Reviewerly/exHarmony huggingface dataset:
Viewport-Driven Adaptive 360◦ Live Streaming Optimization Framework

Virtual reality (VR) video streaming and 360◦ panoramic video have received extensive attention in recent years, which can bring users an immersive experience. However, the ultra-high bandwidth and ultra-low latency requirements of virtual reality video or 360◦ panoramic video also put tremendous pressure on the carrying capacity of the current network. In fact, since the user’s field of view (a.k.a viewport) is limited when watching a panoramic video and users can only watch about 20%∼30% of the video content, it is not necessary to directly transmit all high-resolution content to the user. Therefore, predicting the user’s future viewing viewport can be crucial for selective streaming and further bitrate decisions. Combined with the tile-based adaptive bitrate (ABR) algorithm for panoramic video, video content within the user’s viewport can be transmitted at a higher resolution, while areas outside the viewport can be transmitted at a lower resolution. This paper mainly proposes a viewport-driven adaptive 360◦ live streaming optimization framework, which combines viewport prediction and ABR algorithm to optimize the transmission of live 360◦ panoramic video. However, existing viewport prediction always suffers from low prediction accuracy and does not support real-time performance. With the advantage of convolutional network (CNN) in image processing and long short-term memory (LSTM) in temporal series processing, we propose an online-updated viewport prediction model called LiveCL which mainly utilizes CNN to extract the spatial characteristics of video frames and LSTM to learn the temporal characteristics of the user’s viewport trajectories. With the help of the viewport prediction and ABR algorithm, unnecessary bandwidth consumption can be effectively reduced. The main contributions of this work include: (1) a framework for 360◦ video transmission is proposed; (2) an online real-time viewport prediction model called LiveCL is proposed to optimize 360◦ video transmission combined with a novel ABR algorithm, which outperforms the existing model. Based on the public 360◦ video dataset, the tile accuracy, recall, precision, and frame accuracy of LiveCL are better than those of the latest model. Combined with related adaptive bitrate algorithms, the proposed viewport prediction model can reduce the transmission bandwidth by about 50%.
Design and Practice of Hybrid Course Support Guarantee System Based on Learning Power Model

To promote the "learning-centered, teaching-led" classroom teaching reform, promote the integration of information technology and the teaching process, requires vigorous promotion of hybrid course construction.The questionnaire survey shows that there are problems of unclear construction goals, weak curriculum capacity of teachers, and unsuitable basic support guarantee of schools in the construction of hybrid courses.So this study introduces the learning power theory, organizes a survey on the current situation of learning power and the influencing factors, uses SPSS software to empirically analyze the survey results, and constructs a learning power model through stepwise multiple regression.According to the model, we congshturct the hybrid course construction support system of "three competency development and four guarantee support".The system will give full play to the role of teacher-led and school-based support, promote the transformation of teaching and learning styles, create a new type of teacher-student learning community, and enhance students' learning power.
Evaluation of Immature Platelet Fraction in Patients with Fever and Thrombocytopenia and its Clinical Utility

Introduction: Thrombocytopenia is very common in dengue and other haematological disorders and the aetiology is multifactorial. Immature Platelet Fraction (IPF) is a novel parameter which is a measure of reticulated platelets and it reflects the rate of regeneration of platelets. Aim: To evaluate the relationship between the IPF and platelet recovery and the consistency of its expression. Materials and Methods: The present study was a retrospective observational study done at Institute of Pathology, Madras Medical College between November 2018 to December 2018. Total 37 patients having fever with thrombocytopenia in dengue and other haematological causes were included for analysis. The platelet count, IPF, Mean Platelet Volume (MPV), Platelet Distribution Width (PDW) and Plateletcrit (PCT) was evaluated at the time of admission and once in every 24 hours and plotted in excel spread sheet. Results: About 86.4% were recovered in 24 hours after attaining the peak, 89.1% showed recovery in 24-48 hours of the rise of the IPF compared to the previous value and 94.5% recovered within 24 hours after the fall in the IPF value. It was observed that 81.8% were recovered when the IPF value ≥10% within 24- 48 hours. IPF readings are able to appreciate even in low platelet count levels but the other platelet recovery parameters did not. Conclusion: IPF is a consistent and reliable marker which can be measured even when the platelet count is low and it also predicts the platelets recovery. It is a promising marker that helps in guiding the decision towards platelet transfusion.
Virtual Simulation Teaching Course Design based on Internet of Things Data Transmission

