Reviewerly/exHarmony huggingface dataset:
Adoption of AI Chatbots in Travel and Tourism Services

AI chatbots are possessing significant importance due to the advent of emerging technologies, such as artificial intelligence. They are used in many industries to interact with customers, respond to frequently asked inquiries, and carry out specific duties for which they were designed. More AI enabled chatbots are being used in the tourism industry to provide better customer service to the travellers. In the literature review, researches relating to adoption of AI chatbots in tourism are scarce. The paper extends the UTAUT2 model and examines the aspects that impact consumer adoption of AI chatbots in the travel and tourism segment. We used a quantitative research approach and the data was collected through online surveys. A total of 466 responses were collected from frequent travellers. Partial least square approach was utilized to do the data analysis. The emanating outcome of this research study divulge the aspects of performance expectancy, effort expectancy, habit, personal innovativeness, and awareness of chatbot service optimistically influenced the behavioural intention to make use of AI chatbots for tourism services. Awareness of service also indirectly influenced intention to use through performance and effort expectancies. This paper offers unique insights for executives, practitioners, and employees at managerial level in the tourism sector, along with system designers and creators of AI chatbot technologies.
Metaverse and Tourism Industry: A Conceptual Proposition

The physical universe has a very limited range of possibilities. To access a virtual world with new opportunities for interactions and living, we must develop a brand-new gateway. The idea of a virtual world where individuals can communicate with each other without leaving the actual world is essentially what the metaverse is. Future versions of the metaverse will be built on blockchain technology and provide everyone with equal opportunities to participate. The tourism sector has greatly benefited from the opportunities generated by the metaverse. By engaging in a brand-new, more immersive type of virtual travel, customers can enjoy the advantages of metaverse tourism. Additionally, hotels and other hospitality businesses can leverage the metaverse to improve operational efficiency, including event planners. The metaverse offers an excellent platform for enhancing the marketing efforts of travel and hospitality companies.
A self-supervised language model selection strategy for biomedical question answering

Large neural-based Pre-trained Language Models (PLM) have recently gained much attention due to their noteworthy performance in many downstream Information Retrieval (IR) and Natural Language Processing (NLP) tasks. PLMs can be categorized as either general-purpose, which are trained on resources such as large-scale Web corpora, and domain-specific which are trained on in-domain or mixed-domain corpora. While domain-specific PLMs have shown promising performance on domain-specific tasks, they are significantly more computationally expensive compared to general-purpose PLMs as they have to be either retrained or trained from scratch. The objective of our work in this paper is to explore whether it would be possible to leverage general-purpose PLMs to show competitive performance to domain-specific PLMs without the need for expensive retraining of the PLMs for domain-specific tasks. By focusing specifically on the recent BioASQ Biomedical Question Answering task, we show how different general-purpose PLMs show synergistic behaviour in terms of performance, which can lead to overall notable performance improvement when used in tandem with each other. More concretely, given a set of general-purpose PLMs, we propose a self-supervised method for training a classifier that systematically selects the PLM that is most likely to answer the question correctly on a per-input basis. We show that through such a selection strategy, the performance of general-purpose PLMs can become competitive with domain-specific PLMs while remaining computationally light since there is no need to retrain the large language model itself. We run experiments on the BioASQ dataset, which is a large-scale biomedical question-answering benchmark. We show that utilizing our proposed selection strategy can show statistically significant performance improvements on general-purpose language models with an average of 16.7% when using only lighter models such as DistilBERT and DistilRoBERTa, as well as 14.2% improvement when using relatively larger models such as BERT and RoBERTa and so, their performance become competitive with domain-specific large language models such as PubMedBERT.
Intelligent detection and behavior tracking under ammonia nitrogen stress

In this paper, a novel YOLO-based detection model with deformable convolution network (DCN-YOLOv5) is developed, which is concerned with the object and key points detection and behavior tracking problem for Oplegnathus punctatus in the ammonia nitrogen environment. The proposed model can adapt to the posture change of the object by deforming the receptive field, which solves the problem of false and missed detection caused by the movement and occlusion. Moreover, a new multi-object multi-category tracking algorithm (MOMC-Tracking) is proposed to track and plot the trajectory and calculate the key behavioral characteristics parameters. In addition, an executable software which integrates the proposed DCN-YOLOv5 model and the MOMC-Tracking algorithm is proposed. Extensive experiments show that compared with the typical YOLO series of algorithms, the proposed model in this paper performs the best with the highest accuracy and the fastest convergence speed, where the mAP@0.5 and mAP@0.5:0.95 of the proposed DCN-YOLOv5 model are 93.71% and 57.45%, which are respectively improved by 1.78% and 24.77% as compared with those obtained by the original YOLOv5 model.
Dual Swin-transformer based mutual interactive network for RGB-D salient object detection

