Reviewerly/exHarmony huggingface dataset:
Realtime gray-box algorithm configuration using cost-sensitive classification

Abstract A solver’s runtime and the quality of the solutions it generates are strongly influenced by its parameter settings. Finding good parameter configurations is a formidable challenge, even for fixed problem instance distributions. However, when the instance distribution can change over time, a once effective configuration may no longer provide adequate performance. Realtime algorithm configuration (RAC) offers assistance in finding high-quality configurations for such distributions by automatically adjusting the configurations it recommends based on instances seen so far. Existing RAC methods treat the solver as a black box, meaning the solver is given a configuration as input, and it outputs either a solution or runtime as an objective function for the configurator. However, analyzing intermediate output from the solver can enable configurators to avoid wasting time on poorly performing configurations. We propose a gray-box approach that utilizes intermediate output during evaluation and implement it within the RAC method Contextual Preselection with Plackett-Luce (CPPL blue). We apply cost-sensitive machine learning with pairwise comparisons to determine whether ongoing evaluations can be terminated to free resources. We compare our approach to a black-box equivalent on several experimental settings and show that our approach reduces the total solving time in several scenarios and improves solution quality in an additional scenario.
Methodology for Early Childhood Education and Care Research Premises and Principles of Scientific Knowledge Building

Click to increase image sizeClick to decrease image size AcknowledgmentsThe authors would like to express their heartfelt appreciation and gratitude to the Lembaga Pengelola Dana Pendidikan- LPDP (Indonesia Endowment Fund for Education) under the Ministry of Finance of the Republic of Indonesia for sponsoring the author master's degree as well as for supporting this publication and collaboration.
Live Coding and Music Production as Hybrid Practice

This article discusses incorporating live coding as part of a new Foundation pathway for music production at a UK university that started in September 2022. The inclusion of live coding, using the application Sonic Pi, is situated alongside music production using a DAW, initially through the process of drum programming. The role of Sonic Pi is also to provide a means for producers to take their productions into the live performance space. This article’s contribution is in three areas. The first is to provide a short history of live coding at the current institution coupled with a longer account of my fragmented journey into live coding to provide some context. For the second discussion area, information about the foundation, its structure and how it fits into the overall degree programme is discussed. This section also includes some short code examples to illustrate the approach and links to video materials. For the last discussion area I outline an area of crossover between production and live coding which opens up a number of critical discussion points. This concerns the use of a breakbeat, what this means when used in productions, in live coding and when shipped with paid for or free software.
On the Integration of Machine Agents into Live Coding

Co-creation strategies for human–machine collaboration have recently been explored in various creative disciplines and more opportunities for human–machine collaborations are materialising. In this article, we outline how to augment musical live coding by considering how human live coders can effectively collaborate with a machine agent imbued with the ability to produce its own patterns of executable code. Using machine agents allows live coders to explore not-yet conceptualised patterns of code and supports them in asking new questions. We argue that to move away from scenarios where machine agents are used in a merely generative way, or only as creative impetus for the human, and towards a more collaborative relationship with the machine agent, consideration is needed for system designers around the aspects of reflection, aesthetics and evaluation. Furthermore, owing to live coding’s close relationship with exposing processes, using agents in such a way can be a useful manner to explore how to make artificial intelligence processes more open and explainable to an audience. Finally, some speculative futures of co-creative and artificially intelligent systems and what opportunities they might afford the live coder are discussed.
Team Building in Business Relations: Myths, Paradoxes, and Prospects of Development

The article points out the value of teamwork and highlights the conditions for its effectiveness. There is also a growing doubt about the universality of team building in relation to solving all possible problems. On this basis, the most common myths (mythologemes) of team building and the corresponding paradoxes of team work are singled out, as well as the ways to overcome these myths. The article proposes an expanded understanding of teams, taking into account the possibility of including participants in modern teams who work not only remotely, but also the so-called “virtual” participants, whose ideas and methods can be used in teamwork. The article also discusses the main mistakes on the part of psychologists coaches, managers and teachers at team building training sessions. As the main idea, which is “team-building”, we emphasise not only the orientation towards common goals and values, but also the adoption by the participants of a common style and method for solving certain problems. The style itself is a special value, which determines the effectiveness of teamwork.
Live Coding the Global Hyperorgan: The Paragraph environment in the indeterminate place

