Reviewerly/exHarmony huggingface dataset:
Visual Odometry with Convolutional Neural Networks for Trajectory Learning in Mobile Robots

The development of robots capable of performing tasks autonomously is a topic of great inter-est mainly motivated by the wide field of promising applications that it offers. Particularly robotswith learning capacity, such as those that use Teach and Repeat (T&R) systems, which consist ofthe repetition, by the robot, of a taught task. Robot learning can be approached with differenttechniques. One of those that is obtaining surprising results is the one that uses Deep NeuralNetworks (DNN), perhaps because they allow parallel processing of information, recognition andmemory of large volumes of data. Increasing advances in image sensor technology, on the otherhand, have led to the development of robotic systems which only sensing hardware is a camera,as for example in Visual Odometry (VO). Thus, in order to adopt these emerging technologies forour autonomous robot prototypes, we have designed, developed and tested a VO system using amonocular camera and Convolutional Neural Networks (CNN) without the need to use a Graphics Processing Unit (GPU). This makes our system more computationally economical, withoutharming the quality of the results obtained. The experimental evaluation was divided into twostages: the first one was carried out using datasets from two very different environments, oneterrestrial and the other underwater, in conditions of good and poor visibility. The robustnessachieved in the Teach phase is demonstrated through these experiments. In addition, the abilityof the developed system to work with images with a wide range of features was observed. Withthe present approach, the system does not require the use of the camera’s intrinsic parameters,eliminating the dependence on a certain hardware model and the need for calibration. The secondstage of experimental evaluation consists of testing the Repeat phase in real time, which was carried out in computer simulation using the Pioneer 3AT terrestrial robot in the Gazebo simulationenvironment.
The potential and pitfalls of machine learning in the Congruence Engine context

This article considers the role of machine learning (ML) in the Congruence Engine project. The authors bring their digital and ML experience to the project and here reflect on existing tools and approaches, the particular challenges of Congruence Engine endeavour, and possible solutions within and beyond the project. Although these ideas will develop as the project progresses, the authors draw on knowledge of the existing ML landscape and current digital collections practice as well as learnings from Heritage Connector, the Science Museum Group’s previous project in the same funding scheme as Congruence Engine. The authors propose that while significant advances in ML and the availability of open datasets such as Wikidata offer huge opportunities for linking heritage collections, this will require a pipeline model with iterative stages of human intervention. Closer relationships will need to be developed between human curators, researchers and users of ML and the technology and processes it requires and this article points to the likely areas of collaboration that Congruence Engine will explore and test.
SENTIMENT ANALYSIS OF ONLINE GOJEK TRANSPORTATION SERVICES ON TWITTER USING THE NAÏVE BAYES METHOD

Abstract— Social media is the most accessed internet content by internet users in Indonesia. This is not surprising, given the many benefits that social media provides, one of which is the benefit of self-expression. Self-expression can include many things, including emotional openness, which is the openness of a person in conveying the emotions he is feeling. Along with the development of social media, emotional disclosure is ubiquitous in social media, one of which is social media Twitter. With the development of information technology, means of transportation are also developing with the existence of online transportation services. Currently, the use of online transportation services has become a necessity, so it is necessary to conduct a sentiment analysis on online transportation services to find out how the public responds to these online transportation services. The purpose of this study is to analyze community responses by analyzing data in the form of tweets and then classifying them into positive, negative, and neutral classes using the Naïve Bayes method because the error rate obtained is lower when the dataset is large, besides that the accuracy of Naive Bayes and the speed is higher. high when applied to a larger dataset. The results of this study indicate that the neutral sentiment level of public tweets is greater than the level of positive sentiment and negative sentiment with an accuracy of 25.00%.
Message from the Winner

We are very honored to receive the JDR Award for the Most Downloaded Article 2022, a new award from the Journal of Disaster Research (JDR). After the 2011 Great Tohoku Earthquake, it is now said that a magnitude 9-class earthquake could occur in the Nankai Trough, and it is necessary for us to know how strong ground motion (shaking) we will experience during such a great earthquake in order to take countermeasures. In this paper, we developed a new seismic motion prediction model that is applicable to M9-class earthquakes. We did this by adding the strong-motion records of the 2011 Great Tohoku Earthquake to the many records accumulated by various organizations since the 1950s. We would like to express our gratitude to all the organizations that provided us with their valuable records. Nearly 10 years have passed since the publication of the paper, and the records accumulated during that time have helped us to recognize the usefulness of our proposed model. We understand that our paper is still being downloaded in large numbers today and that our model is now being used not only in research but also in practice. As natural disasters occur more frequently in Japan and around the world, we hope that JDR will continue to grow and develop as an international source of useful knowledge on natural disasters.
Analysis of Students to Follow Computer Extracurricular Activities using Fuzzy Logic Method

