OpenRaiser/Intern-Atlas huggingface dataset:
Design of robust H_inf fuzzy output feedback controller for affine nonlinear systems:Fuzzy Lyapunov function approach
Venue: arXiv cs.SY 2018
Year: 2018

In this paper, we propose a new systematic approach based on nonquadratic Lyapunov function and technique of introducing slack matrices, for a class of affine nonlinear systems with disturbance. To achieve the goal, first, the affine nonlinear system is represented via Takagi-Sugeno (T-S) fuzzy bilinear model. Subsequently, the robust H_inf controller is designed based on parallel distributed compensation (PDC) scheme. Then, the stability conditions are derived in terms of linear matrix inequalities (LMIs) by utilizing Lyapunov function. Moreover, some slack matrices are proposed to reduce the conservativeness of the LMI stability conditions. Finally, for illustrating the merits and verifying the effectiveness of the proposed approach, the application of an isothermal continuous stirred tank reactor (CSTR) for Van de Vusse reactor is discussed in details.
Attribute-aware Collaborative Filtering: Survey and Classification
Venue: arXiv cs.IR 2018
Year: 2018

Attribute-aware CF models aims at rating prediction given not only the historical rating from users to items, but also the information associated with users (e.g. age), items (e.g. price), or even ratings (e.g. rating time). This paper surveys works in the past decade developing attribute-aware CF systems, and discovered that mathematically they can be classified into four different categories. We provide the readers not only the high level mathematical interpretation of the existing works in this area but also the mathematical insight for each category of models. Finally we provide in-depth experiment results comparing the effectiveness of the major works in each category.
Enriched Interpretation
Venue: arXiv cs.AI 2018
Year: 2018

The theory introduced, presented and developed in this paper, is concerned with an enriched extension of the theory of Rough Sets pioneered by Zdzislaw Pawlak. The enrichment discussed here is in the sense of valuated categories as developed by F.W. Lawvere. This paper relates Rough Sets to an abstraction of the theory of Fuzzy Sets pioneered by Lotfi Zadeh, and provides a natural foundation for "soft computation". To paraphrase Lotfi Zadeh, the impetus for the transition from a hard theory to a soft theory derives from the fact that both the generality of a theory and its applicability to real-world problems are substantially enhanced by replacing various hard concepts with their soft counterparts. Here we discuss the corresponding enriched notions for indiscernibility, subsets, upper/lower approximations, and rough sets. Throughout, we indicate linkages with the theory of Formal Concept Analysis pioneered by Rudolf Wille. We pay particular attention to the all-important notion of a "linguistic variable" - developing its enriched extension, comparing it with the notion of conceptual scale from Formal Concept Analysis, and discussing the pragmatic issues of its creation and use in the interpretation of data. These pragmatic issues are exemplified by the discovery, conceptual analysis, interpretation, and categorization of networked information resources in WAVE, the Web Analysis and Visualization Environment currently being developed for the management and interpretation of the universe of resource information distributed over the World-Wide Web.
Empirically Assessing Opportunities for Prefetching and Caching in Mobile Apps
Venue: arXiv cs.SE 2018
Year: 2018

Network latency in mobile software has a large impact on user experience, with potentially severe economic consequences. Prefetching and caching have been shown effective in reducing the latencies in browser-based systems. However, those techniques cannot be directly applied to the emerging domain of mobile apps because of the differences in network interactions. Moreover, there is a lack of research on prefetching and caching techniques that may be suitable for the mobile app domain, and it is not clear whether such techniques can be effective or whether they are even feasible. This paper takes the first step toward answering these questions by conducting a comprehensive study to understand the characteristics of HTTP requests in over 1000 popular Android apps. Our work focuses on the prefetchability of requests using static program analysis techniques and cacheability of resulting responses. We find that there is a substantial opportunity to leverage prefetching and caching in mobile apps, but that suitable techniques must take into account the nature of apps' network interactions and idiosyncrasies such as untrustworthy HTTP header information. Our observations provide guidelines for developers to utilize prefetching and caching schemes in app development, and motivate future research in this area.
Sleep Arousal Detection from Polysomnography using the Scattering Transform and Recurrent Neural Networks
Venue: arXiv cs.LG 2018
Year: 2018

Sleep disorders are implicated in a growing number of health problems. In this paper, we present a signal-processing/machine learning approach to detecting arousals in the multi-channel polysomnographic recordings of the Physionet/CinC Challenge2018 dataset. Methods: Our network architecture consists of two components. Inputs were presented to a Scattering Transform (ST) representation layer which fed a recurrent neural network for sequence learning using three layers of Long Short-Term Memory (LSTM). The STs were calculated for each signal with downsampling parameters chosen to give approximately 1 s time resolution, resulting in an eighteen-fold data reduction. The LSTM layers then operated at this downsampled rate. Results: The proposed approach detected arousal regions on the 10% random sample of the hidden test set with an AUROC of 88.0% and an AUPRC of 42.1%.
Learning from the Kernel and the Range Space
Venue: arXiv cs.LG 2018
Year: 2018

