Reviewerly/exHarmony huggingface dataset:
A framework study on the use of immersive XR technologies in the cultural heritage domain

Most cultural promotion and dissemination are nowadays performed through the digitization of heritage sites and museums, a necessary requirement to meet the new needs of the public. Augmented Reality (AR), Mixed Reality (MR), and Virtual Reality (VR) have the potential to improve the experience quality and educational effect of these sites by stimulating users’ senses in a more natural and vivid way. In this respect, head-mounted display (HMD) devices allow visitors to enhance the experience of cultural sites by digitizing information and integrating additional virtual cues about cultural artifacts, resulting in a more immersive experience that engages the visitor both physically and emotionally. This study contributes to the development and incorporation of AR, MR, and VR applications in the cultural heritage domain by providing an overview of relevant studies utilizing fully immersive systems, such as headsets and CAVE systems, emphasizing the advantages that they bring when compared to handheld devices. We propose a framework study to identify the key features of headset-based Extended Reality (XR) technologies used in the cultural heritage domain that boost immersion, sense of presence, and agency. Furthermore, we highlight core characteristics that favor the adoption of these systems over more traditional solutions (e.g., handheld devices), as well as unsolved issues that must be addressed to improve the guests’ experience and the appreciation of the cultural heritage. An extensive search of Google Scholar, Scopus, IEEE Xplore, ACM Digital Library, and Wiley Online Library databases was conducted, including papers published from January 2018 to September 2022. To improve review reporting, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were used. Sixty-five papers met the inclusion criteria and were classified depending on the study’s purpose: education, entertainment, edutainment, touristic guidance systems, accessibility, visitor profiling, and management. Immersive cultural heritage systems allow visitors to feel completely immersed and present in the virtual environment, providing a stimulating and educational cultural experience that can improve the quality and learning purposes of cultural visits. Nonetheless, the analyzed studies revealed some limitations that must be faced to give a further impulse to the adoption of these technologies in the cultural heritage domain.
SculptMate: Personalizing cultural heritage experience using fuzzy weights

Virtual Environment (VE) technology has become increasingly popular in the cultural heritage field, providing new ways to experience and interact with cultural artifacts and sites. By creating immersive and realistic virtual environments, VE technology allows users to explore and engage with cultural heritage in a more dynamic and engaging way. Towards this direction, this study introduces SculptMate, a cutting-edge mobile application that uses advanced personalization features (fuzzy logic) to enhance the appreciation and understanding of sculptures from various eras and artistic styles. The application aims to provide users with an immersive and interactive experience, both within and beyond museum settings, by allowing them to explore and interact with an extensive collection of virtual sculptures from museums and galleries worldwide. The paper's objectives are to investigate the potential of SculptMate, examine the effectiveness of fuzzy logic in personalizing the user experience, and assess the impact of the personalized experience on user engagement and satisfaction. The novelty of this study lies in the utilization of fuzzy logic in VE for personalizing the cultural heritage experience. SculptMate has been evaluated with very promising results.
Non‐Volatile Optical Switch Element Enabled by Low‐Loss Phase Change Material

Abstract Mach–Zehnder interferometers (MZIs) integrated with phase‐change materials have attracted great interest due to their low power consumption and ultra‐compact size, which are favored for reconfigurable photonic processors. However, they suffer from a low optical extinction ratio and limited switching cycles due to high material loss and poor reversible repeatability caused by material degradation. Here a non‐volatile electrically reconfigurable 2 × 2 MZI integrated with a low‐loss phase‐change material Sb 2 Se 3 encapsulated in Al 2 O 3 layers is demonstrated. The phase change is electrically actuated by a forward‐biased silicon p‐i‐n diode. The switch extinction ratio is more than 20 dB due to the low‐loss Sb 2 Se 3 ‐based phase shifter. By dividing the Sb 2 Se 3 patch into small sub‐cells to restrict the material reflow, more than 10 000 reversible phase‐change cycles and 6‐bit multilevel switching states are achieved by programming the electrical pulses. Its non‐volatility, high endurance, and fine‐tuning capability makes the device promising in large‐scale low‐power reconfigurable photonic processors.
Manta ray foraging optimization based on mechanics game and progressive learning for multiple optimization problems

