Reviewerly/exHarmony huggingface dataset:
SISTEM PENDUKUNG KEPUTUSAN PEGAWAI PENERIMA INSENTIF COVID-19 DENGAN METODE SIMPLE MULTI ATTRIBUTE RATING TECNIQUE (SMART):

The Covid-19 pandemic that has hit the world, especially Indonesia starting from the end of February 2020, has caused many people to need health care, so that at this time, health workers are required to work extra and the risk of disease transmission is high, therefore the government provides incentives to officers health as an incentive to support these activities in the form of money, in the distribution of these incentives, a decision-making system that is more effective, fast and on target is needed in order to accelerate the disbursement of funds according to the specified ceiling and the preparation of incentive data reports period. Therefore, researchers designed a decision support system for covid-19 incentives at Port Health Office Jambi Class III which is considered important to assist the administration section at Port Health Office Jambi Class III. In the Port Health Office Class III Jambi, incentives disbursement activities use the manual method, namely by recording using excel. This is what underlies the research at the Port Health Office Jambi Class III. In conducting research, the researcher uses several research methodologies, including field research as the main source in obtaining input data such as information on incentive recipient data for data processing purposes on the system, ceiling, and object of assessment. This application was built with the support of PHP, and a MySql database with the final result in the form of reports and data information on the recipient of the covid19 incentive value at the Port Health Office Class III Jambi. Furthermore, the application that has been built is expected to further assist the Port Health Office Jambi Class III in building the initial foundation of digital data archives. From the results of the study it can be concluded that this application was made with the aim of helping the Port Health Office Class III Jambi in processing data related to the Decision Support System.
Research on Artificial Neural Networks in Bulgarian Academy of Sciences

The paper presents a review of research on Artificial Neural Networks (ANNs) done by scientists from the Bulgarian Academy of Sciences (BAS). It summarizes mainly papers refereed in world famous data bases with scientific literature such as Scopus that are considered as representative collection of the work on ANNs done by teams of researchers including at least one scientist form an institute at BAS. The check up in Scopus revealed more than 200 research items published during last 30 years having h factor of 18 without self citations.
On the Euclidian Metric for Undirected Graphs and Exact Calculations

The Euclidian metric gives better results when organizing a multi-agent interaction environment. The analytical basis for this metric is the matrix inverse to a simple matrix description of an undirected graph. In many applied tasks, the usual matrix inversion and real numbers in the framework of finite bit-depth calculations are quite sufficient. However, the unweighted undirected graph is a discrete object, and traditional metrics are able to support processing in the field of rational numbers. Here we show that the Euclidian metric has the same property. Moreover, the space with the dot product is much richer in possibilities in comparison to the spaces where only norms are introduced. Here these possibilities are at the heart of a simple algorithm for calculating the rational entries of the required inverse matrix. Also in the Euclidian space one can use the most important relationship between its elements - orthogonality. The results of numerical experiments are presented.
Mobile Augmented Reality Integrating Fingertip Haptic Devices and Wrist-Worn Visual Displays

In the context of Mobile Augmented Reality (MAR), hand-based interaction with virtual environments is still an open challenge. Virtual objects projected on the smartphone screen are on a different layer than the camera-captured images, hence the user's hand cannot get in contact with virtual entities. In this work, we present an improvement to a previously published proof-of-concept that aims at overcoming current MAR limitations by mapping the hand in the virtual reference. To this end, we developed an ad hoc ring interface for finger tracking and haptic feedback, designed with wearability in mind. A wrist-worn display compacts the hardware on a single-arm, leaving the other completely free. The overall system is affordable thanks to the use of the smart-phone as core technology and provides "on-demand" augmented reality, tailored for daily activities scenarios. Eighteen users were enrolled to compare the wristband based system and the standard hand-held smartphone montage. Quantitative and qualitative results show that our system was positively received by the subjects, that completed the experimental task achieving similar performance in the two conditions but expressed their preference for the wrist-worn display.
The Corpora They Are a-Changing: a Case Study in Italian Newspapers

The use of automatic methods for the study of lexical semantic change (LSC) has led to the creation of evaluation benchmarks.Benchmark datasets, however, are intimately tied to the corpus used for their creation questioning their reliability as well as the robustness of automatic methods.This contribution investigates these aspects showing the impact of unforeseen social and cultural dimensions.We also identify a set of additional issues (OCR quality, named entities) that impact the performance of the automatic methods, especially when used to discover LSC.
Deep Convolutional Feature-Based Fluorescence-to-Color Image Registration

