OpenRaiser/Intern-Atlas huggingface dataset:
Maximizing Revenues for Online-Dial-a-Ride
Venue: arXiv cs.DS 2013
Year: 2013

In the classic Dial-a-Ride Problem, a server travels in some metric space to serve requests for rides. Each request has a source, destination, and release time. We study a variation of this problem where each request also has a revenue that is earned if the request is satisfied. The goal is to serve requests within a time limit such that the total revenue is maximized. We first prove that the version of this problem where edges in the input graph have varying weights is NP-complete. We also prove that no algorithm can be competitive for this problem. We therefore consider the version where edges in the graph have unit weight and develop a 2-competitive algorithm for this problem.
Optimizing scanning strategies: Selecting scanning bandwidth in adversarial RF environments
Venue: arXiv cs.GT 2013
Year: 2013

In this paper we investigate the problem of designing a spectrum scanning strategy to detect an intelligent Invader who wants to utilize spectrum undetected for his/her unapproved purposes. To deal with this problem we apply game-theoretical tools. We model the situation as a game between a Scanner and an Invader where the Invader faces a dilemma: the more bandwidth the Invader attempts to use leads to a larger payoff if he is not detected, but at the same time also increases the probability of being detected and thus fined. Similarly, the Scanner faces a dilemma: the wider the bandwidth scanned, the higher the probability of detecting the Invader, but at the expense of increasing the cost of building the scanning system. The equilibrium strategies are found explicitly and reveal interesting properties. In particular, we have found a discontinuous dependence of the equilibrium strategies on the network parameters, fine and the type of the Invader's award. This discontinuity on fine means that the network provider has to take into account a human factor since some threshold values of fine could be very sensible for the Invader, while in other situations simply increasing the fine has minimal deterrence impact. Also we show how different reward types for the Invader (e.g. motivated by using different type of application, say, video-streaming or downloading files) can be incorporated into scanning strategy to increase its efficiency.
Optimized Markov Chain Monte Carlo for Signal Detection in MIMO Systems: an Analysis of Stationary Distribution and Mixing Time
Venue: arXiv cs.IT 2013
Year: 2013

In this paper we introduce an optimized Markov Chain Monte Carlo (MCMC) technique for solving the integer least-squares (ILS) problems, which include Maximum Likelihood (ML) detection in Multiple-Input Multiple-Output (MIMO) systems. Two factors contribute to the speed of finding the optimal solution by the MCMC detector: the probability of the optimal solution in the stationary distribution, and the mixing time of the MCMC detector. Firstly, we compute the optimal value of the "temperature" parameter, in the sense that the temperature has the desirable property that once the Markov chain has mixed to its stationary distribution, there is polynomially small probability ($1/\mbox{poly}(N)$, instead of exponentially small) of encountering the optimal solution. This temperature is shown to be at most $O(\sqrt{SNR}/\ln(N))$, where $SNR$ is the signal-to-noise ratio, and $N$ is the problem dimension. Secondly, we study the mixing time of the underlying Markov chain of the proposed MCMC detector. We find that, the mixing time of MCMC is closely related to whether there is a local minimum in the lattice structures of ILS problems. For some lattices without local minima, the mixing time of the Markov chain is independent of $SNR$, and grows polynomially in the problem dimension; for lattices with local minima, the mixing time grows unboundedly as $SNR$ grows, when the temperature is set, as in conventional wisdom, to be the standard deviation of noises. Our results suggest that, to ensure fast mixing for a fixed dimension $N$, the temperature for MCMC should instead be set as $Ω(\sqrt{SNR})$ in general. Simulation results show that the optimized MCMC detector efficiently achieves approximately ML detection in MIMO systems having a huge number of transmit and receive dimensions.
Determinants of a Successful Migration to Cloud Computing in Iranian Telecommunication Industry
Venue: arXiv cs.CY 2013
Year: 2013

Although the research support for Cloud Computing (CC) is still developing, the concept of this paper has provided comprehensive frameworks for a successful migration to cloud computing (SMCC) in telecommunication industry in Iran. Using an academic orientation, the conceptual research is focusing on the determinants of a successful migration from legacy to cloud computing. The study attempts to reveal the constructive effects and deconstructive defects of migration to cloud computing with a close regard into prior literature and practical practices all around the world. Conceptual frameworks are deducted from the literature and Telco's revolutionary movements toward cloud computing. The confirmatory quantitative approach tries to verify or reject the validity of determinants of successful migration. The study reports that there are some success and failure factors which are influencing a successful migration of data centres and servers of Iranian Telecommunication to the cloud. Considering these approved factors before any migration decision would be valuable for engaged project members and finally for Telecommunication organization. This paper can be used as reliable model for any migration beforehand taking any action. Enforcing proper determinants in accordance with the prior success stories and academia viewpoints in Telecommunication industry in Iran as a first mover research in this field provides a precious insight for policy and decision makers to change their mindset and grant a proper space for cloud computing to grow in this industry due to its advantages. Obviously, like any other big Telco in the world, Iranian Telco might start cloud projects to sustain its presence in the global market.
Formulation and Steady-state Analysis of LMS Adaptive Networks for Distributed Estimation in the Presence of Transmission Errors
Venue: arXiv cs.SY 2013
Year: 2013

