Reviewerly/exHarmony huggingface dataset:
Determinantal Beam Search

Beam search is a go-to strategy for decoding neural sequence models.The algorithm can naturally be viewed as a subset optimization problem, albeit one where the corresponding set function does not reflect interactions between candidates.Empirically, this leads to sets often exhibiting high overlap, e.g., strings may differ by only a single word.Yet in use-cases that call for multiple solutions, a diverse or representative set is often desired.To address this issue, we propose a reformulation of beam search, which we call determinantal beam search.Determinantal beam search has a natural relationship to determinantal point processes (DPPs), models over sets that inherently encode intra-set interactions.By posing iterations in beam search as a series of subdeterminant maximization problems, we can turn the algorithm into a diverse subset selection process.In a case study, we use the string subsequence kernel to explicitly encourage n-gram coverage in text generated from a sequence model.We observe that our algorithm offers competitive performance against other diverse set generation strategies in the context of language generation, while providing a more general approach to optimizing for diversity.
Empirical Investigation of Critical Requirements Engineering Practices for Global Software Development

There is a need to identify requirements engineering (RE) practices that are important to global software development (GSD) project success. The objective of this paper is to report our recent empirical study results which aimed to identify the RE practices that are important to GSD projects. This study used an online survey questionnaire to elicit data from 56 RE experts of GSD projects. The survey included 66 RE practices identified by Sommerville et al. for non-GSD projects. The participants were asked to rank each RE practice on a four-point scale to determine the degree of importance of each practice in the context of GSD projects. This research identified a set of six key RE practices that mainly focuses on GSD project stakeholders, scope, standards and requirements traceability management. One common theme that is evident from the RE experts' feedback analysis is the standardization of requirements documents to reduce requirements inconsistencies and improve communication in diverse and distributed GSD project environments Our results show that not all 66 RE best practices are important for GSD projects. We believe that a good understanding of the identified RE practices is vital in developing and implementing the situation-specific RE processes for GSD projects.
Subgoal-Based Reward Shaping to Improve Efficiency in Reinforcement Learning

Reinforcement learning, which acquires a policy maximizing long-term rewards, has been actively studied. Unfortunately, this learning type is too slow and difficult to use in practical situations because the state-action space becomes huge in real environments. Many studies have incorporated human knowledge into reinforcement Learning. Though human knowledge on trajectories is often used, a human could be asked to control an AI agent, which can be difficult. Knowledge on subgoals may lessen this requirement because humans need only to consider a few representative states on an optimal trajectory in their minds. The essential factor for learning efficiency is rewards. Potential-based reward shaping is a basic method for enriching rewards. However, it is often difficult to incorporate subgoals for accelerating learning over potential-based reward shaping. This is because the appropriate potentials are not intuitive for humans. We extend potential-based reward shaping and propose a subgoal-based reward shaping. The method makes it easier for human trainers to share their knowledge of subgoals. To evaluate our method, we obtained a subgoal series from participants and conducted experiments in three domains, four-rooms(discrete states and discrete actions), pinball(continuous and discrete), and picking(both continuous). We compared our method with a baseline reinforcement learning algorithm and other subgoal-based methods, including random subgoal and naive subgoal-based reward shaping. As a result, we found out that our reward shaping outperformed all other methods in learning efficiency.
Dialogue-oriented Pre-training

Pre-trained language models (PrLM) has been shown powerful in enhancing a broad range of downstream tasks including various dialogue related ones.However, PrLMs are usually trained on general plain text with common language model (LM) training objectives, which cannot sufficiently capture dialogue exclusive features due to the limitation of such training setting, so that there is an immediate need to fill the gap between a specific dialogue task and the LM task.As it is unlikely to collect huge dialogue data for dialogueoriented pre-training, in this paper, we propose three strategies to simulate the conversation features on general plain text.Our proposed method differs from existing posttraining methods that it may yield a generalpurpose PrLM and does not individualize to any detailed task while keeping the capability of learning dialogue related features including speaker awareness, continuity and consistency.The resulted Dialog-PrLM is fine-tuned on three public multi-turn dialogue datasets and helps achieve significant and consistent improvement over the plain PrLMs.
CDRNN: Discovering Complex Dynamics in Human Language Processing

The human mind is a dynamical system, yet many analysis techniques used to study it are limited in their ability to capture the complex dynamics that may characterize mental processes.This study proposes the continuoustime deconvolutional regressive neural network (CDRNN), a deep neural extension of continuous-time deconvolutional regression (CDR, Shain and Schuler, 2021) that jointly captures time-varying, non-linear, and delayed influences of predictors (e.g.word surprisal) on the response (e.g.reading time).Despite this flexibility, CDRNN is interpretable and able to illuminate patterns in human cognition that are otherwise difficult to study.Behavioral and fMRI experiments reveal detailed and plausible estimates of human language processing dynamics that generalize better than CDR and other baselines, supporting a potential role for CDRNN in studying human language processing.
Defending Pre-trained Language Models from Adversarial Word Substitution Without Performance Sacrifice

