Reviewerly/exHarmony huggingface dataset:
Hype or Hope: Digital Technologies in Auditing Process

Manuscript type: Research paper Research aims: This study aims to examine how technological,organisational and environmental (TOE) factors affect digital technologies’ utilisation and its impact on auditors’ performance. Design/Methodology/Approach: A survey was conducted on 4 large and non-large companies in Jordan, and data were retrieved from 168 external auditors. The partial least squares (PLS-3) structural equations modeling was used to analyse data and to test the proposed model. Research findings: Results show that the TOE factors have a significant and positive effect on digital technologies utilisation. It also appears to enhance auditors’ performance. Theoretical contribution/Originality: This study fills a gap in the literature of accounting by empirically assessing how digital technologies utilisation impacts the performance of auditors in Jordan. It also expands on previous literature by determining how the TOE factors affect such technology usage in the auditing process. This study also has a methodological contribution in the form of measuring the client’s complexity of IT system from a different perspective, which is from the external auditor’s perspective. Practitioner/Policy implications: This study is expected to help regulators and audit companies to update their policies and regulations so that auditors are trained in using advanced technologies, and in protecting the security of accounting information. In this regard, efficient auditors will be able to protect investors, thereby creating an attractive environment for investment which can boost economic growth. Research limitation/Implications: This study is based on the Jordanian context. There was no comparison made with developing or developed countries, hence no distinctions can be detected. Future studies should focus on looking at other countries so as to provide new insights into the auditing profession, and specifically, auditors’ performance. Finally, this study is a practical guide for external and internal auditors who have not considered digital technologies, in the context of Jordan. Future studies may thus consider internal auditors and their relationship with external auditors in digital technologies utilisation.
Space Efficient Context Encoding for Non-Task-Oriented Dialogue Generation with Graph Attention Transformer

To improve the coherence and knowledge retrieval capabilities of non-task-oriented dialogue systems, recent Transformer-based models aim to integrate fixed background context.This often comes in the form of knowledge graphs, and the integration is done by creating pseudo utterances through paraphrasing knowledge triples, added into the accumulated dialogue context.However, the context length is fixed in these architectures, which restricts how much background or dialogue context can be kept.In this work, we propose a more concise encoding for background context structured in the form of knowledge graphs, by expressing the graph connections through restrictions on the attention weights.The results of our human evaluation show that this encoding reduces space requirements without negative effects on the precision of reproduction of knowledge and perceived consistency.Further, models trained with our proposed context encoding generate dialogues that are judged to be more comprehensive and interesting.
Learning Model-Based Privacy Protection under Budget Constraints

Protecting privacy in gradient-based learning has become increasingly critical as more sensitive information is being used. Many existing solutions seek to protect the sensitive gradients by constraining the overall privacy cost within a constant budget, where the protection is hand-designed and empirically calibrated to boost the utility of the resulting model. However, it remains challenging to choose the proper protection adapted for specific constraints so that the utility is maximized. To this end, we propose a novel Learning-to-Protect algorithm that automatically learns a model-based protector from a set of non-private learning tasks. The learned protector can be applied to private learning tasks to improve utility within the specific privacy budget constraint. Our empirical studies on both synthetic and real datasets demonstrate that the proposed algorithm can achieve a superior utility with a given privacy constraint and generalize well to new private datasets distributed differently as compared to the hand-designed competitors.
Inverse Reinforcement Learning From Like-Minded Teachers

We study the problem of learning a policy in a Markov decision process (MDP) based on observations of the actions taken by multiple teachers. We assume that the teachers are like-minded in that their reward functions -- while different from each other -- are random perturbations of an underlying reward function. Under this assumption, we demonstrate that inverse reinforcement learning algorithms that satisfy a certain property -- that of matching feature expectations -- yield policies that are approximately optimal with respect to the underlying reward function, and that no algorithm can do better in the worst case. We also show how to efficiently recover the optimal policy when the MDP has one state -- a setting that is akin to multi-armed bandits.
Learning General Planning Policies from Small Examples Without Supervision

