Reviewerly/exHarmony huggingface dataset:
Image Style Transferred to Graphical User Interfaces

This paper presents an approach for permitting the restyling of an application by automatic analysis of a considered image. The approach relies on the analysis of color distribution from a query image and a color-to-component mapping in order to perform its style transfer to the GUI. No deep learning technique is used, e.g., making the approach applicable without investigating image style learning through training stages. We show how our approach can (i) be used to directly and dynamically restyle GUI of applications, (ii) be adopted as an end user functionality toward the restyling of mobile app, desktop app, web app, and so on, (iii) serve to developers, e.g., for facilitating the selection of appropriate reference graphic charter during the design stage of their applications by exploring GUI appearances from a list of considered query images. Experimental results show the efficiency of the approach and its high potential of generalization.
Super-Attention for Exemplar-Based Image Colorization

In image colorization, exemplar-based methods use a reference color image to guide the colorization of a target grayscale image. In this article, we present a deep learning framework for exemplar-based image colorization which relies on attention layers to capture robust correspondences between high-resolution deep features from pairs of images. To avoid the quadratic scaling problem from classic attention, we rely on a novel attention block computed from superpixel features, which we call super-attention. Super-attention blocks can learn to transfer semantically related color characteristics from a reference image at different scales of a deep network. Our experimental validations highlight the interest of this approach for exemplar-based colorization. We obtain promising results, achieving visually appealing colorization and outperforming state-of-the-art methods on different quantitative metrics.
Accurate classification of liver nuclei based on AI models for diagnostic support of hepatocellular carcinoma

Introduction: Hepatocellular carcinoma (HCC) is the most common primary liver cancer worldwide. Early diagnosis is crucial to improve management and provide survival benefits to patients with HCC. Liver biopsy coupled to histopathology remains the “gold standard”. Visual inspection of biopsy images is a crucial examination to identify tissue and cellular alterations related to HCC, but it is limited by long timelines and by the variability in interpretation. Therefore, recent advances in digital microscopy and automatic image analysis have been developed for HCC classification, thus facilitating the final diagnosis.
TRAINING FOR PAINTING WASTE BAGS FOR THE CREATIVITY OF WANGKIS SHILDREN CHILDREN

Abstract : Efforts to plant Clean and Healthy Behavior (PHBS) need to be instilled in children from an early age. Trash painting training aims to foster Clean and Healthy Behavior (PHBS) because seeing existing trash cans tends to be dull and less attractive to children. The purpose of painting a trash can, which is to make children interested in throwing trash in its place, a moral message that is easily understood through the pictures depicted, and able to make the trash can product as a place of entrepreneurship. The activity carried out involved 90 students, consisting of all students of Large and Small Kindergarten. Implementation techniques use “brainstorming” and persuasive methods. Through these two methods children can be encouraged to bring up their ideas and imagination. With that in mind, the students’ creativity is honed and developed well, as evidenced by the results of painting on trash cans that are colorful and contain certain moral messages. Keywords: Training, Barrel Painting, Creativity.
Trading Strategies for All Stock Programs

Market traders buy and sell volatile assets frequently, with a goal to maximize their total return. There is usually a commission for each purchase and sale. Two such assets are gold and bitcoin. In order to solve the existing issues of purchases between gold and bitcoin, given that we have 1,000 USD, what strategies should we take to maximize our profits? In this article, the authors established seven models to predict the value of gold and bitcoins and how you should buy them, as the trends of value fluctuate, our models must be accurate enough to avoid being influenced. Targeted at that, the content is divided into three parts. For part 1: The authors selected several indicators that feature how the stock runs. For instance, price of gold and profit of gold to build first two models, which are the risk of investment model and the judgment on bull-or-bear market model. Then we use these models to evaluate whether it is safe to invest. The models are as follows: bear-bull market judgment model, risk of investment evaluation model, prediction model, trade model. For part 2: Based on the data concerned, the authors established the time series model to predict the way the market fluctuates. Meanwhile, the result of this model can be applied in correcting the results of former two models so as to make it more accurate. For part 3: The authors combined models above to give the best trading strategy. In addition, we improved the models by adding more indicators to make it more precise. We hope that by applying our models and strategies, you can successfully maximize your profit.
Three-Dimensional Multi-Target Tracking Using Dual-Orthogonal Baseline Interferometric Radar

