Reviewerly/exHarmony huggingface dataset:
Improved Glass Composition Analysis and Identification of Cultural Heritage with Limited Data Using Data Augmentation and CatBoost

Glass artifacts play a significant role in cultural heritage, offering valuable insights into ancient craftsmanship and cultural exchange. However, accurately analyzing and identifying ancient glass objects presents challenges due to limited data. This study aims to enhance the analysis and identification of glass compositions in cultural heritage by employing data augmentation techniques and the CatBoost prediction model. Firstly, data augmentation techniques are applied to expand the limited dataset, increasing sample quantity and diversity to improve the model’s generalization capability. The TOPSIS method is employed to comprehensively evaluate different augmentation factors and select the most suitable ones. Subsequently, the CatBoost prediction model is utilized, and the model parameters are optimized using a random search method to further enhance predictive performance. Experimental research on ancient glass artifacts validates the effectiveness and feasibility of the proposed methods. The final model demonstrates high predictive performance and a good fit on the training set, cross-validation set, and test set. For example, when predicting the sodium oxide content before weathering in glass artifacts, the average R-squared(R2) reaches 0.998, and the Mean Squared Error(MSE) is 0.003. These results signify the accurate prediction of glass artifact compositions and the model’s stable predictive capabilities across different datasets. Utilizing the predicted chemical composition, the identification of glass artifacts achieves a classification accuracy of 100%, indicating the excellence of the model. In conclusion, this study presents an improved approach for analyzing and identifying glass compositions by overcoming the limitations posed by limited data through data augmentation and the CatBoost model. These advancements provide valuable tools and methods for preserving and researching cultural heritage, contributing to the progress of ancient civilization studies and technological development.
Monitoring the Workflow of Earthquake by Using Machine Learning

This study explores the innovative application of machine learning techniques in monitoring and analyzing the workflow associated with earthquakes. The primary objective is to enhance the accuracy and efficiency of earthquake detection, prediction, and response mechanisms the development of machine learning technology enables more robust real-time earthquake monitoring through automated implementations. However, the application of machine learning to earthquake location problems faces challenges in regions with limited available training data. This paper proposes a machine learning-based approach to monitor seismic workflows efficiently. We apply various Machine Learning algorithms, including deep learning neural networks and decision trees, to process and interpret vast amounts of seismic data. These algorithms were trained on historical earthquake records to identify patterns and anomalies that precede seismic events. Our approach not only focused on the detection of seismic activities but also on the prediction of potential aftershock locations and magnitudes. Earthquake monitoring and response systems play a crucial role in mitigating potential damages and ensuring rapid response to seismic events. Traditional methods rely on sensor networks and manual analysis, often leading to delays in detection and response. The system leverages real-time data from seismic sensors, incorporating advanced machine learning algorithms for data analysis and pattern recognition. By employing deep learning models, such as convolution neural networks (CNNs) and recurrent neural networks (RNNs), the system can detect and classify seismic activities accurately. Additionally, anomaly detection techniques enable the identification of unusual seismic patterns that might indicate impending earthquakes or aftershocks. The system integrates geographical information systems (GIS) to map the seismic activities spatially, aiding in the visualization and understanding of seismic patterns across regions. The machine learning models are trained on historical seismic data to enhance their predictive capabilities and adaptability to varying seismic behaviors. This proposed system offers a real-time monitoring solution that can assist in early earthquake detection, accurate event classification, and timely response coordination. Its ability to continuously learn from incoming data ensures adaptability to changing seismic behaviors, thereby improving overall efficiency and reliability in earthquake monitoring workflows.
Cloud Based Data Centre for Managing Logistics in the Disaster

Considering the many man-made and natural disasters that have occurred in recent years, such as the September 2001 earthquake and the tsunami in Japan, the importance of problem solving processes cannot be ignored. The total damage caused by the disaster in Japan alone is estimated at more than $300 billion. Transportation and communications play an important role in disaster response and management to minimize loss of life, economic costs and impacts. Our research includes the development of emergency response systems for disasters of all sizes, focused on transportation systems designed to use and communicate information and technology. In this article, we discuss the use of technology for disaster management using smart transportation, including mobile, mobile and cloud. The system is intelligent because it can collect information from many places and locations, including the situation and make decisions regarding the use of the communication process, making the vehicle more efficient. Hybrid vehicle communications based on vehicle-to-vehicle and vehicle-to-infrastructure protocols are being exploited opportunistically. The effectiveness of our system has been demonstrated by simulating the impact of disasters on the real urban environment and comparing the situation with our existing disaster management systems. We declare that the adoption of our plan has led to the improvement and stabilization of traffic and evacuation.
Earthquake Early Warning Systems by Using Cloud and IoT

