Reviewerly/exHarmony huggingface dataset:
3D Human Motion Prediction Based Deep Learning

A growing number of established methods utilising deep neural networks to handle the problem of human motion prediction have been developed as a result of deep learning's success in a wide range of computer vision and computer graphics jobs. Modern motion prediction techniques concentrate on resolving numerous problems to forecast accurately and regular human movement across time. We give a thorough analysis of deep learning-based human motion prediction techniques in this paper. The goal of this work and the human motion prediction challenge are first defined. On the basis of our suggested classification, we then present pertinent background information and a thorough list of motion prediction techniques. Next, we give a thorough analysis of the traits frequently employed in the literature and describe the evaluation procedures. We concluded by providing a quantitative comparison.
The effect of perceived organizational support on the prohibitive voice behavior of knowledgeable talents during the COVID-19 pandemic: Exploring moderating role of the digitalization level

This study focuses on the effect of perceived organizational support on the prohibitive voice of knowledgeable talents and on the factors influencing that voice during the COVID-19 pandemic in China. Grounding our research in social exchange theory and the Chinese sociocultural background, we collected data from 714 questionnaires from universities in Zhejiang Province and analysize by linear regression and found, first, that perceived organizational support significantly increased the prohibitive voice of knowledgeable talents and, second, that in the relationship between perceived organizational support and the prohibitive voice of those talents, the level of digitalization level of an organization has a positive the effect of perceived organizational support on prohibitive voice behavior. This study enriches the theory of voice behavior, helps universities understand the behavioral patterns of knowledgeable talents and guides the cultivation of an atmosphere with prohibitive voice behavior.
Domain-Adaptive Self-Supervised Pre-Training for Face &amp; Body Detection in Drawings

Drawings are powerful means of pictorial abstraction and communication. Understanding diverse forms of drawings, including digital arts, cartoons, and comics, has been a major problem of interest for the computer vision and computer graphics communities. Although there are large amounts of digitized drawings from comic books and cartoons, they contain vast stylistic variations, which necessitate expensive manual labeling for training domain-specific recognizers. In this work, we show how self-supervised learning, based on a teacher-student network with a modified student network update design, can be used to build face and body detectors. Our setup allows exploiting large amounts of unlabeled data from the target domain when labels are provided for only a small subset of it. We further demonstrate that style transfer can be incorporated into our learning pipeline to bootstrap detectors using a vast amount of out-of-domain labeled images from natural images (i.e., images from the real world). Our combined architecture yields detectors with state-of-the-art (SOTA) and near-SOTA performance using minimal annotation effort. Our code can be accessed from https://github.com/barisbatuhan/DASS_Detector.
VectorFusion: Text-to-SVG by Abstracting Pixel-Based Diffusion Models

Diffusion models have shown impressive results in text-to-image synthesis. Using massive datasets of captioned images, diffusion models learn to generate raster images of highly diverse objects and scenes. However, designers frequently use vector representations of images like Scalable Vector Graphics (SVGs) for digital icons or art. Vector graphics can be scaled to any size, and are compact. We show that a text-conditioned diffusion model trained on pixel representations of images can be used to generate SVG-exportable vector graphics. We do so without access to large datasets of captioned SVGs. By optimizing a differentiable vector graphics rasterizer, our method, VectorFusion, distills abstract semantic knowledge out of a pretrained diffusion model. Inspired by recent text-to-3D work, we learn an SVG consistent with a caption using Score Distillation Sampling. To accelerate generation and improve fidelity, VectorFusion also initializes from an image sample. Experiments show greater quality than prior work, and demonstrate a range of styles including pixel art and sketches. See our project webpage at https://ajayj.com/vectorfusion .
A virtual surgical prototype system based on gesture recognition for virtual surgical training in maxillofacial surgery

