Reviewerly/exHarmony huggingface dataset:
Perceived benefits of open data are improving but scientists still lack resources, skills, and rewards

Abstract Addressing global scientific challenges requires the widespread sharing of consistent and trustworthy research data. Identifying the factors that influence widespread data sharing will help us understand the limitations and potential leverage points. We used two well-known theoretical frameworks, the Theory of Planned Behavior and the Technology Acceptance Model, to analyze three DataONE surveys published in 2011, 2015, and 2020. These surveys aimed to identify individual, social, and organizational influences on data-sharing behavior. In this paper, we report on the application of multiple factor analysis (MFA) on this combined, longitudinal, survey data to determine how these attitudes may have changed over time. The first two dimensions of the MFA were named willingness to share and satisfaction with resources based on the contributing questions and answers. Our results indicated that both dimensions are strongly influenced by individual factors such as perceived benefit, risk, and effort. Satisfaction with resources was significantly influenced by social and organizational factors such as the availability of training and data repositories. Researchers that improved in willingness to share are shown to be operating in domains with a high reliance on shared resources, are reliant on funding from national or federal sources, work in sectors where internal practices are mandated, and live in regions with highly effective communication networks. Significantly, satisfaction with resources was inversely correlated with willingness to share across all regions. We posit that this relationship results from researchers learning what resources they actually need only after engaging with the tools and procedures extensively.
Authorship of Artificial Intelligence-Generated Works and Possible System Improvement in China

With the rapid development of artificial intelligence technology, artificial intelligence (AI) is able to create "original" literature, artistic or musical works that do not differ significantly from human works.This raises a series of new questions for copyright theory and practice.AI-generated works should be considered works protected by copyright law; it can meet a minimal degree of originality.Since giving authorship to AI would be contrary to civil law and the theory of natural person author, AI cannot be regarded as an author.The authorship of its creations should be vested in its users.For AI-generated works, there are some certain gaps in China's current copyright system that need to be filled, such as fair use and term of protection.
Virtuality or Physicality? Supporting Memorization Through Augmented Reality Gamification

Augmented reality (AR) is evolving to become a pervasive tool for interacting with virtual objects. We conducted a comparative study to explore the impact of virtuality and physicality in supporting human memorization through gamification. A head-mounted display (HMD) AR memory matching game and a corresponding physical version game with paper boards were harnessed. The proof-of-concept version was demonstrated in an initial user study (n=12) with counterbalancing design to determine that our proposed gamified HMD AR system with virtuality could support better human memorization compared to the physical version game in reducing task time, improving usability, becoming more recommendable, and decreasing cognitive task workload. The study was then followed by quantitative analysis of the respective four metrics: game completion time (GCT), system usability scale (SUS), recommendation level, and NASA task load index (TLX). A brief qualitative analysis is presented. The results show that in our case, the virtuality outperformed the physicality in supporting human memorization in a gamified context through HMD AR in an evident range.
A multiple gated boosting network for multi‐organ medical image segmentation

Abstract Segmentations provide important clues for diagnosing diseases. U‐shaped neural networks with skip connections have become one of popular frameworks for medical image segmentation. Skip connections really reduce loss of spatial details caused by down‐sampling, but they cannot handle well semantic gaps between low‐ and high‐level features. It is quite challenging to accurately separate out long, narrow, and small organs from human bodies. To solve these problems, the authors propose a Multiple Gated Boosting Network (MGB‐Net). To boost spatial accuracy, the authors first adopt Gated Recurrent Units (GRU) to design multiple Gated Skip Connections (GSC) at different levels, which efficiently reduce the semantic gap between the shallow and deep features. The Update and Reset gates of GRUs enhance features beneficial to segmentation and suppress information adverse to final results in a recurrent way. To obtain more scale invariances, the authors propose a module of Multi‐scale Weighted Channel Attention (MWCA). The module first uses convolutions with different kernel sizes and group numbers to generate multi‐scale features, and then adopts learnable weights to emphasize the importance of each scale for capturing attention features. Blocks of Transformer Self‐Attention (TSA) are sequentially stacked to extract long‐range dependency features. To effectively fuse and boost the features of MWCA and TSA, the authors use GRUs again to propose a Gated Dual Attention module (GDA), which enhances beneficial features and suppresses adverse information in a gated learning way. Experiments show that the authors’ method achieves an average Dice coefficient of 80.66% on the Synapse multi‐organ segmentation dataset. The authors’ method outperforms the state‐of‐the‐art methods on medical images. In addition, the authors’ method achieves a Dice segmentation accuracy of 62.77% on difficult objects such as pancreas, significantly exceeding the current average accuracy, so multiple gated boosting (MGB) methods are reliably effective for improving the ability of feature representations. The authors’ code is publicly available at https://github.com/DAgalaxy/MGB‐Net .
Deep learning for deterioration prediction of COVID-19 patients based on time-series of three vital signs

