Reviewerly/exHarmony huggingface dataset:
DESIGN AND NEED ANALYSIS OF COMPUTER DEVICES’ EXPERT SYSTEM USING FORWARD CHAINING METHOD

This study aimed to: 1) Analyzing description of Core Competency and Basic Competency on Assembly of Computer Devices subject, establishing the concept of the subject and analyzing the students; 2) to know what was the obstacles that were faced by teacher during teach Assembly of Computer Devices subject 3) to know what was the instructional media that needed to be designed on Assembly of Computer Devices subject; 4) designing instructional media that was designed on Assembly of Computer Devices subject.The method in this study used SDLC (System Development Life Cycle), Waterfall.From the results of this study, it can be concluded, were follows: Assembly of Computer Devices subject was one subject that is difficult to teach through lecturing because it was practicing.So, the teacher needed instructional media that could help students so that could use it on practicing of Assembly of Computer Devices subject.That is designing instructional media based on an expert system, it could help the student to identify computer damage on Assembly of Computer Devices subject.Design of instructional media based on the expert system used UML (Unified Modelling Language) process.
Exploration and Research on Teaching Mode for the Basic Courses of College Computer under the New Engineering Background of Colleges and Universities

In the 1960s or 1970s, Chinese colleges and universities had begun to set up the computer courses successively. In the next several decades, the new information technology is constantly emerging with the rapid development of information technology. As for the engineering colleges, the content and system setting of basic computer courses should meet the development needs of the times and should also meet the specialty needs. The rapid development of engineering and information technology raises new requirements for the computer course teaching of new engineering colleges. Based on the current situation of the basic computer course teaching in Chinese colleges and universities, this paper puts forward some suggestions for the computer teaching mode of engineering colleges.
Empathy in a Technology-Driven Design Process: Designing for users without a voice of their own

Smart textiles are often developed in sportsoriented contexts through technology-driven processes.In the medical context, practitioners themselves also invent and develop technological aids in response to needs that emerge in practice.In these cases, novel technology may be the first driver for design to secure functionality and reliability, but our study shows that these processes benefit from human-centric and empathic design approaches.The project develops smart textiles for infants with medical adversities, such as preterm birth, neonatal infections, or birth asphyxia, collaboratively with medical researchers.Our pilot research illuminates the need to use the interest group's empathic understanding as a starting point for design, as the user of the garments does not yet have a voice of their own.In this paper, we develop the argument for empathic design in a technology-driven design process in the medical context.
Algorithms Based on Trigonometric Interpolation for Signal Reconstruction with Even Number of Sampling Points

Research results of the finite-length discrete-time signal reconstruction accuracy by means of the trigonometric interpolation are discussed. The research is performed for even number of sampling points by means of the well-known algorithm as well as the new one. We demonstrate that the discrete-time signal reconstruction error is the sum of the monotonically decreasing function (the one the number of samples of increases) and a specific periodic component. The periodic component occurrence reasons are described.
Drones Fueled Revolutions

Advances in Robotics, AI, and Data Science are affecting the human journey as never before. Several productive sectors are experiencing second industrial revolution. Production methods and process are changing under the pressure of advanced automation and the job-market is becoming essentially knowledge based. Drone technologies are one of the driving forces towards a completely new landscape in many human activities including traditional labor-intensive sectors such as farming, agriculture, maintenance, etc.
Advantages of Giraph over Hadoop in Graph Processing

This article presents a comparison of the computing performance of the MapReduce tool Hadoop and Giraph on large-scale graphs. The main ideas of MapReduce and bulk synchronous parallel (BSP) are reviewed as big data computing approaches to highlight their applicability in large-scale graph processing. This paper reviews the execution performance of Hadoop and Giraph on the PageRank algorithm to classify web pages according to their relevance, and on a few other algorithms to find the minimum spanning tree in a graph with the primary goal of finding the most efficient computing approach to work on large-scale graphs. Experimental results show that the use of Giraph for processing large-size graphs reduces the execution time by 25% in comparison with the results obtained using the Hadoop for the same experiments. Giraph represents the optimal option thanks to its in-memory computing approach that avoids secondary memory direct interaction.
DPSR: A Differentially Private Social Recommender System for Mobile Users

