OpenRaiser/Intern-Atlas huggingface dataset:
Opinion mining of text documents written in Macedonian language
Venue: arXiv cs.CL 2014
Year: 2014

The ability to extract public opinion from web portals such as review sites, social networks and blogs will enable companies and individuals to form a view, an attitude and make decisions without having to do lengthy and costly researches and surveys. In this paper machine learning techniques are used for determining the polarity of forum posts on kajgana which are written in Macedonian language. The posts are classified as being positive, negative or neutral. We test different feature metrics and classifiers and provide detailed evaluation of their participation in improving the overall performance on a manually generated dataset. By achieving 92% accuracy, we show that the performance of systems for automated opinion mining is comparable to a human evaluator, thus making it a viable option for text data analysis. Finally, we present a few statistics derived from the forum posts using the developed system.
A Parallel Genetic Algorithm for Three Dimensional Bin Packing with Heterogeneous Bins
Venue: arXiv cs.DC 2014
Year: 2014

This paper presents a parallel genetic algorithm for three dimensional bin packing with heterogeneous bins using Hadoop Map-Reduce framework. The most common three dimensional bin packing problem which packs given set of boxes into minimum number of equal sized bins is proven to be NP Hard. The variation of three dimensional bin packing problem that allows heterogeneous bin sizes and rotation of boxes is computationally more harder than common three dimensional bin packing problem. The proposed Map-Reduce implementation helps to run the genetic algorithm for three dimensional bin packing with heterogeneous bins on multiple machines parallely and computes the solution in relatively short time.
Pseudorandomness for concentration bounds and signed majorities
Venue: arXiv cs.CC 2014
Year: 2014

The problem of constructing pseudorandom generators that fool halfspaces has been studied intensively in recent times. For fooling halfspaces over the hypercube with polynomially small error, the best construction known requires seed-length O(log^2 n) (MekaZ13). Getting the seed-length down to O(log(n)) is a natural challenge in its own right, which needs to be overcome in order to derandomize RL. In this work we make progress towards this goal by obtaining near-optimal generators for two important special cases: 1) We give a near optimal derandomization of the Chernoff bound for independent, uniformly random bits. Specifically, we show how to generate a x in {1,-1}^n using $\tilde{O}(\log (n/ε))$ random bits such that for any unit vector u, <u,x> matches the sub-Gaussian tail behaviour predicted by the Chernoff bound up to error eps. 2) We construct a generator which fools halfspaces with {0,1,-1} coefficients with error eps with a seed-length of $\tilde{O}(\log(n/ε))$. This includes the important special case of majorities. In both cases, the best previous results required seed-length of $O(\log n + \log^2(1/ε))$. Technically, our work combines new Fourier-analytic tools with the iterative dimension reduction techniques and the gradually increasing independence paradigm of previous works (KaneMN11, CelisRSW13, GopalanMRTV12).
Assume-Guarantee Synthesis for Concurrent Reactive Programs with Partial Information
Venue: arXiv cs.LO 2014
Year: 2014

Synthesis of program parts is very useful for concurrent systems. However, most synthesis approaches do not support common design tasks, like modifying a single process without having to re-synthesize or verify the whole system. Assume-guarantee synthesis (AGS) provides robustness against modifications of system parts, but thus far has been limited to the perfect information setting. This means that local variables cannot be hidden from other processes, which renders synthesis results cumbersome or even impossible to realize. We resolve this shortcoming by defining AGS in a partial information setting. We analyze the complexity and decidability in different settings, showing that the problem has a high worst-case complexity and is undecidable in many interesting cases. Based on these observations, we present a pragmatic algorithm based on bounded synthesis, and demonstrate its practical applicability on several examples.
Relations World: A Possibilistic Graphical Model
Venue: arXiv cs.CL 2014
Year: 2014

We explore the idea of using a "possibilistic graphical model" as the basis for a world model that drives a dialog system. As a first step we have developed a system that uses text-based dialog to derive a model of the user's family relations. The system leverages its world model to infer relational triples, to learn to recover from upstream coreference resolution errors and ambiguities, and to learn context-dependent paraphrase models. We also explore some theoretical aspects of the underlying graphical model.
On The Modeling of OpenFlow-based SDNs: The Single Node Case
Venue: arXiv cs.PF 2014
Year: 2014

