Miracle-dz/UniFAR-FTUs huggingface dataset:
classes conditions Monitor Step 1 : Identify Classes Step 3 : Generate Monitor Step 2

Field data is an invaluable source of information for testers and developers because it witnesses how software systems operate in real environments, capturing scenarios and configurations relevant to end-users. Unfortunately, collecting traces might be resource-consuming and can significantly affect the user experience, for instance causing annoying slowdowns. Existing monitoring techniques can control the overhead introduced in the applications by reducing the amount of collected data, for instance by collecting each event only with a given probability. However, collecting fewer events limits the amount of information extracted from the field and may fail in providing a comprehensive picture of the behavior of a program. In this paper we present fragmented monitoring, a monitoring technique that addresses the issue of collecting information from the field without annoying users. The key idea of fragmented monitoring is to reduce the overhead by recording partial traces (fragments) instead of full traces, while annotating the beginning and the end of each fragment with state information. These annotations are exploited offline to derive traces that might be likely observed in the field and that could not be collected directly due to the overhead that would be introduced in a program.
Explicit algorithms to solve a class of state constrained parabolic optimal control problems

Abstract We consider an optimal control problem of a system governed by a linear parabolic equation with the following features: control is distributed, observation is either distributed or final, there are constraints on the state function and on its time derivative. Iterative solution methods are proposed and investigated for the finite difference approximations of these optimal control problems. Due to explicit in time approximation of the state equation and the appropriate choice of the preconditioners in the iterative methods, the implementation of all constructed methods is carried out by explicit formulae. Computational experiments confirm the theoretical results.
Spacetime Numerical Techniques for the Wave and Schrödinger Equations

The most common tool for solving spacetime problems using finite elements is based on semidiscretization: discretizing in space by a finite element method and then advancing in time by a numerical scheme. Contrary to this standard procedure, in this dissertation we consider formulations where time is another coordinate of the domain. Therefore, spacetime problems can be studied as boundary value problems, where initial conditions are considered as part of the spacetime boundary conditions. When seeking solutions to these problems, it is natural to ask what are the correct spaces of functions to choose, to obtain wellposedness. This motivates the study of an abstract theory for unbounded partial differential operators associated with a general boundary value problem on a bounded domain. A framework for choosing the spaces is introduced, and conditions for the solvability of weak formulations are provided. We apply this framework to study wave problems on tents and to study wellposed discontinuous Petrov-Galerkin (DPG) formulations for the Schrödinger and wave equations. Several numerical issues are also discussed.
Automatic Description Generation from Images: A Survey of Models, Datasets, and Evaluation Measures (Extended Abstract)

Automatic image description generation is a challenging problem that has recently received a large amount of interest from the computer vision and natural language processing communities. In this survey, we classify the known approaches based on how they conceptualise this problem and provide a review of existing models, highlighting their advantages and disadvantages. Moreover, we give an overview of the benchmark image-text datasets and the evaluation measures that have been developed to assess the quality of machine-generated descriptions. Finally we explore future directions in the area of automatic image description.
Neural Symbolic Machines : Learning Semantic Parsers on Freebase with Weak Supervision

Extending the success of deep neural networks to high level tasks like natural language understanding and symbolic reasoning requires program induction and learning with weak supervision. Recent neural program induction approaches have either used primitive computation component like Turing machine or differentiable operations and memory trained by backpropagation. In this work, we propose the Manager-Programmer-Computer framework to integrate neural networks with operations and memory that are abstract, scalable and precise but non-differentiable, and a friendly neural computer interface. Specifically, we introduce the Neural Symbolic Machines. It contains a sequence-to-sequence neural "programmer" that takes in natural language input and outputs a program as a sequence of tokens, and a non-differentiable "computer" that is a Lisp interpreter with code assistance using syntax check and denotations of partial programs. This integration enables the model to effectively learn a semantic parser from weak supervision over a large knowledge base. Our model obtained new state-of-the-art performance on WEBQUESTIONSSP, a challenging semantic parsing dataset.
Numerical Comparison of Leja and Clenshaw-Curtis Dimension-Adaptive Collocation for Stochastic Parametric Electromagnetic Field Problems

