OpenRaiser/Intern-Atlas huggingface dataset:
Interference Localization for Uplink OFDMA Systems in Presence of CFOs
Venue: arXiv cs.IT 2014
Year: 2014

Multiple carrier frequency offsets (CFOs) present in the uplink of orthogonal frequency division multiple access (OFDMA) systems adversely affect subcarrier orthogonality and impose a serious performance loss. In this paper, we propose the application of time domain receiver windowing to concentrate the leakage caused by CFOs to a few adjacent subcarriers with almost no additional computational complexity. This allows us to approximate the interference matrix with a quasi-banded matrix by neglecting small elements outside a certain band which enables robust and computationally efficient signal detection. The proposed CFO compensation technique is applicable to all types of subcarrier assignment techniques. Simulation results show that the quasi-banded approximation of the interference matrix is accurate enough to provide almost the same bit error rate performance as that of the optimal solution. The excellent performance of our proposed method is also proven through running an experiment using our FPGA-based system setup.
Can Image-Level Labels Replace Pixel-Level Labels for Image Parsing
Venue: arXiv cs.CV 2014
Year: 2014

This paper presents a weakly supervised sparse learning approach to the problem of noisily tagged image parsing, or segmenting all the objects within a noisily tagged image and identifying their categories (i.e. tags). Different from the traditional image parsing that takes pixel-level labels as strong supervisory information, our noisily tagged image parsing is provided with noisy tags of all the images (i.e. image-level labels), which is a natural setting for social image collections (e.g. Flickr). By oversegmenting all the images into regions, we formulate noisily tagged image parsing as a weakly supervised sparse learning problem over all the regions, where the initial labels of each region are inferred from image-level labels. Furthermore, we develop an efficient algorithm to solve such weakly supervised sparse learning problem. The experimental results on two benchmark datasets show the effectiveness of our approach. More notably, the reported surprising results shed some light on answering the question: can image-level labels replace pixel-level labels (hard to access) as supervisory information for image parsing.
Optimal Patching in Clustered Malware Epidemics
Venue: arXiv cs.CR 2014
Year: 2014

Studies on the propagation of malware in mobile networks have revealed that the spread of malware can be highly inhomogeneous. Platform diversity, contact list utilization by the malware, clustering in the network structure, etc. can also lead to differing spreading rates. In this paper, a general formal framework is proposed for leveraging such heterogeneity to derive optimal patching policies that attain the minimum aggregate cost due to the spread of malware and the surcharge of patching. Using Pontryagin's Maximum Principle for a stratified epidemic model, it is analytically proven that in the mean-field deterministic regime, optimal patch disseminations are simple single-threshold policies. Through numerical simulations, the behavior of optimal patching policies is investigated in sample topologies and their advantages are demonstrated.
Rigid-Motion Scattering for Texture Classification
Venue: arXiv cs.CV 2014
Year: 2014

A rigid-motion scattering computes adaptive invariants along translations and rotations, with a deep convolutional network. Convolutions are calculated on the rigid-motion group, with wavelets defined on the translation and rotation variables. It preserves joint rotation and translation information, while providing global invariants at any desired scale. Texture classification is studied, through the characterization of stationary processes from a single realization. State-of-the-art results are obtained on multiple texture data bases, with important rotation and scaling variabilities.
Privacy Failures in Encrypted Messaging Services: Apple iMessage and Beyond
Venue: arXiv cs.CR 2014
Year: 2014

Instant messaging services are quickly becoming the most dominant form of communication among consumers around the world. Apple iMessage, for example, handles over 2 billion message each day, while WhatsApp claims 16 billion messages from 400 million international users. To protect user privacy, these services typically implement end-to-end and transport layer encryption, which are meant to make eavesdropping infeasible even for the service providers themselves. In this paper, however, we show that it is possible for an eavesdropper to learn information about user actions, the language of messages, and even the length of those messages with greater than 96% accuracy despite the use of state-of-the-art encryption technologies simply by observing the sizes of encrypted packet. While our evaluation focuses on Apple iMessage, the attacks are completely generic and we show how they can be applied to many popular messaging services, including WhatsApp, Viber, and Telegram.
Text Entry Method Affects Password Security
Venue: arXiv cs.CR 2014
Year: 2014

