OpenRaiser/Intern-Atlas huggingface dataset:
Cloud Radio Access meets Heterogeneous Small Cell Networks: A Cognitive Hierarchy Perspective
Venue: arXiv cs.IT 2016
Year: 2016

In this paper, the problem of distributed power allocation is considered for the downlink of a cloud radio access network (CRAN) that is coexisting with a heterogeneous network. In this multi-tier system, the heterogeneous network base stations (BSs) as well as the CRAN remote radio heads seek to choose their optimal power to maximize their users' rates. The problem is formulated as a noncooperative game in which the players are the CRAN's cloud and the BSs. Given the difference of capabilities between the CRAN and the various BSs, the game is cast within the framework of cognitive hierarchy theory. In this framework, players are organized in a hierarchy in such a way that a player can choose its strategy while considering players of only similar or lower hierarchies. Using such a hierarchical design, one can reduce the interference caused by the CRAN and high-powered base stations on low-powered BSs. For this game, the properties of the Nash equilibrium and the cognitive hierarchy equilibrium are analyzed. Simulation results show that the proposed cognitive hierarchy model yields significant performance gains, in terms of the total rate, reaching up to twice the rate achieved by a classical noncooperative game's Nash equilibrium.
Spreadsheet Probabilistic Programming
Venue: arXiv cs.AI 2016
Year: 2016

Spreadsheet workbook contents are simple programs. Because of this, probabilistic programming techniques can be used to perform Bayesian inversion of spreadsheet computations. What is more, existing execution engines in spreadsheet applications such as Microsoft Excel can be made to do this using only built-in functionality. We demonstrate this by developing a native Excel implementation of both a particle Markov Chain Monte Carlo variant and black-box variational inference for spreadsheet probabilistic programming. The resulting engine performs probabilistically coherent inference over spreadsheet computations, notably including spreadsheets that include user-defined black-box functions. Spreadsheet engines that choose to integrate the functionality we describe in this paper will give their users the ability to both easily develop probabilistic models and maintain them over time by including actuals via a simple user-interface mechanism. For spreadsheet end-users this would mean having access to efficient and probabilistically coherent probabilistic modeling and inference for use in all kinds of decision making under uncertainty.
Learning Term Weights for Ad-hoc Retrieval
Venue: arXiv cs.IR 2016
Year: 2016

Most Information Retrieval models compute the relevance score of a document for a given query by summing term weights specific to a document or a query. Heuristic approaches, like TF-IDF, or probabilistic models, like BM25, are used to specify how a term weight is computed. In this paper, we propose to leverage learning-to-rank principles to learn how to compute a term weight for a given document based on the term occurrence pattern.
For-loops in Logic Programming
Venue: arXiv cs.PL 2016
Year: 2016

Logic programming has traditiLogic programming has traditionally lacked devices for expressing iterative tasks. To overcome this problem, this paper proposes iterative goal formulas of the form $\seqandq{x}{L} G$ where $G$ is a goal, $x$ is a variable, and $L$ is a list. $\seqandq{x}{L}$ is called a parallel bounded quantifier. These goals allow us to specify the following task: iterate $G$ with $x$ ranging over all the elements of $L$. onally lacked devices for expressing iterative tasks. To overcome this problem, this paper proposes iterative goal formulas of the form $\seqandq{x}{L} G$ where $G$ is a goal, $x$ is a variable, and $L$ is a list. $\seqandq{x}{L}$ is called a parallel bounded quantifier. These goals allow us to specify the following task: iterate $G$ with $x$ ranging over all the elements of $L$.
Business Matter Experts do Matter: A Model-Driven Approach for Domain Specific Process Design and Monitoring
Venue: arXiv cs.SE 2016
Year: 2016

