Reviewerly/exHarmony huggingface dataset:
Using Medical Device Standards for Design and Risk Management of Immersive Virtual Reality for At-Home Therapy and Remote Patient Monitoring

Numerous virtual reality (VR) systems have received regulatory clearance as therapeutic medical devices for in-clinic and at-home use. These systems enable remote patient monitoring of clinician-prescribed rehabilitation exercises, although most of these systems are nonimmersive. With the expanding availability of affordable and easy-to-use head-mounted display (HMD)-based VR, there is growing interest in immersive VR therapies. However, HMD-based VR presents unique risks. Following standards for medical device development, the objective of this paper is to demonstrate a risk management process for a generic immersive VR system for remote patient monitoring of at-home therapy. Regulations, standards, and guidance documents applicable to therapeutic VR design are reviewed to provide necessary background. Generic requirements for an immersive VR system for home use and remote patient monitoring are identified using predicate analysis and specified for both patients and clinicians using user stories. To analyze risk, failure modes and effects analysis, adapted for medical device risk management, is performed on the generic user stories and a set of risk control measures is proposed. Many therapeutic applications of VR would be regulated as a medical device if they were to be commercially marketed. Understanding relevant standards for design and risk management early in the development process can help expedite the availability of innovative VR therapies that are safe and effective.
Gaussian Context Transformer

Recently, a large number of channel attention blocks are proposed to boost the representational power of deep convolutional neural networks (CNNs). These approaches commonly learn the relationship between global contexts and attention activations by fully-connected layers or linear transformations. However, we empirically find that though many parameters are introduced, these attention blocks may not learn the relationship well. In this paper, we hypothesize that the relationship is predetermined. Based on this hypothesis, we propose a simple yet extremely efficient channel attention block, called Gaussian Context Transformer (GCT), which achieves contextual feature excitation using a Gaussian function that satisfies the presupposed relation-ship. According to whether the standard deviation of the Gaussian function is learnable, we develop two versions of GCT: GCT-B0 and GCT-B1. GCT-B0 is a parameter-free channel attention block by fixing the standard deviation. It directly maps global contexts to attention activations with-out learning. In contrast, GCT-B1 is a parameterized version, which adaptively learns the standard deviation to enhance the mapping ability. Extensive experiments on ImageNet and MS COCO benchmarks demonstrate that our GCTs lead to consistent improvements across various deep CNNs and detectors. Compared with a bank of state-of-the-art channel attention blocks, such as SE [17] and ECA [42], our GCTs are superior in effectiveness and efficiency.
Explaining Neural Network Predictions on Sentence Pairs via Learning Word-Group Masks

Explaining neural network models is important for increasing their trustworthiness in realworld applications.Most existing methods generate post-hoc explanations for neural network models by identifying individual feature attributions or detecting interactions between adjacent features.However, for models with text pairs as inputs (e.g., paraphrase identification), existing methods are not sufficient to capture feature interactions between two texts and their simple extension of computing all word-pair interactions between two texts is computationally inefficient.In this work, we propose the Group Mask (GMASK) method to implicitly detect word correlations by grouping correlated words from the input text pair together and measure their contribution to the corresponding NLP tasks as a whole.The proposed method is evaluated with two different model architectures (decomposable attention model and BERT) across four datasets, including natural language inference and paraphrase identification tasks.Experiments show the effectiveness of GMASK in providing faithful explanations to these models 1 .
Greedy adversarial equilibrium: an efficient alternative to nonconvex-nonconcave min-max optimization

Min-max optimization of an objective function f: ℝd × ℝd → ℝ is an important model for robustness in an adversarial setting, with applications to many areas including optimization, economics, and deep learning. In many applications f may be nonconvex-nonconcave, and finding a global min-max point may be computationally intractable. There is a long line of work that seeks computationally tractable algorithms for alternatives to the min-max optimization model. However, many of the alternative models have solution points which are only guaranteed to exist under strong assumptions on f, such as convexity, monotonicity, or special properties of the starting point. We propose an optimization model, the ε-greedy adversarial equilibrium, and show that it can serve as a computationally tractable alternative to the min-max optimization model. Roughly, we say that a point (x⋆, y⋆) is an ε-greedy adversarial equilibrium if y⋆ is an ε-approximate local maximum for f(x⋆,·), and x⋆ is an ε-approximate local minimum for a "greedy approximation" to the function maxz f(x, z) which can be efficiently estimated using second-order optimization algorithms. We prove the existence of such a point for any smooth function which is bounded and has Lipschitz Hessian. To prove existence, we introduce an algorithm that converges from any starting point to an ε-greedy adversarial equilibrium in a number of evaluations of the function f, the max-player's gradient ∇y f(x,y), and its Hessian ∇y2 f(x,y), that is polynomial in the dimension d, 1/ε, and the bounds on f and its Lipschitz constant.
Experimental evaluation of unsupervised image retrieval application using hybrid feature extraction by integrating deep learning and handcrafted techniques

