Reviewerly/exHarmony huggingface dataset:
Study of Inflation using Stationary Test with Augmented Dickey Fuller &amp; Phillips-Peron Unit Root Test (Case in Bukittinggi City Inflation for 2014-2019)

This classical regression model is designed to handle the relationship between stationary variables and should not be applied to non-stationary series. A time series data is said to be stationary if the mean, variance, and covariance remain constant over time. The problem associated with non-stationary variables, and often encountered by researchers when dealing with time series data, is spurious regression. A clear indicator of false regression is the low Durbin-Watson statistic but has a higher coefficient of determination (R2). Therefore, before doing modeling or forecasting using time series data, it is very important to do a stationary test. In this study, we use inflation data in the City of Bukittinggi from January 2014 to December 2019 as a case study. The data shows an uptrend and correlated error terms. Empirical results show that inflation data in Bukittinggi City is a stationary series.
Multimodal Dialogue Response Timing Estimation Using Dialogue Context Encoder

Spoken dialogue systems need to determine when to respond to a user in addition to the response. Various cues, such as prosody, gaze, and facial expression are known to affect response timing. Recent studies have revealed that using the representation of a system response improves the performance of response timing prediction. However, it is difficult to directly use a future response with dialogue systems that require an entire user utterance to generate a response. This study proposes a neural-based response timing estimation model using past utterances to alleviate this problem. The proposed model is expected to consider the intention of the system response implicitly.
Applying digital technologies in the inclusive education at universities

The article analyzes theoretical and practical aspects of the use of digital technologies in a university environment, notes the main problems and opportunities for integrating digital technologies into inclusive education. On the basis of the studied material, the author compiled the visual structure of the digital inclusive educational environment, demonstrating the multidimensionality and complexity of the digitalization process of inclusive education. A high level of preparation of students with special educational needs in the digital environment is an urgent task in the educational system, which can be ensured through the implementation of certain pedagogical conditions. The purpose of the article is to confirm the effectiveness of the implementation of pedagogical conditions for the formation of information competence of students with special educational needs by means of e-learning. As the designated pedagogical conditions, the following are described: the development of electronic information and educational environment and the development of interactive educational content by means of information technology. The scientific novelty of the research lies in the fact that pedagogical conditions have been developed and substantiated, contributing to the effective formation of information competence of students with special educational needs by means of e-learning. The theoretical significance of the work is presented by the results of the selection and systematization of the results of scientific research on the problem under study, which provides an objective and comprehensive view when assessing the prospects for the use of digital technologies in the context of inclusive education at a university. The practical significance of the work is presented by the developed pedagogical conditions that can be applied in the practice of teaching universities in order to improve the quality of training for students with special educational needs. Research methods: generalization, analysis and synthesis of information, comparison.
2D GRAVITY AND MAGNETIC MODELLING OF THE MACKENZIE LARGE IGNEOUS PROVINCE PLUME CENTER REGION, NORTHWEST TERRITORRIES, CANADA.

The proposed plume center of the 1270Ma Mackenzie large igneous province (LIP) is defined by the focus of the Mackenzie radiating dyke swarm and is located off the coast of Victoria Island in NWT, Canada.It has been proposed that the intracratonic Amundsen Basin, which partially surrounds the Mackenzie plume center, may have formed because of decay of an unidentified mantle plume [1][2][3] which we suggest is the Mackenzie plume.This study conducts 2D gravity and magnetic modelling of the circumferential gravitational low of the Amundsen Basin, along with radial gravitational highs partially circumscribing the plume center.The radial gravitational highs are interpreted as being feeder dykes of the Mackenzie LIP [4][5].This modelling will help understand the setting of the Muskox intrusion, the Mackenzie plumbing system, and its other layered intrusions.The intrusions' geological context is key in understanding the different magma paths, batches, mineralization, and geochemical evolution of the Mackenzie LIP.The proximity of the Muskox mineralized intrusion to one of the radial gravitational highs suggests possible greenfield exploration targets along other radial gravitational highs.We propose the thermal decay of the Mackenzie plume possibly caused the Amundsen Basin subsidence through three mechanisms.First, the thermal decay of the plume which occurred over ~200 Ma or more [6][7].Second, the lateral redistribution of large amounts of magma through the Mackenzie dyke swarm from the plume centre [6][8][9].Third, crustal loading due to the emplacement of massive mafic/ultramafic intrusions and eclogitized ultramafic underplating [10]
Bounds on Oblivious Multiparty Quantum Communication Complexity

