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

共 718 条记录，以下是521-530 订阅

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CareD: Caregiver’s Experience with cognitive Decline in Reddit Posts

CareD: Caregiver’s Experience with Cognitive Decline in Red...

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IEEE international conference on Healthcare Informatics (ICHI)

作者： Muskan Garg Sunghwan Sohn Artificial Intelligence and Informatics Mayo Clinic Rochester MN USA

With advancements in analysis of cognitive decline in electronic health records, the research community witnesses a recent surge in social media posting by caregivers and/or loved ones of people with cognitive decline. The major challenges in this area are availability of large and diverse datasets, ethics of data collection and sharing, diagnostic specificity and clinical acceptability. To this end, we construct a new dataset, CAREgivers experiences with cognitive Decline (CareD), of 1005 posts with more than 194K words and 9541 sentences, highlighting discussions on people with dementia and Alzheimer’s disease on Reddit. We discuss the changing trends of discussions on cognitive decline in social media and open challenges for natural language processing and social computing. We first identify the Reddit posts reflecting substantial information as candidate posts. We further formulate the annotation guidelines, handle perplexities to investigate the existence of experiences, self-reported articles and potential caregiver in candidate posts, resulting in the discovery of latent symptoms, firsthand information, and prospective source of longitudinal information about the patient, respectively.

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The Study of complex Manipulation via Kinematic Hand Synergies: The Effects of data Pre-Processing

The Study of Complex Manipulation via Kinematic Hand Synergi...

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IEEE international conference on Rehabilitation Robotics (ICORR)

作者： A. Michael West Federico Tessari Neville Hogan Department of Mechanical Engineering Laboratory for Biomechanics and Human Rehabilitation Massachusetts Institute of Technology Cambridge MA USA Department of Brain and Cognitive Sciences Massachusetts Institute of Technology Cambridge MA USA

The study of kinematic hand synergies through matrix decomposition techniques, such as singular value decomposition, supports the theory that humans might control a subspace of predefined motions during manipulation tasks. These subspaces are often referred to as synergies. However, different data pre-processing methods lead to quantitatively different conclusions about these synergies. In this work, we shed light on the role of data pre-processing on the study of hand synergies by analyzing both numerical simulation and real kinematic data from a complex manipulation task, i.e., piano playing. The results obtained suggest that centering the data, by removing the mean, appears to be the most appropriate preprocessing technique for studying kinematic hand synergies.

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cognitive and Emotional Monitoring with Inexpensive Wrist-Worn Consumer-Grade Wearables

Cognitive and Emotional Monitoring with Inexpensive Wrist-Wo...

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

作者： Yunan Wu Roxana Valdez Clifton Forlines Northwestern University Evanston IL USA Bowdoin College Brunswick ME USA Northeastern University Portland ME USA

High-cost and physical-limitations limit the use of Electroencephalogram sensing (EEG) with respect to Affective computing. Wrist-worn Wearable health devices, an intriguing alternative, are becoming cheap, inconspicuous, and common-place - driving down the cost of heart-rate, accelerometer, respiration, and similar sensing. In this paper, we describe a quantitative empirical study investigating the use of low-cost sensing as a replacement for EEG. Our results show significant correlation between wrist-sensed, low-cost physiological data features and established EEG measures of emotion and cognition. This work demonstrates the value of inexpensive sensors capable of inferring emotion and cognition in a wrist-worn form-factor.

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Advanced Educational Assessments: Automated Question Classification Based on Bloom’s cognitive Level

Advanced Educational Assessments: Automated Question Classif...

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Evolutionary Algorithms and Soft computing Techniques (EASCT), international conference on

作者： Shelin Vankawala Amit Thakkar Nikita Bhatt Department of Computer Science & Engg CSPIT Charusat India U & P U. Patel Dept. of Computer Engg CSPIT Charusat India

In the contemporary education system, the quality of question papers plays a pivotal role in evaluating students' knowledge and comprehension. To ensure the validity of assessment outcomes, it is imperative to assess the quality of these question papers, taking into account factors such as question clarity, alignment with learning objectives, structural coherence, and conformity with intended educational outcomes. This study is centered around the development of a predictive model that employs Bloom’s taxonomy—a framework for categorizing learning objectives—to gauge the difficulty level of questions. To optimize performance, we have harnessed the power of Bidirectional Long Short-Term Memory Network (BiLSTM), renowned for effectively preserving intricate dependencies within data. Through extensive experimentation on widely recognized datasets, our results showcased the superior accuracy of BiLSTM, with an overall accuracy rate of 80%, outperforming existing methods by a substantial margin of 5.44%. These findings represent a significant advancement in the realm of educational assessment, empowering educators with advanced machine learning techniques for more precise evaluation of students' cognitive capabilities.

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Bug Predictive Models based on data Analytics and Soft computing Techniques: A Survey

Bug Predictive Models based on Data Analytics and Soft Compu...

