Miracle-dz/UniFAR-FTUs huggingface dataset:
Forecasting the Behavior of Gas Furnace Multivariate Time Series Using Ridge Polynomial Based Neural Network Models

In this paper, a new application of ridge polynomial based neural network models in multivariate time series forecasting is presented. The existing ridge polynomial based neural network models can be grouped into two groups. Group A consists of models that use only autoregressive inputs, whereas Group B consists of models that use autoregressive and moving-average (i.e., error feedback) inputs. The well-known Box-Jenkins gas furnace multivariate time series was used in the forecasting comparison between the two groups. Simulation results show that the models in Group B achieve significant forecasting performance as compared to the models in Group A. Therefore, the Box-Jenkins gas furnace data can be modeled better using neural networks when error feedback is used.
A Language-oriented Approach for the Maintenance of Megamodel-based Complex Systems

Model Driven Engineering (MDE) provides the concept of a runtime megamodel to represent the dynamic structure of a given system, to which it is causally connected. A system changes at runtime therefore implies frequent and dynamic changes of its related megamodel. In a previous work we have proposed to automate change management through a runtime megamodel evolution management approach. In such an approach, a megamodel manipulation, a kind of programming in-the-large activity, is considered as a mega-program which is modified throughout Global Operation Models (GOMs). Then we proposed a safe execution of GOMs as the solution for megamodel consistency preserving during evolution. In this work, we propose LAMEME, a domain-specific language for the management and the evolution of megamodels, and its axiomatic semantics. LAMEME gives the possibility to express an evolving megamodel as a mega-program and therefore defines a framework that supports our previously proposed approach.
Hunting for Bugs in Code Coverage Tools via Randomized Differential Testing

Reliable code coverage tools are critically important as it is heavily used to facilitate many quality assurance activities, such as software testing, fuzzing, and debugging. However, little attention has been devoted to assessing the reliability of code coverage tools. In this study, we propose a randomized differential testing approach to hunting for bugs in the most widely used C code coverage tools. Specifically, by generating random input programs, our approach seeks for inconsistencies in code coverage reports produced by different code coverage tools, and then identifies inconsistencies as potential code coverage bugs. To effectively report code coverage bugs, we addressed three specific challenges: (1) How to filter out duplicate test programs as many of them triggering the same bugs in code coverage tools; (2) how to automatically reduce large test programs to much smaller ones that have the same properties; and (3) how to determine which code coverage tools have bugs? The extensive evaluations validate the effectiveness of our approach, resulting in 42 and 28 confirmed/fixed bugs for gcov and llvm-cov, respectively. This case study indicates that code coverage tools are not as reliable as it might have been envisaged. It not only demonstrates the effectiveness of our approach, but also highlights the need to continue improving the reliability of code coverage tools. This work opens up a new direction in code coverage validation which calls for more attention in this area.
Toward Detection and Characterization of Variability Bugs in Configurable C Software: An Empirical Study

Variability in C software is a useful tool, but critical bugs that only exist in certain configurations are easily missed by conventional debugging techniques. Even with a small number of features, the configuration space of configurable software is too large to analyze exhaustively. Variability-aware static analysis for bug detection is being developed, but remains at too early a stage to be fully usable in real-world C programs. In this work, we present a methodology of finding variability bugs by combining variability-oblivious bug detectors, static analysis of build processes, and dynamic feature interaction inference. We further present an empirical study in which we test our methodology on two highly configurable C programs. We found our methodology to be effective, finding 88 true bugs between the two programs, of which 64 were variability bugs.
Variational Russian Roulette for Deep Bayesian Nonparametrics

Bayesian nonparametric models provide a principled way to automatically adapt the complexity of a model to the amount of the data available, but computation in such models is difficult. Amortized variational approximations are appealing because of their computational efficiency, but current methods rely on a fixed finite truncation of the infinite model. This truncation level can be difficult to set, and also interacts poorly with amortized methods due to the over-pruning problem. Instead, we propose a new variational approximation, based on a method from statistical physics called Russian roulette sampling. This allows the variational distribution to adapt its complexity during inference, without relying on a fixed truncation level, and while still obtaining an unbiased estimate of the gradient of the original variational objective. We demonstrate this method on infinite sized variational auto-encoders using a Beta-Bernoulli (Indian buffet process) prior.
Moving Object Detection and Tracking with Doppler LiDAR

