OpenRaiser/Intern-Atlas huggingface dataset:
Energy Efficiency Optimization for MIMO-OFDM Mobile Multimedia Communication Systems with QoS Constraints
Venue: arXiv cs.IT 2014
Year: 2014

It is widely recognized that besides the quality of service (QoS), the energy efficiency is also a key parameter in designing and evaluating mobile multimedia communication systems, which has catalyzed great interest in recent literature. In this paper, an energy efficiency model is first proposed for multiple-input multiple-output orthogonal-frequency-division-multiplexing (MIMO-OFDM) mobile multimedia communication systems with statistical QoS constraints. Employing the channel matrix singular-value-decomposition (SVD) method, all subchannels are classified by their channel characteristics. Furthermore, the multi-channel joint optimization problem in conventional MIMO-OFDM communication systems is transformed into a multi-target single channel optimization problem by grouping all subchannels. Therefore, a closed-form solution of the energy efficiency optimization is derived for MIMO-OFDM mobile mlutimedia communication systems. As a consequence, an energy-efficiency optimized power allocation (EEOPA) algorithm is proposed to improve the energy efficiency of MIMO-OFDM mobile multimedia communication systems. Simulation comparisons validate that the proposed EEOPA algorithm can guarantee the required QoS with high energy efficiency in MIMO-OFDM mobile multimedia communication systems.
Narrowband Interference Mitigation in SC-FDMA Using Bayesian Sparse Recovery
Venue: arXiv cs.IT 2014
Year: 2014

This paper presents a novel narrowband interference (NBI) mitigation scheme for SC-FDMA systems. The proposed NBI cancellation scheme exploits the frequency domain sparsity of the unknown signal and adopts a low complexity Bayesian sparse recovery procedure. At the transmitter, a few randomly chosen sub-carriers are kept data free to sense the NBI signal at the receiver. Further, it is noted that in practice, the sparsity of the NBI signal is destroyed by a grid mismatch between NBI sources and the system under consideration. Towards this end, first an accurate grid mismatch model is presented that is capable of assuming independent offsets for multiple NBI sources. Secondly, prior to NBI reconstruction, the sparsity of the unknown signal is restored by employing a sparsifying transform. To improve the spectral efficiency of the proposed scheme, a data-aided NBI recovery procedure is outlined that relies on adaptively selecting a subset of data carriers and uses them as additional measurements to enhance the NBI estimation. Finally, the proposed scheme is extended to single-input multi-output systems by performing a collaborative NBI support search over all antennas. Numerical results are presented that depict the suitability of the proposed scheme for NBI mitigation.
Selective Watchdog Technique for Intrusion Detection in Mobile Ad-Hoc Network
Venue: arXiv cs.NI 2014
Year: 2014

Mobile ad-hoc networks(MANET) is the collection of mobile nodes which are self organizing and are connected by wireless links where nodes which are not in the direct range communicate with each other relying on the intermediate nodes. As a result of trusting other nodes in the route, a malicious node can easily compromise the security of the network. A black-hole node is the malicious node which drops the entire packet coming to it and always shows the fresh route to the destination, even if the route to destination doesn't exist. This paper describes a scheme that will detect the intrusion in the network in the presence of black-hole node and its performance is compared with the previous technique. This novel technique helps to increase the network performance by reducing the overhead in the network.
FLBRA: Fuzzy Logic Based Routing Algorithm for Indoor Wireless Sensor Networks
Venue: arXiv cs.NI 2014
Year: 2014

Considering the context of building management systems with wireless sensor networks monitoring environmental features, this paper presents a proposal of a Fuzzy Logic Based Routing Algorithm (FLBRA) to determine the cost of each link and the identification of the best routes for packet forwarding. We describe the parameters (Received Signal Strength Indicator - RSSI, Standard Deviation of the RSSI and Packet Error Rate - PER) for the cost definition of each path, the sequence of identifying best routes and the results obtained in simulation. As expected in this proposal, the simulation results showed an increase in the packet delivery rate compared to RSSI-based forward protocol (RBF).
Effective persistent homology of digital images
Venue: arXiv cs.CV 2014
Year: 2014