OpenFlow is one of the most commonly used protocols for communication between the controller and the forwarding element in a software defined network (SDN). A model based on M/M/1 queues is proposed in [1] to capture the communication between the forwarding element and the controller. Albeit the model provides useful insight, it is accurate only for the case when the probability of expecting a new flow is small. Secondly, it is not straight forward to extend the model in [1] to more than one forwarding element in the data plane. In this work we propose a model which addresses both these challenges. The model is based on Jackson assumption but with corrections tailored to the OpenFlow based SDN network. Performance analysis using the proposed model indicates that the model is accurate even for the case when the probability of new flow is quite large. Further we show by a toy example that the model can be extended to more than one node in the data plane.
The Art of Data Hiding with Reed-Solomon Error Correcting Codes
Venue: arXiv cs.MM 2014
Year: 2014

With the tremendous advancements in technology and the Internet, data security has become a major issue around the globe. To guarantee that data is protected and does not go to an unintended endpoint, the art of data hiding (steganography) emerged. Steganography is the art of hiding information such that it is not detectable to the naked eye. Various techniques have been proposed for hiding a secret message in a carrier document. In this paper, we present a novel design that applies Reed-Solomon (RS) error correcting codes in steganographic applications. The model works by substituting the redundant RS codes with the steganographic message. The experimental results show that the proposed design is satisfactory with the percentage of decoded information 100% and percentage of decoded secret message 97. 36%. The proposed model proved that it could be applied in various steganographic applications.
Information Theoretical Analysis of Identification based on Active Content Fingerprinting
Venue: arXiv cs.IT 2014
Year: 2014

Content fingerprinting and digital watermarking are techniques that are used for content protection and distribution monitoring. Over the past few years, both techniques have been well studied and their shortcomings understood. Recently, a new content fingerprinting scheme called {\em active content fingerprinting} was introduced to overcome these shortcomings. Active content fingerprinting aims to modify a content to extract robuster fingerprints than the conventional content fingerprinting. Moreover, contrary to digital watermarking, active content fingerprinting does not embed any message independent of contents thus does not face host interference. The main goal of this paper is to analyze fundamental limits of active content fingerprinting in an information theoretical framework.
Linguistic Descriptions for Automatic Generation of Textual Short-Term Weather Forecasts on Real Prediction Data
Venue: arXiv cs.AI 2014
Year: 2014

We present in this paper an application which automatically generates textual short-term weather forecasts for every municipality in Galicia (NW Spain), using the real data provided by the Galician Meteorology Agency (MeteoGalicia). This solution combines in an innovative way computing with perceptions techniques and strategies for linguistic description of data together with a natural language generation (NLG) system. The application, named GALiWeather, extracts relevant information from weather forecast input data and encodes it into intermediate descriptions using linguistic variables and temporal references. These descriptions are later translated into natural language texts by the natural language generation system. The obtained forecast results have been thoroughly validated by an expert meteorologist from MeteoGalicia using a quality assessment methodology which covers two key dimensions of a text: the accuracy of its content and the correctness of its form. Following this validation GALiWeather will be released as a real service offering custom forecasts for a wide public.
Integrated canopy, building energy and radiosity model for 3D urban design
Venue: arXiv cs.CY 2014
Year: 2014

We present an integrated, three dimensional, model of urban canopy, building energy and radiosity, for early stage urban designs and test it on four urban morphologies. All sub-models share a common descriptions of the urban morphology, similar to 3D urban design master plans and have simple parameters. The canopy model is a multilayer model, with a new discrete layer approach that does not rely on simplified geometry such as canyon or regular arrays. The building energy model is a simplified RC equivalent model, with no hypotheses on internal zoning or wall composition. We use the CitySim software for the radiosity model. We study the effects of convexity, the number of buildings and building height, at constant density and thermal characteristics. Our results suggest that careful three dimensional morphology design can reduce heat demand by a factor of 2, especially by improving insolation of lower levels. The most energy efficient morphology in our simulations has both the highest surface/volume ratio and the biggest impact on the urban climate.
Designing Deep Networks for Surface Normal Estimation
Venue: arXiv cs.CV 2014
Year: 2014

In the past few years, convolutional neural nets (CNN) have shown incredible promise for learning visual representations. In this paper, we use CNNs for the task of predicting surface normals from a single image. But what is the right architecture we should use? We propose to build upon the decades of hard work in 3D scene understanding, to design new CNN architecture for the task of surface normal estimation. We show by incorporating several constraints (man-made, manhattan world) and meaningful intermediate representations (room layout, edge labels) in the architecture leads to state of the art performance on surface normal estimation. We also show that our network is quite robust and show state of the art results on other datasets as well without any fine-tuning.
Performance-oriented Cloud Provisioning: Taxonomy and Survey
Venue: arXiv cs.DC 2014
Year: 2014

