OpenRaiser/Intern-Atlas huggingface dataset:
Unsupervised Temporal Segmentation of Repetitive Human Actions Based on Kinematic Modeling and Frequency Analysis
Venue: arXiv cs.CV 2015
Year: 2015

In this paper, we propose a method for temporal segmentation of human repetitive actions based on frequency analysis of kinematic parameters, zero-velocity crossing detection, and adaptive k-means clustering. Since the human motion data may be captured with different modalities which have different temporal sampling rate and accuracy (e.g., optical motion capture systems vs. Microsoft Kinect), we first apply a generic full-body kinematic model with an unscented Kalman filter to convert the motion data into a unified representation that is robust to noise. Furthermore, we extract the most representative kinematic parameters via the primary frequency analysis. The sequences are segmented based on zero-velocity crossing of the selected parameters followed by an adaptive k-means clustering to identify the repetition segments. Experimental results demonstrate that for the motion data captured by both the motion capture system and the Microsoft Kinect, our proposed algorithm obtains robust segmentation of repetitive action sequences.
JoKER: Trusted Detection of Kernel Rootkits in Android Devices via JTAG Interface
Venue: arXiv cs.CR 2015
Year: 2015

Smartphones and tablets have become prime targets for malware, due to the valuable private and corporate information they hold. While Anti-Virus (AV) program may successfully detect malicious applications (apps), they remain ineffective against low-level rootkits that evade detection mechanisms by masking their own presence. Furthermore, any detection mechanism run on the same physical device as the monitored OS can be compromised via application, kernel or boot-loader vulnerabilities. Consequentially, trusted detection of kernel rootkits in mobile devices is a challenging task in practice. In this paper we present JoKER - a system which aims at detecting rootkits in the Android kernel by utilizing the hardware's Joint Test Action Group (JTAG) interface for trusted memory forensics. Our framework consists of components that extract areas of a kernel's memory and reconstruct it for further analysis. We present the overall architecture along with its implementation, and demonstrate that the system can successfully detect the presence of stealthy rootkits in the kernel. The results show that although JTAG's main purpose is system testing, it can also be used for malware detection where traditional methods fail.
Joint User Association and Interference Mitigation for D2D-Enabled Heterogeneous Cellular Networks
Venue: arXiv cs.IT 2015
Year: 2015

The heterogeneous cellular networks (HCNs) with device-to-device (D2D) communications have been a promising solution to cost-efficient delivery of high data rates. A key challenge in such D2D-enabled HCNs is how to design an effective association scheme with D2D model selection for load balancing. Moreover, the offloaded users and D2D receivers (RXs) would suffer strong interference from BSs, especially from high-power BSs. Evidently, a good association scheme should integrate with interference mitigation. Thus, we first propose an effective resource partitioning strategy that can mitigate the interference received by offloaded users from high-power BSs and the one received by D2D RXs from BSs. Based on this, we then design a user association scheme for load balancing, which jointly considers user association and D2D model selection to maximize network-wide utility. Considering that the formulated problem is in a nonlinear and mixted-integer form and hard to tackle, we adopt a dual decomposition method to develop an efficient distributed algorithm. Simulation results show that the proposed scheme provides a load balancing gain and a resource partitioning gain.
Steps in creation of educational and research web-portal of nuclear knowledge BelNET
Venue: arXiv cs.CY 2015
Year: 2015

Belarusian State University is currently developing the educational and research web portal of nuclear knowledge BelNET (Belarusian Nuclear Education and Training Portal). In the future, this specialized electronic portal could grow into a national portal of nuclear knowledge. The concept, structure and taxonomy of BelNET portal are developed. The requirements and conditions for its functioning are analyzed. The information model and architecture of the portal, as well as algorithms and methods of software are realized. At present, BelNET software implemented all the basic functions of this portal, including the ability to remotely (via the Internet) open content editing, sorting, filtering, etc. Filling the BelNET by knowledge is at the beginning.
Clustering time series under the Fréchet distance
Venue: arXiv cs.CG 2015
Year: 2015

