OpenRaiser/Intern-Atlas huggingface dataset:
Observationally Cooperative Multithreading
Venue: arXiv cs.PL 2015
Year: 2015

Despite widespread interest in multicore computing, concur- rency models in mainstream languages often lead to subtle, error-prone code. Observationally Cooperative Multithreading (OCM) is a new approach to shared-memory parallelism. Programmers write code using the well-understood cooperative (i.e., nonpreemptive) multithreading model for uniprocessors. OCM then allows threads to run in parallel, so long as results remain consistent with the cooperative model. Programmers benefit because they can reason largely sequentially. Remaining interthread interactions are far less chaotic than in other models, permitting easier reasoning and debugging. Programmers can also defer the choice of concurrency-control mechanism (e.g., locks or transactions) until after they have written their programs, at which point they can compare concurrency-control strategies and choose the one that offers the best performance. Implementers and researchers also benefit from the agnostic nature of OCM -- it provides a level of abstraction to investigate, compare, and combine a variety of interesting concurrency-control techniques.
Affective Music Information Retrieval
Venue: arXiv cs.IR 2015
Year: 2015

Much of the appeal of music lies in its power to convey emotions/moods and to evoke them in listeners. In consequence, the past decade witnessed a growing interest in modeling emotions from musical signals in the music information retrieval (MIR) community. In this article, we present a novel generative approach to music emotion modeling, with a specific focus on the valence-arousal (VA) dimension model of emotion. The presented generative model, called \emph{acoustic emotion Gaussians} (AEG), better accounts for the subjectivity of emotion perception by the use of probability distributions. Specifically, it learns from the emotion annotations of multiple subjects a Gaussian mixture model in the VA space with prior constraints on the corresponding acoustic features of the training music pieces. Such a computational framework is technically sound, capable of learning in an online fashion, and thus applicable to a variety of applications, including user-independent (general) and user-dependent (personalized) emotion recognition and emotion-based music retrieval. We report evaluations of the aforementioned applications of AEG on a larger-scale emotion-annotated corpora, AMG1608, to demonstrate the effectiveness of AEG and to showcase how evaluations are conducted for research on emotion-based MIR. Directions of future work are also discussed.
Dengue disease prediction using weka data mining tool
Venue: arXiv cs.CY 2015
Year: 2015

Dengue is a life threatening disease prevalent in several developed as well as developing countries like India.In this paper we discuss various algorithm approaches of data mining that have been utilized for dengue disease prediction. Data mining is a well known technique used by health organizations for classification of diseases such as dengue, diabetes and cancer in bioinformatics research. In the proposed approach we have used WEKA with 10 cross validation to evaluate data and compare results. Weka has an extensive collection of different machine learning and data mining algorithms. In this paper we have firstly classified the dengue data set and then compared the different data mining techniques in weka through Explorer, knowledge flow and Experimenter interfaces. Furthermore in order to validate our approach we have used a dengue dataset with 108 instances but weka used 99 rows and 18 attributes to determine the prediction of disease and their accuracy using classifications of different algorithms to find out the best performance. The main objective of this paper is to classify data and assist the users in extracting useful information from data and easily identify a suitable algorithm for accurate predictive model from it. From the findings of this paper it can be concluded that Naïve Bayes and J48 are the best performance algorithms for classified accuracy because they achieved maximum accuracy= 100% with 99 correctly classified instances, maximum ROC = 1, had least mean absolute error and it took minimum time for building this model through Explorer and Knowledge flow results
Cross-Modality Hashing with Partial Correspondence
Venue: arXiv cs.CV 2015
Year: 2015

Learning a hashing function for cross-media search is very desirable due to its low storage cost and fast query speed. However, the data crawled from Internet cannot always guarantee good correspondence among different modalities which affects the learning for hashing function. In this paper, we focus on cross-modal hashing with partially corresponded data. The data without full correspondence are made in use to enhance the hashing performance. The experiments on Wiki and NUS-WIDE datasets demonstrates that the proposed method outperforms some state-of-the-art hashing approaches with fewer correspondence information.
NEFI: Network Extraction From Images
Venue: arXiv cs.CV 2015
Year: 2015