Recommender systems, which provide users with suggestions for selecting items that is of potential interest to them, are widely used to assist mobile users in reducing information overload and making better choice quickly in their daily life. Social recommender systems, which have the potential to mitigate the new user cold-start problem, utilize social relationships as an extra source of information. As recommendation results depend on users’ individual data, privacy breaches may occur. Although several differentially private social recommender systems have been proposed, their application scopes or protection strengths are limited. In this paper, we propose a differentially private social recommender system for mobile users named $$\mathcal D\!P\!S\!R$$ to block curious users from inferring the existence of someone else’s numeric rating or social relationship. Empirical evaluations on two real-world datasets are conducted, and the results show that $$\mathcal D\!P\!S\!R$$ can balance the utility of recommendations with the privacy of users’ data in both normal and cold-start test view.
Visualization &amp; Storytelling Workshop

Can a single chart change the course of a policy? What chart can help you win a policy argument? If data is the new oil, what is data visualization? In this hands-on workshop, you will learn how data visualization principles can help you communicate and conjugate facts, data, and policies to convince an audience.
Classification of Firm External Audit Using Ensemble Support Vector Machine Method

Financial fraud is an important problem because it can detrimental firm in the modern business world.An audit is carried out to prevent and be responsible for detecting fraud.External audit is one of the audit practices conducted outside of the firm internal audit by visiting firms in carrying out the work of financial report audit data.The application of machine learning can be used as a solution in the use of data analysis methods needed to solve these problems.This study proposes a Support Vector Machine (SVM) method by combining the Ensemble Bagging model to improve single classification performance.Data comes from 14 different corporate sectors with 777 records.The results showed that the Ensemble Bagging model could improve the accuracy of classification performance from the Support Vector Machine (SVM) method and achieved the highest accuracy of 89.95%.Based on the results of the accuracy obtained, the Support Vector Machine (SVM) method with the Ensemble Bagging model can be used to detect fraud in the firm.
Obstacle Avoidance for Mobile Robots Using End-to-End Learning

The purpose of autonomous driving is to help a mobile robot move from a starting point to a target point without any collisions. A mobile robot requires global path planning and local path planning to generate a path, where the local path planning guides the robot along the global path while taking action to avoid obstacles. In this paper, end-to-end approach to obstacle avoidance for a mobile robot is proposed. The approach uses a depth data to produce a straight motion, left rotation, or right rotation. The only data that the learning model requires is the depth image and control commands from the human operator. The method does not perform any additional operations like feature extraction on the raw sensor input values, resulting in a relatively low computing burden and intuitive learning without needing to know the robot model. Experimental results show that the proposed method successfully avoids obstacles.
Latent Feature Group Learning for High-Dimensional Data Clustering

In this paper, we propose a latent feature group learning (LFGL) algorithm to discover the feature grouping structures and subspace clusters for high-dimensional data. The feature grouping structures, which are learned in an analytical way, can enhance the accuracy and efficiency of high-dimensional data clustering. In LFGL algorithm, the Darwinian evolutionary process is used to explore the optimal feature grouping structures, which are coded as chromosomes in the genetic algorithm. The feature grouping weighting k-means algorithm is used as the fitness function to evaluate the chromosomes or feature grouping structures in each generation of evolution. To better handle the diverse densities of clusters in high-dimensional data, the original feature grouping weighting k-means is revised with the mass-based dissimilarity measure rather than the Euclidean distance measure and the feature weights are optimized as a nonnegative matrix factorization problem under the orthogonal constraint of feature weight matrix. The genetic operations of mutation and crossover are used to generate the new chromosomes for next generation. In comparison with the well-known clustering algorithms, LFGL algorithm produced encouraging experimental results on real world datasets, which demonstrated the better performance of LFGL when clustering high-dimensional data.
simTPM: User-centric TPM for Mobile Devices

Trusted Platform Modules are valuable building blocks for security solutions and have also been recognized as beneficial for security on mobile platforms, like smartphones and tablets. However, strict space, cost, and power constraints of mobile devices prohibit an implementation as dedicated on-board chip and the incumbent implementations are software TPMs protected by Trusted Execution Environments. In this paper, we present simTPM, an alternative implementation of a mobile TPM based on the SIM card available in mobile platforms. We solve the technical challenge of implementing a TPM2.0 in the resource-constrained SIM card environment and integrate our simTPM into the secure boot chain of the ARM Trusted Firmware on a HiKey960 reference board. Most notably, we address the challenge of how a removable TPM can be bound to the host device’s root of trust for measurement. As such, our solution not only provides a mobile TPM that avoids additional hardware while using a dedicated, strongly protected environment, but also offers promising synergies with co-existing TEE-based TPMs. In particular, simTPM offers a user-centric trusted module. Using performance benchmarks, we show that our simTPM has competitive speed with a reported TEE-based TPM and a hardware-based TPM.
The Utilization Management of Path Analysis Methods to Improve Quality in Writing Research Reports at Higher Education

