OpenRaiser/Intern-Atlas huggingface dataset:
A Deeper Look at Dataset Bias
Venue: arXiv cs.CV 2015
Year: 2015

The presence of a bias in each image data collection has recently attracted a lot of attention in the computer vision community showing the limits in generalization of any learning method trained on a specific dataset. At the same time, with the rapid development of deep learning architectures, the activation values of Convolutional Neural Networks (CNN) are emerging as reliable and robust image descriptors. In this paper we propose to verify the potential of the DeCAF features when facing the dataset bias problem. We conduct a series of analyses looking at how existing datasets differ among each other and verifying the performance of existing debiasing methods under different representations. We learn important lessons on which part of the dataset bias problem can be considered solved and which open questions still need to be tackled.
The History of Mobile Augmented Reality
Venue: arXiv cs.HC 2015
Year: 2015

This document summarizes the major milestones in mobile Augmented Reality between 1968 and 2014. Major parts of the list were compiled by the member of the Christian Doppler Laboratory for Handheld Augmented Reality in 2010 (author list in alphabetical order) for the ISMAR society. Later in 2013 it was updated, and more recent work was added during preparation of this report. Permission is granted to copy and modify.
Analyzing readerships of International Iranian publications in Mendeley: an altmetrics study
Venue: arXiv cs.DL 2015
Year: 2015

In this study, the presence and distribution of both Mendeley readerships and Web of Science citations for the publications published in the 43 Iranian international journals indexed in Journal Citation Reports have been investigated. The aim was to determine the impact, visibility and use of the publications published by the Iranian international journals in Mendeley compared to their citation impact; furthermore, to explore if there is any relation between these two impact indicators (Mendeley readership counts and WoS citation counts) for these publications. The DOIs of the 1,884 publications used to extract the readerships data from Mendeley REST API in February 2014 and citations data until end of 2013 calculated using CWTS in-house WoS database. SPSS (version 21) used to analyze the relationship between the readerships and citations for those publications. The Mendeley usage distribution both at the publication level (across publications years, fields and document types) and at the user level (across users disciplines, academic status and countries) have been investigated. These information will help to understand the visibility and usage vs citation pattern and impact of Iranian scientific outputs.
Classification of Occluded Objects using Fast Recurrent Processing
Venue: arXiv cs.CV 2015
Year: 2015

Recurrent neural networks are powerful tools for handling incomplete data problems in computer vision, thanks to their significant generative capabilities. However, the computational demand for these algorithms is too high to work in real time, without specialized hardware or software solutions. In this paper, we propose a framework for augmenting recurrent processing capabilities into a feedforward network without sacrificing much from computational efficiency. We assume a mixture model and generate samples of the last hidden layer according to the class decisions of the output layer, modify the hidden layer activity using the samples, and propagate to lower layers. For visual occlusion problem, the iterative procedure emulates feedforward-feedback loop, filling-in the missing hidden layer activity with meaningful representations. The proposed algorithm is tested on a widely used dataset, and shown to achieve 2$\times$ improvement in classification accuracy for occluded objects. When compared to Restricted Boltzmann Machines, our algorithm shows superior performance for occluded object classification.
Improved Approximation Algorithms for Stochastic Matching
Venue: arXiv cs.DS 2015
Year: 2015

In this paper we consider the Stochastic Matching problem, which is motivated by applications in kidney exchange and online dating. We are given an undirected graph in which every edge is assigned a probability of existence and a positive profit, and each node is assigned a positive integer called timeout. We know whether an edge exists or not only after probing it. On this random graph we are executing a process, which one-by-one probes the edges and gradually constructs a matching. The process is constrained in two ways: once an edge is taken it cannot be removed from the matching, and the timeout of node $v$ upper-bounds the number of edges incident to $v$ that can be probed. The goal is to maximize the expected profit of the constructed matching. For this problem Bansal et al. (Algorithmica 2012) provided a $3$-approximation algorithm for bipartite graphs, and a $4$-approximation for general graphs. In this work we improve the approximation factors to $2.845$ and $3.709$, respectively. We also consider an online version of the bipartite case, where one side of the partition arrives node by node, and each time a node $b$ arrives we have to decide which edges incident to $b$ we want to probe, and in which order. Here we present a $4.07$-approximation, improving on the $7.92$-approximation of Bansal et al. The main technical ingredient in our result is a novel way of probing edges according to a random but non-uniform permutation. Patching this method with an algorithm that works best for large probability edges (plus some additional ideas) leads to our improved approximation factors.
Taking back control of HPC file systems with Robinhood Policy Engine
Venue: arXiv cs.DC 2015
Year: 2015

