Reviewerly/exHarmony huggingface dataset:
DLDENet: Deep Local Directional Embeddings with Increased Foreground Focal Loss for object detection

We propose modifications to the RetinaNet architecture and Focal Loss [1] to improve the effectiveness of them in the context of object detection. We show that through normalizing the embeddings generated by each receptive field in the feature map, classifying them using cosine similarity, increasing the loss for the foreground anchors and forcing the dispersion of the classification vectors will improve the performance of the model. We test our proposal with the FlickrLogos-32 [2] dataset that contains 40 examples for each one of the 32 classes, achieving competitive results with respect to the state-of-the-art approaches with a precision of 0.975, a recall of 0.944, an accuracy of 0.949 and improving the mAP over the dataset from 0.657 with RetinaNet [1] to 0.775 using our new architecture DLDENet.
Efficient Noise Level Estimation Using Orientational Gradient Statistics

noise level is an important parameter to improve the performance of many image processing applications. Natural scenes follow special statistics independent of the image contents. These statistical characteristics change under the effects of distortions. Therefore, they can help image processing algorithms to estimate the noise level of input images. We devised a noise level estimation method based on the statistics of orientational differences between image pixel values and those of their neighbors. The proposed approach outperforms the state-of-the-art especially for higher noise levels. This variance estimation approach is effective for both Gaussian and non-Gaussian additive noises. In addition to the high accuracy, because of not using any normalization or image transformation, the proposed method is quite fast and completely useful for real-time applications.
The Implementation of Local Culture on the Development of Reception Orale Teaching Materials through YouTube Media

The development of receptional orale teaching materials based on local culture through YouTube aims to help learners understand French language with the situation of local culture. The development of teaching materials using YouTube which is implemented in learning is very important to improve listening skills by French language learners. Besides this local culture-based video uploaded on YouTube will be very useful for French tourists visiting Indonesia, especially Medan. Therefore this video will present the real situation in Medan, for example, when making a reservation through the application for transportation or for others. The research method uses Research and Development that will be implemented in the French Language Education Study Program by developing teaching materials based on local culture to improve listening skills by Unimed French language students.
Correction to: Collaborative immersive authoring tool for real-time creation of multisensory VR experiences

In the original publication, Figs. 1 and 2 were interchange and the citation of Fig. 1 in the third paragraph of section 2.2 Authoring tools for multisensory VR experiences should be removed.
Beijing IP Court clarifies standard of infringement for moral right of integrity

Abstract Muye Zhang v China Film Co. LTD, Chuan Lu, Dream Author Film Co., Letv Film Co., No 587 (2016), Civil Final Instance, Beijing Intellectual Property Court, 8 August 2019 In Muye Zhang v China Film Co. LTD, et al, the Beijing Intellectual Property Court reversed the decision of Beijing Xicheng District Court and reiterated that ‘prejudice to author’s honour or reputation’ is not a prerequisite for infringement of the right of integrity under Article 10(4) of Copyright Law; in so doing, the court provided some much-needed guidance on evaluating a claim for infringement of the said right.
PGAN: Prediction Generative Adversarial Nets for Meshes

Unlike images, the topology similarity among meshes can hardly be handled with traditional signal processing tools because of their irregular structures. Geometry image parameterization provides a way to represent 3D meshes in the form of 2D geometry and normal images. However, most existing methods, including the CoGAN are not suitable for such unnatural images corresponding to meshes. To solve this problem, we propose a Prediction Generative Adversarial Network (PGAN) to learn a joint distribution of geometry and normal images for generating meshes. Particularly, we enforce a prediction constraint on the geometry GAN and normal GAN in our PGAN utilizing the inherent relationship between the geometry and normal. The experimental results on face mesh generation indicate that our PGAN outperforms in generating realistic face models with rich facial attributes such as facial expression and retaining the geometry of the faces.
Metasearch engine result optimization using reformed genetic algorithm

Metasearch engine is a system that applies several different search engines, merges the returned results from the search engines and presents the best results. Principal component of the metasearch engine is the method applied for merging the given results. The most of existing merging algorithms are relied on the information achieved by ranking scores which is integrated with the results of different search engines. In this paper, a reformed genetic algorithm (RGA) is proposed for aggregating results of different search engines. In the RGA, a chaotic sequence is applied to select the parents to mate, preventing the RGA to get stuck in local optima. The combination of pitch adjustment rule and uniform crossover (CPARU) is also proposed to further mutate of chromosomes. In the problem of optimizing search engine results, the proposed method tries to find weights of documents’ place to allocate each document to the best place. Therefore, the only required information is to know the number of the search engines that finds each document in the corresponding place. Accordingly, this technique works independently of the different search engines’ ranking scores. The experimental results have depicted that the RGA outperforms the genetic algorithm (GA), Borda method, Kendall-tau genetic algorithm (GKTu) and Spearmen's footrule genetic algorithm (GSFD) methods.
Performance of distance-based k-nearest neighbor classification method using local mean vector and harmonic distance

