Reviewerly/exHarmony huggingface dataset:
Education reform and change driven by digital technology: a bibliometric study from a global perspective

Authors:
raw_author_name: Xiaojiao Chen
raw_affiliation_string: College of Educational Science and Technology, Zhejiang University of Technology, Zhejiang, China
raw_author_name: Yidan Liu
raw_affiliation_string: College of Educational Science and Technology, Zhejiang University of Technology, Zhejiang, China

Abstract: Abstract Amidst the global digital transformation of educational institutions, digital technology has emerged as a significant area of interest among scholars. Such technologies have played an instrumental role in enhancing learner performance and improving the effectiveness of teaching and learning. These digital technologies also ensure the sustainability and stability of education during the epidemic. Despite this, a dearth of systematic reviews exists regarding the current state of digital technology application in education. To address this gap, this study utilized the Web of Science Core Collection as a data source (specifically selecting the high-quality SSCI and SCIE) and implemented a topic search by setting keywords, yielding 1849 initial publications. Furthermore, following the PRISMA guidelines, we refined the selection to 588 high-quality articles. Using software tools such as CiteSpace, VOSviewer, and Charticulator, we reviewed these 588 publications to identify core authors (such as Selwyn, Henderson, Edwards), highly productive countries/regions (England, Australia, USA), key institutions (Monash University, Australian Catholic University), and crucial journals in the field ( Education and Information Technologies , Computers &amp; Education , British Journal of Educational Technology ). Evolutionary analysis reveals four developmental periods in the research field of digital technology education application: the embryonic period, the preliminary development period, the key exploration, and the acceleration period of change. The study highlights the dual influence of technological factors and historical context on the research topic. Technology is a key factor in enabling education to transform and upgrade, and the context of the times is an important driving force in promoting the adoption of new technologies in the education system and the transformation and upgrading of education. Additionally, the study identifies three frontier hotspots in the field: physical education, digital transformation, and professional development under the promotion of digital technology. This study presents a clear framework for digital technology application in education, which can serve as a valuable reference for researchers and educational practitioners concerned with digital technology education application in theory and practice.
The metaverse digital environments: A scoping review of the techniques, technologies, and applications

Authors:
raw_author_name: Muhammad Tukur
raw_affiliation_string: College of Science and Engineering, Hamad Bin Khalifa University, Education City, P.O. Box: 34110, Doha, Qatar; Computer Science Department, Gombe State University, Tudun Wada, P.M.B 127, Gombe, Nigeria
raw_author_name: Jens Schneider
raw_affiliation_string: College of Science and Engineering, Hamad Bin Khalifa University, Education City, P.O. Box: 34110, Doha, Qatar
raw_author_name: Mowafa Househ
raw_affiliation_string: College of Science and Engineering, Hamad Bin Khalifa University, Education City, P.O. Box: 34110, Doha, Qatar
raw_author_name: Ahmed Haruna Dokoro
raw_affiliation_string: Computer Science Department, Gombe State Polytechnic, Bajoga, 762102, Gombe, Nigeria
raw_author_name: Usman Idris Ismail
raw_affiliation_string: Computer Science Department, Federal University of Kashere, 771103, Gombe, Nigeria
raw_author_name: Muhammad Dawaki
raw_affiliation_string: Computer Science Department, Gombe State University, Tudun Wada, P.M.B 127, Gombe, Nigeria
raw_author_name: Marco Agus
raw_affiliation_string: College of Science and Engineering, Hamad Bin Khalifa University, Education City, P.O. Box: 34110, Doha, Qatar