Earthquake early warning systems play a crucial role in minimizing the impact of seismic events on human life and infrastructure. This research paper explores the integration of Cloud Computing and Internet of Things (IoT) technologies to enhance the effectiveness of earthquake early warning systems. The proposed system aims to provide real-time data collection, analysis, and dissemination of alerts through a distributed network, leveraging the scalability and flexibility of cloud-based platforms. This paper discusses the architecture, components, and benefits of the proposed system, along with a case study to illustrate its potential in earthquake-prone regions.
Chapter 9: Audiovisual sample 9.1 -9.4

&lt;p&gt;Audiovisual sample 9.1:&lt;/p&gt; &lt;p&gt;Recorded by Luting Hsia in Longgang Grand Sports Field during the Rice Noodles Festival, April 29, 2018. Used with permission by the Yunnanese Dage Association.&lt;/p&gt; &lt;p&gt; &lt;/p&gt; &lt;p&gt;Audiovisual sample 9.2: &lt;/p&gt; &lt;p&gt;Videotaped by Luting Hsia in Longgang Grand Sports Field during the Rice Noodles Festival, April 20, 2019. Used with permission by the Yunnanese Dage Association.&lt;/p&gt; &lt;p&gt;&lt;br&gt;&lt;/p&gt; &lt;p&gt;Audiovisual sample 9.3: &lt;/p&gt; &lt;p&gt;Videotaped by the author in Yunnanese Cultural Park during the Rice Noodles Festival, 14 April 2017. With thanks to the Yunnanese Dage Association.&lt;/p&gt; &lt;p&gt;&lt;br&gt;&lt;/p&gt; &lt;p&gt;Audiovisual sample 9.4:&lt;br&gt; Videotaped by Luting Hsia in Longgang Grand Sports Field during the Rice Noodles Festival, April 13, 2019. Thanks to the Yunnanese Dage Association.&lt;/p&gt;
FAST AND SMART 3D MODELLING: AN ALGORITHMIC TOOL BASED ON CHURCH TYPOLOGY

Abstract. Increasingly advanced technological development and the broad possibilities introduced by computer graphics and parametric-semantic modelling force us to reflect on the concept of smart models, especially in relation to the purposes for which the models themselves are created, directing research towards in-depth studies linked to the type of information that the model is intended to convey. This research shows the results obtained in the development and experimentation of a generative modelling algorithm dedicated to the rapid and semi-automatic modelling of churches. The work stems from the need to elaborate a useful tool to prepare, in a short time, an intelligent database - graphic and informative - rapidly visualisable, dedicated to the management of churches in post-earthquake emergency conditions.The main objective is to make efficient the processes of data management and visualisation based on the seismic damage assessment sheets [D.P.C.M. 23 February 2006 (G.U. 7.3.2006, no. 55)] through procedures capable of expanding the information patrimony and, at the same time, optimising documentation and intervention times, costs and resource management (Chevrier, et al., 2009). The procedure for realising the parametric model is based on the concept of shape grammar. It allows different types of churches to be generated from the modification of basic shapes prepared according to the concept of a macro-element. It makes it possible to generate different types of churches from the modification of basic shapes prepared according to the macro-element concept (Lanzara, et al., 2021). The algorithm was tested by applying it to several case studies to evaluate its effectiveness and future implementations.
Students' Thriving and Well-Being in Online Learning Environments in Vocational Education and Training

Context: Recent research on students' experiences with the quality of online learning during the COVID-19 pandemic has increased the challenge to the development of online learning. During the pandemic, anxiety, depression, and fatigue occurred in online studies, which also weakened students' well-being. In this quantitative study, we examined how students thrive in synchronous and asynchronous online implementations. The goal of the research was to support students' well-being in online studies; therefore, the study uses the PERMA well-being theory. Approach: Finnish vocational education and training students (N = 363) participated in the study and answered the questions on positive emotions, engagement, relationships, meaning, and accomplishment from both asynchronous and synchronous online delivery perspectives using a questionnaire. Findings: This study highlighted the importance of the teacher's online learning facilitation skills and task design skills, and the emotions generated in an online course. Based on the results, the respondents were divided into three student profiles: (1) Students thriving in online courses in general; (2) Students thriving, especially in asynchronous online courses; and (3) Students not thriving in online courses in general. Conclusions: According to the findings, students need different options for completing online courses. The role of the teacher in creating a positive and supportive atmosphere, fostering relationships, and creating clear work-related and versatile tasks is of great importance in the generation of emotions, whether it is an asynchronous or synchronous online implementation.
Denoising GNSS Velocities for Earthquake Ground Motions with Deep Learning

