Reviewerly/exHarmony huggingface dataset:
A Comparison of Uncertainty Quantification of Deep Learning models for Time Series

With the advancement of artificial intelligence, machine learning, and deep learning, their applications in various industries, particularly finance, have increased. However, interpreting predictions from deep learning models poses challenges, especially in finance where result interpretation is important. This study aims to determine the uncertainty of stable deep learning models, despite changes in the model like dropout, to establish standards for reliable models and identify those detecting anomal data through model uncertainty. In the experiment, the traditional statistical model ARIMA and deep learning models mainly used for time series analysis, CNN, LSTM, MLP, and CNN-LSTM. Uncertainty was measured from a Bayesian perspective using MC Dropout. The experimental results confirmed that deep learning models performed better than statistical models for various patterns of time series data. It was observed that, even if the performance was not the best, LSTM based models exhibited low uncertainty, indicating stability. Consequently, this study highlights the importance of considering uncertainty along with accuracy in model selection. Moreover, it was confirmed that models with higher uncertainty are suitable for anomaly detection, making CNN based models particularly fitting for this purpose.
Anomaly Trajectory Detection Model Using LSTM Auto Encoder

With the rise in aviation demand and the emergence of Urban Air Mobility, developing a safe aviation system in urban areas is becoming increasingly important. This study addresses the challenge of detecting anomalous flight trajectories, which can be influenced by environmental factors. We propose a novel Long Short-Term Memory-Auto Encoder (LSTM-AE) model that processes both environmental and trajectory data but only reconstructs trajectory data in its output. This approach was validated by assessing the average reconstruction error for specific trajectories. Additionally, the model's ability to identify anomalies was confirmed by evaluating the Area under the ROC curve (AUC) for typical anomalous trajectories, such as go-around maneuvers. Our findings indicate that the proposed LSTM-AE model effectively learns trajectory patterns in relation to environmental variables and shows enhanced anomaly detection capabilities compared to traditional AE and LSTM-AE models. These results contribute to the development of advanced models that incorporate a wider range of environmental factors, enhancing safety in urban air travel.
Legislative review of the use and regulation of generative artificial intelligence ChatGPT: Focusing on copyright law and personal information protection legislation

With the advent of ChatGPT, the boundary between humans and machines has disappeared, making it increasingly difficult for people to discern whether the person they are dealing with is a machine or a human. Nevertheless, ChatGPT is bringing about changes in the way we work, communicate, and play in the digital world by bringing about a shift in information search and access methods and creating products such as books and art.&#x0D; ChatGPT optimizes conversations using Reinforcement Learning from Human Feedback (RLHF). Reinforcement Learning is a type of machine learning technique that learns how to perform a task through repeated trial and error interactions. A person scores ChatGPT's responses and reflects them in the reinforcement learning rewards algorithm to learn ChatGPT's reward model. It is learned that the more a human user judges a given query to be a good response, the greater the reward, and the more harmful text is generated, the less reward it receives. It is thanks to this RLHF technology that when talking with ChatGPT, you feel like you are talking with a logical and fluent person rather than a machine.&#x0D; ChatGPT will enable the commercialization of a generative AI service that enables interactive conversation rather than focusing on one-sided commands, and since it is a very large-scale model, versatility is its core competitiveness. In other words, ChatGPT allows you to exchange conversations and finds or converts results such as writing, composing, coding, or drawing with a single text input. As ChatGPT significantly lowers the threshold of time and limitations associated with content production, it could gain attention in a variety of fields including advertising, publishing, and gaming. Additionally, ChatGPT has great potential for growth in the field of education as it can unravel and explain complex concepts and correct errors through conversation.&#x0D; This study reviewed the technological development process and general-purpose use of generative artificial intelligence (ChatGPT) and the legal regulations of ChatGPT, focusing on the legal issues surrounding the emergence of generative artificial intelligence (ChatGPT) in the AI era.
Elastic moduli prediction of a short fiber composite with interface debonding at fiber ends

This work deals with the fiber ends debonding-induced elastic moduli degradation of a short fiber reinforced composite (SFRC). The strain fields of the matrix in a representative volume element (RVE) of the SFRC are determined from an imaginary fiber technique. By using the debonding boundary conditions, the debonded modulus of the composite is derived, which is affected by not only the far-field but also the transient solutions for the longitudinal strain of the matrix. Particularly, the imaginary fiber technique provides unbounded solution for a moderately large fiber aspect ratio. This is resolved by separating the RVE along the fiber direction into two domains. Combining the longitudinal strains in both domains, the longitudinal modulus is determined, and the longitudinal bridging tensor element in the micromechanics Bridging Model is amended. Five elastic moduli of an SFRC after the fiber end debonding are obtained on the amended Bridging Model. The effects of the fiber volume fraction, fiber aspect ratio, and fiber-to-matrix modulus ratio on the end-debonded longitudinal modulus, transverse modulus, and longitudinal Poisson’s ratio are investigated.
Variational quantum algorithm for experimental photonic multiparameter estimation

