Reviewerly/exHarmony huggingface dataset:
“ARRES” Augmented Reality for the human respiratory system

Abstract Technology is advancing at a brisk pace as many things that were not possible a few years ago are possible now. Augmented Reality is a synthetic, computer simulated reality or recreation of a real-time environment where a user can interact with the replicated real environments. Biology is only taught in conventional way through books so it makes the students only pay their attention to texts and pictures to catch their interest, the lesson will get more interesting and interactive if the students can look at the process directly in the form of 3D as it makes students memorize it at ease. Respiratory and blood circulation system are examples from Biology for second grade of Junior High School. By applying Augmented Reality, they can take a lesson interactively through digital object in a reality. The design of Augmented Reality in respiratory and blood circulation system for Junior High School students is made with Blender application. The making of AR application with android platform uses game engine unity. Based on the functional test conducted by the researchers, it can be concluded that by applying the application, the users can gain the information related to the human respiratory and blood circulation system can be conveyed interestingly and interactively to the students.
A Simple Approach to Attributed Graph Embedding via Enhanced Autoencoder

Network Representation Learning (NRL) aims at learning a low-dimensional representation of nodes in a graph such that its properties are preserved in the learned embedding. NRL methods may exploit different sources of information such as the structural or attribute information of the graph. Recent efforts have shown that jointly using both structure and attributes helps in learning a better representation. Most of these methods rely on highly complex procedures, such as sampling, which makes them non-scalable to large graphs. In this paper, we propose a simple and scalable deep neural network model that learns an embedding by jointly incorporating the network structure and the attribute information. Specifically, the model employs an enhanced decoder that preserves global network structure and also handles the non-linearities of both the network structure and network attributes. We discuss node classification, link prediction, and network reconstruction experiments on four real-world datasets, demonstrating that our approach achieves better performance against the state-of-the-art baselines.
Faulty-data detection and data quality measure in cyber–physical systems through Weibull distribution

The preeminent challenge when data is compiled from cyber–physical system is the detection and filtering of faulty data. When the storage, processing, and communication of a reasonable amount of data migrate to those mobile and embedded devices, it becomes strenuous to apply conventional powerful algorithms for identifying, eliminating, and tolerating the faulty data because of the resource constraints of these devices. Further, the real-time requirements of CPS applications require CPSs to respond quickly and the aggregation method that is long-established in CPSs makes it challenging to ascertain the value of the original data. Thus, a systematic approach is essential to accomplish the goal of faulty data detection and filtering. In this paper, we present several approaches that we can deploy to detect and filter faulty data efficiently and cost-effectively, to improve the quality of the collected data from a system’s perspective to match the specific requirements of CPSs.
Readability and suitability of oral health education pamphlets produced by the Ministry of Health Malaysia.

Printed health education materials can only be effective if they are readable and suitable for the target audience. This study examined the readability and suitability of oral health education (OHE) pamphlets produced by the Oral Health Program (OHP), Ministry of Health (MOH) Malaysia.The Khadijah Rohani's Readability Formula (KRRF) and Suitability Assessment of Materials (SAM) instrument were used to assess the readability and suitability of the pamphlets respectively. All 23 Bahasa Malaysia pamphlets retrieved from the official portal of OHP on the 31st January 2019 were assessed for suitability. However, only five pamphlets were found to be eligible for readability assessment because the KRRF, the single formula available for Bahasa Malaysia text is applicable only for materials with 300 words or more. The readability is interpreted based on the level of formal education in Malaysia.All pamphlets achieved superior suitability rating with a minimum and maximum score of 75% and 95% respectively. However, a few pamphlets did not fulfil SAM superior and adequate criteria for the following factors and were rated not suitable: did not include summary (73.9%), have few or no headers (4.3%), did not use captions to explain graphics (17.4%), and did not provide interactive learning (21.7%). Readability of the pamphlets eligible for assessment ranged from primary six to secondary three.OHE pamphlets produced by the MOH are readable by most Malaysians. Most pamphlets are generally suitable for the intended audience although a few performed poorly in several areas.
A non-contact swing-arm profilometer with the spectrally-resolved-interferometry distance sensor

