OpenRaiser/Intern-Atlas huggingface dataset:
Coded Path Protection Part 2: Design, Implementation, and Performance
Venue: arXiv cs.NI 2012
Year: 2012

In Part 1 of this paper, we introduced a coding-based proactive network protection scheme, named Coded Path Protection (CPP). In CPP, a backup stream of the primary data is encoded with other data streams, resulting in capacity savings. In addition to being a systematic approach of building valid coding structures, CPP is an optimal and simple capacity placement and coding group formation algorithm. It converts the sharing structure of any solution of a Shared Path Protection (SPP) technique into a coding structure with minimum extra capacity. In this Part 2 of the paper, we describe the implementation of our algorithm using Integer Linear Programming (ILP), its timing and synchronization requirements, and implementation issues in networks. We present simulation results which confirm that CPP provides faster link failure recovery than SPP while it incurs marginal extra capacity beyond that of SPP.
Minimizing the Time of Spam Mail Detection by Relocating Filtering System to the Sender Mail Server
Venue: arXiv cs.IR 2012
Year: 2012

Unsolicited Bulk Emails (also known as Spam) are undesirable emails sent to massive number of users. Spam emails consume the network resources and cause lots of security uncertainties. As we studied, the location where the spam filter operates in is an important parameter to preserve network resources. Although there are many different methods to block spam emails, most of program developers only intend to block spam emails from being delivered to their clients. In this paper, we will introduce a new and efficient approach to prevent spam emails from being transferred. The result shows that if we focus on developing a filtering method for spams emails in the sender mail server rather than the receiver mail server, we can detect the spam emails in the shortest time consequently to avoid wasting network resources.
Route Extrapolation for Source and Destination Camouflage in Wireless Ad Hoc Networks
Venue: arXiv cs.NI 2012
Year: 2012

In wireless ad hoc networks, protecting source and destination nodes location privacy is a challenging task due to malicious traffic analysis and privacy attacks. Existing solutions, such as incorporating fake source destination pairs in the network, provide some privacy of real source and destination nodes against attackers. Moreover, ad hoc networks need stronger privacy protection against powerful global attacker which has knowledge of overall network topology and, that can also eavesdrop and visualize network wide data transmissions. In this paper, we present a novel privacy technique, (EXTROUT) Route Extrapolation to camouflage the real source and destination nodes along an extended path in an ad hoc network. We demonstrate that the privacy level achieved with EXTROUT is higher and more effective against a global attacker, when compared to fake source destination nodes privacy scheme.
Modified Temporal Key Integrity Protocol For Efficient Wireless Network Security
Venue: arXiv cs.CR 2012
Year: 2012

Temporal Key Integrity Protocol (TKIP) is the IEEE TaskGroupi solution for the security loop holes present in the already widely deployed 802.11 hardware. It is a set of algorithms that wrap WEP to give the best possible solution given design constraints such as paucity of the CPU cycles, hardwiring of the WEP encryption algorithm and software upgrade dependent. Thus, TKIP is significantly more difficult and challenging to implement and optimise than WEP. The objective of this research is to examine the cost and benefit of TKIP security mechanisms and optimise its implementation to reduce security overhead for better performance. We propose a modified TKIP (MoTKIP) with improved packet encapsulation and decapsulation procedure that reduces computation and packet overhead in classic TKIP substantially and optimises total wireless network throughput rates.
Document Clustering Evaluation: Divergence from a Random Baseline
Venue: arXiv cs.IR 2012
Year: 2012

Divergence from a random baseline is a technique for the evaluation of document clustering. It ensures cluster quality measures are performing work that prevents ineffective clusterings from giving high scores to clusterings that provide no useful result. These concepts are defined and analysed using intrinsic and extrinsic approaches to the evaluation of document cluster quality. This includes the classical clusters to categories approach and a novel approach that uses ad hoc information retrieval. The divergence from a random baseline approach is able to differentiate ineffective clusterings encountered in the INEX XML Mining track. It also appears to perform a normalisation similar to the Normalised Mutual Information (NMI) measure but it can be applied to any measure of cluster quality. When it is applied to the intrinsic measure of distortion as measured by RMSE, subtraction from a random baseline provides a clear optimum that is not apparent otherwise. This approach can be applied to any clustering evaluation. This paper describes its use in the context of document clustering evaluation.
Weak Alternating Timed Automata
Venue: arXiv cs.LO 2012
Year: 2012

