OpenRaiser/Intern-Atlas huggingface dataset:
An alternative use of the NetLogo modeling environment, where the student thinks and acts like an Agent, in order to teach concepts of Ecology
Venue: arXiv cs.CY 2015
Year: 2015

The Multi Agent Based programming, modeling and simulation environment of NetLogo has been used extensively during the last fifteen years for educational among other purposes. The learning subject, upon interacting with the Users Interface of NetLogo, can easily study properties of the simulated natural systems, as well as observe the latters response, when altering their parameters. In this research, NetLogo was used under the perspective that the learning subject (student or prospective teacher)interacts with the model in a deeper way, obtaining the role of an agent. This is not achieved by obliging the learner to program (write NetLogo code) but by interviewing them, together with applying the choices that they make on the model. The scheme was carried out, as part of a broader research, with interviews, and web page like interface menu selections, in a sample of 17 University students in Athens (prospective Primary School teachers) and the results were judged as encouraging. At a further stage, the computers were set as a network, where all the agents performed together. In this way the learners could watch onscreen the overall outcome of their choices and actions on the modeled ecosystem. This seems to open a new, small, area of research in NetLogo educational applications.
A Symbolic Execution Algorithm for Constraint-Based Testing of Database Programs
Venue: arXiv cs.SE 2015
Year: 2015

In so-called constraint-based testing, symbolic execution is a common technique used as a part of the process to generate test data for imperative programs. Databases are ubiquitous in software and testing of programs manipulating databases is thus essential to enhance the reliability of software. This work proposes and evaluates experimentally a symbolic ex- ecution algorithm for constraint-based testing of database programs. First, we describe SimpleDB, a formal language which offers a minimal and well-defined syntax and seman- tics, to model common interaction scenarios between pro- grams and databases. Secondly, we detail the proposed al- gorithm for symbolic execution of SimpleDB models. This algorithm considers a SimpleDB program as a sequence of operations over a set of relational variables, modeling both the database tables and the program variables. By inte- grating this relational model of the program with classical static symbolic execution, the algorithm can generate a set of path constraints for any finite path to test in the control- flow graph of the program. Solutions of these constraints are test inputs for the program, including an initial content for the database. When the program is executed with respect to these inputs, it is guaranteed to follow the path with re- spect to which the constraints were generated. Finally, the algorithm is evaluated experimentally using representative SimpleDB models.
On Generalized Rectangular Fuzzy Model for Assessment
Venue: arXiv cs.AI 2015
Year: 2015

The article is dedicated to the analysis of the existing models for assessment based of the fuzzy logic centroid technique. A new Generalized Rectangular Model were developed. Some generalizations of the existing models are offered.
A New Efficient Method for Calculating Similarity Between Web Services
Venue: arXiv cs.AI 2015
Year: 2015

Web services allow communication between heterogeneous systems in a distributed environment. Their enormous success and their increased use led to the fact that thousands of Web services are present on the Internet. This significant number of Web services which not cease to increase has led to problems of the difficulty in locating and classifying web services, these problems are encountered mainly during the operations of web services discovery and substitution. Traditional ways of search based on keywords are not successful in this context, their results do not support the structure of Web services and they consider in their search only the identifiers of the web service description language (WSDL) interface elements. The methods based on semantics (WSDLS, OWLS, SAWSDL...) which increase the WSDL description of a Web service with a semantic description allow raising partially this problem, but their complexity and difficulty delays their adoption in real cases. Measuring the similarity between the web services interfaces is the most suitable solution for this kind of problems, it will classify available web services so as to know those that best match the searched profile and those that do not match. Thus, the main goal of this work is to study the degree of similarity between any two web services by offering a new method that is more effective than existing works.
Learning Execution Contexts from System Call Distributions for Intrusion Detection in Embedded Systems
Venue: arXiv cs.CR 2015
Year: 2015

Existing techniques used for intrusion detection do not fully utilize the intrinsic properties of embedded systems. In this paper, we propose a lightweight method for detecting anomalous executions using a distribution of system call frequencies. We use a cluster analysis to learn the legitimate execution contexts of embedded applications and then monitor them at run-time to capture abnormal executions. We also present an architectural framework with minor processor modifications to aid in this process. Our prototype shows that the proposed method can effectively detect anomalous executions without relying on sophisticated analyses or affecting the critical execution paths.
Tsallis entropy of complex networks
Venue: arXiv cs.SI 2015
Year: 2015

