Reviewerly/exHarmony huggingface dataset:
Wheelchair Assistance and Guidance Using IOT

A Smart Wheelchair is a Power Wheelchair with numerous sensors, assistive technology, and computers built in that allow a disabled person to travel around freely and safely. People with disabilities, such as impairments, handicaps, and life-altering injuries, are the users. The conventional wheelchairs are being increasingly replaced by these new models. Only 5 to 15% of the 70 million disabled individuals worldwide, according to the World Health Organization (WHO), have access to wheelchairs. As a result, we must supply a cost-effective Smart that uses cutting-edge components and technology while keeping costs to a minimum. They have embraced a number of technologies, including artificial intelligence, where they created an autonomous wheelchair that used machine learning principles to travel. Some of them have also used the Internet of Things to manage the wheelchair using a voice recognition system. This report will outline an inexpensive Smart Wheelchair-based Arduino Nano microcontroller and Internet of Things (IoT) technology that has a number of features to help disabled people, particularly poor people who cannot afford an expensive Smart Wheelchair, get the assistance they need to complete daily tasks without help. As a result of this project, a variety of disabled people will be able to afford smart wheelchairs. It will be powered by Arduino Nano, ESP-12e module for Wi-Fi access, MPU6050 for fall detection with voice message notification using IFTTT platform, obstacle detection with buzzer and LED for hazard detection, voice recognition system, and joysticks for wheelchair control.
Axiomatization of Some Basic and Modal Boolean Connexive Logics

Boolean connexive logic is an extension of Boolean logic that is closed under Modus Ponens and contains Aristotle's and Boethius' theses. According to these theses (i) a sentence cannot imply its negation and the negation of a sentence cannot imply the sentence; and (ii) if the antecedent implies the consequent, then the antecedent cannot imply the negation of the consequent and if the antecedent implies the negation of the consequent, then the antecedent cannot imply the consequent. Such a logic was first introduced by Jarmużek and Malinowski, by means of so-called relating semantics and tableau systems. Subsequently its modal extension was determined by means of the combination of possible-worlds semantics and relating semantics. In the following article we present axiomatic systems of some basic and modal Boolean connexive logics. Proofs of completeness will be carried out using canonical models defined with respect to maximal consistent sets.
Mixnet optimization methods

We propose a method to optimally select mix network parameters for a given deployment context and adversarial model. Our method considers both worstcase and average-case anonymity and selects configurations that meet worst-case constraints while maximizing average anonymity. We apply our methods to mixnet size optimization to determine the number and width of mixnet layers, and provide results for various deployment and adversarial scenarios. For cases where the deployment context suddenly changes (drop in user traffic) we evaluate countermeasures based on mix-generated dummy traffic and show that inexpensive link dummies can significantly boost protection in some of these cases.
AWARENESS OF ACADEMIC INTEGRITY IN SOCIAL SCIENCE STUDENTS AT REGIONAL HIGHER EDUCATION INSTITUTIONS IN LATVIA

AWARENESS OF ACADEMIC INTEGRITY IN SOCIAL SCIENCE STUDENTS AT REGIONAL HIGHER EDUCATION INSTITUTIONS IN LATVIA
Investment in Bitcoin : A Delusion or Diligence?

Bitcoin investment gained great research interest, especially after the onset of the COVID-19 pandemic, a period marked by huge volatility in this asset class. This study investigated Bitcoin’s persistence and hedging properties in the pre-COVID era to establish its efficiency and safety by testing relevant data. We evaluated the role of persistence in Bitcoin trading to highlight its efficiency. The GPH estimator and ARFIMA were used to map the evolving efficiency of the Bitcoin price. Our analysis of intra-day data exhibited the presence of an anti-persistence effect, following the popular conclusion of momentum and speculative trading in the Bitcoin market. The second section of this study evaluated whether Bitcoin played the role of a hedge and an asset of protection in a global portfolio manager’s portfolio during extreme market volatility. Using the Threshold GARCH (TGARCH), we evaluated the trading correlation between Bitcoin prices and four major indices, namely S &amp; P 500, FTSE, Hang Seng, and Nikkei, on daily and weekly data. We identified the time-varying hedge and safety properties of Bitcoin: volatility, speculation, less-traded history, and lack of regulatory infrastructure. Our findings added to the literature by testing the efficiency of Bitcoin in major developed economies using returns of high-frequency data, along with daily returns. We also considered extreme movements in the currency to check its hedging and protection properties in a portfolio of developed market stocks. We recommended that investors be cautious when combining this currency with different stock markets based on our findings.
A Study on the Instructional Design Strategy of University-based Regional Win-Win Convergence Subjects

