OpenRaiser/Intern-Atlas huggingface dataset:
Power laws in citation distributions: Evidence from Scopus
Venue: arXiv cs.DL 2014
Year: 2014

Modeling distributions of citations to scientific papers is crucial for understanding how science develops. However, there is a considerable empirical controversy on which statistical model fits the citation distributions best. This paper is concerned with rigorous empirical detection of power-law behaviour in the distribution of citations received by the most highly cited scientific papers. We have used a large, novel data set on citations to scientific papers published between 1998 and 2002 drawn from Scopus. The power-law model is compared with a number of alternative models using a likelihood ratio test. We have found that the power-law hypothesis is rejected for around half of the Scopus fields of science. For these fields of science, the Yule, power-law with exponential cut-off and log-normal distributions seem to fit the data better than the pure power-law model. On the other hand, when the power-law hypothesis is not rejected, it is usually empirically indistinguishable from most of the alternative models. The pure power-law model seems to be the best model only for the most highly cited papers in "Physics and Astronomy". Overall, our results seem to support theories implying that the most highly cited scientific papers follow the Yule, power-law with exponential cut-off or log-normal distribution. Our findings suggest also that power laws in citation distributions, when present, account only for a very small fraction of the published papers (less than 1% for most of science fields) and that the power-law scaling parameter (exponent) is substantially higher (from around 3.2 to around 4.7) than found in the older literature.
Context-driven Software Project Estimation
Venue: arXiv cs.SE 2014
Year: 2014

Using quantitative data from past projects for software project estimation requires context knowledge that characterizes its origin and indicates its applicability for future use. This article sketches the SPRINT I technique for project planning and controlling. The underlying prediction mechanism is based on the identification of similar past projects and the building of so-called clusters with typical data curves. The article focuses on how to characterize these clusters with context knowledge and how to use context information from actual projects for prediction. The SPRINT approach is tool-supported and first evaluations have been conducted.
Design of a Hybrid Robot Control System using Memristor-Model and Ant-Inspired Based Information Transfer Protocols
Venue: arXiv cs.RO 2014
Year: 2014

It is not always possible for a robot to process all the information from its sensors in a timely manner and thus quick and yet valid approximations of the robot's situation are needed. Here we design hybrid control for a robot within this limit using algorithms inspired by ant worker placement behaviour and based on memristor-based non-linearity.
Friendship Prediction in Composite Social Networks
Venue: arXiv cs.SI 2014
Year: 2014

Friendship prediction is an important task in social network analysis (SNA). It can help users identify friends and improve their level of activity. Most previous approaches predict users' friendship based on their historical records, such as their existing friendship, social interactions, etc. However, in reality, most users have limited friends in a single network, and the data can be very sparse. The sparsity problem causes existing methods to overfit the rare observations and suffer from serious performance degradation. This is particularly true when a new social network just starts to form. We observe that many of today's social networks are composite in nature, where people are often engaged in multiple networks. In addition, users' friendships are always correlated, for example, they are both friends on Facebook and Google+. Thus, by considering those overlapping users as the bridge, the friendship knowledge in other networks can help predict their friendships in the current network. This can be achieved by exploiting the knowledge in different networks in a collective manner. However, as each individual network has its own properties that can be incompatible and inconsistent with other networks, the naive merging of all networks into a single one may not work well. The proposed solution is to extract the common behaviors between different networks via a hierarchical Bayesian model. It captures the common knowledge across networks, while avoiding negative impacts due to network differences. Empirical studies demonstrate that the proposed approach improves the mean average precision of friendship prediction over state-of-the-art baselines on nine real-world social networking datasets significantly.
Boolean Logic Gates From A Single Memristor Via Low-Level Sequential Logic
Venue: arXiv cs.ET 2014
Year: 2014

By using the memristor's memory to both store a bit and perform an operation with a second input bit, simple Boolean logic gates have been built with a single memristor. The operation makes use of the interaction of current spikes (occasionally called current transients) found in both memristors and other devices. The sequential time-based logic methodology allows two logical input bits to be used on a one-port by sending the bits separated in time. The resulting logic gate is faster than one relying on memristor's state switching, low power and requires only one memristor. We experimentally demonstrate working OR and XOR gates made with a single flexible Titanium dioxide sol-gel memristor.
Conservative collision prediction and avoidance for stochastic trajectories in continuous time and space
Venue: arXiv cs.AI 2014
Year: 2014

