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International Conference on Wireless Communications and Signal Processing (WCSP)

作者： Jiasheng Xu Luoyi Fu Xiaoying Gan Bo Zhu School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China School of Agriculture and Biology Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9781728172361

ISBN: (纸本)9781728172378

Community detection, referred as the clustering procedure for similar nodes in the network, is an important research topic in the area of big data network. Distributed community detection algorithm, with its simplicity and high-efficiency, has drawn extensive attention recently. The existing distributed algorithm can only be applied on networks with non-overlapping communities, however, overlapping communities widely exist in real networks, which cannot be detected by the distributed algorithm. We carefully study the overlapping community structure and introduce the overlapping stochastic block model, in which each node may belong to multiple communities. Based on the existing distributed algorithm, we further propose an improved algorithm with an extra stage to discover nodes belonging to the overlapping community, which can be applied on the overlapping stochastic block model. Through theoretical analysis, we show that the proposed algorithm achieves effective detection results on both overlapping and non-overlapping communities. We also conduct various simulations on the overlapping stochastic block model, showing that the proposed algorithm outperforms the existing distributed algorithm and maintains effectiveness in a wide range of system parameters.

关键词： Wireless communication Analytical models Simulation Stochastic processes Signal processing algorithms Distributed algorithms detection algorithms

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Early Fusion of Multi-level Saliency Descriptors for Image Classification

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REVISTA IBEROAMERICANA DE AUTOMATICA E INFORMATICA INDUSTRIAL 2019年第3期16卷 358-368页

作者： Fidalgo, E. Alegre, E. Fernandez-Robles, L. Gonzalez-Castro, V Univ Leon Dept Ingn Elect & Sistemas & Automat Leon Spain Univ Leon Dept Ingn Mecan Informat & Aeroespacial Leon Spain INCIBE Spanish Natl Inst Cybersecur Leon Spain

In this paper, we propose a method that improves the classification of images. Considering saliency maps as if they were topographic maps and filtering the characteristics of the image's background, the Bag of Visual Words (BoVW) coding is improved. First, we evaluated six known algorithms to generate saliency maps and we selected GBVS and SIM because they are the ones that retain most of the information of the object. Next, we eliminated the extracted SIFT descriptors belonging to the background by filtering features based on binary images obtained at various levels of the selected saliency maps. We filtered the descriptors by obtaining layers at various levels of the saliency maps, and we evaluated the early fusion of the SIFT descriptors contained in these layers into five different datasets. The results obtained indicate that the proposed method always improves the reference method when combining the first two layers of GBVS or SIM and the dataset contains images with a single object.

关键词： Computer Vision detection algorithms Machine Learning Image processing Encoding Classifiers

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Detecting User-Perceived Failure in Mobile Applications via Mining User Traces

Detecting User-Perceived Failure in Mobile Applications via ...

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International Conference on Software Engineering - Companion, ICSE, Companion

作者： Deyu Tian Key Lab of High-Confidence Software Technology (MoE) Peking University

ISBN: (纸本)9781665429757

Mobile applications (apps) often suffer from failure nowadays. Developers usually pay more attention to the failure that is perceived by users and compromises the user experience. Existing approaches focus on mining large volume logs to detect failure, however, to our best knowledge, there is no approach focusing on detecting whether users have actually perceived failure, which directly influence the user experience. In this paper, we propose a novel approach to detecting user-perceived failure in mobile apps. By leveraging the frontend user traces, our approach first builds an app page model, and applies an unsupervised detection algorithm to detect whether a user has perceived failure. Our insight behind the algorithm is that when user-perceived failure occurs on an app page, the users will backtrack and revisit the certain page to retry. Preliminary evaluation results show that our approach can achieve good detection performance on a dataset collected from real world users.

关键词： Focusing User experience Mobile applications detection algorithms Software engineering

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Community detection Algorithm Based on Single Target PSO

Community Detection Algorithm Based on Single Target PSO

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Information Technology and Computer Application (ITCA), International Conference on

作者： Chao Wang Jilian Guo Anwei Shen Shujiang Huang Air Force Engineering University Xi’an China Harbin Flight Academy of the Air Force Harbin China

With the development of information technology, complex networks have become more common in people's lives, and methods based on modularity optimization have attracted more and more attention. Because the traditional particle swarm algorithm is used to solve continuous optimization problems, the community structure detection problem is a discrete optimization problem based on graphs. We applied a new coding strategy and a particle update strategy to overcome this problem. In the update strategy, we introduced a method based on neighbor update to ensure that the update of the particles is guided to a certain extent by following the neighborhood information, which is in line with the characteristics of real complex networks. In addition, the expanded module density function is used for optimization to overcome the resolution limitation problem of the traditional module density function and to ensure that the community structure of complex networks is found at different resolutions.