Business process design and monitoring are essential elements of Business Process Management (BPM), often relying on Service Oriented Architectures (SOA). However the current BPM approaches and standards have not sufficiently reduced the Business-IT gap. Today's solutions are mostly domain-independent and platform-dependent, which limits the ability of business matter experts to express business intent and enact process change. In contrast, the approach presented in this paper focuses on BPM and SOA environments in a domain-dependent and platform-independent way. We propose to add a domain specific-layer on top of current solutions so business stakeholders can design and understand their processes in a more intuitive way. We rely on previously proposed technical solutions and integrate them in an end-to-end methodology (from design to monitoring and back). The appropriateness and the feasibility of the approach is justified through a use case and a complete prototype implementation.
BDDT-SCC: A Task-parallel Runtime for Non Cache-Coherent Multicores
Venue: arXiv cs.DC 2016
Year: 2016

This paper presents BDDT-SCC, a task-parallel runtime system for non cache-coherent multicore processors, implemented for the Intel Single-Chip Cloud Computer. The BDDT-SCC runtime includes a dynamic dependence analysis and automatic synchronization, and executes OpenMP-Ss tasks on a non cache-coherent architecture. We design a runtime that uses fast on-chip inter-core communication with small messages. At the same time, we use non coherent shared memory to avoid large core-to-core data transfers that would incur a high volume of unnecessary copying. We evaluate BDDT-SCC on a set of representative benchmarks, in terms of task granularity, locality, and communication. We find that memory locality and allocation plays a very important role in performance, as the architecture of the SCC memory controllers can create strong contention effects. We suggest patterns that improve memory locality and thus the performance of applications, and measure their impact.
Problems on One Way Road Networks
Venue: arXiv cs.CG 2016
Year: 2016

Let $OWRN = \left\langle W_x,W_y \right\rangle$ be a One Way Road Network where $W_x$ and $W_y$ are the sets of directed horizontal and vertical roads respectively. $OWRN$ can be considered as a variation of directed grid graph. The intersections of the horizontal and vertical roads are the vertices of $OWRN$ and any two consecutive vertices on a road are connected by an edge. In this work, we analyze the problem of collision free traffic configuration in a $OWRN$. A traffic configuration is a two-tuple $TC=\left\langle OWRN, C\right\rangle$, where $C$ is a set of cars travelling on a pre-defined path. We prove that finding a maximum cardinality subset $C_{sub}\subseteq C$ such that $TC=\left\langle OWRN, C_{sub}\right\rangle$ is collision-free, is NP-hard. Lastly we investigate the properties of connectedness, shortest paths in a $OWRN$.
Fundamentals of Modeling Finite Wireless Networks using Binomial Point Process
Venue: arXiv cs.IT 2016
Year: 2016

Modeling the locations of nodes as a uniform binomial point process (BPP), we present a generic mathematical framework to characterize the performance of an arbitrarily-located reference receiver in a finite wireless network. Different from most of the prior works where the serving transmitter (TX) node is located at the fixed distance from the reference receiver, we consider two general TX-selection policies: i) uniform TX-selection: the serving node is chosen uniformly at random amongst transmitting nodes, and ii) k-closest TX-selection: the serving node is the k-th closest node out of transmitting nodes to the reference receiver. The key intermediate step in our analysis is the derivation of a new set of distance distributions that lead not only to the tractable analysis of coverage probability but also enable the analyses of wide range of classical and currently trending problems in wireless networks. Using this new set of distance distributions, we first investigate the diversity loss due to SIR correlation in a finite network. We then obtain the optimal number of links that can be simultaneously activated to maximize network spectral efficiency. Finally, we evaluate optimal caching probability to maximize the total hit probability in cache-enabled finite networks.
The Infinity Mirror Test for Analyzing the Robustness of Graph Generators
Venue: arXiv cs.SI 2016
Year: 2016

