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IEEE International Conference on Automated Software engineering (ASE)

作者： Yueming Wu Xiaodi Li Deqing Zou Wei Yang Xin Zhang Hai Jin Cluster and Grid Computing Lab Services Computing Technology and System Lab National Engineering Research Center for Big Data Technology and System School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China University of Texas at Dallas Shenzhen Huazhong University of Science and Technology Research Institute School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

Malware scanning of an app market is expected to be scalable and effective. However, existing approaches use either syntax-based features which can be evaded by transformation attacks or semantic-based features which are usually extracted by performing expensive program analysis. Therefor, in this paper, we propose a lightweight graph-based approach to perform Android malware detection. Instead of traditional heavyweight static analysis, we treat function call graphs of apps as social networks and perform social-network-based centrality analysis to represent the semantic features of the graphs. Our key insight is that centrality provides a succinct and fault-tolerant representation of graph semantics, especially for graphs with certain amount of inaccurate information (e.g., inaccurate call graphs). We implement a prototype system, MalScan, and evaluate it on datasets of 15,285 benign samples and 15,430 malicious samples. Experimental results show that MalScan is capable of detecting Android malware with up to 98% accuracy under one second which is more than 100 times faster than two state-of-the-art approaches, namely MaMaDroid and Drebin. We also demonstrate the feasibility of MalScan on market-wide malware scanning by performing a statistical study on over 3 million apps. Finally, in a corpus of dataset collected from Google-Play app market, MalScan is able to identify 18 zero-day malware including malware samples that can evade detection of existing tools.

关键词： Malware Social networking (online) Feature extraction Semantics Analysis of variance Static analysis Robustness

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Domain Knowledge Guided Deep Learning with Electronic Health Records

Domain Knowledge Guided Deep Learning with Electronic Health...

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IEEE International Conference on data Mining (ICDM)

作者： Changchang Yin Rongjian Zhao Buyue Qian Xin Lv Ping Zhang School of Computer Science and Technology Xi’an Jiaotong University Xi’an China National Engineering Lab for Big Data Analytics Xi’an Jiaotong University Xi’an China The Ohio State University Columbus Ohio USA

Due to their promising performance in clinical risk prediction with Electronic Health Records (EHRs), deep learning methods have attracted significant interest from healthcare researchers. However, there are 4 challenges: (i) data insufficiency. Many methods require large amounts of training data to achieve satisfactory results. (ii) Interpretability. Results from many methods are hard to explain to clinicians (e.g., why the models make particular predictions and which events cause clinical outcomes). (iii) Domain knowledge integration. No existing method dynamically exploits complicated medical knowledge (e.g., relations such as cause and is-caused-by between clinical events). (iv) Time interval information. Most existing methods only consider the relative order of visits from EHRs, but ignore the irregular time intervals between neighboring visits. In the study, we propose a new model, Domain Knowledge Guided Recurrent Neural Networks (DG-RNN), by directly introducing domain knowledge from the medical knowledge graph into an RNN architecture, as well as taking the irregular time intervals into account. Experimental results on heart failure risk prediction tasks show that our model not only outperforms state-of-the-art deep-learning based risk prediction models, but also associates individual medical events with heart failure onset, thus paving the way for interpretable accurate clinical risk predictions.

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Robust unsupervised feature selection by nonnegative sparse subspace learning

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Neurocomputing 2019年 334卷 156-171页

作者： Wei Zheng Hui Yan Jian Yang School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing PR China School of Computer Engineering Jinling Institute of Technology Nanjing PR China PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education and Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Key Laboratory of Trusted Cloud Computing and Big Data Analysis Nanjing Xiaozhuang University Nanjing PR China

Sparse subspace learning has been demonstrated to be effective in data mining and machine learning. In this paper, we cast the unsupervised feature selection scenario as a matrix factorization problem from the viewpoint of sparse subspace learning. By minimizing the reconstruction residual, the learned feature weight matrix with the l 2,1 -norm and the non-negative constraints not only removes the irrelevant features, but also captures the underlying low dimensional structure of the data points. Meanwhile in order to enhance the model's robustness, l 1 -norm error function is used to resistant to outliers and sparse noise. An efficient iterative algorithm is introduced to optimize this non-convex and non-smooth objective function and the proof of its convergence is given. Although, there is a subtraction item in our multiplicative update rule, we validate its non-negativity. The superiority of our model is demonstrated by comparative experiments on various original datasets with and without malicious pollution.

关键词： Subspace learning Non-negative matrix factorization Unsupervised feature selection

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The Circular DNA Model of 0–1 Programming Problem Based on DNA Strand Displacement

The Circular DNA Model of 0–1 Programming Problem Based on ...