This study dealt with the design process of a class on how a university and its region develop together through the use of local resources, collaboration with residents, and communication with the local community. The instructional design model of this study focused on learnercentered PBL, and consisted of steps such as problem development preparation, problem selection, problem and learning activity development, learning activity determination, and teaching and learning process. This class derived facilitating competencies through these steps. facilitating competencies consist of task solving competency, communication competency, creativity, responsibility, self-directedness, time management competency, collaboration competency, occupational ethics, and emotional artistry. In this class, guide questions were proposed so that instructors and learners can fully understand the facilitating competencies in the class and perform their roles. The results of this class are as follows. First, this class developed a project that can be applied to local cultural resources and small business commercial spaces. Second, this class contributed to revitalizing the local community through cooperation with local members in the planning and progress of the project. Third, through this class, students can become interested in the local community and acquire practical skills to solve pending issues and problems.&#x0D;
A Study of the Anti-Cyclicity in the Derivational Operation of the Informal Style Constructions in the Hearer-Oriented Honorific System

The purpose of this paper is to show that the types of sentences in Korean may be determined not only by the syntactic information but also by the phonological information, especially the prosody structure. In the informal style constructions in the Hearer-oriented Honorific System, we conducted phonetic experiments to prove this phenome- non. As a result, it is observed that the types of sentences can be distinguished by intonation. Since the sentence type of Korean is determined at the end of the sentence, it can be confirmed that each sentence type is divided according to IP boundary tones. Our observation is significant because it is an example of the anti-cyclicity of the derivational operation in generative grammar. This does not mean, however, that all types of sentences will be related to anti- cyclicity.
Knowledge management as a modern educational trend

This article discusses the essence of the educational process. The subject of the study is knowledge management as an emerging trend of systematization of effective factors in the development of education. Philosophical ideas of self-organization and involvement of holistic thinking, contextual vision of the situation in the educational process play a special role. The perspective of the development of education is connected with the peculiarities of the influence of globalist and postmodern worldviews on approaches to educational activities and knowledge management in general. The methodological basis of the study is a holistic approach to education. The formation of the unity of the educational process is connected with the distribution of educational initiatives on an equal footing between the teacher and the students of the higher school. The results of the study emphasize the need for the teacher to master the position of a creative manager in taking into account the many positions of self-education of students in the space of social communication management. In response, students as students need philosophical and psychological preparation for managing their education and a high level of reflection in controlling their ways of mastering knowledge and accompanying social relations. The novelty of the work lies in determining the role, necessity, area of responsibility and list of competencies of a creative manager – an expert in educational activities. It arises when there is a figurative connection among students and their adjustment to a contextual vision of the situation. The scope of application of the results is any educational process in which students are involved – from higher education to corporate training. The modern educational system must meet the challenges of digital reality with its ubiquity of information. The approach to student education should be refocused on the constant exchange of teacher-student roles between student and teacher. For the competent implementation of the exchange of roles, students must be properly prepared. The quality of education should be monitored by a special expert with the necessary competencies in the field of knowledge management.
Preface

The 2022 International Conference on Applied Mathematics and Physics (ICAMP 2022) was held on April 15-17, 2022 as a virtual conference due to the growing concerns over the coronavirus outbreak (COVID-19), and in order to protect the well-being of our attendees, partners, and staff as our number one priority. The conference focused on the research areas of mathematics, applied mathematics and physics and aimed to provide a platform for experts and scholars from home and abroad who engaged in mathematics and physics research to share scientific research results and cutting-edge technologies, understand academic development trends, broaden research ideas, strengthen academic research and discussion, and promote cooperation in the industrialization of academic results. The conference will be held once a year as scheduled, inviting experts, scholars, entrepreneurs and other relevant personnel from universities and research institutions at home and abroad to attend the conference. This scientific event brings together more than 60 national and international researchers in applied mathematics and physics. During the conference, the conference model was divided into three sessions, including oral presentations, keynote speeches, and online Q&amp;A discussion. In the first part, some scholars, whose submissions were selected as the excellent papers, were given about 5-10 minutes to perform their oral presentations one by one. Then in the second part, keynote speakers were each allocated 30-45 minutes to hold their speeches. List of Committee member, Conference Chairmen and Program Committees are available in this pdf.
Robust Fuzzy Active Contour Model for Mixed Noise Image Segmentation

