BNoteToDanmu: Category-Guided Note-to-Danmu Conversion Method for Learning on Video Sharing Platforms

This chapter takes a regulatory perspective on transparency in commercial communication by social media influencers with a focus on the new European Audiovisual Media Services Directive 2018 (AVMS Directive 2018) in the context of existing rather successful (self-) regulatory models in the Netherlands. In the AVMS Directive 2018 influencers’ video channels are explicitly made subject to top-down regulation. Influencers can be held accountable by law in the same way as Video On Demand (VOD) services such as Netflix. Furthermore, Video Sharing Platforms (VSPs) such as YouTube will have to uphold a system of new standards of transparency towards its users and can be held accountable by the regulatory authority under whose jurisdiction it falls, should its system prove to be ineffective. The deadline for implementation of the AVMS Directive 2018 in Member States’ national law is September 2020. This contribution carefully examines the different but soon to be co-existing regimes for influencers aiming at the improvement of transparency regarding commercial interests in videos, a form of strict self-regulation, public law top-down regulation by regulators, and the private (co-regulatory) regime aimed at video sharing platforms. After a diligent evaluation of these regimes we shall conclude by looking ahead at the necessity of firm self-regulatory approaches to guarantee transparency in the best interests of consumers, whilst duly taking note of the influencers’ fundamental rights.

The progressing convergence of television and Internet services has caused a dynamic development of the audiovisual market. The decision to regulate the subject matter of video-sharing platforms (VSPs) in the amended Audiovisual Media Services Directive (AVMSD) was dictated by recognition that such platforms compete for the same viewers and incomes as other audiovisual media services. Coordination of legislation on the European level led to the necessity to introduce amendments to Polish law. The main purpose of this article is to present selected provisions of AVMSD and Poland’s Broadcasting Act concerning VSPs as an area of media policy, which previously had fallen outside the scope of institutional intervention and regulatory restrictions relating to the traditional media market. The central question that author attempted to resolve was whether measures used in relation to VSP providers are legitimate, necessary and proportional. The study explains the definition of video delivery services which is fundamental from the point of view of imposing some obligations on these types of services.

Evaluation of educational value of YouTube videos addressing robotic pyeloplasty in children

This paper presents a method of evaluating singing skills that does not require score information of the sung melody. This requires an approach that is different from existing systems, such as those currently used for Karaoke systems. Previous research on singing evaluation has focused on analyzing the characteristics of singing voice, but were not aimed at developing an automatic evaluation method. The approach presented in this study uses pitch inter- val accuracy and vibrato as acoustic features which are indepen- dent from specific characteristics of the singer or melody. The ap- proach was tested by a 2-class (good/poor) classification test with 600 song sequences, and achieved an average classification rate of 83.5%. The aim of this study is to explore a method of automatic evalua- tion of singing skills without score information. Our interest lies in identifying the criteria that human subjects use in judging the quality of singing for unknown melodies, using acoustic features which are independent from specific characteristics of the singer or melody. Such evaluation systems can be useful tools for im- proving singing skills, and also can be applied to broadening the scope of music information retrieval and singing voice synthesis. Previous work related to singing skills include those based on a control model of fundamental frequency (F0) trajectory (1), gen- eral characteristics (2, 3), as well as work on automatic discrimi- nation of singing and speaking voices (4), and acoustic differences between trained and untrained singers' voices (5, 6, 7). None of these work have gone as far as presenting an automatic evaluation method. This paper presents a singing skill evaluation scheme based on pitch interval accuracy and vibrato, which are regarded as features that function independently from the individual characteristics of singer or melody. To test the validity of these features, an experi- ment of automatic evaluation of singing performance by a 2-class classification (good/poor) was conducted. The following sections describe our approach and the exper- imental results of its evaluation. Section 2 presents discussion of features. Section 3 describes the classification experiment and its evaluation. Section 4 concludes the paper, with discussion on di- rections for future work.

In a wireless channel above microwave frequency, rain attenuation is very important. In order to predict rain attenuation, 1- min. rain rate distribution is required. This paper discusses appropriate conversion methods to estimate 1-minute rain rate from that of other integration time. Based on the measurement data filed in ITU-R WP3J including ETRI data for 6 consecutive years, distributions of rain rate with 1-, 5-, 10-, 20-, 30-minute integration time were analyzed, both on the global and regional basis, and the parametric relationship between the statistical characteristics of 1-minute and other measurement data were investigated to deduce the conversion methods. It is shown that the global model works good with good accuracy for 5-, 10-, 20-min integration time, and the global model is also applicable globally with good accuracy for 5-, 10-, 20-min integration time. The global conversion model was adopted last year as an ITU-R document for new recommendation. The regional conversion model would also be very useful for locations of similar climatic zone.

