Reviewerly/exHarmony huggingface dataset:
A decision-theoretic approach for model interpretability in Bayesian framework

A salient approach to interpretable machine learning is to restrict modeling to simple models. In the Bayesian framework, this can be pursued by restricting the model structure and prior to favor interpretable models. Fundamentally, however, interpretability is about users' preferences, not the data generation mechanism; it is more natural to formulate interpretability as a utility function. In this work, we propose an interpretability utility, which explicates the trade-off between explanation fidelity and interpretability in the Bayesian framework. The method consists of two steps. First, a reference model, possibly a black-box Bayesian predictive model which does not compromise accuracy, is fitted to the training data. Second, a proxy model from an interpretable model family that best mimics the predictive behaviour of the reference model is found by optimizing the interpretability utility function. The approach is model agnostic -- neither the interpretable model nor the reference model are restricted to a certain class of models -- and the optimization problem can be solved using standard tools. Through experiments on real-word data sets, using decision trees as interpretable models and Bayesian additive regression models as reference models, we show that for the same level of interpretability, our approach generates more accurate models than the alternative of restricting the prior. We also propose a systematic way to measure stability of interpretabile models constructed by different interpretability approaches and show that our proposed approach generates more stable models.
Vision-Based Nonlinear Control of Quadrotors Using the Photogrammetric Technique

This paper presents a controller designed via the backstepping technique, for the tracking of a reference trajectory obtained via the photogrammetric technique. The dynamic equations used to represent the motion of the quadrotor helicopter are based on the Newton–Euler model. The resulting quadrotor model has been divided into four subsystems for the altitude, longitudinal, lateral, and yaw motions. A control input is designed for each subsystem. Furthermore, the photogrammetric technique has been used to obtain the reference trajectory to be tracked. The performance and effectiveness of the proposed nonlinear controllers have been tested via numerical simulations using the Pixhawk Pilot Support Package developed for Matlab/Simulink.
Robustness of Bayesian Neural Networks to Gradient-Based Attacks

Vulnerability to adversarial attacks is one of the principal hurdles to the adoption of deep learning in safety-critical applications. Despite significant efforts, both practical and theoretical, the problem remains open. In this paper, we analyse the geometry of adversarial attacks in the large-data, overparametrized limit for Bayesian Neural Networks (BNNs). We show that, in the limit, vulnerability to gradient-based attacks arises as a result of degeneracy in the data distribution, i.e., when the data lies on a lower-dimensional submanifold of the ambient space. As a direct consequence, we demonstrate that in the limit BNN posteriors are robust to gradient-based adversarial attacks. Experimental results on the MNIST and Fashion MNIST datasets with BNNs trained with Hamiltonian Monte Carlo and Variational Inference support this line of argument, showing that BNNs can display both high accuracy and robustness to gradient based adversarial attacks.
Howl: A Deployed, Open-Source Wake Word Detection System

We describe Howl, an open-source wake word detection toolkit with native support for open speech datasets such as Mozilla Common Voice (MCV) and Google Speech Commands (GSC).We report benchmark results of various models supported by our toolkit on GSC and our own freely available wake word detection dataset, built from MCV.One of our models is deployed in Firefox Voice, a plugin enabling speech interactivity for the Firefox web browser.Howl represents, to the best of our knowledge, the first fully productionized, open-source wake word detection toolkit with a web browser deployment target.Our codebase is at howl.ai.
AUTH @ CLSciSumm 20, LaySumm 20, LongSumm 20

We present the systems we submitted for the shared tasks of the Workshop on Scholarly Document Processing at EMNLP 2020.Our approaches to the tasks are focused on exploiting large Transformer models pre-trained on huge corpora and adapting them to the different shared tasks.For tasks 1A and 1B of CL-SciSumm we are using different variants of the BERT model to tackle the tasks of "cited text span" and "facet" identification.For the summarization tasks 2 of CL-SciSumm, LaySumm and LongSumm we make use of different variants of the PEGASUS model, with and without fine-tuning, adapted to the nuances of each one of those particular tasks.
Message-passing Algorithm of Quantum Annealing with Nonstoquastic Hamiltonian

