Reviewerly/exHarmony huggingface dataset:
Biosecurity, crisis management, automation technologies and economic performance of travel, tourism and hospitality companies – A conceptual framework

The COVID-19 pandemic of 2020 will have a significant influence on travel, tourism and hospitality (TTH) globally. With a massive reduction in tourism globally because of the health crisis, the industry will have to plan recovery and rebuilding of the industry with greater consideration of the biosecurity of customers. A critical element in the reorganisation of the industry will involve the increased incorporation of automation technologies. This article adopts a supply-side perspective and elaborates on the opportunities that automation technologies provide to TTH companies to mitigate the negative impacts of biosecurity threats on their economic performance. It discusses the costs and benefits of the incorporation of increased levels of automation, especially in regard to the benefits of the safety and health of the consumer. It elaborates on how the current pandemic would stimulate the adoption of automation technologies. Finally, the article discusses how this fit into the business models of tourism-related industries and outlines the micro- and macroeconomic implications of the greater incorporation of automation into the industry.
Blind Omnidirectional Image Quality Assessment With Viewport Oriented Graph Convolutional Networks

Quality assessment of omnidirectional images has become increasingly urgent due to the rapid growth of virtual reality applications. Different from traditional 2D images and videos, omnidirectional contents can provide consumers with freely changeable viewports and a larger field of view covering the 360 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">°</sup> ×180 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">°</sup> spherical surface, which makes the objective quality assessment of omnidirectional images more challenging. In this paper, motivated by the characteristics of the human vision system (HVS) and the viewing process of omnidirectional contents, we propose a novel Viewport oriented Graph Convolution Network (VGCN) for blind omnidirectional image quality assessment (IQA). Generally, observers tend to give the subjective rating of a 360-degree image after passing and aggregating different viewports information when browsing the spherical scenery. Therefore, in order to model the mutual dependency of viewports in the omnidirectional image, we build a spatial viewport graph. Specifically, the graph nodes are first defined with selected viewports with higher probabilities to be seen, which is inspired by the HVS that human beings are more sensitive to structural information. Then, these nodes are connected by spatial relations to capture interactions among them. Finally, reasoning on the proposed graph is performed via graph convolutional networks. Moreover, we simultaneously obtain global quality using the entire omnidirectional image without viewport sampling to boost the performance according to the viewing experience. Experimental results demonstrate that our proposed model outperforms state-of-the-art full-reference and no-reference IQA metrics on two public omnidirectional IQA databases.
Efficient semantic segmentation with pyramidal fusion

Emergence of large datasets and resilience of convolutional models have enabled successful training of very large semantic segmentation models. However, high capacity implies high computational complexity and therefore hinders real-time operation. We therefore study compact architectures which aim at high accuracy in spite of modest capacity. We propose a novel semantic segmentation approach based on shared pyramidal representation and fusion of heterogeneous features along the upsampling path. The proposed pyramidal fusion approach is especially effective for dense inference in images with large scale variance due to strong regularization effects induced by feature sharing across the resolution pyramid. Interpretation of the decision process suggests that our approach succeeds by acting as a large ensemble of relatively simple models, as well as due to large receptive range and strong gradient flow towards early layers. Our best model achieves 76.4% mIoU on Cityscapes test and runs in real time on low-power embedded devices.
Ensemble mutation-driven salp swarm algorithm with restart mechanism: Framework and fundamental analysis

This research proposes a reinforced salp swarm algorithm (SSA) variant with an ensemble mutation strategy and a restart mechanism, which is named CMSRSSSA for short, to enhance exploration and exploitation capacity of SSA and conquer the restriction of a single search mechanism of the SSA in tackling continuous optimization problems. In this variant, an ensemble/composite mutation strategy (CMS) can boost the exploitation and exploration trends of SSA, as well as restart strategy (RS) is capable of assisting salps in getting away from local optimum. To investigate the performance of the proposed optimizer, firstly, IEEE CEC2017 benchmark problems are used to estimate the capability of the presented CMSRSSSA in solving continuous optimization problems in comparison to other advanced algorithms; furthermore, IEEE CEC2011 real-world benchmark problems and constrained engineering optimization problems are also utilized to assess the performance of CMSRSSSA for practical ideas. Experimental and statistical results reveal that the CMSRSSSA outperforms all the competitors, including winners of the related IEEE CEC competition; therefore, it will be able to be treated as a promising method in resolving both constrained and unconstrained optimization problems. For post-publication supports and guides on the idea of the paper, please be in touch with the hosting website: http://aliasgharheidari.com.
Anomaly detection based on machine learning in IoT-based vertical plant wall for indoor climate control

