Reviewerly/exHarmony huggingface dataset:
The metaverse as virtual heterotopia

William Gibson's quasi-prophetic vision of cyberspace as a 'consensual hallucination' may have predated the commercial inception of the worldwide web by nearly a decade, but given the emergence of online worlds and the blurred virtual/actual (or 'phygital') interface, contemporary social and cultural investigation seems more appropriate than ever.The proliferation of online environments making up the shared virtual worlds now dubbed the 'metaverse' is bringing us one step closer to this vision.By applying Foucault's six qualifying requirements of a heterotopia to this largely ungoverned digital realm, it becomes apparent that the illusory nature of the online environments users inhabit makes them heterotopic in nature.This in turn asks how these non-places should be governed, financed and regulated or indeed whether their virtual nature precludes them from these 'real life' (RL) constraints.Furthermore, the amount of time and effort invested in these blockchain-powered metaverse environments and the construction of virtual identities to inhabit themrunning the gamut of online social networks, gaming, commerce, blogs or otherwiselends credence to the relative importance of these environments in comparison to RL interactions.The Heideggerian concept of Dasein can subsequently be applied to the metaverse and its heterotopic nature to question larger concerns at play regarding the essential nature of personhood and relationships within the metaverse.
Combined improved A* and greedy algorithm for path planning of multi-objective mobile robot

With the development of artificial intelligence, path planning of Autonomous Mobile Robot (AMR) has been a research hotspot in recent years. This paper proposes the improved A* algorithm combined with the greedy algorithm for a multi-objective path planning strategy. Firstly, the evaluation function is improved to make the convergence of A* algorithm faster. Secondly, the unnecessary nodes of the A* algorithm are removed, meanwhile only the necessary inflection points are retained for path planning. Thirdly, the improved A* algorithm combined with the greedy algorithm is applied to multi-objective point planning. Finally, path planning is performed for five target nodes in a warehouse environment to compare path lengths, turn angles and other parameters. The simulation results show that the proposed algorithm is smoother and the path length is reduced by about 5%. The results show that the proposed method can reduce a certain path length.
The effect of online consumer reviews on purchasing intention through product mental image

The aim of the study is to examine the impact of online consumer reviews on purchasing intention through product mental image. The primary sources represented by the instrument were distributed to the study population consisting of consumers of cosmetics in Jordan. Recently, the phenomenon of selling cosmetics through Internet websites and social media platforms has spread widely in Jordan. Therefore, Google Forms was used to design the research instrument and ask consumers of these products to respond to it by publishing it on the official websites of the sellers and with their approval. Structural equation modeling (CB-SEM) technique using the AMOS software was used to test the study hypotheses. The study results show that product mental image mediates the relationship between online consumer review and purchasing intention. Based on the results of the study, the researchers provided recommendations.
Application of augmented reality technologies for education projects preparation

After analysis of scientific literature, we defined that concept of “augmented reality” has following synonyms: “advanced reality”, “improved reality”, “enriched reality”, “mixed reality” and “hybrid reality”. Analysis of scientific literature and own practical experience of the use of augmented reality technologies application in educational practices allowed to state next: augmented reality technologies have a great potential for application in education; there are some cases of augmented reality use for school education; positive aspects of augmented reality technologies application in higher education institutions are confirmed by experiments (isolated cases); only few universities in Ukraine apply augmented reality technologies to educate students; only few universities in Ukraine have special subjects or modules in schedule to teach students to develop augmented reality technologies; various scientific events, mass events, competitions are held in Ukraine, and specialized training on the augmentation of augmented reality technologies is carried out, but this is non-systematic and does not have special state orientation and support. Features of introduction of virtual and augmented reality technologies at Sumy State University (Ukraine) are identified: “e-learning ecosystems” was created; in 2019, augmented and virtual reality research laboratory was established. Advantages and disadvantages of project activity in education are described: project activity is one of the most important components of educational process; it promotes creative self-development and self-realization of project implementers and forms various life competencies. It is determined that augmented reality application for implementation of educational projects will help: to increase students’ interest for educational material; formation of new competences; increase of students’ motivation for independent educational and cognitive activity; activation of educational activities; formation of positive motivation for personal and professional growth; conditions creation for development of personal qualities (creativity, teamwork, etc.). Current trends in implementation of educational projects were identified: most of the winner projects were implemented using augmented reality technology; augmented reality technologies were used in projects to teach different disciplines in higher education institutions. Augmented reality technology application for project activity has positive impact on learning outcomes and competitiveness of the national workforce; it will enhance the country’s position in the global economic space.
CEAM-YOLOv7: Improved YOLOv7 Based on Channel Expansion and Attention Mechanism for Driver Distraction Behavior Detection