Unrecognized deterioration of COVID-19 patients can lead to high morbidity and mortality. Most existing deterioration prediction models require a large number of clinical information, typically collected in hospital settings, such as medical images or comprehensive laboratory tests. This is infeasible for telehealth solutions and highlights a gap in deterioration prediction models based on minimal data, which can be recorded at a large scale in any clinic, nursing home, or even at the patient's home. In this study, we develop and compare two prognostic models that predict if a patient will experience deterioration in the forthcoming 3 to 24 h. The models sequentially process routine triadic vital signs: (a) oxygen saturation, (b) heart rate, and (c) temperature. These models are also provided with basic patient information, including sex, age, vaccination status, vaccination date, and status of obesity, hypertension, or diabetes. The difference between the two models is the way that the temporal dynamics of the vital signs are processed. Model #1 utilizes a temporally-dilated version of the Long-Short Term Memory model (LSTM) for temporal processes, and Model #2 utilizes a residual temporal convolutional network (TCN) for this purpose. We train and evaluate the models using data collected from 37,006 COVID-19 patients at NYU Langone Health in New York, USA. The convolution-based model outperforms the LSTM based model, achieving a high AUROC of 0.8844-0.9336 for 3 to 24 h deterioration prediction on a held-out test set. We also conduct occlusion experiments to evaluate the importance of each input feature, which reveals the significance of continuously monitoring the variation of the vital signs. Our results show the prospect for accurate deterioration forecast using a minimum feature set that can be relatively easily obtained using wearable devices and self-reported patient information.
Research on the Development of a Proctoring System for Conducting Online Exams in Kazakhstan

The demand for online education is gradually growing. Most universities and other institutions are faced with the fact that it is almost impossible to track how honestly test takers take exams remotely. In online formats, there are many simple opportunities that allow for cheating and using the use of outside help. Online proctoring based on artificial intelligence technologies in distance education is an effective technological solution to prevent academic dishonesty. This article explores the development and implementation of an online control proctoring system using artificial intelligence technology for conducting online exams. The article discusses the proctoring systems used in Kazakhstan, compares the functional features of the selected proctoring systems, and describes the architecture of Proctor SU. A prototype of the Proctor SU proctoring system has been developed. As a pilot program, the authors used this system during an online university exam and examined the results of the test. According to the author’s examination, students have a positive attitude towards the use of Proctor SU online proctoring. The proposed proctor system includes features of face detection, face tracking, audio capture, and the active capture of system windows. Models CNN, R-CNN, and YOLOv3 were used in the development process. The YOLOv3 model processed images in real time at 45 frames per second, and CNN and R-CNN processed images in real time at 30 and 38 frames per second. The YOLOv3 model showed better results in terms of real-time face recognition. Therefore, the YOLOv3 model was implemented into the Proctor SU proctoring system.
Deep Generative Knowledge Distillation by Likelihood Finetuning

Knowledge Distillation (KD) is designed to train smaller student models using a larger pretrained teacher model. However, in decentralized data systems such as blockchain, privacy concerns may arise, making the data inaccessible. To address this issue, Data-Free KD (DFKD) methods have been proposed, which extract prior knowledge from teacher networks and use it to synthesize data for KD. Previous DFKD methods faced challenges due to the large search space of data generation. Recently, deep generative models (DGMs) have been proposed to learn data distribution using deep networks, which provides an efficient way to reduce the search space by generating a set of pseudo data. In this paper, we explore the performance of KD trained using pseudo samples generated by pretrained DGMs and find that the correlation with image quality is not always positive. Based on this observation, we propose a new DFKD framework called Generative Knowledge Distillation (GenKD) that reduces the search space by constructing a prior distribution modeled by DGMs for their power of likelihood estimation. Specifically, we use energy-based models (EBM) to generate data from the Maximum Likelihood Estimation (MLE) of the EBM and gradients from downstream KD tasks by policy gradient. We then train the student model using the pretrained teacher model and pseudo samples. We also implement our GenKD framework on several widely-used benchmarks, including CIFAR100, CIFAR10, and SVHN. Our experiments demonstrate that we can generate high-quality pseudo samples quantitatively and qualitatively using GenKD. Additionally, the top-1 accuracy of the student network can approach state-of-the-art (SOTA) DFKD methods trained using fewer pseudo samples and less generation time.
Task type's effect on attitudes towards voice assistants

