Reviewerly/exHarmony huggingface dataset:
Real-Time Collaboration in Code Development: A Comprehensive Review of Code Jett

CodeJett is a pioneering collaborative real-time code editor that serves as a beacon of innovation in the field of software development. With the increasing prevalence of remote work and distributed development teams, the need for tools that facilitate seamless collaboration among developers is paramount. CodeJett rises to this challenge by offering an array of features, including real-time synchronization, syntax highlighting, version control integration, and a built-in chat system. This paper provides a comprehensive overview of CodeJett, highlighting its significance and the evolving landscape of collaborative development tools. In a world where software development transcends borders and time zones, CodeJett acts as a unifying force, enabling developers to work together harmoniously on coding projects. The real-time nature of the tool eliminates the need for manual synchronization, enhancing productivity and reducing the likelihood of errors. CodeJett's versatility extends to its support for multiple programming languages, making it accessible to a broad spectrum of developers. This abstract underscore the role of CodeJett in facilitating collaboration, real-time development, and efficient communication within the software development community. The paper also explores the technical challenges faced in the development of CodeJett and offers insights into its future potential as a transformative tool in an ever-changing software development landscape. With keywords such as collaboration, real-time synchronization, syntax highlighting, and version control, this abstract encapsulates the essence of CodeJett's importance and its enduring impact on the field of software development.
Human Action Recognition with Skeleton and Infrared Fusion Model

Skeleton-based human action recognition conveys interesting information about the dynamics of a human body. In this work, we develop a method that uses a multi-stream model with connections between the parallel streams. This work is inspired by a state-of-the-art method called FUSIONCPA that merges different modalities: infrared input and skeleton input. Because we are interested in investigating improvements related to the skeleton-branch backbone, we used the Spatial-Temporal Graph Convolutional Networks (ST-GCN) model and an EfficientGCN attention module. We aim to provide improvements when capturing spatial and temporal features. In addition, we exploited a Graph Convolutional Network (GCN) implemented in the ST-GCN model to capture the graphic connectivity in skeletons. This paper reports interesting accuracy on a large-scale dataset (NTU-RGB+D 60), over 91% and 93% on respectively crosssubject, and cross-view benchmarks. This proposed model is lighter by 9 million training parameters compared with the model FUSION-CPA.
AICRID: AI-Empowered CR For Interior Design

Augmented Reality (AR) technologies have been utilized for interior design for years. Normally 3D furniture models need to be created manually or by scanning with specialized devices and this is usually a costly process. Additionally, users need controllers or hands for manipulating the virtual furniture which may lead to fatigue for long-time usage. Artificial Intelligence (AI) technologies have made it possible to generate 3D models from texts, images or both and show potential to automate interactions through the user’s voice. We propose a novel design, AICRID in short, which aims to automate the 3D model generation and to facilitate the interactions for interior design AR by leveraging on AI technologies. Specifically, our design will allow the users to directly generate 3D furniture models with generative AI, enabling them to directly interact with the virtual objects through their voices.
The Correlation Between Students’ Self Efficacy, Motivation, And English Learning Outcomes

The recent decade has witnessed the worse condition of Indonesian students’ learning loss which commonly caused by lack of self control. In handling that, students need the confidence to enhance their self-control which has been referred in literature as self-efficacy and motivation. The research aims to measure the correlation of self-efficacy and motivation towards students’ English learning outcomes. 34 students in grade 10 at MA Darul Ulum Palangka Raya were polled quantitatively using 24 self-efficacy and motivational questions. Multiple linear regressions was aplied to examine the data. The finding is, both variables are positively correlated with students' English test scores (F=38.025, p=0.000). students’ english result was correlated 67,20% by self-efficacy(x1), and motivation (x2) 3,80%, The total contribution was 71%. Other variables not under examination had an impact on the remaining 29%.
Keynote Speech 4: AI/ML Applications in Computer Networks

Computer networks are the backbone of the modern world, connecting people and devices across the globe. AL/ML are rapidly transforming many industries, and computer networks are no exception. This talk will discuss how AI/ML is being used to improve computer networks. We will cover topics such as current affairs of networks, functions, AL/ML trends and their application in next generation networks. This industry based talk is intended for a general audience with an interest in application of AI/ML in computer networks.
Message from the ISMAR 2023 General Chairs

It is our great pleasure to welcome you to the 22nd IEEE International Symposium on Mixed and Augmented Reality (ISMAR), held from 16 to 20 October 2023 in Sydney, Australia. ISMAR stands as the foremost international academic conference in the fields of Augmented Reality (AR), Mixed Reality (MR) and Virtual Reality (VR). Over the years, it has continually expanded its horizon, delving into the latest developments in AR, MR, and VR within both commercial and research domains. The conference is organized and supported by IEEE, IEEE Computer Society, and IEEE VGTC.
Penerapan model InceptionV3 dalam klasifikasi penyakit ayam

