Reviewerly/exHarmony huggingface dataset:
Levenshtein Transformer

Modern neural sequence generation models are built to either generate tokens step-by-step from scratch or (iteratively) modify a sequence of tokens bounded by a fixed length. In this work, we develop Levenshtein Transformer, a new partially autoregressive model devised for more flexible and amenable sequence generation. Unlike previous approaches, the basic operations of our model are insertion and deletion. The combination of them facilitates not only generation but also sequence refinement allowing dynamic length changes. We also propose a set of new training techniques dedicated at them, effectively exploiting one as the other's learning signal thanks to their complementary nature. Experiments applying the proposed model achieve comparable or even better performance with much-improved efficiency on both generation (e.g. machine translation, text summarization) and refinement tasks (e.g. automatic post-editing). We further confirm the flexibility of our model by showing a Levenshtein Transformer trained by machine translation can straightforwardly be used for automatic post-editing.
ANU-Net: Attention-based nested U-Net to exploit full resolution features for medical image segmentation

Organ cancer have a high mortality rate. In order to help doctors diagnose and treat organ lesion, an automatic medical image segmentation model is urgently needed as manually segmentation is time-consuming and error-prone. However, automatic segmentation of target organ from medical images is a challenging task because of organ’s uneven and irregular shapes. In this paper, we propose an attention-based nested segmentation network, named ANU-Net. Our proposed network has a deep supervised encoder-decoder architecture and a redesigned dense skip connection. ANU-Net introduces attention mechanism between nested convolutional blocks so that the features extracted at different levels can be merged with a task-related selection. Besides, we redesign a hybrid loss function combining with three kinds of losses to make full use of full resolution feature information. We evaluated proposed model on MICCAI 2017 Liver Tumor Segmentation (LiTS) Challenge Dataset and ISBI 2019 Combined Healthy Abdominal Organ Segmentation (CHAOS) Challenge. ANU-Net achieved very competitive performance for four kinds of medical image segmentation tasks.
Artificial intelligence system reduces false-positive findings in the interpretation of breast ultrasound exams

Abstract Though consistently shown to detect mammographically occult cancers, breast ultrasound has been noted to have high false-positive rates. In this work, we present an AI system that achieves radiologist-level accuracy in identifying breast cancer in ultrasound images. Developed on 288,767 exams, consisting of 5,442,907 B-mode and Color Doppler images, the AI achieves an area under the receiver operating characteristic curve (AUROC) of 0.976 on a test set consisting of 44,755 exams. In a retrospective reader study, the AI achieves a higher AUROC than the average of ten board-certified breast radiologists (AUROC: 0.962 AI, 0.924 ± 0.02 radiologists). With the help of the AI, radiologists decrease their false positive rates by 37.3% and reduce requested biopsies by 27.8%, while maintaining the same level of sensitivity. This highlights the potential of AI in improving the accuracy, consistency, and efficiency of breast ultrasound diagnosis.
VR Locomotion in the New Era of Virtual Reality: An Empirical Comparison of Prevalent Techniques

The latest technical and interaction advancements within the virtual reality (VR) field have marked a new era, not only for VR, but also for VR locomotion. In this era, well-established, prevalent VR locomotion techniques are mostly used as points of comparison for benchmarking of new VR locomotion designs. At the same time, there is the need for more exploratory, comparative studies of contemporary VR locomotion techniques, so that their distinguished interaction aspects can be documented and guide the design process of new techniques. This article presents a comparative, empirical evaluation study of contemporary and prevalent VR locomotion techniques, examining the user experience (UX) they offer. First, the prevalent VR locomotion techniques are identified based on literature, i.e., walking-in-place, controller/joystick, and teleportation. Twenty-six adults are enrolled in the study and perform a game-like task using the techniques. The study follows a mixed methods approach, utilising the System Usability Scale survey, the Game Experience Questionnaire, and a semistructured interview to assess user experiences. Results indicate that the walking-in-place technique offers the highest immersion but also presents high levels of psychophysical discomfort. Controller/joystick VR locomotion is perceived as easy-to-use due to the users’ familiarity with controllers, whereas teleportation is considered to be effective due to its fast navigation, although its visual ‘jumps’ do break the users’ sense of immersion. Based on the interviews, the users focused on the following interaction dimensions to describe their VR locomotion experiences: (i) immersion and flow, (ii) ease-of-use and mastering, (iii) competence and sense of effectiveness, and (iv) psychophysical discomfort. The study implications for VR locomotion are discussed, along with the study limitations and the future direction for research.
Improved Deep Hashing With Soft Pairwise Similarity for Multi-Label Image Retrieval

