Reviewerly/exHarmony huggingface dataset:
PENGGUNAAN ALAT PRAKTIKUM MESIN ATWOOD BERBASIS ARDUINO TERHADAP HASIL BELAJAR DAN KETERAMPILAN ARGUMENTASI MAHASISWA PENDIDIKAN FISIKA PADA MATA PRAKTIKUM MEKANIKA TAHUN AKADEMIK 2017/2018

Abstract: The results of the presurvey conducted at the Laboratory of Physics Education, University of Muhammadiyah Metro that there is an arduino-based atwood machine practicum tool that has been made by Rohmah (2017) and has been validated with good accuracy but this tool has not been used in learning and has several advantages one of which is automatic detect the time needed for objects to move, so that the tool can work well and can be used to find out learning outcomes and argumentation skills. The research subjects were 12 students of Muhammadiyah Metro University physics education. Data collection instruments use student worksheets and test questions. The results showed that the use of arduino-based atwood machine practicum tools could affect the learning outcomes and argumentation skills of physics education students. Learning outcomes were calculated using a t-test with a significance level of 5%, so the results showed that thit abel t table was 5.66 ≥ 2.07, thus rejecting hypothesis H0 and accept H1: (there is a difference between the pretest and posttest scores) the disputes are 21.61. Then for student argumentation skills obtained an average percentage value of 83.33% from three aspects, namely claim, evidence and reasoning. Suggestions for readers, can be used as reference media for atwood machine practicum tools in learning with different materials and other learning methods.
Joint Multiple Efficient Neighbors and Graph Learning for Multi-view Clustering

Graph-based multi-view clustering is a popular method for identifying informative graphs for e.g. computer vision applications. Nevertheless, optimizing sparsity and connectivity simultaneously is challenging. Multi-view clustering (MC) integrates complementary information from different views. However, most existing methods introduce noise or ignore relevant data structures. This paper introduces a Joint multiple efficient neighbors and Graph (JMEG) learning method for MC. Our approach includes a post-processing technique to optimize sparsity and connectivity by means of identifying neighbors efficiently. JMEG also uses partition space and consensus graph learning to uncover data structures efficiently. Experiments show that JMEG outperforms state-of-the-art methods with negligible additional computation cost.
Maturity-based taxonomy of extended reality technologies in aircraft lifecycle

Abstract. EXtended Reality (XR) is a fast growing and rapidly evolving technology. In the aeronautical sector, XR can be exploited for the entire aircraft lifecycle, however, different levels of maturity can be identified for applications in each one of the lifecycle’s phases. This paper, by outlining the TRL of current XR applications over the aircraft lifecycle, aims to be a foundation to identify the possible future improvements and applications of immersive technologies in the aeronautical sector.
The fireball ghosts, in three major countries of Southeast Asia culture

The mystical mindset is not only common among Indonesians, who frequently associate all events with the supernatural. In neighboring countries, such as Malaysia and Thailand, there is a belief in the existence of mystical or occult things. This research aims to describe the mystery of Banaspati in ASEAN countries, specifically Indonesia, Thailand, and Malaysia. Furthermore, this study will describe the story of Islamization in Java, which is inextricably linked to the influence of mysticism, one of which is Banaspati. This study employs a descriptive-qualitative methodology with a content analysis strategy. According to the study's findings, Indonesians believe that Banaspati, particularly in Java, is a supernatural ghost or spirit that manifests as a blazing fire with great power. In Thailand. Bong fai phayanak (dragon fireballs) is a supernatural phenomenon that occurs in the Mekong River and is caused by the Mekong River's guardian dragon in October. In Malaysia, it is known as polong or "fireball." People in Malaysia believe that a pod is a form of a mystical pet item of humans who have black magic, which functions to bring disaster to someone they don't like. The community's concept of fire elemental ghosts is related to the beliefs of Buddhists in Thailand and Muslims in Indonesia, especially in Java.
Position Estimation of Unmanned Aerial Vehicles in Contested Environments using Dense Matching Networks

This paper presents an approach to predicting the trajectory and the position of unmanned aerial vehicles (UAVs) in contested global navigation satellite system (GNSS) environments. Our approach utilizes dense matching networks to analyze and predict patterns in UAV movement and then effectively recovers temporal trajectory information and accurately predicts UAV position changes using images and altitude data. This problem was part of the KDDBR 2022 Kaggle Competition, where we obtained a Root Mean Squared Error (RMSE) of 0.29603, which made it the runner-up solution. This could have significant implications for the future of UAV navigation, potentially leading to safer and more efficient operations in various applications.
International Commercial Arbitration and Technology - An Authors’ Interview with Generative Artificial Intelligence

