OpenRaiser/Intern-Atlas huggingface dataset:
Automatic Conversion of Relational Databases into Ontologies: A Comparative Analysis of Protégé Plug-ins Performances
Venue: arXiv cs.DB 2016
Year: 2016

Constructing ontologies from relational databases is an active research topic in the Semantic Web domain. While conceptual mapping rules/principles of relational databases and ontology structures are being proposed, several software modules or plug-ins are being developed to enable the automatic conversion of relational databases into ontologies. However, the correlation between the resulting ontologies built automatically with plug-ins from relational databases and the database-to-ontology mapping principles has been given little attention. This study reviews and applies two Protégé plug-ins, namely, DataMaster and OntoBase to automatically construct ontologies from a relational database. The resulting ontologies are further analysed to match their structures against the database-to-ontology mapping principles. A comparative analysis of the matching results reveals that OntoBase outperforms DataMaster in applying the database-to-ontology mapping principles for automatically converting relational databases into ontologies.
Statistical Assessment of PLC Networking for Front-Hauling in Small Radio Cells
Venue: arXiv cs.NI 2016
Year: 2016

The employment of power lines for communications (PLC) has been theorized almost a century ago; although the physical medium is not meant for data transmission, recent technical developments pushed the capacity boundary much higher than expected, allowing to consider PLC for new applications as the one considered in this contribution, i.e., as a solution for small cell back/front-hauling. In principle, back-hauling for cellular networks via PLC is very attractive given how pervasive the power line infrastructure is. Telecom operators are looking for solutions that can handle the traffic increase which is doublying every year. This calls for improvements of both cellular systems and front-hauling technologies that have to bridge the radio network with the core network. Small cells are currently advocated as the solution to spatially fragment the network and offer high capacity in densely populated areas; this is also due to the availability of spectrum at high frequency (mmWave links) which inherently offers large bandwidth but forces the cells to become smaller due to the short distance coverage. Therefore, it appears that mobile operators may eventually have to deploy an order of magnitude more (small) cells compared to existing networks having macro cells. This translates in a high density cellular network that poses more challenges for the back-haul.
Helping HPC Users Specify Job Memory Requirements via Machine Learning
Venue: arXiv cs.DC 2016
Year: 2016

Resource allocation in High Performance Computing (HPC) settings is still not easy for end-users due to the wide variety of application and environment configuration options. Users have difficulties to estimate the number of processors and amount of memory required by their jobs, select the queue and partition, and estimate when job output will be available to plan for next experiments. Apart from wasting infrastructure resources by making wrong allocation decisions, overall user response time can also be negatively impacted. Techniques that exploit batch scheduler systems to predict waiting time and runtime of user jobs have already been proposed. However, we observed that such techniques are not suitable for predicting job memory usage. In this paper we introduce a tool to help users predict their memory requirements using machine learning. We describe the integration of the tool with a batch scheduler system, discuss how batch scheduler log data can be exploited to generate memory usage predictions through machine learning, and present results of two production systems containing thousands of jobs.
SLA-aware Interactive Workflow Assistant for HPC Parameter Sweeping Experiments
Venue: arXiv cs.DC 2016
Year: 2016

A common workflow in science and engineering is to (i) setup and deploy large experiments with tasks comprising an application and multiple parameter values; (ii) generate intermediate results; (iii) analyze them; and (iv) reprioritize the tasks. These steps are repeated until the desired goal is achieved, which can be the evaluation/simulation of complex systems or model calibration. Due to time and cost constraints, sweeping all possible parameter values of the user application is not always feasible. Experimental Design techniques can help users reorganize submission-execution-analysis workflows to bring a solution in a more timely manner. This paper introduces a novel tool that leverages users' feedback on analyzing intermediate results of parameter sweeping experiments to advise them about their strategies on parameter selections tied to their SLA constraints. We evaluated our tool with three applications of distinct domains and search space shapes. Our main finding is that users with submission-execution-analysis workflows can benefit from their interaction with intermediate results and adapt themselves according to their domain expertise and SLA constraints.
Energy and Bursty Packet Loss Tradeoff over Fading Channels: A System Level Model
Venue: arXiv cs.IT 2016
Year: 2016

