Reviewerly/exHarmony huggingface dataset:
The Use of Augmented Reality Technology in Preserving Cultural Heritage: A Case Study of Old Jami Mosque of Palopo

This research aims to explore the use of Augmented Reality (AR) technology in the context of cultural heritage preservation, with a particular focus on the Old Jami Mosque of Palopo. This research uses a qualitative case study approach to investigate the application of AR in preserving the mosque's cultural heritage. Data were collected through observation, interviews, documentary analysis, and system testing using the Unified Theory of Acceptance and Use of Technology (UTAUT) model. The findings from a questionnaire survey conducted with 50 respondents representing the general public, students, college students, traditional leaders, and tourists indicated a positive acceptance of the AR system, with an overall approval rating of 87% across various aspects. The results demonstrate that the developed AR application for the Old Jami Mosque Palopo enhances visitors' experience and understanding of the mosque's historical, architectural, and cultural values. The integration of AR technology has the potential to foster greater visitor engagement and contribute to the preservation of cultural heritage. This research provides insights into the potential and advantages of leveraging AR technology for cultural heritage preservation and provides an overview of the development of effective AR applications in cultural contexts.
Implementing Privacy-Preserving and Collaborative Industrial Artificial Intelligence

Despite the growing connectivity and availability of sensor data boosted by the Industry 4.0 paradigm, data scarcity remains one of the biggest challenges for the widespread adoption of industrial AI, particularly regarding failure or defect data required for automated quality inspection solutions.Data sharing among different stakeholders is difficult due to data privacy, ownership, and cybersecurity concerns.Traditional centralized Machine Learning is already an essential part of several sectors, including smart manufacturing; however, it may lead to several issues regarding security and performance over time.In response to these problems, federated learning can be seen as a potential catalyst and enabler for the adoption of collaborative industrial artificial intelligence.It is an innovative and decentralized approach to machine learning, focused on collaboration and data privacy, shifting the information flow from the data to the models.Although considerable research efforts have been put forth towards this topic, there are still many unresolved issues concerning the application of federated learning approaches in smart manufacturing scenarios, including the lack of a common holistic reference framework and implementation.In this light, the present work aims to provide the foundation for a common framework for collaborative Industrial AI in smart manufacturing, addressing not only the collaboration aspect but also the main challenges of data privacy, ownership, and security.Additionally, we present and make publicly available a curated dataset focused on a manufacturing quality inspection scenario to further promote research on this topic.
Exploring Humor as a Repair Strategy During Communication Breakdowns with Voice Assistants

Voice assistants are becoming increasingly useful and support realistic conversations, yet communication breakdowns occur. We investigate the use of humor as a repair strategy in an experiment where the voice assistant makes a mistake and then utilizes one of four humorous personalities to repair the breakdown in the conversation. We conducted a study with 30 participants, each of whom took the Humor Style Questionnaire (HSQ) to understand their predisposition to humor type, and then engaged in conversation with each of the four humorous personalities and one that was designed to give neutral repair responses (non-humorous). Aggressive personalities were rated as the funniest, yet there was no clear connection between the participant's humor style and their preferred voice assistant personality. While humorous responses were successful in repairing communication breakdowns, participants overall preferred non-humorous responses. This research provides insight into the role of humor in communication breakdown repair with voice assistants.
Reconstruction and completion of high-resolution 3D cardiac shapes using anisotropic CMRI segmentations and continuous implicit neural representations

Since the onset of computer-aided diagnosis in medical imaging, voxel-based segmentation has emerged as the primary methodology for automatic analysis of left ventricle (LV) function and morphology in cardiac magnetic resonance images (CMRI). In standard clinical practice, simultaneous multi-slice 2D cine short-axis MR imaging is performed under multiple breath-holds resulting in highly anisotropic 3D images. Furthermore, sparse-view CMRI often lacks whole heart coverage caused by large slice thickness and often suffers from inter-slice misalignment induced by respiratory motion. Therefore, these volumes only provide limited information about the true 3D cardiac anatomy which may hamper highly accurate assessment of functional and anatomical abnormalities. To address this, we propose a method that learns a continuous implicit function representing 3D LV shapes by training an auto-decoder. For training, high-resolution segmentations from cardiac CT angiography are used. The ability of our approach to reconstruct and complete high-resolution shapes from manually or automatically obtained sparse-view cardiac shape information is evaluated by using paired high- and low-resolution CMRI LV segmentations. The results show that the reconstructed LV shapes have an unconstrained subvoxel resolution and appear smooth and plausible in through-plane direction. Furthermore, Bland-Altman analysis reveals that reconstructed high-resolution ventricle volumes are closer to the corresponding reference volumes than reference low-resolution volumes with bias of [limits of agreement] -3.51 [-18.87, 11.85] mL, and 12.96 [-10.01, 35.92] mL respectively. Finally, the results demonstrate that the proposed approach allows recovering missing shape information and can indirectly correct for limited motion-induced artifacts.
Opportunities and Challenges for Official Statistics in Digital Bangladesh

