OpenRaiser/Intern-Atlas huggingface dataset:
Analysis of Intelligent Classifiers and Enhancing the Detection Accuracy for Intrusion Detection System
Venue: arXiv cs.CR 2015
Year: 2015

In this paper we discuss and analyze some of the intelligent classifiers which allows for automatic detection and classification of networks attacks for any intrusion detection system. We will proceed initially with their analysis using the WEKA software to work with the classifiers on a well-known IDS (Intrusion Detection Systems) dataset like NSL-KDD dataset. The NSL-KDD dataset of network attacks was created in a military network by MIT Lincoln Labs. Then we will discuss and experiment some of the hybrid AI (Artificial Intelligence) classifiers that can be used for IDS, and finally we developed a Java software with three most efficient classifiers and compared it with other options. The outputs would show the detection accuracy and efficiency of the single and combined classifiers used.
Towards a complete DMT classification of division algebra codes
Venue: arXiv cs.IT 2015
Year: 2015

This work aims at providing new bounds for the diversity multiplexing gain trade-off of a general class of division algebra based lattice codes. In the low multiplexing gain regime, some bounds were previously obtained from the high signal-to-noise ratio estimate of the union bound for the pairwise error probabilities. Here these results are extended to cover a larger range of multiplexing gains. The improvement is achieved by using ergodic theory in Lie groups to estimate the behavior of the sum arising from the union bound. In particular, the new bounds for lattice codes derived from Q-central division algebras suggest that these codes can be divided into two subclasses based on their Hasse invariants at the infinite places. Algebras with ramification at the infinite place seem to provide better diversity-multiplexing gain tradeoff.
Multi-threaded Graph Coloring Algorithm for Shared Memory Architecture
Venue: arXiv cs.DC 2015
Year: 2015

In this paper, we present multi-threaded algorithms for graph coloring suitable to the shared memory programming model. We modify an existing algorithm widely used in the literature and prove the correctness of the modified algorithm. We also propose a new approach to solve the problem of coloring using locks. Using datasets from real world graphs, we evaluate the performance of the algorithms on the Intel platform. We compare the performance of the sequential approach v/s our proposed approach and analyze the speedup obtained against the existing algorithm from the literature. The results show that the speedup obtained is consequential. We also provide a direction for future work towards improving the performance further in terms of different metrics.
A hybrid COA$ε$-constraint method for solving multi-objective problems
Venue: arXiv cs.NE 2015
Year: 2015

In this paper, a hybrid method for solving multi-objective problem has been provided. The proposed method is combining the ε-Constraint and the Cuckoo algorithm. First the multi objective problem transfers into a single-objective problem using $ε$-Constraint, then the Cuckoo optimization algorithm will optimize the problem in each task. At last the optimized Pareto frontier will be drawn. The advantage of this method is the high accuracy and the dispersion of its Pareto frontier. In order to testing the efficiency of the suggested method, a lot of test problems have been solved using this method. Comparing the results of this method with the results of other similar methods shows that the Cuckoo algorithm is more suitable for solving the multi-objective problems.
Optimal Energy Allocation Policies for a High Altitude Flying Wireless Access Point
Venue: arXiv cs.NI 2015
Year: 2015

Inspired by recent industrial efforts toward high altitude flying wireless access points powered by renewable energy, an online resource allocation problem for a mobile access point (AP) travelling at high altitude is formulated. The AP allocates its resources (available energy) to maximize the total utility (reward) provided to a sequentially observed set of users demanding service. The problem is formulated as a 0/1 dynamic knapsack problem with incremental capacity over a finite time horizon, the solution of which is quite open in the literature. We address the problem through deterministic and stochastic formulations. For the deterministic problem, several online approximations are proposed based on an instantaneous threshold that can adapt to short-time-scale dynamics. For the stochastic model, after showing the optimality of a threshold based solution on a dynamic programming (DP) formulation, an approximate threshold based policy is obtained. The performances of proposed policies are compared with that of the optimal solution obtained through DP.
Efficient Discriminative Nonorthogonal Binary Subspace with its Application to Visual Tracking
Venue: arXiv cs.CV 2015
Year: 2015

One of the crucial problems in visual tracking is how the object is represented. Conventional appearance-based trackers are using increasingly more complex features in order to be robust. However, complex representations typically not only require more computation for feature extraction, but also make the state inference complicated. We show that with a careful feature selection scheme, extremely simple yet discriminative features can be used for robust object tracking. The central component of the proposed method is a succinct and discriminative representation of the object using discriminative non-orthogonal binary subspace (DNBS) which is spanned by Haar-like features. The DNBS representation inherits the merits of the original NBS in that it efficiently describes the object. It also incorporates the discriminative information to distinguish foreground from background. However, the problem of finding the DNBS bases from an over-complete dictionary is NP-hard. We propose a greedy algorithm called discriminative optimized orthogonal matching pursuit (D-OOMP) to solve this problem. An iterative formulation named iterative D-OOMP is further developed to drastically reduce the redundant computation between iterations and a hierarchical selection strategy is integrated for reducing the search space of features. The proposed DNBS representation is applied to object tracking through SSD-based template matching. We validate the effectiveness of our method through extensive experiments on challenging videos with comparisons against several state-of-the-art trackers and demonstrate its capability to track objects in clutter and moving background.
CARMA: Collective Adaptive Resource-sharing Markovian Agents
Venue: arXiv cs.PL 2015
Year: 2015