In this paper, three Computational Topology methods (namely effective homology, persistent homology and discrete vector fields) are mixed together to produce algorithms for homological digital image processing. The algorithms have been implemented as extensions of the Kenzo system and have shown a good performance when applied on some actual images extracted from a public dataset.
Gradual training of deep denoising auto encoders
Venue: arXiv cs.LG 2014
Year: 2014

Stacked denoising auto encoders (DAEs) are well known to learn useful deep representations, which can be used to improve supervised training by initializing a deep network. We investigate a training scheme of a deep DAE, where DAE layers are gradually added and keep adapting as additional layers are added. We show that in the regime of mid-sized datasets, this gradual training provides a small but consistent improvement over stacked training in both reconstruction quality and classification error over stacked training on MNIST and CIFAR datasets.
Nano-artifact metrics based on random collapse of resist
Venue: arXiv cs.CR 2014
Year: 2014

Artifact metrics is an information security technology that uses the intrinsic characteristics of a physical object for authentication and clone resistance. Here, we demonstrate nano-artifact metrics based on silicon nanostructures formed via an array of resist pillars that randomly collapse when exposed to electron-beam lithography. The proposed technique uses conventional and scalable lithography processes, and because of the random collapse of resist, the resultant structure has extremely fine-scale morphology with a minimum dimension below 10 nm, which is less than the resolution of current lithography capabilities. By evaluating false match, false non-match and clone-resistance rates, we clarify that the nanostructured patterns based on resist collapse satisfy the requirements for high-performance security applications.
From dependency to causality: a machine learning approach
Venue: arXiv cs.LG 2014
Year: 2014

The relationship between statistical dependency and causality lies at the heart of all statistical approaches to causal inference. Recent results in the ChaLearn cause-effect pair challenge have shown that causal directionality can be inferred with good accuracy also in Markov indistinguishable configurations thanks to data driven approaches. This paper proposes a supervised machine learning approach to infer the existence of a directed causal link between two variables in multivariate settings with $n>2$ variables. The approach relies on the asymmetry of some conditional (in)dependence relations between the members of the Markov blankets of two variables causally connected. Our results show that supervised learning methods may be successfully used to extract causal information on the basis of asymmetric statistical descriptors also for $n>2$ variate distributions.
Semi-Stochastic Coordinate Descent
Venue: arXiv cs.NA 2014
Year: 2014

We propose a novel stochastic gradient method---semi-stochastic coordinate descent (S2CD)---for the problem of minimizing a strongly convex function represented as the average of a large number of smooth convex functions: $f(x)=\tfrac{1}{n}\sum_i f_i(x)$. Our method first performs a deterministic step (computation of the gradient of $f$ at the starting point), followed by a large number of stochastic steps. The process is repeated a few times, with the last stochastic iterate becoming the new starting point where the deterministic step is taken. The novelty of our method is in how the stochastic steps are performed. In each such step, we pick a random function $f_i$ and a random coordinate $j$---both using nonuniform distributions---and update a single coordinate of the decision vector only, based on the computation of the $j^{th}$ partial derivative of $f_i$ at two different points. Each random step of the method constitutes an unbiased estimate of the gradient of $f$ and moreover, the squared norm of the steps goes to zero in expectation, meaning that the stochastic estimate of the gradient progressively improves. The complexity of the method is the sum of two terms: $O(n\log(1/ε))$ evaluations of gradients $\nabla f_i$ and $O(\hatκ\log(1/ε))$ evaluations of partial derivatives $\nabla_j f_i$, where $\hatκ$ is a novel condition number.
An Empirical Study on Refactoring Activity
Venue: arXiv cs.SE 2014
Year: 2014

