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检索条件"任意字段=2023 International Conference on Cognitive Computing and Complex Data, ICCD 2023"

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Testing Neurofeedback System for Studying cognitive Tests

Testing Neurofeedback System for Studying Cognitive Tests

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international conference on Communication, computing and Digital Systems (C-CODE)

作者： Rukhsar Ali Dept. of Electrical Engineering National University of Computer and Emerging Sciences Karachi Pakistan

The ongoing advancement in the multidisciplinary fields of engineering, neuroscience, microelectronics, bioengineering, and neurophysiology have decreased the gap among human and machine intelligence. Numerous techniques and methods have been produced for examination and grouping of bio-signals (i.e., one or two dimensional) both in time and frequency domain. The combination of signal processing with the surgical recording devices fills in as primary tool for the improvement of different biomedical applications. The research area has been very active and is continually improvising to build an efficient human-machine framework. Electroencephalography (EEG) is non-invasive and professional method to record the activity of brain in terms of electrical minute pulses. The development of EEG in the biomedical applications help in identifying various other problems related to human brain such as tumors, seizures, Alzheimer’s malady, epilepsy, and different failures of human brain. The fundamental goal of this study is to obtain and analyze EEG signals by using a single electrode provided by NeuroSky MindWave Mobile, that electrode is placed on the frontal lobe (more specifically forehead). The headset provides the raw EEG signal, and the distinctive parameters of EEG from different subjects in order to study the relationship, while accurately categorizing these values which are displayed on the provided Graphics User Interface (GUI). After obtaining the data of subjects’ different techniques are used to study the cognitive function of individuals, and we have observed the improvement in attentions shown by subjects after the neurofeedback training.

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Knowledge Graph enhanced Aspect-Based Sentiment Analysis Incorporating External Knowledge

Knowledge Graph enhanced Aspect-Based Sentiment Analysis Inc...

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IEEE international conference on data Mining Workshops (ICDM Workshops)

作者： Autumn Teo Zhaoxia Wang Haibo Pen Budhitama Subagdja Seng-Beng Ho Boon Kiat Quek School of Computing and Information Systems Singapore Management University Singapore Singapore School of Electrical and Information Engineering Tianjin University Tianjin China Social and Cognitive Computing Department Institute of High Performance Computing (IHPC) Agency for Science Technology and Research (A*STAR) Singapore Singapore

Aspect-Based Sentiment Analysis (ABSA) is a type of sentiment analysis that could identify and extract various aspects or features from text and determining the sentiment associated with each aspect. ABSA has significant real-world applications, such as providing deeper insights about specific strengths and weaknesses of each aspect contained within text data. Despite notable advancements, ABSA still has room for improvement in completeness, accuracy, and performance efficacy. To tackle these challenges, this research introduces an approach to ABSA that leverages knowledge graphs to improve completeness, accuracy, and performance efficacy. Our key novelty is in being able to incorporate enhancements across multiple stages, including utilising knowledge graphs together with dataset processing, and architectural modelling. Additionally, we offer a complementary overview and analysis of various deep learning heuristics and optimization strategies that could further enhance ABSA performance. Our validation results demonstrate the effectiveness of the proposed knowledge graph enhanced ABSA method across multiple benchmark datasets, with notable boosts to model performance. Importantly, in being model-agnostic, our dataset processing approach could potentially enhance the performance of other ABSA methods in the future.

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Combination of Translation and Rotation in Dual Quaternion Space for Temporal Knowledge Graph Completion

Combination of Translation and Rotation in Dual Quaternion S...