Abstract There is an emerging interest in examining user attitudes towards voice assistants (VAs); however, there is limited research on how user attitudes are formulated in different contexts. Drawing from the stereotype content models, the current study attempts to investigate how users perceive and evaluate voice assistants (VAs) in different contexts (i.e., functional vs. social tasks) based on warmth, competence and trustworthiness. Study 1 ( N = 123) employs a within‐subjects design to examine how task type (functional vs. social) affects user perceptions and attitudes towards a VA (i.e., Google Assistant). Study 2 ( N = 116) and Study 3 ( N = 61) examine the boundary effect of perceived psychological power and ease of use. The findings show that attitude is significantly more positive in functional tasks (vs. social), and this effect is mediated by perceived competence. This indirect effect is also significantly moderated by perceived ease of use. Perceived warmth does not mediate the effect of social tasks on attitude, and trust in VAs is a direct outcome of functional tasks. Taken together, this study contributes to both theory and practice in many ways. Specifically, the findings are the first to demonstrate a direct effect of task type on consumer perceptions and attitudes. Additionally, the findings indicate that user evaluations of VAs are still dominated by user perceptions of the competence of the VAs.
Area-Efficient digital filtering based on truncated multiply-accumulate units in residue number system <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si22.svg"><mml:mrow><mml:msup><mml:mn>2</mml:mn><mml:mi>n</mml:mi></mml:msup><mml:mo linebreak="badbreak" linebreakstyle="after">-</mml:mo><mml:mn>1</mml:mn><mml:mo>,</mml:mo><mml:msup><mml:mn>2</mml:mn><mml:mi>n</mml:mi></mml:msup><mml:mo>,</mml:mo><mml:msup><mml:mn>2</mml:mn><mml:mi>n</mml:mi></mml:msup><mml:mo linebreak="…

High resource consumption of digital filtering devices is one of the main practical problems of digital signal processing. Parallel data processing is one of the most effective approaches to improve the quantitative characteristics of such devices. This paper proposes a method for filtering signals using Truncated Multiply-and-Accumulate units (TMAC) in the residue number system (RNS) using moduli of a special type 2n-1,2n,2n+1. These moduli reduced hardware costs and power consumption compared to state-of-the-art approaches for a 32-bit RNS filter by up to 23% and up to 22.3%, respectively, with a delay increase of 131%. The proposed approach allowed to reduce the device area by up to 14% with comparable energy expended for a 48-bit RNS filter, but the device delay increased by 81%. The results obtained open up the possibility of creating area-efficient and power-efficient devices.
A Classification Study in High-Dimensional Data of Linear Discriminant Analysis and Regularized Discriminant Analysis

The objective of this work is to compare linear discriminant analysis (LDA) and regularized discriminant analysis (RDA) for classification in high-dimensional data. This dataset consists of the response variable as a binary or dichotomous variable and the explanatory as a continuous variable. The LDA and RDA methods are well-known in statistical and probabilistic learning classification. The LDA has created the decision boundary as a linear function where the covariance of two classes is equal. Then the RDA is extended from the LDA to resolve the estimated covariance when the number of observations exceeds the explanatory variables, or called high-dimensional data. The explanatory dataset is generated from the normal distribution, contaminated normal distribution, and uniform distribution. The binary of the response variables is computed from the logit function depending on the explanatory variable. The highest average accuracy percentage evaluates to propose the performance of the classification methods in several situations. Through simulation results, the LDA was successful when using large sample sizes, but the RDA performed when using the most sample sizes.
Semi-Supervised Learning for Robust Emotional Speech Synthesis with Limited Data

Emotional speech synthesis is an important branch of human–computer interaction technology that aims to generate emotionally expressive and comprehensible speech based on the input text. With the rapid development of speech synthesis technology based on deep learning, the research of affective speech synthesis has gradually attracted the attention of scholars. However, due to the lack of quality emotional speech synthesis corpus, emotional speech synthesis research under low-resource conditions is prone to overfitting, exposure error, catastrophic forgetting and other problems leading to unsatisfactory generated speech results. In this paper, we proposed an emotional speech synthesis method that integrates migration learning, semi-supervised training and robust attention mechanism to achieve better adaptation to the emotional style of the speech data during fine-tuning. By adopting an appropriate fine-tuning strategy, trade-off parameter configuration and pseudo-labels in the form of loss functions, we efficiently guided the learning of the regularized synthesis of emotional speech. The proposed SMAL-ET2 method outperforms the baseline methods in both subjective and objective evaluations. It is demonstrated that our training strategy with stepwise monotonic attention and semi-supervised loss method can alleviate the overfitting phenomenon and improve the generalization ability of the text-to-speech model. Our method can also enable the model to successfully synthesize different categories of emotional speech with better naturalness and emotion similarity.
What Happens When the President Calls You an “Enemy of the People?” Election Officials and Public Sentiment

