OpenRaiser/Intern-Atlas huggingface dataset:
Acoustic Scene Classification
Venue: arXiv cs.SD 2014
Year: 2014

In this article we present an account of the state-of-the-art in acoustic scene classification (ASC), the task of classifying environments from the sounds they produce. Starting from a historical review of previous research in this area, we define a general framework for ASC and present different imple- mentations of its components. We then describe a range of different algorithms submitted for a data challenge that was held to provide a general and fair benchmark for ASC techniques. The dataset recorded for this purpose is presented, along with the performance metrics that are used to evaluate the algorithms and statistical significance tests to compare the submitted methods. We use a baseline method that employs MFCCS, GMMS and a maximum likelihood criterion as a benchmark, and only find sufficient evidence to conclude that three algorithms significantly outperform it. We also evaluate the human classification accuracy in performing a similar classification task. The best performing algorithm achieves a mean accuracy that matches the median accuracy obtained by humans, and common pairs of classes are misclassified by both computers and humans. However, all acoustic scenes are correctly classified by at least some individuals, while there are scenes that are misclassified by all algorithms.
Holistic Collaborative Privacy Framework for Users' Privacy in Social Recommender Service
Venue: arXiv cs.CR 2014
Year: 2014

The current business model for existing recommender services is centered around the availability of users' personal data at their side whereas consumers have to trust that the recommender service providers will not use their data in a malicious way. With the increasing number of cases for privacy breaches, different countries and corporations have issued privacy laws and regulations to define the best practices for the protection of personal information. The data protection directive 95/46/EC and the privacy principles established by the Organization for Economic Cooperation and Development (OECD) are examples of such regulation frameworks. In this paper, we assert that utilizing third-party recommender services to generate accurate referrals are feasible, while preserving the privacy of the users' sensitive information which will be residing on a clear form only on his/her own device. As a result, each user who benefits from the third-party recommender service will have absolute control over what to release from his/her own preferences. We proposed a collaborative privacy middleware that executes a two stage concealment process within a distributed data collection protocol in order to attain this claim. Additionally, the proposed solution complies with one of the common privacy regulation frameworks for fair information practice in a natural and functional way -which is OECD privacy principles. The approach presented in this paper is easily integrated into the current business model as it is implemented using a middleware that runs at the end-users side and utilizes the social nature of content distribution services to implement a topological data collection protocol.
Cryptographically Blinded Games: Leveraging Players' Limitations for Equilibria and Profit
Venue: arXiv cs.GT 2014
Year: 2014

In this work we apply methods from cryptography to enable any number of mutually distrusting players to implement broad classes of mediated equilibria of strategic games without the need for trusted mediation. Our implementation makes use of a (standard) pre-play "cheap talk" phase, in which players engage in free and non-binding communication prior to playing in the original game. In our cheap talk phase, the players execute a secure multi-party computation protocol to sample an action profile from an equilibrium of a "cryptographically blinded" version of the original game, in which actions are encrypted. The essence of our approach is to exploit the power of encryption to selectively restrict the information available to players about sampled action profiles, such that these desirable equilibria can be stably achieved. In contrast to previous applications of cryptography to game theory, this work is the first to employ the paradigm of using encryption to allow players to benefit from hiding information \emph{from themselves}, rather than from others; and we stress that rational players would \emph{choose} to hide the information from themselves.
Pouring Cloud Virtualization Security Inside Out
Venue: arXiv cs.CR 2014
Year: 2014

In this article, virtualization security concerns in the cloud computing domain are reviewed. The focus is toward virtual machine (VM) security where attacks and vulnerabilities such as VM escape, VM hopping, cross-VM side-channel, VM-based rootkits (VMBRs), VM mobility, and VM remote are mentioned and discussed according to their relevance in the clouds. For each attack we outline how they affect the security of cloud systems. Countermeasures and security measures to detect or prevent them through techniques such as VM detection, GuardHype, VM introspection, VM image scanning, etc. are also discussed. Through the surveyed work we present a classification of VM threats within the clouds. Finally, we include our observations and those of other researchers on this matter of cloud virtualization security.
Performance Evaluation of Virtualized Hadoop Clusters
Venue: arXiv cs.DC 2014
Year: 2014

In this report we investigate the performance of Hadoop clusters, deployed with separated storage and compute layers, on top of a hypervisor managing a single physical host. We have analyzed and evaluated the different Hadoop cluster configurations by running CPU bound and I/O bound workloads. The report is structured as follows: Section 2 provides a brief description of the technologies involved in our study. An overview of the experimental platform, setup test and configurations are presented in Section 3. Our benchmark methodology is defined in Section 4. The performed experiments together with the evaluation of the results are presented in Section 5. Finally, Section 6 concludes with lessons learned.
On the Performance of Secure Full-Duplex Relaying under Composite Fading Channels
Venue: arXiv cs.IT 2014
Year: 2014