Content Based Image Retrieval is ever growing technology for many applications such as medical, remote sensing, social media search engines and surveillance monitoring, etc,. Representing image content with appropriate features is tedious task using traditional low level feature extraction methods. Deep learning models achieved high precision in classification and object detection algorithms by extracting automated high level feature extraction process. This paper proposed a hybrid feature extraction technique by combining the high level features and low level features to improve the robustness of the feature vector. The proposed model used pre-trained Googlenet model as feature extractor and combined with Gabor multiscale texture features. The final feature vector will be used for retrieving the relevant image data from the large scale image dataset. It has achieved the precision of 91 percent which shows better than state of art methods.
Attention-Guided Semantic Hashing for Unsupervised Cross-Modal Retrieval

Recently, due to the low storage consumption and high search efficiency of hashing methods and the powerful feature extraction capability of deep neural networks, deep cross-modal hashing has received extensive attention in the field of multi-media retrieval. However, existing methods tend to ignore the latent relationships between heterogeneous data when learning a common semantic subspace, and cannot retain more important semantic information when mining deep correlations. In this paper, an attention mechanism which focuses on the characteristics of the associated features is employed to propose an attention-aware semantic fusion matrix that integrates important information from different modalities. We introduce a novel network that can pass the extracted features through the attention module to efficiently encode rich and relevant features, and can also generate hash codes under the self-supervision of the proposed attention-aware semantic fusion matrix. Our experimental results and detailed analysis prove that our method can achieve better retrieval performance on the three popular datasets, compared with the recent unsupervised cross-modal hashing methods.
Learning Across Tasks for Zero-Shot Domain Adaptation From a Single Source Domain

Domain adaptation techniques learn transferable knowledge from a source domain to a target domain and train models that generalize well in the target domain. Unfortunately, a majority of the existing techniques are only applicable to scenarios that the target-domain data in the task of interest is available for training, yet this is not often true in practice. In general, human beings are experts in generalization across domains. For example, a baby can easily identify the bear from a clipart image after learning this category of animal from the photo images. To reduce the gap between the generalization ability of human and that of machines, we propose a new solution to the challenging zero-shot domain adaptation (ZSDA) problem, where only a single source domain is available and the target domain for the task of interest is not accessible. Inspired by the observation that the knowledge about domain correlation can improve our generalization ability, we explore the correlation between source domain and target domain in an irrelevant knowledge task ([Formula: see text]-task), where dual-domain samples are available. We denote the task of interest as the question task ([Formula: see text]-task) and synthesize its non-accessible target-domain as such that these two tasks have the shared domain correlation. In order to realize our idea, we introduce a new network structure, i.e., conditional coupled generative adversarial networks (CoCoGAN), by extending the coupled generative adversarial networks (CoGAN) into a conditioning model. With a pair of coupling GANs, our CoCoGAN is able to capture the joint distribution of data samples across two domains and two tasks. For CoCoGAN training in a ZSDA task, we introduce three supervisory signals, i.e., semantic relationship consistency across domains, global representation alignment across tasks, and alignment consistency across domains. Experimental results demonstrate that our method can learn a suitable model for the non-accessible target domain and outperforms the existing state of the arts in both image classification and semantic segmentation.
Topology and channel affinity reinforced global attention for person re‐identification

Person re-identification (re-id), with the goal to recognize persons from images captured by nonoverlapping cameras, is a challenging topic in computer vision. It has been studied extensively in recent years, and the attention mechanism is widely applied in person re-id. But many works mainly focus on extracting discriminative features from local saliency regions, while ignoring some potentially global information between whole-body features and body-part features. In this study, we first proposed two effective global information for extracting discriminative features: spatial topology information (STI) and channel affinity information (CAI). On this basis, we further propose a Multi-information Fusion reinforced Global Attention (MIFGA) module which can effectively fuse a variety of information and utilize more comprehensive information to guide the learning of attention, so as to obtain pedestrian features that are conducive to clustering. Specifically, the proposed MIFGA module includes spatial attention (MIFGA-S) and channel attention (MIFGA-C). MIFGA-S mainly utilizes local feature semantic information and STI to guide the learning of spatial attention. Furthermore, to mine the potential topology information in original feature maps, the self-learning graph convolution network is proposed. MIFGA-C fuses channel semantic information and CAI to guide the learning of channel attention. Extensive ablation studies demonstrate that our proposed MIFGA significantly enhances the baseline model and achieves a competitive performance compared with the state-of-the-art person re-id methods on standard data sets Market-1501, DukeMTMC-reID, and CUHK03.
Computing Russian Morphological distribution patterns using RusAC Online Server