Quantum annealing (QA) is a generic method for solving optimization problems using fictitious quantum fluctuation.The current device performing QA involves controlling the transverse field; it is classically simulatable by using the standard technique for mapping the quantum spin systems to the classical ones.In this sense, the current system for QA is not powerful despite utilizing quantum fluctuation.Hence, we developed a system with a timedependent Hamiltonian consisting of a combination of the formulated Ising model and the "driver" Hamiltonian with only quantum fluctuation.In the previous study, for a fully connected spin model, quantum fluctuation can be addressed in a relatively simple way.We proved that the fully connected antiferromagnetic interaction can be transformed into a fluctuating transverse field and is thus classically simulatable at sufficiently low temperatures.Using the fluctuating transverse field, we established several ways to simulate part of the nonstoquastic Hamiltonian on classical computers.We formulated a message-passing algorithm in the present study.This algorithm is capable of assessing the performance of QA with part of the nonstoquastic Hamiltonian having a large number of spins.In other words, we developed a different approach for simulating the nonstoquastic Hamiltonian without using the quantum Monte Carlo technique.Our results were validated by comparison to the results obtained by the replica method.
Dynamically Updating Event Representations for Temporal Relation Classification with Multi-category Learning

Temporal relation classification is a pair-wise task for identifying the relation of a temporal link (TLINK) between two mentions, i.e. event, time and document creation time (DCT).It leads to two crucial limits: 1) Two TLINKs involving a common mention do not share information.2) Existing models with independent classifiers for each TLINK category (E2E, E2T and E2D) 1 hinder from using the whole data.This paper presents an event centric model that allows to manage dynamic event representations across multiple TLINKs.Our model deals with three TLINK categories with multi-task learning to leverage the full size of data.The experimental results show that our proposal outperforms state-of-the-art models and two transfer learning baselines on both the English and Japanese data.
Less is More: Attention Supervision with Counterfactuals for Text Classification

We aim to leverage human and machine intelligence together for attention supervision.Specifically, we show that human annotation cost can be kept reasonably low, while its quality can be enhanced by machine selfsupervision.Specifically, for this goal, we explore the advantage of counterfactual reasoning, over associative reasoning typically used in attention supervision.Our empirical results show that this machine-augmented human attention supervision is more effective than existing methods requiring a higher annotation cost, in text classification tasks, including sentiment analysis and news categorization.
Deterministic single-photon subtraction based on a coupled single quantum dot-cavity system

We describe a scheme of deterministic single-photon subtraction in a solid-state system consisting of a charged quantum dot coupled to a bimodal photonic-crystal cavity with a moderate magnetic field applied in a Voigt configuration. We numerically simulate injection of optical pulses into one of the modes of the bimodal cavity and show that the system deterministically transfers one photon into the second cavity mode for input pulses in the form of both Fock states and coherent states.
Absence of wave operators for one-dimensional quantum walks

We show that there exist pairs of two time evolution operators which do not have wave operators in a context of one-dimensional discrete time quantum walks. As a consequence, the borderline between existence and nonexistence of wave operators is decided.
A Benchmark for Structured Procedural Knowledge Extraction from Cooking Videos

Watching instructional videos are often used to learn about procedures.Video captioning is one way of automatically collecting such knowledge.However, it provides only an indirect, overall evaluation of multimodal models with no finer-grained quantitative measure of what they have learned.We propose instead, a benchmark of structured procedural knowledge extracted from cooking videos.This work is complementary to existing tasks, but requires models to produce interpretable structured knowledge in the form of verb-argument tuples.Our manually annotated open-vocabulary resource includes 356 instructional cooking videos and 15,523 video clip/sentence-level annotations.Our analysis shows that the proposed task is challenging and standard modeling approaches like unsupervised segmentation, semantic role labeling, and visual action detection perform poorly when forced to predict every action of a procedure in structured form.
Teaching a Formalized Logical Calculus

Classical first-order logic is in many ways central to work in mathematics, linguistics, computer science and artificial intelligence, so it is worthwhile to define it in full detail.We present soundness and completeness proofs of a sequent calculus for first-order logic, formalized in the interactive proof assistant Isabelle/HOL.Our formalization is based on work by Stefan Berghofer, which we have since updated to use Isabelle's declarative proof style Isar (Archive of Formal Proofs, Entry FOL-Fitting, August 2007 / July 2018).We represent variables with de Bruijn indices; this makes substitution under quantifiers less intuitive for a human reader.However, the nature of natural numbers yields an elegant solution when compared to implementations of substitution using variables represented by strings.The sequent calculus considered has the special property of an always empty antecedent and a list of formulas in the succedent.We obtain the proofs of soundness and completeness for the sequent calculus as a derived result of the inverse duality of its tableau counterpart.We strive to not only present the results of the proofs of soundness and completeness, but also to provide a deep dive into a programming-like approach to the formalization of first-order logic syntax, semantics and the sequent calculus.We use the formalization in a bachelor course on logic for computer science and discuss our experiences.
Continuity of Topic, Interaction, and Query: Learning to Quote in Online Conversations