Driver distraction behavior is prone to induce traffic accidents. Therefore, it is necessary to detect them to caution drivers in time for traffic safety. In driver behavior recognition, the variety of behaviors and the diversity of the driving environment can have a certain effect on detection accuracy, and the information loss is severe in most existing methods. These make it challenging to improve the real-time accuracy of driver distraction behavior. In this paper, we propose an improved YOLOv7 based on the channel expansion and attention mechanism for driver distraction behavior detection, named CEAM-YOLOv7. The global attention mechanism (GAM) module focuses on the key information to improve accuracy. With the insertion of GAM into the Backbone and Head of YOLOv7, the global dimensional interaction features are scaled up to enable the network to extract key features. Furthermore, In the CEAM-YOLOv7 architecture, the convolution computation has been significantly simplified, which is conducive to increasing the detection speed. Combined with the Inversion and contrast limited adaptive histogram equalization (CLAHE) image enhancement algorithm, a channel expansion (CE) algorithm for data augmentation is presented to further optimize the detection effect of infrared (IR) images. On the driver distraction IR dataset of Hunan University of Science and Technology (HNUST) and Hunan University (HNU), the verification results show that CEAM-YOLOv7 achieved 20.26% higher <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mAP</i> compared to the original YOLOv7 model, and the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FPS</i> reaches 156, which illustrate that CEAM-YOLOv7 outperforms state-of-the-art methods in both accuracy and speed.
Blockchain and Machine Learning for Future Smart Grids: A Review

Developments such as the increasing electrical energy demand, growth of renewable energy sources, cyber–physical security threats, increased penetration of electric vehicles (EVs), and unpredictable behavior of prosumers and EV users pose a range of challenges to the electric power system. To address these challenges, a decentralized system using blockchain technology and machine learning techniques for secure communication, distributed energy management and decentralized energy trading between prosumers is required. Blockchain enables secure distributed trust platforms, addresses optimization and reliability challenges, and allows P2P distributed energy exchange as well as flexibility services between customers. On the other hand, machine learning techniques enable intelligent smart grid operations by using prediction models and big data analysis. Motivated from these facts, in this review, we examine the potential of combining blockchain technology and machine learning techniques in the development of smart grid and investigate the benefits achieved by using both techniques for the future smart grid scenario. Further, we discuss research challenges and future research directions of applying blockchain and machine learning techniques for smart grids both individually as well as combining them together. The identified areas that require significant research are demand management in power grids, improving the security of grids with better consensus mechanisms, electric vehicle charging systems, scheduling of the entire grid system, designing secure microgrids, and the interconnection of different blockchain networks.
R₂FD₂: Fast and Robust Matching of Multimodal Remote Sensing Images via Repeatable Feature Detector and Rotation-Invariant Feature Descriptor

Identifying feature correspondences between multimodal images is facing enormous challenges because of the significant differences both in radiation and geometry. To address these problems, we propose a novel feature matching method (named R <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> FD <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> ) that is robust to radiation and rotation differences, which consists of a repeatable feature detector and a rotation-invariant feature descriptor. In the first stage, a repeatable feature detector called the Multi-channel Auto-correlation of the Log-Gabor (MALG) is presented for feature detection, which combines the multi-channel auto-correlation strategy with the Log-Gabor wavelets to detect interest points (IPs) with high repeatability and uniform distribution. In the second stage, a rotation-invariant feature descriptor is constructed, named the Rotation-invariant Maximum index map of the Log-Gabor (RMLG), which includes fast assignment of dominant orientation and construction of feature representation. In the process of fast assignment of dominant orientation, a Rotation-invariant Maximum Index Map (RMIM) is built to address rotation deformations. Then, the proposed RMLG incorporates the rotation-invariant RMIM with the spatial configuration of DAISY to improve RMLG’s resistance to radiation and rotation variances. Finally, we conduct experiments to validate the matching performance of our R <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> FD <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> utilizing different types of multimodal image datasets. Experimental results show that the proposed R <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> FD <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> outperforms five state-of-the-art feature matching methods. Moreover, our R <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> FD <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> achieves the accuracy of matching within two pixels and has a great advantage in matching efficiency over contrastive methods.
Factors Affecting Medical Students’ Acceptance of the Metaverse System in Medical Training in the United Arab Emirates

