Reviewerly/exHarmony huggingface dataset:
The Theoretical Background of Virtual Reality and Its Implications for the Tourism Industry

Fueled by the Internet and artificial intelligence, virtual tourism represents an innovative integration of advanced technologies within the tourism industry. The 360 degree panoramic video has emerged as the most prevalent form of virtual tourism both domestically and internationally, owing to its affordability, ease of use, and mild impact. Nevertheless, the advent and proliferation of virtual reality (VR) has unlocked new opportunities in the tourism sector, as VR offers enhanced immersion and a more authentic user experience. Although VR is already employed in various capacities within the industry, it holds the potential to elevate virtual tourism to unprecedented heights. Diverging from prior research, this study examines virtual tourism as a distinct category of tourism that may materialize in the future. A focus group interview and a survey involving 243 respondents were conducted to gauge individuals’ receptiveness to virtual tourism. Participants responded to questions regarding their travel habits and their perceptions of virtual tourism. Data analysis revealed concerns surrounding the technological aspects of virtual tourism: over 20% of respondents were entirely unfamiliar with VR, and a mere 17% utilized the technology. A larger proportion, nearly 40%, lacked awareness of VR’s application in the tourism industry, and only 11.20% had personal experience with it. Concurrently, the Likert scale indicated that respondents recognized the advantages of virtual tourism in comparison to traditional travel, but they expressed skepticism about the technological advancements. Overall, most respondents displayed curiosity and a willingness to explore virtual tourism. These findings hold significant implications for both theoretical understanding and practical implementation within the tourism industry.
An empirical study of vulnerabilities in edge frameworks to support security testing improvement

Abstract Edge computing is a distributed computing paradigm aiming at ensuring low latency in modern data intensive applications (e.g., video streaming and IoT). It consists of deploying computation and storage nodes close to the end-users. Unfortunately, being distributed and close to end-users, Edge systems have a wider attack surface (e.g., they may be physically reachable) and are more complex to update than other types of systems (e.g., Cloud systems) thus requiring thorough security testing activities, possibly tailored to be cost-effective. To support the development of effective and automated Edge security testing solutions, we conducted an empirical study of vulnerabilities affecting Edge frameworks. The study is driven by eight research questions that aim to determine what test triggers, test harnesses, test oracles, and input types should be considered when defining new security testing approaches dedicated to Edge systems. preconditions and inputs leading to a successful exploit, the security properties being violated, the most frequent vulnerability types, the software behaviours and developer mistakes associated to these vulnerabilities, and the severity of Edge vulnerabilities. We have inspected 147 vulnerabilities of four popular Edge frameworks. Our findings indicate that vulnerabilities slip through the testing process because of the complexity of the Edge features. Indeed, they can’t be exhaustively tested in-house because of the large number of combinations of inputs, outputs, and interfaces to be tested. Since we observed that most of the vulnerabilities do not affect the system integrity and, further, only one action (e.g., requesting a URL) is sufficient to exploit a vulnerability
Data-informed reservoir computing for efficient time-series prediction

We propose a new approach to dynamical system forecasting called data-informed-reservoir computing (DI-RC) that, while solely being based on data, yields increased accuracy, reduced computational cost, and mitigates tedious hyper-parameter optimization of the reservoir computer (RC). Our DI-RC approach is based on the recently proposed hybrid setup where a knowledge-based model is combined with a machine learning prediction system, but it replaces the knowledge-based component by a data-driven model discovery technique. As a result, our approach can be chosen when a suitable knowledge-based model is not available. We demonstrate our approach using a delay-based RC as the machine learning component in conjunction with sparse identification of nonlinear dynamical systems for the data-driven model component. We test the performance on two example systems: the Lorenz system and the Kuramoto-Sivashinsky system. Our results indicate that our proposed technique can yield an improvement in the time-series forecasting capabilities compared with both approaches applied individually, while remaining computationally cheap. The benefit of our proposed approach, compared with pure RC, is most pronounced when the reservoir parameters are not optimized, thereby reducing the need for hyperparameter optimization.
USMLP: U-shaped Sparse-MLP network for mass segmentation in mammograms

Convolutional neural networks (CNN) are widely used in computer-aided diagnosis (CAD). However, there are several limitations of this structure for mass segmentation in mammograms, which may lead to incorrect segmentation results. Recently, multi-layer perceptron (MLP) based methods introduce a new way to solve computer vision (CV) problems. In this paper we propose U-shaped Sparse-MLP (USMLP), which is a MLP-based segmentation model with U-shaped architecture. Our proposed method consists of CNN layers and sparse MLP (sMLP) blocks. Detailed experiments on two public datasets show that our method achieves state-of-the-art performance while remaining efficiency, compared with other benchmarks. We hope the investigation of new network architecture can be beneficial to mass segmentation tasks as well as CAD systems.
Geochemical Baseline and Pre-Mining Environmental Assessment of Heavy Metals at Iron Exploration Area, Northeastern Aswan, Egypt

