OpenRaiser/Intern-Atlas huggingface dataset:
Privacy Impacts of Data Encryption on the Efficiency of Digital Forensics Technology
Venue: arXiv cs.CR 2013
Year: 2013

Owing to a number of reasons, the deployment of encryption solutions are beginning to be ubiquitous at both organizational and individual levels. The most emphasized reason is the necessity to ensure confidentiality of privileged information. Unfortunately, it is also popular as cyber-criminals' escape route from the grasp of digital forensic investigations. The direct encryption of data or indirect encryption of storage devices, more often than not, prevents access to such information contained therein. This consequently leaves the forensics investigation team, and subsequently the prosecution, little or no evidence to work with, in sixty percent of such cases. However, it is unthinkable to jeopardize the successes brought by encryption technology to information security, in favour of digital forensics technology. This paper examines what data encryption contributes to information security, and then highlights its contributions to digital forensics of disk drives. The paper also discusses the available ways and tools, in digital forensics, to get around the problems constituted by encryption. A particular attention is paid to the Truecrypt encryption solution to illustrate ideas being discussed. It then compares encryption's contributions in both realms, to justify the need for introduction of new technologies to forensically defeat data encryption as the only solution, whilst maintaining the privacy goal of users.
Error-Correcting Regenerating and Locally Repairable Codes via Rank-Metric Codes
Venue: arXiv cs.IT 2013
Year: 2013

This paper presents and analyzes a novel concatenated coding scheme for enabling error resilience in two distributed storage settings: one being storage using existing regenerating codes and the second being storage using locally repairable codes. The concatenated coding scheme brings together a maximum rank distance (MRD) code as an outer code and either a globally regenerating or a locally repairable code as an inner code. Also, error resilience for combination of locally repairable codes with regenerating codes is considered. This concatenated coding system is designed to handle two different types of adversarial errors: the first type includes an adversary that can replace the content of an affected node only once; while the second type studies an adversary that is capable of polluting data an unbounded number of times. The paper establishes an upper bound on the resilience capacity for a locally repairable code and proves that this concatenated coding coding scheme attains the upper bound on resilience capacity for the first type of adversary. Further, the paper presents mechanisms that combine the presented concatenated coding scheme with subspace signatures to achieve error resilience for the second type of errors.
A Uniform Self-Stabilizing Minimum Diameter Spanning Tree Algorithm
Venue: arXiv cs.DC 2013
Year: 2013

We present a uniform self-stabilizing algorithm, which solves the problem of distributively finding a minimum diameter spanning tree of an arbitrary positively real-weighted graph. Our algorithm consists in two stages of stabilizing protocols. The first stage is a uniform randomized stabilizing {\em unique naming} protocol, and the second stage is a stabilizing {\em MDST} protocol, designed as a {\em fair composition} of Merlin--Segall's stabilizing protocol and a distributed deterministic stabilizing protocol solving the (MDST) problem. The resulting randomized distributed algorithm presented herein is a composition of the two stages; it stabilizes in $O(nΔ+{\cal D}^2 + n \log\log n)$ expected time, and uses $O(n^2\log n + n \log W)$ memory bits (where $n$ is the order of the graph, $Δ$ is the maximum degree of the network, $\cal D$ is the diameter in terms of hops, and $W$ is the largest edge weight). To our knowledge, our protocol is the very first distributed algorithm for the (MDST) problem. Moreover, it is fault-tolerant and works for any anonymous arbitrary network.
A Distributed Deadlock Free Quorum Based Algorithm for Mutual Exclusion
Venue: arXiv cs.DC 2013
Year: 2013

Quorum based mutual exclusion algorithms enjoy many advantages such as low message complexity and high failure resiliency. The use of quorums is a well known approach to achieving mutual exclusion in distributed environments. Several distributed based quorum mutual exclusion was presented.
On-the-fly Fast Mean-Field Model-Checking: Extended Version
Venue: arXiv cs.LO 2013
Year: 2013

