Reviewerly/exHarmony huggingface dataset:
Simultaneously Sorting Overlapping Quantum States of Light

The efficient manipulation, sorting, and measurement of optical modes and single-photon states is fundamental to classical and quantum science. Here, we realize simultaneous and efficient sorting of nonorthogonal, overlapping states of light, encoded in the transverse spatial degree of freedom. We use a specifically designed multiplane light converter to sort states encoded in dimensions ranging from d=3 to d=7. Through the use of an auxiliary output mode, the multiplane light converter simultaneously performs the unitary operation required for unambiguous discrimination and the basis change for the outcomes to be spatially separated. Our results lay the groundwork for optimal image identification and classification via optical networks, with potential applications ranging from self-driving cars to quantum communication systems.
The interplay of manufacturer encroachment and blockchain adoption to combat counterfeits in a platform supply chain

Consumers’ perception of product value when purchasing is a critical issue relative to online platform commerce. This is especially true when the brand manufacturer authorises only a third-party reseller to sell through the platform, a practice that may provide opportunities to counterfeiters. To address this issue, we establish a theoretical model in which one brand manufacturer wholesales its products to a third-party reseller who then sells through an online platform. The platform acts as a marketplace, while multiple counterfeiters also exist on the platform, which may deceive consumers. In particular, we consider that the manufacturer can encroach with a direct channel and that the platform can adopt blockchain technology to help consumers identify authenticity. The results show that the platform could induce the brand manufacturer to encroach by deploying blockchain technology. Furthermore, an interesting result indicates that although either manufacturer encroachment or blockchain technology used alone can effectively boycott counterfeits, the coexistence of two strategies can raise the demand for counterfeit products. Finally, we find that when the brand manufacturer encroaches on the platform and the platform adopts blockchain technology, the brand manufacturer, platform, reseller and social welfare can achieve Pareto improvement, while the consumer surplus may be worse off.
Software-defined nanophotonic devices and systems empowered by machine learning

Nanophotonic devices, such as metasurfaces and silicon photonic components, have been progressively demonstrated to be efficient and versatile alternatives to their bulky counterparts, enabling compact and light-weight systems for the application of imaging, sensing, communication and computing. The tremendous advances in machine learning provide new design methods, metrology and functionalities for nanophotonic devices and systems. Specifically, machine learning has fundamentally changed automatic design, measurement and result processing of highly application-specific nanophotonic systems without the need of extensive expert experience. This trend can be well described by the popular concept of “software-defined” infrastructure in information technology, which can decouple specific hardware from end users by virtualizing physical components using software interfaces, making the entire system faster, more flexible and more scalable. In this review, we introduce the concept of software-defined nanophotonics and summarize the interdisciplinary research that bridges nanophotonics and intelligence algorithms, especially machine learning algorithms, in the device design, measurement and system setup. The review is organized in an application-oriented manner, showing how the software-defined scheme is utilized in solving both forward and inverse problems for various nanophotonic devices and systems.
Exploring problem-based learning curricula in the metaverse: The hospitality students' perspective

As the metaverse has been introduced into the education sector, higher education institutions have further promoted metaverse platforms for implementing problem-based learning (PBL) to enhance students' competencies. This research explored hospitality students’ experiences of a PBL curriculum using grounded theory to understand the educational effect of PBL in the metaverse. The findings indicated that while participants were fascinated by the fact that the metaverse platform had novel features and functions with the creation of virtual space, they faced difficulties in using it due to linguistic and technical limitations in class. However, the participants actively participated in collaboration with the group members as their willingness to adhere to the lessons grew. Therefore, the core category was derived as “enhancing learning effectiveness through active participation in changing educational environments.” Based on the research results, this study provides theoretical and practical foundations to create innovative and effective PBL educational environments in the field of hospitality education.
How do consumers react to chatbots' humorous emojis in service failures

Prior research has shown that humor can positively impact service recovery in face-to-face interactions. However, the efficacy of using humor in virtual environments for chatbots to address service failures remains unclear. Through three experiments in different populations, this paper found that using humorous emojis by chatbots can help increase consumers' willingness to continue using chatbots after service failures (i.e., reuse intention) and the underlying mechanism; that is, the level of consumers perceiving the degree of the chatbot's intelligence (i.e., perceived intelligence) partially mediates the relationship between humorous emojis use and consumers' reuse intention. Further, how people form impressions about others based on limited information (i.e., implicit personality) significantly moderates the influence path from humorous emojis use to perceived intelligence, and perceived intelligence is more likely to mediate for people who see challenging situations as opportunities (i.e., incremental theorists). In conclusion, this paper provides empirical evidence supporting the potential benefits of using humorous emojis in chatbot service recovery, and offers guidance to online retailers to leverage digital technology for effective consumer engagement.
Multi scale pixel attention and feature extraction based neural network for image denoising

