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DisCANTree: A Distributed Algorithm for Incremental Frequent Itemset Mining based on MapReduce

Journal of Physics: Conference Series 2020年第1期1682卷

作者： Wen Xiao Juan Hu Key Laboratory of Unmanned Aerial Vehicle Development & Data Application of Anhui Higher Education Institutes Wanjiang University of Technology Maanshan 243000 China Ma'anshan Engineering Technology Research Center for Wireless Sensor Network and IntelliSense Wanjiang University of Technology Maanshan 243000 China

Frequent itemset mining is one of the most important data mining tasks. Classical frequent itemset mining algorithms need to store data in a centralized way and run in a batch way, which cannot meet the requirements of fast updating big data mining. In this paper, we propose a distributed incremental frequent itemset mining algorithm, DisCANTree, which uses CANTree to store the conditional database, achieves the load balance between nodes by grouping all items, updates the new transaction to the existing CANTree to avoid the load of tree reconstruction, and uses the efficient FPGrowth algorithm to mine CANTree to generate frequent itemsets. The popular distributed programming model MapReduce and its open source system Hadoop are used to implement the DisCANTree algorithm. The experimental results show that the DisCANTree algorithm has more advantages than the most popular PFP algorithm in performance as well as the number of transferred records between nodes, and especially suits for the fast updating sparse big data.

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Micro-Expression Recognition based on 2D Gabor Filter and Sparse Representation

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Journal of Physics: Conference Series 2017年第1期787卷

作者： Hao Zheng Key Laboratory of Trusted Cloud Computing and Big Data Analysis School of Information Engineering Nanjing XiaoZhuang University Nanjing China MOE Key Laboratory of Computer Network and Information Integration School of Computer Science and Engineering Southeast University Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

Micro-expression recognition is always a challenging problem for its quick facial expression. This paper proposed a novel method named 2D Gabor filter and Sparse Representation (2DGSR) to deal with the recognition of micro-expression. In our method, 2D Gabor filter is used for enhancing the robustness of the variations due to increasing the discrimination power. While the sparse representation is applied to deal with the subtlety, and cast recognition as a sparse approximation problem. We compare our method to other popular methods in three spontaneous micro-expression recognition databases. The results show that our method has more excellent performance than other methods.

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Microfiber Assisted Bidirectional Wavelength-Locked Ultrafast Erbium-Doped Fiber Laser

SSRN

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SSRN 2022年

作者： Mou, Chengbo Dai, Lilong Huang, Zinan Huang, Qianqian AlAraimi, Mohammed Rozhin, Aleksey Liang, Xindong Key Lab of Specialty Fiber Optics and Optical Access Network Shanghai Institute for Advanced Communication and Data Science Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication Shanghai University Shanghai200444 China Department of Engineering University of Technology and Applied Sciences Higher College of Tech-nology PO Box 74 Al-Khuwair Postal Code 133 Oman Aston Institute of Photonic Technologies Aston University BirminghamB4 7ET United Kingdom Key Laboratory of Gravitational Wave Precision Measurement of Zhejiang Province Hangzhou Institute for Advanced Study UCAS Hangzhou310024 China Zhejiang Lab Hangzhou311100 China

Bidirectional mode-locked fiber lasers are promising media to implement intracavity phase interferometry (IPI) due to the ability to produce ultrashort pulses in a compact design. A typical bidirectional ultrafast fiber laser will generate counter-propagating pulses with different repetition rates due to the asymmetric constructure. In this paper, by using the filtering effect of microfiber, an asymmetric bidirectional fiber laser that emits two pulses with same operation wavelength is demonstrated. The utilization of microfiber in locking output wavelength from two directions may directly simplify the design of laser cavity suitable for IPI applications. Moreover, continuous wavelength tuning in a single-cavity bidirectional ultrafast fiber laser has been realized for the first time by applying strain on the microfiber. In the clockwise (CW) direction, wavelength tuning ranges from 1558.95 nm to 1567.3 nm and from 1558.93 nm to 1567.25 nm in the counter clockwise (CCW) direction. The counter-circulating pulses maintain spectral locking state during the whole wavelength tuning process with a good linear sensitivity of 0.2 nm/µm. Such features may further expand the scope and field of fiber lasers for sensing. © 2022, The Authors. All rights reserved.

