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A dynamic level interaction method based on trusted value

Journal of computers (Taiwan) 2019年第1期30卷 142-152页

作者： Zhang, Yong-sheng Fan, Yue-qin Cui, Ran-ran Wei, Yu Department of Information Science and Engineering Shandong Normal University Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan China

With the rapid development of the cloud computing, there is a large amount of service providers with low quality in the marketplace. The mendacious service providers cause a serious loss for the users. In order to select an efficient cloud service, it can be trust and meet the user’s privacy protection requirements. In this paper, a dynamic level interaction method based on credible value is indicated. The cloud service providers and cloud users are divided into different dynamic levels according to their historical records. The security level of privacy data is decided according to the users’ trust level. This model introduces the center degree factor to filter the mendacious service and it can get a more accurate service evaluation by limiting the malicious users to make false evaluation for the service and it can control the providers’ access rights to protect the users’ information. The results of simulation experiment show that this model can improve the interaction successful rate and strengthen the safety of user privacy data. It demonstrates the efficiency and effectiveness of our approach in trusted interaction with other counterparts. 2019 © computer Society of the Republic of China. All Rights Reserved.

关键词： Grading

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Towards a programming framework for activity-oriented context-aware applications

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Frontiers of computer science 2017年第6期11卷 987-1006页

作者： Xuansong LI Xianping TAO Jian LU State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Nanjing 210000 China

Context-aware system is an emerging research area in recent years. Context plays an important role in these systems. In most existing work, context is treated as all rel- ative elements in the environment of an application, and the scope of context is predefined by the developers during the development. However, it is difficult to analyze, specify, and organize everything in the environment accurately and com- pletely; and even when it is possible, the developed applica- tions are difficult to extend or modify as the requests for en- vironment may change over time. In this paper, we focus on activity-oriented context-aware （AOCA） applications where the requests for environment are highly dependent on user activities, and propose a programming framework for devel- oping AOCA applications. In particular, we first present a concept model for describing the notions of activity-oriented context. Next, based on the concept model, we describe the details of the programming framework as well as a develop- ment tool. Moreover, we provide a platform to support the runtime of AOCA applications, and demonstrate the advan- tages of our programming framework through experimental evaluations.

关键词： pervasive computing context-awareness,context-aware programming framework activity-orientedcontext

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Chasing errors using biasing automata 8th

Chasing errors using biasing automata

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8th International Symposium on Leveraging Applications of Formal Methods, Verification and Validation, ISoLA 2018

作者： Bu, Lei Peled, Doron Shen, Dashuan Tzirulnikov, Yael State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Department of Computer Science Bar Ilan University Ramat Gan Israel

ISBN: (纸本)9783030034207

Randomized testing is a lightweight approach for searching for bugs. It presents a tradeoff between the number of testing experiments performed and the probability to find errors. An important challenge in random testing is when the errors that we try to detect are scattered with very low probability among the different executions, forming a "rare event". We suggest here the use of a "biasing automaton", which observes the tested sequence and controls the distribution of the different choices of extending it. By the careful selection of a biasing automaton, we can increase the chance of errors to be found and consequently reduce the number of tests we need to perform. The biasing automaton is constructed through repeated testing of variants of the system under test. We show how to construct biasing automata based on genetic programming. © Springer Nature Switzerland AG 2018.

关键词： Genetic programming

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Gated group self-attention for answer selection

arXiv

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arXiv 2019年

作者： Xu, Dong Ji, Jianhui Huang, Haikuan Deng, Hongbo Li, Wu-Jun National Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China Department of Computer Science and Technology Nanjing University Nanjing210023 China Alibaba Group

Answer selection (answer ranking) is one of the key steps in many kinds of question answering (QA) applications, where deep models have achieved state-of-the-art performance. Among these deep models, recurrent neural network (RNN) based models are most popular, typically with better performance than convolutional neural network (CNN) based models. Nevertheless, it is difficult for RNN based models to capture the information about long-range dependency among words in the sentences of questions and answers. In this paper, we propose a new deep model, called gated group selfattention (GGSA), for answer selection. GGSA is inspired by global self-attention which is originally proposed for machine translation and has not been explored in answer selection. GGSA tackles the problem of global self-attention that local and global information cannot be well distinguished. Furthermore, an interaction mechanism between questions and answers is also proposed to enhance GGSA by a residual structure. Experimental results on two popular QA datasets show that GGSA can outperform existing answer selection models to achieve state-of-the-art performance. Furthermore, GGSA can also achieve higher accuracy than global self-attention for the answer selection task, with a lower computation cost. Copyright © 2019, The Authors. All rights reserved.

