Reviewerly/exHarmony huggingface dataset:
Research on the Influencing Factors of Emotional Well-Being of the Elderly in Rural Areas of Shaanxi Province

At present, the degree of aging is deepening, and there are problems such as "getting old before getting rich" and "getting old before getting ready".The happiness of the elderly has become a key issue for scholars, and improving the well-being of the elderly has also become a serious challenge facing our country.In recent years, researchers have proposed that the happiness of the elderly in rural areas in our country is lower than that of the elderly in urban areas, but most studies take the elderly in urban areas as the subject of subjective well-being, and few studies focus on the subjective well-being of the elderly in rural areas.Studying the influencing factors of the well-being of the elderly in rural areas can inspire the society to pay attention to the material and spiritual needs of the elderly in rural areas.Based on the CHARLS 2015 national tracking survey data, research the influence of demographic characteristics, health status, intergenerational relationship, economic status, and social factors on the emotional well-being of the elderly in rural Shaanxi Province, and put forward countermeasures and suggestions to improve the emotional well-being of the elderly in rural areas of Shaanxi Province
Towards Simple and Accurate Human Pose Estimation With Stair Network

In this paper, we focus on tackling the precise keypoint coordinates regression task.Most existing approaches adopt complicated networks with a large number of parameters, leading to a heavy model with poor cost-effectiveness in practice.To overcome this limitation, we develop a small yet discrimicative model called STair Network, which can be simply stacked towards an accurate multi-stage pose estimation system.Specifically, to reduce computational cost, STair Network is composed of novel basic feature extraction blocks which focus on promoting feature diversity and obtaining rich local representations with fewer parameters, enabling a satisfactory balance on efficiency and performance.To further improve the performance, we introduce two mechanisms with negligible computational cost, focusing on feature fusion and replenish.We demonstrate the effectiveness of the STair Network on two standard datasets, e.g., 1-stage STair Network achieves a higher accuracy than HRNet by 5.5% on COCO test dataset with 80% fewer parameters and 68% fewer GFLOPs.
Homomorphic multi-label classification of virus strains

Detecting the gene sequence of virus strains from patients and classifying them into specific strains are very important to provide effective treatment. However, there are significant barriers to sharing the virus strains' gene data in plaintext to the privacy concerns of the patients. Homomorphic encryption is a form of encryption that allows users to calculate encrypted data without decrypting it. Achieving highly accurate viral strain prediction while safeguarding user privacy is a challenge. We develop a secure multi-label virus strains classification method using the homomorphic encryption scheme. We first used the method of statistical genotype frequencies for preprocessing to reduce the gene dimension of viral strains. Second, we improved the TFHE library proposed by Chillotti et al. to accommodate the floating-point input of the neural network to make the homomorphic calculation result more accurate. Finally, we improve computational speed and reduce storage usage by a data packing method that packs multiple feature information into one ciphertext. We successfully calculated 2000 virus strains classification inference steps on 128-bit encrypted test data in 0.09 seconds, reaching an accuracy of 100 %.
Secure and Distributed IoT Data Storage in Clouds Based on Secret Sharing and Collaborative Blockchain

With the rapid development of 5G/6G, most Internet of Things (IoT) devices will embrace wireless connection in the near future. A public concern is how to securely organize, store and retrieve data generated from IoT devices. Many cloud-based IoT data storage schemes have been proposed recently. However, for an untrusted or vulnerable cloud server, the stored IoT data can be easily accessed, modified and even destroyed given that the IoT data are stored in total centralization. Moreover, the servers in a cloud are generally homogeneous and thus vulnerable to attacks. For improvements, we design a novel framework for secure and efficient IoT data storage based on secret sharing and a collaborative blockchain. First, an ultra-lightweight secret sharing algorithm is designed to map original messages generated by IoT devices to a set of shorter message shares. Second, all the shares of IoT messages are separately delivered to different clouds for storage. To guarantee the security of shares, the delivery is notarized on a proposed blockchain. Specifically, both hash values of the shares and their information of location are embedded in blocks which are then chained to form a blockchain. Third, we create a balanced index structure about the shares for each cloud storage node based on the information in the blockchain, and we also propose a depth-first data search algorithm to improve IoT data retrieval efficiency. Theoretical analysis and simulation results illustrate that our scheme can store and retrieve the IoT data securely and efficiently.
Lap, not app?

