Dialogue Relation Extraction (DRE) is a task that aims to identify all entity pair relations within a dialogue. However, it is difficult to establish direct associations between inter-sentence entity pairs and the lac...
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ISBN:
(纸本)9789819794423;9789819794430
Dialogue Relation Extraction (DRE) is a task that aims to identify all entity pair relations within a dialogue. However, it is difficult to establish direct associations between inter-sentence entity pairs and the lack of path information makes identifying inter-sentence entity pair relations challenging. To address this issue, we proposes an effective inference model that constructs an entity co-occurrence graph of dialogue documents to model inter-sentence entity pair associations, incorporates multiple entity pair information to enrich path semantics, and employs attention mechanism to capture the semantics associated with each relation in the path information. These enhancements lead to improved inter-sentence inference and increase the effectiveness of dialogue relation extraction.
Functional magnetic resonance imaging (fMRI) leverages the blood-oxygen-level-dependent (BOLD) signals to gauge functional brain activation. Specifically, task-fMRI has become a predominant tool to investigate specifi...
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ISBN:
(纸本)9798350344868;9798350344851
Functional magnetic resonance imaging (fMRI) leverages the blood-oxygen-level-dependent (BOLD) signals to gauge functional brain activation. Specifically, task-fMRI has become a predominant tool to investigate specific cerebral regions associated with diverse cognitive processes. A myriad of studies employing task-fMRI have harnessed both static functional connectivity (FC) and dynamic functional connectivity (dFC) to identify task-related biomarkers. However, while FC and dFC have proven their efficacy, they mostly necessitate manual determination of factors, including window size, stride, and FC metrics, which potentially undermine their analysis reliability. In response to these challenges, one can detect and classify task-related patterns directly from the fMRI signals without using connectivity matrices (i.e., FC and dFC). Here, we propose a novel eigendecomposition-based attention mechanism (EAM) that emphasizes task-related regions and time points in the original signal space. By virtue of this approach, our proposed model allow us to effectively extract a refined integrated feature representation for identifying the brain cognitive states from the fMRI signals and also lessens the burden of making heuristic decisions in measuring both FC and dFC. We validate the superiority and effectiveness of our proposed method with comprehensive evaluations conducted on the Human Connectome Project (HCP) dataset, which covers seven distinct cognitive tasks.
The rapid advancements in Generative Artificial Intelligence (AI) have revolutionized domains such as natural language processing, computer vision, and creative content generation. Simultaneously, cognitive Science se...
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The travel industry is undergoing profound changes with the continuous development of information technology. One such emerging technology is cloud computing, which boasts powerful computing power, massive storage spa...
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A collection of information technology (IT) services known as "cloud computing" is offered to customers across a network on a subscription basis with the flexibility to scale up or down based on their servic...
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ISBN:
(纸本)9781665475785
A collection of information technology (IT) services known as "cloud computing" is offered to customers across a network on a subscription basis with the flexibility to scale up or down based on their service needs. The emergence of cloud computing technology, which has tremendous advantages, is one of the major advancements in recent times. Many computers and servers are specifically devoted to meeting the demands of businesses in a cloud computing system for internal communications. Users can access their services via an internet connection. Registered users have remote access to both hardware and software, thanks to the cloud service, which has made essential adjustments to how information is stored and made accessible. This paper investigates the use of Amazon Web Services (AWS) for big data processing and analytics in South Korea. We collected several domestic journal and conference papers that studied local cloud services based on AWS to introduce distributed systems and cloud computing technologies. This study can provide researchers with a compact version of the extensive AWS-based data processing literature and potential future insights. It can also provide stakeholders tailored services, information about cutting-edge solutions that can influence academics, and details about current research needs.
High mobility of Internet of Vehicles (IoV) brings rapidly changing network topology, and massive data produced by vehicles aggravate heavy burden to the network. These may lead unreliable and high latency of data tra...
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ISBN:
(纸本)9781538674628
High mobility of Internet of Vehicles (IoV) brings rapidly changing network topology, and massive data produced by vehicles aggravate heavy burden to the network. These may lead unreliable and high latency of data transmission and processing, which is not facilitate the application and popularization of automatic driving. Evolutions of intelligent vehicles and edge intelligence promising technologies enable vehicles as agents. Vehicles have abilities to act as aided Mobile Edge computing (MEC) servers to support ultra-low communication and computing latency and super-high reliability data transmission and processing. In this paper, we elect some vehicles as aided MEC servers and design an improved Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm to involve more scattered vehicles for clustering. We adopt a Multi-Multi matching algorithm to pair vehicles and the aided MEC server, and designed a multi-agent-based task offloading mechanism to reduce latency and improve resource utilization efficiency. Furthermore, a reward mechanism is proposed to stimulate vehicles to be aided MEC servers instead of refusing to provide services. Evaluation results verify the proposed offloading method could effectively reduce the average delay, improve computing resources utilization and increase the benefit of aided MEC servers and service providers.
cognitive decline and structural alterations in the brain are hallmarks of Alzheimer's disease (AD). It is a neurodegenerative illness which progresses over time. One of the most important diagnostics and tracking...
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ISBN:
(纸本)9783031744426;9783031744433
cognitive decline and structural alterations in the brain are hallmarks of Alzheimer's disease (AD). It is a neurodegenerative illness which progresses over time. One of the most important diagnostics and tracking tools for AD is Magnetic Resonance Imaging (MRI). The image processing method used to identify Alzheimer disease involves multiple steps such as pre-processing, segmentation, feature extraction, and classification. A crucial part of understanding illness causes and facilitating early diagnosis is feature extraction from MRI images. This paper reviews a variety of feature extraction approaches and a thorough assessment is conducted on several research papers that address feature extraction and classification methods used in image processing for identification of Alzheimer disease. The importance of feature extraction in Alzheimer's MRI analysis along with the steps for detection of Alzheimer disease are briefly touched upon in the paper.
Transfer learning is frequently utilized in scenarios with limited labeled examples, where a crucial step is to identify a related task to the target task. CogTaskonomy (Luo et al., 2022) was proposed to acquire a tax...
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ISBN:
(纸本)9798891760189
Transfer learning is frequently utilized in scenarios with limited labeled examples, where a crucial step is to identify a related task to the target task. CogTaskonomy (Luo et al., 2022) was proposed to acquire a taxonomy of NLP tasks, specifically focusing on assessing the similarities between tasks. This method, inspired by cognitive processes, exhibits notable time efficiency. Nevertheless, it does not fully exploit the task-related information present in cognitive data and lacks a comprehensive evaluation of various types of cognitive data. To address these limitations, this paper proposes a comprehensive neural and behavioral method to investigate the relationship among NLP tasks. Our approach utilizes cognitive data, encompassing both neural data such as fMRI and EEG, as well as behavioral data including eye-tracking and semantic feature ratings. Each data modality is employed to establish a common representation space with Representation Similarity Analysis for projecting task-related representations. To fully leverage the cognitiveinformation, we effectively extract the task-relevant information extracted from neural data through feature ranking. Experimental results on 12 NLP tasks demonstrate that our proposed method outperforms state-of-the-art methods on evaluating task similarity.
This paper proposes an innovative Deep Learning (DL) architecture for classifying cognitive stress using a novel hybrid model, StressGAN-GP. The increasing prevalence of stress-related disorders in modern society nece...
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In this paper, evolutionary game theory is used to study the boundary conditions of different stable states of knowledge alliances in distributed computing environments and the specific factors that influence the evol...
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