This paper reports an empirical study on refactoring activity in three Java software systems. We investigated some questions on refactoring activity, to confirm or disagree on conclusions that have been drawn from previous empirical studies. Unlike previous empirical studies, our study found that it is not always true that there are more refactoring activities before major project release date than after. In contrast, we were able to confirm that software developers perform different types of refactoring operations on test code and production code, specific developers are responsible for refactorings in the project, refactoring edits are not very well tested. Further, floss refactoring is more popular among the developers, refactoring activity is frequent in the projects, majority of bad smells once occurred they persist up to the latest version of the system. By confirming assumptions by other researchers we can have greater confidence that those research conclusions are generalizable.
A Python-based Post-processing Toolset For Seismic Analyses
Venue: arXiv cs.CE 2014
Year: 2014

This paper discusses the design and implementation of a Python-based toolset to aid in assessing the response of the UK's Advanced Gas Reactor nuclear power stations to earthquakes. The seismic analyses themselves are carried out with a commercial Finite Element solver, but understanding the raw model output this produces requires customised post-processing and visualisation tools. Extending the existing tools had become increasingly difficult and a decision was made to develop a new, Python-based toolset. This comprises of a post-processing framework (aftershock) which includes an embedded Python interpreter, and a plotting package (afterplot) based on numpy and matplotlib. The new toolset had to be significantly more flexible and easier to maintain than the existing code-base, while allowing the majority of development to be carried out by engineers with little training in software development. The resulting architecture will be described with a focus on exploring how the design drivers were met and the successes and challenges arising from the choices made.
An Extensible Benchmarking Infrastructure for Motion Planning Algorithms
Venue: arXiv cs.RO 2014
Year: 2014

Sampling-based planning algorithms are the most common probabilistically complete algorithms and are widely used on many robot platforms. Within this class of algorithms, many variants have been proposed over the last 20 years, yet there is still no characterization of which algorithms are well-suited for which classes of problems. This has motivated us to develop a benchmarking infrastructure for motion planning algorithms. It consists of three main components. First, we have created an extensive benchmarking software framework that is included with the Open Motion Planning Library (OMPL), a C++ library that contains implementations of many sampling-based algorithms. Second, we have defined extensible formats for storing benchmark results. The formats are fairly straightforward so that other planning libraries could easily produce compatible output. Finally, we have created an interactive, versatile visualization tool for compact presentation of collected benchmark data. The tool and underlying database facilitate the analysis of performance across benchmark problems and planners.
SVEN: Informative Visual Representation of Complex Dynamic Structure
Venue: arXiv cs.SI 2014
Year: 2014

Graphs change over time, and typically variations on the small multiples or animation pattern is used to convey this dynamism visually. However, both of these classical techniques have significant drawbacks, so a new approach, Storyline Visualization of Events on a Network (SVEN) is proposed. SVEN builds on storyline techniques, conveying nodes as contiguous lines over time. SVEN encodes time in a natural manner, along the horizontal axis, and optimizes the vertical placement of storylines to decrease clutter (line crossings, straightness, and bends) in the drawing. This paper demonstrates SVEN on several different flavors of real-world dynamic data, and outlines the remaining near-term future work.
Off-grid DOA Estimation Based on Analysis of the Convexity of Maximum Likelihood Function
Venue: arXiv cs.IT 2014
Year: 2014

Spatial compressive sensing (SCS) has recently been applied to direction-of-arrival (DOA) estimation owing to advantages over conventional ones. However the performance of compressive sensing (CS)-based estimation methods decreases when true DOAs are not exactly on the discretized sampling grid. We solve the off-grid DOA estimation problem using the deterministic maximum likelihood (DML) estimation method. In this work, we analyze the convexity of the DML function in the vicinity of the global solution. Especially under the condition of large array, we search for an approximately convex range around the ture DOAs to guarantee the DML function convex. Based on the convexity of the DML function, we propose a computationally efficient algorithm framework for off-grid DOA estimation. Numerical experiments show that the rough convex range accords well with the exact convex range of the DML function with large array and demonstrate the superior performance of the proposed methods in terms of accuracy, robustness and speed.
Cooperative Equilibrium: A solution predicting cooperative play
Venue: arXiv cs.GT 2014
Year: 2014