Existing work in multi-agent collision prediction and avoidance typically assumes discrete-time trajectories with Gaussian uncertainty or that are completely deterministic. We propose an approach that allows detection of collisions even between continuous, stochastic trajectories with the only restriction that means and variances can be computed. To this end, we employ probabilistic bounds to derive criterion functions whose negative sign provably is indicative of probable collisions. For criterion functions that are Lipschitz, an algorithm is provided to rapidly find negative values or prove their absence. We propose an iterative policy-search approach that avoids prior discretisations and yields collision-free trajectories with adjustably high certainty. We test our method with both fixed-priority and auction-based protocols for coordinating the iterative planning process. Results are provided in collision-avoidance simulations of feedback controlled plants.
Application of Pseudo-Transient Continuation Method in Dynamic Stability Analysis
Venue: arXiv cs.SY 2014
Year: 2014

In this paper, pseudo-transient continuation method has been modified and implemented in power system long-term stability analysis. This method is a middle ground between integration and steady state calculation, thus is a good compromise between accuracy and efficiency. Pseudo-transient continuation method can be applied in the long-term stability model directly to accelerate simulation speed and can also be implemented in the QSS model to overcome numerical difficulties. Numerical examples show that pseudo-transient continuation method can provide correct approximations for the long-term stability model in terms of trajectories and stability assessment.
Information Theory of Matrix Completion
Venue: arXiv cs.IT 2014
Year: 2014

Matrix completion is a fundamental problem that comes up in a variety of applications like the Netflix problem, collaborative filtering, computer vision, and crowdsourcing. The goal of the problem is to recover a k-by-n unknown matrix from a subset of its noiseless (or noisy) entries. We define an information-theoretic notion of completion capacity C that quantifies the maximum number of entries that one observation of an entry can resolve. This number provides the minimum number m of entries required for reliable reconstruction: m=kn/C. Translating the problem into a distributed joint source-channel coding problem with encoder restriction, we characterize the completion capacity for a wide class of stochastic models of the unknown matrix and the observation process. Our achievability proof is inspired by that of the Slepian-Wolf theorem. For an arbitrary stochastic matrix, we derive an upper bound on the completion capacity.
Downlink and Uplink Energy Minimization Through User Association and Beamforming in Cloud RAN
Venue: arXiv cs.IT 2014
Year: 2014

The cloud radio access network (C-RAN) concept, in which densely deployed access points (APs) are empowered by cloud computing to cooperatively support mobile users (MUs), to improve mobile data rates, has been recently proposed. However, the high density of active ("on") APs results in severe interference and also inefficient energy consumption. Moreover, the growing popularity of highly interactive applications with stringent uplink (UL) requirements, e.g. network gaming and real-time broadcasting by wireless users, means that the UL transmission is becoming more crucial and requires special attention. Therefore in this paper, we propose a joint downlink (DL) and UL MU-AP association and beamforming design to coordinate interference in the C-RAN for energy minimization, a problem which is shown to be NP hard. Due to the new consideration of UL transmission, it is shown that the two state-of-the-art approaches for finding computationally efficient solutions of joint MU-AP association and beamforming considering only the DL, i.e., group-sparse optimization and relaxed-integer programming, cannot be modified in a straightforward way to solve our problem. Leveraging on the celebrated UL-DL duality result, we show that by establishing a virtual DL transmission for the original UL transmission, the joint DL and UL optimization problem can be converted to an equivalent DL problem in C-RAN with two inter-related subproblems for the original and virtual DL transmissions, respectively. Based on this transformation, two efficient algorithms for joint DL and UL MU-AP association and beamforming design are proposed, whose performances are evaluated and compared with other benchmarking schemes through extensive simulations.
Precoding by Priority: A UEP Scheme for RaptorQ Codes
Venue: arXiv cs.IT 2014
Year: 2014

Raptor codes are the first class of fountain codes with linear time encoding and decoding. These codes are recommended in standards such as Third Generation Partnership Project (3GPP) and digital video broadcasting. RaptorQ codes are an extension to Raptor codes, having better coding efficiency and flexibility. Standard Raptor and RaptorQ codes are systematic with equal error protection of the data. However, in many applications such as MPEG transmission, there is a need for Unequal Error Protection (UEP): namely, some data symbols require higher error correction capabilities compared to others. We propose an approach that we call Priority Based Precode Ratio (PBPR) to achieve UEP for systematic RaptorQ and Raptor codes. Our UEP assumes that all symbols in a source block belong to the same importance class. The UEP is achieved by changing the number of precode symbols depending on the priority of the information symbols in the source block. PBPR provides UEP with the same number of decoding overhead symbols for source blocks with different importance classes. We demonstrate consistent improvements in the error correction capability of higher importance class compared to the lower importance class across the entire range of channel erasure probabilities. We also show that PBPR does not result in a significant increase in decoding and encoding times compared to the standard implementation.
New Method for Accurate Parameter Estimation of Induction Motors Based on Artificial Bee Colony Algorithm
Venue: arXiv cs.SY 2014
Year: 2014