The article presents findings of distribution patterns of Russian grammatical categories computed with the help of MyStem.3 tagger and a proprietary Russian language processor, ETAP-3. The corpus of over 1.1 mln tokens compiled for the study comprises two types of academic textbooks used in Russian schools: Science and Humanities. We computed descriptive metrics of each textbooks with the help of the text analyzer RusAC (http://tykau.pythonanywhere.com/) and pursued the contrastive analysis of Science and Humanities textbook features. Significant differences of two types of the texts were found in distribution patterns of noun cases and verbs tenses, while morphological categories of nouns, adjectives, verbs, and adverbs demonstrate similarities. The specifics of grammatical patterns defined for classroom textbooks can be used in further studies on distribution of morphological patterns and text complexity of Russian academic texts.
Image Enhancement in Low-Light Conditions using Volume-based Subspace Interpretation

The job of low light picture upgrade is to improve the nature of photographs, fundamentally used to upgrade the photograph which was caught under terrible brightening conditions like low brilliance, low differentiation, shading contortion, a thin dark reach, and furthermore impressive commotion. These are the variables which significantly limit the presentation of machine vision framework just as it influences the enhanced visualization on natural eye. Henceforth, this paper is particularly focused on low light picture improvement, especially utilizing subspace. Subspace can let out enlightenment and commotion, raised from comparable picture patches, named volume, at every pixel position. Contingent upon this volume-based subspace, superfluous commotion is stifled, and simultaneously enlightening part will be derived from the given picture. This will assist with uncovering the under construction in low light zones. The exploratory consequence of the proposed strategy has effectively upgraded and robust the corrupted photographs.
Learning Relaxed Neighborhood Consistency for Feature Matching

Feature matching is a critical prerequisite in many applications of remote sensing, and its aim is to establish reliable correspondences between two sets of features. Existing attempts typically involve estimating the underlying image transformations to remove false matches in putative matches. However, the image transformation could vary with different application scenarios, which means that using a predefined geometrical model may lead to inferior matching accuracy, especially if the image transformation is nonrigid. This article casts the mismatch removal into a neighborhood consistency evaluation problem under a customized learning framework. With only seven training image pairs involving approximately 8000 putative matches, our method can handle different types of images or transformation models (affine, homography, piecewise-linear transformation, and others). Extensive experiments on feature matching and image registration are conducted to demonstrate the superiority of our method over the eight state-of-the-art competitors.
The Methods and Approaches of Explainable Artificial Intelligence

Artificial Intelligence has found innumerable applications, becoming ubiquitous in the contemporary society. From making unnoticeable, minor choices to determining people’s fates (the case of predictive policing). This fact raises serious concerns about the lack of explainability of those systems. Finding ways to enable humans to comprehend the results provided by AI is a blooming area of research right now. This paper explores the current findings in the field of Explainable Artificial Intelligence (xAI), along with xAI methods and solutions that realise them. The paper provides an umbrella perspective on available xAI options, sorting them into a range of levels of abstraction, starting from community-developed code snippets implementing facets of xAI research all the way up to comprehensive solutions utilising state-of-the-art achievements in the domain.
Extreme Low-Resolution Activity Recognition Using a Super-Resolution-Oriented Generative Adversarial Network

Activity recognition is a fundamental and crucial task in computer vision. Impressive results have been achieved for activity recognition in high-resolution videos, but for extreme low-resolution videos, which capture the action information at a distance and are vital for preserving privacy, the performance of activity recognition algorithms is far from satisfactory. The reason is that extreme low-resolution (e.g., 12 × 16 pixels) images lack adequate scene and appearance information, which is needed for efficient recognition. To address this problem, we propose a super-resolution-driven generative adversarial network for activity recognition. To fully take advantage of the latent information in low-resolution images, a powerful network module is employed to super-resolve the extremely low-resolution images with a large scale factor. Then, a general activity recognition network is applied to analyze the super-resolved video clips. Extensive experiments on two public benchmarks were conducted to evaluate the effectiveness of our proposed method. The results demonstrate that our method outperforms several state-of-the-art low-resolution activity recognition approaches.
Visual recognition of gymnastic exercise sequences. Application to supervision and robot learning by demonstration

