Reviewerly/exHarmony huggingface dataset:
Multi-viewpoints visual models for efficient modeling and analysis of Twitter based health-care services

Purpose The purpose of this study for eHealth text mining domains, cosine-based visual methods (VM) assess the clusters more accurately than Euclidean; which are recommended for tweet data models for clusters assessment. Such VM determines the clusters concerning a single viewpoint or none, which are less informative. Multi-viewpoints (MVP) were used for addressing the more informative clusters assessment of health-care tweet documents and to demonstrate visual analysis of cluster tendency. Design/methodology/approach In this paper, the authors proposed MVP-based VM by using traditional topic models with visual techniques to find cluster tendency, partitioning for cluster validity to propose health-care recommendations based on tweets. The authors demonstrated the effectiveness of proposed methods on different real-time Twitter health-care data sets in the experimental study. The authors also did a comparative analysis of proposed models with existing visual assessment tendency (VAT) and cVAT models by using cluster validity indices and computational complexities; the examples suggest that MVP VM were more informative. Findings In this paper, the authors proposed MVP-based VM by using traditional topic models with visual techniques to find cluster tendency, partitioning for cluster validity to propose health-care recommendations based on tweets. Originality/value In this paper, the authors proposed multi-viewpoints distance metric in topic model cluster tendency for the first time and visual representation using VAT images using hybrid topic models to find cluster tendency, partitioning for cluster validity to propose health-care recommendations based on tweets.
A Logarithmic Bayesian Approach to Quantum Error Detection

We consider the problem of continuous quantum error correction from a Bayesian perspective, proposing a pair of digital filters using logarithmic probabilities that are able to achieve near-optimal performance on a three-qubit bit-flip code, while still being reasonable to implement on low-latency hardware. These practical filters are approximations of an optimal filter that we derive explicitly for finite time steps, in contrast with previous work that has relied on stochastic differential equations such as the Wonham filter. By utilizing logarithmic probabilities, we are able to eliminate the need for explicit normalization and can reduce the Gaussian noise distribution to a simple quadratic expression. The state transitions induced by the bit-flip errors are modeled using a Markov chain, which for log-probabilties must be evaluated using a LogSumExp function. We develop the two versions of our filter by constraining this LogSumExp to have either one or two inputs, which favors either simplicity or accuracy, respectively. Using simulated data, we demonstrate that the single-term and two-term filters are able to significantly outperform both a double threshold scheme and a linearized version of the Wonham filter in tests of error detection under a wide variety of error rates and time steps.
CT-Video Matching for Retrograde Intrarenal Surgery Based on Depth Prediction and Style Transfer

Retrograde intrarenal surgery (RIRS) is a minimally invasive endoscopic procedure for the treatment of kidney stones. Traditionally, RIRS is usually performed by reconstructing a 3D model of the kidney from preoperative CT images in order to locate the kidney stones; then, the surgeon finds and removes the stones with experience in endoscopic video. However, due to the many branches within the kidney, it can be difficult to relocate each lesion and to ensure that all branches are searched, which may result in the misdiagnosis of some kidney stones. To avoid this situation, we propose a convolutional neural network (CNN)-based method for matching preoperative CT images and intraoperative videos for the navigation of ureteroscopic procedures. First, a pair of synthetic images and depth maps reflecting preoperative information are obtained from a 3D model of the kidney. Then, a style transfer network is introduced to transfer the ureteroscopic images to the synthetic images, which can generate the associated depth maps. Finally, the fusion and matching of depth maps of preoperative images and intraoperative video images are realized based on semantic features. Compared with the traditional CT-video matching method, our method achieved a five times improvement in time performance and a 26% improvement in the top 10 accuracy.
Graph-Based Network for Depth Completion with Ground Completion

The purpose of depth completion is to recover a dense depth map from the sparse depth data and the corresponding single RGB image. This paper proposes a depth completion method that separates ground and non-ground data, and use the equation of the ground directly predict the depth of ground. Specifically, we first separate the ground point cloud and calculate the equation of the ground. Since the point cloud is sparse on the depth map, we use the rasterization method to identify the point cloud area. For the ground area in the image, we combine the ground equation to make depth prediction and use the ground-completed data as training data to train the network. In addition, we also designed a smoothing loss based on depth changes to ensure the stability of the output. Our method has proved its effectiveness on the KITTI dataset.
Human Activity Recognition of Exoskeleton Robot Based on Adaptive DTW Classifier