Energy efficiency and quality of service (QoS) guarantees are the key design goals for the 5G wireless communication systems. In this context, we discuss a multiuser scheduling scheme over fading channels for loss tolerant applications. The loss tolerance of the application is characterized in terms of different parameters that contribute to quality of experience for the application. The mobile users are scheduled opportunistically such that a minimum QoS is guaranteed. We propose an opportunistic scheduling scheme and address the cross layer design framework when channel state information is not perfectly available at the transmitter and the receiver. We characterize the system energy as a function of different QoS and channel state estimation error parameters. The optimization problem is formulated using Markov chain framework and solved using stochastic optimization techniques. The results demonstrate that the parameters characterizing the packet loss are tightly coupled and relaxation of one parameter does not benefit the system much if the other constraints are tight. We evaluate the energy-performance trade-off numerically and show the effect of channel uncertainty on the packet scheduler design.
NP-Hardness of Reed-Solomon Decoding, and the Prouhet-Tarry-Escott Problem
Venue: arXiv cs.IT 2016
Year: 2016

Establishing the complexity of {\em Bounded Distance Decoding} for Reed-Solomon codes is a fundamental open problem in coding theory, explicitly asked by Guruswami and Vardy (IEEE Trans. Inf. Theory, 2005). The problem is motivated by the large current gap between the regime when it is NP-hard, and the regime when it is efficiently solvable (i.e., the Johnson radius). We show the first NP-hardness results for asymptotically smaller decoding radii than the maximum likelihood decoding radius of Guruswami and Vardy. Specifically, for Reed-Solomon codes of length $N$ and dimension $K=O(N)$, we show that it is NP-hard to decode more than $ N-K- c\frac{\log N}{\log\log N}$ errors (with $c>0$ an absolute constant). Moreover, we show that the problem is NP-hard under quasipolynomial-time reductions for an error amount $> N-K- c\log{N}$ (with $c>0$ an absolute constant). These results follow from the NP-hardness of a generalization of the classical Subset Sum problem to higher moments, called {\em Moments Subset Sum}, which has been a known open problem, and which may be of independent interest. We further reveal a strong connection with the well-studied Prouhet-Tarry-Escott problem in Number Theory, which turns out to capture a main barrier in extending our techniques. We believe the Prouhet-Tarry-Escott problem deserves further study in the theoretical computer science community.
Fractal Art Generation using GPUs
Venue: arXiv cs.GR 2016
Year: 2016

Fractal image generation algorithms exhibit extreme parallelizability. Using general purpose graphics processing unit (GPU) programming to implement escape-time algorithms for Julia sets of functions,parallel methods generate visually attractive fractal images much faster than traditional methods. Vastly improved speeds are achieved using this method of computation, which allow real-time generation and display of images. A comparison is made between sequential and parallel implementations of the algorithm. An application created by the authors demonstrates using the increased speed to create dynamic imaging of fractals where the user may explore paths of parameter values corresponding to a given function's Mandelbrot set. Examples are given of artistic and mathematical insights gained by experiencing fractals interactively and from the ability to sample the parameter space quickly and comprehensively.
VR 'SPACE OPERA': Mimetic Spectralism in an Immersive Starlight Audification System
Venue: arXiv cs.SD 2016
Year: 2016