A novel, scalable, on-the-fly model-checking procedure is presented to verify bounded PCTL properties of selected individuals in the context of very large systems of independent interacting objects. The proposed procedure combines on-the-fly model checking techniques with deterministic mean-field approximation in discrete time. The asymptotic correctness of the procedure is shown and some results of the application of a prototype implementation of the FlyFast model-checker are presented.
On the power of randomization in network interdiction
Venue: arXiv cs.GT 2013
Year: 2013

Network interdiction can be viewed as a game between two players, an "interdictor" and a "flow player". The flow player wishes to send as much material as possible through a network, while the interdictor attempts to minimize the amount of transported material by removing a certain number of arcs, say $Γ$ arcs. We introduce the randomized network interdiction problem that allows the interdictor to use randomness to select arcs to be removed. We model the problem in two different ways: arc-based and path-based formulations, depending on whether flows are defined on arcs or paths, respectively. We present insights into the modeling power, complexity, and approximability of both formulations. In particular, we prove that $Z_{\text{NI}}/Z_{\text{RNI}}\leq Γ+1$, $Z_{\text{NI}}/Z_{\text{RNI}}^{\text{Path}}\leq Γ+1$, $Z_{\text{RNI}}/Z_{\text{RNI}}^{\text{Path}}\leq Γ$, where $Z_{\text{NI}}$, $Z_{\text{RNI}}$, and $Z_{\text{RNI}}^{\text{Path}}$ are the optimal values of the network interdiction problem and its randomized versions in arc-based and path-based formulations, respectively. We also show that these bounds are tight. We show that it is NP-hard to compute the values $Z_{\text{RNI}}$ and $Z_{\text{RNI}}^{\text{Path}}$ for a general $Γ$, but they are computable in polynomial time when $Γ=1$. Further, we provide a $(Γ+1)$-approximation for $Z_{\text{NI}}$, a $Γ$-approximation for $Z_{\text{RNI}}$, and a $\big(1+\lfloor Γ/2\rfloor \cdot \lceil Γ/2\rceil/(Γ+1)\big)$-approximation for $Z_{\text{RNI}}^{\text{Path}}$.
Randomness of D Sequences via Diehard Testing
Venue: arXiv cs.NA 2013
Year: 2013

This paper presents a comparison of the quality of randomness of D sequences based on diehard tests. Since D sequences can model any random sequence, this comparison is of value beyond this specific class.
Beaconing-Aware Optimal Policies for Two-Hop Routing in Multi-Class Delay Tolerant Networks
Venue: arXiv cs.NI 2013
Year: 2013

In Delay Tolerant Networks (DTNs), two-hop routing compromises energy versus delay more conveniently than epidemic routing. Literature provides comprehensive results on optimal routing policies for mobile nodes with homogeneous mobility, often neglecting signaling costs. Routing policies are customarily computed by means of fluid approximation techniques, which assure solutions to be optimal only when the number of nodes is infinite, while they provide a coarse approximation otherwise. This work addresses heterogeneous mobility patterns and multiple wireless transmission technologies; moreover, we explicitly consider the beaconing/signaling costs to support routing and the possibility for nodes to discard packets after a local time. We theoretically characterize the optimal policies by deriving their formal properties. Such analysis is leveraged to define two algorithmic approaches which allow to trade off optimality with computational efficiency. Theoretical bounds on the approximation guarantees of the proposed algorithms are derived. We then experimentally evaluated them in realistic scenarios of multi-class DTNs.
Efficient Baseline-free Sampling in Parameter Exploring Policy Gradients: Super Symmetric PGPE
Venue: arXiv cs.LG 2013
Year: 2013