In this paper, we present a model-free detection-based tracking approach for detecting and tracking moving objects in street scenes from point clouds obtained via a Doppler LiDAR that can not only collect spatial information (e.g., point clouds) but also Doppler images by using Doppler-shifted frequencies. Using our approach, Doppler images are used to detect moving points and determine the number of moving objects followed by complete segmentations via a region growing technique. The tracking approach is based on Multiple Hypothesis Tracking (MHT) with two extensions. One is that a point cloud descriptor, Oriented Ensemble of Shape Function (OESF), is proposed to evaluate the structure similarity when doing object-to-track association. Another is to use Doppler images to improve the estimation of dynamic state of moving objects. The quantitative evaluation of detection and tracking results on different datasets shows the advantages of Doppler LiDAR and the effectiveness of our approach.
Intelligent Content Management System for Tourism Smart Mobility: Approach and Cloud-based Android Application

Intelligent content management systems have become more popular over the last few years in the tourism industry due to the significantly increasing impact on revenue. Such systems are the part of the smart mobility concept. Smart mobility allows tourists to become more comfortable with transportation in an unknown city by providing interesting information about places seen during their trip. Traditional taxies provide quick transportation from the point A to point B but people sometimes are interested in seeing attractions during their trips and are willing to spend more time and money to do so. Integration of the smartphone application with the vehicle infotainment system provides opportunities of new smart services construction that is based on information from vehicle sensors and Internet connection as well as utilization of in-cabin infrastructure and communication with the driver (suggesting the route preferred by the tourist, speed while going around attractions, adjust the temperature, music, and etc.). The paper presents an approach to smart mobility system development and its evaluation by showing the tourist video information about attractions around.
A Tree-Based Method for Fast Repeated Sampling of Determinantal Point Processes

It is often desirable in recommender systems and other information retrieval applications to provide diverse results, and determinantal point processes (DPPs) have become a popular way to capture the trade-off between the quality of individual results and the diversity of the overall set. However, sampling from a DPP is inherently expensive: if the underlying collection contains N items, then generating each DPP sample requires time linear in N following a one-time preprocessing phase. Additionally, results often need to be personalized to a user, but standard approaches to personalization invalidate the preprocessing, making personalized samples especially expensive. In this work we address both of these shortcomings. First, we propose a new algorithm for generating DPP samples in time logarithmic in N , following a slightly more expensive preprocessing phase. We then extend the algorithm to support arbitrary query-time feature weights, allowing us to generate samples customized to individual users while still retaining logarithmic runtime; experiments show our approach runs over 300 times faster than traditional DPP sampling on collections of 100,000 items for samples of size 10.
Identifying Dry-Season Rice-Planting Patterns in Bangladesh Using the Landsat Archive

In many countries, in situ agricultural data is not available and cost-prohibitive to obtain. While remote sensing provides a unique opportunity to map agricultural areas and management characteristics, major efforts are needed to expand our understanding of cropping patterns and the potential for remotely monitoring crop production because this could support predictions of food shortages and improve resource allocation. In this study, we demonstrate a new method to map paddy rice using Google Earth Engine (GEE) and the Landsat archive in Bangladesh during the dry (boro) season. Using GEE and Landsat, dry-season rice areas were mapped at 30 m resolution for approximately 90,000 km2 annually between 2014 and 2018. The method first reconstructs spectral vegetation indices (VIs) for individual pixels using a harmonic time series (HTS) model to minimize the effect of any sensor inconsistencies and atmospheric noise, and then combines the time series indices with a rule-based algorithm to identify characteristics of rice phenology to classify rice pixels. To our knowledge, this is the first time an annual pixel-based time series model has been applied to Landsat at the national level in a multiyear analysis of rice. Findings suggest that the harmonic-time-series-based vegetation indices (HTS-VIs) model has the potential to map rice production across fragmented landscapes and heterogeneous production practices with comparable results to other estimates, but without local management or in situ information as inputs. The HTS-VIs model identified 4.285, 4.425, 4.645, 4.117, and 4.407 million rice-producing hectares for 2014, 2015, 2016, 2017, and 2018, respectively, which correlates well with national and district estimates from official sources at an average R-squared of 0.8. Moreover, accuracy assessment with independent validation locations resulted in an overall accuracy of 91% and a kappa coefficient of 0.83 for the boro/non-boro stable rice map from 2014 to 2018. We conclude with a discussion of potential improvements and future research pathways for this approach to spatiotemporal mapping of rice in heterogeneous landscapes.
Dichotomic Selection on Words: A Probabilistic Analysis

The paper studies the behaviour of selection algorithms that are based on dichotomy principles. On the entry formed by an ordered list L and a searched element x 6∈ L , they return the interval of the list L the element x belongs to. We focus here on the case of words, where dichotomy principles lead to a selection algorithm designed by Crochemore, Hancart and Lecroq, which appears to be “quasi-optimal”. We perform a probabilistic analysis of this algorithm that exhibits its quasi-optimality on average. 2012
Curiosity-Bottleneck: Exploration By Distilling Task-Specific Novelty

