Reviewerly/exHarmony huggingface dataset:
Decentralized Authentication of Distributed Patients in Hospital Networks Using Blockchain

In any interconnected healthcare system (e.g., those that are part of a smart city), interactions between patients, medical doctors, nurses and other healthcare practitioners need to be secure and efficient. For example, all members must be authenticated and securely interconnected to minimize security and privacy breaches from within a given network. However, introducing security and privacy-preserving solutions can also incur delays in processing and other related services, potentially threatening patients lives in critical situations. A considerable number of authentication and security systems presented in the literature are centralized, and frequently need to rely on some secure and trusted third-party entity to facilitate secure communications. This, in turn, increases the time required for authentication and decreases throughput due to known overhead, for patients and inter-hospital communications. In this paper, we propose a novel decentralized authentication of patients in a distributed hospital network, by leveraging blockchain. Our notion of a healthcare setting includes patients and allied health professionals (medical doctors, nurses, technicians, etc), and the health information of patients. Findings from our in-depth simulations demonstrate the potential utility of the proposed architecture. For example, it is shown that the proposed architecture's decentralized authentication among a distributed affiliated hospital network does not require re-authentication. This improvement will have a considerable impact on increasing throughput, reducing overhead, improving response time, and decreasing energy consumption in the network. We also provide a comparative analysis of our model in relation to a base model of the network without blockchain to show the overall effectiveness of our proposed solution.
Single-shot absolute 3D shape measurement with deep-learning-based color fringe projection profilometry

Recovering the high-resolution three-dimensional (3D) surface of an object from a single frame image has been the ultimate goal long pursued in fringe projection profilometry (FPP). The color fringe projection method is one of the technologies with the most potential towards such a goal due to its three-channel multiplexing properties. However, the associated color imbalance, crosstalk problems, and compromised coding strategy remain major obstacles to overcome. Inspired by recent successes of deep learning for FPP, we propose a single-shot absolute 3D shape measurement with deep-learning-based color FPP. Through “learning” on extensive data sets, the properly trained neural network can “predict” the high-resolution, motion-artifact-free, crosstalk-free absolute phase directly from one single color fringe image. Compared with the traditional approach, our method allows for more accurate phase retrieval and more robust phase unwrapping. Experimental results demonstrate that the proposed approach can provide high-accuracy single-frame absolute 3D shape measurement for complicated objects.
Multi-View Clustering in Latent Embedding Space

Previous multi-view clustering algorithms mostly partition the multi-view data in their original feature space, the efficacy of which heavily and implicitly relies on the quality of the original feature presentation. In light of this, this paper proposes a novel approach termed Multi-view Clustering in Latent Embedding Space (MCLES), which is able to cluster the multi-view data in a learned latent embedding space while simultaneously learning the global structure and the cluster indicator matrix in a unified optimization framework. Specifically, in our framework, a latent embedding representation is firstly discovered which can effectively exploit the complementary information from different views. The global structure learning is then performed based on the learned latent embedding representation. Further, the cluster indicator matrix can be acquired directly with the learned global structure. An alternating optimization scheme is introduced to solve the optimization problem. Extensive experiments conducted on several real-world multi-view datasets have demonstrated the superiority of our approach.
Older Adults With Cognitive and/or Physical Impairments Can Benefit From Immersive Virtual Reality Experiences: A Feasibility Study

Background: Older adults living in long term care, rehabilitation hospitals, and seniors' residences often experience reduced mobility, sometimes resulting in confinement indoors and isolation, which can introduce or aggravate symptoms of depression, anxiety, loneliness, and apathy. As Virtual Reality (VR) technologies become increasingly accessible and affordable, there is a unique opportunity to enable older adults to escape their restricted physical realities and be transported to both stimulating and calming places which may improve their general well-being. To date no robust evaluations of the use of immersive VR therapy [experienced through a head-mounted-display (HMD)] for older adults within these settings have been reported. VR-therapy may prove to be a safe, inexpensive, non-pharmacological means of managing depressive symptoms and providing engagement and enjoyment to this rapidly growing demographic. Objectives: Establish whether it is feasible to use immersive VR technology as therapy for older adults who have reduced sensory, mobility and/or impaired cognition. This includes evaluation of tolerability, comfort, and ease of use of the HMD, and of the potential for immersive VR to provide enjoyment/relaxation and reduce anxiety and depressive symptoms. Methods: Sixty-six older adults (mean age 80.5, SD = 10.5) with varying cognitive abilities (normal = 28, mild impairment = 17, moderate impairment = 12, severe impairment = 3, unknown cognitive score = 6), and/or physical impairments, entered a multi-site non-randomized interventional study in Toronto, Canada. Participants experienced 3 to 20 min of 360°-video footage of nature scenes displayed on Samsung GearVR HMD. Data was collected through pre/post-intervention surveys, standardized observations during intervention, and post-intervention semi-structured interviews addressing the VR experience. Results: All participants completed the study with no negative side-effects reported (e.g., No dizziness, disorientation, interference with hearing aids); the average time spent in VR was 8 min and 76% of participants viewed the entire experience at least once. Participants tolerated the HMD very well; most had positive feedback, feeling more relaxed and adventurous; 76% wanted to try VR again. Better image quality and increased narrative video content were suggested to improve the experience. Conclusion: It is feasible and safe to expose older adults with various levels of cognitive and physical impairments to immersive VR within these settings. Further research should evaluate the potential benefits of VR in different settings (e.g., home/community based) and explore better customization/optimization of the VR content and equipment for the targeted populations.
Learning Relationships between Text, Audio, and Video via Deep Canonical Correlation for Multimodal Language Analysis