Text-based passwords continue to be the prime form of authentication to computer systems. Today, they are increasingly created and used with mobile text entry methods, such as touchscreens and mobile keyboards, in addition to traditional physical keyboards. This raises a foundational question for usable security: whether text entry methods affect password generation and password security. This paper presents results from a between-group study with 63 participants, in which each group generated passwords for multiple virtual accounts using a different text entry method. Participants were also asked to recall their passwords afterwards. We applied analysis of structures and probabilities, with standard and recent security metrics and also performed cracking attacks on the collected data. The results show a significant effect of text entry methods on passwords. In particular, one of the experimental groups created passwords with significantly more lowercase letters per password than the control group ($t(60) = 2.99, p = 0.004$). The choices for character types in each group were also significantly different ($p=0.048, FET$). Our cracking attacks consequently expose significantly different resistance across groups ($p=0.031, FET$) and text entry method vulnerabilities. Our findings contribute to the understanding of password security in the context of usable interfaces.
An IAC Approach for Detecting Profile Cloning in Online Social Networks
Venue: arXiv cs.SI 2014
Year: 2014

Nowadays, Online Social Networks are popular websites on the internet, which millions of users register on and share their own personal information with others. Privacy threats and disclosing personal information are the most important concerns of OSNs users. Recently, a new attack which is named Identity Cloned Attack is detected on OSNs. In this attack the attacker tries to make a fake identity of a real user in order to access to private information of the users friends which they do not publish on the public profiles. In today OSNs, there are some verification services, but they are not active services and they are useful for users who are familiar with online identity issues. In this paper, Identity cloned attacks are explained in more details and a new and precise method to detect profile cloning in online social networks is proposed. In this method, first, the social network is shown in a form of graph, then, according to similarities among users, this graph is divided into smaller communities. Afterwards, all of the similar profiles to the real profile are gathered (from the same community), then strength of relationship (among all selected profiles and the real profile) is calculated, and those which have the less strength of relationship will be verified by mutual friend system. In this study, in order to evaluate the effectiveness of proposed method, all steps are applied on a dataset of Facebook, and finally this work is compared with two previous works by applying them on the dataset.
Era of Big Data Processing: A New Approach via Tensor Networks and Tensor Decompositions
Venue: arXiv cs.ET 2014
Year: 2014

Many problems in computational neuroscience, neuroinformatics, pattern/image recognition, signal processing and machine learning generate massive amounts of multidimensional data with multiple aspects and high dimensionality. Tensors (i.e., multi-way arrays) provide often a natural and compact representation for such massive multidimensional data via suitable low-rank approximations. Big data analytics require novel technologies to efficiently process huge datasets within tolerable elapsed times. Such a new emerging technology for multidimensional big data is a multiway analysis via tensor networks (TNs) and tensor decompositions (TDs) which represent tensors by sets of factor (component) matrices and lower-order (core) tensors. Dynamic tensor analysis allows us to discover meaningful hidden structures of complex data and to perform generalizations by capturing multi-linear and multi-aspect relationships. We will discuss some fundamental TN models, their mathematical and graphical descriptions and associated learning algorithms for large-scale TDs and TNs, with many potential applications including: Anomaly detection, feature extraction, classification, cluster analysis, data fusion and integration, pattern recognition, predictive modeling, regression, time series analysis and multiway component analysis. Keywords: Large-scale HOSVD, Tensor decompositions, CPD, Tucker models, Hierarchical Tucker (HT) decomposition, low-rank tensor approximations (LRA), Tensorization/Quantization, tensor train (TT/QTT) - Matrix Product States (MPS), Matrix Product Operator (MPO), DMRG, Strong Kronecker Product (SKP).
Application of Asynchronous Weak Commitment Search in Autonomous Quality of Service Provision in Cognitive Radio Networks
Venue: arXiv cs.NI 2014
Year: 2014

This article presents a distributed solution to autonomous quality of service provision in cognitive radio networks. Specifically, cognitive STDMA and CDMA communication networks are studied. Based on asynchronous weak commitment search the task of QoS provision is distributed among different network nodes. Simulation results verify this scheme converges very fast to optimal solution, which makes it suitable for practical real time systems. This application of artificial intelligence in wireless and mobile communications can be used in home automation and networking, and vehicular technology. The generalizations and extensions of this approach can be used in Long Term Evolution Self Organizing Networks (LTE-SONs). In addition, it can pave the way for decentralized and autonomous QoS provision in capillary networks that reach end nodes at Internet of Things, where central management is either unavailable or not efficient.
Diversity of Linear Transceivers in MIMO AF Half-duplex Relaying Channels
Venue: arXiv cs.IT 2014
Year: 2014