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International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Zhen Tang Zhixiang Yin Jing Yang Xia Sun Jianzhong Cui School of Mathematics and Big Data AnHui University of Science and Technology Huainan AnHui china School of Electronic and Information Engineering AnHui University of Science and Technology Huainan AnHui china

ISBN: (纸本)9781538680988;9781538680971

DNA strand displacement is a new molecular biological technology with strong operability and simple experimental conditions. In this paper, DNA strand displacement is applied to solve the 0-1 programming problem, and a model is constructed using the class closed-loop DNA structure (known as “circular DNA” in this paper). The model fixes the DNA strands which represent the variables on the circular DNA single strand in counterclockwise order. The DNA strand 5' end is attached with the primers which are applied to the displacement of the DNA strand. The 3' end is attached with the colored fluorophore. Taking the possible solutions as the input, the results are detected by different fluorescent colors resulting from the DNA strand displacement. The DNA computing model has better flexibility, realizability and applicability.

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WORD: A large scale dataset, benchmark and clinical applicable study for abdominal organ segmentation from CT image

arXiv

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arXiv 2021年

作者： Luo, Xiangde Liao, Wenjun Xiao, Jianghong Chen, Jieneng Song, Tao Zhang, Xiaofan Li, Kang Metaxas, Dimitris N. Wang, Guotai Zhang, Shaoting School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu China Department of Radiation Oncology Sichuan Cancer Hospital and Institute Sichuan Cancer Center Chengdu China Department of Radiation Oncology Cancer Center West China Hospital Sichuan University Chengdu China Shanghai Artificial Intelligence Laboratory Shanghai China School of Medicine University of Electronic Science and Technology of China Chengdu China Department of Computer Science Johns Hopkins University Baltimore United States SenseTime Research Shanghai China West China Hospital-SenseTime Joint Lab West China Biomedical Big Data Center Sichuan University Chengdu China Department of Computer Science Rutgers University PiscatawayNJ United States

Whole abdominal organ segmentation is important in diagnosing abdomen lesions, radiotherapy, and follow-up. However, oncologists’ delineating all abdominal organs from 3D volumes is time-consuming and very expensive. Deep learning-based medical image segmentation has shown the potential to reduce manual delineation efforts, but it still requires a large-scale fine annotated dataset for training, and there is a lack of large-scale datasets covering the whole abdomen region with accurate and detailed annotations for the whole abdominal organ segmentation. In this work, we establish a new large-scale Whole abdominal ORgan dataset (WORD) for algorithm research and clinical application development. This dataset contains 150 abdominal CT volumes (30495 slices). Each volume has 16 organs with fine pixel-level annotations and scribble-based sparse annotations, which may be the largest dataset with whole abdominal organ annotation. Several state-of-the-art segmentation methods are evaluated on this dataset. And we also invited three experienced oncologists to revise the model predictions to measure the gap between the deep learning method and oncologists. Afterwards, we investigate the inference-efficient learning on the WORD, as the high-resolution image requires large GPU memory and a long inference time in the test stage. We further evaluate the scribble-based annotation-efficient learning on this dataset, as the pixel-wise manual annotation is time-consuming and expensive. The work provided a new benchmark for the abdominal multi-organ segmentation task, and these experiments can serve as the baseline for future research and clinical application development. © 2021, CC BY.

关键词： Large dataset

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Automatic Generation of Medical Imaging Diagnostic Report with Hierarchical Recurrent Neural Network

Automatic Generation of Medical Imaging Diagnostic Report wi...

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IEEE International Conference on data Mining (ICDM)

作者： Changchang Yin Buyue Qian Jishang Wei Xiaoyu Li Xianli Zhang Yang Li Qinghua Zheng School of Computer Science and Technology Xi’an Jiaotong University Xi’an Shaanxi China National Engineering Lab for Big Data Analytics Xian Jiaotong University Xi’an China HP Labs Palo Alto CA USA

Medical images are widely used in the medical domain for the diagnosis and treatment of diseases. Reading a medical image and summarizing its insights is a routine, yet nonetheless time-consuming task, which often represents a bottleneck in the clinical diagnosis process. Automatic report generation can relieve the issues. However, generating medical reports presents two major challenges: (i) it is hard to accurately detect all the abnormalities simultaneously, especially the rare diseases; (ii) a medical image report consists of many paragraphs and sentences, which are longer than natural image captions. We present a new framework to accurately detect the abnormalities and automatically generate medical reports. The report generation model is based on hierarchical recurrent neural network (HRNN). We introduce a topic matching mechanism to HRNN, so as to make generated reports more accurate and diverse. The soft attention mechanism is also introduced to HRNN model. Experimental results on two image-paragraph pair datasets show that our framework outperforms all the state-of-art methods.

关键词： Medical diagnostic imaging Feature extraction Task analysis Diseases Recurrent neural networks Deep learning

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A machine learning aided method for GNSS-R permittivity retrieval VIM analysis

A machine learning aided method for GNSS-R permittivity retr...