Networks and network-like structures are amongst the central building blocks of many technological and biological systems. Given a mathematical graph representation of a network, methods from graph theory enable a precise investigation of its properties. Software for the analysis of graphs is widely available and has been applied to graphs describing large scale networks such as social networks, protein-interaction networks, etc. In these applications, graph acquisition, i.e., the extraction of a mathematical graph from a network, is relatively simple. However, for many network-like structures, e.g. leaf venations, slime molds and mud cracks, data collection relies on images where graph extraction requires domain-specific solutions or even manual. Here we introduce Network Extraction From Images, NEFI, a software tool that automatically extracts accurate graphs from images of a wide range of networks originating in various domains. While there is previous work on graph extraction from images, theoretical results are fully accessible only to an expert audience and ready-to-use implementations for non-experts are rarely available or insufficiently documented. NEFI provides a novel platform allowing practitioners from many disciplines to easily extract graph representations from images by supplying flexible tools from image processing, computer vision and graph theory bundled in a convenient package. Thus, NEFI constitutes a scalable alternative to tedious and error-prone manual graph extraction and special purpose tools. We anticipate NEFI to enable the collection of larger datasets by reducing the time spent on graph extraction. The analysis of these new datasets may open up the possibility to gain new insights into the structure and function of various types of networks. NEFI is open source and available http://nefi.mpi-inf.mpg.de.
How to Bootstrap Anonymous Communication
Venue: arXiv cs.CR 2015
Year: 2015

We ask whether it is possible to anonymously communicate a large amount of data using only public (non-anonymous) communication together with a small anonymous channel. We think this is a central question in the theory of anonymous communication and to the best of our knowledge this is the first formal study in this direction. To solve this problem, we introduce the concept of anonymous steganography: think of a leaker Lea who wants to leak a large document to Joe the journalist. Using anonymous steganography Lea can embed this document in innocent looking communication on some popular website (such as cat videos on YouTube or funny memes on 9GAG). Then Lea provides Joe with a short key $k$ which, when applied to the entire website, recovers the document while hiding the identity of Lea among the large number of users of the website. Our contributions include: - Introducing and formally defining anonymous steganography, - A construction showing that anonymous steganography is possible (which uses recent results in circuits obfuscation), - A lower bound on the number of bits which are needed to bootstrap anonymous communication.
The Price of Local Power Control in Wireless Scheduling
Venue: arXiv cs.NI 2015
Year: 2015

We consider the problem of scheduling wireless links in the physical model, where we seek an assignment of power levels and a partition of the given set of links into the minimum number of subsets satisfying the signal-to-interference-and-noise-ratio (SINR) constraints. Specifically, we are interested in the efficiency of local power assignment schemes, or oblivious power schemes, in approximating wireless scheduling. Oblivious power schemes are motivated by networking scenarios when power levels must be decided in advance, and not as part of the scheduling computation. We first show that the known algorithms fail to obtain sub-logarithmic bounds; that is, their approximation ratio are $\tildeΩ(\log \max(Δ,n))$, where $n$ is the number of links, $Δ$ is the ratio of the maximum and minimum link lengths, and $\tildeΩ$ hides doubly-logarithmic factors. We then present the first $O(\log{\logΔ})$-approximation algorithm, which is known to be best possible (in terms of $Δ$) for oblivious power schemes. We achieve this by representing interference by a conflict graph, which allows the application of graph-theoretic results for a variety of related problems, including the weighted capacity problem. We explore further the contours of approximability, and find the choice of power assignment matters; that not all metric spaces are equal; and that the presence of weak links makes the problem harder. Combined, our result resolve the price of oblivious power for wireless scheduling, or the value of allowing unfettered power control.
Controlled Data Sharing for Collaborative Predictive Blacklisting
Venue: arXiv cs.CR 2015
Year: 2015

Although sharing data across organizations is often advocated as a promising way to enhance cybersecurity, collaborative initiatives are rarely put into practice owing to confidentiality, trust, and liability challenges. In this paper, we investigate whether collaborative threat mitigation can be realized via a controlled data sharing approach, whereby organizations make informed decisions as to whether or not, and how much, to share. Using appropriate cryptographic tools, entities can estimate the benefits of collaboration and agree on what to share in a privacy-preserving way, without having to disclose their datasets. We focus on collaborative predictive blacklisting, i.e., forecasting attack sources based on one's logs and those contributed by other organizations. We study the impact of different sharing strategies by experimenting on a real-world dataset of two billion suspicious IP addresses collected from Dshield over two months. We find that controlled data sharing yields up to 105% accuracy improvement on average, while also reducing the false positive rate.
On learning k-parities with and without noise
Venue: arXiv cs.DS 2015
Year: 2015