Multimodal language analysis often considers relationships between features based on text and those based on acoustical and visual properties. Text features typically outperform non-text features in sentiment analysis or emotion recognition tasks in part because the text features are derived from advanced language models or word embeddings trained on massive data sources while audio and video features are human-engineered and comparatively underdeveloped. Given that the text, audio, and video are describing the same utterance in different ways, we hypothesize that the multimodal sentiment analysis and emotion recognition can be improved by learning (hidden) correlations between features extracted from the outer product of text and audio (we call this text-based audio) and analogous text-based video. This paper proposes a novel model, the Interaction Canonical Correlation Network (ICCN), to learn such multimodal embeddings. ICCN learns correlations between all three modes via deep canonical correlation analysis (DCCA) and the proposed embeddings are then tested on several benchmark datasets and against other state-of-the-art multimodal embedding algorithms. Empirical results and ablation studies confirm the effectiveness of ICCN in capturing useful information from all three views.
P-Tuning: Prompt Tuning Can Be Comparable to Fine-tuning Across Scales and Tasks

Prompt tuning, which only tunes continuous prompts with a frozen language model, substantially reduces per-task storage and memory usage at training.However, in the context of NLU, prior work reveals that prompt tuning does not perform well for normal-sized pretrained models.We also find that existing methods of prompt tuning cannot handle hard sequence labeling tasks, indicating a lack of universality.We present a novel empirical finding that properly optimized prompt tuning can be universally effective across a wide range of model scales and NLU tasks.It matches the performance of finetuning while having only 0.1%-3% tuned parameters.Our method P-Tuning v2 is an implementation of Deep Prompt Tuning (Li and Liang, 2021;Qin and Eisner, 2021) optimized and adapted for NLU.Given the universality and simplicity of P-Tuning v2, we believe it can serve as an alternative to finetuning and a strong baseline for future research. 1
A Study on Sentiment Analysis Techniques of Twitter Data

The entire world is transforming quickly under the present innovations.The Internet has become a basic requirement for everybody with the Web being utilized in every field.With the rapid increase in social network applications, people are using these platforms to voice them their opinions with regard to daily issues.Gathering and analyzing peoples' reactions toward buying a product, public services, and so on are vital.Sentiment analysis (or opinion mining) is a common dialogue preparing task that aims to discover the sentiments behind opinions in texts on varying subjects.In recent years, researchers in the field of sentiment analysis have been concerned with analyzing opinions on different topics such as movies, commercial products, and daily societal issues.Twitter is an enormously popular microblog on which clients may voice their opinions.Opinion investigation of Twitter data is a field that has been given much attention over the last decade and involves dissecting "tweets" (comments) and the content of these expressions.As such, this paper explores the various sentiment analysis applied to Twitter data and their outcomes.
A Hierarchical Framework for Relation Extraction with Reinforcement Learning

Most existing methods determine relation types only after all the entities have been recognized, thus the interaction between relation types and entity mentions is not fully modeled. This paper presents a novel paradigm to deal with relation extraction by regarding the related entities as the arguments of a relation. We apply a hierarchical reinforcement learning (HRL) framework in this paradigm to enhance the interaction between entity mentions and relation types. The whole extraction process is decomposed into a hierarchy of two-level RL policies for relation detection and entity extraction respectively, so that it is more feasible and natural to deal with overlapping relations. Our model was evaluated on public datasets collected via distant supervision, and results show that it gains better performance than existing methods and is more powerful for extracting overlapping relations1.
Exploiting Subspace Relation in Semantic Labels for Cross-Modal Hashing