In this article, a novel approach to learning a complex function which can be written as the system of linear equations is introduced. This learning is grounded upon the observation that solving the system of linear equations by a manipulation in the kernel and the range space boils down to an estimation based on the least squares error approximation. The learning approach is applied to learn a deep feedforward network with full weight connections. The numerical experiments on network learning of synthetic and benchmark data not only show feasibility of the proposed learning approach but also provide insights into the mechanism of data representation.
A general learning system based on neuron bursting and tonic firing
Venue: arXiv cs.NE 2018
Year: 2018

This paper proposes a framework for the biological learning mechanism as a general learning system. The proposal is as follows. The bursting and tonic modes of firing patterns found in many neuron types in the brain correspond to two separate modes of information processing, with one mode resulting in awareness, and another mode being subliminal. In such a coding scheme, a neuron in bursting state codes for the highest level of perceptual abstraction representing a pattern of sensory stimuli, or volitional abstraction representing a pattern of muscle contraction sequences. Within the 50-250 ms minimum integration time of experience, the bursting neurons form synchrony ensembles to allow for binding of related percepts. The degree which different bursting neurons can be merged into the same synchrony ensemble depends on the underlying cortical connections that represent the degree of perceptual similarity. These synchrony ensembles compete for selective attention to remain active. The dominant synchrony ensemble triggers episodic memory recall in the hippocampus, while forming new episodic memory with current sensory stimuli, resulting in a stream of thoughts. Neuromodulation modulates both top-down selection of synchrony ensembles, and memory formation. Episodic memory stored in the hippocampus is transferred to semantic and procedural memory in the cortex during rapid eye movement sleep, by updating cortical neuron synaptic weights with spike timing dependent plasticity. With the update of synaptic weights, new neurons become bursting while previous bursting neurons become tonic, allowing bursting neurons to move up to a higher level of perceptual abstraction. Finally, the proposed learning mechanism is compared with the back-propagation algorithm used in deep neural networks, and a proposal of how the credit assignment problem can be addressed by the current proposal is presented.
Observing the Evolution of QUIC Implementations
Venue: arXiv cs.NI 2018
Year: 2018

The QUIC protocol combines features that were initially found inside the TCP, TLS and HTTP/2 protocols. The IETF is currently finalising a complete specification of this protocol. More than a dozen of independent implementations have been developed in parallel with these standardisation activities. We propose and implement a QUIC test suite that interacts with public QUIC servers to verify their conformance with key features of the IETF specification. Our measurements, gathered over a semester, provide a unique viewpoint on the evolution of a protocol and of its implementations. They highlight the arrival of new features and some regressions among the different implementations.
Ensemble Method for Censored Demand Prediction
Venue: arXiv cs.LG 2018
Year: 2018

Many economic applications including optimal pricing and inventory management requires prediction of demand based on sales data and estimation of sales reaction to a price change. There is a wide range of econometric approaches which are used to correct a bias in estimates of demand parameters on censored sales data. These approaches can also be applied to various classes of machine learning models to reduce the prediction error of sales volume. In this study we construct two ensemble models for demand prediction with and without accounting for demand censorship. Accounting for sales censorship is based on the idea of censored quantile regression method where the model estimation is splitted on two separate parts: a) prediction of zero sales by classification model; and b) prediction of non-zero sales by regression model. Models with and without accounting for censorship are based on the predictions aggregations of Least squares, Ridge and Lasso regressions and Random Forest model. Having estimated the predictive properties of both models, we empirically test the best predictive power of the model that takes into account the censored nature of demand. We also show that machine learning method with censorship accounting provide bias corrected estimates of demand sensitivity for price change similar to econometric models.
Automatic acoustic identification of individual animals: Improving generalisation across species and recording conditions
Venue: arXiv cs.SD 2018
Year: 2018

Many animals emit vocal sounds which, independently from the sounds' function, embed some individually-distinctive signature. Thus the automatic recognition of individuals by sound is a potentially powerful tool for zoology and ecology research and practical monitoring. Here we present a general automatic identification method, that can work across multiple animal species with various levels of complexity in their communication systems. We further introduce new analysis techniques based on dataset manipulations that can evaluate the robustness and generality of a classifier. By using these techniques we confirmed the presence of experimental confounds in situations resembling those from past studies. We introduce data manipulations that can reduce the impact of these confounds, compatible with any classifier. We suggest that assessment of confounds should become a standard part of future studies to ensure they do not report over-optimistic results. We provide annotated recordings used for analyses along with this study and we call for dataset sharing to be a common practice to enhance development of methods and comparisons of results.
Real-time Neural-based Input Method
Venue: arXiv cs.CL 2018
Year: 2018

