OpenRaiser/Intern-Atlas huggingface dataset:
Enforcing Label and Intensity Consistency for IR Target Detection
Venue: arXiv cs.CV 2014
Year: 2014

This study formulates the IR target detection as a binary classification problem of each pixel. Each pixel is associated with a label which indicates whether it is a target or background pixel. The optimal label set for all the pixels of an image maximizes aposteriori distribution of label configuration given the pixel intensities. The posterior probability is factored into (or proportional to) a conditional likelihood of the intensity values and a prior probability of label configuration. Each of these two probabilities are computed assuming a Markov Random Field (MRF) on both pixel intensities and their labels. In particular, this study enforces neighborhood dependency on both intensity values, by a Simultaneous Auto Regressive (SAR) model, and on labels, by an Auto-Logistic model. The parameters of these MRF models are learned from labeled examples. During testing, an MRF inference technique, namely Iterated Conditional Mode (ICM), produces the optimal label for each pixel. The detection performance is further improved by incorporating temporal information through background subtraction. High performances on benchmark datasets demonstrate effectiveness of this method for IR target detection.
A Scalable Framework for Spatiotemporal Analysis of Location-based Social Media Data
Venue: arXiv cs.SI 2014
Year: 2014

In the past several years, social media (e.g., Twitter and Facebook) has been experiencing a spectacular rise and popularity, and becoming a ubiquitous discourse for content sharing and social networking. With the widespread of mobile devices and location-based services, social media typically allows users to share whereabouts of daily activities (e.g., check-ins and taking photos), and thus strengthens the roles of social media as a proxy to understand human behaviors and complex social dynamics in geographic spaces. Unlike conventional spatiotemporal data, this new modality of data is dynamic, massive, and typically represented in stream of unstructured media (e.g., texts and photos), which pose fundamental representation, modeling and computational challenges to conventional spatiotemporal analysis and geographic information science. In this paper, we describe a scalable computational framework to harness massive location-based social media data for efficient and systematic spatiotemporal data analysis. Within this framework, the concept of space-time trajectories (or paths) is applied to represent activity profiles of social media users. A hierarchical spatiotemporal data model, namely a spatiotemporal data cube model, is developed based on collections of space-time trajectories to represent the collective dynamics of social media users across aggregation boundaries at multiple spatiotemporal scales. The framework is implemented based upon a public data stream of Twitter feeds posted on the continent of North America. To demonstrate the advantages and performance of this framework, an interactive flow mapping interface (including both single-source and multiple-source flow mapping) is developed to allow real-time, and interactive visual exploration of movement dynamics in massive location-based social media at multiple scales.
Free software, Open source software, licenses. A short presentation including a procedure for research software and data dissemination
Venue: arXiv cs.CY 2014
Year: 2014

The main goal of this document is to help the research community to understand the basic concepts of software distribution: Free software, Open source software, licenses. This document also includes a procedure for research software and data dissemination.
Test Case Purification for Improving Fault Localization
Venue: arXiv cs.SE 2014
Year: 2014

Finding and fixing bugs are time-consuming activities in software development. Spectrum-based fault localization aims to identify the faulty position in source code based on the execution trace of test cases. Failing test cases and their assertions form test oracles for the failing behavior of the system under analysis. In this paper, we propose a novel concept of spectrum driven test case purification for improving fault localization. The goal of test case purification is to separate existing test cases into small fractions (called purified test cases) and to enhance the test oracles to further localize faults. Combining with an original fault localization technique (e.g., Tarantula), test case purification results in better ranking the program statements. Our experiments on 1800 faults in six open-source Java programs show that test case purification can effectively improve existing fault localization techniques.
Counterexample to the $l$-modular Belfiore-Solé Conjecture
Venue: arXiv cs.IT 2014
Year: 2014

We show that the secrecy function conjecture that states that the maximum of the secrecy function of an $l$-modular lattice occurs at $1/\sqrt{l}$ is false, by proving that the 4-modular lattice $C^(4) = \mathbb{Z} \oplus \sqrt{2}\mathbb{Z} \oplus 2\mathbb{Z}$ fails to satisfy this conjecture. We also indicate how the secrecy function must be modified in the $l$-modular case to have a more reasonable chance for it to have a maximum at $1/\sqrt{l}$, and show that the conjecture, modified with this new secrecy function, is true for various odd 2-modular lattices.
Reinforcement-based data transmission in temporally-correlated fading channels: Partial CSIT scenario
Venue: arXiv cs.IT 2014
Year: 2014

