Reviewerly/exHarmony huggingface dataset:
Spatial HuBERT: Self-supervised Spatial Speech Representation Learning for a Single Talker from Multi-channel Audio

Self-supervised learning has been used to leverage unlabelled data, improving accuracy and generalisation of speech systems through the training of representation models. While many recent works have sought to produce effective representations across a variety of acoustic domains, languages, modalities and even simultaneous speakers, these studies have all been limited to single-channel audio recordings. This paper presents Spatial HuBERT, a self-supervised speech representation model that learns both acoustic and spatial information pertaining to a single speaker in a potentially noisy environment by using multi-channel audio inputs. Spatial HuBERT learns representations that outperform state-of-the-art single-channel speech representations on a variety of spatial downstream tasks, particularly in reverberant and noisy environments. We also demonstrate the utility of the representations learned by Spatial HuBERT on a speech localisation downstream task. Along with this paper, we publicly release a new dataset of 100 000 simulated first-order ambisonics room impulse responses.
Proper Laplacian Representation Learning

The ability to learn good representations of states is essential for solving large reinforcement learning problems, where exploration, generalization, and transfer are particularly challenging. The Laplacian representation is a promising approach to address these problems by inducing informative state encoding and intrinsic rewards for temporally-extended action discovery and reward shaping. To obtain the Laplacian representation one needs to compute the eigensystem of the graph Laplacian, which is often approximated through optimization objectives compatible with deep learning approaches. These approximations, however, depend on hyperparameters that are impossible to tune efficiently, converge to arbitrary rotations of the desired eigenvectors, and are unable to accurately recover the corresponding eigenvalues. In this paper we introduce a theoretically sound objective and corresponding optimization algorithm for approximating the Laplacian representation. Our approach naturally recovers both the true eigenvectors and eigenvalues while eliminating the hyperparameter dependence of previous approximations. We provide theoretical guarantees for our method and we show that those results translate empirically into robust learning across multiple environments.
General approach to realize optimized nonadiabatic holonomic quantum computation

The nonadiabatic holonomic quantum computation has attracted much attention in the quantum computation realm, however it is required to satisfy the cyclic evolution and parallel transport conditions strictly. In order to relax the parallel condition, the optimized nonadiabatic holonomic quantum computation was proposed which can be more possible to combine with most of the optimization schemes. In this paper, we put forward the general form of reverse Hamiltonian to realize the optimized nonadiabatic holonomic computation gate. The Hamiltonian is only the function of path parameters in projective Hilbert space, hence, we can realize arbitrary holonomic gates with any desired evolution path. As same as other reverse approach, by using our reverse Hamiltonian, we also can select some special evolution path to decrease the path length and the pulse area to decrease the evolution time. Therefore our approach might give a new horizon to realize high-fidelity quantum gate.
A Local Graph Limits Perspective on Sampling-Based GNNs

We propose a theoretical framework for training Graph Neural Networks (GNNs) on large input graphs via training on small, fixed-size sampled subgraphs. This framework is applicable to a wide range of models, including popular sampling-based GNNs, such as GraphSAGE and FastGCN. Leveraging the theory of graph local limits, we prove that, under mild assumptions, parameters learned from training sampling-based GNNs on small samples of a large input graph are within an $\epsilon$-neighborhood of the outcome of training the same architecture on the whole graph. We derive bounds on the number of samples, the size of the graph, and the training steps required as a function of $\epsilon$. Our results give a novel theoretical understanding for using sampling in training GNNs. They also suggest that by training GNNs on small samples of the input graph, practitioners can identify and select the best models, hyperparameters, and sampling algorithms more efficiently. We empirically illustrate our results on a node classification task on large citation graphs, observing that sampling-based GNNs trained on local subgraphs 12$\times$ smaller than the original graph achieve comparable performance to those trained on the input graph.
Bound for Gaussian-state quantum illumination using a direct photon measurement

It is important to find feasible measurement bounds for quantum information protocols. We present analytic bounds for quantum illumination with Gaussian states when using an on-off detection or a photon number resolving (PNR) detection, where its performance is evaluated with signal-to-noise ratio. First, for coincidence counting measurement, the best performance is given by the two-mode squeezed vacuum (TMSV) state which outperforms the coherent state and the classically correlated thermal (CCT) state. However, the coherent state can beat the TMSV state with increasing signal mean photon number in the case of the on-off detection. Second, the performance is enhanced by taking Fisher information approach with all counting probabilities including non-detection events. In the Fisher information approach, the TMSV state still presents the best performance but the CCT state can beat the TMSV state with increasing signal mean photon number in the case of the on-off detection. Furthermore, we show that it is useful to take the PNR detection on the signal mode and the on-off detection on the idler mode, which reaches similar performance of using PNR detection on both modes.
Fault diagnosis method for intelligent substation secondary system communication network based on improved TCN