This article presents several scenarios in which a live coding environment called Paragraph was utilised to telematically play networked and geographically distributed hyperorgans. Situated within the framework of the Global Hyperorgan project, the TCP/Indeterminate Place Quartet have explored the affordances of the organ network through the concept of Tele-Copresence. By outsourcing certain dimensions of the parameter space of the Paragraph language to other members of the quartet, a shared instrumentality is enabled, where the organs are collaboratively controlled by means of this system. Rooted in a personal composer-performer practice and studied from the perspective of the live coder, the Paragraph system, adapted to the TCP/Indeterminate Place environment, can be understood as a modular system of human and non-human agents, into which the other musicians are patched. The distributed parameter space utilised, thus resembles a shared cantus firmus , a foundational, but dynamically changing, ecology for the live coder to play within.
Study on Kotaro Oshio's Finger-style Guitar

Finger-style guitar is a very emerging style of guitar playing. Based on the author's playing experience and understanding of music and guitar techniques, this paper analyzes the playing techniques of Kotaro Oshio's finger-style guitar song, Fight, and adapts it to Merry Christmas Mr. Lawrence. This paper is aimed to promote the spread of finger-style guitar.
Review of Recent Laser Technology of Development Multi Qubit Gates Using Ion Trap Method

The qubit technology using Trapped ions are taking the systems for practical quantum computing (QC). The normal requirements to achieve quantum supremacy have all been studied with ions, and quantum algorithms use ion-qubit systems have been implemented. I cover in this study many points regarding the concept of Qubit through Ion Trap, near application, and experiments also explore the Multi gates, Hybrid gates implementations
Factors influencing the employment intention of private college graduates based on robot control system design

INTRODUCTION: Robotics is currently the most cutting-edge international science and technology, as well as a high-value-added core technology. Robots are widely used in a variety of industrial fields, as a new direction in the development of robotics, and play an important role in solving the current employment problems in China.&#x0D; OBJECTIVES: This paper combines its research results, introduces the machine learning method in the robot control system, and establishes the employment index system in the robot working environment by combining the employment factors with the environmental relationship analysis.&#x0D; METHODS: This paper combines its research results, introduces the machine learning method in the robot control system, and establishes the employment index system in the robot working environment by combining the employment factors with the environmental relationship analysis.&#x0D; RESULTS: The study found that the willingness of university students to choose a job gradually increases as their education level rises; the lower the level of education, the weaker their willingness to look for a job; the higher the level of education the more sensitive they are to the quality of education and educational specialities, the higher their willingness to work.&#x0D; CONCLUSION: Based on the robot control system design the factors that have an impact on the environment in real economic activities (e.g., age, gender, occupation, education level, etc.) play a role in promoting the future application and development of robotics in China.
Feasibility Analysis of VR Virtual Technology Application in Art Design Teaching

VR virtual reality technology is now used in various fields, in recent years, the technology is also gradually applied to teaching. VR virtual technology can build a simulation learning environment, so that students have a comprehensive and systematic understanding of the courses of art design, and cultivate students' creative ability and imagination. This paper analyzes the status quo of VR virtual reality education technology and the advantages of VR technology in teaching, puts forward the teaching method of integrating VR virtual reality technology into classroom teaching, and discusses the feasibility of VR virtual reality technology, so that the technology can be widely used in the teaching of art design major.
Blind Restoration of a Single Real Turbulence-Degraded Image Based on Self-Supervised Learning

Turbulence-degraded image frames are distorted by both turbulent deformations and space–time varying blurs. Restoration of the atmospheric turbulence-degraded image is of great importance in the state of affairs, such as remoting sensing, surveillance, traffic control, and astronomy. While traditional supervised learning uses lots of simulated distorted images for training, it has poor generalization ability for real degraded images. To address this problem, a novel blind restoration network that only inputs a single turbulence-degraded image is presented, which is mainly used to reconstruct the real atmospheric turbulence distorted images. In addition, the proposed method does not require pre-training, and only needs to input a single real turbulent degradation image to output a high-quality result. Meanwhile, to improve the self-supervised restoration effect, Regularization by Denoising (RED) is introduced to the network, and the final output is obtained by averaging the prediction of multiple iterations in the trained model. Experiments are carried out with real-world turbulence-degraded data by implementing the proposed method and four reported methods, and we use four non-reference indicators for evaluation, among which Average Gradient, NIQE, and BRISQUE have achieved state-of-the-art effects compared with other methods. As a result, our method is effective in alleviating distortions and blur, restoring image details, and enhancing visual quality. Furthermore, the proposed approach has a certain degree of generalization, and has an excellent restoration effect for motion-blurred images.
Construction of Teaching and Research Linkage Mechanism for College English Team Building of Military Academies