App's the way to do it?! The subject divides opinion, but dedicated story apps do offer a wealth of benefits and added storytelling dimensions, explains Andy McCormack
Formation of digital professional competencies of a modern teacher of Chinese as a foreign language

The purpose of the study is to consider the formation and development of digital professional competencies of foreign language teachers. The article briefly analyzes the digital professional competencies that modern teachers of Chinese as a foreign language should master, and the factors influencing the development of digital professional competencies for modern teachers of Chinese as a foreign language. The scientific novelty lies in the fact that the author begins with the study of the recommendations of the Ministry of Education and Science of Russia on the inclusion of modern digital technologies in the basic educational programs and analyzes the formation and application of digital professional competencies of teachers of Chinese as a foreign language in Chinese classes with the support and guidance of relevant laws and regulations on the example of the Peoples' Friendship University of Russia. The study summarizes some digital professional competencies that modern Chinese as a foreign language teachers should master, as well as some factors influencing the development of modern Chinese foreign language teachers.
WALOR: Workload-Driven Adaptive Layout Optimization of Raft Groups for Heterogeneous Distributed Key-Value Stores

In a heterogeneous cluster based on the Raft protocol, in order to solve the problem of slow performance caused by the leader on a slow node, someone proposed ALOR. However, the leader distribution of ALOR is not optimal. In this paper, we propose Workload-driven Adaptive Layout Optimization of Raft groups (WALOR), which changes the leader distribution of ALOR to promote the performance further by more fitting the read-write request ratio of the system’s workload. Our experiments on an actual heterogeneous cluster show that, on average, WALOR improves throughput by 82.96% and 32.42% compared to the even distribution (ED) solution and ALOR, respectively.
Real-Time K-Prototypes for Incremental Attribute Learning Using Feature Selection

Mixed data streams escorted with new features are continuously generated at a very giant speed and volume. This makes it difficult for conventional clustering algorithms to create and to maintain groups of similar entities. Nonetheless, not all emerged attributes are relevant for mining and decision-making. In this paper, we propose an incremental attribute and object learning clustering method based on k-prototypes algorithm and using feature selection in a streaming data environment. Actually, we would like to investigate the impact of applying feature selection preprocessing technique in incremental unsupervised attribute learning space since it has not been evaluated so far. The obtained results highlight that our proposal achieves consistently better performance and gains more coherent clustering results in less time consuming. Also, performing feature selection before modeling data, in incremental attribute learning context, eases the learning process and speeds it up.
Spatio-temporal graph neural networks for multi-site PV power forecasting

Accurate forecasting of solar power generation with fine temporal and spatial resolution is vital for the operation of the power grid. However, state-of-the-art approaches that combine machine learning with numerical weather predictions (NWP) have coarse resolution. In this paper, we take a graph signal processing perspective and model multi-site photovoltaic (PV) production time series as signals on a graph to capture their spatio-temporal dependencies and achieve higher spatial and temporal resolution forecasts. We present two novel graph neural network models for deterministic multi-site PV forecasting dubbed the graph-convolutional long short term memory (GCLSTM) and the graph-convolutional transformer (GCTrafo) models. These methods rely solely on production data and exploit the intuition that PV systems provide a dense network of virtual weather stations. The proposed methods were evaluated in two data sets for an entire year: 1) production data from 304 real PV systems, and 2) simulated production of 1000 PV systems, both distributed over Switzerland. The proposed models outperform state-of-the-art multi-site forecasting methods for prediction horizons of six hours ahead. Furthermore, the proposed models outperform state-of-the-art single-site methods with NWP as inputs on horizons up to four hours ahead.
A New Poisoning Attacks on Deep Neural Networks

In healthcare field, many machine learning schemes have been applied in analyzing image content dataset. Among them, deep neural networks (DNNs), also known as deep learning, catches much attention. However, if deep neural networks are compromised by the attacker, medical diagnosis may be wrong, which leads to vital result. Recently, we find a new poisoning attack on DNNs may possibly happens due to poisoning dataset. This new poisoning attack, Category Diverse attack, has better ability to paralyze DNNs. Our performance experiments show our Category diverse attack actually leads to large accuracy drop of DNNs. We hope this discovery can help the information experts can improve the medical dataset quality in the future.
TEACHERS' DIGITAL COMPETENCES BEFORE AND DURING THE COVID-19 PANDEMIC FOR THE IMPROVEMENT OF SECURITY AND DEFENCE HIGHER EDUCATION

