Reviewerly/exHarmony huggingface dataset:
Privacy Protection Strategy Based on Federated Learning for Smart Park Multi Energy Fusion System

In order to realize the promotion of clean energy, the energy Internet which supports the efficient energy utilization has been widely concerned. While, the smart Park energy system to realize the highly coupling of energy, intelligent processing and self-response has become an important part of realizing the energy Internet. In the multi energy integration system of smart Park, the grid needs to collect nearly real-time power consumption data from the user side to analyze the power consumption behavior of the whole park, so as to formulate power dispatching strategy. However, this kind of nearly real-time power consumption data is easy to reveal the status of electric devices, which poses a threat to the user's privacy. This paper proposes a privacy protection strategy based on Federated learning for smart Park multifunctional fusion system. Through federal learning technology, users in the park participate in energy consumption modeling while don't need to send their privacy-sensitive data to the power grid. Therefore, individual privacy can be protected while power grid can obtain the overall power consumption behavior of the park.
A study on impact of gender in investment decisions

John Grey's classic book "Men are from Mars, Women are from Venus" illustrates how they view the world through different lenses.Women differ from men in many ways including their attitude toward money.The literature supports the notion of gender differences in investing, pointing out that male investors are the more risk tolerant and self-confident investors.Yet few studies have empirically connected these conditions to differences in the information acquisition behavior of male and female investors.This paper examines the causes which make men and women to behave differently in financial matters.The paper also studies if there exists differences in the information source and is this the cause for women exhibiting greater financial risk aversion than men.
Mediating Effect of Grit in the Relationship between Body Image and Health Promotion Lifestyle of Nursing Students

Purpose: This study aims to investigate the mediating effect of grit in the relationship between body image and health promotion lifestyle for nursing students. It was implemented to present basic data for the development of educational programs to establish the health promotion lifestyle of nursing students. Methods: The participants of this study were 169 nursing students in three universities in G City, J Province. Data were descriptive statistics, t-test, ANOVA, Pearson correlation analysis. Also mediation analysis was performed according to the Baron and Kenny method and Sobel test. Results: Health promotion lifestyle is positively correlated with body image(r=.53, p<.001), grit (r=.37, p<.001), and body image is positively correlated with grit(r=.33, p<.001). The grit showed a partial mediating effect between the body image and the health promotion lifestyle(Z=3.21, p<.001), the positive the body image(β=.46, p<.001), the higher the grit(β=.22, p<.001), the healthier the health. The level of health promotion lifestyle increased, and the explanatory power to explain the health promotion lifestyle was 33%. Conclusion: Therefore, in order to improve the health promoting lifestyle level of nursing students, it is important to change their perception so that they can view their body image positively. In addition, it suggests that nursing students need to cultivate grit so that they can continuously pay attention to and strive for their health promotion lifestyle.
Deep Learning and Uniform LBP Histograms for Position Recognition of Elderly People with Privacy Preservation

For the elderly population, falls are a vital health problem especially in the current context of home care for COVID-19 patients. Given the saturation of health structures, patients are quarantined, in order to prevent the spread of the disease. Therefore, it is highly desirable to have a dedicated monitoring system to adequately improve their independent living and significantly reduce assistance costs. A fall event is considered as a specific and brutal change of pose. Thus, human poses should be first identified in order to detect abnormal events. Prompted by the great results achieved by the deep neural networks, we proposed a new architecture for image classification based on local binary pattern (LBP) histograms for feature extraction. These features were then saved, instead of saving the whole image in the series of identified poses. We aimed to preserve privacy, which is highly recommended in health informatics. The novelty of this study lies in the recognition of individuals’ positions in video images avoiding the convolution neural networks (CNNs) exorbitant computational cost and Minimizing the number of necessary inputs when learning a recognition model. The obtained numerical results of our approach application are very promising compared to the results of using other complex architectures like the deep CNNs.
Medical Image Character Recognition Based on Multi-scale Neural Convolutional Network

Generally, medical images are usually characterized by complex background and low resolution. Also, text's location is sometimes random. Therefore, traditional Optical Character Recognition (OCR) technology cannot be well applied to recognize medical images which contain many factors. Aiming at this problem, a character recognition algorithm for medical images is proposed in this paper. The Efficient and Accurate Scene Text (EAST) scene detection algorithm is used in text detection and the Convolutional Recurrent Neural Network (CRNN) model is used in character recognition. The problem of low recognition rate that caused by the low resolution will be solved by adding a multi-scale convolution kernel to the model. The designed algorithm is tested by Medpix datasets, and the experimental results show that the proposed method has a higher precision and a higher recall than the traditional recognition methods.
Improving scalability of parallel CNN training by adaptively adjusting parameter update frequency

