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3rd International Conference on Smart Computing and Communications, SmartCom 2018

作者： Cai, Shubin Feng, Xiaogang Deng, Ziwei Ming, Zhong Shan, Zhiguang College of Computer Science and Software Engineering Shenzhen University Shenzhen China State Information Center of China Beijing China

ISBN: (纸本)9783030057541

The changes of stock market and the predictions of the price have become hot topics. When machine learning emerged, it has been used in the stock market forecast research. In recent years, the vertical development of machine learning has led to the emergence of deep learning. Therefore, this paper proposes and realizes the CNN and LSTM forecasting model with financial news and historical data of stock market, which uses deep learning methods to quantify text and mine the laws of stock market changes and analyze whether they can predict changes. According to the results from this paper, this method has certain accuracy in predicting the future changes of the stock market, which provides help to study the inherent laws of stock market changes. © 2018, Springer Nature Switzerland AG.

关键词： Commerce

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Optimization of Neural Network Parameters Based on a Genetic Algorithm for Prediction of Time Series

Optimization of Neural Network Parameters Based on a Genetic...

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International Multi-Conference on Industrial engineering and Modern Technologies (FarEastCon)

作者： V.S. Tormozov A.L. Zolkin K.A. Vasilenko Senior lecturer of the Software engineering for computers and computer-based systems sub-faculty Pacific National University Khabarovsk Tikhookeanskaya St. 136 Ph.D. in Engineering Science Povolzhskiy State University of Telecommunications and Informatics Samara Highest category lecturer Service and design college of the Vladivostok State University of Economics and Service Service and design college of the Vladivostok State University of Economics and Service Vladivostok

ISBN: (数字)9781728169514

ISBN: (纸本)9781728169521

The article proposes a method that allows to solve the complex combinatorial problem of structural optimization of an artificial neural network with a large dimension of the space of optimization parameters using the stochastic mutation function with a constant coefficient of variation. A study on assessment of applicability of this approach to solve the problem is given. This method is proposed to be used for the forecasting of time series values.

关键词： Genetic algorithms Sociology Neurons Biological cells Artificial neural networks Time series analysis Optimization

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Research on arm motion capture of virtual reality based on kinematics 3rd

Research on arm motion capture of virtual reality based on k...

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3rd International Conference on Smart Computing and Communications, SmartCom 2018

作者： Cai, Shubin Deng, Dihui Wen, Jinchun Chen, Chaoqin Ming, Zhong Shan, Zhiguang College of Computer Science and Software Engineering Shenzhen University Shenzhen China State Information Center of China Beijing China

ISBN: (纸本)9783030057541

Virtual reality needs to simulate interaction scenes that are as consistent as possible with reality. Motion capture is the key to address this need. In this paper, a kinematic-based virtual reality arm motion capture scheme is designed on the HTC VIVE platform to achieve low-cost and high-precision motion capture. Based on the human skeleton model, an arm kinematic chain model suitable for VR environment is designed. The above human structure data is redirected to the VR arm to drive the VR arm movement in the virtual environment. Compared with existing motion capture solutions, the experimental results and user survey results show that the method proposed in this paper is able to restore the actual arm movements in virtual reality, showing higher accuracy, and the average satisfaction of the survey object reaches 85%. © 2018, Springer Nature Switzerland AG.

关键词： Kinematics

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ANDROIDOFF:Offloading android application based on cost estimation

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Journal of Systems and software 2019年 158卷 110418-000页

作者： Chen, Xing Chen, Jiaqing Liu, Bichun Ma, Yun Zhang, Ying Zhong, Hao College of Mathematics and Computer Science Fuzhou University Fuzhou350116 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou350116 China School of Software Tsinghua University Beijing100084 China National Engineering Research Center of Software Engineering Peking University China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China

Computation offloading is a promising way of improving the performance and reducing the battery power consumption, since it moves some time-consuming computation activities to nearby servers. Although various approaches have been proposed to support computation offloading, we argue that there is still sufficient space for improvements, since existing approaches cannot accurately estimate the execution costs. As a result, we find that their offloading plans are less optimized. To handle the problem, in this paper, given an Android application, we propose a novel approach, called ANDROIDOFF, that supports offloading at the granularity of objects. Supporting such capability is challenging due to the two reasons: (1) through dynamic execution, it is feasible to collect the execution costs of only partial methods, and (2) it is difficult to accurately estimate the execution costs of the remaining methods. To overcome the challenges, given an Android application, ANDROIDOFF first combines static and dynamic analysis to predict the execution costs of all its methods. After all the costs are estimated, ANDROIDOFF synthesizes an offloading plan, in which determines the offloading details. We evaluate ANDROIDOFF on a real-world application, with two mobile devices. Our results show that, compared with other approaches, ANDROIDOFF saves the response time by 8%–49% and reduces the energy consumption by 12%–49% on average for computation-intensive applications. © 2019 Elsevier Inc.

