Miracle-dz/UniFAR-FTUs huggingface dataset:
Parent experiences with a nurse-supervised community health worker asthma home-visiting program

Abstract Objective: This study seeks to identify helpful components of a nurse-supervised Community Health Worker (CHW) asthma home-visiting program, obtain feedback from parents and families about their experiences, and receive suggestions for new services that the program could provide. Methods: Likert scale ratings and semi-structured qualitative interviews were conducted with parents who were selected from a representative sample and previously participated in the program. Five-point Likert scale ratings from 1 (not helpful) to 5 (very helpful) were obtained for 11 program components. Interviews were analyzed using a grounded theory participatory approach. Data were analyzed and themes were identified by two different coders using Dedoose software. Results: A total of 22 participants were enrolled and 20 participants completed Likert scale ratings and qualitative interviews. Likert scale ratings (mean standard deviation [SD]) show that program strengths include asthma education (4.75 [0.55]), supplies (4.65 [0.99]), help with housing conditions (3.94 [1.56], pest management (3.79 [1.69]) and greater access to community resources (3.70 [1.30]). The ratings suggest that families need more help with other social determinants of health, such as school, lack of enough money or food, and mental health and behavioral concerns (3.05 [1.78]). Interviews echoed these ratings and revealed several themes about family and parental stress, children’s activity limitations, desire for outreach after the 12-month intervention, a need for help with other social determinants and more emotional support. Conclusions: This study shows that the program was well received and reveals the importance of addressing social determinants of health and behavioral health concerns.
Musical Gesture Recognition Using Machine Learning and Audio Descriptors

We report preliminary results of an ongoing project on automatic recognition and classification of musical “gestures” from audio extracts. We use a machine learning tool designed for motion tracking and recognition, applied to labeled vectors of audio descriptors in order to recognize hypothetical gestures formed by these descriptors. A hypothesis is that the classes detected in audio descriptors can be used to identify higher-level/abstract musical structures which might not be described easily using standard/symbolic representations.
Simple, Distributed, and Accelerated Probabilistic Programming

We describe a simple, low-level approach for embedding probabilistic programming in a deep learning ecosystem. In particular, we distill probabilistic programming down to a single abstraction---the random variable. Our lightweight implementation in TensorFlow enables numerous applications: a model-parallel variational auto-encoder (VAE) with 2nd-generation tensor processing units (TPUv2s); a data-parallel autoregressive model (Image Transformer) with TPUv2s; and multi-GPU No-U-Turn Sampler (NUTS). For both a state-of-the-art VAE on 64x64 ImageNet and Image Transformer on 256x256 CelebA-HQ, our approach achieves an optimal linear speedup from 1 to 256 TPUv2 chips. With NUTS, we see a 100x speedup on GPUs over Stan and 37x over PyMC3.
Towards Scalable and Accurate Online Feature Selection for Big Data

Feature selection is important in many big data applications. There are at least two critical challenges. Firstly, in many applications, the dimensionality is extremely high, in millions, and keeps growing. Secondly, feature selection has to be highly scalable, preferably in an online manner such that each feature can be processed in a sequential scan. In this paper, we develop SAOLA, a Scalable and Accurate On Line Approach for feature selection. With a theoretical analysis on a low bound on the pair wise correlations between features in the currently selected feature subset, SAOLA employs novel online pair wise comparison techniques to address the two challenges and maintain a parsimonious model over time in an online manner. An empirical study using a series of benchmark real data sets shows that SAOLA is scalable on data sets of extremely high dimensionality, and has superior performance over the state-of-the-art feature selection methods.
The RWTH Aachen University English-German and German-English Unsupervised Neural Machine Translation Systems for WMT 2018

This paper describes the unsupervised neural machine translation (NMT) systems of the RWTH Aachen University developed for the English ↔ German news translation task of the EMNLP 2018 Third Conference on Machine Translation (WMT 2018). Our work is based on iterative back-translation using a shared encoder-decoder NMT model. We extensively compare different vocabulary types, word embedding initialization schemes and optimization methods for our model. We also investigate gating and weight normalization for the word embedding layer.
Gradual Type Theory (Extended Version)