This paper describes a system designed as part of an interactive VR opera, which immerses a real-time composer and an audience (via a network) in the historical location of Gobeklitepe, in southern Turkey during an imaginary scenario set in the Pre-Pottery Neolithic period (8500-5500 BCE), viewed by some to be the earliest example of a temple, or observatory. In this scene music is generated, where the harmonic material is determined based on observations of light variation from pulsating stars, that would have theoretically been overhead on the 1st of October 8000 BC at 23:00 and animal calls based on the reliefs in the temple. Based on the observations of the stars V465 Per, HD 217860, 16 Lac, BG CVn and KIC 6382916, frequency collections were derived and applied to the generation of musical sound and notation sequences within a custom VR environment using a novel method incorporating spectralist techniques. Parameters controlling this 'resynthesis' can be manipulated by the performer using a Leap Motion controller and Oculus Rift HMD, yielding both sonic and visual results in the environment. The final opera is to be viewed via Google Cardboard and delivered over the Internet. This entire process aims to pose questions about real-time composition through time distortion and invoke a sense of wonder and meaningfulness through a ritualistic experience.
Tracing metaphors in time through self-distance in vector spaces
Venue: arXiv cs.CL 2016
Year: 2016

From a diachronic corpus of Italian, we build consecutive vector spaces in time and use them to compare a term's cosine similarity to itself in different time spans. We assume that a drop in similarity might be related to the emergence of a metaphorical sense at a given time. Similarity-based observations are matched to the actual year when a figurative meaning was documented in a reference dictionary and through manual inspection of corpus occurrences.
Fast Algorithm of High-resolution Microwave Imaging Using the Non-parametric Generalized Reflectivity Model
Venue: arXiv cs.CV 2016
Year: 2016

This paper presents an efficient algorithm of high-resolution microwave imaging based on the concept of generalized reflectivity. The contribution made in this paper is two-fold. We introduce the concept of non-parametric generalized reflectivity (GR, for short) as a function of operational frequencies and view angles, etc. The GR extends the conventional Born-based imaging model, i.e., single-scattering model, into that accounting for more realistic interaction between the electromagnetic wavefield and imaged scene. Afterwards, the GR-based microwave imaging is formulated in the convex of sparsity-regularized optimization. Typically, the sparsity-regularized optimization requires the implementation of iterative strategy, which is computationally expensive, especially for large-scale problems. To break this bottleneck, we convert the imaging problem into the problem of physics-driven image processing by introducing a dual transformation. Moreover, this image processing is performed over overlapping patches, which can be efficiently solved in the parallel or distributed manner. In this way, the proposed high-resolution imaging methodology could be applicable to large-scale microwave imaging problems. Selected simulation results are provided to demonstrate the state-of-art performance of proposed methodology.
On the Impact of Entity Linking in Microblog Real-Time Filtering
Venue: arXiv cs.IR 2016
Year: 2016

Microblogging is a model of content sharing in which the temporal locality of posts with respect to important events, either of foreseeable or unforeseeable nature, makes applica- tions of real-time filtering of great practical interest. We propose the use of Entity Linking (EL) in order to improve the retrieval effectiveness, by enriching the representation of microblog posts and filtering queries. EL is the process of recognizing in an unstructured text the mention of relevant entities described in a knowledge base. EL of short pieces of text is a difficult task, but it is also a scenario in which the information EL adds to the text can have a substantial impact on the retrieval process. We implement a start-of-the-art filtering method, based on the best systems from the TREC Microblog track realtime adhoc retrieval and filtering tasks , and extend it with a Wikipedia-based EL method. Results show that the use of EL significantly improves over non-EL based versions of the filtering methods.
Approximately Sampling Elements with Fixed Rank in Graded Posets
Venue: arXiv cs.DS 2016
Year: 2016