In this paper we present CARMA, a language recently defined to support specification and analysis of collective adaptive systems. CARMA is a stochastic process algebra equipped with linguistic constructs specifically developed for modelling and programming systems that can operate in open-ended and unpredictable environments. This class of systems is typically composed of a huge number of interacting agents that dynamically adjust and combine their behaviour to achieve specific goals. A CARMA model, termed a collective, consists of a set of components, each of which exhibits a set of attributes. To model dynamic aggregations, which are sometimes referred to as ensembles, CARMA provides communication primitives that are based on predicates over the exhibited attributes. These predicates are used to select the participants in a communication. Two communication mechanisms are provided in the CARMA language: multicast-based and unicast-based. In this paper, we first introduce the basic principles of CARMA and then we show how our language can be used to support specification with a simple but illustrative example of a socio-technical collective adaptive system.
Probabilistic Output Analysis by Program Manipulation
Venue: arXiv cs.PL 2015
Year: 2015

The aim of a probabilistic output analysis is to derive a probability distribution of possible output values for a program from a probability distribution of its input. We present a method for performing static output analysis, based on program transformation techniques. It generates a probability function as a possibly uncomputable expression in an intermediate language. This program is then analyzed, transformed, and approximated. The result is a closed form expression that computes an over approximation of the output probability distribution for the program. We focus on programs where the possible input follows a known probability distribution. Tests in programs are not assumed to satisfy the Markov property of having fixed branching probabilities independently of previous history.
High Probability Guarantees in Repeated Games: Theory and Applications in Information Theory
Venue: arXiv cs.GT 2015
Year: 2015

We introduce a "high probability" framework for repeated games with incomplete information. In our non-equilibrium setting, players aim to guarantee a certain payoff with high probability, rather than in expected value. We provide a high probability counterpart of the classical result of Mertens and Zamir for the zero-sum repeated games. Any payoff that can be guaranteed with high probability can be guaranteed in expectation, but the reverse is not true. Hence, unlike the average payoff case where the payoff guaranteed by each player is the negative of the payoff by the other player, the two guaranteed payoffs would differ in the high probability framework. One motivation for this framework comes from information transmission systems, where it is customary to formulate problems in terms of asymptotically vanishing probability of error. An application of our results to a class of compound arbitrarily varying channels is given.
Probabilistic Threshold Indexing for Uncertain Strings
Venue: arXiv cs.DB 2015
Year: 2015

Strings form a fundamental data type in computer systems. String searching has been extensively studied since the inception of computer science. Increasingly many applications have to deal with imprecise strings or strings with fuzzy information in them. String matching becomes a probabilistic event when a string contains uncertainty, i.e. each position of the string can have different probable characters with associated probability of occurrence for each character. Such uncertain strings are prevalent in various applications such as biological sequence data, event monitoring and automatic ECG annotations. We explore the problem of indexing uncertain strings to support efficient string searching. In this paper we consider two basic problems of string searching, namely substring searching and string listing. In substring searching, the task is to find the occurrences of a deterministic string in an uncertain string. We formulate the string listing problem for uncertain strings, where the objective is to output all the strings from a collection of strings, that contain probable occurrence of a deterministic query string. Indexing solution for both these problems are significantly more challenging for uncertain strings than for deterministic strings. Given a construction time probability value $τ$, our indexes can be constructed in linear space and supports queries in near optimal time for arbitrary values of probability threshold parameter greater than $τ$. To the best of our knowledge, this is the first indexing solution for searching in uncertain strings that achieves strong theoretical bound and supports arbitrary values of probability threshold parameter. We also propose an approximate substring search index that can answer substring search queries with an additive error in optimal time. We conduct experiments to evaluate the performance of our indexes.
Active Eavesdropping via Spoofing Relay Attack
Venue: arXiv cs.IT 2015
Year: 2015

