OpenRaiser/Intern-Atlas huggingface dataset:
Self-organization on social media: endo-exo bursts and baseline fluctuations
Venue: arXiv cs.SI 2014
Year: 2014

A salient dynamic property of social media is bursting behavior. In this paper, we study bursting behavior in terms of the temporal relation between a preceding baseline fluctuation and the successive burst response using a frequency time series of 3,000 keywords on Twitter. We found that there is a fluctuation threshold up to which the burst size increases as the fluctuation increases and that above the threshold, there appears a variety of burst sizes. We call this threshold the critical threshold. Investigating this threshold in relation to endogenous bursts and exogenous bursts based on peak ratio and burst size reveals that the bursts below this threshold are endogenously caused and above this threshold, exogenous bursts emerge. Analysis of the 3,000 keywords shows that all the nouns have both endogenous and exogenous origins of bursts and that each keyword has a critical threshold in the baseline fluctuation value to distinguish between the two. Having a threshold for an input value for activating the system implies that Twitter is an excitable medium. These findings are useful for characterizing how excitable a keyword is on Twitter and could be used, for example, to predict the response to particular information on social media.
Real-Time and Efficient Method for Accuracy Enhancement of Edge Based License Plate Recognition System
Venue: arXiv cs.CV 2014
Year: 2014

License Plate Recognition plays an important role on the traffic monitoring and parking management. Administration and restriction of those transportation tools for their better service becomes very essential. In this paper, a fast and real time method has an appropriate application to find plates that the plat has tilt and the picture quality is poor. In the proposed method, at the beginning, the image is converted into binary mode with use of adaptive threshold. And with use of edge detection and morphology operation, plate number location has been specified and if the plat has tilt; its tilt is removed away. Then its characters are distinguished using image processing techniques. Finally, K Nearest Neighbour (KNN) classifier was used for character recognition. This method has been tested on available data set that has different images of the background, considering distance, and angel of view so that the correct extraction rate of plate reached at 98% and character recognition rate achieved at 99.12%. Further we tested our character recognition stage on Persian vehicle data set and we achieved 99% correct recognition rate.
Convolutional Neural Associative Memories: Massive Capacity with Noise Tolerance
Venue: arXiv cs.NE 2014
Year: 2014

The task of a neural associative memory is to retrieve a set of previously memorized patterns from their noisy versions using a network of neurons. An ideal network should have the ability to 1) learn a set of patterns as they arrive, 2) retrieve the correct patterns from noisy queries, and 3) maximize the pattern retrieval capacity while maintaining the reliability in responding to queries. The majority of work on neural associative memories has focused on designing networks capable of memorizing any set of randomly chosen patterns at the expense of limiting the retrieval capacity. In this paper, we show that if we target memorizing only those patterns that have inherent redundancy (i.e., belong to a subspace), we can obtain all the aforementioned properties. This is in sharp contrast with the previous work that could only improve one or two aspects at the expense of the third. More specifically, we propose framework based on a convolutional neural network along with an iterative algorithm that learns the redundancy among the patterns. The resulting network has a retrieval capacity that is exponential in the size of the network. Moreover, the asymptotic error correction performance of our network is linear in the size of the patterns. We then ex- tend our approach to deal with patterns lie approximately in a subspace. This extension allows us to memorize datasets containing natural patterns (e.g., images). Finally, we report experimental results on both synthetic and real datasets to support our claims.
2-Server PIR with sub-polynomial communication
Venue: arXiv cs.CC 2014
Year: 2014

A 2-server Private Information Retrieval (PIR) scheme allows a user to retrieve the $i$th bit of an $n$-bit database replicated among two servers (which do not communicate) while not revealing any information about $i$ to either server. In this work we construct a 1-round 2-server PIR with total communication cost $n^{O({\sqrt{\log\log n/\log n}})}$. This improves over the currently known 2-server protocols which require $O(n^{1/3})$ communication and matches the communication cost of known 3-server PIR schemes. Our improvement comes from reducing the number of servers in existing protocols, based on Matching Vector Codes, from 3 or 4 servers to 2. This is achieved by viewing these protocols in an algebraic way (using polynomial interpolation) and extending them using partial derivatives.
CrowdSTAR: A Social Task Routing Framework for Online Communities
Venue: arXiv cs.SI 2014
Year: 2014

The online communities available on the Web have shown to be significantly interactive and capable of collectively solving difficult tasks. Nevertheless, it is still a challenge to decide how a task should be dispatched through the network due to the high diversity of the communities and the dynamically changing expertise and social availability of their members. We introduce CrowdSTAR, a framework designed to route tasks across and within online crowds. CrowdSTAR indexes the topic-specific expertise and social features of the crowd contributors and then uses a routing algorithm, which suggests the best sources to ask based on the knowledge vs. availability trade-offs. We experimented with the proposed framework for question and answering scenarios by using two popular social networks as crowd candidates: Twitter and Quora.
Ordenação Baseada em Árvores de Fusão
Venue: arXiv cs.DS 2014
Year: 2014