Abstract Variational quantum metrology represents a powerful tool to optimize estimation strategies, resulting particularly beneficial for multiparameter estimation problems that often suffer from limitations due to the curse of dimensionality and computational complexity. To overcome these challenges, we develop a variational approach able to efficiently optimize a quantum multiphase sensor. Leveraging the reconfigurability of an integrated photonic device, we implement a hybrid quantum-classical feedback loop able to enhance the estimation performances. The quantum circuit evaluations are used to compute the system partial derivatives by applying the parameter-shift rule, and thus reconstruct experimentally the Fisher information matrix. This in turn is adopted as the cost function of a classical learning algorithm run to optimize the measurement settings. Our experimental results showcase significant improvements in estimation accuracy and noise robustness, highlighting the potential of variational techniques for practical applications in quantum sensing and more generally in quantum information processing using photonic circuits.
Real-time displacement monitoring using camera video records with camera motion correction

Structure from Motion (SfM) method can reconstruct the story drift ratio, but the camera motion produced by earthquakes reduces the measuring accuracy. This paper improves the applicability of the traditional method by correcting camera motion according to the identification of translation and rotation of camera. Experiments at different levels are designed to prove the proposed method. First, one set of experiments proves the motion correction method by the Single-degree-of-freedom (SDOF) system. The error of maximum response is 4.5% for the case with less rotation. As for the camera motion with larger rotation, the average error increases to 7.9%, which still meets the practical utilization. After that, the accuracy of using SfM method is confirmed by the Multi-degree-of-freedom (MDOF) system with the average error of 4.9%. This paper is expected to extend approaches for the application of the SfM method in the case of huge earthquakes.
Image captioning: Semantic selection unit with stacked residual attention

Semantic information and attention mechanism play important roles in the task of image captioning. Semantic information can strengthen the relationship between images and languages, while attention operation can steer the relevant regions spatially in the image. However, in most current works, semantic attributes are always confined to be learned from pairs of images and sentences, which ignore to fully utilize more semantic attributes and the structure information of sentences, thus limit the variety of sentences to be generated. Meanwhile, current attention models usually lack the ability to learn the positional information in an explicit way during attention generation, and have the problem of vanishing gradient in the training process. This paper proposes a Semantic Selection Unit (SSU) and a Stacked Residual Attention (SRA) to remedy these drawbacks. Specifically, the SSU is designed to capture selectively semantic information from expanding attributes or guidance sentences. With the help of expanding vocabulary and the structure information in sentences, the SSU can improve the quality of the generated sentences. The SRA is constructed to solve the problem of positional information missing and vanishing gradient problem during attention generation. Architecturally, the SSU and SRA work together in a jointed framework with end-to-end learning for image captioning. Extensive experiments have been conducted on the public dataset of the MS COCO, achieving 139.7 CIDEr score on the test set.
MENGISI WAKTU LUANG DENGAN MENUMBUHKAN DAN MENINGKATKAN BUDAYA LITERASI MELALUI SI PITUNG DI DESA SIRNAGALIH KECAMATAN CILAKU KABUPATEN CIANJUR

This activity was motivated by the low reading interest of children in Sirnagalih Village, Cilaku District, Cianjur Regency. Apart from that, children spend most of their free time playing gadgets. Gadget addiction will have an impact on children's cognitive, motoric and social development. Thus, one of the KKN work programs is by holding an integrated and superior science literacy training center or abbreviated as "Si Pitung". The purpose of carrying out this activity is to fill free time while at the same time driving children's literacy using the Fun Learning method in Sirnagalih village. The results of this activity affect the community's awareness of the importance of literacy, strengthen ties among the community and form a cohesive community so as to facilitate village community access to provide information related to Sirnagalih village.
The Effects of Learner Characteristics on Learning Flow and Learning Satisfaction in the Flipped Learning of Chinese Beauty Major College Students

