OpenRaiser/Intern-Atlas huggingface dataset:
Penetration Testing == POMDP Solving?
Venue: arXiv cs.AI 2013
Year: 2013

Penetration Testing is a methodology for assessing network security, by generating and executing possible attacks. Doing so automatically allows for regular and systematic testing without a prohibitive amount of human labor. A key question then is how to generate the attacks. This is naturally formulated as a planning problem. Previous work (Lucangeli et al. 2010) used classical planning and hence ignores all the incomplete knowledge that characterizes hacking. More recent work (Sarraute et al. 2011) makes strong independence assumptions for the sake of scaling, and lacks a clear formal concept of what the attack planning problem actually is. Herein, we model that problem in terms of partially observable Markov decision processes (POMDP). This grounds penetration testing in a well-researched formalism, highlighting important aspects of this problem's nature. POMDPs allow to model information gathering as an integral part of the problem, thus providing for the first time a means to intelligently mix scanning actions with actual exploits.
Felzenszwalb-Baum-Welch: Event Detection by Changing Appearance
Venue: arXiv cs.CV 2013
Year: 2013

We propose a method which can detect events in videos by modeling the change in appearance of the event participants over time. This method makes it possible to detect events which are characterized not by motion, but by the changing state of the people or objects involved. This is accomplished by using object detectors as output models for the states of a hidden Markov model (HMM). The method allows an HMM to model the sequence of poses of the event participants over time, and is effective for poses of humans and inanimate objects. The ability to use existing object-detection methods as part of an event model makes it possible to leverage ongoing work in the object-detection community. A novel training method uses an EM loop to simultaneously learn the temporal structure and object models automatically, without the need to specify either the individual poses to be modeled or the frames in which they occur. The E-step estimates the latent assignment of video frames to HMM states, while the M-step estimates both the HMM transition probabilities and state output models, including the object detectors, which are trained on the weighted subset of frames assigned to their state. A new dataset was gathered because little work has been done on events characterized by changing object pose, and suitable datasets are not available. Our method produced results superior to that of comparison systems on this dataset.
The potential of preprints to accelerate scholarly communication - A bibliometric analysis based on selected journals
Venue: arXiv cs.DL 2013
Year: 2013

This paper quantifies to which extent preprints in arXiv accelerate scholarly communication. The following subject fields were investigated up to the year 2012: High Energy Physics (HEP), Mathematics, Astrophysics, Quantitative Biology, and Library and Information Science (LIS). Publication and citation data was downloaded from Scopus and matched with corresponding preprints in arXiv. Furthermore, the INSPIRE HEP database was used to retrieve citation data for papers related to HEP. The bibliometric analysis deals with the growth in numbers of articles published having a previous preprint in arXiv and the publication delay, which is defined as the chronological distance between the deposit of a preprint in arXiv and its formal journal publication. Likewise, the citation delay is analyzed, which describes the time it takes until the first citation of preprints, and articles, respectively. Total citation numbers are compared for sets of articles with a previous preprint and those without. The results show that in all fields but biology a significant citation advantage exists in terms of speed and citation rates for articles with a previous preprint version on arXiv.
Information embedding and the triple role of control
Venue: arXiv cs.IT 2013
Year: 2013

We consider the problem of information embedding where the encoder modifies a white Gaussian host signal in a power-constrained manner to encode a message, and the decoder recovers both the embedded message and the modified host signal. This partially extends the recent work of Sumszyk and Steinberg to the continuous-alphabet Gaussian setting. Through a control-theoretic lens, we observe that the problem is a minimalist example of what is called the "triple role" of control actions. We show that a dirty-paper-coding strategy achieves the optimal rate for perfect recovery of the modified host and the message for any message rate. For imperfect recovery of the modified host, by deriving bounds on the minimum mean-square error (MMSE) in recovering the modified host signal, we show that DPC-based strategies are guaranteed to attain within a uniform constant factor of 16 of the optimal weighted sum of power required in host signal modification and the MMSE in the modified host signal reconstruction for all weights and all message rates. When specialized to the zero-rate case, our results provide the tightest known lower bounds on the asymptotic costs for the vector version of a famous open problem in decentralized control: the Witsenhausen counterexample. Numerically, this tighter bound helps us characterize the asymptotically optimal costs for the vector Witsenhausen problem to within a factor of 1.3 for all problem parameters, improving on the earlier best known bound of 2.
3-SAT Problem A New Memetic-PSO Algorithm
Venue: arXiv cs.AI 2013
Year: 2013

