Miracle-dz/UniFAR-FTUs huggingface dataset:
Accented Speech Recognition Inspired by Human Perception

While improvements have been made in automatic speech recognition performance over the last several years, machines continue to have significantly lower performance on accented speech than humans. In addition, the most significant improvements on accented speech primarily arise by overwhelming the problem with hundreds or even thousands of hours of data. Humans typically require much less data to adapt to a new accent. This paper explores methods that are inspired by human perception to evaluate possible performance improvements for recognition of accented speech, with a specific focus on recognizing speech with a novel accent relative to that of the training data. Our experiments are run on small, accessible datasets that are available to the research community. We explore four methodologies: pre-exposure to multiple accents, grapheme and phoneme-based pronunciations, dropout (to improve generalization to a novel accent), and the identification of the layers in the neural network that can specifically be associated with accent modeling. Our results indicate that methods based on human perception are promising in reducing WER and understanding how accented speech is modeled in neural networks for novel accents.
Action-Conditioned 3D Human Motion Synthesis with Transformer VAE

We tackle the problem of action-conditioned generation of realistic and diverse human motion sequences. In contrast to methods that complete, or extend, motion sequences, this task does not require an initial pose or sequence. Here we learn an action-aware latent representation for human motions by training a generative variational autoencoder (VAE). By sampling from this latent space and querying a certain duration through a series of positional encodings, we synthesize variable-length motion sequences conditioned on a categorical action. Specifically, we design a Transformer-based architecture, ACTOR, for encoding and decoding a sequence of parametric SMPL human body models estimated from action recognition datasets. We evaluate our approach on the NTU RGB+D, HumanAct12 and UESTC datasets and show improvements over the state of the art. Furthermore, we present two use cases: improving action recognition through adding our synthesized data to training, and motion denoising. Code and models are available on our project page [53].
Developing Annotated Resources for Internal Displacement Monitoring

This paper describes in details the design and development of a novel annotation framework and of annotated resources for Internal Displacement, as the outcome of a collaboration with the Internal Displacement Monitoring Centre, aimed at improving the accuracy of their monitoring platform IDETECT. The schema includes multi-faceted description of the events, including cause, quantity of people displaced, location and date. Higher-order facets aimed at improving the information extraction, such as document relevance and type, are proposed. We also report a case study of machine learning application to the document classification tasks. Finally, we discuss the importance of standardized schema in dataset benchmark development and its impact on the development of reliable disaster monitoring infrastructure.
On probabilistic termination of functional programs with continuous distributions

We study termination of higher-order probabilistic functional programs with recursion, stochastic conditioning and sampling from continuous distributions. Reasoning about the termination probability of programs with continuous distributions is hard, because the enumeration of terminating executions cannot provide any non-trivial bounds. We present a new operational semantics based on traces of intervals, which is sound and complete with respect to the standard sampling-based semantics, in which (countable) enumeration can provide arbitrarily tight lower bounds. Consequently we obtain the first proof that deciding almost-sure termination (AST) for programs with continuous distributions is Π20-complete (for CbN). We also provide a compositional representation of our semantics in terms of an intersection type system. In the second part, we present a method of proving AST for non-affine programs, i.e., recursive programs that can, during the evaluation of the recursive body, make multiple recursive calls (of a first-order function) from distinct call sites. Unlike in a deterministic language, the number of recursion call sites has direct consequences on the termination probability. Our framework supports a proof system that can verify AST for programs that are well beyond the scope of existing methods. We have constructed prototype implementations of our methods for computing lower bounds on the termination probability, and AST verification.
Fool Me Twice: Entailment from Wikipedia Gamification

We release FoolMeTwice (FM2 for short), a large dataset of challenging entailment pairs collected through a fun multi-player game. Gamification encourages adversarial examples, drastically lowering the number of examples that can be solved using “shortcuts” compared to other popular entailment datasets. Players are presented with two tasks. The first task asks the player to write a plausible claim based on the evidence from a Wikipedia page. The second one shows two plausible claims written by other players, one of which is false, and the goal is to identify it before the time runs out. Players “pay” to see clues retrieved from the evidence pool: the more evidence the player needs, the harder the claim. Game-play between motivated players leads to diverse strategies for crafting claims, such as temporal inference and diverting to unrelated evidence, and results in higher quality data for the entailment and evidence retrieval tasks. We open source the dataset and the game code.
Beyond Pointwise Submodularity: Non-Monotone Adaptive Submodular Maximization subject to a Knapsack Constraint