Metaheuristic algorithms are currently being studied in depth by many scholars, and it is an important task to improve the learning and adaptive capabilities of the algorithms so that they can be of great use in a wide range of optimization problems. The Manta ray foraging optimization (MRFO) has made certain achievements in optimization problems, but it still has shortcomings, such as its insufficient intra-population communication and poor learning ability, so the optimization capability of MRFO needs further improvement. To address these shortcomings, this paper proposes a manta ray foraging optimization based on mechanics game and progressive learning for multiple optimization problems, which is abbreviated as MGL-MRFO, introducing variable spiral factors and dynamic adjustment of internal parameters; then proposes progressive learning to enhance the learning ability of MRFO; and finally proposes a matching game-based update mechanism to eliminate intra-population repetition as a feasible solution. It is validated in terms of move step size and time complexity. The results are compared with variants of the algorithm proposed in recent years in 18 benchmark test functions and the CEC 2022 test set, as well as with the TOP algorithm in the CEC competition, and show that MGL-MRFO has strong advantages, validating the novelty and competitiveness of MGL-MRFO. At the end of the paper, the feasibility and practicality of MGL-MRFO is further validated by three engineering optimization problems.
The impact of industrial Internet of things on sustainable performance: the indirect effect of supply chain visibility

Purpose Sustainable management has become a strategic and popular requirement for stakeholders, as the ability to create competitive advantages in the current dynamic business environment and the pressures of digital transformation need to be increased attention in achieving the highest level of sustainable performance. The main objective of this paper is to explore the direct effects of the industrial Internet of things and supply chain visibility on sustainable performance (economic performance, environmental performance and social performance), in addition to evaluating the indirect effects that supply chain visibility plays in these relationships. Design/methodology/approach To achieve the purposes and objectives of this work, a conceptual model was developed by referring to the current works in the literature, and the theoretical model was developed, and then data were collected through the questionnaire and by the snowball sampling method, as 380 questionnaires were used. In the data analysis, the multivariate analysis technique was used via Covariance-based Structural Equation Modeling (CB-SEM), as hypothesized relationships were evaluated through confirmatory factor analysis (CFA), and then the hypotheses were tested through a structural model. Findings The results reached the acceptance of all hypotheses, and this means that all hypothesized relationships were positive, as the impact of the industrial Internet of things was positive on each of the supply chain visibility and economic performance, environmental performance and social performance, and also the supply chain visibility mediated the relationship between each of the industrial Internet of things and economic performance, environmental performance and social performance. Originality/value The current paper reported a range of theoretical and practical contributions, as this study is the first to examine these hypothesized relationships together in this way and thus helped this study to develop new perceptions about Industry 4.0 technologies and their potential impacts on supply chain and sustainable performance. Also, this study had a variety of management recommendations that allow managers of pharmaceutical manufacturing companies operating in Jordan to develop their technological capabilities and use them to enhance sustainable performance.
Detection of Foreign Objects Intrusion Into Transmission Lines Using Diverse Generation Model

Foreign objects intrusion into transmission lines can lead to serious troubles, using deep learning technology for foreign object detection has good performance and can reduce losses. Due to the complexity and diversity of the surrounding environment of the transmission lines, and the limitations of data acquisition methods, the image data of foreign objects invading transmission lines used in current research are extremely rare, and the types of foreign objects and background features are single. Deep learning requires large image data as a research driving force, Rare number of images leads to insufficient model fitting and affects the detection accuracy. We propose a Diverse Generation model, which can generate many images of foreign objects invading the transmission lines to provide support for deep learning, thereby solving the shortcomings of existing models. The results show that the dataset generated by our model has high quality and diversity, and can cover different scenes including those that are not convenient for data acquisition in reality. Thus, the accuracy of foreign objects detection can be effectively improved. This achievement provides a preliminary guarantee for abnormal detection of transmission lines, and helps to promote the integration of artificial intelligence technology in the power system.
Personalized fashion outfit generation with user coordination preference learning

This paper focuses on personalized outfit generation, aiming to generate compatible fashion outfits catering to given users. Personalized recommendation by generating outfits of compatible items is an emerging task in the recommendation community with great commercial value but less explored. The task requires to explore both user-outfit personalization and outfit compatibility, any of which is challenging due to the huge learning space resulted from large number of items, users, and possible outfit options. To specify the user preference on outfits and regulate the outfit compatibility modeling, we propose to incorporate coordination knowledge in fashion. Inspired by the fact that users might have coordination preference in terms of category combination, we first define category combinations as templates and propose to model user-template relationship to capture users' coordination preferences. Moreover, since a small number of templates can cover the majority of fashion outfits, leveraging templates is also promising to guide the outfit generation process. In this paper, we propose Template-guided Outfit Generation (TOG) framework, which unifies the learning of user-template interaction, user–item interaction and outfit compatibility modeling. The personal preference modeling and outfit generation are organically blended together in our problem formulation, and therefore can be achieved simultaneously. Furthermore, we propose new evaluation protocols to evaluate different models from both the personalization and compatibility perspectives. Extensive experiments on two public datasets have demonstrated that the proposed TOG can achieve preferable performance in both evaluation perspectives, namely outperforming the most competitive baseline BGN by 7.8% and 10.3% in terms of personalization precision on iFashion and Polyvore datasets, respectively, and improving the compatibility of the generated outfits by over 2%.
Quantum Key Distribution Using a Quantum Emitter in Hexagonal Boron Nitride