Hashing methods have been extensively applied to efficient multimedia data indexing and retrieval on account of the explosion of multimedia data. Cross-modal hashing usually learns binary codes by mapping multi-modal data into a common Hamming space. Most supervised methods utilize relation information like class labels as pairwise similarities of cross-modal data pair to narrow intra-modal and inter-modal gap. In this paper, we propose a novel supervised cross-modal hashing method dubbed Subspace Relation Learning for Cross-modal Hashing (SRLCH), which exploits relation information of labels in semantic space to make similar data from different modalities closer in the low-dimension Hamming subspace. SRLCH preserves the modality relationships, the discrete constraints and nonlinear structures, while admitting a closed-form binary codes solution, which effectively enhances the training efficiency. An iterative alternative optimization algorithm is developed to simultaneously learn both hash functions and unified binary codes. With these binary codes and hash functions, we can index multimedia data and search them in an efficient way. Evaluations in two cross-modal retrieval tasks on several widely-used datasets show that the proposed SRLCH outperforms most cross-modal hashing methods. Theoretical analysis also illustrates reasons for our method’s promotion in subspace relation learning.
Pyramid Channel-based Feature Attention Network for image dehazing

Traditional deep learning-based image dehazing methods usually use the high-level features (which contain more semantic information) to remove haze in the input image, while ignoring the low-level features (which contain more detail information). In this paper, a Pyramid Channel-based Feature Attention Network (PCFAN) is proposed for single image dehazing, which leverages complementarity among different level features in a pyramid manner with channel attention mechanism. PCFAN consists of three modules: a three-scale feature extraction module, a pyramid channel-based feature attention module (PCFA), and an image reconstruction module. The three-scale feature extraction module simultaneously captures the low-level spatial structural features and the high-level contextual features in different scales. The PCFA module utilizes the feature pyramid and the channel attention mechanism, which effectively extracts interdependent channel maps and selectively aggregates the more important features in a pyramid manner for image dehazing. The image reconstruction module is used to reconstruct features to recover a clear image. Meanwhile, a loss function that combines a mean square error loss part and an edge loss part is employed in PCFAN, which can better preserve image details. Experimental results demonstrate that the proposed PCFAN outperforms existing state-of-the-art algorithms on standard benchmark datasets in terms of accuracy, efficiency, and visual effect. The code will be made publicly available.
Quantum error correction: an introductory guide

Quantum error correction protocols will play a central role in the realisation of quantum computing; the choice of error correction code will influence the full quantum computing stack, from the layout of qubits at the physical level to gate compilation strategies at the software level. As such, familiarity with quantum coding is an essential prerequisite for the understanding of current and future quantum computing architectures. In this review, we provide an introductory guide to the theory and implementation of quantum error correction codes. Where possible, fundamental concepts are described using the simplest examples of detection and correction codes, the working of which can be verified by hand. We outline the construction and operation of the surface code, the most widely pursued error correction protocol for experiment. Finally, we discuss issues that arise in the practical implementation of the surface code and other quantum error correction codes.
I Know the Relationships: Zero-Shot Action Recognition via Two-Stream Graph Convolutional Networks and Knowledge Graphs

Recently, with the ever-growing action categories, zero-shot action recognition (ZSAR) has been achieved by automatically mining the underlying concepts (e.g., actions, attributes) in videos. However, most existing methods only exploit the visual cues of these concepts but ignore external knowledge information for modeling explicit relationships between them. In fact, humans have remarkable ability to transfer knowledge learned from familiar classes to recognize unfamiliar classes. To narrow the knowledge gap between existing methods and humans, we propose an end-to-end ZSAR framework based on a structured knowledge graph, which can jointly model the relationships between action-attribute, action-action, and attribute-attribute. To effectively leverage the knowledge graph, we design a novel Two-Stream Graph Convolutional Network (TS-GCN) consisting of a classifier branch and an instance branch. Specifically, the classifier branch takes the semantic-embedding vectors of all the concepts as input, then generates the classifiers for action categories. The instance branch maps the attribute embeddings and scores of each video instance into an attribute-feature space. Finally, the generated classifiers are evaluated on the attribute features of each video, and a classification loss is adopted for optimizing the whole network. In addition, a self-attention module is utilized to model the temporal information of videos. Extensive experimental results on three realistic action benchmarks Olympic Sports, HMDB51 and UCF101 demonstrate the favorable performance of our proposed framework.
Residual Networks for Light Field Image Super-Resolution