Indoor climate is closely related to human health, comfort and productivity. Vertical plant wall systems, embedded with sensors and actuators, have become a promising application for indoor climate control. In this study, we explore the possibility of applying machine learning based anomaly detection methods to vertical plant wall systems so as to enhance the automation and improve the intelligence to realize predictive maintenance of the indoor climate. Two categories of anomalies, namely point anomalies and contextual anomalies are researched. Prediction-based and pattern recognition-based methods are investigated and applied to indoor climate anomaly detection. The results show that neural network-based models, specifically the autoencoder (AE) and the long short-term memory encoder decoder (LSTM-ED) model surpass the others in terms of detecting point anomalies and contextual anomalies, respectively, therefore can be deployed into vertical plant walls systems in industrial practice. Based on the results, a new data cleaning method is proposed and a prediction-based method is deployed to the cloud in practice as a proof-of-concept. This study showcases the advancements in machine learning and Internet of things can be fully utilized by researches on building environment to accelerate the solution development.
A Self-Reasoning Framework for Anomaly Detection Using Video-Level Labels

Anomalous event detection in surveillance videos is a challenging and practical research problem among image and video processing community. Compared to the frame-level annotations of anomalous events, obtaining video-level annotations is quite fast and cheap though such high-level labels may contain significant noise. More specifically, an anomalous labeled video may actually contain anomaly only in a short duration while the rest of the video frames may be normal. In the current work, we propose a weakly supervised anomaly detection framework based on deep neural networks which is trained in a self-reasoning fashion using only video-level labels. To carry out the self-reasoning based training, we generate pseudo labels by using binary clustering of spatio-temporal video features which helps in mitigating the noise present in the labels of anomalous videos. Our proposed formulation encourages both the main network and the clustering to complement each other in achieving the goal of more accurate anomaly detection. The proposed framework has been evaluated on publicly available real-world anomaly detection datasets including UCF-crime, ShanghaiTech and UCSD Ped2. The experiments demonstrate superiority of our proposed framework over the current state-of-the-art methods.
Local Levenberg-Marquardt Algorithm for Learning Feedforwad Neural Networks

Abstract This paper presents a local modification of the Levenberg-Marquardt algorithm (LM). First, the mathematical basics of the classic LM method are shown. The classic LM algorithm is very efficient for learning small neural networks. For bigger neural networks, whose computational complexity grows significantly, it makes this method practically inefficient. In order to overcome this limitation, local modification of the LM is introduced in this paper. The main goal of this paper is to develop a more complexity efficient modification of the LM method by using a local computation. The introduced modification has been tested on the following benchmarks: the function approximation and classification problems. The obtained results have been compared to the classic LM method performance. The paper shows that the local modification of the LM method significantly improves the algorithm’s performance for bigger networks. Several possible proposals for future works are suggested.
A Brief Survey on Breast Cancer Diagnostic With Deep Learning Schemes Using Multi-Image Modalities

Patients with breast cancer are prone to serious health-related complications with higher mortality.The primary reason might be a misinterpretation of radiologists in recognizing suspicious lesions due to technical issues in imaging qualities and heterogeneous breast densities which increases the false-(positive and negative) ratio.Early intervention is significant in establishing an up-to-date prognosis process which can successfully mitigate complications of disease with higher recovery.The manual screening of breast abnormalities through traditional machine learning schemes misinterpret the inconsistent feature-extraction process which poses a problem, i.e., patients being called-back for biopsies to eliminates the suspicions.However, several deep learning-based methods have been developed for reliable breast cancer prognosis and classification but very few of them provided a comprehensive overview of lesions segmentation.This research focusses on providing benefits and risks of breast multi-imaging modalities, segmentation schemes, feature extraction, classification of breast abnormalities through state-of-the-art deep learning approaches.This research also explores various well-known databases using "Breast Cancer" keyword to present a comprehensive survey on existing diagnostic schemes to open-up new research challenges for radiologists and researchers to intervene as early as possible to develop an efficient and reliable breast cancer prognosis system using prominent deep learning schemes.Breast cancer, computer- INDEX TERMS
Luminance-Aware Pyramid Network for Low-Light Image Enhancement

