Reviewerly/exHarmony huggingface dataset:
Intolerant Analysis: A Proposed Method for Measuring Intolerance Level in Text-Based Social Media Profiles

Intolerance is a problem that arises with the development of social media. These problems sometimes arise if they are related to elements of ethnicity, religion, race, or certain groups. According to some literature, in general, intolerance is a person's feeling that they cannot accept other groups in public discussions that occur on social media. Many researchers have carried out sentiment analysis but have not studied intolerance analysis, even though, in many cases, intolerance is often characterized by negative sentiment or even hate speech. In this study, a classification of intolerance sentences using machine learning is proposed. Furthermore, several calculations of the level and percentage of intolerance for a social media account are also described. The accuracy of machine learning calculations is 97% for intolerance analysis. The calculation of the percentage of intolerance for a social media account is obtained from the number of data dictionaries (corpus) that appear in all text posts. So, the percentage level of intolerance can be calculated to categorize the level of intolerance of a social media account based on text.
Classification of Cyber Crime Cases in Surabaya, Indonesia Based on ITE Law Violation

As a state of law, Indonesia is obliged to protect every citizen from harmful actions, including crimes that occur virtually, commonly called cybercrime. This paper classifies cybercrimes registered on 2021 that took place in the District Court of Surabaya to recognize which ITE law article has the highest number of violations committed by the people living in Surabaya. Information related to the verdict was obtained by accessing the official website of the local District Court. Details such as which article was violated, verdicts, and accusations are provided and can be publicly accessed. The classification commences by determining the article violated and listing the accusation given to the defendant. The results are presented in tables and graphs along with three articles that have the highest number of violations, including a brief description of the cases.
A Perceptual Shape Loss for Monocular 3D Face Reconstruction

Abstract Monocular 3D face reconstruction is a wide‐spread topic, and existing approaches tackle the problem either through fast neural network inference or offline iterative reconstruction of face geometry. In either case carefully‐designed energy functions are minimized, commonly including loss terms like a photometric loss, a landmark reprojection loss, and others. In this work we propose a new loss function for monocular face capture, inspired by how humans would perceive the quality of a 3D face reconstruction given a particular image. It is widely known that shading provides a strong indicator for 3D shape in the human visual system. As such, our new ‘perceptual’ shape loss aims to judge the quality of a 3D face estimate using only shading cues. Our loss is implemented as a discriminator‐style neural network that takes an input face image and a shaded render of the geometry estimate, and then predicts a score that perceptually evaluates how well the shaded render matches the given image. This ‘critic’ network operates on the RGB image and geometry render alone, without requiring an estimate of the albedo or illumination in the scene. Furthermore, our loss operates entirely in image space and is thus agnostic to mesh topology. We show how our new perceptual shape loss can be combined with traditional energy terms for monocular 3D face optimization and deep neural network regression, improving upon current state‐of‐the‐art results.
K-Means Algorithm in Illiteracy Clustering

Illiteracy is a condition where a person cannot read and write to communicate and express opinions in social life. Illiteracy is a problem that exists in almost all countries, including Indonesia. Even though the illiteracy rate continues to decrease annually, several regions still have a high quantity of illiteracy. Additionally, there are fewer illiterates in any area. One way to find out the distribution of illiterate population data is to map the population based on the level of illiteracy so that it can be prioritized for handling in areas with high illiteracy rates. In data mining, the technique for mapping the illiterate population can be done using the clustering method, namely by grouping the population of each region by giving labels based on their illiterate class. This study aims to cluster illiterate population data based on their illiteracy level and find the optimal number of classes in the illiteracy level clustering process. The class clustering algorithm uses k-means clustering. This study applies three scenarios with different k values, namely k=2, k=3, and k=5. The clustering results are validated using the cluster validation process to find the optimal number of clusters with a silhouette coefficient (SC). The results showed that the SC values at k=2, k=3, and k=5 were 0.24, 0.59, and 0.35. The results of this validation show that when k=3 produces the most excellent SC value, the study concludes that k=3 is the most ideal number of clusters.
Classification Based on Texture Analysis for Synthetic Hazy Image from Mount Kelud Haze Image Density