Graded posets frequently arise throughout combinatorics, where it is natural to try to count the number of elements of a fixed rank. These counting problems are often $\#\textbf{P}$-complete, so we consider approximation algorithms for counting and uniform sampling. We show that for certain classes of posets, biased Markov chains that walk along edges of their Hasse diagrams allow us to approximately generate samples with any fixed rank in expected polynomial time. Our arguments do not rely on the typical proofs of log-concavity, which are used to construct a stationary distribution with a specific mode in order to give a lower bound on the probability of outputting an element of the desired rank. Instead, we infer this directly from bounds on the mixing time of the chains through a method we call $\textit{balanced bias}$. A noteworthy application of our method is sampling restricted classes of integer partitions of $n$. We give the first provably efficient Markov chain algorithm to uniformly sample integer partitions of $n$ from general restricted classes. Several observations allow us to improve the efficiency of this chain to require $O(n^{1/2}\log(n))$ space, and for unrestricted integer partitions, expected $O(n^{9/4})$ time. Related applications include sampling permutations with a fixed number of inversions and lozenge tilings on the triangular lattice with a fixed average height.
Location and Position Estimation in Wireless Sensor Networks
Venue: arXiv cs.NI 2016
Year: 2016

A wireless sensor network comprises of small sensor nodes each of which consists of a processing device, small amount of memory, battery and radio transceiver for communication. The sensor nodes are autonomous and spatially distributed in an area of investigation. Certain applications and protocols of wireless sensor networks require that the sensor nodes should be aware of their position relative to the sensor network. For it to be significant and to be of value, the data such as temperature, humidity and pressure, gathered by sensor nodes must be ascribed to the relative position from where it was collected. For this to happen, the sensor nodes must be aware of their relative positions. Traditional location finding solutions, such as Global Positioning System, are not feasible for wireless sensor nodes due to multiple reasons. Therefore, new methods, techniques and algorithms need to be developed to solve the problem of location and position estimation of wireless sensor nodes. A number of algorithms and techniques based upon different characteristics and properties of sensor nodes have already been proposed for this purpose. This chapter discusses the basic principles and techniques used in the localization algorithms, categories of these algorithms and also takes a more closer look at a few of the representative localization schemes.
Evaluating Urbanization from Satellite and Aerial Images by means of a statistical approach to the texture analysis
Venue: arXiv cs.CV 2016
Year: 2016

Statistical methods are usually applied in the processing of digital images for the analysis of the textures displayed by them. Aiming to evaluate the urbanization of a given location from satellite or aerial images, here we consider a simple processing to distinguish in them the 'urban' from the 'rural' texture. The method is based on the mean values and the standard deviations of the colour tones of image pixels. The processing of the input images allows to obtain some maps from which a quantitative evaluation of the textures can be obtained.
Song From PI: A Musically Plausible Network for Pop Music Generation
Venue: arXiv cs.SD 2016
Year: 2016

We present a novel framework for generating pop music. Our model is a hierarchical Recurrent Neural Network, where the layers and the structure of the hierarchy encode our prior knowledge about how pop music is composed. In particular, the bottom layers generate the melody, while the higher levels produce the drums and chords. We conduct several human studies that show strong preference of our generated music over that produced by the recent method by Google. We additionally show two applications of our framework: neural dancing and karaoke, as well as neural story singing.
Neural Networks Models for Entity Discovery and Linking
Venue: arXiv cs.CL 2016
Year: 2016

This paper describes the USTC_NELSLIP systems submitted to the Trilingual Entity Detection and Linking (EDL) track in 2016 TAC Knowledge Base Population (KBP) contests. We have built two systems for entity discovery and mention detection (MD): one uses the conditional RNNLM and the other one uses the attention-based encoder-decoder framework. The entity linking (EL) system consists of two modules: a rule based candidate generation and a neural networks probability ranking model. Moreover, some simple string matching rules are used for NIL clustering. At the end, our best system has achieved an F1 score of 0.624 in the end-to-end typed mention ceaf plus metric.
Dynamic Teams and Decentralized Control Problems with Substitutable Actions
Venue: arXiv cs.SY 2016
Year: 2016

