OpenRaiser/Intern-Atlas huggingface dataset:
Rigorous Description Of Design Components Functionality: An Approach Based Contract
Venue: arXiv cs.SE 2014
Year: 2014

Current models for software components have made component-based software engineering practical. However, these models are limited in the sense that their support for the characterization/specification of design components primarily deals with syntactic issues. To avoid mismatch and misuse of components, more comprehensive specification of software components is required, In this paper, we present a contract-based approach to analyze and model the both aspects (functional and non-functional) properties of design components and their composition in order to detect and correct composition errors. This approach permits to characterize the structural, interface and behavioural aspects of design component. To enable this we present a pattern contract language that captures the structural and behavioral requirements associated with a range of patterns, as well as the system properties that are guaranteed as a result. In addition, we propose the use of the LOTOS language as an ADL for formalizing these aspects. We illustrate the approach by applying it to a standard design
Privacy and National Security Issues in Social Networks: The Challenges
Venue: arXiv cs.SI 2014
Year: 2014

Online social networks are becoming a major growth point of the internet, as individuals, companies and governments constantly desire to interact with one another, the ability of the internet to deliver this networking capabilities grows stronger. In this paper, we looked at the structure and components of the member profile and the challenges of privacy issues faced by individuals and governments that participate in social networking. We also looked at how it can be used to distort national security, how it became the new weapons of mass mobilization and also how social networks have became the rallying forces for revolutions and social justice.
Scientific works citation analysis
Venue: arXiv cs.DL 2014
Year: 2014

Several questions of scientometrics parameters organization are considered. Two new indices for scientific works citation analysis are proposed. They provide more detailed and reliable scientific significance assessment of individual authors and scientific groups basing on the publication activity
Learning-assisted Theorem Proving with Millions of Lemmas
Venue: arXiv cs.AI 2014
Year: 2014

Large formal mathematical libraries consist of millions of atomic inference steps that give rise to a corresponding number of proved statements (lemmas). Analogously to the informal mathematical practice, only a tiny fraction of such statements is named and re-used in later proofs by formal mathematicians. In this work, we suggest and implement criteria defining the estimated usefulness of the HOL Light lemmas for proving further theorems. We use these criteria to mine the large inference graph of the lemmas in the HOL Light and Flyspeck libraries, adding up to millions of the best lemmas to the pool of statements that can be re-used in later proofs. We show that in combination with learning-based relevance filtering, such methods significantly strengthen automated theorem proving of new conjectures over large formal mathematical libraries such as Flyspeck.
An Energy Efficient Neighbour Node Discovery Method for Wireless Sensor Networks
Venue: arXiv cs.NI 2014
Year: 2014

The discovery of neighbouring nodes in multihop wireless networks has become a key challenge. Due to tribulations in communication, synchronization loss between nodes, disparity in transmission power etc, the connectivity of nodes will always experience disruptions. On the other hand, the energy utilization by the nodes also became critical . In this paper, we propose a new method for neighbour discovery in wireless sensor networks (WSNs) which pays an eminent consideration for energy utilization and QoS parameters like latency, throughput, error rate etc. In the proposed method, the network routing is enhanced using AOMDV protocol which can accurately discover the neighbour nodes and power management with HMAC protocol which reduces the energy utilization significantly. A complete analysis is being performed to estimate how the Q o S metrics varies in various scenarios of power consumption in wireless networks.
Resolution of Linear Algebra for the Discrete Logarithm Problem Using GPU and Multi-core Architectures
Venue: arXiv cs.CR 2014
Year: 2014

In cryptanalysis, solving the discrete logarithm problem (DLP) is key to assessing the security of many public-key cryptosystems. The index-calculus methods, that attack the DLP in multiplicative subgroups of finite fields, require solving large sparse systems of linear equations modulo large primes. This article deals with how we can run this computation on GPU- and multi-core-based clusters, featuring InfiniBand networking. More specifically, we present the sparse linear algebra algorithms that are proposed in the literature, in particular the block Wiedemann algorithm. We discuss the parallelization of the central matrix--vector product operation from both algorithmic and practical points of view, and illustrate how our approach has contributed to the recent record-sized DLP computation in GF($2^{809}$).
(Co)recursion in Logic Programming: Lazy vs Eager
Venue: arXiv cs.PL 2014
Year: 2014