关键词： Carbon nanotubes

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AI exposure predicts unemployment risk

arXiv

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arXiv 2023年

作者： Frank, Morgan R. Ahn, Yong-Yeol Moro, Esteban Department of Informatics and Networked Systems University of Pittsburgh PittsburghPA15216 United States Digital Economy Lab Institute for Human-Centered Artificial Intelligence Stanford University StanfordCA94305 United States Media Laboratory Massachusetts Institute of Technology CambridgeMA02139 United States Connection Science Massachusetts Institute of Technology CambridgeMA United States Center for Complex Networks and Systems Research Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN United States Indiana University Network Science Institute Indiana University BloomingtonIN United States Department of Mathematics & GISC Universidad Carlos III de Madrid Leganes28911 Spain Institute for Data Systems and Society Massachusetts Institute of Technology CambridgeMA United States

Is artificial intelligence (AI) disrupting jobs and creating unemployment? Despite many attempts to quantify occupations' exposure to AI, inconsistent validation obfuscates the relative benefits of each approach. A lack of disaggregated labor outcome data, including unemployment data, further exacerbates the issue. Here, we assess which models of AI exposure predict job separations and unemployment risk using new occupation-level unemployment data by occupation from each US state's unemployment insurance office spanning 2010 through 2020. Although these AI exposure scores have been used by governments and industry, we find that individual AI exposure models are not predictive of unemployment rates, unemployment risk, or job separation rates. However, an ensemble of those models exhibits substantial predictive power suggesting that competing models may capture different aspects of AI exposure that collectively account for AI's variable impact across occupations, regions, and time. Our results also call for dynamic, context-aware, and validated methods for assessing AI exposure. Interactive visualizations for this study are available at https://***/~mrfrank/uiRiskDemo/. © 2023, CC BY.

关键词： Visualization

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Revisiting Cloud Migration: Strategies and Methods

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Journal of Physics: Conference Series 2020年第1期1575卷

作者： Zhixiao Wang Wenyao Yan Wendong Wang School of Computer Science and Engineering Xi'an University of Technology Xi'an 710048 China Shaanxi Key Laboratory Xi'an of Network Computing and Security Technology China School of Data Science and Computer Xi'an innovation college of Yan'an University Xi'an 710100 China Yan'an University Xi'an 716000 China

Cloud computing attracts users with its advantage of unlimited resource supply where resources can be elastically expanded on demand and balanced-load at the same time. This means that the application in the cloud environment should run in the way of elastic expansion cluster. At present, most of the elastic expansion and load balancing technologies provided by IaaS level are oriented to virtual machines (VM), with little consideration of application level, which does not fundamentally meet the needs of users. Based on this, starting from the motivation of cloud migration, we first analyse the cloud migration technology from three aspects: migration object, migration means and migration objectives, then explore the cloud migration criteria and four cloud migration strategies at the application level in the cloud environment, and point out that cloud migration should be optimized and maintained in terms of elastic expansion, load balancing, security, etc.

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Deep learning for depression recognition with audiovisual cues: A review

arXiv

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arXiv 2021年

作者： He, Lang Niu, Mingyue Tiwari, Prayag Marttinen, Pekka Su, Rui Jiang, Jiewei Guo, Chenguang Wang, Hongyu Ding, Songtao Wang, Zhongmin Pan, Xiaoying Dang, Wei School of Computer Science and Technology Xi'an University of Posts and Telecommunications Shaanxi Xi'an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Shaanxi Xi'an710121 China Beijing100190 China Beijing100049 China Department of Computer Science Aalto University Espoo Finland School of Foreign Languages Northwest University Shaanxi Xi'an China School of Electronic Engineering Xi'an University of Posts and Telecommunications Xi'an China School of Electronics and Information Northwestern Polytechnical University Shaanxi Xi'an China Shaanxi Mental Health Center Shaanxi Xi'an710061 China

With the acceleration of the pace of work and life, people have to face more and more pressure, which increases the possibility of suffering from depression. However, many patients may fail to get a timely diagnosis due to the serious imbalance in the doctor-patient ratio in the world. Promisingly, physiological and psychological studies have indicated some differences in speech and facial expression between patients with depression and healthy individuals. Consequently, to improve current medical care, many scholars have used deep learning to extract a representation of depression cues in audio and video for automatic depression detection. To sort out and summarize these works, this review introduces the databases and describes objective markers for automatic depression estimation (ADE). Furthermore, we review the deep learning methods for automatic depression detection to extract the representation of depression from audio and video. Finally, this paper discusses challenges and promising directions related to automatic diagnosing of depression using deep learning technologies. © 2021, CC BY.