Pre-trained contextualized language models (PrLMs) have led to strong performance gains in downstream natural language understanding tasks.However, PrLMs can still be easily fooled by adversarial word substitution, which is one of the most challenging textual adversarial attack methods.Existing defence approaches suffer from notable performance loss and complexities.Thus, this paper presents a compact and performance-preserved framework, Anomaly Detection with Frequency-Aware Randomization (ADFAR).In detail, we design an auxiliary anomaly detection classifier and adopt a multi-task learning procedure, by which PrLMs are able to distinguish adversarial input samples.Then, in order to defend adversarial word substitution, a frequency-aware randomization process is applied to those recognized adversarial input samples.Empirical results show that AD-FAR significantly outperforms those newly proposed defense methods over various tasks with much higher inference speed.Remarkably, ADFAR does not impair the overall performance of PrLMs.
Scalable and Safe Multi-Agent Motion Planning with Nonlinear Dynamics and Bounded Disturbances

We present a scalable and effective multi-agent safe motion planner that enables a group of agents to move to their desired locations while avoiding collisions with obstacles and other agents, with the presence of rich obstacles, high-dimensional, nonlinear, nonholonomic dynamics, actuation limits, and disturbances. We address this problem by finding a piecewise linear path for each agent such that the actual trajectories following these paths are guaranteed to satisfy the reach-and-avoid requirement. We show that the spatial tracking error of the actual trajectories of the controlled agents can be pre-computed for any qualified path that considers the minimum duration of each path segment due to actuation limits. Using these bounds, we find a collision-free path for each agent by solving Mixed Integer-Linear Programs and coordinate agents by using the priority-based search. We demonstrate our method by benchmarking in 2D and 3D scenarios with ground vehicles and quadrotors, respectively, and show improvements over the solving time and the solution quality compared to two state-of-the-art multi-agent motion planners.
CiteWorth: Cite-Worthiness Detection for Improved Scientific Document Understanding

Scientific document understanding is challenging as the data is highly domain specific and diverse.However, datasets for tasks with scientific text require expensive manual annotation and tend to be small and limited to only one or a few fields.At the same time, scientific documents contain many potential training signals, such as citations, which can be used to build large labelled datasets.Given this, we present an in-depth study of cite-worthiness detection in English, where a sentence is labelled for whether or not it cites an external source.To accomplish this, we introduce CITEWORTH, a large, contextualized, rigorously cleaned labelled dataset for cite-worthiness detection built from a massive corpus of extracted plaintext scientific documents.We show that CITEWORTH is high-quality, challenging, and suitable for studying problems such as domain adaptation.Our best performing citeworthiness detection model is a paragraphlevel contextualized sentence labelling model based on Longformer, exhibiting a 5 F1 point improvement over SciBERT which considers only individual sentences.Finally, we demonstrate that language model fine-tuning with cite-worthiness as a secondary task leads to improved performance on downstream scientific document understanding tasks.
Voice Recognition and Inverse Kinematics Control for a Redundant Manipulator Based on a Multilayer Artificial Intelligence Network

This study presents the construction of a Vietnamese voice recognition module and inverse kinematics control of a redundant manipulator by using artificial intelligence algorithms. The first deep learning model is built to recognize and convert voice information into input signals of the inverse kinematics problem of a 6-degrees-of-freedom robotic manipulator. The inverse kinematics problem is solved based on the construction and training. The second deep learning model is built using the data determined from the mathematical model of the system’s geometrical structure, the limits of joint variables, and the workspace. The deep learning models are built in the PYTHON language. The efficient operation of the built deep learning networks demonstrates the reliability of the artificial intelligence algorithms and the applicability of the Vietnamese voice recognition module for various tasks.
Addressing Domain Gap via Content Invariant Representation for Semantic Segmentation

