This work proposes a personalized mobile learning system using smart glasses which include outward and inward facing cameras. By using the outward facing camera, the proposed system recognizes the QR code, and then di...
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ISBN:
(纸本)9783030004101;9783030004095
This work proposes a personalized mobile learning system using smart glasses which include outward and inward facing cameras. By using the outward facing camera, the proposed system recognizes the QR code, and then discovers the front view of a wearer. Additionally, our system employs an inward facing camera to capture eye images, find out the centers of irises, and then derive visual focal points. According to the exhibit of high interest, the audiovisual clips associated with the baseball background knowledge and stories were designed for learners visiting the baseball museum. The experimental results reveal that the proposed system can achieve a view angel deviation below 3.20 degrees, and identify the 13.5 cm x 13.5 cm QR code at a distance of 2.3 m and a view angle of 40 degrees. Therefore, the personalized mobile learning system proposed herein effectively provides learners with attention tracking, interest cultivation, and immersive engagement.
作者:
Dong CaoRan HeZhenan SunTieniu TanCASIA
National Laboratory of Pattern Recognition CASIA
Center for Research on Intelligent Perception and Computing CAS
Center for Excellence in Brain Science and Intelligence Technology
Popularity of surveillance and mobile cameras provides great opportunities to video-based face recognition (VFR) in less-controlled conditions. This paper proposes a joint space learning method to simultaneously ident...
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ISBN:
(纸本)9781479961016
Popularity of surveillance and mobile cameras provides great opportunities to video-based face recognition (VFR) in less-controlled conditions. This paper proposes a joint space learning method to simultaneously identify the most representative samples and discriminative features from facial videos for reliable face recognition. Specifically, we use a mixture modal by learning multiple feature spaces to capture the data variations where the representative samples in each subspace are learned. Actually, this procedure is a chick to egg problem and an alternate algorithm is developed to monotonically optimize the joint task. In addition, randomized techniques are applied to kernel approximations for capturing the nonlinear structure in data, so that both accuracy and efficiency of our method can be improved. The proposed method performs better than the state-of-the-art video based face recognition methods on Honda, Mobo and YouTube Celebrities databases.
The development of societies of human and machine agents should benefit from an understanding of human group decision processes. Political Scientist and Professor, Bruce Bueno De Mesquita has made significant claims f...
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ISBN:
(纸本)9789898425409
The development of societies of human and machine agents should benefit from an understanding of human group decision processes. Political Scientist and Professor, Bruce Bueno De Mesquita has made significant claims for the predictive accuracy of his computational model of group decision making, receiving much popular press including newspaper articles, books and a television documentary entitled "The New Nostradamus". Despite these and many journal and conference publications related to the topic, no clear elicitation of the model exists in the open literature. We expose and present the model by careful navigation of the literature and illustrate the soundness of our interpretation by replicating De Mesquita's own results. We also discuss concerns regarding model sensitivity and convergence.
We present the design and implementation of a card game architecture for mulit-user interactive tabletop surfaces. Our system is built on the DiamondTouch, a touch-sensitive input surface that allows several users to ...
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The internationalconference on Intelligent Computing (ICIC) was formed to provide an annual forum dedicated to the emerging and challenging topics in artificial intel- gence, machine learning, patternrecognition, im...
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ISBN:
(数字)9783642148316
ISBN:
(纸本)9783642148309
The internationalconference on Intelligent Computing (ICIC) was formed to provide an annual forum dedicated to the emerging and challenging topics in artificial intel- gence, machine learning, patternrecognition, image processing, bioinformatics, and computational biology. It aims to bring together researchers and practitioners from both academia and industry to share ideas, problems, and solutions related to the m- tifaceted aspects of intelligent computing. ICIC 2010, held in Changsha, China, August 18-21, 2010, constituted the 6th - ternational conference on Intelligent Computing. It built upon the success of ICIC 2009, ICIC 2008, ICIC 2007, ICIC 2006, and ICIC 2005, that were held in Ulsan, Korea, Shanghai, Qingdao, Kunming and Hefei, China, respectively. This year, the conference concentrated mainly on the theories and methodologies as well as the emerging applications of intelligent computing. Its aim was to unify the picture of contemporary intelligent computing techniques as an integral concept that highlights the trends in advanced computational intelligence and bridges theoretical research with applications. Therefore, the theme for this conference was “Advanced Intelligent Computing Technology and Applications.” Papers focusing on this theme were solicited, addressing theories, methodologies, and applications in science and technology.
The proceedings contain 43 papers. The special focus in this conference is on Biomedical and Health Informatics. The topics include: Towards harmonized data processing in SMBG;notarization of knowledge retrieval from ...