Governments, businesses, and the public, in general, are expected to receive information from national systems of official statistics regarding the economic, demographic, social, and environmental situation. Therefore, digitizing data collection for government statistics could have a significant social impact. Slowly but surely, government administrative data is being used more. There is scant evidence, but significant data access through satellites, point-of-sale systems, and social networks is now being researched and tested. New needs for official statistics and pressures on traditional data collection from households and companies drove these projects. New requirements for official statistics have brought about these requirements and demands. The efficient use of new data sources by the government can lay the most important evidential foundation for policies that are intended to enhance people's lives, and data that has been anonymized is also being made available to other researchers. The study aims to explore the opportunities and challenges for official statistics in digital Bangladesh and identify the potential benefits and drawbacks of using digital technologies and data sources for official statistics. By understanding these opportunities and challenges, policymakers, statisticians, and other stakeholders can develop strategies and solutions that address the challenges and leverage the opportunities to improve official statistics' quality, timeliness, and relevance. Additionally, the objective is to highlight the importance of investing in capacity building, data governance frameworks, and technology and infrastructure to promote the development of official statistics in digital Bangladesh.
The Relationship Between the Implementation Levels of Industry 4.0 Technologies and Advanced Manufacturing Technologies - PROOF READING

Industry 4.0 is expected to provide high quality and customized products at lower costs by increasing efficiency, and hence create a competitive advantage in the manufacturing industry.As the emergence of Industry 4.0 is deeply rooted in the past industrial revolutions, Advanced Manufacturing Technologies of Industry 3.0 are the precursors of the latest Industry 4.0 technologies.This study aims to contribute to the understanding of technological evolution of manufacturing industry based on the relationship between the usage levels of Advanced Manufacturing Technologies and Industry 4.0 technologies.To this end, a survey was conducted with Turkish manufacturers to assess and compare their manufacturing technology usage levels.The survey data collected from 424 companies was analyzed by machine learning approach.The results of the study reveal that the implementation level of each Industry 4.0 technology is positively associated with the implementation level of a set of Advanced Manufacturing Technologies.
SentimentGPT: Exploiting GPT for Advanced Sentiment Analysis and its Departure from Current Machine Learning

This study presents a thorough examination of various Generative Pretrained Transformer (GPT) methodologies in sentiment analysis, specifically in the context of Task 4 on the SemEval 2017 dataset. Three primary strategies are employed: 1) prompt engineering using the advanced GPT-3.5 Turbo, 2) fine-tuning GPT models, and 3) an inventive approach to embedding classification. The research yields detailed comparative insights among these strategies and individual GPT models, revealing their unique strengths and potential limitations. Additionally, the study compares these GPT-based methodologies with other current, high-performing models previously used with the same dataset. The results illustrate the significant superiority of the GPT approaches in terms of predictive performance, more than 22\% in F1-score compared to the state-of-the-art. Further, the paper sheds light on common challenges in sentiment analysis tasks, such as understanding context and detecting sarcasm. It underscores the enhanced capabilities of the GPT models to effectively handle these complexities. Taken together, these findings highlight the promising potential of GPT models in sentiment analysis, setting the stage for future research in this field. The code can be found at https://github.com/DSAatUSU/SentimentGPT
Recovering lost artworks by deep neural networks: Motivations, methodology, and proof-of-concept simulations

We discuss the problem of computationally generating images resembling those of lost cultural patrimony, specifically two-dimensional artworks such as paintings and drawings.We view the problem as one of computing an estimate of the image in the lost work that best conforms to surviving information in a variety of forms: works by the source artist, including preparatory works such as cartoons for the target work; copies of the target by other artists; other works by these artists that reveal aspects of their style; historical knowledge of art methods and materials; stylistic conventions of the relevant era; textual descriptions of the lost work and images associated with the target's title.Some of the general information linking images and text can be learned from large corpora of natural photographs and accompanying text scraped from the web.We present some preliminary proof-of-concept simulations for recovering lost artworks with a special focus on textual information about target artworks.We outline our future directions, such as methods for assessing the contributions of different forms of information in the overall task of recovering lost artworks.
Adaptive surface code for quantum error correction in the presence of temporary or permanent defects