Graph generators learn a model from a source graph in order to generate a new graph that has many of the same properties. The learned models each have implicit and explicit biases built in, and its important to understand the assumptions that are made when generating a new graph. Of course, the differences between the new graph and the original graph, as compared by any number of graph properties, are important indicators of the biases inherent in any modelling task. But these critical differences are subtle and not immediately apparent using standard performance metrics. Therefore, we introduce the infinity mirror test for the analysis of graph generator performance and robustness. This stress test operates by repeatedly, recursively fitting a model to itself. A perfect graph generator would have no deviation from the original or ideal graph, however the implicit biases and assumptions that are cooked into the various models are exaggerated by the infinity mirror test allowing for new insights that were not available before. We show, via hundreds of experiments on 6 real world graphs, that several common graph generators do degenerate in interesting and informative ways. We believe that the observed degenerative patterns are clues to future development of better graph models.
Neural Networks and Continuous Time
Venue: arXiv cs.NE 2016
Year: 2016

The fields of neural computation and artificial neural networks have developed much in the last decades. Most of the works in these fields focus on implementing and/or learning discrete functions or behavior. However, technical, physical, and also cognitive processes evolve continuously in time. This cannot be described directly with standard architectures of artificial neural networks such as multi-layer feed-forward perceptrons. Therefore, in this paper, we will argue that neural networks modeling continuous time are needed explicitly for this purpose, because with them the synthesis and analysis of continuous and possibly periodic processes in time are possible (e.g. for robot behavior) besides computing discrete classification functions (e.g. for logical reasoning). We will relate possible neural network architectures with (hybrid) automata models that allow to express continuous processes.
Lifted Convex Quadratic Programming
Venue: arXiv cs.AI 2016
Year: 2016

Symmetry is the essential element of lifted inference that has recently demon- strated the possibility to perform very efficient inference in highly-connected, but symmetric probabilistic models models. This raises the question, whether this holds for optimisation problems in general. Here we show that for a large class of optimisation methods this is actually the case. More precisely, we introduce the concept of fractional symmetries of convex quadratic programs (QPs), which lie at the heart of many machine learning approaches, and exploit it to lift, i.e., to compress QPs. These lifted QPs can then be tackled with the usual optimization toolbox (off-the-shelf solvers, cutting plane algorithms, stochastic gradients etc.). If the original QP exhibits symmetry, then the lifted one will generally be more compact, and hence their optimization is likely to be more efficient.
Directional Modulation via Symbol-Level Precoding: A Way to Enhance Security
Venue: arXiv cs.IT 2016
Year: 2016

Wireless communication provides a wide coverage at the cost of exposing information to unintended users. As an information-theoretic paradigm, secrecy rate derives bounds for secure transmission when the channel to the eavesdropper is known. However, such bounds are shown to be restrictive in practice and may require exploitation of specialized coding schemes. In this paper, we employ the concept of directional modulation and follow a signal processing approach to enhance the security of multi-user MIMO communication systems when a multi-antenna eavesdropper is present. Enhancing the security is accomplished by increasing the symbol error rate at the eavesdropper. Unlike the information-theoretic secrecy rate paradigm, we assume that the legitimate transmitter is not aware of its channel to the eavesdropper, which is a more realistic assumption. We examine the applicability of MIMO receiving algorithms at the eavesdropper. Using the channel knowledge and the intended symbols for the users, we design security enhancing symbol-level precoders for different transmitter and eavesdropper antenna configurations. We transform each design problem to a linearly constrained quadratic program and propose two solutions, namely the iterative algorithm and one based on non-negative least squares, at each scenario for a computationally-efficient modulation. Simulation results verify the analysis and show that the designed precoders outperform the benchmark scheme in terms of both power efficiency and security enhancement.
Addressing the Regression Test Problem with Change Impact Analysis for Ada
Venue: arXiv cs.SE 2016
Year: 2016

