Reviewerly/exHarmony huggingface dataset:
SVM Method for Classification of Primary School Teacher Education Journal Articles

Increasing search for a journal article should be supported by appropriate journal articles search results. Searching for appropriate journal articles can be done by grouping journal articles or information presented based on certain categories. The categorization problem that is often faced is the unbalanced data categorization. So that the settlement of the problem of categorizing unbalanced journal articles in each group will be solved by over-sampling and text classification methods. The purpose of this study is to compare the text classification for the journal articles of Primary School Teacher Education (PGSD) with the Support Vector Machine (SVM) classification method by conducting over-sampling techniques or methods with Synthetic Minority Over-Sampling Technique (SMOTE) and classification of PGSD journal articles using the SVM method only. This research includes text preprocessing processes such as tokenizing and case folding which produce a set of terms or words. The results of words will be weighted using TFIDF. After obtaining the TFIDF value, SMOTE will be conducted to overcome the data imbalance. The data that has been through SMOTE will be classified by the SVM method. The comparison of performance for each method is measured by 3 test outputs, which are accuracy, recall, and precision. From the test results, it was proved that the SVM using SMOTE after the TFIDF value obtained is better for classifying than using the SVM method only. The test value for the SVM method using SMOTE has a precision value of 97.3%, recall of 97.23% and accuracy of 97.22%, while the SVM method has a precision value of 72.55%, recall of 50.71% and accuracy of 80.56%.
Red-Green-Blue Augmented Reality Tags for Retail Stores

In this paper, we introduce a new Augmented Reality (AR) Tag to enhance detection rates, accuracy and also user experiences in marker-based AR technologies. The tag is a colour printed card, divided into three colour channels: red, blue, and green; to label the three components: (1) an oriented marker, (2) a bar-code and (3) a graphic image, respectively. In this tag, the oriented marker is used for tag detection and orientation identification, the bar-code is for storing and achieving numerical information (IDs of the models), and the texture image is to provide the users with an original sight of what the tag is displaying. When our new AR tags are placed in front of the camera, the corresponding 3D graphics (models of figures or products) will appear directly on top of it. Also, we can rotate the tags to rotate the 3D graphics; and move the camera to zoom in/out or view it from a different angle. The embedded bar-code could be 1D or 2D bar-codes; the currently popular QR code could be used. Fortunately, QR codes include position detection patterns that could be used to identify the orientation for the code. Thus, the oriented marker is not needed for QR code, and one channel is saved and used for presenting the initially displaying image. Some experiments have been carried out to identify the robustness of the proposed tags. The results show that our tags and its orientations (marker stored in the blue colour channel) are relatively easy to detect using commodity webcams. The embedded QR code (painted in blue) is readable in most test cases. Compared to the ordinary QR tag (black and white), our embedded QR code has the detection rates of 95%. The image texture is stored in the red and green channel is relatively visible. However, the blue channel is missing, which makes it not visually correctly in some cases. Application-wise, this could be used in many AR applications such as shopping. Thanks to the large storage of QR Code, this AR Tag is capable of storing and displaying virtual products of a much wider variety. The user could see its 3D figure, zoom and rotate using intuitive on-hand controls.
A Novel Genetic Algorithm Selection Method and Implementation in IoT Domain

In Internet of Things (IoT) domain, there are many NP-hard problems that are required to be solved efficiently by some meta-heuristic algorithms such as genetic algorithm. There are many genetic algorithm selection methods which aim to find the optimal chromosome by populating and varying them by some mutation and cross-over methodologies. Most of these algorithms process all of the genes of chromosomes into fitness function to decide whether to pass them to new generation or to apply some genetic operations or to abandon them. If chromosomes are ultimately long or fitness functions are difficult to compute, it will have a great overhead and will consume so much time. In this paper, a novel genetic algorithm selection method is proposed and promises some intelligence to keep these processes short and efficient by taking some remarkable risks. It is based on analogy of a football league with multiple groups and it uses the idea of partially processing the chromosome genes after adopting a decision mechanism based on Bayesian game theory. We implement the proposed selection algorithm to solve a complex problem in IoT domain and illustrate its performance compared to other generic selection methods.
Online Dynamic Resource Procurements via Cloud Brokerage with Multiple Reserved Instance Terms

