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arXiv 2017年

作者： Jin, Xin Wu, Le Li, Xiaodong Chen, Siyu Peng, Siwei Chi, Jingying Ge, Shiming Song, Chenggen Zhao, Geng Department of Computer Science and Technology Beijing Electronic Science and Technology Institute Beijing100070 China Department of Information Security Beijing Electronic Science and Technology Institute Beijing100070 China College of Information Science and Technology Beijing University of Chemical Technology Beijing100029 China Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China

Aesthetic quality prediction is a challenging task in the computer vision community because of the complex interplay with semantic contents and photographic technologies. Recent studies on the powerful deep learning based aesthetic quality assessment usually use a binary high-low label or a numerical score to represent the aesthetic quality. However the scalar representation cannot describe well the underlying varieties of the human perception of aesthetics. In this work, we propose to predict the aesthetic score distribution (i.e., a score distribution vector of the ordinal basic human ratings) using Deep Convolutional Neural Network (DCNN). Conventional DCNNs which aim to minimize the difference between the predicted scalar numbers or vectors and the ground truth cannot be directly used for the ordinal basic rating distribution. Thus, a novel CNN based on the Cumulative distribution with Jensen-Shannon divergence (CJS-CNN) is presented to predict the aesthetic score distribution of human ratings, with a new reliability-sensitive learning method based on the kurtosis of the score distribution, which eliminates the requirement of the original full data of human ratings (without normalization). Experimental results on large scale aesthetic dataset demonstrate the effectiveness of our introduced CJS-CNN in this task. Copyright © 2017, The Authors. All rights reserved.

关键词： Forecasting

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Energy efficient genetic inspired scheduling for data aggregation

International Journal of Control and Automation

引用

International Journal of Control and Automation 2016年第3期9卷 227-234页

作者： Madhavi, S. Kim, Tai Honn Department of Computer Science and Engineering P.V.P. Siddhartha Institute of Technology Andhra Pradesh India Department of Convergence Security Sungshin Women's University 249-1 Dongseon-dong 3 - ga Seoul Korea Republic of

Energy efficient Data Aggregation Scheduling is essential in Ubiquitous sensor networks. Optimal communication distances between the nodes and the base station improves the network lifetime. Clustering improves the efficiency of the data aggregation problem. Many clustering techniques exist to find the optimal number of clusters in the network. The computation complexity of the methods to obtain optimal number of clusters is fair when genetic approaches are employed instead of the classical approaches. Since nodes in USN are dynamic, finding data aggregation schedules is difficult in a USN. Concurrent transmissions improve the throughput of the network, but the SINR perceived at the receiver should be greater than or equal to a certain threshold value for a successful transmission. Also the cluster heads and the member nodes dissipate energy. This power dissipation is more when there are more number of cluster heads. The noise and the power dissipation play a major role in decreasing the network life time. Hence in this paper we designed an efficient energy dissipation algorithm for data aggregation scheduling using the principles of Genetic algorithm and SINR. Since the battery power and bandwidth are the limited resources for the nodes in the USN our data aggregation scheduling algorithm gives equal priority for all of the following while electing a node as a cluster head. They are (a) Total power dissipation from all cluster heads in the USN, (b) The total power dissipation from the nodes at each cluster heads, (c) The SINR perceived at the cluster head should be more than a threshold value at each cluster head for the transmission to be successful, (d) The optimal schedule for the nodes in the USN so that all the nodes transmits their data finally to the base station quickly. With the application of the genetic methods our algorithm proved to be efficient when compared with the existing algorithms in obtaining maximum network life time with minimum number of clu

关键词： Base stations

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A consequence of invertibility on the normal form of a MIMO nonlinear systems

引用

IFAC-PapersOnLine 2016年第18期49卷 856-861页

作者： Wang, Lei Isidori, Alberto Marconi, Lorenzo Su, Hongye State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China CASY - DEI University of Bologna Italy Department of Computer Control and Management Engineering University of Rome "La Sapienza" Rome Italy

In this paper, a relevant consequence of invertibility on the normal form of MIMO nonlinear systems is presented. Making full use of the definition of uniform invertibility in the sense of Singh, the multipliers δi+1(x)’s in the normal form are shown to possess a special property, that is to be triangularly dependent on the x2,i’s. As a matter of fact, this property turns out to be necessary and sufficient for uniform invertibility in the case of a two-input two-output systems having normal form. Moreover, we also show that invertibility, or this special property of the multipliers, is a sufficient condition of uniform complete observability. © 2016