Fluorescence imaging has been widely utilized in various clinical applications. As a functional imaging modality, NIR fluorescence imaging often does not offer sufficient structural details. Therefore, structural imaging such as color reflectance overlaid with fluorescence imaging represents a superior approach for surgical visualization. Image registration of color reflectance and NIR fluorescence is needed for accurate overlay. In this study, we have implemented a deep convolutional algorithm for feature-based fluorescence-to-color image registration. Software-hardware codesign was conducted. Several sets of experiments were performed on biological tissues to compare the performance of our algorithm and traditional methods. We have demonstrated the feasibility of deep convolutional feature-based fluorescence-to-color image registration. To our best knowledge, this is the first demonstration of deep learning-based image registration between fluorescence and color imageries.
A Coupled Spring Forced Bat Searching Algorithm for Energy Resource Management

Abstract Swarm intelligence-based optimization algorithms show great success to solve complex problems with high efficiency. Recently, a novel and heuristic algorithm, Bat searching algorithm (BA) has been proposed. Moreover, numerical evaluation has already demonstrated the better performance of BA compared with other algorithms variations. In this paper, we propose a coupled spring forced BA (SFBA) algorithm by considering that each particle is a spring and is coupled with the optimal solution found so far as the second abstract spring. The synergistic integration of the coupled springs, the bat’s behavior, and swarm intelligence governs and navigates the new algorithm in the searching process. Moreover, the convergence of the SFBA is studied via Jury’s Test. Numerical evaluation is provided for the proposed SFBA algorithm by conducting comparison with other variations of BA in the literature, which indicates that the performance of SFBA surpasses all the listed variations of BA significantly. Moreover, the proposed SFBA is applied so solve a large-scale energy resource management in uncertain environments, and the results are numerically compared with other BA algorithms.
Visual Writing at the State-of-the-Art?

On the digital road to visual writing, pictograms, icons and signs are becoming available for everyone, following the example of emojis. They can be installed in digital fonts and written by keyboard in a line with letters, to illustrate or replace words. Emojis are close to achieving this state-of-the-art. Pictograms first require modified typography. This allows them to be reduced to letter-size and to embed in digital fonts like emojis. While, considering the advantages and disadvantages of pictograms and emojis, an experimental design will demonstrate how both types of visual characters can be combined in a hybrid writing system. A further step in visualizing language is based on an almost self-evident grammar that can apply equally to emojis, pictograms, and signs. In theory, the project refers to the fact that digital technology is creating a new economy of image processing, and to the realization that the Pictorial Turn, the rehabilitation of visual thinking, is now also being expressed in everyday language, in emails, chats and tweets. The feasibility of this project will currently benefit in particular from an input method that is already used for Chinese words and was implemented by Apple in 2020 with iOS 14. With this method, emojis (and possibly pictograms, icons, and signs) appear in a menu bar when words are entered, and a single tap can replace the letters. Future perspectives are concerned with the emotional and semantic possibilities of animated emojis and pictograms.
A Multi Task Approach to Improve Face and Body Generation in Pose Transfer

Recent advancement in generative adversarial networks (GANs) and conditional GANs has inspired researchers to come up with a new and unique application. One such innovative use case is pose transfer where the poses of the source person are transferred over images of the target person. Using conditional GANs one can create images of a target person doing similar actions as those of the source subject. Unfortunately, the generated images often contain a lot of artifacts that drastically impact the quality of the generated output. In the paper, we propose a novel GAN architecture that uses the concepts of multitask learning to focus on generating a better quality of face and body. Further, a multi-discriminator coarse-to-fine UNet architecture is proposed which helps in controlling and mitigating finer artifacts present in the image
On Analysis of Security and Elasticity Dependency in IIoT Platform Services

Modern IIoT (Industrial Internet of Things) systems rely on highly decomposed platform services that are distributed across the edge and cloud. Security attacks in these systems can cause indirect and unwanted impacts on the elasticity of platform services. These impacts can often manifest across subsystems and not necessarily remain localized. However, there is a lack of techniques to identify and model cross-layered, cross-system concrete dependencies between specific security attacks and their corresponding elasticity impacts. This paper presents a novel framework SEDAICO that can model the elasticity relationship among IIoT platform services due to a security attack. Secondly, the framework supports the developer to identify concrete dependency between specific security attacks and the corresponding impacts on the elasticity of the platform services. We further illustrate our work with experiments and evaluate the framework through a real-world example.
Multiclass Classification of Histology Images of Breast Cancer Using Improved Deep Learning Approach