Gradually typed languages are designed to support both dynamically typed and statically typed programming styles while preserving the benefits of each. While existing gradual type soundness theorems for these languages aim to show that type-based reasoning is preserved when moving from the fully static setting to a gradual one, these theorems do not imply that correctness of type-based refactorings and optimizations is preserved. Establishing correctness of program transformations is technically difficult, and is often neglected in the metatheory of gradual languages. In this paper, we propose an axiomatic account of program equivalence in a gradual cast calculus, which we formalize in a logic we call gradual type theory (GTT). Based on Levy's call-by-push-value, GTT gives an axiomatic account of both call-by-value and call-by-name gradual languages. We then prove theorems that justify optimizations and refactorings in gradually typed languages. For example, uniqueness principles for gradual type connectives show that if the $\beta\eta$ laws hold for a connective, then casts between that connective must be equivalent to the lazy cast semantics. Contrapositively, this shows that eager cast semantics violates the extensionality of function types. As another example, we show that gradual upcasts are pure and dually, gradual downcasts are strict. We show the consistency and applicability of our theory by proving that an implementation using the lazy cast semantics gives a logical relations model of our type theory, where equivalence in GTT implies contextual equivalence of the programs. Since GTT also axiomatizes the dynamic gradual guarantee, our model also establishes this central theorem of gradual typing. The model is parametrized by the implementation of the dynamic types, and so gives a family of implementations that validate type-based optimization and the gradual guarantee.
Intrinsic robustness of the price of anarchy

The price of anarchy (POA) is a worst-case measure of the inefficiency of selfish behavior, defined as the ratio of the objective function value of a worst Nash equilibrium of a game and that of an optimal outcome. This measure implicitly assumes that players successfully reach some Nash equilibrium. This drawback motivates the search for inefficiency bounds that apply more generally to weaker notions of equilibria, such as mixed Nash and correlated equilibria; or to sequences of outcomes generated by natural experimentation strategies, such as successive best responses or simultaneous regret-minimization. We prove a general and fundamental connection between the price of anarchy and its seemingly stronger relatives in classes of games with a sum objective. First, we identify a "canonical sufficient condition" for an upper bound of the POA for pure Nash equilibria, which we call a smoothness argument. Second, we show that every bound derived via a smoothness argument extends automatically, with no quantitative degradation in the bound, to mixed Nash equilibria, correlated equilibria, and the average objective function value of regret-minimizing players (or "price of total anarchy"). Smoothness arguments also have automatic implications for the inefficiency of approximate and Bayesian-Nash equilibria and, under mild additional assumptions, for bicriteria bounds and for polynomial-length best-response sequences. We also identify classes of games --- most notably, congestion games with cost functions restricted to an arbitrary fixed set --- that are tight, in the sense that smoothness arguments are guaranteed to produce an optimal worst-case upper bound on the POA, even for the smallest set of interest (pure Nash equilibria). Byproducts of our proof of this result include the first tight bounds on the POA in congestion games with non-polynomial cost functions, and the first structural characterization of atomic congestion games that are universal worst-case examples for the POA.
Visual Sentiment Analysis Based on on Objective Text Description of Images

Visual Sentiment Analysis aims to estimate the polarity of the sentiment evoked by images in terms of positive or negative sentiment. To this aim, most of the state of the art works exploit the text associated to a social post provided by the user. However, such textual data is typically noisy due to the subjectivity of the user which usually includes text useful to maximize the diffusion of the social post. In this paper we extract and employ an Objective Text description of images automatically extracted from the visual content rather than the classic Subjective Text provided by the users. The proposed method defines a multimodal embedding space based on the contribute of both visual and textual features. The sentiment polarity is then inferred by a supervised Support Vector Machine trained on the representations of the obtained embedding space. Experiments performed on a representative dataset of 47235 labelled samples demonstrate that the exploitation of the proposed Objective Text helps to outperform state-of-the-art for sentiment polarity estimation.
The Power of Bots

Leveraging the pull request model of social coding platforms, Open Source Software (OSS) integrators review developers' contributions, checking aspects like license, code quality, and testability. Some projects use bots to automate predefined, sometimes repetitive tasks, thereby assisting integrators' and contributors' work. Our research investigates the usage and impact of such bots. We sampled 351 popular projects from GitHub and found that 93 (26%) use bots. We classified the bots, collected metrics from before and after bot adoption, and surveyed 228 developers and integrators. Our results indicate that bots perform numerous tasks. Although integrators reported that bots are useful for maintenance tasks, we did not find a consistent, statistically significant difference between before and after bot adoption across the analyzed projects in terms of number of comments, commits, changed files, and time to close pull requests. Our survey respondents deem the current bots as not smart enough and provided insights into the bots' relevance for specific tasks, challenges, and potential new features. We discuss some of the raised suggestions and challenges in light of the literature in order to help GitHub bot designers reuse and test ideas and technologies already investigated in other contexts.
Active Temporal Action Detection in Untrimmed Videos Via Deep Reinforcement Learning