Cloud computing is being viewed as the technology of today and the future. Through this paradigm, the customers gain access to shared computing resources located in remote data centers that are hosted by cloud providers (CP). This technology allows for provisioning of various resources such as virtual machines (VM), physical machines, processors, memory, network, storage and software as per the needs of customers. Application providers (AP), who are customers of the CP, deploy applications on the cloud infrastructure and then these applications are used by the end-users. To meet the fluctuating application workload demands, dynamic provisioning is essential and this article provides a detailed literature survey of dynamic provisioning within cloud systems with focus on application performance. The well-known types of provisioning and the associated problems are clearly and pictorially explained and the provisioning terminology is clarified. A very detailed and general cloud provisioning classification is presented, which views provisioning from different perspectives, aiding in understanding the process inside-out. Cloud dynamic provisioning is explained by considering resources, stakeholders, techniques, technologies, algorithms, problems, goals and more.
Optimal DVB-S2 spectral efficiency with hierarchical modulation
Venue: arXiv cs.IT 2014
Year: 2014

We study the design of a DVB-S2 system in order to maximise spectral efficiency. This task is usually challenging due to channel variability. The solution adopted in modern satellite communications systems such as DVB-SH and DVB-S2 relies mainly on a time sharing strategy. Recently, we proposed to combine time sharing with hierarchical modulation to increase the transmission rate of broadcast systems. However, the optimal spectral efficiency remained an open question. In this paper, we show that the optimal transmission rate is the solution of a linear programming problem. We also study the performance of the optimal scheme for a DVB-S2 use case.
Towards a Theory of Societal Co-Evolution: Individualism versus Collectivism
Venue: arXiv cs.SI 2014
Year: 2014

Substantial empirical research has shown that the level of individualism vs. collectivism is one of the most critical and important determinants of societal traits, such as economic growth, economic institutions and health conditions. But the exact nature of this impact has thus far not been well understood in an analytical setting. In this work, we develop one of the first theoretical models that analytically studies the impact of individualism-collectivism on the society. We model the growth of an individual's welfare (wealth, resources and health) as depending not only on himself, but also on the level of collectivism, i.e. the level of dependence on the rest of the individuals in the society, which leads to a co-evolutionary setting. Based on our model, we are able to predict the impact of individualism-collectivism on various societal metrics, such as average welfare, average life-time, total population, cumulative welfare and average inequality. We analytically show that individualism has a positive impact on average welfare and cumulative welfare, but comes with the drawbacks of lower average life-time, lower total population and higher average inequality.
Mutually Exclusive Procedures in Imperative Languages
Venue: arXiv cs.PL 2014
Year: 2014

To represent mutually exclusive procedures, we propose a choice-conjunctive declaration statement of the form $uchoo(S,R)$ where $S, R$ are the procedure declaration statements within a module. This statement has the following semantics: request the machine to choose a successful one between $S$ and $R$. This statement is useful for representing objects with mutually exclusive procedures. We illustrate our idea via C^uchoo, an extension of the core C with a new statement.
A Complete Refinement Procedure for Regular Separability of Context-Free Languages
Venue: arXiv cs.FL 2014
Year: 2014

Often, when analyzing the behaviour of systems modelled as context-free languages, we wish to know if two languages overlap. To this end, we present an effective semi-decision procedure for regular separability of context-free languages, based on counter-example guided abstraction refinement. We propose two refinement methods, one inexpensive but incomplete, and the other complete but more expensive. We provide an experimental evaluation of this procedure, and demonstrate its practicality on a range of verification and language-theoretic instances.
Measuring Urban Deprivation from User Generated Content
Venue: arXiv cs.SI 2014
Year: 2014

Measuring socioeconomic deprivation of cities in an accurate and timely fashion has become a priority for governments around the world, as the massive urbanization process we are witnessing is causing high levels of inequalities which require intervention. Traditionally, deprivation indexes have been derived from census data, which is however very expensive to obtain, and thus acquired only every few years. Alternative computational methods have been proposed in recent years to automatically extract proxies of deprivation at a fine spatio-temporal level of granularity; however, they usually require access to datasets (e.g., call details records) that are not publicly available to governments and agencies. To remedy this, we propose a new method to automatically mine deprivation at a fine level of spatio-temporal granularity that only requires access to freely available user-generated content. More precisely, the method needs access to datasets describing what urban elements are present in the physical environment; examples of such datasets are Foursquare and OpenStreetMap. Using these datasets, we quantitatively describe neighborhoods by means of a metric, called {\em Offering Advantage}, that reflects which urban elements are distinctive features of each neighborhood. We then use that metric to {\em (i)} build accurate classifiers of urban deprivation and {\em (ii)} interpret the outcomes through thematic analysis. We apply the method to three UK urban areas of different scale and elaborate on the results in terms of precision and recall.
Online placement test based on Item Response Theory and IMS Global standards
Venue: arXiv cs.CY 2014
Year: 2014