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international Joint conference on Neural Networks (IJCNN)

作者： Ruiguo Yu Tao Liu Jian Yu Wenbin Zhang Yue Zhao Ming Yang Mankun Zhao Jiujiang Guo College of Intelligence and Computing Tianjin University Tianjin China Tianjin Key Laboratory of Advanced Networking(TANK Lab) Tianjin China Tianjin Key Laboratory of Cognitive Computing and Application Tianjin China Information and Network Center Tianjin University Tianjin China College of Computing and Software Engineering Kennesaw State University Marietta GA USA

Compared with static knowledge graphs (KGs) temporal KGs record the dynamic relations between entities over time, therefore, research on temporal Knowledge Graph Completion (KGC) attracts much attention. Temporal KGs exhibit complex temporal relation patterns, such as multiple relations. However, existing methods can hardly model all the relation patterns and apply to the temporal KGs. In this paper, we propose a novel temporal KGC method that Combining Translation and Rotation (ComTR) in Dual Quaternion Space for temporal KGC. Specifically, we use dual-quaternion-based multiplication to model timestamps and relations as the combination of translation and rotation operations. We analyze the relation patterns of temporal KGs in detail and demonstrate that our method can model all the relation patterns in temporal KGs. Empirically, we show that ComTR can achieve the state-of-the-art performances over four temporal KGC benchmarks datasets.

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Composite AI for Behavior Analysis in Social Interactions 25

Composite AI for Behavior Analysis in Social Interactions

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25th international conference on Multimodal Interaction, ICMI 2023 Companion

作者： Dos Santos Melicio, Bruno Carlos Xiang, Linyun Dillon, Emily Soorya, Latha Chetouani, Mohamed Sarkany, Andras Kun, Peter Fenech, Kristian Lorincz, Andras Department of Artificial Intelligence Eotvos Lorand University Budapest Hungary Department of Psychiatry Rush University Medical Center ChicagoIL United States Institute for Intelligent Systems and Robotics Cnrs Umr 7222 Sorbonne University Paris France Argus Cognitive Inc. HanoverNH United States

ISBN: (纸本)9798400703218

Social interactions are fundamental to human life. Accurately identifying and interpreting verbal and non-verbal cues is essential for analyzing human behavior and human-machine interactions. The complexity of these interactions, along with the different communication signals, and their varying frequencies is a challenge that Deep Neural Networks cannot yet address. Composite AI, which combines Deep Learning (DL) and traditional AI methods, emerges as a potential approach. We propose a framework comprising three main modules: feature extraction, episode detection, and activity interpretation. The feature extraction module uses DL methods to extract relevant information about the environment, interacting partners, and context of the interaction. By combining these features with rule-based systems, we restrict the analysis to key episodic components, effectively reducing the false positive detection rate. Classifiers are then used to identify and recognize specific sub-events. We show our system's effectiveness in detecting communication cues on recorded sessions of a standardized autism diagnostic test (ADOS-2) involving autistic children, where detecting such cues can be highly challenging. In this scenario, we achieve 87% and 89% accuracy for verbal and non-verbal requests, respectively, and zero false positives. This comprehensive framework can significantly enhance social interaction analysis, enabling more effective automated event analysis. We also highlight the importance of sub-event segmentation and the components that could be improved with additional algorithmic combinations or data-driven improvements of the detection systems. © 2023 Owner/Author.

关键词： Feature extraction

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Emotions Studied by Computer Scientists and Psychologists — A Complementary Perspective

Emotions Studied by Computer Scientists and Psychologists —...

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IEEE Annual conference on Pervasive computing and Communications Workshops (PerCom)

作者： Maciej Behnke Stanislaw Saganowski Łukasz D. Kaczmarek Przernysław Kazienko Faculty of Psychology and Cognitive Science Adam Mickiewicz University Poznan Poland Department of Artificial Intelligence Wroclaw University of Science and Technology Wrocł aw Poland

Affective scientists from different fields work together to describe, understand, and harness human emotions. One specific collaboration between computer scientists and psychologists created the new field of affective computing. Computer scientists bring to the field perspective resulting from education in Physical Sciences and Engineering, whereas psychologists from Social Sciences and Humanities. This article first addresses three issues in studying emotions that are usually approached differently in psychology and computer science: defining emotions, ethical considerations, and exploring emotional data. With the bibliometric data, we show that interdisciplinary teams are exceptions, which may limit the scope of research on emotions. Next, we argue that presented different perspectives complement rather than exclude each other. Thus, we advocate for creating multidisciplinary initiatives that bring unique benefits. Our perspective provides an additional impulse to bring our fields together and encourage multidisciplinary discussions on advancing the science of emotions.