This article presents the formulation and steady-state analysis of the distributed estimation algorithms based on the diffusion cooperation scheme in the presence of errors due to the unreliable data transfer among nodes. In particular, we highlight the impact of transmission errors on the least-mean squares (LMS) adaptive networks. We develop the closed-form expressions of the steady-state mean-square deviation (MSD) which is helpful to assess the effects of the imperfect information flow on on the behavior of the diffusion LMS algorithm in terms of the steady-state error. The model is then validated by performing Monte Carlo simulations. It is shown that local and global MSD curves are not necessarily monotonic increasing functions of the error probability. We also assess sufficient conditions that ensure mean and mean-square stability of diffusion LMS strategies in the presence of transmission errors. Moreover, issues such as scalability in the sense of network size and regressor size, spatially correlated observations, as well as the effect of the distribution of the noise variance are studied. While the proposed theoretical framework is general in the sense that it is not confined to a particular source of error during information diffusion, for practical reasons we additionally study a specific scenario where errors occur at the medium access control (MAC) level. We develop a model to quantify the MAC-level transmission errors according to the network topology and system parameters for a set of nodes employing a backoff procedure to access the channel. To overcome the problem of unreliable data exchange, we propose an enhanced combining rule that can be deployed in order to improve the performance of diffusion estimation algorithms by using the knowledge of the properties of the transmission errors.
H Index Communication Journals according to Google Scholar Metrics (2008-2012)
Venue: arXiv cs.DL 2013
Year: 2013

The aim of this report is to present a ranking of Communication journals covered in Google Scholar Metrics for the period 2008-2012. It corresponds to the H Index update made last year for the period 2007-2011 (Delgado López-Cózar and Repiso 2013). Google Scholar Metrics doesnt currently allow to group and sort all journals belonging to a scientific discipline. In the case of Communication, in the ten listings displayed by GSM we can only locate 46 journals. Therefore, in an attempt to overcome this limitation, we have used the diversity of search procedures allowed by GSM to identify the greatest number of scientific journals of Communication with H Index calculated by this bibliometric tool. The result is a ranking of 354 communication journals sorted by the same H Index, and mean as discriminating value. Journals are also grouped by quartiles.
Neural perceptual model to global-local vision for recognition of the logical structure of administrative documents
Venue: arXiv cs.CV 2013
Year: 2013

This paper gives the definition of Transparent Neural Network "TNN" for the simulation of the globallocal vision and its application to the segmentation of administrative document image. We have developed and have adapted a recognition method which models the contextual effects reported from studies in experimental psychology. Then, we evaluated and tested the TNN and the multi-layer perceptron "MLP", which showed its effectiveness in the field of the recognition, in order to show that the TNN is clearer for the user and more powerful on the level of the recognition. Indeed, the TNN is the only system which makes it possible to recognize the document and its structure.
New Constructions of Codes for Asymmetric Channels via Concatenation
Venue: arXiv cs.IT 2013
Year: 2013

We present new constructions of codes for asymmetric channels for both binary and nonbinary alphabets, based on methods of generalized code concatenation. For the binary asymmetric channel, our methods construct nonlinear single-error-correcting codes from ternary outer codes. We show that some of the Varshamov-Tenengol'ts-Constantin-Rao codes, a class of binary nonlinear codes for this channel, have a nice structure when viewed as ternary codes. In many cases, our ternary construction yields even better codes. For the nonbinary asymmetric channel, our methods construct linear codes for many lengths and distances which are superior to the linear codes of the same length capable of correcting the same number of symmetric errors. In the binary case, Varshamov has shown that almost all good linear codes for the asymmetric channel are also good for the symmetric channel. Our results indicate that Varshamov's argument does not extend to the nonbinary case, i.e., one can find better linear codes for asymmetric channels than for symmetric ones.
Evaluating Cache Coherent Shared Virtual Memory for Heterogeneous Multicore Chips
Venue: arXiv cs.AR 2013
Year: 2013

The trend in industry is towards heterogeneous multicore processors (HMCs), including chips with CPUs and massively-threaded throughput-oriented processors (MTTOPs) such as GPUs. Although current homogeneous chips tightly couple the cores with cache-coherent shared virtual memory (CCSVM), this is not the communication paradigm used by any current HMC. In this paper, we present a CCSVM design for a CPU/MTTOP chip, as well as an extension of the pthreads programming model, called xthreads, for programming this HMC. Our goal is to evaluate the potential performance benefits of tightly coupling heterogeneous cores with CCSVM.
Conditional Entropy based User Selection for Multiuser MIMO Systems
Venue: arXiv cs.IT 2013
Year: 2013