Abstract K-Nearest Neighbor was one of the top ten algorithms data mining in the classification process. The low accuracy results in the K-Nearest Neighbor classification method was caused of this method used the system of majority vote which allowed the selection of outliers as the closest neighbors and in the distance model used as a method of determining similarity between data. In this process it is evident that local mean vector and harmonic distance can improve accuracy, where the highest increase in average accuracy obtained in the set data wine is equal to 6.29% and the highest accuracy increase for LMKNN is obtained in set data glass identification which is 16.18%. Based on the tests that had been conducted on all data sets used, it could be seen that the proposed method was able to provide a better value of accuracy than the value of accuracy produced by traditional K-Nearest Neighbor and LMKNN.
An expert system for equipment selection of thin coal seam mining

an expert system for equipment selection of thin coal seam mining introductionThin coal seam is abundant in China, of which the thickness is less than 1.3 m.According to incomplete statistics in 95 state-owned coal enterprises, there are about 445 mines with the occurrence of TCS.The recoverable reserves of TCS is about 6.5 billion tons, occupying 19% of the total recoverable reserves (he et al. 2012;Jiang et al. 2009;han 2013;Sheng et al. 2007).In recent years, coal mining keeps high intensity in China.As a result of the predatory mining, thick seam mined with thin seam abandoned, thick and min-thick seam are becoming gradually exhausted in mid-eastern and some old mines.In order to balance productivity and prolong the service life of coal mine, the TCS are to be mined urgently in many mining areas, such as huaibei, huainan, Zibo, Yanzhou, Xinwen, etc.The intensity of TCS mining is increasing yearly.however, TCS mining will be situated in an
Increased accuracy in the classification method of backpropagation neural network using principal component analysis

Abstract Water and air in life are needed in every human and living creature on earth, especially with the status of water quality and air quality status that must be known to humans. Water and air quality status has 120 records with 8 attributes consisting of 4 classes and 1096 records with 5 attributes consisting of 6 classes. Water and air quality classification can affect performance in data grouping. So from that the author tries to increase accuracy in classification by using the Neural Network Backpropagation algorithm with PCA. In this study, it is expected that the Backpropagation Neural Network algorithm using PCA is able to increase accuracy in the classification method.
An Approach to Adding Knowledge Constraints to a Data-driven Generative Model for Carnatic Rhythm Sequence

Computational models for generative music have been a recent trend in AI based technology developments. However, an entirely data-driven strategy often falls short of capturing the naturally occurring rhythmic grouping. Guedes et al. [1,2] had proposed dictionary based and stroke-grouping based approaches to generate novel sequences in the 8-beat cycle of Aditala. More recently an attempt of incorporating arithmetic partitioning, as conceived by performers, was made [3] to get rid of the drawback of the former model being failure to capture long-term structure and grammar of this particular idiom and being only successful in capturing local and short-term phrasing. One way of solving this issue would be to consider a rhythmic phrase as a gestalt i.e. to hypothesize three rationales: (i) a sequence of strokes, when played in a faster speed, behaves as an independent unit and not a mere compressed version of the reference; (ii) context influences the accent – the same phrase is played differently when as a part of a composition versus as a filler (ornamentation) during improvisation; (iii) phrases show a co-articulation effect – the gesture differs in anticipation of the forthcoming stroke/pattern. Initial experiments show that a time-compressed version of the reference phrase played in 4x speed sounds perceivably different from the same reference phrase played at 4x speed by the same musician. This indicates that there is a gestural difference in articulating the same phrase at different speeds. We extract timbral features to understand the differences, though there is a context-dependence that seems to have been captured in a supra-segmental way, motivating us to investigate prosodic features. This indicates that a syntactically correct sequence may not serve as a semantically plausible one to a musician’s expectancy. As the qualitative evaluation of CAMel [1] involves expert listening, we believe, adding the proposed knowledge constraints would add to the naturalness, hence acceptability, of the generated sequences. Keywords – Carnatic rhythm, Gestural modeling, Gestalt principle, Generative model.
A Brief Analysis of Privacy Protection Strategy for Block Chain-based Internet of Things System

Block chain technology can provide a new storage mode for sensitive data in the Internet of Things. However, due to the structural characteristics of block chains, block chains also have the risk of privacy leakage. This paper starts with the analysis of the background of block chain technology and the characteristics of block chain technology, further analyses the privacy problems faced by block chain technology at present, and elaborates the corresponding protection and solution strategies.
[The Development of Early Warning Systems for Home/Community Elderly Care].