Hash coding has been widely used in the approximate nearest neighbor search for large-scale image retrieval. Recently, many deep hashing methods have been proposed and shown largely improved performance over traditional feature-learning methods. Most of these methods examine the pairwise similarity on the semantic-level labels, where the pairwise similarity is generally defined in a hard-assignment way. That is, the pairwise similarity is “1” if they share no less than one class label and “0” if they do not share any. However, such similarity definition cannot reflect the similarity ranking for pairwise images that hold multiple labels. In this paper, an improved deep hashing method is proposed to enhance the ability of multi-label image retrieval. We introduce a pairwise quantified similarity calculated on the normalized semantic labels. Based on this, we divide the pairwise similarity into two situations-“hard similarity” and “soft similarity,” where cross-entropy loss and mean square error loss are adapted respectively for more robust feature learning and hash coding. Experiments on four popular datasets demonstrate that the proposed method outperforms the competing methods and achieves the state-of-the-art performance in multi-label image retrieval.
Word2vec convolutional neural networks for classification of news articles and tweets

Big web data from sources including online news and Twitter are good resources for investigating deep learning. However, collected news articles and tweets almost certainly contain data unnecessary for learning, and this disturbs accurate learning. This paper explores the performance of word2vec Convolutional Neural Networks (CNNs) to classify news articles and tweets into related and unrelated ones. Using two word embedding algorithms of word2vec, Continuous Bag-of-Word (CBOW) and Skip-gram, we constructed CNN with the CBOW model and CNN with the Skip-gram model. We measured the classification accuracy of CNN with CBOW, CNN with Skip-gram, and CNN without word2vec models for real news articles and tweets. The experimental results indicated that word2vec significantly improved the accuracy of the classification model. The accuracy of the CBOW model was higher and more stable when compared to that of the Skip-gram model. The CBOW model exhibited better performance on news articles, and the Skip-gram model exhibited better performance on tweets. Specifically, CNN with word2vec models was more effective on news articles when compared to that on tweets because news articles are typically more uniform when compared to tweets.
Hierarchical Long Short-Term Concurrent Memory for Human Interaction Recognition

In this work, we aim to address the problem of human interaction recognition in videos by exploring the long-term inter-related dynamics among multiple persons. Recently, Long Short-Term Memory (LSTM) has become a popular choice to model individual dynamic for single-person action recognition due to its ability to capture the temporal motion information in a range. However, most existing LSTM-based methods focus only on capturing the dynamics of human interaction by simply combining all dynamics of individuals or modeling them as a whole. Such methods neglect the inter-related dynamics of how human interactions change over time. To this end, we propose a novel Hierarchical Long Short-Term Concurrent Memory (H-LSTCM) to model the long-term inter-related dynamics among a group of persons for recognizing human interactions. Specifically, we first feed each person's static features into a Single-Person LSTM to model the single-person dynamic. Subsequently, at one time step, the outputs of all Single-Person LSTM units are fed into a novel Concurrent LSTM (Co-LSTM) unit, which mainly consists of multiple sub-memory units, a new cell gate, and a new co-memory cell. In the Co-LSTM unit, each sub-memory unit stores individual motion information, while this Co-LSTM unit selectively integrates and stores inter-related motion information between multiple interacting persons from multiple sub-memory units via the cell gate and co-memory cell, respectively. Extensive experiments on several public datasets validate the effectiveness of the proposed H-LSTCM by comparing against baseline and state-of-the-art methods.
Novel artificial intelligence system increases the detection of prostate cancer in whole slide images of core needle biopsies

Prostate cancer (PrCa) is the second most common cancer among men in the United States. The gold standard for detecting PrCa is the examination of prostate needle core biopsies. Diagnosis can be challenging, especially for small, well-differentiated cancers. Recently, machine learning algorithms have been developed for detecting PrCa in whole slide images (WSIs) with high test accuracy. However, the impact of these artificial intelligence systems on pathologic diagnosis is not known. To address this, we investigated how pathologists interact with Paige Prostate Alpha, a state-of-the-art PrCa detection system, in WSIs of prostate needle core biopsies stained with hematoxylin and eosin. Three AP-board certified pathologists assessed 304 anonymized prostate needle core biopsy WSIs in 8 hours. The pathologists classified each WSI as benign or cancerous. After ~4 weeks, pathologists were tasked with re-reviewing each WSI with the aid of Paige Prostate Alpha. For each WSI, Paige Prostate Alpha was used to perform cancer detection and, for WSIs where cancer was detected, the system marked the area where cancer was detected with the highest probability. The original diagnosis for each slide was rendered by genitourinary pathologists and incorporated any ancillary studies requested during the original diagnostic assessment. Against this ground truth, the pathologists and Paige Prostate Alpha were measured. Without Paige Prostate Alpha, pathologists had an average sensitivity of 74% and an average specificity of 97%. With Paige Prostate Alpha, the average sensitivity for pathologists significantly increased to 90% with no statistically significant change in specificity. With Paige Prostate Alpha, pathologists more often correctly classified smaller, lower grade tumors, and spent less time analyzing each WSI. Future studies will investigate if similar benefit is yielded when such a system is used to detect other forms of cancer in a setting that more closely emulates real practice.
The impact of employee satisfaction on customer satisfaction: Theoretical and empirical underpinning

