Reviewerly/exHarmony huggingface dataset:
Understanding by Understanding Not: Modeling Negation in Language Models

Negation is a core construction in natural language.Despite being very successful on many tasks, state-of-the-art pre-trained language models often handle negation incorrectly.To improve language models in this regard, we propose to augment the language modeling objective with an unlikelihood objective that is based on negated generic sentences from a raw text corpus.By training BERT with the resulting combined objective we reduce the mean top 1 error rate to 4% on the negated LAMA dataset.We also see some improvements on the negated NLI benchmarks. QueryTop 3 words with log probs from BERT Top 3 words with log probs from BERTNOT iOS is developed by [MASK].Apple (-1.8),Google (-2.6), Microsoft (-2.8)Apple (-1.8),Google (-2.5),Microsoft (-2.7) iOS is not developed by [MASK].Apple (-1.8),Google (-2.6), Microsoft (-2.8)Microsoft (-1.8),Google (-2.4),Apple (-3.1)The majority of the amazon forest is in [MASK].Brazil (-2.6), Bolivia (-2.7),Madagascar (-3.1)Brazil (-2.9),Bolivia (-3.1),Mexico (-3.2) The majority of the amazon forest is not in [MASK].cultivation (-1.0),Brazil (-3.5),Mexico (-3.5) cultivation (-2.0),Mexico (-4.1),France (-4.3)Charles Nodier died in [MASK].Paris (-1.35),Rome (-3.2), office (-3.4)Paris (-1.5),Rome (-3.3),France (-3.6)Charles Nodier did not die in [MASK].Paris (-2.4), office (-2.7),France (-2.8) vain (-3.5), error (-4.0), doubt (-4.5)Mac OS is developed by [MASK].Apple (-1.9),Microsoft (-2.0),Intel (-2.0)Apple (-2.0), Microsoft (-2.0),Intel (-2.1)Mac OS is not developed by [MASK].Apple (-1.3),Microsoft (-1.5),IBM (-2.3)Microsoft (-2.1),IBM (-2.7), itself (-3.4)
Exploring opportunities and challenges to the adoption of blockchain technology in the fresh produce value chain

<abstract> This study investigates the attitudes and perceptions of businesses and consumers towards the application of Blockchain technology (BCT) to fresh produce supply chains. Semi-structured interviews and focus groups (FG) were performed with fresh produce company managers and consumers, respectively. The fresh produce sector, in this context, concerns the fresh and chill chain, value-added vegetables, fruits and salads and the ingredients of these used in further food value-added processes and products. A total of five themes were identified as factors influencing BCT adoption: the novelty of the technology, supply chain characteristics, open data issues, cost-benefit analysis, and role of public stakeholders. From these, twelve sub-themes were developed and discussed. Findings provide a framework with opportunities and challenges that the fresh produce system must face in adopting decentralised systems for managing supply chain data like BCT. </abstract>
SpatialProto: Exploring Real-World Motion Captures for Rapid Prototyping of Interactive Mixed Reality

Spatial computing devices that blend virtual and real worlds have the potential to soon become ubiquitous. Yet, creating experiences for spatial computing is non-trivial and needs skills in programming and 3D content creation, rendering them inaccessible to a wider group of users. We present SpatialProto, an in-situ spatial prototyping system for lowering the barrier to engage in spatial prototyping. With a depth-sensing capable mixed reality headset, SpatialProto lets users record animated objects of the real-world environment (e.g. paper, clay, people, or any other prop), extract only the relevant parts, and directly place and transform these recordings in their physical environment. We describe the design and implementation of SpatialProto, a user study evaluating the system's prototype with non-expert users (n = 9), and demonstrate applications where multiple captures are fused for compelling augmented reality experiences.
EMaQ: Expected-Max Q-Learning Operator for Simple Yet Effective Offline and Online RL

Off-policy reinforcement learning (RL) holds the promise of sample-efficient learning of decision-making policies by leveraging past experience. However, in the offline RL setting -- where a fixed collection of interactions are provided and no further interactions are allowed -- it has been shown that standard off-policy RL methods can significantly underperform. Recently proposed methods often aim to address this shortcoming by constraining learned policies to remain close to the given dataset of interactions. In this work, we closely investigate an important simplification of BCQ~\citep{fujimoto2018off} -- a prior approach for offline RL -- which removes a heuristic design choice and naturally restrict extracted policies to remain \emph{exactly} within the support of a given behavior policy. Importantly, in contrast to their original theoretical considerations, we derive this simplified algorithm through the introduction of a novel backup operator, Expected-Max Q-Learning (EMaQ), which is more closely related to the resulting practical algorithm. Specifically, in addition to the distribution support, EMaQ explicitly considers the number of samples and the proposal distribution, allowing us to derive new sub-optimality bounds which can serve as a novel measure of complexity for offline RL problems. In the offline RL setting -- the main focus of this work -- EMaQ matches and outperforms prior state-of-the-art in the D4RL benchmarks~\citep{fu2020d4rl}. In the online RL setting, we demonstrate that EMaQ is competitive with Soft Actor Critic (SAC). The key contributions of our empirical findings are demonstrating the importance of careful generative model design for estimating behavior policies, and an intuitive notion of complexity for offline RL problems. With its simple interpretation and fewer moving parts, such as no explicit function approximator representing the policy, EMaQ serves as a strong yet easy to implement baseline for future work.
Visual Tracking via Hierarchical Deep Reinforcement Learning