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From E-Commerce to Virtual Commerce: The Exciting Opportunities of Virtual Shopping

Abstract Virtual commerce has the potential to transform the shopping experience. It is highly interactive and can create immersive experiences when consumers wear head-mounted displays. Benefits include increased telepresence, better size and proportion judgement, customizable shopping environments, highly personalized decision support and social interaction opportunities. Technological advances in VR hardware, such as improved display resolution and reduced motion sickness, contribute to a more seamless and enjoyable shopping experience. These benefits can lead to higher purchase rates. However, there are also privacy concerns due to the many ways in which data can be collected. While the future of VR shopping is difficult to predict, AR applications are already well established. Tech giants, research institutions, and startups will play a critical role in shaping the future of virtual commerce.
Development of smart-speech enabling system for mute people using hand gesture

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Azlinda Ramli, M. Aliff A. Salim, Jasrina Jaffar; Development of smart-speech enabling system for mute people using hand gesture. AIP Conf. Proc. 26 October 2023; 2564 (1): 060002. https://doi.org/10.1063/5.0124157 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
In-Place Virtual Exploration Using a Virtual Cane: An Initial Study

In this initial study, we addressed the challenge of assisting individuals who are blind or have low vision (BLV) in familiarizing themselves with new environments. Navigating unfamiliar areas presents numerous challenges for BLV individuals. We sought to explore the potential of Virtual Reality (VR) technology to replicate real-world settings, thereby allowing users to virtually experience these spaces at their convenience, often from the comfort of their homes. As part of our preliminary investigation, we designed an interface tailored to facilitate movement for BLV users without needing physical mobility. Our study involved six blind participants. Early findings revealed that participants encountered difficulties adapting to the interface. Post-experiment interviews illuminated the reasons for these challenges, including issues with interface usage, the complexity of managing multiple interface elements, and the disparity between physical movement and interface use. Given the early stage of this research, these findings provide valuable insights for refining the approach and improving the interface in future iterations.
Saliency Detection Algorithm of Targets in Rainy City Traffic Images

In rainy days, many features of targets are obscured in city traffic images, and the utility of the outdoor traffic system is severely affected. To improve the image quality, the features of targets in city traffic rainy images are comprehensively analyzed. The brightness feature, the color difference feature, and the dark channel prior feature are extracted to map the target saliency features in rainy images. The target saliency detection model based on the above features is constructed. Finally, performance evaluation experiments are performed on ten state-of-the-art algorithms through seven standard evaluation indicators. The experimental comparison results verify the effectiveness of the proposed algorithm.
Automated Grading System for Breast Cancer Histopathological Images Using Histogram of Oriented Gradients (HOG) Algorithm

Breast cancer is the most common type of cancer in the world, affecting both men and women. In 2023, the American Cancer Society's reported that there will be approximately 297,800 new cases of invasive breast cancer in women and 2,850 in men, along with 55,750 cases of ductal carcinoma in situ (DCIS) in women. Further, an estimated 43,750 deaths are expected from breast cancer, of which approximately 43,180 are among women and 570 are among men. In this paper, we propose an automated grading system for breast cancer based on tumor's histopathological images using a combination of the Histogram of Oriented Gradients (HOG) for feature extraction and machine learning algorithms. The proposed system has four main phases: image preprocessing and segmentation, feature extraction, classification, and integration with a website. Grayscale conversion, enhancement, noise and artifact removal methods are used during the image preprocessing stage. Then the image is segment during the segmentation phase to extract regions of interest. And then, features are extracted from the obtained region of interest using the Histogram of Oriented Gradients (HOG) algorithm. The next, the images are classified into three distinct breast cancer grades based on the extracted features using machine learning algorithms. Moreover, the effectiveness of the proposed system was evaluated and reported using vary evaluation methods and the results showed a remarkable accuracy of up to 97% by the SVM classifier. Finally, the machine learning model is integrated into a website to improve the detection and diagnosis of breast cancer disease and facilitate the access and use of patient data. This will make the work easier for physicians to enhance breast cancer detection and treatment
Eye Contact Matters for Consumer Trust – Even with Robots

Abstract The integration of AI into consumer services is transforming the way people make decisions. AI is becoming more human-like, with chatbots, voice assistants and robots adopting human features and behavior. Consumers react differently to assistants with a human-like appearance than to advice from a web page, and the behavior of AI advisors influences consumer trust and decision-making. The results of an experiment comparing human advisors, robotic advisors with and without eye contact, and text-based services show that human advisors are trusted the most, but robotic advisors are preferred over text-based services. Human-like advisors increase trust and satisfaction. Extended human features like eye contact are essential for establishing trust and positive consumer responses. Companies should consider using humanoid advisors and incorporating eye contact to enhance customer experience. Consumers should be aware of the influence of human-like AI and stay informed about AI developments.
Weakly Nonlinear Time Invariant Circuits

