Reviewerly/exHarmony huggingface dataset:
Design Of Attendance Information System With Web Based QrCode In PT.Andalas Prima Teknologi.

Data technology urges organizational sectors or other institutions to be able to use it as a support for work activities so that they can create accurate data. The development of technology and data resulted in the entry of Western culture and resulted in a change in the attitude pattern of citizens who tilted towards westernization. PT Salim Ivomas Pratama Tbk is an industry engaged in the agribusiness sector. PT. Andalas Prima Teknologi is an industry that produces and markets cooking oil, margarine, and shortening products. The employee attendance system at PT. Andalas Prima Teknologi, currently uses a finger scan attendance, which when carrying out attendance and detecting there are still errors in carrying out identification. When the personnel wanted to carry out the retrieval of information, the attendance system was still using the manual method, in which the personnel were still required to check it again, because the files taken were still in the form of a spreadsheet file. Until the author created a Website-Based Employee Attendance system using a QR Code Scan that practiced the QR Code technology that is on each Employee ID Card to make it easier for employees to carry out absences. As well as a database for storing employee information and reports that can be viewed by personnel. This attendance system can generate reports every day, month, and yearly employee attendance.
Creating Fulfillment Webhooks

Fulfillment typically means that you are connecting your conversation to a web service. Companies often have web services and APIs to display content on websites in place already. In that case, although the majority of the work (like connecting to databases, security and business rules, etc.) has been done before, you will still need to work on an additional code that takes care of routing (to all your web services), and the conversational part, how you return your dialogue back to the user.
Image Processing: Comparative Analysis of Face Processing by AI and Humans

We deal with dozens of human faces every day including those of our relatives, friends, colleagues, and the many familiar faces of media celebrities. Dealing with faces is quite a complex task involving a plethora of factors that affect its recognition such as viewing angle, illumination, face expressions affected by emotions and speech movements. Despite considerable empirical study, the human face is still in the focus of researchers’ attention, and the development of technical and methodological research methods has led to an increase in the number of fundamental and applied research.Face perception is a rather extensive area that requires an interdisciplinary approach uniting social, biological and technical sciences. Deep understanding of face perception will, in its turn, help us create more precise and applicable computer algorithms aimed at face processing. For this reason, experimental research is required to deeper understand the face perception mechanisms.Our short investigation showed that the main distinction between human and AI face processing lies in the way people and machines deal with the information they receive. While AI works with the image as a whole, humans distinguish certain aspects of the face image and work with them. It is clearly seen when memorizing something. We hope that our conclusions could be corroborated by creating algorithms that will imitate the human mind and conducting comparative studies involving humans and AI.
국내 제약 산업 수출지원서비스 품질이 기업의 수출 기대치에 미치는 영향

Objectives: This study aimed to provide a comprehensive analysis of the relationship between the service quality of the Korea Health Industry Development Institute (KHIDI), along with the utilization of and customer satisfaction with its export supporting program, and the export performance of Korean pharmaceutical companies. Methods: An online and offline survey was conducted on Korean pharmaceutical companies that have used the export supporting program provided by KHIDI. Data on the service quality of KHIDI, utilization of and customer satisfaction with the export supporting program, and company export performance were collected via survey. Results: Results indicated that systemizing and reliability service factors of the export supporting program have a significant impact on customer satisfaction, and customer satisfaction has a significant impact on utilization of the export supporting program. Furthermore, customer satisfaction and utilization of the program have a significant impact on company expectations regarding future export performance. Conclusions: As a result, service quality, utilization, and customer satisfaction were found to affect Korean pharmaceutical company’s expectations regarding their future export performance. The results of this study can significantly impact the development of export supporting programs.
CoSimGNN: Towards Large-scale Graph Similarity Computation

The ability to compute similarity scores between graphs based on metrics such as Graph Edit Distance (GED) is important in many real-world applications. Computing exact GED values is typically an NP-hard problem and traditional algorithms usually achieve an unsatisfactory trade-off between accuracy and efficiency. Recently, Graph Neural Networks (GNNs) provide a data-driven solution for this task, which is more efficient while maintaining prediction accuracy in small graph (around 10 nodes per graph) similarity computation. Existing GNN-based methods, which either respectively embeds two graphs (lack of low-level cross-graph interactions) or deploy cross-graph interactions for whole graph pairs (redundant and time-consuming), are still not able to achieve competitive results when the number of nodes in graphs increases. In this paper, we focus on similarity computation for large-scale graphs and propose the "embedding-coarsening-matching" framework CoSimGNN, which first embeds and coarsens large graphs with adaptive pooling operation and then deploys fine-grained interactions on the coarsened graphs for final similarity scores. Furthermore, we create several synthetic datasets which provide new benchmarks for graph similarity computation. Detailed experiments on both synthetic and real-world datasets have been conducted and CoSimGNN achieves the best performance while the inference time is at most 1/3 of that of previous state-of-the-art.
Interactive Multi-level Stroke Control for Neural Style Transfer

