Reviewerly/exHarmony huggingface dataset:
LSTM based video stabilization for object tracking

The object tracking accuracy may be decreased because of the camera jitter, making it extremely hard for object tracking and trajectory analyzation. To achieve accurate video stabilization, the movement of camera can be analyzed and predicted based on the previous camera jitter sequence. In the area of sequence prediction, the long-short term memory (LSTM) network shows the potential in sequence forecasting, here we use LSTM network in camera jitter prediction and video stabilization. In this paper, we propose a video stabilization algorithm based on multi-region grey projection method and LSTM encoder-decoder network. Our algorithm calculates the motion of the camera through the gray projection of four areas in each frame, then filters out the main movement direction and jitter of the camera. The LSTM encoder-decoder network receives the camera jitter sequence, predicts the camera jitter then stabilizes the video. We to verify the performance of the proposed video stabilization method. We tested the proposed video stabilization algorithm on the jitter videos, which is made by the VisDrone dataset video modified with our recorded camera jitter. Experimental results demonstrate that the proposed method can achieve the video stabilization in real time, and increase the accuracy of object tracking and trajectory analyzation.
The Credit Problem in parametric stress: A probabilistic approach

In this paper, we introduce a novel domain-general, statistical learning model for P&amp;P grammars: the Expectation Driven Parameter Learner (EDPL). We show that the EDPL provides a mathematically principled solution to the Credit Problem (Dresher 1999). We present the first systematic tests of the EDPL and an existing and closely related model, the Naïve Parameter Learner (NPL), on a full stress typology, the one generated by Dresher &amp; Kaye’s (1990) stress parameter framework. This framework has figured prominently in the debate about the necessity of domain-specific mechanisms for learning of parametric stress. The essential difference between the two learning models is that the EDPL incorporates a mechanism that directly tackles the Credit Problem, while the NPL does not. We find that the NPL fails to cope with the ambiguity of this stress system both in terms of learning success and data complexity, while the EDPL performs well on both metrics. Based on these results, we argue that probabilistic inference provides a viable domain-general approach to parametric stress learning, but only when learning involves an inferential process that directly addresses the Credit Problem. We also present in-depth analyses of the learning outcomes, showing how learning outcomes depend crucially on the structural ambiguities posited by a particular phonological theory, and how these learning difficulties correspond to typological gaps.
Block Chain + Accounting: Applications, Challenges and Countermeasures Research

Block chain is a new application model integrating distributed data storage, peer-to-peer transmission, consensus mechanism, encryption algorithm and other computer technologies born under the background of rapid development of the Internet, and its emergence has aroused the attention of various countries and discussions in various industries. The application of blockchain technology to the accounting field has been a hot topic of research and exploration for scholars in recent years. Its features of decentralization, de-trust, immutability, data traceability and smart contracts can solve the trust problem faced by the accounting field. The article focuses on the nature, working principle and characteristics of blockchain, discusses the advantages of applying blockchain to accounting, and proposes some problems faced by &#x201C;block chain &#x002B; accounting&#x201D; today as well as strategies to solve the problems.
Research on Online Course Information Acquisition of Engineering Cost Based on Virtual Simulation Technology

Abstract The Ministry of Education proposed to deeply integrate higher education and information technology, and the traditional education mode also changed qualitatively with the introduction of virtual simulation technology, making educational technology more suitable for the development of modern society and the goal of talent training. Through the research on the application and practice of virtual simulation technology in the core course teaching of engineering cost specialty, virtual simulation teaching has the advantages of high efficiency, low cost, rich content, effective performance and safety, etc., and it can make the whole building change the observation through the 3d simulation animation display. By summarizing the teaching process of introducing virtual simulation technology into engineering cost course, this paper puts forward some problems that need to be further studied in the teaching process of introducing virtual simulation technology into engineering cost course, points out its advantages in the teaching process of engineering cost course, and summarizes the full text.
LEGO MINDSTORMS EV3 Robot in TRICK Studio environment as a new performer for teaching algorithmization and the basics of robotics in a school informatics course