This paper proposes a method based on adaptive Dynamic Time Warping (DTW) classifier to recognize human activities of exoskeleton robots. First, the path constraint restriction of the traditional DTW algorithm is improved. Then a multi-voting weight distribution mechanism is designed for the DTW classifier when multi-sensor fusion. Finally, we propose a strategy to update the standard templates for classifier using the recognition result and current data. The experimental results show that the adaptive strategy can make the classifier gradually adapt to the user’s behavior habits, thereby improving the recognition accuracy.
Learning Single/Multi-Attribute of Object With Symmetry and Group

Attributes and objects can compose diverse compositions. To model the compositional nature of these concepts, it is a good choice to learn them as transformations, e.g., coupling and decoupling. However, complex transformations need to satisfy specific principles to guarantee rationality. Here, we first propose a previously ignored principle of attribute-object transformation: Symmetry. For example, coupling peeled-apple with attribute peeled should result in peeled-apple, and decoupling peeled from apple should still output apple. Incorporating the symmetry, we propose a transformation framework inspired by group theory, i.e., SymNet. It consists of two modules: Coupling Network and Decoupling Network. We adopt deep neural networks to implement SymNet and train it in an end-to-end paradigm with the group axioms and symmetry as objectives. Then, we propose a Relative Moving Distance (RMD) based method to utilize the attribute change instead of the attribute pattern itself to classify attributes. Besides the compositions of single-attribute and object, our RMD is also suitable for complex compositions of multiple attributes and objects when incorporating attribute correlations. SymNet can be utilized for attribute learning, compositional zero-shot learning and outperforms the state-of-the-art on four widely-used benchmarks. Code is at https://github.com/DirtyHarryLYL/SymNet.
Quantum algorithm based on the $$\varepsilon $$-random linear disequations for the continuous hidden shift problem

There have been several research works on the hidden shift problem, quantum algorithms for the problem, and their applications. However, all the results have focused on discrete groups with discrete oracle functions. In this paper, we define the continuous hidden shift problem on $${\mathbb {R}}^n$$ with a continuous oracle function as an extension of the hidden shift problem, and also define the $$\varepsilon $$ -random linear disequations which is a generalization of the random linear disequations. By employing the newly defined concepts, we show that there exists a quantum computational algorithm which solves this problem in time polynomial in n.
SARS-CoV-2/covid-19 and the crisis of the care: some keys for its understanding

This article incorporates a reflection on care, as action and relationship, which operates fundamentally in the private sphere and which has historically been assigned to women. The arrival of SARS-CoV-2 -better known as COVID-19- and forced confinement intensified various inequalities associated with care, which aggravated the conditions of women. This was expressed in task overloads for women because of the lack of co-responsibility in care work, the absence of public policies, the lack of incorporation of men to this work; all these were maximized by job insecurity that many women are facing.
Additive Schwarz methods for convex optimization with backtracking

This paper presents a novel backtracking strategy for additive Schwarz methods for general convex optimization problems as an acceleration scheme. The proposed backtracking strategy is independent of local solvers, so that it can be applied to any algorithms that can be represented in an abstract framework of additive Schwarz methods. Allowing for adaptive increasing and decreasing of the step size along the iterations, the convergence rate of an algorithm is greatly improved. Improved convergence rate of the algorithm is proven rigorously. In addition, combining the proposed backtracking strategy with a momentum acceleration technique, we propose a further accelerated additive Schwarz method. Numerical results for various convex optimization problems that support our theory are presented.
Information Bottleneck Theory Based Exploration of Cascade Learning

In solving challenging pattern recognition problems, deep neural networks have shown excellent performance by forming powerful mappings between inputs and targets, learning representations (features) and making subsequent predictions. A recent tool to help understand how representations are formed is based on observing the dynamics of learning on an information plane using mutual information, linking the input to the representation (I(X;T)) and the representation to the target (I(T;Y)). In this paper, we use an information theoretical approach to understand how Cascade Learning (CL), a method to train deep neural networks layer-by-layer, learns representations, as CL has shown comparable results while saving computation and memory costs. We observe that performance is not linked to information–compression, which differs from observation on End-to-End (E2E) learning. Additionally, CL can inherit information about targets, and gradually specialise extracted features layer-by-layer. We evaluate this effect by proposing an information transition ratio, I(T;Y)/I(X;T), and show that it can serve as a useful heuristic in setting the depth of a neural network that achieves satisfactory accuracy of classification.
A Feature Selection Approach Based on Improved Binary Coyote Optimization Algorithm