We consider the problem of user subset selection for maximizing the sum rate of downlink multi-user MIMO systems. The brute-force search for the optimal user set becomes impractical as the total number of users in a cell increase. We propose a user selection algorithm based on conditional differential entropy. We apply the proposed algorithm on Block diagonalization scheme. Simulation results show that the proposed conditional entropy based algorithm offers better alternatives than the existing user selection algorithms. Furthermore, in terms of sum rate, the solution obtained by the proposed algorithm turns out to be close to the optimal solution with significantly lower computational complexity than brute-force search.
Smartphone as a Personal, Pervasive Health Informatics Services Platform: Literature Review
Venue: arXiv cs.CY 2013
Year: 2013

Objectives: The article provides an overview of current trends in personal sensor, signal and imaging informatics, that are based on emerging mobile computing and communications technologies enclosed in a smartphone and enabling the provision of personal, pervasive health informatics services. Methods: The article reviews examples of these trends from the PubMed and Google scholar literature search engines, which, by no means claim to be complete, as the field is evolving and some recent advances may not be documented yet. Results: There exist critical technological advances in the surveyed smartphone technologies, employed in provision and improvement of diagnosis, acute and chronic treatment and rehabilitation health services, as well as in education and training of healthcare practitioners. However, the most emerging trend relates to a routine application of these technologies in a prevention/wellness sector, helping its users in self-care to stay healthy. Conclusions: Smartphone-based personal health informatics services exist, but still have a long way to go to become an everyday, personalized healthcare-provisioning tool in the medical field and in a clinical practice. Key main challenge for their widespread adoption involve lack of user acceptance striving from variable credibility and reliability of applications and solutions as they a) lack evidence-based approach; b) have low levels of medical professional involvement in their design and content; c) are provided in an unreliable way, influencing negatively its usability; and, in some cases, d) being industry-driven, hence exposing bias in information provided, for example towards particular types of treatment or intervention procedures.
Description and Evaluation of Semantic Similarity Measures Approaches
Venue: arXiv cs.CL 2013
Year: 2013

In recent years, semantic similarity measure has a great interest in Semantic Web and Natural Language Processing (NLP). Several similarity measures have been developed, being given the existence of a structured knowledge representation offered by ontologies and corpus which enable semantic interpretation of terms. Semantic similarity measures compute the similarity between concepts/terms included in knowledge sources in order to perform estimations. This paper discusses the existing semantic similarity methods based on structure, information content and feature approaches. Additionally, we present a critical evaluation of several categories of semantic similarity approaches based on two standard benchmarks. The aim of this paper is to give an efficient evaluation of all these measures which help researcher and practitioners to select the measure that best fit for their requirements.
Herbrand-Confluence
Venue: arXiv cs.LO 2013
Year: 2013

We consider cut-elimination in the sequent calculus for classical first-order logic. It is well known that this system, in its most general form, is neither confluent nor strongly normalizing. In this work we take a coarser (and mathematically more realistic) look at cut-free proofs. We analyze which witnesses they choose for which quantifiers, or in other words: we only consider the Herbrand-disjunction of a cut-free proof. Our main theorem is a confluence result for a natural class of proofs: all (possibly infinitely many) normal forms of the non-erasing reduction lead to the same Herbrand-disjunction.
Para-active learning
Venue: arXiv cs.LG 2013
Year: 2013

Training examples are not all equally informative. Active learning strategies leverage this observation in order to massively reduce the number of examples that need to be labeled. We leverage the same observation to build a generic strategy for parallelizing learning algorithms. This strategy is effective because the search for informative examples is highly parallelizable and because we show that its performance does not deteriorate when the sifting process relies on a slightly outdated model. Parallel active learning is particularly attractive to train nonlinear models with non-linear representations because there are few practical parallel learning algorithms for such models. We report preliminary experiments using both kernel SVMs and SGD-trained neural networks.
Reinforcement Learning Framework for Opportunistic Routing in WSNs
Venue: arXiv cs.NI 2013
Year: 2013

Routing packets opportunistically is an essential part of multihop ad hoc wireless sensor networks. The existing routing techniques are not adaptive opportunistic. In this paper we have proposed an adaptive opportunistic routing scheme that routes packets opportunistically in order to ensure that packet loss is avoided. Learning and routing are combined in the framework that explores the optimal routing possibilities. In this paper we implemented this Reinforced learning framework using a customer simulator. The experimental results revealed that the scheme is able to exploit the opportunistic to optimize routing of packets even though the network structure is unknown.
Optimal Impartial Selection
Venue: arXiv cs.GT 2013
Year: 2013