The article discusses the methodological aspects of teaching algorithmization and the basics of robotics using the educational constructor LEGO MINDSTORMS EV3 in the TRIK Studio environment. The advantages of using the TRIK Studio programming environment to control both a real robot "live" and a virtual one in simulation mode are shown. Based on the methodology for introducing basic algorithmic constructions and types of algorithms by I.G. Semakin, examples of solving a system of problems using commands to control the EV3 robot in the TRIK Studio environment are given. The LEGO MINDSTORMS EV3 Robot is seen as a new performer in the TRIK Studio environment. The article highlights directions and provides examples of tasks for further mastering the basics of robotics and algorithmization. The parallel study of the basics of robotics with the study of the algorithmization section based on the task approach allows you to organize the learning process in the informatics course based on interdisciplinary and STEM approaches, to implement the requirements of the Approximate Basic Educational Program of Basic General Education (2015).
Contact Tracing Apps: A Lesson in Societal Aspects of Technological Development

Abstract Overall, there might be more important aspects of the COVID-19 pandemic and the global fight against it than contact tracing apps. But the case of the contact tracing apps tells us an interesting story in the context of Digital Humanism. It shows us that the principle of privacy by design has reached practice and what it can mean and achieve. Unfortunately, it is also a lesson about the societal limitations of privacy by design in practice. It is a good thing that people are skeptical and ask questions about privacy and data protection. However, it is necessary to differentiate and try to make educated decisions or trust experts and civil society.
Comparison on unsupervised person re-identification: methods and experiments

Many existing methods have achieved the efficiency and speed of person re-identification deep neural models. However, due to the expensive cost of annotations, semi-supervised and unsupervised models have been treating seriously nowadays. The normal process of the unsupervised re-id model can be sum up into five steps and categorized into purely unsupervised learning person re-ID and unsupervised domain adaptation person re-ID. This paper will discuss some extant unsupervised re-id methods and address some existing problems about Person Re-identification. We also sort out the code of each paper mentioned and compare their performance on the existed dataset.
Shadow and Highlight Removal

Shadow removal is a challenging task as it usually requires the detection or annotation of shadows as well as semantic understanding of the scene. In this chapter, we introduce a shadow removal algorithm based on the shadow linear model and then introduce an end-to-end shadow removal algorithm based on deep learning without relying on shadow detection. Our deep neural network (DeshadowNet) is designed with a multi-context architecture, where the output shadow matte is predicted by embedding information from three different perspectives, i.e., high-level semantic context, mid-level appearance information, and local image details. Experimental results show that DeshadowNet is adaptive to shadows with widely varying penumbra widths and works well for shadows cast on different semantic regions. For highlight removal, in this chapter, we derive a global color-lines constraint from dichromatic reflection model to robustly recover specular and diffuse reflection. Our key observation is that each image pixel lies along a color line in normalized RGB space and the different color lines representing distinct diffuse chromaticities intersect at one point, namely the illumination chromaticity. For pixels along the same color line, they spread over the entire image and their distances to the illumination chromaticity reflect the amount of specular reflection components. With global (non-local) information from these color lines, our method can effectively separate specular and diffuse reflection components in a pixel-wise manner for a single image, and it is suitable for real-time applications. Experimental results on synthetic and real images show that our method performs better than the existing methods in terms of both specular reflection removal accuracy and computational efficiency.
The Measuring Engagement (ME™) Tool

Studies on human engagement behavior whilst interacting with multimedia environment has now become very important due to the era of convergences of immersive technology in various digital platforms. This paper will describe a system to measure human engagement behavior in NORMA (Natural Observation and Reflection of Multimedia Application) experimental setting. The tool was developed as a Proof of Concept of The NEMD MODEL (Norma™ Engagement Multimedia Design Model) a renamed of An Engaging Multimedia Design Model that could explain the engagement phenomenon that has enticed the use of virtual spaces to complement its physical form. This paper is a description of The Measuring Engagement (ME™) Tool automated system in comparison to a system done manually. The system has proven to be a useful tool to enable us knows more about engagement and the phenomenon surrounding it. Findings could help us know more about engagement that may lead to excessive, immersive and addictive use of innovative technologies.
Application of BS's Knowledge Base Model in Scientific Research and Development

This article analyzes the application technology of the knowledge base model. The research content of this paper includes large file upload technology, document conversion to HTML technology and big data technology. The author analyzes the overall frame structure design, knowledge base system design, knowledge base function design, simplified model network design, and model network loop elimination. This paper studies the specific application of the knowledge base model in data management, emergency management, and development monitoring. The purpose of this article is to give play to the application value of the knowledge base model and accelerate the speed of product research and development.
Forecasting Of Rupiah Exchange Rate in ASEAN throughout the COVID-19 Pandemic using ARIMA