We assume a full-duplex (FD) cooperative network subject to hostile attacks and undergoing composite fading channels. We focus on two scenarios: \textit{a)} the transmitter has full CSI, for which we derive closed-form expressions for the \textit{average secrecy rate}; and \textit{b)} the transmitter only knows the CSI of the legitimate nodes, for which we obtain closed-form expressions for the \textit{secrecy outage probability}. We show that secure FD relaying is feasible, even under strong self-interference and in the presence of sophisticated multiple antenna eavesdropper.
Statistical physics of random binning
Venue: arXiv cs.IT 2014
Year: 2014

We consider the model of random binning and finite-temperature decoding for Slepian-Wolf codes, from a statistical-mechanical perspective. While ordinary random channel coding is intimately related to the random energy model (REM) - a statistical-mechanical model of disordered magnetic materials, it turns out that random binning (for Slepian-Wolf coding) is analogous to another, related statistical mechanical model of strong disorder, which we call the random dilution model (RDM). We use the latter analogy to characterize phase transitions pertaining to finite- temperature Slepian-Wolf decoding, which are somewhat similar, but not identical, to those of finite-temperature channel decoding. We then provide the exact random coding exponent of the bit error rate (BER) as a function of the coding rate and the decoding temperature, and discuss its properties. Finally, a few modifications and extensions of our results are outlined and discussed.
Overlapping Community Discovery Methods: A Survey
Venue: arXiv cs.SI 2014
Year: 2014

The detection of overlapping communities is a challenging problem which is gaining increasing interest in recent years because of the natural attitude of individuals, observed in real-world networks, to participate in multiple groups at the same time. This review gives a description of the main proposals in the field. Besides the methods designed for static networks, some new approaches that deal with the detection of overlapping communities in networks that change over time, are described. Methods are classified with respect to the underlying principles guiding them to obtain a network division in groups sharing part of their nodes. For each of them we also report, when available, computational complexity and web site address from which it is possible to download the software implementing the method.
A convex formulation for hyperspectral image superresolution via subspace-based regularization
Venue: arXiv cs.CV 2014
Year: 2014

Hyperspectral remote sensing images (HSIs) usually have high spectral resolution and low spatial resolution. Conversely, multispectral images (MSIs) usually have low spectral and high spatial resolutions. The problem of inferring images which combine the high spectral and high spatial resolutions of HSIs and MSIs, respectively, is a data fusion problem that has been the focus of recent active research due to the increasing availability of HSIs and MSIs retrieved from the same geographical area. We formulate this problem as the minimization of a convex objective function containing two quadratic data-fitting terms and an edge-preserving regularizer. The data-fitting terms account for blur, different resolutions, and additive noise. The regularizer, a form of vector Total Variation, promotes piecewise-smooth solutions with discontinuities aligned across the hyperspectral bands. The downsampling operator accounting for the different spatial resolutions, the non-quadratic and non-smooth nature of the regularizer, and the very large size of the HSI to be estimated lead to a hard optimization problem. We deal with these difficulties by exploiting the fact that HSIs generally "live" in a low-dimensional subspace and by tailoring the Split Augmented Lagrangian Shrinkage Algorithm (SALSA), which is an instance of the Alternating Direction Method of Multipliers (ADMM), to this optimization problem, by means of a convenient variable splitting. The spatial blur and the spectral linear operators linked, respectively, with the HSI and MSI acquisition processes are also estimated, and we obtain an effective algorithm that outperforms the state-of-the-art, as illustrated in a series of experiments with simulated and real-life data.
LAMP: A Locally Adapting Matching Pursuit Framework for Group Sparse Signatures in Ultra-Wide Band Radar Imaging
Venue: arXiv cs.IT 2014
Year: 2014

It has been found that radar returns of extended targets are not only sparse but also exhibit a tendency to cluster into randomly located, variable sized groups. However, the standard techniques of Compressive Sensing as applied in radar imaging hardly considers the clustering tendency into account while reconstructing the image from the compressed measurements. If the group sparsity is taken into account, it is intuitive that one might obtain better results both in terms of accuracy and time complexity as compared to the conventional recovery techniques like Orthogonal Matching Pursuit (OMP). In order to remedy this, techniques like Block OMP have been used in the existing literature. An alternate approach is via reconstructing the signal by transforming into the Hough Transform Domain where they become point-wise sparse. However, these techniques essentially assume specific size and structure of the groups and are not always effective if the exact characteristics of the groups are not known, prior to reconstruction. In this manuscript, a novel framework that we call locally adapting matching pursuit (LAMP) have been proposed for efficient reconstruction of group sparse signals from compressed measurements without assuming any specific size, location, or structure of the groups. The recovery guarantee of the LAMP and its superiority compared to the existing algorithms has been established with respect to accuracy, time complexity and flexibility in group size. LAMP has been successfully used on a real-world, experimental data set.
Decremental All-Pairs ALL Shortest Paths and Betweenness Centrality
Venue: arXiv cs.DS 2014
Year: 2014

