Reviewerly/exHarmony huggingface dataset:
Joint and Constrained Inversion as Hypothesis Testing Tools

: In this chapter, I discuss an alternative perspective on interpreting the results of joint and constrained inversions of geophysical data. Typically such inversions are performed based on inductive reasoning (i.e. we fit a limited set of observations and conclude that the resulting model is representative of the Earth). While this has seen many successes, it is less useful when, for example, the specified relationship between different physical parameters is violated in parts of the inversion domain. I argue that in these cases a hypothesis testing perspective can help to learn more about the properties of the Earth. I present joint and constrained inversion examples that show how we can use violations of the assumptions specified in the inversion to study the subsurface. In particular I focus on the combination of gravity and magnetic data with seismic constraints in the western United States. There I see that high velocity structures in the crust are associated with relatively low density anomalies, a possible indication of the presence of melt in a strong rock matrix. The concepts, however, can be applied to other types of data and other regions and offer an extra dimension of analysis to interpret the results of geophysical inversion algorithms.
Deep Learning based Multi Object Tracking with Improved Appearance Feature Extraction

Multi-Object Tracking (MOT) aims to track the trajectories of multiple objects simultaneously in a video over time. In MOT, object detection is used to locate instances of objects in images or videos, and re-identification is employed to match objects with the same ID. Among recent MOT methods, one-shot MOT, which relies on a single neck and head for object detection and appearance feature extraction, faces a conflict between object category classification and unique ID classification. To address this challenge, we propose a solution that integrates an FPN neck-based appearance feature extraction module into YOLOX. This method effectively mitigates the conflict between object category classification and unique ID classification in one-shot MOT. Furthermore, the proposed variable offset mechanism enables correction of the extraction position, even in cases of object occlusion. The proposed technique achieved 83.7% mIDF1 and 75% mMOTA in the experimental results for the TITAN dataset. This improved mIDF1 and mMOTA by 0.2% and 0.2% respectively over the IOU-based MOT, and by 1.2% and 0.7% respectively over the one-shot MOT.
Explorative Analyses and Discussions to Compose Design Principles of Building and Spaces

The term metaverse was first used in the novel 'Snow Crash.' However, it has come into people's lives with the development of 3D design and visual techniques, machine computations, blockchain, and the internet. Companies and communities worldwide are launching their metaverse platform with diverse functions like trading, gaming, and socialization. The virtual spaces are designed, and virtual buildings or reconstructions of existing ones can be seen. The facts and laws of the universe that people live in directly affect their understanding, so developers generally tend to implement these into their virtual environments. However, the factors which shape and govern the real world are not valid in virtual environments. Thus, there are diverse possibilities in the metaverse. This chapter presents the metaverse and its platforms with definitions, usage, interfaces, and problems. It explores the facts of the universe and discusses gaps and lack of designing spaces and buildings in virtual environments by thinking about the possibilities in the metaverse.
Analysis of predisposition in levels of individual digital competence among Spanish university students

The objective of this study was to identify university student profiles with different levels of predisposition and usage of digital competences in social communication and collaborative learning (CSCCL) as well as technology use in information search and treatment (CSTI). The sample comprised 383 students from three state universities in Spain. The study employed a questionnaire called “basic digital competences 2.0 in university students” (COBADI). Chi-squared automatic interaction detection (CHAID) algorithm was used for data analysis due to its capability to handle both quantitative and qualitative variables, enabling profiling and the generation of predictive models with easily interpretable graphical representations (decision trees). The results revealed a high level of digital competence in socialization and execution of tasks online, managing digital tools for planning study time, and using resources for information searching and browsing. These findings align with previous works on collaborative writing on the Internet and digital competence. However, students demonstrated low digital competence in data analysis processes and image production using social software apps, which has been linked to task complexity and heavy workload in other studies. Interestingly, the students’ sociodemographic characteristics (age, sex, and university attended) did not influence their predisposition towards the analyzed digital competences. In conclusion, enhancing effective digital teaching in higher education can be achieved by incorporating the teaching of critical analysis of information, addressing information overload, providing instruction on social software apps, and emphasizing collaborative learning. These strategies aim to help students acquire and apply knowledge relevant to the current job market.
Pervasive Art Museum Education: Online and Onsite Learning as Network Effect in the Postdigital Era