How complex of the complex networks has attracted many researchers to explore it. The entropy is an useful method to describe the degree of the $complex$ of the complex networks. In this paper, a new method which is based on the Tsallis entropy is proposed to describe the $complex$ of the complex networks. The results in this paper show that the complex of the complex networks not only decided by the structure property of the complex networks, but also influenced by the relationship between each nodes. In other word, which kinds of nodes are chosen as the main part of the complex networks will influence the value of the entropy of the complex networks. The value of q in the Tsallis entropy of the complex networks is used to decided which kinds of nodes will be chosen as the main part in the complex networks. The proposed Tsallis entropy of the complex networks is a generalised method to describe the property of the complex networks.
Between Pure and Approximate Differential Privacy
Venue: arXiv cs.DS 2015
Year: 2015

We show a new lower bound on the sample complexity of $(\varepsilon, δ)$-differentially private algorithms that accurately answer statistical queries on high-dimensional databases. The novelty of our bound is that it depends optimally on the parameter $δ$, which loosely corresponds to the probability that the algorithm fails to be private, and is the first to smoothly interpolate between approximate differential privacy ($δ> 0$) and pure differential privacy ($δ= 0$). Specifically, we consider a database $D \in \{\pm1\}^{n \times d}$ and its \emph{one-way marginals}, which are the $d$ queries of the form "What fraction of individual records have the $i$-th bit set to $+1$?" We show that in order to answer all of these queries to within error $\pm α$ (on average) while satisfying $(\varepsilon, δ)$-differential privacy, it is necessary that $$ n \geq Ω\left( \frac{\sqrt{d \log(1/δ)}}{α\varepsilon} \right), $$ which is optimal up to constant factors. To prove our lower bound, we build on the connection between \emph{fingerprinting codes} and lower bounds in differential privacy (Bun, Ullman, and Vadhan, STOC'14). In addition to our lower bound, we give new purely and approximately differentially private algorithms for answering arbitrary statistical queries that improve on the sample complexity of the standard Laplace and Gaussian mechanisms for achieving worst-case accuracy guarantees by a logarithmic factor.
Analyzing data citation practices using the Data Citation Index
Venue: arXiv cs.DL 2015
Year: 2015

We present an analysis of data citation practices based on the Data Citation Index from Thomson Reuters. This database launched in 2012 aims to link data sets and data studies with citations received from the other citation indexes. The DCI harvests citations to research data from papers indexed in the Web of Science. It relies on the information provided by the data repository as data citation practices are inconsistent or inexistent in many cases. The findings of this study show that data citation practices are far from common in most research fields. Some differences have been reported on the way researchers cite data: while in the areas of Science and Engineering and Technology data sets were the most cited, in Social Sciences and Arts and Humanities data studies play a greater role. A total of 88.1 percent of the records have received no citation, but some repositories show very low uncitedness rates. Although data citation practices are rare in most fields, they have expanded in disciplines such as crystallography and genomics. We conclude by emphasizing the role that the DCI could play in encouraging the consistent, standardized citation of research data; a role that would enhance their value as a means of following the research process from data collection to publication.
A new Definition and Classification of Physical Unclonable Functions
Venue: arXiv cs.CR 2015
Year: 2015

A new definition of "Physical Unclonable Functions" (PUFs), the first one that fully captures its intuitive idea among experts, is presented. A PUF is an information-storage system with a security mechanism that is 1. meant to impede the duplication of a precisely described storage-functionality in another, separate system and 2. remains effective against an attacker with temporary access to the whole original system. A novel classification scheme of the security objectives and mechanisms of PUFs is proposed and its usefulness to aid future research and security evaluation is demonstrated. One class of PUF security mechanisms that prevents an attacker to apply all addresses at which secrets are stored in the information-storage system, is shown to be closely analogous to cryptographic encryption. Its development marks the dawn of a new fundamental primitive of hardware-security engineering: cryptostorage. These results firmly establish PUFs as a fundamental concept of hardware security.
On Secrecy Performance of Antenna Selection Aided MIMO Systems Against Eavesdropping
Venue: arXiv cs.IT 2015
Year: 2015

In this paper, we consider a multiple-input multiple-output (MIMO) system consisting of one source, one destination and one eavesdropper, where each node is equipped with an arbitrary number of antennas. To improve the security of source-destination transmissions, we investigate the antenna selection at the source and propose the optimal antenna selection (OAS) and suboptimal antenna selection (SAS) schemes, depending on whether the source node has the global channel state information (CSI) of both the main link (from source to destination) and wiretap link (from source to eavesdropper). Also, the traditional space-time transmission (STT) is studied as a benchmark. We evaluate the secrecy performance of STT, SAS, and OAS schemes in terms of the probability of zero secrecy capacity. Furthermore, we examine the generalized secrecy diversity of STT, SAS, and OAS schemes through an asymptotic analysis of the probability of zero secrecy capacity, as the ratio between the average gains of the main and wiretap channels tends to infinity. This is different from the conventional secrecy diversity which assumes an infinite signal-to-noise ratio (SNR) received at the destination under the condition that the eavesdropper has a finite received SNR. It is shown that the generalized secrecy diversity orders of STT, SAS, and OAS schemes are the product of the number of antennas at source and destination. Additionally, numerical results show that the proposed OAS scheme strictly outperforms both the STT and SAS schemes in terms of the probability of zero secrecy capacity.
Critical pairs for the Product Singleton Bound
Venue: arXiv cs.IT 2015
Year: 2015