Feature selection is one of the essential approaches for machine learning and data mining to eliminate the irrelevant and redundant features from the datasets. As the wrapper-based feature selection can be considered as a binary optimization problem, an improved binary coyote optimization algorithm (IBCOA) is proposed and applied for solving the feature selection problems in this paper. In the proposed algorithm, two Sigmoidal transfer functions are utilized respectively for updating the social condition of each coyote and dealing with the birth of new coyotes. An alpha based mutation strategy is applied for updating and binarizing the coyotes in a pack. The proposed approach is evaluated using twelve well-known datasets from the UCI machine learning repository. The experimental results confirm that the proposed approach outperforms the other two feature selection approaches based on the conventional COA and a V-shaped binary COA.
Survey of Action Recognition Based on Attention Mechanism

Attention mechanism for action recognition is to enhance data by using attention mechanism so as to recognize human action in video. This research has been intensely investigated in the field of computer vision, which has attracted extensive attention from academia and industry. It is also the premise of intelligent interaction and human-computer cooperation, which helps the machine to perceive the external environment. In the past decade, especially the emergence of deep learning technology, great progress has been made in this field. Therefore, it is necessary to make a comprehensive review of the recent work. This paper summarizes the research background and application fields, typical datasets and experimental evaluation, and compares various attention mechanism for human action feature representation and action classification algorithms. Finally, we look forward to the development prospect of human action recognition in self-driving vehicles. This paper provides ideas for the follow-up research on the understanding of the behavior of self-driving vehicles, and makes self-driving vehicles become interactive wheeled robots.
Image Comes Dancing With Collaborative Parsing-Flow Video Synthesis

Transferring human motion from a source to a target person poses great potential in computer vision and graphics applications. A crucial step is to manipulate sequential future motion while retaining the appearance characteristic. Previous work has either relied on crafted 3D human models or trained a separate model specifically for each target person, which is not scalable in practice. This work studies a more general setting, in which we aim to learn a single model to parsimoniously transfer motion from a source video to any target person given only one image of the person, named as Collaborative Parsing-Flow Network (CPF-Net). The paucity of information regarding the target person makes the task particularly challenging to faithfully preserve the appearance in varying designated poses. To address this issue, CPF-Net integrates the structured human parsing and appearance flow to guide the realistic foreground synthesis which is merged into the background by a spatio-temporal fusion module. In particular, CPF-Net decouples the problem into stages of human parsing sequence generation, foreground sequence generation and final video generation. The human parsing generation stage captures both the pose and the body structure of the target. The appearance flow is beneficial to keep details in synthesized frames. The integration of human parsing and appearance flow effectively guides the generation of video frames with realistic appearance. Finally, the dedicated designed fusion network ensure the temporal coherence. We further collect a large set of human dancing videos to push forward this research field. Both quantitative and qualitative results show our method substantially improves over previous approaches and is able to generate appealing and photo-realistic target videos given any input person image. All source code and dataset will be released at https://github.com/xiezhy6/CPF-Net.
Study and analysis of various sentiment classification strategies: A challenging overview

In large-scale social media, sentiment classification is a significant one for connecting gaps among social media contents as well as real-world actions, including public emotional status monitoring, political election prediction, and so on. On the other hand, textual sentiment classification is well studied by various platforms, like Instagram, Twitter, etc. Sentiment classification has many advantages in various fields, like opinion polls, education, and e-commerce. Sentiment classification is an interesting and progressing research area due to its applications in several areas. The information is collected from various people about social, products, and social events by web in sentiment analysis. This review provides a detailed survey of 50 research papers presenting sentiment classification schemes such as active learning-based approach, aspect learning-based method, and machine learning-based approach. The analysis is presented based on the categorization of sentiment classification schemes, the dataset used, software tools utilized, published year, and the performance metrics. Finally, the issues of existing methods considering conventional sentiment classification strategies are elaborated to obtain improved contribution in devising significant sentiment classification strategies. Moreover, the probable future research directions in attaining efficient sentiment classification are provided.
Inverse design for integrated photonics using deep neural network

Focusing on nanophotonic power splitters, we show that a generative neural network can design a series of devices that achieve nearly arbitrary target performance, with an excellent capability to generalize training data produced by the adjoint method.
LIDSNet: A Lightweight on-device Intent Detection model using Deep Siamese Network