To enhance the performance of swing arm profilometer (SAP) for measuring aspheric surfaces in their ground or initial polishing state, a spectrally-resolved interferometry sensor (SRIS) is introduced. The calibrated SRIS has high resolution and good linearity as well as large angular range. Besides, each distance measurement can be realized in less than 10 ms with SRIS, which greatly shortens the measuring time of SAP. For the system setup, the air table is designed to work on the horizon while the aspheric optics to be tested is tilted to decrease the impact of swing rotary table motion error on measurement results. Procedure of system adjustment is given out before measurement. Results of validation experiment show that the SAP with SRIS can measure aspheric surface with an accuracy of 0.076 μm in root-mean-square (RMS). It takes only 8 s to finish a line profile scanning of one mm hyperboloid, which certifies that the SRIS has a good capability to raise the measuring efficiency of aspheric surface with SAP.
Detecting Kuzushiji Characters from Historical Documents by Two-Dimensional Context Box Proposal Network

Detecting characters from historical documents is a challenging problem due to the cursive and connected characters. It is a fundamental task for recognizing historical documents. In this paper, we propose a two-dimensional context box proposal network. The network has three parts: feature extraction, two-dimensional context, and box proposal regression. For feature extraction, we employ VGG16 to extract features from an input image. Then, the extracted features are processed by two-dimensional context. We employ two Bidirectional Long Short Term Memory (BLSTM) to explore vertical and horizontal meaningful context. Finally, the bounding box is predicted from the output of the two-dimensional context. We tested our proposed system on 28 books of Kuzushiji documents. The results of the experiments show the effectiveness of our proposed two-dimensional context for character detection. Our system achieved 88.50% and 92.46% of F1 score on validation and testing sets, respectively, which outperforms the baseline system (Connectionist Text Proposal Network).
Assessment of geospatial capability index for siting waste dump/landfill to control groundwater geopollution using geographic information system (GIS) approach: case study of Abakaliki area and environs, Southeastern Nigeria

Abstract Understanding spatial variation of rock and soil is important for capability precision as well as groundwater contamination or geopollution management. In view of waste dump/landfills, geoenvironmental hazards ravaging our land including surface and groundwater contamination, site suitability indexing, spatial distribution and characteristics of underlying rock and soil matter are studied. Rock and soil samples were collected in a systematic grid pattern using simple drill core rig and hand auger. Geostatistical and soil property analyses were conducted on each grid. Groundwater aquifer vulnerability to leachate was studied using depth to water table, recharge (precipitation), aquifer material, topographic slope, impact on vadose zone/vadose zone material and hydraulic conductivity (DRASTIC) and geophysical methods. Human population growth analysis indicates tremendous waste generation. Flash points of waste generation and dumping were noted. Geographical positioning system was used to take record of sampling point coordinates. Using a sufficient dataset, each grid indicative factor is objectively scaled, weighed and assigned a numerical rating according to their relative importance employing site suitability (S) analysis approach in an empirical equation. Results were imported into a geographic information system (GIS) platform to generate thematic maps. The results showed that subsurface rock and soil characteristics are neither spatially nor vertically homogeneously distributed. Thematic maps were generated and further interpolated in the GIS domain to produce a composite waste dump/landfill suitability index map.
Graph-Based Fraud Detection with the Free Energy Distance

This paper investigates a real-world application of the free energy distance between nodes of a graph [14, 20] by proposing an improved extension of the existing Fraud Detection System named APATE [36]. It relies on a new way of computing the free energy distance based on paths of increasing length, and scaling on large, sparse, graphs. This new approach is assessed on a real-world large-scale e-commerce payment transactions dataset obtained from a major Belgian credit card issuer. Our results show that the free-energy based approach reduces the computation time by one half while maintaining state-of-the art performance in term of Precision@100 on fraudulent card prediction.
Review and Prospect of Research on Generative Adversarial Networks

Since it was proposed in 2014, generative adversarial networks (GAN) has been highly concerned and widely studied by industrial circles and artificial intelligence researchers. It provides a new idea for the construction of the generative model. This paper reviews the research progress of GAN and prospects its development trend. Section 2 describes the basic idea and model structure of GAN. Section 3 introduces several typical derivative models of GAN. Section 4 lists GAN's applications in many fields, such as image, vision, voice, language, and etc. Section 5 makes a forward look and thinks on the development trend of GAN, and discusses the application of GAN in the field of communication countermeasures. Finally, section 6 summarizes this paper.
MaaFace: Multiplicative and Additive Angular Margin Loss for Deep Face Recognition