We characterize Product-MDS pairs of linear codes, i.e.\ pairs of codes $C,D$ whose product under coordinatewise multiplication has maximum possible minimum distance as a function of the code length and the dimensions $\dim C, \dim D$. We prove in particular, for $C=D$, that if the square of the code $C$ has minimum distance at least $2$, and $(C,C)$ is a Product-MDS pair, then either $C$ is a generalized Reed-Solomon code, or $C$ is a direct sum of self-dual codes. In passing we establish coding-theory analogues of classical theorems of additive combinatorics.
Weather forecasting using Convex hull & K-Means Techniques An Approach
Venue: arXiv cs.DB 2015
Year: 2015

Data mining is a popular concept of mined necessary data from a large set of data. Data mining using clustering is a powerful way to analyze data and gives prediction. In this paper non structural time series data is used to forecast daily average temperature, humidity and overall weather conditions of Kolkata city. The air pollution data have been taken from West Bengal Pollution Control Board to build the original dataset on which the prediction approach of this paper is studied and applied. This paper describes a new technique to predict the weather conditions using convex hull which gives structural data and then apply incremental K-means to define the appropriate clusters. It splits the total database into four separate databases with respect to different weather conditions. In the final step, the result will be calculated on the basis of priority based protocol which is defined based on some mathematical deduction.
Interference Calculation in Asynchronous Random Access Protocols using Diversity
Venue: arXiv cs.IT 2015
Year: 2015

The use of Aloha-based Random Access protocols is interesting when channel sensing is either not possible or not convenient and the traffic from terminals is unpredictable and sporadic. In this paper an analytic model for packet interference calculation in asynchronous Random Access protocols using diversity is presented. The aim is to provide a tool that avoids time-consuming simulations to evaluate packet loss and throughput in case decodability is still possible when a certain interference threshold is not exceeded. Moreover the same model represents the groundbase for further studies in which iterative Interference Cancellation is applied to received frames.
Compressed Support Vector Machines
Venue: arXiv cs.LG 2015
Year: 2015

Support vector machines (SVM) can classify data sets along highly non-linear decision boundaries because of the kernel-trick. This expressiveness comes at a price: During test-time, the SVM classifier needs to compute the kernel inner-product between a test sample and all support vectors. With large training data sets, the time required for this computation can be substantial. In this paper, we introduce a post-processing algorithm, which compresses the learned SVM model by reducing and optimizing support vectors. We evaluate our algorithm on several medium-scaled real-world data sets, demonstrating that it maintains high test accuracy while reducing the test-time evaluation cost by several orders of magnitude---in some cases from hours to seconds. It is fair to say that most of the work in this paper was previously been invented by Burges and Schölkopf almost 20 years ago. For most of the time during which we conducted this research, we were unaware of this prior work. However, in the past two decades, computing power has increased drastically, and we can therefore provide empirical insights that were not possible in their original paper.
Coordination in State-Dependent Distributed Networks: The Two-Agent Case
Venue: arXiv cs.IT 2015
Year: 2015

This paper addresses a coordination problem between two agents (Agents $1$ and $2$) in the presence of a noisy communication channel which depends on an external system state $\{x_{0,t}\}$. The channel takes as inputs both agents' actions, $\{x_{1,t}\}$ and $\{x_{2,t}\}$ and produces outputs that are observed strictly causally at Agent $2$ but not at Agent $1$. The system state is available either causally or non-causally at Agent $1$ but unknown at Agent $2$. Necessary and sufficient conditions on a joint distribution $\bar{Q}(x_0,x_1,x_2)$ to be implementable asymptotically (i.e, when the number of taken actions grows large) are provided for both causal and non-causal state information at Agent $1$. Since the coordination degree between the agents' actions, $x_{1,t}$ and $x_{2,t}$, and the system state $x_{0,t}$ is measured in terms of an average payoff function, feasible payoffs are fully characterized by implementable joint distributions. In this sense, our results allow us to derive the performance of optimal power control policies on an interference channel and to assess the gain provided by non-causal knowledge of the system state at Agent $1$. The derived proofs readily yield new results also for the problem of state-amplification under a causality constraint at the decoder.
Practical Foundations of History Independence
Venue: arXiv cs.CR 2015
Year: 2015