Accelerating the construction of educational informatization and leading the construction of virtual simulation experiment teaching center is an important reform plan put forward by the Ministry of education in the information age.Today's era has entered the information age.Advanced science and technology not only provides convenience for our life, but also plays a great role in the development of education.At present, China's colleges and universities have deficiencies in teaching mode and teaching experimental equipment, especially in interior design teaching and architectural design teaching, which makes it difficult to cultivate compound talents with both technology and theory needed in the new era.Therefore, it is necessary to apply the rapidly developing and powerful VR technology to teaching design.This paper briefly introduces the application of virtual simulation teaching mode in the teaching of virtual reality conference interpretation.By analyzing the key points of its longterm management, this paper explores the reform scheme of teaching mode, in order to achieve the ultimate goal of co construction and sharing of teaching resources.
The Dengue Hemorrhagic Fever Modeling in Banyumas Regency by Using CAR-BYM, Generalized Poisson, and Negative Binomial

The research studied disease mapping of dengue haemorrhagic fever (DHF) in Banyumas Regency.The generalized Poisson (GP), negative binomial (NB), and CAR-BYM models are then used to modelling the DHF.The predictor variables used in this research are the number of health worker, altitude, and population density.We choose the best model for further analysis and estimate relative risk.The criteria for choosing the best model is root mean square error (RMSE).The result showed that the CAR-BYM is the best model.This is due to the RMSE of CAR-BYS is the lowest (1.65) among others (GP = 3.17 and NB=3.25).. Here, we also presented several analysis of the region case, such as Kembaran District is the highest of DFH.The West Purwokerto is the highest relative risk value.This mean that transmission DHF in West Purwokerto district is highest.We also found that the lowest relative risk value is Gumelar.This mean that transmission DHF in Gumelar district is the lowest.
Methods of data mining and educational analytics

The relevance of the study is due to the fact that there are currently more questions than specific answers on the topic in the context of intellectual analysis of educational data: how it is done, for what and how we can use it, what metrics to include in the sample and how to make forecasts. Undoubtedly, in the coming years there will be a transition from discussions to the practical implementation of educational analytics in educational processes. The purpose of the study is to systematize the methods of intellectual analysis of educational data in the context of the difference between educational analytics and pedagogical diagnostics and other methods of data collection. The results of the study will help to build a learning strategy and combine the objectives of the training program with the effectiveness of the educational process and the expected results from the students. In this regard, the author considers the types of educational analytics. The scientific novelty of the research lies in the systematization of the areas of research interests related to data mining in education and educational analytics. It is proved that educational analytics in combination with intellectual analysis of educational data makes it possible to develop accurate models that characterize the behavior of students, their properties, weaknesses and strengths of content and interaction with it, team and group dynamics. The practical significance of the study lies in the fact that the methods considered will allow to assess the current state of the training system or program, predict the desired results and draw up a roadmap of planned changes. For pedagogical designers and methodologists, the presented methods will become the foundation for optimizing the program. Thanks to the presented methods, students receive the most relevant, engaging and meaningful educational experience.
Skin Segmentation Based on Improved DeepLabV3+

In recent years, the number of elderly people and people with disabilities has been increasing.However, the intelligence of the bathing equipment used to assist the elderly and disabled lags far behind that of other medical equipment.The first task of developing an intelligent autonomous bathing aid system without human intervention is to achieve automatic segmentation and localization of skin.The traditional image segmentation model has low accuracy in the case of insufficient light, foam, and water mist occlusion, while the deep learning model exemplified by DeepLabV3+ has high accuracy and robustness.Replacing the backbone feature extraction network in DeepLabV3+ with the lightweight neural network MobileNetV2 can effectively solve the problem of computing power limitation when the deep learning algorithm is deployed to embedded devices, and the generalization performance ofthe improved model is optimized by five scenario-specific data enhancement methods and migration learning ideas.Experiments show that the proposed improved method reduces the weight file size by 93% and 87% with only 1.1% and 1.2% reduction in MIoU compared with the original and Unet models, respectively, achieving a better balance in accuracy and performance.
Connected Components Labeling on Bitonal Images

Several algorithmic solutions for the optimization of Connected Components Labeling have been proposed in literature. Among them, one of the most effective is a block-based mask to drastically reduce the number of memory accesses during the labeling procedure. This paper proposes a systematic approach for labeling multiple pixels at once, automatically generating the actions to be performed on the current pixel/block given the mask values. The proposed strategy allows to extend existing techniques for the generation of optimal decision trees to much more complex masks, where the connectivity between pixels inside a block is not guaranteed. A showcase application, consisting in the design of an efficient CCL algorithm for bitonal images, demonstrates the effectiveness of our proposal in terms of speed and memory footprint.
Digital Literacy in Education: From Local to Global