Because convolutional neural networks can extract discriminative features, they are widely used in face recognition and significantly improve the performance in face recognition. In order to improve the accuracy of the face recognition, in addition to improving the structures of convolutional neural networks, many new loss functions have been proposed to enhance the distinguishing ability to extract features, such as SphereFace and ArcFace. Inspired by SphereFace and ArcFace, we propose a new loss function called MaaFace, in which the angular multiplier and the angular addition are introduced into the loss function simultaneously. We give a detailed derivation of MaaFace and conduct extensive experiments on different networks and different data sets. Experiments show that our proposed loss function can achieve an out performance than the latest face recognition loss functions in face recognition accuracy. Finally, we explain why MaaFace can achieve better performance through statistical analysis of the experimental data.
Leukemia Cell Segmentation from Microscopic Blood Smear Image Using C-Mode

Blood-related diseases such as leukemia are very dreadful diseases and detection of such diseases must be carried out at very early stage. In manual method of leukemia detection, experts check the microscopic images. This is time-consuming process which depends on the person’s skill and does not have standard accuracy. The automated leukemia detection system analyzes the microscopic blood smear image and overcomes these drawbacks of manual detection. Many literature surveys are done, and it was found that average accuracy up to 84–87% is achieved to date. In this technique for automating leukemia, we are applying a new algorithm called C-mode as a method of segmentation which is required in the process of detecting early symptoms of diseases via medical imaging analysis. Improving the present method by application of soft computing to provide accuracy is the focus of the paper. Next, we can achieve by applying C-mode on RGB image, this removes number of steps which was earlier needed to process. Third, the execution speed increases as C-mode reduces the number of iteration. The result obtained provides more accuracy and is used for feature extraction.
Interrelation of career anchors with education and labor activity motives of students

The article is devoted to the research of career orientations (career “anchors”) with education and labor activity motivation of students. 114 students of “Psychological and pedagogical education” took part in the study. It is revealed that the most adequate motivation of education ex-pressed in the predominance of motives for acquiring knowledge and mastering the profession is associated with career orientations “service / dedication to a cause”, “pure challenge”, “technical / functional competence”. In other words, it is connected with aspiration to realize basic values in their careers, to improve the world, to develop and apply professional skills, to solve difficult problems, to overcome obstacles, to win. The career anchor of “service / dedication to a cause” is also associated with an increase of the communicative motive and a decrease of the competitive motive of labor activity. The more students are guided by the formal motive of getting a diploma of higher education, the less the stability of a career is valuable for them. The more utilitarian and pragmatic motive of la-bor activity is expressed in students, the less they are inclined to managerial or entrepreneurial career. The more autonomy and independence in career is valuable for students, the less social usefulness of labor (cooperative motive) is important for them.
End to End Trainable Active Contours via Differentiable Rendering

We present an image segmentation method that iteratively evolves a polygon. At each iteration, the vertices of the polygon are displaced based on the local value of a 2D shift map that is inferred from the input image via an encoder-decoder architecture. The main training loss that is used is the difference between the polygon shape and the ground truth segmentation mask. The network employs a neural renderer to create the polygon from its vertices, making the process fully differentiable. We demonstrate that our method outperforms the state of the art segmentation networks and deep active contour solutions in a variety of benchmarks, including medical imaging and aerial images. Our code is available at this https URL.
Research Progress in Image Denoising Algorithms Based on Deep Learning

Abstract Images constitute the main source of information to people. Digital images are vital means of obtaining, processing, analyzing, and sharing information in the era of information. Now they have been deeply incorporated into every aspect of people’s production and life, generating considerable social and economic benefits. Thus improving the quality of images and reducing the negative impact of image noises to subsequent image processing have been two important research topics. Image processing technology has been combined with such research fields as cognitive psychology, machine learning, machine vision, and deep learning in recent years, which lead to an unprecedented development level and breakthroughs. Therefore, the study of image denoising technology has profound theoretical significance and promising prospects in practical application. The paper mainly discusses the development course of applying deep learning technology to the image denoising field. Meanwhile, it introduces various classical denoising algorithms and focuses on the thoughts, advantages, and disadvantages of each algorithm. Furthermore, it discusses the challenges faced by deep learning in the denoising field and puts forward possible solutions.
Path Planning with Discrete Geometric Shape Patterns

This paper describes a novel technique for generating vehicle trajectories motivated by the need for fast and robust path planning in hazardous traffic situations. We use the well–known approach of assembling trajectories from circular arcs, line segments, and clothoids to construct curves and s- bends aligned to a given target line. To this end, we use a special geometric technique based on discrete shape patterns built from elementary blocks. This enables us to derive complex maneuvers from simple design specifications. We can use it to create curves that enclose a random angle and build s-bends that are aligned with a parallel line at any distance. If these two methods are combined, curves and s-bends can be generated which are aligned to lines of arbitrary orientation. Simulations prove the correct operation of the proposed algorithm. The applicability of the approach was demonstrated in real driving tests using a collision evasion scenario.
Reliability analysis of distributed storage systems considering data loss and theft