This paper classifies the simulations of homogeneous synthetic images, heterogeneous synthetic hazy images, and original hazy images taken from CCTV (Close Circuit Television) of Mt. Kelud crater using the GLCM (Gray Level Co-Occurrence Matrix) method. The average feature values obtained using the GLCM (Gray Level Co-Occurrence Matrix) method are used to compare the similarity of gray feature values of the three and then classify thin, medium, and thick images. The results for classifying thin haze, medium haze, and thick haze on the homogeneous synthetic hazy image test data obtained an accuracy value of 50%, a precision value of 46%, and a sensitivity value of 65%. As for the classification of thin, medium, and thick fog on heterogeneous synthetic hazy images, test data obtained an accuracy value of 42%, a precision value of 32%, and a sensitivity value of 48%.
Performance Evaluation of EfficientNetB0, EfficientNetV2, and MobileNetV3 for American Sign Language Classification

Sign language is a type of communication system that involves the gestures of our hands and our facial expressions to communicate with people who are deaf. Unfortunately, it is estimated that 1.5 billion people in the world have this disability. The task of sign language identification has attracted a lot of scientific attention over the years, resulting in advancements and insightful findings to the field. In keeping with the ongoing search for enhanced accuracy and efficiency, our research examines the utility of transfer learning by using pre-trained CNN models, notably EfficientNetB0, EfficientNetV2, and MobileNetV3. Our main goal is to compare and assess how well these models perform when it comes to the recognition of American Sign Language (ASL). The dataset used consists of a varied collection of 13,000 photos divided into 29 different ASL groups. In this endeavor, we find that EfficientNetB0, MobileNetV3, and EfficientNetV2 have accuracy rates of 99.54%, 93.93%, and 74.62%, respectively. These findings shed substantial insight into these models' potential to advance ASL recognition technology.
Book Spine Matching with Library Collection Using Speeded Up Robust Features

Misplaced books in libraries are an ongoing issue that impedes the efficient retrieval and organizing of books. However, the traditional way of regularly arranging a lot of books according to the classification number in the library costs a lot of time and the implementation of technology such as RFID (Radio Frequency Identification) is expensive. To provide insight and recommendations for libraries with the convenient and cheap method, the use of SURF (Speeded Up Robust Features) is implemented. SURF was implemented by analyzing the image data of some books on a shelf to know the location of the misplaced book when it is projected onto a bookshelf. The result, SURF could match the book spine of the misplaced book on the bookshelf. On the other hand, if it applied to the bookshelf there are a lot of similar books even though they come from different authors. It concludes that SURF could find the location of the book we want by matching the image.
Researching the body and movement through artistic performative installations for collaborative digital sensemaking

When tackling digital transformation, Digital Sensemaking, as researched in the DIGI-Sense project, empowers humans to reflect on the meaning from an intertwined cognitive, aesthetic, and body perspective. Sensemaking is fundamental for meaningful (work) experience of individuals and organizations. Digital means can play a central role to give meaning to processes, shared experiences, and to rationalize established routines. As embodiment, materialities, movements, and aesthetics are core to sensemaking, we have designed and explored a corresponding artscience installation. It implements the idea that collective digital drawing is a form of participatory sense-making that emerges from embodied, dynamical and collaborative interactions between co-performers. Performers could exert control over expressing drawings, by coordinating their movements with one another. This functionality is integrated in a setting with increasing task complexity – to further stimulate social collaboration and create rich visual feedback. In this way digital sensemaking could become an essential means for the encounter between two persons to sketch a common ground of their activity spaces.
Experiencing the Architectural Evolution of a Heritage Museum in Extended Reality Application

Although the use of Mixed Reality technology in knowledge, leisure, and cultural domains is ubiquitous, it has remained limited in heritage contexts. Museums are the DNA of each culture and contain exquisite heritage collections worth premier exhibit to be perceived, absorbed, and appreciated. When the heritage of the past is located within an architectural heritage, the connection and attachment to the roots of a culture are magnified. Hence, the heritage building of museums necessitates documentation and demonstration to visitors as much as the heritage artifacts. This paper uses a case study from DIKE (Digitization of Indigenous Knowledge for Extended Reality (XR) and Culture), German Summer School, 2023 to present a novel approach to utilizing Augmented Reality (AR) to develop a mobile application that facilitates the experience of the architectural evolution of Sarawak Museum. The APP »Museum Time Machine« showcases the architectural changes of Sarawak's heritage museum from 1891 to 2022.
CNN Image Classification Model Comparison Between Single-Label and Multi-Label CAPTCHA Dataset