CoAlgebraic Logic Programming (CoALP) is a dialect of Logic Programming designed to bring a more precise compile-time and run-time analysis of termination and productivity for recursive and corecursive functions in Logic Programming. Its second goal is to introduce guarded lazy (co)recursion akin to functional theorem provers into logic programming. In this paper, we explain lazy features of CoALP, and compare them with the loop-analysis and eager execution in Coinductive Logic Programming (CoLP). We conclude by outlining the future directions in developing the guarded (co)recursion in logic programming.
A Framework for Developing Real-Time OLAP algorithm using Multi-core processing and GPU: Heterogeneous Computing
Venue: arXiv cs.DC 2014
Year: 2014

The overwhelmingly increasing amount of stored data has spurred researchers seeking different methods in order to optimally take advantage of it which mostly have faced a response time problem as a result of this enormous size of data. Most of solutions have suggested materialization as a favourite solution. However, such a solution cannot attain Real- Time answers anyhow. In this paper we propose a framework illustrating the barriers and suggested solutions in the way of achieving Real-Time OLAP answers that are significantly used in decision support systems and data warehouses.
Throughput Maximization in Multiprocessor Speed-Scaling
Venue: arXiv cs.DS 2014
Year: 2014

We are given a set of $n$ jobs that have to be executed on a set of $m$ speed-scalable machines that can vary their speeds dynamically using the energy model introduced in [Yao et al., FOCS'95]. Every job $j$ is characterized by its release date $r_j$, its deadline $d_j$, its processing volume $p_{i,j}$ if $j$ is executed on machine $i$ and its weight $w_j$. We are also given a budget of energy $E$ and our objective is to maximize the weighted throughput, i.e. the total weight of jobs that are completed between their respective release dates and deadlines. We propose a polynomial-time approximation algorithm where the preemption of the jobs is allowed but not their migration. Our algorithm uses a primal-dual approach on a linearized version of a convex program with linear constraints. Furthermore, we present two optimal algorithms for the non-preemptive case where the number of machines is bounded by a fixed constant. More specifically, we consider: {\em (a)} the case of identical processing volumes, i.e. $p_{i,j}=p$ for every $i$ and $j$, for which we present a polynomial-time algorithm for the unweighted version, which becomes a pseudopolynomial-time algorithm for the weighted throughput version, and {\em (b)} the case of agreeable instances, i.e. for which $r_i \le r_j$ if and only if $d_i \le d_j$, for which we present a pseudopolynomial-time algorithm. Both algorithms are based on a discretization of the problem and the use of dynamic programming.
Performance Evaluation of Machine Learning Classifiers in Sentiment Mining
Venue: arXiv cs.LG 2014
Year: 2014

In recent years, the use of machine learning classifiers is of great value in solving a variety of problems in text classification. Sentiment mining is a kind of text classification in which, messages are classified according to sentiment orientation such as positive or negative. This paper extends the idea of evaluating the performance of various classifiers to show their effectiveness in sentiment mining of online product reviews. The product reviews are collected from Amazon reviews. To evaluate the performance of classifiers various evaluation methods like random sampling, linear sampling and bootstrap sampling are used. Our results shows that support vector machine with bootstrap sampling method outperforms others classifiers and sampling methods in terms of misclassification rate.
Simulating Congestion Dynamics of Train Rapid Transit using Smart Card Data
Venue: arXiv cs.MA 2014
Year: 2014

Investigating congestion in train rapid transit systems (RTS) in today's urban cities is a challenge compounded by limited data availability and difficulties in model validation. Here, we integrate information from travel smart card data, a mathematical model of route choice, and a full-scale agent-based model of the Singapore RTS to provide a more comprehensive understanding of the congestion dynamics than can be obtained through analytical modelling alone. Our model is empirically validated, and allows for close inspection of the dynamics including station crowdedness, average travel duration, and frequency of missed trains---all highly pertinent factors in service quality. Using current data, the crowdedness in all 121 stations appears to be distributed log-normally. In our preliminary scenarios, we investigate the effect of population growth on service quality. We find that the current population (2 million) lies below a critical point; and increasing it beyond a factor of $\sim10\%$ leads to an exponential deterioration in service quality. We also predict that incentivizing commuters to avoid the most congested hours can bring modest improvements to the service quality provided the population remains under the critical point. Finally, our model can be used to generate simulated data for analytical modelling when such data are not empirically available, as is often the case.
New Mechanism for Multiagent Extensible Negotiations
Venue: arXiv cs.MA 2014
Year: 2014