The main conceptual contribution of this paper is investigating quantum multiparty communication complexity in the setting where communication is oblivious. This requirement, which to our knowledge is satisfied by all quantum multiparty protocols in the literature, means that the communication pattern, and in particular the amount of communication exchanged between each pair of players at each round is fixed independently of the input before the execution of the protocol. We show, for a wide class of functions, how to prove strong lower bounds on their oblivious quantum k-party communication complexity using lower bounds on their two-party communication complexity. We apply this technique to prove tight lower bounds for all symmetric functions with AND gadget, and in particular obtain an optimal $$\varOmega (k\sqrt{n})$$ lower bound on the oblivious quantum k-party communication complexity of the n-bit Set-Disjointness function. We also show the tightness of these lower bounds by giving (nearly) matching upper bounds.
A Robust NIfTI Image Authentication Framework Based on DST and Multi-Scale Otsu Thresholding

Telemedicine has been intensely promoted in the present pandemic situation of COVID-19 to maintain a strategic distance from the infected person.Several medical tests were used to detect the coronavirus, including antigen, RT-PCR, and a lung CT scan.Only a lung CT-Scan can detect the coronavirus and provide information about the lung infection.As a result, digital imaging plays a critical role in the current pandemic situation.Teleradiology allows for the communication of digital medical images of patients over the internet for diagnosis.A lung CT-Scan test is currently being performed on billions of people to detect COVID-19.These images were sent via the internet for diagnosis and research purposes.The NIfTI image file (.nii extension) was created by the CT-Scanner and contains multiple slices of the lungs.As a result, radiologists determine that the received image has not been tempered during transmission, posing a critical authenticity problem when transmitting these images over the internet.As a result, the researchers are more concerned about the integrity and authenticity of these images in teleradiology.This paper proposes a blind, robust watermarking scheme for lung CT-Scan NIfTI images to address this issue.We use Otsu's image segmentation algorithm in the proposed scheme to identify the slice with the least amount of medical information for watermark embedding.The proposed scheme employs the Discrete Shearlet Transform (DST), Lifting Wavelet Transform (LWT), and Schur decomposition to embed the encrypted watermark.Watermarks are encrypted using the Affine Transform.The experimental results show that watermarked slice has been tainted by the addition of various sorts of noise, including salt-and-pepper noise, compression, Gaussian noise, speckle noise, and motion blur.After an attack, a watermark is retrieved, and the NC values of extracted watermarks are 0.99623 for Salt and pepper noise, 0.96964 for Gaussian noise, 0.99014 for Speckle noise.The proposed scheme was put to the test with a variety of attacks and produced significant results.
An empirical study on audit quality and financial fraud of audited companies

In recent years, the endless cases of corporate financial fraud revealed, set off a wave, the negative impact on the maintenance of the order of the market economy is huge, while causing widespread concern among scholars, in order to study financial fraud behavior in depth, this paper selects the violation information of listed companies in 2010-2020 as the research sample, through the establishment of a binary logistic model, data for processing The relationship between audit quality and corporate financial fraud is studied, and it is found that the occurrence of financial fraud can be effectively suppressed by improving audit quality.
Learning Intrinsically Motivated Transition Models for Autonomous Systems

To support long-term autonomy and rational decision making, robotic systems should be risk aware and actively maintain the fidelity of critical state information. This is particularly difficult in natural environments that are dynamic, noisy, and partially observable. To support autonomy, predictive probabilistic models of robot-object interaction can be used to guide the agent toward rewarding and controllable outcomes with high probability while avoiding undesired states and allowing the agent to be aware of the amount of risk associated with acting. In this paper, we propose an intrinsically motivated learning technique to model probabilistic transition functions in a manner that is task-independent and sample efficient. We model them as Aspect Transition Graphs (ATGs)—a state-dependent control roadmap that depends on transition probability functions grounded in the sensory and motor resources of the robot. Experimental data that changes the relative perspective of an actively-controlled RGB-D camera is used to train empirical models of the transition probability functions. Our experiments demonstrate that the transition function of the underlying Partially Observable Markov Decision Process (POMDP) can be acquired efficiently using intrinsically motivated structure learning approach.
ANDROID-BASED D’LAUNDRY APPLICATION USING MODEL DESIGN THINKING

Research problems felt by urban communities, the majority of whom are workers, both in government agencies, or in companies, are working 8 hours a day, unfavorable weather and returning home to rest. Especially during the COVID-19 pandemic and the PPKM (Enforce Activity Restrictions Offices in non-essential sectors) that is running, it makes people extra full to manage their time and prioritize between home and office. This busyness is the problem raised in this study. The solution offered is to a laundry delivery message application that is able to answer challenges with an excellent service model, namely pick-up and drop-off to the customer's location so that users can work quietly without having to be preoccupied with picking them up to the laundry location. gadgets on the Android operating system, administration and delivery services can be accessed more practically and are very easy to use, and other advantages can save time and costs. This application is an information system based on mobile devices which was developed with the Design Thinking model. The purpose of Design Thinking is process or method of thinking to empathize with human-centered problems, and also associated with product and Innovation service business and social
MEANING OF SOFT-SKILLS IN THE CONTEXT OF HUMANISTIC, CIVIC AND UTILITARIAL OBJECTIVES OF HIGHER EDUCATION