Chicken disease is one of the problems that can have a very significant impact on chicken farmers, in addition to having an impact on the farm itself, chicken disease can also have an impact on the surrounding environment. Lack of knowledge about the symptoms and diseases that occur in chickens, makes some chicken breeders treat and treat diseases in a traditional way. This method often takes a long time and is prone to errors. In this study, technology will be used to classify chicken diseases by utilizing a deep learning model from the Convolutional Neural Network (CNN) architecture, namely InceptionV3. In carrying out the process of classifying chicken diseases, using a dataset of chicken feces images with a number of 8067 Healthy, Salmonella, Coccidiosis, and Newcastle disease. In the research process, three experimental scenarios were carried out using 20 epochs, 50 epochs and 100 epochs. From the experimental results, using a value of 100 epochs produces the highest accuracy value with a value of 94.05%.
Improving Accuracy of Pseudo-LiDAR for 3D Object Detection by Accurate Depth Estimation

With the advancement of autonomous driving technology, traditional 2D object detection approaches no longer fulfill the Safety of The Intended Functionality (SOTIF) standards. LiDAR sensors are efficient in giving precise point cloud information for accurate and reliable 3D object detection. Alternative sensors also are researched by researchers if they have lower costs but are limited in environmental circumstances. Notably, pseudo-LiDAR-based monocular 3D object detection has emerged as a viable option, as it generates a depth map from an RGB picture before translating it into a point cloud for 3D object detection. However, because the majority of depth estimation algorithms do not account for the loss of boundary information during feature extraction processing, depth artifacts occur in the periphery of objects in the depth map. These artifacts introduce long-tail problems in pseudo-LiDAR data, undermining the accuracy of 3D object detection. Therefore, we suggested a depth estimation method using Laplacian pyramid-based depth residuals to correctly capture object depth bounds. This improved the estimate and revised the depth map, which was converted into pseudo-LiDAR point cloud data. The pseudo-LiDAR was then used to recognize 3D objects. The proposed method, in particular, significantly mitigated the common border Long-tail problems in pseudo-LiDAR data, hence improving the precision of 3D object detection. Experiment validations demonstrated the method's efficiency and enhanced performance to improve the reliability of autonomous driving systems.
Usage Trend and Patterns of Google Meet as an e-Leadership Tool: An Evidence from Malaysian Public Organisations

Video conference technology (VCT) is a multimedia application that enables sound and image transmission, as well as real-time, duplex, and data communication. It facilitates communication between people in different places by using a camera, microphone, and monitor. In Malaysia, the use of this technology is rather recent and the spike usages of it were seen during the outbreak of Covid-19 in 2020. Organisations were fast to adopted VCT in order to facilitate the organizational communications as employees were away from the office. In Malaysian public organisations, the Government have introduced the use of Google Meet as their main VCT tool. However, not all employees in the organisations were ready to adopt the technology and even some are still refusing it. Driven by the noted disparities in preparedness to embrace this technology, the study attempts to identify the variables impacting Google Meet's usage for leadership communication through an examination of usage patterns and demographic traits. Overall, the paper examines the demographic characteristics of 353 survey participants, their responses regarding Google Meet usage trends in a leadership communication context, and substantiates the widespread utilization of Google Meet for official purposes by managers in Malaysia through the presentation of data using charts and tables. The study also explores the subtle aspects of usage, including trends, frequency, and types of activities that supervisors, subordinates, and coworkers engage in on Google Meet. It is interesting to note that managers in Malaysian public organisations prefer to utilise Google Meet as an e-leadership tool, and they have expressed interest in using it going forward. The study's conclusions offer compelling new perspectives on how things work of this communication channel. Moreover, understanding the preferences of managers on e-leadership tools able to help to the development of policies that align with organizational goals. For this, the findings of this study can be used for policy makers in strategic decision-making within organizations, helping leaders tailor their approaches to technological integration.
Peningkatan Kompetensi Penulisan Karya Ilmiah Bagi Pengajar Bipa Di Yala Rajabath University Thailand

Teachers are required to have various competencies in the process of transferring knowledge to their students, as are BIPA teachers. Quality teaching competency is an important aspect in the successful achievement of student competency. Therefore, discussions on teacher competence are important as supporting the success of second language learning, in the context that will be carried out, namely the competence of BIPA teachers in writing scientific papers. This is done because writing skills are skills that are difficult to learn and teach. Additionally, consider BIPA which is increasingly popular for non-native speakers to follow. The objectives of this PKM are (1) to provide training in an effort to increase the competency in writing scientific papers of BIPA teachers, (2) to provide insight related to the use of technology in writing scientific papers. This activity was carried out at Yala Rajabhat University, Thailand. This location was chosen because it held BIPA learning and there were problems regarding inadequate insight in writing scientific papers in the context of Indonesian language and culture.The solution offered is to provide academic writing material (1) paraphrasing and quoting skills, (2) word or lexical variations, (3) active and inactive sentences, (4) thinking processes which include ability to understand written information, (5) structure and development of arguments in micro and macro stages. In addition, to achieve the second goal, provide insights related to the use of Mendeley, Vos Viewer, and Sciendiret and Scihub. It is hoped that these two indicators will be able to solve the problems faced by BIPA teachers
Investigating the Impact of Partial Shading on Photovoltaic Panels and Enhancing their Efficiency using the Python

