OpenRaiser/Intern-Atlas huggingface dataset:
Efficient Multi-User Computation Offloading for Mobile-Edge Cloud Computing
Venue: arXiv cs.NI 2015
Year: 2015

Mobile-edge cloud computing is a new paradigm to provide cloud computing capabilities at the edge of pervasive radio access networks in close proximity to mobile users. In this paper, we first study the multi-user computation offloading problem for mobile-edge cloud computing in a multi-channel wireless interference environment. We show that it is NP-hard to compute a centralized optimal solution, and hence adopt a game theoretic approach for achieving efficient computation offloading in a distributed manner. We formulate the distributed computation offloading decision making problem among mobile device users as a multi-user computation offloading game. We analyze the structural property of the game and show that the game admits a Nash equilibrium and possesses the finite improvement property. We then design a distributed computation offloading algorithm that can achieve a Nash equilibrium, derive the upper bound of the convergence time, and quantify its efficiency ratio over the centralized optimal solutions in terms of two important performance metrics. We further extend our study to the scenario of multi-user computation offloading in the multi-channel wireless contention environment. Numerical results corroborate that the proposed algorithm can achieve superior computation offloading performance and scale well as the user size increases.
Single Image Dehazing through Improved Atmospheric Light Estimation
Venue: arXiv cs.CV 2015
Year: 2015

Image contrast enhancement for outdoor vision is important for smart car auxiliary transport systems. The video frames captured in poor weather conditions are often characterized by poor visibility. Most image dehazing algorithms consider to use a hard threshold assumptions or user input to estimate atmospheric light. However, the brightest pixels sometimes are objects such as car lights or streetlights, especially for smart car auxiliary transport systems. Simply using a hard threshold may cause a wrong estimation. In this paper, we propose a single optimized image dehazing method that estimates atmospheric light efficiently and removes haze through the estimation of a semi-globally adaptive filter. The enhanced images are characterized with little noise and good exposure in dark regions. The textures and edges of the processed images are also enhanced significantly.
GPU-Based Computation of 2D Least Median of Squares with Applications to Fast and Robust Line Detection
Venue: arXiv cs.CV 2015
Year: 2015

The 2D Least Median of Squares (LMS) is a popular tool in robust regression because of its high breakdown point: up to half of the input data can be contaminated with outliers without affecting the accuracy of the LMS estimator. The complexity of 2D LMS estimation has been shown to be $Ω(n^2)$ where $n$ is the total number of points. This high theoretical complexity along with the availability of graphics processing units (GPU) motivates the development of a fast, parallel, GPU-based algorithm for LMS computation. We present a CUDA based algorithm for LMS computation and show it to be much faster than the optimal state of the art single threaded CPU algorithm. We begin by describing the proposed method and analyzing its performance. We then demonstrate how it can be used to modify the well-known Hough Transform (HT) in order to efficiently detect image lines in noisy images. Our method is compared with standard HT-based line detection methods and shown to overcome their shortcomings in terms of both efficiency and accuracy.
Performance Peculiarities of Viterbi Decoder in Mathworks Simulink, GNU Radio and Other Systems with Likewise Implementation
Venue: arXiv cs.IT 2015
Year: 2015

The performance of convolutional codes decoding by the Viterbi algorithm should not depend on the particular distribution of zeros and ones in the input messages, as they are linear. However, it was identified that specific implementations of Add-Compare-Select unit for the Viterbi Algorithm demonstrate the decoding performance that depends on proportion of elements in the input message. It is conjectured that the modern commercial hard- and software defined communication equipment may also feature similar implementation and as such their decoding performance could also vary.
Design and Management of Vehicle Sharing Systems: A Survey of Algorithmic Approaches
Venue: arXiv cs.DS 2015
Year: 2015

Vehicle (bike or car) sharing represents an emerging transportation scheme which may comprise an important link in the green mobility chain of smart city environments. This chapter offers a comprehensive review of algorithmic approaches for the design and management of vehicle sharing systems. Our focus is on one-way vehicle sharing systems (wherein customers are allowed to pick-up a vehicle at any location and return it to any other station) which best suits typical urban journey requirements. Along this line, we present methods dealing with the so-called asymmetric demand-offer problem (i.e. the unbalanced offer and demand of vehicles) typically experienced in one-way sharing systems which severely affects their economic viability as it implies that considerable human (and financial) resources should be engaged in relocating vehicles to satisfy customer demand. The chapter covers all planning aspects that affect the effectiveness and viability of vehicle sharing systems: the actual system design (e.g. number and location of vehicle station facilities, vehicle fleet size, vehicles distribution among stations); customer incentivisation schemes to motivate customer-based distribution of bicycles/cars (such schemes offer meaningful incentives to users so as to leave their vehicle to a station different to that originally intended and satisfy future user demand); cost-effective solutions to schedule operator-based repositioning of bicycles/cars (by employees explicitly enrolled in vehicle relocation) based on the current and future (predicted) demand patterns (operator-based and customer-based relocation may be thought as complementary methods to achieve the intended distribution of vehicles among stations).
Energy-Efficient Data Transmission with Non-FIFO Packets
Venue: arXiv cs.NI 2015
Year: 2015