Depth information for RGB-D Salient Object Detection(SOD) is important and conventional deep models are usually relied on the CNN feature extractors. The long-range contextual dependencies, dense modeling on the saliency decoder, and multi-task learning assistance are usually ignored. In this work, we propose a Dual Swin-Transformer-based Mutual Interactive Network (DTMINet), aiming to learn contextualized, dense, and edge-aware features for RGB-D SOD. We adopt the Swin-Transformer as the visual backbone to extract contextualized features. A self-attention-based Cross-Modality Interaction module is proposed to strengthen the visual backbone for cross-modal interaction. In addition, a Gated Modality Attention module is designed for cross-modal fusion. At different decoding stages, enhanced with dense connections and progressively merge the multi-level encoding features with the proposed Dense Saliency Decoder. Considering the depth quality issue, a Skip Convolution module is introduced to provide guidance to the RGB modality for the saliency prediction. In addition, we add the edge prediction to the saliency predictor to regularize the learning process. Comprehensive experiments on five standard RGB-D SOD benchmark datasets over four evaluation metrics demonstrate the superiority of the proposed method.
WAID: A Large-Scale Dataset for Wildlife Detection with Drones

Drones are widely used for wildlife monitoring. Deep learning algorithms are key to the success of monitoring wildlife with drones, although they face the problem of detecting small targets. To solve this problem, we have introduced the SE-YOLO model, which incorporates a channel self-attention mechanism into the advanced real-time object detection algorithm YOLOv7, enabling the model to perform effectively on small targets. However, there is another barrier; the lack of publicly available UAV wildlife aerial datasets hampers research on UAV wildlife monitoring algorithms. To fill this gap, we present a large-scale, multi-class, high-quality dataset called WAID (Wildlife Aerial Images from Drone), which contains 14,375 UAV aerial images from different environmental conditions, covering six wildlife species and multiple habitat types. We conducted a statistical analysis experiment, an algorithm detection comparison experiment, and a dataset generalization experiment. The statistical analysis experiment demonstrated the dataset characteristics both quantitatively and intuitively. The comparison and generalization experiments compared different types of advanced algorithms as well as the SE-YOLO method from the perspective of the practical application of UAVs for wildlife monitoring. The experimental results show that WAID is suitable for the study of wildlife monitoring algorithms for UAVs, and SE-YOLO is the most effective in this scenario, with a mAP of up to 0.983. This study brings new methods, data, and inspiration to the field of wildlife monitoring by UAVs.
Preventing Crimes Through Gunshots Recognition Using Novel Feature Engineering and Meta-Learning Approach

Gunshot sounds are common in crimes, particularly those involving threats, harassment, or killing. The gunshot sounds in crimes can create fear and panic among victims, often leading to psychological trauma. Gunshot sounds are associated with a significant mortality rate, especially in cases of gun violence. The sound of gunshots can serve as evidence in criminal investigations, allowing law enforcement officials to determine the number of shots fired, the caliber of the gun used, and the distance from which the shots were fired. Efficient gunshot detection is necessary to address the issue of gun violence in society. This study aims to detect gunshot sounds using an efficient approach to prevent crimes. The frequency-time domain spectrum analysis is performed to understand the patterns of signals related to each target class. A novel Discrete Wavelet Transform Random Forest Probabilistic (DWT-RFP) feature engineering approach is proposed, which takes Mel-frequency cepstral coefficients (MFCC) extracted from gunshot sound data as input for feature extraction. A novel meta-learning-based Meta-RF-KN (MRK) is proposed to detect gunshots based on newly created ensemble features with a DWT-RFP approach. For experiments, the gunshot sounds dataset containing 851 audio clips collected from public videos on YouTube from eight kinds of gun models, is used. Advanced machine learning and deep learning techniques are applied in comparison to evaluate the performance of the proposed approach. Extensive experiments show that the proposed MRK approach achieves 99% k-fold accuracy for detecting gunshots and outperforms state-of-the-art approaches. The proposed approach can potentially be used for accurate gunshot detection and to help prevent crimes.
An Interpretable Dynamic Inference System Based on Fuzzy Broad Learning

