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arXiv 2022年

作者： He, Tongyue Chen, Yiming Chen, Junxin Wang, Wei Zhou, Yicong College of Medicine and Biological Information Engineering Northeastern University Shenyang China School of Software Dalian University of Technology Dalian China School of Intelligent Systems Engineering Sun Yat-sen University Shenzhen China Department of Computer and Information Science University of Macau China

This paper presents a fused deep learning algorithm for ECG classification. It takes advantages of the combined convolutional and recurrent neural network for ECG classification, and the weight allocation capability of attention mechanism. The input ECG signals are firstly segmented and normalized, and then fed into the combined VGG and LSTM network for feature extraction and classification. An attention mechanism (SE block) is embedded into the core network for increasing the weight of important features. Two databases from different sources and devices are employed for performance validation, and the results well demonstrate the effectiveness and robustness of the proposed algorithm. Copyright © 2022, The Authors. All rights reserved.

关键词： Electrocardiograms

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Big networks: A survey

arXiv

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arXiv 2020年

作者： Bedru, Hayat Dino Yu, Shuo Xiao, Xinru Zhang, Da Wan, Liangtian Guo, He Xiaa, Feng Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province School of Software Dalian University of Technology Dalian116620 China Department of Electrical and Computer Engineering University of Miami United States School of Science Engineering and Information Technology Federation University Australia Australia

A network is a typical expressive form of representing complex systems in terms of vertices and links, in which the pattern of interactions amongst components of the network is intricate. The network can be static that does not change over time or dynamic that evolves through time. The complication of network analysis is different under the new circumstance of network size explosive increasing. In this paper, we introduce a new network science concept called big network. Big networks are generally in large-scale with a complicated and higher-order inner structure. This paper proposes a guideline framework that gives an insight into the major topics in the area of network science from the viewpoint of a big network. We first introduce the structural characteristics of big networks from three levels, which are micro-level, meso-level, and macro-level. We then discuss some state-of-the-art advanced topics of big network analysis. Big network models and related approaches, including ranking methods, partition approaches, as well as network embedding algorithms are systematically introduced. Some typical applications in big networks are then reviewed, such as community detection, link prediction, recommendation, etc. Moreover, we also pinpoint some critical open issues that need to be investigated further. Copyright © 2020, The Authors. All rights reserved.

关键词： Complex networks

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A Hierarchical Image Annotation Method Based on SVM and Semi-supervised EM

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自动化学报 2010年第7期36卷 960-967页

作者： GAO Yan-Yu YIN Yi-Xin UOZUMI Takashi School of Information Engineering University of Science and Technology Beijing Beijing 100083 P.R.China Key Laboratory of Advanced Control of Iron and Steel Process (Ministry of Education) University of Science and Technology Beijing Beijing 100083 P. R. China Department of Computer Science and Systems Engineering Muroran Institute of Technology Muroran 050- 8585 Japan

Automatic image annotation, which aims at automatically identifying and then assigning semantic keywords to the meaningful objects in a digital image, is not a very difficult task for human but has been regarded as a difficult and challenging problem to machines. In this paper, we present a hierarchical annotation scheme considering that generally human s visual identification to a scenery object is a rough-to-fine hierarchical process. First, the input image is segmented into multiple regions and each segmented region is roughly labeled with a general keyword using the multi-classification support vector machine. Since the results of rough annotation affect fine annotation directly, we construct the statistical contextual relationship to revise the improper labels and improve the accuracy of rough annotation. To obtain reasonable fine annotation for those roughly classified regions, we propose an active semi-supervised expectation-maximization algorithm, which can not only find the representative pattern of each fine class but also classify the roughly labeled regions into corresponded fine classes. Finally, the contextual relationship is applied again to revise the improper fine labels. To illustrate the effectiveness of the presented approaches, a prototype image annotation system is developed, the preliminary results of which showed that the hierarchical annotation scheme is effective.

关键词：数字图像自动化系统准确性意义图像标注

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An investigation into the PNGV battery model with the addition of a dynamic temperature range

An investigation into the PNGV battery model with the additi...

