OpenRaiser/Intern-Atlas huggingface dataset:
Dynamic Collective Choice: Social Optima
Venue: arXiv cs.SY 2016
Year: 2016

We consider a dynamic collective choice problem where a large number of players are cooperatively choosing between multiple destinations while being influenced by the behavior of the group. For example, in a robotic swarm exploring a new environment, a robot might have to choose between multiple sites to visit, but at the same time it should remain close to the group to achieve some coordinated tasks. We show that to find a social optimum for our problem, one needs to solve a set of Linear Quadratic Regulator problems, whose number increases exponentially with the size of the population. Alternatively, we develop via the Mean Field Games methodology a set of decentralized strategies that are independent of the size of the population. When the number of agents is sufficiently large, these strategies qualify as approximately socially optimal. To compute the approximate social optimum, each player needs to know its own state and the statistical distributions of the players' initial states and problem parameters. Finally, we give a numerical example where the cooperative and noncooperative cases have opposite behaviors. Whereas in the former the size of the majority increases with the social effect, in the latter, the existence of a majority is disadvantaged.
A Comparison of Big Data Frameworks on a Layered Dataflow Model
Venue: arXiv cs.DC 2016
Year: 2016

In the world of Big Data analytics, there is a series of tools aiming at simplifying programming applications to be executed on clusters. Although each tool claims to provide better programming, data and execution models, for which only informal (and often confusing) semantics is generally provided, all share a common underlying model, namely, the Dataflow model. The Dataflow model we propose shows how various tools share the same expressiveness at different levels of abstraction. The contribution of this work is twofold: first, we show that the proposed model is (at least) as general as existing batch and streaming frameworks (e.g., Spark, Flink, Storm), thus making it easier to understand high-level data-processing applications written in such frameworks. Second, we provide a layered model that can represent tools and applications following the Dataflow paradigm and we show how the analyzed tools fit in each level.
Can Machine Learn Steganography? - Implementing LSB Substitution and Matrix Coding Steganography with Feed-Forward Neural Networks
Venue: arXiv cs.MM 2016
Year: 2016

In recent years, due to the powerful abilities to deal with highly complex tasks, the artificial neural networks (ANNs) have been studied in the hope of achieving human-like performance in many applications. Since the ANNs have the ability to approximate complex functions from observations, it is straightforward to consider the ANNs for steganography. In this paper, we aim to implement the well-known LSB substitution and matrix coding steganography with the feed-forward neural networks (FNNs). Our experimental results have shown that, the used FNNs can achieve the data embedding operation of the LSB substitution and matrix coding steganography. For steganography with the ANNs, though there may be some challenges to us, it would be very promising and valuable to pay attention to the ANNs for steganography, which may be a new direction for steganography.
Covariance of Motion and Appearance Featuresfor Spatio Temporal Recognition Tasks
Venue: arXiv cs.CV 2016
Year: 2016

In this paper, we introduce an end-to-end framework for video analysis focused towards practical scenarios built on theoretical foundations from sparse representation, including a novel descriptor for general purpose video analysis. In our approach, we compute kinematic features from optical flow and first and second-order derivatives of intensities to represent motion and appearance respectively. These features are then used to construct covariance matrices which capture joint statistics of both low-level motion and appearance features extracted from a video. Using an over-complete dictionary of the covariance based descriptors built from labeled training samples, we formulate low-level event recognition as a sparse linear approximation problem. Within this, we pose the sparse decomposition of a covariance matrix, which also conforms to the space of semi-positive definite matrices, as a determinant maximization problem. Also since covariance matrices lie on non-linear Riemannian manifolds, we compare our former approach with a sparse linear approximation alternative that is suitable for equivalent vector spaces of covariance matrices. This is done by searching for the best projection of the query data on a dictionary using an Orthogonal Matching pursuit algorithm. We show the applicability of our video descriptor in two different application domains - namely low-level event recognition in unconstrained scenarios and gesture recognition using one shot learning. Our experiments provide promising insights in large scale video analysis.
D2O - a distributed data object for parallel high-performance computing in Python
Venue: arXiv cs.MS 2016
Year: 2016

We introduce D2O, a Python module for cluster-distributed multi-dimensional numerical arrays. It acts as a layer of abstraction between the algorithm code and the data-distribution logic. The main goal is to achieve usability without losing numerical performance and scalability. D2O's global interface is similar to the one of a numpy.ndarray, whereas the cluster node's local data is directly accessible for use in customized high-performance modules. D2O is written in pure Python which makes it portable and easy to use and modify. Expensive operations are carried out by dedicated external libraries like numpy and mpi4py. The performance of D2O is on a par with numpy for serial applications and scales well when moving to an MPI cluster. D2O is open-source software available under the GNU General Public License v3 (GPL-3) at https://gitlab.mpcdf.mpg.de/ift/D2O
Anonymized e-mail interviews with R package maintainers active on CRAN and GitHub
Venue: arXiv cs.SE 2016
Year: 2016