Fuzzy broad learning system (FBLS) is a fuzzy neural model proposed in recent years, which combines the efficient performance of broad learning and the interpretability of fuzzy systems. However, the current research on the interpretability of fuzzy broad learning is only limited to the partial network layer, which hinders the model’s ability to express rules and extract knowledge. A Takagi-Sugeno-Kang (TSK) fuzzy inference system with strong expressive power and adaptive network structure is explored and developed under the fuzzy broad learning framework in this paper. It improves the accuracy of neural fuzzy models and addresses the challenge of poor interpretability in fuzzy broad learning. Firstly, to build an expressive power model, a set of TSK fuzzy system outputs is used as a mapped layer and interpretable linguistic fuzzy rules (ILFR) are embedded in the enhancement layer. Secondly, an incremental learning method is proposed to allow the parameters to be adaptively adjusted and the network structure to be optimized for the rule explosion problem and network structure redundancy. Finally, the Schur complement method is used instead of the ridge regression method to find the pseudo-inverse to improve the model learning efficiency. The experiments show that the proposed model has a compact structure and good classifiably while having interpretability.
High-fidelity parametric beamsplitting with a parity-protected converter

Fast, high-fidelity operations between microwave resonators are an important tool for bosonic quantum computation and simulation with superconducting circuits. An attractive approach for implementing these operations is to couple these resonators via a nonlinear converter and actuate parametric processes with RF drives. It can be challenging to make these processes simultaneously fast and high fidelity, since this requires introducing strong drives without activating parasitic processes or introducing additional decoherence channels. We show that in addition to a careful management of drive frequencies and the spectrum of environmental noise, leveraging the inbuilt symmetries of the converter Hamiltonian can suppress unwanted nonlinear interactions, preventing converter-induced decoherence. We demonstrate these principles using a differentially-driven DC-SQUID as our converter, coupled to two high-Q microwave cavities. Using this architecture, we engineer a highly-coherent beamsplitter and fast (~100 ns) swaps between the cavities, limited primarily by their intrinsic single-photon loss. We characterize this beamsplitter in the cavities' joint single-photon subspace, and show that we can detect and post-select photon loss events to achieve a beamsplitter gate fidelity exceeding 99.98%, which to our knowledge far surpasses the current state of the art.
Reinforcement Learning in Education: A Literature Review

The utilization of reinforcement learning (RL) within the field of education holds the potential to bring about a significant shift in the way students approach and engage with learning and how teachers evaluate student progress. The use of RL in education allows for personalized and adaptive learning, where the difficulty level can be adjusted based on a student’s performance. As a result, this could result in heightened levels of motivation and engagement among students. The aim of this article is to investigate the applications and techniques of RL in education and determine its potential impact on enhancing educational outcomes. It compares the various policies induced by RL with baselines and identifies four distinct RL techniques: the Markov decision process, partially observable Markov decision process, deep RL network, and Markov chain, as well as their application in education. The main focus of the article is to identify best practices for incorporating RL into educational settings to achieve effective and rewarding outcomes. To accomplish this, the article thoroughly examines the existing literature on using RL in education and its potential to advance educational technology. This work provides a thorough analysis of the various techniques and applications of RL in education to answer questions related to the effectiveness of RL in education and its future prospects. The findings of this study will provide researchers with a benchmark to compare the usefulness and effectiveness of commonly employed RL algorithms and provide direction for future research in education.
Impact of Deep Convolutional Neural Network Structure on Photovoltaic Array Extraction from High Spatial Resolution Remote Sensing Images

Accurate information on the location, shape, and size of photovoltaic (PV) arrays is essential for optimal power system planning and energy system development. In this study, we explore the potential of deep convolutional neural networks (DCNNs) for extracting PV arrays from high spatial resolution remote sensing (HSRRS) images. While previous research has mainly focused on the application of DCNNs, little attention has been paid to investigating the influence of different DCNN structures on the accuracy of PV array extraction. To address this gap, we compare the performance of seven popular DCNNs—AlexNet, VGG16, ResNet50, ResNeXt50, Xception, DenseNet121, and EfficientNetB6—based on a PV array dataset containing 2072 images of 1024 × 1024 size. We evaluate their intersection over union (IoU) values and highlight four DCNNs (EfficientNetB6, Xception, ResNeXt50, and VGG16) that consistently achieve IoU values above 94%. Furthermore, through analyzing the difference in the structure and features of these four DCNNs, we identify structural factors that contribute to the extraction of low-level spatial features (LFs) and high-level semantic features (HFs) of PV arrays. We find that the first feature extraction block without downsampling enhances the LFs’ extraction capability of the DCNNs, resulting in an increase in IoU values of approximately 0.25%. In addition, the use of separable convolution and attention mechanisms plays a crucial role in improving the HFs’ extraction, resulting in a 0.7% and 0.4% increase in IoU values, respectively. Overall, our study provides valuable insights into the impact of DCNN structures on the extraction of PV arrays from HSRRS images. These findings have significant implications for the selection of appropriate DCNNs and the design of robust DCNNs tailored for the accurate and efficient extraction of PV arrays.
Learning Spaces in Higher Education: A State-of-the-Art Review