Employee satisfaction is significant when it comes to define organizational success, particularly in the service industry.The need to enhance employee satisfaction is critical because it is the key to better business operations as it increases long-term employee productivity and retains profitable customers.The purpose of this study is to observe and test practically the relationship between employee satisfaction and customer satisfaction.The study discusses five employee variables that impact on customer satisfaction, namely, communication and rewards as well as employee loyalty, retention and commitment.A set of hypotheses were then developed theoretically and tested practically using the SEM-PLS approach.In conclusion, it was found that customer satisfaction had a causal relationship with employee satisfaction and an understanding of the employees' satisfaction role was extremely important in this context.The paper also discusses further findings from the study as well as suggests future related research areas.
Object as Hotspots: An Anchor-Free 3D Object Detection Approach via Firing of Hotspots

Accurate 3D object detection in LiDAR based point clouds suffers from the challenges of data sparsity and irregularities. Existing methods strive to organize the points regularly, e.g. voxelize, pass them through a designed 2D/3D neural network, and then define object-level anchors that predict offsets of 3D bounding boxes using collective evidences from all the points on the objects of interest. Contrary to the state-of-the-art anchor-based methods, based on the very nature of data sparsity, we observe that even points on an individual object part are informative about semantic information of the object. We thus argue in this paper for an approach opposite to existing methods using object-level anchors. Inspired by compositional models, which represent an object as parts and their spatial relations, we propose to represent an object as composition of its interior non-empty voxels, termed hotspots, and the spatial relations of hotspots. This gives rise to the representation of Object as Hotspots (OHS). Based on OHS, we further propose an anchor-free detection head with a novel ground truth assignment strategy that deals with inter-object point-sparsity imbalance to prevent the network from biasing towards objects with more points. Experimental results show that our proposed method works remarkably well on objects with a small number of points. Notably, our approach ranked $$1^{st}$$ on KITTI 3D Detection Benchmark for cyclist and pedestrian detection, and achieved state-of-the-art performance on NuScenes 3D Detection Benchmark.
A quantitative discriminant method of elbow point for the optimal number of clusters in clustering algorithm

Abstract Clustering, a traditional machine learning method, plays a significant role in data analysis. Most clustering algorithms depend on a predetermined exact number of clusters, whereas, in practice, clusters are usually unpredictable. Although the Elbow method is one of the most commonly used methods to discriminate the optimal cluster number, the discriminant of the number of clusters depends on the manual identification of the elbow points on the visualization curve. Thus, experienced analysts cannot clearly identify the elbow point from the plotted curve when the plotted curve is fairly smooth. To solve this problem, a new elbow point discriminant method is proposed to yield a statistical metric that estimates an optimal cluster number when clustering on a dataset. First, the average degree of distortion obtained by the Elbow method is normalized to the range of 0 to 10. Second, the normalized results are used to calculate the cosine of intersection angles between elbow points. Third, this calculated cosine of intersection angles and the arccosine theorem are used to compute the intersection angles between elbow points. Finally, the index of the above-computed minimal intersection angles between elbow points is used as the estimated potential optimal cluster number. The experimental results based on simulated datasets and a well-known public dataset (Iris Dataset) demonstrated that the estimated optimal cluster number obtained by our newly proposed method is better than the widely used Silhouette method.
Online Foreign Language Learning in Higher Education and Its Correlates during the COVID-19 Pandemic

During the COVID-19 pandemic, the online learning of foreign languages at higher education level has represented a way to adapt to the restrictions imposed worldwide. The aim of the present article is to analyse university students’ behaviours, emotions and perceptions associated to online foreign language learning during the pandemic and their correlates by using a mixed approach. The research used the Foreign Language Enjoyment (FLE) scale and tools developed by the authors, focusing on task value, self-perceived foreign language proficiency, stressors and responses in online foreign language learning during the pandemic. Some of the results, such as the negative association between anxiety and FLE, are consistent with those revealed in studies conducted in normal times. Other results are novel, such as the protective role of retrospective enjoyment in trying times or the higher level of enjoyment with lower-achieving students. Reference is made to students’ preferences for certain online resources during the pandemic (e.g., preference for PowerPoint presentations) and to their opinions regarding the use of entirely or partially online foreign language teaching in the post-COVID period. The quantitative results are fostered by the respondents’ voices in the qualitative research. The consequences of these results are discussed with respect to the teacher-student relationship in the online environment and to the implications for sustainable online foreign language learning.
WARP: Word-level Adversarial ReProgramming

