OpenRaiser/Intern-Atlas huggingface dataset:
Réseaux de radio cognitive : Allocation des ressources radio et accès dynamique au spectre
Venue: arXiv cs.MA 2014
Year: 2014

In the first chapter of this report, we provide an overview on mobile and wireless networks, with special focus on the IEEE 802.22 norm, which is a norm dedicated to cognitive radio (CR). Chapter 2 goes into detail about CR and Chapter 3 is devoted to the presentation of the concept of agents and in particular the concept of multi-agent systems (MAS). Finally, Chapter 4 provides a state of the art on the use of artificial intelligence techniques, particularly MAS for radio resource allocation and dynamic spectrum access in the field of CR.
A Comparative Review Of Internet Protocol Version 4 (IPv4) and Internet Protocol Version 6 (IPv6)
Venue: arXiv cs.NI 2014
Year: 2014

Many computers and devices are becoming more connected to the internet in recent years; the use of the Internet Protocol (IP) has made the connectivity and identification of these devices possible in large scale. In this paper, we will discuss the evolution of Internet Protocol version 4 (IPv4), its features, issues and limitations and how Internet Protocol version 6 (IPv6) tends to solve some of these issues including the differences and transition between these two protocols.
Google Scholar Metrics 2014: a low cost bibliometric tool
Venue: arXiv cs.DL 2014
Year: 2014

We analyse the main features of the third edition of Google Scholar Metrics (GSM), released in June 2014, focusing on its more important changes, strengths, and weaknesses. Additionally, we present some figures that outline the dimensions of this new edition, and we compare them to those of previous editions. Principal among these figures are the number of visualized publications, publication types, languages, and the maximum and minimum h5-index and h5-median values by language, subject area, and subcategory. This new edition is marked by continuity. There is nothing new other than the updating of the time frame (2009-2013) and the removal of some redundant subcategories (from 268 to 261) for English written publications. Google has just updated the data, which means that some of the errors discussed in previous studies still persist. To sum up, GSM is a minimalist information product with few features, closed (it cannot be customized by the user), and simple (navigating it only takes a few clicks). For these reasons, we consider it a 'low cost' bibliometric tool, and propose a list of features it should incorporate in order to stop being labeled as such. Notwithstanding the above, this product presents a stability in its bibliometric indicators that supports its ability to measure and track the impact of scientific publications.
Visualizing criminal networks reconstructed from mobile phone records
Venue: arXiv cs.SI 2014
Year: 2014

In the fight against the racketeering and terrorism, knowledge about the structure and the organization of criminal networks is of fundamental importance for both the investigations and the development of efficient strategies to prevent and restrain crimes. Intelligence agencies exploit information obtained from the analysis of large amounts of heterogeneous data deriving from various informative sources including the records of phone traffic, the social networks, surveillance data, interview data, experiential police data, and police intelligence files, to acquire knowledge about criminal networks and initiate accurate and destabilizing actions. In this context, visual representation techniques coordinate the exploration of the structure of the network together with the metrics of social network analysis. Nevertheless, the utility of visualization tools may become limited when the dimension and the complexity of the system under analysis grow beyond certain terms. In this paper we show how we employ some interactive visualization techniques to represent criminal and terrorist networks reconstructed from phone traffic data, namely foci, fisheye and geo-mapping network layouts. These methods allow the exploration of the network through animated transitions among visualization models and local enlargement techniques in order to improve the comprehension of interesting areas. By combining the features of the various visualization models it is possible to gain substantial enhancements with respect to classic visualization models, often unreadable in those cases of great complexity of the network.
Graph-TSP from Steiner Cycles
Venue: arXiv cs.DS 2014
Year: 2014

We present an approach for the traveling salesman problem with graph metric based on Steiner cycles. A Steiner cycle is a cycle that is required to contain some specified subset of vertices. For a graph $G$, if we can find a spanning tree $T$ and a simple cycle that contains the vertices with odd-degree in $T$, then we show how to combine the classic "double spanning tree" algorithm with Christofides' algorithm to obtain a TSP tour of length at most $\frac{4n}{3}$. We use this approach to show that a graph containing a Hamiltonian path has a TSP tour of length at most $4n/3$. Since a Hamiltonian path is a spanning tree with two leaves, this motivates the question of whether or not a graph containing a spanning tree with few leaves has a short TSP tour. The recent techniques of Mömke and Svensson imply that a graph containing a depth-first-search tree with $k$ leaves has a TSP tour of length $4n/3 + O(k)$. Using our approach, we can show that a $2(k-1)$-vertex connected graph that contains a spanning tree with at most $k$ leaves has a TSP tour of length $4n/3$. We also explore other conditions under which our approach results in a short tour.
Enabling High-Dimensional Hierarchical Uncertainty Quantification by ANOVA and Tensor-Train Decomposition
Venue: arXiv cs.CE 2014
Year: 2014