Synchronous SGD with data parallelism, the most popular parallelization strategy for CNN training, suffers from the expensive communication cost of averaging gradients among all workers. The iterative parameter updates of SGD cause frequent communications and it becomes the performance bottleneck. In this paper, we propose a lazy parameter update algorithm that adaptively adjusts the parameter update frequency to address the expensive communication cost issue. Our algorithm accumulates the gradients if the difference of the accumulated gradients and the latest gradients is sufficiently small. The less frequent parameter updates reduce the per-iteration communication cost while maintaining the model accuracy. Our experimental results demonstrate that the lazy update method remarkably improves the scalability while maintaining the model accuracy. For ResNet50 training on ImageNet, the proposed algorithm achieves a significantly higher speedup (739.6 on 2048 Cori KNL nodes) as compared to the vanilla synchronous SGD (276.6) while the model accuracy is almost not affected (<0.2% difference).
A Survey on Efficient Consensus Mechanism for Electricity Information Acquisition System

The current electricity information acquisition system has been able to meet some basic needs of Power system data sharing, but with the development of Power system, data sharing is faced with some problems of scale expansion, and the data acquisition method is complex, which puts forward new challenges to the timeliness and security of data. As one of the core technologies of blockchain technology, consensus mechanism has been used in various fields widely. It is a very suitable way to use consensus algorithm for data sharing in electricity information acquisition system and make use of the high efficiency and expansibility of some consensus algorithms to realize the efficient collection of electricity information acquisition system and the real-time query of data by users while saving energy. In this article, analyzing the actuality of electricity information acquisition system, We select 3 consensus algorithms suitable for power field to meet its efficient data synchronization, including Raft, RSP and PoS. After analysis and comparison, the results show that the improved algorithm for three kinds of algorithms can solve some efficient electricity information acquisition system, storage, energy saving and safety problems.
Deep Learning for Historical Data Analysis

This presentation will give an overview of projects on leveraging deep learning for historical data analysis my group did in the last 3 years, partly in the context of the ANR EnHerit project. I will first discuss how deep learning can be used to retrieve and analyze repeated details in artworks in artwork collections [5, 6]. I will then present several problems related to historical document analysis: historical watermarks recognition [7], document images segmentation [2], clustering for text modelling [3, 4], and scientific illustration propagation in historical manuscripts analysis [1]. In all cases, I will show that standard approaches can give useful baseline results when tuned adequately, but that developing dedicated approaches that take into account the specificity of the data and the problem significantly improves the results.
THE MECHANISM OF PRACTICAL APPLICATION OF THE COMPETENCE MODEL OF A UNIVERSITY GRADUATE BASED ON DIGITAL TECHNOLOGIES

Today, the issues of mastering the current training program at the university are of concern to school graduates, applicants and students of the universities. Often, the question of choosing a promising profession arises even at school – after all, which exams the student will have to prepare for depends on it. At the same time, often, the choice of a future profession is made more and more intuitively, on the recommendation of close relative; employers and universities are very little involved in this process today. In addition, the employer does not have any significant tools to influence the formation of the competencies of a future university graduate, only indirectly influencing the training process. The article analyzes the shortcomings of the existing interaction between students, employers and the university. The scientific novelty of the research consists in the formation of a mechanism for the practical application of the competence model of training students based on the use of the capabilities of modern digital technologies, which is supposed to be implemented by developing and creating a digital platform for the employment of graduates, providing training of specialists on request of specific organizations. This will make it possible to satisfy the organization's demand for highly qualified personnel with targeted practice-oriented training, influence the formation of the required competencies "on request", as well as more effectively provide employment opportunities for graduates. Despite the significant experience of interaction between employers and job seekers on various electronic resources, modern platforms do not presuppose the influence of the employers on the competence of the student, and force them to deal with the finished product of training - a graduate, spending time and other resources on his targeted training at the workplace. The proposed digital platform is designed to solve this problem.
4D Doodling: Free Creation of Shape-Changing Decoration with A 3D Printing Pen