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international conference on computing for Sustainable Global Development (INDIACom)

作者： Veena Jadhav Prakash Devale Rohini Jadhav Mayuri Molawade Sagar Mohite Ranjeet Vasant Bidwe Department of Computer Engineering Bharati Vidyapeeth (Deemed to be University)College of Engineering Pune India Symbiosis Institute of Technology (SIT) Symbiosis International (Deemed) University (SIU) Lavale Pune India

As software projects become larger and more complex, bugs pose a significant threat to their consistency and efficiency. During early phases of software development, bug prediction essentially enhances vital aspects such as software efficiency reliability quality, cost of development. Hence, various software bug prediction techniques have been presented previously but there is no research that focuses on surveying logical analysis and Just-In-Time (JIT) bug predictions with the consideration of data analytics and soft computing techniques. Hence this research investigates Soft computing and hybrid logistic model by determining imperfect range of data, class labels with heuristics prediction of bugs. Also, survey data analytics techniques such as clustering and oversampling techniques with JIT prediction for forecasting software bug in future. The study also compares various software bug prediction techniques to determine their effectiveness. Then, mentioned the basic benefits and drawbacks of the various bug prediction techniques. Moreover, future directions are provided to overcome the existing impact of various techniques in software bug prediction.

关键词： data analysis Costs Computational modeling Computer bugs Predictive models Software data models

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emsReACT: A Real-Time Interactive cognitive Assistant for Cardiac Arrest Training in Emergency Medical Services

emsReACT: A Real-Time Interactive Cognitive Assistant for Ca...

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international conference on Distributed computing in Sensor Systems (DCOSS)

作者： M Arif Rahman Leon Jia Eimara Mirza Sarah Masud Preum Homa Alemzadeh Ronald D. Williams John A. Stankovic Dept. of Computer Science University of Virginia Charlottesville USA School of Medicine University of Virginia Charlottesville USA Dept. of Electrical and Computer Engineering University of Virginia Charlottesville USA Dept. of Computer Science Dartmouth College Hanover USA

EMS (emergency medical services) deals with cardiac arrest cases more frequently than any other fatal health conditions all over the world. In this situation, a sequence of complex and time sensitive interventions is performed to ensure the safe recovery of the patient. We have developed emsReACT, a real-time interactive cognitive assistant, to train EMS providers for cardiac arrest cases in an emergency situation. This customized tool interacts in real-time with the first-responder and collects critical information. Using the conversational audio data available at EMS training sessions, emsReACT provides responder-specific decision support during the training based on domain specific information extraction, context-aware tracking of cardiac arrest protocols, and the dynamically changing condition of the patient. emsReACT leverages a dynamic behavioral model and a task-graph of two frequently used cardiac arrest EMS protocols. We have developed an intelligent abstraction mechanism with a critical risk-rating that drives an anytime algorithm to meet time requirements for regular and critical situations. Our thorough experimentation reveals an average end-to-end time of 2.7 seconds and 1.8 seconds for regular and critical interventions, thereby meeting the time requirements of 7 and 3 seconds, respectively. A qualitative study also reflects that over 70% of the 31 surveyed EMS providers rate the system as helpful to properly train the first-responders for executing cardiac arrest protocols.

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Analysis of ATC-type Stair Structure by Axonometric Projection Drawing Demo

Analysis of ATC-type Stair Structure by Axonometric Projecti...

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Electrical Engineering, Big data and Algorithms (EEBDA), IEEE international conference on

作者： Fang Yuchu Luo Huan Yang Qian The School of Civil and Architectural Engineering Guangxi Vocational Normal University Nanning China

In the teaching of architectural engineering drawing, beginners usually need a sense of space and help to understand the graphic expression content and building entities. The typical way to solve this problem is to use software to build a three-dimensional model for demonstration. However, this method has some disadvantages, such as difficulty operating a single demonstration, lack of change, and test demonstrating according to the cognitive process. In this paper, axial projection drawing is used for demonstration in the teaching of architectural engineering drawing recognition. Take ATC-type stairs as an example; step-by-step analysis is carried out with the help of MSPaint drawing tools. As a result, the drawing rules of progressive cognition, the study of factor changes can be carried out from easy to complex in line with the cognitive habits of the human brain-friendly to beginners and has achieved good results in actual teaching.

关键词： Electrical engineering Solid modeling cognitive processes Engineering drawings Education Layout Stairs

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Virtual Museum and the Phenomenon of Digital Heritage: Challenges of the 21st Century 2

Virtual Museum and the Phenomenon of Digital Heritage: Chall...