The advancement of information and communication technology, a central element in the Fourth Industrial Revolution, is influencing the way people think and act with the introduction of artificial intelligence, augmented reality, and the Internet of Things. In education, there's a notable shift from a teacher-centric approach to a learner-centric environment, emphasizing the flipped learning teaching method that facilitates self-directed learning. This study examined how learner characteristics influence learning flow (classroom class and online class) and learning satisfaction in order to establish a foundation for the effective implementation of beauty education in a flipped learning environment. An empirical study was conducted from July 10 to August 10, 2023, involving 166 surveys administered to beauty-related students in China. The data were analyzed using SPSS 25.0, employing techniques such as frequency analysis, factor analysis, reliability analysis, descriptive statistics, correlation analysis, and multiple regression analysis. The research findings indicated high reliability scores for learner characteristics (3.98), learning flow (3.75), and learning satisfaction (3.77) within the flipped learning environment. Correlation analyses between learner characteristics, learning flow, and learning satisfaction revealed statistically significant correlation, especially in terms of the correlation between learning attachment of learner characteristics and learning self-efficacy in learning satisfaction. In particular, a higher level of learning attachment in learner characteristics was associated with a statistically significant positive effect on learning satisfaction, whether in online or classroom learning contexts. The research findings suggest that flipped learning classes can improve students' satisfaction by boosting their learning attachment, leading to increased learning self-efficacy. This study highlights the importance of a flipped learning instructional approach when designing courses to enhance learning flow and attachment, addressing academic self-regulation and self-determination motivation.
Effects of Classroom Atmosphere on the Quality of Learning (QoL) for Students Learning

Learning is the most important issue in teaching and regarding class atmosphere is one of the main issues that has special place in partial quality of learning. Teacher creates attractive class by applying proper management skills that makes positive and active relationships between him/her and his pupils. This study was performed to investigate the relationship of Atmosphere on the Quality of Learning (QoL). This study was done in Guilan University of Guilan that about 0.5 of students were selected through cluster sampling method with regard to the size of population (7200 students) and through systematic random sampling. 360 persons were selected and 357 questionnaires were filled and returned among 360 distributed questionnaires in various disciplines. Also 35 academic staffs (about 0.1) were participated in this research for conducting interview. Instruments for collecting the information of questionnaire have been researcher-made and interview. Interview with dear faculty members was about the impact of mental state of on quality of learning. Descriptive- analytic method has been used in this research. In all cases null hypotheses rejected and research hypotheses were confirmed. Also in analysis of interview with academic staffs that questions were related to research main hypotheses, much emphasis has been put on the effect of factors influencing on class atmosphere and quality of teaching and learning. It should be stated that there is a positive and direct relation- ship between class atmosphere and its factors (order and organization in class, clear- ness of rules in class academic staff control over the class, academic staffs and student mutual respect) and quality of learning. Clearness of rules of class results in relaxation in professor and student and in fact helps both of them to move toward goals of class and qualitative improvement of learning.
Perceptual Quality-Oriented Rate Allocation via Distillation from End-to-End Image Compression

Mainstream image/video coding standards, exemplified by the state-of-the-art H.266/VVC, AVS3, and AV1, follow the block-based hybrid coding framework. Due to the block-based framework, encoders designed for these standards are easily optimized for peak signal-to-noise ratio (PSNR) but have difficulties optimizing for the metrics more aligned to perceptual quality, e.g. multi-scale structural similarity (MS-SSIM), since these metrics cannot be accurately evaluated at the small block level. We address this problem by leveraging inspiration from the end-to-end image compression built on deep networks, which is easily optimized through network training for any metric as long as the metric is differentiable. We compared the trained models using the same network structure but different metrics and observed that the models allocate rates in different ratios. We then propose a distillation method to obtain the rate allocation rule from end-to-end image compression models with different metrics and to utilize such a rule in the block-based encoders. We implement the proposed method on the VVC reference software – VTM and the AVS3 reference software – HPM, focusing on intraframe coding. Experimental results show that the proposed method on top of VTM achieves more than 10% BD-rate reduction than the anchor when evaluated with MS-SSIM or LPIPS, which leads to concrete perceptual quality improvement.
A Blockchain-Based Fairness Guarantee Approach for Privacy-Preserving Collaborative Training in Computing Force Network