Virtual reality (VR) technology is an ideal alternative of operation training and surgical teaching. However, virtual surgery is usually carried out using the mouse or data gloves, which affects the authenticity of virtual operation. A virtual surgery system with gesture recognition and real-time image feedback was explored to realize more authentic immersion.Gesture recognition technology proposed with an efficient and real-time algorithm and high fidelity was explored. The recognition of hand contour, palm and fingertip was firstly realized by hand data extraction. Then, an Support Vector Machine classifier was utilized to classify and recognize common gestures after extraction of feature recognition. The algorithm of collision detection adopted Axis Aligned Bounding Box binary tree to build hand and scalpel collision models. What's more, nominal radius theorem (NRT) and separating axis theorem (SAT) were applied for speeding up collision detection. Based on the maxillofacial virtual surgical system we proposed before, the feasibility of integration of the above technologies in this prototype system was evaluated.Ten kinds of signal static gestures were designed to test gesture recognition algorithms. The accuracy of gestures recognition is more than 80%, some of which were over 90%. The generation speed of collision detection model met the software requirements with the method of NRT and SAT. The response time of gesture] recognition was less than 40 ms, namely the speed of hand gesture recognition system was greater than 25 Hz. On the condition of integration of hand gesture recognition, typical virtual surgical procedures including grabbing a scalpel, puncture site selection, virtual puncture operation and incision were carried out with realization of real-time image feedback.Based on the previous maxillofacial virtual surgical system that consisted of VR, triangular mesh collision detection and maxillofacial biomechanical model construction, the integration of hand gesture recognition was a feasible method to improve the interactivity and immersion of virtual surgical operation training.
College Students Service Feedback Based on a Complex Network

&lt;p&gt;The ideas of others always influence people because they are social animals. They will evaluate a movie based on the rating level and change their decision based on someone&amp;rsquo;s advice. It is expected that the comments on the news are reversed suddenly because of a few people, especially in the context of the communication wave set off by the Internet as a new media. It is worth noting that there is a relationship between the deviation of public opinion and the intimacy between people, and confidence and openness also play a role. Recently, there has been renewed interest in dynamic models of research opinions. Our goal is to build a dynamic model of opinion offset based on various influencing factors and then use it to control public opinion more accurately and reduce the loss caused by them. We analyzed existing models and found that few articles considered people&amp;rsquo;s confidence, openness, and intimacy together. Therefore, we designed new models that considered all the influencing factors. We tested the model with actual data and achieved high accuracy. Finally, we found that opinions would eventually converge to a peak value, and the time needed for convergence was affected by intimacy, openness, and confidence.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
Passive Detection Method Based on Phase-frequency Conversion

On the basis of the existing research, the passive detection method based on Doppler shift is further converted to the passive detection method based on the non-fuzzy phase difference measurement. Based on the phase-frequency conversion, the passive detection methods of two different models, that is a method of detecting a fixed target by a moving station, are presented in this chapter for improving the ranging accuracy. The first one studies the single station passive ranging method which takes the motion trajectory as the baseline of the detection array. The second one studies the ranging method which transforms the single base array into the virtual double base array by the relation between frequency shift and path difference. The research in this chapter provides some reference information on how to improve the performance of single or dual station passive positioning. This chapter proves that the accuracy of the phase difference ranging is better than that of the Doppler frequency difference. The significance of phase-frequency conversion lies in the first use of Doppler positioning method to give the location method which is difficult to obtain by using the phase difference equation, and then through phase-frequency conversion, further obtain better measurement accuracy than Doppler frequency difference measurement.
Semantic-aware One-shot Face Re-enactment with Dense Correspondence Estimation

One-shot face re-enactment is a challenging task due to the identity mismatch between source and driving faces. Specifically, the suboptimally disentangled identity information of driving subjects would inevitably interfere with the re-enactment results and lead to face shape distortion. To solve this problem, this paper proposes to use 3D Morphable Model (3DMM) for explicit facial semantic decomposition and identity disentanglement. Instead of using 3D coefficients alone for re-enactment control, we take the advantage of the generative ability of 3DMM to render textured face proxies. These proxies contain abundant yet compact geometric and semantic information of human faces, which enable us to compute the face motion field between source and driving images by estimating the dense correspondence. In this way, we could approximate re-enactment results by warping source images according to the motion field, and a Generative Adversarial Network (GAN) is adopted to further improve the visual quality of warping results. Extensive experiments on various datasets demonstrate the advantages of the proposed method over existing start-of-the-art benchmarks in both identity preservation and re-enactment fulfillment.
Integration of formal and informal education as a didactic condition for the professional growth of future specialists in the information activities of the enterprise

The article represents the mechanisms of implementation of one of the didactic conditions for the professional growth of future specialists in the information activities of the enterprise while studying at an educational institution. It is the activation of students' educational activities through the integration of formal and informal education. Formal education is considered as a process of professional training of future specialists in the information activities of the enterprise, informal education is seen as a process of students’ self-education.
PRAGMATISM AS A TEACHING APPROACH FOR ENGAGING STUDENTS IN COMPUTING EDUCATION

Appears in: ICERI2022 Proceedings Publication year: 2022Pages: 4270-4273ISBN: 978-84-09-45476-1ISSN: 2340-1095doi: 10.21125/iceri.2022.1040Conference name: 15th annual International Conference of Education, Research and InnovationDates: 7-9 November, 2022Location: Seville, Spain
AUGMENTED REALITY FOR THE TEACHING OF STEM COURSES IN SENIOR HIGH SCHOOL