Linear transceiving schemes between the relay and the destination have recently attracted much interest in MIMO amplify-and-forward (AF) relaying systems due to low implementation complexity. In this paper, we provide comprehensive analysis on the diversity order of the linear zero-forcing (ZF) and minimum mean squared error (MMSE) transceivers. Firstly, we obtain a compact closed-form expression for the diversity-multiplexing tradeoff (DMT) through tight upper and lower bounds. While our DMT analysis accurately predicts the performance of the ZF transceivers, it is observed that the MMSE transceivers exhibit a complicated rate dependent behavior, and thus are very unpredictable via DMT for finite rate cases. Secondly, we highlight this interesting behavior of the MMSE transceivers and characterize the diversity order at all finite rates. This leads to a closed-form expression for the diversity-rate tradeoff (DRT) which reveals the relationship between the diversity, the rate, and the number of antennas at each node. Our DRT analysis compliments our previous work on DMT, thereby providing a complete understanding on the diversity order of linear transceiving schemes in MIMO AF relaying channels.
Non-linear mass-spring system for large soft tissue deformations modeling
Venue: arXiv cs.NA 2014
Year: 2014

Implant placement under soft tissues operation is described. In this operation tissues can reach such deformations that nonlinear properties are appeared. A mass-spring model modification for modeling nonlinear tissue operation is developed. A method for creating elasticity module using splines is described. For Poisson ratio different stiffness for different types of springs in cubic grid is used. For stiffness finding an equation system that described material tension is solved. The model is verified with quadratic sample tension experiment. These tests show that sample tension under external forces is equal to defined nonlinear elasticity module. The accuracy of Poisson ratio modeling is thirty five percent that is better the results of available ratio modeling method.
Generalised Mixability, Constant Regret, and Bayesian Updating
Venue: arXiv cs.LG 2014
Year: 2014

Mixability of a loss is known to characterise when constant regret bounds are achievable in games of prediction with expert advice through the use of Vovk's aggregating algorithm. We provide a new interpretation of mixability via convex analysis that highlights the role of the Kullback-Leibler divergence in its definition. This naturally generalises to what we call $Φ$-mixability where the Bregman divergence $D_Φ$ replaces the KL divergence. We prove that losses that are $Φ$-mixable also enjoy constant regret bounds via a generalised aggregating algorithm that is similar to mirror descent.
Optimal interval clustering: Application to Bregman clustering and statistical mixture learning
Venue: arXiv cs.IT 2014
Year: 2014

We present a generic dynamic programming method to compute the optimal clustering of $n$ scalar elements into $k$ pairwise disjoint intervals. This case includes 1D Euclidean $k$-means, $k$-medoids, $k$-medians, $k$-centers, etc. We extend the method to incorporate cluster size constraints and show how to choose the appropriate $k$ by model selection. Finally, we illustrate and refine the method on two case studies: Bregman clustering and statistical mixture learning maximizing the complete likelihood.
Theoretical Evaluation of Offloading through Wireless LANs
Venue: arXiv cs.PF 2014
Year: 2014

Offloading of cellular traffic through a wireless local area network (WLAN) is theoretically evaluated. First, empirical data sets of the locations of WLAN internet access points are analyzed and an inhomogeneous Poisson process consisting of high, normal, and low density regions is proposed as a spatial point process model for these configurations. Second, performance metrics, such as mean available bandwidth for a user and the number of vertical handovers, are evaluated for the proposed model through geometric analysis. Explicit formulas are derived for the metrics, although they depend on many parameters such as the number of WLAN access points, the shape of each WLAN coverage region, the location of each WLAN access point, the available bandwidth (bps) of the WLAN, and the shape and available bandwidth (bps) of each subregion identified by the channel quality indicator in a cell of the cellular network. Explicit formulas strongly suggest that the bandwidth a user experiences does not depend on the user mobility. This is because the bandwidth available by a user who does not move and that available by a user who moves are the same or approximately the same as a probabilistic distribution. Numerical examples show that parameters, such as the size of regions where placement of WLAN access points is not allowed and the mean density of WLANs in high density regions, have a large impact on performance metrics. In particular, a homogeneous Poisson process model as the WLAN access point location model largely overestimates the mean available bandwidth for a user and the number of vertical handovers. The overestimated mean available bandwidth is, for example, about 50% in a certain condition.
Detection of Node Clones in Wireless Sensor Network Using Detection Protocols
Venue: arXiv cs.NI 2014
Year: 2014

Wireless sensor networks consist of hundreds to thousands of sensor nodes and are widely used in civilian and security applications. One of the serious physical attacks faced by the wireless sensor network is node clone attack. Thus two node clone detection protocols are introduced via distributed hash table and randomly directed exploration to detect node clones. The former is based on a hash table value which is already distributed and provides key based facilities like checking and caching to detect node clones. The later one is using probabilistic directed forwarding technique and border determination. The simulation results for storage consumption, communication cost and detection probability is done using NS2 and obtained randomly directed exploration is the best one having low communication cost and storage consumption and has good detection probability. Keywords: wireless sensor networks (wsn), distributed hash table, randomly directed exploration.
Handling Large and Complex Data in a Photovoltaic Research Institution Using a Custom Laboratory Information Management System
Venue: arXiv cs.DL 2014
Year: 2014