In this paper, a novel region based active contour model is proposed to segment images with mixed noise. The energy function of the proposed model is composed of global fuzzy energy term and local fuzzy energy term. The global term is constructed by using grayscale statistical characteristics of the global region, mainly used to improve evolution efficiency of the model and to avoid unsatisfactory results caused by inappropriate initial position. In order to improve model robustness to mixed noise, using local spatial and grayscale information to establish local term, which can adaptively adjust the influence of the neighborhood on the membership of the central pixel according to the intensity of the mixed noise in the local region. In addition, this model provides a fast convergence to the desired object edge by directly calculating the change of fuzzy energy in place of solving Euler–Lagrange equations corresponding to the energy function. Experiments are carried out with synthetic data sets and real data sets. The experimental results show that the proposed model has better performance than the currently commonly used active contour model.
Classical and quantum passive systems

The term "passive" is used in both the classical control community and the quantum physics community. In particular, classical passive systems focus on the relation between the output and the input of the systems while quantum passive systems require that the internal components do not produce energy and moreover, the systems dissipate energy to the outside environment. From this viewpoint, classical passive systems and quantum passive systems share some common feature. In this paper, we investigate if quantum passive systems are passive from the perspective of classical passive systems. We find that quantum passive systems satisfy a Riccati equation condition characterizing classical passive systems. The problem is that the Riccati equation condition has a real symmetric matrix solution for classical passive systems. However, for quantum passive systems, we find that the corresponding solution is iI which is symmetric but not real. This fact indicates that a direct generalization of classical passivity by admitting Hermitian solutions may not work for quantum passive systems.
State Estimation with Differential Privacy under Scale Enlargement of the Sensor Network

In this paper, we consider networked control systems consisting of a data aggregator and multiple sensors, where the data aggregator collects the data from the sensors and runs a Kalman filter to estimate the plant’s states. To preserve the privacy of the plant’s states, the sensors send differentially private measurements of the states to the aggregator. We consider the scenario that an extra sensor is available and discuss the estimation performance of the Kalman filter after adding the extra sensor while still preserving differential privacy. We consider two schemes of sensor addition: integral scheme and separate scheme. In the integral scheme, we design the privacy noises for all sensors in the new sensor group, which means that the privacy noises of the original sensors will be updated. In the separate scheme, the privacy noises of the original sensor group are keep unchanged and we only design the privacy noise of the additional sensor. For both schemes, we analyze the effect of adding an extra sensor on estimation accuracy while ensuring differential privacy. We propose a differential privacy mechanism for sensor addition and show that using this mechanism, the estimation accuracy can always be improved after adding an extra sensor.
A variable selection and structure optimization algorithm for online sequence extreme learning machine

In the study, an online prediction algorithm with variable selection and optimization for nonlinear modeling of fast-changing data steams is proposed. The proposed prediction algorithm is an iterative three-step approach. Firstly, an extreme learning machine (ELM) is trained with the initial dataset. Secondly, the garrote shrinkage and selection operator are introduced to shrink the weights of input and hidden neurons of the ELM. Thirdly, through the online update mechanism of the online sequence extreme learning machine (OS-ELM), the output value of the ELM after variable selection and network structural parameters are updated iteratively to adapt new sample data. Finally, the effectiveness of the proposed algorithm is verified by the comparative simulations of an artificial data set. Simulation results demonstrate that the developed algorithm is more adaptable in modeling the data stream that has data drift in input variables.
SYSTEM FOR DETECTING FAUX PRODUCTS

As a disruptive technology, blockchain technology has the potential to eliminate the problem of product counterfeiting in supply chains. A blockchainbased platform, on the other hand, charges legal producers and retailers an operational fee for product tracking and authentication. We use enterprise profitdriven analytical models based on Stackelberg equilibrium theory in this work to demonstrate the benefits of blockchain-supported e-commerce platforms in combating product counterfeiting. We evaluate the profitability of all actors in two separate supply chains, traditional and blockchain-supported, to determine the true benefits of blockchain technology. Manufacturers, merchants, and customers do not necessarily benefit from the use of blockchain technology, according to the findings. When a genuine company's manufacturing costs are sufficiently high, however, blockchain technology allows the manufacturer to make more money. Furthermore, in a price-sensitive market, a retailer is more likely to trade on a blockchainsupported e-commerce platform if the retailer's qualification in the platform is lower than in a traditional supply chain and the counterfeit manufacturer's manufacturing cost in the platform is higher than in a traditional supply chain.
Evaluation Model of IAP Education(IPM) in Colleges and Universities (CAU) Based on BP Neural Network and Grey Correlation Theory

