Reviewerly/exHarmony huggingface dataset:
Advertisement Extraction Using Deep Learning

This paper presents a novel deep learning model for extracting advertisements in images, PTPNet, and multiple loss functions that capture the extracted object’s shape. The PTPNet model extracts features using Convolutional Neural Network (CNN), feeds them to a regression model to predict polygon vertices, which are passed to a rendering model to generate a mask corresponding to the predicted polygon. The loss function takes into account the predicted vertices and the generated mask. In addition, this paper presents a new dataset, AD dataset, that includes annotated advertisement images, which could be used for training and testing deep learning models. In our current implementation, we focus on quadrilateral advertisements. We conducted an extensive experimental study to evaluate the performance of common deep learning models in extracting advertisement from images and compare their performance with our proposed model. We show that our model manages to extract advertisements at high accuracy and outperforms other deep learning models.
Methodology and organisation of health-improving work with students with impaired health

The article analyses the features of the methodology for organising physical culture and health-improving work with students with impaired health in Russian universities. The work experience in this direction of physical culture teachers of the Institute of Pedagogy and Psychology of Kostroma State University is presented. The methodology of organising physical culture and health-improving work with students with impaired health in terms of distance education via e-mail is considered; the WhatsApp system popular among young people; Zoom platform; YouTube video hosting. Within the framework of this problem, a digital self-observation diary of students with impaired health has been developed and tested. The tables provide information about students with weakened health of the Institute of Pedagogy and Psychology of KSU, those who started and did not start physical excercises in a distance format and the main reasons for their refusal to practice in a distance format. The author's model of the organisation of distance physical culture and health-improving work with students with health problems is described, including: purpose, objectives, principles, means of physical culture and health-improving work with students with impaired health, as well as features of distance interaction between students and teachers; factors ensuring the implementation of this type of activity and elements of a comprehensive assessment. In the conclusion of the article, conclusions are drawn about the results of the study.
19.2: Accurate and Fast Eye‐box Measurement for Near‐eye Display Based on Image Recognition Algorithm

The measurement of eye‐box is very important for near‐eye display. This paper introduces a fast and accurate measurement scheme based on image recognition algorithm by a automatic robot measuring system. The influence of tilt angle and focus sharpness on eye‐box measurement is also analyzed
IMPORTANT FACTORS FOR THE PARTICIPATION OF REGIONAL MOTHERS IN CHILDCARE SUPPORT PROGRAMS AT BIRTH CENTERS

In this study, the management methods practiced in birth centers, how to use space and their assessment by participant mothers are analyzed through case studies. The aim was to investigate the factors that guide regional mothers to participate in birth centers’childcare support programs.
The Effective Research of Computer Multimedia Technology in Auxiliary Higher Vocational Physical Education Teaching

In this era of computer network technology, the combination of physical education and computer network technology can increase the interest and depth of the school in the process of physical education, which has aroused the attention of many people. There are also some sports researchers who link physical education with computer network, and they also explore the original ideas of physical education in the process of contact. Through the analysis of physical education in higher vocational schools, this paper explores the characteristics and functions of physical education. At the same time, this paper puts forward the difficulties faced by the integration of physical education and computer network technology. On this basis, this paper puts forward the countermeasures of physical education under computer network education, so as to further enrich and perfect the physical education teaching in higher vocational colleges.
A Scalable Approximation Algorithm for Weighted Longest Common Subsequence

This work introduces novel parallel methods for weighted longest common subsequence (WLCS) and its generalization, all-substrings WLCS. Previous work developed efficient algorithms for these problems via Monge matrix multiplication, which is a limiting factor for further improvement. Diverging from these approaches, we relax the algorithm’s optimality guarantee in a controlled way, using a different, natural dynamic program which can be sketched and solved in a divide-and-conquer manner that is efficient to parallelize.Additionally, to compute the base case of our algorithm, we develop a novel and efficient method for all-substrings WLCS inspired by previous work on unweighted all-substrings LCS, exploiting the typically small range of weights.Our method fits in most parallel models of computation, including the PRAM and the BSP model. To the best of our knowledge this is the fastest \((1-\epsilon )\)-approximation algorithm for all-substrings WLCS and WLCS in BSP. Further, this is the asymptotically fastest parallel algorithm for weighted LCS as the number of processors increases.
Optimal modeling analysis and intelligent calculation of a class of stochastic signal systems

Abstract Information flow optimization is very important for decision-making problems in various fields. Using an innovative method of information decision-making optimization, this paper makes a thorough study and analysis of the optimization modeling and intelligent calculation of a kind of stochastic signal system in science and technology by using the big data analysis method. Thus, it provides an ideal decision-making analysis method for effectively realizing the information optimization and processing of such problems.
UCB-ENAS based on Reinforcement Learning