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Signal, Information and data Processing (ICSIDP), IEEE International Conference on

作者： Yan Jia Yuekun Pei Wenmei Li Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province Nanjing University of Posts and Telecommunications Nanjing China Beidou High Precision Positioning Service Technology Engineering Laboratory of Liaoning Province Dalian Universitiy Dalian China

ISBN: (数字)9781728123455

ISBN: (纸本)9781728123462

GNSS-Reflectometry is a new type of remote sensing technology for soil moisture retrieval. The popularity of GNSS-R can be explained by its plenty of signal sources, all weathers and 24-hours conditions and so on. Up until now, various GNSS-R soil moisture retrieval methods have been reported. However, it still faces some problems due to the uncertainty of the experimental environment, complex modeling, and retrieval process. To investigate the behavior of GNSS-R soil moisture retrieval process, we utilized the Random Forest algorithm that is a popular ensemble machine learning method in computer subject, which just emerged now for the classification of remote sensing and geographic data. In this paper, the variable importance (VI) was evaluated. It showed that the input variable SNR is much more sensitive to the obtained permittivity with the importance of a minimum of 40%. Moreover, the variable theta showed the least importance (below 10%) in the GNSS-R soil moisture retrieval. In some extreme cases (changing the parameter of the algorithm), the importance of theta shows nearly zero means it is not sensitive to the obtained permittivity and soil moisture content. This paper provides some experimental insights into the behavior of the GNSS-R soil moisture retrieval procedure.

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The Chinese Postman Problem Based on Molecular Beacon Strand Displacement

The Chinese Postman Problem Based on Molecular Beacon Strand...

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International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Jing Yang Kaifeng Huang Zhixiang Yin Jianzhong Cui School of mathematics and big data Anhui University of Science and Technology Anhui China School of mechanical and electrical engineering Huainan Normal University Anhui China

ISBN: (纸本)9781538680988;9781538680971

With the help of the special structure of the molecular beacon, a solution model of the Chinese postman problem is established by using the DNA strand displacement reaction principle. First, the problem is mapped into an undirected weighting graph. Then the vertex and arc of the graph are coded according to the coding principle. Secondly, structure probe, and make use of stand displacement to find solutions to satisfy the problem. Finally, the optimum solution was extracted by gel electrophoresis. The model has high sensitivity, specificity, and the lower error rate. The solution reaction can be carried out at normal temperature. The model calculation shows that the complexity of the problem can be reduced by using the molecular beacon strand displacement reaction to solve the Chinese postman problem.

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Simulation and modeling of a solidly mounted resonator 1st

Simulation and modeling of a solidly mounted resonator

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1st International Conference on Smart Vehicular technology, Transportation, Communication and Applications, VTCA 2017

作者： Meng, Sheng-Hui Huang, An-Chi Chen, Ying-Chung Fujian Provincial Key Lab of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China Department of Electrical Engineering National Sun Yat-Sen University Kaohsiung Taiwan

ISBN: (纸本)9783319707297

Solidly mounted resonators (SMRs) have a stable structure, high frequency range, and good process yield, and thus are increasingly being used in various applications, including radiofrequency (RF) communication devices. In this study, we simulated SMR structures and thin films of the piezoelectric materials AlN and ZnO using the commonly used RF design program Advanced Design System. © Springer International Publishing AG 2018.

关键词： Zinc oxide

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Classify EEG and Reveal Latent Graph Structure with Spatio-Temporal Graph Convolutional Neural Network

Classify EEG and Reveal Latent Graph Structure with Spatio-T...

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IEEE International Conference on data Mining (ICDM)

作者： Xiaoyu Li Buyue Qian Jishang Wei An Li Xuan Liu Qinghua Zheng School of Computer Science and Technology Xi’an Jiaotong University Xi’an Shaanxi China HP Labs Palo Alto CA USA National Engineering Lab for Big Data Analytics Xi’an Jiaotong University Xi’an Shaanxi China

Electroencephalogram(EEG) is a test that detect brain activities using multiple electrodes placed on the scalp. Multiple channels of EEG signals are recorded through the electrodes and are widely used in applications such as neurological disease diagnosis, emotion recognition, and behavior modeling. Recently, deep learning methods have been applied to classify EEG signals, where the different EEG channels are almost treated as a 2D grid input to the machine learning model. This data formation doesn't consider The complex connection among the EEG channels is not considered in such data formation. In our work, we treat EEG signals as frames of graph, and propose an end-to-end edge-aware spatio-temporal graph convolutional neural network for EEG classification. Specifically, we iteratively apply graph convolutional layer spatially and standard convolutional layer temporally. Since there is no prior knowledge about the exact connection among EEG channels, in our model, we initialize the connection as complete graph and apply learnable mask to capture graph structure at different levels. Furthermore, we also propose an iterative method based on information aggregation in graph convolution mechanism to reveal the latent graph structure. Empirical evaluation shows that our model achieves superior performance over state-of-the-art methods for EEG classification, and the learnt and revealed latent EEG graph structure is verified to be meaningful by neuroscientists.

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