Whether it is at the fabrication stage or during the course of the quantum computation, e.g. because of high-energy events like cosmic rays, the qubits constituting an error correcting code may be rendered inoperable. Such defects may correspond to individual qubits or to clusters and could potentially disrupt the code sufficiently to generate logical errors. In this paper, we explore a novel adaptive approach for surface code quantum error correction on a defective lattice. We show that combining an appropriate defect detection algorithm and a quarantine of the identified zone allows one to preserve the advantage of quantum error correction at finite code sizes, at the cost of a qubit overhead that scales with the size of the defect. Our numerics indicate that the code's threshold need not be significantly affected; for example, for a certain scenario where small defects repeatedly arise in each logical qubit, the noise threshold is $2.7\%$ (versus the defect-free case of $2.9\%$). These results pave the way to the experimental implementation of large-scale quantum computers where defects will be inevitable.
Prediction of Stem Cell State Using Cell Image‐Based Deep Learning

AI-Based Cell Morphology Recognition In article number 2300017, Sunghoi Hong and colleagues present that AI accurately detects the cells that are difficult to distinguish with the naked eye based on their shape. This technology classifies the subtle changes of the cell shape caused by different cell culture conditions, as well as genetic defects using only the cell images. It is expected to establish a stable and reproducible cell culture strategy.
EvolveNet: Evolving Networks by Learning Scale of Depth and Width

Convolutional Neural Networks (CNNs) are largely hand-crafted, which leads to inefficiency in the constructed network. Various other algorithms have been proposed to address this issue, but the inefficiencies resulting from human intervention have not been addressed. Our proposed EvolveNet algorithm is a task-agnostic evolutionary search algorithm that can find optimal depth and width scales automatically in an efficient way. The optimal configurations are not found using grid search, instead evolved from an existing network. This eliminates inefficiencies that emanate from hand-crafting, thus reducing the drop in accuracy. The proposed algorithm is a framework to search through a large search space of subnetworks until a suitable configuration is found. Extensive experiments on the ImageNet dataset demonstrate the superiority of the proposed method by outperforming the state-of-the-art methods.
Navigating the Virtual Frontier: The Convergence of Decentralized Finance and the Metaverse

This study delves into the integration of Decentralized Finance (DeFi) within metaverses, probing its implications and potential. It elucidates DeFi protocols and the intricacies of metaverses before inspecting their intersection and the resulting economic prospects. The analysis further appraises inherent risks such as financial volatility, security, and regulatory issues. Case studies provide tangible insights into DeFi applications in metaverses. Future trajectories of DeFi in metaverses are projected, underlining the possible impact on the broader financial sector. This paper contributes to burgeoning research at the nexus of blockchain technology, finance, and virtual reality.
Efficient Augmentation for Imbalanced Deep Learning

Deep learning models may not effectively generalize across under-represented or minority classes. We empirically study a convolutional neural network’s (CNN) internal representation of imbalanced image data and measure the generalization gap between a model’s feature embeddings in the training and test sets, showing that the gap is wider for minority classes. This insight enables us to design an efficient three-phase CNN training framework for imbalanced data. The framework involves training the network end-to-end on imbalanced data to learn feature embeddings, performing data augmentation in the learned embedding space to balance the training data distribution, and fine-tuning the classifier head on the embedded balanced training data. We develop Expansive Over-Sampling (EOS) as a data augmentation technique to utilize in the training framework. EOS forms synthetic training instances as convex combinations between the minority class samples and their nearest adversaries in the embedding space to reduce the generalization gap. The proposed framework improves the accuracy over leading cost-sensitive and resampling methods commonly used in imbalanced learning. Moreover, it is more computationally efficient than standard data pre-processing methods, such as SMOTE and GAN-based over-sampling, as it requires fewer parameters and less training time. The source code for the proposed framework is available at: https://github.com/dd1github/EOS.
Large Language Models Understand and Can be Enhanced by Emotional Stimuli