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Traumatic Brain Injury Classification Based on Slice Feature Selection and Transformer

Traumatic Brain Injury Classification Based on Slice Feature...

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6th international conference on Intelligent computing and Signal Processing (ICSP)

作者： Jinyu Liu Tianhui Ye Lili Huang Chuanfu Li Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei China Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University & Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China Hefei Comprehensive National Science Center The First Affiliated Hospital of Anhui University of Chinese Medicine & Institute of Artificial Intelligence Hefei China

Traumatic brain injury (TBI) usually originates from primary injury, has a high rate of death and disability, and can lead to temporary or permanent neurological deficit in severe cases. Therefore, it is very important to make an accurate imaging diagnosis as early as possible. Deep learning is widely used in medical image analysis, and the use of convolutional neural networks for initial diagnosis of traumatic brain trauma has been proven effective. Most of the traumatic brain injury recognition work in recent years is to classify a single image. We propose a new approach for diagnosing traumatic brain injuries from CT images that takes advantage of the sequence relationship between slices. Unlike natural images, medical images require doctors to synthesize information from multiple slices to make an accurate diagnosis. To address this, we combine convolutional neural networks (CNN) and Transformers to extract complex features and classify brain CT images by leveraging the correlation between slices. We also introduce an embeddable slice feature selection module that further improves accuracy. We evaluated our approach on a hospital-provided dataset and achieved superior performance compared to existing methods.

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Few-Shot Custom Speech Synthesis with Multi-Angle Fusion

Few-Shot Custom Speech Synthesis with Multi-Angle Fusion

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6th international conference on Intelligent computing and Signal Processing (ICSP)

作者： Haifeng Zhao Hao Liu Mingwei Cao Yi Liu Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei Anhui China IMSL Shenzhen Key Lab PKU-HKUST Shenzhen Hong Kong School of Computer Science and Technology Anhui University Hefei China

In this paper, we propose the TDNN-VITS model: an efficient custom speech synthesis system that can synthesize the speech of arbitrary target speakers, well-improving speech quality as well as reducing the amount of adaptive data. Our model consists of a speaker encoding module, which aims to extract speaker timbre information, and a TTS module, which is based on the fully end-to-end VITS model, but improved according to the problems of custom speech. To better improve the speech quality and speaker similarity, we propose a multi-angle fusion speaker embedding approach. With only 10 speech data and about one minute, good results can be achieved by just fine-tuning for half an hour. The experimental results show that our model is improved compared to the previous classical model, and good speech naturalness and speaker similarity are obtained. The ablation experiments also show the effectiveness of our multi-angle fusion.

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Functional Brain Subnetwork Analysis in Alzheimer's Disease and Mild cognitive Impairment

Functional Brain Subnetwork Analysis in Alzheimer's Disease ...

引用

complex Systems and Intelligent Science (CSIS-IAC), international Annual conference on

作者： He Huang Jinhua Sheng Pu Huang Xiaofan Yang Jialei Wang Ziyi Ying College of Computer Science Hangzhou Dianzi University Hangzhou China

