Reviewerly/exHarmony huggingface dataset:
R-PMA: Rapid &amp; Efficient Statistical Reporting on Heterogeneous Resources

In contemporary organizations, different units of the corporation use a diverse system to produce, save, and search their important data. However, these enterprises synchronize the diversified data of these different systems to understand the full value of the data. Data federation is an approach to integrate the data of heterogeneous data sources. R-PMA is a web-based reporting tool, that federates the heterogeneous data sources e.g. RDF, Relational Databases, and text data in different formats. R-PMA imports all features of datasets and builds interactive reports, based on the imported data resources. It can interact with sparkR for statistical analysis of large datasets. R- PMA also provides an SQL interface to query SparkSQL and MySQL for procuring the required information for reporting. Finally, we have developed it as a plugin in PHPMy Admin to facilitate the organizations that have been using rational databases for years.
Pembelajaran Daring Sebagai Upaya Study From Home (SFH) Selama Pandemi COVID-19

This study aims to identify online learning activities at the homes of students as an effort to support government programs namely study from home during the covid 19 pandemic. This type of research is library research, so that the collection of information and data obtained with the documentation technique that is searching for data about relevant matters from various sources in the library such as books, magazines, news, or other documents. The results showed that online learning conducted at each home made students more independent and created motivation to learn. In addition, online learning has become one of the successes in creating social distancing behavior so as to minimize the emergence of crowds that are considered to have the potential to further spread covid 19 in the school environment.
Mobile Platform Speech Intelligent Recognition and Translation APP

This paper analyzes the function of voice translation software on mobile platform based on the existing technology. To improve the quality of translation, a phrase based translation statistical model is proposed and the training process is analyzed in detail. Then the architecture of the oral translation system is designed by using the speech recognition function of Android framework and the strategy of multi translation engine coordination. For the problem of data sparseness, we adopt a modeling method which combines the language model based on words and the language model with parts of speech. The log linear model is used to analyze the semantic of corpus annotation. The parameters obtained from training reflect the structural information of sentences. Finally, the actual test on the mobile client shows that the real-time speech translation system can not only effectively speed up the speed of data check and audit, but also appropriately improve the accuracy of speech recognition and ensure the quality of translation work.
Lightweight Authenticated Encryption for Cloud-assisted IoT Applications

In an increasingly connected cyberspace where cloud-enabled Internet of things (IoT) applications are exploding, ensuring trust and privacy are two major requirements but often neglected. In this paper, we discuss a lightweight authenticated encryption for simultaneously protecting authenticity and privacy of messages in the cloud-enabled IoT platforms.
Implementing a Distributed Certificate Authority Using Elliptic Curve Cryptography for Big Data Environment

Improving security is necessary to secure the exchange of confidential documents and protection against unauthorized accesses in the big data environment. Sharing data across a large-scale distributed system by a group of non-trusted parties can lead to illegal modification and unauthorized access. Thus, a secure and fast authentication service to authenticate the user connecting to the system is imperative. Based on Elliptic Curve Cryptography (ECC), a Distributed Certificate Authority (DCA) scheme has been designed and implemented which can efficiently improve the authentication of big data in a distributed environment. The role of authentication is distributed amongst the many nodes included in groups. Each group has its own revocation and signature list instead of each node, and this way reduces system overhead and avoids consuming much time when verifying the nodes without a centralized server. The proposed scheme is demonstrated on a big dataset of social networks. The scheme has been analyzed on the basis of security criteria and compare it with previous schemes to evaluate its performance. The experiment shows that the proposed scheme is capable of consumes less memory and reduces time consumption.
Social Media Analysis for Sentiment Classification Using Gradient Boosting Machines

Sentiment analysis deals with the emotions of users on social media discussions and reviews. Gradient boosting machine has shown improved results significantly on many standard classification benchmarks. This paper illustrates the process of text classification for social media to perform sentiment analysis using machine learning (ML) techniques: gradient boosting machines (GBM), AdaBoost, and eXtreme GBM (XGBM) for analyzing online reviews. The classifiers are trained on a benchmark dataset, and performance is assessed in terms of accuracy. A set of systematic experiments are conducted on a social media dataset extracted from the Kaggle. Experimental results reveal that XGBM outperforms in terms of both training and testing accuracy. Sentiment analysis would provide substantial clues about services and product reviews leading to better marketing strategies for branding the products and maximize the level of customer satisfaction and helping in policy-making decisions.
High-Resolution Virtual Try-On Network with Coarse-to-Fine Strategy

