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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311-320 订阅

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ICMI 2023 - Proceedings of the 25th international conference on Multimodal Interaction

ICMI 2023 - Proceedings of the 25th International Conference...

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25th international conference on Multimodal Interaction, ICMI 2023

ISBN: (纸本)9798400700552

The proceedings contain 105 papers. The topics discussed include: multimodal information processing in communication: the nature of faces and voices;a robot just for you: multimodal personalized human-robot interaction and the future of work and care;a multimodal approach to investigate the role of cognitive workload and user interfaces in human-robot collaboration;acoustic and visual knowledge distillation for contrastive audio-visual localization;analyzing and recognizing interlocutors’ gaze functions from multimodal nonverbal cues;analyzing synergetic functional spectrum from head movements and facial expressions in conversations;annotations from speech and heart rate: impact on multimodal emotion recognition;augmented immersive viewing and listening experience based on arbitrarily angled interactive audiovisual representation;breathing new life into COPD assessment: multisensory home-monitoring for predicting severity;and classification of Alzheimer’s disease with deep learning on eye-tracking data.

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Extending Actor Models in data Spaces

Extending Actor Models in Data Spaces

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32nd World Wide Web conference (WWW)

作者： zum Felde, Hendrik Meyer Bellebaum, Thomas Brost, Gerd Kollenstart, Maarten Dalmolen, Simon Fraunhofer AISEC Garching Germany TNO Groningen Netherlands

ISBN: (纸本)9781450394161

In today's internet almost any party can share sets of data with each other. However, creating frameworks and regulated realms for the sharing of data is very complex when multiple parties are involved and complicated regulation comes into play. As solution data spaces were introduced to enable participating parties to share data among themselves in an organized, regulated and standardized way. However, contract data processors, acting as data space participants, are currently unable to execute data requests on behalf of their contract partners. Here we show that an on-behalf-of actor model can be easily added to existing data spaces. We demonstrate how this extension can be realized using verifable credentials. We provide a sample use case, a detailed sequence diagram and discuss necessary architectural adaptations and additions to established protocols. Using the extensions explained in this work numerous real life use cases which previously could technically not be realized can now be covered. This enables future data spaces to provide more dynamic and complex real world use cases.

关键词： data Spaces Contract data Processing Actor Models Self-Sovereign Identities international data Spaces Gaia-X On-behalf-of Model

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An AI-Centred Adaptive cognitive Network Traffic Modeling & Measurement Technique for EIS 3

An AI-Centred Adaptive Cognitive Network Traffic Modeling & ...

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3rd international conference on Advance computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Bharathi, L. Vasoya, Nimish H. Kansal, Lavish Gehlot, Anita Swathi, Bajila Ahmed Khan, Suyeb Ramachandra College of Engineering Department of Electronics and Communication Engineering Andhra Pradesh Eluru India Children's University Department of Student Startup Gujarat Gandhinagar382021 India Lovely Professional University Phagwara India Uttaranchal University Uttaranchal Institute of Technology India SR University School of Computer Science and Artificial Intelligence Telangana Warangal India Baba Kuma Singh Ji Engineering College Amritsar India

ISBN: (纸本)9798350399264

The IoT is the foundation of the Enterprise Information System (EIS), which collects a lot of business information. For EIS, timely, accurate data analysis & transfer are crucial. For net management & traffic optimization in EIS, Network infrastructure traffic is crucial. Nevertheless, the needs for fine-grain traffic monitoring are in conflict with the monitoring overheads & precision. Long-standing promises of artificial intelligence (AI) include the ability to recognise the physical attributes of network activity & take appropriate steps in response. In order to decrease network monitoring overheads & increase measurement precision, we present an AI centred between light Adapt Measuring System (ALAMM) for SDN in this study. Initially, we simulate & forecast the network congestion information using AI. We suggest an adaptive strategy to choose the rate of traffic sample centred on the outcomes of traffic forecasting. Next, we instruct switching devices to evaluate the coarse-grain traffic of flow pattern & networks using the measuring primitives. In order to retrieve the Fine grained (FG) assessment & optimise the measured values, the matrices refilling & optimizing approach is finally suggested. Simulated outcomes demonstrate that our method can accurately and efficiently determine network activity. © 2023 IEEE.