Different infrastructure resources are utilized by the Cloud broker to meet the demands of the users so that the monetary cost is minimized. To a certain extent, the demands of the users can be collected or forecasted in advance, and some off-line algorithms can be used to plan the schemes to use the resources. But it is unavoidable some demands will be unpredictable and thus the input information will be inaccurate. This blunts greatly the practical effectiveness the off-line algorithms. It is impracticable to apply the off-line algorithm with great fluctuation caused by the sudden demands. This paper addresses the challenge by an online algorithm. Extensive real world traces driven evaluations show that the proposed algorithm can save cost up to 14%.
GRAPHIC DISCIPLINES AND DIGITAL PRODUCTION INTERDISCIPLINARY CONNECTIONS

In the article it is considered a topical issue of identification and ordering graphic disciplines and digital production (additive) technologies interdisciplinary connections. It is proposed to include these interdisciplinary connections in the course work program content for graphic disciplines in the technical universities. A new analytical model of the educational process for graphic disciplines is developed. Based on this model interdisciplinary connections with digital production (additive) technologies knowledge area are formulated.
Global Bank: A Guided Pathway of Encoding and Decoding for Pathological Image Analysis

The encoder-decoder architecture of convolutional neural networks (CNNs) is widely used in computer vision tasks and various analyses of medical images. However, extracting semantic features from regions of interest (RoIs) in pathological images remains a challenging task because RoIs of different morphologies and scales are embedded in a blurred background. Additionally, it is well known that the classic encoder-decoder architecture is vulnerable to interference from a blurred background and is thus not entirely suitable for precise analysis of pathological images. In this paper, we propose a pathway named global bank (GLB) to guide the encoder and decoder to focus more on the RoIs by providing the decoder with additional effective features of the RoIs. We extend the U-Net and feature pyramid network (FPN) with GLB and evaluate the resulting models on gland segmentation and cancer embolus detection tasks, respectively. Extensive experiments demonstrate that our proposal can significantly improve the performance of the encoder-decoder architecture. The U-Net with GLB achieves the best semantic segmentation performance on the 2015 MICCAI Gland Challenge dataset. Additionally, the FPN with GLB achieves improvements of 2% in average precision and 3.4% in recall on the embolus detection task.
Variational functionals for the driven quantum harmonic oscillator

We study some variational problems related to the controllability of the transition between two quantum states of the driven quantum harmonic oscillator. We focus on the existence of minimum points of the total energy functional which is obtained by adding to the cost of the control a term penalizing the distance from the maximum transition probability between given energy states.
Detection And Localization Of Splicing Attacks On Videos Using Block Correlation

Temporal attacks (such as copy-move and splicing attacks) are some of the most common tampering techniques in digital video due to their simplicity. While copy-move attacks have been widely studied and different approaches for this task have been proposed in recent years, splicing attacks have been less studied and the works on splicing detection are almost only based on exploiting the sensor pattern noise. This work proposes a straightforward yet effective approach to detect splicing attacks on videos using peak detection over temporal correlation of blocks. This approach allows to detect both the spatial and temporal location of the splicing attacks even when the two videos used in the attack were recorded with the same camera, a case that cannot be addressed with those approaches based on noise patterns.
Web-Based Application Design Rn HAPS Ior Rural and Disaster Affected Areas

In document will explain a web-based application that is stored in HAPS (High Altitude Platform Station) to facilitate communication and evacuation of disaster in rural areas called TeleHAPS. TeleHAPS has three main services, namely TeleChat, TeleData and TeleKamera. TeleChat is a service that serves to send and receive text messages, images and videos that are running locally. TeleData is a real time disaster collection service and the results of the data collection are presented in tables. While TeleCamera is a camera access service at HAPS to facilitate the observation of rural areas and affected by the disaster. The TeleHAPS application is built using HTML, CSS, PHP and JavaScript programming languages, using Sublime software, using Apache as a web server, and also using MySQL as a database. The TeleHAPS application is embedded in Raspberry Pi 3 and transmitted by TP Link Adapter WN 722 to be accessible to users via IP (Internet Protocol) provided by HAPS. In addition, the TeleHAPS application is built on the standards of the W3C (World Wide Web Consortium) on Web Content Accessibility Guidelines 1.0.
Conventional and nonconventional exploration techniques–principles

In this chapter, the main emphasis is given on elucidation of basic principles governing selected nonconventional mineral exploration techniques. These techniques include mineral predictivity mapping, lithogeochemistry, mineral zoning, factor analysis, concentration gradient modeling, fractal models, concentration area, concentration number, spectrum area model, local singularity analysis model, concentration perimeter, concentration volume, concentration distance, and number size. Others are remote sensing, the use of portable X-ray fluorescence spectrometry, the use of whole-rock analysis, and how to determine optimum number of samples to be collected in the field during geochemical sampling. Where possible, limitations of the techniques are given at the end of each subchapter. Conventional techniques have also been presented at the first part of the chapter.
Reform and Practice of Advanced Mathematics Course in the Context of New Engineering

