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检索条件"任意字段=Proceedings of the 2019 8th International Conference on Computing and Pattern Recognition"

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INSIDER: A framework for assessing confidence in psychological scales based on multi-modal physiological signal fusion 19

INSIDER: A framework for assessing confidence in psychologic...

引用

2022 IEEE SmartWorld, 19th IEEE international conference on Ubiquitous Intelligence and computing, 2022 IEEE international conference on Autonomous and Trusted Vehicles conference, 22nd IEEE international conference on Scalable computing and Communications, 2022 IEEE international conference on Digital Twin, 8th IEEE international conference on Privacy computing and 2022 IEEE international conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Wang, Youmian Hu, Bin Chen, Zhenxiang Jiang, Xiaoqing Liu, Wenjuan Wang, Peicheng University of Jinan Shandong Provincial Key Laboratory of NetWork Based Intelligent Computing Jinan China

ISBN: (纸本)9798350346558

the majority of polygraph questions used in psychometric objectivity assessments have inadequate applicability, low validity, and lengthy assessment times. In order to improve the Trusted Multi-View Classification (TMC) and produce more objective scale confidence results, this paper proposes a framework for assessing confidence in psychological scales based on multi-modal physiological signal fusion (INSIDER). this framework uses physiological signals (i.e. ECG, Eye Movement, and ECG) to identify lying response behaviors. By using the CNN (Temporal&Spatial) deep learning framework, in the three modalities, the EEG has the best accuracy (92.67%) in detecting lying answer behaviors;the TMC-based hybrid fusion method had both high accuracy (96.32%) and high noise immunity;and high accuracy (93.59%) can be attained by using LR classifier in the confidence assessment of the combined scale. Experimental resluts demonstrated that it can replace polygraph questions. When choosing the product, INSIDER takes into account the necessity for portability, which might serve as a model for the development of future mental health scale assessment systems. © 2022 IEEE.

关键词： Electrocardiograms

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Resume Parsing and Skill Extraction using Custom pattern Matching algorithm and Gemini API

Resume Parsing and Skill Extraction using Custom Pattern Mat...

引用

international conference on computing Communication Control and Automation (ICCUBEA)

作者： Atharva Joshi Shailaja Pede Prathmesh Kolekar Aditya Metkar Department of Computer Science Engineering (Artificial Intelligence and Machine Learning) PCCOE SPPU India Pune India

ISBN: (数字)9798350391770

ISBN: (纸本)9798350391787

In the realm of contemporary recruitment, efficient resume parsing is essential for talent acquisition. this paper investigates resume parsing using advanced OCR technology and NLP methods. We begin with preprocessing, enhancing scanned resumes for legibility. Leveraging the Tesseract OCR engine, we convert images to machine-readable text. through NLP techniques with spaCy, we extract key information like personal details, education, experience, and *** approach integrates the Gemini Pro Vision API for semantic analysis and question-answering. this enriches our framework, enabling deeper insights and implicit attribute inference. Empirical validation across diverse datasets demonstrates the robustness of our methodology. this work sheds light on performance metrics and challenges in automated resume parsing, paving the way for future enhancements.

关键词： Visualization Technological innovation Machine learning algorithms Resumes Optical character recognition Semantics Robustness Data mining pattern matching Recruitment

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Attention-based Deep Learning Methods for Document Layout Analysis 22

Attention-based Deep Learning Methods for Document Layout An...

