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

Authors:
raw_author_name: Ziyang Wang
raw_affiliation_string: School of Computer Science and Technology, Jilin University, Changchun, China

Abstract: 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

Authors:
raw_author_name: Kumar Aman

Abstract: 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

Authors:
raw_author_name: Abhishek Yadav

Abstract: 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

Authors:
raw_author_name: Monu Kumar

Abstract: 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.
FAST AND SMART 3D MODELLING: AN ALGORITHMIC TOOL BASED ON CHURCH TYPOLOGY

Authors:
raw_author_name: S. Scandurra
raw_affiliation_string: Department of Architecture, University Federico II, Naples, Italy
raw_author_name: D. Palomba
raw_affiliation_string: Department of Architecture, University Federico II, Naples, Italy

Abstract: 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.
Denoising GNSS Velocities for Earthquake Ground Motions with Deep Learning

Authors:
raw_author_name: Tim Dittmann
raw_author_name: Jade Morton
raw_author_name: David Mencin

Abstract: 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.
Orthogonal Neural Network: An Analytical Model for Deep Learning

Authors:
raw_author_name: Yonghao Pan
raw_affiliation_string: Information Technology Institute, Information Engineering University, Zhengzhou 450001, China
raw_author_name: Hongtao Yu
raw_affiliation_string: Information Technology Institute, Information Engineering University, Zhengzhou 450001, China
raw_author_name: Shaomei Li
raw_affiliation_string: Information Technology Institute, Information Engineering University, Zhengzhou 450001, China
raw_author_name: Ruiyang Huang
raw_affiliation_string: Information Technology Institute, Information Engineering University, Zhengzhou 450001, China

Abstract: 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

Authors:
raw_author_name: Daniel Amyot
raw_affiliation_string: School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON K1N 6N5, Canada
raw_author_name: Masoud Bashari
raw_affiliation_string: IEMS Solution Ltd., Communitech, Kitchener, ON N2G 1H6, Canada
raw_author_name: John Mylopoulos
raw_affiliation_string: School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON K1N 6N5, Canada

Abstract: 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.
Resilient Strategies for Internet-Based Education: Investigating Engineering Students in the Canary Islands in the Aftermath of COVID-19

Authors:
raw_author_name: Carlos Boente
raw_affiliation_string: Laboratorio de Estratigrafía Biomolecular, ETSI Minas y Energía, Universidad Politécnica de Madrid, 28003 Madrid, Spain
raw_author_name: Enrique Rosales-Asensio
raw_affiliation_string: Departamento de Ingeniería Eléctrica, Universidad de Las Palmas de Gran Canaria, Tarifa s/n, 35017 Las Palmas de Gran Canaria, Spain

Abstract: The COVID-19 pandemic has brought about notable changes in the education sector, specifically the shift towards online learning. This study examined the experiences of 124 engineering students in the Canary Islands, an EU ultra-peripheral region, as they adapted to online education during the pandemic. A comprehensive survey assessed students' experiences in five key dimensions, including satisfaction with traditional face-to-face learning, perceptions of the engineering department's transition to online learning, module-specific adaptations, personal adaptation strategies, and the adaptation of teaching staff. The study's methodology involved statistical analyses using Microsoft Excel v16.0 and SPSS 27 tools to identify patterns and draw conclusions. The findings indicate a nuanced landscape. Students demonstrated strong technological literacy and readiness for online learning. However, they expressed concerns about educators' digital proficiency and perceived a decline in educational quality. These results emphasize the critical need for sustainable, adaptable, and inclusive educational strategies, particularly in regions like the Canary Islands that face unique challenges. The implications of the study have broader relevance to digital education. It is essential to note the need for educators to receive comprehensive training in digital tools and methodologies to improve the quality of online learning.
Social Media and Higher Education: A Linguistic Analysis of Identity Construction of Pakistani Universities

Authors:
raw_author_name: Noor Aqsa Nabila Mat Isa
raw_affiliation_string: Universiti Malaya