Generalized planning is concerned with the computation of general policies that solve multiple instances of a planning domain all at once. It has been recently shown that these policies can be computed in two steps: first, a suitable abstraction in the form of a qualitative numerical planning problem (QNP) is learned from sample plans, then the general policies are obtained from the learned QNP using a planner. In this work, we introduce an alternative approach for computing more expressive general policies which does not require sample plans or a QNP planner. The new formulation is very simple and can be cast in terms that are more standard in machine learning: a large but finite pool of features is defined from the predicates in the planning examples using a general grammar, and a small subset of features is sought for separating “good” from “bad” state transitions, and goals from non-goals. The problems of finding such a “separating surface” while labeling the transitions as “good” or “bad” are jointly addressed as a single combinatorial optimization problem expressed as a Weighted Max-SAT problem. The advantage of looking for the simplest policy in the given feature space that solves the given examples, possibly non-optimally, is that many domains have no general, compact policies that are optimal. The approach yields general policies for a number of benchmark domains.
Modeling Transitions of Focal Entities for Conversational Knowledge Base Question Answering

Conversational KBQA is about answering a sequence of questions related to a KB.Follow-up questions in conversational KBQA often have missing information referring to entities from the conversation history.In this paper, we propose to model these implied entities, which we refer to as the focal entities of the conversation.We propose a novel graph-based model to capture the transitions of focal entities and apply a graph neural network to derive a probability distribution of focal entities for each question, which is then combined with a standard KBQA module to perform answer ranking.Our experiments on two datasets demonstrate the effectiveness of our proposed method.
Differentially Private and Communication Efficient Collaborative Learning

Collaborative learning has received huge interests due to its capability of exploiting the collective computing power of the wireless edge devices. However, during the learning process, model updates using local private samples and large-scale parameter exchanges among agents impose severe privacy concerns and communication bottleneck. In this paper, to address these problems, we propose two differentially private (DP) and communication efficient algorithms, called Q-DPSGD-1 and Q-DPSGD-2. In Q-DPSGD-1, each agent first performs local model updates by a DP gradient descent method to provide the DP guarantee and then quantizes the local model before transmitting it to neighbors to improve communication efficiency. In Q-DPSGD-2, each agent injects discrete Gaussian noise to enforce DP guarantee after first quantizing the local model. Moreover, we track the privacy loss of both approaches under the Renyi DP and provide convergence analysis for both convex and non-convex loss functions. The proposed methods are evaluated in extensive experiments on real-world datasets and the empirical results validate our theoretical findings.
IntelliCAT: Intelligent Machine Translation Post-Editing with Quality Estimation and Translation Suggestion

We present IntelliCAT, an interactive translation interface with neural models that streamline the post-editing process on machine translation output.We leverage two quality estimation (QE) models at different granularities: sentence-level QE, to predict the quality of each machine-translated sentence, and wordlevel QE, to locate the parts of the machinetranslated sentence that need correction.Additionally, we introduce a novel translation suggestion model conditioned on both the left and right contexts, providing alternatives for specific words or phrases for correction.Finally, with word alignments, IntelliCAT automatically preserves the original document's styles in the translated document.The experimental results show that post-editing based on the proposed QE and translation suggestions can significantly improve translation quality.Furthermore, a user study reveals that three features provided in IntelliCAT significantly accelerate the post-editing task, achieving a 52.9% speedup in translation time compared to translating from scratch.The interface is publicly available at https://intellicat.beringlab.com/.
HyKnow: End-to-End Task-Oriented Dialog Modeling with Hybrid Knowledge Management

Task-oriented dialog (TOD) systems typically manage structured knowledge (e.g.ontologies and databases) to guide the goal-oriented conversations.However, they fall short of handling dialog turns grounded on unstructured knowledge (e.g.reviews and documents).In this paper, we formulate a task of modeling TOD grounded on both structured and unstructured knowledge.To address this task, we propose a TOD system with hybrid knowledge management, HyKnow.It extends the belief state to manage both structured and unstructured knowledge, and is the first end-to-end model that jointly optimizes dialog modeling grounded on these two kinds of knowledge.We conduct experiments on the modified version of MultiWOZ 2.1 dataset, where dialogs are grounded on hybrid knowledge.Experimental results show that HyKnow has strong end-to-end performance compared to existing TOD systems.It also outperforms the pipeline knowledge management schemes, with higher unstructured knowledge retrieval accuracy.
Bringing Structure into Summaries: a Faceted Summarization Dataset for Long Scientific Documents

Faceted summarization provides briefings of a document from different perspectives.Readers can quickly comprehend the main points of a long document with the help of a structured outline.However, little research has been conducted on this subject, partially due to the lack of large-scale faceted summarization datasets.In this study, we present FacetSum, a faceted summarization benchmark built on Emerald journal articles, covering a diverse range of domains.Different from traditional documentsummary pairs, FacetSum provides multiple summaries, each targeted at specific sections of a long document, including the purpose, method, findings, and value.Analyses and empirical results on our dataset reveal the importance of bringing structure into summaries.We believe FacetSum will spur further advances in summarization research and foster the development of NLP systems that can leverage the structured information in both long texts and summaries.
Spatial-temporal Causal Inference for Partial Image-to-video Adaptation