Aim: Medical training activities have been disrupted in many regions following the outbreak and rapid spread of the coronavirus disease 2019 (COVID-19) across the globe. The most affected areas include organizations’ process of leveraging high-tech medical equipment from abroad to facilitate a practical approach to learning. Also, as countries implemented COVID-19 safety regulations, it became difficult for organizations to conduct face-to-face training. Consequently, non-face-to-face learning methods have been introduced in the medical field to enable instructors to remotely engage with learners. The current research investigated the students' perceptions of the use of metaverse systems in medical training within the medical community of the United Arab Emirates (UAE).&#x0D; Methods: A conceptual model comprising the adoption properties of personal innovativeness, perceived enjoyment, and Technology Acceptance Model concepts was utilised. The current research targeted students in UAE medical universities. Data was obtained by conducting online surveys that were implemented in the winter semester of 2021/2022 between 15th February and 15th May 2022. 500 questionnaires were issued to students following their voluntary participation and 435 questionnaire responses were obtained i.e. an 87% response rate. The research team tested the measurement model employing Structural Equation Modeling using Smart Partial Least Squares Version (3.2.7).&#x0D; Results: Statistically significant associations were confirmed to exist between Personal Innovativeness (PI) influenced by both the Perceived Ease of Use (PEOU), and Perceived Usefulness (PU) (β= 0.456) and (β= 0.563) at P&lt;0.001. The statistically significant associations involving Perceived Enjoyment (EJ) and PEOU and PU (β= 0.554, P&lt;0.05), (β= 0.571, P&lt;0.05) were further confirmed. Additionally, PEOU had a relationship with PU (β= 0.863, P&lt;0.001). Eventually, PEOU and PU significantly influenced the participants’ inclination to use the metaverse technology with (β= 0.745, P&lt;0.001) and (β= 0.416, P&lt;0.001), respectively.
Comparing Code Explanations Created by Students and Large Language Models

Reasoning about code and explaining its purpose are fundamental skills for computer scientists. There has been extensive research in the field of computing education on the relationship between a student's ability to explain code and other skills such as writing and tracing code. In particular, the ability to describe at a high-level of abstraction how code will behave over all possible inputs correlates strongly with code writing skills. However, developing the expertise to comprehend and explain code accurately and succinctly is a challenge for many students. Existing pedagogical approaches that scaffold the ability to explain code, such as producing exemplar code explanations on demand, do not currently scale well to large classrooms. The recent emergence of powerful large language models (LLMs) may offer a solution. In this paper, we explore the potential of LLMs in generating explanations that can serve as examples to scaffold students' ability to understand and explain code. To evaluate LLM-created explanations, we compare them with explanations created by students in a large course (n ≈ 1000) with respect to accuracy, understandability and length. We find that LLM-created explanations, which can be produced automatically on demand, are rated as being significantly easier to understand and more accurate summaries of code than student-created explanations. We discuss the significance of this finding, and suggest how such models can be incorporated into introductory programming education.
Optimized Thermal-Aware Job Scheduling and Control of Data Centers

Analyzing data centers with thermal-aware optimization techniques is a viable approach to reduce energy consumption of data centers. By taking into account thermal consequences of job placements among the servers of a data center, it is possible to reduce the amount of cooling necessary to keep the servers below a given safe temperature threshold. We set up an optimization problem to analyze and characterize the optimal set points for the workload distribution and the supply temperature of the cooling equipment. Furthermore, under mild assumptions, we design and analyze controllers that regulate the system to the optimal state without knowledge of the current total workload to be handled by the data center. The response of our controller is validated by simulations and convergence to the optimal set points is achieved under varying workload conditions.
Typology of phase transitions in Bayesian inference problems

