OpenRaiser/Intern-Atlas huggingface dataset:
Geographical Asynchronous Information Access for Distributed Systems
Venue: arXiv cs.DC 2014
Year: 2014

Non-relational databases are the common means of data storage in the Cloud, and optimizing the data access is of paramount importance into determining the overall Cloud system performance. In this paper, we present GAIA, a novel model for retrieving and managing correlated geo-localized data in the cloud environment. We survey and compare the existing models used mostly in Geographical Information Systems (GIS), mainly the Grid model and the Coordinates Projection model. Besides, we present a benchmark comparing the efficiency of the models. Using extensive experimentation, we show that GAIA outperforms the existing models by its high efficiency which is of O(log(n)), and this mainly thanks to its combination of projection with cell decomposition. The other models have a linear efficiency of O(n). The presented model is designed from the ground up to support GIS and is designed to suit both cloud and parallel computing.
The Effect of Social Learning on Individual Learning and Evolution
Venue: arXiv cs.AI 2014
Year: 2014

We consider the effects of social learning on the individual learning and genetic evolution of a colony of artificial agents capable of genetic, individual and social modes of adaptation. We confirm that there is strong selection pressure to acquire traits of individual learning and social learning when these are adaptive traits. We show that selection pressure for learning of either kind can supress selection pressure for reproduction or greater fitness. We show that social learning differs from individual learning in that it can support a second evolutionary system that is decoupled from the biological evolutionary system. This decoupling leads to an emergent interaction where immature agents are more likely to engage in learning activities than mature agents.
On bipartization of cubic graphs by removal of an independent set
Venue: arXiv cs.DM 2014
Year: 2014

We study a new problem for cubic graphs: bipartization of a cubic graph $Q$ by deleting sufficiently large independent set $I$. It can be expressed as follows: \emph{Given a connected $n$-vertex tripartite cubic graph $Q=(V,E)$ with independence number $α(Q)$, does $Q$ contain an independent set $I$ of size $k$ such that $Q-I$ is bipartite?} We are interested for which value of $k$ the answer to this question is affirmative. We prove constructively that if $α(Q) \geq 4n/10$, then the answer is positive for each $k$ fulfilling $\lfloor (n-α(Q))/2 \rfloor \leq k \leq α(Q)$. It remains an open question if a similar construction is possible for cubic graphs with $α(Q)<4n/10$. Next, we show that this problem with $α(Q)\geq 4n/10$ and $k$ fulfilling inequalities $\lfloor n/3 \rfloor \leq k \leq α(Q)$ can be related to semi-equitable graph 3-coloring, where one color class is of size $k$, and the subgraph induced by the remaining vertices is equitably 2-colored. This means that $Q$ has a coloring of type $(k, \lceil(n-k)/2\rceil, \lfloor (n-k)/2 \rfloor)$.
Deep Epitomic Convolutional Neural Networks
Venue: arXiv cs.CV 2014
Year: 2014

Deep convolutional neural networks have recently proven extremely competitive in challenging image recognition tasks. This paper proposes the epitomic convolution as a new building block for deep neural networks. An epitomic convolution layer replaces a pair of consecutive convolution and max-pooling layers found in standard deep convolutional neural networks. The main version of the proposed model uses mini-epitomes in place of filters and computes responses invariant to small translations by epitomic search instead of max-pooling over image positions. The topographic version of the proposed model uses large epitomes to learn filter maps organized in translational topographies. We show that error back-propagation can successfully learn multiple epitomic layers in a supervised fashion. The effectiveness of the proposed method is assessed in image classification tasks on standard benchmarks. Our experiments on Imagenet indicate improved recognition performance compared to standard convolutional neural networks of similar architecture. Our models pre-trained on Imagenet perform excellently on Caltech-101. We also obtain competitive image classification results on the small-image MNIST and CIFAR-10 datasets.
Are 140 Characters Enough? A Large-Scale Linkability Study of Tweets
Venue: arXiv cs.IR 2014
Year: 2014