We consider the problem of approximating the output of a parametric electromagnetic field model in the presence of a large number of uncertain input parameters. Given a sufficiently smooth output with respect to the input parameters, such problems are often tackled with interpolation-based approaches, such as the stochastic collocation method on tensor-product or isotropic sparse grids. Due to the so-called curse of dimensionality, those approaches result in increased or even forbidding computational costs. In order to reduce the growth in complexity with the number of dimensions, we employ a dimension-adaptive, hierarchical interpolation scheme, based on nested univariate interpolation nodes. Clenshaw-Curtis and Leja nodes satisfy the nestedness property and have been found to provide accurate interpolations when the parameters follow uniform distributions. The dimension-adaptive algorithm constructs the approximation based on the observation that not all parameters or interactions among them are equally important regarding their impact on the model's output. Our goal is to exploit this anisotropy in order to construct accurate polynomial surrogate models at a reduced computational cost compared to isotropic sparse grids. We apply the stochastic collocation method to two electromagnetic field models with medium- to high-dimensional input uncertainty. The performances of isotropic and adaptively constructed, anisotropic sparse grids based on both Clenshaw-Curtis and Leja interpolation nodes are examined. All considered approaches are compared with one another regarding the surrogate models' approximation accuracies using a cross-validation error metric.
Topic Model : Extracting Product Opinions from Tweets by Leveraging Hashtags and Sentiment Lexicon

Aspect-based opinion mining is widely applied to review data to aggregate or summarize opinions of a product, and the current stateof-the-art is achieved with Latent Dirichlet Allocation (LDA)-based model. Although social media data like tweets are laden with opinions, their “dirty” nature (as natural language) has discouraged researchers from applying LDA-based opinion model for product review mining. Tweets are often informal, unstructured and lacking labeled data such as categories and ratings, making it challenging for product opinion mining. In this paper, we propose an LDA-based opinion model named Twitter Opinion Topic Model (TOTM) for opinion mining and sentiment analysis. TOTM leverages hashtags, mentions, emoticons and strong sentiment words that are present in tweets in its discovery process. It improves opinion prediction by modeling the target-opinion interaction directly, thus discovering target specific opinion words, neglected in existing approaches. Moreover, we propose a new formulation of incorporating sentiment prior information into a topic model, by utilizing an existing public sentiment lexicon. This is novel in that it learns and updates with the data. We conduct experiments on 9 million tweets on electronic products, and demonstrate the improved performance of TOTM in both quantitative evaluations and qualitative analysis. We show that aspect-based opinion analysis on massive volume of tweets provides useful opinions on products.
Product-Based Neural Networks for User Response Prediction over Multi-Field Categorical Data

User response prediction is a crucial component for personalized information retrieval and filtering scenarios, such as recommender system and web search. The data in user response prediction is mostly in a multi-field categorical format and transformed into sparse representations via one-hot encoding. Due to the sparsity problems in representation and optimization, most research focuses on feature engineering and shallow modeling. Recently, deep neural networks have attracted research attention on such a problem for their high capacity and end-to-end training scheme. In this article, we study user response prediction in the scenario of click prediction. We first analyze a coupled gradient issue in latent vector-based models and propose kernel product to learn field-aware feature interactions. Then, we discuss an insensitive gradient issue in DNN-based models and propose Product-based Neural Network, which adopts a feature extractor to explore feature interactions. Generalizing the kernel product to a net-in-net architecture, we further propose Product-network in Network (PIN), which can generalize previous models. Extensive experiments on four industrial datasets and one contest dataset demonstrate that our models consistently outperform eight baselines on both area under curve and log loss. Besides, PIN makes great click-through rate improvement (relatively 34.67%) in online A/B test.
The Communicative Multiagent Team Decision Problem: Analyzing Teamwork Theories and Models

Despite the significant progress in multiagent teamwork, existing research does not address the optimality of its prescriptions nor the complexity of the teamwork problem. Without a characterization of the optimality-complexity tradeoffs, it is impossible to determine whether the assumptions and approximations made by a particular theory gain enough efficiency to justify the losses in overall performance. To provide a tool for use by multiagent researchers in evaluating this tradeoff, we present a unified framework, the COMmunicative Multiagent Team Decision Problem (COM-MTDP). The COM-MTDP model combines and extends existing multiagent theories, such as decentralized partially observable Markov decision processes and economic team theory. In addition to their generality of representation, COM-MTDPs also support the analysis of both the optimality of team performance and the computational complexity of the agents' decision problem. In analyzing complexity, we present a breakdown of the computational complexity of constructing optimal teams under various classes of problem domains, along the dimensions of observability and communication cost. In analyzing optimality, we exploit the COM-MTDP's ability to encode existing teamwork theories and models to encode two instantiations of joint intentions theory taken from the literature. Furthermore, the COM-MTDP model provides a basis for the development of novel team coordination algorithms. We derive a domain-independent criterion for optimal communication and provide a comparative analysis of the two joint intentions instantiations with respect to this optimal policy. We have implemented a reusable, domain-independent software package based on COM-MTDPs to analyze teamwork coordination strategies, and we demonstrate its use by encoding and evaluating the two joint intentions strategies within an example domain.
The Application of Deep Learning in Airport Visibility Forecast