Sorting is one of the most important problem in the computer science. After more than 60 years of studies, there are still many research devoted to develop faster sorting algorithms. This work aims to explain the Fusion Tree data structure. Fusion Tree was responsible for the first sorting algorithm with time $o(n \ lg n) $. ----- O problema da ordenação é sem dúvida um dos mais estudados na Ciência da Computação. No escopo da computação moderna, depois de mais de 60 anos de estudos, ainda existem muitas pesquisas que objetivam o desenvolvimento de algoritmos que solucionem uma ordenação mais rápida ou com menos recursos comparados a outros algoritmos já conhecidos. Há vários tipos de algoritmos de ordenação, alguns mais rápidos, outros mais econômicos em relação ao espaço e outros com algumas restrições com relação à entrada de dados. O objetivo deste trabalho é explicar a estrutura de dados Ávore de Fusão, responsável pelo primeiro algoritmo de ordenação com tempo inferior a $ n \lg n $, tempo esse que criou certa confusão, gerando uma errada crença de ser o menor possível para esse tipo de problema.
Substitute Based SCODE Word Embeddings in Supervised NLP Tasks
Venue: arXiv cs.CL 2014
Year: 2014

We analyze a word embedding method in supervised tasks. It maps words on a sphere such that words co-occurring in similar contexts lie closely. The similarity of contexts is measured by the distribution of substitutes that can fill them. We compared word embeddings, including more recent representations, in Named Entity Recognition (NER), Chunking, and Dependency Parsing. We examine our framework in multilingual dependency parsing as well. The results show that the proposed method achieves as good as or better results compared to the other word embeddings in the tasks we investigate. It achieves state-of-the-art results in multilingual dependency parsing. Word embeddings in 7 languages are available for public use.
An Easy yet Effective Method for Detecting Spatial Domain LSB Steganography
Venue: arXiv cs.MM 2014
Year: 2014

Digitization of image was a revolutionary step for the fields of photography and Image processing as this made the editing of images much effortless and easier. Image editing was not an issue until it was limited to corrective editing procedures used to enhance the quality of an image such as, contrast stretching, noise filtering, sharpening etc. But, it became a headache for many fields when image editing became manipulative. Digital images have become an easier source of tampering and forgery during last few decades. Today users and editing specialists, equipped with easily available image editing software, manipulate digital images with varied goals. Photo journalists often tamper photographs to give dramatic effect to their stories. Scientists and researchers use this trick to get theirs works published. Patients' diagnoses are misrepresented by manipulating medical imageries. Lawyers and Politicians use tampered images to direct the opinion of people or court to their favor. Terrorists, anti-social groups use manipulated Stego images for secret communication. In this paper we present an effective method for detecting spatial domain Steganography.
Survey of Parallel Computing with MATLAB
Venue: arXiv cs.DC 2014
Year: 2014

Matlab is one of the most widely used mathematical computing environments in technical computing. It has an interactive environment which provides high performance computing (HPC) procedures and easy to use. Parallel computing with Matlab has been an interested area for scientists of parallel computing researches for a number of years. Where there are many attempts to parallel Matlab. In this paper, we present most of the past,present attempts of parallel Matlab such as MatlabMPI, bcMPI, pMatlab, Star-P and PCT. Finally, we expect the future attempts.
OMP2HMPP: HMPP Source Code Generation from Programs with Pragma Extensions
Venue: arXiv cs.DC 2014
Year: 2014

High-performance computing are based more and more in heterogeneous architectures and GPGPUs have become one of the main integrated blocks in these, as the recently emerged Mali GPU in embedded systems or the NVIDIA GPUs in HPC servers. In both GPGPUs, programming could become a hurdle that can limit their adoption, since the programmer has to learn the hardware capabilities and the language to work with these. We present OMP2HMPP, a tool that, automatically trans-lates a high-level C source code(OpenMP) code into HMPP. The generated version rarely will differs from a hand-coded HMPP version, and will provide an important speedup, near 113%, that could be later improved by hand-coded CUDA. The generated code could be transported either to HPC servers and to embedded GPUs, due to the commonalities between them.
Ultra accurate collaborative information filtering via directed user similarity
Venue: arXiv cs.IR 2014
Year: 2014