This technical report accompanies a research article that empirically studies the problems related to inter-repository package dependencies in the R ecosystem of statistical computing, with a focus on R packages hosted on CRAN and GitHub. The current report provides supplementary material, reproducing an anonymised and sanitised version of e-mail interviews that have been conducted in November 2015 with five active R package maintainers. The goal was to gain a better understanding in how R package maintainers develop and distribute their packages through GitHub and CRAN. All five interviewees were actively maintaining packages on GitHub, some were also active on CRAN. They have been selected based on their profile (the number of R packages they maintain on GitHub and/or CRAN) as well as their gender (three interviewees were male, two were female).
Efficient Verification of Concurrent Programs Over TSO Memory Model
Venue: arXiv cs.LO 2016
Year: 2016

We address the problem of efficient verification of multi-threaded programs running over Total Store Order (TSO) memory model. It has been shown that even with finite data domain programs, the complexity of control state reachability under TSO is non-primitive recursive. In this paper, we first present a bounded-buffer verification approach wherein a bound on the size of buffers is placed; verification is performed incrementally by increasing the size of the buffer with each iteration of the verification procedure until the said bound is reached. For programs operating on finite data domains, we also demonstrate the existence of a buffer bound k such that if the program is safe under that bound, then it is also safe for unbounded buffers. We have implemented this technique in a tool ProofTraPar. Our results against memorax [2], a state-of-the-art sound and complete verifier for TSO memory model, have been encouraging.
Public Key Protocols over the Ring E_p(m)
Venue: arXiv cs.IT 2016
Year: 2016

In this paper we use the nonrepresentable ring E_p(m)to introduce public key cryptosystems in noncommutative settings and based on the Semigrouop Action Problem and the Decomposition Problem respectively.
Sampling and Distortion Tradeoffs for Indirect Source Retrieval
Venue: arXiv cs.IT 2016
Year: 2016

Consider a continuous signal that cannot be observed directly. Instead, one has access to multiple corrupted versions of the signal. The available corrupted signals are correlated because they carry information about the common remote signal. The goal is to reconstruct the original signal from the data collected from its corrupted versions. The information theoretic formulation of the remote reconstruction problem assumes that the corrupted signals are uniformly sampled and the focus is on optimal compression of the samples. In this paper we revisit this problem from a sampling perspective. We look at the problem of finding the best sampling locations for each signal to minimize the total reconstruction distortion of the remote signal. In finding the sampling locations, one can take advantage of the correlation among the corrupted signals. Our main contribution is a fundamental lower bound on the reconstruction distortion for any arbitrary nonuniform sampling strategy. This lower bound is valid for any sampling rate. Furthermore, it is tight and matches the optimal reconstruction distortion in low and high sampling rates. Moreover, it is shown that in the low sampling rate region, it is optimal to use a certain nonuniform sampling scheme on all the signals. On the other hand, in the high sampling rate region, it is optimal to uniformly sample all the signals. We also consider the problem of finding the optimal sampling locations to recover the set of corrupted signals, rather than the remote signal. Unlike the information theoretic formulation of the problem in which these two problems were equivalent, we show that they are not equivalent in our setting.
Tensor Ring Decomposition
Venue: arXiv cs.NA 2016
Year: 2016

Tensor networks have in recent years emerged as the powerful tools for solving the large-scale optimization problems. One of the most popular tensor network is tensor train (TT) decomposition that acts as the building blocks for the complicated tensor networks. However, the TT decomposition highly depends on permutations of tensor dimensions, due to its strictly sequential multilinear products over latent cores, which leads to difficulties in finding the optimal TT representation. In this paper, we introduce a fundamental tensor decomposition model to represent a large dimensional tensor by a circular multilinear products over a sequence of low dimensional cores, which can be graphically interpreted as a cyclic interconnection of 3rd-order tensors, and thus termed as tensor ring (TR) decomposition. The key advantage of TR model is the circular dimensional permutation invariance which is gained by employing the trace operation and treating the latent cores equivalently. TR model can be viewed as a linear combination of TT decompositions, thus obtaining the powerful and generalized representation abilities. For optimization of latent cores, we present four different algorithms based on the sequential SVDs, ALS scheme, and block-wise ALS techniques. Furthermore, the mathematical properties of TR model are investigated, which shows that the basic multilinear algebra can be performed efficiently by using TR representaions and the classical tensor decompositions can be conveniently transformed into the TR representation. Finally, the experiments on both synthetic signals and real-world datasets were conducted to evaluate the performance of different algorithms.
Data-driven HR - Résumé Analysis Based on Natural Language Processing and Machine Learning
Venue: arXiv cs.CL 2016
Year: 2016