The Fréchet distance is a popular distance measure for curves. We study the problem of clustering time series under the Fréchet distance. In particular, we give $(1+\varepsilon)$-approximation algorithms for variations of the following problem with parameters $k$ and $\ell$. Given $n$ univariate time series $P$, each of complexity at most $m$, we find $k$ time series, not necessarily from $P$, which we call \emph{cluster centers} and which each have complexity at most $\ell$, such that (a) the maximum distance of an element of $P$ to its nearest cluster center or (b) the sum of these distances is minimized. Our algorithms have running time near-linear in the input size for constant $\varepsilon$, $k$ and $\ell$. To the best of our knowledge, our algorithms are the first clustering algorithms for the Fréchet distance which achieve an approximation factor of $(1+\varepsilon)$ or better. Keywords: time series, longitudinal data, functional data, clustering, Fréchet distance, dynamic time warping, approximation algorithms.
Secure message transmission in the presence of a fully generalised adversary
Venue: arXiv cs.CR 2015
Year: 2015

We investigate the problem of secure message transmission in the presence of a "fully generalised" adversary, who disrupts and listens to separate sets of communication wires. We extend previous results by considering the case when these sets may have arbitrary size, providing necessary and sufficient conditions for both one-way and two-way communication.
Interactive Volumetry Of Liver Ablation Zones
Venue: arXiv cs.CV 2015
Year: 2015

Percutaneous radiofrequency ablation (RFA) is a minimally invasive technique that destroys cancer cells by heat. The heat results from focusing energy in the radiofrequency spectrum through a needle. Amongst others, this can enable the treatment of patients who are not eligible for an open surgery. However, the possibility of recurrent liver cancer due to incomplete ablation of the tumor makes post-interventional monitoring via regular follow-up scans mandatory. These scans have to be carefully inspected for any conspicuousness. Within this study, the RF ablation zones from twelve post-interventional CT acquisitions have been segmented semi-automatically to support the visual inspection. An interactive, graph-based contouring approach, which prefers spherically shaped regions, has been applied. For the quantitative and qualitative analysis of the algorithm's results, manual slice-by-slice segmentations produced by clinical experts have been used as the gold standard (which have also been compared among each other). As evaluation metric for the statistical validation, the Dice Similarity Coefficient (DSC) has been calculated. The results show that the proposed tool provides lesion segmentation with sufficient accuracy much faster than manual segmentation. The visual feedback and interactivity make the proposed tool well suitable for the clinical workflow.
Using Pi-calculus to Model Dynamic Web Services Composition Based on the Authority Model
Venue: arXiv cs.SE 2015
Year: 2015

There are lots of research works on web service, composition, modeling, verification and other problems. Theses research works are done on the basis of formal methods, such as petri-net, pi-calculus, automata theory, and so on. Pi-calculus is a natural vehicle to model mobility aspect in dynamic web services composition (DWSC). However, it has recently been shown that pi-calculus needs to be extended suitably to specify and verify DWSC. In this paper, we considers the authority model for DWSC, extends pi-calculus in order to model dynamic attributes of system, and proposes a automatic method for modeling DWSC based on extended pi-calculus.
Joint Image-Text News Topic Detection and Tracking with And-Or Graph Representation
Venue: arXiv cs.IR 2015
Year: 2015