Microblogging is a very popular Internet activity that informs and entertains great multitudes of people world-wide via quickly and scalably disseminated terse messages containing all kinds of newsworthy utterances. Even though microblogging is neither designed nor meant to emphasize privacy, numerous contributors hide behind pseudonyms and compartmentalize their different incarnations via multiple accounts within the same, or across multiple, site(s). Prior work has shown that stylometric analysis is a very powerful tool capable of linking product or service reviews and blogs that are produced by the same author when the number of authors is large. In this paper, we explore linkability of tweets. Our results, based on a very large corpus of tweets, clearly demonstrate that, at least for relatively active tweeters, linkability of tweets by the same author is easily attained even when the number of tweeters is large. We also show that our linkability results hold for a set of actual Twitter users who tweet from multiple accounts. This has some obvious privacy implications, both positive and negative.
Out Performance Of Cuckoo Search Algorithm Among Nature Inspired Algorithms in Planar Antenna Arrays
Venue: arXiv cs.ET 2014
Year: 2014

In this modern era a great deal of metamorphism is observed around us which eventuate due to some minute modifications and innovations in the area of Science and Technology. This paper deals with the application of a meta heuristic optimization algorithm namely the Cuckoo Search Algorithm in the design of an optimized planar antenna array which ensures high gain,directivity, suppression of side lobes, increased efficiency and improves other antenna parameters as well.
The Mean Interference-to-Signal Ratio and its Key Role in Cellular and Amorphous Networks
Venue: arXiv cs.IT 2014
Year: 2014

We introduce a simple yet powerful and versatile analytical framework to approximate the SIR distribution in the downlink of cellular systems. It is based on the mean interference-to-signal ratio and yields the horizontal gap (SIR gain) between the SIR distribution in question and a reference SIR distribution. As applications, we determine the SIR gain for base station silencing, cooperation, and lattice deployment over a baseline architecture that is based on a Poisson deployment of base stations and strongest-base station association. The applications demonstrate that the proposed approach unifies several recent results and provides a convenient framework for the analysis and comparison of future network architectures and transmission schemes, including amorphous networks where a user is served by multiple base stations and, consequently, (hard) cell association becomes obsolete.
On the Impact of Geometry on Ad Hoc Communication in Wireless Networks
Venue: arXiv cs.DC 2014
Year: 2014

In this work we address the question how important is the knowledge of geometric location and network density to the efficiency of (distributed) wireless communication in ad hoc networks. We study fundamental communication task of broadcast and develop well-scalable, randomized algorithms that do not rely on GPS information, and which efficiency formulas do not depend on how dense the geometric network is. We consider two settings: with and without spontaneous wake-up of nodes. In the former setting, in which all nodes start the protocol at the same time, our algorithm accomplishes broadcast in $O(D\log n + \log^2 n)$ rounds under the SINR model, with high probability (whp), where $D$ is the diameter of the communication graph and $n$ is the number of stations. In the latter setting, in which only the source node containing the original message is active in the beginning, we develop a slightly slower algorithm working in $O(D\log^2 n)$ rounds whp. Both algorithms are based on a novel distributed coloring method, which is of independent interest and potential applicability to other communication tasks under the SINR wireless model.
A Literature Review on Information Systems Supporting the Physical Wellbeing of Elderly People
Venue: arXiv cs.CY 2014
Year: 2014

This paper reviews multi-disciplinary research on information systems supporting the physical wellbeing of the elderly population. By taking a systematic approach, it screens journals, conference proceedings and books on how computer-based systems are used for improving both the health and wellbeing of the most senior individuals. By using different Internet-databases indexing academic publications and a set of conceptual keywords the authors searched for and identified 62 major publications on the topic that were carefully reviewed. Each publication item was classified according different category sets and the aggregated data was then analyzed from different socio-technological perspectives. Our findings suggest that research on the topic is very focused on diseases over health and wellbeing, since most of the studied information systems focused much more on the protective and curative medical procedures over other important dimensions such as prevention, education and health promotion. An overview on what and where the studied systems are used is presented and a new information systems research agenda is proposed.
Explicit Computation of Input Weights in Extreme Learning Machines
Venue: arXiv cs.NE 2014
Year: 2014