ISBN:
(纸本)9789811074189
The proceedings contain 43 papers. The special focus in this conference is on Biomedical and Health Informatics. The topics include: Towards harmonized data processing in SMBG;notarization of knowledge retrieval from biomedical repositories using blockchain technology;Gap analysis for information security in interoperable solutions at a systemic level: The KONFIDO approach;Identification of barriers and facilitators for eHealth Acceptance: The KONFIDO study;A personalized cloud-based platform for AAL support to cognitively impaired elderly people;enhanced healthcare system based on mobile communication;Experience of using the WELCOME remote monitoring system on patients with COPD and comorbidities;adipose tissue as a biomarker in data mining predictive models of metabolic pathophysiologies;portable near-infrared spectroscopy for detecting peripheral arterial occlusion;epileptic seizure prediction with stacked auto-encoders: Lessons from the evaluation on a large and collaborative database;physiological monitoring of cold-air stimulated rhinitis;association between SpO2signal characteristics and sleep architecture with insulin resistance in patients with obstructive sleep apnea syndrome;Preprocessing and filtration techniques of BSPM signals in a small-scale study;blood vessel segmentation from microcirculation images;active learning for semi-automated sleep scoring;human fall detection from acceleration measurements using a recurrent neural network;optimal threshold selection for acceleration-based fall detection;camera based real time fall detection using pattern classification;Epileptic seizures classification based on long-term EEG signal wavelet analysis;heartrate variability comparison between electrocardiogram, photoplethysmogram and ballistic pulse waveforms at fiducial points;Deep learning techniques on sparsely sampled multichannel data—Identify deterioration in ICU patients;emotion recognition from haptic touch on android device screens.
The proceedings contain 67 papers. The special focus in this conference is on Next Generation Wired/Wireless Networking. The topics include: Detecting PII Leakage Using DPI and machine Learning in an En...
ISBN:
(纸本)9783031609930
The proceedings contain 67 papers. The special focus in this conference is on Next Generation Wired/Wireless Networking. The topics include: Detecting PII Leakage Using DPI and machine Learning in an Enterprise Environment;deep Learning for Preventing Botnet Attacks on IoT;Design of Multi-polarisation MIMO Antenna for Heterogeneous Smartphone Applications;potential Possibilities of Voice patternrecognition by a Distributed Fiber Optic Sensor;Enhancing Wireless Connectivity Through Bayesian-Optimized UAV-BS Positioning and Charging;comparison of Centralized and Federated machine Learning Techniques for Beamtracking in 5G/6G Systems;exploring the Efficiencies and Vulnerabilities of Smart Door Control Systems: A Systematic Review;System-Level Model for SINR and HPBW Evaluation in 5G mmWave UDN with Location-Aware Beamforming;reinforcement Learning Based Power Allocation for 6G Heterogenous Networks;The Impact of Capacity Averaging in Packet-Level Modeling of 5G NR with Blockage and Micromobility;Interference Mitigation for Reconfigurable Intelligent Surface (RIS)-Aided Non-terrestrial Base Station (NTBS) in NOMA Downlink HetNets;modeling a Digital Avatar of a Car Drivers Based on the Quantification of the Information Environment;A New Blockage Detection Approach for 6G THz Systems;real-Time Anomaly Detection in Network Traffic Using Graph Neural Networks and Random Forest;addressing Security and Privacy Issues in a Smart Environment by Using Blockchain as a Preemptive Technique;the Role of Cryptocurrencies in the Development of Countries and the Fight Against Financial Problems;Designing the UzBCS Lending Platform Network Based on Blockchain Technology and Ensure Transaction Security;decentralized Blockchain Networks and Economic Security: Balancing Scalability and Security Tradeoffs;study of machine Learning Models for IoT Based Efficient Classroom Usage;transforming Higher Education: A Comprehensive Analysis of Blockchain Technologies and Digitalization.
The paper describes an object-oriented implementation of the algorithm advanced by Wang and Mendel (1992). Numerical results are presented both for time series with seasonal changes and time series corresponding to ch...
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The paper describes an object-oriented implementation of the algorithm advanced by Wang and Mendel (1992). Numerical results are presented both for time series with seasonal changes and time series corresponding to chaotic situations, such as encountered in the context of strange attractors. In the latter case, the effect of noise on predictive power of the fuzzy controller are explored. In addition, by introducing a distance between an observed and predicted data, one can apply the results of this study to a patternrecognition of temporal signatures.< >
This book constitutes the refereed post-conference proceedings of the 8th international Workshop on machine Learning and Data Mining for Sports Analytics, MLSA 2021, held as virtual event in September 2021.;The 12 ful...
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ISBN:
(数字)9783031020445
ISBN:
(纸本)9783031020438
This book constitutes the refereed post-conference proceedings of the 8th international Workshop on machine Learning and Data Mining for Sports Analytics, MLSA 2021, held as virtual event in September 2021.;The 12 full papers and 4 short papers presented were carefully reviewed and selected from 29 submissions. The papers present a variety of topics within the area of sports analytics, including tactical analysis, outcome predictions, data acquisition, performance optimization, and player evaluation.
Nowadays copy-move attack is one of the most obvious ways of digital image forgery in order to hide the information contained in images. Copy-move process consists of copying the fragment from one place of an image, c...
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ISBN:
(纸本)9783319415017;9783319415000
Nowadays copy-move attack is one of the most obvious ways of digital image forgery in order to hide the information contained in images. Copy-move process consists of copying the fragment from one place of an image, changing it and pasting it to another place of the same image. However, only a few existing studies reached high detection accuracy for a narrow range of transform parameters. In this paper, we propose a copy-move detection algorithm that uses features based on binary gradient contours that are robust to contrast enhancement, additive noise and JPEG compression. The proposed solution showed high detection accuracy and the results are supported by conducted experiments for wide ranges of transform parameters. A comparison of features based on binary gradient contours and based on various forms of local binary patterns showed a significant 20-30 % difference in detection accuracy, corresponding to an improvement with the proposed solution.
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