Policy Gradient methods that explore directly in parameter space are among the most effective and robust direct policy search methods and have drawn a lot of attention lately. The basic method from this field, Policy Gradients with Parameter-based Exploration, uses two samples that are symmetric around the current hypothesis to circumvent misleading reward in \emph{asymmetrical} reward distributed problems gathered with the usual baseline approach. The exploration parameters are still updated by a baseline approach - leaving the exploration prone to asymmetric reward distributions. In this paper we will show how the exploration parameters can be sampled quasi symmetric despite having limited instead of free parameters for exploration. We give a transformation approximation to get quasi symmetric samples with respect to the exploration without changing the overall sampling distribution. Finally we will demonstrate that sampling symmetrically also for the exploration parameters is superior in needs of samples and robustness than the original sampling approach.
Data Hiding and Retrieval Using Permutation Index Method
Venue: arXiv cs.CR 2013
Year: 2013

In this paper a novel approach for matrix manipulation and indexing is proposed .Here the elements in a row of matrix are designated by numeric value called permutation index followed by the elements of the row being randomised. This is done for all the rows of the matrix and in the end the set of permutation indices are put in the parent matrix and random locations depending on a pre decided scheme called passkey. This passkey is used to put back the elements of all the rows back in the correct sequence. This approach finds application in data encapsulation and hiding.
Eugene Garfield, Francis Narin, and PageRank: The Theoretical Bases of the Google Search Engine
Venue: arXiv cs.IR 2013
Year: 2013

This paper presents a test of the validity of using Google Scholar to evaluate the publications of researchers by comparing the premises on which its search engine, PageRank, is based, to those of Garfield's theory of citation indexing. It finds that the premises are identical and that PageRank and Garfield's theory of citation indexing validate each other.
Algorithmic Diversity for Software Security
Venue: arXiv cs.PL 2013
Year: 2013

Software diversity protects against a modern-day exploits such as code-reuse attacks. When an attacker designs a code-reuse attack on an example executable, it relies on replicating the target environment. With software diversity, the attacker cannot reliably replicate their target. This is a security benefit which can be applied to massive-scale software distribution. When applied to large-scale communities, an invested attacker may perform analysis of samples to improve the chances of a successful attack (M. Franz). We present a general NOP-insertion algorithm which can be expanded and customized for security, performance, or other costs. We demonstrate an improvement in security so that a code-reuse attack based on any one variant has minimal chances of success on another and analyse the costs of this method. Alternately, the variants may be customized to meet performance or memory overhead constraints. Deterministic diversification allows for the flexibility to balance these needs in a way that doesn't exist in a random online method.
Domain adaptation for sequence labeling using hidden Markov models
Venue: arXiv cs.CL 2013
Year: 2013

Most natural language processing systems based on machine learning are not robust to domain shift. For example, a state-of-the-art syntactic dependency parser trained on Wall Street Journal sentences has an absolute drop in performance of more than ten points when tested on textual data from the Web. An efficient solution to make these methods more robust to domain shift is to first learn a word representation using large amounts of unlabeled data from both domains, and then use this representation as features in a supervised learning algorithm. In this paper, we propose to use hidden Markov models to learn word representations for part-of-speech tagging. In particular, we study the influence of using data from the source, the target or both domains to learn the representation and the different ways to represent words using an HMM.
An introduction to synchronous self-learning Pareto strategy
Venue: arXiv cs.NE 2013
Year: 2013

In last decades optimization and control of complex systems that possessed various conflicted objectives simultaneously attracted an incremental interest of scientists. This is because of the vast applications of these systems in various fields of real life engineering phenomena that are generally multi modal, non convex and multi criterion. Hence, many researchers utilized versatile intelligent models such as Pareto based techniques, game theory (cooperative and non cooperative games), neuro evolutionary systems, fuzzy logic and advanced neural networks for handling these types of problems. In this paper a novel method called Synchronous Self Learning Pareto Strategy Algorithm (SSLPSA) is presented which utilizes Evolutionary Computing (EC), Swarm Intelligence (SI) techniques and adaptive Classical Self Organizing Map (CSOM) simultaneously incorporating with a data shuffling behavior. Evolutionary Algorithms (EA) which attempt to simulate the phenomenon of natural evolution are powerful numerical optimization algorithms that reach an approximate global maximum of a complex multi variable function over a wide search space and swarm base technique can improved the intensity and the robustness in EA. CSOM is a neural network capable of learning and can improve the quality of obtained optimal Pareto front. To prove the efficient performance of proposed algorithm, authors utilized some well known benchmark test functions. Obtained results indicate that the cited method is best suit in the case of vector optimization.
Towards slime mould colour sensor: Recognition of colours by Physarum polycephalum
Venue: arXiv cs.ET 2013
Year: 2013