온오프라인의 공존이 생소하지 않고 디지털이 일상의 문화로 재편성된 포스트디지털 시대가 도래했으며 미술관 경험 또한 온전히 오프라인의 경험일 수만은 없다. 포스트디지털 시대 미술관 교육의 재개념화를 위해 ‘어디에나 있는 미술관’ 모델을 ‘온라인 미술관’이나 ‘분산된 미술관’ 모델과 비교하며, 네트워크된 디지털 이미지의 교육적 가능성을 탐구하고 미술관 학습을 인간과 비인간 행위자의 탈중심화된 네트워크 효과로서 파악한다. 다중 사례 연구를 위해 의도적으로 다양성을 최대한 드러내는 세 사례를 선정하여 어디에나 있는 미술관 학습을 ‘시공간의 재편성’, ‘권위의 중심 이동’, ‘번역을 통한 새로운 학습 연결망의 생성’의 세 가지 키워드로 다룬다. 세 사례의 교차 분석을 토대로 포스트디지털 시대 미술관 교육의 과제를 미술관의 주체성에 대한 재고, 온오프라인의 당위성, 접근가능성에 대한 논의로 제안한다. 결과적으로 본 연구는 포스트디지털 시대 담론을 활용해 기존 미술관 교육의 논의를 확장하며 온라인을 포함한 미술관 학습이 ‘비대면’의 이유가 아닌 적절한 당위성을 지니고 지속될 수 있음을 제안한다.The postdigital era has arrived where digital has been integrated as an everyday culture and it is no longer sufficient to conceive of art museum experiences as soley onsite events. To reconceptualize art museum education in the postdigital era, this study compares the ‘pervasive art museum’ model with the ‘online art museum’ and the ‘distributed art museum’ models. Furthermore, the pedagogical possibilities of networked digital images are explored and reintroduces art museum learning as a decentralized network effect of human and non-human actors. For this multiple case study research, three cases were selected using maximum variation purposeful sampling. The cases are discussed under the following three key words: 1) rearrangement of time and space, 2) shifting centers of authority, and 3) emergence of new learning network through translation. Based on the cross-analysis of the three cases, this study proposes three issues of art museum education in the postdigital era as follows: 1) rethinking art museum’s positionality, 2) the necessity of onsite and online practices, and 3) accessibility. In conclusion, this study aims to expand upon existing discussions of art museum education by tracing postdigital-related discourses and suggests that art museum education practices incorporating online experiences should continue while highlighting its necessity other than the reason of lockdown.
A Survey on the Current Status and Teachers’ Perception of Early Childhood English Teacher Education

The purpose of this study is to examine the current state of early childhood English teacher education and assess teachers’ attitudes towards online teacher education. To achieve this, a questionnaire was to distributed to 80 early childhood English teachers. The closed-ended questions were analyzed using frequency analysis, while the open-ended questions were subjected to qualitative analysis. The key findings of the study are outlined below. Firstly, it was discovered that a majority of early childhood English teacher education has shifted from face-to-face to online delivery in response to the COVID-19 pandemic. Secondly, the most significant advantages of online teacher education are the flexibility in terms of time and space, high attendance rate, and the availability of data sharing. However, the disadvantages of online teacher education include the lack of interaction and communication, low concentration, and a lack of realism in the educational field. Additionally, online teacher education lacks the limited opportunity for classroom demonstrations. Based on the study’s results, the following educational implications can be drawn. Both theory-centered online teacher education and practice-centered face-to-face teacher education should be provided in appropriate proportions. Furthermore, providing a video that demonstrates the process of creating teaching aids could be beneficial.
The Possibilities of a Reflective Approach to Play-centered Art Education: Focusing on H.-G. Gadamer’s Concept of Play