关键词： Heuristic algorithms Complex networks Computer applications Density functional theory Encoding detection algorithms Particle swarm optimization

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Automatic detection of Shoulder Bending Defects in Tire X-ray Images

Automatic Detection of Shoulder Bending Defects in Tire X-ra...

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International Conference on Computer Engineering and Applications (ICCEA)

作者： Chuanwen Lin Gang Chen Yuxiang Zhang Fei Sun School of Artificial Intelligence and Big Data Hefei University Hefei China People’s Liberation Army Guangzhou China Department of Computer Science and Technology University of Science and Technology of China Hefei China

ISBN: (数字)9781728159041

ISBN: (纸本)9781728159058

The quality of tire determines the stability of vehicles. Among the various defects of tires, shoulder bending is a common and serious one. A novel method for shoulder bending detection in tire X-ray images is introduced in this paper. Firstly, a series of pre-processing operations including image inpainting, image blurring, adaptive binarization and image segmentation will be done on the images. Then a seed based cords searching algorithm is proposed to find the cords in pixel level. Finally, a feature extraction algorithm is designed to extract the feature vector of each cords which will be fed into the final judgement. In contrast with deep learning methods, the proposed solution does not require many samples to train the network and it is more adaptable when processing images produced from the factory assembly lines. Experimental results show that when processing different types of tire X-ray images, our method is not only more robust but also with even better precision and recall.

关键词： Tires Feature extraction X-ray imaging Microsoft Windows Classification algorithms Gray-scale detection algorithms

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Scalable Impact Range detection against Newly Added Rules for Smart Network Verification

Scalable Impact Range Detection against Newly Added Rules fo...

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IEEE Annual International Computer Software and Applications Conference (COMPSAC)

作者： Yutaka Takita Masatake Miyabe Hiroshi Tomonaga Naoki Oguchi Fujitsu Laboratories Limited

ISBN: (数字)9781728173030

ISBN: (纸本)9781728173047

Technological progress in cloud networking, 5G networks, and the IoT (Internet of Things) are remarkable. In addition, demands for flexible construction of SoEs (Systems on Engagement) for various type of businesses are increasing. In such environments, dynamic changes of network rules, such as access control (AC) or packet forwarding, are required to ensure function and security in networks. On the other hand, it is becoming increasingly difficult to grasp the exact situation in such networks by utilizing current well-known network verification technologies since a huge number of network rules are complexly intertwined. To mitigate these issues, we have proposed a scalable network verification approach utilizing the concept of "Packet Equivalence Class (PEC)," which enable precise network function verification by strictly recognizing the impact range of each network rule. However, this approach is still not scalable for very large-scale networks which consist of tens of thousands of routers. In this paper, we enhanced our impact range detection algorithm for practical large-scale networks. Through evaluation in the network with more than 80,000 AC rules, we confirmed that our enhanced algorithm can achieve precise impact range detection in under 600 seconds.

关键词： Firewalls (computing) Internet of Things Access control Servers IP networks detection algorithms Cloud computing

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Supraharmonics detection Algorithm Based on Bimodal Spectral Line Interpolation Algorithm

Supraharmonics Detection Algorithm Based on Bimodal Spectral...

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IEEE Conference on Energy Internet and Energy System Integration (EI2)

作者： Minxuan Shen Zhe An Tongxun Wang Shengjun Zhou Shun Tao State Key Laboratory of Advanced Power Transmission Technology Global Energy Interconnection Research Institute Co. Ltd Beijing China State Key Laboratory of New Energy Power Systems North China Electric Power University Beijing China

ISBN: (数字)9781728196060

ISBN: (纸本)9781728196077

The wide application of high-frequency power electronic equipment leads to the increasingly serious problem of supraharmonics in the power grid. However, the existing hamionic detection methods have some problems such as slow detection speed and more data processing when used to detect supraharmonics in a wider frequency range. In this paper, based on a comprehensive comparison of existing harmonic detection algorithms, a detection algorithm for supraharmonics, bimodal spectral line interpolation algorithm, is proposed. The algorithm can effectively suppress the spectrum leakage, and the detection accuracy is good, which provides a new idea for the accurate detection of supraharmonics.