In this paper, we study the non-monotone adaptive submodular maximization problem subject to a knapsack constraint. The input of our problem is a set of items, where each item has a particular state drawn from a known prior distribution. However, the state of an item is initially unknown, one must select an item in order to reveal the state of that item. Moreover, each item has a ﬁxed cost. There is a utility function which is deﬁned over items and states. Our objective is to sequentially select a group of items to maximize the expected utility subject to a knapsack constraint. Although the cardinality-constrained, as well as the more general matroid-constrained, adaptive submodular maximization has been well studied in the literature, whether there exists a constant approximation solution for the knapsack-constrained adaptive submodular maximization problem remains an open problem. We ﬁll this gap by proposing the ﬁrst constant approximation solution. In particular, our main contribution is to develop a sampling-based randomized algorithm that achieves a 110 approximation for maximizing an adaptive submodular function subject to a knapsack constraint.
Deep Time Series Forecasting With Shape and Temporal Criteria

This paper addresses the problem of multi-step time series forecasting for non-stationary signals that can present sudden changes. Current state-of-the-art deep learning forecasting methods, often trained with variants of the MSE, lack the ability to provide sharp predictions in deterministic and probabilistic contexts. To handle these challenges, we propose to incorporate shape and temporal criteria in the training objective of deep models. We define shape and temporal similarities and dissimilarities, based on a smooth relaxation of Dynamic Time Warping (DTW) and Temporal Distortion Index (TDI), that enable to build differentiable loss functions and positive semi-definite (PSD) kernels. With these tools, we introduce DILATE (DIstortion Loss including shApe and TimE), a new objective for deterministic forecasting, that explicitly incorporates two terms supporting precise shape and temporal change detection. For probabilistic forecasting, we introduce STRIPE++ (Shape and Time diverRsIty in Probabilistic for Ecasting), a framework for providing a set of sharp and diverse forecasts, where the structured shape and time diversity is enforced with a determinantal point process (DPP) diversity loss. Extensive experiments and ablations studies on synthetic and real-world datasets confirm the benefits of leveraging shape and time features in time series forecasting.
What We Measure in Mixed Reality Experiments

There are many potential measures that one might use when evaluating mixed-reality experiences. In this position paper I will argue that there are various stances to take for evaluation, depending on the framing of the experience within a larger body of work. I will draw upon various types of work that my team has been involved with in order to illustrate these different stances. I will then sketch out some directions for developing more robust measures that can help the field move forward.
Machine Translation Decoding beyond Beam Search

Beam search is the go-to method for decoding auto-regressive machine translation models. While it yields consistent improvements in terms of BLEU, it is only concerned with finding outputs with high model likelihood, and is thus agnostic to whatever end metric or score practitioners care about. Our aim is to establish whether beam search can be replaced by a more powerful metric-driven search technique. To this end, we explore numerous decoding algorithms, including some which rely on a value function parameterised by a neural network, and report results on a variety of metrics. Notably, we introduce a Monte-Carlo Tree Search (MCTS) based method and showcase its competitiveness. We provide a blueprint for how to use MCTS fruitfully in language applications, which opens promising future directions. We find that which algorithm is best heavily depends on the characteristics of the goal metric; we believe that our extensive experiments and analysis will inform further research in this area.
Image-Level or Object-Level? A Tale of Two Resampling Strategies for Long-Tailed Detection

Training on datasets with long-tailed distributions has been challenging for major recognition tasks such as classification and detection. To deal with this challenge, image resampling is typically introduced as a simple but effective approach. However, we observe that long-tailed detection differs from classification since multiple classes may be present in one image. As a result, image resampling alone is not enough to yield a sufficiently balanced distribution at the object level. We address object-level resampling by introducing an object-centric memory replay strategy based on dynamic, episodic memory banks. Our proposed strategy has two benefits: 1) convenient object-level resampling without significant extra computation, and 2) implicit feature-level augmentation from model updates. We show that image-level and object-level resamplings are both important, and thus unify them with a joint resampling strategy (RIO). Our method outperforms state-of-the-art long-tailed detection and segmentation methods on LVIS v0.5 across various backbones. Code is available at https://github.com/NVlabs/RIO.
Deep Learning for Prominence Detection In Children’s Read Speech

The detection of perceived prominence in speech has attracted approaches ranging from the design of knowledge-based linguistic and acoustic features to the automatic feature learning from suprasegmental attributes such as pitch and intensity contours. We present here, in contrast, a system that operates directly on segmented speech waveforms to learn features relevant to prominent word detection for children’s oral fluency assessment. The chosen CRNN (convolutional recurrent neural network) framework, incorporating both word-level features and sequence information, is found to benefit from the perceptually motivated SincNet filters as the first convolutional layer. We further explore the benefits of the linguistic association between the prosodic events of phrase boundary and prominence with different multi-task architectures. Surpassing the previously reported performance on the same dataset of a random forest ensemble predictor trained on carefully chosen hand-crafted acoustic features, we evaluate further the possibly complementary information from hand-crafted acoustic and pre-trained lexical features.
Virtual Barriers in Augmented Reality for Safe and Effective Human-Robot Cooperation in Manufacturing