Abstract: The “metaverse” has gained immense attention as the next internet frontier, holding significant economic implications, especially for the IT sector. To establish the groundwork for immersive metaverse spaces, our comprehensive scoping review uncovers the current techniques and technologies in metaverse development. This study investigates post-COVID-19 metaverse techniques, technologies, and applications, spanning from January 2020 to December 2022. Findings reveal potential metaverse applications across 11 industries, with education, manufacturing, healthcare, and real estate being notable. We identified 12 major technologies and four key development techniques that have gained prominence in the metaverse landscape. Notably, “Extended Reality (XR),” “Artificial Intelligence (AI),” and “Decentralized Technologies” were the three most frequently mentioned technologies, appearing in 73%, 40%, and 30% of the reviewed articles, respectively. Furthermore, “VR space convergence” and “fundamental technology” emerged as primary enablers in 73% and 43% of the selected articles, with “object connection” and “communication computing infrastructure” playing supportive roles. Integrating these technologies is vital for holistic metaverse development. Our report serves as a catalyst for discussions among stakeholders, promoting further research and the transformative potential of the metaverse. It highlights the need for advancing understanding and technological evolution in this emerging digital realm.
Kernel-based mapping of reliability in predictions for consensus modelling

Authors:
raw_author_name: Roberto Todeschini
raw_affiliation_string: Milano Chemometrics and QSAR Research Group, Department of Earth and Environmental Sciences, University of Milano - Bicocca, Milano, Italy
raw_author_name: Davide Ballabio
raw_affiliation_string: Milano Chemometrics and QSAR Research Group, Department of Earth and Environmental Sciences, University of Milano - Bicocca, Milano, Italy

Abstract: Approaches of high-level data fusion, also known as consensus, combine predictions of individual models to increase reliability and overcome limitations of single models. Consensus strategies are frequently applied in the framework of Quantitative Structure - Activity Relationships (QSARs) to reduce the uncertainties in the prediction of molecular activities and provide better accuracy of the model outcomes. However, specific regions of the chemical space may systematically be associated with low accuracy and even consensus modelling cannot improve prediction reliability through the multiple outcomes of individual models. In this study, a new heuristic metric to assess the degree of accuracy of consensus predictions in the chemical space is proposed. This metric can assist the mapping of reliability in prediction and enhance the delineation of a safe zone, where consensus predictions are expected to have better accuracy. The new metric is calculated by kernel-based potential functions and it can be used in the framework of both classification and regression consensus modelling. Four case studies, including extensive datasets for consensus modelling, were used to test the proposed approach. Results demonstrated that a potential can be associated with regions of the chemical space as a function of accuracy of consensus modelling and it can be used to enable the mapping of reliability in prediction and the definition of specific regions where predictions are expected to be more reliable.
Optical computing metasurfaces: applications and advances

Authors:
raw_author_name: Chongli Zhao
raw_affiliation_string: School of Physics and Optoelectronic Engineering , Beijing University of Technology , Beijing 100124 , China
raw_author_name: Cong He
raw_affiliation_string: Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics , Beijing Institute of Technology , Beijing 100081 , China
raw_author_name: Lingling Huang
raw_affiliation_string: Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics , Beijing Institute of Technology , Beijing 100081 , China
raw_author_name: Yuhong Wan
raw_affiliation_string: School of Physics and Optoelectronic Engineering , Beijing University of Technology , Beijing 100124 , China

Abstract: Abstract Integrated photonic devices and artificial intelligence have presented a significant opportunity for the advancement of optical computing in practical applications. Optical computing technology is a unique computing system based on optical devices and computing functions, which significantly differs from the traditional electronic computing technology. On the other hand, optical computing technology offers the advantages such as fast speed, low energy consumption, and high parallelism. Yet there are still challenges such as device integration and portability. In the burgeoning development of micro–nano optics technology, especially the deeply ingrained concept of metasurface technique, it provides an advanced platform for optical computing applications, including edge detection, image or motion recognition, logic computation, and on-chip optical computing. With the aim of providing a comprehensive introduction and perspective for optical computing metasurface applications, we review the recent research advances of optical computing, from nanostructure and computing methods to practical applications. In this work, we review the challenges and analysis of optical computing metasurfaces in engineering field and look forward to the future development trends of optical computing.
Continuous-time quantum walks for MAX-CUT are hot