This paper investigates the problem of energy-efficient packet transmission with arbitrary arrival instants and deadline constraints over a point-to-point Additive White Gaussian Noise (AWGN) channel. This is different from previous work where it is assumed that the packets follow a First-In-First-Out (FIFO) order in that the packets that arrive earlier will have a deadline that is also earlier. We first investigate the necessary and sufficient conditions of the optimal transmission scheduler. We then propose an algorithm which finds the transmission schedule of each packet in the order of the packets with the largest transmission rate to the packets with the smallest transmission rate. Finally, we show that our algorithm satisfies the sufficient conditions of the optimal transmission scheduler and thus, is optimal.
Out-of-Band Radiation Comparison of GFDM, WCP-COQAM and OFDM at Equal Spectral Efficiency
Venue: arXiv cs.IT 2015
Year: 2015

GFDM and WCP-COQAM are amongst the candidate physical layer modulation formats to be used in 5G, whose claimed lower out-of-band (OOB) emissions are important with respect to cognitive radio based dynamic spectrum access solutions. In this study, we compare OFDM, GFDM and WCP-COQAM in terms of OOB emissions in a fair manner such that their spectral efficiencies are the same and OOB emission reduction techniques are applied to all of the modulation types. Analytical PSD expressions are also correlated with the simulation based OOB emission results. Maintaining the same spectral efficiency, carrier frequency offset immunities will also be compared.
Bayesian Inference via Approximation of Log-likelihood for Priors in Exponential Family
Venue: arXiv cs.LG 2015
Year: 2015

In this paper, a Bayesian inference technique based on Taylor series approximation of the logarithm of the likelihood function is presented. The proposed approximation is devised for the case, where the prior distribution belongs to the exponential family of distributions. The logarithm of the likelihood function is linearized with respect to the sufficient statistic of the prior distribution in exponential family such that the posterior obtains the same exponential family form as the prior. Similarities between the proposed method and the extended Kalman filter for nonlinear filtering are illustrated. Furthermore, an extended target measurement update for target models where the target extent is represented by a random matrix having an inverse Wishart distribution is derived. The approximate update covers the important case where the spread of measurement is due to the target extent as well as the measurement noise in the sensor.
Hadamard Product Decomposition and Mutually Exclusive Matrices on Network Structure and Utilization
Venue: arXiv cs.DM 2015
Year: 2015

Graphs are very important mathematical structures used in many applications, one of which is transportation science. When dealing with transportation networks, one deals not only with the network structure, but also with information related to the utilization of the elements of the network, which can be shown using flow and origin-destination matrices. This paper extends an algebraic model used to relate all these components by deriving additional relationships and constructing a more structured understanding of the model. Specifically, the paper introduces the concept of mutually exclusive matrices, and shows their effect when decomposing the components of a Hadamard product on matrices
A Common-Factor Approach for Multivariate Data Cleaning with an Application to Mars Phoenix Mission Data
Venue: arXiv cs.AI 2015
Year: 2015

Data quality is fundamentally important to ensure the reliability of data for stakeholders to make decisions. In real world applications, such as scientific exploration of extreme environments, it is unrealistic to require raw data collected to be perfect. As data miners, when it is infeasible to physically know the why and the how in order to clean up the data, we propose to seek the intrinsic structure of the signal to identify the common factors of multivariate data. Using our new data driven learning method, the common-factor data cleaning approach, we address an interdisciplinary challenge on multivariate data cleaning when complex external impacts appear to interfere with multiple data measurements. Existing data analyses typically process one signal measurement at a time without considering the associations among all signals. We analyze all signal measurements simultaneously to find the hidden common factors that drive all measurements to vary together, but not as a result of the true data measurements. We use common factors to reduce the variations in the data without changing the base mean level of the data to avoid altering the physical meaning.
An extension of the open-source porousMultiphaseFoam toolbox dedicated to groundwater flows solving the Richards' equation
Venue: arXiv cs.CE 2015
Year: 2015

