Reviewerly/exHarmony huggingface dataset:
A Block Compressed Sensing for Images Selective Encryption in Cloud

The theory of compressed sensing (CS) has been proposed to reduce the processing time and accelerate the scanning process. In this paper, the image recovery task is considered to outsource to the cloud server for its abundant... | Find, read and cite all the research you need on Tech Science Press
Fully Convolutional Networks for Text Understanding in Scene Images

Text understanding in scene images has gained plenty of attention in the computer vision community and it is an important task in many applications as text carries semantically rich information about scene content and context.For instance, reading text in a scene can be applied to autonomous driving, scene understanding or assisting visually impaired people.The general aim of scene text understanding is to localize and recognize text in scene images.Text regions are first localized in the original image by a trained detector model and afterwards fed into a recognition module.The tasks of localization and recognition are highly correlated since an inaccurate localization can affect the recognition task.The main purpose of this thesis is to devise efficient methods for scene text understanding.We investigate how the latest results on deep learning can advance text understanding pipelines.Fully Convolutional Networks (FCNs) and derived methods have achieved a significant performance on semantic segmentation and pixel level classification tasks.Therefore, we took benefit of the strengths of FCN approaches in order to detect and recognize text in natural scenes images.
Diseminasi Online Multimedia Pembelajaran Matematika yang Dikembangkan Menggunakan Videoscribe

In the industrial revolution 4.0 era, teacher needs to utilize information and communication technology (ICT) in learning. The professionalism competencies proclaimed by teachers also require teachers to be able to develop interactive and interesting learning media so that there is an increase in the quality of learning. This challenge is inversely proportional to the conditions in the field, namely that there are no teachers in the Mathematics Teacher Association(MGMP) at SMA and SMK in Bintan Regency who develop learning multimedia and utilize technology such as social media as learning media. For this reason, Community Service (PkM) activities needs to be conducted, with theme disseminating learning multimedia with videoscribe on social media (e-learning). The PkM activity was concluded to be going well with indicator 1) 100% participant (teacher) got involved during mentoring; 2) the production of learning video by teachers and the dissemination of the learning videos on the YouTube channel; 3) teacher participants require for more training activities.
Efficient ID-based Key-insulated Multi Signature Scheme without Pairings

Key insulated mechanism mitigates the potential damages caused by secret signing key exposure in public key signature schemes. To minimize the impact of secret key exposure in group-oriented applications, in this paper, we propose a novel Pairing-free Identity-based Key-insulated Multi Signature (PF-IDKIMS) scheme. The strong key insulated security of our PF-IDKIMS primitive is reduced to the classical elliptic curve discrete logarithm problem in random oracle model. To the best of our knowledge, the proposed scheme is the first multi signature scheme without pairings in identity based framework dealing with key exposure problems by employing key insulation mechanism.
An Online Tree-Based Approach for Mining Non-Stationary High-Speed Data Streams

This paper presents a new learning algorithm for inducing decision trees from data streams. In these domains, large amounts of data are constantly arriving over time, possibly at high speed. The proposed algorithm uses a top-down induction method for building trees, splitting leaf nodes recursively, until none of them can be expanded. The new algorithm combines two split methods in the tree induction. The first method is able to guarantee, with statistical significance, that each split chosen would be the same as that chosen using infinite examples. By doing so, it aims at ensuring that the tree induced online is close to the optimal model. However, this split method often needs too many examples to make a decision about the best split, which delays the accuracy improvement of the online predictive learning model. Therefore, the second method is used to split nodes more quickly, speeding up the tree growth. The second split method is based on the observation that larger trees are able to store more information about the training examples and to represent more complex concepts. The first split method is also used to correct splits previously suggested by the second one, when it has sufficient evidence. Finally, an additional procedure rebuilds the tree model according to the suggestions made with an adequate level of statistical significance. The proposed algorithm is empirically compared with several well-known induction algorithms for learning decision trees from data streams. In the tests it is possible to observe that the proposed algorithm is more competitive in terms of accuracy and model size using various synthetic and real world datasets.
MODEL TASK-BASED LEARNING UNTUK MEMBANGUN PEMBELAJARAN MANDIRI PADA TUTORIAL ONLINE