Existing action detection algorithms usually generate action proposals through an extensive search over the video at multiple temporal scales, which brings about huge computational overhead and deviates from the human perception procedure. We argue that the process of detecting actions in the video should be naturally one of observation and refinement: observe the current temporal window and refine the span of attended window to cover true action regions. In this paper, we propose an active action detection model that learns to search actions through continuously adjusting the bounds of temporal attended window in a self-adaptive way. The whole process can be deemed as an exploring procedure, where an agent is first placed at the beginning of the video and then traverses the whole video by adopting a sequence of transformations on the current attended window to discover actions according to a learned policy. We utilize reinforcement learning, especially the deep Q-learning algorithm to learn the agent’s decision policy. Actually, we construct an end-to-end trainable framework for the action detection task, which includes a proposal generation network based on deep Q-learning, and the classification and regression networks responsible for the action category prediction and the action location adjustment, respectively. In addition, we design a long short-term memory structure upon extracted convolutional neural network features of sparsely sampled frames to generate the effective feature representations for video sequences of various durations. We evaluate the action proposal performance of our approach on THUMOS’14 and assess the generalization ability for unseen action categories on ActivityNet. We also compare the action detection performance of ours with other state-of-the-art methods on both data sets. Experiment results validate the effectiveness of the proposed approach, which can achieve comparative or superior performance than other action detection methods via much fewer proposals.
Certainty-based prototype insertion/deletion for classification with metric adaptation

We propose an extension of prototype-based classiﬁcation models to automatically adjust model complexity, thus oﬀering a powerful technique for online, incremental learning tasks. The incremental technique is based on the notion of the certainty of an observed classiﬁcation. Unlike previous work, we can incorporate matrix learning into the framework by relying on the cost function of generalised learning vector quantisation (GLVQ) for prototype insertion, deletion, as well as training. In several benchmarks, we demonstrate that the proposed method provides comparable results to oﬄine counterparts and an incremental support vector machine, while enabling a better control of the required memory.
White lies

When do people tell white lies? In this paper we distinguish between two types of white lies: those that help others at the expense of the person telling the lie, which we term altruistic white lies, and those that help both others and the liar, which we term Pareto white lies. We find a large fraction of participants are reluctant to tell even a Pareto white lie, demonstrating a pure lie aversion independent of any social preferences for outcomes. In contrast, a non-negligible fraction of participants are willing to tell an altruistic white lie that hurts them a bit but significantly helps others. Comparing white lies to those where lying increases the liar's payoff at the expense of another reveals important insights into the interaction of incentives, lying aversion, and social preferences for payoff distributions. Finally, in line with previous findings, women are less likely than men to lie when it is costly to the other side. Interestingly, we find that women are more likely to tell an altruistic lie, but tend to tell fewer Pareto lies.
SCOAP: Sandia controllability/observability analysis program

SCOAP is a program developed at Sandia National Laboratories for the analysis of digital circuit testability. Testability is related to the difficulty of controlling and observing the logical values of internal nodes from circuit inputs and outputs, respectively. This paper reviews the testability analysis algorithms and describes their implementation in the SCOAP program.
Flipper 2.0: A Pragmatic Dialogue Engine for Embodied Conversational Agents

We present anew dialogue engine called Flipper 2.0 (Flipper) which aims to help developers of embodied conversational agents (ECAs) to quickly and flexibly create dialogues. Flipper provides a technically stable and robust dialogue management system to integrate with other components of ECAs such as behaviour realisers. We compare Flipper with state-of-the-art dialogue design systems. We describe the details of our dialogue engine, how it handles dialogue management and how it supports the authoring of dialogues. We demonstrate the use of the dialogue engine with examples of design patterns and discuss practical applications. Finally we give recommendations on the cases in which it is beneficial to use Flipper.
Towards Provably-Secure Analog and Mixed-Signal Locking Against Overproduction

Similar to digital circuits, analog and mixed-signal (AMS) circuits are also susceptible to supply-chain attacks such as piracy, overproduction, and Trojan insertion. However, unlike digital circuits, supply-chain security of AMS circuits is less explored. In this work, we propose to perform “logic locking” on digital section of the AMS circuits. The idea is to make the analog design intentionally suffer from the effects of process variations, which impede the operation of the circuit. Only on applying the correct key, the effect of process variations are mitigated, and the analog circuit performs as desired. We provide the theoretical guarantees of the security of the circuit, and along with simulation results for the band-pass filter, low-noise amplifier, and low-dropout regulator, we also show experimental results of our technique on a band-pass filter.
Computing Exact Vertex Eccentricity on Massive-Scale Distributed Graphs