This paper proposes an effective method for estimating the parameters of double-cage induction motors by using Artificial Bee Colony (ABC) algorithm. For this purpose the unknown parameters in the electrical model of asynchronous machine are calculated such that the sum of the square of differences between full load torques, starting torques, maximum torques, starting currents, full load currents, and nominal power factors obtained from model and provided by manufacturer is minimized. In order to confirm the efficiency of the proposed method the results are also compared with those achieved by using GA, PSO, and PAMP. The simulations show that in the problem under consideration ABC converges considerably faster than other algorithms and the results are as accurate as PAMP.
Symbiosis of Search and Heuristics for Random 3-SAT
Venue: arXiv cs.DS 2014
Year: 2014

When combined properly, search techniques can reveal the full potential of sophisticated branching heuristics. We demonstrate this observation on the well-known class of random 3-SAT formulae. First, a new branching heuristic is presented, which generalizes existing work on this class. Much smaller search trees can be constructed by using this heuristic. Second, we introduce a variant of discrepancy search, called ALDS. Theoretical and practical evidence support that ALDS traverses the search tree in a near-optimal order when combined with the new heuristic. Both techniques, search and heuristic, have been implemented in the look-ahead solver march. The SAT 2009 competition results show that march is by far the strongest complete solver on random k-SAT formulae.
Concurrent Cube-and-Conquer
Venue: arXiv cs.DS 2014
Year: 2014

Recent work introduced the cube-and-conquer technique to solve hard SAT instances. It partitions the search space into cubes using a lookahead solver. Each cube is tackled by a conflict-driven clause learning (CDCL) solver. Crucial for strong performance is the cutoff heuristic that decides when to switch from lookahead to CDCL. Yet, this offline heuristic is far from ideal. In this paper, we present a novel hybrid solver that applies the cube and conquer steps simultaneously. A lookahead and a CDCL solver work together on each cube, while communication is restricted to synchronization. Our concurrent cube-and-conquer solver can solve many instances faster than pure lookahead, pure CDCL and offline cube-and-conquer, and can abort early in favor of a pure CDCL search if an instance is not suitable for cube-and-conquer techniques.
The Power of Duples (in Self-Assembly): It's Not So Hip To Be Square
Venue: arXiv cs.ET 2014
Year: 2014

In this paper we define the Dupled abstract Tile Assembly Model (DaTAM), which is a slight extension to the abstract Tile Assembly Model (aTAM) that allows for not only the standard square tiles, but also "duple" tiles which are rectangles pre-formed by the joining of two square tiles. We show that the addition of duples allows for powerful behaviors of self-assembling systems at temperature 1, meaning systems which exclude the requirement of cooperative binding by tiles (i.e., the requirement that a tile must be able to bind to at least 2 tiles in an existing assembly if it is to attach). Cooperative binding is conjectured to be required in the standard aTAM for Turing universal computation and the efficient self-assembly of shapes, but we show that in the DaTAM these behaviors can in fact be exhibited at temperature 1. We then show that the DaTAM doesn't provide asymptotic improvements over the aTAM in its ability to efficiently build thin rectangles. Finally, we present a series of results which prove that the temperature-2 aTAM and temperature-1 DaTAM have mutually exclusive powers. That is, each is able to self-assemble shapes that the other can't, and each has systems which cannot be simulated by the other. Beyond being of purely theoretical interest, these results have practical motivation as duples have already proven to be useful in laboratory implementations of DNA-based tiles.
Power Allocation in Compressed Sensing of Non-uniformly Sparse Signals
Venue: arXiv cs.IT 2014
Year: 2014

This paper studies the problem of power allocation in compressed sensing when different components in the unknown sparse signal have different probability to be non-zero. Given the prior information of the non-uniform sparsity and the total power budget, we are interested in how to optimally allocate the power across the columns of a Gaussian random measurement matrix so that the mean squared reconstruction error is minimized. Based on the state evolution technique originated from the work by Donoho, Maleki, and Montanari, we revise the so called approximate message passing (AMP) algorithm for the reconstruction and quantify the MSE performance in the asymptotic regime. Then the closed form of the optimal power allocation is obtained. The results show that in the presence of measurement noise, uniform power allocation, which results in the commonly used Gaussian random matrix with i.i.d. entries, is not optimal for non-uniformly sparse signals. Empirical results are presented to demonstrate the performance gain.
IEEE 802.11ah: The Wi-Fi Approach for M2M Communications
Venue: arXiv cs.NI 2014
Year: 2014