Appears in: ICERI2022 Proceedings Publication year: 2022Pages: 4213-4219ISBN: 978-84-09-45476-1ISSN: 2340-1095doi: 10.21125/iceri.2022.1021Conference name: 15th annual International Conference of Education, Research and InnovationDates: 7-9 November, 2022Location: Seville, Spain
Decoding Process of RS Codes with Errors and Erasures: An Overview

This paper provides a comprehensive overview of the hard-decision decoding process of Reed-Solomon codes for error-and-erasure decoding. For an [n, k] RS code, the decoder can correct simultaneously v errors and µ erasures in the received data if 2v + µ ≤n - k (correctable range) and will fail otherwise (uncorrectable range). We give detailed reviews of both Berlekamp-Massy and Continued-Fraction algorithms for error-and-erasure decoding. Berlekamp-Massy algorithm has long been known but sometimes appeared incorrectly in some references. Continued-Fraction algorithm has been recently applied for error-and-erasure decoding. Finally, we verify by simulation that two algorithms work exactly the same even in the uncorrectable range.
Slow Science and Slow Education in a Digitalized University: an Anthropopractic Approach

Introduction. The slow movement in universities is a reaction to the economic-centricity and commodification of higher education and science, the standardization and quantifiability of research and educational activities, the expansion of digital technologies` use in the generation and assimilation of knowledge, the imposition of the cult of speed on the academic and student communities. Rapid knowledge practices are becoming a trend in the context of digitalization of higher education. At the same time, the slow movement in Russian universities remains marginal and poorly institutionalized. The article analyzes the philosophical concepts that underlie the slow movement in universities and substantiates the importance of an anthropopractical approach to the study of slow science and slow education. Methodology and sources. The technocratic approach to the digitalization of higher education contributes to the spread of high-speed science and learning practices based on the reduction of knowledge to information, the growth of knowledge – to extension and cumulation, the cognitive motivation – to competition, greed and ambition. Turning to an anthropopractic approach allows us to overcome this reductionism. Slow science and slow education as anthropological practices are based on the desire of subjects of cognition for dialogue, reflection, improvisation and creative self-realization, self-organization and autodesign, intellectual enjoyment, sustainable, ecological coexistence. Digital technologies are only driving tools for this practice. Results and discussion. Slow practices in education and science are considered in this article, firstly, as an antithesis to the practices of posthumanism and futuro design, overcoming imperfect human nature and movement towards a posthuman with the help of nano-, bio-, information, cognitive and social technologies. Secondly, slow science and slow education as anthropological practices are opposed to managerial practices that form educational environments hostile to a person, the characteristic features of which are competition and hierarchy in the system “effective – ineffective”, “winners – losers”, “successful – catching up – lagging behind”. Conclusion. The authors see the optimal scenario for the transfer of slow practices to the sphere of higher education in Russia in achieving a balance between pragmatism, a shortterm policy and effectivenes, and a reflective position that ensures an anthropological orientation and sustainable development of modern universities.
Edge-Cloud Cooperative Image Processing by Partially Streaming ROI Data for Metaverse Applications

Recently, interest in the metaverse has been increasing. In the multi-edge-based structure of the metaverse, the edge consumes a lot of power during image processing and data transmission. These problems make the metaverse system difficult to use in streaming, which leads to an increase in the edge's size and performance. We propose a collaborative edge-cloud of image processing and data transmission to reduce the edge's size and power consumption. We use a Region of Interest (ROI) and distribute edge-image processing to make streamable and runtime executable AI software. With this proposed structure, we confirmed the reduction in edge time, power consumption, and network communication.
An Adaptive Beetle Swarm Optimization Algorithm with Novel Opposition-Based Learning

The Beetle Swarm Optimization (BSO) algorithm is a high-performance swarm intelligent algorithm based on beetle behaviors. However, it suffers from poor search speeds and is prone to local optimization due to the size of the step length. To address this further, a novel improved opposition-based learning mechanism is utilized, and an adaptive beetle swarm optimization algorithm with novel opposition-based learning (NOBBSO) is proposed. In the proposed NOBBSO algorithm, the novel opposition-based learning is designed as follows. Firstly, according to the characteristics of the swarm intelligence algorithms, a new opposite solution is obtained to generate the current optimal solution by iterations in the current population. The novel opposition-based learning strategy is easy to converge quickly. Secondly, an adaptive strategy is used to make NOBBSO parameters self-adaptive, which makes the results tend to converge more easily. Finally, 27 CEC2017 benchmark functions are tested to verify its effectiveness. Comprehensive numerical experiment outcomes demonstrate that the NOBBSO algorithm has obtained faster convergent speed and higher convergent accuracy in comparison with other outstanding competitors.
Sustaining Education by Actualizing Affordances of Social Media Platforms During the Pandemic