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IEEE Southeastcon

作者： Kenneth Leslie Ilteris Demirkiran Eric Rask Henning Lohse-Busch Electrical Computer Software and Systems Engineering Embry-Riddle Aeronautical University Daytona Beach FL USA Argonne National Laboratory Advanced Powertrain Research Facility Argonne IL USA

The Partnership for Next Generation-Vehicles Hybrid Pulse Power Characterization (HPPC) battery model was used to develop a base-line estimation. Within the HPPC is a method to establish an estimated open circuit voltage (VOC) from a linear calculation of internal resistances and currents. The flexibility of the parameterization was tested over a changing temperature range. Algorithms were developed that predict the VOC and state of charge (SOC) based on the battery's temperature, current draw, terminal voltage, and sampling time step; therefore, increasing the accuracy of the battery parameterization estimator (BPE). Three production vehicles with lithium based battery chemistries were used in the study. A least square method is used as in the PNGV battery model to determine initial parameters; an improvement was shown over this base-line by using a non-linear algorithm. The estimates from both algorithms were compared to the measured data as verification.

关键词： System-on-chip Batteries Temperature measurement Mathematical model Schedules Resistance Vehicles

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Enhancing fairness: A novel C2C e-commerce community member reputation algorithm based on transaction social networks

Enhancing fairness: A novel C2C e-commerce community member ...

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IADIS International Conference WWW/Internet 2010

作者： Feng, Wangsen Zhang, Bei Xia, Yunni Li, Jiajing Meng, Tao Key Laboratory of Network and Software Security Assurance of Ministry of Education Computing Center Peking University Beijing 100871 China College of Computer Science Chongqing University Chongqing 400044 China School of Mechanical Electronic and Information Engineering China University of Mining and Technology Beijing 100083 China Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China

ISBN: (纸本)9789728939250

Today, almost all the C2C e-commerce community member reputation evaluating algorithms are time sensitive. The reputation of a community member is accumulated after transactions and the increment of reputation after a successful transaction is computed based on the score given by his or her transaction partner. Since people always tend to do business with those with high reputation value, the reputation of members joined in C2C community at different time will vary greatly although they may provide the same commodity quality and service. It is unfair to new comers. In this paper, we propose a novel algorithm to overcome the shortcoming. Based on the transaction social network, our algorithm employs the random walk strategy to compute member reputation and enhance system fairness. © 2010 IADIS.

关键词： Electronic commerce

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Deep Neural Network Enhanced Chip-on-board Hybrid-Packaged Photonic-Electrical Silicon Transceiver for High-speed Optical Interconnects

Deep Neural Network Enhanced Chip-on-board Hybrid-Packaged P...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Qin, Jun Yang, Qipeng Han, Changhao Zhou, Yan Zhang, Yunhao Wu, Yichen Sun, Yu Lu, Junde Liu, Siming Li, Yueqin Sun, Jian Min, Miao Hu, Weiwei He, Zhixue Wang, Lei Yu, Shaohua Shu, Haowen Wang, Xingjun Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument Beijing Information Science and Technology University Beijing100192 China State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Peking University Beijing100871 China Department of Electrical and Computer Engineering University of California Santa BarbaraCA93106 United States Peking University Yangtze Delta Institute of Optoelectronics Nantong226010 China Peng Cheng Laboratory Shenzhen518055 China State Key Laboratory of Information Photonics and Optical Communications BUPT Beijing100876 China

We explore implementing a multilevel deep neural network to enhance the performance of a 4-channel photonic-electrical hybrid-packaged silicon transceiver. Stable transmission and reception of 150 Gbps/λ PAM4 signals... 详细信息

ISBN: (纸本)9781957171395

关键词： Optical interconnects

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A Handwritten Chinese Text Recognizer Applying Multi-level Multimodal Fusion Network

A Handwritten Chinese Text Recognizer Applying Multi-level M...

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International Conference on Document Analysis and Recognition

作者： Yuhuan Xiu Qingqing Wang Hongjian Zhan Man Lan Yue Lu School of Computer Science and Software Engineering East China Normal University Shanghai P.R.China University of Technology Sydney Shanghai Key Laboratory of Multidimensional Information Processing

Handwritten Chinese text recognition (HCTR) has received extensive attention from the community of pattern recognition in the past decades. Most existing deep learning methods consist of two stages, i.e., training a text recognition network on the base of visual information, followed by incorporating language constrains with various language models. Therefore, the inherent linguistic semantic information is often neglected when designing the recognition network. To tackle this problem, in this work, we propose a novel multi-level multimodal fusion network and properly embed it into an attention-based LSTM so that both the visual information and the linguistic semantic information can be fully leveraged when predicting sequential outputs from the feature vectors. Experimental results on the ICDAR-2013 competition dataset demonstrate a comparable result with the state-of-the-art approaches.