关键词： computation offloading

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A Modular software System for 3D Measurement of Acetabulum in Arthritic Dysplasia Hips

A Modular Software System for 3D Measurement of Acetabulum i...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Chulan Chen Peihui Wu Linli Zheng Yuanping Liu Xiaoyan Zhang Yukai Song College of Computer Science & Software Engineering Shenzhen University Shenzhen China First Affiliated Hospital of Sun Yat-sen University Guangzhou China

ISBN: (纸本)9781728118680

In order to help orthopedists evaluate the morphological characteristics of the acetabulum of patients with osteoarthritis of the hip more efficiently and accurately to identify the type of acetabular deformities which benefits the personalized preoperative planning, a 3-dimensional (3D) acetabular morphologic parameters measurements software dedicated to the hip was developed. The system includes four modules: 1) Identify the anterior pelvic plane (APP) of the pelvis model; 2) Identify the circular rim of the acetabular wall; 3) Automatically measure the 3D morphological parameters of the dysplastic acetabulum; 4) Interactively measure the 3D morphological parameters of the dysplastic acetabulum. The automatic parameter measurement function of this software could fast and accurately measure the 3D morphological parameters of the dysplastic acetabulum. These automatically measured parameters were close to those measured manually with error generally less than 2mm, and their average measurement time was nearly 10 times faster than that using the Mimics 17.0 system. For patients with large osteophyte, the 3D morphological parameters of the dysplastic acetabulum could be efficiently measured using the interactive measurement function of this software with simple operations. This software was used to measure acetabular morphological parameters in 61 patients. Two types of dysplastic acetabula were identified by the thickness of the medial wall on the lower margin of the acetabulum Tb: type I was a thin acetabulum (35 cases, Tb \leqslant 10.0 mm) and type II was a thick acetabulum (26 cases, mm). According to the result of the acetabular morphological characteristic analysis, it can be found that the thickness of the medial wall is an important morphological characteristic for the THA preoperative surgical planning, and the thickened medial wall could be a misleading factor for the suboptimal placement of the cup.

关键词： Acetabular morphological characteristics anterior pelvic plane three-dimensional measurements dysplastic acetabula hip Acetabulum Hip Dysplastic dimensional measurement Hip Osteoarthritis computer software RIMS Measuring Function

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An Improved GANs Model for Steel Plate Defect Detection

An Improved GANs Model for Steel Plate Defect Detection

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作者： Renjie Tang Keming Mao School of Computer science and engineering Southeast University China Mobile Group Zhejiang Co. Ltd. College of Software Northeastern University

Automatic steel plate defect detection is very important for it can monitor the product *** paper makes a study on steel plate defect detection based on machine *** main difficult is that there is not enough data to make powerful detection *** propose a Generative Adversarial Networks based method to generate synthetic training image.A novel structure is designed with type related variable incorporated in Generator and a classification branch added to *** expanded dataset,two detection algorithm,Faster R-CNN and YOLO are *** model structures,optimization methods,batch sizes and model execution time are evaluated and the influence of parameters are also *** experimental results show that the proposed novel data generation method can effectively improve the model performance.

关键词： image CNN An Improved GANs Model for Steel Plate Defect Detection Model

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CALPA-NET: Channel-pruning-assisted deep residual network for steganalysis of digital images

arXiv

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arXiv 2019年

作者： Tan, Shunquan Wu, Weilong Shao, Zilong Li, Qiushi Li, Bin Huang, Jiwu College of Computer Science and Software Engineering Shenzhen University College of Electronic and Information Engineering Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen 518060 China

Over the past few years, detection performance improvements of deep-learning based steganalyzers have been usually achieved through structure expansion. However, excessive expanded structure results in huge computational cost, storage overheads, and consequently difficulty in training and deployment. In this paper we propose CALPA-NET, a ChAnneL-Pruning-Assisted deep residual network architecture search approach to shrink the network structure of existing vast, over-parameterized deep-learning based steganalyzers. By providing theoretical rationale, we demonstrate that the broad inverted-pyramid structure of existing deep-learning based steganalyzers contradicts the well-established model diversity oriented philosophy and is not suitable for steganalysis. Then a hybrid criterion combined with two network pruning schemes is introduced to adaptively shrink every involved convolutional layer in a data-driven manner. The resulting network architecture presents a slender bottleneck-like structure. We have conducted extensive experiments on BOSSBase+BOWS2 dataset and even a large-scale subset extracted from ImageNet CLS-LOC dataset. The experimental results show that the model structure generated by our proposed CALPA-NET can achieve comparative detection performance with less than two percent of parameters and about one third FLOPs compared to the original steganalytic model. The new model possesses even better adaptivity, transferability, and scalability. Copyright © 2019, The Authors. All rights reserved.