引用

8th international conference on computing and Artificial Intelligence, ICCAI 2022

作者： Sun, Hao-Yue Zhong, Ying Wang, Da-Han School of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen361024 China

ISBN: (纸本)9781450396110

Deep learning has unique advantages in document layout analysis. In this paper, we propose to use the attention mechanism to improve the deep learning based methods for document layout analysis, and conducted comprehensive experiments to evaluate the effects of different models/modules/backbones. Firstly, the evaluated backbones include ResNet-50 and ResNet-101 for their high performance, and the popularly used Fast RCNN and Faster RCNN based methods. Secondly, for the loss function, the Focal loss function is also adopted to improve the performance of document layout analysis. Most importantly, we propose to integrate the attention mechanism into the deep learning-based methods to improve the performance. Specifically, when calculating the residual using this ResNet network, we use the attention to perform a dot product operation on the degraded accuracy to correct the residual error of the network calculation, which obtains a higher detection accuracy. We conducted experiments on the PubLayNet, DocBank and ICDAR-2013 datasets for evaluation. Experimental results show that, with the attention mechanism, the basic deep learning-based methods has been significantly improved in the task of document layout analysis, demonstrating the effectiveness of the proposed method. the focal loss that is designed to overcome the problem of imbalance number of training samples is also effective for document layout analysis. © 2022 Association for computing Machinery. All rights reserved.

关键词： Deep learning

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Towards using Breathing Features for Multimodal Estimation of Depression Severity 22

Towards using Breathing Features for Multimodal Estimation o...

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24th ACM international conference on Multimodal Interaction (ICMI)

作者： Pessanha, Francisca Kaya, Heysem Salah, Alkim Almila Akdag Salah, Albert Ali Univ Utrecht Utrecht Netherlands Bogazici Univ Istanbul Turkey

ISBN: (纸本)9781450393904

Breathing patterns are shown to have strong correlations with emotional states, and hence have promise for automatic mood order prediction and analysis. An essential challenge here is the lack of ground truth for breathing sounds, especially for medical and archival datasets. In this study, we provide a cross-dataset approach for breathing pattern prediction and analyse the contribution of predicted breath signals for the detection of depressive states, using the DAIC-WOZ corpus. We use interpretable features in our models to provide actionable insights. Our experimental evaluation shows that in participants with higher depression scores (as indicated by the eight-item Patient Health Questionnaire, PHQ-8), breathing events tend to be shallow or slow. We furthermore tested linear and non-linear regression models with breathing, linguistic sentiment and conversational features, and show that these simple models outperform the AVEC17 Real-life Depression recognition Sub-challenge baseline.

关键词： Afective computing Paralinguistics Depression Severity Prediction Breathing Analysis Interpretability DAIC-WOZ Corpus

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Energy-Constrained Partial Offloading in Data Processing Unit (DPU)-Enabled Mobile Edge computing 19

Energy-Constrained Partial Offloading in Data Processing Uni...

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作者： Liu, Jinyu Lin, Tianhao Zhang, Yi Li, Yalong Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210094 China

ISBN: (纸本)9798350346558

As a dedicated data-centric processor, data processing unit (DPU) can release central processing unit (CPU) from the heavy computation load of data-intensive applications such as data encryption and decryption. In mobile edge computing (MEC), the integration of DPU structure into the MEC server can significantly reduce the processing delay of security-critical tasks. this paper studies the partial offloading problem oriented toward a DPU-enabled MEC system, in which the DPUs are responsible for all computation burden of data encryption/decryption. the objective is to minimize the completion time of security-critical tasks under an energy constraint. We introduce a new EDA-based metaheuristic (NEDA) to solve the resultant optimization problem. By using a task sequence to represent an offloading solution, this algorithm employs a task dispatching strategy to make partial offloading decision for each task in the sequence such that the solution quality can be evaluated. To enhance NEDA's solution exploration capability, we develop a new population updating mechanism that relies on the current best solution and a probability matrix to generate a new population of high-quality solutions. Experimental results demonstrate NEDA's effectiveness in solving the partial offloading problem in terms of task completion time. © 2022 IEEE.

关键词： Mobile edge computing

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QiMINT: Quantum-Inspired Mobile Intelligence - Advancing Complex Signal Processing with Machine Learning

QiMINT: Quantum-Inspired Mobile Intelligence - Advancing Com...