We present a closed form expression for initializing the input weights in a multi-layer perceptron, which can be used as the first step in synthesis of an Extreme Learning Ma-chine. The expression is based on the standard function for a separating hyperplane as computed in multilayer perceptrons and linear Support Vector Machines; that is, as a linear combination of input data samples. In the absence of supervised training for the input weights, random linear combinations of training data samples are used to project the input data to a higher dimensional hidden layer. The hidden layer weights are solved in the standard ELM fashion by computing the pseudoinverse of the hidden layer outputs and multiplying by the desired output values. All weights for this method can be computed in a single pass, and the resulting networks are more accurate and more consistent on some standard problems than regular ELM networks of the same size.
A machine-compiled macroevolutionary history of Phanerozoic life
Venue: arXiv cs.DB 2014
Year: 2014

Many aspects of macroevolutionary theory and our understanding of biotic responses to global environmental change derive from literature-based compilations of palaeontological data. Existing manually assembled databases are, however, incomplete and difficult to assess and enhance. Here, we develop and validate the quality of a machine reading system, PaleoDeepDive, that automatically locates and extracts data from heterogeneous text, tables, and figures in publications. PaleoDeepDive performs comparably to humans in complex data extraction and inference tasks and generates congruent synthetic macroevolutionary results. Unlike traditional databases, PaleoDeepDive produces a probabilistic database that systematically improves as information is added. We also show that the system can readily accommodate sophisticated data types, such as morphological data in biological illustrations and associated textual descriptions. Our machine reading approach to scientific data integration and synthesis brings within reach many questions that are currently underdetermined and does so in ways that may stimulate entirely new modes of inquiry.
Truncated Nuclear Norm Minimization for Image Restoration Based On Iterative Support Detection
Venue: arXiv cs.CV 2014
Year: 2014

Recovering a large matrix from limited measurements is a challenging task arising in many real applications, such as image inpainting, compressive sensing and medical imaging, and this kind of problems are mostly formulated as low-rank matrix approximation problems. Due to the rank operator being non-convex and discontinuous, most of the recent theoretical studies use the nuclear norm as a convex relaxation and the low-rank matrix recovery problem is solved through minimization of the nuclear norm regularized problem. However, a major limitation of nuclear norm minimization is that all the singular values are simultaneously minimized and the rank may not be well approximated \cite{hu2012fast}. Correspondingly, in this paper, we propose a new multi-stage algorithm, which makes use of the concept of Truncated Nuclear Norm Regularization (TNNR) proposed in \citep{hu2012fast} and Iterative Support Detection (ISD) proposed in \citep{wang2010sparse} to overcome the above limitation. Besides matrix completion problems considered in \citep{hu2012fast}, the proposed method can be also extended to the general low-rank matrix recovery problems. Extensive experiments well validate the superiority of our new algorithms over other state-of-the-art methods.
Deception with Side Information in Biometric Authentication Systems
Venue: arXiv cs.IT 2014
Year: 2014

In this paper, we study the probability of successful deception of an uncompressed biometric authentication system with side information at the adversary. It represents the scenario where the adversary may have correlated side information, e.g.,~a partial finger print or a DNA sequence of a relative of the legitimate user. We find the optimal exponent of the deception probability by proving both the achievability and the converse. Our proofs are based on the connection between the problem of deception with side information and the rate distortion problem with side information at both the encoder and decoder.
Guarantees and Limits of Preprocessing in Constraint Satisfaction and Reasoning
Venue: arXiv cs.AI 2014
Year: 2014