CAPTCHA is a digital security system that prevents automated bots from accessing an online website or service. With the advancements in Convolutional Neural Networks (CNN), CAPTCHA can be easily bypassed with the correct model and dataset. Using deep learning methods to bypass various types of CAPTCHAs have been researched in previous related papers, while our paper will focus specifically on the image CAPTCHA called reCAPTCHA. This research addresses a gap in existing literature surrounding the effects of utilizing multi-labeled images compared to single-labeled images. To facilitate this endeavor, this research provides a new Multi-Label Image CAPTCHA dataset labeled and collected manually by the researchers. Using a public dataset alongside the newly collected dataset, this paper aims to compare and provide data on the success rate of CNN models trained using two different approaches. The first approach is training the model using a public dataset of single-labeled images acquired from Kaggle. The second approach uses the new dataset that can have multiple labels in each image. The researchers deployed the created models on a reCAPTCHA demonstration website and documented the results.
Design Challenges and Opportunities of Fossil Preparation Tools and Methods

Fossil preparation is the activity of processing paleontological specimens for research and exhibition. Alongside traditional mechanical extraction methods, fossil preparation presently comprises non-destructive digital techniques within the emerging field of virtual paleontology. Despite significant technological advances, traditional and digital preparation remain cumbersome and time-consuming. However, this field has received scarce attention from a human-computer interaction standpoint. This study aims to present the current state of paleontological fossil preparation, highlighting its key challenges and initiating a dialogue about innovative designs to address current issues. Our research comprises a qualitative study involving technical preparators and virtual paleontologists. The study consists of two main parts: Firstly, a focus group session brought together preparators and researchers to discuss their workflows, obtain a preliminary understanding of their issues, and ideate solutions based on their counterparts' experiences. Subsequently, a series of contextual inquiries involved direct observations and semi-structured in-depth interviews. By transcribing and analyzing the data through theoretical and inductive thematic analysis, we clustered emerging themes and applied concepts from human-computer interaction and related fields. Our findings report on challenges traditional and digital fossil preparators encounter, shedding light on potential opportunities to enhance their tools and workflows. This work contributes a novel analysis of fossil preparation from an HCI perspective.
Tracking, visualisation and interaction for virtual reconstruction of cultural heritage in mixed reality

This paper explores the use of mixed reality (MR) technology in the virtual reconstruction of cultural heritage sites. Specifically, it focuses on the areas of tracking, visualisation, and interaction, which are key components in creating an immersive and engaging MR experience. Through a review of the latest developments in the field, the paper presents a state-of-the-art analysis on a few chosen examples of MR technology in cultural heritage applications. The development of an MR application virtualising a digitalised scenography piece of the "Othello" play is explained, consisting of concept design, implementation, demonstration and testing. It discusses the benefits of using MR for cultural heritage in comparison to virtual reality (VR). Additionally, the paper highlights future trends and directions for the field. Overall, a comprehensive overview of the current state and potential of MR technology in the virtual reconstruction of cultural heritage sites is provided.
Design inspiration translated from the “Proud to be Iban” probes

The purpose of this paper is to present the co-design probes in collaboration with the community and apply the semiotic triangle model to analyse the design probes co-created to simulate design ideas that can be used for future prototyping of the community networks' setup. Five (5) members of the community participated in three (3) activities: one online via a WhatsApp group and two in-person workshops at the community longhouse. The community collected twenty-five (25) values and created seven "Proud to be Iban" design probes. Following the semiotic triangle model, the design probes were analysed with the community and then mapped with the 25 values. Through this generative design process, a few good design ideas were generated, which will be recommendation to the technology design for the implementation of the Bawang Assan community networks in the later stage. It is anticipated that the design of technology based on these findings will assist the community in adopting technology as part of their culture and promote the sustainability of community networks.
ConformiTree: an artistic research on the expansion of photography to the third dimension

The artistic research project ConformiTree serves as an example to evaluate the current possibilities of capturing the spatial dimension with generally accessible devices. As several of Hong Kong's roadside trees are unique in their shape and style, a photographic recording with a two-dimensional image of their roots hardly does justice to their sculptural complexity. Therefore, several possibilities have been explored to record these structures in the third dimension – without specialised equipment. With a hybrid derived from archaic photographic media, analogue large format plates and current 3D scanning technologies, the project results in an engaging, concrete but virtual representation with sculptural quality, celebrating the impressive power of those majestic trees.
Exploratory Multimethod Evaluation of Experience and Behaviour in Large Public Total Media Situations

