OpenRaiser/Intern-Atlas huggingface dataset:
Improving Statistical Machine Translation for a Resource-Poor Language Using Related Resource-Rich Languages
Venue: arXiv cs.CL 2014
Year: 2014

We propose a novel language-independent approach for improving machine translation for resource-poor languages by exploiting their similarity to resource-rich ones. More precisely, we improve the translation from a resource-poor source language X_1 into a resource-rich language Y given a bi-text containing a limited number of parallel sentences for X_1-Y and a larger bi-text for X_2-Y for some resource-rich language X_2 that is closely related to X_1. This is achieved by taking advantage of the opportunities that vocabulary overlap and similarities between the languages X_1 and X_2 in spelling, word order, and syntax offer: (1) we improve the word alignments for the resource-poor language, (2) we further augment it with additional translation options, and (3) we take care of potential spelling differences through appropriate transliteration. The evaluation for Indonesian- >English using Malay and for Spanish -> English using Portuguese and pretending Spanish is resource-poor shows an absolute gain of up to 1.35 and 3.37 BLEU points, respectively, which is an improvement over the best rivaling approaches, while using much less additional data. Overall, our method cuts the amount of necessary "real training data by a factor of 2--5.
How the Sando Search Tool Recommends Queries
Venue: arXiv cs.SE 2014
Year: 2014

Developers spend a significant amount of time searching their local codebase. To help them search efficiently, researchers have proposed novel tools that apply state-of-the-art information retrieval algorithms to retrieve relevant code snippets from the local codebase. However, these tools still rely on the developer to craft an effective query, which requires that the developer is familiar with the terms contained in the related code snippets. Our empirical data from a state-of-the-art local code search tool, called Sando, suggests that developers are sometimes unacquainted with their local codebase. In order to bridge the gap between developers and their ever-increasing local codebase, in this paper we demonstrate the recommendation techniques integrated in Sando.
A Fast String Matching Algorithm Based on Lowlight Characters in the Pattern
Venue: arXiv cs.DS 2014
Year: 2014

We put forth a new string matching algorithm which matches the pattern from neither the left nor the right end, instead a special position. Comparing with the Knuth-Morris-Pratt algorithm and the Boyer-Moore algorithm, the new algorithm is more flexible to pick the position for starting comparisons. The option really brings it a saving in cost.
Measuring large-scale social networks with high resolution
Venue: arXiv cs.SI 2014
Year: 2014

This paper describes the deployment of a large-scale study designed to measure human interactions across a variety of communication channels, with high temporal resolution and spanning multiple years - the Copenhagen Networks Study. Specifically, we collect data on face-to-face interactions, telecommunication, social networks, location, and background information (personality, demographic, health, politics) for a densely connected population of 1,000 individuals, using state-of-art smartphones as social sensors. Here we provide an overview of the related work and describe the motivation and research agenda driving the study. Additionally the paper details the data-types measured, and the technical infrastructure in terms of both backend and phone software, as well as an outline of the deployment procedures. We document the participant privacy procedures and their underlying principles. The paper is concluded with early results from data analysis, illustrating the importance of multi-channel high-resolution approach to data collection.
Fuzzy Controller Design for Assisted Omni-Directional Treadmill Therapy
Venue: arXiv cs.AI 2014
Year: 2014

One of the defining characteristic of human being is their ability to walk upright. Loss or restriction of such ability whether due to the accident, spine problem, stroke or other neurological injuries can cause tremendous stress on the patients and hence will contribute negatively to their quality of life. Modern research shows that physical exercise is very important for maintaining physical fitness and adopting a healthier life style. In modern days treadmill is widely used for physical exercises and training which enables the user to set up an exercise regime that can be adhered to irrespective of the weather conditions. Among the users of treadmills today are medical facilities such as hospitals, rehabilitation centres, medical and physiotherapy clinics etc. The process of assisted training or doing rehabilitation exercise through treadmill is referred to as treadmill therapy. A modern treadmill is an automated machine having built in functions and predefined features. Most of the treadmills used today are one dimensional and user can only walk in one direction. This paper presents the idea of using omnidirectional treadmills which will be more appealing to the patients as they can walk in any direction, hence encouraging them to do exercises more frequently. This paper proposes a fuzzy control design and possible implementation strategy to assist patients in treadmill therapy. By intelligently controlling the safety belt attached to the treadmill user, one can help them steering left, right or in any direction. The use of intelligent treadmill therapy can help patients to improve their walking ability without being continuously supervised by the specialists. The patients can walk freely within a limited space and the support system will provide continuous evaluation of their position and can adjust the control parameters of treadmill accordingly to provide best possible assistance.
Superconcentration on a Pair of Butterflies
Venue: arXiv cs.DS 2014
Year: 2014