Multiagent negotiation mechanisms advise original solutions to several problems for which usual problem solving methods are inappropriate. Mainly negotiation models are based on agents' interactions through messages. Agents interact in order to reach an agreement for solving a specific problem. In this work, we study a new variant of negotiations, which has not yet been addressed in existing works. This negotiation form is denoted extensible negotiation. In contrast with current negotiation models, this form of negotiation allows the agents to dynamically extend the set of items under negotiation. This facility gives more acceptable solutions for the agents in their negotiation. The advantage of enlarging the negotiation space is to certainly offer more facilities for the agents for reaching new agreements which would not have been obtained using usual negotiation methods. This paper presents the protocol and the strategies used by the agents to deal with such negotiations.
Does the D.C. Response of Memristors Allow Robotic Short-Term Memory and a Possible Route to Artificial Time Perception?
Venue: arXiv cs.RO 2014
Year: 2014

Time perception is essential for task switching, and in the mammalian brain appears alongside other processes. Memristors are electronic components used as synapses and as models for neurons. The d.c. response of memristors can be considered as a type of short-term memory. Interactions of the memristor d.c. response within networks of memristors leads to the emergence of oscillatory dynamics and intermittent spike trains, which are similar to neural dynamics. Based on this data, the structure of a memristor network control for a robot as it undergoes task switching is discussed and it is suggested that these emergent network dynamics could improve the performance of role switching and learning in an artificial intelligence and perhaps create artificial time perception.
Statistical Noise Analysis in SENSE Parallel MRI
Venue: arXiv cs.CV 2014
Year: 2014

A complete first and second order statistical characterization of noise in SENSE reconstructed data is proposed. SENSE acquisitions have usually been modeled as Rician distributed, since the data reconstruction takes place into the spatial domain, where Gaussian noise is assumed. However, this model just holds for the first order statistics and obviates other effects induced by coils correlations and the reconstruction interpolation. Those effects are properly taken into account in this study, in order to fully justify a final SENSE noise model. As a result, some interesting features of the reconstructed image arise: (1) There is a strong correlation between adjacent lines. (2) The resulting distribution is non-stationary and therefore the variance of noise will vary from point to point across the image. Closed equations for the calculation of the variance of noise and the correlation coefficient between lines are proposed. The proposed model is totally compatible with g-factor formulations.
Selective Sampling with Drift
Venue: arXiv cs.LG 2014
Year: 2014

Recently there has been much work on selective sampling, an online active learning setting, in which algorithms work in rounds. On each round an algorithm receives an input and makes a prediction. Then, it can decide whether to query a label, and if so to update its model, otherwise the input is discarded. Most of this work is focused on the stationary case, where it is assumed that there is a fixed target model, and the performance of the algorithm is compared to a fixed model. However, in many real-world applications, such as spam prediction, the best target function may drift over time, or have shifts from time to time. We develop a novel selective sampling algorithm for the drifting setting, analyze it under no assumptions on the mechanism generating the sequence of instances, and derive new mistake bounds that depend on the amount of drift in the problem. Simulations on synthetic and real-world datasets demonstrate the superiority of our algorithms as a selective sampling algorithm in the drifting setting.
Maximizing the Signal-to-Alias Ratio in Non-Uniform Filter Banks for Acoustic Echo Cancellation
Venue: arXiv cs.SD 2014
Year: 2014

A new method for designing non-uniform filter-banks for acoustic echo cancellation is proposed. In the method, the analysis prototype filter design is framed as a convex optimization problem that maximizes the signal-to-alias ratio (SAR) in the analysis banks. Since each sub-band has a different bandwidth, the contribution to the overall SAR from each analysis bank is taken into account during optimization. To increase the degrees of freedom during optimization, no constraints are imposed on the phase or group delay of the filters; at the same time, low delay is achieved by ensuring that the resulting filters are minimum phase. Experimental results show that the filter bank designed using the proposed method results in a sub-band adaptive filter with a much better echo return loss enhancement (ERLE) when compared with existing design methods.
Extracting Networks of Characters and Places from Written Works with CHAPLIN
Venue: arXiv cs.CY 2014
Year: 2014

We are proposing a tool able to gather information on social networks from narrative texts. Its name is CHAPLIN, CHAracters and PLaces Interaction Network, implemented in VB.NET. Characters and places of the narrative works are extracted in a list of raw words. Aided by the interface, the user selects names out of them. After this choice, the tool allows the user to enter some parameters, and, according to them, creates a network where the nodes are the characters and places, and the edges their interactions. Edges are labelled by performances. The output is a GV file, written in the DOT graph scripting language, which is rendered by means of the free open source software Graphviz.
Discretization of Temporal Data: A Survey
Venue: arXiv cs.DB 2014
Year: 2014