A key challenge of the collaborative filtering (CF) information filtering is how to obtain the reliable and accurate results with the help of peers' recommendation. Since the similarities from small-degree users to large-degree users would be larger than the ones opposite direction, the large-degree users' selections are recommended extensively by the traditional second-order CF algorithms. By considering the users' similarity direction and the second-order correlations to depress the influence of mainstream preferences, we present the directed second-order CF (HDCF) algorithm specifically to address the challenge of accuracy and diversity of the CF algorithm. The numerical results for two benchmark data sets, MovieLens and Netflix, show that the accuracy of the new algorithm outperforms the state-of-the-art CF algorithms. Comparing with the CF algorithm based on random-walks proposed in the Ref.7, the average ranking score could reach 0.0767 and 0.0402, which is enhanced by 27.3\% and 19.1\% for MovieLens and Netflix respectively. In addition, the diversity, precision and recall are also enhanced greatly. Without relying on any context-specific information, tuning the similarity direction of CF algorithms could obtain accurate and diverse recommendations. This work suggests that the user similarity direction is an important factor to improve the personalized recommendation performance.
Efficient Design of Reversible Sequential Circuit
Venue: arXiv cs.ET 2014
Year: 2014

Reversible logic has come to the forefront of theoretical and applied research today. Although many researchers are investigating techniques to synthesize reversible combinational logic, there is little work in the area of sequential reversible logic. Latches and flip-flops are the most significant memory elements for the forthcoming sequential memory elements. In this paper, we proposed two new reversible logic gates MG-1 and MG-2. We then proposed new design techniques for latches and flip-flops with the help of the new proposed gates. The proposed designs are better than the existing ones in terms of number of gates, garbage outputs and delay.
Transmission of packets on a hierarchical network: Avalanches, statistics and explosive percolation
Venue: arXiv cs.SI 2014
Year: 2014

We discuss transport on load bearing branching hierarchical networks which can model diverse systems which can serve as models of river networks, computer networks, respiratory networks and granular media. We study avalanche transmissions and directed percolation on these networks, and on the V lattice, i.e., the strongest realization of the lattice. We find that typical realizations of the lattice show multimodal distributions for the avalanche transmissions, and a second order transition for directed percolation. On the other hand, the V lattice shows power - law behavior for avalanche transmissions, and a first order (explosive) transition to percolation. The V lattice is thus the critical case of hierarchical networks. We note that small perturbations to the V lattice destroy the power-law behavior of the distributions, and the first order nature of the percolation. We discuss the implications of our results.
Conditional Plausibility Measures and Bayesian Networks
Venue: arXiv cs.AI 2014
Year: 2014

A general notion of algebraic conditional plausibility measures is defined. Probability measures, ranking functions, possibility measures, and (under the appropriate definitions) sets of probability measures can all be viewed as defining algebraic conditional plausibility measures. It is shown that the technology of Bayesian networks can be applied to algebraic conditional plausibility measures.
Towards Automatic Migration of ROS Components from Software to Hardware
Venue: arXiv cs.RO 2014
Year: 2014

The use of the ROS middleware is a growing trend in robotics in general, ROS and hard real-time embedded systems have however not been easily uniteable while retaining the same overall communication and processing methodology at all levels. In this paper we present an approach aimed at tackling the schism between high-level, flexible software and low-level, real-time software. The key idea of our approach is to enable software components written for a high-level publish-subscribe software architecture to be automatically migrated to a dedicated hardware architecture implemented using programmable logic. Our approach is based on the Unity framework, a unified software/hardware framework based on FPGAs for quickly interfacing high-level software to low-level robotics hardware.
Chasing Ghosts: Competing with Stateful Policies
Venue: arXiv cs.LG 2014
Year: 2014

We consider sequential decision making in a setting where regret is measured with respect to a set of stateful reference policies, and feedback is limited to observing the rewards of the actions performed (the so called "bandit" setting). If either the reference policies are stateless rather than stateful, or the feedback includes the rewards of all actions (the so called "expert" setting), previous work shows that the optimal regret grows like $Θ(\sqrt{T})$ in terms of the number of decision rounds $T$. The difficulty in our setting is that the decision maker unavoidably loses track of the internal states of the reference policies, and thus cannot reliably attribute rewards observed in a certain round to any of the reference policies. In fact, in this setting it is impossible for the algorithm to estimate which policy gives the highest (or even approximately highest) total reward. Nevertheless, we design an algorithm that achieves expected regret that is sublinear in $T$, of the form $O( T/\log^{1/4}{T})$. Our algorithm is based on a certain local repetition lemma that may be of independent interest. We also show that no algorithm can guarantee expected regret better than $O( T/\log^{3/2} T)$.
Speed-scaling with no Preemptions
Venue: arXiv cs.DS 2014
Year: 2014