Educational learning spaces encompass a spectrum, from traditional classrooms to contemporary online platforms and immersive virtual reality settings, fostering versatile teaching methods like collaboration, project-based, and experiential learning. Our study delves into modern higher education environments, particularly the shift from conventional to innovative spaces. Our focus centers on the benefits and challenges intrinsic to these emerging learning spaces, intending to enlighten educators, policymakers, and researchers. Our exploration commences by revealing the limitations entrenched within traditional learning environments, emphasizing the vital need for inventive solutions to meet evolving educational demands. We further investigate diverse learning scenarios, ranging from hybrid and remote setups to the integration of online platforms and virtual tools. Through this lens we navigate complexities introduced by these novel modalities, including potential reductions in face-to-face interactions and heightened demand for adept instructional and technological support. Lastly, our inquiry underscores the disparities between traditional and contemporary learning spaces, accentuating the potential for innovative settings to elevate higher education quality. Here, we illuminate the anticipated merits of such spaces, notably heightened student engagement, enriched collaboration, and amplified creativity. Concurrently, we explore technology’s pivotal role in shaping learning environments and ultimately influencing pedagogical methodologies. Our future research will explore how Artificial Intelligence can improve higher education.
Why Fixed Costs Matter for Proof-of-Work–Based Cryptocurrencies

We assess how the cost structure of cryptocurrency mining affects the response of miners to exchange rate fluctuations and the immutability of cryptocurrency ledgers that rely on proof-of-work. We show that the amount of mining power supplied to currencies that rely on specialized hardware, such as Bitcoin, responds less to adverse exchange rate shocks than other currencies respond to such shocks, a fact that is instrumental to avoiding double-spending attacks. The results may change if mining equipment used for one cryptocurrency can be transferred to another. For smaller currencies with low exchange rate correlation, transferability eliminates the protection that fixed costs provide. Our results weaken doomsday predictions for Bitcoin and other cryptocurrencies with declining block rewards. This paper was accepted by Bruno Biais, Special Section of Management Science: Blockchains and Crypto Economics. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2023.4901 .
Development of zero-noise extrapolated projective quantum algorithm for accurate evaluation of molecular energetics in noisy quantum devices

The recently developed Projective Quantum Eigensolver (PQE) offers an elegant procedure to evaluate the ground state energies of molecular systems in quantum computers. However, the noise in available quantum hardware can result in significant errors in computed outcomes, limiting the realization of quantum advantage. Although PQE comes equipped with some degree of inherent noise resilience, any practical implementation with apposite accuracy would require additional routines to eliminate or mitigate the errors further. In this work, we propose a way to enhance the efficiency of PQE by developing an optimal framework for introducing Zero Noise Extrapolation (ZNE) in the nonlinear iterative procedure that outlines the PQE, leading to the formulation of ZNE-PQE. Moreover, we perform a detailed analysis of how various components involved in it affect the accuracy and efficiency of the reciprocated energy convergence trajectory. Additionally, we investigate the underlying mechanism that leads to the improvements observed in ZNE-PQE over conventional PQE by performing a comparative analysis of their residue norm landscape. This approach is expected to facilitate practical applications of quantum computing in fields related to molecular sciences, where it is essential to determine molecular energies accurately.
Roadmap for integrating blockchain with Internet of Things (IoT) for sustainable and secured operations in logistics and supply chains: Decision making framework with case illustration

Blockchain and Internet of Things (IoT) technologies have considerable potential to achieve sustainability in logistics and supply chain management. However, the integration of blockchain-IoT has not been explored extensively in past research. This research provides a complete analysis of the enablers for using blockchain-IoT to manage logistics and supply chains. It aims to explore and prioritise various enablers for its adoption. The Diffusion of Innovation and the Technology-Organisation-Environment theories are used in this research to examine blockchain-IoT adoption enablers. The enablers identification is confirmed using the Fuzzy-Delphi technique and then analysed using the fuzzy - Decision-Making Trial and Evaluation Laboratory tool to determine the strength of cause-effect hierarchical framework. The sensitivity analysis is also carried out to address the robustness of the results obtained. In order to measure the extent of a firm's readiness to adopt blockchain-IoT, a framework based on Graph Theory Matrix Method is proposed. It is illustrated with the help of a real life case. The results revealed that 'sustainability focus', 'emphasis on emerging technology by organisation', and 'technology maturity' are the top three enablers for blockchain-IoT adoption. Findings of study may motivate managers to include blockchain-IoT into their strategic planning processes based on their categorization of enablers into cause and effect categories. The research findings may also be used to the creation of efficient Blockchain-IoT deployment methods.