This work presents a novel software architecture to autonomously identify and evaluate the gymnastic activity that people are carrying out. It is composed of three different interconnected layers. The first corresponds to a Multilayer Perceptron (MLP) trained from a set of angular magnitudes derived from the information provided by the OpenPose library. This library works frame by frame, so some postures may be incorrectly detected due to eventual occlusions. The MLP layer makes it possible to accurately identify the posture a person is performing. A second layer, based on a Hidden Markov Model (HMM) and the Viterbi algorithm, filters the incorrect spurious postures. Thus, the accuracy of the algorithm is improved, leading to a precise sequence of postures. A third layer identifies the current exercise and evaluates whether the person is doing it at a correct speed. This layer uses an innovative Modified Levenshtein Distance (MLD), which considers not only the number of operations to transform a given sequence, but also the nature of the elements participating in the comparison. The system works in real time with little delay, thus recognizing sequences of arbitrary length and providing continuous feedback on the exercises being performed. An experiment carried out consisted in reproducing the output of the second layer on an autonomous Pepper robot that can be used in environments where physical exercise is performed, such as a residence for the elderly or others. It has reproduced different exercises previously executed by an instructor so that people can copy the robot. The article analyzes the current situation of the automated gymnastic activities recognition, presents the architecture, the different experiments carried out and the results obtained. The integration of the three components (MLP, HMM and MLD) results in a robust system that has allowed us to improve the results of previous works.
Semi-Supervised Semantic Segmentation via Entropy Minimization

In this paper, we propose a novel entropy minimization based semi-supervised method for semantic segmentation. Entropy minimization has proven to be an effective semi-supervised method for realizing the cluster assumption, where the decision boundary should lie in low-density regions. Inspired by the existing consistency training semi-supervised segmentation networks with encoder-decoder architecture, we found that there tend to be more large gradient values at the object edges than other positions in the feature map of the encoder, and therefore propose a feature gradient map regularization to enlarge inter-class distance in the feature space for low-entropy of segmentation prediction. Additionally, we introduce an adaptive sharpening scheme with aleatoric uncertainty, and a class consistency constraint regularization, to alleviate the interference of noise with pseudo labels. Extensive experiments on PASCAL VOC, PASCAL-Context, and Leukocyte datasets show that the proposed method achieves state-of-the-art semi-supervised semantic segmentation performance without almost additional calculations and network structures.
Near-optimal learning of tree-structured distributions by Chow-Liu

We provide finite sample guarantees for the classical Chow-Liu algorithm (IEEE Trans. Inform. Theory, 1968) to learn a tree-structured graphical model of a distribution. For a distribution P on Σn and a tree T on n nodes, we say T is an ε-approximate tree for P if there is a T-structured distribution Q such that D(P || Q) is at most ε more than the best possible tree-structured distribution for P. We show that if P itself is tree-structured, then the Chow-Liu algorithm with the plug-in estimator for mutual information with O(|Σ|3 nε−1) i.i.d. samples outputs an ε-approximate tree for P with constant probability. In contrast, for a general P (which may not be tree-structured), Ω(n2ε−2) samples are necessary to find an ε-approximate tree. Our upper bound is based on a new conditional independence tester that addresses an open problem posed by Canonne, Diakonikolas, Kane, and Stewart (STOC, 2018): we prove that for three random variables X,Y,Z each over Σ, testing if I(X; Y ∣ Z) is 0 or ≥ ε is possible with O(|Σ|3/ε) samples. Finally, we show that for a specific tree T, with O(|Σ|2nε−1) samples from a distribution P over Σn, one can efficiently learn the closest T-structured distribution in KL divergence by applying the add-1 estimator at each node.
A Machine Learning Approach for Suggesting Feedback in Textual Exercises in Large Courses

Open-ended textual exercises facilitate the comprehension of problem-solving skills. Students can learn from their mistakes when teachers provide individual feedback. However, courses with hundreds of students cause a heavy workload for teachers: providing individual feedback is mostly a manual, repetitive, and time-consuming activity.
Adv-Emotion: The Facial Expression Adversarial Attack

Artificial intelligence is developing rapidly in the direction of intellectualization and humanization. Recent studies have shown the vulnerability of many deep learning models to adversarial examples, but there are fewer studies on adversarial examples attacking facial expression recognition systems. Human–computer interaction requires facial expression recognition, so the security demands of artificial intelligence humanization should be considered. Inspired by facial expression recognition, we want to explore the characteristics of facial expression recognition adversarial examples. In this paper, we are the first to study facial expression adversarial examples (FEAEs) and propose an adversarial attack method on facial expression recognition systems, a novel measurement method on the adversarial hardness of FEAEs, and two evaluation metrics on FEAE transferability. The experimental results illustrate that our approach is superior to other gradient-based attack methods. Finding FEAEs can attack not only facial expression recognition systems but also face recognition systems. The transferability and adversarial hardness of FEAEs can be measured effectively and accurately.