This paper aims to present an online placement test. It is based on the Item Response Theory to provide relevant estimates of learner competences. The proposed test is the entry point of our e-Learning system. It gathers the learner response to a set of questions and uses a specific developed algorithm to estimate its level. This algorithm identifies learning gaps, which allows tutors to conceive sequence of courses and remediation adapted to each case of learner, in order to achieve a competence.
Correlation of Scholarly Networks and Social Networks
Venue: arXiv cs.SI 2014
Year: 2014

In previous studies, much attention from multidisciplinary fields has been devoted to understand the mechanism of underlying scholarly networks including bibliographic networks, citation networks and co-citation networks. Particularly focusing on networks constructed by means of either authors affinities or the mutual content. Missing a valuable dimension of network, which is an audience scholarly paper. We aim at this paper to assess the impact that social networks and media can have on scholarly papers. We also examine the process of information flow in such networks. We also mention some observa- tions of attractive incidents that our proposed network model revealed.
ConceptLearner: Discovering Visual Concepts from Weakly Labeled Image Collections
Venue: arXiv cs.CV 2014
Year: 2014

Discovering visual knowledge from weakly labeled data is crucial to scale up computer vision recognition system, since it is expensive to obtain fully labeled data for a large number of concept categories. In this paper, we propose ConceptLearner, which is a scalable approach to discover visual concepts from weakly labeled image collections. Thousands of visual concept detectors are learned automatically, without human in the loop for additional annotation. We show that these learned detectors could be applied to recognize concepts at image-level and to detect concepts at image region-level accurately. Under domain-specific supervision, we further evaluate the learned concepts for scene recognition on SUN database and for object detection on Pascal VOC 2007. ConceptLearner shows promising performance compared to fully supervised and weakly supervised methods.
On Heterogeneous Neighbor Discovery in Wireless Sensor Networks
Venue: arXiv cs.NI 2014
Year: 2014

Neighbor discovery plays a crucial role in the formation of wireless sensor networks and mobile networks where the power of sensors (or mobile devices) is constrained. Due to the difficulty of clock synchronization, many asynchronous protocols based on wake-up scheduling have been developed over the years in order to enable timely neighbor discovery between neighboring sensors while saving energy. However, existing protocols are not fine-grained enough to support all heterogeneous battery duty cycles, which can lead to a more rapid deterioration of long-term battery health for those without support. Existing research can be broadly divided into two categories according to their neighbor-discovery techniques---the quorum based protocols and the co-primality based protocols.In this paper, we propose two neighbor discovery protocols, called Hedis and Todis, that optimize the duty cycle granularity of quorum and co-primality based protocols respectively, by enabling the finest-grained control of heterogeneous duty cycles. We compare the two optimal protocols via analytical and simulation results, which show that although the optimal co-primality based protocol (Todis) is simpler in its design, the optimal quorum based protocol (Hedis) has a better performance since it has a lower relative error rate and smaller discovery delay, while still allowing the sensor nodes to wake up at a more infrequent rate.
Adaptive tracking of representative cycles in regular and zigzag persistent homology
Venue: arXiv cs.CG 2014
Year: 2014

Persistent homology and zigzag persistent homology are techniques which track the homology over a sequence of spaces, outputting a set of intervals corresponding to birth and death times of homological features in the sequence. This paper presents a method for choosing a homology class to correspond to each of the intervals at each time point. For each homology class a specific representative cycle is stored, with the choice of homology class and representative cycle being both geometrically relevant and compatible with the birth-death interval decomposition. After describing the method in detail and proving its correctness, we illustrate the utility of the method by applying it to the study of coverage holes in time-varying sensor networks.
The Social Name-Letter Effect on Online Social Networks
Venue: arXiv cs.SI 2014
Year: 2014

The Name-Letter Effect states that people have a preference for brands, places, and even jobs that start with the same letter as their own first name. So Sam might like Snickers and live in Seattle. We use social network data from Twitter and Google+ to replicate this effect in a new environment. We find limited to no support for the Name-Letter Effect on social networks. We do, however, find a very robust Same-Name Effect where, say, Michaels would be more likely to link to other Michaels than Johns. This effect persists when accounting for gender, nationality, race, and age. The fundamentals behind these effects have implications beyond psychology as understanding how a positive self-image is transferred to other entities is important in domains ranging from studying homophily to personalized advertising and to link formation in social networks.