IPM in CAU is an important way for schools to teach and educate people, promote students' all-around development and improve their comprehensive quality. The ideological and moral level of college students determines the quality of talent training in the future. Therefore, it is of great significance to analyze and study the factors affecting the effect of Ideological and political (IAP) work in CAU. In this paper, the grey correlation theory and neural network technology are combined to establish the evaluation index system, and the BP neural network mapping method is used to determine the implicit node degree value, confidence interval value, and output error threshold to calculate the advantages and disadvantages of the results. Firstly, we introduce the role, educational channels, and educational principles of IPM in CAU, then study BP neural network and grey correlation theory and the evaluation model of IPM in CAU based on this theory, and investigate the application status of the model. The survey results show that the evaluation model of IPM in CAU based on BP neural network and grey correlation theory is favored by teachers in the three dimensions of education content, educational method, and educational goal.
Lesson Planning of Smart Classroom Based on Genetic Algorithm

Smart classrooms with smart devices and AI applications increase the efficiency of course study. However, smart lesson planning is a time-consuming and confusing task for teachers. Thus, a lesson planning algorithm based on a genetic algorithm is proposed for promising lesson plans. Teaching activities, such as lectures, exercises, and classroom questioning are modeled as an event sequence to form a lesson plan. The mastery degree of students is selected as the fitness function of the genetic algorithm, and the estimated attention degree is calculated. Crossover and mutation genetic operators are designed for lesson planning. The algorithm is tested and optimal solutions or suboptimal solutions are found.
BUILDING A PROFILE OF HIGHER EDUCATION INSTITUTIONS IN THE DIGITAL ENVIRONMENT: INTERNATIONAL EXPERIENCE

modern universities, in response to general digitalization, not only transfer educational processes into a digital environment and actively implement digital technologies, but also create around their activities a special media space on the Internet. This media space is a set of communications with stakeholders, carried out through various communication forms and types, such as social networks, the university’s website, etc. At the same time, universities, given the growing audience of social networks, tend to follow modern trends and intensify work in this direction, which is especially attractive to young people as a core of applicants. The paper considers the problem of determining the feasibility of using social networks to attract applicants and the development of the university, using a media-metric analysis of the resources of foreign universities.
Accelerating Vertical Federated Learning

Privacy, security and data governance constraints rule out a brute force process in the integration of cross-silo data, which inherits the development of the Internet of Things. Federated learning is proposed to ensure that all parties can collaboratively complete the training task while the data is not out of the local. Vertical federated learning is a specialization of federated learning for distributed features. To preserve privacy, homomorphic encryption is applied to enable encrypted operations without decryption. Nevertheless, together with a robust security guarantee, homomorphic encryption brings extra communication and computation overhead. In this paper, we analyze the current bottlenecks of vertical federated learning under homomorphic encryption comprehensively and numerically. We propose a straggler-resilient and computation-efficient accelerating system that reduces the communication overhead in heterogeneous scenarios by 65.26&#x0025; at most and reduces the computation overhead caused by homomorphic encryption by 40.66&#x0025; at most. Our system can improve the robustness and efficiency of the current vertical federated learning framework without loss of security.
Using waveform correlation and template event metadata to reduce analyst workload.

We use waveform templates from stations that are within regional distance of aftershock sequences to detect subsequent events , then use template ev ent metadata to discover what stations are likely to record corroborating arrival waveforms for recurring aftershock events at the same location , and develop additional waveform templates to seek corroborating detections. We evaluate the results with the goal of determining whether applying the method to aftershock events will improve the choice of waveform correlation detections that lead to bulletin - worthy events and reduction of analyst effort.
Hierarchical off-diagonal Hessian approximation for Bayesian inverse problems with application to the flow of the Greenland ice sheet.

data misfit term has a spectrum that collapses rapidly to zero. We present a fast method for computation of an approximation to the posterior covariance that exploits the lowrank structure of the preconditioned (by the prior covariance) Hessian of the data misfit. Analysis of an infinite-dimensional model convection-diffusion problem, and numerical experiments on large-scale 3D convection-diffusion inverse problems with up to 1.5 million parameters, demonstrate that the number of forward PDE solves required for an accurate low-rank approximation is independent of the problem dimension. This permits scalable estimation of the uncertainty in large-scale ill-posed linear inverse problems at a small multiple (independent of the problem dimension) of the cost of solving the forward problem.