Reviewerly/exHarmony huggingface dataset:
Using blockchain technology to drive operational excellence in perishable food supply chains during outbreaks

Purpose This paper aims to explore the potential of blockchain technology (BT) to support the operational excellence in perishable food supply chain (PFSC) during outbreaks, by doing use-case analysis. Design/methodology/approach A systematic literature review is performed to determine the dimensions of operational excellence in the food supply chain (FSC), then a single use-case analysis is conducted to explore the potential of blockchain in order to achieve operational excellence for PFSC during the pandemics by applying context, interventions, mechanism and outcomes (CIMO) logic. Findings The findings of this study reveal that blockchain capabilities such as immutability and transparency, visibility, traceability, integration and interoperability, disintermediation and decentralisation, smart contracts and consensus mechanism provide better sustainable operational excellence outcomes for PFSCs to be more responsive, flexible, efficient and collaborative to cope with the impacts of COVID-19. Research limitations/implications This research employs only one real case with multiple PFSC participants. Statistical generalisation is not possible at this stage of the research. However, the findings are not restricted to this single use-case. Practical implications This study provides a research direction to explore the potential of BT to achieve operational excellence in the PFSC during outbreaks and generates prescriptive knowledge for better managerial decision-making across the PFSC during outbreaks. Originality/value This research conducts semi-structured interviews with different participants in one blockchain ecosystem to understand multiple participants' perspectives of operational excellence within PFSC.
Human Activity Recognition Using an Ensemble Learning Algorithm with Smartphone Sensor Data

Human activity recognition (HAR) can monitor persons at risk of COVID-19 virus infection to manage their activity status. Currently, many people are isolated at home or quarantined in some specified places due to the spread of COVID-19 virus all over the world. This situation raises the requirement of using the HAR to observe physical activity levels to assess physical and mental health. This study proposes an ensemble learning algorithm (ELA) to perform activity recognition using the signals recorded by smartphone sensors. The proposed ELA combines a gated recurrent unit (GRU), a convolutional neural network (CNN) stacked on the GRU and a deep neural network (DNN). The input samples of DNN were an extra feature vector consisting of 561 time-domain and frequency-domain parameters. The full connected DNN was used to fuse three models for the activity classification. The experimental results show that the precision, recall, F1-score and accuracy achieved by the ELA are 96.8%, 96.8%, 96.8%, and 96.7%, respectively, which are superior to the existing schemes.
Eliciting positive emotion through strategic responses to COVID-19 crisis: Evidence from the tourism sector

While social media are effective means of communicating with adverse customer emotions during a crisis, it remains unclear how tourism organisations can respond to pandemic crisis on social media to prevent negative aftermaths. Using a set-theoretical approach, we investigate how COVID-19 response strategies and linguistic cues of responses are intertwined to evoke positive emotions among consumers. This study entails a qualitative content analysis of tourism organisations' COVID-19 announcements and a social media analytics approach that captures consumers' emotional reactions to these announcements via their Twitter replies. Our results extend some well-established findings in the tourism crisis literature by suggesting that combining innovative response strategy, argument quality, and assertive language can reinforce positive emotions during the COVID-19 crisis. Taking organisational characteristics into consideration, we suggest that young established hotels utilise innovative response strategies, whereas retrenchment response strategies for all types of restaurants should be avoided during the COVID-19 crisis.
Methods and tools for causal discovery and causal inference

Abstract Causality is a complex concept, which roots its developments across several fields, such as statistics, economics, epidemiology, computer science, and philosophy. In recent years, the study of causal relationships has become a crucial part of the Artificial Intelligence community, as causality can be a key tool for overcoming some limitations of correlation‐based Machine Learning systems. Causality research can generally be divided into two main branches, that is, causal discovery and causal inference. The former focuses on obtaining causal knowledge directly from observational data. The latter aims to estimate the impact deriving from a change of a certain variable over an outcome of interest. This article aims at covering several methodologies that have been developed for both tasks. This survey does not only focus on theoretical aspects. But also provides a practical toolkit for interested researchers and practitioners, including software, datasets, and running examples. This article is categorized under: Algorithmic Development &gt; Causality Discovery Fundamental Concepts of Data and Knowledge &gt; Explainable AI Technologies &gt; Machine Learning
Gated attention fusion network for multimodal sentiment classification