Low-light image enhancement based on deep convolutional neural networks (CNNs) has revealed prominent performance in recent years. However, it is still a challenging task since the underexposed regions and details are always imperceptible. Moreover, deep learning models are always accompanied by complex structures and enormous computational burden, which hinders their deployment on mobile devices. To remedy these issues, in this paper, we present a lightweight and efficient Luminance-aware Pyramid Network (LPNet) to reconstruct normal-light images in a coarse-to-fine strategy. The architecture is comprised of two coarse feature extraction branches and a luminance-aware refinement branch with an auxiliary subnet learning the luminance map of the input and target images. Besides, we propose a multi-scale contrast feature block (MSCFB) that involves channel split, channel shuffle strategies, and contrast attention mechanism. MSCFB is the essential component of our network, which achieves an excellent balance between image quality and model size. In this way, our method can not only brighten up low-light images with rich details and high contrast but also significantly ameliorate the execution speed. Extensive experiments demonstrate that our LPNet outperforms state-of-the-art methods both qualitatively and quantitatively.
Antecedents of blockchain adoption: An integrative framework

Abstract Important technological, organizational, environmental, individual, and task‐related blockchain considerations are identified and discussed. The framework combines five salient adoption considerations and presents cases of the most important antecedents of organizational blockchain adoption. Further, it provides the theoretical foundation of blockchain adoption by integrating existing frameworks and applying them to a novel technology.
Person Detection for Social Distancing and Safety Violation Alert based on Segmented ROI

In addressing the worldwide Covid-19 pandemic situation, the process of flattening the curve for coronavirus cases will be difficult if the citizens do not take action to prevent the spread of the virus. One of the most important practices in these outbreaks is to ensure a safe distance between people in public. This paper presents the detection of people with social distance monitoring as a precautionary measure in reducing physical contact between people. This study focuses on detecting people in areas of interest using the MobileNet Single Shot Multibox Detector (SSD) object tracking model and OpenCV library for image processing. The distance will be computed between the persons detected in the captured footage and then compared to a fixed pixels' values. The distance is measured between the central points and the overlapping boundary between persons in the segmented tracking area. With the detection of unsafe distances between people, alerts or warnings can be issued to keep the distance safe. In addition to social distance measure, another key feature of the system is detecting the presence of people in restricted areas, which can also be used to trigger warnings. Some analysis has been performed to test the effectiveness of the program for both purposes. From the results obtained, the distance tracking system achieved between 56.5% to 68% accuracy for testing performed on outdoor and challenging input videos, while 100% accuracy was achieved for the controlled environment on indoor testing. Whereas for the safety violation alert feature based on segmented ROI, it was found to have achieved better accuracy, i.e. between 95.8% to 100% for all tested input videos.
Blockchain adoptions in the maritime industry: a conceptual framework

Blockchain technology has become one of the emerging technologies set to disrupt the maritime industry. Maritime companies are increasingly exploring to adopt blockchain to stay ahead of competition. However, studies on blockchain applications in the maritime sector have been scarce and most of them are confined to a specific sector like the maritime shipping sector. Therefore, this study is motivated to provide a thorough analysis of blockchain applications from the perspectives of different sectors in the industry. It also aims to develop a novel conceptual framework to provide a holistic view of blockchain adoption in the industry and guide future research. The implication analysis of blockchain adoption indicates that for industry organisations, a good understanding of blockchain and their own specific problems and requirements is key before adopting the technology. For government agencies, technical code for blockchain can be utilised to govern blockchain innovation with the same effects as legal code. Lastly, recommendations are provided to various maritime stakeholders to seize the emerging opportunities provided by blockchain and mitigate relevant risks.
When Explainability Meets Adversarial Learning: Detecting Adversarial Examples using SHAP Signatures

State-of-the-art deep neural networks (DNNs) are highly effective in solving many complex real-world problems. However, these models are vulnerable to adversarial perturbation attacks, and despite the plethora of research in this domain, to this day, adversaries still have the upper hand in the cat and mouse game of adversarial example generation methods vs. detection and prevention methods. In this research, we present a novel detection method that uses Shapley Additive Explanations (SHAP) values computed for the internal layers of a DNN classifier to discriminate between normal and adversarial inputs. We evaluate our method by building an extensive dataset of adversarial examples over the popular CIFAR-10 and MNIST datasets, and training a neural network-based detector to distinguish between normal and adversarial inputs. We evaluate our detector against adversarial examples generated by diverse state-of-the-art attacks and demonstrate its high detection accuracy and strong generalization ability to adversarial inputs generated with different attack methods.
Uncertainty Aware Temporal-Ensembling Model for Semi-Supervised ABUS Mass Segmentation