Alternating timed automata on infinite words are considered. The main result is a characterization of acceptance conditions for which the emptiness problem for these automata is decidable. This result implies new decidability results for fragments of timed temporal logics. It is also shown that, unlike for MITL, the characterisation remains the same even if no punctual constraints are allowed.
The Cycle Consistency Matrix Approach to Absorbing Sets in Separable Circulant-Based LDPC Codes
Venue: arXiv cs.IT 2012
Year: 2012

For LDPC codes operating over additive white Gaussian noise channels and decoded using message-passing decoders with limited precision, absorbing sets have been shown to be a key factor in error floor behavior. Focusing on this scenario, this paper introduces the cycle consistency matrix (CCM) as a powerful analytical tool for characterizing and avoiding absorbing sets in separable circulant-based (SCB) LDPC codes. SCB codes include a wide variety of regular LDPC codes such as array-based LDPC codes as well as many common quasi-cyclic codes. As a consequence of its cycle structure, each potential absorbing set in an SCB LDPC code has a CCM, and an absorbing set can be present in an SCB LDPC code only if the associated CCM has a nontrivial null space. CCM-based analysis can determine the multiplicity of an absorbing set in an SCB code and CCM-based constructions avoid certain small absorbing sets completely. While these techniques can be applied to an SCB code of any rate, lower-rate SCB codes can usually avoid small absorbing sets because of their higher variable node degree. This paper focuses attention on the high-rate scenario in which the CCM constructions provide the most benefit. Simulation results demonstrate that under limited-precision decoding the new codes have steeper error-floor slopes and can provide one order of magnitude of improvement in the low FER region.
The Period of the subtraction games
Venue: arXiv cs.GT 2012
Year: 2012

Subtraction games is a class of impartial combinatorial games, They with finite subtraction sets are known to have periodic nim-sequences. So people try to find the regular of the games. But for specific of Sprague-Grundy Theory, it is too difficult to find, they obtained some conclusions just by simple observing. This paper used PTFN algorithm to analyze the period of the Subtraction games. It is more suitable than Sprague-Grundy Theory, and this paper obtained four conclusions by PTFN algorithm . This algorithm provide a new direction to study the subtraction games' period.
Checking Robustness against TSO
Venue: arXiv cs.PL 2012
Year: 2012

We present algorithms for checking and enforcing robustness of concurrent programs against the Total Store Ordering (TSO) memory model. A program is robust if all its TSO computations correspond to computations under the Sequential Consistency (SC) semantics. We provide a complete characterization of non-robustness in terms of so-called attacks: a restricted form of (harmful) out-of-program-order executions. Then, we show that detecting attacks can be parallelized, and can be solved using state reachability queries under SC semantics in a suitably instrumented program obtained by a linear size source-to-source translation. Importantly, the construction is valid for an arbitrary number of addresses and an arbitrary number of parallel threads, and it is independent from the data domain and from the size of store buffers in the TSO semantics. In particular, when the data domain is finite and the number of addresses is fixed, we obtain decidability and complexity results for robustness, even for an arbitrary number of threads. As a second contribution, we provide an algorithm for computing an optimal set of fences that enforce robustness. We consider two criteria of optimality: minimization of program size and maximization of its performance. The algorithms we define are implemented, and we successfully applied them to analyzing and correcting several concurrent algorithms.
The Boost.Build System
Venue: arXiv cs.SE 2012
Year: 2012

Boost.Build is a new build system with unique approach to portability. This paper discusses the underlying requirements, the key design decisions, and the lessons learned during several years of development. We also review other contemporary build systems, and why they fail to meet the same requirements.
First Clinical Experience in Urologic Surgery with a Novel Robotic Lightweight Laparoscope Holder
Venue: arXiv cs.RO 2012
Year: 2012