关键词： Recurrent neural networks

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Cyan Droid: stable and effective energy inefficiency diagnosis for Android apps

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science China(Information sciences) 2017年第1期60卷 51-68页

作者： Qiwei LI Chang XU Yepang LIU Chun CAO Xiaoxing MA Jian LU¨ State Key Laboratory for Novel Software Technology Nanjing University Department of Computer Science and Technology Nanjing University Department of Computer Science and Engineering The Hong Kong University of Science and Technology

Smartphones are an indispensable part of people’s daily lives. Smartphone apps often use phone sensors to probe their users’ physical environmental conditions to provide services. However, sensing operations can be energy-consumptive, and thus the obtained sensory data should be effectively utilized by apps for their users’ benefits. Existing studies disclosed that many real-world smartphone apps have poor utilization of sensory data, and this causes serious energy waste. To diagnose such energy bugs, a recent technique GreenD roid automatically generates sensory data, tracks their propagation and analyzes their utilization in an app. However,we observe that GreenD roid’s sensory data generation is random and this can negatively affect its stability and effectiveness. Our study reported that GreenD roid might miss energy bugs that require specific sensory data to manifest. To address this problem, we propose a novel approach to systematically generating multi-dimensional sensory data. For effective diagnosis, we also propose to consider app state changes at a finer granularity. We implemented our approach as a prototype tool CyanD roid, and evaluated it using four real-world Android apps and hundreds of their mutants. Our results confirmed that CyanD roid is more stable and effective in energy inefficiency diagnosis for sensory data underutilization issues.

关键词： smartphone sensory data utilization energy inefficiency white-box sampling dynamic tainting

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TagSheet: Sleeping Posture Recognition with an Unobtrusive Passive Tag Matrix

TagSheet: Sleeping Posture Recognition with an Unobtrusive P...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Jia Liu Xingyu Chen Shigang Chen Xiulong Liu Yanyan Wang Lijun Chen State Key Laboratory for Novel Software Technology Nanjing University China Department of Computer & Information Science & Engineering University of Florida USA Department of Computing The Hong Kong Polytechnic University China

Sleep monitoring plays an important role in many medical applications, including SIDS prevention, care of patients with pressure ulcers, and assistance to patients with sleep apnea, where studies have shown that autonomous and continuous monitoring of sleep postures provides useful information for lowering health risk. Existing systems are designed based on electrocardiogram, cameras and pressure sensors, which are expensive to deploy, intrusive to privacy, or uncomfortable to use. This paper presents TagSheet, the first sleep monitoring system based on passive RFID tags, which provides a convenient, non-intrusive, and comfortable way of monitoring the sleeping postures. It does not require attaching any tag directly to a patient's body. Tags are taped under a bed sheet. With a combination of hierarchical recognition, image processing and polynomial fitting, the proposed system identifies body postures based on the observed variation caused by the patient body to the backscattered signals from tags. The system does not require any personalized data training, making it plug-n-play in use. One additional advantage is that the system can also estimate the patient's respiration rate. This is particularly helpful in assisting patients with sleep apnea. We have implemented a prototype system, and experiments show that the system performs posture identification with an accuracy up to 96.7% and in the meantime it measures the respiration rate with a small error of about 0.7 bpm (breath per minute).

关键词： Sleep apnea Monitoring Diversity reception Cameras Pressure sensors Training Pediatrics

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A Real-Time Interpretable Ai Model for the Cholangioscopic Diagnosis of Malignant Biliary Stricture

