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keystroke dynamics in password authentication for multi-user account

Journal of Computational Information systems 2015年第1期11卷 321-331页

作者： Zhang, Xiaomeng Zhao, Peihai Wang, Mimi Department of Computer Science and Technology Tongji University Shanghai China The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China

In the field of identity authentication, existing researches on user keystroke authentication only focus on the situation in which a single user uses a single account. When multiple users share one account, there emerges the problem of inaccurate modeling. The key to construct the keyboard behaviour pattern in a single-account-for-multiple-users case is to cluster and build the unique keyboard behaviour pattern for each user under the shared account. In this paper, the original K-means clustering algorithm is improved by adding variance determination conditions. And when the number of users is unknown, the algorithm will determine whether the account is a multi-user account or not and identify the number of users under the account to carry out keyboard behaviour clustering in a single-account-for-multiple-users situation. We build a model based on keyboard behaviour data after clustering. Compared with other methods, experiments arrive at the expected results. This method can be used for keyboard behaviour authentication in real-world scenarios in which multiple users share one account at home or in companies. ©, 2014, Journal of Computational Information systems. All right reserved.

关键词： Authentication

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An Enhanced Scheduling Solution to Support Quality of service for Broadband Wireless Access Networks

An Enhanced Scheduling Solution to Support Quality of Servic...

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第三届IEEE无线通讯、网络技术暨移动计算国际会议

作者： Fuqiang Liu Yuhui Huang Shangzhi Xu The Key Laboratory of Embedded System and Service Computing supported by Ministry of Education Broadband Wireless Communication and Multimedia Laboratory Tongji University Shanghai China

This paper proposes an enhanced scheduling solution to support different quality of service (QoS) for IEEE 802.16e broadband wireless access network. Based on the existing scheduling algorithm M-LWDF, we do deeper research into how to integrate it into IEEE 802.16e. The proposed scheduling solution combines the key service flow parameters defined by IEEE 802.16e standard with M-LWDF scheme. Simulation results show that the delay character is improved for real-time services using the proposed method.

关键词： scheduling QoS BWA

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A New One-Class Classification Method with Multiple Encoder-Decoder Pairs for Images

A New One-Class Classification Method with Multiple Encoder-...

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International Conference on Intelligent Human-Machine systems and Cybernetics, IHMSC

作者： Dongxiang Chen Chungang Yan Mimi Wang Department of Electronics and Information Engineering Tongji University The Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai China

One of the main destinations of image classification methods is to screen out the images belonging to the target class (positive) and identify the images of other classes (negative). Although most classifiers are trained on both positive samples and negative samples, in reality, negative samples are often unavailable. One-class classifiers trained only on positive samples, are proposed to solve this problem. However, how to train effectively a classifier remains a daunting challenge. Considering the success of deep learning in the field of computer vision in recent years, we propose a one-class classification model for images based on convolutional neural network (CNN). The model consists of two parts of networks with different responsibilities. The network of the first part works as the discriminator, used to identify whether the images are positive. The networks of the second part, each of which consists of an encoder-decoder pair work as the guiders of the discriminator. They guide the discriminator to learn what images it should identify to be negative. Different encoder-decoder pairs can restore images to different degrees. Images restored to different degrees can be used to train the discriminator. The discriminator learns that images under a specific restored degree don't belong to the target class. The experiment results on CIFAR-10 show that our method can achieve good performance, with less difficulty in training than the GAN-based counterparts'.

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Spatial-Temporal Flow Holistic Interaction Graph Convolution Network for Bidirectional Traffic Flow Forecasting

Spatial-Temporal Flow Holistic Interaction Graph Convolution...

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IEEE International Conference on Big Data

作者： Canyang Zhang Qi Zheng Yaying Zhang Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China Shanghai Artificial Intelligence Laboratory Shanghai China

Traffic flow forecasting is indispensable in modern urban life. Considering the complexity, variability and strong timeliness of traffic flow, traffic flow forecasting is a worth exploring but challenging research field. To achieve better traffic flow forecasting effect, we focus on two critical aspects that assume noteworthy importance: i) the features inside the traffic outflows and inflows. ii) the supplementary information regarding exterior region which is the area outside the grid division regions. To address these challenges, we propose a novel deep learning model Spatial-Temporal Flow Holistic Interaction Graph Convolution Network (STHGCN). In STHGCN, graph convolution based modules are applied through multi-step simulation. An exterior region feature estimation module is designed to estimate the influence of the special exterior region through the characteristics of complete trajectories, which enables a more comprehensive reasoning for traffic flow forecasting in grid division regions. Furthermore, a flow feature fusion integrator and stackable convolution modules are proposed to aggregate the intermediate features extracted from various perspectives, which simulate the constantly-updating and interlinked states of traffic flows through the process of multi-layer feature separation and fusion. We conduct extensive experiments on real-world traffic datasets and our proposed model outperforms all baselines.