Distributed measurements of earthquake ground shaking are critical to understanding seismic processes and hazards. High rate, continuous GNSS reference stations are alternative sources of these ground motion observations that complement the dynamic range of traditional inertial-based seismic sensors. GNSS time differenced carrier phase velocity processing (TDCP) has shown to have increased sensitivity to coseismic dynamics when compared to precise point positioning (PPP) while also offering distinct computational advantages. Furthermore, data-driven machine learning (ML) models have proven beneficial for detection and phase picking of seismic waveforms embedded in high-dimensionality noise environments. In this study, we address this challenging detection problem by modifying existing domain-similar deep learning strategies to take advantage of an established labeled TDCP 5Hz earthquake catalog. We present the results of training a U-net convolutional network architecture used for image processing on denoising temporal windows from a catalog of synthetic 5Hz TDCP waveforms. Unlike traditional fixed bandwidth frequency filtering, the neural network learns a sparse representation mask of time-frequency domain features to separate complex, time-variant noise signatures from a range of signal inputs. These signal inputs from the existing catalog consist of over 2000 observed strong motion velocity waveforms, each augmented with 7 unique noise time series. We use time-frequency domain features from 30 second windows to train a mask on noise-free target waveforms. We report on results of testing denoising unseen synthetic noisy GNSS TDCP time series. We then validate this strategy against both real TDCP waveforms and co-located lower-noise inertial instrumentation. We find the trained neural network mask filters background noise across a range of time- and spatially-dependent GNSS noise environments. This deep-learned mask permits a spectrum of strong-motion seismic signal signatures to pass through for rapid event characterization and destructive ground motion now-casting. This denoising has the potential to expand the operational role of GNSS in seismic hazard monitoring by increasing signal-to-noise (SNR) of computationally-lightweight TDCP waveforms.
Public Library Websites in the Reaching Across Illinois Library System: A Webometric Examination

A public library’s website is often the first introduction that a user has to the library. Users come to the website looking for information about the library’s location, hours, how to get a library card, library programs, search the library catalog, or other services. As Poll writes, “Libraries have started to offer a new virtual ‘entrance’ to their services: the library website” (2007, p. 1). As such, a library’s website must provide users with the information they are seeking simply and clearly. People expect websites to be simple to use and to be able to find the information they are seeking quickly; simply put “if it’s convenient, they will use it; if not, they won’t” (Nielsen &amp; Loranger, 2006, Preface, xxi). Ideally, a public library’s website will fulfill these needs for their community. The library website should provide information to patrons in a way that the majority of its users will understand. Providing access to information is a key component of the mission of public libraries and the library website is often the first stop for patrons looking to access library resources. This study is a web analysis of Chicago area public libraries in RAILS (Reaching Across Illinois Library System) using checklists based on the work of Chow, Commander, and Bridges (2014), Powers (2011) and Vargas Ochoa (2020).
College Virtual Tour using Virtual Reality

Abstract: Virtual reality has become one of the highly interesting and popular fields. Also in recent times, the concept of the metaverse and its idea of a virtual world is being spread rapidly. As the technological connotation of the term VR has been changing and encompasses various VR systems with different capabilities, it has also paved a way for virtual tourism. The current pandemic situation made people around the world stay inside their houses, which has led to the growth of virtual tourism. Yet the usage of VR is not as wide as its reach. The main reason for VR’s less usage is its affordability, not everyone could afford a VR headset. The objective of this project is to create an immersive virtual tour of the college that can be used by even an individual without any proper gear. Virtual tours are a way for people to experience and explore a place that exists in reality as a physical space via the internet. The old techniques of the virtual tour are either of a pre-recorded video form or just some collections of 360-degree images. To improve the experience, we are creating a 3D virtual environment that the user can access without any restrictions. The user can download the file and can run the program on their desired platform
The Effects of Augmented Reality on Very Young Learners’ Motivation and Learning of the Alphabet and Vocabulary