With the advance of digital technology and the process of globalization, digital literacy becmoes a more important role in education, especially in global context.Digital literacy education is a continuous process from kindergarten to college and even in work places.It can be defined as the ability to use digital tools in education and in work.However, the difference in scope and focus makes digital literacy into two types: local one and global on.Local digital literacy refers basic skills app applied in local life while global one emphasize the active role of learners in global education issues, stable connections with other participants globally and relations to other literacy like policy and transnational literacy.As values and attitudes, global digital literacy benefits students'' learning and future career.Students can move from local digital literacy to global digital literacy with the help from government and teachers.
Constructing automatic work analysis system for order picking process

Work analysis is one of critical tools for improving order picking efficiency in logistics centers. Traditionally, video analysis has been used to analyze work time for various tasks. However, video analysis generally requires a substantial amount of time and labor, which makes it too inefficient for real-time work improvement. To address this, a novel automatic work analysis method is proposed for analyzing the order picking process in logistics centers in this study. The automatic work analysis method can be divided into two parts: dynamic positioning and motion analysis. With the proposed method, an ultrasonic network and a tracking camera are used for measuring a worker’s real-time position while he/she is moving, and an acceleration sensor is used for checking the acceleration of the dominant working hand. Using the position and acceleration data, the worker’s motion is then estimated by an estimation model pre-tested for accuracy. To test the effectiveness of the proposed method, an experiment was conducted in which the measurement error of positioning was found to be approximately 0.01±0.19 m for the x-coordinate values and 0.03±0.45 m for the y-coordinate values. The total measurement error was confirmed to be within a one-step stride. The results of the motion analysis were more than 90% consistent with those obtained by traditional video analysis.
Corrections to “A Secure and Efficient Trust Model for Wireless Sensor IoTs Using Blockchain”

In the above article <xref ref-type="bibr" rid="ref1">[1]</xref>, reference <xref ref-type="bibr" rid="ref2">[2]</xref> is updated and the missing DOI is provided. In Section IV &#x201C;Proposed System Model&#x201D; of the article, a two-point distance formula is added which is taken from <xref ref-type="bibr" rid="ref3">[3]</xref>. The text is updated as follows: &#x201C;In order to send the sensed data, the OSN follows the shortest path. The OSN finds the shortest distance between itself and nearby SN using the x and y coordinates. As we have deployed a two-dimensional (2D) network. So, the above-mentioned distance is being calculated with the help of the two-point distance formula:
Application of wearable motion sensor in business English teaching

With the advancement of science and technology, portable motion sensors are becoming increasingly popular in life and have become a research point for improving life and learning, and are widely used in medical care and smart terminals. Based on the advantages of portable motion sensors, this paper focuses on its application in learning English business. Collects speech information through special motion sensors, analyzes the accuracy of students' reading through speech recognition, to help students better learn business English. Firstly, the wearable sensor is used to collect and preprocess the speech information of students' business English reading as the input of speech recognition. Secondly, the linear predictive cepstrum coefficient (LPCC) and Meier frequency cepstrum coefficient (MFCC) of students' business English reading speech are extracted, and the mixed parameters of LPCC and MFCC are taken as speech features. Finally, the correctness of reading speech is recognized by combining HMM and WNN. Through the simulation analysis of students' reading speech recognition, it is shown that the speech recognition based on wearable motion sensor is feasible and the recognition method has good performance. In addition, the feasibility of wearable motion sensors in business English teaching is verified by the establishment of an experimental classes, which can promote students' English learning better.
Risk Analysis of Developing Lifestyle-Related Diseases Based on the Content of Social Networking Service Posts

Japan’s leading causes of death are cancer, cardiovascular disease, and diabetes, which account for approximately 60% of all deaths. All these diseases are closely related to lifestyle habits such as diet, exercise, and sleep and are called lifestyle-related diseases. Lifestyle-related diseases are characterized by a lack of subjective symptoms, making it difficult to notice the deterioration of the disease. In this study, we analyzed tweets related to diet, exercise, physical condition, and mental condition from the contents posted by users on social networking services (SNS) to construct a system that can prevent lifestyle-related diseases more simply. We found that the results of this work can lead to the construction of a system than can detect risk factors leading to the development of lifestyle-related diseases, from the contents of users’ posts.
On a possible embedding of Lisp into the C language

An implementation of the Lisp computational model is proposed, written in the C language and intended for use in projects whose main language is C. The implementation allows Lisp S-expressions to be written in the form of a superposition of C function calls, thus syntax and semantics of Lisp are modeled with C language capabilities, without the need for additional preprocessing of the source code.
Eigenvalues of the solution of the Lyapunov tensor equation