In this paper, we aim to develop a method for automatically detecting and tracking topics in broadcast news. We present a hierarchical And-Or graph (AOG) to jointly represent the latent structure of both texts and visuals. The AOG embeds a context sensitive grammar that can describe the hierarchical composition of news topics by semantic elements about people involved, related places and what happened, and model contextual relationships between elements in the hierarchy. We detect news topics through a cluster sampling process which groups stories about closely related events. Swendsen-Wang Cuts (SWC), an effective cluster sampling algorithm, is adopted for traversing the solution space and obtaining optimal clustering solutions by maximizing a Bayesian posterior probability. Topics are tracked to deal with the continuously updated news streams. We generate topic trajectories to show how topics emerge, evolve and disappear over time. The experimental results show that our method can explicitly describe the textual and visual data in news videos and produce meaningful topic trajectories. Our method achieves superior performance compared to state-of-the-art methods on both a public dataset Reuters-21578 and a self-collected dataset named UCLA Broadcast News Dataset.
Principled Evaluation of Differentially Private Algorithms using DPBench
Venue: arXiv cs.DB 2015
Year: 2015

Differential privacy has become the dominant standard in the research community for strong privacy protection. There has been a flood of research into query answering algorithms that meet this standard. Algorithms are becoming increasingly complex, and in particular, the performance of many emerging algorithms is {\em data dependent}, meaning the distribution of the noise added to query answers may change depending on the input data. Theoretical analysis typically only considers the worst case, making empirical study of average case performance increasingly important. In this paper we propose a set of evaluation principles which we argue are essential for sound evaluation. Based on these principles we propose DPBench, a novel evaluation framework for standardized evaluation of privacy algorithms. We then apply our benchmark to evaluate algorithms for answering 1- and 2-dimensional range queries. The result is a thorough empirical study of 15 published algorithms on a total of 27 datasets that offers new insights into algorithm behavior---in particular the influence of dataset scale and shape---and a more complete characterization of the state of the art. Our methodology is able to resolve inconsistencies in prior empirical studies and place algorithm performance in context through comparison to simple baselines. Finally, we pose open research questions which we hope will guide future algorithm design.
Group Mobility: Detection, Tracking and Characterization
Venue: arXiv cs.SI 2015
Year: 2015

In the era of mobile computing, understanding human mobility patterns is crucial in order to better design protocols and applications. Many studies focus on different aspects of human mobility such as people's points of interests, routes, traffic, individual mobility patterns, among others. In this work, we propose to look at human mobility through a social perspective, i.e., analyze the impact of social groups in mobility patterns. We use the MIT Reality Mining proximity trace to detect, track and investigate group's evolution throughout time. Our results show that group meetings happen in a periodical fashion and present daily and weekly periodicity. We analyze how groups' dynamics change over day hours and find that group meetings lasting longer are those with less changes in members composition and with members having stronger social bonds with each other. Our findings can be used to propose meeting prediction algorithms, opportunistic routing and information diffusion protocols, taking advantage of those revealed properties.
On the free, safe, and timely execution of component-based systems
Venue: arXiv cs.SE 2015
Year: 2015

Traditional real-time systems are reluctant to integrate dynamic behavior since it challenges predictability and timeliness. Current efforts are starting to address the inclusion of a controllable level of dynamicity in real-time systems to increase the degree of freedom or flexibility in the execution of real-time systems. This is mainly achieved by imposing a set of bounds and limitations to the allowed system structure and operations during a transition. The ultimate goal is to time-bound the duration and result of the system state transitions. One of the main obstacles for run-time transitions is the difficulty in characterizing the different operations and phases of a run-time system transition to guarantee a time bound for each phase. In this paper, an infrastructure is described to ensure the timely execution of state transitions that can be safely incorporated and performed at run-time in a component-based real-time system, preserving its temporal properties. The infrastructure allows to perform different management operations to modify components at run-time ensuring the overall schedulability of the system. The infrastructure is validated by its implementation in a specific component-based framework.
Approximating the Simplicial Depth
Venue: arXiv cs.CG 2015
Year: 2015

