Reviewerly/exHarmony huggingface dataset:
Literature Review Process:Measuring the Effective Usage of Knowledge Management Systems in Customer Support Organizations

Customer support organizations cannot be successful without a knowledge management system.The customer support world will come to a standstill without these wonderful knowledge management systems.Hence knowledge management systems are backbone to any customer support organizations.In order for the customer support to function smoothly, the relationship with knowledge management systems should be mutual.Therefore, measuring the effective usage of knowledge system is necessary to understand their demand in customer support organizations.This study aims to conduct a literature review processas a starting point to understand the variables associated with knowledge management system.This process is necessary to conduct the actual research.Initial idea of the research problem and conceptual framework is discussed in introduction section.Detailed literature review for sixteen research papers is explained in review of literature.This section starts with each independent variable and its corresponding research paper.Each research's literature review process is explained as research problem (what, so what, because why, goal), data collection and analysis, results, conclusions, supporting studies, limitations, implications, and impact.Finally, it is explained how the problem was revisited.Future research is to implement this research idea with the proposed methodology.
A Neural Network-Based Navigation Approach for Autonomous Mobile Robot Systems

A mobile robot is a futuristic technology that is changing the industry of automobiles as well as boosting the operations of on-demand services and applications. The navigation capability of mobile robots is a crucial task and one of the complex processes that guarantees moving from a starting position to a destination. To prevent any potential incidents or accidents, navigation must focus on the obstacle avoidance issue. This paper considers the navigation scenario of a mobile robot with a finite number of motion types without global environmental information. In addition, appropriate human decisions on motion types were collected in situations involving various obstacle features, and the corresponding environmental information was also recorded with the human decisions to establish a database. Further, an algorithm is proposed to train a neural network model via supervising learning using the collected data to replicate the human decision-making process under the same navigation scenario. The performance of the neural network-based decision-making method was cross-validated using both training and testing data to show an accuracy level close to 90%. In addition, the trained neural network model was installed on a virtual mobile robot within a mobile robot navigation simulator to interact with the environment and to make the decisions, and the results showed the effectiveness and efficacy of the proposed algorithm.
Plug‐and‐Play Single‐Photon Devices with Efficient Fiber‐Quantum Dot Interface

Incorporating solid-state quantum emitters into optical fiber networks enables the long-distance transmission of quantum information and the remote connection of distributed quantum nodes. However, interfacing quantum emitters with fiber optics encounters several challenges, including low coupling efficiency and stability. Here, we demonstrate a highly efficient fiber-interfacing photonic device that directly launches single photons from quantum dots into a standard FC/PC-connectorized single-mode fiber (SMF28). Optimally designed photonic structures based on hole gratings produce an ultra-narrow directional beam that matches the small numerical aperture of a single-mode fiber. A pick-and-place technique selectively integrates a single miniaturized device into the core of the fiber. Our approach realizes a plug-and-play single-photon device that does not require any optical alignment and thus guarantees long-term stability. The results thus represent a major step toward practical and reliable quantum lights across a fiber network.
An enabling Framework for Blockchain in Tourism

Abstract This viewpoint article proposes an enabling framework that identifies the use of various blockchain technologies in tourism and their applications (digitalization, automation, disintermediation, and intelligent environment) across the different stages of travel (pre-trip, during the trip, and post-trip). As we know, the tourism sector contributes immensely to world GDP and job creation. However, the COVID-19 pandemic, even after two years since it first appeared, continues to adversely impact the tourism prospects of countries across the world due to nationwide lockdowns and travel restrictions. As the world tries to adapt to the “new normal,“ the tourism sector is forced to re-think its ways of doing business and bring about innovations to facilitate the new norms of contactless and safe transactions. Also, the sector, more than ever, need to effectively deal with its inherent challenges such as transparency and credibility of information, fraudulent practices, opportunistic behavior of intermediaries, and foreign currency risks. Blockchain technology can transform the tourism sector by offering innovative solutions that address its pressing issues. However, our current understanding of blockchain application in tourism is quite limited, with previous work being largely fragmented and narrow in terms of both scope and application. We foresee that the insights offered in this viewpoint, including the framework, will advance both theory and practice and facilitate the implementation of blockchain-enabled solutions across different travel stages.
Investigating the Fidelity of Explainable Artificial Intelligence Methods for Applications of Convolutional Neural Networks in Geoscience