Today, the largest Lustre file systems store billions of entries. On such systems, classic tools based on namespace scanning become unusable. Operations such as managing file lifetime, scheduling data copies, and generating overall filesystem statistics become painful as they require collecting, sorting and aggregating information for billions of records. Robinhood Policy Engine is an open source software developed to address these challenges. It makes it possible to schedule automatic actions on huge numbers of filesystem entries. It also gives a synthetic understanding of file systems contents by providing overall statistics about data ownership, age and size profiles. Even if it can be used with any POSIX filesystem, Robinhood supports Lustre specific features like OSTs, pools, HSM, ChangeLogs, and DNE. It implements specific support for these features, and takes advantage of them to manage Lustre file systems efficiently.
Differences in Personal and Professional Tweets of Scholars
Venue: arXiv cs.DL 2015
Year: 2015

Purpose: This study shows that there were differences in the use of Twitter by professors at universities in the Association of American Universities (AAU). Affordance use differed between the personal and professional tweets of professors. Framing behaviors were described that could impact the interpretation of tweets by audience members. Design/methodology/approach: A three phase research design was used that included surveys of professors, categorization of tweets by Amazon's Mechanical Turk workers (i.e., turkers), and categorization of tweets by active professors on Twitter. Findings: There were significant differences found between professors that reported having a Twitter account, significant differences found between types of Twitter accounts (personal, professional, or both), and significant differences in the affordances used in personal and professional tweets. Framing behaviors were described that may assist altmetric researchers in distinguishing between personal and professional tweets. Research limitations/implications (if applicable): The study is limited by the sample population, survey instrument, low survey response rate, and low Cohen's kappa. Practical implications (if applicable): An overview of various affordances found in Twitter is provided and a novel use of Amazon's Mechanical Turk for the categorization of tweets is described that can be applied to future altmetric studies. Originality/value: This work utilizes a socio-technical framework integrating social and psychological theories to interpret results from the tweeting behavior of professors and the interpretation of tweets by workers in Amazon's Mechanical Turk.
Learning and Optimization with Submodular Functions
Venue: arXiv cs.LG 2015
Year: 2015

In many naturally occurring optimization problems one needs to ensure that the definition of the optimization problem lends itself to solutions that are tractable to compute. In cases where exact solutions cannot be computed tractably, it is beneficial to have strong guarantees on the tractable approximate solutions. In order operate under these criterion most optimization problems are cast under the umbrella of convexity or submodularity. In this report we will study design and optimization over a common class of functions called submodular functions. Set functions, and specifically submodular set functions, characterize a wide variety of naturally occurring optimization problems, and the property of submodularity of set functions has deep theoretical consequences with wide ranging applications. Informally, the property of submodularity of set functions concerns the intuitive "principle of diminishing returns. This property states that adding an element to a smaller set has more value than adding it to a larger set. Common examples of submodular monotone functions are entropies, concave functions of cardinality, and matroid rank functions; non-monotone examples include graph cuts, network flows, and mutual information. In this paper we will review the formal definition of submodularity; the optimization of submodular functions, both maximization and minimization; and finally discuss some applications in relation to learning and reasoning using submodular functions.
Filter characteristics in image decomposition with singular spectrum analysis
Venue: arXiv cs.CV 2015
Year: 2015

Singular spectrum analysis is developed as a nonparametric spectral decomposition of a time series. It can be easily extended to the decomposition of multidimensional lattice-like data through the filtering interpretation. In this viewpoint, the singular spectrum analysis can be understood as the adaptive and optimal generation of the filters and their two-step point-symmetric operation to the original data. In this paper, we point out that, when applied to the multidimensional data, the adaptively generated filters exhibit symmetry properties resulting from the bisymmetric nature of the lag-covariance matrices. The eigenvectors of the lag-covariance matrix are either symmetric or antisymmetric, and for the 2D image data, these lead to the differential-type filters with even- or odd-order derivatives. The dominant filter is a smoothing filter, reflecting the dominance of low-frequency components of the photo images. The others are the edge-enhancement or the noise filters corresponding to the band-pass or the high-pass filters. The implication of the decomposition to the image denoising is briefly discussed.
Location-Quality-aware Policy Optimisation for Relay Selection in Mobile Networks
Venue: arXiv cs.NI 2015
Year: 2015

Relaying can improve the coverage and performance of wireless access networks. In presence of a localisation system at the mobile nodes, the use of such location estimates for relay node selection can be advantageous as such information can be collected by access points in linear effort with respect to number of mobile nodes (while the number of links grows quadratically). However, the localisation error and the chosen update rate of location information in conjunction with the mobility model affect the performance of such location-based relay schemes; these parameters also need to be taken into account in the design of optimal policies. This paper develops a Markov model that can capture the joint impact of localisation errors and inaccuracies of location information due to forwarding delays and mobility; the Markov model is used to develop algorithms to determine optimal location-based relay policies that take the aforementioned factors into account. The model is subsequently used to analyse the impact of deployment parameter choices on the performance of location-based relaying in WLAN scenarios with free-space propagation conditions and in an measurement-based indoor office scenario.
Software as a Service: Analyzing Security Issues
Venue: arXiv cs.CR 2015
Year: 2015