The Advanced Mathematics course is a highly applied and closely related course to modern economics, finance, statistics, and management. This course provides the necessary foundation for students to learn professional knowledge. It has strong application and is also of great significance for the study of subsequent courses. Therefore, it is particularly important to improve the effect of teaching Advanced Mathematics course. This paper analyzes the necessity and importance of Advanced Mathematics teaching reform in private colleges and universities and points out the ways and methods of Advanced Mathematics teaching reform.
Design of Quality Evaluation and Deduction System for Requirement Schema

The deduction support system for quality evaluation of requirement scheme is a method for evaluating the quality of generated requirement scheme by simulation deduction. In order to effectively evaluate the quality of requirements, based on the analysis of the related functions of the evaluation and deduction system, an entity-oriented system analysis method is used to construct the evaluation process deduction model, and the simulation and deduction process information is extracted by using the information acquisition ability of XML. The use of the system improves the reliability of the evaluation conclusion.
Remodeling Numerical Representation for Text Generation on Small Corpus

Data-to-text generation aims to generate natural language descriptions from non-linguistic data. Recent research on data-to-text generation often uses a neural encoder-decoder architecture due to its simplicity to work across multiple domain. In this study, we aim to investigate two input encoding strategies: (1) numeral encoding as baseline, and (2) numeral as sequence-of-character tokens as proposed solution in financial data-to-text systems. An empirical study on the financial dataset validates our initial hypothesis that the character-based representation performs comparable results in content selection and diversity towards the generated text descriptions.
The application of image encryption method based on chaotic mapping in the field of radar transmitter remote monitor system

Because keeping technical secrets are essential when it comes to radar technology, the parameters and images captured from the equipment should be transmitted in a safe channel. The security of image data must be taken into account. In this paper, a new image encryption method based on chaos map is proposed, which is the permutation and diffusion framework. In the replacement of this step, according to the permutation matrix with the generation of the original image and the original image of the same size, use the matrix to completely disrupted of original image pixel location so as to reduce the correlation between neighboring pixels. And with the help of S-box, use continuously updated encryption coefficients are encrypted. The results of the simulation show that the image encryption scheme has high security and efficiency.
Augmenting Embodied Sensemaking using VR-Enabled New and Unusual perspectives

Since the launch of the first Oculus Developer Kit in 2013, consumer and commercial adoption of VR and AR technology has arrived beyond the early-adopter stage. This widespread availability of VR and AR headsets raises challenging and exciting questions for researchers in the field of embodied interaction: how do we design embodied interactions in VR? Can we improve (social) sensemaking beyond the natural body? What new opportunities for embodied interaction have presented themselves, thanks to this new technology, and how can we best use them? To address these questions, my research focuses on designing new interactions with VR systems that go beyond the (digital) gaming context, especially including tangible interactions from new and unusual perspectives, made possible by the new developments in the field of VR technologies. In my thesis, I aspire to present a framework of embodied sensemaking informed by new and unusual perspectives, enabled by virtual reality technology, developed in a Research through Design process.
Sensing by Light: Introduction

In this session, we discuss on the sensing teclmology using laser and light rangin g the detection of neutrinos and gravitational wave, LiDAR for mobiles, 3D face recognition, bio-photonic imaging.
Statistical MT Training for the translation of English-Arabic UN Resolutions

Machine Translation (MT) systems deliver translations instantly; however, users can easily identify MT output given that it may contain inaccurate word combinations, literal incorrect and ambiguous translations.For mitigating the inaccuracies associated with MT, the dissertation explores the feasibility of training a Customised Machine Translation (CMT) system in the context of Languages for Specific Purposes (LSP) for the legal domain.KantanMT a cloud-based CMT platform was deployed to translate English-Arabic UN Resolutions.UN Resolutions, a type of LSP, have specific characteristics including standardized style, fixed expressions and specialized terminology.The study adopts empirical and qualitative approaches to analyze and evaluate the translation produced by CMT in terms of the overall translation quality and recurring linguistic errors.The analysis of the study concludes that LSP is highly feasible with CMT systems as shown by the highly accurate automatic and manual evaluations.Furthermore, the linguistic analysis of LSP can be reused as a reference for training CMT, particularly in the context of Statistical Machine Translation (SMT), as well as performing manual evaluation and adopting pre-editing and post-editing strategies.
DP-Pose: Multi-Person Pose Estimation in Video Sequence through Dynamic Programming