We consider the all pairs all shortest paths (APASP) problem, which maintains the shortest path dag rooted at every vertex in a directed graph G=(V,E) with positive edge weights. For this problem we present a decremental algorithm (that supports the deletion of a vertex, or weight increases on edges incident to a vertex). Our algorithm runs in amortized O(\vstar^2 \cdot \log n) time per update, where n=|V|, and \vstar bounds the number of edges that lie on shortest paths through any given vertex. Our APASP algorithm can be used for the decremental computation of betweenness centrality (BC), a graph parameter that is widely used in the analysis of large complex networks. No nontrivial decremental algorithm for either problem was known prior to our work. Our method is a generalization of the decremental algorithm of Demetrescu and Italiano [DI04] for unique shortest paths, and for graphs with \vstar =O(n), we match the bound in [DI04]. Thus for graphs with a constant number of shortest paths between any pair of vertices, our algorithm maintains APASP and BC scores in amortized time O(n^2 \log n) under decremental updates, regardless of the number of edges in the graph.
Cost-Aware Green Cellular Networks with Energy and Communication Cooperation
Venue: arXiv cs.IT 2014
Year: 2014

Energy cost of cellular networks is ever-increasing to match the surge of wireless data traffic, and the saving of this cost is important to reduce the operational expenditure (OPEX) of wireless operators in future. The recent advancements of renewable energy integration and two-way energy flow in smart grid provide potential new solutions to save the cost. However, they also impose challenges, especially on how to use the stochastically and spatially distributed renewable energy harvested at cellular base stations (BSs) to reliably supply time- and space-varying wireless traffic over cellular networks. To overcome these challenges, in this article we present three approaches, namely, {\emph{energy cooperation, communication cooperation, and joint energy and communication cooperation}}, in which different BSs bidirectionally trade or share energy via the aggregator in smart grid, and/or share wireless resources and shift loads with each other to reduce the total energy cost.
Diversity Handling In Evolutionary Landscape
Venue: arXiv cs.NE 2014
Year: 2014

The search ability of an Evolutionary Algorithm (EA) depends on the variation among the individuals in the population. Maintaining an optimal level of diversity in the EA population is imperative to ensure that progress of the EA search is unhindered by premature convergence to suboptimal solutions. Clearer understanding of the concept of population diversity, in the context of evolutionary search and premature convergence in particular, is the key to designing efficient EAs. To this end, this paper first presents a comprehensive analysis of the EA population diversity issues. Next we present an investigation on a counter-niching EA technique that introduces and maintains constructive diversity in the population. The proposed approach uses informed genetic operations to reach promising, but un-explored or under-explored areas of the search space, while discouraging premature local convergence. Simulation runs on a number of standard benchmark test functions with Genetic Algorithm (GA) implementation shows promising results.
Combining contextual and local edges for line segment extraction in cluttered images
Venue: arXiv cs.CV 2014
Year: 2014

Automatic extraction methods typically assume that line segments are pronounced, thin, few and far between, do not cross each other, and are noise and clutter-free. Since these assumptions often fail in realistic scenarios, many line segments are not detected or are fragmented. In more severe cases, i.e., many who use the Hough Transform, extraction can fail entirely. In this paper, we propose a method that tackles these issues. Its key aspect is the combination of thresholded image derivatives obtained with filters of large and small footprints, which we denote as contextual and local edges, respectively. Contextual edges are robust to noise and we use them to select valid local edges, i.e., local edges that are of the same type as contextual ones: dark-to-bright transition of vice-versa. If the distance between valid local edges does not exceed a maximum distance threshold, we enforce connectivity by marking them and the pixels in between as edge points. This originates connected edge maps that are robust and well localized. We use a powerful two-sample statistical test to compute contextual edges, which we introduce briefly, as they are unfamiliar to the image processing community. Finally, we present experiments that illustrate, with synthetic and real images, how our method is efficient in extracting complete segments of all lengths and widths in several situations where current methods fail.
What They Do in Shadows: Twitter Underground Follower Market
Venue: arXiv cs.SI 2014
Year: 2014