Convolutional neural networks (CNNs) have recently attracted great attention in geoscience due to their ability to capture non-linear system behavior and extract predictive spatiotemporal patterns. Given their black-box nature however, and the importance of prediction explainability, methods of explainable artificial intelligence (XAI) are gaining popularity as a means to explain the CNN decision-making strategy. Here, we establish an intercomparison of some of the most popular XAI methods and investigate their fidelity in explaining CNN decisions for geoscientific applications. Our goal is to raise awareness of the theoretical limitations of these methods and gain insight into the relative strengths and weaknesses to help guide best practices. The considered XAI methods are first applied to an idealized attribution benchmark, where the ground truth of explanation of the network is known a priori, to help objectively assess their performance. Secondly, we apply XAI to a climate-related prediction setting, namely to explain a CNN that is trained to predict the number of atmospheric rivers in daily snapshots of climate simulations. Our results highlight several important issues of XAI methods (e.g., gradient shattering, inability to distinguish the sign of attribution, ignorance to zero input) that have previously been overlooked in our field and, if not considered cautiously, may lead to a distorted picture of the CNN decision-making strategy. We envision that our analysis will motivate further investigation into XAI fidelity and will help towards a cautious implementation of XAI in geoscience, which can lead to further exploitation of CNNs and deep learning for prediction problems.
Entrepreneurial Competency, Financial Literacy, and Sustainable Performance—Examining the Mediating Role of Entrepreneurial Resilience among Saudi Entrepreneurs

This paper investigated the influence of entrepreneurial competency and financial literacy on sustainable small enterprise performance. We simultaneously investigated the mediating role of entrepreneurial resilience on the association between competence and financial literacy with sustainable business performance. We used a quantitative method for collecting data from 220 small business entrepreneurs in Saudi Arabia using an online questionnaire. The collected data were analysed with the help of PLS-SEM. The results reveal that financial literacy significantly positively affects entrepreneurial competency and resilience. Entrepreneurial competency has a significant positive effect on Saudi SMEs’ entrepreneurial resilience and sustainable performance. Finally, entrepreneurial resilience was simultaneously found to have a considerable impact on sustainable performance while mediating the effect of financial literacy and competency on sustainable performance across Saudi SMEs. In line with Saudi Vision 2030, with its profound focus on entrepreneurship and developing a financially independent entrepreneurial community, this research contributes to creating economic opportunities for local entrepreneurs managing small enterprises. Apart from extending the body of knowledge, this research can support policymakers in formulating relevant strategies to encourage SMEs that have been worst hit by the COVID-19 pandemic to perform sustainably. Several other implications for financial institutions and entrepreneurs have also been drawn.
The students’ brainwork intensification via the computer visualization of study materials

The paper the approaches to the intensification of the students’ brainwork by means of computer visualization of study material have been disclosed. In general, the content of students’ brainwork has been presented as a type of activity providing the cognitive process, mastering the techniques and ways of thinking, developing the capabilities and abilities of the individual, the product of which is a certain form of information, as a result of the brainwork the outlook of the subject of work is enriched. It is shown the visualization is the process of presenting data in the form of an image with the aim of maximum ease of understanding; the giving process of visual form to any mental object. In the paper the content, techniques, methods and software for creating visualization tools for study material has exposed. The essence and computer tools for creating such types of visualization of educational material like mind maps, supporting notes and infographics have been illustrated; they have been concretized from the point of view of application in the course of studying the mathematical sciences. It is proved the use of visualization tools for study materials helps to increase the intensity and effectiveness of students’ brainwork. Based on the results of an empirical study, it has been concluded the visualization of study materials contributes to the formation of students’ key intellectual competencies and forming their brainwork culture.
Self-regularized prototypical network for few-shot semantic segmentation

The deep CNNs in image semantic segmentation typically require a large number of densely-annotated images for training and have difficulties in generalizing to unseen object categories. Therefore, few-shot segmentation has been developed to perform segmentation with just a few annotated examples. In this work, we tackle the few-shot segmentation using a self-regularized prototypical network (SRPNet) based on prototype extraction for better utilization of the support information. The proposed SRPNet extracts class-specific prototype representations from support images and generates segmentation masks for query images by a distance metric - the fidelity. A direct yet effective prototype regularization on support set is proposed in SRPNet, in which the generated prototypes are evaluated and regularized on the support set itself. The extent to which the generated prototypes restore the support mask imposes an upper limit on performance. The performance on the query set should never exceed the upper limit no matter how complete the knowledge is generalized from support set to query set. With the specific prototype regularization, SRPNet fully exploits knowledge from the support and offers high-quality prototypes that are representative for each semantic class and meanwhile discriminative for different classes. The query performance is further improved by an iterative query inference (IQI) module that combines a set of regularized prototypes. Our proposed SRPNet achieves new state-of-art performance on 1-shot and 5-shot segmentation benchmarks.
Building a Smart Education Ecosystem from a Metaverse Perspective