SSRN

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SSRN 2022年

作者： Zhang, Xiang Tang, Dehua Zhou, Jindong Ni, Muhan Yan, Peng Zhang, Zhenyu Yu, Tao Zhan, Qiang Shen, Yonghua Zhou, Lin Zheng, Ruhua Zou, Xiaoping Zhang, Bin Li, Wu-Jun Wang, Lei Department of Gastroenterology Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University Nanjing Medical University Jiangsu Nanjing210008 China Department of Gastroenterology Nanjing Drum Tower Hospital Affiliated Drum Tower Hospital Medical School of Nanjing University Jiangsu Nanjing210008 China National Institute of Healthcare Data Science Nanjing University Jiangsu Nanjing210008 China National Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Jiangsu Nanjing210008 China Departments of Gastroenterology Qilu Hospital of Shandong University Shandong Jinan China Department of Gastroenterology Wuxi People’s Hospital Affiliated to Nanjing Medical University Jiangsu Wuxi214023 China Department of Gastroenterology Taikang Xianlin Drum Tower Hospital Jiangsu Nanjing210046 China Center for Medical Big Data Nanjing Drum Tower Hospital Affiliated Drum Tower Hospital Medical School of Nanjing University Jiangsu Nanjing210008 China Department of Gastroenterology Nanjing Drum Tower Hospital Affiliated Drum Tower Hospital Medical School of Nanjing University 321 Zhongshan Road Jiangsu Nanjing210008 China

Background: It is crucial to accurately determine malignant biliary strictures (MBSs) for early curative treatment. Therefore, an effective and accessible modality is needed to facilitate the diagnosis of MBSs. The study aimed to develop a real-time interpretable artificial intelligent (AI) system to predict MBSs under digital single-operator cholangioscopy (DSOC).Methods: A novel interpretable AI system called MBSDeiT was developed, consisting of two models to identify qualified images and then predict MBS in real time. The overall efficiency of MBSDeiT was validated at the image level on internal, external, prospective testing datasets and subgroups analyses, and at the video level on the prospective test datasets, and compared with that of endoscopists. The association between AI predictions and endoscopic features was evaluated to increase the ***: MBSDeiT can first automatically select qualified DSOC images with an AUC of 0.904 and 0.921–0.927 on the internal testing dataset and the external testing datasets, and then identify MBSs with an AUC of 0.971 on the internal testing dataset, an AUC of 0.978–0.999 on the external testing datasets, and an AUC of 0.976 on the prospective testing dataset, respectively. MBSDeiT accurately identified 92.3% MBS in prospective testing videos. Subgroups analyses confirmed the stability and robustness of MBSDeiT. MBSDeiT achieved superior performance to that of expert and novice endoscopists. The AI predictions were significantly associated with four endoscopic features [including mass (OR 25.17, 95% CI 8.73–72.61);friability (70.70, 16.89–296.03);raised intraductal lesion (10.94, 5.68–21.04);and abnormal vessels (103.08, 13.94–762.09)] under DSOC, which is consistent with the endoscopists’ ***: The findings suggest that MBSDeiT could be a promising approach for the accurate diagnosis of MBS under *** Information: The project is funded by Jiangsu Provincial Health Commission

关键词： Artificial intelligence

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Publisher Correction: A skin-integrated multimodal haptic interface for immersive tactile feedback

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Nature Electronics 2023年第12期6卷 1041页

作者： Ya Huang Jingkun Zhou Chun Ki Yiu Kuanming Yao Dengfeng Li Jian Li Tsz Hung Wong Yiming Liu Yuyu Gao Xingcan Huang Hu Li Jiyu Li Binbin Zhang Zhenlin Chen Xinge Yu Pingchuan Ke Shaoyu Cai Xingyu Yang Haichen Gao Kening Zhu Xu Guo Zichen Zhao Zhaoqian Xie Yu Zhou Lei Li Huanxi Zheng Zuankai Wang Enming Song Hui Wu Department of Biomedical Engineering City University of Hong Kong Hong Kong China Hong Kong Centre for Cerebro-Cardiovascular Health Engineering Hong Kong Science Park Hong Kong China Shenzhen Research Institute City University of Hong Kong Shenzhen China Department of Sociology Hong Kong Shue Yan University Hong Kong China School of Creative Media City University of Hong Kong Hong Kong China Engineering Design and Innovation Centre College of Design and Engineering National University of Singapore Singapore Singapore State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial Equipment Dalian University of Technology Dalian China Department of Engineering Mechanics Dalian University of Technology Dalian China Ningbo Institute of Dalian University of Technology Ningbo China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong China National Engineering Research Center of Electric Vehicles Beijing Institute of Technology Beijing China Department of Mechanical Engineering Hong Kong Polytechnic University Hong Kong China Shanghai Frontiers Science Research Base of Intelligent Optoelectronics and Perception Institute of Optoelectronics Fudan University Shanghai China State Key Laboratory of New Ceramics and Fine Processing School of Materials Science and Engineering Tsinghua University Beijing China