Multi-target tracking (MTT) generally needs either a Doppler radar network with spatially separated receivers or a single radar equipped with costly phased array antennas. However, Doppler radar networks have high computational complexity, attributed to the multiple receivers in the network. Moreover, array signal processing techniques for phased array radar also increase the computational burden on the processing unit. To resolve this issue, this paper investigates the problem of the detection and tracking of multiple targets in a three-dimensional (3D) Cartesian space based on range and 3D velocity measurements extracted from dual-orthogonal baseline interferometric radar. The contribution of this paper is twofold. First, a nonlinear 3D velocity measurement function, defining the relationship between the state of the target and 3D velocity measurements, is derived. Based on this measurement function, the design of the proposed algorithm includes the global nearest neighbor (GNN) technique for data association, an interacting multiple model estimator with a square-root cubature Kalman filter (IMM-SCKF) for state estimation, and a rule-based M/N logic for track management. Second, Monte Carlo simulation results for different multi-target scenarios are presented to demonstrate the performance of the algorithm in terms of track accuracy, computational complexity, and IMM mean model probabilities.
Method and Device of All-in-Focus Imaging with Overexposure Suppression in an Irregular Pipe

To avoid depth-of-field mismatches caused by the changes in pipe structure and image overexposures caused by highly reflective surfaces while radial imaging irregular pipes, this paper proposes a novel all-in-focus, adaptable, and low scene-coupling method that suppresses overexposures in support of fault detection. Firstly, the pipeline’s radial depth distribution data are obtained by sensors, and an optimal all-in-focus imaging scheme is established by combining camera parameters. Secondly, using digital imaging technology, the high reflection effect produced by disparate light sources is comprehensively evaluated for overexposure suppression. Thirdly, a device is designed for imaging non-Lambertian free-form surface scenes under low illumination, providing the sequence images needed for the next step. Lastly, specific digital fusions are made to the sequential images to obtain an all-in-focus final image without overexposure. An image-quality analysis method is then used to measure the efficacy of the system in obtaining the characteristic information of the inner surfaces of an irregular pipe. Results of the experiment show that the method and device used are able to distinguish small 0.5 mm wide lines ranging from 40–878 mm depth and are capable of providing efficient image support for defect inspection of irregular pipes and free-form surfaces amongst other irregular surfaces.
ACADEMIC TEXT AS A COMPONENT OF PROFESSIONAL COMMUNICATION OF THE STUDENTS WHO MAJOR IN NATURAL SCIENCES

The aim of the study is to identify the characteristics of an academic text and consider its role as a component of professional communication of students of natural sciences. The research is specifically concerned with the ways of forming and developing the skills of the students who major in natural sciences to create effective academic text of different types. The paper makes an attempt to study and generalize the research of the national and foreign scientists and practitioners dealing with different aspects of an academic text: its organization, lexical and grammatical specific features and editing. The practical recommendations for students have been given and the effective techniques have been offered. Special attention has been paid to cross-cultural differences when it comes to writing essays by American and Ukrainian students. The role of reading, selecting, and citing scientific sources has been revealed. The examples given in the study have provided the support to the fact that the formation of the skills of academic writing is a necessary component of professional communication of students. It contributes greatly to development of scientific skills, creativity, critical thinking of students and their academic mobility.
Laser-Aided Profile Measurement and Cluster Analysis of Ceramic Shapes

Ceramics are one of the commonest sources of archaeological information, yet their abundance often confounds documentation and analysis. This article presents a new method of documenting and analyzing ceramics that includes laser-aided profile measurement to capture ceramic shape and other information quickly and accurately, resulting in digital outputs suitable for both publication and morphometric analysis. Linked software and database solutions enable unsupervised machine learning to cluster shapes based on similarity, eventually assisting typological analysis. Following an overview of current practices in ceramic recording and both standard and computational shape classification analyses, the new approach is discussed in full as a documentary and analytical tool. A case study from the Middle and Late Bronze Age site of Kaymakçı in western Anatolia demonstrates the benefits of the recording method and helps show that a combination of automated and manual shape clustering techniques currently remains the best practice in ceramic shape classification.
CAPTCHA the Flag

A live diagram of locations identified from the IP addresses of participants. By solving a CAPTCHA participants can contribute to the drawing, holding the 'flag' until it is taken by somebody else.
A Dual-Masked Auto-Encoder for Robust Motion Capture with Spatial-Temporal Skeletal Token Completion