Recruiters usually spend less than a minute looking at each résumé when deciding whether it's worth continuing the recruitment process with the candidate. Recruiters focus on keywords, and it's almost impossible to guarantee a fair process of candidate selection. The main scope of this paper is to tackle this issue by introducing a data-driven approach that shows how to process résumés automatically and give recruiters more time to only examine promising candidates. Furthermore, we show how to leverage Machine Learning and Natural Language Processing in order to extract all required information from the résumés. Once the information is extracted, a ranking score is calculated. The score describes how well the candidates fit based on their education, work experience and skills. Later this paper illustrates a prototype application that shows how this novel approach can increase the productivity of recruiters. The application enables them to filter and rank candidates based on predefined job descriptions. Guided by the ranking, recruiters can get deeper insights from candidate profiles and validate why and how the application ranked them. This application shows how to improve the hiring process by giving an unbiased hiring decision support.
Equi-depth Histogram Construction for Big Data with Quality Guarantees
Venue: arXiv cs.DB 2016
Year: 2016

The amount of data generated and stored in cloud systems has been increasing exponentially. The examples of data include user generated data, machine generated data as well as data crawled from the Internet. There have been several frameworks with proven efficiency to store and process the petabyte scale data such as Apache Hadoop, HDFS and several NoSQL frameworks. These systems have been widely used in industry and thus are subject to several research. The proposed data processing techniques should be compatible with the above frameworks in order to be practical. One of the key data operations is deriving equi-depth histograms as they are crucial in understanding the statistical properties of the underlying data with many applications including query optimization. In this paper, we focus on approximate equi-depth histogram construction for big data and propose a novel merge based histogram construction method with a histogram processing framework which constructs an equi-depth histogram for a given time interval. The proposed method constructs approximate equi-depth histograms by merging exact equi-depth histograms of partitioned data by guaranteeing a maximum error bound on the number of items in a bucket (bucket size) as well as any range on the histogram.
Linear Classification of data with Support Vector Machines and Generalized Support Vector Machines
Venue: arXiv cs.LG 2016
Year: 2016

In this paper, we study the support vector machine and introduced the notion of generalized support vector machine for classification of data. We show that the problem of generalized support vector machine is equivalent to the problem of generalized variational inequality and establish various results for the existence of solutions. Moreover, we provide various examples to support our results.
Design and Implementation of a Novel Compatible Encoding Scheme in the Time Domain for Image Sensor Communication
Venue: arXiv cs.OH 2016
Year: 2016

This paper presents a modulation scheme in the time domain based on On-Off-Keying and proposes various compatible supports for different types of image sensors. The content of this article is a sub-proposal to the IEEE 802.15.7r1 Task Group (TG7r1) aimed at Optical Wireless Communication (OWC) using an image sensor as the receiver. The compatibility support is indispensable for Image Sensor Communications (ISC) because the rolling shutter image sensors currently available have different frame rates, shutter speeds, sampling rates, and resolutions. However, focusing on unidirectional communications (i.e., data broadcasting, beacons), an asynchronous communication prototype is also discussed in the paper. Due to the physical limitations associated with typical image sensors (including low and varying frame rates, long exposures, and low shutter speeds), the link speed performance is critically considered. Based on the practical measurement of camera response to modulated light, an operating frequency range is suggested along with the similar system architecture, decoding procedure, and algorithms. A significant feature of our novel data frame structure is that it can support both typical frame rate cameras (in the oversampling mode) as well as very low frame rate cameras (in the error detection mode for a camera whose frame rate is lower than the transmission packet rate). A high frame rate camera, i.e., no less than 20 fps, is supported in an oversampling mode in which a majority voting scheme for decoding data is applied. A low frame rate camera, i.e., when the frame rate drops to less than 20 fps at some certain time, is supported by an error detection mode in which any missing data sub-packet is detected in decoding and later corrected by external code. Numerical results and valuable analysis are also included to indicate the capability of the proposed schemes.
User-Centric Mobility Management in Ultra-Dense Cellular Networks under Spatio-Temporal Dynamics
Venue: arXiv cs.IT 2016
Year: 2016