Suppose we concatenate two directed graphs, each isomorphic to a $d$ dimensional butterfly (but not necessarily identical to each other). Select any set of $2^k$ input and $2^k$ output nodes on the resulting graph. Then there exist node disjoint paths from the input nodes to the output nodes. If we take two standard butterflies and permute the order of the layers, then the result holds on sets of any size, not just powers of two.
How Best to Handle a Dicey Situation
Venue: arXiv cs.DS 2014
Year: 2014

We introduce the {Destructive Object Handling} (DOH) problem, which models aspects of many real-world allocation problems, such as shipping explosive munitions, scheduling processes in a cluster with fragile nodes, re-using passwords across multiple websites, and quarantining patients during a disease outbreak. In these problems, objects must be assigned to handlers, but each object has a probability of destroying itself and all the other objects allocated to the same handler. The goal is to maximize the expected value of the objects handled successfully. We show that finding the optimal allocation is $\mathsf{NP}$-$\mathsf{complete}$, even if all the handlers are identical. We present an FPTAS when the number of handlers is constant. We note in passing that the same technique also yields a first FPTAS for the weapons-target allocation problem \cite{manne_wta} with a constant number of targets. We study the structure of DOH problems and find that they have a sort of phase transition -- in some instances it is better to spread risk evenly among the handlers, in others, one handler should be used as a ``sacrificial lamb''. We show that the problem is solvable in polynomial time if the destruction probabilities depend only on the handler to which an object is assigned; if all the handlers are identical and the objects all have the same value; or if each handler can be assigned at most one object. Finally, we empirically evaluate several heuristics based on a combination of greedy and genetic algorithms. The proposed heuristics return fairly high quality solutions to very large problem instances (upto 250 objects and 100 handlers) in tens of seconds.
The parametrized probabilistic finite-state transducer probe game player fingerprint model
Venue: arXiv cs.GT 2014
Year: 2014

Fingerprinting operators generate functional signatures of game players and are useful for their automated analysis independent of representation or encoding. The theory for a fingerprinting operator which returns the length-weighted probability of a given move pair occurring from playing the investigated agent against a general parametrized probabilistic finite-state transducer (PFT) is developed, applicable to arbitrary iterated games. Results for the distinguishing power of the 1-state opponent model, uniform approximability of fingerprints of arbitrary players, analyticity and Lipschitz continuity of fingerprints for logically possible players, and equicontinuity of the fingerprints of bounded-state probabilistic transducers are derived. Algorithms for the efficient computation of special instances are given; the shortcomings of a previous model, strictly generalized here from a simple projection of the new model, are explained in terms of regularity condition violations, and the extra power and functional niceness of the new fingerprints demonstrated. The 2-state deterministic finite-state transducers (DFTs) are fingerprinted and pairwise distances computed; using this the structure of DFTs in strategy space is elucidated.
Smoothed Low Rank and Sparse Matrix Recovery by Iteratively Reweighted Least Squares Minimization
Venue: arXiv cs.LG 2014
Year: 2014

This work presents a general framework for solving the low rank and/or sparse matrix minimization problems, which may involve multiple non-smooth terms. The Iteratively Reweighted Least Squares (IRLS) method is a fast solver, which smooths the objective function and minimizes it by alternately updating the variables and their weights. However, the traditional IRLS can only solve a sparse only or low rank only minimization problem with squared loss or an affine constraint. This work generalizes IRLS to solve joint/mixed low rank and sparse minimization problems, which are essential formulations for many tasks. As a concrete example, we solve the Schatten-$p$ norm and $\ell_{2,q}$-norm regularized Low-Rank Representation (LRR) problem by IRLS, and theoretically prove that the derived solution is a stationary point (globally optimal if $p,q\geq1$). Our convergence proof of IRLS is more general than previous one which depends on the special properties of the Schatten-$p$ norm and $\ell_{2,q}$-norm. Extensive experiments on both synthetic and real data sets demonstrate that our IRLS is much more efficient.
On Performance of Logical-Clustering Of Flow-Sensors
Venue: arXiv cs.NI 2014
Year: 2014

In state-of-the-art Pervasive Computing, it is envisioned that unlimited access to information will be facilitated for anyone and anything. Wireless sensor networks will play a pivotal role in the stated vision. This reflects the phenomena where any situation can be sensed and analyzed anywhere. It makes heterogeneous context ubiquitous. Clustering context is one of the techniques to manage ubiquitous context information efficiently to maximize its potential. Logical-clustering is useful to share real-time context where sensors are physically distributed but logically clustered. This paper investigates the network performance of logical-clustering based on ns-3 simulations. In particular reliability, scalability, and reachability in terms of delay, jitter, and packet loss for the logically clustered network have been investigated. The performance study shows that jitter demonstrates 40 % and 44 % fluctuation for 200 % increase in the node per cluster and 100 % increase in the cluster size respectively. Packet loss exhibits only 18 % increase for 83 % increase in the packet flow-rate.
Online Social Media in the Syria Conflict: Encompassing the Extremes and the In-Betweens
Venue: arXiv cs.SI 2014
Year: 2014