Quotations are crucial for successful explanations and persuasions in interpersonal communications.However, finding what to quote in a conversation is challenging for both humans and machines.This work studies automatic quotation generation in an online conversation and explores how language consistency affects whether a quotation fits the given context.Here, we capture the contextual consistency of a quotation in terms of latent topics, interactions with the dialogue history, and coherence to the query turn's existing content.Further, an encoder-decoder neural framework is employed to continue the context with a quotation via language generation.Experiment results on two large-scale datasets in English and Chinese demonstrate that our quotation generation model outperforms the state-of-the-art models.Further analysis shows that topic, interaction, and query consistency are all helpful to learn how to quote in online conversations.
Joint Modeling of Arguments for Event Understanding

We recognize the task of event argument linking in documents as similar to that of intent slot resolution in dialogue, providing a Transformer-based model that extends from a recently proposed solution to resolve references to slots.The approach allows for joint consideration of argument candidates given a detected event, which we illustrate leads to state-of-the-art performance in multi-sentence argument linking. 1
Improving QA Generalization by Concurrent Modeling of Multiple Biases

Existing NLP datasets contain various biases that models can easily exploit to achieve high performances on the corresponding evaluation sets.However, focusing on dataset-specific biases limits their ability to learn more generalizable knowledge about the task from more general data patterns.In this paper, we investigate the impact of debiasing methods for improving generalization and propose a general framework for improving the performance on both in-domain and out-of-domain datasets by concurrent modeling of multiple biases in the training data.Our framework weights each example based on the biases it contains and the strength of those biases in the training data.It then uses these weights in the training objective so that the model relies less on examples with high bias weights.We extensively evaluate our framework on extractive question answering with training data from various domains with multiple biases of different strengths.We perform the evaluations in two different settings, in which the model is trained on a single domain or multiple domains simultaneously, and show its effectiveness in both settings compared to state-of-the-art debiasing methods. 1
Masked Linear Regression for Learning Local Receptive Fields for Facial Expression Synthesis

Compared to facial expression recognition, expression synthesis requires a very high-dimensional mapping. This problem exacerbates with increasing image sizes and limits existing expression synthesis approaches to relatively small images. We observe that facial expressions often constitute sparsely distributed and locally correlated changes from one expression to another. By exploiting this observation, the number of parameters in an expression synthesis model can be significantly reduced. Therefore, we propose a constrained version of ridge regression that exploits the local and sparse structure of facial expressions. We consider this model as masked regression for learning local receptive fields. In contrast to the existing approaches, our proposed model can be efficiently trained on larger image sizes. Experiments using three publicly available datasets demonstrate that our model is significantly better than $$\ell _0, \ell _1$$ and $$\ell _2$$-regression, SVD based approaches, and kernelized regression in terms of mean-squared-error, visual quality as well as computational and spatial complexities. The reduction in the number of parameters allows our method to generalize better even after training on smaller datasets. The proposed algorithm is also compared with state-of-the-art GANs including Pix2Pix, CycleGAN, StarGAN and GANimation. These GANs produce photo-realistic results as long as the testing and the training distributions are similar. In contrast, our results demonstrate significant generalization of the proposed algorithm over out-of-dataset human photographs, pencil sketches and even animal faces.
Relations between comprehensibility and adequacy errors in machine translation output

This work presents a detailed analysis of translation errors perceived by readers as comprehensibility and/or adequacy issues.The main finding is that good comprehensibility, similarly to good fluency, can mask a number of adequacy errors.Of all major adequacy errors, 30% were fully comprehensible, thus fully misleading the reader to accept the incorrect information.Another 25% of major adequacy errors were perceived as almost comprehensible, thus being potentially misleading.Also, a vast majority of omissions (about 70%) is hidden by comprehensibility.Further analysis of misleading translations revealed that the most frequent error types are ambiguity, mistranslation, noun phrase error, word-by-word translation, untranslated word, subject-verb agreement, and spelling error in the source text.However, none of these error types appears exclusively in misleading translations, but are also frequent in fully incorrect (incomprehensible inadequate) and discarded correct (incomprehensible adequate) translations.Deeper analysis is needed to potentially detect underlying phenomena specifically related to misleading translations.
Steady-State Sine Cosine Genetic Algorithm Based Chaotic Search for Nonlinear Programming and Engineering Applications

This paper proposes a newly meta-heuristic approach, steady-state sine cosine genetic algorithm-based chaotic search, for solving nonlinear programming and engineering applications. It is a combination of sine cosine approach (SCA), steady-state genetic algorithm (SSGA), and chaotic search (CS), and named as chaos-enhanced SCA with SSGA. The proposed approach integrates SSGA's exploitation ability and SCA's exploration ability and local search capability of CS. The performance of the new approach works in two different stages. Firstly, SCA and SSGA start together to increase exploration capability and exploitation tendencies. Secondly, CS used to improve the approximate solution obtained from the first stage and reach the global solution. Hence, the proposed new approach will be more robust as it avoids trapping into local minima in addition to the speed of the search process and rapid convergence towards the global solution. The efficiency of the proposed approach is verified by using it to solve 32 well-known benchmark problems and different engineering design problems. Simulation results show that the proposed approach is competitive and better in most cases as a comparison to others.
Analysis of student motivation towards body expression through the use of formative and share assessment (Análisis de la motivación del alumnado hacia la expresión corporal a través del uso de la evaluación formativa y compartida)