Deep learning has achieved good results in many practical applications, but the network architecture is largely dependent on manual design. In order to liberate the network architecture from manual design, the Neural Architecture Search (NAS) came into being. NAS is mainly divided into three parts: search space, search strategy and performance estimation strategy. Because of the huge search space of NAS, search process becomes extremely long. A good search strategy can search out the high-performance network architecture in a short time. In this paper, we study the search strategy for NAS problems and propose the UCB-ENAS algorithm based on reinforcement learning, which significantly improves search efficiency in a flexible manner. NAS problem can be regarded as a stateless Multi-armed Bandit problem, so we use long short-term memory (LSTM) and Upper Confidence Bounds (UCB) to jointly build a controller that generates a network architecture, and then use the policy-based REINFORCE algorithm to update the controller parameters to maximize the expected reward. Controller parameters and model parameters are alternately optimized. A large number of experiments show that the proposed algorithm can quickly and efficiently search the network architecture, which is faster than ENAS in search speed, and the performance is higher than the architecture searched by DARTS (first order). For example: 56.54% perplexity is obtained on the PTB dataset.
Learning Accounting Using E-Learning In The Covid-19 Period As An Effort To Improve Student Understanding And Academic Quality

This study aims to determine how accounting learning uses e-learning during the Covid-19 period as an effort to increase student understanding and academic quality. The data analysis technique used in this study is to use a form of pattern matching analysis. The results of the research obtained based on data analysis said that the e-learning infrastructure used in UMSIDA, especially the Accounting study program, was good, the e-learning ceiling used in UMSIDA was moodle. In the e-learning learning process at UMSIDA, there are content standards that must be met. Learning accounting using e-learning during the Covid-19 period can be used as an effort to increase student understanding and academic quality, it's just that there needs to be a commitment between students and lecturers.
강세 음절과 비강세 음절의 음조와 음장의 실현 분석

Most English learners desire to have native-like English pronunciation, but is it necessary to have it? Intelligible English pronunciation is what we have to focus on in teaching and learning English and it requires some level of accuracy, which is suprasegmental aspect, especially, stress. English stress consists of three components: duration, pitch, and intensity. Duration and pitch will be dealt in this paper. Twenty four college students and four native speakers participated in the current experiment. Speech samples included eight words and twenty four sentences with the various stressed positions. Each sample appeared twice randomly on the monitor to be read. The Praat software program was used to analyze the recorded samples. The results are: 1) there was not a specific generalization in stress realization from two groups, 2) both of the groups used pitch more than duration on stressed syllables, and 3) native participants produced twice bigger ratio of stressed and unstressed syllables than Korean participants did
Sentiment Analysis on Beauty Product Reviews using LSTM Method

A review is an opinion that contains value on the job or event being reviewed. Many sites provide reviews of products or goods in the modern era to users, such as the femaledaily.com site, which provides a platform for users to review products purchased. The sentiments contained in these reviews are valuable information for business owners. Thanks to product reviews, business owners get insights and data related to the products they sell to improve their products' quality. However, getting opinion information from an unstructured review text is quite difficult. This study aims to classify these reviews as "positive" or "negative". The model proposed for classification is LSTM. Long Short-Term Memory (LSTM) was used in the previously trained model to classify this review. The model designed for the model focuses on preprocessing reviews as follows: data cleansing, case folding, normalization, tokenization, stopword, and stemming. Once classified, this review is visualized as a graph. The best-case scenario results with an accuracy of 95,10% on the sentiment towards the price aspect.
Qualitative Assessment of Cyber Risk Exposures in India

Abstract Increased dependency on technology and data usage has simultaneously enhanced the risk exposures and vulnerabilities of organizations globally. This paper investigates this issue in India by analyzing the survey-based data we collect and provides a framework for qualitative assessment of cyber risks. The paper uses the Structural Equation Model (SEM) to validate the conceptual cyber-risk model that we have developed using the key-risk factors, such as the level of awareness, perceived risk likelihood, level of cyber-security, and cyber-risk exposures. The study results show that the risk vulnerability and the perceived risk likelihood have a positive impact on risk exposure, while risk awareness, cybersecurity, and control measures have a negative relationship. This study also identifies the causal factors among the exposures and the findings can help organizations prioritize their cybersecurity protection investments and caution insurers to take necessary mitigation measures for effective cyber-risk management and financing for their clients.
Report of the Case of Constructing Dialogue Design Patterns for Agent System Based on the Standard Dialogue Structure