Acellular slime mould Physarum polycephalum is a popular now user-friendly living substrate for designing of future and emergent sensing and computing devices. P. polycephalum exhibits regular patterns of oscillations of its surface electrical potential. The oscillation patterns are changed when the slime mould is subjected to mechanical, chemical, electrical or optical stimuli. We evaluate feasibility of slime-mould based colour sensors by illuminating Physarum with red, green, blue and white colours and analysing patterns of the slime mould's electrical potential oscillations. We define that the slime mould recognises a colour if it reacts to illumination with the colour by a unique changes in amplitude and periods of oscillatory activity. In laboratory experiments we found that the slime mould recognises red and blue colour. The slime mould does not differentiate between green and white colours. The slime mould also recognises when red colour is switched off. We also map colours to diversity of the oscillations: illumination with a white colour increases diversity of amplitudes and periods of oscillations, other colours studied increase diversity either of amplitude or period.
Feature Graph Architectures
Venue: arXiv cs.LG 2013
Year: 2013

In this article we propose feature graph architectures (FGA), which are deep learning systems employing a structured initialisation and training method based on a feature graph which facilitates improved generalisation performance compared with a standard shallow architecture. The goal is to explore alternative perspectives on the problem of deep network training. We evaluate FGA performance for deep SVMs on some experimental datasets, and show how generalisation and stability results may be derived for these models. We describe the effect of permutations on the model accuracy, and give a criterion for the optimal permutation in terms of feature correlations. The experimental results show that the algorithm produces robust and significant test set improvements over a standard shallow SVM training method for a range of datasets. These gains are achieved with a moderate increase in time complexity.
Modification of Contract Net Protocol(CNP) : A Rule-Updation Approach
Venue: arXiv cs.MA 2013
Year: 2013

Coordination in multi-agent system is very essential, in order to perform complex tasks and lead MAS towards its goal. Also, the member agents of multi-agent system should be autonomous as well as collaborative to accomplish the complex task for which multi-agent system is designed specifically. Contract-Net Protocol (CNP) is one of the coordination mechanisms which is used by multi-agent systems which prefer coordination through interaction protocols. In order to overcome the limitations of conventional CNP, this paper proposes a modification in conventional CNP called updated-CNP. Updated-CNP is an effort towards updating of a CNP in terms of its limitations of modifiability and communication overhead. The limitation of the modification of tasks, if the task requirements change at any instance, corresponding to tasks which are allocated to contractor agents by manager agents is possible in our updated-CNP version, which was not possible in the case of conventional-CNP, as it has to be restarted in the case of task modification. This in turn will be reducing the communication overhead of CNP, which is time taken by various agents using CNP to pass messages to each other. For the illustration of the updated CNP, we have used a sound predator-prey case study.
GLC actors, artificial chemical connectomes, topological issues and knots
Venue: arXiv cs.DC 2013
Year: 2013

Based on graphic lambda calculus, we propose a program for a new model of asynchronous distributed computing, inspired from Hewitt Actor Model, as well as several investigation paths, concerning how one may graft lambda calculus and knot diagrammatics.
Small Universal Petri Nets with Inhibitor Arcs
Venue: arXiv cs.FL 2013
Year: 2013