关键词： Diagnosis

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A Novel Method for ECG Signal Classification via One-Dimensional Convolutional Neural network

arXiv

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arXiv 2020年

作者： Hua, Xuan Han, Jungang Zhao, Chen Tang, Haipeng He, Zhuo Tang, Jinshan Chen, Qing-Hui Tang, Shaojie Zhou, Weihua College of Electronic Engineering Xi’an University of Posts and Telecommunications Xi’an710121 China Key Laboratory of Network Data Analysis and Intelligent Processing in Shaanxi Xi’an710121 China College of Computer Science and Technology Xi’an University of Posts and Telecommunications Xi’an710121 China College of Computing Michigan Technological University HoughtonMI49931 United States School of Computing University of Southern Mississippi Long Beach39560 United States Deprtment of Kinesiology and Integrative Physiology Michigan Technological University HoughtonMI49931 United States School of Automation Xi’an University of Posts and Telecommunications Xi’an710121 China

This paper presents an end-to-end ECG signal classification method based on a novel segmentation strategy via 1D Convolutional Neural networks (CNN) to aid the classification of ECG signals. The ECG segmentation strategy named R-R-R strategy (i.e., retaining ECG data between the R peaks just before and after the current R peak) for segmenting the original ECG data into segments in order to train and test the 1D CNN models. The novel strategy mimics physicians in scanning ECG to a greater extent, and maximizes the inherent information of ECG segments. The performance of the classification models for 5-class and 6-class are verified with ECG signals from 48 records of the MIT-BIH arrhythmia database. As the heartbeat types are divided into 5 classes (i.e., normal beat, left bundle branch block beat, right bundle branch block beat, ventricular ectopic beat, and paced beat) in the MIT-BIH, the best classification accuracy, the area under the curve (AUC), the sensitivity and the F1-score reach 99.24%, 0.9994, 0.99 and 0.99, respectively. As the heartbeat types are divided into 6 classes (i.e., normal beat, left bundle branch block beat, right bundle branch block beat, ventricular ectopic beat, paced beat and other beats) in the MIT-BIH, the beat classification accuracy, the AUC, the sensitivity, and the F1-score reach 97.02%, 0.9966, 0.97, and 0.97, respectively. Meanwhile, according to the recommended practice from the Association for Advancement of Medical Instrumentation (AAMI), the heartbeat types are divided into 5 classes (i.e., normal beat, supraventricular ectopic beats, ventricular ectopic beats, fusion beats, and unclassifiable beats), the beat classification accuracy, the sensitivity, and the F1-score reach 97.45%, 0.97, and 0.97, respectively. The experimental results show that the proposed method achieves better performance than the state-of-the-art methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Support vector machines

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Early environmental quality and life-course mental health effects: The Equal-Life project 24

Early environmental quality and life-course mental health ef...

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24th International Congress on Acoustics, ICA 2022

作者： van Kamp, Irene van Wijk, Kim Waye, Kerstin Persson Kanninen, Katja Gulliver, John Bolte, Gabriele Bozzon, Alessandro Psyllidis, Achilleas Boshuizen, Hendriek Selander, Jenny van den Hazel, Peter Brambilla, Marco Balduini, Marco Foraster, Maria Klatte, Maria Spilski, Jan Jeram, Sonja Botteldooren, Dick Lercher, Peter Ristovska, Gordana Kaprio, Jaakko Schreckenberg, Dirk Hornikx, Maarten Fels, Janina Weber, Miriam Braat-Eggen, Ella Calvo, Jordi Julvez Clark, Charlotte Vrijkotte, Tanja Centre for Sustainability Environment and Health National Institute for Public Health and the Environment Bilthoven Netherlands School of Public Health and community medicine Gothenburg University Gothenburg Sweden A.I.Virtanen Institute for Molecular Sciences University of Eastern Finland Kuopio Finland Centre for Environmental Health and Sustainability School of Geography Geology and the Environment University of Leicester United Kingdom Institute of Public Health and Nursing Research University of Bremen Bremen Germany Faculty of Industrial Design Engineering Delft University of Technology Delft Netherlands Centre for Nutrition Prevention and Health Services National Institute for Public Health and the Environment Bilthoven Netherlands Unit of Occupational Medicine The Institute of Environmental Medicine Karolinska Institutet Stockholm Sweden International Network on Children's Health Environment and Safety xx Netherlands Data Science Laboratory Politecnico di Milano Milan Italy Barcelona Institute for Global Health ISGlobal Barcelona Spain Department of Cognitive and Developmental Psychology Technical University Kaiserslautern Germany National Institute of Public Health Ljubljana Slovenia Department of Information Technology Faculty of Engineering and Architecture Ghent University Ghent Belgium Institute for Highway Engineering and Transport Planning Graz University of Technology Graz Austria Institute of Public Health of the Republic of North Macedonia Skopje Macedonia Institute for Molecular Medicine Finland Department of Public Health University of Helsinki Helsinki Finland Bochum Germany Built Environment Technical University Eindhoven Eindhoven Netherlands Hearing Technology and Acoustics RWTH Aachen University Aachen Germany City of Utrecht Utrecht Netherlands Avans University of applied Science Tilburg Netherlands Barcelona Spain Population Health Research Institute St George's University of London London United Kingdom Department

