outperformed the other techniques, with RMSE, MAE, and MAPE scores of 0.9298, 0.6099, and 13.9443, respectively. Despite ARIMA's lower accuracy, it had the fastest inference time, followed by LSTM, CNN-BI-LSTM, and BI-LSTM. Results indicate that using comprehensive data with DL models significantly improves stock price forecast accuracy, highlighting the potential of technological innovation in enhancing financial market decision-making. These DL algorithms can provide valuable insights for investors and analysts in the PSX, leading to better decision-making and more accurate economic predictions. © 2024 IEEE.

关键词： Deep learning

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A Brain-computer Interface for Controlling IoT Devices using EEG Signals 5

A Brain-Computer Interface for Controlling IoT Devices using...

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5th IEEE Ecuador Technical Chapters Meeting, ETCM 2021

作者： Chicaiza, Kelvin Ortiz Benalcázar, Marco E. Artificial Intelligence and Computer Vision Research Lab Department of Computer Science and Informatics Escuela Politécnica Nacional Quito Ecuador

ISBN: (纸本)9781665441414

Motor disability is the loss of the ability to move a limb of the body. Motor disabilities make difficult the interaction between a disabled person and her/his environment. Recent research has focused on developing innovative technologies that could be used by disabled people to improve their life quality. In this paper, a brain-computer interface for controlling IoT devices is proposed. This system is based on the use of the Muse-Headband sensor which captures EEG signals when a person blinks. This sensor is placed on the forehead of the user of the system. The EEGs are preprocessed and then classified into short and long blinks by computing their signal envelopes and using the k-NN algorithm. The classified blinks are then used to form control commands that are sent to a mosquitto server hosted in the cloud. This server is responsible for sending the control action to the connected IoT devices. The accuracy of the classifier designed in this work is 99.53%. Usability tests show that the probability of a user sending a wrong command, with the proposed system, is 4.83%. However, this probability decreases when the time of use of the system proposed in this work increases. © 2021 IEEE.

关键词： Brain computer interface

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UCPM: Uncertainty-Guided Cross-Modal Retrieval with Partially Mismatched Pairs

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IEEE Transactions on Image Processing 2025年 34卷 3622-3634页

作者： Zha, Quanxing Liu, Xin Cheung, Yiu-Ming Peng, Shu-Juan Xu, Xing Wang, Nannan Huaqiao University Department of Computer Science Xiamen 361021 China Key Laboratory of Pattern Recognition and Computer Vision Xiamen 361021 China Huaqiao University Fujian Key Laboratory of Big Data Intelligence and Security Xiamen 361021 China Hong Kong Baptist University Department of Computer Science Hong Kong Huaqiao University Department of Artificial Intelligence Xiamen 361021 China Fujian Province University Key Laboratory of Computer Vision and Machine Learning (Huaqiao University) Xiamen 361021 China University of Electronic Science and Technology of China Center for Future Multimedia School of Computer Science and Engineering Chengdu 610051 China Xidian University State Key Laboratory of Integrated Services Networks Xi’an 710071 China

The manual annotation of perfectly aligned labels for cross-modal retrieval (CMR) is incredibly labor-intensive. As an alternative, the collection of co-occurring data pairs from the Internet is a remarkably cost-effective way, but which, inevitably induces the Partially Mismatched Pairs (PMPs) and therefore significantly degrades the retrieval performance without particular treatment. Previous efforts often utilize the pair-wise similarity to filter out the mismatched pairs, and such operation is highly sensitive to mismatched or ambiguous data and thus leads to sub-optimal performance. To alleviate these concerns, we propose an efficient approach, termed UCPM, i.e., Uncertainty-guided Cross-modal retrieval with Partially Mismatched pairs, which can significantly reduce the adverse impact of mismatched data pairs. Specifically, a novel Uncertainty Guided Division (UGD) strategy is sophisticatedly designed to divide the corrupted training data into confident matched (clean), easily-identifiable mismatched (noisy) and hardly-determined hard subsets, and the derived uncertainty can simultaneously guide the informative pair learning while reducing the negative impact of potential mismatched pairs. Meanwhile, an effective Uncertainty Self-Correction (USC) mechanism is concurrently presented to accurately identify and rectify the fluctuated uncertainty during the training process, which further improves the stability and reliability of the estimated uncertainty. Besides, a Trusted Margin Loss (TML) is newly designed to enhance the discriminability between those hard pairs, by dynamically adjusting their soft margins to amplify the positive contributions of matched pairs while suppressing the negative impacts of mismatched pairs. Extensive experiments on three widely-used benchmark datasets, verify the effectiveness and reliability of UCPM compared with the existing SOTA approaches, and significantly improve the robustness in both synthetic and real-world PMPs. The code i

关键词： Cross-modal retrieval partially mismatched pairs trusted margin loss uncertainty guided division uncertainty self-correction

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Advancing Logic in AI: Insights from the LNGAI 2021 Workshop

Advancing Logic in AI: Insights from the LNGAI 2021 Workshop

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作者： Liao, Beishui Liu, Fenrong van der Torre, Leendert Zhou, Yi School of Philosophy Zhejiang University Hangzhou310058 China The State Key Lab of Brain-Machine Intelligence Hangzhou310058 China Department of Philosophy Tsinghua University Beijing100084 China Department of Computer Science University of Luxembourg Esch-sur-AlzetteL-4365 Luxembourg School of Information Science and Technology University of Science and Technology of China HeFei230026 China

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Constructing A Theoretical Model to Bridge Neural Transition with a State Switch in Bipolar Disorder

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Neuroscience Bulletin 2025年第1期41卷 181-185页

作者： Xiaonan Guo Lizichen Chen Jianbo Lai Shaohua Hu Department of Psychiatry the First Affiliated HospitalZhejiang University School of MedicineHangzhou310003China Department of Engineering Mechanics Zhejiang UniversityHangzhou310027China Nanhu Brain-computer Interface Institute Hangzhou311100China The Zhejiang Key Laboratory of Precision Psychiatry Hangzhou310003China MOE Frontier Science Center for Brain Science and Brain-Machine Integration Zhejiang University School of MedicineHangzhou310003China Brain Research Institute of Zhejiang University Hangzhou310003China The State Key Lab of Brain-Machine Intelligence Zhejiang UniversityHangzhouChina Zhejiang Engineering Center for Mathematical Mental Health Hangzhou310003China

Reward or stress,which exists extensively,causes resilient emotional fluctuations under common ***,reward or stress is a typical trigger for manic or depressive episodes of bipolar disorder(BD),which is corroborated by psychological theory,biological findings,and psychosocial treatment approaches[1,2].During an episode of BD,the affective aberration can be persistent and switchable,accompanied by opposite constellations of cognitive and psychomotor *** by uncontrollable mood ranging in severity,duration,and polarity,to disentangle the pathophysiology mechanism of BD is to delineate the mystery of affective fluctuations driven by reward or stress.

关键词： bipolar Transition finding

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