The eccentricity of a vertex is defined as the length of the longest shortest path to any other vertex. While eccentricity is an important measure of vertex centrality, directly computing exact eccentricity for all vertices on large-scale graphs is prohibitively costly. Takes and Kosters proposed an iterative algorithm that uses multiple runs of single-source shortest path (SSSP) to compute lower and upper bounds on eccentricity at every vertex. Their technique converges to exact eccentricity by performing SSSP from only a small percentage of vertices, when sources are efficiently selected. However, their source selection strategies do not always yield rapid convergence. We propose a pincer movement source selection algorithm that efficiently selects source vertices based on analysis of the lower and upper bounds produced by SSSP. We also leverage k-BFS, which runs breadth-first search (BFS) from multiple sources concurrently on HavoqGT, a high-performance vertex-centric message-passing graph processing framework, to achieve an additional significant performance improvement on distributed-memory systems. We demonstrate that our novel source vertex selection strategy has better performance on various real-world graph datasets compared with the previous strategy. In addition, we compute exact eccentricity for graphs with more than 1000X more edges (112B undirected edges) than graphs in the previous literature.
Sport Action Recognition with Siamese Spatio-Temporal CNNs: Application to Table Tennis

Human action recognition in video is one of the key problems in visual data interpretation. Despite intensive research, the recognition of actions with low inter-class variability remains a challenge. This paper presents a new Siamese Spatio-Temporal Convolutional neural network (SSTC) for this purpose. When applied to table tennis, it is possible to detect and recognize 20 table tennis strokes. The model has been trained on a specific dataset, TTStroke-21, recorded in natural condition (markerless) at the Faculty of Sports of the University of Bordeaux. Our model takes as inputs a RGB image sequence and its computed Optical Flow. After 3 spatio-temporal convolutions, data are fused in a fully connected layer of a proposed siamese network architecture. Our method reaches an accuracy of 91.4% against 43.1% for our baseline.
“Wow! You Are So Beautiful Today!”

Beauty e-Experts, a fully automatic system for makeover recommendation and synthesis, is developed in this work. The makeover recommendation and synthesis system simultaneously considers many kinds of makeover items on hairstyle and makeup. Given a user-provided frontal face image with short/bound hair and no/light makeup, the Beauty e-Experts system not only recommends the most suitable hairdo and makeup, but also synthesizes the virtual hairdo and makeup effects. To acquire enough knowledge for beauty modeling, we built the Beauty e-Experts Database, which contains 1,505 female photos with a variety of attributes annotated with different discrete values. We organize these attributes into two different categories, beauty attributes and beauty-related attributes. Beauty attributes refer to those values that are changeable during the makeover process and thus need to be recommended by the system. Beauty-related attributes are those values that cannot be changed during the makeup process but can help the system to perform recommendation. Based on this Beauty e-Experts Dataset, two problems are addressed for the Beauty e-Experts system: what to recommend and how to wear it, which describes a similar process of selecting hairstyle and cosmetics in daily life. For the what-to-recommend problem, we propose a multiple tree-structured supergraph model to explore the complex relationships among high-level beauty attributes, mid-level beauty-related attributes, and low-level image features. Based on this model, the most compatible beauty attributes for a given facial image can be efficiently inferred. For the how-to-wear-it problem, an effective and efficient facial image synthesis module is designed to seamlessly synthesize the recommended makeovers into the user facial image. We have conducted extensive experiments on testing images of various conditions to evaluate and analyze the proposed system. The experimental results well demonstrate the effectiveness and efficiency of the proposed system.
Recognizing Human Needs During Critical Events Using Machine Learning Powered Psychology-Based Framework

We propose a psychological need detection framework which automatically identifies people needs and measures their satisfaction level. The framework employs three need models, which are developed using psychological, linguistic, and Twitter-specific features. We evaluate the performance of the proposed models on psychological need data sets which are annotated by psychologists. The models obtained encouraging results: 78.71% in recognizing need content, 81.96% in identifying need type, and 93.56% in measuring need satisfaction level. We use the proposed framework to recognize individual needs and measure their satisfaction level in response to the Florida shooting event, which occurred on February 14, 2018, and the related March for Our Lives event which followed on March 24, 2018. Timeline-based visual and textual representations are generated to explain the motivation behind public reaction and behavior.
BENGAL: An Automatic Benchmark Generator for Entity Recognition and Linking

The manual creation of gold standards for named entity recognition and entity linking is time- and resource-intensive. Moreover, recent works show that such gold standards contain a large proportion of mistakes in addition to being difficult to maintain. We hence present Bengal, a novel automatic generation of such gold standards as a complement to manually created benchmarks. The main advantage of our benchmarks is that they can be readily generated at any time. They are also cost-effective while being guaranteed to be free of annotation errors. We compare the performance of 11 tools on benchmarks in English generated by Bengal and on 16 benchmarks created manually. We show that our approach can be ported easily across languages by presenting results achieved by 4 tools on both Brazilian Portuguese and Spanish. Overall, our results suggest that our automatic benchmark generation approach can create varied benchmarks that have characteristics similar to those of existing benchmarks. Our approach is open-source. Our experimental results are available at http://faturl.com/bengalexpinlg and the code at https://github.com/dice-group/BENGAL.