The meaning of soft skills for the three objectives of higher education – humanistic, civic and utilitarian – is discussed in terms of historical and philosophical analysis. It is found that soft skills facilitate mutual strengthening of the objectives and help the competence approach in overcoming the tensions between them. It is demonstrated that the issue of ambivalence of these values is updated due to soft skills. It is proved that particularly soft skills become a new challenger for the integrator role providing integrality and balance of the intellectual, integral and existential development of the student and make it possible to transfer the specifics of the classical higher education (humanism, civics and universality) to the profile (specialized) higher schools on the one hand, and prevent universities from turning into “large colleges” (that massovization and professionalization may involve), on the other hand.
Designing a Website for Adolescents to Introduce Human Factors Methods and Concepts

Human factors researchers and practitioners have awareness of, and access to, various sources covering methods commonly used in their field. However, human factors curriculum is underrepresented within high school education. In accordance with an effort to produce a website that is geared towards those with, at best, a limited understanding of the field, we wanted to also emphasize universal accessibility.
Research of Pedestrian Object Tracking Algorithms

High-quality pedestrian tracking algorithms are the basis for solving many problems including video surveillance, action recognition, and crowd behaviour analysis. The problem of pedestrian target detection and tracking is to identify and track the trajectories of different targets in video sequences. It can be divided into single target and tracking, multi-target and tracking, pedestrian re-identification, and so on. In recent years, with improvement of benchmarking, pedestrian object tracking methods have also developed from traditional feature learning to neural network modelling. This paper mainly summarizes and analyses the latest proposed deep learning algorithm models for pedestrian detection and tracking and the corresponding improvement for existing problems. In addition, possible future research directions are also discussed at the end.
What Is the Education Becoming?

Education should be based on the inclinations, abilities and aspirations of each child: to learn and create new things, to communicate, cooperate, learn and teach. The actualization and development of this human potential, considering the personal and cognitive traits of each child, is an ever-greater need. In the world of accelerating, unpredictable and irreversible changes the ways of becoming an advanced, pre-adaptive education are being formed. We cannot stand by and wait. We must not catch up but get ahead of events.
CIOFL: Collaborative Inference-Based Online Federated Learning for UAV Object Detection

Federated learning (FL) has great potential in visual applications such as object detection by unmanned aerial vehicles (UAVs), since different UAVs can capture diverse characteristics of targeted objects from different angles. More importantly, how to collaboratively train an accurate object detection model via FL in an online manner is critical to UAV object detection in practice. In this demonstration, we present a working prototype of CIOFL on embedded computers, Collaborative Inference-based Online Federated Learning for object detection within a UAV swarm. In essence, the proposed CIOFL enables online FL for object detection among multiple nodes, by continuously adding high-quality real-world samples inferred just by these nodes with a complex and large-scale object detection model. Our evaluation results show that, compared to the traditional FL, CIOFL improves the convergence rate and accuracy by ~1.3✗ and ~ 1.34✗, respectively. We envision that, the CIOFL can effectively enhance the applicability of UAVs conducting object detection in practice.
Research on Electromagnetic Interference Protection of Nuclear Power Unit Equipment Under the Application of New Wireless Technology in Smart Nuclear Power

Abstract Intelligence is the trend of industrial development in today’s world, which will inevitably affect the nuclear power field, thus promoting nuclear power industry from digital to intelligent forward.5G and Wi-Fi6, as a new generation of wireless communication technologies, have important advantages in terms of technical performance and security due to the characteristics of dense personnel, wide equipment distribution, complex facility environment and special materials used in nuclear power scenarios. Considering the high requirement of electromagnetic compatibility in nuclear power environment, it is urgent to further study the electromagnetic interference and protection requirements of 5G, Wi-Fi6 and other new technologies on nuclear power unit equipment in nuclear power environment. In this paper, combined with relevant national document standards and industry standards, we will sort out the requirements of Immunity about Radio Frequency Radiation (RFR) for the important equipment in nuclear power plants, and use signal generators to simulate new radio signals such as 5G and Wi-Fi6 to test the immunity of nuclear power instruments and meters. Combined the results of test, we will study the Electro Magnetic Compatibility (EMC) between new wireless technologies and nuclear power plant equipment from different factors, such as Power, bandwidth, modulation, distance, direction. And then, we will summarize the protection requirements about nuclear power equipment.
Classification of Competition Content on Web Pages using a Machine Learning Algorithm