The present article aims to investigate the influence of the use of formative and shared assessment processes on students’ motivation and their level of commitment towards body expression contents in the Physical Education subject. 182 students with ages ranging from 12-13 years old participated in a study that was developed along a didactic unit of corporal expression. Following a qualitative methodology, data was obtained through the observation of individual student diaries, the teacher's diary and a focus group. The results show that a correct use of formative and shared evaluation increases students' motivation to develop expressive contents as well as their individual and group commitment. Students being able to choose their own teams, the greater amount of freedom that the creative methodology offers them, the permanent knowledge of what is demanded from the student, the playful aspect of the contents, and the provision of feedback are some of the factors that influence increased motivation and commitment levels from students.&#x0D; Resumen: El presente artículo tiene como objetivo investigar la influencia del uso de los procesos de evaluación formativa y compartida en la motivación de los estudiantes y su nivel de compromiso con los contenidos de expresión corporal en la asignatura de Educación Física. 182 estudiantes con edades comprendidas entre los 12 y 13 años participaron en un estudio que se desarrolló a lo largo de una unidad didáctica de expresión corporal. Siguiendo una metodología cualitativa, los datos se obtuvieron mediante la observación de los diarios individuales de los estudiantes, el diario del profesor y un grupo de discusión. Los resultados muestran que un uso correcto de la evaluación formativa y compartida aumenta la motivación de los estudiantes para desarrollar contenidos expresivos, así como su compromiso individual y grupal. La posibilidad de que los estudiantes elijan sus propios equipos, la mayor libertad que les ofrece la metodología creativa, el conocimiento permanente de lo que se exige al estudiante, el aspecto lúdico de los contenidos y la administración de retroalimentación son algunos de los factores que influyen en el aumento de los niveles de motivación y compromiso de los estudiantes.
Effective Emotion Recognition from Partially Occluded Facial Images Using Deep Learning

Effective expression analysis hugely depends upon the accurate representation of facial features. Proper identification and tracking of different facial muscles irrespective of pose, face shape, illumination, and image resolution is very much essential for serving the purpose. However, extraction and analysis of facial and appearance based features fails with improper face alignment and occlusions. Few existing works on these problems mainly determine the facial regions which contribute towards discrimination of expressions based on the training data. However, in these approaches, the positions and sizes of the facial patches vary according to the training data which inherently makes it difficult to conceive a generic system to serve the purpose. This paper proposes a novel facial landmark detection technique as well as a salient patch based facial expression recognition framework based on ACNN with significant performance at different image resolutions.
A dual hybrid recommender system based on SCoR and the random forest

We propose a Dual Hybrid Recommender System based on SCoR, the Synthetic Coordinate Recommendation system, and the Random Forest method. By combining user ratings and user/item features, SCoR is initially employed to provide a recommendation which is fed into the Random Forest. The two systems are initially combined by splitting the training set into two ?equivalent? parts, one of which is used to train SCoR while the other is used to train the Random Forest. This initial approach does not exhibit good performance due to reduced training. The resulted drawback is alleviated by the proposed dual training system which, using an innovative splitting method, exploits the entire training set for SCoR and the Random Forest, resulting to two recommender systems that are subsequently efficiently combined. Experimental results demonstrate the high performance of the proposed system on the Movielens datasets.
A Coarse-to-Fine Model for Geolocating Chinese Addresses

Address geolocation aims to associate address texts to the geographic locations. In China, due to the increasing demand for LBS applications such as take-out services and express delivery, automatically geolocating the unstructured address information is the key issue that needs to be solved first. Recently, a few approaches have been proposed to automate the address geolocation by directly predicting geographic coordinates. However, such point-based methods ignore the hierarchy information in addresses which may cause poor geolocation performance. In this paper, we propose a hierarchical region-based approach for geolocating Chinese addresses. We model the address geolocation as a Sequence-to-Sequence (Seq2Seq) learning task, that is, the input sequence is a textual address, and the output sequence is a GeoSOT grid code which exactly represents multi-level regions covered by the address. A novel coarse-to-fine model, which combines BERT and LSTM, is designed to learn the task. The experimental results demonstrate that our model correctly understands the Chinese addresses and achieves the highest geolocation accuracy among all the baselines.