We present a first theoretical analysis of the power of polynomial-time preprocessing for important combinatorial problems from various areas in AI. We consider problems from Constraint Satisfaction, Global Constraints, Satisfiability, Nonmonotonic and Bayesian Reasoning under structural restrictions. All these problems involve two tasks: (i) identifying the structure in the input as required by the restriction, and (ii) using the identified structure to solve the reasoning task efficiently. We show that for most of the considered problems, task (i) admits a polynomial-time preprocessing to a problem kernel whose size is polynomial in a structural problem parameter of the input, in contrast to task (ii) which does not admit such a reduction to a problem kernel of polynomial size, subject to a complexity theoretic assumption. As a notable exception we show that the consistency problem for the AtMost-NValue constraint admits a polynomial kernel consisting of a quadratic number of variables and domain values. Our results provide a firm worst-case guarantees and theoretical boundaries for the performance of polynomial-time preprocessing algorithms for the considered problems.
A hybrid neuro--wavelet predictor for QoS control and stability
Venue: arXiv cs.NE 2014
Year: 2014

For distributed systems to properly react to peaks of requests, their adaptation activities would benefit from the estimation of the amount of requests. This paper proposes a solution to produce a short-term forecast based on data characterising user behaviour of online services. We use \emph{wavelet analysis}, providing compression and denoising on the observed time series of the amount of past user requests; and a \emph{recurrent neural network} trained with observed data and designed so as to provide well-timed estimations of future requests. The said ensemble has the ability to predict the amount of future user requests with a root mean squared error below 0.06\%. Thanks to prediction, advance resource provision can be performed for the duration of a request peak and for just the right amount of resources, hence avoiding over-provisioning and associated costs. Moreover, reliable provision lets users enjoy a level of availability of services unaffected by load variations.
Solving the "Isomorphism of Polynomials with Two Secrets" Problem for all Pairs of Quadratic Forms
Venue: arXiv cs.SC 2014
Year: 2014

We study the Isomorphism of Polynomial (IP2S) problem with m=2 homogeneous quadratic polynomials of n variables over a finite field of odd characteristic: given two quadratic polynomials (a, b) on n variables, we find two bijective linear maps (s,t) such that b=t . a . s. We give an algorithm computing s and t in time complexity O~(n^4) for all instances, and O~(n^3) in a dominant set of instances. The IP2S problem was introduced in cryptography by Patarin back in 1996. The special case of this problem when t is the identity is called the isomorphism with one secret (IP1S) problem. Generic algebraic equation solvers (for example using Gröbner bases) solve quite well random instances of the IP1S problem. For the particular cyclic instances of IP1S, a cubic-time algorithm was later given and explained in terms of pencils of quadratic forms over all finite fields; in particular, the cyclic IP1S problem in odd characteristic reduces to the computation of the square root of a matrix. We give here an algorithm solving all cases of the IP1S problem in odd characteristic using two new tools, the Kronecker form for a singular quadratic pencil, and the reduction of bilinear forms over a non-commutative algebra. Finally, we show that the second secret in the IP2S problem may be recovered in cubic time.
Compact Indexes for Flexible Top-k Retrieval
Venue: arXiv cs.DS 2014
Year: 2014

We engineer a self-index based retrieval system capable of rank-safe evaluation of top-k queries. The framework generalizes the GREEDY approach of Culpepper et al. (ESA 2010) to handle multi-term queries, including over phrases. We propose two techniques which significantly reduce the ranking time for a wide range of popular Information Retrieval (IR) relevance measures, such as TFxIDF and BM25. First, we reorder elements in the document array according to document weight. Second, we introduce the repetition array, which generalizes Sadakane's (JDA 2007) document frequency structure to document subsets. Combining document and repetition array, we achieve attractive functionality-space trade-offs. We provide an extensive evaluation of our system on terabyte-sized IR collections.
An eigenvector-based hotspot detection
Venue: arXiv cs.AI 2014
Year: 2014