Abstract In this paper, we propose a high-resolution virtual try-on network model based on 2D images, which can seamlessly put on given clothing to a target person with any pose. Under the coarse-to-fine strategy, we firstly transform the given normal clothes to warped clothes to well match the pose of the person by a clothing matching module, then these two generated images are combined to generate one fitting image of the person put on the given clothes by a try-on module, lastly utilize a Very Deep Super Resolution (VDSR) module to refine the generated fitting image. Compared to the 3D based methods that are computationally prohibitive, our method only needs 2D images, which is much faster. We evaluate our proposed model both quantitatively (i.e., in terms of SSIM) and qualitatively on a public virtual try-on dataset (i.e, Zalando). The experimental results demonstrate the effectiveness of the proposed method: generating visually better quality of images, our new method can improve the SSIM by 1.5%.
Point of Interest Recommendation Acceleration Using Clustering

Point of Interest (POI) recommendation systems exploit information in location-based social networks to predict locations that users may be interested in. POI recommendations have been widely adopted in many applications, which are helpful for daily life. POI recommendation services receive a huge volume of visit history data generated by users' daily lives with mobile devices. However, POI recommendation systems require long time to build a model from such a huge volume of check-in data and recommend suitable POIs to users. Thus, it is indispensable to shorten the execution time in a big data era. In this study, we propose a clustering-based method to divide the data into multiple subsets to accelerate the POI recommendation's execution while maintaining accuracy. Our proposed method can be adapted to any general POI recommendation algorithm. We divide the whole data, that is, users and POIs, into subsets with a tree structure to balance the size of subsets according to both geographical information and user check-in distribution. Evaluation results show that we successfully accelerate the base algorithms over 17 to 39 times faster while keeping the accuracy almost the same.
Social isolation: a leisure perspective

The feeling of social isolation can be uncomfortable, painful even, and as modernity continues to shift from a period of ‘solidity’ into one of ‘fluidity’ individuals are certain to encounter it more frequently. However, a way of dealing with this circumstance is to view social isolation as something that can be directly embraced and exploited. That is to say, with enough creativity, imagination, and determination social isolation can be reimagined to open up new possibilities of enchantment. With this in mind, this paper focuses on a group of urban explorers who manage to invent themselves through a process of self-creation known as anthropotechnics. What this means is that pursuits such as urban exploration can be used to transform carceral archipelagos of isolation into carceral archipelagos of leisure. In short, these can be understood as occasions where individuals harness an ‘inner chaos’ that is normally contained and controlled in everyday life.
MISNet: Multi-Resolution Level Feature Interpolating Ultralight-Weight Residual Image Super Resolution Network

The design of ultralight-weight super-resolution convolutional neural networks capable of providing images with high visual quality is crucial in many real-world applications with limited power and storage capacity, such as mobile devices and portable cameras. In this paper, a new ultralight-weight super-resolution network, based on the idea of using multiresolution level feature interpolation in a residual framework, is developed. In the proposed network, the multiple resolution level interpolated features generated are fused and the resulting feature maps are added to the residual features obtained from a shallow convolutional neural network. The proposed network is applied to various benchmark datasets and is shown to outperform the state-of-the-art ultralight-weight image super-resolution networks existing in the literature.
Multiple Time Scale Motion Images for Action Recognition

This paper proposes a simple and effective approach for RGB-based action recognition using multiple streams Convolutional Neural Networks (ConvNets). We utilize a new method to represent temporal structure of RGB videos, named Motion Image (MI), which is constructed from the difference between frames with a certain time scale. Considering the different duration of different actions, multiple time scale sampling MIs can obtain more temporal information. Furthermore, we adopt multiple streams ConvNets, including MIs and RGB streams, to learn spatial-temporal features for action recognition. Our approach has been evaluated on UCF-101 and HMDB-51, and the experimental results demonstrate the effectiveness and significantly improve action recognition rate at a small computational cost.
Research on the causes of false comments based on principal component factor analysis model

Online comments usually express consumers' actual use experience and subjective views of goods, and have become one of the important factors influencing consumers' purchasing decisions, thus affecting the promotion and sales of products. However, driven by economic interests, some enterprises or individuals intentionally publish false online reviews in order to promote or denigrate specific products. In this paper, SPSS20.0 was used to conduct principal component analysis on the collected questionnaire data to obtain the most important influencing factors. In order to study the effect evaluation model of e-commerce industry false comments on consumer intentions in detail, this paper establishes a principal component analysis model. Through the analysis of ten factors and the collection of statistics and mathematical statistics and analysis of questionnaires on consumers' intentions based on false comments in the e-commerce industry, the results can be obtained. The contribution rate of X1 index is 0.312, X2 index is 0.126, X3 contribution rate is 0.106, X4 contribution rate is 0.061, and X8 contribution rate is 0.172. So the main reasons for the proliferation of fake reviews are their lack of legal knowledge, lack of economic income, lax supervision on the platform and the vengeful mentality, which directly influences consumers' buying intentions.
Trends in Cloud Computing

