Reviewerly/exHarmony huggingface dataset:
Label big data compression in Internet of things based on piecewise linear regression

Abstract In order to solve the key problem that most of the energy of wireless sensor network nodes is consumed in wireless data modulation, which is an extremely important and limited resource. The energy efficiency evaluation scheme of data compression algorithm based on the separation of hardware factor and algorithm factor is proposed; In order to improve the running efficiency of the compression algorithm and reduce the energy consumption of the algorithm itself, a program level energy-saving optimization method for the data compression algorithm is proposed; In order to keep the energy-saving benefits of the data compression algorithm when the wireless transmission power is adjusted, an adjustment mechanism of the compression algorithm which can adapt to the change of transmission power is proposed. The experiment shows that when the wireless transmission power is - 7dBm and below (k &lt; 178.4), the data should be compressed by S-LZW algorithm, and when the wireless transmission power is - 5dBm and above (k &gt; 178.4), the b ~ RLE algorithm should be used for compression. The validity of the method is verified.
A Finger Sleeve with Fabricated Electrodes that Generates Multi-touch Interactions on Touch Screen

Most of the latest smartphones and laptops are equipped with capacitive touch panels, that enable users to directly input data by touching the screen with a finger or stylus. There are two input methods for such touch panels: single touch using one finger and multi-touch using multiple fingers. Currently, multi-touch requires the use of at least two fingers. In this paper, we propose a device that generates multi-touch by using only a thumb, even when the user is holding a smartphone with one hand. The proposed device consists of an Arduino Uno, a circuit with a mechanical relay, an electrode array, and a power supply. Each electrode is connected to an individual relay. When the relay is on, the corresponding electrode is connected to ground (GND). Multi-touch is generated by continuously grounding multiple electrodes and moving the center of the capacitance change. When the electrode array is brought into contact with the touch panel, it is recognized as if it were a finger, and a touch is generated. We fabricated a prototype device, and implemented it after preliminary experiments to determine the appropriate electrode spacing for generating touch interaction. The device can generate six touch interactions: single touch, swipe, pinch-in, pinch-out, press-and-touch, and rotation. The results of our evaluation showed that single touch, swipe, pinch-in, and pinch-out each had a success rate of 100%, press-and-touch had 80%, and rotation had 42%. We also created and implemented a device specifically designed for rotation.
Legal Regulation in the Field of Internet News Using Artificial Intelligence

Digital transformation has affected all spheres of public and social life, and the media is no exception. The development of online news platforms has qualitatively changed the picture of the legal regulation of the media, and the use of artificial intelligence (AI) to generate news content has required specialists to develop effective methods of legal regulation and prevent the risks of spreading prohibited materials, content, or false information. In this regard, the authors of the article pay special attention to the analysis of the key problems associated with the placement of AI-generated news on online platforms. They pay special attention to the consideration of the main legal approaches to the definition of news content, the legal protection of AI products, and the search for the optimal legal status of news platforms in European, American, and Russian legislation.
An Explainable Approach to Semantic Link Mining in Multi-sourced Dynamic Data

It is challenging to mine semantic links among multi-sourced data. Knowledge graphs can capture the semantics of data to support implicit links (cross-data sources) to be inferred through reasoning, known as the link prediction task. However, existing link prediction approaches are limited in their adaptability to data changes and cannot provide explanations for the predictions. In this work, we introduce a framework for semantic link mining through knowledge graphs and rule-based link prediction. In particular, rules representing higher-order patterns in the data are automatically mined and updated according to the dynamics of the data. We present a practical use case and a system for the semantic link mining of aviation data from multiple online sources. Besides, we evaluate our system by comparing it with several link prediction models to demonstrate the effectiveness of our approach in both static and dynamic link prediction and explanation.
Application of fuzzy AHP in the evaluation of students' cognitive ability

Cognitive theory holds that each individual's cognitive ability is different. If teachers can adopt an appropriate evaluation method, they will be able to accurately know each student's cognitive ability and characteristics, which is conducive to personalised teaching implementation. Therefore, based on the revised Bloom cognitive theory, this paper establishes the evaluation index system of students' cognitive ability by adopting the analytic hierarchy process (AHP) method, carries out fuzzy evaluation of students' test scores with the fuzzy theory, works out the weight of each indicator of cognitive ability by expert scoring method, and finally obtains the comprehensive evaluation of students' cognitive ability. It is proved that this method can be more objective and comprehensive in evaluation of the students' cognitive ability, and it can reflect their achievement degree of each goal, show their strengths and weaknesses, offering some valuable information for students' later study.
BLS based authentication and token-based authorization for Smart Home

