OpenRaiser/Intern-Atlas huggingface dataset:
Crowd Saliency Detection via Global Similarity Structure
Venue: arXiv cs.CV 2014
Year: 2014

It is common for CCTV operators to overlook inter- esting events taking place within the crowd due to large number of people in the crowded scene (i.e. marathon, rally). Thus, there is a dire need to automate the detection of salient crowd regions acquiring immediate attention for a more effective and proactive surveillance. This paper proposes a novel framework to identify and localize salient regions in a crowd scene, by transforming low-level features extracted from crowd motion field into a global similarity structure. The global similarity structure representation allows the discovery of the intrinsic manifold of the motion dynamics, which could not be captured by the low-level representation. Ranking is then performed on the global similarity structure to identify a set of extrema. The proposed approach is unsupervised so learning stage is eliminated. Experimental results on public datasets demonstrates the effectiveness of exploiting such extrema in identifying salient regions in various crowd scenarios that exhibit crowding, local irregular motion, and unique motion areas such as sources and sinks.
Optimizing the For loop: Comparison of For loop and micro For loop
Venue: arXiv cs.PL 2014
Year: 2014

Looping is one of the fundamental logical instructions used for repeating a block of code. It is used in programs across all programming languages. Traditionally, in languages like C, the for loop is used extensively for repeated execution of a block of code, due to its ease for use and simplified representation. This paper proposes a new way of representing the for loop to improve its runtime efficiency and compares the experimental statistics with the traditional for loop representation. It is found that for small number of iterations, the difference in computational time may not be considerable. But given any large number of iterations, the difference is noticeable.
Refinement Checking for Multirate Hybrid ZIA
Venue: arXiv cs.LO 2014
Year: 2014

A hybrid system is a dynamical system with both discrete and continuous components. In order to study the modeling and verification aspects of hybrid system, in this paper we first introduce a specification approach combining interface automata, initialized multirate hybrid automata and Z language, which is named MZIA. Meanwhile we propose a refinement relation on MZIAs. Then we give an algorithm for checking refinement relation between MZIAs with finite domain and demonstrate the correctness of the algorithm.
POLYGLOT-NER: Massive Multilingual Named Entity Recognition
Venue: arXiv cs.CL 2014
Year: 2014

The increasing diversity of languages used on the web introduces a new level of complexity to Information Retrieval (IR) systems. We can no longer assume that textual content is written in one language or even the same language family. In this paper, we demonstrate how to build massive multilingual annotators with minimal human expertise and intervention. We describe a system that builds Named Entity Recognition (NER) annotators for 40 major languages using Wikipedia and Freebase. Our approach does not require NER human annotated datasets or language specific resources like treebanks, parallel corpora, and orthographic rules. The novelty of approach lies therein - using only language agnostic techniques, while achieving competitive performance. Our method learns distributed word representations (word embeddings) which encode semantic and syntactic features of words in each language. Then, we automatically generate datasets from Wikipedia link structure and Freebase attributes. Finally, we apply two preprocessing stages (oversampling and exact surface form matching) which do not require any linguistic expertise. Our evaluation is two fold: First, we demonstrate the system performance on human annotated datasets. Second, for languages where no gold-standard benchmarks are available, we propose a new method, distant evaluation, based on statistical machine translation.
Google Scholar makes it Hard - the complexity of organizing one's publications
Venue: arXiv cs.DL 2014
Year: 2014

With Google Scholar, scientists can maintain their publications on personal profile pages, while the citations to these works are automatically collected and counted. Maintenance of publications is done manually by the researcher herself, and involves deleting erroneous ones, merging ones that are the same but which were not recognized as the same, adding forgotten co-authors, and correcting titles of papers and venues. The publications are presented on pages with 20 or 100 papers in the web page interface from 2012--2014. The interface does not allow a scientist to merge two version of a paper if they appear on different pages. This not only implies that a scientist who wants to merge certain subsets of publications will sometimes be unable to do so, but also, we show in this note that the decision problem to determine if it is possible to merge given subsets of papers is NP-complete.
High Order Structure Descriptors for Scene Images
Venue: arXiv cs.CV 2014
Year: 2014

Structure information is ubiquitous in natural scene images and it plays an important role in scene representation. In this paper, third order structure statistics (TOSS) and fourth order structure statistics (FOSS) are exploited to encode higher order structure information. Afterwards, based on the radial and normal slice of TOSS and FOSS, we propose the high order structure feature: third order structure feature (TOSF) and fourth order structure feature (FOSF). It is well known that scene images are well characterized by particular arrangements of their local structures, we divide the scene image into the non-overlapping sub-regions and compute the proposed higher order structural features among them. Then a scene classification is performed by using SVM classifier with these higher order structure features. The experimental results show that higher order structure statistics can deliver image structure information well and its spatial envelope has strong discriminative ability.
Bernstein-based polynomial approach to study the stability of switched systems and formal verification using HOL Light
Venue: arXiv cs.SY 2014
Year: 2014