The input method is an essential service on every mobile and desktop devices that provides text suggestions. It converts sequential keyboard inputs to the characters in its target language, which is indispensable for Japanese and Chinese users. Due to critical resource constraints and limited network bandwidth of the target devices, applying neural models to input method is not well explored. In this work, we apply a LSTM-based language model to input method and evaluate its performance for both prediction and conversion tasks with Japanese BCCWJ corpus. We articulate the bottleneck to be the slow softmax computation during conversion. To solve the issue, we propose incremental softmax approximation approach, which computes softmax with a selected subset vocabulary and fix the stale probabilities when the vocabulary is updated in future steps. We refer to this method as incremental selective softmax. The results show a two order speedup for the softmax computation when converting Japanese input sequences with a large vocabulary, reaching real-time speed on commodity CPU. We also exploit the model compressing potential to achieve a 92% model size reduction without losing accuracy.
Coupled Longitudinal and Lateral Control of a Vehicle using Deep Learning
Venue: arXiv cs.LG 2018
Year: 2018

This paper explores the capability of deep neural networks to capture key characteristics of vehicle dynamics, and their ability to perform coupled longitudinal and lateral control of a vehicle. To this extent, two different artificial neural networks are trained to compute vehicle controls corresponding to a reference trajectory, using a dataset based on high-fidelity simulations of vehicle dynamics. In this study, control inputs are chosen as the steering angle of the front wheels, and the applied torque on each wheel. The performance of both models, namely a Multi-Layer Perceptron (MLP) and a Convolutional Neural Network (CNN), is evaluated based on their ability to drive the vehicle on a challenging test track, shifting between long straight lines and tight curves. A comparison to conventional decoupled controllers on the same track is also provided.
OS Scheduling Algorithms for Improving the Performance of Multithreaded Workloads
Venue: arXiv cs.DC 2018
Year: 2018

Major chip manufacturers have all introduced multicore microprocessors. Multi-socket systems built from these processors are used for running various server applications. However to the best of our knowledge current commercial operating systems are not optimized for multi-threaded workloads running on such servers. Cache-to-cache transfers and remote memory accesses impact the performance of such workloads. This paper presents a unified approach to optimizing OS scheduling algorithms for both cache-to-cache transfers and remote DRAM accesses that also takes cache affinity into account. By observing the patterns of local and remote cache-to-cache transfers as well as local and remote DRAM accesses for every thread in each scheduling quantum and applying different algorithms, we come up with a new schedule of threads for the next quantum taking cache affinity into account. This new schedule cuts down both remote cache-to-cache transfers and remote DRAM accesses for the next scheduling quantum and improves overall performance. We present two algorithms of varying complexity for optimizing cache-to-cache transfers. One of these is a new algorithm which is relatively simpler and performs better when combined with algorithms that optimize remote DRAM accesses. For optimizing remote DRAM accesses we present two algorithms. Though both algorithms differ in algorithmic complexity we find that for our workloads they perform equally well. We used three different synthetic workloads to evaluate these algorithms. We also performed sensitivity analysis with respect to varying remote cache-to-cache transfer latency and remote DRAM latency. We show that these algorithms can cut down overall latency by up to 16.79% depending on the algorithm used.
Learning Probabilistic Trajectory Models of Aircraft in Terminal Airspace from Position Data
Venue: arXiv cs.LG 2018
Year: 2018

Models for predicting aircraft motion are an important component of modern aeronautical systems. These models help aircraft plan collision avoidance maneuvers and help conduct offline performance and safety analyses. In this article, we develop a method for learning a probabilistic generative model of aircraft motion in terminal airspace, the controlled airspace surrounding a given airport. The method fits the model based on a historical dataset of radar-based position measurements of aircraft landings and takeoffs at that airport. We find that the model generates realistic trajectories, provides accurate predictions, and captures the statistical properties of aircraft trajectories. Furthermore, the model trains quickly, is compact, and allows for efficient real-time inference.
A Fully Attention-Based Information Retriever
Venue: arXiv cs.CL 2018
Year: 2018

Recurrent neural networks are now the state-of-the-art in natural language processing because they can build rich contextual representations and process texts of arbitrary length. However, recent developments on attention mechanisms have equipped feedforward networks with similar capabilities, hence enabling faster computations due to the increase in the number of operations that can be parallelized. We explore this new type of architecture in the domain of question-answering and propose a novel approach that we call Fully Attention Based Information Retriever (FABIR). We show that FABIR achieves competitive results in the Stanford Question Answering Dataset (SQuAD) while having fewer parameters and being faster at both learning and inference than rival methods.
Source Code Properties of Defective Infrastructure as Code Scripts
Venue: arXiv cs.SE 2018
Year: 2018