This paper studies a new active eavesdropping technique via the so-called spoofing relay attack, which could be launched by the eavesdropper to significantly enhance the information leakage rate from the source over conventional passive eaves-dropping. With this attack, the eavesdropper acts as a relay to spoof the source to vary transmission rate in favor of its eavesdropping performance by either enhancing or degrading the effective channel of the legitimate link. The maxi-mum information leakage rate achievable by the eavesdropper and the corresponding optimal operation at the spoofing relay are obtained. It is shown that such a spoofing relay attack could impose new challenges from a physical-layer security perspective since it leads to significantly higher information leakage rate than conventional passive eavesdropping.
Censoring Diffusion for Harvesting WSNs
Venue: arXiv cs.SY 2015
Year: 2015

In this paper, we analyze energy-harvesting adaptive diffusion networks for a distributed estimation problem. In order to wisely manage the available energy resources, we propose a scheme where a censoring algorithm is jointly applied over the diffusion strategy. An energy-aware variation of a diffusion algorithm is used, and a new way of measuring the relevance of the estimates in diffusion networks is proposed in order to apply a subsequent censoring mechanism. Simulation results show the potential benefit of integrating censoring schemes in energy-constrained diffusion networks.
On the Scalability and Message Count of Trickle-based Broadcasting Schemes
Venue: arXiv cs.NI 2015
Year: 2015

As the use of wireless sensor networks increases, the need for efficient and reliable broadcasting algorithms grows. Ideally, a broadcasting algorithm should have the ability to quickly disseminate data, while keeping the number of transmissions low. In this paper, we analyze the popular Trickle algorithm, which has been proposed as a suitable communication protocol for code maintenance and propagation in wireless sensor networks. We show that the broadcasting process of a network using Trickle can be modeled by a Markov chain and that this chain falls under a class of Markov chains, closely related to residual lifetime distributions. It is then shown that this class of Markov chains admits a stationary distribution of a special form. These results are used to analyze the Trickle algorithm and its message count. Our results prove conjectures made in the literature concerning the effect of a listen-only period. Besides providing a mathematical analysis of the algorithm, we propose a generalized version of Trickle, with an additional parameter defining the length of a listen-only period.
LINOEP vectors, spiral of Theodorus, and nonlinear time-invariant system models of mode decomposition
Venue: arXiv cs.IT 2015
Year: 2015

In this paper, we propose a general method to obtain a set of Linearly Independent Non-Orthogonal yet Energy (square of the norm) Preserving (LINOEP) vectors using iterative filtering operation and we refer it as Filter Mode Decomposition (FDM). We show that the general energy preserving theorem (EPT), which is valid for both linearly independent (orthogonal and nonorthogonal) and linearly dependent set of vectors, proposed by Singh P. et al. is a generalization of the discrete spiral of Theodorus (or square root spiral or Einstein spiral or Pythagorean spiral). From the EPT, we obtain the (2D) discrete spiral of Theodorus and show that the multidimensional discrete spirals (e.g. a 3D spiral) can be easily generated using a set of multidimensional energy preserving unit vectors. We also establish that the recently proposed methods (e.g. Empirical Mode Decomposition (EMD), Synchrosqueezed Wavelet Transforms (SSWT), Variational Mode Decomposition (VMD), Eigenvalue Decomposition (EVD), Fourier Decomposition Method (FDM), etc.), for nonlinear and nonstationary time series analysis, are nonlinear time-invariant (NTI) system models of filtering. Simulation and numerical results demonstrate the efficacy of LINOEP vectors.
Tolerance Analysis by Polytopes
Venue: arXiv cs.CG 2015
Year: 2015

To determine the relative position of any two surfaces in a system, one approach is to useoperations (Minkowski sum and intersection) on sets of constraints. These constraints aremade compliant with half-spaces of R^n where each set of half-spaces defines an operandpolyhedron. These operands are generally unbounded due to the inclusion of degrees ofinvariance for surfaces and degrees of freedom for joints defining theoretically unlimiteddisplacements. To solve operations on operands, Minkowski sums in particular, "cap" halfspacesare added to each polyhedron to make it compliant with a polytope which is bydefinition a bounded polyhedron. The difficulty of this method lies in controlling the influenceof these additional half-spaces on the topology of polytopes calculated by sum or intersection.This is necessary to validate the geometric tolerances that ensure the compliance of amechanical system in terms of functional requirements.
The Evolution of eSports: An Analysis of its origin and a look at its prospective future growth as enhanced by Information Technology Management tools
Venue: arXiv cs.CY 2015
Year: 2015

