OpenRaiser/Intern-Atlas huggingface dataset:
On-Demand Big Data Integration: A Hybrid ETL Approach for Reproducible Scientific Research
Venue: arXiv cs.DB 2018
Year: 2018

Scientific research requires access, analysis, and sharing of data that is distributed across various heterogeneous data sources at the scale of the Internet. An eager ETL process constructs an integrated data repository as its first step, integrating and loading data in its entirety from the data sources. The bootstrapping of this process is not efficient for scientific research that requires access to data from very large and typically numerous distributed data sources. a lazy ETL process loads only the metadata, but still eagerly. Lazy ETL is faster in bootstrapping. However, queries on the integrated data repository of eager ETL perform faster, due to the availability of the entire data beforehand. In this paper, we propose a novel ETL approach for scientific data integration, as a hybrid of eager and lazy ETL approaches, and applied both to data as well as metadata. This way, Hybrid ETL supports incremental integration and loading of metadata and data from the data sources. We incorporate a human-in-the-loop approach, to enhance the hybrid ETL, with selective data integration driven by the user queries and sharing of integrated data between users. We implement our hybrid ETL approach in a prototype platform, Obidos, and evaluate it in the context of data sharing for medical research. Obidos outperforms both the eager ETL and lazy ETL approaches, for scientific research data integration and sharing, through its selective loading of data and metadata, while storing the integrated data in a scalable integrated data repository.
Genesis of Basic and Multi-Layer Echo State Network Recurrent Autoencoders for Efficient Data Representations
Venue: arXiv cs.NE 2018
Year: 2018

It is a widely accepted fact that data representations intervene noticeably in machine learning tools. The more they are well defined the better the performance results are. Feature extraction-based methods such as autoencoders are conceived for finding more accurate data representations from the original ones. They efficiently perform on a specific task in terms of 1) high accuracy, 2) large short term memory and 3) low execution time. Echo State Network (ESN) is a recent specific kind of Recurrent Neural Network which presents very rich dynamics thanks to its reservoir-based hidden layer. It is widely used in dealing with complex non-linear problems and it has outperformed classical approaches in a number of tasks including regression, classification, etc. In this paper, the noticeable dynamism and the large memory provided by ESN and the strength of Autoencoders in feature extraction are gathered within an ESN Recurrent Autoencoder (ESN-RAE). In order to bring up sturdier alternative to conventional reservoir-based networks, not only single layer basic ESN is used as an autoencoder, but also Multi-Layer ESN (ML-ESN-RAE). The new features, once extracted from ESN's hidden layer, are applied to classification tasks. The classification rates rise considerably compared to those obtained when applying the original data features. An accuracy-based comparison is performed between the proposed recurrent AEs and two variants of an ELM feed-forward AEs (Basic and ML) in both of noise free and noisy environments. The empirical study reveals the main contribution of recurrent connections in improving the classification performance results.
Estimate and Replace: A Novel Approach to Integrating Deep Neural Networks with Existing Applications
Venue: arXiv cs.LG 2018
Year: 2018

Existing applications include a huge amount of knowledge that is out of reach for deep neural networks. This paper presents a novel approach for integrating calls to existing applications into deep learning architectures. Using this approach, we estimate each application's functionality with an estimator, which is implemented as a deep neural network (DNN). The estimator is then embedded into a base network that we direct into complying with the application's interface during an end-to-end optimization process. At inference time, we replace each estimator with its existing application counterpart and let the base network solve the task by interacting with the existing application. Using this 'Estimate and Replace' method, we were able to train a DNN end-to-end with less data and outperformed a matching DNN that did not interact with the external application.
On Optimal Index Codes for Interlinked Cycle Structures with Outer Cycles
Venue: arXiv cs.IT 2018
Year: 2018