关键词： Software defined networking

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Neuro-Symbolic AI: Integrating Symbolic Reasoning with Deep Learning 10

Neuro-Symbolic AI: Integrating Symbolic Reasoning with Deep ...

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10th IEEE Uttar Pradesh Section international conference on Electrical, Electronics and Computer Engineering, UPCON 2023

作者： Himabindu, Modi Revathi, V. Gupta, Manish Rana, Ajay Chandra, Pradeep Kumar Abdulaali, Hayder Saadoon Institute of Aeronautical Engineering Hyderabad Dundigal India New Horizon College of Engineering Department of Applied Sciences Bangalore India Lovely Professional University Phagwara India Director General Amity University Greater Noida India Lloyd Institute of Engineering & Technology Uttar Pradesh Greater Noida India College of Engineering Technology National University of Science and Technology Dhi Qar Iraq

ISBN: (纸本)9798350382471

Neuro-symbolic artificial intelligence (AI) stands at the frontier of machine learning by amalgamating the interpretability and structured knowledge representation of symbolic reasoning with the adaptive learning capabilities of deep neural networks. This paper presents a comprehensive framework for neuro-symbolic integration, outlining a harmonized architecture that leverages the strengths of both domains. The proposed system utilizes symbolic AI to impose structural constraints and inject domain knowledge into the learning process, enhancing the reasoning capabilities of deep learning models. Concurrently, it capitalizes on the proficiency of deep learning in handling high-dimensional, noisy data, enabling the symbolic components to operate beyond discrete, well-defined environments. The architecture is validated through a series of experiments demonstrating enhanced performance in tasks requiring complex reasoning, generalization, and knowledge transfer. The framework showcases a significant reduction in data dependency for model training, increased interpretability of the decision-making process, and robustness to noise and ambiguity. This integration marks a stride towards the development of AI systems with advanced cognitive abilities, akin to human-like understanding and reasoning. The paper concludes with a discussion on the implications of neuro-symbolic AI in advancing the field and its potential to transform future AI applications. © 2023 IEEE.

关键词： cognitive AI Systems Deep Learning Knowledge Representation Neuro-symbolic AI Symbolic Reasoning

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The Smart Application of data Mining in the Detection of Fraudulent Transactions 3

The Smart Application of Data Mining in the Detection of Fra...

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3rd international conference on Smart Generation computing, Communication and Networking, SMART GENCON 2023

作者： Bodhankar, Aniruddha Aswal, Upendra Singh Arivazhagn, D. Pund, Sachin S. Kulkarni, Nandini Chaturvedi, Sudhir Kumar Dr. Ambedkar Institute of Management Studies and Research- Nagpur Department of Data Science & Information Systems Nagpur India Shri Ramdeobaba College of Engineering and Management Department of Industrial Engineering Maharashtra Nagpur India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India Symbiosis International University Department of Symbiosis School of Planning Architecture and Design Nagpur Nagpur India Business School Academy of Maritime Education and Training Deemed to Be University Department of Amet Chennai India Upes Department of School of Engineering Dehradun India

ISBN: (纸本)9798350319125

Records mining is a powerful analytical device used to find out styles, correlations, and traits in large facts sets. Smart application of data mining methods within the detection of fraudulent transactions has finished an extensive quantity of success. It normally entails statistical algorithms, facts evaluation, and machine getting to know techniques to stumble on suspicious sports associated with fraud. Using statistics mining techniques, it's far possible to music the behaviour of clients, discover anomalous transaction styles and developments in huge datasets, come across frauds and generate signals on suspicious activities. It also allows to identify which forms of merchandise are frequently centered, which price methods are used, which places are problem to danger, and which traders have capacity credit card fraud. The utility of facts mining within the detection of fraudulent sports entails principal techniques. The first method makes use of conventional techniques used by banks, credit groups and other retailers, which includes account reconciliation, annualized billing and complex question processing. Those methods are used to generate reviews that are used by investigators to perceive probably suspicious activities. The second technique entails the usage of superior analytical techniques such as neural networks, decision timber, and clustering algorithms. These strategies have the functionality to stumble on facts irregularities in several conditions that were formerly unseen or unknown. © 2023 IEEE.