Reinforcement algorithms refer to the schemes where the results of the previous trials and a reward-punishment rule are used for parameter setting in the next steps. In this paper, we use the concept of reinforcement algorithms to develop different data transmission models in wireless networks. Considering temporally-correlated fading channels, the results are presented for the cases with partial channel state information at the transmitter (CSIT). As demonstrated, the implementation of reinforcement algorithms improves the performance of communication setups remarkably, with the same feedback load/complexity as in the state-of-the-art schemes.
YoMo - The Arduino based Smart Metering Board
Venue: arXiv cs.OH 2014
Year: 2014

Smart meters are an enabling technology for many smart grid applications. This paper introduces a design for a low-cost smart meter system as well as the fundamentals of smart metering. The smart meter platform, provided as open hardware, is designed with a connector interface compatible to the Arduino platform, thus opening the possibilities for smart meters with flexible hardware and computation features, starting from low-cost 8 bit micro controllers up to powerful single board computers that can run Linux. The metering platform features a current transformer which allows a non-intrusive installation of the current measurement unit. The suggested design can switch loads, offers a variable sampling frequency, and provides measurement data such as active power, reactive and apparent power. Results indicate that measurement accuracy and resolution of the proposed metering platform are sufficient for a range of different applications and loads from a few watts up to five kilowatts.
Consensus-Based Modelling using Distributed Feature Construction
Venue: arXiv cs.LG 2014
Year: 2014

A particularly successful role for Inductive Logic Programming (ILP) is as a tool for discovering useful relational features for subsequent use in a predictive model. Conceptually, the case for using ILP to construct relational features rests on treating these features as functions, the automated discovery of which necessarily requires some form of first-order learning. Practically, there are now several reports in the literature that suggest that augmenting any existing features with ILP-discovered relational features can substantially improve the predictive power of a model. While the approach is straightforward enough, much still needs to be done to scale it up to explore more fully the space of possible features that can be constructed by an ILP system. This is in principle, infinite and in practice, extremely large. Applications have been confined to heuristic or random selections from this space. In this paper, we address this computational difficulty by allowing features to be constructed in a distributed manner. That is, there is a network of computational units, each of which employs an ILP engine to construct some small number of features and then builds a (local) model. We then employ a consensus-based algorithm, in which neighboring nodes share information to update local models. For a category of models (those with convex loss functions), it can be shown that the algorithm will result in all nodes converging to a consensus model. In practice, it may be slow to achieve this convergence. Nevertheless, our results on synthetic and real datasets that suggests that in relatively short time the "best" node in the network reaches a model whose predictive accuracy is comparable to that obtained using more computational effort in a non-distributed setting (the best node is identified as the one whose weights converge first).
Descriptive Control Theory: A Proposal
Venue: arXiv cs.LO 2014
Year: 2014

Logic is playing an increasingly important role in the engineering of real-time, hybrid, and cyber-physical systems, but mostly in the form of posterior verification and high-level analysis. The core methodology in the design of real-world systems consists mainly of control theory and numerical analysis, and has remained mostly free of logic and formal approaches. As a result, besides facing extreme difficulty in guaranteeing the reliability of these systems, engineers are also missing out the computational power of logic-based methods that has greatly advanced in the past decades. To change this situation, we need a logical and computational foundation for control theory. The name "descriptive control theory" emphasizes the overarching theme of using logic to express, analyze, and solve problems in control theory. If the program is successfully carried out, logical approaches will significantly extend existing engineering methods towards a unified methodology for handling nonlinear and hybrid systems, and bring design automation and reliability to an unprecedented level in the broad field of engineering.
An Advanced Approach On Load Balancing in Grid Computing
Venue: arXiv cs.DC 2014
Year: 2014

With the rapid development in wide area networks and low cost, powerful computational resources, grid computing has gained its popularity. With the advent of grid computing, space limitations of conventional distributed systems can be overcome and underutilized computing resources at different locations around the world can be put to distributed jobs. Workload and resource management is the main key grid services at the service level of grid infrastructures, out of which load balancing in the main concern for grid developers. It has been found that load is the major problem which server faces, especially when the number of users increases. A lot of research is being done in the area of load management. This paper presents the various mechanisms of load balancing in grid computing so that the readers will get an idea of which algorithm would be suitable in different situations. Keywords: wide area network, distributed computing, load balancing.
Computing Approximate Nash Equilibria in Polymatrix Games
Venue: arXiv cs.GT 2014
Year: 2014