Emotional intelligence significantly impacts our daily behaviors and interactions. Although Large Language Models (LLMs) are increasingly viewed as a stride toward artificial general intelligence, exhibiting impressive performance in numerous tasks, it is still uncertain if LLMs can genuinely grasp psychological emotional stimuli. Understanding and responding to emotional cues gives humans a distinct advantage in problem-solving. In this paper, we take the first step towards exploring the ability of LLMs to understand emotional stimuli. To this end, we first conduct automatic experiments on 45 tasks using various LLMs, including Flan-T5-Large, Vicuna, Llama 2, BLOOM, ChatGPT, and GPT-4. Our tasks span deterministic and generative applications that represent comprehensive evaluation scenarios. Our automatic experiments show that LLMs have a grasp of emotional intelligence, and their performance can be improved with emotional prompts (which we call "EmotionPrompt" that combines the original prompt with emotional stimuli), e.g., 8.00% relative performance improvement in Instruction Induction and 115% in BIG-Bench. In addition to those deterministic tasks that can be automatically evaluated using existing metrics, we conducted a human study with 106 participants to assess the quality of generative tasks using both vanilla and emotional prompts. Our human study results demonstrate that EmotionPrompt significantly boosts the performance of generative tasks (10.9% average improvement in terms of performance, truthfulness, and responsibility metrics). We provide an in-depth discussion regarding why EmotionPrompt works for LLMs and the factors that may influence its performance. We posit that EmotionPrompt heralds a novel avenue for exploring interdisciplinary knowledge for human-LLMs interaction.
Archangel: A Hybrid UAV-Based Human Detection Benchmark With Position and Pose Metadata

Learning to detect objects, such as humans, in imagery captured by an unmanned aerial vehicle (UAV) usually suffers from tremendous variations caused by the UAV's position towards the objects.In addition, existing UAV-based benchmark datasets do not provide adequate dataset metadata, which is essential for precise model diagnosis and learning features invariant to those variations.In this paper, we introduce Archangel, the first UAV-based object detection dataset composed of real and synthetic subsets captured with similar imagining conditions and UAV position and object pose metadata.A series of experiments are carefully designed with a state-of-the-art object detector to demonstrate the benefits of leveraging the metadata during model evaluation.Moreover, several crucial insights involving both real and synthetic data during model optimization are presented.In the end, we discuss the advantages, limitations, and future directions regarding Archangel to highlight its distinct value for the broader machine learning community.
Implementing quality function deployment using service quality and Kano model to the quality of e-learning

&lt;span lang="EN-US"&gt;E-learning has become an important aspect of university education. System evaluation must be carried out by higher education management to identify the number of e-learning successes and future improvement efforts. Three research methodologies were used in this study to measure, classify, and translate service quality into an improvement process, namely SERVQUAL, Kano model, and quality functional deployment (QFD). A survey involving 219 UPN “Veteran” Jakarta students as e-learning users were done to examine the developed methodology. The partial least square (PLS) method was used to process the data. This study employed the quantitative method. Website content, assurance, empathy, responsiveness, tangibles, reliability, and learning content are variables in this approach. This study provided knowledge on identifying and analyzing the quality of e-learning services using the service quality (SERVQUAL) method by calculating the score gap between the perceived service quality assessment (performance) and the expected service quality using website content, assurance, empathy research variables, responsiveness, tangibles, reliability, learning content, e-learning quality, satisfaction, and behavioral intention. This study also evaluates what improvements can be made to improve the quality of e-learning services.&lt;/span&gt;
A novel lorenz-sine coupling chaotic system and its application on color image encryption

Abstract The chaos-based image encryption has been extensively studied for image security owing to its high efficiency. However, both the complexity of the chaotic system and the chaos-based image encryption method must be enhanced. A Lorenz-Sine coupling chaotic system with complex behavior and a novel image encryption scheme is presented in this work. By adding a nonlinear controller containing sinusoidal function to the Lorenz system, we obtain smooth chaotic attractors with a unique appearance. Analysis of the dynamical properties shows that it has a multi-symmetry stranger attractor and a large chaotic range. Based on the proposed chaotic system, a pseudorandom sequence generator is designed to generate keys with high randomness. Furthermore, an image encryption scheme using spiral-rotation and random permutation is presented. Cipher-feedback is employed to increase the avalanche effect. The encryption efficiency and the security analysis are simulated. The result shows that the correlation coefficient between the adjacent pixels of the proposed scheme achieves <?CDATA ${10}^{-4}.$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msup> <mml:mrow> <mml:mn>10</mml:mn> </mml:mrow> <mml:mrow> <mml:mo>−</mml:mo> <mml:mn>4</mml:mn> </mml:mrow> </mml:msup> <mml:mo>.</mml:mo> </mml:math> The NPCR of the proposed scheme is larger than 0.9661 and the UACI falls within the theoretical value of UACI with a significance level of 0.05. Thus, the proposed scheme is more capable against differential attacks. Furthermore, it is more robust against noise attacks and cropping attacks.