Sentiment classification can explore the opinions expressed by people and help them make better decisions. With the increasing of multimodal contents on the web, such as text, image, audio and video, how to make full use of them is important in many tasks, including sentiment classification. This paper focuses on the text and image. Previous work cannot capture the fine-grained features of images, and those models bring a lot of noise during feature fusion. In this work, we propose a novel multimodal sentiment classification model based on gated attention mechanism. The image feature is used to emphasize the text segment by the attention mechanism and it allows the model to focus on the text that affects the sentiment polarity. Moreover, the gating mechanism enables the model to retain useful image information while ignoring the noise introduced during the fusion of image and text. The experiment results on Yelp multimodal dataset show that our model outperforms the previous SOTA model. And the ablation experiment results further prove the effectiveness of different strategies in the proposed model.
Factors affecting hotel managers’ intentions to adopt robotic technologies: A global study

The objective of this study that was conducted with 1077 hotel managers in 11 countries in North and South America, Europe, Asia and the Middle East, was to identify the effects of technological, organizational, and environmental (TOE) factors on hotel managers’ intentions to adopt robotic technologies in their hotels. Structural equation modeling (SEM) was utilized to test the study hypotheses. The results indicated that hotel managers’ intention to adopt robotic technologies were positively influenced by their perceived relative advantage, competitive pressure and top management support and negatively influenced by their perceived complexity of the technology. The study results further demonstrated that the impacts of relative advantage, complexity, top management support, and competitive advantage on intention to adopt were moderated by innovativeness. The current study also addressed the theoretical and practical implications to existing knowledge and practice in the hotel industry.
Pathway of Trends and Technologies in Fall Detection: A Systematic Review

Falling is one of the most serious health risk problems throughout the world for elderly people. Considerable expenses are allocated for the treatment of after-fall injuries and emergency services after a fall. Fall risks and their effects would be substantially reduced if a fall is predicted or detected accurately on time and prevented by providing timely help. Various methods have been proposed to prevent or predict falls in elderly people. This paper systematically reviews all the publications, projects, and patents around the world in the field of fall prediction, fall detection, and fall prevention. The related works are categorized based on the methodology which they used, their types, and their achievements.
Geometric SMOTE for regression

Learning from imbalanced data sets is known to be a challenging task. There are many proposals to tackle the challenge for classification problems, but regarding regression the solutions are few. In the context of regression, imbalanced learning means that there is a concern with the accurate prediction of the target values in a subset of the continuous target variable, considering that these values rarely occur in the data set. In this article, we extend the G-SMOTE algorithm that is used in classification to regression tasks. G-SMOTE is a pre-processing algorithm that differs from the SMOTE algorithm as it allows the generation of synthetic instances in a geometric region around the selected instances rather than in the line segment that joins the two selected instances. The performance of G-SMOTE for regression was compared against other methods, and the empirical results show that our proposal outperformed those methods.
Prioritizing adoption barriers of platforms based on blockchain technology from balanced scorecard perspectives in healthcare industry: a structural approach

Since the advent of blockchain technology (BT), extensive research has explored using this technology in non-financial cases. The healthcare industry is one of the non-financial sectors that BT has significantly impacted. In this paper, for the first time, barriers to implement BT-based platforms from a balanced scorecard perspective in the healthcare sector are introduced, and these barriers are prioritized using a structural approach based on the weighted influence non-linear gauge system. Unlike other structural models such as decision-making trial and evaluation laboratory (DEMATEL), in this approach, both strength and influence intensity of components are considered in the ranking process. The proposed approach is applied in networks that have a hierarchical structure and intertwined components. The performance of the proposed approach is evaluated using the ranking of BT-based platform adoption barriers in the healthcare industry in Iran. The results show that financial issues, security issues, lack of expertise and knowledge, and uncertain government policies are the most important barriers to BT adoption in the healthcare industry.
ETHOS: a multi-label hate speech detection dataset

Online hate speech is a recent problem in our society that is rising at a steady pace by leveraging the vulnerabilities of the corresponding regimes that characterise most social media platforms. This phenomenon is primarily fostered by offensive comments, either during user interaction or in the form of a posted multimedia context. Nowadays, giant corporations own platforms where millions of users log in every day, and protection from exposure to similar phenomena appears to be necessary in order to comply with the corresponding legislation and maintain a high level of service quality. A robust and reliable system for detecting and preventing the uploading of relevant content will have a significant impact on our digitally interconnected society. Several aspects of our daily lives are undeniably linked to our social profiles, making us vulnerable to abusive behaviours. As a result, the lack of accurate hate speech detection mechanisms would severely degrade the overall user experience, although its erroneous operation would pose many ethical concerns. In this paper, we present 'ETHOS', a textual dataset with two variants: binary and multi-label, based on YouTube and Reddit comments validated using the Figure-Eight crowdsourcing platform. Furthermore, we present the annotation protocol used to create this dataset: an active sampling procedure for balancing our data in relation to the various aspects defined. Our key assumption is that, even gaining a small amount of labelled data from such a time-consuming process, we can guarantee hate speech occurrences in the examined material.
A representation coefficient-based k-nearest centroid neighbor classifier