In this preliminary work, we propose to use a polynomial approach in order to study the stability of switched systems. The proposed strategy is based on the Bernstein interpolation method that may transform a switched system into a polynomial expression from which an associated "simple" Lyapunov function can be eventually built. The HOL Light proof assistant allows verifying formally the Lyapunov functions that are identified from the proposed switching structure. Our approach is illustrated by numerical examples.
An Automated Group Key Authentication System Using Secret Image Sharing Scheme
Venue: arXiv cs.CR 2014
Year: 2014

In an open network environment, privacy of group communication and integrity of the communication data are the two major issues related to secured information exchange. The required level of security may be achieved by authenticating a group key in the communication channel, where contribution from each group member becomes a part of the overall group key. In the current work, we have developed an authentication system through Central Administrative Server (CAS) for automatic integration and validation of the group key. For secured group communication, the CAS generates a secret alphanumeric group key image. Using secret image sharing scheme, this group key image shares are distributed among all the participating group members in the open network. Some or all the secret shares may be merged to reconstruct the group key image at CAS. A k-nearest neighbor classifier with 48 features to represent the images, is used to validate the reconstructed image with the one stored in the CAS. 48 topological features are used to represent the reconstructed group key image. We have achieved 99.1% classification accuracy for 26 printed English uppercase characters and 10 numeric digits.
Towards Development of Correct Software using Views
Venue: arXiv cs.SE 2014
Year: 2014

This paper aims at integrating heterogeneous documents used in pragmatic software develpoment methods to describe views with a formal refinement based software development process. Therefore we propose an integrated semantics of heterogeneous documents based on a common system model and a set of syntactic development steps with a well-defined semantics for document evolution. The use of the development steps is demonstrated in a small example.
Mutual Dimension
Venue: arXiv cs.CC 2014
Year: 2014

We define the lower and upper mutual dimensions $mdim(x:y)$ and $Mdim(x:y)$ between any two points $x$ and $y$ in Euclidean space. Intuitively these are the lower and upper densities of the algorithmic information shared by $x$ and $y$. We show that these quantities satisfy the main desiderata for a satisfactory measure of mutual algorithmic information. Our main theorem, the data processing inequality for mutual dimension, says that, if $f:\mathbb{R}^m \rightarrow \mathbb{R}^n$ is computable and Lipschitz, then the inequalities $mdim(f(x):y) \leq mdim(x:y)$ and $Mdim(f(x):y) \leq Mdim(x:y)$ hold for all $x \in \mathbb{R}^m$ and $y \in \mathbb{R}^t$. We use this inequality and related inequalities that we prove in like fashion to establish conditions under which various classes of computable functions on Euclidean space preserve or otherwise transform mutual dimensions between points.
Tweets as impact indicators: Examining the implications of automated bot accounts on Twitter
Venue: arXiv cs.DL 2014
Year: 2014

This brief communication presents preliminary findings on automated Twitter accounts distributing links to scientific papers deposited on the preprint repository arXiv. It discusses the implication of the presence of such bots from the perspective of social media metrics (altmetrics), where mentions of scholarly documents on Twitter have been suggested as a means of measuring impact that is both broader and timelier than citations. We present preliminary findings that automated Twitter accounts create a considerable amount of tweets to scientific papers and that they behave differently than common social bots, which has critical implications for the use of raw tweet counts in research evaluation and assessment. We discuss some definitions of Twitter cyborgs and bots in scholarly communication and propose differentiating between different levels of engagement from tweeting only bibliographic information to discussing or commenting on the content of a paper.
Efficient HTTP based I/O on very large datasets for high performance computing with the libdavix library
Venue: arXiv cs.PF 2014
Year: 2014

Remote data access for data analysis in high performance computing is commonly done with specialized data access protocols and storage systems. These protocols are highly optimized for high throughput on very large datasets, multi-streams, high availability, low latency and efficient parallel I/O. The purpose of this paper is to describe how we have adapted a generic protocol, the Hyper Text Transport Protocol (HTTP) to make it a competitive alternative for high performance I/O and data analysis applications in a global computing grid: the Worldwide LHC Computing Grid. In this work, we first analyze the design differences between the HTTP protocol and the most common high performance I/O protocols, pointing out the main performance weaknesses of HTTP. Then, we describe in detail how we solved these issues. Our solutions have been implemented in a toolkit called davix, available through several recent Linux distributions. Finally, we describe the results of our benchmarks where we compare the performance of davix against a HPC specific protocol for a data analysis use case.
An Efficient Algorithm for Unweighted Spectral Graph Sparsification
Venue: arXiv cs.DS 2014
Year: 2014