We present StyleTune, a mobile app for interactive multi-level control of neural style transfers that facilitates creative adjustments of style elements and enables high output fidelity. In contrast to current mobile neural style transfer apps, StyleTune supports users to adjust both the size and orientation of style elements, such as brushstrokes and texture patches, on a global as well as local level. To this end, we propose a novel stroke-adaptive feed-forward style transfer network, that enables control over stroke size and intensity and allows a larger range of edits than current approaches. For additional level-of-control, we propose a network agnostic method for stroke-orientation adjustment by utilizing the rotation-variance of CNNs. To achieve high output fidelity, we further add a patch-based style transfer method that enables users to obtain output resolutions of more than 20 Megapixel. Our approach empowers users to create many novel results that are not possible with current mobile neural style transfer apps.
On the Capacity of Quantum Private Information Retrieval from MDS-Coded and Colluding Servers.zip

In quantum private information retrieval (QPIR), a user retrieves a classical file from multiple servers by downloading quantum systems without revealing the identity of the file. The QPIR capacity is the maximal achievable ratio of the retrieved file size to the total download size. In this paper, the capacity of QPIR from MDS-coded and colluding servers is studied. Two classes of QPIR, called stabilizer QPIR and dimension squared QPIR induced from classical strongly linear PIR are defined, and the related QPIR capacities are derived. For the non-colluding case, the general QPIR capacity is derived when the number of files goes to infinity. The capacities of symmetric and non-symmetric QPIR with coded and colluding servers are proved to coincide, being double to their classical counterparts. A general statement on the converse bound for QPIR with coded and colluding servers is derived showing that the capacities of stabilizer QPIR and dimension squared QPIR induced from any class of PIR are upper bounded by twice the classical capacity of the respective PIR class. The proposed capacity-achieving scheme combines the star-product scheme by Freij-Hollanti et al. and the stabilizer QPIR scheme by Song et al. by employing (weakly) self-dual Reed--Solomon codes.
Cost Optimization of Top Three Cloud Services Google, Azure and AWS using HybridCloud

The process of reducing your overall cloud spending by using your own available resources as add-onsfor the cloud infrastructure you are using for your business is Cloud cost optimization.This paper discusses the benefitsin terms of cost of applying distributed cloud computing technologies in the development of business. First, the conception of cloud computing is introduced, and then a hybrid cloud computing platform for business is designed.The distinguished characteristics of the proposed platform are then explained in detail, followed with the introductionto the new system of storage using remote hardware infrastructure and resources. An introduction to state-of-the-art products which can be used to build the proposed platform is given.
RECOGNITION OF THE TEXT BY MEANS OF DEEP LEARNING

Using neural networks, various variations of the classification of objects can be performed. Neural networks are used in many areas of recognition. A big area in this area is text recognition. The paper considers the optimal way to build a network for text recognition, the use of optimal methods for activation functions, and optimizers. Also, the article checked the correctness of text recognition with different optimization methods. This article is devoted to the analysis of convolutional neural networks. In the article, a convolutional neural network model will be trained with a teacher. Teaching with a teacher is a type of training for neural networks in which you provide the input data and the desired result, that is, the student looking at the input data will understand that you need to strive for the result that was provided to him.
COMPILING VOCABULARY LISTS FOR CORPUS-BASED ARABIC FOR TOURISM TEACHING

Arabic for Tourism is a course that can be facilitated with innovative teaching materials along with the availability of learning resources on the internet rich in vocabulary and terms typical to the field of Arabic tourism. Therefore, the use of Arabic tourism websites is an effective way to compile vocabulary lists. The articles published in this kind of websites can be collected to be used as a corpus, then processed using a corpus processing software, AntConc. This study used a combination of qualitative and quantitative approaches with descriptive and comparative methods using a corpus linguistic approach. Teachers can take advantage of the features in this application to identify vocabularies commonly used in the world of tourism. These features are word frequency, concordance, collocation, and N-Gram. The results can be used as a reference in compiling an Arabic for Tourism vocabulary list.
Multi-Class Classification of Blood Cells -- End to End Computer Vision based diagnosis case study