Hierarchical uncertainty quantification can reduce the computational cost of stochastic circuit simulation by employing spectral methods at different levels. This paper presents an efficient framework to simulate hierarchically some challenging stochastic circuits/systems that include high-dimensional subsystems. Due to the high parameter dimensionality, it is challenging to both extract surrogate models at the low level of the design hierarchy and to handle them in the high-level simulation. In this paper, we develop an efficient ANOVA-based stochastic circuit/MEMS simulator to extract efficiently the surrogate models at the low level. In order to avoid the curse of dimensionality, we employ tensor-train decomposition at the high level to construct the basis functions and Gauss quadrature points. As a demonstration, we verify our algorithm on a stochastic oscillator with four MEMS capacitors and 184 random parameters. This challenging example is simulated efficiently by our simulator at the cost of only 10 minutes in MATLAB on a regular personal computer.
Near-optimal Keypoint Sampling for Fast Pathological Lung Segmentation
Venue: arXiv cs.CV 2014
Year: 2014

Accurate delineation of pathological lungs from computed tomography (CT) images remains mostly unsolved because available methods fail to provide a reliable generic solution due to high variability of abnormality appearance. Local descriptor-based classification methods have shown to work well in annotating pathologies; however, these methods are usually computationally intensive which restricts their widespread use in real-time or near-real-time clinical applications. In this paper, we present a novel approach for fast, accurate, reliable segmentation of pathological lungs from CT scans by combining region-based segmentation method with local descriptor classification that is performed on an optimized sampling grid. Our method works in two stages; during stage one, we adapted the fuzzy connectedness (FC) image segmentation algorithm to perform initial lung parenchyma extraction. In the second stage, texture-based local descriptors are utilized to segment abnormal imaging patterns using a near optimal keypoint analysis by employing centroid of supervoxel as grid points. The quantitative results show that our pathological lung segmentation method is fast, robust, and improves on current standards and has potential to enhance the performance of routine clinical tasks.
Boolean Algebraic Programs as a Methodology for Symbolically Demonstrating Lower and Upper Bounds of Algorithms and Determinism
Venue: arXiv cs.DS 2014
Year: 2014

The lower and upper bound of any given algorithm is one of the most crucial pieces of information needed when evaluating the computational effectiveness for said algorithm. Here a novel method of Boolean Algebraic Programming for symbolic manipulation of Machines, Functions, and Inputs is presented which allows for direct analysis of time complexities and proof of deterministic methodologies. It is demonstrated through the analysis of a particular problem which is proven and solved through the application of Boolean algebraic programming.
Examples for counterintuitive behavior of the new citation-rank indicator P100 for bibliometric evaluations
Venue: arXiv cs.DL 2014
Year: 2014

A new percentile-based rating scale P100 has recently been proposed to describe the citation impact in terms of the distribution of the unique citation values. Here I investigate P100 for 5 example datasets, two simple fictitious models and three larger empirical samples. Counterintuitive behavior is demonstrated in the model datasets, pointing to difficulties when the evolution with time of the indicator is analyzed or when different fields or publication years are compared. It is shown that similar problems can occur for the three larger datasets of empirical citation values. Further, it is observed that the performance evalution result in terms of percentiles can be influenced by selecting different journals for publication of a manuscript.
Multiple chaotic central pattern generators with learning for legged locomotion and malfunction compensation
Venue: arXiv cs.AI 2014
Year: 2014

An originally chaotic system can be controlled into various periodic dynamics. When it is implemented into a legged robot's locomotion control as a central pattern generator (CPG), sophisticated gait patterns arise so that the robot can perform various walking behaviors. However, such a single chaotic CPG controller has difficulties dealing with leg malfunction. Specifically, in the scenarios presented here, its movement permanently deviates from the desired trajectory. To address this problem, we extend the single chaotic CPG to multiple CPGs with learning. The learning mechanism is based on a simulated annealing algorithm. In a normal situation, the CPGs synchronize and their dynamics are identical. With leg malfunction or disability, the CPGs lose synchronization leading to independent dynamics. In this case, the learning mechanism is applied to automatically adjust the remaining legs' oscillation frequencies so that the robot adapts its locomotion to deal with the malfunction. As a consequence, the trajectory produced by the multiple chaotic CPGs resembles the original trajectory far better than the one produced by only a single CPG. The performance of the system is evaluated first in a physical simulation of a quadruped as well as a hexapod robot and finally in a real six-legged walking machine called AMOSII. The experimental results presented here reveal that using multiple CPGs with learning is an effective approach for adaptive locomotion generation where, for instance, different body parts have to perform independent movements for malfunction compensation.
Offline to Online Conversion
Venue: arXiv cs.LG 2014
Year: 2014

