Reviewerly/exHarmony huggingface dataset:
PENGEMBANGAN GAME ACTION-ADVENTURE BERDASARKAN CERITA RAKYAT SULTAN SURIANSYAH BERMUATAN KEARIFAN LOKAL BANJARMASIN BERBASIS PERANGKAT BERGERAK

Sultan Suriansyah was the King of Bandarmasih (the First King of Banjar) who ruled from 1520-1540 and was the first king to embrace Islam. Tourism-related to the tomb of Sultan Suriansyah is very popular among the people of Banjar. However, the folklore that tells about Sultan Suriansyah is not as popular as his tomb, many young people today don't know it, even across Indonesia it is also not well known for this folklore from South Kalimantan. The purpose of this research is to design and build a folklore game with an action-adventure genre that adapts the story of Sultan Suriansyah to preserve Banjarmasin folklore and as a means to understand grammar in the Banjar language. The research method used is the Game Development Life Cycle with stages in the form of Pre-Production, Production, Testing, and Post-Production. The results show that the game design has been produced in the form of a storyboard and then developed using Godot Engine software with the output of the (.apk) extension application that can be installed on mobile devices. Then the Playtesting and Gameflow testing was also carried out which involved 50 respondents as players with an age range of 13-15 years, the results showed that the games built and tested had met the elements and criteria with an overall average value of 4.84 and no bugs were found in the game. Tests related to player knowledge and justification for using games as a media to preserve folklore and a means to understand the Banjarese language have also been carried out with very good results.
Federated Research Profile Management for Researcher in India: Indian Research Information Network System (IRINS)

The article describes Indian Research Information Network System and its impact in the higher education system in Indian. The article also, challenges for implementing the research information management in the academic environment.
Visually exploring a Collaborative Augmented Reality Taxonomy

Augmented Reality (AR) has been explored with the objective to assist in scenarios of co-located or remote collaboration. To help understand how well collaborative work can be addressed with AR, it is important to foster harmonization of perspectives and create a common ground for systematization and discussion. In this vein, understand relationships among existing dimensions of collaboration, as well as identify research opportunities, is of paramount importance and thus tools that allow visually exploring information associated with Collaborative AR may be most valuable. In this paper, we present a first effort towards the creation of such an interactive visualization tool for exploration and analysis of collaborative AR research. It allows visualize data of selected papers organized according to a human-centered taxonomy on collaborative AR. In order to get insights into whether the structure was understood and if the representation was clear and efficient to use, we evaluated the proposed tool through a user study with 40 participants. Results suggest the tool has potential towards the creation of a shared understanding and identification of existing patterns, trends and opportunities within the field of collaborative AR.
Phishing Websites Detection using Python

Phishing websites is a problem on internet that target the people amenabilitiesrather than software vulnerabilities. It can be described as it is the processof collecting sensitive information such as usernames and passwords. Types ofweb pages are different in terms of their features. Phishing occurs when amalicious site acts as a legitimate for obtaining sensitive information such aspasswords, account information, or credit card numbers. Phishing attacks can beprevented by detecting websites and notifying users to detect phishing websites.Machine learning algorithms have been one of the most effective techniques fordetecting phishing websites. Nowadays, information and communication tools areused in a manner that is very dense with information. For this purpose, varioussolution methods are developed for various problem. Machine Learning (ML) methodsbe used in application development for in security purpose. Prediction anddecision support system and great benefits can be provided to the person who isresponsible for information security. Today, it has become an increasinglypopular subject in developing intelligent application. Hence, we use a web pagefeatures set to avoid phishing attacks. We proposed a model based on machinelearning techniques to detect phishing web pages. We have suggested some rulesto have efficient features. The model has 13 inputs and 1 output. The averageclassification accuracy was measured as 75%.
A Threat of Deepfakes as a Weapon on Digital Platform and their Detection Methods

Advances in machine learning, deep learning, and Artificial Intelligence(AI) allows people to exchange other people's faces and voices in videos to make it look like what they did or say whatever you want to say. These videos and photos are called “deepfake” and are getting more complicated every day and this has lawmakers worried. This technology uses machine learning technology to provide computers with real data about images, so that we can make forgeries. The creators of Deepfake use artificial intelligence and machine learning algorithms to mimic the work and characteristics of real humans. It differs from counterfeit traditional media because it is difficult to identify. As In the 2020 elections loomed, AI-generated deepfakes were hit the news cycle. DeepFakes threatens facial recognition and online content. This deception can be dangerous, because if used incorrectly, this technique can be abused. Fake video, voice, and audio clips can do enormous damage. This paper examines the algorithms used to generate deepfakes as well as the methods proposed to detect them. We go through the threats, research patterns, and future directions for deepfake technologies in detail. This research provides a detailed description of deep imitation technology and encourages the creation of new and more powerful methods to deal with increasingly severe deep imitation by studying the history of deep imitation.
Minimizing the Cosine Loss in a Solar Tower Power Plant by a Change in the Heliostat Position and Number