Usage of machine learning methods for early detection of money laundering schemes

To develop a multi-turn dialogue-based conversational recommender system (DCRS), it is important to predict users' intents behind their utterances and their satisfaction with the recommendation, so as to allow the system to incrementally refine user preference model and adjust its dialogue strategy. However, little work has investigated these issues so far. In this paper, we first contribute with two hierarchical taxonomies for classifying user intents and recommender actions respectively based on grounded theory. We then define various categories of feature considering content, discourse, sentiment, and context to predict users' intents and satisfaction by comparing different machine learning methods. The experimental results for user intent prediction task show that some models (such as XGBoost and SVM) can perform well in predicting user intents, and incorporating context features into the prediction model can significantly boost the performance. Our empirical study also demonstrates that leveraging dialogue behavior features (i.e., including both user intents and recommender actions) can achieve good results in predicting user satisfaction.

Sentiment Analysis (SA) research has increased tremendously in recent times. Sentiment analysis deals with the methods that automatically process the text contents and extract the opinion of the users. In this paper, unigram and bi-grams are extracted from the text, and composite features are created using them. Part of Speech (POS) based features adjectives and adverbs are also extracted. Information Gain (IG) and Minimum Redundancy Maximum Relevancy (mRMR) feature selection methods are used to extract prominent features. Further, effect of various feature sets for sentiment classification is investigated using machine learning methods. Effects of different categories of features are investigated on four standard datasets i.e. Movie review, product (book, DVD and electronics) review dataset. Experimental results show that composite features created from prominent features of unigram and bi-gram perform better than other features for sentiment classification. mRMR is better feature selection method as compared to IG for sentiment classification. Boolean Multinomial Naïve Bayes (BMNB) algorithm performs better than Support Vector Machine (SVM) classifier for sentiment analysis in terms of accuracy and execution time.KeywordsSentiment Analysisfeature selection methodsmachine learningInformation GainMinimum Redundancy Maximum Relevancy (mRMR)composite features

Neural networks have been widely used in color space conversion in the digital printing process. The shallow neural network easily obtains the local optimal solution when establishing multi-dimensional nonlinear mapping. In this paper, an improved high-precision deep belief network (DBN) algorithm is proposed to achieve the color space conversion from CMYK to L*a*b*. First, the PANTONE TCX color card is used as sample data, in which the CMYK value of the color block is used as input and the L*a*b* value is used as output; then, the conversion model from CMYK to L*a*b* color space is established by using DBN. To obtain better weight and threshold, DBN is optimized by a particle swarm optimization algorithm. Experimental results show that the proposed method has the highest conversion accuracy compared with Back Propagation Neural Network, Generalized Regression Neural Network, and traditional DBN color space conversion methods. It can also adapt to the actual production demand of color management in digital printing.

Fuzzy rule base systems are expert systems rely on fuzzy set theory. Here the knowledge of human expert is transfered to the artificial model via fuzzy rules. Therefore, preciseness, completeness and coverage of fuzzy rules in a fuzzy system is vital for the accuracy and plausibility of fuzzy reasoning. However, in such cases where the human expert is unable to supply the rules sufficiently, data-based automatic rule generation methods attract attention. In this study, 2 linear and 2 evolutionary approaches of automatic fuzzy rule generation methods are investigated. The investigated linear solutions contain Wang-Mendel Method and E2E-HFS, while MOGUL and IVTURS-FARC are the selected evolutionary approaches. Wang-Mendel and MOGUL is commonly considered as basic methods of the group they belong to. IVTURS-FARC is distinguished with its ability to handle interval valued fuzzy sets. Among the rest of the algorithms, E2E-HFS is unique with its weak dependency to data. Because it only use some simple properties of corresponding input variable. In order to compare the completeness and the accuracy of automatically generated fuzzy rules, several experiments are performed on different software defect prediction datasets, and the classification performance of resulting fuzzy systems is evaluated. Provided results show that even if training of evolutionary approaches seem to be more precise, similar accuracy can be achieved by linear approaches, and they perform better regarding the experiments on unseen data.

Aiming at the low confidence of traditional spoken English automatic evaluation methods, this study designs an automatic evaluation method of spoken English based on multimodal discourse analysis theory. This evaluation method uses sound sensors to collect spoken English pronunciation signals, decomposes the spoken English speech signals by multilayer wavelet feature scale transform, and carries out adaptive filter detection and spectrum analysis on spoken English speech signals according to the results of feature decomposition. Based on multimodal discourse analysis theory, this evaluation method can extract the automatic evaluation features of spoken English and automatically recognize the speech quality according to the results. The experimental results show that, compared with the control group, the designed evaluation method has obvious advantages in confidence evaluation and can solve the problem of low confidence of traditional oral automatic evaluation methods.