Authors:
raw_author_name: Robert J. Banks
raw_affiliation_string: London Centre for Nanotechnology, UCL, London WC1H 0AH, UK
raw_author_name: Yahya Ali
raw_affiliation_string: Newham Collegiate Sixth Form Centre, 326 Barking Rd, London, E6 2BB, UK
raw_author_name: Dan E. Browne
raw_affiliation_string: Department of Physics and Astronomy, UCL, London WC1E 6BT, UK
raw_author_name: P. A. Warburton
raw_affiliation_string: London Centre for Nanotechnology, UCL, London WC1H 0AH, UK

Abstract: By exploiting the link between time-independent Hamiltonians and thermalisation, heuristic predictions on the performance of continuous-time quantum walks for MAX-CUT are made. The resulting predictions depend on the number of triangles in the underlying MAX-CUT graph. We extend these results to the time-dependent setting with multi-stage quantum walks and Floquet systems. The approach followed here provides a novel way of understanding the role of unitary dynamics in tackling combinatorial optimisation problems with continuous-time quantum algorithms.
Assessment framework for deepfake detection in real-world situations

Authors:
raw_author_name: Yuhang Lu
raw_affiliation_string: Multimedia Signal Processing Group (MMSPG), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
raw_author_name: Touradj Ebrahimi
raw_affiliation_string: Multimedia Signal Processing Group (MMSPG), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland

Abstract: Abstract Detecting digital face manipulation in images and video has attracted extensive attention due to the potential risk to public trust. To counteract the malicious usage of such techniques, deep learning-based deepfake detection methods have been employed and have exhibited remarkable performance. However, the performance of such detectors is often assessed on related benchmarks that hardly reflect real-world situations. For example, the impact of various image and video processing operations and typical workflow distortions on detection accuracy has not been systematically measured. In this paper, a more reliable assessment framework is proposed to evaluate the performance of learning-based deepfake detectors in more realistic settings. To the best of our acknowledgment, it is the first systematic assessment approach for deepfake detectors that not only reports the general performance under real-world conditions but also quantitatively measures their robustness toward different processing operations. To demonstrate the effectiveness and usage of the framework, extensive experiments and detailed analysis of four popular deepfake detection methods are further presented in this paper. In addition, a stochastic degradation-based data augmentation method driven by realistic processing operations is designed, which significantly improves the robustness of deepfake detectors.
Hardness of Random Optimization Problems for Boolean Circuits, Low-Degree Polynomials, and Langevin Dynamics

Authors:
raw_author_name: David Gamarnik
raw_affiliation_string: Sloan School of Management and Operations Research Center, MIT, Boston, MA 02140 USA.
raw_author_name: Aukosh Jagannath
raw_affiliation_string: Department of Statistics and Actuarial Science, Department of Applied Mathematics, and Cheriton School of Computer Science, University of Waterloo, Waterloo, ON, Canada.
raw_author_name: Alexander S. Wein
raw_affiliation_string: Department of Mathematics, University of California, Davis, CA 95616 USA.

Abstract: .We consider the problem of finding nearly optimal solutions of optimization problems with random objective functions. Such problems arise widely in the theory of random graphs, theoretical computer science, and statistical physics. Two concrete problems we consider are (a) optimizing the Hamiltonian of a spherical or Ising \(p\)-spin glass model and (b) finding a large independent set in a sparse Erdős–Rényi graph. The following families of algorithms are considered: (a) low-degree polynomials of the input—a general framework that captures many prior algorithms; (b) low-depth Boolean circuits; (c) the Langevin dynamics algorithm, a canonical Monte Carlo analogue of the gradient descent algorithm. We show that these families of algorithms cannot have high success probability. For the case of Boolean circuits, our results improve the state-of-the-art bounds known in circuit complexity theory (although we consider the search problem as opposed to the decision problem). Our proof uses the fact that these models are known to exhibit a variant of the overlap gap property (OGP) of near-optimal solutions. Specifically, for both models, every two solutions whose objectives are above a certain threshold are either close to or far from each other. The crux of our proof is that the classes of algorithms we consider exhibit a form of stability (noise-insensitivity): a small perturbation of the input induces a small perturbation of the output. We show by an interpolation argument that stable algorithms cannot overcome the OGP barrier. The stability of Langevin dynamics is an immediate consequence of the well-posedness of stochastic differential equations. The stability of low-degree polynomials and Boolean circuits is established using tools from Gaussian and Boolean analysis—namely hypercontractivity and total influence, as well as a novel lower bound for random walks avoiding certain subsets, which we expect to be of independent interest. In the case of Boolean circuits, the result also makes use of Linial–Mansour–Nisan's classical theorem. Our techniques apply more broadly to low influence functions, and we expect that they may apply more generally.Keywordsspin glassesmaximum independent setrandom graphsrandom optimization problemsaverage-case hardnessBoolean circuitslow-degree methodsLangevin dynamicsoverlap gap propertyMSC codes68Q0668Q1768Q2568Q8782D3058J6590C27
Context Transformer and Adaptive Method with Visual Transformer for Robust Facial Expression Recognition

