Reviewerly/exHarmony huggingface dataset:
Analysis of digital identity transactions with Ethereum blockchain ethereum in a case study of credit applications in banking

Abstract The falsification and embezzlement of personal data are still found in several cases in the past year. The reason is personal data in physical form are easily manipulated and difficult to be distinguished from the original. The most detrimental impact is if a person’s personal data is used for credit application fraud in the banking industry. Implementation of blockchain technologies, one of which is Ethereum, allows the use of contracts as a rule that must be fulfilled by the parties involved. All stored transactions are perpetual (cannot be deleted or changed), easy to be audited, transparent, and distributed at each participating node. This study aimed to develop a smart contract for personal data transactions with a case study of credit submission at the Bank. The authors developed a trial application using the prototype method in the process of assessment. Assessment was done by black box testing method in the scope of the lab with 10 credit submission data to be transacted. It is resulting in all credit submission data can be transacted and stored in the smart contract. The interview resulted in an opinion that blockchain technology can be used to store personal data and submit credit submissions at the Bank. The results of the analysis on assessments and interviews conclude that blockchain technology can be used as a medium to store personal data and secure credit applications. For future research, testing transactions can be done on Testnet networks with changing several blocks of data.
The analysis of learning material on flood disaster based on regional physical condition in Sampang, East Java

Abstract Nowadays, disaster learning materials on SMA don’t usea suitable context based on conditions in the field. One of the material should be well understood by the student is flood. Susceptible area toward flood usually lies near coastal area. SampangRegency physically identified as coastal area. It’s spreadthe entire northern part and southern part of Madura Island. This region is exposed by flood every year especially on the rainy season. Typical of a flood could be divided into three kinds; tidal flood, river flood, and combination of tidal and river overflow. The recent flood was triggered by Kali Kemuning. Kali Kemuningspills over every year on rainy season. It affects local inhabitant who lives along riparian and it’s surrounding. Based on Indonesia Statistical Bureau (BPS) data people who exposed by flood in Sampang 1,793 each km2. Its large amount of people became vulnerable. This research was designed in qualitative research. The data is collected by primary and secondary. Primary data was collected from field observation and in-depth interviews. Meanwhile, the secondary data was collected from government institutions; Regional Disaster Management Agency (BPBD), National Disaster Management Agency (BNPB), Inarisk BNPB, andCentral Bureau of Statistics (BPS). The result represents several parts in Sampangthat are susceptible to flooding especially along the coastal area and river riparian. The recent condition in April 2019 showed that the Government build the river wall to reduce flood impact. Local people, insight view toward flood is a usual phenomenon. The condition above explainsthat before learning mitigation material in SMA, the students should understand well how to cope with flood disaster risk.
Approximating Optimal Bidirectional Macro Schemes

Lempel-Ziv is an easy-to-compute member of a wide family of so-called macro schemes; it restricts pointers to go in one direction only. Optimal bidirectional macro schemes are NP-complete to find, but they may provide much better compression on highly repetitive sequences. We consider the problem of approximating optimal bidirectional macro schemes. We describe a simulated annealing algorithm that usually converges quickly. Moreover, in some cases, we obtain bidirectional macro schemes that are provably a 2-approximation of the optimal. We test our algorithm on a number of artificial repetitive texts and verify that it is efficient in practice and outperforms Lempel-Ziv, sometimes by a wide margin.
Multi-type web image text detection based on the improved EAST algorithm

Abstract Web image character recognition has important application value in the commercial field and text detection is the basis of extracting this text information. Among many text detection algorithms, EAST (efficient and accurate scene text detector) algorithm has a good performance in natural scenes, but it is ordinary in web pictures. Based on this, aiming at the problem that the number of channels in the QUAD output layer of EAST algorithm is redundant, which leads to the low accuracy of web image text detection, it is modified as the distance from the point marked as a positive sample to the four vertices of the text box where it is located instead of the coordinate offset to the vertices. The improvement reduces from the original 8 channels to 4 channels, which restricts the optimization direction of the model, and then by setting a new loss function in order to improve the loss value of the edge area of the text box and the pixels in the image that are difficult to detect, the model is more suitable for the text detection of the web image. The experimental results show that the improved EAST algorithm significantly improves the accuracy of text detection in web images.
Pojok digital: The role of technology to improve learning motivation and literacy of primary school students