Software-as-a-service (SaaS) is a type of software service delivery model which encompasses a broad range of business opportunities and challenges. Users and service providers are reluctant to integrate their business into SaaS due to its security concerns while at the same time they are attracted by its benefits. This article highlights SaaS utility and applicability in different environments like cloud computing, mobile cloud computing, software defined networking and Internet of things. It then embarks on the analysis of SaaS security challenges spanning across data security, application security and SaaS deployment security. A detailed review of the existing mainstream solutions to tackle the respective security issues mapping into different SaaS security challenges is presented. Finally, possible solutions or techniques which can be applied in tandem are presented for a secure SaaS platform.
Weighted Gaussian entropy and determinant inequalities
Venue: arXiv cs.IT 2015
Year: 2015

We produce a series of results extending information-theoretical inequalities (discussed by Dembo--Cover--Thomas in 1989-1991) to a weighted version of entropy. The resulting inequalities involve the Gaussian weighted entropy; they imply a number of new relations for determinants of positive-definite matrices.
Contextual Analysis for Middle Eastern Languages with Hidden Markov Models
Venue: arXiv cs.CL 2015
Year: 2015

Displaying a document in Middle Eastern languages requires contextual analysis due to different presentational forms for each character of the alphabet. The words of the document will be formed by the joining of the correct positional glyphs representing corresponding presentational forms of the characters. A set of rules defines the joining of the glyphs. As usual, these rules vary from language to language and are subject to interpretation by the software developers. In this paper, we propose a machine learning approach for contextual analysis based on the first order Hidden Markov Model. We will design and build a model for the Farsi language to exhibit this technology. The Farsi model achieves 94 \% accuracy with the training based on a short list of 89 Farsi vocabularies consisting of 2780 Farsi characters. The experiment can be easily extended to many languages including Arabic, Urdu, and Sindhi. Furthermore, the advantage of this approach is that the same software can be used to perform contextual analysis without coding complex rules for each specific language. Of particular interest is that the languages with fewer speakers can have greater representation on the web, since they are typically ignored by software developers due to lack of financial incentives.
Wireless Multicast for Zoomable Video Streaming
Venue: arXiv cs.NI 2015
Year: 2015

Zoomable video streaming refers to a new class of interactive video applications, where users can zoom into a video stream to view a selected region of interest in higher resolutions and pan around to move the region of interest. The zoom and pan effects are typically achieved by breaking the source video into a grid of independently decodable tiles. Streaming the tiles to a set of heterogeneous users using broadcast is challenging, as users have different link rates and different regions of interest at different resolution levels. In this paper, we consider the following problem: given the subset of tiles that each user requested, the link rate of each user, and the available time slots, at which resolution should each tile be sent, to maximize the overall video quality received by all users. We design an efficient algorithm to solve the problem above, and evaluate the solution on a testbed using 10 mobile devices. Our method is able to achieve up to 12dB improvements over other heuristic methods.
Adaptive System Identification using Markov Chain Monte Carlo
Venue: arXiv cs.SY 2015
Year: 2015

One of the major problems in adaptive filtering is the problem of system identification. It has been studied extensively due to its immense practical importance in a variety of fields. The underlying goal is to identify the impulse response of an unknown system. This is accomplished by placing a known system in parallel and feeding both systems with the same input. Due to initial disparity in their impulse responses, an error is generated between their outputs. This error is set to tune the impulse response of known system in a way that every change in impulse response reduces the magnitude of prospective error. This process is repeated until the error becomes negligible and the responses of both systems match. To specifically minimize the error, numerous adaptive algorithms are available. They are noteworthy either for their low computational complexity or high convergence speed. Recently, a method, known as Markov Chain Monte Carlo (MCMC), has gained much attention due to its remarkably low computational complexity. But despite this colossal advantage, properties of MCMC method have not been investigated for adaptive system identification problem. This article bridges this gap by providing a complete treatment of MCMC method in the aforementioned context.
A Survey on Detection of Sinkhole Attack in Wireless Sensor Network
Venue: arXiv cs.CR 2015
Year: 2015