M2M communications are projected to be one of the fastest growing technology segments of the IT sector in the next years. Sensor and actuator networks connect communication machines and devices so that they automatically transmit information, serving the growing demand for environmental data acquisition. IEEE 802.11ah Task Group addresses the creation of a new standard for giving response to the particular requirements of this type of networks: large number of power-constrained stations, long transmission range, small and infrequent data messages, low data-rates and non-critical delay. This article explores the key features of this new standard under development, especially those related to the reduction of energy consumption in the MAC Layer. In this direction, a performance assessment of IEEE 802.11ah in four typical M2M scenarios has been performed.
A Fast Compressive Sensing Based Digital Image Encryption Technique using Structurally Random Matrices and Arnold Transform
Venue: arXiv cs.CR 2014
Year: 2014

A new digital image encryption method based on fast compressed sensing approach using structurally random matrices and Arnold transform is proposed. Considering the natural images to be compressed in any domain, the fast compressed sensing based approach saves computational time, increases the quality of the image and reduces the dimension of the digital image by choosing even 25 % of the measurements. First, dimension reduction is utilized to compress the digital image with scrambling effect. Second, Arnold transformation is used to give the reduced digital image into more complex form. Then, the complex image is again encrypted by double random phase encoding process embedded with a host image; two random keys with fractional Fourier transform are been used as a secret keys. At the receiver, the decryption process is recovered by using TwIST algorithm. Experimental results including peak-to-peak signal-to-noise ratio between the original and reconstructed image are shown to analyze the validity of this technique and demonstrated our proposed method to be secure, fast, complex and robust.
A normative account of defeasible and probabilistic inference
Venue: arXiv cs.LO 2014
Year: 2014

In this paper, we provide more evidence for the contention that logical consequence should be understood in normative terms. Hartry Field and John MacFarlane covered the classical case. We extend their work, examining what it means for an agent to be obliged to infer a conclusion when faced with uncertain information or reasoning within a non-monotonic, defeasible, logical framework (which allows e. g. for inference to be drawn from premises considered true unless evidence to the contrary is presented).
Bitcoin: a Money-like Informational Commodity
Venue: arXiv cs.CY 2014
Year: 2014

The question "what is Bitcoin" allows for many answers depending on the objectives aimed at when providing such answers. The question addressed in this paper is to determine a top-level classification, or type, for Bitcoin. We will classify Bitcoin as a system of type money-like informational commodity (MLIC).
Shortest Paths in Intersection Graphs of Unit Disks
Venue: arXiv cs.CG 2014
Year: 2014

Let $G$ be a unit disk graph in the plane defined by $n$ disks whose positions are known. For the case when $G$ is unweighted, we give a simple algorithm to compute a shortest path tree from a given source in $O(n\log n)$ time. For the case when $G$ is weighted, we show that a shortest path tree from a given source can be computed in $O(n^{1+\varepsilon})$ time, improving the previous best time bound of $O(n^{4/3+\varepsilon})$.
Survey on Sparse Coded Features for Content Based Face Image Retrieval
Venue: arXiv cs.IR 2014
Year: 2014

Content based image retrieval, a technique which uses visual contents of image to search images from large scale image databases according to users' interests. This paper provides a comprehensive survey on recent technology used in the area of content based face image retrieval. Nowadays digital devices and photo sharing sites are getting more popularity, large human face photos are available in database. Multiple types of facial features are used to represent discriminality on large scale human facial image database. Searching and mining of facial images are challenging problems and important research issues. Sparse representation on features provides significant improvement in indexing related images to query image.
Enhanced Secure Algorithm for Fingerprint Recognition
Venue: arXiv cs.CV 2014
Year: 2014

Fingerprint recognition requires a minimal effort from the user, does not capture other information than strictly necessary for the recognition process, and provides relatively good performance. A critical step in fingerprint identification system is thinning of the input fingerprint image. The performance of a minutiae extraction algorithm relies heavily on the quality of the thinning algorithm. So, a fast fingerprint thinning algorithm is proposed. The algorithm works directly on the gray-scale image as binarization of fingerprint causes many spurious minutiae and also removes many important features. The performance of the thinning algorithm is evaluated and experimental results show that the proposed thinning algorithm is both fast and accurate. A new minutiae-based fingerprint matching technique is proposed. The main idea is that each fingerprint is represented by a minutiae table of just two columns in the database. The number of different minutiae types (terminations and bifurcations) found in each track of a certain width around the core point of the fingerprint is recorded in this table. Each row in the table represents a certain track, in the first column, the number of terminations in each track is recorded, in the second column, the number of bifurcations in each track is recorded. The algorithm is rotation and translation invariant, and needs less storage size. Experimental results show that recognition accuracy is 98%, with Equal Error Rate (EER) of 2%. Finally, the integrity of the data transmission via communication channels must be secure all the way from the scanner to the application. After applying Gaussian noise addition, and JPEG compression with high and moderate quality factors on the watermarked fingerprint images, recognition accuracy decreases slightly to reach 96%.