To design a dialogue well and easily implement it into an agent dialogue system, we constructed and verified typical dialogue patterns as “ Dialogue Design Patterns ”. To analyze the dialogues, we defined the “ Standard Dialogue Structure ”that represents simple components of dialogues. This is constructed using the“ Turn ”that is one turn-taking between the dialogue participants, the“ Topic ”that is a series of Turns in which a single question is resolved, the“Topic-shift”that is short interaction in which a consensus is built before moving to the next Topic, and the“ Scene ”that puts together each component. We investigated the frequent patterns of dialogue in the Topic. We analyzed the content of each Turn’s utterances on two axes:“convey-receive”and“logical-emotional”. These Turns were labeled on a four-quadrant impression as“ teach ”(logical convey),“ empathy ”(emotional convey),“ survey ” (logical receive), and“ listen ”(emotional receive). We analyzed the frequency of appearance for these labels in the Turns for each Topic using frequent pattern analysis, and we found several typical patterns that order these labels. We named these the patterns and determined their tendency scores. The tendencies of these patterns were categorized into“ information amount ”,“ cooperation ”, and“ balance ”. Finally, we implemented a dialogue scenario created using the Standard Dialogue Structures and Dialogue Design Patterns in our system. In the feasibility study with local government employees, the achievement rate of agent dialogue was more than 50%, and it higher than our previous system. This shows that dialogues based on Dialogue Design Patterns can facilitate agent dialogue. In conclusion, we showed that applying the Standard Dialogue Structure and Dialogue Design Patterns to dialogues in a human-agent dialogue system is a practical and possible way of easily implementing effective dialogues that facilitate interaction even for novices in dialogue design.
A Behavior-Driven Forum Spammer Recognition Method with Its Application in Automobile Forums

Forum comments are valuable information for enterprises to discover public preferences and market trends. However, extensive marketing and malicious attack behaviors in forums are always an obstacle for enterprises to make effective use of this information. And these forum spammers are constantly updating technology to prevent detection. Therefore, how to accurately recognize forum spammers has become an important issue. Aiming to accurately recognize forum spammers, this paper changes the research target from understanding abnormal reviews and the suspicious relationship among forum spammers to discover how they must behave (follow or be followed) to achieve their monetary goals. First, we classify forum spammers into automated forum spammers and marketing forum spammers based on different behavioral features. Then, we propose a support vector machine-based automated spammer recognition (ASR) model and a k-means clustering-based marketing spammer recognition (MSR) model. The experimental results on the real-world labelled dataset illustrate the effectiveness of our methods on classification spammer from common users. To the best of our knowledge, this work is among the first to construct behavior-driven recognition models according to the different behavioral patterns of forum spammers.
How to Construct the Online Teaching Quality Monitoring System of Design Courses Driven by Data Analysis Technology

Big data and cloud computing technology play an important role in online teaching, educational data mining and learning behavior analysis. Taking environmental design as an example, based on the analysis of online teaching characteristics, this research uses the Delphi method to construct a digital monitoring system for online teaching quality of engineering design courses, and uses the analytic hierarchy process to determine the weights of the monitoring system indicators. Finally, the teaching data is extracted and quantitatively analyzed through online teaching empirical cases to verify the effectiveness of the monitoring system. The research results could provide technical reference for the monitoring and management of online teaching quality of engineering design courses.
The Influence Between Attitude, Learning Independence, And The Application Of Learning On Students 'Learning Style

This study aims to determine how the influence of trust, preceived ease quality of information towards online shopping decision. The research was carried out for three months, starting in March to June 2020. The research method used was survey method with approach of causality. The population in this study is the millennial population totaling 200 respondents. Data collection techniques using literature techniques and questionnaires. Data were analyzed using IBM SPSS Statistics 22.0 and AMOS 22.0. All independent variables are influence the dependent variable with critical value ratio (CR), but only one independent variables that accepted with P value. H1 shows that the trust variable has a critical value ratio (CR) of 2.815> 1.96 and a P value of 0.005 < 0.05, so there is an influence between trust and online shopping decisions but the variable cannot be accepted. H2 shows that the ease variable has a critical value ratio (CR) of 4.224> 1.96 and a P value of 0.000 <0.05 then has a positive relationship between ease with online shopping decisions and the variable can be accepted. H3 shows that the quality of information variable has a critical value ratio (CR) of 0.572> 1.96 and a P value of 0.567> 0.05 then has a positive relationship between trust and online shopping decisions but the variable cannot be accepted.
Neural Lyrical Poetry in the Context of Postmodernist Aesthetics