For index coding problems with special structure on the side-information graphs called Interlinked Cycle (IC) structures index codes have been proposed in the literature (C. Thapa, L. Ong, and S. Johnson, "Interlinked Cycles for Index Coding: Generalizing Cycles and Cliques", in \textit{IEEE Trans. Inf. Theory, vol. 63, no. 6, Jun. 2017} with a correction in "Interlinked Cycles for Index Coding: Generalizing Cycles and Cliques", in arxiv (arxiv:1603.00092v2 [cs.IT] 25 Feb 2018)). In this paper we consider a generalization of IC structures called {\it IC structures with interlocked outer cycles}. For IC structures with interlocked outer cycles we show that the optimal length (also known as the minrank of the index coding problem) depends on the maximum number of disjoint outer cycles. We give two sufficient conditions such that if any of these is satisfied then we provide explicit optimal index code construction. The conditions mentioned above are shown to be not necessary by an explicit example.
Parallel Approximation of the Maximum Likelihood Estimation for the Prediction of Large-Scale Geostatistics Simulations
Venue: arXiv cs.NA 2018
Year: 2018

Maximum likelihood estimation is an important statistical technique for estimating missing data, for example in climate and environmental applications, which are usually large and feature data points that are irregularly spaced. In particular, the Gaussian log-likelihood function is the \emph{de facto} model, which operates on the resulting sizable dense covariance matrix. The advent of high performance systems with advanced computing power and memory capacity have enabled full simulations only for rather small dimensional climate problems, solved at the machine precision accuracy. The challenge for high dimensional problems lies in the computation requirements of the log-likelihood function, which necessitates ${\mathcal O}(n^2)$ storage and ${\mathcal O}(n^3)$ operations, where $n$ represents the number of given spatial locations. This prohibitive computational cost may be reduced by using approximation techniques that not only enable large-scale simulations otherwise intractable but also maintain the accuracy and the fidelity of the spatial statistics model. In this paper, we extend the Exascale GeoStatistics software framework (i.e., ExaGeoStat) to support the Tile Low-Rank (TLR) approximation technique, which exploits the data sparsity of the dense covariance matrix by compressing the off-diagonal tiles up to a user-defined accuracy threshold. The underlying linear algebra operations may then be carried out on this data compression format, which may ultimately reduce the arithmetic complexity of the maximum likelihood estimation and the corresponding memory footprint. Performance results of TLR-based computations on shared and distributed-memory systems attain up to 13X and 5X speedups, respectively, compared to full accuracy simulations using synthetic and real datasets (up to 2M), while ensuring adequate prediction accuracy.
Automated Detection of Adverse Drug Reactions in the Biomedical Literature Using Convolutional Neural Networks and Biomedical Word Embeddings
Venue: arXiv cs.CL 2018
Year: 2018

Monitoring the biomedical literature for cases of Adverse Drug Reactions (ADRs) is a critically important and time consuming task in pharmacovigilance. The development of computer assisted approaches to aid this process in different forms has been the subject of many recent works. One particular area that has shown promise is the use of Deep Neural Networks, in particular, Convolutional Neural Networks (CNNs), for the detection of ADR relevant sentences. Using token-level convolutions and general purpose word embeddings, this architecture has shown good performance relative to more traditional models as well as Long Short Term Memory (LSTM) models. In this work, we evaluate and compare two different CNN architectures using the ADE corpus. In addition, we show that by de-duplicating the ADR relevant sentences, we can greatly reduce overoptimism in the classification results. Finally, we evaluate the use of word embeddings specifically developed for biomedical text and show that they lead to a better performance in this task.
An Integrated Framework for AI Assisted Level Design in 2D Platformers
Venue: arXiv cs.AI 2018
Year: 2018

The design of video game levels is a complex and critical task. Levels need to elicit fun and challenge while avoiding frustration at all costs. In this paper, we present a framework to assist designers in the creation of levels for 2D platformers. Our framework provides designers with a toolbox (i) to create 2D platformer levels, (ii) to estimate the difficulty and probability of success of single jump actions (the main mechanics of platformer games), and (iii) a set of metrics to evaluate the difficulty and probability of completion of entire levels. At the end, we present the results of a set of experiments we carried out with human players to validate the metrics included in our framework.
SRCLock: SAT-Resistant Cyclic Logic Locking for Protecting the Hardware
Venue: arXiv cs.CR 2018
Year: 2018