Safety is a basic requirement in any human-robot collaboration scenario. To ensure user safety, from both physical and psychological aspects, we propose a novel Virtual Barrier system facilitated by an augmented reality interface1. Our system provides two types of Virtual Barriers to ensure safety: 1) a Virtual Person Barrier which encapsulates and follows the user to protect them from collisions with the robot, and 2) Virtual Obstacle Barriers which users can spawn to protect objects or regions that the robot should not enter. Our system utilizes augmented reality to visually display these protective barriers to the user during operation. To enable effective human-robot collaboration, our system automatically replans the robot’s motion when potential collisions are detected as a result of a barrier intersecting the robot’s planned path. Comparing our novel system with a standard 2D display interface in a user study with a mock industrial manufacturing task showed that our system increases both physical and psychological safety, task efficiency and interaction intuitiveness.
A Phase Transition Phenomenon for Ruin Probabilities in a Network of Agents and Objects

The classical Cramér-Lundberg risk process models the ruin probability of an insurance company experiencing an incoming cash flow the premium income, and an outgoing cash flow the claims. From a system’s viewpoint, the web of insurance agents and risk objects can be represented by a bipartite network. In such a bipartite network setting, it has been shown that joint ruin of a group of agents may be avoided even if individual agents would experience ruin in the classical Cramér-Lundberg model. This paper describes and examines a phase transition phenomenon for these ruin probabilities.
A MeerKAT view on galaxy clusters: a radio–optical study of Abell 1300 and MACS J1931.8−2634

In this paper, we present results from a radio–optical study of the galaxy populations of the galaxy clusters Abell 1300 and MACS J1931.8−2634, a merger and a relaxed system respectively both located at z ∼ 0.3, aimed at finding evidence of merger-induced radio emission. Radio observations are taken at 1.28 GHz with the MeerKAT interferometer during its early-stage commissioning phase, and combined with archive optical data. We generated catalogues containing 107 and 162 radio sources in the A 1300 and MACS J1931.8−2634 cluster fields, respectively, above a 0.2 mJy threshold and within a 30 arcmin radius from the cluster centre (corresponding to 8.1 and 8.8 Mpc, respectively). By cross-correlating the radio and optical catalogues, and including spectroscopic information, nine and six sources were found to be cluster members and used to construct the radio luminosity functions, respectively, for both clusters. The comparison of the radio source catalogues between the two cluster fields leads to a marginal difference, with a 2σ statistical significance. We derived the radio luminosity function at 1.28 GHz in both clusters, in the power range of $22.81 \lt \rm {log~\mathit{ P}_{1.28~GHz}~(W\,Hz^{-1})} \lt 25.95$, and obtained that in A 1300 the radio luminosity function averaged over the full radio power interval is only 3.3 ± 1.9 times higher than the MACS J1931.8−2634 one, suggesting no statistical difference in their probability to host nuclear radio emission. We conclude that, at least for the two clusters studied here, the role of cluster mergers in affecting the statistical properties of the radio galaxy population is negligible.
Identifying and Categorizing Offensive Language in Social Media

Offensive language is pervasive in social media. Individuals frequently take advantage of the perceived anonymity of computer-mediated communication, using this to engage in behavior that many of them would not consider in real life. The automatic identification of offensive content online is an important task that has gained more attention in recent years. This task can be modeled as a supervised classification problem in which systems are trained using a dataset containing posts that are annotated with respect to the presence of some form(s) of abusive or offensive content. The objective of this study is to provide a description of a classification system built for SemEval-2019 Task 6: OffensEval. This system classifies a tweet as either offensive or not offensive (Sub-task A) and further classifies offensive tweets into categories (Sub-tasks B \&C). We trained machine learning and deep learning models along with data preprocessing and sampling techniques to come up with the best results. Models discussed include Naive Bayes, SVM, Logistic Regression, Random Forest and LSTM.
A search for signatures of interactions of X-ray binary outflows with their environments with ALMA