Concentrating Solar Power (CSP) focuses sunlight in order to use the heat energy of the sun. In a central receiver system configuration, many mirrors (heliostats) individually track the sun and reflect the concentrated solar energy onto a receiver on top of a tower. The receiver contains the working fluid which is heated by the concentrated solar radiation. The useful energy that absorbed by the water flows through the receiver in solar tower plant depending on the angle between the solar rays and the position of heliostat in the region of work. Heliostat will reflect the incident solar radiation in the direction of the receiver founded in the top of the tower, in order to get a maximum incident solar radiation on the heliostat reflection area. Because of the cosine factor loss effect due to the sun position is variable along the day from sunrise to sunset, which must be in a minimum value, therefore an automated tracking system with dual axes as a control system with sensors had been built and used to stay the sunrays incident on the receiver, and enable the heliostat to flow the sun where it was
Passenger Detection, Counting, and Action Recognition for Self-Driving Public Transport Vehicles

Due to the recent progress on autonomous driving, some technologies have been gradually deployed to the public transport vehicles. The passenger safety under the unmanned operating environment has become an emerging issue which requires much more attention. This paper presents a method for passenger detection, counting and action recognition inside a minibus. A top-view camera system is mounted on the ceiling to have a full coverage of the interior. 2D and 3D convolutional neural networks are developed for pose recognition and action classification of the passengers. The experiments are carried out in a self-driving minibus, and the results have demonstrated the feasibility of the proposed technique.
Image Enhancement and Corrosion Detection for UAV Visual Inspection of Pressure Vessels

The condition of a pressure vessel is normally checked by human operators, which have health and safety risks as well as low working efficiency and high inspection cost. Visual inspection for pressure vessels can be done by an Unmanned Aerial Vehicle (UAV) with the sensing module. Image enhancement techniques and image processing techniques are vital in the UAV inspection of pressure vessels. However, there are several issues to be overcome in the UAV visual inspection of pressure vessels. The images captured by the UAV are of low quality under the cluttered environment due to poor lighting, noises and vibrations caused by the UAV. In this research, a system is developed for UAV visual inspection of pressure vessels using image processing and image enhancement techniques. In the developed system, the input image is captured by the UAV first. Next, efficient image enhancement techniques are applied to the images in order to enhance image qualities. After that, the corrosion part is detected and the percentage of the corrosion area in the entire image is measured. The proposed system has the potential to be implemented for the autonomous correction detection with the image enhancement techniques in UAV visual inspection for the pressure vessels.
Improved Categorical Cross-Entropy Loss for Training Deep Neural Networks with Noisy Labels

Deep neural networks (DNNs) have achieved impressive success in a variety of classification tasks. However, the presence of noisy labels in training dataset adversely affects the performance of DNNs. Recently, numerous noise-robust loss functions have been proposed to combat the noisy label problem. However, we find that these loss functions are either slow to learn the potential data pattern or not sufficiently robust against noisy labels. Here, we propose an improved categorical cross entropy (ICCE) to deal with this challenge. The ICCE can automatically adjust the weighting scheme based on the predicted probability distribution of DNNs by an exponential item, which makes it gain strong noise robustness and fast learning ability. A theoretical analysis of the ICCE is presented in the context of noisy labels. Experiments on datasets indicate that the ICCE can better improve the performance of DNNs even under high-level noise.
Research on Moving Target Capture and Trajectory Reconstruction of Orthogonal Double Vision System

In a certain space, reproducing the trajectory of a moving target can provide a reliable data source for motion analysis. This paper uses two cameras with the same parameters to construct an orthogonal dual-view system, taking the table tennis movement in the table tennis space as the research object Carry out research on moving target capture and trajectory reproduction. Firstly, an orthogonal dual-view system is constructed, and the camera is calibrated, and a three-dimensional coordinate system based on a ping-pong table is constructed through dual-view; secondly, a HSV and RGB hybrid model is constructed for coarse filtering, and the background is filtered by the Gaussian mixture model. Morphological processing was used to filter the moving target region; finally, the two-dimensional coordinate of the ping-pong ball is obtained by edge extraction and Hough circle transformation. Aiming at the existence of smear in the image, the ellipse fitting method is used to make optimization. After obtaining the coordinates of the ping-pong ball, Kalman filtering is used to predict, supplement and correct the obtained series of coordinating. The experimental results show that this method can eliminate most of the interference, accurately obtain the three-dimensional trajectory of the table tennis ball and make short-term predictions.
HPCReg-Net: Unsupervised U-Net Integrating Dilated Convolution and Residual Attention for Hippocampus Registration