Purpose: To report the feasibility and the safety of a surgeon-controlled robotic endoscope holder in laparoscopic surgery. Materials and methods: From March 2010 to September 2010, 20 patients were enrolled prospectively to undergo a laparoscopic surgery using an innovative robotic endoscope holder. Two surgeons performed 6 adrenalectomies, 4 sacrocolpopexies, 5 pyeloplasties, 4 radical prostatectomies and 1 radical nephrectomy. Demographic data, overall set-up time, operative time, number of assistants needed were reviewed. Surgeon's satisfaction regarding the ergonomics was assessed using a ten point scale. Postoperative clinical outcomes were reviewed at day 1 and 1 month postoperatively. Results: The per-protocol analysis was performed on 17 patients for whom the robot was effectively used for surgery. Median age was 63 years, 10 patients were female (59%). Median BMI was 26.8. Surgical procedures were completed with the robot in 12 cases (71 %). Median number of surgical assistant was 0. Overall set-up time with the robot was 19 min, operative time was 130 min) during which the robot was used 71% of the time. Mean hospital stay was 6.94 days $\pm$ 2.3. Median score regarding the easiness of use was 7. Median pain level was 1.5/10 at day 1 and 0 at 1 month postoperatively. Open conversion was needed in 1 case (6 %) and 4 minor complications occurred in 2 patients (12%). Conclusion: This use of this novel robotic laparoscope holder is safe, feasible and it provides a good comfort to the surgeon.
A two-stage denoising filter: the preprocessed Yaroslavsky filter
Venue: arXiv cs.CV 2012
Year: 2012

This paper describes a simple image noise removal method which combines a preprocessing step with the Yaroslavsky filter for strong numerical, visual, and theoretical performance on a broad class of images. The framework developed is a two-stage approach. In the first stage the image is filtered with a classical denoising method (e.g., wavelet or curvelet thresholding). In the second stage a modification of the Yaroslavsky filter is performed on the original noisy image, where the weights of the filters are governed by pixel similarities in the denoised image from the first stage. Similar prefiltering ideas have proved effective previously in the literature, and this paper provides theoretical guarantees and important insight into why prefiltering can be effective. Empirically, this simple approach achieves very good performance for cartoon images, and can be computed much more quickly than current patch-based denoising algorithms.
Efficient Computation of the Permanent of Block Factorizable Matrices
Venue: arXiv cs.DM 2012
Year: 2012

We present an efficient algorithm for computing the permanent for matrices of size N that can written as a product of L block diagonal matrices with blocks of size at most 2. For fixed L, the time and space resources scale linearly in N, with a prefactor that scales exponentially in L. This class of matrices contains banded matrices with banded inverse. We show that such a factorization into a product of block diagonal matrices gives rise to a circuit acting on a Hilbert space with a tensor product structure and that the permanent is equal to the transition amplitude of this circuit and a product basis state. In this correspondence, a block diagonal matrix gives rise to one layer of the circuit, where each block to a gate acting either on a single tensor component or on two adjacent tensor components. This observation allows us to adopt matrix product states, a computational method from condensed matter physics and quantum information theory used to simulate quantum systems, to evaluate the transition amplitude.
An Improved Bound for the Nystrom Method for Large Eigengap
Venue: arXiv cs.LG 2012
Year: 2012

We develop an improved bound for the approximation error of the Nyström method under the assumption that there is a large eigengap in the spectrum of kernel matrix. This is based on the empirical observation that the eigengap has a significant impact on the approximation error of the Nyström method. Our approach is based on the concentration inequality of integral operator and the theory of matrix perturbation. Our analysis shows that when there is a large eigengap, we can improve the approximation error of the Nyström method from $O(N/m^{1/4})$ to $O(N/m^{1/2})$ when measured in Frobenius norm, where $N$ is the size of the kernel matrix, and $m$ is the number of sampled columns.
Autoregressive short-term prediction of turning points using support vector regression
Venue: arXiv cs.LG 2012
Year: 2012

