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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591-600 订阅

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Deep Multi-kernel Clustering Network

Deep Multi-kernel Clustering Network

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IEEE international conference on data Mining (ICDM)

作者： Lina Ren Ruizhang Huang Shengwei Ma Yongbin Qin Yanping Chen Chuan Lin State Key Laboratory of Public Big Data Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry College of Computer Science and Technology Guizhou University Guiyang China Department of Information Engineering Guizhou Light Industry Technical College Guiyang China

In this paper, a deep multi-kernel clustering network, named DMKCN, is proposed to learn a high-quality and structurally separable kernel representation for the clustering task. Specifically, a multi-kernel learner is proposed to choose a suitable kernel function by learning a suitable combination of kernel functions automatically. A kernel-aid encoder module, consisting of a series of multi-kernel learners, is proposed to learn the structurally separable kernel representation. Besides, a dual self-supervised mechanism, consisting of a kernel self-supervised strategy and a representation self-supervised strategy, is designed to uniformly optimize the kernel representation learning and structural partition. The kernel self-supervised strategy is developed to supervise the multi-kernel learners with the consideration of an objective of clustering task, the representation self-supervised strategy is developed to guide the optimization of kernel representation learning by reconstructing the raw data. Extensive experiments on six real-world datasets demonstrate the outstanding performance of our proposed DMKCN.

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14th international conference on Parallel Processing and Applied Mathematics, PPAM 2022

14th International Conference on Parallel Processing and App...

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14th international conference on Parallel Processing and Applied Mathematics, PPAM 2022

ISBN: (纸本)9783031304446

The proceedings contain 77 papers. The special focus in this conference is on Parallel Processing and Applied Mathematics. The topics include: Neural Nets with a Newton Conjugate Gradient Method on Multiple GPUs;Exploring Techniques for the Analysis of Spontaneous Asynchronicity in MPI-Parallel Applications;Cost and Performance Analysis of MPI-Based SaaS on the Private Cloud Infrastructure;building a Fine-Grained Analytical Performance Model for complex Scientific Simulations;evaluation of Machine Learning Techniques for Predicting Run Times of Scientific Workflow Jobs;Smart Clustering of HPC Applications Using Similar Job Detection Methods;distributed Work Stealing in a Task-Based dataflow Runtime;task Scheduler for Heterogeneous data Centres Based on Deep Reinforcement Learning;Shisha: Online Scheduling of CNN Pipelines on Heterogeneous Architectures;General Framework for Deriving Reproducible Krylov Subspace Algorithms: BiCGStab Case;proactive Task Offloading for Load Balancing in Iterative Applications;language Agnostic Approach for Unification of Implementation Variants for Different computing Devices;high Performance dataframes from Parallel Processing Patterns;global Access to Legacy data-Sets in Multi-cloud Applications with Onedata;MD-Bench: A Generic Proxy-App Toolbox for State-of-the-Art Molecular Dynamics Algorithms;Breaking Down the Parallel Performance of GROMACS, a High-Performance Molecular Dynamics Software;GPU-Based Molecular Dynamics of Turbulent Liquid Flows with OpenMM;a Novel Parallel Approach for Modeling the Dynamics of Aerodynamically Interacting Particles in Turbulent Flows;reliable Energy Measurement on Heterogeneous Systems–on–Chip Based Environments;distributed Objective Function Evaluation for Optimization of Radiation Therapy Treatment Plans;a Generalized Parallel Prefix Sums Algorithm for Arbitrary Size Arrays;GPU4SNN: GPU-Based Acceleration for Spiking Neural Network Simulations;Ant System Inspired Heuristic Optimization of UAVs Depl

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14th international conference on Parallel Processing and Applied Mathematics, PPAM 2022

14th International Conference on Parallel Processing and App...

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14th international conference on Parallel Processing and Applied Mathematics, PPAM 2022

ISBN: (纸本)9783031304415

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Ontology-Based Generation of data Platform Assets

Ontology-Based Generation of Data Platform Assets

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IEEE international conference on Big data

作者： Vincenzo De Leo Gianni Fenu David Greco Nicolò Bidotti Paolo Platter Enrico Motta Andrea Giovanni Nuzzolese Francesco Osborne Diego Reforgiato Recupero Department of Math and Computer Science University of Cagliari Cagliari Italy Big Data Laboratory AgileLab s.r.l Rome Italy Knowledge Media Institute Open University Milton Keynes UK Institute of Cognitive Sciences and Technologies National Council of Research Bologna Italy

The design and management of modern big data platforms are extremely complex. It requires carefully integrating multiple storage and computational platforms as well as implementing approaches to protect and audit data access. Therefore, onboarding new data and implementing new data transformation processes is typically time-consuming and expensive. In many cases, enterprises construct their data platforms without a clear distinction between logical and technical concerns. Consequently, these platforms lack sufficient abstraction and are closely tied to particular technologies, making the adaptation to technological evolution very costly. This paper illustrates a novel approach to designing data platform models based on a formal ontology that structures various domain components into an accessible knowledge graph. We also describe the preliminary version of AGILE-DM, a novel ontology that we built for this purpose. Our solution is flexible, technologically agnostic, and more adaptable to changes and technical advancements.