The research aims to reflect on specificity of generative poetry, its place in contemporary postmodernist culture. Scientific novelty of the research lies in studying neural lyrical poetry and its forerunner, cybernetic poetry, which has been cultural reality since the second half of the XX century, as phenomena of contemporary literary process in the context of postmodernist aesthetics. As a result, it is proved that the phenomenon of neural lyrical poetry logically fits into contemporary cultural process: prerequisites for cybernetic art development appeared in philosophical reflections and artistic experiments of the modernists; almost all the principles of postmodernism are absolutised in aesthetics of neural art. Consequently, neural literature will inevitably develop as a movement in which trends of technologisation of art and aestheticisation of technologies, co-authorship of the artist and artificial intelligence are realised.
Correlation Filtering Tracking Algorithm with Joint Scale Estimation and Occlusion processing

An improved correlation filtering algorithm is proposed to address the problems of the traditional correlation filtering algorithm in solving the scale changes and occlusion problems. First, by establishing a scale pyramid, the algorithm can effectively adapt to the change of target scale. In addition, an occlusion detection mechanism is added in the model update stage, and the peak-to-edge ratio is used to determine whether the model needs to be updated, so that the algorithm can better handle the occlusion scenes. Based on what mentioned above, the improved algorithm can achieve adaptive target tracking under scale changes and occlusion. The simulation results show that the algorithm can effectively improve the scale estimation and occlusion performance compared with other related algorithms.
The use of 3D printing for the development of a learning tool for the visually impaired.

3D printing is a technology that allows manufacturing objects from a virtual model. The field of education has appropriated its use due to its potential to contribute to the teaching-learning process, especially for the education of the visually impaired. Visually impaired people develop their skills through alternative means, mainly using auditory and tactile landmarks as a way of obtaining information. Within the innovative perspectives for education, this study aims to propose a learning tool using 3D printing technology to build a board game employed for the use of unplugged programming language in the education of children visually impaired for their development of logical thinking skills. The learning tool production started in its modeling performed using specific software that generated 3D virtual models. Later, these files were sent to a 3D printer for printing, using the fused filament fabrication, originating physical copies of modeled objects. Then, the objects, now printed, went through a finishing process for their completion. Part of the game components received braille indications accordingly to the proposed in this study. As a result, we obtained a game prototype adapted for the visually impaired, made through 3D printing, with the potential to assist in the teaching-learning process.
Sentiment Intensity Prediction using Neural Word Embeddings

Sentiment analysis is central to the process of mining opinions and attitudes from online texts. While much attention has been paid to the sentiment classification problem, much less work has tried to tackle the problem of predicting the intensity of the sentiment. The go to method is VADER --- an unsupervised lexicon based approach to scoring sentiment. However, such approaches are limited because of the vocabulary mismatch problem. In this paper, we present in detail and evaluate our AWESSOME framework (A Word Embedding Sentiment Scorer Of Many Emotions) for sentiment intensity scoring, that capitalizes on pre-existing lexicons, does not require training and provides fine grained and accurate sentiment intensity scores of words, phrases and text. In our experiments, we used seven Sentiment Collections to evaluate the proposed approach, against lexicon based approaches (e.g., VADER), and supervised methods such as deep learning based approaches (e.g., SentiBERT). The results show that despite not surpassing supervised approaches, the AWESSOME unsupervised approach significantly outperforms existing lexicon approaches and therefore provides a simple and effective approach for sentiment analysis. The AWESSOME framework can be flexibly adapted to cater for different seed lexicons and different neural word embeddings models in order to produce corpus specific lexicons -- without the need for extensive supervised learning or retraining.
Segmentation of Brain MR Image Using Modified Student’s t-Mixture Model

In conventional brain image analysis, it is a critical step to segment brain magnetic resonance (MR) image into three major tissues: Gray Matter (GM), White Matter (WM) and Cerebrospinal Fluid (CSF). The main difficulties for segmenting brain MR image are partial volume effect, intensity inhomogeneity and noise, which result in challenging segmentation task. In this paper, we propose a novel modified method based on the basis of the conventional Student’s t-Mixture Model (SMM), for segmentation of brain MR image and correction of bias field. The advantages of our model are introduced as follows. First, we take account of the influence on the probabilities of the pixels in the adjacent region and take full advantage of the local spatial information and class information. Second, our modified SMM is derived from the traditional finite mixture model (FMM) by adding the bias field correction model; the logarithmic likelihood function of traditional FMM is revised. Third, the noise and bias field can be easily extended to combine with the SMM model and EM algorithm. Last but not least, the exponential coefficients are employed to control the results of segmentation details. As a result, our effective and highly accurate method exhibits high robustness on both simulated and real MR image segmentation, compared to the state-of-the-art algorithms.