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Identification of hosts behind a NAT device utilizing multiple fields of IP and TCP

Identification of hosts behind a NAT device utilizing multip...

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International Conference on Information and Communication Technology Convergence (ICTC)

作者： Hanbyeol Park Seung-hun Shin Byeong-hee Roh Cheolho Lee Graduate School of Software University College Dept. of Computer Engineering Ajou University Suwon Korea Attached Institute of ETRI Daejeon Korea

ISBN: (纸本)9781509013265

NAT provides a function to translate private IP addresses into a public IP address. With NAT functions, hosts with private IP addresses can be connected to the Internet, but they are hidden on the Internet. When malicious hosts behind a NAT device attack service providers on the Internet, firewalls attached on the services may detect the attack. After the attacks are detected, they may block all the traffic including the malicious hosts and other normal ones from the NAT, since all the traffic from the NAT have a same source IP address and the hosts behind the NAT cannot be identified individually by them. In this paper, we propose an effective method to identify hosts behind a NAT device by utilizing multiple fields of IP and TCP such as IPID, TTL, SYN flag, and timestamp. The proposed method can identify the number of hosts behind a NAT device, and their OSs with very high accuracy compared to existing works utilizing only one field.

关键词： Linux Internet Sensitivity Ports (computers) Object recognition TCPIP

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Application recommendation at places for mobile users

Application recommendation at places for mobile users

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IEEE International Symposium on World of Wireless Mobile and Multimedia Networks (WoWMoM)

作者： Yanliang Liu Yupeng Hu Ruiyun Yu Yonghe Liu Dept. of Computer Science and Engineering The University of Texas at Arlington Arlington TX College of Computer Science and Electronic Engineering Hunan University Changsha China School of Software Northeastern University Shenyang China

ISBN: (纸本)9781509021864

With the ever expanding mobile device ecosystem, mobile users face a vast and constantly growing application pool. At the same time, in our daily life, waiting occurs regularly at different places such as shopping centers, where mobile applications become the de facto means to consume the time periods. In this paper, we propose a novel application recommendation system that utilizes human activity information at different places, to better match the applications with the characteristics of the users current contexts. Specifically, we design a place/application matching model and present two application list recommending algorithms with bounded approximation ratio. We also implement the recommendation system on real mobile phones and conduct field studies to show its feasibility. Our experimental and simulation results show that the proposed schemes can achieve satisfactory results.

关键词： Mobile handsets Motion pictures Encyclopedias Electronic publishing Internet Mobile applications

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Integrated feature preprocessing for classification based on neural incremental attribute learning

Integrated feature preprocessing for classification based on...

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International Conference on Information Fusion

作者： Ting Wang Wei Zhou Xiaoyan Zhu Fangzhou Liu Sheng-Uei Guan Wuxi Research Institute of Applied Technologies Tsinghua University Wuxi China Dept. of Computer Science University of Liverpool Liverpool UK Dept. of Computer Science and Software Engineering Xi'an Jiaotong-Liverpool University Suzhou China

Incremental Attribute Learning (IAL) is a feasible machine learning strategy for solving high-dimensional pattern classification problems. It gradually trains features one by one, which is quite different from those conventional machine learning approaches where features are trained in one batch. Preprocessing, such as feature selection, feature ordering and feature extraction, has been verified as useful steps for improving classification performance by previous IAL studies. However, in the previous research, these preprocessing approaches were individually employed and they have not been applied for training simultaneously. Therefore, it is still unknown whether the classification results can be further improved by these different preprocess approaches when they are used at the same time. This study integrates different feature preprocessing steps for IAL, where feature extraction, feature selection and feature ordering are simultaneously employed. Experimental results indicate that such an integrated preprocessing approach is applicable for pattern classification performance improvement. Moreover, statistical significance testing also verified that such an integrated preprocessing approach is more suitable for the datasets with high-dimensional inputs.