An educational institution will carry out activities for the development of human resources. In improving good human resources, additional lessons are needed to increase students' skills, especially in the computer field. The current obstacle is that schools have difficulty measuring students' interest in participating in computer extracurricular, as a result the development of computer hardware needs is hampered. This study aims to provide knowledge to schools to find out what percentage of students are interested in learning computers. The method used is a Likert scale and fuzzy logic by measuring four variables of student feelings, student attention, student interest and student involvement. Measurement of student interest by compiling a questionnaire filled in by students and students there are 14 questions with a choice of each question 5 answer choices, obtained an average weight on the Likert scale variable feeling 74.0%, attention 76.5%, interest 75.7% and the involvement of 78.0% of the weight was tested with fuzzy inference with a firmness value of 38, where the value is included in the output domain not interested. By measuring student interest, it is hoped that it will be useful for institutions to consider implementing computer extracurricular.
Automatic Clothes Finder for the Colorblind

One challenge for people who are colorblind is finding clothes that match or are according to their needs. This system serves as a solution to that problem by providing an automated clothes finder system based on user input of the preferred color. The paper discusses the design of a color-based sensing system using modules to help colorblind people determine the color of their clothes. The paper justifies why each component is selected based on their tested performances. The paper also documents the software and hardware modifications in the design process. The test results show that the device can accurately detect multiple colors and coordinate the mechanical system to present the user with the clothing item with the correct color. In conclusion, the paper proposes a clothes finder system that is able to provide support for colorblind people when choosing their clothes. The device is able to record the colors of all clothing items in front of its camera, and save the items list so that it can show the user the right item color the next time they use the device. In addition, because of the use of the color detection camera, the clothing items do not need to be put in the same order every time. The user can simply reset the device for it to rescan all clothing items and save their colors. The proposed system design from the design method passes multiple tests, and is able to perform most functions as stated in the criteria and specifications. While specific in function, the implementation of this device may be quite useful for colorblind people in one of their day-to-day tasks.
Incorporating User Item Similarity in Hybrid Neighborhood-Based Recommendation Systems

Recommendation systems have been developed in many domains to help users with information overload from the large volume of online multimedia content by providing them with appropriate options. Recently developed hybrid recommendation systems require a large amount of data to understand users’ interests and give appropriate suggestions. However, several internet privacy issues make users skeptical about sharing their personal information with online service providers, limiting the potential of these systems. The study in this paper introduces various novel methods utilizing the baseline estimate to learn user interests in specific item features from their past interactions. Subsequently, extracted user feature vectors are implemented to estimate the user-item correlations, providing an additional fine-tuning factor for neighborhood-based collaborative filtering systems. Comprehensive experiments show that utilizing the user-item similarity scores in the rating prediction task can improve the accuracy of hybrid neighborhood-based systems by at least 2.11% compared to traditional methods while minimizing the need for tracking users' digital footprints.
ReAFM: A Reconfigurable Nonlinear Activation Function Module for Neural Networks

Deep neural networks (DNNs) with various nonlinear activation functions (NAFs) have achieved unprecedented successes, sparking interest in efficient DNN hardware implementation. However, most existing NAF implementations focus on one type of functions with dedicated architectures, which is not suited for supporting versatile DNN accelerators. In this brief, based on a proposed reciprocal approximation optimization (RAO) method, an efficient reconfigurable nonlinear activation function module (ReAFM) is devised to implement various NAFs. The computational logic and dataflow of certain NAFs are merged and reused to minimize hardware consumption by leveraging the correlations among different NAFs. In addition, a precision adjustable exponential unit (PAEU) is developed to obtain a good tradeoff between the approximation accuracy and hardware cost. Compared to the prior art, the experimental results demonstrate that the proposed ReAFM can support many more NAF types with comparable or even better performance. Furthermore, evaluation results on some prevalent neural networks show that the proposed approximation method causes negligible accuracy loss (< 0.1%).
Steganography: A Flexible Embedded Randomization Technique