The paper is concerned with H(Z) -eigenvalues of the solution of the Lyapunov tensor equation.According to the Lyapunov algebraic theorem on tensors, bounds for H(Z)eigenvalues of the solution are given firstly, then based on the relationship between the Lyapunov tensor equation and the continuous-time linear uncertain system, conditional inequalities for the asymptotic stability of the system are shown by H(Z) -eigenvalues of the solution of the Lyapunov tensor equation.
Regional Heterogeneity Analysis of Macroprudential Policy Responses

The global financial crisis in 2008 made people realize that the role of monetary policy in maintaining financial system stability is limited.In that case, macro-prudential policy came into being.At present, there is a lack of related research on the evaluation of this policy effect.Given this, this paper constructs a macro-prudential policy response index, which scales the policy responses from 31 provincial-level administrative regions for 45 quarters from 2010 to 2021.Besides, a fixed-effect model was used to attribute the differences of performance results.The results show four: (1) The macroprudential policy responses of China regions can be divided into four: the response invalid region, the typical response region, the excellent response region, and the excessive response region.(2) The provincial macro-prudential policy response is related to financial development: The higher the financial development, the stronger the macro-prudential policy response.(3) The macro-prudential policy response of provincial-level administrative regions is also related to the advanced industrial structure.The higher the provincial administrative region, the stronger the macro-prudential policy response.(4) The difference in the macro-prudential policy response is also affected by the rationality of the regional industrial structure.The lower the need for intervention, the more likely the market's self-regulatory effects are offsetting each other.
Design of the Selection Mechanism of CPC Members in Higher Vocational Colleges

Under the considering the ideological and political, ability and quality, moral conduct, realistic performance, and other standards, an evaluation indicators for the selection mechanism of CPC members in higher vocational colleges based on seven second-level indicators such as intra-Party activities, academic achievements, play the cutting edge exemplary role, student cadres, outstanding contributions, Party class achievements, and volunteer service, and fourteen third-level indicators such as the attendance rate of regular activities within the Party, has been designed.Through the questionnaire survey, the weight of evaluation indicators was determined, and the design of the selection mechanism of CPC members in higher vocational colleges was finally completed.Through this selection mechanism, each the activist of Party application is evaluated and sorted, and the Party preparation is selected according to the score from high to low in turn, so as to ensure the fairness, impartiality and openness of the selection of the Party preparation.
More Than Words: Collocation Retokenization for Latent Dirichlet Allocation Models

Traditionally, Latent Dirichlet Allocation (LDA) ingests words in a collection of documents to discover their latent topics using word-document co-occurrences.Previous studies show that representing bigrams collocations in the input can improve topic coherence in English.However, it is unclear how to achieve the best results for languages without marked word boundaries such as Chinese and Thai.Here, we explore the use of retokenization based on chi-squared measures, tstatistics, and raw frequency to merge frequent token ngrams into collocations when preparing input to the LDA model.Based on the goodness of fit and the coherence metric, we show that topics trained with merged tokens result in topic keys that are clearer, more coherent, and more effective at distinguishing topics than those of unmerged models.
Parallel Improved Genetic Algorithm for the Quadratic Assignment Problem

Quadratic Assignment Problem is one of the most common combinatorial optimization problems that represents many real-life problems.Many techniques are applied to solve Quadratic Assignment Problem, these include exact, heuristic, and metaheuristic methods.A Genetic Algorithm is a powerful heuristic approach used to find optimal solutions or near-tooptimal for Quadratic Assignment smelborp.In this paper, we developed a Genetic Algorithm with a new crossover operator with new technology closer to that found in nature without a crossover point and a new suggested intelligent mutation operator, then we developed a Parallel Genetic Algorithm using the same crossover and mutation.The sequential Genetic Algorithm will be implemented in the Central Processing Unit (CPU), and the Parallel Genetic Algorithm will be implemented in the Graphical Processing Unit (GPU).This paper presents two comparisons, first calculates elapsed time for crossover, mutation, and selection in both CPU and GPU, then compares the results.This comparison clearly shows the enhancement degree of computation time in the parallel environment, which is around half the time executed in the sequential environment.The second comparison, iterates these operators into several generations, using twenty benchmark instances reported in Quadratic Assignment Problem Library with sizes from (12-70), population size equal to 600, the number of generations equal to 2000, and the maximum number of parallel threads will be 600.Proposed crossover and mutation give the optimal solutions with ten benchmarks with problem sizes from 12 to 32 in both Sequential Genetic Algorithm and Parallel Genetic Algorithm, the next ten benchmarks give solutions closed to the optimal solution with a small error rate.