COVID-19 hastened a trend that was already ongoing before the pandemic outbreak: the progressively increasing use of distance and online teaching and learning, alongside with lectures and classes.The potentialities of online teaching allowed a didactic continuity that would have been impossible otherwise, and this approach is likely to be maintained even after COVID-19 related restrictions end.From these remarks, it immediately follows that it is of great importance that teachers, students and other personnel, such as technicians and program managers, possess digital skills devoted to education.In the context of security and defence, areas with a strong international vocation, these skills are even more valuable.This research investigates the impact of COVID-19 on education in these contexts: the changes caused by the pandemic, the teachers' perception about some aspects of their job, such as the way they relate with students, and their ability to perform the same commitments in a different scenario.The research has been conducted based on the analysis of an online anonymous questionnaire with more than 500 responses.Results suggested the importance of the development of a training devoted to improving teachers' digital skills, since they live frontline in education, and they have been directly impacted by disruptive changes.This study is part of the European project Digital Competences for Improving Security and Defence Education -DIGICODE.Pursuing to the Digital Education Action Plan, the project aims at improving education quality in security and defence, by means of digital tools in didactics, and the development of teachers' professional competences.
User Consumption Behavior Recognition Based on SMOTE and Improved AdaBoost

The sudden outbreak of COVID-19 has dealt a huge blow to traditional education and training companies. Institutions use the WeChat platform to attract users, but how to identify high-quality users has always been a difficult point for enterprises. In this paper, researchers proposed a classification algorithm based on SMOTE and the improved AdaBoost, which fuses feature information weights and sample weights to effectively solve the problems of overfitting and sample imbalance. To justify the study, it was compared with other traditional machine-learning algorithms. The accuracy and recall of the model increased by 19% and 36%, respectively, and the AUC value reached 0.98, indicating that the model could effectively identify the user's purchase intention. The proposed algorithm also ensures that it works well in spam identification and fraud detection. This research is of great significance for educational institutions to identify high-quality users of the WeChat platform and increase purchase conversion rate.
On wavelet type generalized Bézier operators

This paper deals with construction and studying wavelet type generalized Bézier operators by using the compactly supported Daubechies wavelets of the given function $ f $. The basis used in this construction are the wavelet expansion of the function $ f $ instead of its rational sampling values $ f\left( \frac{k}{n}\right) $. By using the Chanturiya modulus of variation we estimate the rate of pointwise convergence of $ \left( WB_{n, \alpha }f\right) (x)\ $at those $ x&gt;0 $ at which the one-sided limits $ f(x+) $, $ f(x-) $ exist.Clearly our wavelet type operators contain at least classical version, Durrmeyer and the Kantorovich form of the generalized Bézier operators and hence our results extend some of the previous results on generalized B ézier operators, such as [1], [11] and [12].
Who Regulates Our Regulators? Regulating Social Media Platforms’ Use of Artificial Intelligence to Regulate Us

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Copy DOI
Conceiving and Creating a Diagnosis Chatbot to Back Up Primary Health Services

The use of technology in the medical field is rapidly expanding.The delivery of healthcare and the nature of the patient-doctor relationship have both been revolutionized by technological advancements.Both patients and physicians have benefited from the revolutionary effects of artificial intelligence and chatbots in the medical field.An interactive diagnostic tool is needed to improve the healthcare system.Chabot is built on top of the newest machine learning technologies, including the decision tree algorithm, to aid users in self-diagnosis.The system will be trained to interpret user queries about different ailments through natural language processing and provide accurate answers.Like Siri, Alexa, etc., the system may be utilized for efficient information extraction, but its focus will be narrowed to illness diagnosis.
Pelatihan Publikasi Karya Ilmiah di Jurnal Nasional Bagi Guru SMP Negeri 2 Buay Madang Timur OKU Timur

A community service has been carried out with the topic of Scientific Publication Training in the National Journal for Teachers of SMP Negeri 2 Buay Madang Timur which is embodied in an agenda with the theme of Publication Management. This service aims to provide training and understanding of scientific publications in the teacher's morning national journal. The method implemented is using training and mentoring methods regarding scientific publications in national journals. In the activity carried out several stages of material topics that lead to the main goal. The stages of the material presented include the importance of scientific publications to teachers, then continued with an explanation of what components are contained in scientific publications, and finally the practice of scientific publication training. After the activity took place the teacher felt helped by this training. Teachers become more aware of the importance of scientific publications for teachers, one of which is for functional promotions, teachers become more aware of how to make good and correct scientific papers, and teachers know and know how to publish scientific papers through the online submission process..
Spatial and Visual Perspective-Taking via View Rotation and Relation Reasoning for Embodied Reference Understanding