We investigate the problem of construction of small-size universal Petri nets with inhibitor arcs. We consider four descriptional complexity parameters: the number of places, transitions, inhibitor arcs, and the maximal degree of a transition, each of which we try to minimize. We give six constructions having the following values of parameters (listed in the above order): $(30,34,13,3)$, $(14, 31, 51, 8)$, $(11, 31, 79, 11)$, $(21,25,13,5)$, $(67, 64, 8, 3)$, $(58, 55, 8, 5)$ that improve the few known results on this topic. Our investigation also highlights several interesting trade-offs.
Extremality for Gallager's Reliability Function $E_0$
Venue: arXiv cs.IT 2013
Year: 2013

We describe certain extremalities for Gallager's $E_0$ function evaluated under the uniform input distribution for binary input discrete memoryless channels. The results characterize the extremality of the $E_0(ρ)$ curves of the binary erasure channel and the binary symmetric channel among all the $E_0(ρ)$ curves that can be generated by the class of binary discrete memoryless channels whose $E_0(ρ)$ curves pass through a given point $(ρ_0, e_0)$, for some $ρ_0 > -1$.
GCG: Mining Maximal Complete Graph Patterns from Large Spatial Data
Venue: arXiv cs.DB 2013
Year: 2013

Recent research on pattern discovery has progressed from mining frequent patterns and sequences to mining structured patterns, such as trees and graphs. Graphs as general data structure can model complex relations among data with wide applications in web exploration and social networks. However, the process of mining large graph patterns is a challenge due to the existence of large number of subgraphs. In this paper, we aim to mine only frequent complete graph patterns. A graph g in a database is complete if every pair of distinct vertices is connected by a unique edge. Grid Complete Graph (GCG) is a mining algorithm developed to explore interesting pruning techniques to extract maximal complete graphs from large spatial dataset existing in Sloan Digital Sky Survey (SDSS) data. Using a divide and conquer strategy, GCG shows high efficiency especially in the presence of large number of patterns. In this paper, we describe GCG that can mine not only simple co-location spatial patterns but also complex ones. To the best of our knowledge, this is the first algorithm used to exploit the extraction of maximal complete graphs in the process of mining complex co-location patterns in large spatial dataset.
Tethered Flying Robot for Information Gathering System
Venue: arXiv cs.SY 2013
Year: 2013

Information from the sky is important for rescue activity in large-scale disaster or dangerous areas. Observation system using a balloon or an airplane has been studied as an information gathering system from the sky. A balloon observation system needs helium gas and relatively long time to be ready. An airplane observation system can be prepared in a short time and its mobility is good. However, a long time flight is difficult because of limited amount of fuel. This paper proposes a kite-based observation system that complements activities of balloon and airplane observation systems by short preparation time and long time flight. This research aims at construction of the autonomous flight information gathering system using a tethered flying unit that consists of the kite and the ground tether line control unit with a winding machine. This paper reports development of the kite type tethered flying robot and an autonomous flying control system inspired by how to fly a kite by a human.
Evolution and Computational Learning Theory: A survey on Valiant's paper
Venue: arXiv cs.LG 2013
Year: 2013

Darwin's theory of evolution is considered to be one of the greatest scientific gems in modern science. It not only gives us a description of how living things evolve, but also shows how a population evolves through time and also, why only the fittest individuals continue the generation forward. The paper basically gives a high level analysis of the works of Valiant[1]. Though, we know the mechanisms of evolution, but it seems that there does not exist any strong quantitative and mathematical theory of the evolution of certain mechanisms. What is defined exactly as the fitness of an individual, why is that only certain individuals in a population tend to mutate, how computation is done in finite time when we have exponentially many examples: there seems to be a lot of questions which need to be answered. [1] basically treats Darwinian theory as a form of computational learning theory, which calculates the net fitness of the hypotheses and thus distinguishes functions and their classes which could be evolvable using polynomial amount of resources. Evolution is considered as a function of the environment and the previous evolutionary stages that chooses the best hypothesis using learning techniques that makes mutation possible and hence, gives a quantitative idea that why only the fittest individuals tend to survive and have the power to mutate.