This research project focuses on investigating the impact of partial shading on photovoltaic (PV) panels and proposes methods to enhance their efficiency using Python programming. Partial shading can significantly reduce the performance of PV panels by creating imbalances in current and voltage outputs. By leveraging Python's computational capabilities, this study aims to develop simulation models and algorithms that accurately capture the behavior of shaded PV panels. The objectives of this research include building a comprehensive understanding of partial shading effects, developing a Python-based simulation framework, analyzing the impact of different shading patterns on PV panel efficiency, investigating novel techniques to enhance efficiency using Python, and evaluating proposed approaches through simulations and experimental validation. Through this investigation, we aim to contribute to the development of improved strategies for the design and operation of PV systems. By mitigating the negative effects of shading and enhancing PV panel efficiency, we can further promote the adoption of sustainable solar energy solutions. The outcomes of this research have the potential to advance the field of solar energy and facilitate a greener and cleaner future. Also, this paper aims to investigate the impact of partial shading on photovoltaic (PV) panels and explores methods to enhance their efficiency using Python. Partial shading can significantly reduce the overall output of PV systems, leading to suboptimal performance. By analyzing shading patterns and implementing intelligent algorithms, we can optimize PV panel configuration and improve their efficiency. In this study, the Python programming language will be utilized to develop simulation models, perform data analysis, and propose solutions for increasing the efficiency of shaded PV panels.
Moving to a Data Driven Organisation

Summary Oil and gas has a long rich history in the industry. As such, there is significant reliance on legacy system as part of a mature business process. There is a growing shift towards low-code or self manage solution, it becomes important ensure there is an ecosystem to support the change to fully leverage the trend.
Preliminary Study on Soil Mineral Identification in Erbil Province‎

This article presents a preliminary study conducted to identify soil minerals within Erbil Province. Soil minerals play a crucial role in soil fertility, nutrient availability, and various soil-related processes. Understanding the mineral composition of soils is essential for effective land management, sustainable agriculture, and environmental conservation. The study area was carefully selected to represent a diverse range of soil types, geological formations, and land use practices. A systematic sampling approach was employed, ensuring adequate coverage of the study area. 80 soil samples were collected from various locations and depths, and a combination of field observations and laboratory analyses was conducted to identify and characterize soil minerals. The soil samples were examined by X-ray diffraction (XRD) method to determine the mineral composition. The identification of minerals allowed for insights into the geological origin, weathering processes, and soil formation mechanisms within the study area. The preliminary findings revealed a diverse range of soil minerals across the study area. Common mineral types, including silicates, carbonates, oxides, and clay minerals, were identified. Variations in mineral composition were observed, reflecting the influence of parent material, geology, and soil development processes. The results of this preliminary study provide a foundation for future research and more in-depth investigations of soil minerals in the study area. The mineral identification data can be utilized for land use planning, soil classification, and targeted soil management strategies
Depth-conditioned GAN for Underwater Image Enhancement

Autonomous underwater vehicles perceive environmental information relying on vision. However, the variety of the underwater environment always leads to image distortion. To effectively obtain color-corrected images that guarantee high sharpness and good content recovery, this paper proposes an algorithm based on deep learning networks. Our method is a modified version of generative adversarial networks for underwater image enhancement. Due to the complexity of the underwater environment, the green hue in the image is uneven, and the existing methods cannot recover the uneven green hue image well. Using our model, we generate corresponding depth estimation maps, along with the real underwater images, as inputs to the generator for color correction of monocular underwater images. The depth estimation maps, which are considered as image noise in the input, helps to distinguish the region of interest from the background. Furthermore, we formulate an objective function through several different loss functions, which can evaluate image quality globally. In addition, we performed training on the EUVP dataset, the comparison results show that our method is superior to other methods. Finally, we conduct extensive experiments on EUVP, UIEB and UFO-120 datasets, where our model not only significantly improves the quality of the underwater images, but also performs well over the state-of-the-art methods.
DEEP LEARNING FOR OBJECT DETECTION USING RADAR DATA