关键词： Nonlinear systems MIMO systems Observability Delta I Invertibility MIMO nonlinear systems Multi input multi output nonlinear systems Normal form Special properties Two input two output systems Uniform complete observability

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JPEG steganalysis based on DenseNet

arXiv

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arXiv 2017年

作者： Yang, Jianhua Shi, Yun-Qing Wong, Edward K. Kang, Xiangui Guangdong Key Lab of Information Security School of Data and Computer Science Sun Yat-Sen University Guangzhou510006 China Department of ECE New Jersey Institute of Technology NewarkNJ07102 United States Department of Computer Science and Engineering New York University Tandon School of Engineering BrooklynNY11201 United States

Different from the conventional deep learning work based on an images content in computer vision, deep steganalysis is an art to detect the secret information embedded in an image via deep learning, pose challenge of detection weak information invisible hidden in a host image thus learning in a very low signal-to-noise (SNR) case. In this paper, we propose a 32- layer convolutional neural Networks (CNNs) in to improve the efficiency of preprocess and reuse the features by concatenating all features from the previous layers with the same featuremap size, thus improve the flow of information and gradient. The shared features and bottleneck layers further improve the feature propagation and reduce the CNN model parameters dramatically. Experimental results on the BOSSbase, BOWS2 and ImageNet datasets have showed that the proposed CNN architecture can improve the performance and enhance the robustness. To further boost the detection accuracy, an ensemble architecture called as CNN-SCA-GFR is proposed, CNN-SCAGFR is also the first work to combine the CNN architecture and conventional method in the JPEG domain. Experiments show that it can further lower detection errors. Compared with the state-of-the-art method XuNet [1] on BOSSbase, the proposed CNN-SCA-GFR architecture can reduce detection error rate by 5.67% for 0.1 bpnzAC and by 4.41% for 0.4 bpnzAC while the number of training parameters in CNN is only 17% of what used by XuNet. It also decreases the detection errors from the conventional method SCA-GFR by 7.89% for 0.1 bpnzAC and 8.06% for 0.4 bpnzAC, respectively. Copyright © 2017, The Authors. All rights reserved.

关键词： Steganography

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Secure mobile technologies for proactive critical infrastructure situational awareness

Secure mobile technologies for proactive critical infrastruc...

引用

IEEE Conference on Technologies for Homeland security

作者： Gabriel Salles-Loustau Vidyasagar Sadhu Dario Pompili Saman Zonouz Vincent Sritapan Department of Electrical and Computer Engineering Rutgers University Cyber Security Division Rutgers University

ISBN: (纸本)9781509007714

Trustworthy operation of our national critical infrastructures, such as the electricity grid, against adversarial parties and accidental failures requires constant and secure monitoring capabilities. In this paper, Eyephone is presented to leverage secure smartphone sensing and data acquisition capabilities and enable pervasive sensing of the national critical infrastructures. The reported information by the smartphone users will notify the control center operators about particular accidental or malicious remote critical infrastructure incidents. The reporting will be proactive regarding potentially upcoming failures given the system's current risky situation, e.g., a tree close to fall on a power grid transmission line. The information will include various modalities such as images, video, audio, time and location. Eyephone will use system-wide information flow analysis and policy enforcement to prevent user privacy violations during the incident reportings. A working proof-of-concept prototype of Eyephone is implemented. Our results show that Eyephone allows secure and effective use of smartphones for real-time situational awareness of our national critical infrastructures.

关键词： Sensors Power grids Smart phones Access control Monitoring

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Author Correction: National identity predicts public health support during a global pandemic