Metaverse is the future of the Internet and integrates a variety of information technologies. It leads future education trends and brings profound changes to education. On the basis of analysis of the development trend of smart education and the connotation and action mechanism of edu-metaverse in the view of metaverse, this paper structures the smart education ecosystem, builds the scenario and modular smart learning space of three education scenarios of resource collaborative interaction, virtuality-reality integration experience, and ubiquitous spatial inquiry by using the six core technologies, and forms the new education mode of virtuality-reality symbiosis, trans-spatial fusion, and collaborative inquiry. Then, it verifies the application effect by AHP. Finally, it creates the smart education ecosystem of “four ecology integration”—resource ecology, interaction ecology, space ecology, and collaboration ecology, which accelerates the organic integration of metaverse and smart education and provides theoretical basis and reference for the new application of future education.
Leveraging IoT-Aware Technologies and AI Techniques for Real-Time Critical Healthcare Applications

Personalised healthcare has seen significant improvements due to the introduction of health monitoring technologies that allow wearable devices to unintrusively monitor physiological parameters such as heart health, blood pressure, sleep patterns, and blood glucose levels, among others. Additionally, utilising advanced sensing technologies based on flexible and innovative biocompatible materials in wearable devices allows high accuracy and precision measurement of biological signals. Furthermore, applying real-time Machine Learning algorithms to highly accurate physiological parameters allows precise identification of unusual patterns in the data to provide health event predictions and warnings for timely intervention. However, in the predominantly adopted architectures, health event predictions based on Machine Learning are typically obtained by leveraging Cloud infrastructures characterised by shortcomings such as delayed response times and privacy issues. Fortunately, recent works highlight that a new paradigm based on Edge Computing technologies and on-device Artificial Intelligence significantly improve the latency and privacy issues. Applying this new paradigm to personalised healthcare architectures can significantly improve their efficiency and efficacy. Therefore, this paper reviews existing IoT healthcare architectures that utilise wearable devices and subsequently presents a scalable and modular system architecture to leverage emerging technologies to solve identified shortcomings. The defined architecture includes ultrathin, skin-compatible, flexible, high precision piezoelectric sensors, low-cost communication technologies, on-device intelligence, Edge Intelligence, and Edge Computing technologies. To provide development guidelines and define a consistent reference architecture for improved scalable wearable IoT-based critical healthcare architectures, this manuscript outlines the essential functional and non-functional requirements based on deductions from existing architectures and emerging technology trends. The presented system architecture can be applied to many scenarios, including ambient assisted living, where continuous surveillance and issuance of timely warnings can afford independence to the elderly and chronically ill. We conclude that the distribution and modularity of architecture layers, local AI-based elaboration, and data packaging consistency are the more essential functional requirements for critical healthcare application use cases. We also identify fast response time, utility, comfort, and low cost as the essential non-functional requirements for the defined system architecture.
Recent advances in use of bio-inspired jellyfish search algorithm for solving optimization problems

The complexity of engineering optimization problems is increasing. Classical gradient-based optimization algorithms are a mathematical means of solving complex problems whose ability to do so is limited. Metaheuristics have become more popular than exact methods for solving optimization problems because of their simplicity and the robustness of the results that they yield. Recently, population-based bio-inspired algorithms have been demonstrated to perform favorably in solving a wide range of optimization problems. The jellyfish search optimizer (JSO) is one such bio-inspired metaheuristic algorithm, which is based on the food-finding behavior of jellyfish in the ocean. According to the literature, JSO outperforms many well-known meta-heuristics in a wide range of benchmark functions and real-world applications. JSO can also be used in conjunction with other artificial intelligence-related techniques. The success of JSO in solving diverse optimization problems motivates the present comprehensive discussion of the latest findings related to JSO. This paper reviews various issues associated with JSO, such as its inspiration, variants, and applications, and will provide the latest developments and research findings concerning JSO. The systematic review contributes to the development of modified versions and the hybridization of JSO to improve upon the original JSO and present variants, and will help researchers to develop superior metaheuristic optimization algorithms with recommendations of add-on intelligent agents.
There’s more than one metaverse