关键词： developments entails reconciliation statistics transaction

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Revisiting Neurological Aspects of Relevance: An EEG Study 8th

Revisiting Neurological Aspects of Relevance: An EEG Study

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8th Annual international conference on Machine Learning, Optimization and data science (LOD)

作者： Pinkosova, Zuzana McGeown, William J. Moshfeghi, Yashar Univ Strathclyde NeuraSearch Lab Glasgow Lanark Scotland Univ Strathclyde Neuroanalyt Lab Glasgow Lanark Scotland

ISBN: (纸本)9783031258909;9783031258916

Relevance is a key topic in Information Retrieval (IR). It indicates how well the information retrieved by the search engine meets the user's information need (IN). Despite research advances in the past decades, the use of brain imaging techniques to investigate complex cognitive processes underpinning relevance is relatively recent, yet has provided valuable insight to better understanding this complex human notion. However, past electrophysiological studies have mainly employed an event-related potential (ERP) component-driven approach. While this approach is effective in exploring known phenomena, it might overlook the key cognitive aspects that significantly contribute to unexplored and complex cognitive processes such as relevance assessment formation. This paper, therefore, aims to study the relevance assessment phenomena using a data-driven approach. To do so, we measured the neural activity of twenty-five participants using electroencephalography (EEG). In particular, the neural activity was recorded in response to participants' binary relevance assessment (relevant vs. non-relevant) within the context of a Question Answering (Q/A) Task. We found significant variation associated with the user's subjective assessment of relevant and non-relevant information within the EEG signals associated with P300/CPP, N400 and, LPC components, which confirms the findings of previous studies. Additionally, the data-driven approach revealed neural differences associated with the previously not reported P100 component, which might play important role in early selective attention and working memory modulation. Our findings are an important step towards a better understanding of the cognitive mechanisms involved in relevance assessment and more effective IR systems.

关键词： Information retrieval Relevance assessment Binary relevance Brain signals EEG ERPs cognitive processes

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AD Classification and Detection Using Neuroimaging data 3

AD Classification and Detection Using Neuroimaging Data

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3rd international Mobile, Intelligent, and Ubiquitous computing conference, MIUCC 2023

作者： Abed, Salma Mourad, Mohamed Abdelkreem, Mohamed Mohamed Ashraf, Mostafa Talaat, Youssef Elashmawi, Walaa H. Misr International University Computer Science Cairo Egypt

ISBN: (纸本)9798350306231

Alzheimer's disease (AD) is the most prevalent type of dementia, a progressive brain illness that typically manifests in old age. Alzheimer's disease will affect 1.2 percent of the world's population by 2046, according to the World Health Organisation. If nothing is done to reduce that figure, the number of fatalities from Alzheimer's and dementia in Egypt will reach 6,975 in 2020. Egypt is rated 148th in the world. Early Alzheimer's diagnosis is critical for informing patients about the severity of their condition and empowering them to take medication and other preventative measures. MRI and PET scans, have been used to aid in diagnosing along with machine learning approaches. Early AD diagnosis was required to accurately distinguish AD cases from standard control (NC) subjects. Naive Bayes (NB), Support Vector Machine (SVM), and k-Nearest Neighbors (KNN) are machine learning algorithms that have been utilized to predict the possibility of Mild cognitive Impairment (MCI) participants getting Alzheimer's, allowing the disease's progression rate to be determined. These strategies, however, were unreliable due to the difficulties of defining the condition as AD or ND, the likelihood of late or seriously demented patients, and a lack of access to large or diverse data sets. A thorough analysis discovered that the proposed workflows might be substantially enhanced by focusing on the constraints of previous studies. The suggested Alzheimer's disease technique categorizes AD patients into one of four stages, using a data set of 1288 MRI images for training and pre-processing procedures such as skull stripping, noise reduction, and motion correction. This method utilizes a convolutional neural network model (CNN) and deep learning architecture in order to enhance accuracy while minimizing the computational resources required to access ROIs (regions of interest). The technology can effectively diagnose patients with minimal work and time while providing much potential. © 2

关键词： Neuroimaging

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HPCAsia 2024 – Proceedings of the international conference on High Performance computing in Asia-Pacific Region

HPCAsia 2024 – Proceedings of the International Conference ...