This work is concerned with autoregressive prediction of turning points in financial price sequences. Such turning points are critical local extrema points along a series, which mark the start of new swings. Predicting the future time of such turning points or even their early or late identification slightly before or after the fact has useful applications in economics and finance. Building on recently proposed neural network model for turning point prediction, we propose and study a new autoregressive model for predicting turning points of small swings. Our method relies on a known turning point indicator, a Fourier enriched representation of price histories, and support vector regression. We empirically examine the performance of the proposed method over a long history of the Dow Jones Industrial average. Our study shows that the proposed method is superior to the previous neural network model, in terms of trading performance of a simple trading application and also exhibits a quantifiable advantage over the buy-and-hold benchmark.
Goldbach Triples and Key Distribution
Venue: arXiv cs.CR 2012
Year: 2012

This paper investigates the use of the number of Goldbach triples, or the number of three prime partitions of an odd number, for use in the generation and distribution of cryptographic keys. In addition to presenting randomness properties of these triples, which turn out to be similar to that of prime partitions of even numbers, we explore the question of restricted partition sets. We propose a protocol for key distribution that is based on these numbers. Two of the three partitions of the randomly chosen number serve as cover to send the third number to the two parties that wish to communicate with each other. This third number can serve as session key and the original number of which it is a partition can be used for audit purposes.
Automatic ECG Beat Arrhythmia Detection
Venue: arXiv cs.NE 2012
Year: 2012

Background: In recent years automated data analysis techniques have drawn great attention and are used in almost every field of research including biomedical. Artificial Neural Networks (ANNs) are one of the Computer- Aided- Diagnosis tools which are used extensively by advances in computer hardware technology. The application of these techniques for disease diagnosis has made great progress and is widely used by physicians. An Electrocardiogram carries vital information about heart activity and physicians use this signal for cardiac disease diagnosis which was the great motivation towards our study. Methods: In this study we are using Probabilistic Neural Networks (PNN) as an automatic technique for ECG signal analysis along with a Genetic Algorithm (GA). As every real signal recorded by the equipment can have different artifacts, we need to do some preprocessing steps before feeding it to the ANN. Wavelet transform is used for extracting the morphological parameters and median filter for data reduction of the ECG signal. The subset of morphological parameters are chosen and optimized using GA. We had two approaches in our investigation, the first one uses the whole signal with 289 normalized and de-noised data points as input to the ANN. In the second approach after applying all the preprocessing steps the signal is reduced to 29 data points and also their important parameters extracted to form the ANN input with 35 data points. Results: The outcome of the two approaches for 8 types of arrhythmia shows that the second approach is superior than the first one with an average accuracy of %99.42.
CAWS - Security Algorithms for Wireless Sensor Networks: A Cellular Automata Based Approach
Venue: arXiv cs.CR 2012
Year: 2012

Security in the Wireless Sensor Networks (WSN) is a very challenging task because of their dissimilarities with the conventional wireless networks. The related works so far have been done have tried to solve the problem keeping in the mind the constraints of WSNs. In this paper we have proposed a set of cellular automata based security algorithms (CAWS) which consists of CAKD, a Cellular Automata (CA) based key management algorithm and CASC, a CA based secure data communication algorithm, which require very small amount of memory as well as simple computation.
Learning Prioritized Control of Motor Primitives
Venue: arXiv cs.RO 2012
Year: 2012

Many tasks in robotics can be decomposed into sub-tasks that are performed simultaneously. In many cases, these sub-tasks cannot all be achieved jointly and a prioritization of such sub-tasks is required to resolve this issue. In this paper, we discuss a novel learning approach that allows to learn a prioritized control law built on a set of sub-tasks represented by motor primitives. The primitives are executed simultaneously but have different priorities. Primitives of higher priority can override the commands of the conflicting lower priority ones. The dominance structure of these primitives has a significant impact on the performance of the prioritized control law. We evaluate the proposed approach with a ball bouncing task on a Barrett WAM.
Reachability of Communicating Timed Processes
Venue: arXiv cs.LO 2012
Year: 2012