Authors:
raw_author_name: Jicun Zhang
raw_affiliation_string: Neusoft Reach Automotive Technology (Dalian) Co., Ltd., Dalian 116000, China
raw_author_name: Xiaojia Zheng
raw_affiliation_string: School of Computer Science and Technology, Dalian University of Technology, Dalian 116000, China
raw_author_name: Yuxin Wang
raw_affiliation_string: School of Computer Science and Technology, Dalian University of Technology, Dalian 116000, China

Abstract: In real-world scenarios, the facial expression recognition task faces several challenges, including lighting variations, image noise, face occlusion, and other factors, which limit the performance of existing models in dealing with complex situations. To cope with these problems, we introduce the CoT module between the CNN and ViT frameworks, which improves the ability to perceive subtle differences by learning the correlations between local area features at a fine-grained level, helping to maintain the consistency between the local area features and the global expression, and making the model more adaptable to complex lighting conditions. Meanwhile, we adopt an adaptive learning method to effectively eliminate the interference of noise and occlusion by dynamically adjusting the parameters of the Transformer Encoder’s self-attention weight matrix. Experiments demonstrate the accuracy of our CoT_AdaViT model in the Oulu-CASIA dataset as (NIR: 87.94%, VL: strong: 89.47%, weak: 84.76%, dark: 82.28%). As well as, CK+, RAF-DB, and FERPlus datasets achieved 99.20%, 91.07%, and 90.57% recognition results, which achieved excellent performance and verified that the model has strong recognition accuracy and robustness in complex scenes.
Sequential quantum simulation of spin chains with a single circuit QED device

Authors:
raw_author_name: Yuxuan Zhang
raw_affiliation_string: Department of Physics and Centre for Quantum Information and Quantum Control, University of Toronto, 60 Saint George Street, Toronto, Ontario, Canada M5S 1A7; Department of Physics, University of Texas at Austin, Austin, Texas 78712, USA; Vector Institute, MaRS Centre, Toronto, Ontario, Canada M5G 1M1
raw_author_name: Shahin Jahanbani
raw_affiliation_string: Department of Physics, University of California, Berkeley, California 94720, USA; Department of Physics, University of Texas at Austin, Austin, Texas 78712, USA; Materials Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA
raw_author_name: Andrew C. Potter
raw_affiliation_string: Department of Physics and Astronomy, and Stewart Blusson Quantum Matter Institute, University of British Columbia, Vancouver, British Columbia, Canada V6T 1Z1

Abstract: Quantum simulations of many-body systems in materials science and chemistry are promising application areas for quantum computers. However, the limited scale and coherence of near-term quantum processors pose a significant obstacle to realizing this potential. Here, we theoretically outline how a single-mode circuit quantum electrodynamics device, consisting of a transmon qubit coupled to a long-lived cavity mode, can be used to simulate the ground state of a highly entangled quantum many-body spin chain. We exploit recently developed methods for implementing quantum operations to sequentially build up a matrix product state (MPS) representation of a many-body state. This approach reuses the transmon qubit to read out the state of each spin in the chain and exploits the large state space of the cavity as a quantum memory encoding intersite correlations and entanglement. We show, through simulation, that analog (pulse-level) control schemes can accurately prepare a known MPS representation of a quantum critical spin chain in significantly less time than digital (gate-based) methods, thereby reducing the exposure to decoherence. We then explore this analog-control approach for the variational preparation of an unknown ground state. We demonstrate that the large state space of the cavity can be used to replace multiple qubits in a qubit-only architecture, and could therefore simplify the design of quantum processors for materials simulation. We explore the practical limitations of realistic noise and decoherence and discuss avenues for scaling this approach to more complex problems that challenge classical computational methods.
Multi-strategy-based adaptive sine cosine algorithm for engineering optimization problems