Nash equilibrium (NE) assumes that players always make a best response. However, this is not always true; sometimes people cooperate even it is not a best response to do so. For example, in the Prisoner's Dilemma, people often cooperate. Are there rules underlying cooperative behavior? In an effort to answer this question, we propose a new equilibrium concept: perfect cooperative equilibrium (PCE), and two related variants: max-PCE and cooperative equilibrium. PCE may help explain players' behavior in games where cooperation is observed in practice. A player's payoff in a PCE is at least as high as in any NE. However, a PCE does not always exist. We thus consider α-PCE, where α takes into account the degree of cooperation; a PCE is a 0-PCE. Every game has a Pareto-optimal max-PCE (M-PCE); that is, an α-PCE for a maximum α. We show that M-PCE does well at predicting behavior in quite a few games of interest. We also consider cooperative equilibrium (CE), another generalization of PCE that takes punishment into account. Interestingly, all Pareto-optimal M-PCE are CE. We prove that, in 2-player games, a PCE (if it exists), a M-PCE, and a CE can all be found in polynomial time using bilinear programming. This is a contrast to Nash equilibrium, which is PPAD complete even in 2-player games [Chen, Deng, and Teng 2009]. We compare M-PCE to the coco value [Kalai and Kalai 2009], another solution concept that tries to capture cooperation, both axiomatically and in terms of an algebraic characterization, and show that the two are closely related, despite their very different definitions.
SENNS: Sparse Extraction Neural NetworkS for Feature Extraction
Venue: arXiv cs.CV 2014
Year: 2014

By drawing on ideas from optimisation theory, artificial neural networks (ANN), graph embeddings and sparse representations, I develop a novel technique, termed SENNS (Sparse Extraction Neural NetworkS), aimed at addressing the feature extraction problem. The proposed method uses (preferably deep) ANNs for projecting input attribute vectors to an output space wherein pairwise distances are maximized for vectors belonging to different classes, but minimized for those belonging to the same class, while simultaneously enforcing sparsity on the ANN outputs. The vectors that result from the projection can then be used as features in any classifier of choice. Mathematically, I formulate the proposed method as the minimisation of an objective function which can be interpreted, in the ANN output space, as a negative factor of the sum of the squares of the pair-wise distances between output vectors belonging to different classes, added to a positive factor of the sum of squares of the pair-wise distances between output vectors belonging to the same classes, plus sparsity and weight decay terms. To derive an algorithm for minimizing the objective function via gradient descent, I use the multi-variate version of the chain rule to obtain the partial derivatives of the function with respect to ANN weights and biases, and find that each of the required partial derivatives can be expressed as a sum of six terms. As it turns out, four of those six terms can be computed using the standard back propagation algorithm; the fifth can be computed via a slight modification of the standard backpropagation algorithm; while the sixth one can be computed via simple arithmetic. Finally, I propose experiments on the ARABASE Arabic corpora of digits and letters, the CMU PIE database of faces, the MNIST digits database, and other standard machine learning databases.
Representation of (Left) Ideal Regular Languages by Synchronizing Automata
Venue: arXiv cs.FL 2014
Year: 2014

We follow language theoretic approach to synchronizing automata and Černý's conjecture initiated in a series of recent papers. We find a precise lower bound for the reset complexity of a principal ideal languages. Also we show a strict connection between principal left ideals and synchronizing automata. We characterize regular languages whose minimal deterministic finite automaton is synchronizing and possesses a reset word belonging to the recognized language.
Connectivity of Millimeter Wave Networks with Multi-hop Relaying
Venue: arXiv cs.IT 2014
Year: 2014