In this paper, we claim that cyclic obfuscation, when properly implemented, poses exponential complexity on SAT or CycSAT attack. The CycSAT, in order to generate the necessary cycle avoidance clauses, uses a pre-processing step. We show that this pre-processing step has to compose its cycle avoidance condition on all cycles in a netlist, otherwise, a missing cycle could trap the SAT solver in an infinite loop or force it to return an incorrect key. Then, we propose several techniques by which the number of cycles is exponentially increased with respect to the number of inserted feedbacks. We further illustrate that when the number of feedbacks is increased, the pre-processing step of CycSAT faces an exponential increase in complexity and runtime, preventing the correct composition of loop avoidance clauses in a reasonable time before invoking the SAT solver. On the other hand, if the pre-processing is not completed properly, the SAT solver will get stuck or return incorrect key. Hence, when the cyclic obfuscation in accordance to the conditions proposed in this paper is implemented, it would impose an exponential complexity with respect to the number of inserted feedback, even when the CycSAT solution is used.
Hybrid LISA for Wideband Multiuser Millimeter Wave Communication Systems under Beam Squint
Venue: arXiv cs.IT 2018
Year: 2018

This work jointly addresses user scheduling and precoder/combiner design in the downlink of a wideband millimeter wave (mmWave) communications system. We consider Orthogonal Frequency Division Multiplexing (OFDM) modulation to overcome channel frequency selectivity and obtain a number of equivalent narrowband channels. Hence, the main challenge is that the analog preprocessing network is frequency flat and common to all the users at the transmitter side. Moreover, the effect of the signal bandwidth over the Uniform Linear Array (ULA) steering vectors has to be taken into account to design the hybrid precoders and combiners. The proposed algorithmic solution is based on Linear Successive Allocation (LISA), which greedily allocates streams to different users and computes the corresponding precoders and combiners. By taking into account the rank limitations imposed by the hardware at transmission and reception, the performance loss in terms of achievable sum rate for the hybrid approach is negligible. Numerical experiments show that the proposed method exhibits excellent performance with reasonable computational complexity.
Transferring Interactive Search-Based Software Testing to Industry
Venue: arXiv cs.SE 2018
Year: 2018

Search-Based Software Testing (SBST) is the application of optimization algorithms to problems in software testing. In previous work, we have implemented and evaluated Interactive Search-Based Software Testing (ISBST) tool prototypes, with a goal to successfully transfer the technique to industry. While SBSE solutions are often validated on benchmark problems, there is a need to validate them in an operational setting. The present paper discusses the development and deployment of SBST tools for use in industry and reflects on the transfer of these techniques to industry. In addition to previous work discussing the development and validation of an ISBST prototype, a new version of the prototype ISBST system was evaluated in the laboratory and in industry. This evaluation is based on an industrial System under Test (SUT) and was carried out with industrial practitioners. The Technology Transfer Model is used as a framework to describe the progression of the development and evaluation of the ISBST system. The paper presents a synthesis of previous work developing and evaluating the ISBST prototype, as well as presenting an evaluation, in both academia and industry, of that prototype's latest version. This paper presents an overview of the development and deployment of the ISBST system in an industrial setting, using the framework of the Technology Transfer Model. We conclude that the ISBST system is capable of evolving useful test cases for that setting, though improvements in the means the system uses to communicate that information to the user are still required. In addition, a set of lessons learned from the project are listed and discussed. Our objective is to help other researchers that wish to validate search-based systems in industry and provide more information about the benefits and drawbacks of these systems.
Segmentation-Free Approaches for Handwritten Numeral String Recognition
Venue: arXiv cs.CV 2018
Year: 2018