To develop quality and human resources in the future, students as an agent of change can increase their potential by participating in competitions. There are so many benefits that can be obtained from participating in competitions such as increasing abilities, improving soft skills, increasing experience, expanding relationships, and of course, getting prizes and certificates. Competition information can be found on the internet, especially in search engines. As the web continues to evolve, it is becoming increasingly difficult to find relevant information using traditional search engines. But even though it has been optimized by SEO, it is not uncommon to find web pages that provide invalid competition information and pass the deadline. So, a web page classification is needed. This paper uses a machine-learning algorithm to classify valid competition information web pages for students with the Gradient Tree Boosting classifier method. The Gradient Boosting Algorithm is one of the more sophisticated ensemble methods for online boosting classifiers, which often improves classification accuracy. To do machine learning with this algorithm we split the dataset into two parts, which are for testing 30% and 70% for training. And the parameters are learning_ rate=0.01, n _ estimators=150. Then, we got 85% accuracy. This result is good for classification and proves that the machine learning algorithm can be used as a good method for web classification because it is more effective and efficient to retrieve valid competition information rather than manually validating. According to the result, our proposed method shows a promising performance to validate the competition information.
Interactive Analysis of Post-Silicon Validation Data

Analyzing post-silicon validation datasets to spot bugs and obtain hints for design improvements challenges validation engineers with complex and sometimes subtle problems. Through experience, engineers gain accuracy and speed in recognizing the cause of a problem, but analyzing the data remains a tedious and time-consuming process involving substantial manual effort. Sometimes, validation engineers just have a gut feeling regarding the cause, and in many cases they can find themselves at a loss in understanding the cause of a problem. This work presents a new approach that visually supports validation engineers in debugging chips during the post-silicon validation phase. It was developed as part of a collaboration between visualization researchers and post-silicon validation domain experts, aiming at tackling complex problems that present subtle differences between test case outcomes.
Scary Stories: Fear Appeals, Hopelessness and the Role of Response Efficacy in Protective Online Behavior

The issue of online threats is a topic of widespread notoriety and the target of voluminous research. In spite of this, recommended treatments do not seem to have been completely effective, as indicated by the prominence of identity theft among complaints to government agencies such as the Federal Trade Commission. The goal of this research is to produce a more complete and nuanced understanding of this problem and thus provide better guidance toward preventing identity theft. This work offers a 2 x 2 experiment that manipulates both Threat conditions and Response Efficacy in concert, testing for an interaction effect of Threat and Response Efficacy on Behavioral Intention, Fear, and Hopelessness. Our results indicate that a high Threat condition increases users’ intentions to take action against an online threat, as expected. However, we also find that Response Efficacy plays a critical role in how individuals react to online threats. Response Efficacy is found to relate negatively to Hopelessness. In addition, Fear is measured and discussed.
VTS+: A VISUAL THINKING STRATEGIES VARIANT FOR LOW-INTERACTIVITY DISTANCE LEARNING

The COVID-19 pandemic has forced governments and educational institutions around the world to look for viable alternatives to campus-based education.However, pedagogical methods such as Visual Thinking Strategies (VTS) might need to be adapted to low-interactivity distance learning environments, such as datacasting.This paper describes VTS+, a version of VTS that, in addition to asking the three VTS foundation questions, involves getting the student to iteratively write down his or her answers to the three foundation questions of VTS until no further details could be observed, and finally writing down what the visual text is communicating to the student.These innovations were designed to increase the degree of learner-self conversation and, through prolonged engagement with the teacher-selected visual text, also the degree of learner-teacher conversation.Quasi-experimental results indicate the effectiveness of VTS+ and a surprising outcome: the conciseness of final interpretations.
IMPLEMENTASI MODEL BLENDED LEARNING DALAM PEMBELAJARAN PADA GURU-GURU SEKOLAH DASAR DI DESA WAIMITAL

This community service activity aims to (1) improve the understanding of teachers of SD Negeri 1 and SD Negeri 3 Waimital about learning models Blended Learning, (2) produce lesson plans by using the learning model Blended Learning. The achievement of these goals is carried out through the provision of materials and training with lecture methods, questions and answers, demonstrations, and exercises (tutorials), when the activity takes place, monitoring is carried out by resource persons to see and assist teachers in making based lesson plans Blended Learning. The results show that the service program is going well and smoothly. This program is able to improve the understanding ability of teachers about models and teachers are able to make lesson plans based Blended Learning. The target of this activity is elementary school teachers in Waimital Village