Abstract Will there only be one Metaverse in the future? Since October 2021, many virtual worlds suddenly called themselves “Metaverse”, but is that justified? What is the meaning behind the pervasive term Metaverse? How can it be approached from a pragmatic and research-oriented point of view, and maybe separated a bit from pure marketing? This paper tries to contextualise the term and thereby offer an orientation for readers from different industries. Based on a review of past and present activities, we will propose our own definition of the term Metaverse. This is applied to analyse different application scenarios before the paper continues with a general discussion. In a direct comparison, we will tabulate some worlds that already call themselves a Metaverse and check them against criteria from our own definition. In conclusion, we present relevant focal points for a potential future Metaverse resulting from this comparison.
Validation and real-world clinical application of an artificial intelligence algorithm for breast cancer detection in biopsies

Breast cancer is the most common malignant disease worldwide, with over 2.26 million new cases in 2020. Its diagnosis is determined by a histological review of breast biopsy specimens, which can be labor-intensive, subjective, and error-prone. Artificial Intelligence (AI)-based tools can support cancer detection and classification in breast biopsies ensuring rapid, accurate, and objective diagnosis. We present here the development, external clinical validation, and deployment in routine use of an AI-based quality control solution for breast biopsy review. The underlying AI algorithm is trained to identify 51 different types of clinical and morphological features, and it achieves very high accuracy in a large, multi-site validation study. Specifically, the area under the receiver operating characteristic curves (AUC) for the detection of invasive carcinoma and of ductal carcinoma in situ (DCIS) are 0.99 (specificity and sensitivity of 93.57 and 95.51%, respectively) and 0.98 (specificity and sensitivity of 93.79 and 93.20% respectively), respectively. The AI algorithm differentiates well between subtypes of invasive and different grades of in situ carcinomas with an AUC of 0.97 for invasive ductal carcinoma (IDC) vs. invasive lobular carcinoma (ILC) and AUC of 0.92 for DCIS high grade vs. low grade/atypical ductal hyperplasia, respectively, as well as accurately identifies stromal tumor-infiltrating lymphocytes (TILs) with an AUC of 0.965. Deployment of this AI solution as a real-time quality control solution in clinical routine leads to the identification of cancers initially missed by the reviewing pathologist, demonstrating both clinical utility and accuracy in real-world clinical application.
Intelligent generation of Peking opera facial masks with deep learning frameworks

Abstract The production of traditional Peking opera facial masks often relies on hand painting by experienced painters, which restricts the inheritance and development of this intangible cultural heritage. Current research mainly focuses on the digital reconstruction and storage of existing Peking opera facial masks, while high-quality facial mask generation technology is still in an infancy stage. In this paper, different deep learning frameworks are improved for learning features of Peking opera facial masks and generating new masks, which can effectively promote the creative application of Peking opera facial masks. First, using different data enhancement methods, an improved Style Generative Adversarial Network-2 (StyleGAN2) can learn implicit and explicit features of Peking opera facial masks and automatically generate new facial masks. In addition, an image translation framework for joint cross-domain communication under weak supervision is used to translate face sketches and color reference maps to an intermediate feature domain, and then synthesize new facial masks through an image generation network. The experimental results show that the generated Peking opera facial masks have good local randomness and excellent visual quality.
MS-YOLOv7:YOLOv7 Based on Multi-Scale for Object Detection on UAV Aerial Photography

A multi-scale UAV aerial image object detection model MS-YOLOv7 based on YOLOv7 was proposed to address the issues of a large number of objects and a high proportion of small objects that commonly exist in the Unmanned Aerial Vehicle (UAV) aerial image. The new network is developed with a multiple detection head and a CBAM convolutional attention module to extract features at different scales. To solve the problem of high-density object detection, a YOLOv7 network architecture combined with the Swin Transformer units is proposed, and a new pyramidal pooling module, SPPFS is incorporated into the network. Finally, we incorporate the SoftNMS and the Mish activation function to improve the network’s ability to identify overlapping and occlusion objects. Various experiments on the open-source dataset VisDrone2019 reveal that our new model brings a significant performance boost compared to other state-of-the-art (SOTA) models. Compared with the YOLOv7 object detection algorithm of the baseline network, the mAP0.5 of MS-YOLOv7 increased by 6.0%, the mAP0.95 increased by 4.9%. Ablation experiments show that the designed modules can improve detection accuracy and visually display the detection effect in different scenarios. This experiment demonstrates the applicability of the MS-YOLOv7 for UAV aerial photograph object detection.