The way data structures organize data is often a function of the sequence of past operations. The organization of data is referred to as the data structure's state, and the sequence of past operations constitutes the data structure's history. A data structure state can therefore be used as an oracle to derive information about its history. As a result, for history-sensitive applications, such as privacy in e-voting, incremental signature schemes, and regulatory compliant data retention; it is imperative to conceal historical information contained within data structure states. Data structure history can be hidden by making data structures history independent. In this paper, we explore how to achieve history independence. We observe that current history independence notions are significantly limited in number and scope. There are two existing notions of history independence -- weak history independence (WHI) and strong history independence (SHI). WHI does not protect against insider adversaries and SHI mandates canonical representations, resulting in inefficiency. We postulate the need for a broad, encompassing notion of history independence, which can capture WHI, SHI, and a broad spectrum of new history independence notions. To this end, we introduce $Δ$history independence ($Δ$HI), a generic game-based framework that is malleable enough to accommodate existing and new history independence notions. As an essential step towards formalizing $Δ$HI, we explore the concepts of abstract data types, data structures, machine models, memory representations and history independence. Finally, to bridge the gap between theory and practice, we outline a general recipe for building end-to-end, history independent systems and demonstrate the use of the recipe in designing two history independent file systems.
Measuring academic influence: Not all citations are equal
Venue: arXiv cs.DL 2015
Year: 2015

The importance of a research article is routinely measured by counting how many times it has been cited. However, treating all citations with equal weight ignores the wide variety of functions that citations perform. We want to automatically identify the subset of references in a bibliography that have a central academic influence on the citing paper. For this purpose, we examine the effectiveness of a variety of features for determining the academic influence of a citation. By asking authors to identify the key references in their own work, we created a data set in which citations were labeled according to their academic influence. Using automatic feature selection with supervised machine learning, we found a model for predicting academic influence that achieves good performance on this data set using only four features. The best features, among those we evaluated, were those based on the number of times a reference is mentioned in the body of a citing paper. The performance of these features inspired us to design an influence-primed h-index (the hip-index). Unlike the conventional h-index, it weights citations by how many times a reference is mentioned. According to our experiments, the hip-index is a better indicator of researcher performance than the conventional h-index.
Codes With Hierarchical Locality
Venue: arXiv cs.IT 2015
Year: 2015

In this paper, we study the notion of {\em codes with hierarchical locality} that is identified as another approach to local recovery from multiple erasures. The well-known class of {\em codes with locality} is said to possess hierarchical locality with a single level. In a {\em code with two-level hierarchical locality}, every symbol is protected by an inner-most local code, and another middle-level code of larger dimension containing the local code. We first consider codes with two levels of hierarchical locality, derive an upper bound on the minimum distance, and provide optimal code constructions of low field-size under certain parameter sets. Subsequently, we generalize both the bound and the constructions to hierarchical locality of arbitrary levels.
Reduced Complexity Decoding of n x n Algebraic Space-Time Codes
Venue: arXiv cs.IT 2015
Year: 2015

Algebraic space-time coding allows for reliable data exchange across fading multiple-input multiple-output channels. A powerful technique for decoding space-time codes in Maximum-Likelihood (ML) decoding, but well-performing and widely-used codes such as the Golden code often suffer from high ML-decoding complexity. In this article, a recursive algorithm for decoding general algebraic space-time codes of arbitrary dimension is proposed, which reduces the worst-case decoding complexity from $O(|S|^{n^2})$ to $O(|S|^n)$.
Time Aware Knowledge Extraction for Microblog Summarization on Twitter
Venue: arXiv cs.IR 2015
Year: 2015

Microblogging services like Twitter and Facebook collect millions of user generated content every moment about trending news, occurring events, and so on. Nevertheless, it is really a nightmare to find information of interest through the huge amount of available posts that are often noise and redundant. In general, social media analytics services have caught increasing attention from both side research and industry. Specifically, the dynamic context of microblogging requires to manage not only meaning of information but also the evolution of knowledge over the timeline. This work defines Time Aware Knowledge Extraction (briefly TAKE) methodology that relies on temporal extension of Fuzzy Formal Concept Analysis. In particular, a microblog summarization algorithm has been defined filtering the concepts organized by TAKE in a time-dependent hierarchy. The algorithm addresses topic-based summarization on Twitter. Besides considering the timing of the concepts, another distinguish feature of the proposed microblog summarization framework is the possibility to have more or less detailed summary, according to the user's needs, with good levels of quality and completeness as highlighted in the experimental results.
Big Data on the Rise: Testing monotonicity of distributions
Venue: arXiv cs.DS 2015
Year: 2015

The field of property testing of probability distributions, or distribution testing, aims to provide fast and (most likely) correct answers to questions pertaining to specific aspects of very large datasets. In this work, we consider a property of particular interest, monotonicity of distributions. We focus on the complexity of monotonicity testing across different models of access to the distributions; and obtain results in these new settings that differ significantly from the known bounds in the standard sampling model.