During the COVID-19 pandemic, there was a spike in the exchange rate of the rupiah against the US dollar. ASEAN countries, as neighbors of Indonesia, also do not escape the same problem. This study discusses which ASEAN countries experienced a spike in exchange rate changes during the COVID19 pandemic by forecasting currency exchange rates in several ASEAN countries against the Indonesian currency. The Autoregressive Integrated Moving Average algorithm or abbreviated as ARIMA has been widely used in forecasting time series data on the Rupiah currency. All ARIMA models in this study can still be accepted using the RMSE and Relative RMSE measurements. The results of this study are in the form of predictive data analysis to describe the condition of the Rupiah exchange rate with the currencies of the ASEAN region during the COVID-19 pandemic. In general, the exchange rates of all ASEAN currencies rose against the Rupiah at the beginning of the pandemic, but then fluctuated. The forecast results show that the COVID-19 pandemic does not have a significant impact on buying and selling exchange rates for almost all ASEAN currencies against the Rupiah.
Development and Application of Online English Education Platform Based on Modern Information Technology

With the continuous deepening of the modern information society and the gradual improvement of campus network construction, online teaching has been accepted by more and more people, and various online English education platforms have also emerged. Online English academies or English training institutions have begun to develop English courses suitable for different devices, and students can use them to learn English anytime, anywhere. Therefore, it is of great significance to develop and implement an online English teaching platform based on modern information technology. The purpose of this article is the development and application of an online English education platform based on modern information technology. This article uses literature data method, questionnaire survey method, mathematical statistics method, logical analysis method and other research methods to conduct on-the-spot survey questionnaires for students of a certain university in this city, and organize, classify and study the collected data accordingly. The research results of the predecessors are summarized and summarized to lay a solid theoretical foundation for this research. This article relies on modern information technology to establish an online English education platform to comprehensively improve students' English learning ability and performance level, test the system, and test the students' performance. From the analysis of test results, it can be seen that the pass rate of the experimental class is 100%, and the excellent rate can reach 26%. Through the use of the online English education platform, the experimental class has greatly improved the excellence rate, average score and pass rate, and the differences are obvious.
Impact of optimization of the structure of rural general education network on improving the quality of education

On the example of the Samara Region, the question of the influence of the processes of optimizing the structure of the rural general education network on improving the quality of education of students in rural areas is investigated. The relevance of the processes, the elaboration of the topic through the presentation of research by Russian and foreign authors devoted to the development of social and educational space, social and educational networks are considered. The definitions of the educational network and optimization of the structure of the general educational network are given; the tendencies of changes in the structure of the rural general education network of the Samara Region are traced since the end of the 90-ies of the last century on the example of 19 schools of the Samara region subjected to reorganization. The analysis of changes in the quality of education of students based on the results of the main state exam and the unified state exam depending on the type of optimization is presented: reorganization of a small school, which is an independent legal entity, into a branch of another school; change in the level of educational programs being implemented.
Handwritten Digit Recognition System

Recently, handwritten digit recognition has become impressively significant with the escalation of the Artificial Neural Networks (ANN). Apart from this, deep learning has brought a major turnaround in machine learning, which was the main reason it attracted many researchers. We can use it in many applications. The main aim of this article is to use the neural network approach for recognizing handwritten digits. The Convolution Neural Network has become the center of all deep learning strategies. Optical character recognition (OCR) is a part of image processing that leads to excerpting text from images. Recognizing handwritten digits is part of OCR. Recognizing the numbers is an important and remarkable subject. In this way, since the handwritten digits are not of same size, thickness, position, various difficulties are faced in determining the problem of recognizing handwritten digits. The unlikeness and structure of the compositional styles of many entities further influences the example and presence of the numbers. This is the strategy for perceiving and organizing the written characters. Its applications are such as programmed bank checks, health, post offices, for education, etc. In this article, to evaluate CNN's performance, we used the MNIST dataset, which contains 60,000 images of handwritten digits. Achieves 98.85% accuracy for handwritten digit. And where 10% of the total images were used to test the data set.
Video action recognition method based on attention residual network and LSTM

A video action recognition method based on attention residual network and long-term memory network(LSTM) is proposed, which is to solve the problems that the existing human action recognition methods are prone to overfitting, susceptible to interference information, and lack of feature expression ability. In the beginning, the traditional data preprocessing method and sampling method are improved to enhance the generalization ability of the model. Then, a residual network with attention is proposed to improve the feature extraction ability of the network. At length, LSTM is used to recognize video actions. Experimental results on UCF YouTube dataset show that the proposed method can recognize the actions in video more effectively than other similar methods in this field, and the recognition rate reaches 95.45%.
Performance of User Behavior-Based Similarity on Top-N Recommendation