Human pose estimation is an important research topic in the field of computer vision. Bottom-up methods such as OpenPose have become prevalent pose estimation methods because of its high efficiency and detection speed. But for poor quality video, the keypoints obtained by the bottom-up method have suffered from jitter and loss. In this paper, we propose DP-Pose, which constructs candidate pose sequence by selecting points in a region of the heatmap, improves constraint function by combining distance and confidence, and solves the optimal keypoints location by dynamic programming. The experimental results on class dataset show that DP-Pose can recover missing keypoint using heatmap data and obtain stable pose from candidate pose sequences.
Image based on histogram and K-Means clustering segmentation algorithm

The traditional K-Means algorithm is mainly used for image segmentation with large differences in color. Since the traditional K-Means clustering algorithm is easy to be sensitive to noise and it is difficult to obtain the optimal initial cluster center position and number, a method based on histogram and K-Means clustering is proposed. The median value is used. Filtering and histogram averaging preprocess the image to reduce the influence of noise on the pixel; then estimate the number of centers based on the number of peaks appearing in the histogram obtained after preprocessing, and then determine the aggregation in the Lab color space. At the center of the class, the cluster center is continuously iterated and updated until the algorithm finally converges to realize the clustering operation. The experimental results show that the improved algorithm shortens the time of the clustering process and can effectively extract the target object and improve the image segmentation effect.
Reasoning about the Cascading Failure of Financial Network Using Prolog

We construct a Prolog script that models the financial network which has the capability to simulate the spread of financial loss due to bankruptcy. Our focus is in the construction of Prolog script that simulates the cascading failure for a previously constructed model pertaining to Sariwangi AEA and Indorub bankruptcy case. The script is capable of finding bankrupt nodes, the amount of financial loss the entire model suffers, and nodes that are vulnerable to bankruptcy. A simulation is run multiple times under different financial failure threshold. From the simulation results, we obtain seven classifications regarding bankrupt nodes associated to a particular failure threshold.
Smart Innovation Engineering Using Set of Experience and Decisional DNA

Knowledge-based Engineering Systems are founded upon integration of knowledge into computer systems and are one of the core requirements for the future Industry 4.0. Since the fourth industrial revolution is passing through the developing phase, implementation of Cyber-Physical Production Systems require more realistic approach. Knowledge Management and Knowledge Engineering plays an important role in manufacturing industries facing global competition. One of the most promising areas where Knowledge Management is studied and applied is product innovation. This chapter presents the idea of Smart Innovation Engineering (SIE) System based on Set of Experience and Decisional DNA that facilitates the process of product innovation. It collects the experiential knowledge from the formal decisional event from the group of similar products having some common functions and features. The SIE system behaves like a group of experts in its domain as it collects, captures and stores the experiential knowledge and reuses this experiential knowledge as well that ultimately enhances the innovation process. The analysis of basic concepts and implementation method proves that SIE system is an advanced form of Cyber Physical Systems. It is flexible, systematic, fast, and supports customization. It can play a vital role towards Industry 4.0 development.
The Optimal Combination Mode of Three-digit Cryptography Based on Letters and Numbers

In order to better protect the file information from being leaked, the best combination mode with one-letter and two-digit passwords was discussed here. We mainly talked over the possible positions of the letters: firstly, according to the position of the letters, three combinations was listed; then, Advanced Archive Password Recovery was used to crack the three possible combinations and the relevant parameters was recorded; finally, the experimental parameters of each combination mode were analyzed by data analysis method, and the most reliable combination method was found according to the analysis results.
Design of leak testing machines for automatic generic drug packaging pressure capacity of 0.4 atm

Abstract The quality of packaging becomes a matter of concern that is packaging because it can produce the quality of the product being packaged, it is very necessary to have supplies for packaging on the packaging automatically. The purpose of this study is to help overcome the problem of packaging during the initial setup process before entering the packaging machine. The research method used is the design and discuss the function of machinery. Manufacture of packaging testing machines with 900 mm x 600 mm x 906 mm. Estimated to be 99,992,268 N/m 2 . The Control Program can be used using the Arduino UNO program. The process of vacuuming the waiting time for 2 minutes, the marking process is complete, then the pressure indicator returns to zero and the alarm sounds.