In an $ε$-Nash equilibrium, a player can gain at most $ε$ by unilaterally changing his behaviour. For two-player (bimatrix) games with payoffs in $[0,1]$, the best-known$ε$ achievable in polynomial time is 0.3393. In general, for $n$-player games an $ε$-Nash equilibrium can be computed in polynomial time for an $ε$ that is an increasing function of $n$ but does not depend on the number of strategies of the players. For three-player and four-player games the corresponding values of $ε$ are 0.6022 and 0.7153, respectively. Polymatrix games are a restriction of general $n$-player games where a player's payoff is the sum of payoffs from a number of bimatrix games. There exists a very small but constant $ε$ such that computing an $ε$-Nash equilibrium of a polymatrix game is \PPAD-hard. Our main result is that a $(0.5+δ)$-Nash equilibrium of an $n$-player polymatrix game can be computed in time polynomial in the input size and $\frac{1}δ$. Inspired by the algorithm of Tsaknakis and Spirakis, our algorithm uses gradient descent on the maximum regret of the players. We also show that this algorithm can be applied to efficiently find a $(0.5+δ)$-Nash equilibrium in a two-player Bayesian game.
The Missing Piece in Complex Analytics: Low Latency, Scalable Model Management and Serving with Velox
Venue: arXiv cs.DB 2014
Year: 2014

To support complex data-intensive applications such as personalized recommendations, targeted advertising, and intelligent services, the data management community has focused heavily on the design of systems to support training complex models on large datasets. Unfortunately, the design of these systems largely ignores a critical component of the overall analytics process: the deployment and serving of models at scale. In this work, we present Velox, a new component of the Berkeley Data Analytics Stack. Velox is a data management system for facilitating the next steps in real-world, large-scale analytics pipelines: online model management, maintenance, and serving. Velox provides end-user applications and services with a low-latency, intuitive interface to models, transforming the raw statistical models currently trained using existing offline large-scale compute frameworks into full-blown, end-to-end data products capable of recommending products, targeting advertisements, and personalizing web content. To provide up-to-date results for these complex models, Velox also facilitates lightweight online model maintenance and selection (i.e., dynamic weighting). In this paper, we describe the challenges and architectural considerations required to achieve this functionality, including the abilities to span online and offline systems, to adaptively adjust model materialization strategies, and to exploit inherent statistical properties such as model error tolerance, all while operating at "Big Data" scale.
Incorporating Semi-supervised Features into Discontinuous Easy-First Constituent Parsing
Venue: arXiv cs.CL 2014
Year: 2014

This paper describes adaptations for EaFi, a parser for easy-first parsing of discontinuous constituents, to adapt it to multiple languages as well as make use of the unlabeled data that was provided as part of the SPMRL shared task 2014.
Coalescing: Syntactic Abstraction for Reasoning in First-Order Modal Logics
Venue: arXiv cs.LO 2014
Year: 2014

We present a syntactic abstraction method to reason about first-order modal logics by using theorem provers for standard first-order logic and for propositional modal logic.
The upper bound of packet transmission capacity in local static routings
Venue: arXiv cs.NI 2014
Year: 2014

We propose a universal analysis for static routings on networks and describe the congestion characteristics by the theory. The relation between average transmission time and transmission capacity is described by inequality T0Rc0<=1. For large scale sparse networks, the non-trivial upper bond of transmission capacity Rc0 is limited by Rc0<=1/< 1/k > in some approximate conditions. the theoretical results agree with simulations on BA Networks.
Impromptu Deployment of Wireless Relay Networks: Experiences Along a Forest Trail
Venue: arXiv cs.NI 2014
Year: 2014

We are motivated by the problem of impromptu or as- you-go deployment of wireless sensor networks. As an application example, a person, starting from a sink node, walks along a forest trail, makes link quality measurements (with the previously placed nodes) at equally spaced locations, and deploys relays at some of these locations, so as to connect a sensor placed at some a priori unknown point on the trail with the sink node. In this paper, we report our experimental experiences with some as-you-go deployment algorithms. Two algorithms are based on Markov decision process (MDP) formulations; these require a radio propagation model. We also study purely measurement based strategies: one heuristic that is motivated by our MDP formulations, one asymptotically optimal learning algorithm, and one inspired by a popular heuristic. We extract a statistical model of the propagation along a forest trail from raw measurement data, implement the algorithms experimentally in the forest, and compare them. The results provide useful insights regarding the choice of the deployment algorithm and its parameters, and also demonstrate the necessity of a proper theoretical formulation.
Design of Novel Algorithm and Architecture for Gaussian Based Color Image Enhancement System for Real Time Applications
Venue: arXiv cs.AR 2014
Year: 2014