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international conference on Trends in Electronics and Informatics (ICEI)

作者： Vinothkumar Kolluru E. Helen Parimala Kokilavani T Sunil Raj Y Bastin Jesuraj Afrig Aminuddin Department of Data Science Stevens Institute of Technology NJ USA Department of Computer Science Gitam Deemed to be University Bangalore India Department of Computer Science School of Sciences Christ (Deemed) University Bangalore India Department of Data Science St. Xavier's College (Autonomous) Palayamkottai India Department of Computer Science Mother Theresa Arts and Science College Theni Tamilnadu India Department of Information System Faculty of Computer Science Universitas Amikom Yogyakarta Sleman Indonesia

ISBN: (数字)9798331544607

ISBN: (纸本)9798331544614

Integrating mobile intelligence with quantum-inspired machine learning (QiML) opens space for challenging mobile signal processing tasks. By utilizing superposition and entanglement, quantum computing principles, QiML boosts the agility of mobile devices by allowing real-time data processing, pattern recognition, and decision making. this work introduces Quantum-Inspired Mobile Intelligence (QiMINT) a new mobile computing framework that integrates quantum-inspired designs with classical machine learning, which increases mobile devices' accuracy, latency, and energy efficiency. Results show that medical QiML based models exceeded traditional machine learning methods in most key performance indicators. the quantum convolutional neural network (QCNN) achieved an accuracy of 92.3% in contrast with the CNNs' 87.5%, but with a processing time of 80 ms as to 120 ms, energy consumption of 10 mJ in comparison to the CNNs' 15mJ. Also, quantum-inspired random forests lowered processing delay by 40%, sustaining superior accuracy than the classical-base system. these results demonstrate that QiML can effectively balance computation complexity while making it suitable for edge computing, IoT, and mobile intelligence systems.

关键词： Quantum computing Accuracy Decision making Machine learning Signal processing Real-time systems Mobile handsets pattern recognition Optimization Edge computing

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An Unsupervised Person Search Method for Video Surveillance 22

An Unsupervised Person Search Method for Video Surveillance

引用

8th international conference on computing and Artificial Intelligence, ICCAI 2022

作者： Feng, Deying Yang, Jie Wei, Yanxia Xiao, Hairong Zhang, Laigang School of Mechanical and Automotive Engineering Liaocheng University China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Liaocheng University China

ISBN: (纸本)9781450396110

We propose an unsupervised person search method for video surveillance. this method considers both the spatial features of persons within each frame and the temporal relationship of the same person among different frames. thus, the spatial features are extracted by region convolutional neural network, and the temporal relationship is organized by gate recurrent unit. the spatio-temporal features are generated by the following average pooling layer and indexed by locality sensitive hashing. A surveillance video database is constructed to evaluate the proposed method, and the experimental results demonstrate that our method improves the search accuracy by utilizing the spatio-temporal features. © 2022 ACM.

关键词： Monitoring

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Joint computing Resource Scheduling and Task Priority Selection in UAV-Enabled MEC 19

Joint Computing Resource Scheduling and Task Priority Select...

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作者： Xu, Tieniu Yu, Zhiwen Song, Yongbo Ren, Jiaju Cui, Helei Guo, Bin Northwestern Polytechnical University ShaanXi Xi'an China