Spectral graph sparsification has emerged as a powerful tool in the analysis of large-scale networks by reducing the overall number of edges, while maintaining a comparable graph Laplacian matrix. In this paper, we present an efficient algorithm for the construction of a new type of spectral sparsifier, the unweighted spectral sparsifier. Given a general undirected and unweighted graph $G = (V, E)$ and an integer $\ell < |E|$ (the number of edges in $E$), we compute an unweighted graph $H = (V, F)$ with $F \subset E$ and $|F| = \ell$ such that for every $x \in \mathbb{R}^{V}$ \[ {\displaystyle \frac{x^T L_G x}κ \leq x^T L_H x \leq x^T L_G x,} \] where $L_G$ and $L_H$ are the Laplacian matrices for $G$ and $H$, respectively, and $κ\geq 1$ is a slowly-varying function of $|V|, |E|$ and $\ell$. This work addresses the open question of the existence of unweighted graph sparsifiers for unweighted graphs. Additionally, our algorithm can efficiently compute unweighted graph sparsifiers for weighted graphs, leading to sparsified graphs that retain the weights of the original graphs.
USBcat - Towards an Intrusion Surveillance Toolset
Venue: arXiv cs.CR 2014
Year: 2014

This paper identifies an intrusion surveillance framework which provides an analyst with the ability to investigate and monitor cyber-attacks in a covert manner. Where cyber-attacks are perpetrated for the purposes of espionage the ability to understand an adversary's techniques and objectives are an important element in network and computer security. With the appropriate toolset, security investigators would be permitted to perform both live and stealthy counter-intelligence operations by observing the behaviour and communications of the intruder. Subsequently a more complete picture of the attacker's identity, objectives, capabilities, and infiltration could be formulated than is possible with present technologies. This research focused on developing an extensible framework to permit the covert investigation of malware. Additionally, a Universal Serial Bus (USB) Mass Storage Device (MSD) based covert channel was designed to enable remote command and control of the framework. The work was validated through the design, implementation and testing of a toolset.
ALICE: An Advanced Logic for Interactive Component Engineering
Venue: arXiv cs.SE 2014
Year: 2014

This paper presents an overview of the verification framework ALICE in its current version 0.7. It is based on the generic theorem prover Isabelle [Pau03a]. Within ALICE a software or hardware component is specified as a state-full black-box with directed communication channels. Components send and receive asynchronous messages via these channels. The behavior of a component is generally described as a relation on the observations in form of streams of messages flowing over its input and output channels. Untimed and timed as well as state-based, recursive, relational, equational, assumption/guarantee, and functional styles of specification are supported. Hence, ALICE is well suited for the formalization and verification of distributed systems modeled with this stream-processing paradigm.
Constrained Runs algorithm as a lifting operator for the Boltzmann equation
Venue: arXiv cs.CE 2014
Year: 2014

Lifting operators play an important role in starting a kinetic Boltzmann model from given macroscopic information. The macroscopic variables need to be mapped to the distribution functions, mesoscopic variables of the Boltzmann model. A well-known numerical method for the initialization of Boltzmann models is the Constrained Runs algorithm. This algorithm is used in literature for the initialization of lattice Boltzmann models, special discretizations of the Boltzmann equation. It is based on the attraction of the dynamics toward the slow manifold and uses lattice Boltzmann steps to converge to the desired dynamics on the slow manifold. We focus on applying the Constrained Runs algorithm to map density, average flow velocity, and temperature, the macroscopic variables, to distribution functions. Furthermore, we do not consider only lattice Boltzmann models. We want to perform the algorithm for different discretizations of the Boltzmann equation and consider a standard finite volume discretization.
A Review of CUDA, MapReduce, and Pthreads Parallel Computing Models
Venue: arXiv cs.DC 2014
Year: 2014

The advent of high performance computing (HPC) and graphics processing units (GPU), present an enormous computation resource for Large data transactions (big data) that require parallel processing for robust and prompt data analysis. While a number of HPC frameworks have been proposed, parallel programming models present a number of challenges, for instance, how to fully utilize features in the different programming models to implement and manage parallelism via multi-threading in both CPUs and GPUs. In this paper, we take an overview of three parallel programming models, CUDA, MapReduce, and Pthreads. The goal is to explore literature on the subject and provide a high level view of the features presented in the programming models to assist high performance users with a concise understanding of parallel programming concepts and thus faster implementation of big data projects using high performance computing.
Reconstructive Sparse Code Transfer for Contour Detection and Semantic Labeling
Venue: arXiv cs.CV 2014
Year: 2014