Aiming at the low accuracy of the Fault Diagnosis (FuDg) methods used in the Intelligent Substation Secondary System Communication Network (ISSSCN), a FuDg method of the ISSSCN based on the improved Time Convolution Network (TCN) is proposed. Firstly, the overall structure of the ISSSCN and the mathematical description method of the fault signal are analyzed. Secondly, a basic model for f FuDg of the ISSSCN is built based on the TCN including the Extended Causal Convolution (ECC) and the ResNet. Finally, the TCN model is optimized by the improved Hybrid Attention Mechanism (H-AM) based on channel and spatial attention, which greatly improves the accuracy of ISSSCN FuDg. The experiment shows that the accuracy of the proposed method for four different types of FuDg reaches 95.72%, 96.83%, 95.32% and 95.28% respectively, which is higher than the other two comparison methods. Therefore, the proposed method has good FuDg ability
Awareness and Intelligence with Emotions

Emotions are part of every human being. There are hundreds of emotions along with variations, mutations, and blends of those emotions. They tell us about what we are experiencing and help us to know how to react. There are no good or bad emotions, but there are good and bad ways of expressing (or acting on) emotions. In working with youth, who have the least experience dealing with their emotions, adults must pay special attention to their interactions with youth. It is like building a pyramid with cups: any small action or interaction can make things tumble downward. As extension professionals and 4-H volunteers working directly with youth, we can see these pyramids in the process of building up and tumbling down, and we also have the ability to use the skills of emotional awareness and emotional intelligence to help deter again the potential negative impacts of youth breaking down with their emotions.
Exploring Deep Cervical Compartments in Head and Neck Surgical Oncology through Augmented Reality Vision: A Proof of Concept

Background: Virtual surgical planning allows surgeons to meticulously define surgical procedures by creating a digital replica of patients’ anatomy. This enables precise preoperative assessment, facilitating the selection of optimal surgical approaches and the customization of treatment plans. In neck surgery, virtual planning has been significantly underreported compared to craniofacial surgery, due to a multitude of factors, including the predominance of soft tissues, the unavailability of intraoperative navigation and the complexity of segmenting such areas. Augmented reality represents the most innovative approach to translate virtual planning for real patients, as it merges the digital world with the surgical field in real time. Surgeons can access patient-specific data directly within their field of view, through dedicated visors. In head and neck surgical oncology, augmented reality systems overlay critical anatomical information onto the surgeon’s visual field. This aids in locating and preserving vital structures, such as nerves and blood vessels, during complex procedures. In this paper, the authors examine a series of patients undergoing complex neck surgical oncology procedures with prior virtual surgical planning analysis. For each patient, the surgical plan was imported in Hololens headset to allow for intraoperative augmented reality visualization. The authors discuss the results of this preliminary investigation, tracing the conceptual framework for an increasing AR implementation in complex head and neck surgical oncology procedures.
Identifikasi Berita Hoax dengan Menerapkan Algoritma Text Mining

News is new information or information about something that is currently happening, presented in the form of print media, broadcasts, social media or word of mouth to third parties or multiple people. The spread of this news is now very much happening both from social media or messages. However, the problem is that no one can guarantee the truth of the news received. Text mining is the application of data mining concepts and techniques to look for patterns in text, the process of analyzing text to find useful information for a particular purpose. Another definition related to text mining is that text mining is data mining in the form of text where the data source is usually obtained from documents and the goal is to find words that can represent the contents of the document so that an analysis of the connectivity between documents can be carried out. To make it easier to identify news hoax requires a text mining algorithm, using a text mining algorithm can be useful to get real news. Based on the calculations of the TF-IDF algorithm, it can be concluded that the similarity of the meaning of the news whose truth is not yet known is closest to the news ofmerahputih.com with a confidence level of 23.11%.
METHODOLOGICAL RECOMMENDATIONS REGARDING THE USE OF SERVICES OF OPEN SCIENCE CLOUD-ORIENTED SYSTEMS IN THE PROCESS OF TRAINING TEACHERS OF NATURAL AND MATHEMATICAL SUBJECTS TO WORK IN SCIENTIFIC LYCEUMS