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SelectCast: Scalable data aggregation scheme in wireless sensor networks

SelectCast: Scalable data aggregation scheme in wireless sen...

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IEEE INFOCOM 2011

作者： Wang, Cheng Tang, Shaojie Li, Xiang-Yang Jiang, Changjun Department of Computer Science Tongji University Shanghai China Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai China Department of Computer Science Illinois Institute of Technology Chicago IL 60616 United States TNLIST School of Software Tsinghua University China

ISBN: (纸本)9781424499212

In this work, for a wireless sensor network (WSN) of n randomly placed sensors with node density λ ∈ [1, n], we study the tradeoffs between the aggregation throughput and gathering efficiency. The gathering efficiency refers to the ratio of the number of the sensors whose data have been gathered to the total number of sensors. Specifically, we design two efficient aggregation schemes, called single-hop-length (SLH) scheme and multiple-hop-length (MLH) scheme. By novelly integrating these two schemes, we theoretically prove that our protocol achieves the optimal tradeoffs, and derive the optimal aggregation throughput depending on a given threshold value (lower bound) on gathering efficiency. Particularly, we show that under the MLH scheme, for a practically important set of symmetric functions called perfectly compressible functions, including the mean, max, or various kinds of indicator functions, etc., the data from Θ(n) sensors can be aggregated to the sink at the throughput of a constant order Θ(1), implying that our MLH scheme is indeed scalable. © 2011 IEEE.

关键词： Efficiency

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Pilot Design for Channel Estimation in OFDM-Based Cognitive Radio systems

Pilot Design for Channel Estimation in OFDM-Based Cognitive ...

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2010 IEEE Global Telecommunications Conference GLOBECOM 2010

作者： Die Hu Lianghua He Embedded System and Computer-service Key Laboratory University of Tongji Shanghai China

In orthogonal frequency division multiplexing (OFDM)-based cognitive radio (CR) systems, the subcarriers used by the licensed users (LUs) have to be deactivated to avoid interference. Thus only part of subcarriers can be used for transmission and the positions of the activated subcarriers may be non-contiguous. The conventional pilot design methods are no longer effective for such systems. In this paper, we propose a new practical pilot design method for OFDM-based CR systems. We first formulate the pilot design as a new optimization problem, where a simple objective function related to the mean-square error (MSE) of the least-squares (LS) channel estimation method is minimized. We then propose an efficient scheme to solve the optimization problem. Specifically, the pilot tones are obtained sequentially by solving some one-dimensional optimization problems. Only real additions are needed in the proposed scheme. The simulation results show that the pilot sequence obtained by the proposed method exhibits better performance than those obtained by existing pilot design methods.

关键词： Channel estimation Optimization OFDM Bit error rate Design methodology Cognitive radio Sensors

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A study on the optimum state for classroom learning from brain cognition perspective-research on relaxed alertness

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Journal of Convergence Information Technology 2012年第23期7卷 564-571页

作者： Gu, Rong Li, Jie Department of Electronic Science and Technology Tongji University Shanghai China The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China

Booming development in brain science injects new vitality into the reform of teaching methods. Based on the perspective of brain cognition, this article addresses the optimum state for classroom learning- the relaxed alertness. This is a good state of mind combined low threats with high challenges. Specially, the vigilance is estimated by extracting useful electroencephalogram (EEG) information in time-domain, frequency-domain and spatial distribution. It is used as an indicator to judge whether a student is in the optimum state. Focusing on how to achieve that ideal state, this article discusses some new teaching methods, including the use of Baroque music to stimulate alpha brainwave, the design of effective pattern of guiding class to arouse positive emotions of students and the conversion of teaching forms as novel stimuli to heip them improve vigilance and tide over thinking trough. The effectiveness of these methods is verified by the relevant experimental results.