We frame the task of predicting a semantic labeling as a sparse reconstruction procedure that applies a target-specific learned transfer function to a generic deep sparse code representation of an image. This strategy partitions training into two distinct stages. First, in an unsupervised manner, we learn a set of generic dictionaries optimized for sparse coding of image patches. We train a multilayer representation via recursive sparse dictionary learning on pooled codes output by earlier layers. Second, we encode all training images with the generic dictionaries and learn a transfer function that optimizes reconstruction of patches extracted from annotated ground-truth given the sparse codes of their corresponding image patches. At test time, we encode a novel image using the generic dictionaries and then reconstruct using the transfer function. The output reconstruction is a semantic labeling of the test image. Applying this strategy to the task of contour detection, we demonstrate performance competitive with state-of-the-art systems. Unlike almost all prior work, our approach obviates the need for any form of hand-designed features or filters. To illustrate general applicability, we also show initial results on semantic part labeling of human faces. The effectiveness of our approach opens new avenues for research on deep sparse representations. Our classifiers utilize this representation in a novel manner. Rather than acting on nodes in the deepest layer, they attach to nodes along a slice through multiple layers of the network in order to make predictions about local patches. Our flexible combination of a generatively learned sparse representation with discriminatively trained transfer classifiers extends the notion of sparse reconstruction to encompass arbitrary semantic labeling tasks.
Factors Influencing Quality of Mobile Apps:Role of Mobile App Development Life Cycle
Venue: arXiv cs.SE 2014
Year: 2014

In this paper, The mobile application field has been receiving astronomical attention from the past few years due to the growing number of mobile app downloads and withal due to the revenues being engendered .With the surge in the number of apps, the number of lamentable apps/failing apps has withal been growing.Interesting mobile app statistics are included in this paper which might avail the developers understand the concerns and merits of mobile apps.The authors have made an effort to integrate all the crucial factors that cause apps to fail which include negligence by the developers, technical issues, inadequate marketing efforts, and high prospects of the users/consumers.The paper provides suggestions to eschew failure of apps. As per the various surveys, the number of lamentable/failing apps is growing enormously, primarily because mobile app developers are not adopting a standard development life cycle for the development of apps. In this paper, we have developed a mobile application with the aid of traditional software development life cycle phases (Requirements, Design, Develop, Test, and, Maintenance) and we have used UML, M-UML, and mobile application development technologies.
Non-parametric Bayesian Learning with Deep Learning Structure and Its Applications in Wireless Networks
Venue: arXiv cs.LG 2014
Year: 2014

In this paper, we present an infinite hierarchical non-parametric Bayesian model to extract the hidden factors over observed data, where the number of hidden factors for each layer is unknown and can be potentially infinite. Moreover, the number of layers can also be infinite. We construct the model structure that allows continuous values for the hidden factors and weights, which makes the model suitable for various applications. We use the Metropolis-Hastings method to infer the model structure. Then the performance of the algorithm is evaluated by the experiments. Simulation results show that the model fits the underlying structure of simulated data.
Accurate Local Estimation of Geo-Coordinates for Social Media Posts
Venue: arXiv cs.IR 2014
Year: 2014

Associating geo-coordinates with the content of social media posts can enhance many existing applications and services and enable a host of new ones. Unfortunately, a majority of social media posts are not tagged with geo-coordinates. Even when location data is available, it may be inaccurate, very broad or sometimes fictitious. Contemporary location estimation approaches based on analyzing the content of these posts can identify only broad areas such as a city, which limits their usefulness. To address these shortcomings, this paper proposes a methodology to narrowly estimate the geo-coordinates of social media posts with high accuracy. The methodology relies solely on the content of these posts and prior knowledge of the wide geographical region from where the posts originate. An ensemble of language models, which are smoothed over non-overlapping sub-regions of a wider region, lie at the heart of the methodology. Experimental evaluation using a corpus of over half a million tweets from New York City shows that the approach, on an average, estimates locations of tweets to within just 2.15km of their actual positions.
Robust Topological Feature Extraction for Mapping of Environments using Bio-Inspired Sensor Networks
Venue: arXiv cs.RO 2014
Year: 2014

In this paper, we exploit minimal sensing information gathered from biologically inspired sensor networks to perform exploration and mapping in an unknown environment. A probabilistic motion model of mobile sensing nodes, inspired by motion characteristics of cockroaches, is utilized to extract weak encounter information in order to build a topological representation of the environment. Neighbor to neighbor interactions among the nodes are exploited to build point clouds representing spatial features of the manifold characterizing the environment based on the sampled data. To extract dominant features from sampled data, topological data analysis is used to produce persistence intervals for features, to be used for topological mapping. In order to improve robustness characteristics of the sampled data with respect to outliers, density based subsampling algorithms are employed. Moreover, a robust scale-invariant classification algorithm for persistence diagrams is proposed to provide a quantitative representation of desired features in the data. Furthermore, various strategies for defining encounter metrics with different degrees of information regarding agents' motion are suggested to enhance the precision of the estimation and classification performance of the topological method.