Internet users and businesses are increasingly using online social networks (OSN) to drive audience traffic and increase their popularity. In order to boost social presence, OSN users need to increase the visibility and reach of their online profile, like - Facebook likes, Twitter followers, Instagram comments and Yelp reviews. For example, an increase in Twitter followers not only improves the audience reach of the user but also boosts the perceived social reputation and popularity. This has led to a scope for an underground market that provides followers, likes, comments, etc. via a network of fraudulent and compromised accounts and various collusion techniques. In this paper, we landscape the underground markets that provide Twitter followers by studying their basic building blocks - merchants, customers and phony followers. We charecterize the services provided by merchants to understand their operational structure and market hierarchy. Twitter underground markets can operationalize using a premium monetary scheme or other incentivized freemium schemes. We find out that freemium market has an oligopoly structure with few merchants being the market leaders. We also show that merchant popularity does not have any correlation with the quality of service provided by the merchant to its customers. Our findings also shed light on the characteristics and quality of market customers and the phony followers provided. We draw comparison between legitimate users and phony followers, and find out key identifiers to separate such users. With the help of these differentiating features, we build a supervised learning model to predict suspicious following behaviour with an accuracy of 89.2%.
Quantified CTL: Expressiveness and Complexity
Venue: arXiv cs.LO 2014
Year: 2014

While it was defined long ago, the extension of CTL with quantification over atomic propositions has never been studied extensively. Considering two different semantics (depending whether propositional quantification refers to the Kripke structure or to its unwinding tree), we study its expressiveness (showing in particular that QCTL coincides with Monadic Second-Order Logic for both semantics) and characterise the complexity of its model-checking and satisfiability problems, depending on the number of nested propositional quantifiers (showing that the structure semantics populates the polynomial hierarchy while the tree semantics populates the exponential hierarchy).
Constructing Strategy of Online Learning in Higher Education: Transaction Cost Economy
Venue: arXiv cs.CY 2014
Year: 2014

The online learning tools and management also known as Learning Management System (LMS) have been adopted by higher education as it allows convenient and flexibility in learning process between students and instructors or tutors with minimal cost. The adoption of online learning tools in university has allowed users (students and instructors) to interact, share and discuss anytime-anywhere conveniently. Many students nowadays rely on online resources based using their mobile devices, substituting traditional learning interactions. Universities need strategy to sustain in providing intensive interactions and spreading word out mouth of good services through online learning tools by focusing on niche markets and creating close relationship with their stakeholders. The study presented in this paper analyses how universities design best practices in adopting LMS and evaluate its current state for future improvement. In fact, with proper strategies of LMS, universities have opportunities to sustain their business by offering interesting packages and to improve their services through intensive interactions with their users. In this study, we deploy Transaction Cost Economics (TCE) to understand the change business environment and to construct a model for higher institution to regulate their scenario on online learning strategies in fast changing and threatening business environment.
Errata: Distant Supervision for Relation Extraction with Matrix Completion
Venue: arXiv cs.CL 2014
Year: 2014

The essence of distantly supervised relation extraction is that it is an incomplete multi-label classification problem with sparse and noisy features. To tackle the sparsity and noise challenges, we propose solving the classification problem using matrix completion on factorized matrix of minimized rank. We formulate relation classification as completing the unknown labels of testing items (entity pairs) in a sparse matrix that concatenates training and testing textual features with training labels. Our algorithmic framework is based on the assumption that the rank of item-by-feature and item-by-label joint matrix is low. We apply two optimization models to recover the underlying low-rank matrix leveraging the sparsity of feature-label matrix. The matrix completion problem is then solved by the fixed point continuation (FPC) algorithm, which can find the global optimum. Experiments on two widely used datasets with different dimensions of textual features demonstrate that our low-rank matrix completion approach significantly outperforms the baseline and the state-of-the-art methods.
Fully Convolutional Neural Networks for Crowd Segmentation
Venue: arXiv cs.CV 2014
Year: 2014

In this paper, we propose a fast fully convolutional neural network (FCNN) for crowd segmentation. By replacing the fully connected layers in CNN with 1 by 1 convolution kernels, FCNN takes whole images as inputs and directly outputs segmentation maps by one pass of forward propagation. It has the property of translation invariance like patch-by-patch scanning but with much lower computation cost. Once FCNN is learned, it can process input images of any sizes without warping them to a standard size. These attractive properties make it extendable to other general image segmentation problems. Based on FCNN, a multi-stage deep learning is proposed to integrate appearance and motion cues for crowd segmentation. Both appearance filters and motion filers are pretrained stage-by-stage and then jointly optimized. Different combination methods are investigated. The effectiveness of our approach and component-wise analysis are evaluated on two crowd segmentation datasets created by us, which include image frames from 235 and 11 scenes, respectively. They are currently the largest crowd segmentation datasets and will be released to the public.