In this paper, we propose a blind Gaussian denoising network that utilize the features of the input image and its negative for generating denoised output of the same. The proposed network is a dual path model which employs a multi-scale pixel attention (MSPA) block on one path and a multi-scale feature extraction (MSFE) block on another. The concept of using the features of a negative image (that it highlights the low contrast region) in blind Gaussian denoising network is, to the best of our knowledge, a first such attempt. The proposed MSPA and MSFE blocks are designed to focus on the features of the image at multiple scales. The MSPA block focuses on the important features of the negative of the input image whereas the MSFE block focuses on extracting features of the input noisy image. The features of both the images are then combined and a final residual noise is obtained, subtracting which from the input noisy image produces the final denoised result. The proposed network is lightweight and fast, due to the low number of convolutional layers involved, and produces superior results (both quantitatively and qualitatively) when compared with various traditional and learning based blind Gaussian denoising techniques. The code of this paper can be downloaded from https://github.com/RTSIR/NIFBGDNet.
THFE: A Triple-hierarchy Feature Enhancement method for tiny boat detection

In boat navigation, especially in complex sea conditions, the detection performance of the tiny boat is related to the safety of boat sailing. However, due to the tiny boat occupying fewer pixels, the effective features of the tiny boat are difficult to obtain. Current tiny object detection methods focus primarily on dataset size matching, feature fusion, and label assignment, lack of attention to texture and detail information loss, and insufficient semantic utilization. Here, we propose a Triple-hierarchy Feature Enhancement (THFE) method to detect tiny boats. The core idea behind THFE is to enhance the semantic information from different layers to supplement the effective features of tiny boats. It consists of three spaces: the super-resolution enhancement space, the semantic enhancement space, and the hierarchical enhancement space. In THFE, sub-pixel convolution, sparse self-attention mechanism, channel attention mechanism, and spatial attention mechanism are adopted to hierarchically enhance each layer’s high-level and low-level semantic features. Finally, each layer’s high-level and low-level semantic features are adaptively fused so that each feature map contains richer high-level and low-level semantic information. Experiments show that our proposed THFE method achieves impressive gains in detection performance. For example, in terms of AP50tiny, our method outperforms state-of-the-art methods by 1.7% on the TinyBoats dataset, 3.1% on the TinyPersons Dataset and 3.9% on the Tiny CityPersons Dataset. To further study the detection of tiny boats, we introduce a tiny boat dataset that will be publicly accessible.
Brain activity during cybersickness: a scoping review

Virtual reality (VR) experiences can cause a range of negative symptoms such as nausea, disorientation, and oculomotor discomfort, which is collectively called cybersickness. Previous studies have attempted to develop a reliable measure for detecting cybersickness instead of using questionnaires, and electroencephalogram (EEG) has been regarded as one of the possible alternatives. However, despite the increasing interest, little is known about which brain activities are consistently associated with cybersickness and what types of methods should be adopted for measuring discomfort through brain activity. We conducted a scoping review of 33 experimental studies in cybersickness and EEG found through database searches and screening. To understand these studies, we organized the pipeline of EEG analysis into four steps (preprocessing, feature extraction, feature selection, classification) and surveyed the characteristics of each step. The results showed that most studies performed frequency or time-frequency analysis for EEG feature extraction. A part of the studies applied a classification model to predict cybersickness indicating an accuracy between 79 and 100%. These studies tended to use HMD-based VR with a portable EEG headset for measuring brain activity. Most VR content shown was scenic views such as driving or navigating a road, and the age of participants was limited to people in their 20 s. This scoping review contributes to presenting an overview of cybersickness-related EEG research and establishing directions for future work.The online version contains supplementary material available at 10.1007/s10055-023-00795-y.
Long term estimation of global horizontal irradiance using machine learning algorithms