4D printing thermoplastic technology has become a common method for fabricating shape-changing interfaces, however, the fully automated 3D printing method also limits user's creative participation in the fabrication process. We present a 4D Doodling method which allow users to create deformable daily items in a free-style way using a 3D printing pen. In the doodling experiment, we summarized the experience of manual printing and provided a practical method to support the user's creation, which can be a way to increase humanity for 4D printing technology.
Improving Learner Experience, Motivation and Knowledge Gain When Using Mulsemedia-Based Technology Enhanced Learning

Lately there is an increasing interest from educators to find solutions to improve student engagement, motivation and academic performance of their students, especially in Science, Technology, Engineering, and Mathematics (STEM) subjects. This is as most students believe that STEM subjects are either too challenging or require disproportionate effort for the returned benefit. Diverse technology-enhanced learning (TEL) methods are employed in order to reverse this student perception and attract them to STEM education. This paper studies the impact of multiple sensorial media (mulsemedia)-based learning on postgraduate students from Dublin City University Ireland. The study analyses the impact of this novel TEL method on learner experience, motivation and learning outcome. The results of the study show that academic performance, learning experience, engagement and motivation of the student improve when employing mulsemedia-enhanced learning. More than 80% of the participants in the study stated they have enjoyed the use of mulsemedia during learning and agreed with the fact that mulsemedia is highly motivating for learning. Additionally, about 70% of the participants indicated that they would want to continue to be exposed to mulsemedia-enhanced learning.
Towards Transparent Interactive Semantic Parsing via Step-by-Step Correction

Existing studies on semantic parsing focus on mapping a natural-language utterance to a logical form (LF) in one turn.However, because natural language may contain ambiguity and variability, this is a difficult challenge.In this work, we investigate an interactive semantic parsing framework that explains the predicted LF step by step in natural language and enables the user to make corrections through naturallanguage feedback for individual steps.We focus on question answering over knowledge bases (KBQA) as an instantiation of our framework, aiming to increase the transparency of the parsing process and help the user trust the final answer.We construct INSPIRED, a crowdsourced dialogue dataset derived from the COMPLEXWEBQUESTIONS dataset.Our experiments show that this framework has the potential to greatly improve overall parse accuracy.Furthermore, we develop a pipeline for dialogue simulation to evaluate our framework w.r.t. a variety of state-of-the-art KBQA models without further crowdsourcing effort.The results demonstrate that our framework promises to be effective across such models.
Born digital workflow tervezése a Petőfi Irodalmi Múzeum Digitális Bölcsészeti Központjában

The Petőfi Literary Museum’s Digital Humanities Centre, established last year, is in the process of creating a digital humanities platform, dHUpla. One of its sub-areas is the planning of the management of born digital materials. The development of a procedure for born digital materials is an urgent need: in addition to the existing collection items in the PIM, it is expected that in the future more and more content of this kind will be created, which will need to be managed professionally. The presentation will describe the research work carried out and the details of the workflow that is being outlined. It will present the workflow developed on the basis of the OAIS model, which covers the process from ingestion of such material to making it searchable and publishable. It covers not only the more manageable text file formats of born digital content, but also the more difficult tasks of emails or social media sites, and how to professionally extract and archive data from external media.
Key Point Analysis via Contrastive Learning and Extractive Argument Summarization.

Key point analysis is the task of extracting a set of concise and high-level statements from a given collection of arguments, representing the gist of these arguments. This paper presents our proposed approach to the Key Point Analysis Shared Task, colocated with the 8th Workshop on Argument Mining. The approach integrates two complementary components. One component employs contrastive learning via a siamese neural network for matching arguments to key points; the other is a graph-based extractive summarization model for generating key points. In both automatic and manual evaluation, our approach was ranked best among all submissions to the shared task.
Probabilistic and Geometric Depth: Detecting Objects in Perspective

3D object detection is an important capability needed in various practical applications such as driver assistance systems. Monocular 3D detection, as an economical solution compared to conventional settings relying on binocular vision or LiDAR, has drawn increasing attention recently but still yields unsatisfactory results. This paper first presents a systematic study on this problem and observes that the current monocular 3D detection problem can be simplified as an instance depth estimation problem: The inaccurate instance depth blocks all the other 3D attribute predictions from improving the overall detection performance. However, recent methods directly estimate the depth based on isolated instances or pixels while ignoring the geometric relations across different objects, which can be valuable constraints as the key information about depth is not directly manifest in the monocular image. Therefore, we construct geometric relation graphs across predicted objects and use the graph to facilitate depth estimation. As the preliminary depth estimation of each instance is usually inaccurate in this ill-posed setting, we incorporate a probabilistic representation to capture the uncertainty. It provides an important indicator to identify confident predictions and further guide the depth propagation. Despite the simplicity of the basic idea, our method obtains significant improvements on KITTI and nuScenes benchmarks, achieving the 1st place out of all monocular vision-only methods while still maintaining real-time efficiency. Code and models will be released at this https URL.
Design of 3D Modeling Face Image Library in Multimedia Film and Television