Interactive large-scale media installations are difficult to evaluate the bigger the venue, larger the audience, more media elements, and shorter the presentation time. We report on a multi-method evaluation of one such event that combined standard evaluation methods (observation, interviews, etc.) and two novel exploratory methods (self-reflective spatial mapping and 5-word elicitation). We describe findings to demonstrate strength and weaknesses of methods when applied to a complex situation. Findings highlight the situation's richness and dynamics, but also inconsistence in results gained from the methods. Among other recommendations, we conclude that, for the time being, researchers should utilize inductive approaches, as we still lack a comprehensive set of strong concepts and dynamic patterns, which are needed to understand such situations better and to develop novel deductive methods for a more cost effective user experience evaluation of large public total media situations.
Extended Reality Authoring System for Creating Immersive Experiences: A Requirements Analysis

This paper discusses the challenges and complex processes related to creating content for Extended Reality applications. More specifically, the paper focuses on the challenges and processes involved in developing mixed reality applications using the Unity engine and the Microsoft HoloLens 2 device. To design and develop immersive Extended Reality experiences, specific skills and knowledge of tools and their interfaces and compatibility are required. The proposed concept could considerably improve the efficiency and effectiveness of XR development by facilitating cross-device collaboration and design. This could be beneficial for creating immersive experiences, such as content-driven art projects, for devices like the HoloLens.
An empirical study to design interactions with historical buildings used for everyday activities

Many historical buildings are challenged to comply with their dual purpose of being society's living memory and being used for everyday activities, such as a place to work or study. Direct interactions with such buildings could counteract this challenge and integrate their historical significance with contemporary needs. However, there is a lack of knowledge about designing this. We approached the gap in an iterative design process by conducting four expert interviews and two building walks with N=16 building users at a public historical building. Our results highlight the importance for building users to integrate historical significance and contemporary needs into their routines to increase their connection to the building and its past users. We provide three design implications for designing the interaction with historical buildings in everyday life and emphasize research implications for future projects. Our findings emphasize the need for interdisciplinary collaboration between Human-computer interaction research areas.
The Effect of Data Splitting Ratio and Vectorizer Method on the Accuracy of the Support Vector Machine and Naïve Bayes Model to Perform Sentiment Analysis

The main information retrieved from public opinions can be understood by carrying out a sentiment analysis. The validity of the retrieved information depends on the performance of the algorithm used to perform the sentiment analysis., where sentiment analysis parameters themselves need to be considered. Thus, it is important to determine the best level parameter and algorithm in order to obtain a valid conclusion from a sentiment analysis. This study aims to determine the effect of the data splitting ratio (0.5, 0.6, 0.7), and vectorizer (CountVectorizer, TF-IDF) on the accuracy of the Support Vector Machine and Naïve Bayes model while performing sentiment analysis. The results of the research show that the data splitting ratio and vectorizer have a significant effect on the accuracy of the SVM and Naïve Bayes model performance. However, the interaction between the data splitting ratio and the vectorizer method does not significantly influence the accuracy of both models.
Improving YOLOv5n for lightweight ship target detection

A lightweight target detection algorithm based on YOLOv5n is proposed for the current problems of low accuracy rate and high hardware and software limitations of ship target detection for near-shore port and river monitoring. First, the neck network of YOLOv5n uses Ghost convolutional structure instead of ordinary convolution and bottleneck structure in the C3 module to reduce network complexity and computation. Second, the SA attention mechanism is introduced in the backbone network to strengthen the feature extraction capability. Finally, the SIoU loss is used as the localization loss to speed up the model convergence and improve the accuracy of the algorithm. The experimental results show that the improved model volume is reduced by 13.2% and the average accuracy is improved by 3.8% on average. Deploying the algorithm acceleration to the edge device NVIDIA Jetson nano, the inference time is as low as 38ms for a single image with 640×640 resolution, which basically meets the requirements of real-time ship detection.
Blind Remote Sensing Images Quality Estimation and Denoising

The Remote Sensing Images are always interfered by various noises for the complex environment of atmosphere. Despite the success of both residual and dense skip connections in image denoising, both aggregation types have drawbacks. In this work, we propose a variational posterior evaluation and blind image denoising (VPE-BID) algorithm. By using the improved Bayesian framework based on the variational inference method and the multi-scale structure similarity (MS-SSIM) loss function, we take the inherent clean image and noise variance as the potential variables of the input noise image to deparameterize a posteriori. This posterior provides explicit parametric forms for all its involved hyper-parameters, and thus can be easily implemented for blind image denoising with automatic noise estimation for the test noisy image. Extensive experiment on UCMerced LandUse database and BSD68 demonstrates the proposed algorithm has the most advanced performance in qualitative and quantitative measurement.