With the expansion of digital communication networks, a considerable number of randomization techniques have been invented and implemented to enhance the different data transmission algorithms' levels of security.Steganography is among the data transmissions techniques used to hide secret data.Nowadays, several randomization techniques have been used in steganography to elevate the security of transmitted data.Unfortunately, the majority of these techniques lack some simplicity, efficiency, and flexibility, in addition to other limitations.This research presents a new randomization technique called Rand-Stego.Rand-Stego could be applied/practiced over any steganography technique.It provides simplicity and efficiency and elevates the security level.Examples of implementing the proposed technique on some steganography algorithms will be explored.The proposed and current techniques will be compared.The obtained results show Rand-Stego's superiority in terms of efficiency and flexibility when compared to the current techniques.
Case study of a semantic library underpinning the four-corner model for document exchange

The four-corner model network topology enables parties using non-declarative data to leverage declarative content in a document exchange network, thus triggering the network effect of enabling each new user to access all existing users without knowing their trading partner's use of non-declarative data.
FFmpeg Tips and Tricks

I like tips and tricks, and I cannot lie. I have written an entire book titled Linux Command-Line Tips and Tricks. (That’s the name of the book.) The tips and tricks in this chapter are mostly about FFmpeg. If you spend a lot of time with FFmpeg, these tips will be useful. Some advanced FFmpeg solutions, which could not be accommodated elsewhere, are also included.
Understanding the History of the Internet, Its Regulation, and How the Same Is Applicable to Blockchain Technology and Its Governance

Blockchain technology has exploded. The world cannot keep up with the latest innovations and developments in the blockchain and finance industry. While the industry is booming, the same is not fully regulated. Authorities and regulatory bodies of different countries are playing catch-up with each other in terms of trying to control this billion-dollar industry. This research paper seeks to discuss the history of the internet, how it was created, and the various layers, components, and functioning of the same. This paper also discusses two schools of thought put forth by two prominent activists in the space of internet regulation. It further goes on to talk about the flaws in the model of governance of the internet as well as how various nations like America, France, South Korea among other places regulated the internet in the 1990s, while the internet was in its nascent stages. This paper then switches gears and comes to discussing the concept of blockchain technology and how the same should be regulated.
The Local Wisdom on Language Politeness Education in the Use of PO-5 Philosophy in Buton Community: Ethnopedagogical Study

This study aims to reveal the relationship between the concept of politeness from experts and the concept of politeness in the PO-5 philosophy along with its implementation and language evidence for the implementation of the PO-5 philosophy. This descriptive qualitative research used 10 conversations as the data of the study. The data collection was carried out in three ways, they are literature study, interview, and observation. The results of this research indicated that the philosophy of PO-5 is part of the local wisdoms of the Buton Islands Community where the findings consisted of 3 data of Pobhinci-bhinciki kuli, 6 data of pomae-maeaka, 4 data of pomaa-masiaka, 2 data of popia-piara, and 1 data of poangka-angkataka. The PO-5 philosophy contains many moral educations for community, one of them is related to language politeness. Philosophy of PO-5 has similarities with the maxim model of Geoffrey Leech (1993). The similarities are found between maxim of tact and Pobhinci-bhinciki kuli, maxim of generosity and pomae-maeaka, maxim of sympathy and Pomaa-maasiaka, maxim of agreement and popia-piara, and maxim of approbation and poangka-angkataka
Building a Criterion-Referenced Test in Measurement and Evaluation and Determining Its Cut-Off Score by Several Methods

The current study aimed to build a criterion-referenced test in measurement and evaluation and determine its cutoff score by several methods.The primary test form had 45 items, which a group of professors and measurement and evaluation experts reviewed; their comments and feedback were taken into account, and the final test form had 40 items.The test has been presented to 174 university students to examine its psychometric characteristics.Multiple statistical techniques were later performed using the SPSS program, and the results show that the discrimination and difficulty coefficients ranged from 0.36 to 0.82.Additionally, the test reliability was calculated using the Kuder-Richardson -20 and Spilt half statistical methods, and the concurrent validity was 0.76.The results showed that the value of the Kuder-Richardson -20 method was 0.81, while the value of the Spilt-Half method was 0.79.Finally, the cut-off score has been calculated using four methods, and the results indicate that the Angoff method value was 65%, the Nedelsky method was 64%, the contrasting groups' method was 68%, and the criterion groups' method was 62%.
Method of software defect prediction based on least absolute shrinkage and selection operator

Software defect prediction is a means of software quality assurance, which aims to find potential defects in software through historical data and software characteristics. Feature selection is an important link in software defect prediction. With the rapid expansion of the number of features and the increase of feature dimensions, there may be multicollinearity problems between multiple types of features, which makes the model unstable and reduces the accuracy of the model. In order to solve the problem of multicollinearity between features, the least absolute shrinkage and selection operator algorithm is introduced into defect prediction. Through this algorithm, feature selection is realized, and the linear regression method is used for defect prediction, which improves the accuracy of classification results, reduces the over fitting of the model, and speeds up the convergence speed of the model.
Revisiting Ontology Evolution Patterns