关键词： Steganography

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Multilingual Recognition Based on Neural Network with High Recognition Degree

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Journal of Physics: Conference Series 2020年第5期1549卷

作者： Yanhong Jiang Xuegang Zhan Yixiao Lan University of Science and Technology LiaoNing School of Computer Science and Software Enginerring Anshan China Northeastern University College of Computer Science and Engineering Shenyang China

Language recognition has become an important branch of natural language processing. Due to the interference of out of vocabulary words and the same words in different languages, it has been a challenging task to accurately identify language categories with high similarity. In this paper, N-gram is combined with word2vec to process the data, MLP and CNN models are selected for improvement and experimental analysis. The experimental results show that the data processing and model training of 20 kinds of highly acquainted languages are used in the above-mentioned way, which reduces the dependence of the model on the size of the data set and the length of the text, the accuracy of the model detection between the languages with high recognition degree is improved, and the robustness of the language recognition model on different data sets is enhanced, with the accuracy rate being over 97.

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An Empirical Evaluation of Machine Learning Algorithms for Identifying software Requirements on Stack Overflow: Initial Results

An Empirical Evaluation of Machine Learning Algorithms for I...

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IEEE International Conference on software engineering and Service sciences (ICSESS)

作者： Arshad Ahmad Chong Feng Adnan Tahir Asif Khan Muhammad Waqas Sadique Ahmad Ayaz Ullah School of Computer Science & Technology Beijing Institute of Technology Beijing China College of Computer Science & Software Engineering Shenzen University Shenzen China Department of Computer Science FAST-NUCES Peshawar Pakistan Faculty of Engineering Sciences & Technology Iqra University Karachi Pakistan Department of Computer Science University of Swabi Anbar Pakistan

ISBN: (数字)9781728109459

ISBN: (纸本)9781728109466

Context: The recent developments made during the last decade or two in requirements engineering (RE) methods have seen a rise in using different machine-learning (ML) algorithms to solve some complex RE problems. One such problem is identifying and classifying software requirements on Stack Overflow (SO). The suitability of ML-based techniques to this tackle problem has shown convincing results, much better than those generated by some traditional natural language processing (NLP) techniques. Nevertheless, a comprehensive and systematic comprehension of these ML based techniques is still deficient. Objective: To identify and classify the type of ML algorithms used for identifying software requirements on SO. Method: This article reports systematic literature review (SLR) gathering evidence published up to August, 2019. Results: This study identified 1073 published papers related to RE and SO. Only 12 primary papers were selected. The data extraction process revealed that; 1) Latent Dirichlet Allocation (LDA) topic modeling is the most widely used ML algorithm in the selected studies, and 2) Precision and recall are the most commonly used evaluation method to measure the performance of these ML algorithms. Conclusion: The SLR finds that while ML algorithms have great potential in the identification of RE on SO, they face some open issues that will ultimately affect their performance and practical application. The SLR calls for the collaboration between RE and ML researchers, to tackle the open issues facing the development of real-world ML systems.

关键词： software Machine learning algorithms software algorithms Classification algorithms Systematics Requirements engineering Data mining

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Joint precoding and power control in small-cell networks with proportional-rate MISO-BC backhaul

arXiv

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arXiv 2019年

作者： Dong, Yanjie Jahangir Hossain, Md. Cheng, Julian Leung, Victor C.M. Department of Electrical and Computer Engineering University of British Columbia VancouverBC Canada School of Engineering University of British Columbia KelownaBC Canada College of Computer Science and Software Engineering Shenzhen University Shenzhen China

In the small-cell networks with multiple-input-single-output broadcasting (MISO-BC) backhauls, the joint dirty-paper coding and power control are investigated for the MISO-BC backhauls and access links in order to minimize the system transmit power. Considering the proportional rates of MISO-BC backhauls and flow-conservation constraints, the formulated optimization problem is non-convex. Moreover, the formulated problem couples the precoding vectors with the power-control variables. In order to handle the non-convex optimization problem and decouple the backhaul and access links, the structure of the formulated problem is investigated such that the optimal precoding vectors and optimal power-control variables are independently obtained. Moreover, the optimal precoding vectors are obtained in closed-form expressions. Simulation results are used to show the performance improvement over the benchmark scheme. Copyright © 2019, The Authors. All rights reserved.

关键词： Power control

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