In today's world cloud computing is widely used in many it industries. Cloud computing is an emerging model of business computing. Cloud computing which is based on internet has the most powerful architecture of computation. Because of the newly in innovative technologies in cloud computing, the number of labs is transformed into cloud named as the virtual labs. It considers a collection of integrated and networked hardware, software and internet infrastructure. Cloud computing is a development of parallel computing, distributed computing, grid computing and virtualisation technologies from cloud computing adoption accept and also focuses on cloud interoperability issues. We also present the popular platforms of cloud computing. In this paper, we explore several cloud computing system providers about their concerns on security and privacy issues. We also present the characteristics and applications of different well-known cloud computing platforms. We also purpose to recognise the challenges and issues of cloud computing. However, security and privacy issues present a strong roadblock for users to adapt into cloud computing systems.
Comparative Analysis of the Impact of Blockchain On Blockchain Based IoT Application

Blockchain is a safe technology for transaction as it keeps the information safe that it is impossible to change, hack or cheat. blockchain is a digital mode of transaction that is spread across the entire network of computer systems in blockchain. IoT devices allows to send information or useful data in the blockchain to the persons that are inter- connected to each other using computer devices. Transaction is verified to stop discussion that each person or member in the blockchain is responsible for their roles. Using of blockchain with IoT reduces the cost as well the transaction is done with high security. So, we are going to do a comparative analysis of different IoT application on blockchain. And at last, we will come to know that which one application is giving more efficiency in terms of cost, manpower, adaptability, performance.
Continuity as Interrelation of Different Stages of School Teaching

Adaptation in secondary schools acts as an indispensable stage in the life of every student. The present article examines, above all, the definition of continuity and its functions. It also describes the system of requirements for the work to ensure continuity.
Image Dehazing via Multilayer Detail Enhancement and Saturation Compensation

Images taken in hazy conditions often suffer from severe quality degradation, such as detail loss, low contrast, and color cast. To solve these problems, this paper proposed a naturalness preserved dehazing method. First, a hue histogram distribution prior is used to determine if the input image is color- casted, and a simple color correction method is provided to restore colors. Second, a multilayer detail enhancement unit is applied to sharpen the intensity channel, and an adaptive local gamma correction is used to compensate saturation. Dehazed results can be achieved by converting back to RGB. Extensive experimental results indicate that the proposed method can effectively improve the visibility of hazy images, bring out the details without undesirable halo effects, and reproduce vivid color. The assessment results demonstrate that our method is capable of achieving outstanding dehazing performance, compared with some state-of-the-art methods.
Fast Temporal Information Retrieval In Videos With Visual Memory

Due to recent increases in video usage, there have been many studies about processing and managing information within huge volumes of videos. Existing methods for video retrieval aim to retrieve only similar frames related to a query image and compare all frames to the query image, which is costly in run-time and memory usage. To resolve these limitations, we propose a fast retrieval method for precise temporal information with visual memory. Our model compresses an input video into a compressed visual memory and applies an attention-based layer to obtain the probability of a given query image's existence. To the best of our knowledge, we are the first to attempt video retrieval for temporal information using visual memory. To show the efficiency and effectiveness of our model, we conducted experiments for temporal information retrieval on 60-second videos from TV shows and dramas. Our model could effectively compress a video to visual memory with space-savings of 93.6% and 99.1% compared to frame features and original video, respectively. Using the compressed visual memory, our method retrieved temporal information at 250K fps, which is 28x and 4,164x faster than retrieval methods using frame features and frames, respectively.
Result-effects of the implementation of foresight technologies in the training of the future pharmacist