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Deep Anomaly Detection for Time-series Data in Industrial IoT: A Communication-Efficient On-device Federated Learning Approach

arXiv

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arXiv 2020年

作者： Liu, Yi Garg, Sahil Nie, Jiangtian Zhang, Yang Xiong, Zehui Kang, Jiawen Shamim Hossain, M. School of Data Science and Technology Heilongjiang University Harbin China Electrical Engineering Department École de technologie supérieure Uni-versité du Québec Montréal Canada Energy Research Institute Interdisciplinary Graduate Programme School of Computer Science and Engineering Nanyang Technological University Singapore Singapore Hubei Key Laboratory of Transportation Internet of Things School of Computer Science and Technology Wuhan University of Technology China Alibaba-NTU Joint Research Institute School of Computer Science and Engineering NTU Singapore Singapore Energy Research Institute Nanyang Technological University Singapore Department of Software Engineering College of Computer and Information Sciences King Saud University Saudi Arabia

Since edge device failures (i.e., anomalies) seriously affect the production of industrial products in Industrial IoT (IIoT), accurately and timely detecting anomalies is becoming increasingly important. Furthermore, data collected by the edge device may contain the user’s private data, which is challenging the current detection approaches as user privacy is calling for the public concern in recent years. With this focus, this paper proposes a new communication-efficient on-device federated learning (FL)-based deep anomaly detection framework for sensing time-series data in IIoT. Specifically, we first introduce a FL framework to enable decentralized edge devices to collaboratively train an anomaly detection model, which can improve its generalization ability. Second, we propose an Attention Mechanism-based Convolutional Neural Network-Long Short Term Memory (AMCNN-LSTM) model to accurately detect anomalies. The AMCNN-LSTM model uses attention mechanism-based CNN units to capture important fine-grained features, thereby preventing memory loss and gradient dispersion problems. Furthermore, this model retains the advantages of LSTM unit in predicting time series data. Third, to adapt the proposed framework to the timeliness of industrial anomaly detection, we propose a gradient compression mechanism based on Top-k selection to improve communication efficiency. Extensive experiment studies on four real-world datasets demonstrate that the proposed framework can accurately and timely detect anomalies and also reduce the communication overhead by 50% compared to the federated learning framework that does not use gradient compression scheme. Copyright © 2020, The Authors. All rights reserved.

关键词： Anomaly detection

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Review of machine-learning methods for RNA secondary structure prediction

arXiv

引用

arXiv 2020年

作者： Zhao, Qi Zhao, Zheng Fan, Xiaoya Yuan, Zhengwei Mao, Qian Yao, Yudong College of Medicine and Biological Information Engineering Northeastern University ShenyangLiaoning110169 China School of Information Science and Technology Dalian Maritime University Dalian Liaoning116026 China School of Software Dalian University of Technology Key Laboratory for Ubiquitous Network Service Software of Liaoning Province DalianLiaoning116620 China Key Laboratory of Health Ministry for Congenital Malformation Shengjing Hospital China Medical University Shenyang Liaoning110004 China College of Light Industry Liaoning University Shenyang Liaoning110036 China Department of Electrical and Computer Engineering Stevens Institute of Technology HobokenNJ07030 United States

Secondary structure plays an important role in determining the function of non-coding RNAs. Hence, identifying RNA secondary structures is of great value to research. Computational prediction is a mainstream approach for predicting RNA secondary structure. Unfortunately, even though new methods have been proposed over the past 40 years, the performance of computational prediction methods has stagnated in the last decade. Recently, with the increasing availability of RNA structure data, new methods based on machine-learning technologies, especially deep learning, have alleviated the issue. In this review, we provide a comprehensive overview of RNA secondary structure prediction methods based on machine-learning technologies and a tabularized summary of the most important methods in this field. The current pending issues in the field of RNA secondary structure prediction and future trends are also *** Codes I.2.0 General Copyright © 2020, The Authors. All rights reserved.

关键词： RNA

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