3-SAT problem is of great importance to many technical and scientific applications. This paper presents a new hybrid evolutionary algorithm for solving this satisfiability problem. 3-SAT problem has the huge search space and hence it is known as a NP-hard problem. So, deterministic approaches are not applicable in this context. Thereof, application of evolutionary processing approaches and especially PSO will be very effective for solving these kinds of problems. In this paper, we introduce a new evolutionary optimization technique based on PSO, Memetic algorithm and local search approaches. When some heuristics are mixed, their advantages are collected as well and we can reach to the better outcomes. Finally, we test our proposed algorithm over some benchmarks used by some another available algorithms. Obtained results show that our new method leads to the suitable results by the appropriate time. Thereby, it achieves a better result in compared with the existent approaches such as pure genetic algorithm and some verified types
Students' Comparison of Their Trigonometric Answers with the Answers of a Computer Algebra System
Venue: arXiv cs.CY 2013
Year: 2013

Comparison of answers offered by a computer algebra system (CAS) with answers derived by a student without a CAS is relevant, for instance, in the context of computer-aided assessment (CAA). The issues of identity, equivalence and correctness emerge in different ways and are important for CAA. These issues are also interesting if a student is charged with the task of comparing the answers. What will happen when students themselves are encouraged to analyse differences, equivalence and correctness of their own answers and CAS answers? What differences do they notice foremost? Would they recognise equivalence/non-equivalence? How do they explain equivalence/non-equivalence? The paper discusses these questions on the basis of lessons where the students solved trigonometric equations. Ten equations were chosen with the aim to ensure that the expected school answer and the CAS answer would differ in various ways. Three of them are discussed more thoroughly in this paper.
Computer Aided ECG Analysis - State of the Art and Upcoming Challenges
Venue: arXiv cs.CV 2013
Year: 2013

In this paper we present current achievements in computer aided ECG analysis and their applicability in real world medical diagnosis process. Most of the current work is covering problems of removing noise, detecting heartbeats and rhythm-based analysis. There are some advancements in particular ECG segments detection and beat classifications but with limited evaluations and without clinical approvals. This paper presents state of the art advancements in those areas till present day. Besides this short computer science and signal processing literature review, paper covers future challenges regarding the ECG signal morphology analysis deriving from the medical literature review. Paper is concluded with identified gaps in current advancements and testing, upcoming challenges for future research and a bullseye test is suggested for morphology analysis evaluation.
SVM based on personal identification system using Electrocardiograms
Venue: arXiv cs.SY 2013
Year: 2013

This paper presents a new algorithm for personal identification from their Electrocardiograms (ECG) which is based on morphological descriptors and Hermite Polynomials Expansion coefficients (HPEc). After preprocessing, we extracted ten morphological descriptors which were divided into homogeneous groups (amplitude, surface interval and slope) and we extracted sixty Hermite Polynomials Expansion coefficients(HPEc) from each heartbeat. For the classification, we employed a binary Support Vector Machines with Gaussian kernel and we adopted a particular strategy: we first classified groups of morphological descriptors separately then we combined them in one system. On the other hand, we classified the Hermite Polynomials Expansion coefficients apart and we associated them with all groups of morphological descriptors in a single system in order to improve overall performance. We tested our algorithm on 18 different healthy signals of the MIT_BIH database. The analysis of different groups separately showed that the best recognition performance is 96.45% for all morphological descriptors and the results of experiments showed that the proposed hybrid approach has led to an overall maximum of 98.97%.
Is the Sample Good Enough? Comparing Data from Twitter's Streaming API with Twitter's Firehose
Venue: arXiv cs.SI 2013
Year: 2013

Twitter is a social media giant famous for the exchange of short, 140-character messages called "tweets". In the scientific community, the microblogging site is known for openness in sharing its data. It provides a glance into its millions of users and billions of tweets through a "Streaming API" which provides a sample of all tweets matching some parameters preset by the API user. The API service has been used by many researchers, companies, and governmental institutions that want to extract knowledge in accordance with a diverse array of questions pertaining to social media. The essential drawback of the Twitter API is the lack of documentation concerning what and how much data users get. This leads researchers to question whether the sampled data is a valid representation of the overall activity on Twitter. In this work we embark on answering this question by comparing data collected using Twitter's sampled API service with data collected using the full, albeit costly, Firehose stream that includes every single published tweet. We compare both datasets using common statistical metrics as well as metrics that allow us to compare topics, networks, and locations of tweets. The results of our work will help researchers and practitioners understand the implications of using the Streaming API.
Active influence in dynamical models of structural balance in social networks
Venue: arXiv cs.SI 2013
Year: 2013

We consider a nonlinear dynamical system on a signed graph, which can be interpreted as a mathematical model of social networks in which the links can have both positive and negative connotations. In accordance with a concept from social psychology called structural balance, the negative links play a key role in both the structure and dynamics of the network. Recent research has shown that in a nonlinear dynamical system modeling the time evolution of "friendliness levels" in the network, two opposing factions emerge from almost any initial condition. Here we study active external influence in this dynamical model and show that any agent in the network can achieve any desired structurally balanced state from any initial condition by perturbing its own local friendliness levels. Based on this result, we also introduce a new network centrality measure for signed networks. The results are illustrated in an international relations network using United Nations voting record data from 1946 to 2008 to estimate friendliness levels amongst various countries.
Song-based Classification techniques for Endangered Bird Conservation
Venue: arXiv cs.LG 2013
Year: 2013