The hippocampus plays a critical role in the human brain, which is mainly responsible for memory and other functions. Accurate and effective hippocampal registration significantly impacts all kinds of hippocampus-related analysis, particularly in the widely-used multi-atlas hippocampus segmentation and the associated clinical decision. However, the existing registration methods suffer from high computational cost and insufficient registration performance. A 3D unsupervised U-Net registration model HPCReg-Net was proposed under a coarse-fine registration strategy in this study, which combined dilated convolution and residual attention module. Specifically, in the coarse registration stage, a new gap-filling mechanism was designed to solve the semantic gap problem in U-Net, and a novel residual attention module was devised to characterize hippocampal deformation. In the fine registration stage, a cascaded dilated convolution module was adopted to capture the considerable deformation and position information of voxels from each pair of images. The experimental results based on ADNI data sets show that our method can improve hippocampal registration greatly in both computational efficiency and registration precision.
Research on Long Jump Posture in School Physical Education Teaching Based on Video Analysis

Based on video, the human movement can be analyzed to achieve scientific training and skill improvement. Specifically, according to the video data during the movement, the human body can be detected and tracked, and relevant trajectory data can be obtained. On this basis, key motion parameters can be obtained and quantitative analysis of motion can be achieved. This paper uses video processing technologies to analyze the long jump posture in physical education. According to the video sequences measured during the athlete’s long jump, the target detection and tracking algorithms are used to obtain the athlete’s trajectory after preprocessing. Afterwards, further processing is carried out to calculate speed, angle, posture, and other related information to assist scientific sports training. The experimental results based on the measured data show that the algorithm can realize the analysis of the long jump scene and complete the quantitative analysis of the key indicators of the athletes. The research results can effectively support school physical education and guidance training and also provide a reference for other competitive video analysis.
An Effective Clustering Optimization Method for Unsupervised Linear Discriminant Analysis

The recent work Unsupervised Linear Discriminant Analysis (Un-LDA) completes its clustering process during the alternating optimization by converting equivalently the objective and finally using the K-means algorithm. However, the K-means algorithm has its inherent drawbacks. It is hard for the K-means algorithm to deal well with some complex clustering cases where there are too many real clusters or non-convex clusters. In this paper, a novel clustering optimization method is presented to accomplish the clustering process in Un-LDA and the resulting method can be named Un-LDA(CD). Specifically, instead of the K-means algorithm, an elaborately designed coordinate descent algorithm is adopted to obtain the clusters after the objective function goes through a series of simple but deft equivalent conversions. Extensive experiments have demonstrated that the coordinate descent clustering solution for Un-LDA can outperform the original K-means based solution on the tested data sets especially those complex data sets with a pretty large number of real clusters.
Developing Language-Specific Models Using a Neural Architecture Search

This paper applies the neural architecture search (NAS) method to Korean and English grammaticality judgment tasks. Based on the previous research, which only discusses the application of NAS on a Korean dataset, we extend the method to English grammatical tasks and compare the resulting two architectures from Korean and English. Since complex syntactic operations exist beneath the word order that is computed, the two different resulting architectures out of the automated NAS language modeling provide an interesting testbed for future research. To the extent of our knowledge, the methodology adopted here has not been tested in the literature. Crucially, the resulting structure of the NAS application shows an unexpected design for human experts. Furthermore, NAS has generated different models for Korean and English, which have different syntactic operations.
Dynamic-IoTrust: A Dynamic Access Control for IoT Based on Smart Contracts

The Internet of things (IoT) is an active, real-world area in need of more investigation. One of the top weaknesses in security challenges that IoTs face, the centralized access control server, which can be a single point of failure. In this paper, Dynamic-IoTrust, a decentralized access control smart contract based aims to overcome distrusted, dynamic, trust and authentication issues for access control in IoT. It also integrates dynamic trust value to evaluate users based on behavior. In particular, the Dynamic-IoTrust contains multiple Main Smart Contract, one Register Contract, and one Judging Contract to achieve efficient distributed access control management. Dynamic-IoTrust provides both static access rights by allowing predefined access control policies and also provides dynamic access rights by checking the trust value and the behavior of the user. The system also provides to detected user misbehavior and make a decision for user trust value and penalty. There are several levels of trusted users to access the IoTs device. Finally, the case study demonstrates the feasibility of the Dynamic-IoTrust model to offer a dynamic decentralized access control system with trust value attribute to evaluate the internal user used IoTs devices.
Exploring researcher preference for the version of record