Visual tracking has achieved great progress due to numerous different algorithms. However, deep trackers based on classification or Siamese network still have their specific limitations. In this work, we show how to teach machines to track a generic object in videos like humans, who can use a few search steps to perform tracking. By constructing a Markov decision process in Deep Reinforcement Learning (DRL), our agents can learn to determine hierarchical decisions on tracking mode and motion estimation. To be specific, our Hierarchical DRL framework is composed of a Siamese-based observation network which models the motion information of an arbitrary target, a policy network for mode switch and an actor-critic network for box regression. This tracking strategy is more in line with human behavior paradigm, and is effective and efficient to cope with fast motion, background clutter and large deformations. Extensive experiments on the GOT-10k, OTB-100, UAV-123, VOT and LaSOT tracking benchmarks, demonstrate that the proposed tracker achieves state-of-the-art performance while running in real-time.
Awakening Latent Grounding from Pretrained Language Models for Semantic Parsing

Recent years pretrained language models (PLMs) hit a success on several downstream tasks, showing their power on modeling language.To better understand and leverage what PLMs have learned, several techniques have emerged to explore syntactic structures entailed by PLMs.However, few efforts have been made to explore grounding capabilities of PLMs, which are also essential.In this paper, we highlight the ability of PLMs to discover which token should be grounded to which concept, if combined with our proposed erasingthen-awakening approach.Empirical studies on four datasets demonstrate that our approach can awaken latent grounding which is understandable to human experts, even if it is not exposed to such labels during training.More importantly, our approach shows great potential to benefit downstream semantic parsing models.Taking text-to-SQL as a case study, we successfully couple our approach with two off-the-shelf parsers, obtaining an absolute improvement of up to 9.8%.
A Gradually Soft Multi-Task and Data-Augmented Approach to Medical Question Understanding

Users of medical question answering systems often submit long and detailed questions, making it hard to achieve high recall in answer retrieval.To alleviate this problem, we propose a novel Multi-Task Learning (MTL) method with data augmentation for medical question understanding.We first establish an equivalence between the tasks of question summarization and Recognizing Question Entailment (RQE) using their definitions in the medical domain.Based on this equivalence, we propose a data augmentation algorithm to use just one dataset to optimize for both tasks, with a weighted MTL loss.We introduce gradually soft parameter-sharing: a constraint for decoder parameters to be close, that is gradually loosened as we move to the highest layer.We show through ablation studies that our proposed novelties improve performance.Our method outperforms existing MTL methods across 4 datasets of medical question pairs, in ROUGE scores, RQE accuracy and human evaluation.Finally, we show that our method fares better than single-task learning under 4 low-resource settings.
Learning Omni-Frequency Region-adaptive Representations for Real Image Super-Resolution

Traditional single image super-resolution (SISR) methods that focus on solving single and uniform degradation (i.e., bicubic down-sampling), typically suffer from poor performance when applied into real-world low-resolution (LR) images due to the complicated realistic degradations. The key to solving this more challenging real image super-resolution (RealSR) problem lies in learning feature representations that are both informative and content-aware. In this paper, we propose a Omni-frequency Region-adaptive Network (OR-Net) to address both challenges, here we call features of all low, middle and high frequencies omni-frequency features. Specifically, we start from the frequency perspective and design a Frequency Decomposition (FD) module to separate different frequency components to comprehensively compensate the information lost for real LR image. Then, considering the different regions of real LR image have different frequency information lost, we further design a Region-adaptive Frequency Aggregation (RFA) module by leveraging dynamic convolution and spatial attention to adaptively restore frequency components for different regions. The extensive experiments endorse the high-efficient, effective, and scenario-agnostic nature of our OR-Net for RealSR.
Measuring and Increasing Context Usage in Context-Aware Machine Translation

