Reviewerly/exHarmony huggingface dataset:
Virtual reality technologies for clinical education: evaluation metrics and comparative analysis

With recent advances of Virtual Reality (VR) technology, the deployment of such will dramatically increase in non-entertainment environments, such as professional education and training, manufacturing, service, or low frequency/high-risk scenarios. Clinical education is an area that especially stands to benefit from VR technology due to the complexity, high cost, and difficult logistics. The effectiveness of the deployment of VR systems in healthcare environments is subject to factors that may not be necessarily considered for devices targeting the entertainment market. In this work, we systematically compare a wide range of VR head-mounted displays (HMDs) technologies and designs by defining a new set of metrics that are 1) relevant to medical VR solutions and 2) are of paramount importance for VR-based education and training. We evaluated 10 technologies based on various criteria, including neck strain, heat development, cleanability, and colour accuracy. Other metrics such as text readability, comfort, and contrast perception were evaluated in a multi-user study on three selected technologies, namely the ones offered by Oculus Rift S, HTC Vive Pro and Samsung Odyssey+. Results indicate that the HTC Vive Pro performs best with regards to comfort, display quality and compatibility with glasses.
Style Attuned Pre-Training and Parameter Efficient Fine-Tuning for Spoken Language Understanding

Neural models have yielded state-of-the-art results in deciphering spoken language understanding (SLU) problems; however, these models require a significant amount of domain-specific labeled examples for training, which is prohibitively expensive. While pre-trained language models like BERT have been shown to capture a massive amount of knowledge by learning from unlabeled corpora and solve SLU using fewer labeled examples for adaption, the encoding of knowledge is implicit and agnostic to downstream tasks. Such encoding results in model inefficiencies in parameter usage: an entirely new model is required for every domain. To address these challenges, we introduce a novel SLU framework, comprising a conversational language modeling (CLM) pre-training task and a light encoder architecture. The CLM pre-training enables networks to capture the representation of the language in conversation style with the presence of ASR errors. The light encoder architecture separates the shared pre-trained networks from the mappings of generally encoded knowledge to specific domains of SLU, allowing for the domain adaptation to be performed solely at the light encoder and thus increasing efficiency. With the framework, we match the performance of state-of-the-art SLU results on Alexa internal datasets and on two public ones (ATIS, SNIPS), adding only 4.4% parameters per task.
Improving Low Resource Code-Switched ASR Using Augmented Code-Switched TTS

Building Automatic Speech Recognition (ASR) systems for code-switched speech has recently gained renewed attention due to the widespread use of speech technologies in multilingual communities worldwide. End-to-end ASR systems are a natural modeling choice due to their ease of use and superior performance in monolingual settings. However, it is well known that end-to-end systems require large amounts of labeled speech. In this work, we investigate improving code-switched ASR in low resource settings via data augmentation using code-switched text-to-speech (TTS) synthesis. We propose two targeted techniques to effectively leverage TTS speech samples: 1) Mixup, an existing technique to create new training samples via linear interpolation of existing samples, applied to TTS and real speech samples, and 2) a new loss function, used in conjunction with TTS samples, to encourage code-switched predictions. We report significant improvements in ASR performance achieving absolute word error rate (WER) reductions of up to 5%, and measurable improvement in code switching using our proposed techniques on a Hindi-English code-switched ASR task.
ClovaCall: Korean Goal-Oriented Dialog Speech Corpus for Automatic Speech Recognition of Contact Centers

Automatic speech recognition (ASR) via call is essential for various applications, including AI for contact center (AICC) services. Despite the advancement of ASR, however, most publicly available call-based speech corpora such as Switchboard are old-fashioned. Also, most existing call corpora are in English and mainly focus on open domain dialog or general scenarios such as audiobooks. Here we introduce a new large-scale Korean call-based speech corpus under a goal-oriented dialog scenario from more than 11,000 people, i.e., ClovaCall corpus. ClovaCall includes approximately 60,000 pairs of a short sentence and its corresponding spoken utterance in a restaurant reservation domain. We validate the effectiveness of our dataset with intensive experiments using two standard ASR models. Furthermore, we release our ClovaCall dataset and baseline source codes to be available via this https URL.
Review on Superpixel Generation Algorithms Based on Clustering