We first consider the problem of learning $k$-parities in the on-line mistake-bound model: given a hidden vector $x \in \{0,1\}^n$ with $|x|=k$ and a sequence of "questions" $a_1, a_2, ...\in \{0,1\}^n$, where the algorithm must reply to each question with $< a_i, x> \pmod 2$, what is the best tradeoff between the number of mistakes made by the algorithm and its time complexity? We improve the previous best result of Buhrman et al. by an $\exp(k)$ factor in the time complexity. Second, we consider the problem of learning $k$-parities in the presence of classification noise of rate $η\in (0,1/2)$. A polynomial time algorithm for this problem (when $η> 0$ and $k = ω(1)$) is a longstanding challenge in learning theory. Grigorescu et al. showed an algorithm running in time ${n \choose k/2}^{1 + 4η^2 +o(1)}$. Note that this algorithm inherently requires time ${n \choose k/2}$ even when the noise rate $η$ is polynomially small. We observe that for sufficiently small noise rate, it is possible to break the $n \choose k/2$ barrier. In particular, if for some function $f(n) = ω(1)$ and $α\in [1/2, 1)$, $k = n/f(n)$ and $η= o(f(n)^{- α}/\log n)$, then there is an algorithm for the problem with running time $poly(n)\cdot {n \choose k}^{1-α} \cdot e^{-k/4.01}$.
Distributed Inference in the Presence of Eavesdroppers: A Survey
Venue: arXiv cs.CR 2015
Year: 2015

The distributed inference framework comprises of a group of spatially distributed nodes which acquire observations about a phenomenon of interest. Due to bandwidth and energy constraints, the nodes often quantize their observations into a finite-bit local message before sending it to the fusion center (FC). Based on the local summary statistics transmitted by nodes, the FC makes a global decision about the presence of the phenomenon of interest. The distributed and broadcast nature of such systems makes them quite vulnerable to different types of attacks. This paper addresses the problem of secure communication in the presence of eavesdroppers. In particular, we focus on efficient mitigation schemes to mitigate the impact of eavesdropping. We present an overview of the distributed inference schemes under secrecy constraints and describe the currently available approaches in the context of distributed detection and estimation followed by a discussion on avenues for future research.
A New Penta-valued Logic Based Knowledge Representation
Venue: arXiv cs.AI 2015
Year: 2015

In this paper a knowledge representation model are proposed, FP5, which combine the ideas from fuzzy sets and penta-valued logic. FP5 represents imprecise properties whose accomplished degree is undefined, contradictory or indeterminate for some objects. Basic operations of conjunction, disjunction and negation are introduced. Relations to other representation models like fuzzy sets, intuitionistic, paraconsistent and bipolar fuzzy sets are discussed.
On Detection Issues in the SC-based Uplink of a MU-MIMO System with a Large Number of BS Antennas
Venue: arXiv cs.IT 2015
Year: 2015

This paper deals with SC/FDE within a MU-MIMO system where a large number of BS antennas is adopted. In this context, either linear or reduced-complexity iterative DF detection techniques are considered. Regarding performance evaluation by simulation, appropriate semi-analytical methods are proposed. This paper includes a detailed evaluation of BER performances for uncoded 4-Quadrature Amplitude Modulation (4-QAM) schemes and a MU-MIMO channel with uncorrelated Rayleigh fading. The accuracy of performance results obtained through the semi-analytical simulation methods is assessed by means of parallel conventional Monte Carlo simulations, under the assumptions of perfect power control and perfect channel estimation. The performance results are discussed in detail, with the help of selected performance bounds. We emphasize that a moderately large number of BS antennas is enough to closely approximate the SIMO MFB performance, especially when using the suggested low-complexity iterative DF technique, which does not require matrix inversion operations. We also emphasize the achievable "massive MIMO" effects, even for strongly reduced-complexity linear detection techniques, provided that the number of BS antennas is much higher than the number of antennas which are jointly employed in the terminals of the multiple autonomous users.
Spread of Influence in Weighted Networks under Time and Budget Constraints
Venue: arXiv cs.SI 2015
Year: 2015

Given a network represented by a weighted directed graph G, we consider the problem of finding a bounded cost set of nodes S such that the influence spreading from S in G, within a given time bound, is as large as possible. The dynamic that governs the spread of influence is the following: initially only elements in S are influenced; subsequently at each round, the set of influenced elements is augmented by all nodes in the network that have a sufficiently large number of already influenced neighbors. We prove that the problem is NP-hard, even in simple networks like complete graphs and trees. We also derive a series of positive results. We present exact pseudo-polynomial time algorithms for general trees, that become polynomial time in case the trees are unweighted. This last result improves on previously published results. We also design polynomial time algorithms for general weighted paths and cycles, and for unweighted complete graphs.
NP-Hardness and Inapproximability of Sparse PCA
Venue: arXiv cs.LG 2015
Year: 2015

We give a reduction from {\sc clique} to establish that sparse PCA is NP-hard. The reduction has a gap which we use to exclude an FPTAS for sparse PCA (unless P=NP). Under weaker complexity assumptions, we also exclude polynomial constant-factor approximation algorithms.
Scale-Free Algorithms for Online Linear Optimization
Venue: arXiv cs.LG 2015
Year: 2015