The integration of solar power into the smart grid has become quite ubiquitous among scientists and researchers to obviate the need of fossil fuels in the wake of environmental and ecological conservation. Therefore, exact and precisive prediction of global horizontal irradiance (GHI) is indispensable for quantifying the production of solar power in future. The main aim of the present study is to develop the time series forecasting models for next five years by using the past data from the period 2017–2019 of four districts of Rajasthan i.e., Bikaner (D1), Jodhpur (D2), Jaisalmer (D3) and Barmer (D4). This paper compares the efficacy of novel time series estimation models such as auto-seasonal autoregressive integrated moving average (auto-SARIMA), Facebook Prophet (FBP) and Neural Prophet (NP) for predicting the future values of GHI. In order to obtain the best fit between the test data and its prediction outcome, optimized parameters of auto-SARIMA models are selected. The performances of the models are assessed using different error metrics such as root mean squared error (RMSE) and mean absolute error (MAE). The RMSE values calculated for districts D1, D2, D3 and D4 by auto-SARIMA model are 18.69, 21.62, 14.473 and 10.70 W/m2 and their MAE values are 15.00, 19.31, 12.99 and 9.222 W/m2 respectively. Conclusively, it is observed that auto-SARIMA model outperforms the other models as far as reduction in RMSE and MAE values are concerned.
Registered Attribute-Based Encryption

Attribute-based encryption (ABE) generalizes public-key encryption and enables fine-grained control to encrypted data. However, ABE upends the traditional trust model of public-key encryption by requiring a single trusted authority to issue decryption keys. If an adversary compromises the central authority and exfiltrates its secret key, then the adversary can decrypt every ciphertext in the system. This work introduces registered ABE, a primitive that allows users to generate secret keys on their own and then register the associated public key with a "key curator" along with their attributes. The key curator aggregates the public keys from the different users into a single compact master public key. To decrypt, users occasionally need to obtain helper decryption keys from the key curator which they combine with their own secret keys. We require that the size of the aggregated public key, the helper decryption keys, the ciphertexts, as well as the encryption/decryption times to be polylogarithmic in the number of registered users. Moreover, the key curator is entirely transparent and maintains no secrets. Registered ABE generalizes the notion of registration-based encryption (RBE) introduced by Garg et al. (TCC 2018), who focused on the simpler setting of identity-based encryption. We construct a registered ABE scheme that supports an a priori bounded number of users and policies that can be described by a linear secret sharing scheme (e.g., monotone Boolean formulas) from assumptions on composite-order pairing groups. Our approach deviates sharply from previous techniques for constructing RBE and only makes black-box use of cryptography. All existing RBE constructions (a weaker notion than registered ABE) rely on heavy non-black-box techniques. The encryption and decryption costs of our construction are comparable to those of vanilla pairing-based ABE. Two limitations of our scheme are that it requires a structured reference string whose size scales quadratically with the number of users (and linearly with the size of the attribute universe) and the running time of registration scales linearly with the number of users. Finally, as a feasibility result, we construct a registered ABE scheme that supports general policies and an arbitrary number of users from indistinguishability obfuscation and somewhere statistically binding hash functions.
Photonic metamaterial analogue of a continuous time crystal

Time crystals are an eagerly sought phase of matter with broken time-translation symmetry. Quantum time crystals with discretely broken time-translation symmetry have been demonstrated in trapped ions, atoms and spins while continuously broken time-translation symmetry has been observed in an atomic condensate inside an optical cavity. Here we report that a classical metamaterial nanostructure, a two-dimensional array of plasmonic metamolecules supported on flexible nanowires, can be driven to a state possessing all of the key features of a continuous time crystal: continuous coherent illumination by light resonant with the metamolecules' plasmonic mode triggers a spontaneous phase transition to a superradiant-like state of transmissivity oscillations, resulting from many-body interactions among the metamolecules, characterized by long-range order in space and time. The phenomenon is of interest to the study of dynamic classical many-body states in the strongly correlated regime and applications in all-optical modulation, frequency conversion and timing.
Predicting Gaze-based Target Selection in Augmented Reality Headsets based on Eye and Head Endpoint Distributions

Target selection is a fundamental task in interactive Augmented Reality (AR) systems. Predicting the intended target of selection in such systems can provide users with a smooth, low-friction interaction experience. Our work aims to predict gaze-based target selection in AR headsets with eye and head endpoint distributions, which describe the probability distribution of eye and head 3D orientation when a user triggers a selection input. We first conducted a user study to collect users' eye and head behavior in a gaze-based pointing selection task with two confirmation mechanisms (air tap and blinking). Based on the study results, we then built two models: a unimodal model using only eye endpoints and a multimodal model using both eye and head endpoints. Results from a second user study showed that the pointing accuracy is improved by approximately 32% after integrating our models into gaze-based selection techniques.
Two Statistical Approaches to Justify the Use of the Logistic Function in Binary Logistic Regression