K-nearest neighbor rule (KNN) has been regarded as one of the top 10 methods in the field of data mining. Due to its simplicity and effectiveness, it has been widely studied and applied to various classification tasks. In this article, we develop a novel representation coefficient-based k-nearest centroid neighbor method (RCKNCN), which aims to further improve the classification performance and reduce the method’s sensitivity to the neighborhood size k, especially in the cases of small sample size. Different from existing KNN-based methods, RCKNCN is able to capture both the proximity and the geometry of k-nearest neighbors, and learn to differentiate the contribution of each neighbor to the classification of a testing sample through a linear representation method. Moreover, under the RCKNCN framework, we also propose a novel weighted majority voting algorithm using the representation coefficients associated with individual nearest centroid neighbors, which are deemed to hold more discriminative information of the neighbors. To fully study the classification performance of RCKNCN, we compare it with the state-of-the-art KNN-based methods on many data sets that are widely used in the literature. The extensive experiments demonstrate the effectiveness and robustness of our method in various classification tasks.
A lightweight deep learning system for automatic detection of blood cancer

Microscopic analysis of blood-cells is an essential and vital task for the early diagnosis of life-threatening hematological disorders like blood cancer (leukemia). We have presented an effective and computationally efficient approach for automatically detecting and classifying Acute Lymphoblastic Leukemia (ALL) and Acute Myeloid Leukemia (AML). Currently, transfer learning has succeeded as a preferred approach in medical image analysis since it achieves excellent performance in a small database. This paper proposes a lightweight transfer-learning-based feature extraction followed by Support Vector Machine (SVM)-based classification technique for efficient ALL and AML detection. It yields a faster and more efficient system due to the depth-wise separable convolution, tunable multiplier, and inverted residual bottleneck structure. Moreover, the SVM-based classification improves the overall performance by optimizing the hyperplane location. Furthermore, the experimental results signify that our proposed system gains superior performance than others in all these three publicly available standard ALLIDB1, ALLIDB2, and ASH databases.
Speckle Noise Removal by SORAMA Segmentation in Digital Image Processing to Facilitate Precise Robotic Surgery

Kidney stones are renal calculi that are formed due to the collection of calcium and uric acid. The major symptom for the existence of these renal calculi is severe pain, especially when it travels down the urethras To detect these renal calculi, ultrasound images are preferable. But these images have speckle noise which makes the detection of stone challenge. To obtain better results, Semantic Object Region and Morphological Analysis (SORAMA) found to be productive. First scanned image undergoes noise removal process Later the image is enhanced. Detection of Region of interest (ROI) in the image is done. Later it undergoes Dilation and Erosion were a part of Morphological analysis which produces a smoothening effect on the image. From the smoothened image, the stone is detected. If the stone is not detected then it again undergoes noise removal technique and the whole process is repeated until the smoothened image with the stone is detected. This novel research paper will be a boon to medical patients suffering from this disease to be detected and diagnose at a very early stage.
Knowledge graph embedding by logical-default attention graph convolution neural network for link prediction

The purpose of link prediction is to predict missing edges of the knowledge graph, or edges that may appear in the future. An abundance of data-driven methods exist, which rely on many labels and only consider the structural information about the graph. To address these problems, we have developed a logical-default attention graph convolution neural network model (DA-GCN). The model consists of three parts. First, the confidence of first-order logic inference with Default (DFLIN) was calculated by defining rules. Second, a local relational attention model was defined to represent self information, which includes first order, second order neighbor node information, and to train a graph convolution neural network based on iterative attention (IAGCN) by using centralized training distributed execution model. Third, iteratively update DFLIN and IAGCN using the Wasserstein distance to express the relation between entities. Finally, we conduct comparative experiments between the DA-GCN model and other models on a self-built dataset and three public datasets. The results show that the Mean Reciprocal Rank (MRR) value is about 36.48% higher than that of the R-GCN model. Our results show that DA-GCN achieves state-of-the-art performance compared to other specific or general integrated methods, especially on small-scale organized knowledge graphs.
DeepFake Detection for Human Face Images and Videos: A Survey