Context: In continuous deployment, software and services are rapidly deployed to end-users using an automated deployment pipeline. Defects in infrastructure as code (IaC) scripts can hinder the reliability of the automated deployment pipeline. We hypothesize that certain properties of IaC source code such as lines of code and hard-coded strings used as configuration values, show correlation with defective IaC scripts. Objective: The objective of this paper is to help practitioners in increasing the quality of infrastructure as code (IaC) scripts through an empirical study that identifies source code properties of defective IaC scripts. Methodology: We apply qualitative analysis on defect-related commits mined from open source software repositories to identify source code properties that correlate with defective IaC scripts. Next, we survey practitioners to assess the practitioner's agreement level with the identified properties. We also construct defect prediction models using the identified properties for 2,439 scripts collected from four datasets. Results: We identify 10 source code properties that correlate with defective IaC scripts. Of the identified 10 properties we observe lines of code and hard-coded string to show the strongest correlation with defective IaC scripts. Hard-coded string is the property of specifying configuration value as hard-coded string. According to our survey analysis, majority of the practitioners show agreement for two properties: include, the property of executing external modules or scripts, and hard-coded string. Using the identified properties, our constructed defect prediction models show a precision of 0.70~0.78, and a recall of 0.54~0.67.
Some Challenges of Specifying Concurrent Program Components
Venue: arXiv cs.PL 2018
Year: 2018

The purpose of this paper is to address some of the challenges of formally specifying components of shared-memory concurrent programs. The focus is to provide an abstract specification of a component that is suitable for use both by clients of the component and as a starting point for refinement to an implementation of the component. We present some approaches to devising specifications, investigating different forms suitable for different contexts. We examine handling atomicity of access to data structures, blocking operations and progress properties, and transactional operations that may fail and need to be retried.
Deep Neural Ranking for Crowdsourced Geopolitical Event Forecasting
Venue: arXiv cs.AI 2018
Year: 2018

There are many examples of 'wisdom of the crowd' effects in which the large number of participants imparts confidence in the collective judgment of the crowd. But how do we form an aggregated judgment when the size of the crowd is limited? Whose judgments do we include, and whose do we accord the most weight? This paper considers this problem in the context of geopolitical event forecasting, where volunteer analysts are queried to give their expertise, confidence, and predictions about the outcome of an event. We develop a forecast aggregation model that integrates topical information about a question, meta-data about a pair of forecasters, and their predictions in a deep siamese neural network that decides which forecasters' predictions are more likely to be close to the correct response. A ranking of the forecasters is induced from a tournament of pair-wise forecaster comparisons, with the ranking used to create an aggregate forecast. Preliminary results find the aggregate prediction of the best forecasters ranked by our deep siamese network model consistently beats typical aggregation techniques by Brier score.
Criticality Aware Soft Error Mitigation in the Configuration Memory of SRAM based FPGA
Venue: arXiv cs.AR 2018
Year: 2018

Efficient low complexity error correcting code(ECC) is considered as an effective technique for mitigation of multi-bit upset (MBU) in the configuration memory(CM)of static random access memory (SRAM) based Field Programmable Gate Array (FPGA) devices. Traditional multi-bit ECCs have large overhead and complex decoding circuit to correct adjacent multibit error. In this work, we propose a simple multi-bit ECC which uses Secure Hash Algorithm for error detection and parity based two dimensional Erasure Product Code for error correction. Present error mitigation techniques perform error correction in the CM without considering the criticality or the execution period of the tasks allocated in different portion of CM. In most of the cases, error correction is not done in the right instant, which sometimes either suspends normal system operation or wastes hardware resources for less critical tasks. In this paper,we advocate for a dynamic priority-based hardware scheduling algorithm which chooses the tasks for error correction based on their area, execution period and criticality. The proposed method has been validated in terms of overhead due to redundant bits, error correction time and system reliability
Two-way Function Computation
Venue: arXiv cs.IT 2018
Year: 2018

We explore the role of interaction for the problem of reliable computation over two-way multicast networks. Specifically we consider a four-node network in which two nodes wish to compute a modulo-sum of two independent Bernoulli sources generated from the other two, and a similar task is done in the other direction. The main contribution of this work lies in the characterization of the computation capacity region for a deterministic model of the network via a novel transmission scheme. One consequence of this result is that, not only we can get an interaction gain over the one-way non-feedback computation capacities, but also we can sometimes get all the way to perfect-feedback computation capacities simultaneously in both directions. This result draws a parallel with the recent result developed in the context of two-way interference channels.