We study the problem of selecting a member of a set of agents based on impartial nominations by agents from that set. The problem was studied previously by Alon et al. and Holzman and Moulin and has important applications in situations where representatives are selected from within a group or where publishing or funding decisions are made based on a process of peer review. Our main result concerns a randomized mechanism that in expectation awards the prize to an agent with at least half the maximum number of nominations. Subject to impartiality, this is best possible.
The PFR Conjecture Holds for Two Opposing Special Cases
Venue: arXiv cs.DM 2013
Year: 2013

Let $A \subseteq F_2^n$ be a set with $|2A| = K|A|$. We prove that if (1) for at least a fraction $1-K^{-9}$ of all $s \in 2A$, the set $(A+s) \cap A$ has size at most $L\cdot|A|/K$, or (2) for at least a fraction $K^{-L}$ of all $s \in 2A$, the set $(A+s) \cap A$ has size at least $|A|\cdot(1- K^{-1/L})$, then there is a subset $B \subseteq A$ of size $|A|/K^{O_L(1)}$ such that $\mathrm{span}(B) \leq K^{O_L(1)}\cdot|A|$.
Convexity in source separation: Models, geometry, and algorithms
Venue: arXiv cs.IT 2013
Year: 2013

Source separation or demixing is the process of extracting multiple components entangled within a signal. Contemporary signal processing presents a host of difficult source separation problems, from interference cancellation to background subtraction, blind deconvolution, and even dictionary learning. Despite the recent progress in each of these applications, advances in high-throughput sensor technology place demixing algorithms under pressure to accommodate extremely high-dimensional signals, separate an ever larger number of sources, and cope with more sophisticated signal and mixing models. These difficulties are exacerbated by the need for real-time action in automated decision-making systems. Recent advances in convex optimization provide a simple framework for efficiently solving numerous difficult demixing problems. This article provides an overview of the emerging field, explains the theory that governs the underlying procedures, and surveys algorithms that solve them efficiently. We aim to equip practitioners with a toolkit for constructing their own demixing algorithms that work, as well as concrete intuition for why they work.
A Novel Term Weighing Scheme Towards Efficient Crawl of Textual Databases
Venue: arXiv cs.IR 2013
Year: 2013

The Hidden Web is the vast repository of informational databases available only through search form interfaces, accessible by therein typing a set of keywords in the search forms. Typically, a Hidden Web crawler is employed to autonomously discover and download pages from the Hidden Web. Traditional hidden web crawlers do not provide the search engines with an optimal search experience because of the excessive number of search requests posed through the form interface so as to exhaustively crawl and retrieve the contents of the target hidden web database. Here in our work, we provide a framework to investigate the problem of optimal search and curtail it by proposing an effective query term selection approach based on the frequency & distribution of terms in the document database. The paper focuses on developing a term-weighing scheme called VarDF (acronym for variable document frequency) that can ease the identification of optimal terms to be used as queries on the interface for maximizing the achieved coverage of the crawler which in turn will facilitate the search engine to have a diversified and expanded index. We experimentally evaluate the effectiveness of our approach on a manually created database of documents in the area of Information Retrieval.
Why robots? A survey on the roles and benefits of social robots in the therapy of children with autism
Venue: arXiv cs.RO 2013
Year: 2013

This paper reviews the use of socially interactive robots to assist in the therapy of children with autism. The extent to which the robots were successful in helping the children in their social, emotional, and communication deficits was investigated. Child-robot interactions were scrutinized with respect to the different target behaviors that are to be elicited from a child during therapy. These behaviors were thoroughly examined with respect to a childs development needs. Most importantly, experimental data from the surveyed works were extracted and analyzed in terms of the target behaviors and how each robot was used during a therapy session to achieve these behaviors. The study concludes by categorizing the different therapeutic roles that these robots were observed to play, and highlights the important design features that enable them to achieve high levels of effectiveness in autism therapy.
Deterministic Sequences for Compressive MIMO Channel Estimation
Venue: arXiv cs.IT 2013
Year: 2013

This paper considers the problem of pilot design for compressive multiple-input multiple-output (MIMO) channel estimation. In particular, we are interested in estimating the channels for multiple transmitters simultaneously when the pilot sequences are shorter than the combined channels. Existing works on this topic demonstrated that tools from compressed sensing theory can yield accurate multichannel estimation provided that each pilot sequence is randomly generated. Here, we propose constructing the pilot sequence for each transmitter from a small set of deterministic sequences. We derive a theoretical lower bound on the length of the pilot sequences that guarantees the multichannel estimation with high probability. Simulation results are provided to demonstrate the performance of the proposed method.