Abstract Wadi Abu Subeira area contains many farms and houses and is one of the promising areas for iron mining. Therefore, 21 surface soil samples were collected and investigated for toxic heavy metals (Pb, Cr, Ni, Cu, Zn, Co, and As) using inductively coupled plasma (ICP) to establish a geochemical baseline for these metals during pre-mining conditions. To decipher the sources of these metals and their interrelationships, multivariate statistical analysis was applied, while to evaluate the degree of pollution and potential environmental risks the environmental indices were used. Abundances of Pb, Cr, Ni, Cu, Zn, Co, and As fluctuated from 17.72 to 0.06, 47.12 to 10.86, 47.88 to 9.25, 45.04 to 6.23, 51.93 to 17.82, 10.55 to 1.24, and 7.04 to 1.66 mg/kg, respectively, displaying a declining trend of Zn &gt; Cr &gt; Ni &gt; Cu &gt; As &gt; Co &gt; Pb. Additionally, the mean concentrations of all studied metals were found to be significantly lower than the selected international reference standards. Pearson correlation coefficient, principal component analysis, and cluster analysis revealed two geogenic geochemical associations for the studied toxic elements: (1) Zn-As-Ni-Cr-Cu-Co; and (2) Pb. Negative I geo values were observed for all metals, which showed that the samples were uncontaminated and can be considered a geochemical baseline for the study area. Moreover, all CF values were lower than or close to 1, suggesting low contamination levels from all studied metals and supporting the association with natural geological processes. Similarly, Er and RI values of all metals were below 40 and 150, respectively, indicating a low-risk environment. Ultimately, the obtained levels of the studied metals can be used as a geochemical baseline for tracking the future changes in their accumulations in soil sediments considering the current assessment of the area as an environmentally safe area.
Multi-scale convolutional attention network for lightweight image super-resolution

Convolutional neural network (CNN) based methods have recently achieved extraordinary performance in single image super-resolution (SISR) tasks. However, most existing CNN-based approaches increase the model's depth by stacking massive kernel convolutions, bringing expensive computational costs and limiting their application in mobile devices with limited resources. Furthermore, large kernel convolutions are rarely used in lightweight super-resolution designs. To alleviate the above problems, we propose a multi-scale convolutional attention network (MCAN), a lightweight and efficient network for SISR. Specifically, a multi-scale convolutional attention (MCA) is designed to aggregate the spatial information of different large receptive fields. Since the contextual information of the image has a strong local correlation, we design a local feature enhancement unit (LFEU) to further enhance the local feature extraction. Extensive experimental results illustrate that our proposed MCAN can achieve better performance with lower model complexity compared with other state-of-the-art lightweight methods.
Peer to Peer Solar Energy Trading Demonstrator Blockchain-enabled

By combining distributed generation, battery storage, and smart meters, microgrid performance can be enhanced. As a result, distributed energy resources (DER) can be intelligently controlled via online platforms. Through integration, prosumers that use decentralized resources like wind power have replaced conventional power customers. Due to the growth and digitalization of the power distribution infrastructure, a new method of exchanging electricity in community microgrids called peer-to-peer (P2P) intratrading has emerged. Blockchain technology is used because it is transparent, secure, and completes transactions quickly. Prosumers, consumers, and owners of renewable energy sources may now trade energy more easily amongst one another. This study develops the P2P paradigm to create a self-sufficient community microgrid system for trading energy. Incorporating peer-to-peer energy trades and a battery backup system, the suggested technique uses blockchain to simulate a decentralized microgrid energy market. The microgrid P2P market's clearing price is established by taking into account customers' expected reactions to price changes, incentivizing consumers and prosumers to alter their patterns of energy usage. A cryptocurrency called Cosmos that is mined and distributed using blockchain technology is also included in the P2P market idea. The results show how automated P2P commerce and adjustable energy storage enable end users to save energy and become more independent. This paper provides a sustainable microgrid energy market model and suggests an approach to improve security by incorporating blockchain technology into current energy management systems. In summary, the paper offers a design paradigm for an independent microgrid system that makes use of distributed energy sources. It demonstrates how end users may gain from energy efficiency and independence while highlighting the possibilities of P2P energy trade and storage variety offered by blockchain. This thorough method provides information on creating a sustainable microgrid energy market and illustrates how blockchain technology can be used to increase the safety of energy management systems.
Impression Network for Video Object Detection