Breast Cancer has become a serious threat to women life in the overall word. It is necessary to diagnosis for Breast cancer early to avoid mortality rate. In the era of medical imagining with the advent of Artificial Intelligence systems, it has become easy to detect breast cancer at an early stage. Histopathology image modality is one of the best ways for breast cancer detection as it is easy to store in digital format for a long time. Breast cancer is classified into two main categories one is benign and malignant. These two classes are further divided into subclasses. In this paper, we have proposed an improved deep learning model for breast cancer multiclass classification. This proposed model uses a two-level approach one is blocked based and image-based. The blocked based approach is used to reduce overhead with lower-cost processing. Here ensemble learning approach is used for feature extraction which is a combined approach of the various pre-trained model without negotiating accuracy of the system. Final classification is done based on image based improved deep learning approach into eight classes.
The Adaptive Features of an Intelligent Tutoring System for Adult Literacy

Adult learners with low literacy skills compose a highly heterogeneous population in terms of demographic variables, educational backgrounds, knowledge and skills in reading, self-efficacy, motivation etc. They also face various difficulties in consistently attending offline literacy programs, such as unstable worktime, transportation difficulties, and childcare issues. AutoTutor for Adult Reading Comprehension (AT-ARC), as an online conversation-based intelligent tutoring system that incorporated a theoretical model of reading comprehension, was developed with great efforts to meet adult learners’ needs and be adaptive to their knowledge, skills, self-efficacy, and motivation. In this paper, we introduced the adaptive features of AT-ARC from four aspects: learning material selection, adaptive branching, trialogues, and interface, as well as the rationale behind these designs. In the end, we suggested further research on improving the adaptivity of AT-ARC.
Local intensity estimation of solar radiation with vision‐based<scp>3D</scp>modeling of local areas through unmanned aerial vehicle

Abstract The analysis of optimal locations for photovoltaic systems within urban regions has been raised as an important issue for increasing the relative efficiency of solar power generation in urban landscapes. This paper suggests a method that includes 3D modeling through unmanned aerial vehicle (UAV) flight strategies, shadow generation, solar radiation estimation, and evaluation of optimal areas to estimate cost‐effective candidate locations for installing solar panels. For testing the proposed method, combining generated shadows with the intensities of solar radiation in Pohang, South Korea, which were from data collected for a year, the average intensities were calculated to estimate regions for installing solar panels in the targeted places. It is expected that the strategy proposed in this study can contribute to the popularization on local intensity estimations of the solar radiation in urban landscape with using conventional UAV, which has high accessibility as well as realizing low estimation cost.
Studying The Foundations Of The Theory Of Relativity In General Education Schools

This article analyzes the methodology for studying the main ideas and scientific provisions of the special theory of relativity in general education schools. Einstein's postulates, Michelson's experiment, and Lorentz transformations are discussed
A Sentiment Analysis Web Platform for Multiple Social Media Types and Language-Specific Customizations

The designed and implemented web platform for extracting sentiment from texts consists of a machine learning model retrieving sentiment from a text by categorizing the input into three different classes: positive, negative or neutral. Additionally, this model can be used via a web application processing two different types of text input, namely tweets and (movie) reviews. The web interface allows users to analyze their sentences and evaluate batches of opinions and comments based on specific keywords, retrieved from not one but from various types of online sources, namely Twitter, Reddit, and Youtube. Customizations for additional languages are an additional target.
Deblocking Filter in Video Denoising

One of the most severe problems in video denoising is its block-based approach, which leads to distortions called blocking artifacts. This study aimed to present a deblocking filter based on motion threshold estimation and boundary determination. The luminance effect of human visual system (HVS) is also considered to adjust filter. The proposed method has smoother deblocking effect and better detail preservation than other methods. Experiments demonstrate that the proposed method can achieve better results in both the subjective and objective performance than other algorithms.
‘Noooooooooooooooooooooo’: word elongation, gender and emotions in Realistic Fiction

This article examines the functions of word elongation, the duplication of the same letter, in four realistic fiction works: Gilbert’s Eat Pray Love , Green’sThe Fault in Our Stars, Flynn’s Gone Girl and Asher’s Thirteen Reasons Why. We contend that the psychological depictions of the protagonists of these novels are manifested in their employment of word elongation so as to express an array of emotions such as happiness, sadness and anger. By employing a combination of sociolinguistic and psychological theories, we argue that word elongation is a linguistic means of manipulation and social resistance as the female protagonists of the above-mentioned novels identify and re-identify themselves depending on context. It has also been found that although this linguistic technique is mostly associated with female characters, the intensified emotions by which the characters reveal their identity are not always stereotypical, suggesting change and power.
Braille Translation System Using Neural Machine Translation Technology II – Code Conversion of Kana-Kanji Mixed Sentences