We consider the problem of converting offline estimators into an online predictor or estimator with small extra regret. Formally this is the problem of merging a collection of probability measures over strings of length 1,2,3,... into a single probability measure over infinite sequences. We describe various approaches and their pros and cons on various examples. As a side-result we give an elementary non-heuristic purely combinatoric derivation of Turing's famous estimator. Our main technical contribution is to determine the computational complexity of online estimators with good guarantees in general.
Selection from read-only memory with limited workspace
Venue: arXiv cs.DS 2014
Year: 2014

Given an unordered array of $N$ elements drawn from a totally ordered set and an integer $k$ in the range from $1$ to $N$, in the classic selection problem the task is to find the $k$-th smallest element in the array. We study the complexity of this problem in the space-restricted random-access model: The input array is stored on read-only memory, and the algorithm has access to a limited amount of workspace. We prove that the linear-time prune-and-search algorithm---presented in most textbooks on algorithms---can be modified to use $Θ(N)$ bits instead of $Θ(N)$ words of extra space. Prior to our work, the best known algorithm by Frederickson could perform the task with $Θ(N)$ bits of extra space in $O(N \lg^{*} N)$ time. Our result separates the space-restricted random-access model and the multi-pass streaming model, since we can surpass the $Ω(N \lg^{*} N)$ lower bound known for the latter model. We also generalize our algorithm for the case when the size of the workspace is $Θ(S)$ bits, where $\lg^3{N} \leq S \leq N$. The running time of our generalized algorithm is $O(N \lg^{*}(N/S) + N (\lg N) / \lg{} S)$, slightly improving over the $O(N \lg^{*}(N (\lg N)/S) + N (\lg N) / \lg{} S)$ bound of Frederickson's algorithm. To obtain the improvements mentioned above, we developed a new data structure, called the wavelet stack, that we use for repeated pruning. We expect the wavelet stack to be a useful tool in other applications as well.
Authenticating Transactions using Bank-Verified Biometrics
Venue: arXiv cs.CR 2014
Year: 2014

In this paper, we propose a scheme by which banks can collect and verify biometric data eg, fingerprints, directly from their customers and use it to authenticate their transactions made through PoS/ ATM/ online console. We propose building a network of computers called BioNet to allow such transactions to be made online across the world. A BioNet server will be able to do 4 million transactions per second using GPU.
Categorical Proof Theory of Co-Intuitionistic Linear Logic
Venue: arXiv cs.LO 2014
Year: 2014

To provide a categorical semantics for co-intuitionistic logic one has to face the fact, noted by Tristan Crolard, that the definition of co-exponents as adjuncts of coproducts does not work in the category Set, where coproducts are disjoint unions. Following the familiar construction of models of intuitionistic linear logic with exponential"!", we build models of co-intuitionistic logic in symmetric monoidal left-closed categories with additional structure, using a variant of Crolard's term assignment to co-intuitionistic logic in the construction of a free category.
Enhanced Out-of-Band Sensing Algorithm for Cognitive Radio Networks
Venue: arXiv cs.NI 2014
Year: 2014

In cognitive radio networks (CRN), Out-of-Band (OoB) spectrum sensing provides seamless communication. Cognitive radio (CR) users, so called secondary users (SUs), should avoid interference with primary users (PUs), the owner of the licensed band, while trying to access the unused licensed or unlicensed band, for spectrum utilization. When PUs request to access their band, SUs need to vacate the band, thus it is inconvenient to provide seamless communication without OoB sensing. In this paper, we suggest an OoB sensing algorithm to guarantee seamless communication and also minimize the interference of SUs on PUs. Also we obtain analysis-based achievable throughput by considering the OoB sensing duration. To verify our algorithm, we perform simulation and find that the effect due to OoB sensing on the aggregate throughput is insignificant.
Collective Tuning Initiative
Venue: arXiv cs.DC 2014
Year: 2014

Computing systems rarely deliver best possible performance due to ever increasing hardware and software complexity and limitations of the current optimization technology. Additional code and architecture optimizations are often required to improve execution time, size, power consumption, reliability and other important characteristics of computing systems. However, it is often a tedious, repetitive, isolated and time consuming process. In order to automate, simplify and systematize program optimization and architecture design, we are developing open-source modular plugin-based Collective Tuning Infrastructure (CTI, http://cTuning.org) that can distribute optimization process and leverage optimization experience of multiple users. CTI provides a novel fully integrated, collaborative, "one button" approach to improve existing underperfoming computing systems ranging from embedded architectures to high-performance servers based on systematic iterative compilation, statistical collective optimization and machine learning. Our experimental results show that it is possible to reduce execution time (and code size) of some programs from SPEC2006 and EEMBC among others by more than a factor of 2 automatically. It can also reduce development and testing time considerably. Together with the first production quality machine learning enabled interactive research compiler (MILEPOST GCC) this infrastructure opens up many research opportunities to study and develop future realistic self-tuning and self-organizing adaptive intelligent computing systems based on systematic statistical performance evaluation and benchmarking. Finally, using common optimization repository is intended to improve the quality and reproducibility of the research on architecture and code optimization.
Robust Network Routing under Cascading Failures
Venue: arXiv cs.SY 2014
Year: 2014