Transfer learning from pretrained language models recently became the dominant approach for solving many NLP tasks.A common approach to transfer learning for multiple tasks that maximize parameter sharing trains one or more task-specific layers on top of the language model.In this paper, we present an alternative approach based on adversarial reprogramming, which extends earlier work on automatic prompt generation.Adversarial reprogramming attempts to learn task-specific word embeddings that, when concatenated to the input text, instruct the language model to solve the specified task.Using up to 25K trainable parameters per task, this approach outperforms all existing methods with up to 25M trainable parameters on the public leaderboard of the GLUE benchmark.Our method, initialized with task-specific human-readable prompts, also works in a few-shot setting, outperforming GPT-3 on two SuperGLUE tasks with just 32 training samples.
One Time of Interaction May Not Be Enough: Go Deep with an Interaction-over-Interaction Network for Response Selection in Dialogues

Currently, researchers have paid great attention to retrieval-based dialogues in opendomain.In particular, people study the problem by investigating context-response matching for multi-turn response selection based on publicly recognized benchmark data sets.State-of-the-art methods require a response to interact with each utterance in a context from the beginning, but the interaction is performed in a shallow way.In this work, we let utterance-response interaction go deep by proposing an interaction-over-interaction network (IoI).The model performs matching by stacking multiple interaction blocks in which residual information from one time of interaction initiates the interaction process again.Thus, matching information within an utterance-response pair is extracted from the interaction of the pair in an iterative fashion, and the information flows along the chain of the blocks via representations.Evaluation results on three benchmark data sets indicate that IoI can significantly outperform state-of-theart methods in terms of various matching metrics.Through further analysis, we also unveil how the depth of interaction affects the performance of IoI.
Spatial and Temporal Mutual Promotion for Video-Based Person Re-Identification

Video-based person re-identification is a crucial task of matching video sequences of a person across multiple camera views. Generally, features directly extracted from a single frame suffer from occlusion, blur, illumination and posture changes. This leads to false activation or missing activation in some regions, which corrupts the appearance and motion representation. How to explore the abundant spatial-temporal information in video sequences is the key to solve this problem. To this end, we propose a Refining Recurrent Unit (RRU) that recovers the missing parts and suppresses noisy parts of the current frame’s features by referring historical frames. With RRU, the quality of each frame’s appearance representation is improved. Then we use the Spatial-Temporal clues Integration Module (STIM) to mine the spatial-temporal information from those upgraded features. Meanwhile, the multilevel training objective is used to enhance the capability of RRU and STIM. Through the cooperation of those modules, the spatial and temporal features mutually promote each other and the final spatial-temporal feature representation is more discriminative and robust. Extensive experiments are conducted on three challenging datasets, i.e., iLIDS-VID, PRID-2011 and MARS. The experimental results demonstrate that our approach outperforms existing state-of-the-art methods of video-based person re-identification on iLIDS-VID and MARS and achieves favorable results on PRID-2011.
Can You Unpack That? Learning to Rewrite Questions-in-Context

Question answering is an AI-complete problem, but existing datasets lack key elements of language understanding such as coreference and ellipsis resolution.We consider sequential question answering: multiple questions are asked one-by-one in a conversation between a questioner and an answerer.Answering these questions is only possible through understanding the conversation history.We introduce the task of question-in-context rewriting: given the context of a conversation's history, rewrite a context-dependent into a selfcontained question with the same answer.We construct, CANARD, a dataset of 40,527 questions based on QUAC (Choi et al., 2018) and train Seq2Seq models for incorporating context into standalone questions.
Evaluating The Search Phase of Neural Architecture Search

Neural Architecture Search (NAS) aims to facilitate the design of deep networks for new tasks. Existing techniques rely on two stages: searching over the architecture space and validating the best architecture. NAS algorithms are currently compared solely based one their results on the downstream task. While intuitive, this fails to explicitly evaluate the effectiveness of their search strategies. In this paper, we propose to evaluate the NAS search phase. To this end, we compare the quality of the solutions obtained by NAS search policies with that of random architecture selection. We find that: (i) On average, the state-of-the-art NAS algorithms perform similarly to the random policy; (ii) the widely-used weight sharing strategy degrades the ranking of the NAS candidates to the point of not reflecting their true performance, thus reducing the effectiveness of the search process. We believe that our evaluation framework will be key to designing NAS strategies that consistently discover architectures superior to random ones.