Abstract Learning is a system consisting of integrated elements that result in act changing. In a learning activity, students’ learning motivation and varieties of literacy are essential to achieve required learning objectives as required by teachers and government based on the national curriculum. In this era, students’ need in the learning process is affected by the development of technology. The existence of technology is intended to improve learning motivation for students and give a new variety of literacy to gain new knowledge. The purpose of this study is to seek the role of technology in learning motivation and literacy of primary school students. Through Pojok Digital located in the Elementary School of Pujiharjo 2, Kabupaten Malang, students are given access to learning material that involves technology. A quantitative approach was used in which 20 students of 5 th grade as a control class and 20 students of 6th grade as an experimental class to completed a questionnaire. The subjects of this study are 20 students of 5th grade as a control class and 20 students of 6th grade as an experimental class. The result shows that the ratio of the control and experimental class through the ANOVA gain test of the output score (P) was 0.045 or less than α 0,05. The result is reinforced by paired sample t-test with sig values output (P) of 0.01 under α 0,05. The mean of motivation score in the control class gains 0.11% whereas experimental class gain significantly by 2.7%. The test result for the gain score for literacy between control class and experimental sig value output 0.006 or less than α. The result for paired sample t-test of literacy achievement experienced increases in the control class of 10.1% while the experimental class reaches 17.6%.
The senses of being an enterostomal therapist nurse: complexities involved in the specialty

Objective: To describe the meanings of being an enterostomal therapist considering the complexity of the process of caring for people with wounds, stomas and incontinence. Method: A qualitative, descriptive-exploratory study, conducted through a semistructured interview, between the months of January to April 2018, with 22 graduates from a specialization course in Nursing in Enterostomal Therapy from a public university in the Southeast region, following the snowball technique. Documental research was also carried out in order to complement the data analysis. Results: The interviewed graduates reported that being an enterostomal therapist is taking care of people who have a wound, stoma and/or incontinence, which means they recognized the essence of the specialty. They emphasized that being an enterostomal therapist is to care for people who may be marginalized/stigmatized by society, to work with a focus on the patient’s rehabilitation, seeking social reintegration and independence, and to know how to be an entrepreneur. Conclusion: The meaning of being an enterostomal therapist involves being inserted in a specialty that provides flexibility in the job market and an entrepreneurial character, which gives professional recognition and even financial satisfaction. It is suggested, therefore, that further studies be carried out in this area, contributing to the fields of assistance, teaching, research and extension.
Class‐aware single image to 3D object translational autoencoder

The performances of generative adversarial network (GAN) and autoencoder (AE) models on images have been gathering a great deal of interest in terms of transferring them to three-dimensional (3D) domain. In this study, single image to object reconstruction problem was focused by presenting a novel 2D-to-3D AE model inspired by the recent improvements. To benefit from middle-level features, a model with skip connections was constructed by transferring 2D features to 3D domain. Moreover, the authors considered class-awareness for obtaining a category-agnostic model using limited class-annotations. Apart from recent 3D reconstruction models, they adapted the intersection-over-union score based objective, which is used in the object segmentation model, for improving reconstruction performance. With all these contributions, they call their model as skipped volumetric class-aware AE (SkipVCAE). According to experimental studies, proposed model obtained higher scores than the state-of-the-art model given. The results have proven its performance as a category-specific and category-agnostic model together owing to its class-aware nature. In the further analysis, it was seen that presented model yielded satisfactory results on a single image to object modelling compared to its multi-view version thanks to class-awareness.
Towards a Semantic Approach for Candidate Answer Generation in Solving Crossword Puzzles

Language games represent one of the most fascinating challenges of research in Artificial Intelligence. One of the problems that lies in this set is the development of an automated solution generator for Crossword puzzles. Automatic resolution of crossword puzzles is an open natural language challenge that demands the filling of puzzle grid with candidate answers, while satisfying the grid constraints. Moreover, providing an accurate list of answer candidates has a major impact on the performance of the automatic crossword resolution task. This paper proposes a semantic-based search approach for generating candidate answer lists for definition type clues by taking advantage of the lexical relations encoded in WordNet, the lexical database for English.
Augmented and Virtual Reality

WearableWearable computing, i.e. virtual and augmented realityAugmented Reality, is a new medium that provides unprecedented user experiences. Eventually, wearableWearable computing systems will redefine communicationCommunication, entertainment, education, collaborative work, simulation, trainingTraining, telesurgery, and basic vision research. Before these systems become practical for the consumer, two major challenges have to be solved: (i) the hardware systems have to be miniaturized into a socially-acceptable, i.e. eyeglasses-like, device form factor while providing sufficient battery life and (ii) the user experiences offered by these systems have to surpass those of existing system, such as phones, tablets, television, etc. This chapter outlines technical challenges to achieving these goals and summarizes the state of the art of related algorithms, optics, and electronics.
Image-Based World-perceiving Knowledge Graph (WpKG) with Imprecision