This paper presents the development of a new algorithm for Gaussian based color image enhancement system. The algorithm has been designed into architecture suitable for FPGA/ASIC implementation. The color image enhancement is achieved by first convolving an original image with a Gaussian kernel since Gaussian distribution is a point spread function which smoothen the image. Further, logarithm-domain processing and gain/offset corrections are employed in order to enhance and translate pixels into the display range of 0 to 255. The proposed algorithm not only provides better dynamic range compression and color rendition effect but also achieves color constancy in an image. The design exploits high degrees of pipelining and parallel processing to achieve real time performance. The design has been realized by RTL compliant Verilog coding and fits into a single FPGA with a gate count utilization of 321,804. The proposed method is implemented using Xilinx Virtex-II Pro XC2VP40-7FF1148 FPGA device and is capable of processing high resolution color motion pictures of sizes of up to 1600x1200 pixels at the real time video rate of 116 frames per second. This shows that the proposed design would work for not only still images but also for high resolution video sequences.
Some remarks on modularity density
Venue: arXiv cs.SI 2014
Year: 2014

A "quantitative function" for community detection called modularity density has been proposed by Li, Zhang, Wang, Zhang, and Chen in $[$Phys. Rev. E 77, 036109 (2008)$]$. We study the modularity density maximization problem and we discuss some features of the optimal solution. More precisely, we show that in the optimal solution there can be communities having negative modularity density, and we propose a modification of the original formulation to overcome this issue. Moreover, we show that a clique can be divided into two or more parts when maximizing the modularity density. We also compare the solution found by maximizing the modularity density with that obtained by maximizing the modularity on the Zachary karate club network.
Cavlectometry: Towards Holistic Reconstruction of Large Mirror Objects
Venue: arXiv cs.CV 2014
Year: 2014

We introduce a method based on the deflectometry principle for the reconstruction of specular objects exhibiting significant size and geometric complexity. A key feature of our approach is the deployment of an Automatic Virtual Environment (CAVE) as pattern generator. To unfold the full power of this extraordinary experimental setup, an optical encoding scheme is developed which accounts for the distinctive topology of the CAVE. Furthermore, we devise an algorithm for detecting the object of interest in raw deflectometric images. The segmented foreground is used for single-view reconstruction, the background for estimation of the camera pose, necessary for calibrating the sensor system. Experiments suggest a significant gain of coverage in single measurements compared to previous methods. To facilitate research on specular surface reconstruction, we will make our data set publicly available.
Metrics for matrix-valued measures via test functions
Venue: arXiv cs.SY 2014
Year: 2014

It is perhaps not widely recognized that certain common notions of distance between probability measures have an alternative dual interpretation which compares corresponding functionals against suitable families of test functions. This dual viewpoint extends in a straightforward manner to suggest metrics between matrix-valued measures. Our main interest has been in developing weakly-continuous metrics that are suitable for comparing matrix-valued power spectral density functions. To this end, and following the suggested recipe of utilizing suitable families of test functions, we develop a weakly-continuous metric that is analogous to the Wasserstein metric and applies to matrix-valued densities. We use a numerical example to compare this metric to certain standard alternatives including a different version of a matricial Wasserstein metric developed earlier.
Equilibria of Plurality Voting: Lazy and Truth-biased Voters
Venue: arXiv cs.GT 2014
Year: 2014

We present a systematic study of Plurality elections with strategic voters who, in addition to having preferences over election winners, have secondary preferences, which govern their behavior when their vote cannot affect the election outcome. Specifically, we study two models that have been recently considered in the literature: lazy voters, who prefer to abstain when they are not pivotal, and truth-biased voters, who prefer to vote truthfully when they are not pivotal. We extend prior work by investigating the behavior of both lazy and truth-biased voters under different tie-breaking rules (lexicographic rule, random voter rule, random candidate rule). Two of these six combinations of secondary preferences and a tie-breaking rule have been studied in prior work. In order to understand the impact of different secondary preferences and tie-breaking rules on the election outcomes, we study the remaining four combinations. We characterize pure Nash equilibria (PNE) of the resulting strategic games and study the complexity of related computational problems. Our results extend to settings where some of the voters may be non-strategic.
NFV: State of the Art, Challenges and Implementation in Next Generation Mobile Networks (vEPC)
Venue: arXiv cs.NI 2014
Year: 2014

As mobile network users look forward to the connectivity speeds of 5G networks, service providers are facing challenges in complying with connectivity demands without substantial financial investments. Network Function Virtualization (NFV) is introduced as a new methodology that offers a way out of this bottleneck. NFV is poised to change the core structure of telecommunications infrastructure to be more cost-efficient. In this paper, we introduce a Network Function Virtualization framework, and discuss the challenges and requirements of its use in mobile networks. In particular, an NFV framework in the virtual environment is proposed. Moreover, in order to reduce signaling traffic and achieve better performance, this paper proposes a criterion to bundle multiple functions of virtualized evolved packet-core in a single physical device or a group of adjacent devices. The analysis shows that the proposed grouping can reduce the network control traffic by 70 percent.