This paper presents a novel stochastic geometry approach to the connectivity of milimeter wave (mmWave) networks with multi-hop relaying. The random positions and shapes of obstacles in the radio environment are modeled as a Boolean model, whose germs are distributed according to a Poisson point process and grains are random rectangles. The derived analytical results shed light on how the connectivity of mmWave networks depends on key system parameters such as the density and size of obstacles as well as relaying route window -- the range of distances in which routing relays are selected. We find that multi-hop relaying can greatly improve the connectivity versus single hop mmWave transmission. We show that to obtain near-optimal connectivity the relaying route window should be about the size of the obstacles.
Computationally Efficient Implementation of a Hamming Code Decoder using a Graphics Processing Unit
Venue: arXiv cs.DC 2014
Year: 2014

This paper presents a computationally efficient implementation of a Hamming code decoder on a graphics processing unit (GPU) to support real-time software-defined radio (SDR), which is a software alternative for realizing wireless communication. The Hamming code algorithm is challenging to parallelize effectively on a GPU because it works on sparsely located data items with several conditional statements, leading to non-coalesced, long latency, global memory access, and huge thread divergence. To address these issues, we propose an optimized implementation of the Hamming code on the GPU to exploit the higher parallelism inherent in the algorithm. Experimental results using a compute unified device architecture (CUDA)-enabled NVIDIA GeForce GTX 560, including 335 cores, revealed that the proposed approach achieved a 99x speedup versus the equivalent CPU-based implementation.
Secondary Spectrum Auctions for Markets with Communication Constraints
Venue: arXiv cs.GT 2014
Year: 2014

Auctions have been proposed as a way to provide economic incentives for primary users to dynamically allocate unused spectrum to other users in need of it. Previously proposed schemes do not take into account the fact that the power constraints of users might prevent them from transmitting their bid prices to the auctioneer with high precision and that transmitted bid prices must travel through a noisy channel. These schemes also have very high overheads which cannot be accommodated in wireless standards. We propose auction schemes where a central clearing authority auctions spectrum to users who bid for it, while taking into account quantization of prices, overheads in bid revelation, and noise in the channel explicitly. Our schemes are closely related to channel output feedback problems and, specifically, to the technique of posterior matching. We consider several scenarios where the objective of the clearing authority is to award spectrum to the bidders who value spectrum the most. We prove theoretically that this objective is asymptotically attained by our scheme when the bidders are non-strategic with constant bids. We propose separate schemes to make strategic users reveal their private values truthfully, to auction multiple sub-channels among strategic users, and to track slowly time-varying bid prices. Our simulations illustrate the optimality of our schemes for constant bid prices, and also demonstrate the effectiveness of our tracking algorithm for slowly time-varying bids.
Fusing Color and Texture Cues to Categorize the Fruit Diseases from Images
Venue: arXiv cs.CV 2014
Year: 2014

The economic and production losses in agricultural industry worldwide are due to the presence of diseases in the several kinds of fruits. In this paper, a method for the classification of fruit diseases is proposed and experimentally validated. The image processing based proposed approach is composed of the following main steps; in the first step K-Means clustering technique is used for the defect segmentation, in the second step color and textural cues are extracted and fused from the segmented image, and finally images are classified into one of the classes by using a Multi-class Support Vector Machine. We have considered diseases of apple as a test case and evaluated our approach for three types of apple diseases namely apple scab, apple blotch and apple rot and normal apples without diseases. Our experimentation points out that the proposed fusion scheme can significantly support accurate detection and automatic classification of fruit diseases.
Toward Refactoring of DMARF and GIPSY Case Studies -- a Team 9 SOEN6471-S14 Project Report
Venue: arXiv cs.SE 2014
Year: 2014

Software architecture consists of series of decisions taken to give a structural solution that meets all the technical and operational requirements. The paper involves code refactoring. Code refactoring is a process of changing the internal structure of the code without altering its external behavior. This paper focuses over open source systems experimental studies that are DMARF and GIPSY. We have gone through various research papers and analyzed their architectures. Refactoring improves understandability, maintainability, extensibility of the code. Code smells were identified through various tools such as JDeodorant, Logiscope, and CodePro. Reverse engineering of DMARF and GIPSY were done for understanding the system. Tool used for this was Object Aid UML. For better understanding use cases, domain model, design class diagram are built.