This paper draws on the affordances theory and investigates the ways in which pre-service teachers are sustaining social media uptake in learning by taking advantage of the opportunities afforded by the new delivery methods during the pandemic. This research is part of a descriptive mixed methods study. A questionnaire and in-depth, qualitative interviews were used to explore pre-service teachers&amp;rsquo; perceptions of the affordances of social media platforms for learning and the actualization of those affordances. The survey and interview data were analyzed through the theoretical lens of affordance theory. The results suggest that the pre-service teachers actualized the affordances of social media by their knowledgeable use in a dynamic learning environment. The key findings are that the teachers perceived the social media applications to be flexible, useful and practical (functional affordances), bridges formal and informal learning and supported self-regulated learning (cognitive affordances), allowed pre-service teachers to assume new identities and discover learning as independent learners who had control over their learning (identity creation affordances), and fostered interaction and collaborative feedback (social affordances). Lack of guidance or lack of teacher presence was the main constraint. This research produces new knowledge and analysis and makes new theoretical contributions on the specific affordances of social media technologies. This study adds to and extends existing literature by contributing to an understanding of the actualization of the affordances of social media platforms.
Modeling Sparse Data Using MLE with Applications to Microbiome Data

Modeling sparse data such as microbiome and transcriptomics (RNA-seq) data is very challenging due to the exceeded number of zeros and skewness of the distribution. Many probabilistic models have been used for modeling sparse data, including Poisson, negative binomial, zero-inflated Poisson, and zero-inflated negative binomial models. One way to identify the most appropriate probabilistic models for zero-inflated or hurdle models is based on the p-value of the Kolmogorov-Smirnov (KS) test. The main challenge for identifying the probabilistic model is that the model parameters are typically unknown in practice. This paper derives the maximum likelihood estimator (MLE) for a general class of zero-inflated and hurdle models. We also derive the corresponding Fisher information matrices for exploring the estimator's asymptotic properties. We include new probabilistic models such as zero-inflated beta binomial and zero-inflated beta negative binomial models. Our application to microbiome data shows that our new models are more appropriate for modeling microbiome data than commonly used models in the literature.
IoTGMRFM: Internet of Things-Based Variable-Wise Anomaly Detection

Ray, Partha PratimAnomaly detection for multivariate noisy environments is a challenging task. This paper presents the implementation of the Gaussian Markov random field mixture (GMRFM) for a simulated Internet of Things (IoT)-based sensor dataset. The implied process, i.e., IoTGMRFM aims to find variable-wise anomalies based on the variational Bayesian inference. Thus, a sparse mixture of sparse graphical Gaussian models (GGM) is obtained that can remove nuisance variables from the simulated IoT sensor dataset and handles multi-operational modes. This paper aims to find a number of key parametric assessments based on the GMRFM technique such as, (i) combined multivariate anomaly scores computation, (ii) iteration time of execution, (iii) variation of execution time at constant iteration 500, and (iv) anomaly scoring time measurement. The major contribution of this work is to deploy the technique on IoT-based ecosystem, i.e., Raspberry Pi for assessing various parametric features on such environment.
Clinical Dialogue Transcription Error Correction Using Seq2Seq Models

Good communication is critical to good healthcare. Clinical dialogue is a conversation between health practitioners and their patients, with the explicit goal of obtaining and sharing medical information. This information contributes to medical decision-making regarding the patient and plays a crucial role in their healthcare journey. The reliance on note taking and manual scribing processes are extremely inefficient and leads to manual transcription errors when digitizing notes. Automatic Speech Recognition (ASR) plays a significant role in speech-to-text applications, and can be directly used as a text generator in conversational applications. However, recording clinical dialogue presents a number of general and domain-specific challenges. In this paper, we present a seq2seq learning approach for ASR transcription error correction of clinical dialogues. We introduce a new Gastrointestinal Clinical Dialogue (GCD) Dataset which was gathered by healthcare professionals from a NHS Inflammatory Bowel Disease clinic and use this in a comparative study with four commercial ASR systems. Using self-supervision strategies, we fine-tune a seq2seq model on a mask-filling task using a domain-specific PubMed dataset which we have shared publicly for future research. The BART model fine-tuned for mask-filling was able to correct transcription errors and achieve lower word error rates for three out of four commercial ASR outputs.