Abstract The aim of this chapter is to show the utility of the theory that we developed. This is done by applying it to the analysis of nonlinear effects, that is of deviation from linear behaviour, in analog circuits. The vast majority of analog circuits are limited by noise on the bottom end of their dynamic range and by nonlinear effects on the upper end.
The Effect of Attention Saturating Task on Eyes-Free Gesture Production on Mobile Devices

Touch screens are popular input methods for mobile devices, but they compete for visual attention with users’ real-world tasks, leading to performance hindrances. In this study, we investigated the effect of an attention-saturating task on eyes-free gestures. A user study was conducted with 13 participants who performed eyes-free gestures on a smartphone using their dominant or non-dominant hand, alone or while performing a primary task that saturates attention. The results indicated that performing another task while drawing a gesture shortened the length and size of the gesture, reduced the duration of gesture entry, while the finger maintained the same speed across the touchscreen. Additionally, drawing a gesture with the nondominant hand increased the length of the gesture but generated less directional movements around the z-axis.
Gaze-Centric Metamer Computation Based on Peripheral Encoding

With the wide application of head-mounted display devices in virtual reality display, the information perception and visual fidelity of VR have been further studied. The key and main advantage of VR display is the ability to seamlessly integrate digital and physical space, which is a new media medium that can provide users with unprecedented immersion experience. However, to save computing resources, the existing virtual reality display rendering methods often choose the foveated rendering. The common foveated rendering method uses the characteristics of human vision to save a lot of computing resources, but almost completely ignores the information perception ability of peripheral vision and visual experience it can bring. This paper proposes a method based on peripheral encoding, which improves the quality of images rendered by the foveated rendering method.
Holographic Sports Training

Sport practicing through video can be challenging because of missing spatial information. Hence, we present a holographic sports library of short sports exercises used to practice sports. The sports holograms were captured in a volumetric recording studio. Users can watch the holograms on augmented reality (AR) devices like mobile phones and headsets. The user can take advantage of the spatial information and watch the holograms from multiple angles. Moreover, the user can imitate the hologram’s motion, an innovative method to teach sports.
Effects of Varying Avatar Sizes on Food Choices in Virtual Environments

The escalating global obesity crisis necessitates innovative interventions to promote healthier eating habits. This study investigates the potential of Virtual Reality (VR) as a novel approach to this challenge. We developed a VR simulation of a supermarket shopping experience, where the player’s virtual physique changes immediately based on their dietary choices. The simulation was tested with seven participants, who reported high levels of immersion (mean score: 7.67 out of 10) and presence (mean score: 6.3 out of 10). Initial findings revealed a discrepancy in weight loss between genders, which was addressed by introducing a customization feature for gender-specific dietary adjustments. Notably, participants generally consumed fewer calories within the VR environment compared to their self-reported real-life habits. These preliminary findings suggest VR’s potential as a compelling tool for promoting healthier eating habits and combating obesity. However, these results should be interpreted with caution due to the small sample size, and further research is warranted to substantiate these promising initial findings.
Technological Pedagogical Content Knowledge (TPACK) for Preservice Biology Teachers: Two Insights More Promising

TPACK questionnaires have been extensively developed, but measurements considering biological content insights still need to be explored. This study aimed to develop and validate TPACK questionnaires by assessing core competencies, core content, and interdisciplinary biology knowledge for preservice biology teachers. We performed development and validation through focus group discussions with biological education experts, and survey studies. The survey was conducted in three phases with a total number of 732 preservice biology teachers. In phase 1, descriptive analysis was calculated based on 232 participants. In Phase 2, Exploratory Factor Analysis (EFA) was calculated based on 250 participants. In phase 3, Confirmatory Factor Analysis (CFA) was calculated to validate and confirm the final dimension of the instrument recently developed based on 250 participants. This series of analyses resulted in 44 items with eight dimensions: Technological Pedagogical Biological Content Knowledge of Biology (TPACKB) comprises 12 items; Technological Knowledge (TK) of biology, Biological Knowledge (BK), and Technological Pedagogical Knowledge (TPK) of the biology consist of 6 items respectively; Technological Biological Knowledge (TBK) comprises of 5 items; Pedagogical Knowledge (PK) of biology, Pedagogical Biological Knowledge (PBK), and Biological Context Knowledge (BCxK) comprise of 3 items respectively. Our study recommended a TPACK questionnaire to assess preservice biology teachers' holistically interdisciplinary understanding, core content, and core competencies as necessary steps to empower biological resolution for their students.
An Empirical Analysis of Anomaly Detection Methods for Multivariate Time Series