We study the reachability problem for communicating timed processes, both in discrete and dense time. Our model comprises automata with local timing constraints communicating over unbounded FIFO channels. Each automaton can only access its set of local clocks; all clocks evolve at the same rate. Our main contribution is a complete characterization of decidable and undecidable communication topologies, for both discrete and dense time. We also obtain complexity results, by showing that communicating timed processes are at least as hard as Petri nets; in the discrete time, we also show equivalence with Petri nets. Our results follow from mutual topology-preserving reductions between timed automata and (untimed) counter automata.
In-place associative integer sorting
Venue: arXiv cs.DS 2012
Year: 2012

A novel integer value-sorting technique is proposed replacing bucket sort, distribution counting sort and address calculation sort family of algorithms. It requires only constant amount of additional memory. The technique is inspired from one of the ordinal theories of "serial order in behavior" and explained by the analogy with the three main stages in the formation and retrieval of memory in cognitive neuroscience namely (i) practicing, (ii) storing and (iii) retrieval. Although not suitable for integer rank-sorting where the problem is to put an array of elements into ascending or descending order by their numeric keys, each of which is an integer, the technique seems to be efficient and applicable to rank-sorting, as well as other problems such as hashing, searching, element distinction, succinct data structures, gaining space, etc.
Social Cheesecake: An UX-driven designed interface for managing contacts
Venue: arXiv cs.HC 2012
Year: 2012

Social network management interfaces should consider separation of contexts and tie strength. This paper shows the design process upon building the Social Cheesecake, an interface that addresses both issues. Paper and screen prototyping were used in the design process. Paper prototype interactions helped to explore the metaphors in the domain, while screen prototype consolidated the model. The prototype was finally built using HTML5 and Javascript.
Sensor Webs for Environmental research
Venue: arXiv cs.SY 2012
Year: 2012

The ongoing massive global environmental changes and the past learnings have highlighted the urgency and importance of further detailed understanding of the earth system and implementation of social ecological sustainability measures in a much more effective and transparent manner. This short communication discuss the potential of sensor webs in addressing those research challenges, highlighting it in the context of air pollution issues.
Channel Assignment in Dense MC-MR Wireless Networks: Scaling Laws and Algorithms
Venue: arXiv cs.NI 2012
Year: 2012

We investigate optimal channel assignment algorithms that maximize per node throughput in dense multichannel multi-radio (MC-MR) wireless networks. Specifically, we consider an MC-MR network where all nodes are within the transmission range of each other. This situation is encountered in many real-life settings such as students in a lecture hall, delegates attending a conference, or soldiers in a battlefield. In this scenario, we show that intelligent assignment of the available channels results in a significantly higher per node throughput. We first propose a class of channel assignment algorithms, parameterized by T (the number of transceivers per node), that can achieve $Θ(1/N^{1/T})$ per node throughput using $Θ(TN^{1-1/T})$ channels. In view of practical constraints on $T$, we then propose another algorithm that can achieve $Θ(1/(\log_2 N)^2)$ per node throughput using only two transceivers per node. Finally, we identify a fundamental relationship between the achievable per node throughput, the total number of channels used, and the network size under any strategy. Using analysis and simulations, we show that our algorithms achieve close to optimal performance at different operating points on this curve. Our work has several interesting implications on the optimal network design for dense MC-MR wireless networks.
Minimum Weight Euclidean t-spanner is NP-Hard
Venue: arXiv cs.CG 2012
Year: 2012

Given a set P of points in the plane, an Euclidean t-spanner for P is a geometric graph that preserves the Euclidean distances between every pair of points in P up to a constant factor t. The weight of a geometric graph refers to the total length of its edges. In this paper we show that the problem of deciding whether there exists an Euclidean t-spanner, for a given set of points in the plane, of weight at most w is NP-hard for every real constant t > 1, both whether planarity of the t-spanner is required or not.