The Internet of Things (IoT) is a technology which took huge role in our daily life. For smart home, IoT offers several benefits over traditional communication technologies; yet these implementations are currently uncommon. The focus is on smart home’s security feature technology within the home or a particular network. This can be useful for assessing how smart home technologies operate in real-world settings, as well as in more technical and future approaches to establishing new socio-technology combinations. Light weight Cryptography require less memory, less power supply to provide security solution. By adapting this, we can help conserving natural resources and environment. It leads to eco-friendly environment, and it is more sustainable to smart home. In this research, we have proposed Elliptic Curve Cryptography based two-way factor user authentication scheme that protects the messages and communicating data between device and the user. We explored the privacy and security of the house in this research to defend against any negative events such as theft, fire, or incorrect information, sent by the systems which can happen if the owner inside or outside home.
The Logical Theory of Evidence-Based Interpretation (Scientific Source Analysis)

Today the greatest role in historical processes is played by sources and their data, and also at the logical scientific conclusions which based on drawn sources with the corresponding purposes. This article shows the basic points of view in the field of methodology of historical science and work with sources in historical researches and on the basis of interpretation of the facts logically suitable opinions on the given question are stated.
Machine Learning-Based Model for Effective Resource Provisioning in Cloud

Ever increasing demand of cloud infrastructure services and Global adoption of Cloud Computing by diverse organizations has led to establishment of data centers with huge compute and storage infrastructures. Cloud suffices the consumer requirements by providing on demand access to the resources. Software components of cloud architecture work synchronously in order to fulfill the cloud consumer demands. Cloud resource management is one of the key functionalities that enables seamless access to cloud resources. The resource management in cloud is done considering several QoS parameters like workload balancing, resource utilization, application performance, energy efficiency. Several algorithms and techniques have been developed to target one or two of the above parameters however due to dynamic nature of cloud it is the need of the hour to develop smarter mechanisms that can lead to efficient management of resources in cloud. In this paper, we analyze and compare the clustering-based machine learning (ML) approaches that will help cloud resource manager for efficient management of cloud resources. The paper also proposes the system architecture for integrating ML-based algorithms for cloud resource allocation.
Vectors of the development of youth science in a classical university at the present stage

The development of youth science and the creation of conditions for the research activities of students during their studies are one of the topical areas of higher educational institutions activity at the present stage. The extent to which students are able to form competencies in the research field will largely depend on their ability to continue education and self-education throughout their lives and employment. The announcement by the President of the Russian Federation of the Decade of Science and Technology contributed to the revision and updating of many areas of organization of student research activities. In this work, in accordance with the initiatives of the Plan for the Decade of Science and Technology in the Russian Federation, the authors identify four key vectors for the development of youth science in a classical university: 1) the popularization of science among students and the involvement of talented youth in research and project activities; 2) development of additional professional competencies in students in the field of research and project activities; 3) approbation and dissemination of the results of research activities of students; 4) interaction of youth science, business, state and society. In the work, the authors a) describe the relevance of the development of youth science at the present stage; b) conduct an analysis of pedagogical research devoted to the description of the subject content of the organization of research activities of students and the experience of universities in conducting scientific, educational and educational projects, the purpose of which is the formation of additional professional and supraprofessional competencies among students; c) represent the events of the Derzhavin Tambov State University within each of the four vectors of development of youth science at the university.
PENGARUH MEDIA PEMBELAJARAN BIG BOOK DENGAN METODE DISKUSI UNTUK MENINGKATKAN KEMAMPUAN LITERASI INFORMASI SISWA KELAS IV SEKOLAH DASAR

This research aims to identify and provide empirical evidence of the influence of big book media using a model of discussion about the literacy skills of students to Class IV. The method used in this research is quasi-experimental research design with nonequivalent (pretest and posttest) control group design. Sampling using random sampling techniques. These samples included 68 students consisting of 34 students of class IVA as a control class and 34 students in grade IVC as an experimental class. The research instrument used was a test in the form of a written question in the form description you have tested the validity and reliability.The data analysis technique used is descriptive and inferential analysis techniques. The results showed that the use of media big book discussion model with an effect on the ability of information literacy
Optimizing Exploration-Exploitation Trade-off in Continuous Action Spaces via Q-ensemble