Knowledge graphs are a data format that enables the representation of semantics. Most of the available graphs focus on the representation of facts, their features, and relations between them. However, from the point of view of possible applications of semantically rich data formats in intelligent, real-world scenarios, there is a need for knowledge graphs that describe contextual information regarding realistic and casual relations between items in the real world. In this paper, we present a methodology of generating knowledge graphs addressing such a need. We call them World-perceiving Knowledge Graphs – WpKG. The process of their construction is based on analyzing images. We apply deep learning image processing methods to extract scene graphs. We combine these graphs, and process the obtained graph to determine importance of relations between items detected on the images. The generated WpKG is used as a basis for constructing possibility graphs. We illustrate the process and show some snippets of the generated knowledge and possibility graphs.
Raycast Calibration for Augmented Reality HMDs with Off-Axis Reflective Combiners

Augmented reality overlays virtual objects on the real world. To do so, the head mounted display (HMD) needs to be calibrated to establish a mapping between 3D points in the real world with 2D pixels on display panels. This distortion is a high-dimensional function that also depends on pupil position and varifocal settings. We present Raycast calibration, an efficient approach to geometrically calibrate AR displays with off-axis reflective combiners. Our approach requires a small amount of data to estimate a compact, physics-based, and ray-traceable model of the HMD optics. We apply this technique to automatically calibrate an AR prototype with display, SLAM and eye-tracker, without user in the loop.
ComSupResNet: A Compact Super-Resolution Network for Low-Resolution Face Images

Typically in face recognition based applications, a certain degree of resolution is required for effective feature extraction and comparison. Many practical systems such as surveillance applications violate the requirement by capturing Low-Resolution (LR) face images due to wider angle of imaging or longer stand-off to the camera. Such gap in the requirement versus the practice has lead numerous works to investigate approaches to super-resolve the face images that span from classical dictionary based methods to recent deep learning based approaches. In this work, we propose a compact and computationally efficient Convolutional Neural Network (CNN) to increase the spatial resolution of a LR face image to obtain a High-Resolution (HR) face image with an upscaling factor of up to ×8 which we refer as ComSupResNet. Contrary to other earlier works, the proposed architecture in the compact network comprises of a progressive residual propagating asym-metrical architecture with three modules: low-frequency and high-frequency feature extraction modules and reconstruction module. In addition to designing a new architecture, we also exercise care to reduce the number of parameters to approx. 1 M as compared to similar earlier work which has more than 30 M parameters. As a second aspect, we present the generalization aspect for the learned network in a cross-database setting by training the network on CelebA dataset while evaluating it both on CelebA and LFW datasets. Through empirical evaluations, we demonstrate the gain in high fidelity reconstruction in terms of structural similarity and Peak-Signal-to-Noise Ratio (PSNR) despite the compactness of the model.
Xml and Its Application in Library and Information Retrieval

Abstract the Extensible Mark-Up Language Xml is a Simple Data Storage Language, Using a Series of Simple Tags to Describe Data, and These Markers Can Be Used to Establish a Convenient Way, Although the Xml Space to Occupy More Space Than the Binary Data, But Xml is Extremely Simple and Easy to Master and Use. Xml Organized Management of Web Information Resources Distributed in Logic, Web Information Retrieval System Structure to Design a Professional Content, and Provides a Way for Users to Obtain the Special Contents Oriented Information Resources. a Good Data Storage Format, Scalable, Highly Structured and Easy for Network Transmission Characteristics So, and Xml is Determined by the Excellent Performance. the Paper Presents Xml and Its Application in Library and Information Retrieval.
Evaluation on the Functionality, Usability and Reliability of the Developed Application for Predicting Students' Performance in Cisco Certification Exam