Using multivariate time series (MTS) data for anomaly detection is widely adopted in service systems, such as web services and financial businesses. Researchers have recently proposed some well-performed algorithms for MTS anomaly detection from different perspectives. When applied to the real world, we observe that none of the algorithms is adaptable to all scenarios due to the complex data and anomaly characteristics. Moreover, there is currently a lack of comprehensive analysis work of these algorithms to guide operators in selecting the appropriate one in practice. To bridge this gap, we conduct an empirical study using various real-world data to gain an in-depth understanding of state-of-the-art anomaly detection algorithms. First, we provide general recommendations to guide operators in selecting suitable models based on the volume of training data, computational resources, and effectiveness requirements. Then, we summarize the typical data characteristics and types of anomalies and offer tailored model selection suggestions for different data characteristics and anomaly types. At last, we apply the summarized model selection suggestions to all the datasets we collected. The results show that most of our suggestions can achieve better than any single algorithm alone, demonstrating the effectiveness and generalization of our recommendations.
Towards Multi-Language Static Code Analysis

Industrial software systems are composed of components implemented in different programming languages, which are often analyzed for potential errors using static analysis engines. While these components frequently evolve due to addition of new features in, and migration to, newer programming languages, the program analysis engines do not correspondingly evolve to support the newer languages. Thus, static analyzers often are incapable of providing comprehensive analysis for evolving software systems. This limitation is difficult to address because (1) building a scalable static analyzer for a programming language requires enormous engineering efforts, and (2) reusing an existing static analyzer is hard due to the different programming models of different languages. To address this, we propose a hybrid translation architecture that uses both the source program AST and low-level IR to efficiently analyze multi-language systems, leveraging existing analyzers while accommodating language-specific semantics and potential loss of exhibited behaviors.
Multi-scale feature flow alignment fusion with Transformer for the microscopic images segmentation of activated sludge

Abstract Accurate microscopic images segmentation of activated sludge is essential for monitoring wastewater treatment processes. However, it is a challenging task due to poor contrast, artifacts, morphological similarities, and distribution imbalance. A novel image segmentation model (FafFormer) was developed in the work based on Transformer that incorporated pyramid pooling and flow alignment fusion. Pyramid Pooling Module was used to extract multi-scale features of flocs and filamentous bacteria with different morphology in the encoder. Multi-scale features were fused by flow alignment fusion module in the decoder. The module used generated semantic flow as auxiliary information to restore boundary details and facilitate fine-grained upsampling. The Focal–Lovász Loss was designed to handle class imbalance for filamentous bacteria and flocs. Image-segmentation experiments were conducted on an activated sludge dataset from a municipal wastewater treatment plant. FafFormer showed relative superiority in accuracy and reliability, especially for filamentous bacteria compared to existing models.
Prediction of Apartment Price Considering Socio Economic and Crime Rates Factors in DKI Jakarta

Investing in real estate properties in Indonesia is highly lucrative due to their consistent appreciation in value. Amongst the various property types, apartments are particularly favored for investment in limited land space. However, determining the value of apartments is often subjective and lacks quantitative measures. To address this issue, this study develops prediction models to predict rental prices and asset value based on apartment specifications, socio-economic factors, and crime rates. Machine learning models employed include Random Forest, Decision Tree, and Gradient Boosting Machine. The findings show Gradient Boosting Machine exhibits the highest accuracy in predicting apartment rental and sale prices, achieving R² values of 0.9230 and 0.8460, respectively. The study also highlights the significant influence of socio-economic factors and crime rates on the performance of the models, contributing between 0.09 and 0.22 with an average of 0.14, as indicated by the improved R² values. This study demonstrate that these models can be valuable tools for real estate investors and professionals seeking quantitative measures to determine the value of apartments. By incorporating apartment specifications, socio-economic factors, and crime rates, the models can provide objective insights into the potential rental income and asset value of apartments.
Maze Solving Using Deep Q-Network

Path planning and obstacle avoidance are crucial for enabling the autonomy of mobile robots to operate in real-world environments. Conventional algorithms are known to be computationally expensive, and they require prior knowledge of the environment. In this paper, instead of using conventional algorithms, we present the usage of DQN (Deep Q-Network), a reinforcement learning algorithm, to solve the path planning problem. The goal was to observe and report the feasibility of using DQN as a path-planning algorithm for mobile robots in maze environments with walls leading to dead-ends. We have also showed the possibility of improvements to the algorithm, which can be used in more challenging maze environments.