Let $P$ be a set of $n$ points in $d$-dimensions. The simplicial depth, $σ_P(q)$ of a point $q$ is the number of $d$-simplices with vertices in $P$ that contain $q$ in their convex hulls. The simplicial depth is a notion of data depth with many applications in robust statistics and computational geometry. Computing the simplicial depth of a point is known to be a challenging problem. The trivial solution requires $O(n^{d+1})$ time whereas it is generally believed that one cannot do better than $O(n^{d-1})$. In this paper, we consider approximation algorithms for computing the simplicial depth of a point. For $d=2$, we present a new data structure that can approximate the simplicial depth in polylogarithmic time, using polylogarithmic query time. In 3D, we can approximate the simplicial depth of a given point in near-linear time, which is clearly optimal up to polylogarithmic factors. For higher dimensions, we consider two approximation algorithms with different worst-case scenarios. By combining these approaches, we compute a $(1+\varepsilon)$-approximation of the simplicial depth in time $\tilde{O}(n^{d/2 + 1})$ ignoring polylogarithmic factor. All of these algorithms are Monte Carlo algorithms. Furthermore, we present a simple strategy to compute the simplicial depth exactly in $O(n^d \log n)$ time, which provides the first improvement over the trivial $O(n^{d+1})$ time algorithm for $d>4$. Finally, we show that computing the simplicial depth exactly is #P-complete and W[1]-hard if the dimension is part of the input.
Increasing the Action Gap: New Operators for Reinforcement Learning
Venue: arXiv cs.AI 2015
Year: 2015

This paper introduces new optimality-preserving operators on Q-functions. We first describe an operator for tabular representations, the consistent Bellman operator, which incorporates a notion of local policy consistency. We show that this local consistency leads to an increase in the action gap at each state; increasing this gap, we argue, mitigates the undesirable effects of approximation and estimation errors on the induced greedy policies. This operator can also be applied to discretized continuous space and time problems, and we provide empirical results evidencing superior performance in this context. Extending the idea of a locally consistent operator, we then derive sufficient conditions for an operator to preserve optimality, leading to a family of operators which includes our consistent Bellman operator. As corollaries we provide a proof of optimality for Baird's advantage learning algorithm and derive other gap-increasing operators with interesting properties. We conclude with an empirical study on 60 Atari 2600 games illustrating the strong potential of these new operators.
An Approach for Design Parameter Optimization of the Triangle Cloud Control System
Venue: arXiv cs.SY 2015
Year: 2015

In this paper, we have proposed the optimization approach of design parameter of generalized cloud control system by hybrid chaos optimization approach. The approach determined the off-line parameters of the cloud control system by the chaos approach and on-line by gradient approach.
From t-closeness to differential privacy and vice versa in data anonymization
Venue: arXiv cs.CR 2015
Year: 2015

k-Anonymity and ε-differential privacy are two mainstream privacy models, the former introduced to anonymize data sets and the latter to limit the knowledge gain that results from including one individual in the data set. Whereas basic k-anonymity only protects against identity disclosure, t-closeness was presented as an extension of k-anonymity that also protects against attribute disclosure. We show here that, if not quite equivalent, t-closeness and ε-differential privacy are strongly related to one another when it comes to anonymizing data sets. Specifically, k-anonymity for the quasi-identifiers combined with ε-differential privacy for the confidential attributes yields stochastic t-closeness (an extension of t-closeness), with t a function of k and ε. Conversely, t-closeness can yield ε- differential privacy when t = exp(ε/2) and the assumptions made by t-closeness about the prior and posterior views of the data hold
On the performance overhead tradeoff of distributed principal component analysis via data partitioning
Venue: arXiv cs.DC 2015
Year: 2015

Principal component analysis (PCA) is not only a fundamental dimension reduction method, but is also a widely used network anomaly detection technique. Traditionally, PCA is performed in a centralized manner, which has poor scalability for large distributed systems, on account of the large network bandwidth cost required to gather the distributed state at a fusion center. Consequently, several recent works have proposed various distributed PCA algorithms aiming to reduce the communication overhead incurred by PCA without losing its inferential power. This paper evaluates the tradeoff between communication cost and solution quality of two distributed PCA algorithms on a real domain name system (DNS) query dataset from a large network. We also apply the distributed PCA algorithm in the area of network anomaly detection and demonstrate that the detection accuracy of both distributed PCA-based methods has little degradation in quality, yet achieves significant savings in communication bandwidth.
Locally Repairable Codes and Matroid Theory
Venue: arXiv cs.IT 2015
Year: 2015