Image-to-video adaptation leverages off-the-shelf learned models in labeled images to help classification in unlabeled videos, thus alleviating the high computation overhead of training a video classifier from scratch. This task is very challenging since there exist two types of domain shifts between images and videos: 1) spatial domain shift caused by static appearance variance between images and video frames, and 2) temporal domain shift caused by the absence of dynamic motion in images. Moreover, for different video classes, these two domain shifts have different effects on the domain gap and should not be treated equally during adaptation. In this paper, we propose a spatial-temporal causal inference framework for image-to-video adaptation. We first construct a spatial-temporal causal graph to infer the effects of the spatial and temporal domain shifts by performing counterfactual causality. We then learn causality-guided bidirectional heterogeneous mappings between images and videos to adaptively reduce the two domain shifts. Moreover, to relax the assumption that the label spaces of the image and video domains are the same by the existing methods, we incorporate class-wise alignment into the learning of image-video mappings to perform partial image-to-video adaptation where the image label space subsumes the video label space. Extensive experiments on several video datasets have validated the effectiveness of our proposed method.
Unsupervised Enrichment of Persona-grounded Dialog with Background Stories

Humans often refer to personal narratives, life experiences, and events to make a conversation more engaging and rich.While personagrounded dialog models are able to generate responses that follow a given persona, they often miss out on stating detailed experiences or events related to a persona, often leaving conversations shallow and dull.In this work, we equip dialog models with 'background stories' related to a persona by leveraging fictional narratives from existing story datasets (e.g.ROC-Stories).Since current dialog datasets do not contain such narratives as responses, we perform an unsupervised adaptation of a retrieved story for generating a dialog response using a gradient-based rewriting technique.Our proposed method encourages the generated response to be fluent (i.e., highly likely) with the dialog history, minimally different from the retrieved story to preserve event ordering and consistent with the original persona.We demonstrate that our method can generate responses that are more diverse, and are rated more engaging and human-like by human evaluators, compared to outputs from existing dialog models.
NoiseGrad — Enhancing Explanations by Introducing Stochasticity to Model Weights

Many efforts have been made for revealing the decision-making process of black-box learning machines such as deep neural networks, resulting in useful local and global explanation methods. For local explanation, stochasticity is known to help: a simple method, called SmoothGrad, has improved the visual quality of gradient-based attribution by adding noise to the input space and averaging the explanations of the noisy inputs. In this paper, we extend this idea and propose NoiseGrad that enhances both local and global explanation methods. Specifically, NoiseGrad introduces stochasticity in the weight parameter space, such that the decision boundary is perturbed. NoiseGrad is expected to enhance the local explanation, similarly to SmoothGrad, due to the dual relationship between the input perturbation and the decision boundary perturbation. We evaluate NoiseGrad and its fusion with SmoothGrad - FusionGrad - qualitatively and quantitatively with several evaluation criteria, and show that our novel approach significantly outperforms the baseline methods. Both NoiseGrad and FusionGrad are method-agnostic and as handy as SmoothGrad using a simple heuristic for the choice of the hyperparameter setting without the need of fine-tuning.
A Taxonomy for Immersive Experience Design

Immersive technology platforms such as virtual reality (VR) are used by many to create experiences that allow for efficient training, visceral encounters, and faithful reproduction of places and times. This work investigates the various elements that contribute to the design of an effective immersive experience and proposes a taxonomy that establishes levels (ranks) for each of these elements.
Affinity-Based Task Scheduling on Heterogeneous Multicore Systems Using CBS and QBICTM

This work presents the grouping of dependent tasks into a cluster using the Bayesian analysis model to solve the affinity scheduling problem in heterogeneous multicore systems. The non-affinity scheduling of tasks has a negative impact as the overall execution time for the tasks increases. Furthermore, non-affinity-based scheduling also limits the potential for data reuse in the caches so it becomes necessary to bring the same data into the caches multiple times. In heterogeneous multicore systems, it is essential to address the load balancing problem as all cores are operating at varying frequencies. We propose two techniques to solve the load balancing issue, one being designated “chunk-based scheduler” (CBS) which is applied to the heterogeneous systems while the other system is “quantum-based intra-core task migration” (QBICTM) where each task is given a fair and equal chance to run on the fastest core. Results show 30–55% improvement in the average execution time of the tasks by applying our CBS or QBICTM scheduler compare to other traditional schedulers when compared using the same operating system.
Model Uncertainty Guides Visual Object Tracking