Abstract Quantum key distribution (QKD) is considered the most immediate application to be widely implemented among a variety of potential quantum technologies. QKD enables sharing secret keys between distant users by using photons as information carriers. An ongoing endeavor is to implement these protocols in practice in a robust, and compact manner so as to be efficiently deployable in a range of real‐world scenarios. Single photon sources (SPS) in solid‐state materials are prime candidates in this respect. This article demonstrates a room temperature, discrete‐variable quantum key distribution system using a bright single photon source in hexagonal‐boron nitride, operating in free‐space. Employing an easily interchangeable photon source system, keys with one million bits length, and a secret key of approximately 70000 bits, at a quantum bit error rate of 6%, with ε‐security of 10 −10 are generated. This study demonstrates the first proof of concept finite‐key BB84 QKD system realized with hBN defects.
Sentiment analysis classification system using hybrid BERT models

Abstract Because of the rapid growth of mobile technology, social media has become an essential platform for people to express their views and opinions. Understanding public opinion can help businesses and political institutions make strategic decisions. Considering this, sentiment analysis is critical for understanding the polarity of public opinion. Most social media analysis studies divide sentiment into three categories: positive, negative, and neutral. The proposed model is a machine-learning application of a classification problem trained on three datasets. Recently, the BERT model has demonstrated effectiveness in sentiment analysis. However, the accuracy of sentiment analysis still needs to be improved. We propose four deep learning models based on a combination of BERT with Bidirectional Long ShortTerm Memory (BiLSTM) and Bidirectional Gated Recurrent Unit (BiGRU) algorithms. The study is based on pre-trained word embedding vectors that aid in the model fine-tuning process. The proposed methods are trying to enhance accuracy and check the effect of hybridizing layers of BIGRU and BILSTM on both Bert models (DistilBERT, RoBERTa) for no emoji (text sentiment classifier) and also with emoji cases. The proposed methods were compared to two pre-trained BERT models and seven other models built for the same task using classical machine learning. The proposed architectures with BiGRU layers have the best results.
Metaverse meets digital entrepreneurship: a practitioner-based qualitative synthesis

Purpose Considering the pervasiveness of technology, this article offers an understanding of how the metaverse can impact digital entrepreneurship. The objective will be to gather professional evidence on how the revival of this new technology can bring entrepreneurial development. Design/methodology/approach Through a qualitative study approach based on applying the metaverse in digital business contexts and analysing 533 practitioner sources from the Nexis Uni database, it will be possible to identify the concepts and application techniques of this emerging technology. The research adopts a qualitative methodology based on a mixed thematic and content review using tools such as ATLAS.ti and Leximancer. Findings This study finds three relevant macro-topics for metaverse and digital entrepreneurship (technology, immersive and design) and eight concepts (private solutions, digital twins, gamification, public solutions, new business worlds, co-design, collaborative spaces and stakeholders' participation). The uncovered elements demonstrate professionals' interest in a new mode of digital entrepreneurship using the metaverse. This interest highlights the commitment of companies and entrepreneurs toward discovering new services delivered in virtual and parallel worlds that find the creation of digital twins as their essence. Therefore, the study explores ongoing relationships for developing increasingly technically complex metaverse platforms and customer service offerings. Research limitations/implications The study has some limitations as the selection of the database and the way the cases are focused on, which may be a stimulus for future studies. The analysis has innumerable theoretical and practical implications. In the first case, this research will shed light on an empirical case concerning the conceptual difference between innovation or greater transformation of business models through the metaverse. This work will directly contribute to the global discussion by identifying a model for applying emerging technology to digital entrepreneurs. Finally, from a practical point of view, the authors will provide new insights to digital entrepreneurs by showing them applications, best practices, and platforms they can use for their businesses. Practical implications On a practical level, the authors show practical opportunities coming from the metaverse for digital entrepreneurs. This study may inspire CEOs, managers, and future entrepreneurs to use the metaverse to expand their businesses by diversifying their services into numerous sectors. Originality/value To the best of the authors' knowledge, this study represents one of the first efforts to study the metaverse by framing it from theoretical and practical perspectives of digital entrepreneurship. Additionally, future research implications may guide researchers in this brilliant research field.
Classical and Heuristic Approaches for Mobile Robot Path Planning: A Survey