The regression test selection problem--selecting a subset of a test-suite given a change--has been studied widely over the past two decades. However, the problem has seen little attention when constrained to high-criticality developments and where a "safe" selection of tests need to be chosen. Further, no practical approaches have been presented for the programming language Ada. In this paper, we introduce an approach to solving the selection problem given a combination of both static and dynamic data for a program and a change-set. We present a change impact analysis for Ada that selects the safe set of tests that need to be re-executed to ensure no regressions. We have implemented the approach in the commercial, unit-testing tool VectorCAST, and validated it on a number of open-source examples. On an example of a fully-functioning Ada implementation of a DNS server (IRONSIDES), the experimental results show a 97% reduction in test-case execution.
Cyclic codes over $\mathbb{Z}_4[u]/\langle u^k\rangle$ of odd length
Venue: arXiv cs.IT 2016
Year: 2016

Let $R=\mathbb{Z}_{4}[u]/\langle u^k\rangle=\mathbb{Z}_{4}+u\mathbb{Z}_{4}+\ldots+u^{k-1}\mathbb{Z}_{4}$ ($u^k=0$) where $k\in \mathbb{Z}^{+}$ satisfies $k\geq 2$. For any odd positive integer $n$, it is known that cyclic codes over $R$ of length $n$ are identified with ideals of the ring $R[x]/\langle x^{n}-1\rangle$. In this paper, an explicit representation for each cyclic code over $R$ of length $n$ is provided and a formula to count the number of codewords in each code is given. Then a formula to calculate the number of cyclic codes over $R$ of length $n$ is obtained. Precisely, the dual code of each cyclic code and self-dual cyclic codes over $R$ of length $n$ are investigated. When $k=4$, some optimal quasi-cyclic codes over $\mathbb{Z}_{4}$ of length $28$ and index $4$ are obtained from cyclic codes over $R=\mathbb{Z}_{4} [u]/\langle u^4\rangle$.
Bidirectional Long-Short Term Memory for Video Description
Venue: arXiv cs.MM 2016
Year: 2016

Video captioning has been attracting broad research attention in multimedia community. However, most existing approaches either ignore temporal information among video frames or just employ local contextual temporal knowledge. In this work, we propose a novel video captioning framework, termed as \emph{Bidirectional Long-Short Term Memory} (BiLSTM), which deeply captures bidirectional global temporal structure in video. Specifically, we first devise a joint visual modelling approach to encode video data by combining a forward LSTM pass, a backward LSTM pass, together with visual features from Convolutional Neural Networks (CNNs). Then, we inject the derived video representation into the subsequent language model for initialization. The benefits are in two folds: 1) comprehensively preserving sequential and visual information; and 2) adaptively learning dense visual features and sparse semantic representations for videos and sentences, respectively. We verify the effectiveness of our proposed video captioning framework on a commonly-used benchmark, i.e., Microsoft Video Description (MSVD) corpus, and the experimental results demonstrate that the superiority of the proposed approach as compared to several state-of-the-art methods.
The essential role of time in network-based recommendation
Venue: arXiv cs.IR 2016
Year: 2016

Random walks on bipartite networks have been used extensively to design personalized recommendation methods. While aging has been identified as a key component in the growth of information networks, most research has focused on the networks' structural properties and neglected the often available time information. Time has been largely ignored both by the investigated recommendation methods as well as by the methodology used to evaluate them. We show that this time-unaware approach overestimates the methods' recommendation performance. Motivated by microscopic rules of network growth, we propose a time-aware modification of an existing recommendation method and show that by combining the temporal and structural aspects, it outperforms the existing methods. The performance improvements are particularly striking in systems with fast aging.
TwinCloud: Secure Cloud Sharing Without Explicit Key Management
Venue: arXiv cs.CR 2016
Year: 2016

