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arXiv 2021年

作者： Wang, Fusen Sang, Jun Wu, Zhongyuan Liu, Qi Sang, Nong Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education Chongqing University Chongqing400044 China School of Big Data and Software Engineering Chongqing University Chongqing401331 China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430000 China

The existing crowd counting methods usually adopted attention mechanism to tackle background noise, or applied multi-level features or multi-scales context fusion to tackle scale variation. However, these ap- proaches deal with these two problems separately. In this paper, we propose an Hybrid Attention Network (HAN) by employing Progressive Embedding Scale-context (PES) information, which enables the network to simultaneously suppress noise and adapt head scale variation. We build the hybrid attention mechanism through paralleling spatial attention and channel attention module, which makes the network to focus more on the human head area and reduce the interference of background objects. Besides, we embed certain scale-context to the hybrid attention along the spatial and channel dimensions for alleviating these count- ing errors caused by the variation of perspective and head scale. Finally, we propose a progressive learning strategy through cascading multiple hybrid attention modules with embedding diαerent scale-context, which can gradually integrate diαerent scale-context information into the current feature map from global to local. Ablation experiments provides that the network architecture can gradually learn multi-scale features and suppress background noise. Extensive experiments demonstrate that HANet obtain state-of-the-art counting performance on four mainstream datasets. Copyright © 2021, The Authors. All rights reserved.

关键词： Embeddings

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Plot2API: Recommending graphic API from plot via semantic parsing guided neural network

arXiv

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arXiv 2021年

作者： Wang, Zeyu Huang, Sheng Liu, Zhongxin Yan, Meng Xia, Xin Wang, Bei Yang, Dan Key Laboratory of Dependable Service Computing in Cyber Physical Society Chongqing University Ministry of Education China School of Big Data and Software Engineering Chongqing University Chongqing China College of Computer Science and Technology Zhejiang University Hangzhou China Faculty of Information Technology Monash University Australia Pengcheng Laboratory Shenzhen China

Plot-based Graphic API recommendation (Plot2API) is an unstudied but meaningful issue, which has several important applications in the context of software engineering and data visualization, such as the plotting guidance of the beginner, graphic API correlation analysis, and code conversion for plotting. Plot2API is a very challenging task, since each plot is often associated with multiple APIs and the appearances of the graphics drawn by the same API can be extremely varied due to the different settings of the parameters. Additionally, the samples of different APIs also suffer from extremely imbalanced. Considering the lack of technologies in Plot2API, we present a novel deep multi-task learning approach named Semantic Parsing Guided Neural Network (SPGNN) which translates the Plot2API issue as a multi-label image classification and an image semantic parsing tasks for the solution. In SPGNN, the recently advanced Convolutional Neural Network (CNN) named EfficientNet is employed as the backbone network for API recommendation. Meanwhile, a semantic parsing module is complemented to exploit the semantic relevant visual information in feature learning and eliminate the appearance-relevant visual information which may confuse the visual-information-based API recommendation. Moreover, the recent data augmentation technique named random erasing is also applied for alleviating the imbalance of API categories. We collect plots with the graphic APIs used to drawn them from Stack Overflow, and release three new Plot2API datasets corresponding to the graphic APIs of R and Python programming languages for evaluating the effectiveness of Plot2API techniques. Extensive experimental results not only demonstrate the superiority of our method over the recent deep learning baselines but also show the practicability of our method in the recommendation of graphic APIs. © 2021, CC BY-NC-SA.

关键词： Data visualization

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Sanitizable signature scheme with privacy protection for electronic medical data sharing

Cyber Security and Applications

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cyber Security and Applications 2023年 1卷

作者： Xu, Zhiyan Luo, Min Peng, Cong Feng, Qi The Hubei Education Cloud Service Engineering Technology Research Center College of Computer Hubei University of Education Hubei province Wuhan 430205 China The Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Hubei province Wuhan 430072 China

Electronic medicine has received more and more attention because of its ability to provide more efficient and better medical services. However, the characteristics of electronic medical networks make them more vulnerable to security threats such as data integrity and user privacy leakage. Traditional digital signatures cannot meet the diversity and privacy requirements of medical data applications. Sanitizable signatures incorporate sanitization capabilities into signatures to allow designated sanitizers to modify variable parts of a message in a controlled manner without the cooperation of the original signer. This paper uses the key-exposure free chameleon hash function to convert the data sanitization operation into using trapdoor keys to find collisions in the key-exposure free chameleon hash function, and builds a privacy-preserving sanitizable signature scheme. Security analysis and performance evaluation demonstrate that our new scheme achieves public verifiability, which greatly reduces computing costs while effectively ensuring data security and user privacy, and is especially suitable for electronic medical data sharing scenarios. © 2023

关键词： Electronic medical records Privacy protection Provable security Sanitizable signature

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Improving Log-Based Anomaly Detection with Component-Aware Analysis

Improving Log-Based Anomaly Detection with Component-Aware A...