Predictive analytics is a technique that utilizes data mining and probability to forecast unknown future outcomes. Historical data can be used to extrapolate and predict future events. This study is focused on predicting students' performance in cisco certification exam. The data sets were analyzed using various classification algorithms. An application that could predict an exam taker's future outcome was developed using the predictive model discovered from the data sets. The application can be used to aid instructors and school administrators in identifying students that may require intervention at an early stage. The researcher adapted the KDD process and RAD model for software development. This study aims to determine the level of acceptability of the Cisco certification exam prediction system in terms of operational evaluation based on ISO/IEC 25010 metrics by end users such as functionality, usability, and reliability. The system was evaluated by cisco networking experts. The level of response for each criterion was based on Five-Point Likert Scale. Overall, the Cisco Certification Exam Prediction System was evaluated by experts as “Acceptable”.
A Variability-Driven Analysis Method for Automatic Extraction of Domain Behaviors

Domain engineering focuses on modeling knowledge in an application domain for supporting systematic reuse in the context of complex and constantly evolving systems. Automatically supporting this task is challenging; most existing methods assume high similarity of variants which limits reuse of the generated domain artifacts, or provide very low-level features rather than actual domain features. As a result, these methods are limited in handling common scenarios such as similarly behaving systems developed by different teams, or merging existing products. To address this gap, we propose a method for extracting domain knowledge in the form of domain behaviors, building on a previously developed framework for behavior-based variability analysis among class operations. Machine learning techniques are applied for identifying clusters of operations that can potentially form domain behaviors. The approach is evaluated on a set of open-source video games, named apo-games.
Graphical Causal Models and Imputing Missing Data: A Preliminary Study

Real-world datasets often contain many missing values due to several reasons. This is usually an issue since many learning algorithms require complete datasets. In certain cases, there are constraints in the real world problem that create difficulties in continuously observing all data. In this paper, we investigate if graphical causal models can be used to impute missing values and derive additional information on the uncertainty of the imputed values. Our goal is to use the information from a complete dataset in the form of graphical causal models to impute missing values in an incomplete dataset. This assumes that the datasets have the same data generating process. Furthermore, we calculate the probability of each missing data value belonging to a specified percentile. We present a preliminary study on the proposed method using synthetic data, where we can control the causal relations and missing values.
Optimization of artificial neural networks to improve accuracy of vocational competence selection of vocational school students using nguyen-widrow

Abstract The present study used Neural Network Backpropagation combined with Nguyen-Widrow to optimize the disadvantages of ANN causes, which is the difficulty in initializing initial weights. The test was conducted on a dataset of values in semesters 1 and 2. The test results show that the best performance in training model of artificial neural networks with Nguyen-widrow is the smallest average MSE error of 0.002 and the highest average accuracy of 96.38%. Training on artificial neural network model training data with Nguyen-widrow has the smallest MSE error, 0.000996 and the highest accuracy is 97.49% on architecture ANN 9-9.1 with training function parameters: traingdx, epoch: 1000, learning rate: 0.1, and error: 0.001. The best performance was also seen in testing the testing of artificial neural network models with Nguyen-widrow with the smallest average error-MSE of 0.026 and the highest average accuracy of 87.85%. Training data testing on artificial neural network models with Nguyen-widrow has the smallest error-MSE which is 0.004436 and the highest accuracy is 94.50% on architecture ANN 9-9.1 with training function parameters: traingdx, epoch: 1000, learning rate: 0.1, and error: 0.001. The artificial neural network model with Nguyen-widrow has an accuracy difference of 8.53% smaller than the artificial neural network model with an accuracy difference of 9.28%. It can be concluded that the Artificial Neural Network with Nguyen-Widrow can overcome the ANN problem in determining initial weights so that it gives an increase in the accuracy of the prediction of students’ competency selection better than the Artificial Neural Network without Nguyen-Widrow.
A Comparison of Supervised and Semi-supervised Training Algorithms of Restricted Boltzmann Machines on Biological Data

Most devices applied nowadays in biological experiments produce large quantities of data. However, due to the cost of human expertise, only a small subset of these data can be examined or labeled by humans, while a huge part of the extracted information remains unlabeled. Therefore, interest in machine learning methods that can automatically make use of unlabeled data (sometimes in parallel with the annotated data parts) have grown steadily in the past years. In this study we show that one approach to handle the issue stated above could be the application of Restricted Boltzmann Machines (RBMs) in a semi-supervised machine learning configuration. RBMs can extract relevant information from the unlabeled data, and use it to improve the classification accuracy achieved by learning only on the labeled data set. We compare semi-supervised classification RBMs with a (deep) Artificial Neural Network and a Support Vector Machine to examine the improvements originating from the use of the unannotated data in three different, biologically inspired data sets. Our results show that even in this modern era of deep learning, semi-supervised classifiers are a viable option in these areas of science where annotating data is a time-consuming and resource-heavy task.