关键词： Interpolation System integration Harmonic analysis Power grids Power electronics Internet detection algorithms

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A Novel Forgery detection Algorithm Based on Mantissa Distribution in Digital Images

A Novel Forgery Detection Algorithm Based on Mantissa Distri...

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International Conference of Signal Processing and Intelligent Systems (ICSPIS)

作者： Arman Parnak Yasser Baleghi Javad Kazemitabar Faculty of Electrical & Computer Engineering Babol Noshirvani University of Technology Babol Iran

ISBN: (数字)9781728186290

ISBN: (纸本)9781728186306

Nowadays, digital image forgery detection is one of important topics in research world. In this paper, we propose a novel forgery detection algorithm using the logarithmic basis of Benford's law which states the mantissa of the logarithm of all practical numbers should be uniformly distributed. Based on this fact, the proposed method uses extracted features from mantissa distribution of discrete cosine transform (DCT) coefficients in JPEG images. Support vector machine (SVM) is used for classification to detect authentic and forged images based on these features. Results show that our proposed algorithm has the highest mean accuracy (99.78%), sensitivity (99.77%) and specificity (99.79%) in comparison with previous works on CASIA V1.0 dataset.

关键词： Support vector machines Digital images Signal processing algorithms Feature extraction Forgery Discrete cosine transforms detection algorithms

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A Proximity Based Community detection in Temporal Graphs

A Proximity Based Community Detection in Temporal Graphs

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IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT)

作者： H L Advaith S Adarsh G Akshay P Sreeram G P Sajeev Dept of Computer Science and Engineering Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (数字)9781728168289

ISBN: (纸本)9781728168296

With very fast global instant messaging services, communication networks of today are changing rapidly. To accurately represent such dynamic networks, a special type of a graph, known as temporal graph is indispensable. Majority of the community detection algorithms are designed for static graphs. So, an efficient conversion of temporal graphs to static graphs, while retaining important temporal information, enables the use of any standard community detection algorithm easily. This paper proposes a novel community detection method by constructing static graphs from temporal networks. The proposed method is validated through experiments. Results show that our technique leads to considerable runtime reduction upon dealing with large graphs comprise multiple community structures.

关键词： Social network services detection algorithms Image edge detection Data mining Instant messaging Numerical models Heuristic algorithms

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P wave detection in Electrocardiogram Based on Wavelet Transform and Differential Correction

P wave Detection in Electrocardiogram Based on Wavelet Trans...

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International Workshop on Advanced Computational Intelligence (IWACI)

作者： Yuchao Wang Lu Wu Chao Chen Zhongyi Jin Zhao Li Yinglong Wang School of computer science and technology Qilu University of Technology(Shandong Academy of Sciences) Jinan China Shandong Computer Science Center(National Supercomputer Center in Jinan) Qilu University of Technology(Shandong Academy of Sciences) Jinan China Shandong Artificial Intelligence Institute Qilu University of Technology(Shandong Academy of Sciences) Jinan China

ISBN: (数字)9781728142487

ISBN: (纸本)9781728142494

The paper presents an improved algorithm of P wave peaks, onsets and offsets detection in Electrocardiogram (ECG). By reducing the impact of information loss during signal processing and detecting the effects of node errors, the algorithm can detect the P peaks, onsets and ends more quickly and accurately. For ECG signals, a denoising algorithm based on the Generalized Minmax Concave (GMC) penalty is used to remove power frequency interference, baseline drift and myoelectric interference. And then, the R wave peaks and the P wave peaks, onsets and ends are detected using the wavelet transform and the adaptive threshold, and the false detection rate is further reduced by the differential correction. The performance of the proposed algorithm is evaluated on QT database and sensitivity and positive predictivity of P wave detection reached 99.12% and 91.14%, respectively. Moreover, the proposed algorithm makes the detection of features such as P wave dispersion and P wave duration more accurate, which is beneficial to the diagnosis of diseases such as atrial fibrillation.

关键词： Electrocardiography Wavelet transforms Noise reduction Filter banks Signal processing algorithms detection algorithms

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