The statement of basic materials. The open science cloud-oriented systems allow solving a wide range of tasks in the course of scientific research, supporting joint work, collaboration, cooperation, high standards of transparency and openness, search for new information and presentation of scientific results. The article deals with the recommendations for the use of a separate group of services of OSCOS – scientometric and abstract databases. The training for improving the qualifications of teachers is described and practical tasks are offered.
Human-Robot Interactive Operating System for Underwater Manipulators Based on Hand Gesture Recognition

Manipulators are the primary tool for robotic operations, and human-robot interaction for manipulators is an effective way in complex environments or dealing with complex operational tasks. This paper focuses on developing a human-robot interactive operating system for underwater ECA ARM 5E-Micro manipulator based on hand gesture recognition. The system used an RGB-D camera to acquire the operator's hand gestures and a hand image segmentation method was proposed based on color images combined with depth information. A convex hull algorithm and image moments were used to extract hand contour features, and then hand gesture was recognized by using Hu invariant moment features and KNN classification algorithm. The experiment results showed that the human-robot interactive operating system for underwater manipulators based on hand gesture recognition has a good hand gesture recognition rate and interaction effectiveness. This study provides a solution for implementing human-robot interaction for underwater robots.
A Study on Hierarchy of Periodicity in Systemic Functional Linguistics: Focusing on newspaper articles and parliamentary meeting texts related to the Lee-Tae-Won tragedy

Objectives The purpose of this study is to explicitly show how the hierarchy of periodicity in Korean texts is ana-lyzed based on Systemic Functional Linguistics.&#x0D; Methods The hierarchy of periodicity is viewed as having an expanding structure, like the flow of water, which expands into small waves, large waves, and tidal waves. It is explained that when the flow of text expands from clause to paragraph (→ chapter → text), it does not continue in a straight line, but expands like a wave in a circle. Assuming that this hierarchy of periodicity has genre differences, this paper analyzed the text of political news-paper articles related to the ‘Itaewon tragedy’ in written language and the text of parliamentary meeting related to the ‘Itaewon tragedy’ in spoken language.&#x0D; Results In terms of the hierarchical structure of the periodical, the newspaper article and the meeting showed different characteristics. The newspaper article (Text-I) was relatively well organized and followed the typical hi-erarchical structure of a periodical. In particular, it showed that it faithfully followed the structure of HyperTheme - (Elaboration) - HyperNew, which is necessary to organize paragraphs (paragraphs). It also followed the structure of MacroTheme - (Paragraph) - MacroNew when organizing units larger than paragraphs. The meeting text, on the other hand, shows a weak hierarchy, as the MacroTheme is sometimes omitted, and when organizing para-graphs, the HyperNew following the HyperTheme is omitted before moving on to the next topic, the MacroTheme.&#x0D; Conclusions This paper introduced the concepts of hierarchy of periodic words and serial word expansion of dis-course as two double-headed wagons for studying discourse meaning. I hope this article to be useful for future research on periodicity in Korean texts of various genres, as it demonstrates how to analyze discourse at the para-graph and text level beyond the analysis of ‘clause’ units, and examines the genre differences between them.
Methods for analyzing and activating village educational community activities in the cultural-historical activity theory: Focusing on K Innovation City S Village School

Objectives The purpose of this study is to analyze the process of creating, continuously growing, and evolving vil-lage schools in Gyeongnam Innovation City. Therefore, this study aims to analyze the activity system of village schools, based on Engestöm's Cultural Historical Activity Theory, and to study the growth and change of village schools which arose from the process of resolving contradictions.&#x0D; Methods We conducted a literature research, in-depth interviews, and participant observation, and analyzed the activity system based onEngestöm's Cultural Historical Activity Theory. Two in-depth interviews were conducted first with all village teachers at S village school and secondly with key managers among village teachers.&#x0D; Results The results of analyzing the village school's activity system from the perspective of cultural-historical ac-tivity theory are as follows. The five-year activity period of the village school is largely divided into two periods ac-cording to the contradictions and differences of the subject, tool, and object of the activity system. The main con-tradiction came from the difference in the individual situation and activity of the village teachers, and the differ-ence in perception between the village teachers who watched the identity and activity of the village school. The contradictions that appeared in the tool began with the practical difficulties of recruiting students, leading to two conflicting opinions: one to expand to resident participation activities and one to focus on student-centered activities. The contradictions in the community elements were expressed as the community's lack of under-standing of the village school, the absence of governance, and the uncertainty of financial support for the public offering project. The factor that led to the occurrence of expanded learning by overcoming major contradictions was that through continuous formal and informal meetings such as general meetings and teacher learning centers within the village, emotional intimacy was formed between village teachers and relationships expanded, thereby overcoming contradictions and conflicts. It appears that a new activity system has been creatively created.&#x0D; Conclusions In the results of this study, the elements necessary for the sustainability of village schools and vital-ization of village educational communities are as follows. First, it is a democratic communication structure and continuity of learning within the members. Second, the direction and activities of the village school can change, and this must be discussed and agreed upon with the village school members. Third, it is necessary to expand the range of contact with residents in the village and to have a network with the community in the village. Finally, it shows the need for the role and support of the Happy Education District and local governments in the formation of public, government, and academic governance for sustainable activities.
The Meaning of Picture-book-making-centered Lessons for Pre-service Early Childhood Teachers