미술교육에 있어서 놀이에 대한 관심과 다양한 교육적 시도가 더 증대되는 가운데, 이 연구는 학생의 자발적인 놀이를 지속적인 예술 경험으로 어떻게 연결할 것인가에 관한 문제의식에서 시작되었다. 전통적 교육관과 포스트모더니즘 교육관 중 어느 쪽도 놀이의 적절한 위치와 교육적 의미를 규정하기 어려운 현실 속에서 가다머의 독특한 놀이 개념은 놀이, 예술 경험, 이해를 서로 연결함으로써 놀이중심 미술교육이 나아가야 할 방향에 대하여 의미 있는 단서를 제공해준다. 이 연구는 가다머의 놀이 개념에 기초한 놀이 중심 미술교육의 새로운 접근 가능성을 탐구하기 위해 가다머의 ‘전통과 권위’, ‘대화’, 자기 도야‘ 개념을 집중적으로 분석하였다. 각각의 개념에 기초하여 (1) 교사의 역할을 존중하는 놀이중심 미술교육, (2) 개변적 상호작용을 통한 놀이중심 미술교육, (3) 자기 교육에 이르는 놀이중심 미술교육의 가능성을 모색하였다. 또한, 그와 관련된 수업 실천 방안 및 예시 활동을 제안하였다. 나아가 이 세 가지 놀이중심 미술교육에서 추구하는 놀이의 성격을 ‘성찰적 놀이’로 규정하면서 놀이중심 미술교육의 성찰적 접근 방향과 시사점에 관해 논하였다. 그것은 놀이중심 미술교육에서 첫째, 학생들의 자발적 놀이를 발전시키는 교사 역할을 재고하기, 둘째, 학생 개인 차원의 놀이를 넘어서 상호작용으로서의 놀이를 격려하기, 셋째, 놀이를 자기 해소의 차원보다는 자기 성장의 차원으로 인식하기이다.This study aims to explore the questions of how to connect students' voluntary play with continuous artistic experiences as interest in play and various educational attempts increases in art education. While neither traditional educational paradigm nor postmodern one can clearly define an appropriate position of play and its educational significance, Gadamer's unique concept of play provides meaningful clues for the direction of play-centered art education by linking play, artistic experience, and understanding. The study explores the new possibilities of play-centered art education based on Gadamer’s concept of play by analyzing his concepts of tradition and authority, dialogue, self training. Based on each concept, the study finds the possibilities of three approaches to play-centered art education: (1) play-centered art education that respects the role of the teacher; (2) play-centered art education through transformative interaction; and (3) play-centered art education leading to self-education. The study also proposed teaching and learning strategies activities. Furthermore, it defines the nature of play pursued in the three approaches as “reflective play,” and the direction and implications of a reflective approach to play-centered art education: Reconsidering the role of teachers for students’ voluntary play; encouraging play as interactions; and perceiving play as self-growth.
Inverse Reinforcement Learning with Graph Neural Networks for IoT Resource Allocation

The rapid development of Internet of Things (IoT) applications requires efficient computing and communication resource allocation strategies to streamline the existing network operations. These strategies could be formulated as mixed-integer nonlinear programming (MINLP) problems, where the optimal branch-and-bound (B&B) with the full strong branching (FSB) variable selection policy features an extremely high complexity. We propose inverse reinforcement learning with graph neural networks (GNNIRL) to generate a new variable selection policy that closely matches the FSB variable selection. Without sacrificing the optimality, the GNNIRL can directly infer the variable selection with a significantly lower complexity, which is also verified by simulation.
Local Graph-Homomorphic Processing for Privatized Distributed Systems

We study the generation of dependent random numbers in a distributed fashion in order to enable privatized distributed learning by networked agents. We propose a method that we refer to as local graph-homomorphic processing; it relies on the construction of particular noises over the edges to ensure a certain level of differential privacy. We show that the added noise does not affect the performance of the learned model. This is a significant improvement to previous works on differential privacy for distributed algorithms, where the noise was added in a less structured manner without respecting the graph topology and has often led to performance deterioration. We illustrate the theoretical results by considering a linear regression problem over a network of agents.
Binary Image Fast Perfect Recovery from Sparse 2D-DFT Coefficients