The diagnosis of blood-based diseases often involves identifying and characterizing patient blood samples. Automated methods to detect and classify blood cell subtypes have important medical applications. Automated medical image processing and analysis offers a powerful tool for medical diagnosis. In this work we tackle the problem of white blood cell classification based on the morphological characteristics of their outer contour, color. The work we would explore a set of preprocessing and segmentation (Color-based segmentation, Morphological processing, contouring) algorithms along with a set of features extraction methods (Corner detection algorithms and Histogram of Gradients(HOG)), dimensionality reduction algorithms (Principal Component Analysis(PCA)) that are able to recognize and classify through various Unsupervised(k-nearest neighbors) and Supervised (Support Vector Machine, Decision Trees, Linear Discriminant Analysis, Quadratic Discriminant Analysis, Naive Bayes) algorithms different categories of white blood cells to Eosinophil, Lymphocyte, Monocyte, and Neutrophil. We even take a step forwards to explore various Deep Convolutional Neural network architecture (Sqeezent, MobilenetV1,MobilenetV2, InceptionNet etc.) without preprocessing/segmentation and with preprocessing. We would like to explore many algorithms to identify the robust algorithm with least time complexity and low resource requirement. The outcome of this work can be a cue to selection of algorithms as per requirement for automated blood cell classification.
Improving Students Reading Comprehension Of Procedural Text Using KWL Strategy

This study employed a classroom action research design to determine how the KWL strategy could be applied successfully in enhancing students' reading comprehension especially in procedural text. There are four stages in this research. Consist of pre-assessment and cyles 1 until 3. Planning, Acting, Observing, and reflecting were all part of each cycle. Student’s reading achievements were analyzed in each cycle to see their reading comprehension, including pre-assessment. Observations, questionnaires, and interviews were used to collect qualitative data, while test were used to collect quantitative data. This study gets some results. The first is that the KWL strategy could improve students reading comprehension of procedural text and increase students score in reading. The strategy could also develop students' reading comprehension skills in terms of activating prior knowledge, explicit knowledge, implicit information, and word references, as well as recognizing word meanings dependent on context. The strategy was also improved the classroom atmosphere.
RANCANG BANGUN APLIKASI MEDIA PEMBELAJARAN TAHSIN AL-QUR’AN BERBASIS ANDROID

This study aims to design and create a media application for learning the Quran that can be used on Android smartphones by reading and listening to the reading of letters according to their meaning and can immediately be practiced using voice speech. The system development method used is the waterfall method. This application is built on the Android platform and implements voice recognition. Voice recognition algorithm used is devide and conquer to convert sound into discrete form so that synchronization of speech recognition time occurs. The application test results obtained suitability between the algorithm and the design of the application that has been made in accordance with the design objectives. Keywords: tahsin, interactive, android, voice recognation
Gaussian Process Priors for View-Aware Inference

While frame-independent predictions with deep neural networks have become the prominent solutions to many computer vision tasks, the potential benefits of utilizing correlations between frames have received less attention. Even though probabilistic machine learning provides the ability to encode correlation as prior knowledge for inference, there is a tangible gap between the theory and practice of applying probabilistic methods to modern vision problems. For this, we derive a principled framework to combine information coupling between camera poses (translation and orientation) with deep models. We proposed a novel view kernel that generalizes the standard periodic kernel in $\mathrm{SO}(3)$. We show how this soft-prior knowledge can aid several pose-related vision tasks like novel view synthesis and predict arbitrary points in the latent space of generative models, pointing towards a range of new applications for inter-frame reasoning.
Monolingual Corpus Driven Vietnamese-Chinese Neural Machine Translation

Neural machine translation (NMT) usually requires a massive parallel corpus of high quality as training data, the lack of which limits the performance of the NMT model for some low-resource languages. This paper aims to investigate how to make full use of readily accessible monolingual corpus to drive NMT model training. Firstly, the method of capturing a massive Vietnamese corpus is presented. Then, with the back-translation technique, the monolingual corpus is converted into the available Vietnamese-Chinese bilingual corpus which is used for NMT model training. Finally, a visual web application of Vietnamese-Chinese NMT is implemented to display the final training effect.
Optimization of E-Commerce Platform Structure based on Blockchain Technology

E-Commerce has become more and more popular because of rich products, fast transactions, and free from time, locations, stores, and so on. However, the disclosure of personal data such as their IDs, addresses, and phone numbers has become a major concern for online activities. The current e-commerce model is at the crossroads of ownership and privacy. To address this, this article creates an enterprise protocol that uses smart personal contracts to protect privacy during the negotiation phase. This protocol allows contracting parties to conduct business without disclosing personal information such as identity, address, and phone number. Furthermore, we employ the zero-knowledge proof to ensure ownership.
USING DIGITAL EDUCATIONAL TECHNOLOGIES IN PEDAGOGICAL RESEARCH