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5th international conference on Big-data Service and Intelligent Computation, BDSIC 2023

ISBN: (纸本)9798400708893

The proceedings contain 17 papers. The topics discussed include: portable implementations of work stealing;sKokkos: enabling Kokkos with transparent device selection on heterogeneous systems using OpenACC;parallelized remapping algorithms for km-scale global weather and climate simulations with icosahedral grid system;approximate block diagonalization of symmetric matrices using quantum annealing;QUBO formulation using inequalities for problems with complex constraints;low-latency communication in RISC-V clusters;flexible systolic array platform on virtual 2-D Multi-FPGA plane;an efficient task-parallel pipeline programming framework;and task-based low-rank hybrid parallel Cholesky factorization for distributed memory environment.

关键词： Systolic arrays

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Explainable Knowledge Reasoning on Power Grid Knowledge Graph 19th

Explainable Knowledge Reasoning on Power Grid Knowledge Grap...

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19th international conference on Advanced Intelligent computing Technology and Applications (ICIC)

作者： Zhang, Yingyue Huang, Qiyao Zheng, Zhou Liao, Feilong Yi, Longqiang Li, Jinhu Huang, Jiangsheng Zhang, Zhihong Xiamen Univ Sch Informat Xiamen 361005 Fujian Peoples R China State Grid Fujian Elect Power Res Inst Fuzhou 350007 Fujian Peoples R China Kehua Data Co Ltd Xiamen 361006 Fujian Peoples R China State Grid Info Telecom Great Power Sci & Technol Fuzhou 350003 Fujian Peoples R China

ISBN: (纸本)9789819947607;9789819947614

The smooth operation of the power grid is closely related to the national economy and people's livelihood. The knowledge graph, as a widely-used technology, has made considerable contributions to power grid dispatching and query answering. However, explainable reasoning on grid defects datasets is still of great challenge, most models cannot balance effectiveness and explainablity. Therefore, their assistance in grid defects diagnosis is minimal. To address this issue, we propose the rule-enhanced cognitive graph for power grid knowledge reasoning. Our model consists of two modules: expansion and reasoning. For the expansion module, we take into consideration that path-based methods often ignore graph structure and global information and combine the local cognitive graph and global degree distribution. For the reasoning module, weprovide reasoning evidence from two aspects: logical rule learning for strong evidence and cognitive reasoning for possible paths. Experiment results on our grid defects dataset make known that our model achieves better performance with explainablity.

关键词： Power Grid Knowledge Graph Knowledge Reasoning cognitive Graph

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Optimization of University Operations By The Means Of Fuzzy cognitive Maps 14

Optimization of University Operations By The Means Of Fuzzy ...

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14th international conference on Information, Intelligence, Systems and Applications, IISA 2023

作者： Panagiotis, Perivolaris Vassilios, Stylianakis Patras University Electrical and Computer Enginnering Department Greece

ISBN: (纸本)9798350318067

The digital transformation of HEIs after the boost it received in the Pandemic period due to the imposing problems that had to be solved, now offers a variety of new possibilities for the improvement of the existing methods of online teaching but for the overall operation of the Institutions as well. The nature of software tools, with their logistic accuracy and accounting capabilities, along with their flexibility and their continuous technological improvement offers options for the monitoring of various indexes of success, in both quantitative and qualitative metrics. Such metrics could involve both objective and subjective indexes. Examples of objective indexes could be the volume of participation;the subsequent dropout rate;the extent of students' involvement;the various levels of success in their texts. Subjective measures could include Teachers', students, or administrative staff's satisfaction. These could be evaluated in various levels of abstraction, starting from general, on university level and being successively detailed to individual Schools, Departments, Classes, all the way to lessons and classes, even specific tests or parts of the teaching material. The automated nature of digital operation makes it feasible to perform continuous monitoring of the trends of such satisfaction indexes, by the use of online questionnaires, offering valuable insight to all parties involved. In the administrative field and day-to-day operations, such measures could include the number of transactions;time length of operations. These measurements taken into forms of curves and time series can then be cross-correlated with various factors and useful conclusions can be drawn. This data can further be elaborated with various advanced tools, such as cognitive tree maps, fuzzy networks, or/and genetic algorithms, to exploit and define the way that the behavior of a complex system, such as a HEI, is affected by the various conditions changing in time. In the proposal, the layout

关键词： Fuzzy cognitive Maps

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