User-based collaborative filtering is one of the techniques in the recommendation systems that generate suggested items using the similarity between users. In some commercial systems, the recommended items’ accuracy is more important than the accuracy in predicting the rating. One of the factors that affect the accuracy of the recommendations is the similarity model. Recently, the development of similarity models has combined rating-based similarity and behavior-based similarity. However, the performance of these similarity models is only measured using rating prediction metrics without measuring the accuracy of the resulting recommendations. Therefore, this study aims to measure the behavior-based similarity performance in generating recommended items and compare it to the implementation of rating-based similarity. The similarity models use UPCF, UPCSim, and Cosine, which were tested on the MovieLens 100k dataset. The experimental results showed that the behavior-based similarity models outperformed the traditional similarity model (Cosine) with increasing precision, recall, and F1 score by 0.36, 0.32, and 0.34, respectively (for UPCSim) and 0.31, 0.25, and 0.28, respectively (for UPCF). In addition, the UPCSim similarity model outperformed the UPCF with an improvement of precision, recall, and F1 score by 0.05, 0.07, and 0.06.
FOIST - Framework for Operating System Testing by Integrating Standards and Tools

In this innovative and collaborative age, information systems have become an integral component of governmental, social, and economic infrastructures. Therefore, ensuring their security is a major concern in this technological era as the incorporation of communication technologies and sophistication of cyber attacks have increased the threat landscape profoundly. Operating System (OS), being the most fundamental component of these information systems, acts as the core intermediary and manages the underlying mechanisms involved in computer processing. Hence, one of the most efficient ways of achieving optimal security is to deploy a secure OS. To protect OS, evaluation standards including Common Criteria (CC), National Institute of Standards and Technology (NIST), National Bureau of Standards (NBSIR) and International Standardization of Organization (ISO) suggest a set of security requirements, whereas OS tools like Microsoft Security Assessment Toolkit (MSAT) and Belarc Advisor ensure security compliance. This research is focused on assembling such security requirements of an OS by analyzing technical modules of the aforementioned standards as well as tools. The mentioned security standards and tools cover all significant aspects of security, hence the proposed framework for OS testing named FOIST, integrates standards and tools to evaluate major security aspects of OS. Validation of the framework has been done by taking Windows 10 as a test case followed by the evaluation of framework. The results demonstrate that the OS taken as a test case, incorporates high security measures.
Modular Attention Network based on Language Model for Referring Expression

Referring expression comprehension is to point out the corresponding object in the image by giving the natural language expression. This requires not only a full understanding of the image content, but also a certain understanding of the relationship between the objects in the text information, such as the understanding of the object appearance, location attributes and the relationship between the objects. In this paper, we propose a module network guided by scene graph and based on pre-training model (pre-training SGAN). The image is modeled as a semantic graph of graph structure representation, and the natural language expression is also analyzed as the representation of language scene diagram. Further, we use attention to extract the feature from different modules. Finally, the image and text are matched by similarity. The experiment shows that our method is better than other algorithms.
Mixed Teaching Mode Based On SPOC Platform in the Internet Age

With the arrival of internet plus era, MOOC has been evolved various online learning forms. At the meanwhile the informationization of higher education has been developed towards mobility, school-based, and fragmentization. The deep integration of the traditional classroom teaching and MOOC has become an inevitable trend. Firstly, the development of MOOC and SPOC was analyzed, and a comparative study of MOOC and SPOC was presented. Afterwards, combined with the actual situation of application-oriented university teaching, the blended teaching mode based on the SPOC platform was designed. After the detail analysis of the teaching mode, the SPOC teaching platform was designed and built. The exploratory practice indicates that the blended teaching mode based on the SPOC platform has significant effects in the improvement of the teaching quality.
Intensity image restoration of lidar based on atmospheric scattering model

In this paper, three algorithms are proposed to restore the fog-containing relative intensity image of lidar based on the atmospheric scattering model and dark channel prior theory. The algorithm was evaluated by analyzing the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) of the two data sets, including the fog-free relative intensity images and the fog-containing relative intensity images and standard fog-free relative intensity images. The experimental results show that the PSNR of the two groups of data can be improved by the three algorithms to varying degrees, and the highest PSNR can reach 35.6%. The structure similarity SSIM was significantly improved, and the effect was up to three orders of magnitude.