Exploration based on state novelty has brought great success in challenging reinforcement learning problems with sparse rewards. However, existing novelty-based strategies become inefficient in real-world problems where observation contains not only task-dependent state novelty of our interest but also task-irrelevant information that should be ignored. We introduce an informationtheoretic exploration strategy named CuriosityBottleneck that distills task-relevant information from observation. Based on the information bottleneck principle, our exploration bonus is quantified as the compressiveness of observation with respect to the learned representation of a compressive value network. With extensive experiments on static image classification, grid-world and three hard-exploration Atari games, we show that Curiosity-Bottleneck learns an effective exploration strategy by robustly measuring the state novelty in distractive environments where stateof-the-art exploration methods often degenerate.
PA-GD: On the Convergence of Perturbed Alternating Gradient Descent to Second-Order Stationary Points for Structured Nonconvex Optimization

Alternating gradient descent (A-GD) is a simple but popular algorithm in machine learning, which updates two blocks of variables in an alternating manner using gradient descent steps. In this paper, we consider a smooth unconstrained nonconvex optimization problem, and propose a p erturbed A - GD (PA-GD) which is able to converge (with high probability) to the second-order stationary points (SOSPs) with a global sublinear rate. Existing analysis on A-GD type algorithm either only guarantees convergence to ﬁrst-order solutions, or converges to second-order solutions asymptotically (without rates). To the best of our knowledge, this is the ﬁrst alternating type algorithm that takes O ( polylog ( d ) /ϵ 2 ) iterations to achieve an ( ϵ, √ ϵ )-SOSP with high probability, where polylog ( d ) denotes the polynomial of the logarithm with respect to problem dimension d .
Effect of divalent cations on permeabilizer‐induced lysozyme lysis of Pseudomonas aeruginosa

The presence of divalent (Mg2+) ions greatly reduced the lysis of Pseudomonas aeruginosa strain G48 in a system at pH 7·8 or 9·0 consisting of ethylenediamine tetraacetic acid (EDTA), lysozyme and tris. Similar reductions in lysis occurred when EDTA was replaced by nitrilotriacetic acid, sodium citrate or sodium polyphosphate. The effect depended on the cation concentration. Mg2+ may replace cations removed from the outer membrane, or may effectively remove the permeabilizer from the system. The results suggest that the permeabilizing activity associated with these agents against this organism has a common basis in affecting the outer membrane.
A Richer-but-Smarter Shortest Dependency Path with Attentive Augmentation for Relation Extraction

To extract the relationship between two entities in a sentence, two common approaches are (1) using their shortest dependency path (SDP) and (2) using an attention model to capture a context-based representation of the sentence. Each approach suffers from its own disadvantage of either missing or redundant information. In this work, we propose a novel model that combines the advantages of these two approaches. This is based on the basic information in the SDP enhanced with information selected by several attention mechanisms with kernel filters, namely RbSP (Richer-but-Smarter SDP). To exploit the representation behind the RbSP structure effectively, we develop a combined deep neural model with a LSTM network on word sequences and a CNN on RbSP. Experimental results on the SemEval-2010 dataset demonstrate improved performance over competitive baselines. The data and source code are available at https://github.com/catcd/RbSP.
An Empirical Study on Leveraging Logs for Debugging Production Failures

In modern software development, maintenance is one of the most expensive processes. When end-users encounter software defects, they report the bug to developers by specifying the expected behavior and error messages (e.g., log message). Then, they wait for a bug fix from the developers. However, on the developers' side, it can be very challenging and expensive to debug the problem. To fix the bugs, developers often have to play the role of detectives: seeking clues in the user-reported logs files or stack trace in a snapshot of specific system execution. This debugging process may take several hours or even days. In this paper, we first look at the usefulness of the user-reported logs. Then, we propose an automated approach to assist the debugging process by reconstructing the execution path. Through the analysis, our investigation shows that 31% of the time, developer further requests logs from the reporter. Moreover, our preliminary results show that the reconducted path illustrates the user's execution. We believe that our approach proposes a novel solution in debugging production failures.
A unique, predominant hepatitis C virus variant found in an infant born to a mother with multiple variants

To demonstrate vertical transmission of hepatitis C virus (HCV) from an HCV-infected, non-human immunodeficiency virus type 1-infected mother to her infant and to assess the distribution of viral species in the mother and infant, the hypervariable region of the gene encoding the putative envelope glycoprotein E2 (E2HV) was sequenced in three mothers and one mother-infant pair. The data indicate that (i) quasi-species distributions of HCV E2HV variants were found in all four mothers, (ii) a single predominant HCV E2HV variant was found in the infant of a mother shown to have nine predominant E2HV variants, and (iii) the infant's E2HV variant was highly related to, but not identical with, the nine variants identified in the mother at the time of birth. These findings indicate that HCV is transmitted from mother to infant and raise the possibility that the transmission occurs in utero.
A Modified Simple Substitution Cipher With Unbounded Unicity Distance