Abstract. Recently, Deep learning algorithms are becoming increasingly instrumental in autonomous driving by identifying and acknowledging road entities to ensure secure navigation and decision-making. Autonomous car datasets play a vital role in developing and evaluating perception systems. Nevertheless, the majority of current datasets are acquired using Light Detection and Ranging (LiDAR) and camera sensors. Utilizing deep neural networks yields remarkable outcomes in object recognition, especially when applied to analyze data from cameras and LiDAR sensors which perform poorly under adverse weather conditions such as rain, fog, and snow due to the sensor wavelengths. This paper aims to evaluate the ability to use RADAR dataset for detecting objects in adverse weather conditions, when LiDAR and Cameras may fail to be effective. This paper presents two experiments for object detection using Faster-RCNN architecture with Resnet-50 backbone and COCO evaluation metrics. Experiment 1 is object detection over only one class, while Experiment 2 is object detection over eight classes. The results show that as expected the average precision (AP) of detecting one class is (47.2) which is better than the results from detecting eight classes (27.4). Comparing my results from experiment 1 to the literature results which achieved an overall AP (45.77), my result was slightly better in accuracy than the literature mainly due to hyper-parameters optimization. The outcomes of object detection and recognition based on RADAR indicate the potential effectiveness of RADAR data in automotive applications particularly in adverse weather conditions, where vision and LiDAR may encounter limitations.
VEHICLE TRACKING AND SPEED ESTIMATION FROM UNMANNED AERIAL VEHICLES USING SEGMENTATION-INITIALISED TRACKERS

Abstract. We propose an effective vehicle tracker and speed estimation method from Unmanned Aerial Vehicles (UAVs) videos that can be deployed on UAV-embedded edge devices. Our tracker uses segmentation-derived vehicle regions to initialise a MOSSE tracker. This enables road operators to make multipurpose use of segmentation outputs while still being able to track the vehicles across frames. The vehicle speed is estimated using flight parameters derived from the UAV's flight computer and the vehicle displacement across frames. We trained CABiNet on the UAVid urban segmentation benchmark dataset and finetuned it on a dataset collected at our study site. A mean Intersection over Union (mIoU) of 0.73 was obtained for the vehicle class. Our segmentation-initialised MOSSE tracker was evaluated on the VisDrone Multi-Object Tracking (MOT) benchmark dataset and compared against traditional methods that utilise object regions for tracker initialisation. Our approach yielded a Multi-Object Tracking Precision (MOTP) of 0.872 compared to 0.830 when using YOLOv4. Our vehicle speed estimations approach was evaluated using a privately collected ground truth vehicle speed dataset. Our approach yielded a Root Mean Square Error (RMSE) between 3.42 and 16.12 km/hr across different flight configurations. Finally, our approach was deployed on an NVIDIA Jetson Xavier NX edge device and could be executed at 8 Frames Per Second (FPS). The results indicate that our approach is a simple yet fast alternative to traditional tracking methods while producing multipurpose segmentation information.
Discussion on Microteaching in Korean Mathematics Education

This study conducted a literature review to scrutinize the landscape of microteaching-related research within the field of mathematics education in South Korea. The research data were collected by searching for keywords such as microteaching, teaching demonstrations, and simulated rehearsal on RISS until July 2023. The final research data consisted of 28 empirical studies, including only those published in academic journals. For data analysis, the study delved into the purposes of microteaching, the specific grade levels being targeted, and the diverse methodologies employed in microteaching. The findings illuminated that a predominant focus of the studies was on secondary preservice teachers, with microteaching being implemented primarily for the purpose of observing teaching behaviors. Qualitative research methodologies prevailed, with a notable prevalence of studies utilizing video recordings to encapsulate the intricacies of the teaching process. Furthermore, it was a customary practice to facilitate peer feedback sessions following the teaching sessions. Finally, a comprehensive discourse was engaged to elucidate the underlying rationales driving the observed trends in these findings.
Histopathological Image Analysis for Breast Cancer Identification Using Gradient Boosting Classifier

When it comes to cancers, breast cancer is by far the most prevalent among females and the second largest cause of cancer death globally. Radiologists frequently make incorrect diagnoses of breast cancer due to the subtlety of Microcalcification and Lumps. Past research has led to the creation of CAD (Computer Aided Diagnosis) tools that enable the radiologist in quickly and accurately identifying problems. This research study proposes a new method for predicting breast cancer, the Gradient Boosting Classifier (GBC), a supervised machine learning algorithm for identifying breast cancer by training its features. Breast Cancer Kaggle dataset repository provides the histopathological images utilized in this study. The proposed system's performance is rigorously assessed, considering accuracy, sensitivity, and specificity. Notably, it consistently delivers outstanding results during both training and testing phases. In the validation phase, the methods excel, demonstrating an impressive accuracy rate of 96.15%, a sensitivity level of 94%, an F1 score of 95%, and an impressive area under the curve (AUC) of 0.96.