This paper considers two problems -- a dynamic team problem and a decentralized control problem. The problems we consider do not belong to the known classes of "simpler" dynamic team/decentralized control problems such as partially nested or quadratically invariant problems. However, we show that our problems admit simple solutions under an assumption referred to as the substitutability assumption. Intuitively, substitutability in a team (resp. decentralized control) problem means that the effects of one team member's (resp. controller's) action on the cost function and the information (resp. state dynamics) can be achieved by an action of another member (resp. controller). For the non-partially-nested LQG dynamic team problem, it is shown that under certain conditions linear strategies are optimal. For the non-partially-nested decentralized LQG control problem, the state structure can be exploited to obtain optimal control strategies with recursively update-able sufficient statistics. These results suggest that substitutability can work as a counterpart of the information structure requirements that enable simplification of dynamic teams and decentralized control problems.
UTCNN: a Deep Learning Model of Stance Classificationon on Social Media Text
Venue: arXiv cs.CL 2016
Year: 2016

Most neural network models for document classification on social media focus on text infor-mation to the neglect of other information on these platforms. In this paper, we classify post stance on social media channels and develop UTCNN, a neural network model that incorporates user tastes, topic tastes, and user comments on posts. UTCNN not only works on social media texts, but also analyzes texts in forums and message boards. Experiments performed on Chinese Facebook data and English online debate forum data show that UTCNN achieves a 0.755 macro-average f-score for supportive, neutral, and unsupportive stance classes on Facebook data, which is significantly better than models in which either user, topic, or comment information is withheld. This model design greatly mitigates the lack of data for the minor class without the use of oversampling. In addition, UTCNN yields a 0.842 accuracy on English online debate forum data, which also significantly outperforms results from previous work as well as other deep learning models, showing that UTCNN performs well regardless of language or platform.
Of Hags and bitches. Ageist attitudes in 2016 presidential debate on twitter
Venue: arXiv cs.CY 2016
Year: 2016

In this article we present our exploratory research into the occurrence of ageist attitudes within the discussion related to the US 2016 presidential election. We use natural processing techniques to analyze the content tweets related to Hillary Clinton and Donald Trump. Content analysis shows that although ageist attitudes are scarce in the discussion, they are mostly focused on Hillary Clinton rather than Donald Trump. Also, ageist arguments against Donald Trump appear mostly as a reply to controversies connected with the health of Hillary Clinton.
Spatio-Temporal Waveform Design for Multi-user Massive MIMO Downlink with 1-bit Receivers
Venue: arXiv cs.IT 2016
Year: 2016

Internet-of-Things (IoT) refers to a high-density network of low-cost low-bitrate terminals and sensors where also low energy consumption is one central feature. As the power-budget of classical receiver chains is dominated by the high-resolution analog-to-digital converters (ADCs), there is a growing interest towards deploying receiver architectures with reduced-bit or even 1-bit ADCs. In this paper, we study waveform design, optimization and detection aspects of multi-user massive MIMO downlink where user terminals adopt very simple 1-bit ADCs with oversampling. In order to achieve spectral efficiency higher than 1 bit/s/Hz per real-dimension, we propose a two-stage precoding, namely a novel quantization precoder followed by maximum-ratio transmission (MRT) or zero-forcing (ZF) type spatial channel precoder which jointly form the multi-user-multiantenna transmit waveform. The quantization precoder outputs are optimized, under appropriate transmitter and receiver filter bandwidth constraints, to provide controlled inter-symbol-interference (ISI) enabling the input symbols to be uniquely detected from 1-bit quantized observations with a low-complexity symbol detector in the absence of noise. An additional optimization constraint is also imposed in the quantization precoder design to increase the robustness against noise and residual inter-user-interference (IUI). The purpose of the spatial channel precoder, in turn, is to suppress the IUI and provide high beamforming gains such that good symbol-error rates (SERs) can be achieved in the presence of noise and interference. Extensive numerical evaluations illustrate that the proposed spatio-temporal precoder based multiantenna waveform design can facilitate good multi-user link performance, despite the extremely simple 1-bit ADCs in the receivers, hence being one possible enabling technology for the future low-complexity IoT networks.