引用

Nature communications 2022年第1期13卷 1949页

作者： Jay J Van Bavel Aleksandra Cichocka Valerio Capraro Hallgeir Sjåstad John B Nezlek Tomislav Pavlović Mark Alfano Michele J Gelfand Flavio Azevedo Michèle D Birtel Aleksandra Cislak Patricia L Lockwood Robert Malcolm Ross Koen Abts Elena Agadullina John Jamir Benzon Aruta Sahba Nomvula Besharati Alexander Bor Becky L Choma Charles David Crabtree William A Cunningham Koustav De Waqas Ejaz Christian T Elbaek Andrej Findor Daniel Flichtentrei Renata Franc Biljana Gjoneska June Gruber Estrella Gualda Yusaku Horiuchi Toan Luu Duc Huynh Agustin Ibanez Mostak Ahamed Imran Jacob Israelashvili Katarzyna Jasko Jaroslaw Kantorowicz Elena Kantorowicz-Reznichenko André Krouwel Michael Laakasuo Claus Lamm Caroline Leygue Ming-Jen Lin Mohammad Sabbir Mansoor Antoine Marie Lewend Mayiwar Honorata Mazepus Cillian McHugh John Paul Minda Panagiotis Mitkidis Andreas Olsson Tobias Otterbring Dominic J Packer Anat Perry Michael Bang Petersen Arathy Puthillam Julián C Riaño-Moreno Tobias Rothmund Hernando Santamaría-García Petra C Schmid Drozdstoy Stoyanov Shruti Tewari Bojan Todosijević Manos Tsakiris Hans H Tung Radu G Umbreș Edmunds Vanags Madalina Vlasceanu Andrew Vonasch Meltem Yucel Yucheng Zhang Mohcine Abad Eli Adler Narin Akrawi Hamza Alaoui Mdarhri Hanane Amara David M Amodio Benedict G Antazo Matthew Apps F Ceren Ay Mouhamadou Hady Ba Sergio Barbosa Brock Bastian Anton Berg Maria P Bernal-Zárate Michael Bernstein Michał Białek Ennio Bilancini Natalia Bogatyreva Leonardo Boncinelli Jonathan E Booth Sylvie Borau Ondrej Buchel C Daryl Cameron Chrissie F Carvalho Tatiana Celadin Chiara Cerami Hom Nath Chalise Xiaojun Cheng Luca Cian Kate Cockcroft Jane Conway Mateo Andres Córdoba-Delgado Chiara Crespi Marie Crouzevialle Jo Cutler Marzena Cypryańska Justyna Dabrowska Michael A Daniels Victoria H Davis Pamala N Dayley Sylvain Delouvee Ognjan Denkovski Guillaume Dezecache Nathan A Dhaliwal Alelie B Diato Roberto Di Paolo Marianna Drosinou Uwe Dulleck Jānis Ekmanis Arhan S Ertan Tom W Etienne Hapsa Hossain Farhana Fahima Farkhari Har Department of Psychology and Neural Science New York University New York NY USA. jay.vanbavel@nyu.edu. School of Psychology University of Kent Canterbury England. Department of Economics Middlesex University London London England. Department of Strategy and Management Norwegian School of Economics Bergen Norway. SWPS University of Social Sciences and Humanities Poznań Poland. Department of Psychological Sciences College of William and Mary Williamsburg VA USA. Institute of Social Sciences Ivo Pilar Zagreb Croatia. Department of Philosophy Macquarie University Sydney NSW Australia. Stanford Graduate School of Business Stanford University Stanford CA USA. Institute of Communication Science Friedrich-Schiller University Jena Jena Germany. flavio.azevedo@uni-jena.de. School of Human Sciences Institute for Lifecourse Development University of Greenwich London England. Department of Experimental Psychology University of Oxford Oxford England. Center for Human Brain Health School of Psychology University of Birmingham Birmingham England. Department of Psychology Macquarie University Sydney NSW Australia. KU Leuven Leuven Belgium. National Research University Higher School of Economics (HSE) Moscow Russia. De La Salle University Manila Philippines. Department of Psychology University of the Witwatersrand Johannesburg South Africa. Department of Political Science Aarhus University Aarhus Denmark. X University Toronto ON Canada. Department of Government Dartmouth College Hanover NH USA. Department of Psychology University of Toronto Toronto ON Canada. Gatton College of Business and Economics University of Kentucky Lexington KY USA. Department of Mass Communication National University of Science and Technology (NUST) Islamabad Pakistan. Department of Management Aarhus University Aarhus Denmark. Faculty of Social and Economic Sciences Comenius University Bratislava Slovakia. IntraMed Buenos Aires Argentina. Macedonian Academy of Sciences and

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Constructing a Science of cyber-Resilience for Military Systems

Constructing a Science of Cyber-Resilience for Military Syst...

引用

2017 NATO IST-153/RWS-21 Workshop on cyber Resilience, IST-153 2017

作者： Alexeev, Alexander Henshel, Diane S. Levitt, Karl McDaniel, Patrick Rivera, Brian Templeton, Steven Weisman, Mike Data Analysis and Modeling Indiana University School of Public and Environmental Affairs 1315 E 10th Street BlooomingtonIN47405 Indiana University School of Public and Environmental Affairs 1315 E 10th Street BloomingtonIN47405 Computer Science College of Engineering University of California Davis 1 Shields Ave. DavisCA05616 School of Electrical Engineering and Computer Science Pennsylvania State University Information Sciences and Technology Building University ParkPA16802-6823 Network Science Division Tactical Network Assurance Branch U.S. Army Research Laboratory 2800 Powder Mill Road AdelphiMD20783 Computer Security Research Lab Department of Computer Science University of California Davis One Shields Avenue DavisCA95616 Network Science Division Network Security Branch U.S. Army Research Laboratory ICF International 2800 Powder Mill Road AdelphiMD20783