Embodied Reference Understanding studies the reference understanding in an embodied fashion, where a receiver requires to locate a target object referred to by both language and gesture of the sender in a shared physical environment. Its main challenge lies in how to make the receiver with the egocentric view access spatial and visual information relative to the sender to judge how objects are oriented around and seen from the sender, i.e., spatial and visual perspective-taking. In this paper, we propose a REasoning from your Perspective (REP) method to tackle the challenge by modeling relations between the receiver and the sender as well as the sender and the objects via the proposed novel view rotation and relation reasoning. Specifically, view rotation first rotates the receiver to the position of the sender by constructing an embodied 3D coordinate system with the position of the sender as the origin. Then, it changes the orientation of the receiver to the orientation of the sender by encoding the body orientation and gesture of the sender. Relation reasoning models both the nonverbal and verbal relations between the sender and the objects by multi-modal cooperative reasoning in gesture, language, visual content, and spatial position. Experiment results demonstrate the effectiveness of REP, which consistently surpasses all existing state-of-the-art algorithms by a large margin, i.e., + $$5.22\%$$ absolute accuracy in terms of Prec@0.5 on YouRefIt. Code is available ( https://github.com/ChengShiest/REP-ERU ).
A three-level QR code sharing scheme based on SLIC and Hamming code

QR code is a typical two-dimensional code tag consisting of square symbols, which is widely used in social life due to the advantages of high readability and large storage capacity. However, its security performance is low, which has caused widespread concern among scholars in various industries. This paper proposes a three-level QR code scheme based on superpixel segmentation in response. Public information can be scanned directly from shares using any standard QR code scanner. Moreover, secret information can only be obtained by the dissimilarity of shares. The reconstructed secret image in this paper retains the main features of the embedded image, and the scheme is resistant to deformation, high load, and robust because it retains the advantages of visual ciphers and QR codes. Experimental results and analysis show this scheme has many advantages over other schemes.
Assistive health systems for home-dwelling elderly: connecting training and monitoring technologies to a data integration platform

Home health applications have evolved over the last few decades. Assistive systems such as a data platform in connection with health devices can allow for health-related data to be automatically transmitted to a database. However, there remain significant challenges concerning intermodular communication. Central among them is the challenge of achieving interoperability, the ability of devices to communicate and share data with each other. A major goal of this project was to extend an existing data platform (COMES®) and establish working interoperability by connecting assistive devices with differing approaches. We describe this process for a sleep monitoring and a physical exercise device. Furthermore, we aimed to test this setup and the implementation with a data platform in both a laboratory and an in-home setting with 11 elderly participants. The platform modification was realized, and the relevant changes were made so that the incoming data could be processed by the data platform, as well as visually displayed in real-time. Data was recorded by the respective device and transmitted into the data server with minor disruptions. Our observations affirmed that difficulties and data loss are far more likely to occur with increasing technical complexity, in the event of instable internet connection, or when the device setup requires (elderly) subjects to take specific steps for proper functioning. We emphasize the importance for tests and evaluations of home health technologies in real-life circumstances.
Automatic Array Transformation to Columnar Storage at Run Time

Today’s huge memories make it possible to store and process large data structures in memory instead of in a database. Hence, accesses to this data should be optimized, which is normally relegated either to the runtimes and compilers or is left to the developers, who often lack the knowledge about optimization strategies. As arrays are often part of the language, developers frequently use them as an underlying storage mechanism. Thus, optimization of arrays may be vital to improve performance of data-intensive applications. While compilers can apply numerous optimizations to speed up accesses, it would also be beneficial to adapt the actual layout of the data in memory to improve cache utilization. However, runtimes and compilers typically do not perform such memory layout optimizations. In this work, we present an approach to dynamically perform memory layout optimizations on arrays of objects to transform them into a columnar memory layout, a storage layout frequently used in analytical applications that enables faster processing of read-intensive workloads. By integration into a state-of-the-art JavaScript runtime, our approach can speed up queries for large workloads by up to 9x, where the initial transformation overhead is amortized over time.
Digital Banking Chatbots related MCDM Problem by TODIM Strategy in Pentagonal Neutrosophic Arena

Chatbots is an amalgamation of two words, Chats and bots. The meaning of Chat is to talk and bots are robots. Thus specifically Chat bots are talkative robots. The machine which is planned and controlled by a computer is called Robot. The banking business has become 247, and hence there arises the requirement to provide 247, 365 days a year services to customers. By any means, this is impossible for any human being, and for this reason, the machine was required to be invented. Even the multifaceted problems can be solved by robots with the help of external human force or with entrenched system rule. Humans are replaced by robots in doing tough, dangerous, and recurring activities that could not be performed by the former because of their restrictions. In banks, Robots Process Automation can reduce operational errors, reduce the cost of human capital, and has the potential to do multitask 247 without fatigue and 100% success rate. The fundamental of this paper is to appreciate the necessity of robotics process automation in the West Bengal banking industry. A pentagonal neutrosophic number (PNN) based Tomada de decisao interativa e multicritévio (TODIM) method multi-criteria decision making (MCDM) problem is included here to uncover the most vital chatbots factors these days. This study would assist in gaining insights on the consequence of the beginning of RPA in the Banking sector and its overall impact on Customer Relationship Management.