With Taiwan now an "aged society", home safety for older individuals has become a very important issue. The purpose of establishing early warning systems in homes and/or communities is to generate and disseminate meaningful warning information to medical institutions or rescue units in a timely manner so that they may take timely and appropriate action. The main purpose of this paper is to introduce the current application of information and communication technology (ICT, especially the Internet of Things and artificial intelligence) in early warning systems for home and community care. Two approaches to developing these systems are introduced: instant detection and prevention monitoring. Instant detection facilitates fall detection and personnel tracking, while the focus of prevention monitoring is on preventing falls and physiological status monitoring. The challenges faced by in incorporating ICT into these early monitoring systems are discussed as well.居家社區照護早期警示系統之發展.隨著高齡社會的來臨，長者的居家安全成為非常重要的議題。居家／社區的早期警示系統之目的在及時產生、傳播有意義的警告資訊，提供醫療院所或救助單位能迅速提供適當的照護行動。本文主要的目的在介紹目前資通訊科技在居家和社區早期警示系統上之應用，特別是物聯網及人工智慧。本文中將分成即時偵測及預防監控兩個層次來探討，即時偵測包含跌倒偵測和人員追蹤兩方面，而預防監控特別著重在預防跌倒和生理狀態監控兩方面。同時本文也探討現今發展資通訊科技在居家社區的早期監視系統所面臨的挑戰，期望透過本文的介紹說明能對早期監視系統的發展提供幫助。.
The Influence of Organizational Culture on Employee Retention: A Case of Google

Google’s organizational culture is one of the main reasons it has cemented its place as the biggest player in the current age of information technology, rapid digital growth, and in the fourth Industrial Revolution which includes the Internet of Things (IoT), virtual reality (VR) and artificial intelligence (AI). This paper investigates how a healthy workplace culture cultivates the best and retains the best and talented employees in the field. How the mission, core values, and flexibility resonate with the employees in building the culture. The paper presents the influence of organizational culture on employee retention in Google. The paper analyzes the pros and cons of such a culture and its impact on the company. The paper concludes with implication and recommendation to the companies and business leaders around the world.
Essay Error Analysis on the 1st Year Students of English Education Department

Analyzing student problems is very important part to help English teachers in developing teaching materials, assessments and teaching methods. Unfortunately, this is not a very easy thing unless the teacher has to analyze student errors. This study discussed common mistakes made by STKIP Muhammadiyah Enrekang students in the first year in writing Essay. Qualitative descriptive research was used to describe data relating to errors used in essay writing on first year of English department of STKIP Muhammadiyah Enrekang. In this paper, researchers discussed the research method namely (1) research design, (2) subject and object of research, (3) method and data collection instruments, and (4) data analysis. The data obtained from assignments collected which have been given for several weeks. Types of errors can be classified into 24 types out of 10 mistakes of students’ essay writing from the highest to the lowest. They are article, preposition, spelling, word choice, subject-verb agreement, auxiliary verb, plural form, verb form, capital letter, and meaningless sentences.
Formation of Future Educators’ Professional Training for Introducing Social Experience by Means of Innovative Technologies of Education to Senior Preschoolers

The purpose of the paper is to reveal the peculiarities of the introduction of innovative education technologies in the process of training future pre-school teachers in the field of the socialization of preschoolers, and to verify their effectiveness by experimental means. During the research, the methods of analysis and synthesis of philosophical, psychological and pedagogical sources in the field of professional training of the future teacher were used: modeling and designing, to determine the theoretical and methodological foundations of the research and development of innovative technology for the training of future teachers to familiarize preschoolers with social environments; studying and generalizing the current state of the professional training of future teachers of pre-school institutions to familiarize preschoolers with social reality; comparison and classification to determine the essential characteristics, criteria and levels of readiness of future pre-school teachers for the socialization of preschoolers; pedagogical experiment with qualitative and quantitative analysis of results, in particular, the statistical criterion of K. Pearson (x2). The results of the diagnosis led to the need to implement innovative educational technologies into the process of the professional training of future teachers of pre-school educational institutions in three stages: diagnostic and propaedeutic, motivational, activity–creative.
A Novel Multi-scale Fourier Descriptor Based on Plane Orthogonal Vectors for Fast Shape Retrieval