&#x0D; Distance learning organized by Universitas Terbuka provides many benefits and opportunities for students in remote areas to get higher education services. For the time being, this learning program is facilitated by e-learning assistance program using technology and information media. This learning process emphasizes unlimited materials for students outside the main source, Buku Materi Pokok, that can be accessed whenever and wherever. The main focus of this tutorial online is to give a chance for students to learn in their free time, give them learning assistance, and measure how they understand the materials given. This emphasizes the learning process independently and autonomously. This article tries to give a new sight in this Tutorial Online about a technique or a model that can be applied. This model encompasses practices, assignments, and tasks can be done independently by students. This learning model will improve and build students’ autonomous learning by providing them with authentic yet simple tasks, practical and independent assignments.&#x0D;
Variational Quantum Support Vector Machine based on Deutsch-Jozsa Ranking

Recently, machine learning algorithms using quantum computers have been actively developed. Among them, the support vector machine (SVM) specialized for the classification problem is one of the algorithms that are attracting the most attention for improving the performance by the quantum computer. In this study, we propose an SVM that classifies test data according to energy level by using time evolution calculation of Ising model. Furthermore, we propose a method to determine the support vector by applying the concept of DBSCAN and Deutsch-Jozsa algorithm and examining the mixture of surrounding data classes. We show that our algorithm can be classified regardless of the difference in the dimensions of the teacher data, and also the difference between linear and nonlinear data.
Public Cloud Security Protection Research

With the rapid development of cloud computing, public cloud as the main form, the attack value is higher, and security is more difficult. This paper expounds the background and concept of public cloud. Starting from the security threat faced by public cloud, proposed public cloud security protection method, and deeply integrates the cybersecurity classified protection 2.0 standard to provide strong support for public cloud security protection.
THE PRACTICALITY OF ENGINEERING PRINCIPLES IN SOFTWARE ENGINEERING

In the early days of computing, software development was all about automating complex scientific calculations requiring several iterations.But with advances in technology resulting in the drastic reduction in the cost of computers, computer programs were designed to automate enterprise activities that required storing large data and developing complex codes to process the same.Unlike the structured scientific problems that computers solved earlier, the application of computers in automating enterprise activities involved problems that were not as structured as in the sciences.The users of enterprise applications were also not technically skilled as scientists.This increased the complexity of software development projects.The complexity associated with software development increased to a level that necessitated the adoption of principles that have been applied in the traditional engineering fields to solve complex problems such as building skyscrapers, highways, electrically wiring a large building among others.Previous studies have documented and categorized the engineering principles that apply to software engineering.This paper goes a step further to illustrate the practical applications of these principles to the software development process using design methodologies.In this way, the study also closes the knowledge gap as to why the name software engineering.Copy Right, IJAR,
CTR Prediction with Deep Neural Networks

Ad effectiveness on online platforms including Facebook and Google is a challenge for businesses.Since, ad networks use algorithms, ad effectiveness as measured by CTRs is not well understood by marketing and sales executives.CTR prediction with deep neural networks can improve ad CTRs.The AI solution for ad CTRs is useful across industry sectors.In the solution, RNN Models learn representations of sequences by maintaining internal states which are updated sequentially and used as proxy for target prediction.Evidence from research shows that deep neural networks could help businesses enhance CTRs.
On software and television: Analyzing a minimal process for software requirements specification by TV broadcasters

In this paper it is addressed the theme of software applications ( apps ) development and supply by TV broadcasters in order to synchronize the ads of TV shows’ sponsors between multiple screens. That sync is required because audience is using multiple gadgets to watch TV (for example, during the materialization of viewers’ second screen experience). Although broadcasters’ supply of apps to the audience constitutes an alternative to the ads sync, such supply requires a previous software development. The execution of a minimal set of activities to software development (particularly to software requirements specification) is analyzed in this paper, observing that such activities should minimize impacts in the typical production processes of TV to mitigate the risk of broadcasters losing their focus on TV content production while developing the apps . The presentation of (i) Software Engineering processes’ tailoring and of (ii) agile methods contributes to analyze a minimal process for software requirements specification.
Renewable Energy Sources and Systems

The chapter describes renewable energy sources, focusing on solar radiation and also addressing geothermal energy, bioenergy, wind energy and hydroenergy. Details on the solar radiation components, on monitoring procedures and equipment and on numerical modelling are discussed as tools for the assessment of the solar energy potential and the available solar energy in the built environment. The factors affecting the solar energy variability in the built environment are outlined considering the available solar radiation on vertical surfaces (facades), on horizontal and on tilted surfaces for buildings specific arrangements. The main solar energy conversion systems (solar thermal and photovoltaic systems) are discussed as individual systems and as part of hybrid thermal systems (including geothermal and bioenergy systems) and hybrid electrical systems (including wind and micro-hydro energy systems) that are recommended to be implemented at building and at the community level. The most common functional schemes of the solar energy conversion systems and of the sustainable energy mixes are detailed considering their components and their role.
Using Class-Dependent Post-Processing to Improve AI for Automotive Front View Camera

