Reviewerly/exHarmony huggingface dataset:
A Novel Data Augmentation Convolutional Neural Network for Detecting Malaria Parasite in Blood Smear Images

Malaria fever is a potentially fatal disease caused by the Plasmodium parasite. Identifying Plasmodium parasites in blood smear images can help diagnose malaria fever rapidly and precisely. According to the World Health Organization (WHO), there were 241 million malaria cases and 627 000 deaths worldwide in 2020, while 95% of malaria cases and 96% of malaria deaths occurred in Africa. Also in Africa, children that are less than five years old accounted for an estimated 80% of all malaria deaths. To address the menace of malaria, this paper proposes a novel deep learning model, called a data augmentation convolutional neural network (DACNN), trained by reinforcement learning to tackle this problem. The performance of the proposed DACNN model is compared with CNN and directed acyclic graph convolutional neural network (DAGCNN) models. Results show that DACNN outperforms previous studies in processing and classification images. It achieved 94.79% classification accuracy in malaria blood sample images of balanced class dataset obtained from the Kaggle dataset. The proposed model can serve as an effective tool for the detection of malaria parasites in blood smear images.
BPGAN: Brain PET synthesis from MRI using generative adversarial network for multi-modal Alzheimer’s disease diagnosis

Multi-modal medical images, such as magnetic resonance imaging (MRI) and positron emission tomography (PET), have been widely used for the diagnosis of brain disorder diseases like Alzheimer's disease (AD) since they can provide various information. PET scans can detect cellular changes in organs and tissues earlier than MRI. Unlike MRI, PET data is difficult to acquire due to cost, radiation, or other limitations. Moreover, PET data is missing for many subjects in the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. To solve this problem, a 3D end-to-end generative adversarial network (named BPGAN) is proposed to synthesize brain PET from MRI scans, which can be used as a potential data completion scheme for multi-modal medical image research.We propose BPGAN, which learns an end-to-end mapping function to transform the input MRI scans to their underlying PET scans. First, we design a 3D multiple convolution U-Net (MCU) generator architecture to improve the visual quality of synthetic results while preserving the diverse brain structures of different subjects. By further employing a 3D gradient profile (GP) loss and structural similarity index measure (SSIM) loss, the synthetic PET scans have higher-similarity to the ground truth. In this study, we explore alternative data partitioning ways to study their impact on the performance of the proposed method in different medical scenarios.We conduct experiments on a publicly available ADNI database. The proposed BPGAN is evaluated by mean absolute error (MAE), peak-signal-to-noise-ratio (PSNR) and SSIM, superior to other compared models in these quantitative evaluation metrics. Qualitative evaluations also validate the effectiveness of our approach. Additionally, combined with MRI and our synthetic PET scans, the accuracies of multi-class AD diagnosis on dataset-A and dataset-B are 85.00% and 56.47%, which have been improved by about 1% and 1%, respectively, compared to the stand-alone MRI.The experimental results of quantitative measures, qualitative displays, and classification evaluation demonstrate that the synthetic PET images by BPGAN are reasonable and high-quality, which provide complementary information to improve the performance of AD diagnosis. This work provides a valuable reference for multi-modal medical image analysis.
Rethinking the consequences of postmodern authenticity: the case of a World Cultural Heritage in Augmented Reality

The emergence of Augmented Reality (AR) enriched and further enhanced the tourism experience. Drawn upon presence theory and a postmodernist perspective, this study rethinks the impact of postmodern authenticity on two different types of presences (Sense of Presence and Telepresence) and hence on visitor satisfaction and travel intention, by focusing on AR-based World Cultural Heritage Site – Mount Tai, China. Data were collected from 225 respondents visiting AR-based Mount Tai. Results demonstrated that postmodern authenticity positively predicts presences, satisfaction, and travel intention, and Postmodern authenticity has a more substantial effect on the Sense of Presence. Moreover, both types of presence positively predict satisfaction, hence travel intention while Sense of Presence has a stronger effect. Theoretical and managerial implications are presented.
Improving Generalization by Commonality Learning in Face Forgery Detection

This paper proposes a commonality learning strategy for face video forgery detection to improve the generalization. Considering various face forgery methods could leave certain similar forgery traces in videos, we attempt to learn the common forgery features from different forgery databases, so as to achieve better generalization in the detection of unknown forgery methods. Firstly, the Specific Forgery Feature Extractors (SFFExtractors) are trained separately for each of given forgery methods. We utilize the U-net structure and consider the triplet loss, location loss, classification loss, and automatic weighted loss to ensure the detection ability of SFFExtractors on the corresponding forgery methods. Next, the Common Forgery Feature Extractor (CFFExtractor) is trained under the supervision of SFFExtractors to explore the commonality of the forgery traces caused by different forgery methods. The extracted common forgery feature is expected to have a good generalization. The experimental results on FaceForensic++ show that the SFFExtractors outperform many state-of-the-arts in face forgery detection. The generalization performance of the CFFExtractor is verified on FaceForensic++, DFDC, and CelebDF. It is proved that commonality learning can be an effective strategy to improve generalization.
Modified continuous Ant Colony Optimisation for multiple Unmanned Ground Vehicle path planning