In real world, the huge amount of temporal data is to be processed in many application areas such as scientific, financial, network monitoring, sensor data analysis. Data mining techniques are primarily oriented to handle discrete features. In the case of temporal data the time plays an important role on the characteristics of data. To consider this effect, the data discretization techniques have to consider the time while processing to resolve the issue by finding the intervals of data which are more concise and precise with respect to time. Here, this research is reviewing different data discretization techniques used in temporal data applications according to the inclusion or exclusion of: class label, temporal order of the data and handling of stream data to open the research direction for temporal data discretization to improve the performance of data mining technique.
Turning Time from Enemy into an Ally Using the Pomodoro Technique
Venue: arXiv cs.SE 2014
Year: 2014

Time is one of the most important factors dominating agile software development processes in distributed settings. Effective time management helps agile teams to plan and monitor the work to be performed, and create and maintain a fast yet sustainable pace. The Pomodoro Technique is one promising time management technique. Its application and adaptation in Sourcesense Milan Team surfaced various benefits, challenges and implications for distributed agile software development. Lessons learnt from the experiences of Sourcesense Milan Team can be useful for other distributed agile teams to turn time from enemy into an ally.
Hybrid SRL with Optimization Modulo Theories
Venue: arXiv cs.LG 2014
Year: 2014

Generally speaking, the goal of constructive learning could be seen as, given an example set of structured objects, to generate novel objects with similar properties. From a statistical-relational learning (SRL) viewpoint, the task can be interpreted as a constraint satisfaction problem, i.e. the generated objects must obey a set of soft constraints, whose weights are estimated from the data. Traditional SRL approaches rely on (finite) First-Order Logic (FOL) as a description language, and on MAX-SAT solvers to perform inference. Alas, FOL is unsuited for con- structive problems where the objects contain a mixture of Boolean and numerical variables. It is in fact difficult to implement, e.g. linear arithmetic constraints within the language of FOL. In this paper we propose a novel class of hybrid SRL methods that rely on Satisfiability Modulo Theories, an alternative class of for- mal languages that allow to describe, and reason over, mixed Boolean-numerical objects and constraints. The resulting methods, which we call Learning Mod- ulo Theories, are formulated within the structured output SVM framework, and employ a weighted SMT solver as an optimization oracle to perform efficient in- ference and discriminative max margin weight learning. We also present a few examples of constructive learning applications enabled by our method.
FPTAS for Weighted Fibonacci Gates and Its Applications
Venue: arXiv cs.DS 2014
Year: 2014

Fibonacci gate problems have severed as computation primitives to solve other problems by holographic algorithm and play an important role in the dichotomy of exact counting for Holant and CSP frameworks. We generalize them to weighted cases and allow each vertex function to have different parameters, which is a much boarder family and #P-hard for exactly counting. We design a fully polynomial-time approximation scheme (FPTAS) for this generalization by correlation decay technique. This is the first deterministic FPTAS for approximate counting in the general Holant framework without a degree bound. We also formally introduce holographic reduction in the study of approximate counting and these weighted Fibonacci gate problems serve as computation primitives for approximate counting. Under holographic reduction, we obtain FPTAS for other Holant problems and spin problems. One important application is developing an FPTAS for a large range of ferromagnetic two-state spin systems. This is the first deterministic FPTAS in the ferromagnetic range for two-state spin systems without a degree bound. Besides these algorithms, we also develop several new tools and techniques to establish the correlation decay property, which are applicable in other problems.
On Coloring Resilient Graphs
Venue: arXiv cs.CC 2014
Year: 2014

We introduce a new notion of resilience for constraint satisfaction problems, with the goal of more precisely determining the boundary between NP-hardness and the existence of efficient algorithms for resilient instances. In particular, we study $r$-resiliently $k$-colorable graphs, which are those $k$-colorable graphs that remain $k$-colorable even after the addition of any $r$ new edges. We prove lower bounds on the NP-hardness of coloring resiliently colorable graphs, and provide an algorithm that colors sufficiently resilient graphs. We also analyze the corresponding notion of resilience for $k$-SAT. This notion of resilience suggests an array of open questions for graph coloring and other combinatorial problems.
Efficient Local Density Estimation Strategy for VANETs
Venue: arXiv cs.NI 2014
Year: 2014

Local vehicle density estimation is increasingly becoming an essential factor of many vehicular ad-hoc network applications such as congestion control and traffic state estimation. This estimation is used to get an approximate number of neighbors within the transmission range since beacons do not give accurate accuracy about neighborhood. These is due to the special characteristics of VANETs such as high mobility, high density variation. To enhance the performance of these applications, an accurate estimation of the local density with minimum of overhead is needed. Most of the proposed strategies address the global traffic density estimation without a big attention on the local density estimation. This paper proposes an improved approach for local density estimation in VANETs in terms of accuracy and overhead. The simulation results showed that our strategy allows an interesting precision of estimation with acceptable overhead.