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International Conference on Software Maintenance (ICSM)

作者： Kun Yin Meng Yan Ling Xu Zhou Xu Zhao Li Dan Yang Xiaohong Zhang Key Laboratory of Dependable Service Computing in Cyber Physical Society (Chongqing University) Ministry of Education China School of Big Data & Software Engineering Chongqing University China

ISBN: (数字)9781728156194

ISBN: (纸本)9781728156200

Logs are universally available in software systems for troubleshooting. They record system run-time states and messages of system activities. Log analysis is an effective way to diagnosis system exceptions, but it will take a long time for engineers to locate anomalies accurately through logs. Many automatic approaches have been proposed for log-based anomaly detection. However, most of the prior approaches did not consider the corresponding system component of a log message. Such component records the log location, which can help detect the location-sequence-related anomalies. In this paper, we propose LogC, a new Log -based anomaly detection approach with Component-aware analysis. LogC contains two phases: (i) turning log messages into log template sequences and component sequences, (ii) feeding such two sequences to train a combined LSTM model for detecting anomalous logs. LogC only needs normal log sequences to train the combined model. We evaluate LogC on two open-source log datasets: HDFS and ThunderBird. Experimental results show that LogC overall outperforms three baselines (i.e., PCA, IM, and DeepLog) in terms of three metrics (precision, recall, and F-measure).

关键词： Measurement Software maintenance Turning Software systems Anomaly detection Open source software Principal component analysis

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SCARE of Secret Ciphers under Rough Leakage Model

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Security and Communication Networks 2023年第1期2023卷

作者： Wang, Mengxing Tang, Ming Zhang, Xige Chen, Qi Ding, Haili Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Hubei Wuhan430000 China Beijing Smartchip Microelectronics Technology Co. Ltd. Beijing100192 China Marketing Service Center State Grid Electric Power Co. Ltd. Ningxia750002 China

Most previousside-channel analysis-based reverse engineering (SCARE) methods were based on collision attack. However, the collision-based methods generally need noise-free traces and can hardly be verified by realistic implementations. This paper proposes a novel SCARE, which is named as OSHDG-SCARE, targeting on secret S-box. We define a graph as OSHDG (one-step HD graph) to recover the parameters of any secret S-box. Due to the redundancy of OSHDG, the multiple paths can filter the correct results and improve the success rate of the reverse method even when the samples deviate from leakage model. We classify the deviation of side channel signal as two types, which are the gap between real leakage and leakage model, and measurement noise. Experiments are performed on real power traces satisfying the inaccurate HW (Hamming Weight) leakage model from a software implementation of AES-like cipher. OSHDG-SCARE recovers the secret S-box when the deviation rate of traces is 1.45%. © 2023 Mengxing Wang et al.

关键词： Side channel attack

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Research on water quality prediction based on SARIMA-LSTM: A case study of beilun estuary 21

Research on water quality prediction based on SARIMA-LSTM: A...

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21st IEEE International Conference on High Performance computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2019

作者： Xu, Rui Xiong, Qingyu Yi, Hualing Wu, Chao Ye, Jianxin Key Laboratory of Dependable Service Computing in Cyber Physical Society Chongqing University Ministry of Education School of Big Data AND Software Engineering Chongqing University Chongqing China

ISBN: (纸本)9781728120584

As water environment is an important part of mangrove ecosystem, an efficient prediction of water quality is the foundation for judging the health of wetland ecosystem. And it also contributes a lot to the smooth development of environmental protection work. Based on the data of water quality and weather in Beilun Estuary, this paper chooses permanganate index and the content of ammonia nitrogen, which can reflect the water quality, as forecasting targets. We propose a multi-feature prediction method called SARIMA-LSTM on the basis of seasonal autoregressive integrated moving average model and long short-term memory. Through the combination of linear and non-linear model, this method can possess a better prediction effect considering the influence of weather on water quality. And the experimental results of four models show that this method has higher accuracy, stability and reliability. © 2019 IEEE.

关键词： Long short-term memory

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Traceable Multiauthority Attribute-Based Encryption with Outsourced Decryption and Hidden Policy for CIoT

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Wireless Communications and Mobile computing 2021年第1期2021卷

作者： Liu, Suhui Yu, Jiguo Hu, Chunqiang Li, Mengmeng School of Computer Science Qufu Normal University Rizhao Shandong276826 China Jinan Shandong250353 China Jinan Shandong250014 China Shandong Laboratory of Computer Networks Jinan250014 China School of Big Data and Software Engineering Chongqing University Chongqing400044 China Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education Chongqing University China