Logistic regression is a commonly used classification algorithm in machine learning. It allows categorizing data into discrete classes by learning the relationship from a given set of labeled data. It learns a linear relationship from the given dataset and then introduces nonlinearity through an activation function to determine a hyperplane that separates the learning points into two subclasses. In the case of logistic regression, the logistic function is the most used activation function to perform binary classification. The choice of logistic function for binary classifications is justified by its ability to transform any real number into a probability between 0 and 1. This study provides, through two different approaches, a rigorous statistical answer to the crucial question that torments us, namely where does this logistic function on which most neural network algorithms are based come from? Moreover, it determines the computational cost of logistic regression, using theoretical and experimental approaches.
Promising Emerging Technologies for Teaching and Learning: Recent Developments and Future Challenges

As time goes on and the number of people who use information and communication technology (ICT) grows, emerging technologies are receiving a lot of attention from academics, researchers, and users. Emerging technologies are changing more quickly than ever, and it is important to start building skills early in education by using the right examples to teach and learn about digital citizenship. New pedagogies support individual teaching and learning methodologies for knowledge acquisition and self-directed learning. Hundreds of digital education tools have been developed to increase student autonomy, enhance academic process management, promote collaboration, and improve communication between teachers and students. This article examines the current state of emerging technologies from a variety of educational viewpoints, highlights a few of them, and discusses both their established and potential educational benefits. Additionally, it offers in-depth debates on recent developments and future challenges from a wide range of perspectives. The analysis focuses on peer-reviewed research articles published in well-renowned publications databases over the last 8 years, drawing upon a bibliometric analysis using VOSviewer. The results of this study are also anticipated to serve as a substantial impetus for other universities and colleges worldwide to utilize innovatively and revolutionized technology for better academic performance.
Understanding the Benefits and Challenges of Deploying Conversational AI Leveraging Large Language Models for Public Health Intervention

Recent large language models (LLMs) have advanced the quality of open-ended conversations with chatbots. Although LLM-driven chatbots have the potential to support public health interventions by monitoring populations at scale through empathetic interactions, their use in real-world settings is underexplored. We thus examine the case of CareCall, an open-domain chatbot that aims to support socially isolated individuals via check-up phone calls and monitoring by teleoperators. Through focus group observations and interviews with 34 people from three stakeholder groups, including the users, the teleoperators, and the developers, we found CareCall offered a holistic understanding of each individual while offloading the public health workload and helped mitigate loneliness and emotional burdens. However, our findings highlight that traits of LLM-driven chatbots led to challenges in supporting public and personal health needs. We discuss considerations of designing and deploying LLM-driven chatbots for public health intervention, including tensions among stakeholders around system expectations.
A local opposition-learning golden-sine grey wolf optimization algorithm for feature selection in data classification

The classification problem is an important research topic in machine learning and data mining. Feature selection can remove irrelevant and redundant features and improve classification accuracy. The traditional Grey Wolf algorithm (GWO) has the defects of low convergence efficiency and easy to falls into local extremes in solving the feature selection process, leading to ineffective removal of irrelevant and redundant features. This paper proposes a binary version of the local opposing learning golden sine grey wolf optimization algorithm (OGGWO). First, the OGGWO algorithm uses local opposing learning mapping to initialize the positions of individual grey wolves to enrich population diversity and improve convergence speed. Secondly, mix the golden sine algorithm and the grey wolf optimization algorithm to control the direction and distance of α wolves by using the golden mean coefficient to improve the autonomous search ability of individual grey wolves and avoid the algorithm from falling into the local optimum. Finally, the updated grey wolf position is binary converted by pre-setting the threshold value to reduce the feature subset's size and improve the classification effect. To verify the effectiveness of the OGGWO algorithm, 18 international standard datasets were selected, and compare the OGGWO algorithm with the improved Grey Wolf algorithm and the popular metaheuristic algorithm for the fitness value comparison test and the simulation comparison experiment for classification accuracy. The results show that: (1) the OGGWO algorithm has good convergence and high search accuracy on all 18 test data; (2) the improved strategy of the OGGWO algorithm can effectively improve the classification accuracy and reduce the number of selected features compared with the traditional GWO algorithm in the classification accuracy simulation. The experimental results show that the superiority and robustness of the OGGWO algorithm in feature selection are verified.