With the advancement of cloud computing and internet of things, data are usually stored on distributed computers and these data may risk being lost or stolen. In this article, we consider a common case where the entirety of the data is partitioned into several parts and each data part can be allocated to one or more computers. In the case where a computer fails, all the data parts on it are lost. Before the failure of any computer, the data parts may also be stolen by hackers. The basic model of computer failure and computer intrusion resulting in the theft of all the data parts on the computer is considered first. Then, the case is extended to a general model where computer failure, as well as data part corruption and theft caused by hacking are embedded. It is essential to study the reliability of distributed storage systems considering both data loss and data theft, which can be a basis for decision making on system structure optimization. In this article, a multi-valued decision diagram–based approach is developed to quantitatively evaluate system reliability for both models considering the time-dependence property of sequential events. The proposed method is applicable to systems where the random time to failure, theft, or corruption follows arbitrary distributions including the commonly used exponential distributions. Illustrative examples are provided to validate the proposed method.
Will Blockchain Be a Big Deal? Reasons for Caution

The initial enthusiasm for implementing blockchain in financial markets has been dampened considerably by its collision with economic realities. Though the author warns against avoiding the Panglossian trap of viewing ours as the best of all possible worlds, he reminds us that trusted institutions have evolved and emerged in a competitive environment as a means of economizing on transaction costs. Such costs arise from the nature of transactions, including crucially the information environment in which they take place. Though new technologies such as blockchain have the potential to reduce some of these costs, they often do so without fundamentally changing the underlying economic conditions that give rise to them. And as a result, institutions such as banks and exchanges that technologists scoff at may well prove surprisingly competitive and durable in the face of technological challengers. The most successful implementation of blockchain—Bitcoin—solves a very basic transactional challenge peculiar to cryptocurrency: the double spend problem. But it does so in a very expensive way, and many other transactions pose far more complex challenges. The three cautionary tales provided by the author—the first involving securities and derivatives trading and clearing, the second commodity trading, and the third proposals to “equitize” assets—all demonstrate the need to confront “Chesterton's Fence” when evaluating the potential of blockchain in any particular application. In other words, to understand the value of a new technology for a given set of functions, one must understand the economic forces that have shaped the processes and institutions that currently perform those functions. When such forces are considered, it often becomes apparent that new technologies like blockchain will not prove superior to existing practices—and may even create adverse unintended consequences that offset and perhaps even eliminate its beneficial effects.
Barriers Towards No-reference Metrics Application to Compressed Video Quality Analysis: on the Example of No-reference Metric NIQE

This paper analyses the application of no-reference metric NIQE to the task of video-codec comparison. A number of issues in the metric behavior on videos was detected and described. The metric has outlying scores on black and solid-colored frames. The proposed averaging technique for metric quality scores helped to improve the results in some cases. Also, NIQE has low-quality scores for videos with detailed textures and higher scores for videos of lower bit rates due to the blurring of these textures after compression. Although NIQE showed natural results for many tested videos, it is not universal and currently can’t be used for video-codec comparisons.
A Survey of Surface Reconstruction Techniques for Line Structured Light Systems

Abstract Reverse engineering has been widely used in industrial production, and surface reconstruction technology is the most important part of reverse engineering. Line structure measurement is a non-contact surface measurement method with high precision, low destructiveness, high data acquisition efficiency and strong anti-interference. This paper focuses on the measurement principle of line structured light, system calibration, strip center extraction and point cloud data processing and surface reconstruction. Finally, the existing surface reconstruction techniques that are widely used are summarized.
Implementation of random forest algorithm with parallel computing in R

Random forest is a method for building models by combining decision trees or decision trees generated from bootstrap samples and random features. A common problem that often occurs when implementing random forest is long processing time because it uses a lot of data and build many tree models to form random trees because it uses single processor. This research proposes random forest method with parallel computing and implemented in R programming language. Some of the cases used in this research are Iris flower dataset, wine quality and diabetes diagnosis data of Pima Indian woman. The results obtained from the entire study show that the computational time used when running random forest with parallel computing is shorter than when running a regular random forest using only a single processor.