Light field cameras are considered to have many potential applications since angular and spatial information is captured simultaneously. However, the limited spatial resolution has brought lots of difficulties in developing related applications and becomes the main bottleneck of light field cameras. In this paper, a learning-based method using residual convolutional networks is proposed to reconstruct light fields with higher spatial resolution. The view images in one light field are first grouped into different image stacks with consistent sub-pixel offsets and fed into different network branches to implicitly learn inherent corresponding relations. The residual information in different spatial directions is then calculated from each branch and further integrated to supplement high-frequency details for the view image. Finally, a flexible solution is proposed to super-resolve entire light field images with various angular resolutions. Experimental results on synthetic and real-world datasets demonstrate that the proposed method outperforms other state-of-the-art methods by a large margin in both visual and numerical evaluations. Furthermore, the proposed method shows good performances in preserving the inherent epipolar property in light field images.
Fast Neural Architecture Search of Compact Semantic Segmentation Models via Auxiliary Cells

Automated design of neural network architectures tailored for a specific task is an extremely promising, albeit inherently difficult, avenue to explore. While most results in this domain have been achieved on image classification and language modelling problems, here we concentrate on dense per-pixel tasks, in particular, semantic image segmentation using fully convolutional networks. In contrast to the aforementioned areas, the design choices of a fully convolutional network require several changes, ranging from the sort of operations that need to be used - e.g., dilated convolutions - to a solving of a more difficult optimisation problem. In this work, we are particularly interested in searching for high-performance compact segmentation architectures, able to run in real-time using limited resources. To achieve that, we intentionally over-parameterise the architecture during the training time via a set of auxiliary cells that provide an intermediate supervisory signal and can be omitted during the evaluation phase. The design of the auxiliary cell is emitted by a controller, a neural network with the fixed structure trained using reinforcement learning. More crucially, we demonstrate how to efficiently search for these architectures within limited time and computational budgets. In particular, we rely on a progressive strategy that terminates non-promising architectures from being further trained, and on Polyak averaging coupled with knowledge distillation to speed-up the convergence. Quantitatively, in 8 GPU-days our approach discovers a set of architectures performing on-par with state-of-the-art among compact models on the semantic segmentation, pose estimation and depth prediction tasks. Code will be made available here: https://github.com/drsleep/nas-segm-pytorch.
Tagged Back-Translation

Recent work in Neural Machine Translation (NMT) has shown significant quality gains from noised-beam decoding during backtranslation, a method to generate synthetic parallel data.We show that the main role of such synthetic noise is not to diversify the source side, as previously suggested, but simply to indicate to the model that the given source is synthetic.We propose a simpler alternative to noising techniques, consisting of tagging back-translated source sentences with an extra token.Our results on WMT outperform noised back-translation in English-Romanian and match performance on English-German, re-defining state-of-the-art in the former.
Pretraining-Based Natural Language Generation for Text Summarization

In this paper, we propose a novel pretrainingbased encoder-decoder framework, which can generate the output sequence based on the input sequence in a two-stage manner.For the encoder of our model, we encode the input sequence into context representations using BERT.For the decoder, there are two stages in our model, in the first stage, we use a Transformer-based decoder to generate a draft output sequence.In the second stage, we mask each word of the draft sequence and feed it to BERT, then by combining the input sequence and the draft representation generated by BERT, we use a Transformer-based decoder to predict the refined word for each masked position.To the best of our knowledge, our approach is the first method which applies the BERT into text generation tasks.As the first step in this direction, we evaluate our proposed method on the text summarization task.Experimental results show that our model achieves new state-of-the-art on both CNN/Daily Mail and New York Times datasets.
Context-Aware Attention Network for Image-Text Retrieval

As a typical cross-modal problem, image-text bi-directional retrieval relies heavily on the joint embedding learning and similarity measure for each image-text pair. It remains challenging because prior works seldom explore semantic correspondences between modalities and semantic correlations in a single modality at the same time. In this work, we propose a unified Context-Aware Attention Network (CAAN), which selectively focuses on critical local fragments (regions and words) by aggregating the global context. Specifically, it simultaneously utilizes global inter-modal alignments and intra-modal correlations to discover latent semantic relations. Considering the interactions between images and sentences in the retrieval process, intra-modal correlations are derived from the second-order attention of region-word alignments instead of intuitively comparing the distance between original features. Our method achieves fairly competitive results on two generic image-text retrieval datasets Flickr30K and MS-COCO.