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Poisson multi-Bernoulli mixture filter: Direct derivation and implementation

arXiv

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arXiv 2017年

作者： García-Fernández, Ángel F. Williams, Jason L. Granström, Karl Svensson, Lennart Department of Electrical Engineering and Electronics University of Liverpool LiverpoolL69 3GJ United Kingdom National Security and ISR Division Defence Science and Technology Group Edinburgh SA5111 Australia School of Electrical Engineering and Computer Science Queensland University of Technology BrisbaneQLD4000 Australia Department of Electrical Engineering Chalmers University of Technology GothenburgSE-412 96 Sweden

We provide a derivation of the Poisson multi-Bernoulli mixture (PMBM) filter for multi-target tracking with the standard point target measurements without using probability generating functionals or functional derivatives. We also establish the connection with the δ-generalised labelled multi-Bernoulli (δ-GLMB) filter, showing that a δ-GLMB density represents a multi-Bernoulli mixture with labelled targets so it can be seen as a special case of PMBM. In addition, we propose an implementation for linear/Gaussian dynamic and measurement models and how to efficiently obtain typical estimators in the literature from the PMBM. The PMBM filter is shown to outperform other filters in the literature in a challenging scenario. Copyright © 2017, The Authors. All rights reserved.

关键词： Clutter (information theory)

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An access control system for intelligent buildings 9

An access control system for intelligent buildings

引用

9th EAI International Conference on Mobile Multimedia Communications, MOBIMEDIA 2016

作者： Xue, Nian Liang, Lulu Zhang, Jie Huang, Xin Department of Computer Science and Software Engineering Xi'an Jiaotong-Liverpool University China School of Electrical Engineering and Electronics and Computer Science University of Liverpool United Kingdom China Information Technology Security Evaluation Center China

Accompanied with accelerated development of the Internet of Things (IoT) and smart home industry, smart access control systems, e.g. smart locks, are getting popular in recent years. In this paper, we propose a smart access control system that allows users to enter buildings via mobile phones. Also, a protocol is designed to secure communications in the system. In addition, some investigation results regarding security and usability are found: (1) most people pay more attention to security in the access control system;(2) most people prefer using fingerprint as the primary unlocking mechanism. © 2016 EAI.

关键词： Access control

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Analysis of Te and TeO<sub>2</sub>on CdZnTe Nuclear Detectors Treated with Hydrogen Bromide and Ammonium-Based Solutions

引用

Journal of Materials Science and Chemical engineering 2017年第4期5卷 9-18页

作者： Mebougna L. Drabo Stephen U. Egarievwe Ifechukwude O. Okwechime Dominique E. Jones Anwar Hossain Ralph B. James Department of Mechanical & Civil Engineering and Construction Management Alabama A&M University Normal AL USA Department of Electrical Engineering and Computer Science Alabama A&M University Normal AL USA Nuclear Engineering and Radiological Science Center Alabama A&M University Normal AL USA Department of Nonproliferation and National Security Brookhaven National Laboratory Upton NY USA Science and Technology Savannah River National Laboratory Aiken SC USA

Surface defects caused during cutting and polishing in the fabrication of cadmium zinc telluride (CdZnTe) nuclear detectors limit their spectral performance. Chemical treatments are often used to remove surface damages and defects. In this paper, we present the analysis of Te and TeO2 species on the surfaces of CdZnTe nuclear detectors treated with hydrogen bromide and ammonium-based solutions. The CdZnTe wafers were chemo-mechanically polished in a mixture of hydrogen bromide in hydrogen peroxide and ethylene glycol, followed by a chemical passivation in a mixture of ammonium fluoride and hydrogen peroxide solution. X-ray photoelectron spectroscopy showed significant conversion of Te to TeO2, thus producing a more chemically stable surface. The resistivity of the CdZnTe samples is in the order of 1010 ohms-cm. The current for a given applied voltage increased following the passivation and decreased after a 3-hour period. Results from spectral response measurements showed that the 59.5-keV gamma-peak of Am-241 was stable under the same channel for the surface treatment processes.

关键词： CdZnTe Chemical Treatment Nuclear Detectors Surface Passivation XPS

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