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Comparative Analysis of Vocal and Textual Emotion Detection and their association with Consumer Preferences: An Empirical Study

Comparative Analysis of Vocal and Textual Emotion Detection ...

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international conference on Affective computing and Intelligent Interaction Workshops and Demos (ACIIW)

作者： Qiurui Chen Laduona Dai Nino Hardt Methodology & Analytics SKIM Rotterdam The Netherlands Cognitive Science and Artificial Intelligence Tilburg University Tilburg The Netherlands Methodology & Analytics SKIM Berlin Germany

This research explored the link between emotions and consumer preferences in the Consumer Packaged Goods (CPG) industry by investigating the relationship between Pleasure, Arousal, and Dominance (PAD) dimensions and consumer utility scores for various concepts. Using two input methods, voice recording, and typing, the findings revealed that detected emotions might differ based on the data collection method and model (voice or text-based). Text-based responses display higher pleasure scores, and voice responses exhibit greater variance in dominance values. The study also identified that typed inputs led to more pronounced emotional responses between the top and bottom concepts. In contrast, voice inputs elicited an increased sense of control for the top concepts. Further analysis found that pleasure and arousal values were significantly higher for the most preferred concept in typed responses, with dominance playing a more significant role in voice responses. These findings underscore the importance of incorporating both voice recordings and text-based responses for analyzing emotions in market preferences and suggest that further research is needed to better understand emotion-based decision-making in various domains.

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Deep Reinforcement Learning for Mapless Navigation of Autonomous Mobile Robot

Deep Reinforcement Learning for Mapless Navigation of Autono...

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international conference on System Theory, Control, and computing (ICSTCC)

作者： Harsh Yadav Honghu Xue Yan Rudall Mohamed Bakr Benedikt Hein Elmar Rueckert Ngoc Thinh Nguyen Inst. for Robotics and Cognitive Systems University of Lübeck Luebeck Germany Robotics and Automation KION Group AG Hamburg Germany Inst. of Automation Technology Helmut-Schmidt-University Hamburg Germany Inst. for Cyber Physical Systems Montanuniversität Leoben Leoben Austria

This paper presents a study on the mapless navigation of autonomous mobile robot using Deep Reinforcement Learning in an intralogistics setting. The task is to make an autonomous mobile robot learn to navigate to a goal without prior knowledge of the environment. In this paper, a controller using the Soft Actor-Critic algorithm is designed, trained, and applied for navigating the robot equipped with $360^{\mathrm{o}}$ LiDAR and front camera sensors. The controller is successfully validated in an almost fully observable environment under extensive simulations. Furthermore, we investigate the performance of the proposed controller in a partially observable environment and possible limitations. We use a 3D Temporal Convolution Network for processing the time series image data from visual observations. Besides Partial Observability, we also address the problem of sparse positive rewards in training the Deep Reinforcement Learning algorithm with a combined approach of Automatic Curriculum Learning and Dual Prioritized Experience Replay.

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Machine Learning-Based Channel Power Analysis inside Aircraft Cabin

Machine Learning-Based Channel Power Analysis inside Aircraf...

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Intelligent computing and Control (IC&C), international conference on

作者： Xijuan Ye Kai Mao Jian Ma Lixin Song Yuxin Liu Qiuming Zhu The Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Ministry of Industry and Information Technology Nanjing University of Aeronautics and Astronautics Nanjing China Nanjing Mechanical and Electrical Hydraulic Engineering Research Center of Aviation Industry Aeronautical Key Laboratory of Aeronautical Science and Technology Nanjing China

Communication inside aircraft cabin has gained more and more attentions to provide in-flight connectivity for high-quality user experience and enable the wireless communication of electromechanical equipment. In this paper, a power prediction method considering the coordinates of transceiver, propagation distance and carrier frequency based on the parameterized channel model and machine learning (ML) framework is proposed for wireless communication inside the aircraft cabin. In the proposed channel model, a random forest (RF) network is trained for channel characteristics based on ray tracing (RT) simulation data and applied to estimate the power parameters. The proposed method is verified inside a typical aircraft cabin at 1GHz and compared with traditional model and background propagation neural network (BPNN). The simulation results show that RF can accurately predict the power parameter and reproduce the channel environment. The prediction results of power distribution can provide valuable reference for the layout and optimization of communication nodes inside the aircraft cabin.