We design algorithms for online linear optimization that have optimal regret and at the same time do not need to know any upper or lower bounds on the norm of the loss vectors. We achieve adaptiveness to norms of loss vectors by scale invariance, i.e., our algorithms make exactly the same decisions if the sequence of loss vectors is multiplied by any positive constant. Our algorithms work for any decision set, bounded or unbounded. For unbounded decisions sets, these are the first truly adaptive algorithms for online linear optimization.
Spike Event Based Learning in Neural Networks
Venue: arXiv cs.NE 2015
Year: 2015

A scheme is derived for learning connectivity in spiking neural networks. The scheme learns instantaneous firing rates that are conditional on the activity in other parts of the network. The scheme is independent of the choice of neuron dynamics or activation function, and network architecture. It involves two simple, online, local learning rules that are applied only in response to occurrences of spike events. This scheme provides a direct method for transferring ideas between the fields of deep learning and computational neuroscience. This learning scheme is demonstrated using a layered feedforward spiking neural network trained self-supervised on a prediction and classification task for moving MNIST images collected using a Dynamic Vision Sensor.
Impact of Metro Cell Antenna Pattern and Downtilt in Heterogeneous Networks
Venue: arXiv cs.IT 2015
Year: 2015

This article discusses the positive impact metro cell antennas with narrow vertical beamwidth and electrical downtilt can have on heterogeneous cellular networks. Using a model of random cell placement based on Poisson distribution, along with an innovative 3D building model that quantifies blockage due to shadowing, it is demonstrated that network spectral efficiency and average user throughput both increase as vertical beamwidth is decreased and downtilt is applied to metro cell transmission. Moreover, the network becomes more energy efficient. Importantly, these additional gains in network performance can be achieved without any cooperation or exchange of information between macro cell base stations and metro cells.
A General Multi-Graph Matching Approach via Graduated Consistency-regularized Boosting
Venue: arXiv cs.CV 2015
Year: 2015

This paper addresses the problem of matching $N$ weighted graphs referring to an identical object or category. More specifically, matching the common node correspondences among graphs. This multi-graph matching problem involves two ingredients affecting the overall accuracy: i) the local pairwise matching affinity score among graphs; ii) the global matching consistency that measures the uniqueness of the pairwise matching results by different chaining orders. Previous studies typically either enforce the matching consistency constraints in the beginning of iterative optimization, which may propagate matching error both over iterations and across graph pairs; or separate affinity optimizing and consistency regularization in two steps. This paper is motivated by the observation that matching consistency can serve as a regularizer in the affinity objective function when the function is biased due to noises or inappropriate modeling. We propose multi-graph matching methods to incorporate the two aspects by boosting the affinity score, meanwhile gradually infusing the consistency as a regularizer. Furthermore, we propose a node-wise consistency/affinity-driven mechanism to elicit the common inlier nodes out of the irrelevant outliers. Extensive results on both synthetic and public image datasets demonstrate the competency of the proposed algorithms.
Annotated Stack Trees
Venue: arXiv cs.FL 2015
Year: 2015

Annotated pushdown automata provide an automaton model of higher-order recursion schemes, which may in turn be used to model higher-order programs for the purposes of verification. We study Ground Annotated Stack Tree Rewrite Systems -- a tree rewrite system where each node is labelled by the configuration of an annotated pushdown automaton. This allows the modelling of fork and join constructs in higher-order programs and is a generalisation of higher-order stack trees recently introduced by Penelle. We show that, given a regular set of annotated stack trees, the set of trees that can reach this set is also regular, and constructible in n-EXPTIME for an order-n system, which is optimal. We also show that our construction can be extended to allow a global state through which unrelated nodes of the tree may communicate, provided the number of communications is subject to a fixed bound.
Wavelet Analysis as an Information Processing Technique
Venue: arXiv cs.IT 2015
Year: 2015

A new interpretation for the wavelet analysis is reported, which can is viewed as an information processing technique. It was recently proposed that every basic wavelet could be associated with a proper probability density, allowing defining the entropy of a wavelet. Introducing now the concept of wavelet mutual information between a signal and an analysing wavelet fulfils the foundations of a wavelet information theory (WIT). Both continuous and discrete time signals are considered. Finally, we showed how to compute the information provided by a multiresolution analysis by means of the inhomogeneous wavelet expansion. Highlighting ideas behind the WIT are presented.
Converting ECG and other paper legated biomedical maps into digital signals
Venue: arXiv cs.OH 2015
Year: 2015

This paper presents a digital signal processing tool developed using MatlabTM, which provides a very low-cost and effective strategy for analog-to-digital conversion of legated paper biomedical maps without requiring dedicated hardware. This software-based approach is particularly helpful for digitalizing biomedical signals acquired from analogical devices equipped with a plottingter. Albeit signals used in biomedical diagnosis are the primary concern, this imaging processing tool is suitable to modernize facilities in a non-expensive way. Legated paper ECG and EEG charts can be fast and efficiently digitalized in order to be added in existing up-to-date medical data banks, improving the follow-up of patients.