关键词： Electroencephalography

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Evaluating feedback systems for residential building energy monitoring

Evaluating feedback systems for residential building energy ...

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2013 IEEE International Conference on Automation Science and Engineering, CASE 2013

作者： Guo, Wenqi Wendy Ullah, Tania Zhou, Mengchu National Institute of Standards and Technology Gaithersburg MD 20899 United States ECE Dept. New Jersey Institute of Technology Newark NJ 07102 United States Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai 201804 China

ISBN: (纸本)9781479915156

Rising global energy demand, increasing electricity prices, and the limitation of natural resources have led to increased interest in energy monitoring of residential buildings. Much research has been done to examine the effect of energy use feedback to building occupants. An important step towards energy awareness is to provide timely and finegrained consumption information of major household energy expenditures through feedback systems, known as residential energy monitoring systems (REMS). To accomplish this, researchers and product designers are intent on improving the residential whole-building energy consumption estimates gained through sensor networks at homes. Additionally, profiling the usage of appliances and devices within a home can be used to determine an occupant's daily activities and to optimize energy consumption. In this work, we take a step toward creating an evaluation procedure for REMS by determining factors that affect their ability to accurately convey energy usage to occupants. Two feedback sensor systems are assessed in a testbed in which electrical loads typical to a home are operated. Preliminary tests show that the accuracy of the REMS measurement is dependent on an electrical load's power factor. Together with other findings, the results will assist researchers in forming a comprehensive test procedure that will characterize feedback systems in the future. © 2013 IEEE.

关键词： Sensor networks

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Research on the Matthews correlation coefficients metrics of personalized recommendation algorithm evaluation

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International Journal of Hybrid Information Technology 2015年第1期8卷 163-172页

作者： Liu, Yingbo Cheng, Jiujun Yan, Chendan Wu, Xiao Chen, Fuzhen Key Laboratory of Embedded System and Service Computing of Ministry of Education Tongji University Shanghai China Department of Computer Science and Technology Tongji University Shanghai China

The personalized recommendation systems could better improve the personalized service for network user and alleviate the problem of information overload in the Internet. As we all know, the key point of being a successful recommendation system is the performance of recommendation algorithm. When scholars put forward some new recommendation algorithms, they claim that the new algorithms have been improved in some respects, better than previous algorithm. So we need some evaluation metrics to evaluate the algorithm performance. Due to the scholar didn’t fully understand the evaluation mechanism of recommendation algorithms. They mainly emphasized some specific evaluation metrics like Accuracy, Diversity. What’s more, the academia did not establish a complete and unified assessment of recommendation algorithms evaluation system which is credibility to do the work of recommendation evaluation. So how to do this work objective and reasonable is still a challengeable task. In this article, we discussed the present evaluation metrics with its respective advantages and disadvantages. Then, we put forward to use the Matthews Correlation Coefficient to evaluate the recommendation algorithm’s performance. All this based on an open source projects called mahout which provides a rich set of components to construct the classic recommendation algorithm. The results of the experiments show that the applicability of Matthews correlation coefficient in the relative evaluation work of recommendation algorithm. © 2015 SERSC.

关键词： Recommender systems

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A generalized model with spatial resources labels

A generalized model with spatial resources labels

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2010 International Conference on Biomedical Engineering and Computer Science, ICBECS 2010

作者： Jun, Niu Zeng, Guosun Department of Computer Science and Technology Tongji University Shanghai China College of Information Engineering Zhejiang Business Technology Institute Ningbo China Embedded System and Service Computing Key Lab. Ministry of Education Shanghai China

ISBN: (纸本)9781424453153

This paper proposes a generalized model by extending Markov chain with spatial resources labels, which can describe the functional and performance properties and some basic characteristics such as nondeterminacy and randomicity. The syntax and semantics of the new model are shown based on the existing temporal logics such as CSRL and pathCSRL. The model checking procedure is discussed and thus the practicability of the new model is intuitive. Based on the existing models, the proposed model includes spatial resources labels only by adding the spatial information to the existing labels of states, so the treatment of the state explosion problem is similar to that in the existing models. ©2010 IEEE.

关键词： Model checking

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