Multi-person motion capture can be challenging due to ambiguities caused by severe occlusion, fast body movement, and complex interactions. Existing frameworks build on 2D pose estimations and triangulate to 3D coordinates via reasoning the appearance, trajectory, and geometric consistencies among multi-camera observations. However, 2D joint detection is usually incomplete and with wrong identity assignments due to limited observation angle, which leads to noisy 3D triangulation results. To overcome this issue, we propose to explore the short-range autoregressive characteristics of skeletal motion using transformer. First, we propose an adaptive, identity-aware triangulation module to reconstruct 3D joints and identify the missing joints for each identity. To generate complete 3D skeletal motion, we then propose a Dual-Masked Auto-Encoder (D-MAE) which encodes the joint status with both skeletal-structural and temporal position encoding for trajectory completion. D-MAE's flexible masking and encoding mechanism enable arbitrary skeleton definitions to be conveniently deployed under the same framework. In order to demonstrate the proposed model's capability in dealing with severe data loss scenarios, we contribute a high-accuracy and challenging motion capture dataset of multi-person interactions with severe occlusion. Evaluations on both benchmark and our new dataset demonstrate the efficiency of our proposed model, as well as its advantage against the other state-of-the-art methods.
Phoneme-Aware Adaptation with Discrepancy Minimization and Dynamically-Classified Vector for Text-independent Speaker Verification

Recent studies show that introducing phonetic information into multi-task learning could significantly improve the performance of speaker embedding extraction. However, benefits of such architectures usually depend largely on the availibility of a well-matched dataset, and domain or language mismatch would result in obvious dropdown in performance. Meanwhile, the utilization of these massive mismatched data and application of these auxiliary tasks may bring many rich features that could be exploited. In this paper, we propose a phoneme-aware adaptation network with discrepancy minimization and dynamically-classified vector for text-independent speaker verification to address these abovementioned challenges. More specifically, our method first utilize the maximum mean discrepancy (MMD) as part of the total loss function to solve the mismatch between training data of the speaker subnet and the phoneme subnet. And then we use a dynamically-classified vector-guided softmax loss (DV-Softmax), which could adaptively emphasize different high-quality features and dynamically change their weights, to guide the discriminative speaker embedding. Experimental results on VoxCeleb1 data set confirmed its superiority against the other state-of-the-art phoneme adaptation methods, providing approximately 15% relative improvements in equal error rate (EER).
Preface

Whilst providing a universal, clear and meaningful definition of ‘Embodied Intelligence’ is challenging, there is however a clear direction and motivation of the research community to further our understanding of systems that exhibit embodied intelligence and to understand how it can be designed and leveraged. This is a challenging topic, and requires an interdisciplinary and cooperative approach from researchers from engineering, social sciences, computer sciences, biological, materials science and more. Furthering our understanding or Embodied Intelligence requires advancement in the philosophical and theoretical domains to provide frameworks intelligence, consciousness and learning. However the inherently physicality of embodied intelligence relies on the creation of physical technologies and real world interactions, requiring increasingly smart and novel technologies. The intersection of these domains, in a world where AI and machine intelligence are playing an ever growing role in our daily lives makes this research field timely, fascinating and significant. In this book we provide a representative collection of papers written by the leading researchers in the field who attended and participated in the Embodied Intelligence Workshop, held virtually in March 2021. This includes short reviews, perspectives and original research articles. These aim to provide thought provoking, interdisciplinary and novel views and perspectives on this field, its future direction and some of the key concepts needed to deliver breakthroughs in our understanding or practical implementation. The book first touches on perspectives and outlooks for Embodied Intelligence before including examples of soft and bio-inspired robotics. This is followed by chapters discussing the principles of Embodied Intelligence in robotics and also examples of embodiment for real-world applications. Finally, the last two chapters focus on cognition and learning and also the philosophical and conceptual issues surrounding Embodied Intelligence. We would like to thank all of the participants of the seminar, the authors, and the reviewers for their excellent contributions to this volume.
Convective Cloud Classification Model for Reconstruction of Heavy Rain That Triggers the Flood and Landslide in Parapat, North Sumatera

Abstract Rainfall in Parapat as a part of Lake Toba, North Sumatera and a super-priority tourist destination in Indonesia on May, 13 2021 causes flood, it exceeds Meteorologi, Climatologi and Geophysics Agency’s threshold about intensity of extreme condition. It is related to dynamic of weather’s parameter, especially with convection process and clouds. The landslide material was very closed the road so that the SiantarParapat access was temporarily closed down. Heavy rains that have occurred since noon are said to be the cause of the floods and landslides that occurred at night. This study aims to describe the distribution of rain in Parapat and surrounding areas during floods and landslides. The data used are measured rainfall at the nearest AWS (Automatic Weather Station), and rainfall estimation data from GPM IMERG (Global Precipitation Measurement – The Integrated Multi-SatellitE Retrievals). The results of the study show that convective cloud classification model can be identify the reconstruction of heavy rain that trigger the flood and landslide in parapet. There has been rain with moderate to heavy intensity since 12 May 2021. Heavy rains that have occurred in the last few days have caused soil conditions to become saturated, resulting in landslides. Cloud classification shows the presence of Cumulonimbus cloud clusters that grow and develop around the Lake Toba area before and during heavy rains.
Index/Impressum: Vakuum in Forschung und Praxis 5/2022