The division of College English team in military academies is mainly based on the talent training program and the curriculum system. This model has optimized the staff structure, but has little effect on coordinated development of individuals and teams, and cannot enhance the teaching and research innovation. Based on a full investigation into the status and needs of College English team building in military academies, this study aims to frame a teaching and research linkage mechanism, and strives to open up ideas for teaching and research approaches, and further improve teaching efficiency, promote the depth of research, and achieve the parallel development pattern of teaching and research for College English team in military academies.
The Development and Opportunity of Educational Mode in the Metaverse Age

The Metaverse is an end-users-oriented integration of various layers of Information Technology (IT), where Human–Computer Interaction (HCI) would be the core technology. With the rapid development of IT, the Metaverse would allow users to connect, work, conduct business, and access educational resources, all in a technology-mediated environment in new interaction ways. The Metaverse can play a major role in the future of online learning and enable a rich active learning environment, where learners have the opportunity to obtain first-hand experiences that might not be accessible in the physical world. While currently there is a severe shortage in Metaverse-Learning studies, such research strands are expected to soon emerge. The main objective of this paper is to investigate challenges and opportunities for human-centric Metaverse technology in the learning sector, hence accelerating research in this field. A phenomenological research method was used, including semi-structured in-depth interviews, essays written by participants, a focus group discussion with 19 experts in the areas of HCI, intelligent interactive technologies, and online learning. Five challenges were identified for the Metaverse-Learning context: immersive design, privacy and security, universal access, physical and psychological health concerns, and governance. While the research provided suggestions to overcome those challenges, three Meta-Learning opportunities were identified: hands-on training and learning, game-based learning, and collaboration in creating knowledge. The findings of this research contribute to understanding the complexity of the online learning in the Metaverse from the Human–Computer Interaction point of view. These findings can be used to further research the Metaverse as a virtual communication environment and potential business and learning platform.
Trajectory estimation

In this chapter, the Rauch-Tung-Striebel equations are derived, since these are well-known as "smoothing" or "retrodiction" for updating past states with current information. Then, the Accumulated State Densities are introduced. They provide the optimal means for trajectory estimation including cross-covariances of individual states. This fact will be used later on for track-to-track fusion methods and for online processing of out-of-sequence methods, which are a regular challenge in distributed multi sensor applications. The chapter ends with aspects on the duality between quantum theory and state estimation based on Accumulated State Densities.
Appendix B: Test code for linear-Gaussian Feynman Kernel

Book DOI: 10.1049/SBRA551E Chapter DOI: 10.1049/SBRA551E_appendixb ISBN: 9781839534393 e-ISBN: 9781839534409 Preview this chapter: Appendix B: Test code for linear-Gaussian Feynman Kernel, Page 1 of 2 < Previous page | Next page > /docserver/preview/fulltext/books/ra/sbra551e/SBRA551E_appendixb-1.gif /docserver/preview/fulltext/books/ra/sbra551e/SBRA551E_appendixb-2.gif
Novel area-efficient and flexible architectures for optimal Ate pairing on FPGA

While FPGA is a suitable platform for implementing cryptographic algorithms, there are several challenges associated with implementing Optimal Ate pairing on FPGA, such as security, limited computing resources, and high power consumption. To overcome these issues, this study introduces three approaches that can execute the optimal Ate pairing on Barreto–Naehrig curves using Jacobean coordinates with the goal of reaching 128-bit security on the Genesys board. The first approach is a pure software implementation utilizing the MicroBlaze processor. The second involves a combination of software and hardware, with key operations in $$F_{p}$$ and $$F_{p^{2}}$$ being transformed into IP cores for the MicroBlaze. The third approach builds on the second by incorporating parallelism to improve the pairing process. The utilization of multiple MicroBlaze processors within a single system offers both versatility and parallelism to speed up pairing calculations. A variety of methods and parameters are used to optimize the pairing computation, including Montgomery modular multiplication, the Karatsuba method, Jacobean coordinates, the Complex squaring method, sparse multiplication, squaring in $$G_{\phi 6}F_{p^{12}}$$ , and the addition chain method. The proposed systems are designed to efficiently utilize limited resources in restricted environments, while still completing tasks in a timely manner.
Bayesian state estimation and target tracking

This chapter exposes the most important basic target tracking and state estimation algorithms (without considering distributed multi sensor application explicitly). Quite naturally, the Kalman filter will be the starting point. We will present three equivalent derivations of the Kalman filter, for two reasons: This provides a profound insight into the mechanism of the Kalman filter by providing complementary views on the same result and on the other hand many of the tools required to reach this objective will also be used in the derivations of track-to-track fusion methods later on. The strict assumptions of the Kalman filter will be relaxed in extensions on data association and track extraction for ambiguous sensor readings. Tracking expert readers may well omit this chapter and come back whenever necessary.
Track-to-track fusion with unknown correlations