关键词： Visualization Semantics Linguistics Convolution Text recognition Feature extraction Hidden Markov models

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Early Experiences with Specifications Grading in Introductory CS Courses

Early Experiences with Specifications Grading in Introductor...

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Frontiers in Education (FIE) Conference

作者： Stephen H. Edwards Manuel A. Pérez-Quiñones Adrienne Decker Bob Edmison Audrey Rorrer Anmol Shukla Computer Science Virginia Tech Blacksburg VA USA Software and Information Systems UNC Charlotte Charlotte NC USA Engineering Education University at Buffalo Buffalo NY USA Computer Science UNC Charlotte Charlotte NC USA

ISBN: (数字)9798350351507

ISBN: (纸本)9798350363067

This innovative practice paper describes our experiences with alternative grading practices in introductory computing courses and two large public universities in the United States. Computing classrooms often use traditional grading practices involving allocating points to assignments, deducting points for mistakes and tardiness, and combining assignment scores using a weighted average to determine grades. Recent research suggests that these practices may diminish achievement, discourage students, and suppress effort to such an extent that they are considered by some as detrimental. We approach our work as an exploratory case study, without predefined research questions or hypotheses. Our experiences began with the adoption of specifications grading. We outline the grading scheme applied to traditional programming assignments and exams/quizzes, and discuss the initial integration of these schemes with conventional auto-grading tools. We delve into student perceptions of alternative grading, their utilization of flexible deadlines, and resubmission opportunities. We conclude with a discussion of two challenges encountered during our exploration: student acceptance of a novel grading form, and the adaptation of tools designed for traditional grading to support alternative grading mechanisms. Our early exploration aims to inspire further research on the use of alternative grading in computing. It is clear from our observations that simply implementing the practices does not ensure the equitable and inclusive outcomes that can be achieved with these practices. If students are not prepared to use these practices, they find them difficult to understand and can feel that they are not being treated fairly. Additionally, we wish to foster a community of practice to assist faculty members exploring these changes, with the goal of creating more equitable and inclusive classrooms.

关键词： Programming profession

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Improving Failure Detection by Automatically Generating Test Cases Near the Boundaries

Improving Failure Detection by Automatically Generating Test...

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IEEE Annual International computer software and Applications Conference (COMPSAC)

作者： Min Zhou Xi Cheng Xinrui Guo Ming Gu Hongyu Zhang Xiaoyu Song School of Software Tsinghua National Laboratory for Information Science and Technology (TNList) Beijing China Microsoft Research Beijing China Electrical and Computer Engineering Portland State University Portland USA

Boundary value analysis is a typical conventional testing technique. However, manually identifying input regions and writing test cases are labor-intensive and time-consuming. In this paper, we propose a search-based random testing approach, which automatically generates test data along the boundaries of semantic regions of the input domain. The experiments on mutated programs confirm the effectiveness and efficiency of the proposed approach. Furthermore, our approach significantly outperforms the conventional ART (Adaptive Random Testing) methods, which sample test cases evenly across the input regions. Our approach also outperforms EvoSuite, a state-of-the-art tool that generates test cases satisfying certain coverage criterion.

关键词： Testing Subspace constraints software Electronic mail Memetics Measurement Semantics

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Modeling Neural Networks Training Process with Markov Decision Process

Modeling Neural Networks Training Process with Markov Decisi...

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Artificial Intelligence and computer engineering (ICAICE), International Conference on

作者： Yantao Bai Wanwei Liu Xinjun Mao Zhen Liang Key Laboratory of Software Engineering for Complex Systems National University of Defense Technology Changsha China College of Computer Science National University of Defense Technology Changsha China State Key Laboratory of High Performance Computing Institute for Quantum Information &#x0026 National University of Defense Technology Changsha China

ISBN: (纸本)9781665421874

With the development of computer technology, statistics-based machine learning method has made great break-throughs, and also improved the development of artificial intelligence. Nevertheless, as a very influential model, neural networks are still treated as “black boxes”. The results of neural networks are extremely sensitive to the training samples, which lead to great challenges to the controllability of the algorithm. With the wide application of machine learning, demand for interpretability and controllability of neural networks algorithms is increasing. As a result, various scholars have tried to explain and verify neural networks algorithms based on formal methods in recent years. In this paper, a method (called MNNTP) is presented to model the training process of neural networks by using a Markov decision process (MDP). Through MNNTP, the neural networks are abstracted into the form of MDP, which makes notable contributions for verifying some mathematical properties of the neural networks.

关键词： Training Machine learning algorithms Computational modeling Neural networks Machine learning Markov processes Controllability

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