Object detection in videos is more challenging than in static images. It’s proved that naively applying object detector frame by frame can be slow and inaccurate - visual clues can get weakened by defocus and motion blur, causing failure on corresponding frames. Multi-frame feature fusion methods proved effective in improving the accuracy, but they largely sacrifice the speed. Feature propagation methods proved effective in improving the speed, at the cost of lower accuracy. So is it possible to improve speed and performance at once?Inspired by how human utilize impression to recognize objects from blurry frames, we propose Impression Network that embodies a natural and efficient feature aggregation mechanism. In our framework, an impression feature is established by iteratively absorbing sparsely extracted frame features. The impression feature is propagated all the way down the video, enhancing features of low-quality frames. This impression mechanism makes it possible to perform long-range multi-frame feature fusion among sparse keyframes with minimum overhead. We demonstrate that Impression Network is significantly more accurate than per-frame detection baseline on ImageNet VID, while being 3 times faster (20 fps).
Understanding the Threat Posed by Chinese Cyber Warfare Units

The emergence of China as a global superpower has presented numerous challenges, among which is the growing threat posed by Chinese cyber warfare units. Operating under the People's Liberation Army (PLA), these units have been implicated in various high-profile cyber-attacks targeting both civilian and military entities worldwide. This paper endeavors to offer a comprehensive analysis of the risks presented by Chinese cyber warfare units, encompassing their organizational structure, tactics, and capabilities. By elucidating the nature and extent of this menace, we hope to enable governments and organizations to implement measures for better protection against Chinese cyber-attacks, as 80% of them target government bodies. In this study, we utilize the MITRE ATT&CK framework as a foundation to deliver an accessible, informative overview.
Teacher educators’ professional agency in facilitating professional digital competence

This study builds on a comprehensive conceptualisation of teachers' professional digital competence (PDC) that goes beyond the use of technology for teaching and learning. We investigate teacher educators' facilitation of not only generic and didactic digital competence but also profession-oriented and transformative aspects. Drawing on professional agency, we analysed interviews with teacher educators in Norway. Most of them neglected the profession-oriented and transformative PDC aspects. The interplay of teacher educators' competence, colleagues, task perception, and course descriptions contributes to their PDC facilitation. The findings imply the need to move beyond the tool focus and increase collaboration among teacher educators.
AI and the social construction of creativity

Artificial Intelligence (AI) encroaches on new terrains of human activity by dint of its efficacy and an expanding ability to autonomously incorporate information from many disciplines and sources. In this paper, we focus specifically on how AI affects the communicative practices associated with creativity. AI has the capacity to reshape discipline and taste communities by providing new content that competes with human production and by mediating between human activity and information sources. To frame these issues, we turn to the influential systems model of creativity devised by Mihaly Csikszentmihalyi (1996) , which Csikszentmihalyi and Daniel Gruner (2018) recently extended to incorporate AI, redubbing it Creativity 4.0. The model assesses how AI affects the social structure of creative practice without overly accentuating the similarity between humans and AI, or questioning whether computational devices will replace creative jobs. The paper examines Gruner and Csikszentmihalyi’s revised systems model, arguing that it does not sufficiently take into account the variety of ways that AI can be incorporated into creative practice. Prompted by a theoretical reflection on the nature of the model and the emerging features of AI, we propose a new version of the model that highlights how embedded AIs play a key role in filtering and gatekeeping, as well as the importance of generative systems in informing creative practice. We propose that any discussion of AI and the future of creative practice should look at where and how AI supported technologies are used. We examine how AI can reduce and shape the qualitative diversity of sources of inspiration drawn into the creative process, with the associated technological biases, as well as provide an emergent platform for the development of novel ideas.
Characterization and mapping of photovoltaic solar power plants by Landsat imagery and random forest: A case study in Gansu Province, China

Numbers and sizes of photovoltaic solar power plants have grown unprecedentedly over the last few years in China, which aims to achieve a carbon emission peak by 2030 and carbon neutrality by 2060. Thus, timely and accurate monitoring of photovoltaic solar power plants is crucial to the design and management of renewable electricity systems in China. Random forest algorithm has been used to map photovoltaic solar power plants at multiple scales, however, it always causes several salt-and-pepper noises, limiting its application at larger spatial scales. Here we first develop a photovoltaic solar power plant mapping method through integrating time series Landsat imagery, random forest, and morphological characteristics. Then we apply this method in Gansu Province, which has abundant solar and wind energy resources and provide large amounts of potential lands for photovoltaic development, and generate the annual photovoltaic maps from 2015 to 2020. We further analyze the spatial-temporal dynamics of sizes and areas of photovoltaic solar power plants and major land cover conversion of expansive photovoltaic regions. Finally, we discuss the reliability, uncertainties, implications, and future development of our improved methods. We find our photovoltaic mapping method can remove most of salt-and-pepper noises effectively, and the resultant maps in Gansu for 2020 have very high accuracies with user's and producer's accuracies of 97.57% and 99.22%, respectively. There are 165.29 km2 photovoltaic solar power plants in Gansu for 2020, and most of which are located in the northwestern Gansu. In addition, the photovoltaic with patch size > 1 km2 and ≤ 2 km2 (53.4 km2, 32.3%) has largest patch number (39, 15.7%). The improved photovoltaic mapping methods and further analysis in this study provide critical information for accurate and automatic classification of photovoltaic solar power plants in the future, as well as the environmental and sustainable development of solar energy in China.
An Improved LSTM-Based Failure Classification Model for Financial Companies Using Natural Language Processing