ISBN: (纸本)9798350346558

Owing to the outstanding characteristics of unmanned aerial vehicles (UAV), i.e., mobility and flexibility, UAV-enabled mobile edge computing (MEC) has become a widely attractive research direction. this paper studies the computing offload problem of multi UAV auxiliary terminal devices (TDs), in which multiple UAVs equipped with computing resources help the terminal device with limited local computing resources and energy to accomplish computing tasks. there are three computational strategies for the task of terminal devices. Firstly, each terminal device computes the task by itself. Secondly, each task can be offloaded to an UAV for computation. thirdly, each task can be offloaded to nearby base stations (BS) for computation through the UAV relay. In this paper, we propose a partial offloading method and determine the task segmentation threshold. Due to the difference in computing power between heterogeneous UAVs and terminal devices, different tasks have different requirements for the time delay. the research problem is a large-scale nonlinear and complex problem, which is challenging to solve. therefore, the terminal device is divided into different regions. then we assign the UAV with strong computing power to the regions with weak computing power to achieve the complementarity of computing power. In addition, we prioritize the tasks of the end devices. Based on this, we propose the MST algorithm. Simulation results prove that our scheme significantly outperforms the baseline approach in terms of task completion time and compared to the SCA method, Our proposed approach improves performance by 13% on large-scale tasks. © 2022 IEEE.

关键词： Mobile edge computing

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Graph Neural Network Session Recommendation Algorithm Based on Semantic Knowledge and Temporal Encoding 19

Graph Neural Network Session Recommendation Algorithm Based ...

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作者： Chai, Huihui Jiang, Xuesong Wei, Xiumei Li, Yihong Computer Science and Technology shandong Province Jinan city China Queen's University Art and Science-Computing Kingston cityON Canada

ISBN: (纸本)9798350346558

Session-based recommendation aims to match users with corresponding resources, and has been widely used in streaming media, e-commerce and so on. At present, the most widely used session-based graph neural network recommendation model is to construct a session sequence into a session graph as the input of the graph neural network, and use the target attention network to capture the specific user interests associated with the target item, and use the self-attention mechanism to obtain the long-term interest preferences of the user. Although this model has achieved good results, it also has some limitations. (1) It only constructs the session sequence into a session graph, resulting in a single form of constructing the session graph. (2) It is too one-sided to recommend users only according to their session behavior. In order to solve the above problems, this paper proposes a new recommendation algorithm -Graph Neural Network Recommendation Algorithm (KT-GNN) based on semantic knowledge and temporal encoding. First, we introduce dwell time to the session graph and encode the dwell time to solve the session graph single problem. At the same time, the dwell time can reflect the user's preference for the item and improve the efficiency of recommendation. Second, PairRE knowledge translation model is used to process users' semantic knowledge and mine users' semantic preferences. Extensive experiments have demonstrated the superiority of our model on both Yoochoose and TFCD datasets. © 2022 IEEE.

关键词： Graph neural networks

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Effective task offloading heuristics for minimizing energy consumption in edge computing 15

Effective task offloading heuristics for minimizing energy c...

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2022 IEEE Congress on Cybermatics: 15th IEEE international conferences on Internet of things, ithings 2022, 18th IEEE international conferences on Green computing and Communications, GreenCom 2022, 2022 IEEE international conference on Cyber, Physical and Social computing, CPSCom 2022 and 8th IEEE international conference on Smart Data, SmartData 2022

作者： Yin, Guichang Chen, Runze Zhang, Yi Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210094 China

ISBN: (数字)9781665454179

ISBN: (纸本)9781665454179

Mobile edge computing (MEC) allows user devices to offload computing tasks to edge servers, which are deployed close to users, for fast execution, short delay and energy saving. As a result, user devices save energy, but edge servers consume tremendous amount of energy. Accordingly, besides regarding the amount of energy consumed by user devices, it is also necessary to take into account that of energy consumed by edge servers. In this paper, we propose effective heuristics for offloading tasks from multi-user devices to multi-core edge servers with the objective of minimizing the total amount of energy consumption under the deadline constraint. the proposed heuristics start from initial task sequences that are generated by the well-known long and short task first rules, respectively. then, tasks from the initial task sequences are scheduled in a greedy manner so that the considered total amount of energy consumption can be minimized without the violation of the deadline constraint. Simulation experimental results show that the proposed algorithms significantly outperform the compared baseline RoundRobin, indicating the good effectiveness of our heuristics. © 2022 IEEE.

关键词： Energy utilization

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