Superpixel is often used in image preprocessing, which is generated by the feature similarity between pixels. Superpixel can capture redundant information of image and reduce dimension without sacrificing too much precision. These advantages make it receive more and more attention from researchers at home and abroad. In this paper, we summarize the methods of generating superpixel based on clustering iteration and hope to provide some reference for related researchers. On the basis of extensive literature research, this paper firstly introduces the research status of the superpixel generation algorithm, combines with the basic ideas, innovations, advantages and disadvantages of various methods, and focuses on the induction and discussion of the superpixel generation algorithm based on clustering. We then compare the current common superpixel generation algorithms based on clustering and provide the performance and analysis results on the data set. At last, we introduce the research direction of superpixel technology in the future.
Efficent Lane Change Path Planning based on Quintic spline for Autonomous Vehicles

This paper proposes a lane change path planning method for Autonomous Vehicles (AVs) driving in structured environments. Due to the complexity of dynamically changing structured environment, the path planner generally requires to generate a safe and smooth path in a short time when changing lanes. Therefore, we propose an efficient lane change path planning method, which can take into account both the curvature and heading angle of the planned path, ensuring the safety and stability of the lane change maneuver. It builds upon existing method using quintic spline with second-order geometric (G <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -) continuity in a Frenet coordinate system. By calculating the optimal prediction time interval of the lane change maneuver, the heading angle was kept within a safe range. In order to improve the real-time performance, we use road prior information to optimize the selection of sampling points, effectively reducing the number of candidate paths. In addition, beyond the sampling points, the problem of continuous lane change is solved by adding mid-adjust point and target point, so that AVs have the ability to complete complex movements. In an urban environment, the proposed method is tested on our experimental autonomous vehicle equipped with a modular ROS architecture. Experimental results show that the proposed algorithm can generate complex motion paths efficiently, and the curvature and heading angle of the planned path are continuous, which effectively improves the efficiency and stability of path planning.
Characterizing the Value of Information in Medical Notes

Machine learning models depend on the quality of input data.As electronic health records are widely adopted, the amount of data in health care is growing, along with complaints about the quality of medical notes.We use two prediction tasks, readmission prediction and in-hospital mortality prediction, to characterize the value of information in medical notes.We show that as a whole, medical notes only provide additional predictive power over structured information in readmission prediction.We further propose a probing framework to select parts of notes that enable more accurate predictions than using all notes, despite that the selected information leads to a distribution shift from the training data ("all notes").Finally, we demonstrate that models trained on the selected valuable information achieve even better predictive performance, with only 6.8% of all the tokens for readmission prediction.
Research on Lightweight Design of Automobile Collision Safety Structure Based on Multiple Materials

Abstract With the increase in car ownership and the increasing pressure of energy conservation and emission reduction, the lightweighting of cars has become an important development direction for traditional cars to reduce emissions and increase the endurance of electric vehicles. Aiming at the lightweight design of automobile crash safety structure, this paper proposes a variable section and multiple materials vehicle lightweight design framework based on collision safety. Taking a certain type of racing car frame as the research object, the lightweight design is carried out, and the optimal design scheme of racing car frame with good collision safety performance is obtained. Taking the lightest frame mass as the design goal, the optimal latin square design and response surface model are used to optimize the thickness of each pipe frame, and the lightweight frame optimization scheme based on the improvement of collision safety performance is obtained. Finally, under the premise that the peak acceleration of the cockpit is reduced by 20.02% and the amount of intrusion at the brake pedal is reduced by 25.31%, weight reduction of 14.38% is achieved. Based on the actual engineering situation, this paper constructs a lightweight design framework for automobiles based on collision safety, and provides an efficient optimization process for lightweight design of automobiles.
SeNsER: Learning Cross-Building Sensor Metadata Tagger

Sensor metadata tagging, akin to the named entity recognition task, provides key contextual information (e.g., measurement type and location) about sensors for running smart building applications.Unfortunately, sensor metadata in different buildings often follows distinct naming conventions.Therefore, learning a tagger currently requires extensive annotations on a per building basis.In this work, we propose a novel framework, SeNsER, which learns a sensor metadata tagger for a new building based on its raw metadata and some existing fully annotated building.It leverages the commonality between different buildings: At the character level, it employs bidirectional neural language models to capture the shared underlying patterns between two buildings and thus regularizes the feature learning process; At the word level, it leverages as features the k-mers existing in the fully annotated building.During inference, we further incorporate the information obtained from sources such as Wikipedia as prior knowledge.As a result, SeNsER shows promising results in extensive experiments on multiple real-world buildings.
Image Segmentation and the Projections of Graphic Centred Approaches in Medical Image Processing