We revisit the non-preemptive speed-scaling problem, in which a set of jobs have to be executed on a single or a set of parallel speed-scalable processor(s) between their release dates and deadlines so that the energy consumption to be minimized. We adopt the speed-scaling mechanism first introduced in [Yao et al., FOCS 1995] according to which the power dissipated is a convex function of the processor's speed. Intuitively, the higher is the speed of a processor, the higher is the energy consumption. For the single-processor case, we improve the best known approximation algorithm by providing a $(1+ε)^α\tilde{B}_α$-approximation algorithm, where $\tilde{B}_α$ is a generalization of the Bell number. For the multiprocessor case, we present an approximation algorithm of ratio $\tilde{B}_α((1+ε)(1+\frac{w_{\max}}{w_{\min}}))^α$ improving the best known result by a factor of $(\frac{5}{2})^{α-1}(\frac{w_{\max}}{w_{\min}})^α$. Notice that our result holds for the fully heterogeneous environment while the previous known result holds only in the more restricted case of parallel processors with identical power functions.
Hyperspectral Imaging and Analysis for Sparse Reconstruction and Recognition
Venue: arXiv cs.CV 2014
Year: 2014

This thesis proposes spatio-spectral techniques for hyperspectral image analysis. Adaptive spatio-spectral support and variable exposure hyperspectral imaging is demonstrated to improve spectral reflectance recovery from hyperspectral images. Novel spectral dimensionality reduction techniques have been proposed from the perspective of spectral only and spatio-spectral information preservation. It was found that the joint sparse and joint group sparse hyperspectral image models achieve lower reconstruction error and higher recognition accuracy using only a small subset of bands. Hyperspectral image databases have been developed and made publicly available for further research in compressed hyperspectral imaging, forensic document analysis and spectral reflectance recovery.
Security issues for data sharing and service interoperability in eHealth systems: the Nu.Sa. test bed
Venue: arXiv cs.CR 2014
Year: 2014

The aim of the Nu.Sa. project is the definition of national level data standards to collect data coming from General Practitioners' Electronic Health Records and to allow secure data sharing between them. This paper introduces the Nu.Sa. framework and is mainly focused on security issues. A solution for secure data sharing and service interoperability is presented and implemented in the actual system used around Italy. The solution is strongly focused on privacy and correct data sharing with a complete set of tools devoted to authorization, encryption and decryption in a data sharing environment and a distributed architecture. The implemented system with more than one year of experiences in thousands of test cases shows a good feasibility of the approach and a future scalability in a cloud based architecture.
OpenML: networked science in machine learning
Venue: arXiv cs.LG 2014
Year: 2014

Many sciences have made significant breakthroughs by adopting online tools that help organize, structure and mine information that is too detailed to be printed in journals. In this paper, we introduce OpenML, a place for machine learning researchers to share and organize data in fine detail, so that they can work more effectively, be more visible, and collaborate with others to tackle harder problems. We discuss how OpenML relates to other examples of networked science and what benefits it brings for machine learning research, individual scientists, as well as students and practitioners.
Massive Antenna Arrays with Low Front-End Hardware Complexity: An Enabling Technology for the Emerging Small Cell and Distributed Network Architectures
Venue: arXiv cs.NI 2014
Year: 2014

This paper presents the current state-of-the-art of massive antenna array architectures with significant front-end hardware savings, as an enabler for future small and powerful cell nodes that will be able to carry massive MIMO technology. Radio frequency (RF) hardware architectures with a single power amplifier are reviewed, compared, and found superior to conventional MIMO implementations in terms of cost, dissipated heat, and physical size. This progress on the RF-side allows to merge the two competing cellular concepts of virtual and massive MIMO into a hybrid approach of remote radio heads with massive MIMO arrays.
A CUDA-Based Real Parameter Optimization Benchmark
Venue: arXiv cs.NE 2014
Year: 2014

Benchmarking is key for developing and comparing optimization algorithms. In this paper, a CUDA-based real parameter optimization benchmark (cuROB) is introduced. Test functions of diverse properties are included within cuROB and implemented efficiently with CUDA. Speedup of one order of magnitude can be achieved in comparison with CPU-based benchmark of CEC'14.
A Hash-based Co-Clustering Algorithm for Categorical Data
Venue: arXiv cs.LG 2014
Year: 2014

Many real-life data are described by categorical attributes without a pre-classification. A common data mining method used to extract information from this type of data is clustering. This method group together the samples from the data that are more similar than all other samples. But, categorical data pose a challenge when extracting information because: the calculation of two objects similarity is usually done by measuring the number of common features, but ignore a possible importance weighting; if the data may be divided differently according to different subsets of the features, the algorithm may find clusters with different meanings from each other, difficulting the post analysis. Data Co-Clustering of categorical data is the technique that tries to find subsets of samples that share a subset of features in common. By doing so, not only a sample may belong to more than one cluster but, the feature selection of each cluster describe its own characteristics. In this paper a novel Co-Clustering technique for categorical data is proposed by using Locality Sensitive Hashing technique in order to preprocess a list of Co-Clusters seeds based on a previous research. Results indicate this technique is capable of finding high quality Co-Clusters in many different categorical data sets and scales linearly with the data set size.