MRI appears to be particularly attractive for the study of the Sinuses of Valsalva (SV), however there is no global consensus on their suitable measurements. In this paper, we propose a new method, based on the mathematical morphology and combining a numerical geodesic reconstruction with an area estimation, to automatically evaluate the SV from a cine-MRI in a cross-sectional orientation. It consists in the extraction of the shape of the SV, the detection of relevant points (commissures, cusps and the centre of the SV), the measurement of relevant distances and in a classification of the valve as bicuspid or tricuspid by a metric evaluation of the SV. Our method was tested on 23 patient examinations and radii calculations were compared with a manual measurement. The classification of the valve as tricuspid or bicuspid was correct for all the cases. Moreover, there are an excellent correlation and an excellent concordance between manual and automatic measurements for images at diastolic phase (r= 0.97; y = x - 0.02; p=NS; mean of differences = -0.1 mm; standard deviation of differences = 2.3 mm) and at systolic phase (r= 0.96; y = 0.97 x + 0.80; p=NS ; mean of differences = -0.1 mm; standard deviation of differences = 2.4 mm). The cross-sectional orientation of the image acquisition plane conjugated with our automatic method provides a reliable morphometric evaluation of the SV, based on the automatic location of the centre of the SV, the commissure and the cusp positions. Measurements of distances between relevant points allow a precise evaluation of the SV.

The ability to read the Koran is still a serious problem in society, as well as what happens to students, there are many factors that can affect a person's ability to read the Qur'an. Among these factors is the existence of a competent teacher. professional and learning methods that can facilitate students in learning to read the Koran. This type of research used by researchers in this study is quantitative research with multiple regression analysis techniques. The research site is at SDI Child Friendly Depok-West Java. Based on the research results obtained information that; 1) the teacher professional competence variable has a positive and significant effect on students' reading ability of the Al-Qur'an with the Pearson correlation (ry1) correlation coefficient of 0.701. The magnitude of the influence is indicated by the coefficient of determination R2 (R square) = 0.491, which means that the effect of professional competence on students' reading ability of the Al-Qur'an is 49.1% and the remaining 50.1% is determined by other factors. 2) the variable using the Ummi method has a positive and very significant effect on students' reading ability of the Qur'an with the Pearson correlation (ry2) correlation coefficient of 0.795. The magnitude of the influence is indicated by the coefficient of determination R2 (R square) = 0.633, which means that the effect of using the Ummi method on students' reading ability of the Al-Qur'an is 63.3% and the remaining 36.7% is determined by other factors. 3) the variable teacher professional competence and the variable using the ummi method together have a positive and very significant effect on students' reading ability of the Al-Qur'an with the Pearson correlation correlation coefficient (ry1.2) is 0.829. The magnitude of the influence is shown by the coefficient of determination R2 (R square) = 0.688, which means that the effect of the professional competence of teachers and the use of the Ummi method together on students' reading ability of the Koran is 68.8% and the rest is 31.2%. determined by other factors..

This study is a research development of Basic English Grammar teaching materials in the English Language Education Study Program of the Faculty of Literature UMI. This study aims to obtain more effective teaching methods between Oral Approach and Teacher Center Learning or lecture methods. This research was conducted using the pseudo experiment method (quasi experimental research). Quasi Experimental Design in this design was used by two groups of subjects. One group was given a specific treatment (experiment), while the other was used as a control group. Both groups were given pre-test measurements. After that the group given treatment (Situational Based Approach) is an experimental group, then both groups are done measurement (post-test) or acquisition. Pre-test Group - Treatment - Post-test. Research site at UMI Faculty of Literature. The number of respondents as many as 50 students is divided into two groups, namely the experiment group and the control group of 25 people each. The results showed that there was a significant difference between the Oral Approach method and the Teacher Center Learning method or the lecture method in improving the student's learning outcomes in Basic English Grammar learning. The cause is that the Oral Approach method has a higher average and improvement compared to the Teacher Center Learning method or lecture method because the Oral Approach method brings more active students into learning. Although given the same material at the same time, but in oral approach method therefore students are active in the learning process. So, lecturers are just facilitators. While in the method of lecture students are only fixated on the explanation of teachers and students are less active in learning. Therefore, oral approach teaching method is more effective than Teacher Center Learning method on Basic English Grammar teaching.

In industry, it is becoming common to detect and recognize industrial workpieces using deep learning methods. In this field, the lack of datasets is a big problem, and collecting and annotating datasets in this field is very labor intensive. The researchers need to perform dataset annotation if a dataset is generated by themselves. It is also one of the restrictive factors that the current method based on deep learning cannot expand well. At present, there are very few workpiece datasets for industrial fields, and the existing datasets are generated from ideal workpiece computer aided design (CAD) models, for which few actual workpiece images were collected and utilized. We propose an automatic industrial workpiece dataset generation method and an automatic ground truth annotation method. Included in our methods are three algorithms that we proposed: a point cloud based spatial plane segmentation algorithm to segment the workpieces in the real scene and to obtain the annotation information of the workpieces in the images captured in the real scene; a random multiple workpiece generation algorithm to generate abundant composition datasets with random rotation workpiece angles and positions; and a tangent vector based contour tracking and completion algorithm to get improved contour images. With our procedures, annotation information can be obtained using the algorithms proposed in this paper. Upon completion of the annotation process, a json format file is generated. Faster R-CNN (Faster R-convolutional neural network), SSD (single shot multibox detector) and YOLO (you only look once: unified, real-time object detection) are trained using the datasets proposed in this paper. The experimental results show the effectiveness and integrity of this dataset generation and annotation method.