Ensemble-based reinforcement learning methods that combine multiple models of Q-function (i.e., value function) or policy have recently achieved excellent performance on continuous action space benchmarks. However, many ensemble methods only focus on one of exploration or exploitation for improvement, and the computational efficiency and ensemble diversity are also ignored. To mitigate these issues, in this paper, we propose a Q-ensemble-based exploration-exploitation trade-off method (QE3). Specifically, QE3 integrates two key ingredients based on Q-ensemble. Firstly, to improve exploration as well as ensemble diversity, we use an upper-confidence bound and a diversity regularizer in continuous action space, which encourages the agent to visit novel state-action pairs and enforces the diversity between Q-functions. Secondly, to improve exploitation for balancing exploration, we propose the selective repeat update, which improves the sample efficiency and mitigates the error propagation caused by high variance data. Empirical results show that QE3 has better performance than other baselines, and also show that our reuse data trick and the small ensemble of Q-functions make QE3 more computationally efficient than similar methods. Ablation study further shows its ability to balance the exploration and exploitation in continuous action space.
Problems and Optimization of Enterprise Financial Sharing Service Center -- Take Order Group as an Example

Digital economy has become the core element of modern business, among which financial digitization means to change the traditional financial model, and plays a decisive role in the digital transformation of enterprises.The financial sharing service center integrates the financial functions which were scattered at all levels of the enterprise and improves the efficiency of financial management.The construction of financial sharing service center often encounters difficulties in personnel management, information system construction and other aspects.If the existing problems can be analyzed and the solutions can be put forward in time, the operational value of financial sharing service center can be guaranteed.This paper will start with Order Group to analyze the problems from the construction to the landing and operation of its financial sharing service, and analyze the reasons and optimization scheme.
Mask Decoupled Head for Instance Segmentation in Remote Sensing Images

Instance segmentation predicts the categories of all instances and locates them using pixel-level masks. Although existing methods have shown exemplary performance, the poor boundaries due to the lack of fine-grained information re-mains a challenge for RSIs instance segmentation. In this paper, to address the problem, we propose a novel instance segmentation branch, namely Mask Decoupled Head, which is mainly composed of a Feature Enhance Module (FEM) and a Feature Decoupled Module (FDM). FEM enhances the rep-resentation of the body features through the low-frequency component of images. FDM decouples the segmentation task by supervising body and edge separately and leverages fine-grained information to complement the boundary details. We performed comprehensive experiments on NWPU VHR -10 and HRSID datasets to evaluate the effectiveness of our pro-posed method and achieved good performance.
Blockchain and Financial Service

First, this chapter analyzes the application scope and application value of blockchain in six financial fields, including cross-border payment, cross-border trade, supply chain, financing, insurance, and credit reporting, and emphasizes that blockchain has security, Query A credible and efficient advantage. Then, this chapter introduces and analyzes the application trends of blockchain in the financial field of 5 continents including America, Europe, Asia, Oceania and Africa, and focuses on the detailed description of the application of blockchain in China’s financial field by the broad and deep trend. Finally, as an application example, this chapter describes in detail the business pain points, application progress and implementation cases in the fields of digital currency, financial trading market, and supply chain finance.
Instructional Design in the Face of COVID-19: Learned Lessons and Pending Tasks

Abstract Faced with confinement due to COVID-19, educational institutions with face-to-face models had to continue their activities under conditions and with resources not used up to that moment. For this, the institutions formulated and put into operation continuity plans, which involved everything from remote education to hybrids between the latter and online education. Institutions that already had online or hybrid education programs were able to apply that experience to their face-to-face programs, allowing them to respond more quickly than those that did not. The stages of the teaching-learning process that were "adjusted" during this emergency in order to give continuity to educational activities were the last two, namely: the development of instructional material and teaching. In this work, an intervention is proposed in a previous stage of the process, that is, in the instructional design (ID), using the ASSURE model derived from the ADDIE model or approach. This intervention is based on the lessons learned during the pandemic, for the preparation or reformulation of study plans that consider information and communication technologies as a platform to enhance the effectiveness of learning, selecting them and establishing their use strategy from the stage in which the materials are designed, which may be useful considering that even if the students return to the classrooms, a virtual part will be preserved, that is, a hybrid model, in which the face-to-face-virtual ratio will be determined by the educational strategy of the institution.
Commodity-Tra: A Traceable Transaction Scheme Based on FISCO BCOS