Model object trackers largely rely on the online learning of a discriminative classifier from potentially diverse sample frames. However, noisy or insufficient amounts of samples can deteriorate the classifiers' performance and cause tracking drift. Furthermore, alterations such as occlusion and blurring can cause the target to be lost. In this paper, we make several improvements aimed at tackling uncertainty and improving robustness in object tracking. Our first and most important contribution is to propose a sampling method for the online learning of object trackers based on uncertainty adjustment: our method effectively selects representative sample frames to feed the discriminative branch of the tracker, while filtering out noise samples. Furthermore, to improve the robustness of the tracker to various challenging scenarios, we propose a novel data augmentation procedure, together with a specific improved backbone architecture. All our improvements fit together in one model, which we refer to as the Uncertainty Adjusted Tracker (UATracker), and can be trained in a joint and end-to-end fashion. Experiments on the LaSOT, UAV123, OTB100 and VOT2018 benchmarks demonstrate that our UATracker outperforms state-of-the-art real-time trackers by significant margins.
WikiSum: Coherent Summarization Dataset for Efficient Human-Evaluation

Nachshon Cohen, Oren Kalinsky, Yftah Ziser, Alessandro Moschitti. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers). 2021.
A Flexible Framework for Communication-Efficient Machine Learning

With the increasing scale of machine learning tasks, it has become essential to reduce the communication between computing nodes. Early work on gradient compression focused on the bottleneck between CPUs and GPUs, but communication-efficiency is now needed in a variety of different system architectures, from high-performance clusters to energy-constrained IoT devices. In the current practice, compression levels are typically chosen before training and settings that work well for one task may be vastly suboptimal for another dataset on another architecture. In this paper, we propose a flexible framework which adapts the compression level to the true gradient at each iteration, maximizing the improvement in the objective function that is achieved per communicated bit. Our framework is easy to adapt from one technology to the next by modeling how the communication cost depends on the compression level for the specific technology. Theoretical results and practical experiments indicate that the automatic tuning strategies significantly increase communication efficiency on several state-of-the-art compression schemes.
Action Candidate Based Clipped Double Q-learning for Discrete and Continuous Action Tasks

Double Q-learning is a popular reinforcement learning algorithm in Markov decision process (MDP) problems. Clipped Double Q-learning, as an effective variant of Double Q-learning, employs the clipped double estimator to approximate the maximum expected action value. Due to the underestimation bias of the clipped double estimator, performance of clipped Double Q-learning may be degraded in some stochastic environments. In this paper, in order to reduce the underestimation bias, we propose an action candidate based clipped double estimator for Double Q-learning. Specifically, we first select a set of elite action candidates with the high action values from one set of estimators. Then, among these candidates, we choose the highest valued action from the other set of estimators. Finally, we use the maximum value in the second set of estimators to clip the action value of the chosen action in the first set of estimators and the clipped value is used for approximating the maximum expected action value. Theoretically, the underestimation bias in our clipped Double Q-learning decays monotonically as the number of the action candidates decreases. Moreover, the number of action candidates controls the trade-off between the overestimation and underestimation biases. In addition, we also extend our clipped Double Q-learning to continuous action tasks via approximating the elite continuous action candidates. We empirically verify that our algorithm can more accurately estimate the maximum expected action value on some toy environments and yield good performance on several benchmark problems.
Fairness, Semi-Supervised Learning, and More: A General Framework for Clustering with Stochastic Pairwise Constraints

Metric clustering is fundamental in areas ranging from Combinatorial Optimization and Data Mining, to Machine Learning and Operations Research. However, in a variety of situations we may have additional requirements or knowledge, distinct from the underlying metric, regarding which pairs of points should be clustered together. To capture and analyze such scenarios, we introduce a novel family of stochastic pairwise constraints, which we incorporate into several essential clustering objectives (radius/median/means). Moreover, we demonstrate that these constraints can succinctly model an intriguing collection of applications, including among others Individual Fairness in clustering and Must-link constraints in semi-supervised learning. Our main result consists of a general framework that yields approximation algorithms with provable guarantees for important clustering objectives, while at the same time producing solutions that respect the stochastic pairwise constraints. Furthermore, for certain objectives we devise improved results in the case of Must-link constraints, which are also the best possible from a theoretical perspective. Finally, we present experimental evidence that validates the effectiveness of our algorithms.