Locally repairable codes (LRCs) are error correcting codes used in distributed data storage. A traditional approach is to look for codes which simultaneously maximize error tolerance and minimize storage space consumption. However, this tends to yield codes for which error correction requires an unrealistic amount of communication between storage nodes. LRCs solve this problem by allowing errors to be corrected locally. This thesis reviews previous results on the subject presented in [1]. These include that every almost affine LRC induces a matroid such that the essential properties of the code are determined by the matroid. Also, the generalized Singleton bound for LRCs can be extended to matroids as well. Then, matroid theory can be used to find classes of matroids that either achieve the bound, meaning they are optimal in a certain sense, or at least come close to the bound. This thesis presents an improvement to the results of [1] in both of these cases. [1] T. Westerbäck, R. Freij, T. Ernvall and C. Hollanti, "On the Combinatorics of Locally Repairable Codes via Matroid Theory", arXiv:1501.00153 [cs.IT], 2014.
Enhancing the security of caesar cipher substitution method using a randomized approach for more secure communication
Venue: arXiv cs.CR 2015
Year: 2015

Caesar cipher is an ancient, elementary method of encrypting plain text message to protect it from adversaries. However, with the advent of powerful computers there is a need for increasing the complexity of such algorithms. In this paper, we contribute in the area of classical cryptography by providing a modified approach and expanded version for Caesar cipher using knowledge of mathematics and computer science. To increase the strength of this classical encryption technique we use the concepts of affine ciphers, transposition ciphers and randomized substitution techniques to create a cipher text which is nearly impossible to decode. We also increase the domain of characters which Caesar cipher Algorithm can encrypt by including all ASCII and extended ASCII characters in addition to alphabets. A complex key generation technique which generates two keys from a single key is used to provide enhanced security. We aim to propose a modified version of Caesar cipher substitution technique which can overcome all the limitations faced by classical Caesar Cipher.
An Empirical Comparison of Neural Architectures for Reinforcement Learning in Partially Observable Environments
Venue: arXiv cs.NE 2015
Year: 2015

This paper explores the performance of fitted neural Q iteration for reinforcement learning in several partially observable environments, using three recurrent neural network architectures: Long Short-Term Memory, Gated Recurrent Unit and MUT1, a recurrent neural architecture evolved from a pool of several thousands candidate architectures. A variant of fitted Q iteration, based on Advantage values instead of Q values, is also explored. The results show that GRU performs significantly better than LSTM and MUT1 for most of the problems considered, requiring less training episodes and less CPU time before learning a very good policy. Advantage learning also tends to produce better results.
Dynamic Graph Queries
Venue: arXiv cs.LO 2015
Year: 2015

Graph databases in many applications---semantic web, transport or biological networks among others---are not only large, but also frequently modified. Evaluating graph queries in this dynamic context is a challenging task, as those queries often combine first-order and navigational features. Motivated by recent results on maintaining dynamic reachability, we study the dynamic evaluation of traditional query languages for graphs in the descriptive complexity framework. Our focus is on maintaining regular path queries, and extensions thereof, by first-order formulas. In particular we are interested in path queries defined by non-regular languages and in extended conjunctive regular path queries (which allow to compare labels of paths based on word relations). Further we study the closely related problems of maintaining distances in graphs and reachability in product graphs. In this preliminary study we obtain upper bounds for those problems in restricted settings, such as undirected and acyclic graphs, or under insertions only, and negative results regarding quantifier-free update formulas. In addition we point out interesting directions for further research.