The development of 3D modeling technology has promoted the development of the multimedia film and television industry. This article is aimed at studying the design of 3D modeling facial image library in multimedia film and television, at providing a more comprehensive facial image library for the multimedia film and television industry, at breaking the shackles of the traditional film and television industry with 3D technology, and at continuously surpassing traditional film and television media forms. This article deeply explores the background development of multimedia film and television and the characteristics of the development of new media. Starting from 3D technology, it extracts facial features of characters, transforms image data through deep autoencoders, and uses local binarization mode to perform the original facial image is subjected to texture feature extraction. In this paper, a number of experimental subjects were selected, and the subjects were photographed from the left, front, and right from multiple angles. Through the pinhole camera projection imaging process, the internal and external parameters of the camera were adjusted. In the process of 3D image construction, the image is first selected for feature detection, then the corresponding vector information and geometric conditions are matched to construct a 3D matrix, and the facial structure image is obtained by triangulation. This article compares the 3D production software on the market and selects the Maya platform suitable for building this system. The global constraint information is obtained by training some sample images. When searching the test image, find the appropriate feature point position according to the structural matching degree of the local image. When each search is completed, the global information will be used for constraint, so as to output reasonable feature information. The average residual range of the human face image constructed in this paper is 0.25-0.45, and the maximum residual error does not exceed 4.0. The experimental method in this paper has good stability and robustness. Using the COM transmission model can make experimenters not need to think too much about the underlying details. This face animation-driven simulation scheme can achieve more vivid facial expressions.
Stories Inspired Optimization Algorithms

The primary goal of this study is to invent and define a new area called “Stories Inspired Optimization Algorithms (SIOA)”.
Controllable anonymous authentication scheme based on blockchain and decentralized traceable attribute-based signature

The development of information technology and the enhancement of peoples attention to personal privacy security put forward higher requirements for the identity authentication mechanism of traditional Internet applications, which not only needs to realize the users identity authentication, but also protects the users privacy information to the greatest extent. Compared with the traditional digital signature schemes, the attribute-based signature scheme has the characteristics of anonymity, that is, the attribute-based signature can hide the signers identity information, and other users cannot link a signature to the signers identity (attribute set). In practical application scenarios, we usually need to monitor the identity of users to prevent the occurrence of anonymous abuse and dishonesty. Therefore, in order to balance these two seemingly contradictory requirements, this paper proposes a controllable anonymous authentication scheme based on blockchain and decentralized traceable attribute-based signature. By properly improving the existing traceable attribute-based signature scheme, the decentralized attribute authority is realized without affecting the efficiency, so as to prevent the corruption of single attribute authority on system security. The results show that the proposed scheme is suitable for electronic voting systems and other scenarios with decentralized controllable anonymous authentication requirements.
Multimodal Cyberbullying Detection using Hybrid Deep Learning Algorithms

The usage and user of internet and social media is increasing day-by-day and consequently cyberbully vulnerabilities are also growing.Cyberbullying is an aggressive, planned behavior carried out by a group or individual.It is happening by sending, posting, sharing negative, harmful, untrue contents in online.It leads to psychiatric and emotional disorders for those affected.Hence, there is a critical requirement to develop automated methods for cyberbullying detection and prevention.Over the past few years, most existing work on cyberbullying detection has focused on text based analysis.Text and image are the important mediums in cyberbullying incident.This paper presents a hybrid deep neural model for cyberbullying detection in two different modalities of social data, namely text and image.Deep learning methods have achieved state-of-the-art results in various applications.In this paper, hybrid deep learning technique is used in multiple modalities of data to detect cyber bullying.An experiment was conducted on hybrid model (CNN and LSTM) which detects cyberbullying on text and image.The experiments are conducted on publicly available datasets and tested with telegram chat.This paper aims to direct future research on integrating video source with existing multimodal data source to prevent cyberbullying issues.