This paper uses Urumqi International Airport’s hourly observation from 2007 to 2016 and builds regression prediction model for airport visibility with deep learning method. From the results we can see: the absolute error of hourly visibility is 706 m. When the visibility ≤ 1000 m, the absolute error is 325 m, and this method can predict visibility’s trend. So we can use this method to provide the airport visibility’s objective forecast guidance products for aviation meteorological services in the future. In this paper, the Urumqi area is as the research object, to explore the depth of learning in the field of weather forecasting applications, providing a new visibility return forecast for weather forecast personnel so as to improve the visibility of the level of visibility to ensure the safe and stable operation of the airport.
Tracking Triadic Cardinality Distributions for Burst Detection in High-Speed Multigraph Streams

In everyday life, we often observe unusually frequent interactions among people before or during important events, e.g., people send/receive more greetings to/from their friends on holidays than regular days. We also observe that some videos or hashtags suddenly go viral through people's sharing on online social networks (OSNs). Do these seemingly different phenomena share a common structure? All these phenomena are associated with sudden surges of user interactions in networks, which we call "bursts" in this work. We uncover that the emergence of a burst is accompanied with the formation of triangles in some properly defined networks. This finding motivates us to propose a new and robust method to detect bursts on OSNs. We first introduce a new measure, "triadic cardinality distribution", corresponding to the fractions of nodes with different numbers of triangles, i.e., triadic cardinalities, in a network. We show that this distribution not only changes when a burst occurs, but it also has a robustness property that it is immunized against common spamming social-bot attacks. Hence, by tracking triadic cardinality distributions, we can more reliably detect bursts than simply counting interactions on OSNs. To avoid handling massive activity data generated by OSN users during the triadic tracking, we design an efficient "sample-estimate" framework to provide maximum likelihood estimate on the triadic cardinality distribution. We propose several sampling methods, and provide insights about their performance difference, through both theoretical analysis and empirical experiments on real world networks.
High-power femtosecond pulses without a modelocked laser: supplementary material supplementary information to femtosecond pulses without a modelocked laser,"

The gain-switched diode can be electronically triggered at arbitrary repetition rates. Provided the amplifiers are adjusted to maintain the same pulse energy in each stage, the nonlinear evolution remains unchanged, and the same level of performance can be maintained. By way of example, Figure S1 depicts the pulses obtained at 0.8 MHz. Slight differences with the 1 MHz results presented in the main text can be observed, most likely due to the amplifier powers being imperfectly adjusted. However, the main features remain unchanged: the pulse energy at 0.8 MHz is still 2.4-μJ, and the pulses still dechirp to 150 fs (roughly 10% longer than the transform limit).
Identifying Unknown Response Styles: A Latent-Class Bilinear Multinomial Logit Model

Respondents can vary significantly in the way they use rating scales. Specifically, respondents can exhibit varying degrees of response style, which threatens the validity of the responses. The purpose of this article is to investigate to what extent rating scale responses show response style and substantive content of the item. The authors develop a novel model that accounts for possibly unknown kinds of response styles, content of the items, and background characteristics of respondents. By imposing a bilinear structure on the parameters of a multinomial logit model, the authors can visually distinguish the effects on the response behavior of both the characteristics of a respondent and the content of the item. This approach is combined with finite mixture modeling, so that two separate segmentations of the respondents are obtained: one for response style and one for item content. This latent-class bilinear multinomial logit (LC-BML) model is applied to a cross-national data set. The results show that item content is highly influential in explaining response behavior and reveal the presence of several response styles, including the prominent response styles acquiescence and extreme response style.
Revealing Micro-Asperity Rupture Through Finite Source Inversion

Author(s): Parker, JM; Glaser, SD | Abstract: Copyright © 2018 ARMA, American Rock Mechanics Association. Laboratory studies of acoustic emission events with seismic signatures have used the point source assumption. Given the millimeter-scale size and clustering of the asperities and the centimeter-scale distances to the sensors, the records can convey information about the source complexity that is not fully revealed in a point source inversion. We develop a finite source inversion technique following Hartzell and Heaton, 1983 to compute the spatiotemporal distribution of slip during rupture of asperities and asperity clusters. First inversions of purely synthetic signals reveal some of the applicability and limitations of the algorithm. A preliminary inversion of one recorded event is included for demonstration. With much further sensitivity analysis and modeling improvements, the resulting slip profiles will provide a new tool for understanding the frictional strength and rupture behaviors of self-similar rough-rough interfaces at any scale.
Massive stars in young VVV clusters