We propose a dynamical model for cascading failures in single-commodity network flows. In the proposed model, the network state consists of flows and activation status of the links. Network dynamics is determined by a, possibly state-dependent and adversarial, disturbance process that reduces flow capacity on the links, and routing policies at the nodes that have access to the network state, but are oblivious to the presence of disturbance. Under the proposed dynamics, a link becomes irreversibly inactive either due to overload condition on itself or on all of its immediate downstream links. The coupling between link activation and flow dynamics implies that links to become inactive successively are not necessarily adjacent to each other, and hence the pattern of cascading failure under our model is qualitatively different than standard cascade models. The magnitude of a disturbance process is defined as the sum of cumulative capacity reductions across time and links of the network, and the margin of resilience of the network is defined as the infimum over the magnitude of all disturbance processes under which the links at the origin node become inactive. We propose an algorithm to compute an upper bound on the margin of resilience for the setting where the routing policy only has access to information about the local state of the network. For the limiting case when the routing policies update their action as fast as network dynamics, we identify sufficient conditions on network parameters under which the upper bound is tight under an appropriate routing policy. Our analysis relies on making connections between network parameters and monotonicity in network state evolution under proposed dynamics.
Computable Jordan Decomposition of Linear Continuous Functionals on $C[0;1]$
Venue: arXiv cs.LO 2014
Year: 2014

By the Riesz representation theorem using the Riemann-Stieltjes integral, linear continuous functionals on the set of continuous functions from the unit interval into the reals can either be characterized by functions of bounded variation from the unit interval into the reals, or by signed measures on the Borel-subsets. Each of these objects has an (even minimal) Jordan decomposition into non-negative or non-decreasing objects. Using the representation approach to computable analysis, a computable version of the Riesz representation theorem has been proved by Jafarikhah, Lu and Weihrauch. In this article we extend this result. We study the computable relation between three Banach spaces, the space of linear continuous functionals with operator norm, the space of (normalized) functions of bounded variation with total variation norm, and the space of bounded signed Borel measures with variation norm. We introduce natural representations for defining computability. We prove that the canonical linear bijections between these spaces and their inverses are computable. We also prove that Jordan decomposition is computable on each of these spaces.
Finding Motif Sets in Time Series
Venue: arXiv cs.LG 2014
Year: 2014

Time-series motifs are representative subsequences that occur frequently in a time series; a motif set is the set of subsequences deemed to be instances of a given motif. We focus on finding motif sets. Our motivation is to detect motif sets in household electricity-usage profiles, representing repeated patterns of household usage. We propose three algorithms for finding motif sets. Two are greedy algorithms based on pairwise comparison, and the third uses a heuristic measure of set quality to find the motif set directly. We compare these algorithms on simulated datasets and on electricity-usage data. We show that Scan MK, the simplest way of using the best-matching pair to find motif sets, is less accurate on our synthetic data than Set Finder and Cluster MK, although the latter is very sensitive to parameter settings. We qualitatively analyse the outputs for the electricity-usage data and demonstrate that both Scan MK and Set Finder can discover useful motif sets in such data.
Spatiotemporal Stacked Sequential Learning for Pedestrian Detection
Venue: arXiv cs.CV 2014
Year: 2014

Pedestrian classifiers decide which image windows contain a pedestrian. In practice, such classifiers provide a relatively high response at neighbor windows overlapping a pedestrian, while the responses around potential false positives are expected to be lower. An analogous reasoning applies for image sequences. If there is a pedestrian located within a frame, the same pedestrian is expected to appear close to the same location in neighbor frames. Therefore, such a location has chances of receiving high classification scores during several frames, while false positives are expected to be more spurious. In this paper we propose to exploit such correlations for improving the accuracy of base pedestrian classifiers. In particular, we propose to use two-stage classifiers which not only rely on the image descriptors required by the base classifiers but also on the response of such base classifiers in a given spatiotemporal neighborhood. More specifically, we train pedestrian classifiers using a stacked sequential learning (SSL) paradigm. We use a new pedestrian dataset we have acquired from a car to evaluate our proposal at different frame rates. We also test on a well known dataset: Caltech. The obtained results show that our SSL proposal boosts detection accuracy significantly with a minimal impact on the computational cost. Interestingly, SSL improves more the accuracy at the most dangerous situations, i.e. when a pedestrian is close to the camera.