– Medical image segmentation is considered the most precarious element in the analysis and processing of real-life images in the clinical sector. Actually, segmentation effects affect the subsequent procedures of image evaluation, life object illustration and description, feature dimensions and the subsequent considerable task levels such as object categorization. In that case, medical image segmentation is the most essential and crucial aspect for aiding the visualization, delineation and depiction of regions of interest for any particular image. The aspect of physical segmentation of a picture is not just challenging and time-consuming task to do, but also problematic to assure accuracy considering the wide-range image modalities and unguided image quantities which have to be observed. In that case, it now becomes fundamental to evaluate the present approaches of image segmentation based on the application of computing algorithms which require the interaction of users with evaluating medical images. In the process of image segmentation, an anatomic classification requires to be extracted or defined to be projected effectively and independently. In that case, this contribution is focused on image segmentation to extract details for decision-making in the clinical sector. The paper presents generalized and relative techniques that have been categorized into three groups: pixel-centre, edge-centred and region-centred techniques. The paper also provides a highlight of the strengths and weaknesses of these techniques in reference to the appropriateness of a wide-range application in medical image segmentation.
One Solution is Not All You Need: Few-Shot Extrapolation via Structured MaxEnt RL

While reinforcement learning algorithms can learn effective policies for complex tasks, these policies are often brittle to even minor task variations, especially when variations are not explicitly provided during training. One natural approach to this problem is to train agents with manually specified variation in the training task or environment. However, this may be infeasible in practical situations, either because making perturbations is not possible, or because it is unclear how to choose suitable perturbation strategies without sacrificing performance. The key insight of this work is that learning diverse behaviors for accomplishing a task can directly lead to behavior that generalizes to varying environments, without needing to perform explicit perturbations during training. By identifying multiple solutions for the task in a single environment during training, our approach can generalize to new situations by abandoning solutions that are no longer effective and adopting those that are. We theoretically characterize a robustness set of environments that arises from our algorithm and empirically find that our diversity-driven approach can extrapolate to various changes in the environment and task.
Learning the Synthesizability of Dynamic Texture Samples

A dynamic texture (DT) refers to a sequence of images that exhibit temporal regularities and has many applications in computer vision and graphics. Given an exemplar of dynamic texture, it is a dynamic but challenging task to generate new samples with high quality that are perceptually similar to the input exemplar, which is known to be {\em example-based dynamic texture synthesis (EDTS)}. Numerous approaches have been devoted to this problem, in the past decades, but none them are able to tackle all kinds of dynamic textures equally well. In this paper, we investigate the synthesizability of dynamic texture samples: {\em given a dynamic texture sample, how synthesizable it is by using EDTS, and which EDTS method is the most suitable to synthesize it?} To this end, we propose to learn regression models to connect dynamic texture samples with synthesizability scores, with the help of a compiled dynamic texture dataset annotated in terms of synthesizability. More precisely, we first define the synthesizability of DT samples and characterize them by a set of spatiotemporal features. Based on these features and an annotated dynamic texture dataset, we then train regression models to predict the synthesizability scores of texture samples and learn classifiers to select the most suitable EDTS methods. We further complete the selection, partition and synthesizability prediction of dynamic texture samples in a hierarchical scheme. We finally apply the learned synthesizability to detecting synthesizable regions in videos. The experiments demonstrate that our method can effectively learn and predict the synthesizability of DT samples.
Provably Efficient Reward-Agnostic Navigation with Linear Value Iteration

There has been growing progress on theoretical analyses for provably efficient learning in MDPs with linear function approximation, but much of the existing work has made strong assumptions to enable exploration by conventional exploration frameworks. Typically these assumptions are stronger than what is needed to find good solutions in the batch setting. In this work, we show how under a more standard notion of low inherent Bellman error, typically employed in least-square value iteration-style algorithms, we can provide strong PAC guarantees on learning a near optimal value function provided that the linear space is sufficiently explorable. We present a computationally tractable algorithm for the reward-free setting and show how it can be used to learn a near optimal policy for any (linear) reward function, which is revealed only once learning has completed. If this reward function is also estimated from the samples gathered during pure exploration, our results also provide same-order PAC guarantees on the performance of the resulting policy for this setting.
Sequential Span Classification with Neural Semi-Markov CRFs for Biomedical Abstracts