Recent work in neural machine translation has demonstrated both the necessity and feasibility of using inter-sentential context -context from sentences other than those currently being translated.However, while many current methods present model architectures that theoretically can use this extra context, it is often not clear how much they do actually utilize it at translation time.In this paper, we introduce a new metric, conditional cross-mutual information, to quantify the usage of context by these models.Using this metric, we measure how much document-level machine translation systems use particular varieties of context.We find that target context is referenced more than source context, and that conditioning on a longer context has a diminishing effect on results.We then introduce a new, simple training method, context-aware word dropout, to increase the usage of context by context-aware models.Experiments show that our method increases context usage and that this reflects on the translation quality according to metrics such as BLEU and COMET, as well as performance on anaphoric pronoun resolution and lexical cohesion contrastive datasets. 1
Wearable Devices and Low-Power Design for Smart Health Applications: Challenges and Opportunities

Wearable devices can enable affordable and accessible smart health care services with the help of innovative low-power design and edge computing technologies. Indeed, novel wearable devices are already fueling a shift from a hospital-centric setting to more personalized home-based solutions [1]. A wide variety of miniature, flexible, and stretchable sensors enable collecting real-time data without impeding users’ daily routines. For example, inertial measurement units (IMUs) based on MEMS technology integrate a 9-axis accelerometer/gyroscope/magnetometer into a small package. Similarly, bend and stretch sensors embedded into clothes measure knee and hip angles, while biosensors track biopotentials, such as electrocardiogram (ECG) and electromyography (EMG). Then, novel edge-AI algorithms process the real-time data using low-power processors to build smart health applications ranging from health and activity monitoring to early diagnosis and prognosis [2] (Section B). One of the most critical challenges in wearable smart health applications is the stringent energy capacity imposed by size and weight constraints [2], [3]. All the required sensing, processing, and communications tasks must be performed without any manual charging or battery maintenance effort to maximize the user experience (Section C). The rest of this extended abstract discusses the challenges and potential solutions for driver applications and energy management techniques.
A Comprehensive Survey on Image Dehazing Based on Deep Learning

The presence of haze significantly reduces the quality of images. Researchers have designed a variety of algorithms for image dehazing (ID) to restore the quality of hazy images. However, there are few studies that summarize the deep learning (DL) based dehazing technologies. In this paper, we conduct a comprehensive survey on the recent proposed dehazing methods. Firstly, we conclude the commonly used datasets, loss functions and evaluation metrics. Secondly, we group the existing researches of ID into two major categories: supervised ID and unsupervised ID. The core ideas of various influential dehazing models are introduced. Finally, the open issues for future research on ID are pointed out.
Learning Transferable Parameters for Unsupervised Domain Adaptation

Unsupervised domain adaptation (UDA) enables a learning machine to adapt from a labeled source domain to an unlabeled target domain under the distribution shift. Thanks to the strong representation ability of deep neural networks, recent remarkable achievements in UDA resort to learning domain-invariant features. Intuitively, the goal is that a good feature representation and the hypothesis learned from the source domain can generalize well to the target domain. However, the learning processes of domain-invariant features and source hypotheses inevitably involve domain-specific information that would degrade the generalizability of UDA models on the target domain. The lottery ticket hypothesis proves that only partial parameters are essential for generalization. Motivated by it, we find in this paper that only partial parameters are essential for learning domain-invariant information. Such parameters are termed transferable parameters that can generalize well in UDA. In contrast, the rest parameters tend to fit domain-specific details and often cause the failure of generalization, which are termed untransferable parameters. Driven by this insight, we propose Transferable Parameter Learning (TransPar) to reduce the side effect of domain-specific information in the learning process and thus enhance the memorization of domain-invariant information. Specifically, according to the distribution discrepancy degree, we divide all parameters into transferable and untransferable ones in each training iteration. We then perform separate update rules for the two types of parameters. Extensive experiments on image classification and regression tasks (keypoint detection) show that TransPar outperforms prior arts by non-trivial margins. Moreover, experiments demonstrate that TransPar can be integrated into the most popular deep UDA networks and be easily extended to handle any data distribution shift scenarios.
Physics makes the difference: Bayesian optimization and active learning via augmented Gaussian process

Abstract Both experimental and computational methods for the exploration of structure, functionality, and properties of materials often necessitate the search across broad parameter spaces to discover optimal experimental conditions and regions of interest in the image space or parameter space of computational models. The direct grid search of the parameter space tends to be extremely time-consuming, leading to the development of strategies balancing exploration of unknown parameter spaces and exploitation towards required performance metrics. However, classical Bayesian optimization (BO) strategies based on the Gaussian process (GP) do not readily allow for the incorporation of the known physical behaviors or past knowledge. Here we explore a hybrid optimization/exploration algorithm created by augmenting the standard GP with a structured probabilistic model of the expected system’s behavior. This approach balances the flexibility of the non-parametric GP approach with a rigid structure of physical knowledge encoded into the parametric model. The fully Bayesian treatment of the latter allows additional control over the optimization via the selection of priors for the model parameters. The method is demonstrated for a noisy version of a standard univariate test function used to evaluate optimization algorithms and further extended to physical lattice models. This methodology is expected to be universally suitable for injecting prior knowledge in the form of physical models and past data in the BO framework.
A first look at developers’ live chat on Gitter