Techniques for creating and manipulating multimedia information have progressed to the point where they can now ensure a high degree of realism. DeepFake is a generative deep learning algorithm that creates or modifies face features in a superrealistic form, in which it is difficult to distinguish between real and fake features. This technology has greatly advanced and promotes a wide range of applications in TV channels, video game industries, and cinema, such as improving visual effects in movies, as well as a variety of criminal activities, such as misinformation generation by mimicking famous people. To identify and classify DeepFakes, research in DeepFake detection using deep neural networks (DNNs) has attracted increased interest. Basically, DeepFake is the regenerated media that is obtained by injecting or replacing some information within the DNN model. In this survey, we will summarize the DeepFake detection methods in face images and videos on the basis of their results, performance, methodology used and detection type. We will review the existing types of DeepFake creation techniques and sort them into five major categories. Generally, DeepFake models are trained on DeepFake datasets and tested with experiments. Moreover, we will summarize the available DeepFake dataset trends, focusing on their improvements. Additionally, the issue of how DeepFake detection aims to generate a generalized DeepFake detection model will be analyzed. Finally, the challenges related to DeepFake creation and detection will be discussed. We hope that the knowledge encompassed in this survey will accelerate the use of deep learning in face image and video DeepFake detection methods.
Artificial intelligence in mammographic phenotyping of breast cancer risk: a narrative review

Improved breast cancer risk assessment models are needed to enable personalized screening strategies that achieve better harm-to-benefit ratio based on earlier detection and better breast cancer outcomes than existing screening guidelines. Computational mammographic phenotypes have demonstrated a promising role in breast cancer risk prediction. With the recent exponential growth of computational efficiency, the artificial intelligence (AI) revolution, driven by the introduction of deep learning, has expanded the utility of imaging in predictive models. Consequently, AI-based imaging-derived data has led to some of the most promising tools for precision breast cancer screening.This review aims to synthesize the current state-of-the-art applications of AI in mammographic phenotyping of breast cancer risk. We discuss the fundamentals of AI and explore the computing advancements that have made AI-based image analysis essential in refining breast cancer risk assessment. Specifically, we discuss the use of data derived from digital mammography as well as digital breast tomosynthesis. Different aspects of breast cancer risk assessment are targeted including (a) robust and reproducible evaluations of breast density, a well-established breast cancer risk factor, (b) assessment of a woman's inherent breast cancer risk, and (c) identification of women who are likely to be diagnosed with breast cancers after a negative or routine screen due to masking or the rapid and aggressive growth of a tumor. Lastly, we discuss AI challenges unique to the computational analysis of mammographic imaging as well as future directions for this promising research field.We provide a useful reference for AI researchers investigating image-based breast cancer risk assessment while indicating key priorities and challenges that, if properly addressed, could accelerate the implementation of AI-assisted risk stratification to future refine and individualize breast cancer screening strategies.
Binary Artificial Algae Algorithm for feature selection

In this study, binary versions of the Artificial Algae Algorithm (AAA) are presented and employed to determine the ideal attribute subset for classification processes. AAA is a recently proposed algorithm inspired by microalgae’s living behavior, which has not been consistently implemented to determine ideal attribute subset (feature selection) processes yet. AAA can effectively look into the feature space for ideal attributes combination minimizing a designed objective function. The proposed binary versions of AAA are employed to determine the ideal attribute combination that maximizes classification success while minimizing the count of attributes. The original AAA is utilized in these versions while its continuous spaces are restricted in a threshold using an appropriate threshold function after flattening them. In order to demonstrate the performance of the presented binary artificial algae algorithm model, an experimental study was conducted with the latest seven high-performance optimization algorithms. Several evaluation metrics are used to accurately evaluate and analyze the performance of these algorithms over twenty-five datasets with different difficulty levels from the UCI Machine Learning Repository. The experimental results and statistical tests verify the performance of the presented algorithms in increasing the classification accuracy compared to other state-of-the-art binary algorithms, which confirms the capability of the AAA algorithm in exploring the attribute space and deciding the most valuable features for classification problems. • The main motivation for binary AAA for feature selections is that AAA demonstrates successful performance in various problem. • This is the first study used to determine the ideal attribute subset of AAA. • In this study, eight different transfer functions (four V-shaped and four S-shaped) were used for binary AAA. • Results were interpreted in terms of the Wilcoxon signed-rank test and Freidman rank test. • The obtained results outperform the eight state-of-the-art feature selection approaches.