The article is devoted to the application of digital educational technologies in pedagogical research, in particular, the study of the possibilities of modern digital technologies, the description of the feasibility of their application and directions of development and use. The use of digital technologies in research activities is carried out through various forms of elearning. Various information technologies are used in organizing and conducting research using digital educational technologies. The intensive development of digitalization in all spheres of society will raise the organization and quality, of research work in the field of pedagogy to a new level. The use of digital technologies as a means of educational activity is a research and most important task of pedagogical situations. The study of this issue is caused by the need to identify the conditions that most affect the implementation of its target areas.
FORMATION OF SUPRA-PROFESSIONAL COMPETENCES OF STUDENTS IN THE DIGITAL EDUCATIONAL ENVIRONMENT

The transition of Russia to a digital society actualizes the issues of the formation of universal competencies that are in demand in the professions of the future. The article presents the content of supra-professional competencies that allow performing professional tasks at a high level in the context of the digital transformation of the economy. For the formation of meta-competencies, it is necessary to use the resources of the digital educational environment, online tools that provide not only high-quality access to modern knowledge, but change the ways of thinking, which form the ability to interact with a society through the use of information and communication technologies.
Digitalization and study of graphic disciplines in English: current state and prospects

The article discusses the possibility of effective use of various software in the study of graphic disciplines in English in order to compare the quality of classical teaching and learning using various graphic packages in the study of geometric-graphic disciplines in English and its convergence with science. The ways of using graphic editors for training specialists in various industries are described. The teaching methodology is considered, which allows a creative approach to the implementation of two-dimensional and three-dimensional objects. The article describes the use of various software products in the modernized laboratories of “Computer prototyping and reverse engineering of high complexity.” In this regard, the study of digitalization of graphic education in English and its social consequences seems to be a very relevant area of research.
Human Detection in Video Surveillance

Recognition of the human activities in videos has gathered numerous demands in various applications of computer vision like Ambient Assisted Living, intelligent surveillance, Human-Computer interaction. One of the most pioneering techniques for Human Detection in Video Surveillance based on deep learning and this project mainly focuses on various approaches based on that. This paper provides an idea of solution to use video surveillance more effectively, by detecting any humans present and notifying the concerned people. The deep learning model, preferred for fast computation, Convolution Neural Network is used by stacking 3 blocks of layers on fully connected layers. This provided an identification of humans and naïve approach to eliminate inanimate human like objects such as mannequins.
On Learning Deep Models with Imbalanced Data Distribution

The availability of large training data has led to the development of sophisticated deep learning algorithms to achieve state-of-the-art performance on various tasks and several applications have been benefited immensely. Despite the unparalleled success, the performance of deep learning algorithms depends significantly on the training data distribution. An imbalance in training data distribution affects the performance of deep models. Our research focuses on designing and developing solutions for different real-world problems, specifically related to facial analytic tasks, with imbalanced data distribution. These problems include injured face recognition, fake image detection, and estimation and mitigation of bias in model prediction.
GES DISC Status

A 15 minute presentation overview of GES DISC status presented to the Aura Data Systems Working Group since the last meeting in 2016. Status of OMI products in archive, MLS products in archive, GES DISC service and activites status, introduction to new Data Publication System (DPS), and a reminder on Data Preservation.
An Applicability of Blockchain Model in Business use Case - A Technical Approach

Business network brings many organizations close together to achieve their desired goals and profit from it. People from different organizations may or may not know each other but still can be part of a business network. A major challenge with these business networks is how to provide trust among people and data security. Blockchain is another means through which many organizations in the current digital age are overcoming these problems with ease. Blockchains have also changed the way the business transactions with clients take place. Blockchain is a decentralized distributed ledger in a peer to peer network which can be public or private, and it enables individuals or companies to collaborate with each other to achieve trust and transparency between business and its clients. Many implementations of blockchain technology are widely available today. Each of them have their own strengths for a specific application domain. They can fundamentally alter electronic communications with a potential to affect all sorts of transaction processing systems. However, there are still many challenges of blockchain technology waiting to be solved such as scalability and adoptability. In this paper, we provide the knowledge on Blockchain technology and we present the applicability of blockchain in the business models and also discuss the relevant use cases for Banking and Supply Chain models.