Deep learning approaches in artificial intelligence are being increasingly applied to many modern applications, including autonomous driving. Semantic segmentation is one of the promising ways to map vehicle environment utilizing a front view camera. In this paper we propose a post-processing class-dependent method to improve accuracy and configurability of semantic segmentation, arguing that this method is appropriate for front view application. We utilized morphological transformations to overcome the effects of noise and class overlap as well as to improve visibility of classes which had limited presence in the input dataset. Using this method, the output can increase in accuracy to up to 3.88%.
DEVELOPMENT OF INNOVATIVE INSTRUCTIONAL MEDIA BASED ON LECTORA FOR ELEMENTARY SCHOOL STUDENTS

This research is motivated by the problem of the lack of use of school facilities and technology-based learning media in learning activities. This study aims to develop learning media in the form of innovative learning media based on Lectora applications and determine the quality of the media based on assessments by media experts, material experts, class teachers and students. This development research uses a 4D development model that is tailored to the needs of the research so it is only carried out until the third stage, namely define, design, and development. The subject of this product was conducted to 1 media expert, 1 material expert, 1 class teacher and 30 grade IV students at SDN Harjasari 01 with analysis of the research techniques used were quantitative and qualitative. Data collection in this study was carried out by observation, interview, documentation and questionnaire. Based on the analysis of research results in the form of Lectora-based innovative learning media applications which were assessed by 1 learning media expert, 1 mathematical material expert, 1 class teacher and 30 students produced conclusions worthy of use. Analysis of the feasibility assessment by mathematicians gained an average of 4.35, indicating a very good category, evaluations by instructors of instructional media obtained an average of 4.2 aimed at good categories, then an assessment by the class teacher obtained an average of 4.8 indicating very good and learning media developed were tested on 30 students and produced an average of 113.9 showing good categories. Development research that has been done can be concluded that innovative learning media based on Lectora in Mathematics Subjects of Fractional Class IV material is feasible to be used in learning in good categories according to media experts, very good according to material experts, very good according to class teachers and good according to students.
Emotional and psychological well-being of a Person in Difficult Life Situations

The paper explores the construct of emotional and mental well-being, a blend of personal, cognitive-evaluative and emotional features of the positive functioning of a human being. The authors study the role of emotional and mental well-being in the perception of difficult life situations: job loss, children’s life-threating disease, as well as in standard life situations. They use a set of methods to evaluate different aspects of emotional and mental well-being, including K. Riff’s methodology, “Satisfaction with life scale” by E. Diener, self-development orientation tests, a questionnaire of satisfaction with various life aspects, the methodology “Assessment of mental activation, interest, emotional tone, tension and comfort” (N.A. Kurgansky, T.A. Nemchin), integrative test of anxiety (A.P. Bizyuk, L.I. Wasserman, B.V. Iovlev), M. Fordis scale of happiness, “Semantic differential of life situation”, etc. The sample included 214 people aged 24–57 (average age: 37 years old), including 38 unemployed, 52 women with a severely ill child being treated in a hospice. The study revealed significant differences in the self-perception of life situations by people in objectively different situations. It shows the effect of emotional and mental well-being on the self-perception of the situation and the change in its role in different situations. In difficult and poorly controlled situations (child’s severe illness), emotional and mental well-being can change both downwards and upwards. In these situations, high emotional and mental well-being is an internal resource that allows more fully and unbiasedly perceiving the negative and positive aspects of the situation, which can contribute to its fruitful overcoming. In a standard life situation, high emotional and mental well-being with a more negative perception of the situation constitutes a development resource that prompts a person to life changes.
Light Field Reconstruction Based on Compressed Sensing via Deep Learning

The light field has excellent application prospects in immersive media because of the abundant information of the light. Due to the sparsity and redundancy in light field images, light field reconstruction based on compressed sensing is used to recover light field images from only a few measurements. And the light field compressed sensing usually optimizes the measurement matrix and the dictionary and processes each of the light field images separately. Since the high similarity of light field images, the different viewpoints of images can be stacked together and formed as a 4D tensor. In this paper, we propose tensor based on compressed sensing (TCS) method to yield measurements with common characteristics. Besides, a better deep learning network is designed for TCS, the measurement matrix optimization and image reconstruction will be performed simultaneously. Experimental results show that the proposed method gets at least 3 dB gain in PSNR and outperforms state-of-the-art in the reconstruction quality.
Colorwall: An Embedded Temporal Display of Bibliographic Data