In writing a research report requires good management, starting from the start to the end pages must be neatly arranged in accordance with existing rules. In managing the research report method, a researcher can use a variety of methods available, tailored to what he studies. This study discusses the introduction of path analysis, the notion of path analysis, which is a method for studying the direct effects or indirect effects of hypothesized variables as the causes of the effects of variables needed in research, characteristics of path analysis and terms in path analysis . There are 2 (two) completion of path analysis, namely with simple correlation, completion of path analysis with linear regression. Literary research research methods were used in this study to add insight to researchers regarding writing management. With this research, it is expected to be able to help students who are compiling reports in higher education, and can improve the quality of management of research reports using path analysis methods.
Some Variations of Upper Confidence Bound for General Game Playing

Monte Carlo Tree Search (MCTS) is the most used method in General Game Playing, area of the Artificial Intelligence, whose main goal is to develop agents capable of play any board game without preview knowledge. MCTS requires a tree which represents the states and moves of the board game which is visited and expanded using an iterations method. In order to visit the tree, MCTS requires a selection policy which determines which node is visited in each level. Nowdays, Upper Confidence Bound (UCB), is the most popular policy in MCTS due to its simplicity and efficiency. This policy was propose for the Multi-Armed Bandit Problem (MABP) which consists in set of slot machines each of which has a certain probability of give a reward. The goal is to maximize the accumulative reward that is obtained when a machine is played in a series of rounds. Other policy proposed for MCTS is Upper Confidence Bound $$_{\sqrt{.}}$$ (UCB $$_{\sqrt{.}}$$ ) whose goal is to identify the machine with the highest probability to give a reward. This paper shows a comparative between five modifications of UCB and one of UCB $$_{\sqrt{.}}$$ , this comparative has the goal of finding a policy which be able to identify the optimal machine as quickly as possible, this goal in MCTS is equals to identify the node with the highest probability to leading to a victory. The results show that some policies find the optimal machine before UCB, however, with 10,000 rounds UCB is the policy who plays the optimal machine more often.
Extreme Precipitation Expected to Increase with Warming Planet

A new analysis indicates that the frequency and magnitude of extreme precipitation events are expected to increase as Earth continues to warm.
A Complex Image Restoration Method Based on Generating Adversarial Neural Networks

In view of the existing face image restoration methods with occlusion, it is easy to generate problems such as excessive feature learning in the training process and low similarity between the restored face image and the original image. Proposed a based WGAN - GP (Wasserstein Generative Adversarial Networks- gradient penalt) covered face completion method, this method on the model using a generator, a local discriminator and a global discriminator for training the network, to ensure that the generated images and part of the global connectivity. To avoid the single discriminator, network characteristics will study the status of excessive. At the same time, a similarity measurement method is improved to increase the authenticity of the completed image and the original image when the image is completed. Moreover, the similarity measurement of the completed image was conducted through the perception of hash algorithm Dhash and structural similarity SSIM. According to the experiment, in the mixed data set composed of LFW, MegaFace and FaceScrub, the similarity was improved by 4.8%~5.2% compared with other completion methods, which greatly improved the robustness of the image completion method on the blocked face image
A fast ELM-based machine compression scheme for underwater image transmission on a low-bandwidth acoustic channel

Purpose This paper aims to propose a fast machine compression scheme, which can solve the problem of low-bandwidth transmission for underwater images. Design/methodology/approach This fast machine compression scheme mainly consists of three stages. Firstly, raw images are fed into the image pre-processing module, which is specially designed for underwater color images. Secondly, a divide-and-conquer (D&amp;C) image compression framework is developed to divide the problem of image compression into a manageable size. And extreme learning machine (ELM) is introduced to substitute for principal component analysis (PCA), which is a traditional transform-based lossy compression algorithm. The execution time of ELM is very short, thus the authors can compress the images at a much faster speed. Finally, underwater color images can be recovered from the compressed images. Findings Experiment results show that the proposed scheme can not only compress the images at a much faster speed but also maintain the acceptable perceptual quality of reconstructed images. Originality/value This paper proposes a fast machine compression scheme, which combines the traditional PCA compression algorithm with the ELM algorithm. Moreover, a pre-processing module and a D&amp;C image compression framework are specially designed for underwater images.
Gender Identification of Code-Mixed Malayalam–English Data from WhatsApp