The Syria conflict has been described as the most socially mediated in history, with online social media playing a particularly important role. At the same time, the ever-changing landscape of the conflict leads to difficulties in applying analytical approaches taken by other studies of online political activism. Therefore, in this paper, we use an approach that does not require strong prior assumptions or the proposal of an advance hypothesis to analyze Twitter and YouTube activity of a range of protagonists to the conflict, in an attempt to reveal additional insights into the relationships between them. By means of a network representation that combines multiple data views, we uncover communities of accounts falling into four categories that broadly reflect the situation on the ground in Syria. A detailed analysis of selected communities within the anti-regime categories is provided, focusing on their central actors, preferred online platforms, and activity surrounding "real world" events. Our findings indicate that social media activity in Syria is considerably more convoluted than reported in many other studies of online political activism, suggesting that alternative analytical approaches can play an important role in this type of scenario.
Automatic Reference Models Development: A Framework
Venue: arXiv cs.SE 2014
Year: 2014

Software reuse allows the software industry to simultaneously reduce development cost and improve product quality. Reuse of early-stage artifacts has been acknowledged to be more beneficial than reuse of later-stage artifacts. In this regard, early-stage reference models have been considered as good tools to allow reuse across applications within the same domain. However, our literature survey reported in this paper reveals that the problem of automatically developing reference models from given instances has not caught enough researchers attention yet. Accordingly, in this paper we propose a framework for building a reference model that captures the common and variable analysis/design practices, across the different applications in a domain. The framework considers multi-view models in assessing the commonalities and variabilities among given instances. The proposed framework incorporates learning capabilities to allow improving the quality and re-usability of the reference model as it is being used.
Practical Challenges with Spreadsheet Auditing Tools
Venue: arXiv cs.SE 2014
Year: 2014

Just like other software, spreadsheets can contain significant faults. Static analysis is an accepted and well-established technique in software engineering known for its capability to discover faults. In recent years, a growing number of tool vendors started offering tools that allow casual end-users to run various static analyses on spreadsheets as well. We supervised a study where three undergraduate software engineering students examined a selection of 14 spreadsheet auditing tools, trying to give a concrete recommendation for an industry partner. Reflecting on the study's results, we found that most of these tools do provide useful aids in finding problems in spreadsheets, but we have also spotted several areas where tools had significant issues. Some of these issues could be remedied if spreadsheet auditing tool vendors would pick up some ideas of static analysis tools for traditional software development and adopt some of their solution approaches.
Linear-Time Algorithms for the Paired-Domination Problem in Interval Graphs and Circular-Arc Graphs
Venue: arXiv cs.DS 2014
Year: 2014

In a graph $G$, a vertex subset $S\subseteq V(G)$ is said to be a dominating set of $G$ if every vertex not in $S$ is adjacent to a vertex in $S$. A dominating set $S$ of a graph $G$ is called a paired-dominating set if the induced subgraph $G[S]$ contains a perfect matching. The paired-domination problem involves finding a smallest paired-dominating set of $G$. Given an intersection model of an interval graph $G$ with sorted endpoints, Cheng et al. designed an $O(m+n)$-time algorithm for interval graphs and an $O(m(m+n))$-time algorithm for circular-arc graphs. In this paper, to solve the paired-domination problem in interval graphs, we propose an $O(n)$-time algorithm that searches for a minimum paired-dominating set of $G$ incrementally in a greedy manner. Then, we extend the results to design an algorithm for circular-arc graphs that also runs in $O(n)$ time.
A Spectral Framework for Anomalous Subgraph Detection
Venue: arXiv cs.SI 2014
Year: 2014

A wide variety of application domains are concerned with data consisting of entities and their relationships or connections, formally represented as graphs. Within these diverse application areas, a common problem of interest is the detection of a subset of entities whose connectivity is anomalous with respect to the rest of the data. While the detection of such anomalous subgraphs has received a substantial amount of attention, no application-agnostic framework exists for analysis of signal detectability in graph-based data. In this paper, we describe a framework that enables such analysis using the principal eigenspace of a graph's residuals matrix, commonly called the modularity matrix in community detection. Leveraging this analytical tool, we show that the framework has a natural power metric in the spectral norm of the anomalous subgraph's adjacency matrix (signal power) and of the background graph's residuals matrix (noise power). We propose several algorithms based on spectral properties of the residuals matrix, with more computationally expensive techniques providing greater detection power. Detection and identification performance are presented for a number of signal and noise models, including clusters and bipartite foregrounds embedded into simple random backgrounds as well as graphs with community structure and realistic degree distributions. The trends observed verify intuition gleaned from other signal processing areas, such as greater detection power when the signal is embedded within a less active portion of the background. We demonstrate the utility of the proposed techniques in detecting small, highly anomalous subgraphs in real graphs derived from Internet traffic and product co-purchases.
Predictive Green Wireless Access: Exploiting Mobility and Application Information
Venue: arXiv cs.NI 2014
Year: 2014