Modern communication platforms such as Gitter and Slack play an increasingly critical role in supporting software teamwork, especially in open source development.Conversations on such platforms often contain intensive, valuable information that may be used for better understanding OSS developer communication and collaboration. However, little work has been done in this regard. To bridge the gap, this paper reports a first comprehensive empirical study on developers' live chat, investigating when they interact, what community structures look like, which topics are discussed, and how they interact. We manually analyze 749 dialogs in the first phase, followed by an automated analysis of over 173K dialogs in the second phase. We find that developers tend to converse more often on weekdays, especially on Wednesdays and Thursdays (UTC), that there are three common community structures observed, that developers tend to discuss topics such as API usages and errors, and that six dialog interaction patterns are identified in the live chat communities. Based on the findings, we provide recommendations for individual developers and OSS communities, highlight desired features for platform vendors, and shed light on future research directions. We believe that the findings and insights will enable a better understanding of developers' live chat, pave the way for other researchers, as well as a better utilization and mining of knowledge embedded in the massive chat history.
A Novel Server-side Aggregation Strategy for Federated Learning in Non-IID situations

Federated learning has been a promising distributed machine learning approach in many fields like e-economic, autodriving and medical imaging for its privacy-aware manner. However, researchers have discovered that the performance of traditional federated learning approaches such as Federated Averaging (FEDAVG) declines extremely under Non-Independent and Identical (Non-IID) situations. We observed that part of the reason is the improper way of traditional federated learning’s server-side aggregation method.The contributions of clients in federated learning can be distinguished by their trained models’ validated accuracies. Based on that observation, we proposed a new federated learning algorithm, Accuracy Based Averaging (ABAVG), which improves the server-side aggregation method of traditional federated learning so that it can accelerate the convergence speed of federated learning in Non-IID situations. We extensively evaluate our proposed algorithm with FEDAVG as a baseline and we experiment on various Non-IID conditions to demonstrate the robust of our proposed algorithm. Experimental results show that the convergence speed averagely increased by 47% in Mnist dataset, 59% in Fashion-Mnist dataset and 33% in CIFAR-10 dataset in different data distributions by ABAVG.
Kernel-Target Alignment Based Fuzzy Lagrangian Twin Bounded Support Vector Machine

To improve the generalization performance, we develop a new technique for handling the impacts of outliers using Lagrangian twin bounded SVM (TBSVM) with kernel fuzzy membership values, which is termed kernel-target alignment-based fuzzy Lagrangian twin bounded support vector machine (KTA-FLTBSVM). Here, the objective functions are having L2-norm vectors of the slack variable that leads to the optimization problem more convex and yields a unique global solution. Also, the fuzzy membership values are employing the importance of data samples assigned to each sample to minimize the impacts of outlier and noise. Further, we have suggested a linearly convergent iterative approach to obtain the solution of the problem unlike in place to solve the quadratic programming problem in Twin SVM (TSVM) and TBSVM. To investigate the effectiveness of the proposed KTA-FLTBSVM, the comprehensive experiments demonstrate with other reported models on artificial datasets along with benchmark real-life publicly available datasets. Our KTA-FLTBSVM outperforms to other models in terms of better classification accuracy.
Client use of blockchain technology: exploring its (potential) impact on financial statement audits of Australian accounting firms

Purpose The paper reports on a study that investigated the (potential) impact of client use of blockchain technology on financial statement audits of Australian accounting firms. Design/methodology/approach Data were primarily collected from semi-structured interviews with a range of stakeholders including audit partners from first- and second-tier accounting firms in Australia. The interviews focused on the perceived (potential) impact of blockchain on the stages of obtain (retain) engagement, engagement planning, risk assessment, audit evidence and reporting of financial statement audits of clients that use blockchain technology. Perceptions of changes to financial statement audits were interpreted using the logics of professionalism and commercialism. Findings Australian accounting firms have either obtained or considered engagements with clients with a cryptocurrency business or that use a blockchain platform although they are a small group. There is a view that blockchain technology is distinctive and therefore poses risks not encountered before in audit engagements. These risks would most likely shift how firms plan, design audit methodologies and execute financial statement audits. The study showed that the logics of professionalism and commercialism are not conflicting but instead complementary. They present both opportunities and challenges for firms to apply and develop audit expertise in an emerging area in audit. Research limitations/implications Being an exploratory study, the findings are tentative. A case study of an audit engagement with a cryptocurrency business will add to a nuanced understanding of the challenges posed to financial statement audits by blockchain technology. Originality/value This study is novel because of its focus on the impact of an evolving technology on the stages of financial statement audits.