Authors:
raw_author_name: Fengtao Wei
raw_affiliation_string: School of Mechanical & Instrumental Engineering, Xi’an University of Technology, Xi’an, 710048, China
raw_author_name: Yangyang Zhang
raw_affiliation_string: School of Mechanical & Instrumental Engineering, Xi’an University of Technology, Xi’an, 710048, China
raw_author_name: Junyu Li
raw_affiliation_string: School of Mechanical & Instrumental Engineering, Xi’an University of Technology, Xi’an, 710048, China

Abstract: Sine Cosine Algorithm(SCA) is a population-based optimization algorithm, to find the optimal solution. However, SCA has problems such as premature convergence, insufficient solution precision for high-dimensional functions, and slow convergence speed. To solve the problems above, this paper proposes a multi-strategy-based Adaptive Sine Cosine Algorithm (ASCA). Firstly, a more uniform initial population is generated by the Halton sequence so that the initial population covers the entire search space to maintain the diversity of the initial population. Secondly, the adaptive grading strategy is adopted to sort according to the fitness value, and the population dynamics are divided into 4 grades: excellent, good, medium and poor. For the purpose of improving the convergence accuracy of the algorithm and enhancing the ability to jump out of the local optimum, hybrid mutation and elite guidance methods are applied to different levels of populations for perturbing mutations. Finally, in order to improve the convergence speed of the algorithm, a dynamic opposition-based learning global search strategy is proposed. The ASCA is tested on a set of 20 functions in low- dimensional and high-dimensional, and the improved algorithm is compared with Particle Swarm Optimization (PSO), Backtracking Search Algorithm(BSA), Genetic Algorithm(GA)and other improved Sine Cosine Algorithms. The results show the improved convergence accuracy and speed of the ASCA. Moreover, the ASCA proposed in this paper is applied to engineering optimization design. The solution results show that the ASCA is better than other algorithms in superiority-seeking ability, and can effectively solve the optimization problems in engineering.
Connecting through chatbots: residents’ insights on digital storytelling, place attachment, and value co-creation

Authors:
raw_author_name: Petek Tosun
raw_affiliation_string: Business Administration Department, Kadir Has University, Istanbul, Turkey
raw_author_name: Abdullah Uslu
raw_affiliation_string: Tourism Management Department, Manavgat Tourism Faculty, Akdeniz University, Antalya, Turkey

Abstract: Despite the widespread adoption of chatbots in tourism, there is limited research on their role in digital storytelling from residents' perspectives. Drawing on Social Presence Theory, the primary objective of this study was to examine the impact of a chatbot's anthropomorphic conversation style (emotional vs. neutral) on perceived digital storytelling components in destination marketing. Additionally, the research explored how digital storytelling through chatbots influenced residents' place attachment and, consequently, their engagement in value co-creation. To address these research objectives, the study adopted a quantitative perspective and employed an experimental design. Data were collected from 176 residents of Side, Turkey, using convenience sampling, and all hypotheses were confirmed. The perceptions of digital storytelling expressed as emotional (experimental group) were found to be more positive than the neutral (control group). In addition, digital storytelling significantly influenced place attachment, which was a significant predictor of value co-creation. Finally, place attachment partially mediated the relationship between digital storytelling and residents' value co-creation. The findings provided valuable insights for tourism stakeholders such as businesses, policymakers, and researchers, guiding the effective implementation of chatbots in destination marketing, investigating residents' perspectives regarding emotional digital storytelling by chatbots, and enhancing residents' engagement in value co-creation.