This chapter discusses on track fusion methods with unknown cross-covariances. Most of them are approximations to the statistically optimal fusion, but these are often easy to compute and perform well in practical applications. A simplistic approach is to ignore the cross-covariance matrices, that is, to assume that they are zero. This results in an optimistic fused error covariance matrix, which does not cover the actual expected error. A more precise fused error covariance can be obtained by approaches, which are based on geometrical results on the intersection of ellipses. This allows to derive the smallest error covariance matrix, which still covers the optimal fusion for all possible cross-covariances. The last section of the chapter is on a method, which uses joint diagonalization in order to fuse individual parameters of the state based on the maximum information among the sensors.
Knowledge Management in the Context of Toxicity Testing

The chapter presents the knowledge management system, developed in the context of an interdisciplinary project called TOXIN, for the toxicity testing domain to facilitate the safety assessment of new cosmetic ingredients. Tools have been developed to capture existing knowledge captured in Safety Evaluation Opinions documents issued by the Scientific Committee on Consumer Safety in a knowledge graph, to enrich this knowledge with knowledge from other sources, and to access this knowledge efficiently. Ontologies and semantic technology are used to build the toxicological knowledge graph and its tools. The developed knowledge management system is based on the processes for creating, maintaining, and exploiting knowledge graphs defined in the Abstract Reference Architecture. The chapter discusses the approach followed for developing the knowledge management system, and the tools developed to support the different processes of the Abstract Reference Architecture. These tools include end-user tools, as well as more advanced tools for information technology experts.
Multi-Modal Spatio-Temporal Knowledge Graph of Ship Management

In modern maritime activities, the quality of ship communication directly impacts the safety, efficiency, and economic viability of ship operations. Therefore, predicting and analyzing ship communication status has become a crucial task to ensure the smooth operation of ships. Currently, ship communication status analysis heavily relies on large-scale, multi-source heterogeneous data with spatio-temporal and multi-modal features, which presents challenges for ship communication quality prediction tasks. To address this issue, this paper constructs a multi-modal spatio-temporal ontology and a multi-modal spatio-temporal knowledge graph for ship communication, guided by existing ontologies and domain knowledge. This approach effectively integrates multi-modal spatio-temporal data, providing support for subsequent efficient data analysis and applications. Taking the scenario of fishing vessel communication activities as an example, the query tasks for ship communication knowledge are successfully performed using a graph database, and we combine the spatio-temporal knowledge graph with graph convolutional neural network technology to achieve real-time communication quality prediction for fishing vessels, further validating the practical value of the multi-modal spatio-temporal knowledge graph.
LoRA-NCL: Neighborhood-Enriched Contrastive Learning with Low-Rank Dimensionality Reduction for Graph Collaborative Filtering

Graph Collaborative Filtering (GCF) methods have emerged as an effective recommendation approach, capturing users’ preferences over items by modeling user–item interaction graphs. However, these methods suffer from data sparsity in real scenarios, and their performance can be improved using contrastive learning. In this paper, we propose an optimized method, named LoRA-NCL, for GCF based on Neighborhood-enriched Contrastive Learning (NCL) and low-rank dimensionality reduction. We incorporate low-rank features obtained through matrix factorization into the NCL framework and employ LightGCN to extract high-dimensional representations. Extensive experiments on five public datasets demonstrate that the proposed method outperforms a competitive graph collaborative filtering base model, achieving 4.6% performance gains on the MovieLens dataset, respectively.
Shallow quantum circuits for efficient preparation of Slater determinants and correlated states on a quantum computer

Fermionic Ansatz state preparation is a critical subroutine in many quantum algorithms such as the variational quantum eigensolver for quantum chemistry and condensed-matter applications. The shallowest circuit depth needed to prepare Slater determinants and correlated states to date scales at least linearly with respect to the system size $N$. Inspired by data-loading circuits developed for quantum machine learning, we propose an alternate paradigm that provides shallower, yet scalable, $O(d{log}_{2}^{2}N)$ two-qubit gate-depth circuits to prepare such states with $d$ fermions, offering a subexponential reduction in $N$ over existing approaches in second quantization, enabling high-accuracy studies of $d\ensuremath{\ll}O(N/{log}_{2}^{2}N)$ fermionic systems with larger basis sets on near-term quantum devices.