Recently, research on machine translation has been active, and research on the Transformer, which is a translation component of the open source library Tensor2Tensor, is also active. In July 2020, a development research institute called Open AI announced GPT − 3 (Generative Pretrained Transformer), a sentence generation language model, uses 175 billion parameters and is evaluated as an AI tool that generates sentences as if they were written by humans. Using this Transformer, we are also constructing a Braille translation system using a neural network. To translate Japanese into Braille, we devised a three-step process. The first is the step of converting a kana-kanji mixed sentence into a space-inserted sentence, the second is the step of converting the space-inserted sentence into a phonetic kana sentence, and the third is the step of converting the phonetic kana sentence into Braille. Earlier at ICMSEM 2019, we have presented the third step. This time, we have developed the first and second steps. That is, the input data are the kana − kanji mixed sentence of step 1, and the output is the phonetic kana sentence of step 2. The whole picture of this system and the steps developed this time is described.
Discarding Abnormal Measurements

The article explores estimation issues of mixed sample and measurements rejection. The article reveals the content of statistical parametric estimates. The estimation limitation of the maximum likelihood on finite samples is shown. The difference in asymptotic estimate and the estimate for finite sample is shown. The meaning of robust estimates is explained. The situation of measurements is considered in the presence of measured quantities two sets, one of which is unknown for measurements subject. The concept of pure sample and mixed sample is introduced. A mixed sample is formed when an element of the second set falls into dimensions sample of the first set which distorts the statistical estimates. The element of the second set in the sample of the first set is uncountable or abnormal. The article proposes a new method for eliminating uncountable measurements. The method increases the analysis and processing of the sample by n-times #CSOC1120.
One Profile, Many Memories: Projecting Memorials for Instagram via Participatory Design

As the popularity of social networks continue to increase, numerous profiles created daily tend to evolve as software. As they represent people in the online environment, these profiles are digital assets that, after their user’s death, represent a digital legacy, even if it only represents emotional value. In some social networks, these legacies become digital memorials. Currently, on the social network Instagram, there is no difference between an active account and an account that has become a memorial, thus is a source of confusion to anyone who visits these profiles. Instagram moreover does not provide options for users to configure their future memorials, which can affect aspects of a person’s identity after his or her death. A problem therefore emerges: how to maintain the aspects of identity, volition and privacy from the moment of the configuration of a memorial until its creation/transformation. In view of the above, this research has the means to raise questions and propose solutions for an Instagram digital memorial, while maintaining the three aspects. From a methodological point of view, this qualitative exploratory research was based on bibliographic reviews, observation on the web, and a survey and participatory design involving the participation of a focus group. The result includes a set of requirements and interface prototypes that are focused on digital memorials and useful for system developers.
The Local Technical-Based Education Policy Development in China

This chapter examines the local technical universities’ development in China from multiple perspectives. In particular, the historical changes of the educational concept of local technical universities, institutional change and organizational upgrade of local technical universities, interaction between belief and system of local technical universities have been examined in this chapter. In response to the ideological background of the formation of China university of Technology system and the process of its institutional and organizational construction. The different stages of the organizational change of Sino-German College reflect different educational concepts, which are embedded in the social and historical structure. The formation process of modern education system is not a closed system, but a natural open system. The change of educational organization always interacts with the external organizational environment and is influenced by social factors.
Construction of Rural Area Eco-Tourism Resource Optimization Model Under the Background of Internet Plus

Rural Eco-tourism has become an important engine to promote rural revitalization in China. With the improvement of the application of Internet plus technology in rural areas, the characteristic resources of rural Eco-tourism can be optimized and allocated through Internet technology in the development and design. In this paper, using the correlation feature extraction method of rural ecological tourism resources of feature extraction and forecasting, construct Internet plus under the background of big data fusion and adaptive resource allocation of rural Eco-tourism development model, and according to the characteristics of the ecological tourism resources of clustering, subspace model is proposed for rural Eco-tourism resources distribution. The proposed model improves the performance allocation of Eco-tourism resources in rural areas and promotes the sustainable development of rural tourism. Finally, the performance of China’s rural Eco-tourism is tested through simulation experiments. The simulation results show that the rural Eco-tourism resources are well allocated and balanced, and the mining and integration ability of tourism resources is improved. At the same time, this paper also found that the big data mining and feature clustering of Eco-tourism resources can promote the development of rural Eco-tourism.