The problem of unsupervised domain adaptation in semantic segmentation is a major challenge for numerous computer vision tasks because acquiring pixel-level labels is time-consuming with expensive human labor. A large gap exists among data distributions in different domains, which will cause severe performance loss when a model trained with synthetic data is generalized to real data. Hence, we propose a novel domain adaptation approach, called Content Invariant Representation Network, to narrow the domain gap between the source (S) and target (T) domains. The previous works developed a network to directly transfer the knowledge from the S to T. On the contrary, the proposed method aims to progressively reduce the gap between S and T on the basis of a Content Invariant Representation (CIR). CIR is an intermediate domain (I) sharing invariant content with S and having similar data distribution to T. Then, an Ancillary Classifier Module (ACM) is designed to focus on pixel-level details and generate attention-aware results. ACM adaptively assigns different weights to pixels according to their domain offsets, thereby reducing local domain gaps. The global domain gap between CIR and T is also narrowed by enforcing local alignments. Last, we perform self-supervised training in the pseudo-labeled target domain to further fit the distribution of the real data. Comprehensive experiments on two domain adaptation tasks, that is, GTAV → Cityscapes and SYNTHIA → Cityscapes, clearly demonstrate the superiority of our method compared with state-of-the-art methods.
Large Batch Optimization for Deep Learning Using New Complete Layer-Wise Adaptive Rate Scaling

Training deep neural networks using a large batch size has shown promising results and benefits many real-world applications. Warmup is one of nontrivial techniques to stabilize the convergence of large batch training. However, warmup is an empirical method and it is still unknown whether there is a better algorithm with theoretical underpinnings. In this paper, we propose a novel Complete Layer-wise Adaptive Rate Scaling (CLARS) algorithm for large-batch training. We prove the convergence of our algorithm by introducing a new fine-grained analysis of gradient-based methods. Furthermore, the new analysis also helps to understand two other empirical tricks, layer-wise adaptive rate scaling and linear learning rate scaling. We conduct extensive experiments and demonstrate that the proposed algorithm outperforms gradual warmup technique by a large margin and defeats the convergence of the state-of-the-art large-batch optimizer in training advanced deep neural networks (ResNet, DenseNet, MobileNet) on ImageNet dataset.
Towards Knowledge-Grounded Counter Narrative Generation for Hate Speech

Tackling online hatred using informed textual responses -called counter narratives -has been brought under the spotlight recently.Accordingly, a research line has emerged to automatically generate counter narratives in order to facilitate the direct intervention in the hate discussion and to prevent hate content from further spreading.Still, current neural approaches tend to produce generic/repetitive responses and lack grounded and up-to-date evidence such as facts, statistics, or examples.Moreover, these models can create plausible but not necessarily true arguments.In this paper we present the first complete knowledgebound counter narrative generation pipeline, grounded in an external knowledge repository that can provide more informative content to fight online hatred.Together with our approach, we present a series of experiments that show its feasibility to produce suitable and informative counter narratives in in-domain and cross-domain settings.
How is BERT surprised? Layerwise detection of linguistic anomalies

Transformer language models have shown remarkable ability in detecting when a word is anomalous in context, but likelihood scores offer no information about the cause of the anomaly.In this work, we use Gaussian models for density estimation at intermediate layers of three language models (BERT, RoBERTa, and XLNet), and evaluate our method on BLiMP, a grammaticality judgement benchmark.In lower layers, surprisal is highly correlated to low token frequency, but this correlation diminishes in upper layers.Next, we gather datasets of morphosyntactic, semantic, and commonsense anomalies from psycholinguistic studies; we find that the best performing model RoBERTa exhibits surprisal in earlier layers when the anomaly is morphosyntactic than when it is semantic, while commonsense anomalies do not exhibit surprisal at any intermediate layer.These results suggest that language models employ separate mechanisms to detect different types of linguistic anomalies.
How Do Location-Based Augmented Reality Games Improve Physical and Mental Health? Evaluating the Meanings and Values of Pokémon Go Users’ Experiences through the Means-End Chain Theory

New technology has dramatically changed online games and blurred the boundary between active and passive activities. This study aims to explore the meanings and values of augmented reality online games by examining users’ Pokémon Go experiences through the means-end chain theory. Using data from interviews with 34 Pokémon Go users, this study adopts the soft laddering method to identify Pokémon Go’s potential attributes, consequences, and values, and to construct a hierarchical value map. The results indicated that Pokémon Go users pursue social relationships through play, and these relationships are triggered by the benefits of making new friends, maintaining current relationships with friends and family, and the attributes of prevalence, childhood memory, game design, and augmented reality. Subsequently, this study describes how Pokémon Go can be considered an active leisure activity because of its social, mental, and physical benefits and assesses the implications of its findings.
Ordered Counterfactual Explanation by Mixed-Integer Linear Optimization