The most important research area in robotics is navigation algorithms. Robot path planning (RPP) is the process of choosing the best route for a mobile robot to take before it moves. Finding an ideal or nearly ideal path is referred to as “path planning optimization.” Finding the best solution values that satisfy a single or a number of objectives, such as the shortest, smoothest, and safest path, is the goal. The objective of this study is to present an overview of navigation strategies for mobile robots that utilize three classical approaches, namely: the roadmap approach (RM), cell decomposition (CD), and artificial potential fields (APF), in addition to eleven heuristic approaches, including the genetic algorithm (GA), ant colony optimization (ACO), artificial bee colony (ABC), gray wolf optimization (GWO), shuffled frog-leaping algorithm (SFLA), whale optimization algorithm (WOA), bacterial foraging optimization (BFO), firefly (FF) algorithm, cuckoo search (CS), and bat algorithm (BA), which may be used in various environmental situations. Multiple issues, including dynamic goals, static and dynamic environments, multiple robots, real-time simulation, kinematic analysis, and hybrid algorithms, are addressed in a different set of articles presented in this study. A discussion, as well as thorough tables and charts, will be presented at the end of this work to help readers understand what types of strategies for path planning are developed for use in a wide range of ecological contexts. Therefore, this work’s main contribution is that it provides a broad view of robot path planning, which will make it easier for scientists to study the topic in the near future.
A Hybrid Human-in-the-Loop Deep Reinforcement Learning Method for UAV Motion Planning for Long Trajectories with Unpredictable Obstacles

Unmanned Aerial Vehicles (UAVs) can be an important component in the Internet of Things (IoT) ecosystem due to their ability to collect and transmit data from remote and hard-to-reach areas. Ensuring collision-free navigation for these UAVs is crucial in achieving this goal. However, existing UAV collision-avoidance methods face two challenges: conventional path-planning methods are energy-intensive and computationally demanding, while deep reinforcement learning (DRL)-based motion-planning methods are prone to make UAVs trapped in complex environments—especially for long trajectories with unpredictable obstacles—due to UAVs’ limited sensing ability. To address these challenges, we propose a hybrid collision-avoidance method for the real-time navigation of UAVs in complex environments with unpredictable obstacles. We firstly develop a Human-in-the-Loop DRL (HL-DRL) training module for mapless obstacle avoidance and secondly establish a global-planning module that generates a few points as waypoint guidance. Moreover, a novel goal-updating algorithm is proposed to integrate the HL-DRL training module with the global-planning module by adaptively determining the to-be-reached waypoint. The proposed method is evaluated in different simulated environments. Results demonstrate that our approach can rapidly adapt to changes in environments with short replanning time and prevent the UAV from getting stuck in maze-like environments.
Cognitive Network Science Reveals Bias in GPT-3, GPT-3.5 Turbo, and GPT-4 Mirroring Math Anxiety in High-School Students

Large Language Models (LLMs) are becoming increasingly integrated into our lives. Hence, it is important to understand the biases present in their outputs in order to avoid perpetuating harmful stereotypes, which originate in our own flawed ways of thinking. This challenge requires developing new benchmarks and methods for quantifying affective and semantic bias, keeping in mind that LLMs act as psycho-social mirrors that reflect the views and tendencies that are prevalent in society. One such tendency that has harmful negative effects is the global phenomenon of anxiety toward math and STEM subjects. In this study, we introduce a novel application of network science and cognitive psychology to understand biases towards math and STEM fields in LLMs from ChatGPT, such as GPT-3, GPT-3.5, and GPT-4. Specifically, we use behavioral forma mentis networks (BFMNs) to understand how these LLMs frame math and STEM disciplines in relation to other concepts. We use data obtained by probing the three LLMs in a language generation task that has previously been applied to humans. Our findings indicate that LLMs have negative perceptions of math and STEM fields, associating math with negative concepts in 6 cases out of 10. We observe significant differences across OpenAI’s models: newer versions (i.e., GPT-4) produce 5× semantically richer, more emotionally polarized perceptions with fewer negative associations compared to older versions and N=159 high-school students. These findings suggest that advances in the architecture of LLMs may lead to increasingly less biased models that could even perhaps someday aid in reducing harmful stereotypes in society rather than perpetuating them.