There is increasing evidence that a complex interplay of factors within children's environments, including environmental noise, contributes to mental ill-health and suboptimal cognitive development later on in life. The concept of the life-course exposome helps to study the impact of the physical (e.g., acoustic) and social environment on cognitive development and mental health in young people over time. Equal-Life develops and tests combined exposures. data from eleven studies (N=240.000) linked to exposure data will provide insights and policy guidance into aspects of physical and social exposures hitherto untapped at different scale levels and timeframes while accounting for social inequities. Exposure assessment combines GIS-based environmental indicators with omics approaches and new data sources, forming the early-life exposome. Statistical tools integrate data at different spatial and temporal granularity and combine exploratory machine learning models with hypothesis-driven causal modeling. Equal-Life contributes to the exposome concept by 1) integrating the internal, physical and social exposomes, 2) studying life-course effects on a child's development and mental health, 3) characterizing the child's environment at different stages and in different activity spaces, 4) looking at supportive environments for child development, rather than merely pollutants, 5) combining physical, social indicators with novel effect markers. © ICA *** rights reserved

关键词： Children Cognitive development Early Life Exposures Exposome Mental health

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Visual Tracking Algorithm Based on Color Name Histogram

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IOP Conference Series: Materials Science and engineering 2020年第1期790卷

作者： Chenchen Yue Wangsheng Yu Zhiqiang Hou Sugang Ma College of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi'an China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Xi'an China Information and Navigation Institute of Air Force Engineering University Xi'an 710077 China

Aimed at the problem that traditional histogram is sensitive to illumination changes in visual tracking, combined with the CN(Color Name) feature, we proposed a new feature(denotes CNH, Color Name Histogram) based on color name. Firstly, the method projected the original RGB image to CN space to obtain robust 11 feature layers. Then, we counted the each pixel numbers of feature layers. Finally, normalizing the amount of pixels in each layer. In addition, we adopted a feature adaptive fusion method to combine CNH and HOG(Histogram of Oriented Gradient). In order to prove validity of the proposed algorithm, we use Staple(Sum of Template And Pixel-wise Learners) algorithm frame to make a controlled trial. In contrast with the reference algorithms, the success of our algorithm increases by 1.5% and the precision increases by 1.7%. The results show that this method retains the advantages of traditional histogram which is insensitive to target deformation, but also enhances the robustness to illumination change.

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Erratum to “ECMER: Edge-Cloud Collaborative Personalized Multimodal Emotion Recognition Framework in the Internet of Vehicles”

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IEEE network 2024年第3期38卷 292-292页

作者： Puning Zhang Miao Fu Rongjian Zhao Dapeng Wu Hongbin Zhang Zhigang Yang Ruyan Wang School of Communication and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China Advanced Network and Intelligent Connection Technology Key Laboratory of Chongqing Education Commission of China and the Chongqing Key Laboratory of Ubiquitous Sensing and Networking Chongqing China Chongqing Innovation Center of Industrial Big-Data Company Ltd. Chongqing China

This addresses errors in [1] . There is incorrect author affiliation information in this article.

关键词： Emotion recognition Collaboration Internet of Vehicles

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