Many inference problems undergo phase transitions as a function of the signal-to-noise ratio, a prominent example of this phenomenon being found in the stochastic block model (SBM) that generates a random graph with a hidden community structure. Some of these phase transitions affect the information-theoretic optimal (but possibly computationally expensive) estimation procedure, others concern the behavior of efficient iterative algorithms. A computational gap opens when the phase transitions for these two aspects do not coincide, leading to a hard phase in which optimal inference is computationally challenging. In this paper we refine this description in two ways. From a qualitative perspective, we emphasize the existence of more generic phase diagrams with a hybrid-hard phase, in which it is computationally easy to reach a nontrivial inference accuracy but computationally hard to match the information-theoretic optimal one. We support this discussion by quantitative expansions of the functional cavity equations that describe inference problems on sparse graphs, around their trivial solution. These expansions shed light on the existence of hybrid-hard phases, for a large class of planted constraint satisfaction problems, and on the question of the tightness of the Kesten-Stigum (KS) bound for the associated tree reconstruction problem. Our results show that the instability of the trivial fixed point is not generic evidence for the Bayes optimality of the message-passing algorithms. We clarify in particular the status of the symmetric SBM with four communities and of the tree reconstruction of the associated Potts model: In the assortative (ferromagnetic) case the KS bound is always tight, whereas in the disassortative (antiferromagnetic) case we exhibit an explicit criterion involving the degree distribution that separates a large-degree regime where the KS bound is tight and a low-degree regime where it is not. We also investigate the SBM with two communities of different sizes, also known as the asymmetric Ising model, and describe quantitatively its computational gap as a function of its asymmetry, as well as a version of the SBM with two groups of ${q}_{1}$ and ${q}_{2}$ communities. We complement this study with numerical simulations of the belief propagation iterative algorithm, confirming that its behavior on large samples is well described by the cavity method.
Multiphoton Jaynes-Cummings Model: Arbitrary Rotations in Fock Space and Quantum Filters

The multiphoton Jaynes-Cummings model is investigated and applications in quantum information science are explored. Considering the strong atom-field coupling regime and an $N$-photon interaction, a nonlinear driving field can perform an arbitrary rotation in the Fock space of a bosonic mode involving the vacuum and an $M$-Fock state, with $M&lt;N$. In addition, driving a bosonic mode with a linear coherent field (superposition of many Fock states), only the cavity states within the Fock subspace ${|0⟩,|1⟩,\dots{},|N\ensuremath{-}1⟩}$ can be populated; i.e., we show how to implement a Fock state filter, or quantum scissor, that restricts the dynamics of a given bosonic mode to a limited Hilbert space. Such a device can be employed as a generator of finite-dimensional quantum-optical states and also as a quantum-optical intensity limiter, allowing as a special case the generation of single-photon pulses. On the other hand, our system also provides a very rich physics in the weak atom-field coupling regime, in particular, multiphoton electromagnetically induced transparencylike phenomena, inducing a narrow (controllable) reflectivity window for nonlinear probe fields. These results are useful for applications in quantum information processing and also motivate further investigations, e.g., the use of an $N$-photon Jaynes-Cummings system as a qudit with harmonic spectrum and the exploration of multiphoton quantum interference.
Online Graph-Adaptive Learning With Scalability and Privacy

Graphs are widely adopted for modeling complex systems, including financial, biological, and social networks. Nodes in networks usually entail attributes, such as the age or gender of users in a social network. However, real-world networks can have very large size, and nodal attributes can be unavailable to a number of nodes, e.g., due to privacy concerns. Moreover, new nodes can emerge over time, which can necessitate real-time evaluation of their nodal attributes. In this context, the present paper deals with scalable learning of nodal attributes by estimating a nodal function based on noisy observations at a subset of nodes. A multikernel-based approach is developed which is scalable to large-size networks. Unlike most existing methods that re-solve the function estimation problem over all existing nodes whenever a new node joins the network, the novel method is capable of providing real-time evaluation of the function values on newly-joining nodes without resorting to a batch solver. Interestingly, the novel scheme only relies on an encrypted version of each node's connectivity in order to learn the nodal attributes, which promotes privacy. Experiments on both synthetic and real datasets corroborate the effectiveness of the proposed methods.
Environment-Assisted Holonomic Quantum Maps

Holonomic quantum computation uses non-Abelian geometric phases to realize error resilient quantum gates. Nonadiabatic holonomic gates are particularly suitable to avoid unwanted decoherence effects, as they can be performed at high speed. By letting the computational system interact with a structured environment, we show that the scope of error resilience of nonadiabatic holonomic gates can be widened to include systematic parameter errors. Our scheme maintains the geometric properties of the evolution and results in an environment-assisted holonomic quantum map that can mimic the effect of a holonomic gate. We demonstrate that the sensitivity to systematic errors can be reduced in a proof-of-concept spin-bath model.