The work presented in this paper is part of a global framework which long term goal is to design a wireless sensor network able to support the observation of a population of endangered birds. We present the first stage for which we have conducted a knowledge discovery approach on a sample of acoustical data. We use MFCC features extracted from bird songs and we exploit two knowledge discovery techniques. One that relies on clustering-based approaches, that highlights the homogeneity in the songs of the species. The other, based on predictive modeling, that demonstrates the good performances of various machine learning techniques for the identification process. The knowledge elicited provides promising results to consider a widespread study and to elicit guidelines for designing a first version of the automatic approach for data collection based on acoustic sensors.
A Formal Model of QoS-Aware Web Service Orchestration Engine
Venue: arXiv cs.SE 2013
Year: 2013

QoS-aware applications can satisfy not only the functional requirements of the customers, but also the QoS requirements. QoS-aware Web Service orchestration translates the QoS requirements of the customers into those of its component Web Services. In a system viewpoint, we discuss issues on QoS-aware Web Service orchestration and design a typical QoS-aware Web Service orchestration engine called QoS-WSOE. More importantly, we establish a formal model of QoS-WSOE based on actor systems theory. Within the formal model, we use a three-layered pyramidal structure to capture the requirements of the customers with a concept named QoS-Aware WSO Service, characteristics of QoS-WSOE with a concept named QoS-Aware WSO System, and structures and behaviors of QoS-WSOE with a concept named QoS-Aware WSO Behavior. Conclusions showing that a system with QoS-Aware WSO Behavior is a QoS-Aware WSO System and further can provide QoS-Aware WSO Service are drawn.
Spectral redemption: clustering sparse networks
Venue: arXiv cs.SI 2013
Year: 2013

Spectral algorithms are classic approaches to clustering and community detection in networks. However, for sparse networks the standard versions of these algorithms are suboptimal, in some cases completely failing to detect communities even when other algorithms such as belief propagation can do so. Here we introduce a new class of spectral algorithms based on a non-backtracking walk on the directed edges of the graph. The spectrum of this operator is much better-behaved than that of the adjacency matrix or other commonly used matrices, maintaining a strong separation between the bulk eigenvalues and the eigenvalues relevant to community structure even in the sparse case. We show that our algorithm is optimal for graphs generated by the stochastic block model, detecting communities all the way down to the theoretical limit. We also show the spectrum of the non-backtracking operator for some real-world networks, illustrating its advantages over traditional spectral clustering.
A Decomposition of the Max-min Fair Curriculum-based Course Timetabling Problem
Venue: arXiv cs.AI 2013
Year: 2013

We propose a decomposition of the max-min fair curriculum-based course timetabling (MMF-CB-CTT) problem. The decomposition models the room assignment subproblem as a generalized lexicographic bottleneck optimization problem (LBOP). We show that the generalized LBOP can be solved efficiently if the corresponding sum optimization problem can be solved efficiently. As a consequence, the room assignment subproblem of the MMF-CB-CTT problem can be solved efficiently. We use this insight to improve a previously proposed heuristic algorithm for the MMF-CB-CTT problem. Our experimental results indicate that using the new decomposition improves the performance of the algorithm on most of the 21 ITC2007 test instances with respect to the quality of the best solution found. Furthermore, we introduce a measure of the quality of a solution to a max-min fair optimization problem. This measure helps to overcome some limitations imposed by the qualitative nature of max-min fairness and aids the statistical evaluation of the performance of randomized algorithms for such problems. We use this measure to show that using the new decomposition the algorithm outperforms the original one on most instances with respect to the average solution quality.
A State-Space Approach for Optimal Traffic Monitoring via Network Flow Sampling
Venue: arXiv cs.SY 2013
Year: 2013

The robustness and integrity of IP networks require efficient tools for traffic monitoring and analysis, which scale well with traffic volume and network size. We address the problem of optimal large-scale flow monitoring of computer networks under resource constraints. We propose a stochastic optimization framework where traffic measurements are done by exploiting the spatial (across network links) and temporal relationship of traffic flows. Specifically, given the network topology, the state-space characterization of network flows and sampling constraints at each monitoring station, we seek an optimal packet sampling strategy that yields the best traffic volume estimation for all flows of the network. The optimal sampling design is the result of a concave minimization problem; then, Kalman filtering is employed to yield a sequence of traffic estimates for each network flow. We evaluate our algorithm using real-world Internet2 data.
Robust Decentralized Stabilization of Markovian Jump Large-Scale Systems: A Neighboring Mode Dependent Control Approach
Venue: arXiv cs.SY 2013
Year: 2013