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2nd international Workshop on Social Communication and Information Activity in Digital Humanities, SCIA 2023

作者： Myna, Zhanna Banakh, Vasyl Nahirnyak, Andriy Lviv Polytechnic National University S. Bandery st. 12 Lviv79013 Ukraine

The paper presents an analysis of the use of digital technology in the museum industry, the introduction of the term "museum computing" by museum specialists and the emergence of a new phenomenon – the phenomenon of a virtual museum, as a museum that uses up-to-date information and communication technology for the presentation of museum collections. It has been stated that a virtual museum can be interpreted as "an integrated cognitive system, i.e. an accessible interactive collection of complex three-dimensional artefacts or replicas of monuments created by computer methods", as well as a digital unit that shows the features of the museum to complement, improve or expand museum experience through personalization, interactivity and richness of content. It has been emphasized that information technology has made it possible to expand the cognitive and communication potential of cultural heritage and, according to Western researchers, the third cultural (digital) revolution associated with the cyber era allows science, technology and art to be organically intertwined. Therefore, one can speak of cyber heritage, which combines art, culture and intercultural dialogue. It has been stated that the analysis of conceptual and methodical approaches to the development of state-of-the-art digital technologies in the field of cultural heritage, which caused a digital boom in all modern cultural processes, made it possible to trace the leading patterns and trends related to the recording, analysis and transmission of cultural heritage at the present stage. Own research on the popularity of virtual museums and awareness of this issue among different segments of the population have been conducted. Statistical data based on survey results have been traced and analysed. The analysis of the results proved the relevance of the issue under study and the fact that there is no single correct context or environment, in which culture can exist. First of all, it should be where there is an

关键词： cognitive systems

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Fortifying Cyber Defenses: A Deep Dive into the Development of an AI-Powered Network Intrusion Detection System 5th

Fortifying Cyber Defenses: A Deep Dive into the Development ...

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5th international conference on computing, Communications, and Cyber Security, IC4S 2023

作者： Rai, Hari Mohan Galymzada, Alin Almas, Konsbayev Nurzhan, Duzbayev Alibek, Makhayev Department of Cybersecurity International Information Technology University Almaty Kazakhstan

ISBN: (纸本)9789819725496

In the ever-evolving world of cybersecurity, it is essential to strengthen defenses against complex threats. This article provides a comprehensive overview of the development and deployment of an AI-powered network intrusion detection system (NIDS) as a fundamental element in improving organizational cyber resilience. Starting with an examination of the challenges posed by traditional intrusion detection systems (IDS), the article highlights the limitations of rule-based approaches in the face of rapidly evolving cyber threats. Emphasizing the need for real-time threat detection, the article introduces AI-powered NIDS as an adaptive solution. A focus on machine-learning algorithms, including supervised and unsupervised techniques, sheds light on the system's ability to automatically distinguish normal network behavior from potential intrusions. Deep learning models, particularly convolutional neural networks (CNNs) and recurrent neural networks (RNNs), are explored for their transformative impact on accuracy and adaptability in real-time data analysis. The article contextualizes these advancements by tracing the evolution of intrusion detection mechanisms, from rule-based systems to sophisticated AI-driven solutions. It delves into related work, covering machine-learning applications, anomaly detection, and the emergence of ensemble methods and hybrid approaches. Recognizing challenges such as adversarial attacks and interpretability concerns, the article concludes by emphasizing the broader importance of AI-powered NIDS in strengthening digital infrastructures. Serving as a valuable resource for cybersecurity professionals and organizations, this article combines technical depth with practical insights, offering strategic considerations for navigating the complexities of the modern cyber threat landscape. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Network security

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Inter-organizational collaboration and optimal cognitive proximity: Moderating effect of knowledge complexity

Inter-organizational collaboration and optimal cognitive pro...

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IEEE international Technology Management conference

作者： Yeokyung Hwang Junmin Lee Junseok Hwang Integrated Major in Smart City Global Convergence Seoul National University Seoul Korea Department of Public Policy and Management Pusan National University Busan Korea Technology Management Economics and Policy Program Seoul National University Seoul Korea

As a result of increased specialization due to the expansion of human knowledge, collaborative learning to acquire new knowledge is becoming important. In order to find partners for effective learning, organizations need to consider the optimal level of cognitive proximity considering the complexity of the field of collaboration. This study examines the two-sided effects of cognitive proximity in the context of collaborative innovation, focusing on the moderating effect of knowledge complexity. Utilizing USPTO patent data of US biotechnology sector, this research confirmed an inverse U-shaped effect of cognitive proximity on collaborative innovation. The optimal level of cognitive proximity increases with higher level of knowledge complexity, which shows positive moderating effect of complexity on the proximity effect. The marginal effect of cognitive proximity also increases in complex fields, indicating that proximity plays stronger role as knowledge complexity increases. This indicates that while cognitive proximity enhances knowledge absorption, the presence of knowledge complexity hinders knowledge diffusion. Organizations that share more complex knowledge can achieve optimal collaborative performance by partnering with cognitively close counterparts. However, collaboration performance is lower than lower complex fields, even if organization achieve optimal cognitive distance. These insights offer valuable implications for collaboration strategies for organizations and provide guidance for policymakers and stakeholders in fostering innovation networks and creating supportive ecosystems.

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