In recent years, because online shopping has the advantages of abundant products, easy selection, fast door-to-door delivery, and timely information feedback, people are more and more fond of online shopping compared to physical store shopping. With the wave of online shopping, the use of commodity transaction systems has become more and more widespread. However, the traditional commodity transaction system generally adopts centralized management and cannot be traced, which cannot guarantee the openness and transparency of commodity source information, and cannot well meet the commodity purchasing needs of consumers. In order to solve the above problems, we use blockchain technology to propose Commodity-Tra: a traceable transaction scheme based on FISCO BCOS platform. The system Commodity-Tra is suitable for the scene of commodity traceability, and can provide consumers with true and accurate commodity traceability information, which can better meet consumers’ purchasing needs. In addition, we analyze the impact of the traceability on page response time from the aspects of network bandwidth and virtual machine memory, respectively. The experimental results show that the page response time only increases by 2.8 s on average. Therefore, Commodity-Tra is capable of providing the traceability function without affecting users’ experience.
Real Time Customer Satisfaction Analysis using Facial Expressions and Headpose Estimation

One of the most exciting, innovative, and promising topics in marketing research is the quantification of customer interest.This work focuses on interest detection and provides a deep learning-based system that monitors client behaviour.By assessing head position, the recommended method assesses customer attentiveness.Customers whose heads are directed toward the promotion or the item of curiosity are identified by the system, which analyses facial expressions and records client interest.An exclusive method is recommended to recognize frontal face postures first, then splits facial components that are critical for detecting facial expressions into iconized face pictures.Mainly consumer interest monitoring will be executed.Finally, the raw facial images are combined with the iconized face image's confidence ratings to estimate facial emotions.This technique combines local part-based characteristics through holistic face data for precise facial emotion identification.The new method provides the dimension of required marketing and product findings indicate that the suggested architecture has the potential to be implemented because it is efficient and operates in real time.
An Assessment On Genres Of Video Blogs

ABSTRACT&#x0D; Digitalization affects and transforms areas that are an integral part of daily life such as education, economy, entertainment and communication. It is seen that people in both developed and developing countries, where more and more products and services are provided to users in digital environments, are increasingly benefiting from digitally provided environments. It is stated that blogs, which are different from other popular communication protocols based on the Internet, have a democratizing potential. Blogs, which can be a source for sharing news and creating public opinion, can be created under the umbrella of an institution or organization. Blogs, which can also be put forward within the framework of individual purposes, emerge as one of the new ways to communicate with others, and allow personal expressions such as feelings, thoughts and preferences to be shared with a wider audience. The content of blogs, which can be text-based as well as video content, has a great variety. Studies that evaluate blogs according to these differences in terms of species are functional in terms of examining their contents in detail. This study focuses on the video diaries produced by Turkish vloggers. In the study, in which literature review was used as a method, 18 vlog channels with more than two million subscribers were evaluated according to the vlog types determined by Gercely (2018). It was determined that 8 of them were jokes and comedy, 3 of them were gaming, 2 of them were training/teaching, 1 of them was beauty/fashion/fitness, 1 of them was lifestyle, and 1 of them was product review. More than two million Turkish vloggers could not be identified in the movie/TV/book commentary and travel vlogs. Vloggers with the highest followers at the time of the research were evaluated under these headings.
5G mobile wireless network threats and its mitigation methods

In order to achieve higher latency in 4G networks, higher transmission rate and bandwidth are required for end to end communication over digital devices. Wider bandwidth which gives more space for malicious attacks and users experience disruption in the network. These scenarios will lead to exposure of data that are prone to attacks by the hacker. Malicious attacks including Man in the Middle attack (MITM), Jamming, Eavesdropping, Traffic analysis and Denial of service (DoS) which leads us to severe damage to mobile networks. These drawbacks are overcome in 5th Generation networks by improvising latency and best utilization of bandwidth. In this paper we propose an algorithm for the 5G security system. In which Pseudo random number generator (PRNG) and True random number generator (TRNG) methods are used to generate random numbers for OTP authentication. NIST standard techniques are proposed to evaluate the entropy of these random number generators. Based on the evaluation of entropy for the TRNG method we are able to achieve an entropy of 0.8847 for a system of 1000000 bits of binary sequences generated based on the python secret module which uses operating system constraints for noise spectrum and evaluated results using the NIST standard test suite. To avoid MITM attacks occurring in standard Diffie-Hellman key exchange we propose the Station-to-Station (STS) protocol and Software defined networks (SDN) Controllers to achieve more security for 5G applications.