We investigate the reconstruction of a binary image from its sparse 2D discrete Fourier transform (2D-DFT) coefficients. Specifically, we focus on the case where the image height is equal to its width and is a prime number. We derive a lower bound on the number of coefficients required for perfect image recovery and propose a reconstruction algorithm. In our experiment, we demonstrate that the lower bound can be achieved when the height is less than 20. Consequently, we can efficiently reconstruct a 19×19 binary image using only 21 out of the total 361 2D-DFT coefficients, which accounts for approximately 5.81% of the coefficients..
An Effective Anomalous Sound Detection Method Based on Representation Learning with Simulated Anomalies

In this paper, we propose an effective anomalous sound detection (ASD) method based on representation learning with simulated anomalies. Recently, ASD systems have used Outlier Exposure (OE) strategy to achieve promising performance in DCASE challenges. These exploit deep Convolutional Neural Networks (CNNs) to learn discriminative representations by treating the normal samples from different classes as pseudo anomalies. However, since the anomalous sounds occur only rarely, are diversely distributed and are unseen during training, the OE capability of representations learned from normal samples may be limited. To address this issue, we propose a statistics exchange (StEx) method, which constructs simulated anomalies to improve the effectiveness of representation learning via OE strategy. Specifically, the first and second order statistics are extracted from time or frequency axis of input spectrograms, and the simulated anomaly is then obtained by exchanging the statistics of spectrograms from different classes. Furthermore, an out-of-distribution (OOD) metric is introduced as an importance measure to qualitatively analyze OE capability, which enables appropriate simulated anomaly selection for ASD. Extensive experiments on the DCASE2021 challenge task2 development dataset verify the effectiveness of representation learning with simulated anomaly for OE based ASD.
I «Giornali» di Giuliano Passaro (1526ca.). Note filologiche e linguistiche

Abstract The Giornali of the saddler Giuliano Passaro, completed around 1526, have not yet received the attention they deserve. The chronicle, which describes in the Neapolitan vernacular the historical situation of the Realm of Naples, is of considerable interest both from a historiographical and historical-linguistic point of view. Up to now, the printed edition of 1785 has mainly been used for historiographical and linguistic research. However, it presents considerable differences and errors with respect to the manuscripts. A critical edition therefore remains an urgent need. In the present state of our knowledge, the ms. 13324 of the Biblioteca Nacional de España seems to be the closest version to the lost original.
How To Model Users Through Their Abilities: A Methodological Perspective On Ability-Based Design

Ability-Based Design offers a design approach to support practitioners in creating accessible and indivually optimized systems through a shift in focus on abilities instead of disabilities. As such, it could help to minimize moments of exclusion through technology and offer support for people, especially during times of crisis or social distancing as caused by the COVID-19 pandemic. However, in its current theoretically coined state, the approach includes no instruction on how to achieve this shift in mindset and how to apply a focus on abilities when designing systems. Within my thesis, I address this gap in research by analyzing ways for modeling users based on their abilities. For this, I propose ability models as user representation, identify the challenges as well as potentials of such ability models, and examine how this approach for the personalization of systems can help and support user populations. The overall aim and expected benefit of this research is to provide adequate techniques on how to include abilities into design considerations as well as an understanding of what accommodating for abilities might change, achieve or effect in terms of HCI research and design.
Application of SSD network algorithm in panoramic video image vehicle detection system

Abstract Due to the popularity of high-performance cameras and the development of computer video pattern recognition technology, intelligent video monitoring technology is widely used in all aspects of social life. It mainly includes the following: industrial control system uses video monitoring technology for remote monitoring and comprehensive monitoring; in addition, intelligent video monitoring technology is also widely used in the agricultural field, for example, farm administrators can view the activities of animals in real time through smart phones, and agricultural experts can predict future weather changes according to the growth of crops. In the implementation of intelligent monitoring system, automatic detection of vehicles in images is an important topic. The construction of China’s Intelligent Transportation System started late, especially in video traffic detection. Although there are many related studies on video traffic detection algorithms, these algorithms usually only analyze and process information from a single sensor. This article describes the application of the single-shot detector (SSD) network algorithm in a panoramic video image vehicle detection system. The purpose of this article is to investigate the effectiveness of the SSD network algorithm in a panoramic video image vehicle detection system. The experimental results show that the detection accuracy of a single convolutional neural network (CNN) algorithm is only 0.7554, the recall rate is 0.9052, and the comprehensive detection accuracy is 0.8235. The detection accuracy of SSD network algorithm is 0.8720, recall rate is 0.9397, and the comprehensive detection accuracy is 0.9046, which is higher than that of single CNN algorithm. Thus, the proposed SSD network algorithm is compared with a single convolution network algorithm. It is more suitable for vehicle detection, and it plays an important role in panoramic video image vehicle detection.
CHARACTERISTICS OF THE TECHNICAL TASK FOR THE DEVELOPMENT OF A CHATBOT