In this note, the existing porousMultiphaseFoam toolbox, developed initially for any two-phase flow in porous media is extended to the specific case of the Richards' equation which neglect the pressure gradient of the non-wetting phase. This model is typically used for saturated and unsaturated groundwater flows. A Picard's algorithm is implemented to linearize and solve the Richards' equation developed in the pressure head based form. This new solver of the porousMultiphaseFoam toolbox is named groundwaterFoam. The validation of thesolver is achieved by a comparison between numerical simulations and results obtained from the literature. Finally, a parallel efficiency test is performed on a large unstructured mesh and exhibits a super-linear behavior as observed for the other solvers of the toolbox.
Efficient Certificateless Signcryption Tag-KEMs for Resource-constrained Devices
Venue: arXiv cs.CR 2015
Year: 2015

Efficient certificateless one-pass session key establishment protocols can be constructed from key encapsulation mechanisms (KEMs) by making use of tags and signcryption schemes. The resulting primitives are referred to as Certificateless Signcryption Tag Key Encapsulation Mechanisms (CLSC-TKEMs). In this paper we propose two novel CLSC-TKEM protocols, the first, named LSW-CLSC-TKEM, makes use of the signature scheme of Liu et al., the second, named DKTUTS-CLSC-TKEM, is based on the direct key transport using a timestamp (DKTUTS) protocol first described by Zheng. In order to achieve greater efficiency both schemes are instantiated on elliptic curves without making use of pairings and are therefore good candidates for deployment on resource constrained devices.
Directional Global Three-part Image Decomposition
Venue: arXiv cs.CV 2015
Year: 2015

We consider the task of image decomposition and we introduce a new model coined directional global three-part decomposition (DG3PD) for solving it. As key ingredients of the DG3PD model, we introduce a discrete multi-directional total variation norm and a discrete multi-directional G-norm. Using these novel norms, the proposed discrete DG3PD model can decompose an image into two parts or into three parts. Existing models for image decomposition by Vese and Osher, by Aujol and Chambolle, by Starck et al., and by Thai and Gottschlich are included as special cases in the new model. Decomposition of an image by DG3PD results in a cartoon image, a texture image and a residual image. Advantages of the DG3PD model over existing ones lie in the properties enforced on the cartoon and texture images. The geometric objects in the cartoon image have a very smooth surface and sharp edges. The texture image yields oscillating patterns on a defined scale which is both smooth and sparse. Moreover, the DG3PD method achieves the goal of perfect reconstruction by summation of all components better than the other considered methods. Relevant applications of DG3PD are a novel way of image compression as well as feature extraction for applications such as latent fingerprint processing and optical character recognition.
Analyzer and generator for Pali
Venue: arXiv cs.CL 2015
Year: 2015

This work describes a system that performs morphological analysis and generation of Pali words. The system works with regular inflectional paradigms and a lexical database. The generator is used to build a collection of inflected and derived words, which in turn is used by the analyzer. Generating and storing morphological forms along with the corresponding morphological information allows for efficient and simple look up by the analyzer. Indeed, by looking up a word and extracting the attached morphological information, the analyzer does not have to compute this information. As we must, however, assume the lexical database to be incomplete, the system can also work without the dictionary component, using a rule-based approach.
Projection based whole body motion planning for legged robots
Venue: arXiv cs.RO 2015
Year: 2015

In this paper we present a new approach for dynamic motion planning for legged robots. We formulate a trajectory optimization problem based on a compact form of the robot dynamics. Such a form is obtained by projecting the rigid body dynamics onto the null space of the Constraint Jacobian. As consequence of the projection, contact forces are removed from the model but their effects are still taken into account. This approach permits to solve the optimal control problem of a floating base constrained multibody system while avoiding the use of an explicit contact model. We use direct transcription to numerically solve the optimization. As the contact forces are not part of the decision variables the size of the resultant discrete mathematical program is reduced and therefore solutions can be obtained in a tractable time. Using a predefined sequence of contact configurations (phases), our approach solves motions where contact switches occur. Transitions between phases are automatically resolved without using a model for switching dynamics. We present results on a hydraulic quadruped robot (HyQ), including single phase (standing, crouching) as well as multiple phase (rearing, diagonal leg balancing and stepping) dynamic motions.
Language Segmentation
Venue: arXiv cs.CL 2015
Year: 2015

Language segmentation consists in finding the boundaries where one language ends and another language begins in a text written in more than one language. This is important for all natural language processing tasks. The problem can be solved by training language models on language data. However, in the case of low- or no-resource languages, this is problematic. I therefore investigate whether unsupervised methods perform better than supervised methods when it is difficult or impossible to train supervised approaches. A special focus is given to difficult texts, i.e. texts that are rather short (one sentence), containing abbreviations, low-resource languages and non-standard language. I compare three approaches: supervised n-gram language models, unsupervised clustering and weakly supervised n-gram language model induction. I devised the weakly supervised approach in order to deal with difficult text specifically. In order to test the approach, I compiled a small corpus of different text types, ranging from one-sentence texts to texts of about 300 words. The weakly supervised language model induction approach works well on short and difficult texts, outperforming the clustering algorithm and reaching scores in the vicinity of the supervised approach. The results look promising, but there is room for improvement and a more thorough investigation should be undertaken.
Type Reconstruction for the Linear π-Calculus with Composite Regular Types
Venue: arXiv cs.PL 2015
Year: 2015