Accurate breast mass segmentation of automated breast ultrasound (ABUS) images plays a crucial role in 3D breast reconstruction which can assist radiologists in surgery planning. Although the convolutional neural network has great potential for breast mass segmentation due to the remarkable progress of deep learning, the lack of annotated data limits the performance of deep CNNs. In this article, we present an uncertainty aware temporal ensembling (UATE) model for semi-supervised ABUS mass segmentation. Specifically, a temporal ensembling segmentation (TEs) model is designed to segment breast mass using a few labeled images and a large number of unlabeled images. Considering the network output contains correct predictions and unreliable predictions, equally treating each prediction in pseudo label update and loss calculation may degrade the network performance. To alleviate this problem, the uncertainty map is estimated for each image. Then an adaptive ensembling momentum map and an uncertainty aware unsupervised loss are designed and integrated with TEs model. The effectiveness of the proposed UATE model is mainly verified on an ABUS dataset of 107 patients with 170 volumes, including 13382 2D labeled slices. The Jaccard index (JI), Dice similarity coefficient (DSC), pixel-wise accuracy (AC) and Hausdorff distance (HD) of the proposed method on testing set are 63.65%, 74.25%, 99.21% and 3.81mm respectively. Experimental results demonstrate that our semi-supervised method outperforms the fully supervised method, and get a promising result compared with existing semi-supervised methods.
Can We Use Split Learning on 1D CNN Models for Privacy Preserving Training?

A new collaborative learning, called split learning, was recently introduced, aiming to protect user data privacy without revealing raw input data to a server. It collaboratively runs a deep neural network model where the model is split into two parts, one for the client and the other for the server. Therefore, the server has no direct access to raw data processed at the client. Until now, the split learning is believed to be a promising approach to protect the client's raw data; for example, the client's data was protected in healthcare image applications using 2D convolutional neural network (CNN) models. However, it is still unclear whether the split learning can be applied to other deep learning models, in particular, 1D CNN.
The Effect of Work Environment Happiness on Employee Leadership

The concept of leadership has attracted attention of scholars over the recent past as companies struggle to manage stiff competition in the local and international markets. The aim of this systematic review is to critically synthesize the relationship between employee leadership and work environment happiness within the theoretical paradigm of the contemporary academic literature. Primary data was obtained through semi-structured interviews with Secondary data was collected from 21 scholarly sources, including books and journal articles on the subject matter, and further analyzed with the help of mixed quantitative-qualitative methods to answer seven predefined research questions, investigating the role of work environment. Results of the systematic review showed that happiness in the workplace directly influences the ability of employees to ascend to top management positions. The study has identified six independent variables that directly affect the ability of to become leaders: 1) job satisfaction, 2) employee engagement, 3) workplace safety, 4) valued social position, 5) support from work friends and Work-life equalization. Findings provide a foundation for further educational research with potential recommendations for employers. Potential limitations include judgmental sampling and failure to incorporate socioeconomic variables, sensitive to culture.
Breast Cancer Classification Using Deep Learning Approaches and Histopathology Image: A Comparison Study

Convolutional Neural Network (CNN) models are a type of deep learning architecture introduced to achieve the correct classification of breast cancer. This paper has a two-fold purpose. The first aim is to investigate the various deep learning models in classifying breast cancer histopathology images. This study identified the most accurate models in terms of the binary, four, and eight classifications of breast cancer histopathology image databases. The different accuracy scores obtained for the deep learning models on the same database showed that other factors such as pre-processing, data augmentation, and transfer learning methods can impact the ability of the models to achieve higher accuracy. The second purpose of our manuscript is to investigate the latest models that have no or limited examination done in previous studies. The models like ResNeXt, Dual Path Net, SENet, and NASNet had been identified with the most cutting-edge results for the ImageNet database. These models were examined for the binary, and eight classifications on BreakHis, a breast cancer histopathology image database. Furthermore, the BACH database was used to investigate these models for four classifications. Then, these models were compared with the previous studies to find and propose the most state-of-the-art models for each classification. Since the Inception-ResNet-V2 architecture achieved the best results for binary and eight classifications, we have examined this model in our study as well to provide a better comparison result. In short, this paper provides an extensive evaluation and discussion about the experimental settings for each study that had been conducted on the breast cancer histopathology images.