Dividing biomedical abstracts into several segments with rhetorical roles is essential for supporting researchers' information access in the biomedical domain.Conventional methods have regarded the task as a sequence labeling task based on sequential sentence classification, i.e., they assign a rhetorical label to each sentence by considering the context in the abstract.However, these methods have a critical problem: they are prone to mislabel longer continuous sentences with the same rhetorical label.To tackle the problem, we propose sequential span classification that assigns a rhetorical label, not to a single sentence but to a span that consists of continuous sentences.Accordingly, we introduce Neural Semi-Markov Conditional Random Fields to assign the labels to such spans by considering all possible spans of various lengths.Experimental results obtained from PubMed 20k RCT and NICTA-PIBOSO datasets demonstrate that our proposed method achieved the best micro sentence-F 1 score as well as the best micro span-F 1 score.<B> <E> 3 4 5 Semi-Markov CRFs B M R C 1 2 W • v span(3,4) = 0 B B B B @4.5 0.9 2.7 6.3 1.
Speaker or Listener? The Role of a Dialog Agent

For decades, chitchat bots are designed as a listener to passively answer what people ask. This passive and relatively simple dialogue mechanism gains less attention from humans and consumes the interests of human beings rapidly. Therefore some recent researches attempt to endow the bots with proactivity through external knowledge to transform the role from a listener to a speaker with a hypothesis that the speaker expresses more just like a knowledge disseminator. However, along with the proactive manner introduced into a dialogue agent, an issue arises that, with too many knowledge facts to express, the agent starts to talks endlessly, and even completely ignores what the other expresses in dialogue sometimes, which greatly harms the interest of the other chatter to continue the conversation. To the end, we propose a novel model named Initiative-Imitate to interact with adaptive initiative throughout a dialogue. It forces the agent to express in parallel with the appropriate role during the whole conversation. The corresponding experiments show the proposed Initiative-Imitate obtains competitive results both on the automatic and manual metrics. And the fluency and engagement of the chatbot have also been improved significantly. Besides, the case study indicates the Initiative-Imitate can constantly transfer to appropriate role timely and response more properly during the whole continuous conversation.
Fitness for Use of Data Objects Described with Quality Maturity Matrix at Different Phases of Data Production

Fitness for use information should be stored to enable easy identification of data objects that are suitable for re-use – a feature which can only be assessed by the data user. With the described Quality Maturity Matrix (QMM), we want to provide a metric for a discrete measurement of the fitness for use of data objects. We use the data maturity to describe the degree of formalization and standardization of the data with respect to the quality of data and metadata. The data objects mature as they pass through the different post-production steps where they undergo different curation measures. The higher the maturity and the level in the QMM, the easier is it for the user to judge the appropriateness of the data for a possible re-use. For our development of the Quality Maturity Matrix we link the maturity levels to the five phases concept, production/processing, project collaboration/intended use, long-term archiving, and impact re-use. Each of the five levels is measured with regard to the four criteria consistency, completeness, accessibility, and accuracy. For the description we use the terms of the Open Archival Information System (OAIS). We relate our data focused QMM to some existing maturity matrices which put the focus on the maturity of the curation process rather than of the data objects themselves. In addition, we make an attempt to establish a connection between the QMM criteria of data assessment and the FAIR Data principles.
GUIR @ LongSumm 2020: Learning to Generate Long Summaries from Scientific Documents

This paper presents our methods for the LongSumm 2020: Shared Task on Generating Long Summaries for Scientific Documents, where the task is to generatelong summaries given a set of scientific papers provided by the organizers. We explore 3 main approaches for this task: 1. An extractive approach using a BERT-based summarization model; 2. A two stage model that additionally includes an abstraction step using BART; and 3. A new multi-tasking approach on incorporating document structure into the summarizer. We found that our new multi-tasking approach outperforms the two other methods by large margins. Among 9 participants in the shared task, our best model ranks top according to Rouge-1 score (53.11%) while staying competitive in terms of Rouge-2.
A hybrid de-noising method for mammogram images

&lt;span id="docs-internal-guid-0cd3701c-7fff-a024-86c4-cd67dde5c463"&gt;&lt;span&gt;In general, mammogram images contaminated with noise which directly affect images quality. Several methods have been proposed to de-noise these images, however, there is always a risk of losing valuable information. In order to overcome the loss of information, the present study proposed a hybrid denoising method for mammogram images. The proposed hybrid method works in two steps: Firstly, preprocessing with mathematical morphology was applied for image enhancement. Secondly, global unsymmetrical trimmed median filter (GUTM) is applied to de-noise image. Experimental results prove that proposed method work well for mammogram images. Hence, the study provided an alternative method for denoising mammogram images.&lt;/span&gt;&lt;/span&gt;