We extend the linear π-calculus with composite regular types in such a way that data containing linear values can be shared among several processes, if there is no overlapping access to such values. We describe a type reconstruction algorithm for the extended type system and discuss some practical aspects of its implementation.
VBPR: Visual Bayesian Personalized Ranking from Implicit Feedback
Venue: arXiv cs.IR 2015
Year: 2015

Modern recommender systems model people and items by discovering or `teasing apart' the underlying dimensions that encode the properties of items and users' preferences toward them. Critically, such dimensions are uncovered based on user feedback, often in implicit form (such as purchase histories, browsing logs, etc.); in addition, some recommender systems make use of side information, such as product attributes, temporal information, or review text. However one important feature that is typically ignored by existing personalized recommendation and ranking methods is the visual appearance of the items being considered. In this paper we propose a scalable factorization model to incorporate visual signals into predictors of people's opinions, which we apply to a selection of large, real-world datasets. We make use of visual features extracted from product images using (pre-trained) deep networks, on top of which we learn an additional layer that uncovers the visual dimensions that best explain the variation in people's feedback. This not only leads to significantly more accurate personalized ranking methods, but also helps to alleviate cold start issues, and qualitatively to analyze the visual dimensions that influence people's opinions.
Multiple Configurations LT Codes
Venue: arXiv cs.IT 2015
Year: 2015

This paper introduces a new scheme of LT codes, named multiple configurations. In multiple configurations LT codes (MC-LT codes), multiple sets of output symbols are simultaneously provided to receivers for recovering the source data. Each receiver, without the need to send information back to the sender, is capable of receiving the output symbols generated by some configuration chosen according to its own decoding phase. Aiming at the broadcasting scenarios without feedback channels, the proposed MC-LT codes are shown to outperform the optimal pure LT codes at the cost of encoding and transmitting units. In this paper, the inspiration of MC-LT codes is presented, how MC-LT codes work is described by giving examples, in which the optimal pure LT codes are outperformed, and a practical design of MC-LT codes, which is analytically proved to have at least the same performance bound as the pure LT codes, is proposed. The results of numerical simulation experiments demonstrate that the proposed practical design of MC-LT codes can deliver better performance than the LT codes in comparison. In summary, this paper creates new potential research directions for LT codes, and MC-LT codes are a promising variant of LT codes, especially for broadcasting scenarios.
Event-based Camera Pose Tracking using a Generative Event Model
Venue: arXiv cs.CV 2015
Year: 2015

Event-based vision sensors mimic the operation of biological retina and they represent a major paradigm shift from traditional cameras. Instead of providing frames of intensity measurements synchronously, at artificially chosen rates, event-based cameras provide information on brightness changes asynchronously, when they occur. Such non-redundant pieces of information are called "events". These sensors overcome some of the limitations of traditional cameras (response time, bandwidth and dynamic range) but require new methods to deal with the data they output. We tackle the problem of event-based camera localization in a known environment, without additional sensing, using a probabilistic generative event model in a Bayesian filtering framework. Our main contribution is the design of the likelihood function used in the filter to process the observed events. Based on the physical characteristics of the sensor and on empirical evidence of the Gaussian-like distribution of spiked events with respect to the brightness change, we propose to use the contrast residual as a measure of how well the estimated pose of the event-based camera and the environment explain the observed events. The filter allows for localization in the general case of six degrees-of-freedom motions.
VERCE delivers a productive e-Science environment for seismology research
Venue: arXiv cs.DC 2015
Year: 2015

The VERCE project has pioneered an e-Infrastructure to support researchers using established simulation codes on high-performance computers in conjunction with multiple sources of observational data. This is accessed and organised via the VERCE science gateway that makes it convenient for seismologists to use these resources from any location via the Internet. Their data handling is made flexible and scalable by two Python libraries, ObsPy and dispel4py and by data services delivered by ORFEUS and EUDAT. Provenance driven tools enable rapid exploration of results and of the relationships between data, which accelerates understanding and method improvement. These powerful facilities are integrated and draw on many other e-Infrastructures. This paper presents the motivation for building such systems, it reviews how solid-Earth scientists can make significant research progress using them and explains the architecture and mechanisms that make their construction and operation achievable. We conclude with a summary of the achievements to date and identify the crucial steps needed to extend the capabilities for seismologists, for solid-Earth scientists and for similar disciplines.