This article investigates the mobility management of an ultra dense cellular network (UDN) from an energy-efficiency (EE) point of view. Many dormant base stations (BSs) in a UDN do not transmit signals, and thus a received power based handover (HO) approach as in traditional cellular networks is hardly applicable. In addition, the limited front/backhaul capacity compared to a huge number of BSs makes it difficult to implement a centralized HO and power control. For these reasons, a novel user-centric association rule is proposed, which jointly optimizes HO and power control for maximizing EE. The proposed mobility management is able to cope not only with the spatial randomness of user movement but also with temporally correlated wireless channels. The proposed approach is implemented over a HO time window and tractable power control policy by exploiting mean-field game (MFG) and stochastic geometry (SG). Compared to a baseline with a fixed HO interval and transmit power, the proposed approach achieves the 1.2 times higher long-term average EE at a typical active BS.
Potential of EPUB3 for Digital Textbooks in Higher Education
Venue: arXiv cs.CY 2016
Year: 2016

The e-book market is currently in a strong upswing. This research study deals with the question which practical uses the e-book format EPUB3 offers for (higher) education. By means of a didactic content analysis, a range of interactive exercise types were developed as a result of conversations with teachers. For this purpose, a didactic and technical concept has been developed. Different kinds of exercises were prototypically implemented in an e-book. Finally, a brief overview reflects the present state of the current e-book readers. A subsequent discussion illuminates the strengths and weaknesses of the format. In summary, it can be remarked that EPUB3 is suitable for a variety of different exercises and that it is able to serve as a basic format for forthcoming digital textbooks. Furthermore the openness of EPUB3 will assist Open Learning and Teaching in a meaningful way.
DeepStance at SemEval-2016 Task 6: Detecting Stance in Tweets Using Character and Word-Level CNNs
Venue: arXiv cs.CL 2016
Year: 2016

This paper describes our approach for the Detecting Stance in Tweets task (SemEval-2016 Task 6). We utilized recent advances in short text categorization using deep learning to create word-level and character-level models. The choice between word-level and character-level models in each particular case was informed through validation performance. Our final system is a combination of classifiers using word-level or character-level models. We also employed novel data augmentation techniques to expand and diversify our training dataset, thus making our system more robust. Our system achieved a macro-average precision, recall and F1-scores of 0.67, 0.61 and 0.635 respectively.
Query-Focused Opinion Summarization for User-Generated Content
Venue: arXiv cs.CL 2016
Year: 2016

We present a submodular function-based framework for query-focused opinion summarization. Within our framework, relevance ordering produced by a statistical ranker, and information coverage with respect to topic distribution and diverse viewpoints are both encoded as submodular functions. Dispersion functions are utilized to minimize the redundancy. We are the first to evaluate different metrics of text similarity for submodularity-based summarization methods. By experimenting on community QA and blog summarization, we show that our system outperforms state-of-the-art approaches in both automatic evaluation and human evaluation. A human evaluation task is conducted on Amazon Mechanical Turk with scale, and shows that our systems are able to generate summaries of high overall quality and information diversity.
Strategies for Searching Video Content with Text Queries or Video Examples
Venue: arXiv cs.IR 2016
Year: 2016

The large number of user-generated videos uploaded on to the Internet everyday has led to many commercial video search engines, which mainly rely on text metadata for search. However, metadata is often lacking for user-generated videos, thus these videos are unsearchable by current search engines. Therefore, content-based video retrieval (CBVR) tackles this metadata-scarcity problem by directly analyzing the visual and audio streams of each video. CBVR encompasses multiple research topics, including low-level feature design, feature fusion, semantic detector training and video search/reranking. We present novel strategies in these topics to enhance CBVR in both accuracy and speed under different query inputs, including pure textual queries and query by video examples. Our proposed strategies have been incorporated into our submission for the TRECVID 2014 Multimedia Event Detection evaluation, where our system outperformed other submissions in both text queries and video example queries, thus demonstrating the effectiveness of our proposed approaches.
Egyptian Arabic to English Statistical Machine Translation System for NIST OpenMT'2015
Venue: arXiv cs.CL 2016
Year: 2016

The paper describes the Egyptian Arabic-to-English statistical machine translation (SMT) system that the QCRI-Columbia-NYUAD (QCN) group submitted to the NIST OpenMT'2015 competition. The competition focused on informal dialectal Arabic, as used in SMS, chat, and speech. Thus, our efforts focused on processing and standardizing Arabic, e.g., using tools such as 3arrib and MADAMIRA. We further trained a phrase-based SMT system using state-of-the-art features and components such as operation sequence model, class-based language model, sparse features, neural network joint model, genre-based hierarchically-interpolated language model, unsupervised transliteration mining, phrase-table merging, and hypothesis combination. Our system ranked second on all three genres.