Wireless Sensor Network (WSN) consists of large number of low-cost, resource-constrained sensor nodes. The constraints of the wireless sensor node is their characteristics which include low memory, low computation power, they are deployed in hostile area and left unattended, small range of communication capability and low energy capabilities. Base on those characteristics makes this network vulnerable to several attacks, such as sinkhole attack. Sinkhole attack is a type of attack were compromised node tries to attract network traffic by advertise its fake routing update. One of the impacts of sinkhole attack is that, it can be used to launch other attacks like selective forwarding attack, acknowledge spoofing attack and drops or altered routing information. It can also used to send bogus information to base station. This paper is focus on exploring and analyzing the existing solutions which used to detect and identify sinkhole attack in wireless sensor network. The analysis is based on advantages and limitation of the proposed solutions.
Optimization Design and Analysis of Systematic LT codes over AWGN Channel
Venue: arXiv cs.IT 2015
Year: 2015

In this paper, we study systematic Luby Transform (SLT) codes over additive white Gaussian noise (AWGN) channel. We introduce the encoding scheme of SLT codes and give the bipartite graph for iterative belief propagation (BP) decoding algorithm. Similar to low-density parity-check codes, Gaussian approximation (GA) is applied to yield asymptotic performance of SLT codes. Recent work about SLT codes has been focused on providing better encoding and decoding algorithms and design of degree distributions. In our work, we propose a novel linear programming method to optimize the degree distribution. Simulation results show that the proposed distributions can provide better bit-error-ratio (BER) performance. Moreover, we analyze the lower bound of SLT codes and offer closed form expressions.
Direct identification of fault estimation filter for sensor faults
Venue: arXiv cs.SY 2015
Year: 2015

We propose a systematic method to directly identify a sensor fault estimation filter from plant input/output data collected under fault-free condition. This problem is challenging, especially when omitting the step of building an explicit state-space plant model in data-driven design, because the inverse of the underlying plant dynamics is required and needs to be stable. We show that it is possible to address this problem by relying on a system-inversion-based fault estimation filter that is parameterized using identified Markov parameters. Our novel data-driven approach improves estimation performance by avoiding the propagation of model reduction errors originating from identification of the state-space plant model into the designed filter. Furthermore, it allows additional design freedom to stabilize the obtained filter under the same stabilizability condition as the existing model-based system inversion. This crucial property enables its application to sensor faults in unstable plants, where existing data-driven filter designs could not be applied so far due to the lack of such stability guarantees (even after stabilizing the closed-loop system). A numerical simulation example of sensor faults in an unstable aircraft system illustrates the effectiveness of the proposed new method.
Looking at Cellular Networks Through Canonical Domains and Conformal Mapping
Venue: arXiv cs.NI 2015
Year: 2015

In order to cope with the rapidly increasing service demand in cellular networks, more cells are needed with better resource usage efficiency. This poses challenges for the network planning since service demand in practical networks is not geographically uniform and, to cope with the non-uniform service demand, network deployments are becoming increasingly irregular. This paper introduces a new idea to deal with the non-uniform network topology. Rather than capturing the network character (e.g. load distribution) by means of stochastic methods, the proposed novel approach aims at transforming the analysis from the physical (irregular) domain to a canonical/dual (uniform) domain that simplifies the work due to its symmetry. To carry out this task, physical and canonical domains are connected using the conformal (Schwarz-Christoffel) mapping, that makes the rich and mature theory of Complex Analysis available. The main contribution of this paper is to introduce and validate the usability of conformal mapping in the load coupling analysis of cellular networks.
Towards the Formalization of Fractional Calculus in Higher-Order Logic
Venue: arXiv cs.LO 2015
Year: 2015

Fractional calculus is a generalization of classical theories of integration and differentiation to arbitrary order (i.e., real or complex numbers). In the last two decades, this new mathematical modeling approach has been widely used to analyze a wide class of physical systems in various fields of science and engineering. In this paper, we describe an ongoing project which aims at formalizing the basic theories of fractional calculus in the HOL Light theorem prover. Mainly, we present the motivation and application of such formalization efforts, a roadmap to achieve our goals, current status of the project and future milestones.
A lower bound for the determinantal complexity of a hypersurface
Venue: arXiv cs.CC 2015
Year: 2015

We prove that the determinantal complexity of a hypersurface of degree $d > 2$ is bounded below by one more than the codimension of the singular locus, provided that this codimension is at least $5$. As a result, we obtain that the determinantal complexity of the $3 \times 3$ permanent is $7$. We also prove that for $n> 3$, there is no nonsingular hypersurface in $\mathbf{P}^n$ of degree $d$ that has an expression as a determinant of a $d \times d$ matrix of linear forms while on the other hand for $n \le 3$, a general determinantal expression is nonsingular. Finally, we answer a question of Ressayre by showing that the determinantal complexity of the unique (singular) cubic surface containing a single line is $5$.