This paper presents segmentation-free strategies for the recognition of handwritten numeral strings of unknown length. A synthetic dataset of touching numeral strings of sizes 2-, 3- and 4-digits was created to train end-to-end solutions based on Convolutional Neural Networks. A robust experimental protocol is used to show that the proposed segmentation-free methods may reach the state-of-the-art performance without suffering the heavy burden of over-segmentation based methods. In addition, they confirmed the importance of introducing contextual information in the design of end-to-end solutions, such as the proposed length classifier when recognizing numeral strings.
The set of dimensions for which there are no linear perfect 2-error-correcting Lee codes has positive density
Venue: arXiv cs.IT 2018
Year: 2018

The Golomb-Welch conjecture states that there are no perfect $e$-error-correcting Lee codes in $\mathbb{Z}^n$ ($PL(n,e)$-codes) whenever $n\geq 3$ and $e\geq 2$. A special case of this conjecture is when $e=2$. In a recent paper of A. Campello, S. Costa and the author of this paper, it is proved that the set $\mathcal{N}$ of dimensions $n\geq 3$ for which there are no linear $PL(n,2)$-codes is infinite and $\#\{n \in \mathcal{N}: n\leq x\} \geq \frac{x}{3\ln(x)/2} (1+o(1))$. In this paper we present a simple and elementary argument which allows to improve the above result to $\#\{n \in \mathcal{N}: n\leq x\} \geq \frac{4x}{25} (1+o(1))$. In particular, this implies that the set $\mathcal{N}$ has positive (lower) density in $\mathbb{Z}^+$.
Where are we now? A large benchmark study of recent symbolic regression methods
Venue: arXiv cs.NE 2018
Year: 2018

In this paper we provide a broad benchmarking of recent genetic programming approaches to symbolic regression in the context of state of the art machine learning approaches. We use a set of nearly 100 regression benchmark problems culled from open source repositories across the web. We conduct a rigorous benchmarking of four recent symbolic regression approaches as well as nine machine learning approaches from scikit-learn. The results suggest that symbolic regression performs strongly compared to state-of-the-art gradient boosting algorithms, although in terms of running times is among the slowest of the available methodologies. We discuss the results in detail and point to future research directions that may allow symbolic regression to gain wider adoption in the machine learning community.
Analysis of Indoor Uplink Optical Communication Positioning System Exploiting Multipath Reflections
Venue: arXiv cs.IT 2018
Year: 2018

In this paper, we introduce an uplink optical wireless positioning system for indoor applications. This technique uses fingerprints based on the indoor optical wireless channel impulse response for localization. Exploiting the line of sight peak power (LOS), the second power peak (SPP) of the impulse response, and the delay between the LOS and SPP, we present a proof of concept design and theoretical analysis for localization employing a single fixed reference point, i.e., a photodetector (PD) on the ceiling. Adding more PDs leads to more accurate transmitter position estimation. As a benchmark, we present analytical expressions of the Cramer-Rao lower bound (CRLB) for different numbers of PDs and features. We further present closed form analytical approximations for the chosen features of the channel impulse response. Simulation results show a root mean square (RMS) positioning accuracy of 25\,cm and 5\,cm for one and four PDs, respectively, for a typical indoor room at high SNR. Numerical results verify that the derived analytic approximations closely match the simulations.
Learnable Histogram: Statistical Context Features for Deep Neural Networks
Venue: arXiv cs.CV 2018
Year: 2018

Statistical features, such as histogram, Bag-of-Words (BoW) and Fisher Vector, were commonly used with hand-crafted features in conventional classification methods, but attract less attention since the popularity of deep learning methods. In this paper, we propose a learnable histogram layer, which learns histogram features within deep neural networks in end-to-end training. Such a layer is able to back-propagate (BP) errors, learn optimal bin centers and bin widths, and be jointly optimized with other layers in deep networks during training. Two vision problems, semantic segmentation and object detection, are explored by integrating the learnable histogram layer into deep networks, which show that the proposed layer could be well generalized to different applications. In-depth investigations are conducted to provide insights on the newly introduced layer.
Two monads for graphs
Venue: arXiv cs.LO 2018
Year: 2018