关键词： Feature extraction Training Neural networks Principal component analysis Standards Interference Pattern classification

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Fast Image Gradients Using Binary Feature Convolutions

Fast Image Gradients Using Binary Feature Convolutions

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IEEE Conference on computer Vision and Pattern Recognition Workshops

作者： Pierre-Luc St-Charles Guillaume-Alexandre Bilodeau Robert Bergevin LITIV lab. Dept. of Computer & Software Eng. Polytechnique Montréal Montréal QC Canada LVSN - REPARTI Université Laval Québec City QC Canada

The recent increase in popularity of binary feature descriptors has opened the door to new lightweight computer vision applications. Most research efforts thus far have been dedicated to the introduction of new large-scale binary features, which are primarily used for key point description and matching. In this paper, we show that the side products of small-scale binary feature computations can efficiently filter images and estimate image gradients. The improved efficiency of low-level operations can be especially useful in time-constrained applications. Through our experiments, we show that efficient binary feature convolutions can be used to mimic various image processing operations, and even outperform Sobel gradient estimation in the edge detection problem, both in terms of speed and F-Measure.

关键词： Kernel computer vision Image edge detection Convolutional codes Estimation Conferences

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Proactive Healthcare and an Early Warning Mechanism for Coronary Artery Disease Patients using Internet-of-Thing Devices

Proactive Healthcare and an Early Warning Mechanism for Coro...

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IEEE International Conference on Systems, Man, and Cybernetics

作者： Shao-Jie Hsu Shih-Syun Lin Tun-Wen Pai Hamido Fujita Dept. of Computer Science and Engineering National Taiwan Ocean University Keelung Taiwan Intelligent Software Systems Laboratory Iwate Prefectural University Japan

ISBN: (纸本)9781509018987

Ubiquitous Internet environment and matured key technologies of sensing devices provide diversified applications of Internet of Thing (IoT). IoT in intelligent healthcare is one of the major topics for convincing powerful functions of IoT regarding the promotion of national health policies, strategies, and plans. However, according to statistics reports, annual global mortality for cardiovascular diseases remained as high as 29%, of which coronary artery disease (CAD) is accounted for 42%. Unfortunately, almost half of the patients not knowing they are at risk of cardiovascular diseases until serious symptoms occurred. Here, we proposed an early warning system by integrating IoT, big data, and cloud computing technologies which physically linked among personal communication devices of patient/doctor, cloud systems, and hospital medical information systems. The system collects personal heart rate and metabolic equivalent (MET) features from calibrated fitness devices wore by users, and corresponding fitted curves are dynamically calculated and compared to previously trained curve patterns from 213 patients with heart diseases and 124 healthy individuals. The prototype system was already successfully developed and validated by a couple of testing cases with excellent performance. To ensure the robustness of the proposed system, now we are evaluating system reliability and trying to collect more testing samples for further validating effectiveness of the proactive healthcare mechanism.

关键词： proactive healthcare mechanism Internet of medical thing metabolic equivalent(MET) coronary artery disease (CAD)

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Algorithms for determining semantic relations of formal concepts by cognitive machine learning based on concept algebra

Algorithms for determining semantic relations of formal conc...

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IEEE International Conference on Cognitive Informatics

作者： Mehrdad Valipour Yingxu Wang Omar A. Zatarain Marina L. Gavrilova Denotational Mathematics Software Science and Cognitive Systems Dept. of Electrical and Computer Engineering University of Calgary Calgary Alberta Canada Information Systems Lab Packard Dept. of Electrical Engineering Stanford University Stanford CA USA

ISBN: (纸本)9781509038473

It is recognized that the semantic space of knowledge is a hierarchical concept network. This paper presents theories and algorithms of hierarchical concept classification by quantitative semantic relations via machine learning based on concept algebra. The equivalence between formal concepts are analyzed by an Algorithm of Concept Equivalence Analysis (ACEA), which quantitatively determines the semantic similarity of an arbitrary pair of formal concepts. This leads to the development of the Algorithm of Relational Semantic Classification (ARSC) for hierarchically classify any given concept in the semantic space of knowledge. Experiments applying Algorithms ACEA and ARSC on 20 formal concepts are successfully conducted, which encouragingly demonstrate the deep machine understanding of semantic relations and their quantitative weights beyond human perspectives on knowledge learning and natural language processing.