We observed the X-ray binaries Cir X-1, Sco X-1, GRS 1915+105, GX 13+1, and Cyg X-1 with the Atacama Large Millimeter/submillimeter Array (ALMA). Unresolved continuum emission is found at the positions of all the sources at a frequency of 92 GHz, with flux densities ranging between 0.8 and 10 mJy beam−1. In all cases the emission can be associated with jets that have been extensively observed at lower frequencies. We searched for line emission from Hα recombination, SiO, H2O, and CH3OH at the positions of all the sources and, for Cir X-1 and Cyg X-1, also at regions where shocks associated with an interaction between the jet and the interstellar medium had previously been observed. The search did not yield any significant detection, resulting in 3σ upper limits between 0.65 and 3.7 K km s−1 for the existence of line emission in these regions. In contrast, we detected spatially unresolved SiO emission in the field of view of GX 13+1, and we tentatively associate this emission with a SiO maser in a potential young stellar object or evolved star. We also found spatially extended line emission at two additional sites in the field of view of GX 13+1 that we tentatively associate with emission from SO and CH3OH; we speculate that it may be associated with a star-forming region, but again we cannot rule out alternative origins such as emission from evolved stars.
Automated Meta-Analysis: A Causal Learning Perspective

Meta-analysis is a systematic approach for understanding a phenomenon by analyzing the results of many previously published experimental studies. It is central to deriving conclusions about the summary effect of treatments and interventions in medicine, poverty alleviation, and other applications with social impact. Unfortunately, meta-analysis involves great human effort, rendering a process that is extremely inefficient and vulnerable to human bias. To overcome these issues, we work toward automating meta-analysis with a focus on controlling for risks of bias. In particular, we first extract information from scientific publications written in natural language. From a novel causal learning perspective, we then propose to frame automated meta-analysis -- based on the input of the first step -- as a multiple-causal-inference problem where the summary effect is obtained through intervention. Built upon existing efforts for automating the initial steps of meta-analysis, the proposed approach achieves the goal of automated meta-analysis and largely reduces the human effort involved. Evaluations on synthetic and semi-synthetic datasets show that this approach can yield promising results.
FRAKE: Fusional Real-time Automatic Keyword Extraction

Keyword extraction is the process of identifying the words or phrases that express the main concepts of text to the best of one's ability. Electronic infrastructure creates a considerable amount of text every day and at all times. This massive volume of documents makes it practically impossible for human resources to study and manage them. Nevertheless, the need for these documents to be accessed efficiently and effectively is evident in numerous purposes. A blog, news article, or technical note is considered a relatively long text since the reader aims to learn the subject based on keywords or topics. Our approach consists of a combination of two models: graph centrality features and textural features. The proposed method has been used to extract the best keyword among the candidate keywords with an optimal combination of graph centralities, such as degree, betweenness, eigenvector, closeness centrality and etc, and textural, such as Casing, Term position, Term frequency normalization, Term different sentence, Part Of Speech tagging. There have also been attempts to distinguish keywords from candidate phrases and consider them on separate keywords. For evaluating the proposed method, seven datasets were used: Semeval2010, SemEval2017, Inspec, fao30, Thesis100, pak2018, and Wikinews, with results reported as Precision, Recall, and F- measure. Our proposed method performed much better in terms of evaluation metrics in all reviewed datasets compared with available methods in literature. An approximate 16.9% increase was witnessed in F-score metric and this was much more for the Inspec in English datasets and WikiNews in forgone languages.
Evaluation and Control of Opinion Polarization and Disagreement: A Review

Purpose The purpose of this paper is to review the recent studies on opinion polarization and disagreement. Design/methodology/approach In this work, recent advances in opinion polarization and disagreement and pay attention to how they are evaluated and controlled are reviewed. Findings In literature, three metrics: polarization, disagreement and polarization-disagreement index are usually adopted and there is a tradeoff between polarization and disagreement. Different strategies have been proposed in literature which can significantly control opinion polarization and disagreement based on these metrics. Originality/value This review is of crucial importance to summarize works on opinion polarization and disagreement and to the better understanding and control of them.
An Efficient Algorithm for Deep Stochastic Contextual Bandits

In stochastic contextual bandit (SCB) problems, an agent selects an action based on certain observed context to maximize the cumulative reward over iterations. Recently there have been a few studies using a deep neural network (DNN) to predict the expected reward for an action, and the DNN is trained by a stochastic gradient based method. However, convergence analysis has been greatly ignored to examine whether and where these methods converge. In this work, we formulate the SCB that uses a DNN reward function as a non-convex stochastic optimization problem, and design a stage-wise stochastic gradient descent algorithm to optimize the problem and determine the action policy. We prove that with high probability, the action sequence chosen by this algorithm converges to a greedy action policy respecting a local optimal reward function. Extensive experiments have been performed to demonstrate the effectiveness and efficiency of the proposed algorithm on multiple real-world datasets.