An introduction to algebras for graphs, based on Courcelle's algebras of hyperedge replacement and vertex replacement. The paper uses monad notation.
Compositional Equivalence with actor attributes: Positional analysis of the the Florentine Families network
Venue: arXiv cs.SI 2018
Year: 2018

This paper extends Compositional Equivalence ---which is a structural correspondence type aimed for multiplex networks--- by incorporating actor attributes in the modelling of the network relational structure as diagonal matrices. As an illustration, we construct the positional system of the Florentine families' network in the 15th century with Business and Marriage ties together with relevant characteristics acquired from the actors such as families' financial Wealth and their number of Priorates. Different representations of the cumulated person hierarchies reveal that adding Wealth in the modelling provides a more accurate picture of what the substantial narrative says about this network.
Optimized Resource Provisioning and Operation Control for Low-power Wide-area IoT Networks
Venue: arXiv cs.IT 2018
Year: 2018

The tradeoff between cost of the access network and quality of offered service to IoT devices, in terms of reliability and durability of communications, is investigated. We first develop analytical tools for reliability evaluation in uplink-oriented large-scale IoT networks. These tools comprise modeling of interference from heterogeneous interfering sources with time-frequency asynchronous radio-resource usage patterns, and benefit from realistic distribution processes for modeling channel fading and locations of interfering sources. We further present a cost model for the access network as a function of provisioned resources like density of access points (APs), and a battery lifetime model for IoT devices as a function of intrinsic parameters, e.g. level of stored energy, and network parameters, e.g. reliability of communication. The derived models represent the ways in which a required level of reliability can be achieved by either sacrificing battery lifetime (durability), e.g. increasing number of replica transmissions, or sacrificing network cost and increasing provisioned resources, e.g. density of the APs. Then, we investigate optimal resource provisioning and operation control strategies, where the former aims at finding the optimized investment in the access network based on the cost of each resource; while the latter aims at optimizing data transmission strategies of IoT devices. The simulation results confirm tightness of derived analytical expressions, and show how the derived expressions can be used in finding optimal operation points of IoT networks.
The Future of Prosody: It's about Time
Venue: arXiv cs.CL 2018
Year: 2018

Prosody is usually defined in terms of the three distinct but interacting domains of pitch, intensity and duration patterning, or, more generally, as phonological and phonetic properties of 'suprasegmentals', speech segments which are larger than consonants and vowels. Rather than taking this approach, the concept of multiple time domains for prosody processing is taken up, and methods of time domain analysis are discussed: annotation mining with timing dispersion measures, time tree induction, oscillator models in phonology and phonetics, and finally the use of the Amplitude Envelope Modulation Spectrum (AEMS). While frequency demodulation (in the form of pitch tracking) is a central issue in prosodic analysis, in the present context it is amplitude envelope demodulation and frequency zones in the long time-domain spectra of the demodulated envelope which are focused. A generalised view is taken of oscillation as iteration in abstract prosodic models and as modulation and demodulation of a variety of rhythms in the speech signal.
Feedback Synthesis For Underactuated Systems Using Sequential Second-Order Needle Variations
Venue: arXiv cs.RO 2018
Year: 2018

This paper derives nonlinear feedback control synthesis for general control affine systems using second-order actions---the second-order needle variations of optimal control---as the basis for choosing each control response to the current state. A second result of the paper is that the method provably exploits the nonlinear controllability of a system by virtue of an explicit dependence of the second-order needle variation on the Lie bracket between vector fields. As a result, each control decision necessarily decreases the objective when the system is nonlinearly controllable using first-order Lie brackets. Simulation results using a differential drive cart, an underactuated kinematic vehicle in three dimensions, and an underactuated dynamic model of an underwater vehicle demonstrate that the method finds control solutions when the first-order analysis is singular. Lastly, the underactuated dynamic underwater vehicle model demonstrates convergence even in the presence of a velocity field.