Post-hoc explanation methods for machine learning models have been widely used to support decision-making. One of the popular methods is Counterfactual Explanation (CE), also known as Actionable Recourse, which provides a user with a perturbation vector of features that alters the prediction result. Given a perturbation vector, a user can interpret it as an "action" for obtaining one's desired decision result. In practice, however, showing only a perturbation vector is often insufficient for users to execute the action. The reason is that if there is an asymmetric interaction among features, such as causality, the total cost of the action is expected to depend on the order of changing features. Therefore, practical CE methods are required to provide an appropriate order of changing features in addition to a perturbation vector. For this purpose, we propose a new framework called Ordered Counterfactual Explanation (OrdCE). We introduce a new objective function that evaluates a pair of an action and an order based on feature interaction. To extract an optimal pair, we propose a mixed-integer linear optimization approach with our objective function. Numerical experiments on real datasets demonstrated the effectiveness of our OrdCE in comparison with unordered CE methods.
Interpretable Graph Capsule Networks for Object Recognition

Capsule Networks, as alternatives to Convolutional Neural Networks, have been proposed to recognize objects from images. The current literature demonstrates many advantages of CapsNets over CNNs. However, how to create explanations for individual classifications of CapsNets has not been well explored. The widely used saliency methods are mainly proposed for explaining CNN-based classifications; they create saliency map explanations by combining activation values and the corresponding gradients, e.g., Grad-CAM. These saliency methods require a specific architecture of the underlying classifiers and cannot be trivially applied to CapsNets due to the iterative routing mechanism therein. To overcome the lack of interpretability, we can either propose new post-hoc interpretation methods for CapsNets or modifying the model to have build-in explanations. In this work, we explore the latter. Specifically, we propose interpretable Graph Capsule Networks (GraCapsNets), where we replace the routing part with a multi-head attention-based Graph Pooling approach. In the proposed model, individual classification explanations can be created effectively and efficiently. Our model also demonstrates some unexpected benefits, even though it replaces the fundamental part of CapsNets. Our GraCapsNets achieve better classification performance with fewer parameters and better adversarial robustness, when compared to CapsNets. Besides, GraCapsNets also keep other advantages of CapsNets, namely, disentangled representations and affine transformation robustness.
An easy-to-explain decision support framework for forensic analysis of dynamic signatures

Forensic handwriting examination is often criticized for its lack of objective standards and rigorous scientific validation. On the other hand, cutting-edge techniques for biometric handwriting and signature verification are often perceived as perfect black boxes and are not used by forensic handwriting examiners in their work environment. This paper presents an easy-to-explain yet effective framework to support semi-automatic signature verification in forensic settings. The proposed approach is based on measuring similarities between signatures by applying Dynamic Time Warping on easy-to-derive dynamic features. The goal is to provide forensic handwriting examiners with a decision support tool for making reproducible and less questionable inferences, while being both intuitive and easy to explain. The method is tested on a newly proposed dataset that also takes into account the so-called disguised signatures which are of extreme importance in this scenario. • An easy to explain method is proposed to support signature verification in forensic settings. • The proposed method is based on measuring similarities between signatures using Dynamic Time Warping. • The method proved effective on a newly proposed dataset that also takes into account disguised signatures. • The goal is to foster synergies between the forensic and biometric communities, which are currently sometimes lacking.
Enhancing the generalization for Intent Classification and Out-of-Domain Detection in SLU

Intent classification is a major task in spoken language understanding (SLU).Since most models are built with pre-collected in-domain (IND) training utterances, their ability to detect unsupported out-of-domain (OOD) utterances has a critical effect in practical use.Recent works have shown that using extra data and labels can improve the OOD detection performance, yet it could be costly to collect such data.This paper proposes to train a model with only IND data while supporting both IND intent classification and OOD detection.Our method designs a novel domain-regularized module (DRM) to reduce the overconfident phenomenon of a vanilla classifier, achieving a better generalization in both cases.Besides, DRM can be used as a drop-in replacement for the last layer in any neural network-based intent classifier, providing a low-cost strategy for a significant improvement.The evaluation on four datasets shows that our method built on BERT and RoBERTa models achieves state-of-the-art performance against existing approaches and the strong baselines we created for the comparisons.
Single-Qubit Fidelity Assessment of Quantum Annealing Hardware

As a wide variety of quantum computing platforms become available, methods for assessing and comparing the performance of these devices are of increasing interest and importance.Inspired by the success of single-qubit error rate computations for tracking the progress of gate-based quantum computers, this work proposes a quantum annealing single-qubit assessment (QASA) protocol for quantifying the performance of individual qubits in quantum annealing computers.The proposed protocol scales to large quantum annealers with thousands of qubits and provides unique insights into the distribution of qubit properties within a particular hardware device.The efficacy of the QASA protocol is demonstrated by analyzing the properties of a D-Wave 2000Q system, revealing unanticipated correlations in the qubit performance of that device.A study repeating the QASA protocol at different annealing times highlights how the method can be utilized to understand the impact of annealing parameters on qubit performance.Overall, the proposed QASA protocol provides a useful tool for assessing the performance of current and emerging quantum annealing devices.