The professional training of a medical student is determined and predicted by the challenges of modern society. The active professional self-development of a pharmacist is aimed at the continuous development of new competencies in the field of scientific, practical and preventive medicine and pharmacy. Social priorities determine the student's professional self-development in modern pharmaceutical education, focused on the future needs of health care, presented in the predictive models of the future pharmacist. The concept of professional self-development of a future pharmacist has been developed and introduced into practice, which characterizes the goals, benchmarks, structural content, patterns, professional regulations, dynamics, result-effects. The pedagogical potential of a medical university in the professional training of a future pharmacist as an integrator of traditions and modern trends in the development of pharmacy has been determined; opportunities for the professional and personal growth of educational subjects; personification of scientific and pedagogical resources of teachers; an innovatively rich environment; modern network information and educational technologies. In the process of experimental work, we note the following result-effects: axiological dominants in the professional worldview of the future pharmacist on health management; interiorization of knowledge in the process of pharmaceutical practices; modeling the ethical and deontological foundations of the pharmacist's behavior; forecasting the professional future in the process of project implementation.
Combined Invariants to Gaussian Blur and Affine Transformation

The paper presents a new theory of combined moment invariants to Gaussian blur and spatial affine transformation. The blur kernel may be arbitrary oriented, scaled and elongated. No prior information about the kernel parameters and about the underlaying affine transform is required. The main idea, expressed by the Substitution Theorem, is to substitute pure blur invariants into traditional affine moment invariants. Potential applications of the new descriptors are in blur-invariant image recognition and in robust template matching.
A Framework for predicting item ratings based on Aspect Level Sentiment Analysis

With the increase of review snippets on the internet, review rating prediction has become an interesting research problem which if solved will lead to a wide range of applications. Existing methods like Sentiment strength-based methods, Matrix Factorization, Deep neural network-based methods can solve this problem satisfactorily with supervision. However, these techniques suffer from data sparsity problem, and need extensive training using manually curated training sets. In order to overcome these limitations, we propose an unsupervised framework for item rating prediction. This framework uses Latent Dirichlet Allocation to extract topics from reviews and labels each topic with the aspect name. We estimate aspect ratings and aspect importance scores of each product, which are then used to predict the overall rating of a product as a linear combination of these aspect ratings weighted by corresponding aspect importance scores. We validate our model on a review dataset comprising of thousand reviews about two products. The performance metrics used to validate the framework are Confusion Matrix, F-measure, Root Mean Squared Error.
An Empirical Evaluation of Collapsible Panel Interfaces

Although collapsible panel widgets have been widely used in industry for many years in various contexts, there have been no formal evaluations conducted to compare user performance and satisfaction with these interfaces. There is also a lack of formal guidance for user interface designers about how to choose among the many variations of collapsible panel schemes in existence. In this paper, we develop a classification framework and propose an empirical method for evaluating user performance with these interfaces. An experiment was conducted comparing accordions, collapsible panels, and flat panel layouts. This found a clear preference for accordions, with the need to scroll to find targets in the other interfaces cited as a key disadvantage of those approaches. Implications of these findings are discussed.
PRELIMINARY STUDY OF MULTIMEDIA LEARNING IN ELECTRONIC ENGINEERING BASED ON MOBILE AUGMENTED REALITY (MAR) AT THE TECHNOLOGY ACADEMY OF AUB SURAKARTA

This preliminary research is focused on finding out the learning media used in the electronics engineering study program at the AUB Technology Academy (AT AUB) Surakarta. This research was conducted in three stages, namely: (1) collecting data on the types of learning media used at the AUB Technology Academy at the moment; (2) giving final exams to students to find out students' understanding of the material given using existing learning media; (3) giving questionnaires to students to find out whether new learning media are needed, and what kind of media are needed by students to increase understanding of learning material. The learning media used for this research are for the Computer Assembly and Installation Practicum course, the purpose of this course is to make students understand the components of the computer system, the ability to assemble a computer, and the competency of a computer installation. The results of this preliminary study are (1) Types of learning media used today, still using books and display screens; (2) the average score of student competence is still low; (3) Student needs for learning multimedia that are easy to use and can be accessed anywhere, encourage students to learn independently and learn independently. For this reason, it is necessary to create mobile-based learning multimedia Augmented Reality to improve student understanding of learning material.
Improving Question Answering over Knowledge Base with External Linguistic Knowledge

Semantic parsing is an important method to question answering over knowledge base (KBQA), which transforms a question into logical queries to retrieve answers. Existing works largely focus on fine-grained relations representation while ignoring the latent semantic information behind the implicit meaning of relations. In this paper, we leverage an external linguistic knowledge (ELK) to enhance relation semantics where ELK is used to remove the ambiguity of words occurring in a relation. Moreover, we present a sense-based attention for word-level relation representation and a Graph-Attention-Network (GAT)-based question encoder. Experiments evaluated on two data sets show that our model outperforms existing approaches.