The ever increasing mobile data traffic and dense deployment of wireless networks have made energy efficient radio access imperative. As networks are designed to satisfy peak user demands, radio access energy can be reduced in a number of ways at times of lower demand. This includes putting base stations (BSs) to intermittent short sleep modes during low load, as well as adaptively powering down select BSs completely where demand is low for prolonged time periods. In order to fully exploit such energy conserving mechanisms, networks should be aware of the user temporal and spatial traffic demands. To this end, this article investigates the potential of utilizing predictions of user location and application information as a means to energy saving. We discuss the development of a predictive green wireless access (PreGWA) framework and identify its key functional entities and their interaction. To demonstrate the potential energy savings we then provide a case study on stored video streaming and illustrate how exploiting predictions can minimize BS resource consumption within a single cell, and across a network of cells. Finally, to emphasize the practical potential of PreGWA, we present a distributed heuristic that reduces resource consumption significantly without requiring considerable information or signaling overhead.
Security Evaluation of Support Vector Machines in Adversarial Environments
Venue: arXiv cs.LG 2014
Year: 2014

Support Vector Machines (SVMs) are among the most popular classification techniques adopted in security applications like malware detection, intrusion detection, and spam filtering. However, if SVMs are to be incorporated in real-world security systems, they must be able to cope with attack patterns that can either mislead the learning algorithm (poisoning), evade detection (evasion), or gain information about their internal parameters (privacy breaches). The main contributions of this chapter are twofold. First, we introduce a formal general framework for the empirical evaluation of the security of machine-learning systems. Second, according to our framework, we demonstrate the feasibility of evasion, poisoning and privacy attacks against SVMs in real-world security problems. For each attack technique, we evaluate its impact and discuss whether (and how) it can be countered through an adversary-aware design of SVMs. Our experiments are easily reproducible thanks to open-source code that we have made available, together with all the employed datasets, on a public repository.
SPRING: speech and pronunciation improvement through games, for Hispanic children
Venue: arXiv cs.HC 2014
Year: 2014

Lack of proper English pronunciations is a major problem for immigrant population in developed countries like U.S. This poses various problems, including a barrier to entry into mainstream society. This paper presents a research study that explores the use of speech technologies merged with activity-based and arcade-based games to do pronunciation feedback for Hispanic children within the U.S. A 3-month long study with immigrant population in California was used to investigate and analyze the effectiveness of computer aided pronunciation feedback through games. In addition to quantitative findings that point to statistically significant gains in pronunciation quality, the paper also explores qualitative findings, interaction patterns and challenges faced by the researchers in dealing with this community. It also describes the issues involved in dealing with pronunciation as a competency.
Spectrum Sensing Via Reconfigurable Antennas: Is Cooperation of Secondary Users Indispensable?
Venue: arXiv cs.NI 2014
Year: 2014

This work presents an analytical framework for characterizing the performance of cooperative and noncooperative spectrum sensing schemes by figuring out the tradeoff between the achieved diversity and coding gains in each scheme. Based on this analysis, we try to answer the fundamental question: can we dispense with SUs cooperation and still achieve an arbitrary diversity gain? It is shown that this is indeed possible via a novel technique that can offer diversity gain for a single SU using a single antenna. The technique is based on the usage of a reconfigurable antenna that changes its propagation characteristics over time, thus creating an artificial temporal diversity. It is shown that the usage of reconfigurable antennas outperforms cooperative as well as non-cooperative schemes at low and high Signal-to-Noise Ratios (SNRs). Moreover, if the channel state information is available at the SU, an additional SNR gain can also be achieved.
Spatial Modelling Techniques in Microsoft Excel
Venue: arXiv cs.HC 2014
Year: 2014

We begin by considering the expectations of the creators of VisiCalc, the first spreadsheet. The emphasis is on the nature of the spreadsheet grid. The grid is taken as a presentational method for showing a solution to a Sudoku puzzle. We consider methods or approaches for the solution. We look at the relationship between this model and academic papers on the methods for describing and categorising end-user models generally. We consider whether the type of model described here should be categorised separately. The complexity of the model is reviewed in the context of commendations to minimise overly sophisticated presentational constructs and formulae.