Abstract: This study rigorously explores the linguistic strategies employed by Pakistani universities for identity construction on social media platforms, focusing primarily on X (formerly known as Twitter). Utilizing a robust methodology of corpus-assisted discourse analysis, we scrutinized a substantial dataset comprising 2861 tweets. These were collected from the official X accounts of two leading Pakistani universities, National University of Sciences and Technology (NUST) and Lahore University of Management Sciences (LUMS), during the academic year 2021/2022. Our in-depth analysis uncovered distinctive linguistic patterns, particularly in the use of pronouns and specific multi-word key terms that each university employs to construct its identity. Intriguingly, the linguistic choices made by these institutions were found to significantly shape their digital identities, reflecting both individuality and broader cultural themes. The study augments existing research in the field by offering novel and context-specific insights into the dynamics of identity construction via social media. It underscores the pivotal role language plays in the organizational branding landscape. Furthermore, the findings from this research serve as a valuable resource for informing and refining communication strategies, not only for educational institutions but also for organizations at large that aim to construct and effectively manage their digital identities on social media platforms.
Special Issue on Transportation Big Data and Its Applications

Authors:
raw_author_name: Xiaolei Ma
raw_affiliation_string: Key Laboratory of Intelligent Transportation Technology and System, Ministry of Education, Beijing 100191, China; School of Transportation Science and Engineering, Beihang University, Beijing 100191, China
raw_author_name: Xinqiang Chen
raw_affiliation_string: Institute of Institute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai 201306, China

Abstract: Large-scale traffic sensors are strategically deployed across various infrastructures and modes of transportation (e [...]
Research on Imbalanced Data Regression Based on Confrontation

Authors:
raw_author_name: Huixin Tian
raw_affiliation_string: School of Control Science and Engineering, Tiangong University, Tianjin 300387, China; Tianjin Key Laboratory of Intelligent Control of Electrical Equipment, Tiangong University, Tianjin 300387, China

Abstract: The regression model has higher requirements for the quality and balance of data to ensure the accuracy of predictions. However, there is a common problem of imbalanced distribution in real datasets, which directly affects the prediction accuracy of regression models. In order to solve the problem of data imbalance regression, considering the continuity of the target value and the correlation of the data and using the idea of optimization and confrontation, we propose an IRGAN (imbalanced regression generative adversarial network) algorithm. Considering the context information of the target data and the disappearance of the deep network gradient, we constructed a generation module and designed a composite loss function. In the early stages of training, the gap between the generated samples and the real samples is large, which easily causes the problem of non-convergence. A correction module is designed to train the internal relationship between the state and action as well as the subsequent state and reward of the real samples, guide the generation module to generate samples, and alleviate the non-convergence of the training process. The corrected samples and real samples are input into the discriminant module. On this basis, the confrontation idea is used to generate high-quality samples to balance the original samples. The proposed method is tested in the fields of aerospace, biology, physics, and chemistry. The similarity between the generated samples and the real samples is comprehensively measured from multiple perspectives to evaluate the quality of the generated samples, which proves the superiority of the generated module. Regression prediction is performed on the balanced samples processed by the IRGAN algorithm, and it is proven that the proposed algorithm can improve the prediction accuracy in terms of the imbalanced data regression problem.
Interval Type-2 Fuzzy-Model-Based Sampled-Data Control of an AUV Depth System with Input Saturation

Authors:
raw_author_name: Han Sol Kim
raw_affiliation_string: Department of Electronics and Electrical Engineering, Dankook University, 152, Jukjeon-ro, Suji-gu, Yongin-si 16890, Republic of Korea

Abstract: This paper proposes a sampled-data fuzzy controller design technique for an autonomous underwater vehicle (AUV) depth system represented by an interval type-2 (IT-2) fuzzy model, considering input saturation. In the Takagi–Sugeno (T–S) fuzzy model of an AUV depth system, surge velocity is chosen as a premise variable. To address the associated uncertainty with this variable, we employ the IT-2 fuzzy modeling technique. Also, the controller proposed in this paper utilizes time-varying gains, ensuring superior exponential stability compared with traditional fixed gain approaches. Furthermore, a membership function-dependent (MFD) H∞ criterion is employed to enhance robustness for each subsystem individually. Taking into account the mentioned aspects, the controller design condition is derived in the form of linear matrix inequalities (LMIs). Finally, the effectiveness of the proposed method is validated through simulation examples.