A bibliographical data set is often visualized as a network to depict relationships among authors. However, static networks only display minimal information when a dataset accommodates temporal features. This paper proposes an embedded network visualization to present concealed temporal patterns in a data set and leverage multiple intelligent filters to reduce occlusion. We compare different graphing styles, such as feature representation and time direction, then determine the best approach for displaying temporal features. We demonstrate the usability of our approach with case studies and an evaluation of the IEEE InfoVis and VAST conference dataset.
Development of professional communities and their role in solving socio-economic problems

Humanitarian workers operate in complex environments with various challenges and demanding working conditions.These challenges put aid workers in a range of risks and under the pressure.However, human resources are crucial for success of humanitarian operations in general.At the same time, each humanitarian operation is reliant on logistics and logistics activities are always connected with logistic staff.Understanding what motivates logisticians to join the humanitarian sector is essential information for humanitarian organizations and for recruiters within.Also, knowing which factors influence motivation and job satisfaction of humanitarian logisticians could help the organizations to struggle with the extremely turnover they have to face.Up to this moment, needed skills and the performance of humanitarian logisticians were examined.Also, the motivators of humanitarian workers are covered in previous research.Therefore, the additional aim of this research is to extend the knowledge about the human resources in humanitarian sector as well.
ENVIRONMENT AND HABITUS IN KALIMANTAN FOLKLORE AND PAPUAN FOLK STORIES ECOCRITICAL STUDY

There are a lot of environmental problems that occur today. Ecocritics has a basic paradigm that every object can be seen in ecological networks, and ecology can be a helpful science in this critical approach. Environmental insight can be obtained through a literary work, one of which is folklore. In Indonesia, folklore makes nature and the environment a part of the story. In addition to literary works such as poetry and novels, folklore has first explored nature. In folklore, nature is described as the place of life of the characters. That way, folklore indirectly invites the audience to come into contact with environmental insights that need to be learned. The approach of this research is qualitative with the content analysis method. The research data sources are Kalimantan Folklore and Papuan Folk Stories. The folklore that is the source of this research data is the folklore of the Legend of the Stone of Bagaung (Kalimantan) and the Sasori Country, the Land of Prudence (Papua). The study of the perspective of literature (environment) in ecocritics can construct environmental wisdom itself and study the perspective of environmental literature, it can be focused on ethical studies on (1) respect for nature, (2) attitude of responsibility to nature, (3) attitude of solidarity with nature, (4) an attitude of affection and concern for nature, and (5) a non-disruptive attitude towards nature which is displayed in literary works. Through these studies, several findings regarding pollution of the water environment, habitat destruction, and management of nature or the environment have been obtained.
Image rights, creativity and videogames

Videogames characters and avatars have been inspired by real people for decades, but now, technology and hyperrealism have increased the creative potential and consequentially the artistic and economic value of image rights 4 , thus creating new opportunities for celebrities to monetize image rights through licensing.The link between the videogame character and the real person from whom the developers have drawn inspiration may often not even be immediately recognisable, for instance, when only a few traits of the celebrity are reproduced and the level of decontextualization of the avatar is significant: it is hard to see Theodore Roosevelt behind Mr. Robotic, the arch enemy of Sonic, the iconic SEGA hedgehog, even in its most realistically designed version 5 .Conversely, in other instances, developers might aim for the avatar to be easily and immediately linked to the celebrity, and this may, and often does, serve a specific creative purpose.The inclusion of a celebrity in a videogame may be intended to generate greater involvement and players' participation 6 , or to manifest endorsement and promote the game.For instance, the presence of Kevin Spacey as a villain in Call of Duty: Advanced Warfare, while not being necessary for narrative reasons, attracted the public's attention in very much the same way as the presence of Cristiano Ronaldo on the cover of FIFA served to promote the game within his supporters and followers.Using images of real people may in some instances serve a parody purpose.A character might be a caricature of a persona that may be seen as representing a certain group culture.In World of Warcraft you might come across the archaeologist Harrison Jones, which combines Harrison Ford traits with the very essence of Indiana Jones, and the blood elf vendor "Haris Pilton" accompanied by her inseparable pet Tinkerbell.Beyond the examples of cameos, videogames can also be entirely created for political parody such as the third person shooter Rise of Trump, where players help winning the presidency an orange tanned Donald Trump endowed with small hands, by gunning down his political enemies and convincing moustached Mexicans to pay for the wall.Similarly, several popular mobile games allow users to relax by punching and slapping Donald Trump, Ted Cruz and several other political figures.