This study aspires to contribute some initial results to the growing area of research regarding technology potential in the field of early foreign language literacy. An experiment was conducted to examine very young learners’ alphabet and vocabulary learning and retention in an early foreign language (FL) learning context when implementing augmented reality (AR) applications, while very young learners’ motivation was also assessed. A pilot intervention was implemented in a state school in northern Greece. The participants (n = 26) were primary school first-graders (5.5–6 years old) and were assigned into two groups, experimental (13) and control (13). To examine the effects of the intervention, this current study employed two instruments: (a) a pre-test–post-test model to assess young learners’ alphabet and vocabulary learning during three phases and (b) a questionnaire to assess their motivation during the learning process. The findings of this study reveal that both groups displayed significant improvements in FL alphabet and vocabulary learning; however, there are statistical differences in favor of the experimental group regarding long-term alphabet and vocabulary learning and retention. Furthermore, qualitative results regarding children’s perceptions of the technology used indicate that AR was highly appealing and motivating to participating students.
Orthogonal Neural Network: An Analytical Model for Deep Learning

In the current deep learning model, the computation between each feature and parameter is defined in the real number field. This, together with the nonlinearity of the deep learning model, makes it difficult to analyze the relationship between the values of the computational process and the original features from computation in the real number field. We extend the operational rules of the deep learning model in space and propose the orthogonal neural network (ONN) model, in which the features are set orthogonally to each other in space by “modulating” each input feature of the deep learning model to different orthogonal bases. Because the modulated numerical features are orthogonal to each other, they can be separated from the computations of the ONN model. By “demodulating” the model during and after the calculation, we can obtain a numerical relationship between the results and the original features, which can further provide theoretical and computational support for our analysis of the model. Finally, we compute the weights for each input feature as an interpretable deep learning approach, and describe how the model focuses attention on each feature based on the application of the orthogonal neural network model on two typical models: convolutional neural networks and graph neural networks.
Cyclic Homomorphic Encryption Aggregation (CHEA)—A Novel Approach to Data Aggregation in the Smart Grid

The transactive energy market is an emerging development in energy economics built on advanced metering infrastructure. Data generated in this context is often required for market operations, while also being privacy sensitive. This dual concern has necessitated the development of various methods of obfuscation in order to maintain privacy while still facilitating operations. While data aggregation is a common approach in this context, many of the existing aggregation methods rely on additional network components or lack flexibility. In this paper, we introduce Cyclic Homomorphic Encryption Aggregation (CHEA), a secure aggregation protocol that eliminates the need for additional network components or complicated key distribution schemes, while providing additional capabilities compared to similar protocols. We validate our scheme with formal security analysis as well as a software simulation of a transactive energy network running the scheme. Results indicate that CHEA performs well in comparison to similar works, with minimal communication overheads. Additionally, CHEA retains all standard security properties held by other aggregation schemes, while improving flexibility and reducing infrastructural requirements. Our scheme operates on similar assumptions as other works, but current smart metering hardware lags in terms of processing power, making the scheme infeasible on the current generation of hardware. However, these capabilities should quickly advance to an accommodating state. With this in mind, and given the results, we believe CHEA is a strong candidate for aggregating transactive energy data.
Analytical Listening and Aesthetic Experience in Music Criticism

In this article, I discuss the methodological and contextual aspects of writing music criticism, drawing cues from applied musicology and autoethnography. The challenge for any music critic is the question of the relationship between objective and subjective approaches. I analyze the relationship between analytical listening and aesthetic experience, using the examples of two music reviews of Ivo Pogorelić’s piano recitals that I wrote. The interpretations of this pianist are suitable for the analysis precisely because he is commonly seen as an unconventional, even controversial pianist, and his interpretations of romantic music are often regarded as examples of anti-academicism and even deconstruction of pianistic canons accumulated during the 20th century. Against that term, I will talk about liberation, which is perhaps a more suitable label for Pogorelić’s modernist approach to performance.
The Impact of ChatGPT on Tourists' Trust and Travel Planning Intention: International Researches and Current Situation in Vietnam

This research investigates the impact of ChatGPT on tourists' trust and travel planning intention, with a focus on international perspectives and the current situation in Vietnam. The study comprises three main sections: Introduction, where the significance of ChatGPT in tourism is highlighted; Literature Review, encompassing an exploration of ChatGPT, its application in tourism, and the influence of trust and intention in utilizing ChatGPT for travel planning; and Current Situation, providing insights into the existing scenario. By examining factors affecting trust and intention in ChatGPT-based travel planning, this research contributes valuable insights for the tourism industry. The study concludes with a synthesis of findings and potential implications for future research and industry practices.
Test Deployed

The CNTs also could enhance the resistance to photo degradation by UV irradiation during cyclical ageing tests