The boom in social mediaChacko, Vineetha Rebecca has been aAnand Kumar, M. topic of discussionSoman, K. P. among all generations of this era. It most certainly has its positives, such as real-time communication, and a platform for all to voice their opinions. There are a few shady sides to it too, such as anonymity of those communicating. Such anonymity, especially in mediums of messaging such as WhatsApp, can turn out dangerous. Here, comes the crucial role of author profiling. This paper describes the analysis of code-mixed Malayalam–English data, collected from WhatsApp, and its classification based on the basic demographic, the gender, of the author. The text has been represented as Term Frequency–Inverse Document Frequency (TFIDF) matrix and as Term Document Matrix (TDM). The classifiers used are SVM, Naive Bayes, Logistic Regression, Decision Tree, and Random Forest.
A Hybrid Method for Underwater Image Preprocessing

This paper provides a hybrid method for underwater image restoration.Firstly, for the color distortion caused by different light attenuation characteristics, the automatic color balance algorithm which based on dynamic threshold is used to correct the color of underwater images.Secondly, the image dehazing based on the dark primary color principle is adopted, which transmissivity and background light are modified according to the propagation characteristics of underwater light.Finally, aiming at the over exposure of underwater images after dehazing, the gray world light compensation algorithm is employed to improve the visual display of underwater images.Experiments have shown the effectiveness of the system designed in this paper.
Discover better Optical Sensors - by Exploring and Exploiting Nature’s Limits

Nature's limits are precious: they often reveal uncertainty products connecting coherence, resolution, precision, channel capacity... So we can bargain with nature for "optimal" 3D sensors with novel features or just better precision, resolution, speed.
Extending FragOP Domain Reusable Components to Support Product Customization in the Context of Software Product Lines

Software product lines (SPL) have become an efficient paradigm for systematic reuse. SPL engineering is about the planned reuse of common assets for the rapid production of a software systems family. In SPL, an effective product derivation process is key to ensure that the effort required to develop the common assets will be lower than the benefits achieved through their use. While several approaches and tools are available on SPL engineering activities such as, variability management, component assembling, and product testing; most of the existing approaches do not present detailed information on the strategies for product customization (which affects the product derivation effectiveness). In a previous work, we introduced fragment-oriented programming (FragOP), which is a framework used to design, implement, and reuse domain components. In this paper, we enhanced the FragOP approach through the use of customization points and customization files to support the product customization activity. In order to gain preliminary insights into how VariaMos (the tool in which the approach is implemented) supports the FragOP approach, we designed a usability test by following the ISO/IEC 25062:2006 Common Industry Format for usability tests. Eight graduate students from the Universidad Nacional de Colombia took part and were asked to carry out a series of modifications to an e-commerce SPL. The usability test reported high subject performance results; however, we found some usability flaws that should be addressed.
Commissioning and Clinical Use of the Uronav Therapy System with the Electromagnetic Tracking Technology for Intra-Op US Guided Prostate HDR

To commission, evaluate and test clinical workflows for the new real-time prostate HDR brachytherapy treatment system Uronav Therapy from InVivo (Philips).
Survey on Association Rule Hiding Techniques

Data mining process extracts useful information from a large amount of data. The most interesting part of data mining is discovering the unseen patterns without unpacking sensitive knowledge. Privacy Preserving Data Mining abbreviated as PPDM deals with the issue of sustaining the privacy of information. This methodology covers the sensitive information from disclosure. PPDM techniques are established for hiding the sensitive information even after performing the data mining. One of the practices to hide the sensitive association rules is termed as association rule hiding. The main objective of association rule hiding algorithm is to slightly adjust the original database so that no sensitive association rule is derived from it. The following article presents a detailed survey of various association rule hiding techniques for preserving privacy in data mining. At first, different techniques developed by previous researchers are studied in detail. Then, a comparative analysis is carried out to know the limitations of each technique and then providing a suggestion for future improvement in association rule hiding for privacy preservation.