Path planning for multiple Unmanned Ground Vehicles (UGVs) is a critical problem for UGV autonomy and is increasingly attracting attention due to its wide applications. This paper presents a continuous ant colony-based multi-UGV path planner, which consists of UGV path planning and multi-UGV coordination. A continuous Ant Colony Optimisation with a Probability-based random-walk strategy and an Adaptive waypoints-repair method (ACOPAR) is proposed to optimise the path for each UGV. Collision avoidance among the UGVs for the multi-agent coordination problem is then resolved via a velocity shifting optimisation algorithm. In ACOPAR, exploration and exploitation are balanced using a probability-based random-walk strategy switching between a Brownian and a Cauchy motion to modify the construction process of new solutions. An adaptive waypoints-repair strategy and a re-initialisation strategy are designed to improve the algorithm's performance in finding feasible paths. A test suite of multi-UGV path planning with 12 cases is proposed to evaluate the search capability and scalability of the proposed ACOPAR compared to other algorithms. Experimental results validate the superiority of ACOPAR, especially when solving complex, high-dimensional problems.
Satellite Telemetry Data Anomaly Detection Using Causal Network and Feature-Attention-Based LSTM

Most of the data-driven satellite telemetry data anomaly detection methods suffer from high false positive rate (FPR) and poor interpretability. To solve the above problems, we propose an anomaly detection framework using causal network and feature-attention-based long short-term memory (CN-FA-LSTM). In our method, a causal network of telemetry parameters is constructed by calculating normalized modified conditional transfer entropy (NMCTE) and optimized by conditional independence tests based on the conditional mutual information (CMI). Then, a CN-FA-LSTM is established to predict telemetry data, and a nonparametric dynamic <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -sigma threshold updating method is proposed to set thresholds. A case study on a real satellite demonstrates that anomaly detection using the CN-FA-LSTM and nonparametric dynamic <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -sigma threshold updating has an average F1-score of 0.9462 and an FPR of 0.0021, which are better than the baseline methods. Furthermore, CN-FA-LSTM has a stronger interpretability than other commonly used prediction models. Supplementary experiment on two public datasets verifies the universal applicability of our method.
Artificial intelligence for intrusion detection systems in Unmanned Aerial Vehicles

Unmanned Aerial Vehicles (UAVs) are seeing increased use in critical operations for law enforcement, military, industrial control surveillance and more. These hostile operating environments combined with the UAVs reliance on wireless protocols produces an increased threat level. Many attacks against the UAV are becoming commonplace as they are simple to conduct with inexpensive hardware, such as spoofing and jamming. Unfortunately, as many of these vulnerabilities exist within underlying technologies, securing the UAV becomes a difficult task. A promising approach to identifying and mitigating these attacks is the development of an intelligent intrusion detection system (IDS). The proposed approach uses principal component analysis (PCA) and one-class classifiers to detect attacks. This allows for the use of flight logs for training data, providing a versatile and ubiquitous approach. The proposed detection method is integrated into a fully developed IDS called MAVIDS. This IDS operates onboard the UAV within a resource-constrained agent device, allowing it to detect and potentially mitigate attacks even when communication to the ground control station is lost from jamming. The approach shows to be effective against GPS spoofing and jamming with macro averaged F1 scores of 90.57% and 94.3% respectively.
Deep learning networks for real-time regional domestic waste detection

Waste sorting is highly labor intensive because the wide variety of waste items prohibits automation. More recently, deep learning (DL) and computer vision technology has presented an opportunity to streamline the sorting process, but many important developmental steps remain. If computer vision technology can increase the efficiency of automated waste sorting, this would be beneficial for society and the environment. Accordingly, this study used the You Only Look Once-v3 (Yolo-v3) detection model based on DL to enhance recognition performance of household waste products. TrashNet, a commonly used waste image database, was used to train an initial Yolo-v3 model, however each image used for training only had a single waste object, and this study found that the detection model trained with a single object dataset was not only unsuitable for sorting multiple waste objects, but that this has rarely been addressed in academic literature. It was also discovered that nations and regions will need to develop their own unique databases that reflect the types of waste products found. Samples images need to account for the various appearances and colors and be combined in multiple waste object images when training the system. This paper documents the training and testing of an object detection model suitable for detecting domestic waste specific to Taiwan; however, the approach taken would be of use to other countries seeking to automate waste sorting. To achieve this, it was necessary to compile the Taiwan Recycled Waste Database (TRWD). This was then used to train the Yolo-v3, and the efficiencies of this, versus the standard TrashNet model were compared. Results showed that the TRWD-trained Yolo-v3 achieved mAP@0.5 of 92.12% and could detect waste in real-time. Relative to the TrashNet-trained Yolo-v3, the TRWD counterpart performed better due to the multiple waste objects and more relevant image repository. Further studies are recommended to investigate the effect of combining additional sensors that would enable improved detection of specific wastes. Combining the TRWD-trained Yolo-v3 with a robot system for waste sorting would potentially be another rewarding avenue of research.
Efficient Visual Pretraining with Contrastive Detection