Vakuum in Forschung und PraxisVolume 34, Issue 5 p. 59-59 Magazin Index/Impressum: Vakuum in Forschung und Praxis 5/2022 First published: 10 October 2022 https://doi.org/10.1002/vipr.202270509AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume34, Issue5October/November 2022Pages 59-59 RelatedInformation
PENGELOMPOKAN PENYANDANG MASALAH KESEJAHTERAAN SOSIAL DI JAWA BARAT MENGGUNAKAN K-MEANS DAN FUZZY C-MEANS

Social welfare problems still occur in some provinces in Indonesia, including in West Java. Social welfare problems cannot be completely overcome, but according to policy perceptions, they can be reduced, therefore analyses are required. Grouping data on people with social welfare problems to find out the best group based on the data will provide alternative policies and appropriate methods. The purpose of this study was to find the best group of people with social welfare problems using the k-means and fuzzy c-means methods based on the results of the DBI evaluation. The methods used for this grouping were the k-means and fuzzy c-means algorithm methods. From the results of this study, it was obtained the best 2 groups from the experiment of fuzzy c-means algorithms based on the smallest DBI assessment or close to 0 between the k-means and fuzzy c-means algorithms from each DBI value, they were k-means algorithm with value of 0.029 and fuzzy c-means algorithm with value of 0.006.
"The Effect of Education and Training Program Characteristics of the Work-learning Dual System on Learning Performance"

Purpose An empirical analysis was conducted on the process through which factors affect learning performance for blue collar workers in companies adopting Work-learning Dual System. This study is focusing on the mediating effect of informal learning activities and the moderating effect of spiritual leadership. Methods Data collected through the survey were analyzed as follows using SPSS 25.0 and SPSS PROCESS Macro 3.4 of Hayes (2018). Model 4 of PROCESS macro was used to verify the mediating effect. For the moderating effect, model 1 of PROCESS macro was used. Results The characteristics of dual program have a significantly positive effect on learning performance. Informal learning activities were found to mediate significantly the relationship between program characteristics and learning performance, while program characteristics have a positive significant effect on informal learning activities, and informal learning activities on learning performance. Spirituality leadership had a significant moderating effect on the relationship between program characteristics and informal learning activities. Finally program characteristics were found to have a moderated mediating effect on learning performance. Conclusion Companies participating in Work-learning Dual System should strengthen formal and informal learning activities to improve learning performance, and develop high-quality educational programs and preemptively promote informal learning activities. Supervisors as a teacher should provide mental support for learning workers as well as knowledge or technology transferor.
APPLICATION FOR DIAGNOSTIC DENTAL AND MOUTH DISEASE USING FORWARD CHAINING METHOD

The mouth is the entrance to the digestive system and contains accessory organs that are in the early stages of digestion, while teeth are made of a very hard material, namely dentin. (Indah Irma &amp; Ayu Intan, 2013. Many people complain of pain in the teeth and mouth due to lack of hygiene in maintaining the health of this organ. So when people are attacked by dental and oral diseases, people try to do their own treatment, for example buying toothache medicine in the shop. According to Turban (in Vincent, 2011) an expert system is a system that uses human knowledge where this knowledge is entered into a computer and then used to solve problems that usually require human expertise or expertise. The method used is the forward chaining method, namely forward chaining starting from a set of facts (there are) by looking for rules that match the assumptions/hypotheses that exist towards conclusions (Siswanto 2010).&#x0D; &#x0D;
FRONT-END DEVELOPMENT OF WEB-BASED E-MAIL MANAGEMENT SYSTEM IN TELKOM ACCESS KEDATON BANDAR LAMPUNG

The development of information has penetrated into the digital realm, by utilizing computer technology as an information technology tool in an organization or company will provide added value to the data processing process. The process of data processing in the form of incoming and outgoing letters at PT. Telkom Access is still done conventionally as well as manual archiving, with higher correspondence intensity and demands for speed and punctuality. In this study, we will develop a website-based e-mail management system display using the CodeIgniter3 framework and Bootstrap with the scrum methodology. It is hoped that by developing the appearance of the system, it can make it easier and more comfortable for users to use the system.