It appears from this analysis that with ordinary languages and the usual types of ciphers (not codes) this “unicity distance” is approximately H(K) / D. Here H(K) is a number measuring the “size” of the key space. If all keys are a priori equally likely H(K) is the logarithm of the number of possible keys. D is the redundancy of the language and measures the amount of “statistical constraint” imposed by the language. In simple substitution with random key H(K) is log1026! or about 20 and D (in decimal digits per letter) is about . 7 for English. Thus unicity occurs at about 30 letters.
Discovering Latent Covariance Structures for Multiple Time Series

Analyzing multivariate time series data is important to predict future events and changes of complex systems in finance, manufacturing, and administrative decisions. The expressiveness power of Gaussian Process (GP) regression methods has been significantly improved by compositional covariance structures. In this paper, we present a new GP model which naturally handles multiple time series by placing an Indian Buffet Process (IBP) prior on the presence of shared kernels. Our selective covariance structure decomposition allows exploiting shared parameters over a set of multiple, selected time series. We also investigate the well-definedness of the models when infinite latent components are introduced. We present a pragmatic search algorithm which explores a larger structure space efficiently. Experiments conducted on five real-world data sets demonstrate that our new model outperforms existing methods in term of structure discoveries and predictive performances.
Property Oriented Verification Via Iterative Abstract Interpretation

Static analysis by abstract interpretation is one of the widely used automatic approaches to program verification, due to its soundness guarantee and scalability. A key challenge for abstract interpretation with convex and linear abstract domain is verifying complex programs with disjunctive or non-linear behaviors. Our approach is conducting abstract interpretation in an iterative forward and backward manner. It utilizes dynamic input space partitioning to split the input space into sub-spaces. Thus each sub-space involves fewer disjunctive and non-linear program behaviors and is easier to be verified. We have implemented our approach. The experimental results are promising.
Towards Readability Aspects of Probabilistic Mode Automata

This paper presents a new approach and design model targeting hybrid designer- and operator-defined performance budgets for timing and energy consumption. The approach is based on Petri Nets formalism. As the cognitive load is typically high while using formal methods, this increases the chances of mistakes. Our approach is focused on the readability aspects and aims to decrease the cognitive load of developers. We illustrate the proposed approach on example of a sample embedded multi-media system, a modern digital camera.
Conversation Initiation by Diverse News Contents Introduction

In our everyday chit-chat, there is a conversation initiator, who proactively casts an initial utterance to start chatting. However, most existing conversation systems cannot play this role. Previous studies on conversation systems assume that the user always initiates conversation, and have placed emphasis on how to respond to the given user’s utterance. As a result, existing conversation systems become passive. Namely they continue waiting until being spoken to by the users. In this paper, we consider the system as a conversation initiator and propose a novel task of generating the initial utterance in open-domain non-task-oriented conversation. Here, in order not to make users bored, it is necessary to generate diverse utterances to initiate conversation without relying on boilerplate utterances like greetings. To this end, we propose to generate initial utterance by summarizing and chatting about news articles, which provide fresh and various contents everyday. To address the lack of the training data for this task, we constructed a novel large-scale dataset through crowd-sourcing. We also analyzed the dataset in detail to examine how humans initiate conversations (the dataset will be released to facilitate future research activities). We present several approaches to conversation initiation including information retrieval based and generation based models. Experimental results showed that the proposed models trained on our dataset performed reasonably well and outperformed baselines that utilize automatically collected training data in both automatic and manual evaluation.
Learning from Delayed Outcomes via Proxies with Applications to Recommender Systems

Predicting delayed outcomes is an important problem in recommender systems (e.g., if customers will finish reading an ebook). We formalize the problem as an adversarial, delayed online learning problem and consider how a proxy for the delayed outcome (e.g., if customers read a third of the book in 24 hours) can help minimize regret, even though the proxy is not available when making a prediction. Motivated by our regret analysis, we propose two neural network architectures: Factored Forecaster (FF) which is ideal if the proxy is informative of the outcome in hindsight, and Residual Factored Forecaster (RFF) that is robust to a non-informative proxy. Experiments on two real-world datasets for predicting human behavior show that RFF outperforms both FF and a direct forecaster that does not make use of the proxy. Our results suggest that exploiting proxies by factorization is a promising way to mitigate the impact of long delays in human-behavior prediction tasks.