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A Novel Method of Identification of Delirium in Patients from Electronic Health Records Using Machine Learning

A Novel Method of Identification of Delirium in Patients fro...

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Communication & computing (WCONF), World conference on

作者： R. Kavitha Kdv Prasad S Archana Shreee B Maheshwari G Jeevitha Sai V Dankan Gowda Department of Information Technology Velammal College of Engineering and Technology Madurai Tamilnadu India Symbiosis Institute of Business Management Hyderabad Symbiosis International (Deemed University) Pune India Department of Electronics and Communication Engineering BMS Institute of Technology and Management Bangalore Karnataka India

In most cases, the mental impairment caused by delirium may be treated and eventually reversed. Lack of concentration, disorientation, incoherent thought, and fluctuating degrees of awareness (consciousness) are all symptoms. Delirium, an acute neuropsychiatric disorder characterised by inattention and generalised cognitive impairment, is common, hazardous, and generally linked with poor results. Patients with delirium are at increased risk for adverse outcomes throughout their time in the critical care unit. It requires time and medical competence to diagnose delirium. Those at risk of developing delirium should be identified as soon as possible. Once a diagnosis has been made, the treatment process may be lengthy and include several groups working together. This paper’s goal is to show how a model may be built to diagnose delirium using Electronic Health Record data employing a Machine Learning technique.

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Information Security Awareness in Higher Education Institutes: A Work in Progress

Information Security Awareness in Higher Education Institute...

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international conference on Knowledge and Smart Technology (KST)

作者： Rohani Rohan Suree Funilkul Wichian Chutimaskul Prasert Kanthmanon Borworn Papasratorn Debajyoti Pal School of Information Technology King Mongkut’s University of Technology Thonburi Bangkok Thailand Innovative Cognitive Computing Research Center (IC2) School of IT King Mongkut’s University of Technology Thonburi Bangkok Thailand

The demands for information security in Higher Education Institutions (HEIs) are expanding as HEIs are vulnerable because of the involvement of human factors. Hence, maintaining data privacy is paramount, where most individuals interacting with systems and applications are the main stakeholders (lecturers, students, and non-academic staff). In this regard, existing literature and security experts claim that enhancing users’ Information Security Awareness (ISA) is one of the most effective protective techniques. Therefore, this study aims to propose a conceptual security awareness framework consisting of devices, application areas, and security practices and their related activities for HEIs. Moreover, five conceptual dimensions are suggested that affect users’ ISA and are necessary for HEIs while measuring the ISA of their stakeholders. For investigating and understanding these issues, interviews were conducted with IT security experts working in HEIs.

关键词： data privacy Education Information security Human factors Stakeholders Interviews Computer security

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“Accelerating Engineering Education and Workforce Development in Automation & Control for the Semiconductor Industry Based on cognitive Neuroscience”

“Accelerating Engineering Education and Workforce Developme...

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Latin American Electron Devices conference (LAEDC)

作者： Luis Fernando Cruz M. Luis Miguel Quevedo Wilfrido Alejandro Moreno Vicerrectoría de Investigaciones Universidad Manuela Beltran Bogota Colombia JWG S.A.S Tampa FL USA Electrical Engineering Department University of South Florida Tampa FL USA

The rapid development of the semiconductor industry represents a global challenge to meet the needs of Science, Technology, and Innovation in the 21 st century. Advancements in semiconductors optimize computer technology with innovations, among others, in nanotechnology, quantum technology, cognitive computing, Cyber-Physical Human Systems, Artificial Intelligence, and Robotics. The rapid development of semiconductors demands an imperative acceleration of training and the development of a highly skilled workforce. There is a gap between the knowledge and skills of students in the current education system and the fast development of new knowledge. To accelerate comprehensive training in the semiconductor workforce we must start at an early stage, this paper proposes that STEM education must be pedagogically grounded in the advances of cognitive neuroscience and learning. By understanding how the brain functions and how it learns, the process can be enhanced, the education of an Integral Engineer needs to be addressed by forming professionals equipped with adaptability, complex problem-solving, assertive communication, emotional intelligence, interdisciplinary teamwork skills, systems, analytical and critical *** paper proposes curricular transformation processes, ethical formation, pedagogical foundations of interdisciplinary research laboratories, enhanced by an international network of remote laboratories, and a TRUE system of cultural transformation.

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