Twenty-five years ago the desktop computer started becoming ubiquitous in the scientific lab. Researchers were delighted with its ability to both control instrumentation and acquire data on a single system, but they were not completely satisfied. There were often gaps in knowledge that they thought might be gained if they just had more data and they could get the data faster. Computer technology has evolved in keeping with Moore's Law meeting those desires; however those improvement have of late become both a boon and bane for researchers. Computers are now capable of producing high speed data streams containing terabytes of information; capabilities that evolved faster than envisioned last century. Software to handle large scientific data sets has not kept up. How much information might be lost through accidental mismanagement or how many discoveries are missed through data overload are now vital questions. An important new task in most scientific disciplines involves developing methods to address those issues and to create software that can handle large data sets with an eye towards scalability. This software must create archived, indexed, and searchable data from heterogeneous instrumentation for the implementation of a strong data-driven materials development strategy. At the National Center for Photovoltaics in the National Renewable Energy Lab, we began development a few years ago on a Laboratory Information Management System (LIMS) designed to handle lab-wide scientific data acquisition, management, processing, and mining needs for physics and materials science data. and with a specific focus on future scalability for new equipment or research focuses. We will present the decisions, process, and problems we went through while building our LIMS for materials research, its current operational state, and our steps for future development.
A consistent model for cardiac deformation estimation under abnormal ventricular muscle conditions
Venue: arXiv cs.CE 2014
Year: 2014

Deformation modeling of cardiac muscle is an important issue in the field of cardiac analysis. For this reason, many approaches have been developed to best estimate the cardiac muscle deformation, and to obtain a practical model to use in diagnostic procedures. But there are some conditions, like in case of myocardial infarction, in which the regular modeling approaches are not useful. In this section, using a point-wise approach in deformation estimation, we try to estimate the deformation under some abnormal conditions of cardiac muscle. First, the endocardial and epicardial contour points are ordered with respect to the center of gravity of endocardial contour and boundary point displacement vectors are extracted. Then to solve the governing equation of deformation, which is an elliptic equation, we apply boundary conditions in accordance with the computed displacement vectors and then the Finite Element method (FEM) will be used to solve the governing equation. Using obtained displacement field through the cardiac muscle, strain map is extracted to show the mechanical behavior of cardiac muscle. To validate the proposed algorithm in case of infracted muscle, a non-homogeneous ring is modeled using ANSYS under a uniform time varying internal pressure, which is the case in real cardiac muscle deformation and then the proposed algorithm implemented in MATLAB and the results for such problem are extracted.
Towards an Agent-Oriented Modeling and Evaluation Approach For Vehicular Systems Security
Venue: arXiv cs.SE 2014
Year: 2014

Agent technology is a software paradigm that permits to implement large and complex distributed applications. In order to assist the development of multi-agent systems, agent-oriented methodologies (AOM) have been created in the last years to support modeling more and more complex applications in many different domains. By defining in a non-ambiguous way concepts used in a specific domain, Meta modeling may represent a step towards such interoperability. In the Transport domain, this paper propose an agent-oriented meta-model that provides rigorous concepts for conducting transportation system problem modeling. The aim is to allow analysts to produce a transportation system model that precisely captures the knowledge of an organization so that an agent-oriented requirements specification of the system-to-be and its operational corporate environment can be derived from it. To this end, we extend and adapt an existing meta-model, Extended Gaia, to build a meta-model and an adequate model for transportation problems. Our new agent-oriented meta-model aims to allow the analyst to model and specify any transportation system as a multi-agent system. Based on the proposed meta-model, we proposes an approach for modeling and evaluating the Transportation System based on Stochastic Activity Network (SAN) components. The proposed process is based on seven steps from Recognition phase to Quantitative Analysis phase. These analyzes are based on the Dependability models which are built using the formalism Stochastic Activity Network. A real case study of Urban Public Transportation System has been conducted to show the benefits of the approach.
Application of Particle Swarm Optimization to Microwave Tapered Microstrip Lines
Venue: arXiv cs.NE 2014
Year: 2014

Application of metaheuristic algorithms has been of continued interest in the field of electrical engineering because of their powerful features. In this work special design is done for a tapered transmission line used for matching an arbitrary real load to a 50Ω line. The problem at hand is to match this arbitrary load to 50 Ω line using three section tapered transmission line with impedances in decreasing order from the load. So the problem becomes optimizing an equation with three unknowns with various conditions. The optimized values are obtained using Particle Swarm Optimization. It can easily be shown that PSO is very strong in solving this kind of multiobjective optimization problems.