False information about the legitimacy of recent American elections has prompted a barrage of harsh rhetoric against the officials who administer them. This spike in negativity, largely occurring through social media, is driving people out of these essential jobs. This article measures the extent of this negativity, how it has trended over time, which state administrations are targeted by it most, and what sorts of accounts are sending it. By collecting every reply to any Twitter account managed by the agency or person officially responsible for administering a state's elections, we show that the usage of keywords related to election fraud has spiked in recent years, while the sentiments of the replies have grown almost universally harsher. While left-leaning repliers are usually negative towards Republican officials, and vice versa, some officials have begun to receive negative replies from both the left and right, with sustained pile-ons led almost entirely by right-leaning repliers.
Evolutionary Approach for Interpretable Feature Selection Algorithm in Manufacturing Industry

Feature selection techniques in prediction play a role in manufacturing industries of late. However, it is very challenging to achieve an optimal subset of features as well as interpretable relationship among features due to computation complexity and variable diversity. In order to address those difficulties, this paper presents a novel evolutionary approach for feature selection algorithm to improve the effectiveness of existing meta-heuristic approaches. In other words, their optimal combinations with minimal difference between prediction and actual values can be achieved by applying an estimation of distribution algorithms (i.e., extended compact genetic algorithm) on the collected candidate feature sets. The approach discovers a less complicated and more closely related probabilistic-model structure on population space in each generation, thereby encouraging the comprehension power of feature selection results. We tested our method on six real-world data sets from manufacturing industries (open to the public). It demonstrated that higher interpretability on features selection results is achieved in comparison with well-known methods.
A fast and accurate compound collision detector for RRT motion planning

The application of artificial intelligence tools has led to newly developed collision detectors which have better computational efficiency than the kinematics-and-geometry based collision detectors (KCD) to improve robot motion planning strategies. However, new detectors are not very accurate in some cases. To improve the accuracy, a trade-off between efficiency and accuracy is required. We propose a novel compound collision detector (CCD) for collision queries that modifies the planners of rapidly-exploring random tree (RRT) to improve the classical probabilistic collision detector (PCD). It is composed of an exact collision detector (ECD), an inference collision detector (ICD) and a strategy to determine ECD or ICD based on some conditions. In our CCD, we use a sphere-ellipsoidal pseudo distance (SEPD) in the determination strategy to alleviate the problem of highly-frequent outputs of false-positive in narrow passages of PCD, and a node based bounding method (NBB) to increase the speed of data storage and loading for the sub-algorithm ICD. Experiments on a Kinova Jaco assistive robotic arm are taken to evaluate the performance of our CCD, which show an improved accuracy with a small reduction of speed in comparison with PCD. So, it is a promising tool in robot motion planning.
Near-optimal 3D trajectory design in presence of obstacles: A convolutional neural network approach

This paper proposes an approach based on neural networks for designing near-optimal 3D trajectories connecting two points separated by obstacles. A reference path is first built with a novel Theta* algorithm implementation, upgraded to reduce the number of waypoints and the angular variations. Starting from the path, a trajectory based on piecewise Bézier curves is designed with an algorithm relying on two design parameters. The optimal value of these parameters is estimated with a Convolutional Neural Network (CNN) architecture receiving as inputs an image of the path and its properties. The CNN training is performed on a synthetic dataset of optimal parameters built with Differential Evolution optimization for a variety of randomly generated paths. The parameters from CNN are refined with an algorithm capable of providing with high success rate near-optimal trajectories within minimum computation time.
Foveated Spatial Compression for Remote Rendered Virtual Reality

In remote rendered virtual reality (VR), the rendering of the application is moved to the cloud enabling high quality real-time content to be consumed on low-powered standalone head mounted displays (HMDs). The rendered frames are encoded to a video stream and streamed to a thin client which relays user's input to the server and decodes and displays the incoming video. Latency and high bandwidth requirements are key challenges for remote rendered graphics. Foveation can be used to optimize the quality of the transmitted frames to be in line with the human visual system (HVS). In this paper we evaluate multiple different strategies on how to apply foveation to spatially compress video frames, i.e., reduce their resolution, before transmission. We also show how the foveation methods can be used together with super resolution to alleviate the bandwidth usage of real-time remote rendered VR and optimize the perceived image quality.