The Korean e-commerce market represents a large percentage of the global retail distribution market, a market that continues to grow each year, and online payments are rapidly becoming a mainstream payment method. As e-commerce becomes more active, many companies that support electronic payments are increasing the number of franchisees. Electronic payments have become an indispensable part of people’s lives. However, the types of statistical information on the results of electronic payment transactions are not consistent across companies, and it is difficult to automatically determine the error status of a transaction if no one directly confirms the error messages generated during payment. To address these issues, we propose an optimized LSTM model. In this study, we classify the error content in statistical information based on natural language processing to determine the error status of the current failed transaction. We collected 11,865 response messages from various vendors and financial companies and labelled them with an LSTM classifier model to create a dataset. We then trained this dataset with simple RNN, LSTM, and GRU models and compared their performance. The results show that the optimized LSTM model with the attention layer added to the dropout layer and the bidirectional recursive layer achieves an accuracy of about 92% or more. When the model is applied to e-commerce services, any error in the transaction status of the system can be automatically detected by the model.
RICH: A rapid method for image-text cross-modal hash retrieval

Deep cross-modal hash retrieval (DCMHR) methods can effectively analyze the correlation of multimodal data while maintaining efficiency. However, to pursue better accuracy, most existing hash methods forget that the original purpose of introducing hash technology is to reduce training consumption, and overtraining also leads to overfitting. This paper proposes a rapid method for image-text cross-modal hash retrieval (RICH) based on DenseNet and multi-head attention (MHA) BOW (Bag of Words), which makes full use of unlabeled samples and uses EarlyStop in training. To fully extract image case features, we propose multiple dense feature sampling for cross-modal retrieval. It is worth noting that this method applies DenseNet and EarlyStop to unsupervised cross-modal retrieval for the first time and greatly reduces training costs while keeping good results. Then it is discussed that the MHA be carried in the TxtNet, which can extract neglected features. Furthermore, to alleviate the heterogeneous gap between different modalities, we also use the auxiliary similarity metrics. Experiments on three datasets show that the average performance of this method is higher than most of the DCMHR methods. In addition, compared to most of the SoTA unsupervised DCMHR methods, the training cost and stability of RICH are more excellent, which proves the effectiveness and superiority of this method.
UGV Coverage Path Planning: An Energy-Efficient Approach through Turn Reduction

With the advent and rapid growth of automation, unmanned ground vehicles (UGVs) have emerged as a crucial technology, with applications spanning various domains, from agriculture to surveillance, logistics, and military operations. Alongside this surge in the utilization of robotics, novel complications inevitably emerge, posing intriguing questions and challenges to the academic and technological sectors. One such pressing challenge is the coverage path planning (CPP) problem, particularly the notion of optimizing UGV energy utilization during path planning, a significant yet relatively unexplored aspect within the research landscape. While numerous studies have proposed solutions to CPP with a single UGV, the introduction of multiple UGVs within a single environment reveals a unique set of challenges. A paramount concern in multi-UGV CPP is the effective allocation and division of the area among the UGVs. To address this issue, we propose an innovative approach that first segments the area into multiple subareas, which are then allocated to individual UGVs. Our methodology employs fine-tuned spanning trees to minimize the number of turns during navigation, resulting in more efficient and energy-aware coverage paths. As opposed to existing research focusing on models that allocate without optimization, our model utilizes a terrain-aware cost function, and an adaptive path replanning module, leading to a more flexible, effective, and energy-efficient path-planning solution. A series of simulations demonstrated the robustness and efficacy of our approach, highlighting its potential to significantly improve UGV endurance and mission effectiveness, even in challenging terrain conditions. The proposed solution provides a substantial contribution to the field of UGV path planning, addressing a crucial gap and enhancing the body of knowledge surrounding energy-efficient CPP for multi-UGV scenarios.