Mixing formal methods (such as B) and ontology description languages is a promising approach to develop reliable, safe and secure systems. In fact, both techniques have demonstrated their strengths to handle ambiguity, inconsistency and incompleteness of requirements. However, domain knowledge evolves continuously throughout the application life-cycle and hence different change requirements must be addressed. Several problems have to be managed in the ontology evolution process particularly change impact analysis and resolution. Among existing works, pattern-driven techniques have been proposed to provide guidance during the ontology evolution so that it remains consistent. The underlying process is often informal and mostly methodological; and consequently automation efforts are still required. In this paper, we propose a lightweight pattern-driven approach built on an executable formal definition of the Ontology Web Language (OWL), that we call xOWL. In order to ensure the execution, the debugging and the correctness of existing patterns, we instrumented xOWL using the B method and the Meeduse language workbench.
Rendering automatic bokeh recommendation engine for photos using deep learning algorithm

Abstract Automatic bokeh is one of the smartphone’s essential photography effects. This effect enhances the quality of the image where the subject background gets out of focus by providing a soft (i.e., diverse) background. Most smartphones have a single rear camera that is lacking to provide which effects need to be applied to which kind of images. To do the same, smartphones depend on different software to generate the bokeh effect on images. Blur, Color-point, Zoom, Spin, Big Bokeh, Color Picker, Low-key, High-Key, and Silhouette are the popular bokeh effects. With this wide range of bokeh types available, it is difficult for the user to choose a suitable effect for their images. Deep Learning (DL) models (i.e., MobileNetV2, InceptionV3, and VGG16) are used in this work to recommend high-quality bokeh effects for images. Four thousand five hundred images are collected from online resources such as Google images, Unsplash, and Kaggle to examine the model performance. 85% accuracy has been achieved for recommending different bokeh effects using the proposed model MobileNetV2, which exceeds many of the existing models.
A design approach of proactive HMI based on smart interaction

As AI advances, intelligent systems are gaining the ability to collaborate with humans to accomplish everyday tasks proactively. In proactive HMI design, the accuracy of the user intention prediction model in the mechanism becomes the key to affecting the quality of the proactive HMI experience. However, there are three issues that caused the lack of effective ways to improve the prediction accuracy of user prediction models. In this paper, we analyze the Information for improving user prediction accuracy, the Intervention stage, and the required contents for smart interaction. Then, we develop an approach of the proactive HMI based on smart interaction, which is the method that robots learn from the users through interactions. We propose the elements, the framework, and the guidelines. This paper also provides how to use this approach in design case. With this approach, the accuracy of user intention prediction of proactive HMI can be improved and then can be achieved the goal of improving the design effect and the user experience of proactive HMI can be achieved.
Facial acne recognition system based on machine learning

Facial acne plagues many people, causing appearance anxiety and even psychological problems. However, the skin detector or software using traditional image processing technology on the market cannot give consideration to both low cost and high precision. This research aims to develop a low-cost and efficient method to detect facial acne through machine learning. We use hundreds of facial acne patients' pictures collected on the network, use Photoshop to split into thousands of pictures of appropriate size and manually label them as data sets and verification sets, and train them in YOLOX model to finally identify and label skin problems such as facial pustules, acne marks, etc. through one person's facial photos. At present, we have run the system on the desktop (AMD R7 4800H+GTX1650) normally, using the latest YOLOX framework of the open-source YOLO series. In order to improve the learning quality under limited training data, image preprocessing including sharpening and flipping is introduced. The experimental results show that the recognition rate of this method for some skin problems can reach 80%. By further expanding the data set, it can achieve low-cost facial problem recognition. At the same time, this research is also a good case of applying deep learning technology to product design.
Full steam ahead

Early Years EducatorVol. 23, No. 19 EditorialFull steam aheadAngelica CelinskaAngelica CelinskaEditorSearch for more papers by this authorAngelica CelinskaPublished Online:10 Jan 2023https://doi.org/10.12968/eyed.2023.23.19.5AboutSectionsView articleView Full TextPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareShare onFacebookTwitterLinked InEmail View article FiguresReferencesRelatedDetails 2 February 2023Volume 23Issue 19ISSN (print): 1465-931XISSN (online): 2052-4617 Metrics History Published online 10 January 2023 Published in print 2 February 2023 Information© MA Education LimitedPDF download