Space and time are two critical components of many real world systems. For this reason, analysis of anomalies in spatiotemporal data has been a great of interest. In this work, application of tensor decomposition and eigenspace techniques on spatiotemporal hotspot detection is investigated. An algorithm called SST-Hotspot is proposed which accounts for spatiotemporal variations in data and detect hotspots using matching of eigenvector elements of two cases and population tensors. The experimental results reveal the interesting application of tensor decomposition and eigenvector-based techniques in hotspot analysis.
Zero-Correlation Linear Cryptanalysis of Reduced Round ARIA with Partial-sum and FFT
Venue: arXiv cs.CR 2014
Year: 2014

Block cipher ARIA was first proposed by some South Korean experts in 2003, and later, it was established as a Korean Standard block cipher algorithm by Korean Agency for Technology and Standards. In this paper, we focus on the security evaluation of ARIA block cipher against the recent zero-correlation linear cryptanalysis. In addition, Partial-sum technique and FFT (Fast Fourier Transform) technique are used to speed up the cryptanalysis, respectively. We first introduce some 4-round linear approximations of ARIA with zero-correlation, and then present some key-recovery attacks on 6/7-round ARIA-128/256 with Partial-sum technique and FFT technique.The key-recovery attack with Partial-sum technique on 6-round ARIA-128 needs 2^{123.6}known plaintexts (KPs), 2^{121} encryptions and 2^{90.3} bytes memory, and the attack with FFT technique requires 2^{124.1} KPs, 2^{121.5} encryptions and 2^{90.3}bytes memory. Moreover, applying Partial-sum technique, we can attack 7-round ARIA-256 with 2^{124.6} KPs, 2^{203.5} encryptions and 2^{152} bytes and 7-round ARIA-256 employing FFT technique, requires 2^{124.7} KPs, 2^{209.5} encryptions and 2^{152} bytes. Our results are the first zero-correlation linear cryptanalysis results on ARIA.
Integration of Legacy Appliances into Home Energy Management Systems
Venue: arXiv cs.OH 2014
Year: 2014

The progressive installation of renewable energy sources requires the coordination of energy consuming devices. At consumer level, this coordination can be done by a home energy management system (HEMS). Interoperability issues need to be solved among smart appliances as well as between smart and non-smart, i.e., legacy devices. We expect current standardization efforts to soon provide technologies to design smart appliances in order to cope with the current interoperability issues. Nevertheless, common electrical devices affect energy consumption significantly and therefore deserve consideration within energy management applications. This paper discusses the integration of smart and legacy devices into a generic system architecture and, subsequently, elaborates the requirements and components which are necessary to realize such an architecture including an application of load detection for the identification of running loads and their integration into existing HEM systems. We assess the feasibility of such an approach with a case study based on a measurement campaign on real households. We show how the information of detected appliances can be extracted in order to create device profiles allowing for their integration and management within a HEMS.
A Semantic VSM-Based Recommender System
Venue: arXiv cs.IR 2014
Year: 2014

Online forums enable users to discuss together around various topics. One of the serious problems of these environments is high volume of discussions and thus information overload problem. Unfortunately without considering the users interests, traditional Information Retrieval (IR) techniques are not able to solve the problem. Therefore, employment of a Recommender System (RS) that could suggest favorite's topics of users according to their tastes could increases the dynamism of forum and prevent the users from duplicate posts. In addition, consideration of semantics can be useful for increasing the performance of IR based RS. Our goal is study of impact of ontology and data mining techniques on improving of content-based RS. For this purpose, at first, three type of ontologies will be constructed from the domain corpus with utilization of text mining, Natural Language Processing (NLP) and Wordnet and then they will be used as an input in two kind of RS: one, fully ontology-based and one with enriching the user profile vector with ontology in vector space model (VSM) (proposed method). Afterward the results will be compared with the simple VSM based RS. Given results show that the proposed RS presents the highest performance.