关键词： Writing Instruments Decision support systems Ink Animals Handheld computers Lead

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A 110-nW in-channel sigma-delta converter for large-scale neural recording implants

A 110-nW in-channel sigma-delta converter for large-scale ne...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： M. Rezaei E. Maghsoudloo M. Sawan B. Gosselin Dept. of Electrical and Computer Eng. Université Laval Quebec QC Canada Department of Electrical Engineering Polytechnique Montréal Montréal QC Canada

Advancement in wireless and microsystems technology have ushered in new devices that can directly interface with the central nervous system for stimulating and/or monitoring neural circuitry. In this paper, we present an ultra low-power sigma-delta analog-to-digital converter (ADC) intended for utilization into large-scale multi-channel neural recording implants. This proposed design, which provides a resolution of 9 bits using a one-bit oversampled ADC, presents several desirable features that allow for an in-channel ADC scheme, where one sigma-delta converter is provided for each channel, enabling development of scalable systems that can interface with different types of high-density neural microprobes. The proposed circuit, which have been fabricated in a TSMC 180-nm CMOS process, employs a first order noise shaping topology with a passive integrator and a low-supply voltage of 0.6 V to achieve ultra low-power consumption and small size. The proposed ADC clearly outperforms other designs with a power consumption as low as 110 nW for a precision of 9 bits (11-fJ per conversion), a silicon area of only 82 μm × 84 μm and one of the best reported figure of merit among recently published data converters utilized in similar applications.

关键词： Sigma-delta modulation Implants Power demand Wireless communication Topology Feeds Transistors

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Effects of jamming on a multi-agent flocking model with distributed estimation of global features

Effects of jamming on a multi-agent flocking model with dist...

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Future Technologies Conference (FTC)

作者： Zhaoyang Fu Thomas Yang Jing Wang Dept. of Electrical Computer Software & Systems Engr. Embry-Riddle Aeronautical University Daytona Beach FL Department of Electrical and Computer Engineering Bradley University Peoria IL

ISBN: (纸本)9781509041725

In this paper, we study the influence of communication jamming on the operation of multi-agent systems through computer simulations. In our investigation, a simple flocking model with collision avoidance is adopted, and a recently proposed algorithm for distributed estimation of global features is studied as well. The objective of the research is to study the effects of jamming on the achievement of velocity and estimation consensuses. Simulation results indicate that communication jamming delays the achievement of both velocity and estimation consensuses, and the amount of delay roughly increases linearly with the jamming range.

关键词： Jamming Estimation Delays Mathematical model Multi-agent systems Collision avoidance Convergence

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Application of an event-triggered distributed estimation algorithm in a simple multi-agent flocking model

Application of an event-triggered distributed estimation alg...

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IEEE Southeastcon

作者： Thomas Yang Zhaoyang Fu Jing Wang Dept. of Electrical Computer Software & Systems Engr. Embry-Riddle Aeronautical University Daytona Beach FL Department of Electrical and Computer Engineering Bradley University Peoria IL

In this paper, we apply a recently proposed distributed estimation algorithm to a simple multi-agent flocking model with collision avoidance. 3D MATLAB simulations are performed with randomly generated agents' initial positions and velocities. The collision avoidance is realized through a repelling force between agents moderated by an alignment measure. The distributed estimation algorithm estimates certain global feature indicator of the multi-agent systems through consensus of agents' local estimations. The agents exchange information about their local estimations at discrete time instants, which are controlled by an event-triggered mechanism. The algorithm's effectiveness is illustrated through the example of centroid estimation, and simulations show that all agents' local estimations converge to the true position of the group centroid.

关键词： Mathematical model Computational modeling Multi-agent systems Collision avoidance Force Estimation error

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Scheduling for Data Collection in Multi-hop IEEE 802.15.4e TSCH Networks

Scheduling for Data Collection in Multi-hop IEEE 802.15.4e T...

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International Conference on Networking and Network Applications (NaNA)

作者： Tzung-Shi Chen Shu-Yu Kuo Chia-Hsu Kuo Dept. of Computer Sci. and Information Engineering National University of Tainan Tainan Taiwan Department of Software Engineering and Management National Kaohsiung Normal University Kaohsiung Taiwan

ISBN: (纸本)9781467398046

This paper is to develop data collection mechanisms in multi-hop IEEE 802.15.4e Time Slotted Channel Hopping (TSCH) networks. While scheduling, transmission link selection is considered based on the limitations of transmission conflict and transmission interference in ZigBee network topologies. Three scheduling algorithms for data collection are proposed. Furthermore, experiments are made for evaluating the performance of the proposed data scheduling mechanisms for multi-hop IEEE 802.15.4e TSCH networks.

关键词： Internet of things Conferences Data collection ZigBee IEEE Standards Information technology Sensors

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