This whitepaper explores the researcher preference for the version of record (VOR), compared to other article versions such as the accepted manuscript (AM) and preprints.
Implementing a digital system for contact tracing and case investigation during COVID-19 pandemic in San Francisco: a qualitative study

Abstract During the COVID-19 pandemic, many health jurisdictions deployed digital informatics systems to support “manual” case investigation and contact tracing (CICT). This case study evaluates the implementation and use of a digital information system through the experiences of CICT workers in the City and County of San Francisco (CCSF). We conducted semi-structured, 90-min interviews with a sample of the CCSF CICT workforce (n = 37). Participants also completed standardized assessments of the digital system using the System Usability Scale (SUS). Qualitative analyses highlighted (1) the importance of digital tools to ensure rapid onboarding and effective data capture in a public health emergency; (2) the use of digital systems to support culturally sensitive care; and (3) the role of digitals tools in building supportive work environments. The mean SUS score was 70/100 (SD = 17), indicating relative ease of use. In summary, the analysis highlights the importance of digital tools to support manual CICT in the COVID-19 response.
Peningkatan Kompetensi Mahasiswa Melalui Pelatihan Penulisan Karya Ilmiah Dengan Menggunakan Metode Kualitatif Dalam Masa Pandemi Covid-19

The current reality shows that there are many opportunities for scientific writing competitions for students, but there are still very few students who are interested in participating in these activities. This is due to a lack of self-confidence and student interest in participating in competitions. The objectives of the implementation of this service program are: 1) to educate students from an early age about qualitative research methods; 2) to improve the ability of early semester students in and preparation of scientific papers. The method used in this service program is the lecture method, the discussion / question and answer method and ends with an evaluation. Due to the current outbreak of COVID-19, the training method is carried out online. The implementation phase of the training was carried out on May 11, 2021, attended by 126 accounting students in the second and fourth semesters. At the end of the training stage, an evaluation is carried out to measure the level of student understanding of the training that is followed. The results of the evaluation showed that the training was carried out effectively. This is shown through the questionnaires distributed after attending the training, as many as 72.5% of the training participants stated that the preparation of scientific papers using qualitative methods was very easy to do, as many as 60% were interested in participating in scientific writing competitions in the future. 99.2% of the trainees are interested in learning more about scientific works that have been published and 100% of students are interested in further deepening their knowledge of qualitative research methods.
Breast Ultrasound Image Segmentation Model Based Residual Encoder

Artificial intelligence algorithms show promise for various medical imaging applications. Modern computer-aided diagnosis (CAD) systems can potentially be used for the early diagnosis of breast tumors, a leading cause of death in women worldwide. Deep learning algorithms have been applied to readily available breast ultrasound (BUS) images and provided good segmentation and classification performance. Nonetheless, this task remains challenging because US images are very noisy with a class imbalanced data distribution and inhomogenous intensity. To address these issues, we proposed a new variant of the U-Net architecture, which is commonly used for medical image segmentation. We also increased the network depth by using residual blocks (RBs). To resolve the issue of the vanishing gradient while downsampling the features, we expanded the network width by adapting a convolution path instead of a concatenation path in the original U-net. Our proposed model includes a preprocessing stage, feature extraction based on RBs, convolution-concatenation path, and a simple decoder to reconstruct the extracted features. Our proposed model showed better performance than basic U-Net and most recent models like DAL, SK-U-Net, Mobile-U-Net, and Efficient-U-Net. In particular, our model achieved a Dice coefficient and IOU of 91.5% and 84.6% on BUSIS, respectively.
Semantic-relations taxonomy for knowledge representation

Semantic relations in knowledge representation characterizes the association between the concepts of a domain. Concepts are units of knowledge and the relationships that link these units, which gives meaning to the knowledge as represented. In this way, semantic relations allow users to assimilate the purpose of the association between concepts in the presentational context, avoiding misinterpretation of the information, mainly that presented in instruments of knowledge representation.The objective of this paper is to propose a taxonomy of semantic relations that compiles the different approaches on the subject. The methodology applied bibliographic research for theoretical foundation and literature review based on the main bibliographic references on semantic relations. As result, some classifications of semantic relations were found to have been raised by different authors, each offering a singular point of view, which has resulted in a range of discordant terms. To answer this need, a taxonomy was arrived at of sixty-three semantic relations, including a new relation discovered by this study, dubbed here ‘subordinate agent’.