The advent of the big data era has brought unprecedented data demands. The integration of computing resources with network resources in the computing force network enables the possibility of distributed collaborative training. However, unencrypted collaborative training is vulnerable to threats such as gradient inversion attacks and model theft. To address this issue, the data in collaborative training are usually protected by cryptographic methods. However, the semantic meaninglessness of encrypted data makes it difficult to prevent potential data poisoning attacks and free-riding attacks. In this paper, we propose a fairness guarantee approach for privacy-preserving collaborative training, employing blockchain technology to enable participants to share data and exclude potential violators from normal users. We utilize a cryptography-based secure aggregation method to prevent data leakage during blockchain transactions, and employ a contribution evaluation method for encrypted data to prevent data poisoning and free-riding attacks. Additionally, utilizing Shamir’s secret sharing for secret key negotiation within the group, the negotiated key is directly introduced as noise into the model, ensuring the encryption process is computationally lightweight. Decryption is efficiently achieved through the aggregation of encrypted models within the group, without incurring additional computational costs, thereby enhancing the computational efficiency of the encryption and decryption processes. Finally, the experimental results demonstrate the effectiveness and efficiency of our proposed approach.
Kinemon: An inductively shunted transmon artificial atom

We experimentally investigate inductively shunted transmon-type artificial atoms as an alternative to address the challenges of low anharmonicity and the need for strong charge dispersion in superconducting quantum systems. We characterize several devices with varying geometries and parameters (Josephson energies and capacitances), and find a good agreement with calculations. Our approach allows us to retain the benefits of transmon qubit engineering and fabrication technology and high coherence, while potentially increasing anharmonicity. The approach offers an alternative platform for the development of scalable multiqubit systems in quantum computing.
Perceived Body Image, Anti-fat Attitudes and Healthy Lifestyle Practices among Nursing Students towards Obesity Prevention

Obesity rates will continue to grow worldwide for the foreseeable future. Obesity is preventable. To improve the notion of health, everyone in society must practice a healthy lifestyle to prevent obesity and overweight. Recently, Malaysians who practice healthy lifestyles have decreased and the rate of overweight or obesity is increasing. Nurses and nursing students have a significant role and should become role models with good body image in educating and promoting healthy lifestyles to the public. However, a lot of studies show that nurses and nursing students fail to engage in healthy lifestyles. Therefore, the main objective of this study is to determine perceived body image, anti-fat attitudes and healthy lifestyle practices among nursing students at private healthcare university colleges in Malaysia. Methods: Data was collected using valid and reliable questionnaires to determine Perceived Body image, Anti-fat Attitude and Healthy Lifestyle behaviour. Dependent variables were correlated with independent variables. The results of this study showed that nursing students are satisfied with their body image (61.2%), most nursing students engage in unhealthy lifestyles (48.6%) and the majority of nursing students have strong anti-fat attitudes (58.6%). There is no significant relationship between perceived body image, anti-fat attitude and healthy lifestyles. Conclusions: This result could conclude that it confirms that there is no direct link between Perceived body image, anti-fat attitudes, and Healthy lifestyle behaviours, as suggested in the Theory of Planned Behaviour (TPB). There are intermediary factors such as health motivation and perceived control as determining factors to contribute to behaviour prediction. This evidence can help nursing educators improve motivation factors in the nursing curriculum to encourage nursing students to practice healthy lifestyles.
Study of Voice Generation Method Suitable for Characters based on Human Cognitive Characteristics

Previous studies have attempted to estimate existing voices from images of animated characters as a way to generate voices suitable for animated characters, but without good results. Therefore, in this study, to link the voice characteristics to match the animation character with the image, we devised a method to analyze the voice’s tendency to not be uncomfortable and then establish the ratio of voice learning data based on the analyzed tendency data. Specifically, this study prepares multiple voices for one illustration of an anime character, asks subjects to evalu- ate the voices, and calculates an evaluation based on the evaluation values. In experiments, we conducted an evaluation experiment using the one-pair comparison method, calculated the distri- bution of learning data based on the evaluation values obtained, and prepared for the subsequent learning process.
PENERAPAN MODEL PROBLEM BASED LEARNING (PBL) BERBANTUAN LKPD QR-CODE UNTUK MENINGKATKAN HASIL BELAJAR SISWA MATA PELAJARAN IPAS KELAS V SD NEGERI 1 LALANGLINGGAH

This research is an implementation of the use of the Problem Based Learning (PBL) Learning Model assisted by LKPD QR Code as a form of innovation in the learning and teaching process. The aim of this research is to determine the improvement in student learning outcomes in science subjects using the PBL learning model assisted by LKPD QR Code. This research was carried out for 1 month starting from 26 July to 25 August 2023, on 19 class V students of SD Negeri 1 Lalalinggah. This research procedure was carried out in 2 cycles with research methods using quantitative methods. In the initial research, it was found that 13 students out of 19 students scored less than 65. However, in subsequent learning activities through the PBL learning model assisted by LKPD QR Code, student learning outcomes could be improved. This can be seen from the average score before treatment which was 57.63. Meanwhile in cycle I the average score of students increased to 62.00 and in cycle II the average score of students' abilities increased to 72.55.