In studies aimed at diagnosing Alzheimer's Disease (AD) and Mild cognitive Impairment (MCI) using resting-state functional magnetic resonance imaging (fMRI), researchers typically construct whole-brain functional connectivity networks. We investigate whether localized regional networks, rather than the entire brain network, can have a positive impact on the classification of MCI and AD. We utilized T1-weighted data and resting-state fMRI data from 26 AD subjects and 26 age-matched individuals diagnosed with MCI. Employing the Human Connectome Project cortical parcellation, the brain cortex was divided into 360 brain regions. A whole-brain region-wise correlation matrix was established using correlation coefficients. By employing an improved maximal global cost efficiency method, the correlation matrix was binarized to create a 360-node undirected graph representing the functional brain network. Simultaneously, employing a higher-level brain network partitioning, the entire brain was divided into 12 groups, establishing 12 subnetworks. Among these, the Default, Cingulo-Opercular, Language, and Frontoparietal subnetworks demonstrated enhanced discriminative potential for distinguishing AD from MCI compared to the entire brain network. Furthermore, the Frontoparietal subnetwork of the AD group exhibited stronger connectivity than that of the MCI group, outperforming other networks. Our study findings suggest that complex network metrics of functional subnetworks could serve as imaging-based biomarkers for distinguishing AD and MCI.

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Fuzziness-Based Semi-Supervised Learning for Early Detection of Alzheimer’s Disease using MRI data

Fuzziness-Based Semi-Supervised Learning for Early Detection...

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international conference on Computer and Information Technology (ICCIT)

作者： Umme Habiba Bushra Fahmida Chowdhury Priya Muhammed J. A. Patwary Department of Computer Science and Engineering International Islamic University Chittagong Chattogram Bangladesh

Alzheimer’s disease (AD) is a progressive neurodegenerative condition that erodes memory and cognitive abilities. It manifests as a gradual decline in cognitive functions, including memory loss, impaired decision-making and challenges in communication. As the disease advances, individuals may also experience changes in behavior and struggle with daily tasks. Detecting Alzheimer’s in its early stages is crucial, as intervention can slow brain damage. However, diagnosing it can be complex, especially when symptoms are subtle and resemble normal aging. This study’s focus is early AD detection, particularly identifying Mild cognitive Impairment (MCI), a challenging clinical diagnosis from MRI data. A novel approach is proposed where deep features extracted from two different CNN architectures and ORBbased features are combined through feature-level fusion. After performing feature-level fusion, a semi-supervised methodology are incorporated in the concept of fuzziness. The classification task is executed using a SVM classifier, yielding an accuracy of 94.39% for MCI vs CN (cognitively Normal) and 97.90% for AD vs CN. The proposed approach exhibits significant improvements in the classifier’s performance compared to existing methods on the ADNI dataset.

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Efficient MILP Decomposition in Quantum computing for ReLU Network Robustness

Efficient MILP Decomposition in Quantum Computing for ReLU N...

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Quantum computing and Engineering (QCE), IEEE international conference on

作者： Nicola Franco Tom Wollschläger Benedikt Poggel Stephan Günnemann Jeanette Miriam Lorenz Fraunhofer Institute for Cognitive Systems IKS Munich Germany Dept. of Computer Science & Munich Data Science Institute Technical Univ. of Munich Germany

Emerging quantum computing technologies, such as Noisy Intermediate-Scale Quantum (NISQ) devices, offer potential advancements in solving mathematical optimization problems. However, limitations in qubit availability, noise, and errors pose challenges for practical implementation. In this study, we examine two decomposition methods for Mixed-Integer Linear Programming (MILP) designed to reduce the original problem size and utilize available NISQ devices more efficiently. We concentrate on breaking down the original problem into smaller subproblems, which are then solved iteratively using a combined quantum-classical hardware approach. We conduct a detailed analysis for the decomposition of MILP with Benders and Dantzig-Wolfe methods. In our analysis, we show that the number of qubits required to solve Benders is exponentially large in the worst-case, while remains constant for Dantzig-Wolfe. Additionally, we leverage Dantzig-Wolfe decomposition on the use-case of certifying the robustness of ReLU networks. Our experimental results demonstrate that this approach can save up to 90% of qubits compared to existing methods on quantum annealing and gate-based quantum computers.

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