Cloud-assisted Internet of Things (IoT) significantly facilitate IoT devices to outsource their data for high efficient management. Unfortunately, some unsettled security issues dramatically impact the popularity of IoT, such as illegal access and key escrow problem. Traditional public-key encryption can be used to guarantees data confidentiality, while it cannot achieve efficient data sharing. The attribute-based encryption (ABE) is the most promising way to ensure data security and to realize one-to-many fine-grained data sharing simultaneously. However, it cannot be well applied in the cloud-assisted IoT due to the complexity of its decryption and the decryption key leakage problem. To prevent the abuse of decryption rights, we propose a multiauthority ABE scheme with white-box traceability in this paper. Moreover, our scheme greatly lightens the overhead on devices by outsourcing the most decryption work to the cloud server. Besides, fully hidden policy is implemented to protect the privacy of the access policy. Our scheme is proved to be selectively secure against replayable chosen ciphertext attack (RCCA) under the random oracle model. Some theory analysis and simulation are described in the end. © 2021 Suhui Liu et al.

关键词： Internet of things

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Multi-Grained Selection and Fusion for Fine-Grained Image Representation

Multi-Grained Selection and Fusion for Fine-Grained Image Re...

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International Joint Conference on Neural Networks (IJCNN)

作者： Jianrong Jiang Hongxing Wang Key Laboratory of Dependable Service Computing in Cyber Physical Society (Chongqing University) Ministry of Education China School of Big Data & Software Engineering Chongqing University Chongqing China

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

How to learn a good fine-grained image representation is a key problem for fine-grained tasks. Most previous supervised methods suffer from insufficient training data, which require laborious annotations of fine-grained objects. In this paper, we propose an annotation-free method for fine-grained image representation, dubbed Multi-Grained Selection and Fusion (MGSF). The proposed MGSF extracts two types of visual features, i.e., fine-grained discriminative features that highlight informative convolutional parts by spatial selection and channel selection, and coarse-grained scene-level features that provide context information for fine-grained objects. Extensive experiments in fine-grained image retrieval demonstrate the superiority of our proposed representation compared with the state-of-the-art approaches on several fine-grained datasets.

关键词： Feature extraction Image retrieval Image representation Semantics Visualization Image classification Task analysis

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Estimating Origin-Destination Flows Using Radio Frequency Identification Data 13th

Estimating Origin-Destination Flows Using Radio Frequency Id...

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13th International Conference on Green, Pervasive, and Cloud computing, GPC 2018

作者： Chen, Chaoxiong Zheng, Linjiang Cui, Chen Liu, Weining Key Laboratory of Dependable Service Computing in Cyber Physical Society Chongqing University Ministry of Education Chongqing400030 China College of Computer Science Chongqing University Chongqing400030 China

ISBN: (纸本)9783030150921

The origin-destination (OD) demand is a critical information source used in the traffic strategic planning and management. The Radio Frequency Identification (RFID) is an advanced technique to collect traffic data. In this paper, daily origin-destination trips were inferred from the RFID data. Locations of RFID readers are considered as the origins and destinations. However, the sparseness of RFID data leads uncertainty to the destination of trip. To handle this problem, an approach was proposed to estimate the OD matrix. At first, the driving time of trip-legs in all trajectories are calculated by the driving time of taxis, which can be distinguished from the RFID data. And then, the stay, the last pass-by RFID reader of a trip, is inferred based on the calculated driving time. Finally, we extracted daily origin-destination trips for all vehicles. Using the proposed method, a case study was developed employing the real-world data collected in Chongqing, China, which demonstrated the effectiveness of our proposed approach. © 2019, Springer Nature Switzerland AG.

关键词： Data mining

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CTF-Net: Retinal Vessel Segmentation via Deep Coarse-To-Fine Supervision Network

CTF-Net: Retinal Vessel Segmentation via Deep Coarse-To-Fine...

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IEEE International Symposium on Biomedical Imaging

作者： Kun Wang Xiaohong Zhang Sheng Huang Qiuli Wang Feiyu Chen School of Big Data and Software Engineering Chongqing University Chongqing China Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education Chongqing University Chongqing China

ISBN: (数字)9781538693308

ISBN: (纸本)9781538693315

Retinal blood vessels structure plays an important role in the early diagnosis of diabetic retinopathy, which is a cause of blindness globally. However, the precise segmentation of retinal vessels is often extremely challenging due to the low contrast and noise of the capillaries. In this paper, we propose a novel model of deep coarse-to-fine supervision network (CTF-Net) to solve this problem. This model consists of two U-shaped architecture(coarse and fine segNet). The coarse segNet, which learns to predict probability retina map from input patchs, while the fine segNet refines the predicted map. To gain more paths to preserve the multi-scale and rich deep features information, we design an end-to-end training network instead of multi-stage learning framework to segment the retina vessel from coarse to fine. Furthermore, in order to improve feature representation and reduce the number of parameters of model, we introduce a novel feature augmentation module (FAM-residual block). Experiment results confirm that our method achieves the state-of-the-art performances on the popular datasets DRIVE, CHASE_DB1 and STARE.

关键词： Image segmentation Training Decoding Retinal vessels Biomedical imaging Standards

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