This paper is concerned with the decentralized stabilization problem for a class of uncertain large-scale systems with Markovian jump parameters. The controllers use local subsystem states and neighboring mode information to generate local control inputs. A sufficient condition involving rank constrained linear matrix inequalities is proposed for the design of such controllers. A numerical example is given to illustrate the developed theory.
Computation of Diet Composition for Patients Suffering from Kidney and Urinary Tract Diseases with the Fuzzy Genetic System
Venue: arXiv cs.AI 2013
Year: 2013

Determination of dietary food consumed a day for patients with diseases in general, greatly affect the health of the body and the healing process, is no exception for people with kidney disease and urinary tract. This paper presents the determination of diet composition in the form of food subtance for people with kidney and urinary tract diseases with a genetic fuzzy approach. This approach combines fuzzy logic and genetic algorithms, which utilizing fuzzy logic fuzzy tools and techniques to model the components of the genetic algorithm and adapting genetic algorithm control parameters, with the aim of improving system performance. The Mamdani fuzzy inference model and fuzzy rules based on population parameters and generation are used to determine the probability of crossover and mutation, and was using In this study, 400 food survey data along with their substances was used as test material. From the data, a varying amount of population is established. Each chromosome has 10 genes in which the value of each gene indicates the index number of foodstuffs in the database. The fuzzy genetic approach produces 10 best food substance and their compositions. The composition of these foods has nutritional value in accordance with the number of calories needed by people with kidney and urinary tract diseases by type of food.
Rate-Compatible Protograph-based LDPC Codes for Inter-Symbol Interference Channels
Venue: arXiv cs.IT 2013
Year: 2013

This letter produces a family of rate-compatible protograph-based LDPC codes approaching the independent and uniformly distributed (i.u.d.) capacity of inter-symbol interference (ISI) channels. This problem is highly nontrivial due to the joint design of structured (protograph-based) LDPC codes and the state structure of ISI channels. We describe a method to design nested high-rate protograph codes by adding variable nodes to the protograph of a lower rate code. We then design a family of rate-compatible protograph codes using the extension method. The resulting protograph codes have iterative decoding thresholds close to the i.u.d. capacity. Our results are supported by numerical simulations.
Downlink Rate Distribution in Heterogeneous Cellular Networks under Generalized Cell Selection
Venue: arXiv cs.IT 2013
Year: 2013

Considering both small-scale fading and long-term shadowing, we characterize the downlink rate distribution at a typical user equipment (UE) in a heterogeneous cellular network (HetNet), where shadowing, following any general distribution, impacts cell selection while fading does not. Prior work either ignores the impact of channel randomness on cell selection or lumps all the sources of randomness into a single variable, with cell selection based on the instantaneous signal strength, which is unrealistic. As an application of the results, we study the impact of shadowing on load balancing in terms of the optimal per-tier selection bias needed for rate maximization.
One-bit Decentralized Detection with a Rao Test for Multisensor Fusion
Venue: arXiv cs.IT 2013
Year: 2013

In this letter we propose the Rao test as a simpler alternative to the generalized likelihood ratio test (GLRT) for multisensor fusion. We consider sensors observing an unknown deterministic parameter with symmetric and unimodal noise. A decision fusion center (DFC) receives quantized sensor observations through error-prone binary symmetric channels and makes a global decision. We analyze the optimal quantizer thresholds and we study the performance of the Rao test in comparison to the GLRT. Also, a theoretical comparison is made and asymptotic performance is derived in a scenario with homogeneous sensors. All the results are confirmed through simulations.
Clique-width and edge contraction
Venue: arXiv cs.DM 2013
Year: 2013

We prove that edge contractions do not preserve the property that a set of graphs has bounded clique-width. This property is preserved by contractions of edges, one end of which is a vertex of degree 2.
Highlighting Entanglement of Cultures via Ranking of Multilingual Wikipedia Articles
Venue: arXiv cs.SI 2013
Year: 2013

How different cultures evaluate a person? Is an important person in one culture is also important in the other culture? We address these questions via ranking of multilingual Wikipedia articles. With three ranking algorithms based on network structure of Wikipedia, we assign ranking to all articles in 9 multilingual editions of Wikipedia and investigate general ranking structure of PageRank, CheiRank and 2DRank. In particular, we focus on articles related to persons, identify top 30 persons for each rank among different editions and analyze distinctions of their distributions over activity fields such as politics, art, science, religion, sport for each edition. We find that local heroes are dominant but also global heroes exist and create an effective network representing entanglement of cultures. The Google matrix analysis of network of cultures shows signs of the Zipf law distribution. This approach allows to examine diversity and shared characteristics of knowledge organization between cultures. The developed computational, data driven approach highlights cultural interconnections in a new perspective.