With the advent of cloud technologies, there is a growing number of easy-to-use services to store files and share them with other cloud users. By providing security features, cloud service providers try to encourage users to store personal files or corporate documents on their servers. However, their server-side encryption solutions are not satisfactory when the server itself is not trusted. Although, there are several client-side solutions to provide security for cloud sharing, they are not used extensively because of usability issues in key management. In this paper, we propose TwinCloud which is an innovative solution with the goal of providing a secure system to users without compromising the usability of cloud sharing. TwinCloud achieves this by bringing a novel solution to the complex key exchange problem and by providing a simple and practical approach to store and share files by hiding all the cryptographic and key-distribution operations from users. Serving as a gateway, TwinCloud uses two or more cloud providers to store the encryption keys and encrypted files in separate clouds which ease the secure sharing without a need for trust to either of the cloud service providers with the assumption that they do not collude with each other. We implemented TwinCloud as a lightweight application and make it available as open-source. The results of our usability study show the prospect of the secure sharing solution of TwinCloud.
Learning Word Sense Embeddings from Word Sense Definitions
Venue: arXiv cs.CL 2016
Year: 2016

Word embeddings play a significant role in many modern NLP systems. Since learning one representation per word is problematic for polysemous words and homonymous words, researchers propose to use one embedding per word sense. Their approaches mainly train word sense embeddings on a corpus. In this paper, we propose to use word sense definitions to learn one embedding per word sense. Experimental results on word similarity tasks and a word sense disambiguation task show that word sense embeddings produced by our approach are of high quality.
A New Energy Efficient MAC Protocol based on Redundant Radix for Wireless Networks
Venue: arXiv cs.NI 2016
Year: 2016

In this paper, we first propose a redundant radix based number (RBN) representation for encoding the data to be transmitted in a wireless network. This RBN encoding uses three possible values - 0, 1 and $\bar 1$, for each digit to be transmitted. We then propose to use silent periods (zero energy transmission) for transmitting the 0's in the RBN encoded data thus obtained. This is in contrast to most conventional communication strategies that utilize energy based transmission (EbT) schemes, where energy expenditure occurs for transmitting both 0 and 1 bit values. The binary to RBN conversion algorithm presented here offers a significant reduction in the number of non-zero bits in the resulting RBN encoded data. As a result, it provides a highly energy-efficient technique for data transmission with silent periods for transmitting 0's. We simulated our proposed technique with ideal radio device characteristics and also with parameters of various commercially available radio devices. Experimental results on various benchmark suites show that with ideal as well as some commercial device characteristics, our proposed transmission scheme requires 69% less energy on an average, compared to the energy based transmission schemes. This makes it very attractive for application scenarios where the devices are highly energy constrained. Finally, based on this transmission strategy, we have designed a MAC protocol that would support the communication of such RBN encoded data frames.
Combining multiscale features for classification of hyperspectral images: a sequence based kernel approach
Venue: arXiv cs.CV 2016
Year: 2016

Nowadays, hyperspectral image classification widely copes with spatial information to improve accuracy. One of the most popular way to integrate such information is to extract hierarchical features from a multiscale segmentation. In the classification context, the extracted features are commonly concatenated into a long vector (also called stacked vector), on which is applied a conventional vector-based machine learning technique (e.g. SVM with Gaussian kernel). In this paper, we rather propose to use a sequence structured kernel: the spectrum kernel. We show that the conventional stacked vector-based kernel is actually a special case of this kernel. Experiments conducted on various publicly available hyperspectral datasets illustrate the improvement of the proposed kernel w.r.t. conventional ones using the same hierarchical spatial features.
Engineering Tagging Languages for DSLs
Venue: arXiv cs.SE 2016
Year: 2016

To keep a DSL clean, readable and reusable in different contexts, it is useful to define a separate tagging language. A tag model logically adds information to the tagged DSL model while technically keeping the artifacts separated. Using a generic tagging language leads to promiscuous tag models, whereas defining a target DSL-specific tag language has a high initial overhead. This paper presents a systematic approach to define a DSL-specific tag language and a corresponding schema language, combining the advantages of both worlds: (a) the tag language specifically fits to the DSL, (b) the artifacts are kept separated and enabling reuse with different tag decorations, (c) the tag language follows a defined type schema, and (d) systematic derivation considerably reduces the effort necessary to implement the tag language. An example shows that it can at least partially be realized by a generator and applied for any kind of DSL. Index Terms Software Engineering, Modeling, MDE, GSE