As the last years have shown a massive growth within the field of electronic sports (eSports), several questions emerge, such as how much is it growing, and will it continue to grow? This research thesis sees this as its statement of problem, and further aims to define and measure the main factors that caused the growth of eSports. To further enhance the growth, the benefits and disbenefits of implementing Information Technology Management tools is appraised, which additionally gives an understanding of the future of eSports. Through observation of the data collection, the themes games played, games genre, and single-player or team-player was deemed most important. A statistical analysis was additionally done on the numerical data, which showed significant correlation between the themes prize pool, participants and participating countries towards live audience. This indicates that the mentioned themes affect how many people attends live eSport event. The analysis also consists of a trend analysis, which presents that nearly every theme analysed will continue to grow in a five-year period. The trend analysis further expects a 305.51% growth in the live audience from 2014-2019, while the virtual streaming audience is expected to experience a growth of 140.61%. To enhance this growth, the research thesis recommends using the presented trends and patterns found in the short-term, to conduct successful eSport events. However, in the long-term an implementation of a Data-driven Decision Support System can be done, which will periodically gather data to know the best possible way to deliver eSport events to the eSport community.
Replicability and the public/private divide
Venue: arXiv cs.DL 2015
Year: 2015

In a recent letter, Carlos Vilchez-Roman criticizes Bornmann et al. (2015) for using data which cannot be reproduced without access to an in-house version of the Web-of-Science (WoS) at the Max Planck Digital Libraries (MPDL, Munich). We agree with the norm of replicability and therefore returned to our data. Is the problem only a practical one of automation or does the in-house processing add analytical value to the data? Is the newly emerging situation in any sense different from a further professionalization of the field? In our opinion, a political economy of science indicators has in the meantime emerged with a competitive dynamic that affects the intellectual organization of the field.
Automatically Segmenting Oral History Transcripts
Venue: arXiv cs.CL 2015
Year: 2015

Dividing oral histories into topically coherent segments can make them more accessible online. People regularly make judgments about where coherent segments can be extracted from oral histories. But making these judgments can be taxing, so automated assistance is potentially attractive to speed the task of extracting segments from open-ended interviews. When different people are asked to extract coherent segments from the same oral histories, they often do not agree about precisely where such segments begin and end. This low agreement makes the evaluation of algorithmic segmenters challenging, but there is reason to believe that for segmenting oral history transcripts, some approaches are more promising than others. The BayesSeg algorithm performs slightly better than TextTiling, while TextTiling does not perform significantly better than a uniform segmentation. BayesSeg might be used to suggest boundaries to someone segmenting oral histories, but this segmentation task needs to be better defined.
Accelerated Spectral Clustering Using Graph Filtering Of Random Signals
Venue: arXiv cs.SI 2015
Year: 2015

We build upon recent advances in graph signal processing to propose a faster spectral clustering algorithm. Indeed, classical spectral clustering is based on the computation of the first k eigenvectors of the similarity matrix' Laplacian, whose computation cost, even for sparse matrices, becomes prohibitive for large datasets. We show that we can estimate the spectral clustering distance matrix without computing these eigenvectors: by graph filtering random signals. Also, we take advantage of the stochasticity of these random vectors to estimate the number of clusters k. We compare our method to classical spectral clustering on synthetic data, and show that it reaches equal performance while being faster by a factor at least two for large datasets.
A Semi-Supervised Method for Predicting Cancer Survival Using Incomplete Clinical Data
Venue: arXiv cs.LG 2015
Year: 2015

Prediction of survival for cancer patients is an open area of research. However, many of these studies focus on datasets with a large number of patients. We present a novel method that is specifically designed to address the challenge of data scarcity, which is often the case for cancer datasets. Our method is able to use unlabeled data to improve classification by adopting a semi-supervised training approach to learn an ensemble classifier. The results of applying our method to three cancer datasets show the promise of semi-supervised learning for prediction of cancer survival.
Light Field Reconstruction Using Shearlet Transform
Venue: arXiv cs.CV 2015
Year: 2015

In this article we develop an image based rendering technique based on light field reconstruction from a limited set of perspective views acquired by cameras. Our approach utilizes sparse representation of epipolar-plane images in a directionally sensitive transform domain, obtained by an adapted discrete shearlet transform. The used iterative thresholding algorithm provides high-quality reconstruction results for relatively big disparities between neighboring views. The generated densely sampled light field of a given 3D scene is thus suitable for all applications which requires light field reconstruction. The proposed algorithm is compared favorably against state of the art depth image based rendering techniques.
Learning without Recall: A Case for Log-Linear Learning
Venue: arXiv cs.SI 2015
Year: 2015

We analyze a model of learning and belief formation in networks in which agents follow Bayes rule yet they do not recall their history of past observations and cannot reason about how other agents' beliefs are formed. They do so by making rational inferences about their observations which include a sequence of independent and identically distributed private signals as well as the beliefs of their neighboring agents at each time. Fully rational agents would successively apply Bayes rule to the entire history of observations. This leads to forebodingly complex inferences due to lack of knowledge about the global network structure that causes those observations. To address these complexities, we consider a Learning without Recall model, which in addition to providing a tractable framework for analyzing the behavior of rational agents in social networks, can also provide a behavioral foundation for the variety of non-Bayesian update rules in the literature. We present the implications of various choices for time-varying priors of such agents and how this choice affects learning and its rate.