Objectives The purpose of this study is to explore the implications of picture book-making-centered classroom experiences for pre-service early childhood teachers.&#x0D; Methods The subjective of this study were 39 pre-service early childhood teachers who took ‘Picture Book Seminar’ at a college in Seoul. The class was conducted for 15 weeks, focusing on a picture book making activities, and data were collected and analyzed through reflective journals and appreciation and evaluation reports written by the research participants.&#x0D; Results First, pre-service early childhood teachers expanded their knowledge of the objects, values, and charac-teristics of picture books. Second, pre-service early childhood teachers strengthened practical skills such as se-lecting picture books, reading picture books, and engaging in linked activities using picture books. Third, pre-service early childhood teachers developed a pleasant and positive attitude toward reading picture books, and gained ex-pectations and confidence in reading picture books.&#x0D; Conclusions The class centered on picture book making allowed the pre-service early childhood teachers to un-derstand the essential characteristics of picture books, and to create a classroom atmosphere that encouraged free exchange with literary works. The learner to become an active writer as well as a reader, thereby maximizing literary response. It had a positive effect on the teaching capacity of pre-service early childhood teachers' knowl-edge, skills, and attitudes toward picture books.
Exploring the Structural Relationships among Student-Faculty Interactions, Learner-Centered Education, and Major Engagement on Major and College Satisfaction

Objectives The purpose of the study is to explore the structural relationship between learner-centered education based on experience, collaboration, and enquiry based learning, faculty-student interaction, immersion in major classes, and university and major satisfaction.&#x0D; Methods Based on previous studies, three competitive models were set, the optimal model was searched for, and differences by grade were analyzed. Relevant variables were measured through a survey targeting 679 first and second year students at I University located at Kyoungsangnam-do.&#x0D; Results Among the competitive models, the model in which learner-centered education based on experience, collaboration, and enquiry affects university and major satisfaction through faculty-student interaction and im-mersion in major classes was found to be the optimal model. As a result of exploring the difference in path by grade in the optimal model using the multi-group analysis method, it was confirmed that there was no difference by grade in the model path for collaboration and enquiry based learning, but there was a difference in experiential learning.&#x0D; Conclusions The role of faculty-student interaction and immersion in major classes between learner-centered education and satisfaction with colleges and majors, and the importance of active interaction with professors in experiential learning were discussed.
Exploring the Student Perspective: Assessing Technology Readiness and Acceptance for Adopting Large Language Models in Higher Education

Digital technologies are changing and will continue to change how we learn and teach today and in the future. With the latest developments in the field of generative artificial intelligence (AI), particularly large language models (LLMs), the question of using AI-based tools in academic education is ruling the current discussions about the transformative impact of AI in higher education (HE).&#x0D; These discussions range from banning these technologies for learning and teaching in HE to guided study support. This study avoids taking up these multifarious and partly controversial debates. Instead, we show how students perceive using AI-based tools for automated text generation for their studies. Drawing on a synthesis of two theories: the 'Technology Readiness Index' (TRI) and 'Technology Acceptance Model' (TAM). The model is validated based on survey data collected among undergraduate first-semester students (N=111) of a computer science-related study programme in Germany in winter 2022/23. The students had to evaluate their relationship to that new technology focusing on their readiness for technology adoption and acceptance. By analysing the collected data with a partial least squares model, we find that the optimism toward the new technology positively influences technology acceptance, while discomfort with the technology negatively influences perceived ease of use. The paper concludes with recommendations for action for adopting LLMs in HE. A proper investment in building AI skills in academic teaching plays a valuable role in fostering the students' positive attitude and innovativeness towards this new technology. Additionally, there is a need for more education about the risks and challenges of using this technology to reduce the impact of factors such as discomfort on ease of use. This requires a factual discourse, away from the current hype-induced exaggerated and hyperbolic statements, for instance, in developing formal guidance for universities.