Shape retrieval is an interesting and challenging issue in computer vision and robot vision. The recent works employing Fourier-based multi-scale descriptors achieve some success in shape retrieval, especially make real-time application possible by improving the efficiency. However, these methods are commonly adopting really complex shape descriptors to present the shape information which is not intuitive enough. In our work, we propose a new Fourier-based method with novel multi-scale descriptor which is a combination of contour feature and regional feature. This novel descriptor has two main characteristics: First, utilize six different invariant signatures which are based on plane orthogonal vectors; Second, leverage multiple scale structures to describe local and global information of shapes. Except the easiness in understanding, this descriptor approach also produces a more distinct topology of shapes than other approaches which means the property of stability on variations, linear or nonlinear. The method is tested on some popular databases that provides a promising retrieval rate(87.46% and 99.27%) with constant time complexity optimization. The results confirm that our method is more capable of representing the topology of shapes than most other methods based on Fourier descriptor.
An Adaptation Aware Model to Predict Engagement on HTTP Adaptive Live Streaming

Today, video streaming is a well-established application on the internet. Providers are able of transmitting video globally, in large-scale, although there are still some challenges, such as offering high video quality for all clients, particularly in live transmission, due to limited butter size and intense client arrival. Low quality result in low engagement (i.e. client permanence). To increase engagement, the provider can adopt strategies to anticipate (i.e. predict) quality losses. To make this possible, the provider needs to learn what session characteristics lead to an early session abandonment. If a new session presents such characteristics, the provider can presume that it will terminate prematurely. To implement an accurate prediction scheme we must choose a feature set able to explain the user permanence (i.e. engagement). Traditional prediction approaches use user-centered quality metrics such stall rate and average bitrate. In our data-set, these metrics give a prediction accuracy of less than 70%. To improve this, we propose a new set of metrics that explores the client adaptation flow. We model it through Markov chains and show that this increases the prediction accuracy up to 82%. Besides this, grouping similar sessions and train a predictor for each group can increase the accuracy. Finally, we present a use case for the predictor in order to investigate its feasibility in real systems.
Forming Digital Literacy in Students Based on the Experience of EU Countries

Abstract The article analyzes the tendencies in the development of digital literacy of citizens of European Union member states in educational institutions. The urgency of the study is driven by the need to develop the skills needed to communicate effectively in the epoch of 4 – the Industrial Revolution. This study focuses on analyzing the approaches needed to build digital literacy, as well as identifying its key development criteria in the education systems of the UK, Slovenia, the Netherlands, Lithuania and Estonia. Attention is drawn to the existing documents and programs that formulate conceptual trendsways for digital literacy across Europe. At the same time, in the European Union there is no common model that reflects the ways and methods of digital literacy, each country defining its priorities for achieving the goals. It is noted that digital literacy is characterized as one of the key skills for developing the professional competencies of a teacher and a competitive specialist. Based on the study of digital literacy experience abroad, it is possible to define a clear public policy focused on high levels of digital literacy and digital skills. The digitalization status of educational establishments and the population of Ukraine, which is defined as low, is compared. The main directions of the concept of development of the digital economy and society of Ukraine for 2018–2020 are described which aim to bridge the “digital divide” in comparison with the developed EU countries. It is concluded that it is precisely the educational institutions need significant reform. Based on the analysis of digital literacy approaches abroad, this study identifies priority areas for reforming education systems in the European Union in line with current labor market and digital society requirements.
Debris: Intermingling Ethnography with Design and Artistic Practice

This Gallery proposal focuses on the debris left behind by human's daily interactions with non‐human agents, with the end goal of providing arts‐infused lenses to investigate and help untangle our complex relationships with smart systems. Debris is the outcome of a process that intermingles ethnographic tactics with design probing techniques and artistic practice. The Debris – a series of art pieces resulting from this process – are offered to the EPIC 2019 community alongside artefacts that were used to inform art pieces: ethnographic data collected during the interviews, probing toolkit used to augment interview data, and the thought process of the two involved ethnographers/designers/artists, in the form of notes and sketches. This collection of art pieces, objects, visual commentaries, humans‐about‐machines accounts represent the fragments that are left behind by human interactions with smart agents ‐ we offer them to prompt reflections, re‐connections, and re‐discoveries of human‐non‐human hybrid landscapes.
PERILAKU MEMBACA SISWA SMA NEGERI 1 TAKALAR

This research aims to examine the reading behavior of Takalar 1 High School students by using the information selection process analyst . The research was conducted because it was motivated by the low level of literacy in Indonesian society , including in Takalar District in the midst of the huge penetration of information technology . Research carried out by using a qualitative narrative method . Informants from the group of students is determined by random stratification , while the informant of the group of employees of the library is determined based on criteria specified . Results of the study showed that the behavior of reading students SMA Negeri 1 Takalar not support to the direction of the acceleration of literacy because they are actively doing the selection of the information that is up to him . Selection of these include selective exposure , selective attention , and selective retention . Selection is done based on the needs of affective , needs the integration of social , and needs escapist. Suggested perform interference to evoke the need cognitive them in order to conduct read them not just read for the need to look for entertainment and fun self