Abstract. The article describes the general principles of the technical task for the development of chatbots of a modern educational institution. It is noted that chatbots are still a new tool that is constantly developing and improving. Almost any company can integrate a chatbot into its work, because today it has become a universal assistant for various tasks. Depending on the actual needs, it can be added to the site, application or messenger. The process of developing chatbots aimed at ensuring the effectiveness of documentation and communication activities of an educational institution has been studied. Technical tasks for chatbot development are described. The purpose of the study is to consider IT tools and chatbots to activate the process of increasing the efficiency of the documentation and communication activities of the educational institution. The following methods were used: analysis method, analysis of electronic resources, comparative method of review of free online services, synthesis method. The practical significance of the study is in the description of the technical task for the development of chatbots of a modern educational institution using the example of an interactive program created for the department of management information systems of the Donetsk National University, which can be used as a comprehensive platform for interaction with students, applicants and teachers with the possibility of automating a large volume of document flow between the chatbot and its users. Disadvantages of chatbots are shown - limited reactions and the need to develop complex conversation scenarios. Such programs look for answers to customers' questions in a database prepared in advance and do not know how to improvise.
Toward Prototypical Part Interpretable Similarity Learning With ProtoMetric

The Prototypical Part Network (ProtoPNet) is an interpretable deep learning model that combines the strong power of deep learning with the interpretability of case-based reasoning, thereby achieving high accuracy while keeping its reasoning process interpretable without any additional supervision. Thanks to these advantages, ProtoPNet has attracted significant attention and spawned many variants that improve both the accuracy and the computational efficiency. However, since ProtoPNet and its variants (ProtoPNets) adopt a training strategy specific to linear classifiers or decision trees, they run into difficulty when utilized for similarity learning, which is a practically useful technique for cases in which unknown classes exist. To solve this problem, we propose ProtoMetric, an extension of ProtoPNet that is applicable to similarity learning. Extensive experiments on multiple open datasets for fine-grained image classification demonstrate that ProtoMetric achieves a similar accuracy as state-of-the-art ProtoPNets with a smaller number of prototypes. We also demonstrate through case studies that ProtoMetric is applicable to image retrieval tasks where the class labels of the training and test sets are completely different.
TOWARDS EFFECTIVE STRATEGIES FOR MOBILE ROBOT USING REINFORCEMENT LEARNING AND GRAPH ALGORITHMS

Abstract. This research paper explores the use of Reinforcement Learning (RL) and traditional graph algorithms like A* for mobile robots in the field of path planning and strategy development. The paper conducts a comprehensive analysis of these algorithms by evaluating their performance in terms of efficiency, scalability, and applicability in real-world scenarios. The results of the study show that while both RL and A* algorithms have their benefits and limitations, RL algorithms have the potential to provide more effective and scalable solutions for mobile robots in real-world applications. The paper also provides ongoing research directions aimed at improving the performance of these algorithms and concludes by offering valuable insights for researchers and practitioners working in the field of mobile robots.&#x0D; The purpose of this project is to evaluate the performance of these algorithms, identify their benefits and limitations, and contribute to the development of more effective and practical solutions for mobile robots in real-world applications. The results of this study will be valuable for researchers and practitioners working in the field of mobile robots, as it will provide a comprehensive analysis of the use of RL and A* algorithms and offer ongoing research directions for improving their performance.
Mineral chemistry and geothermo-barometry based on amphibole of alkali gabbro dykes of Zarigan, northeast of Bafq

Mineral chemistry and geothermo-barometry based on amphibole of alkali gabbro dykes of Zarigan, northeast of Bafq