The role of massive stars in the Galactic evolution is crucial. During their lifetime these stars change the kinematics around them through stellar winds, affect the formation of new stars, ionise and chemically enrich the media with the final supernova explosion. But the census of both massive stars and their host clusters is still poor. We expect that still ~100 of galactic massive stellar clusters remains unknown (Hanson & Popescu, 2008).
GazeTrain : A case study of an action oriented gaze-controlled game

Creating an engaging and worthwhile gaze-controlled game poses a significant number of challenges. The challenges are compounded by the fact that a number of popular game mechanics rely on quick reactions, and complex input. The straightforward solution to this issue is to avoid these game mechanics, and use the ones that do not require quick reactions. However, avoiding specific mechanics can lead to excluding certain types of game genres, thus reducing the variety of an already meager games selection. With the intention of providing further variety to physically challenged gamers, we present GazeTrain, inspired by a classic action oriented puzzle game Pipe Dream (Wikipedia, 04.03.2009) developed in 1989. We describe the process, necessary compromises and possible pitfalls of redesigning and implementing a game concept for use with gaze control. Preliminary tests and their results are also briefly discussed at the end of the article. The game and associated source is available for free download from The COGAIN Association’s homepage: http://www.cogain. org, in the hopes of motivating further development within the field of leisure applications using gaze-control. In addition to the downloadable game, it will be possible for gamers to contribute feedback and donations towards further development.
Demo: ReCon: Revealing and Controlling PII Leaks in Mobile Network Traffic

It is well known that apps running on mobile devices extensively track and leak users' personally identifiable information (PII) [1] and users are generally unaware and unable to stop them [2]; however, these users have little visibility into PII leaked through the network traffic generated by their devices, and have poor control over how, when and where that traffic is sent and handled by third parties. This demo presents the design, implementation, and evaluation of ReCon: a cross-platform (iOS, Android, Windows) system that reveals PII leaks and gives users control over them without requiring any special privileges or custom OSes. ReCon, which has been accepted as a paper to the Mobisys conference, leverages machine learning to reveal potential PII leaks by inspecting network traffic, and provides a visualization tool to empower users with the ability to control these leaks via blocking or substitution of PII. For this demo, we will show how ReCon detects PII in real time, allows users to provide feedback on detected PII, and provides users with controls for blocking or modifying PII leaks. The system is publicly available and we will also help any interested users install it on their mobile devices.
Effects of the COVID-19 pandemic on the behavior and physical health of dogs in Rio de Janeiro state: Reﬂections on the quality of life of dogs and their owners

The COVID-19 pandemic changed the routines of people, consequently changing the daily lives of their pets. Behavioral and emotional changes caused by the stress resulting from restrictions of social isolation and their consequences in the human-animal relationship have been discussed. However, there are still no studies that identify the factors that affect behavior and which are the most susceptible groups. The purpose of this study is to identify behavioral and emotional changes on dogs during the COVID-19 pandemic and their effects on the quality of life of animals and their owners. The methodology used was online questionnaires, which were posted on social networks aimed to dog owners in Rio de Janeiro state, Brazil. The results showed that age, sex, dog size, type of home, and restrictions imposed differently affected the type of behavioral change. However, the most frequent type of change was the worsening of previous conditions. Neutered behaviors directly interfered in the lives of owners and their pets, as owners managed the situation and sought information without guidance from a veterinarian, with the possibly consequence of worsening the situation in the future. Veterinarians should actively investigate behavioral changes that have occurred through anamnesis to avoid abandonment and instability in the human-animal relationship.
Social Justice Leadership in the Refugees’ Education: Insights of Teachers in Turkish Secondary Schools

The current case study examines secondary school principals’ social justice leadership (SJL) based on teachers’ perceptions. In the study, a qualitative research approach with a phenomenological design was used to explore the teachers’ perceptions regarding their principals’ SJL at Turkish secondary schools. The data were reached through semi-structured interviews. 11 participant teachers were determined as participants of the study with the maximum sampling method. The collected data were analyzed with content analysis, and three themes were reached: limited social justice, inclusive perspectives, and holistic leaders. Based on the results, teachers think that principals have a limited SJL and inclusion enactment. The participant teachers think that SJL should have a holistic perspective, including parents and teachers while ensuring social justice at school.
Wage and Price Dynamics in a Large Emerging Economy:: The Case of China

This study finds that the growth in labour costs in China is not passed through fully to final prices in China, neither in the tradable goods sector nor in the economy as a whole. This probably reflects the strong pressure on profit margins from a highly competitive environment, especially in manufactured goods. The potential implications of labour cost increases in China for global inflation pressures are also discussed.