Intent detection is a crucial task in any Natural Language Understanding (NLU) system and forms the foundation of a task-oriented dialogue system. To build high-quality real-world conversational solutions for edge devices, there is a need for deploying intent detection model on device. This necessitates a light-weight, fast, and accurate model that can perform efficiently in a resource-constrained environment. To this end, we propose LIDSNet, a novel lightweight on-device intent detection model, which accurately predicts the message intent by utilizing a Deep Siamese Network for learning better sentence representations. We use character-level features to enrich the sentence-level representations and empirically demonstrate the advantage of transfer learning by utilizing pre-trained embeddings. Furthermore, to investigate the efficacy of the modules in our architecture, we conduct an ablation study and arrive at our optimal model. Experimental results prove that LIDSNet achieves state-of-the-art competitive accuracy of 98.00% and 95.97% on SNIPS and ATIS public datasets respectively, with under 0.59M parameters. We further benchmark LIDSNet against fine-tuned BERTs and show that our model is at least 41x lighter and 30x faster during inference than MobileBERT on Samsung Galaxy S20 device, justifying its efficiency on resource-constrained edge devices.
A Symbolic Machine Learning Approach for Cybersickness Potential-Cause Estimation

Virtual reality (VR) and head-mounted displays are constantly gaining popularity in various fields such as education, military, entertainment, and bio/medical informatics. Although such technologies provide a high sense of immersion, they can also trigger symptoms of discomfort. This condition is called cybersickness (CS) and is quite popular in recent publications in the virtual reality context. This work proposes a novel experimental analysis using symbolic machine learning that ranks potential causes for CS. We estimate the CS causes and rank them according to their impact on the classification capabilities of CS. The experiments are performed using two distinct virtual reality games. We were able to identify that acceleration triggered cybersickness more frequently in a race game in contrast to a flight game. Furthermore, participants less experienced with VR are more prone to feel discomfort and this variable has a greater impact in the race game in contrast to the flight game, where the acceleration is not controlled by the user.
Deep learning via message passing algorithms based on belief propagation

Abstract Message-passing algorithms based on the belief propagation (BP) equations constitute a well-known distributed computational scheme. They yield exact marginals on tree-like graphical models and have also proven to be effective in many problems defined on loopy graphs, from inference to optimization, from signal processing to clustering. The BP-based schemes are fundamentally different from stochastic gradient descent (SGD), on which the current success of deep networks is based. In this paper, we present and adapt to mini-batch training on GPUs a family of BP-based message-passing algorithms with a reinforcement term that biases distributions towards locally entropic solutions. These algorithms are capable of training multi-layer neural networks with performance comparable to SGD heuristics in a diverse set of experiments on natural datasets including multi-class image classification and continual learning, while being capable of yielding improved performances on sparse networks. Furthermore, they allow to make approximate Bayesian predictions that have higher accuracy than point-wise ones.
Iterative ripple error suppression algorithm for the dynamic interferometry

In dynamic interferometry, the retardation error of quarter-wave plate (QWP) causes inconsistency of the background intensity and the modulation of the four phase shift interferograms, which makes the periodic ripple error in the measurement results. In this paper, an iterative algorithm is proposed to solve this problem. Both simulations and experiments validated that the algorithm can effectively eliminate the ripple error in the measurement results with stable and quick convergence, therefore the measurement accuracy of the dynamic interferometer can be improved without any extra manual operation.
A student-focused checklist for creating infographics

Purpose This paper discusses efforts to produce instructional support objects for undergraduate students engaged in creating infographics, an alternative assignment growing in popularity at the authors' university. Design/methodology/approach The authors examined scholarly, professional, trade and open-web sources to identify best practices for design and data visualization for this type of assignment. They categorized their findings and used a preponderance-of-evidence method for final selection of relevant practices. The authors detail the creation of their support products (instructional checklists and example infographics) and offer recommendations for librarians engaged in similar efforts. Findings Despite the growing popularity of alternative assignments, guidance for best practices in data and design as they relate to student-created infographics is nascent, and best practices for design and data visualization in this context have yet to be concretely identified. Without extant guidance for student-created infographics, the authors developed a checklist of potential best practices for design and data visualization. Practical implications The use of alternative projects assigned in lieu of traditional research papers is growing. Additional guidance may be required for students creating non-traditional works as standards and best practices for these projects are under-developed in the academic setting. Librarians will want to consider their role in supporting students assigned to create an alternative project. Originality/value A consideration of best practices for data and design visualization for students designing research infographics has not yet been written.