Self-supervised pretraining has been shown to yield powerful representations for transfer learning. These performance gains come at a large computational cost however, with state-of-the-art methods requiring an order of magnitude more computation than supervised pretraining. We tackle this computational bottleneck by introducing a new self-supervised objective, contrastive detection, which tasks representations with identifying object-level features across augmentations. This objective extracts a rich learning signal per image, leading to state-of-the-art transfer accuracy on a variety of downstream tasks, while requiring up to 10× less pretraining. In particular, our strongest ImageNet-pretrained model performs on par with SEER, one of the largest self-supervised systems to date, which uses 1000× more pretraining data. Finally, our objective seamlessly handles pretraining on more complex images such as those in COCO, closing the gap with supervised transfer learning from COCO to PASCAL.
Organizational readiness to adopt artificial intelligence in the exhibition sector in Western Europe

This exploratory study explores perceptions of Artificial Intelligence (AI) and organizational readiness to adopt AI, in the exhibition sector of the events industry. A theoretical framework synthesizing the Technology - Organization - Environment framework and the Technology Readiness Index was developed to guide this qualitative study. Seventeen senior managerial representatives from exhibition organizations across nine Western European countries were interviewed, and a reflexive thematic approach was adopted to analyse the data. The findings suggest that the European exhibition industry is a slow adopter of AI, which may impact its future competitiveness, despite the stimulus provided to AI adoption during the COVID-19 pandemic. The degree of confidence in organizational technological practices, financial resources, the size of the organization, and issues of data management and protection, as well as the impacts of the COVID-19 pandemic, motivate or inhibit readiness for AI adoption in the event industry. A new Exhibition Sector Readiness for AI Adoption Model is presented in this research that managers and researchers can use to analyze inhibitors and motivations for AI adoption, which is contextualized for the current challenges facing the exhibitions sector.
Quantum computing challenges in the software industry. A fuzzy AHP-based approach

The current technology revolution has posed unexpected challenges for the software industry. In recent years, the field of quantum computing (QC) technologies has continued to grow in influence and maturity, and it is now poised to revolutionise software engineering. However, the evaluation and prioritisation of QC challenges in the software industry remain unexplored, relatively under-identified and fragmented. The purpose of this study is to identify, examine and prioritise the most critical challenges in the software industry by implementing a fuzzy analytic hierarchy process (F-AHP). First, to identify the key challenges, we conducted a systematic literature review by drawing data from the four relevant digital libraries and supplementing these efforts with a forward and backward snowballing search. Second, we followed the F-AHP approach to evaluate and rank the identified challenges, or barriers. The results show that the key barriers to QC adoption are the lack of technical expertise, information accuracy and organisational interest in adopting the new process. Another critical barrier is the lack of standards of secure communication techniques for implementing QC. By applying F-AHP, we identified institutional barriers as the highest and organisational barriers as the second highest global weight ranked categories among the main QC challenges facing the software industry. We observed that the highest-ranked local barriers facing the software technology industry are the lack of resources for design and initiative while the lack of organisational interest in adopting the new process is the most significant organisational barrier. Our findings, which entail implications for both academicians and practitioners, reveal the emergent nature of QC research and the increasing need for interdisciplinary research to address the identified challenges.
HeadGAN: One-shot Neural Head Synthesis and Editing

Recent attempts to solve the problem of head reenactment using a single reference image have shown promising results. However, most of them either perform poorly in terms of photo-realism, or fail to meet the identity preservation problem, or do not fully transfer the driving pose and expression. We propose HeadGAN, a novel system that conditions synthesis on 3D face representations, which can be extracted from any driving video and adapted to the facial geometry of any reference image, disentangling identity from expression. We further improve mouth movements, by utilising audio features as a complementary input. The 3D face representation enables HeadGAN to be further used as an efficient method for compression and reconstruction and a tool for expression and pose editing.
Systematic Review and Comparison of Publicly Available ICU Data Sets—A Decision Guide for Clinicians and Data Scientists

As data science and artificial intelligence continue to rapidly gain traction, the publication of freely available ICU datasets has become invaluable to propel data-driven clinical research. In this guide for clinicians and researchers, we aim to: 1) systematically search and identify all publicly available adult clinical ICU datasets, 2) compare their characteristics, data quality, and richness and critically appraise their strengths and weaknesses, and 3) provide researchers with suggestions, which datasets are appropriate for answering their clinical question.A systematic search was performed in Pubmed, ArXiv, MedRxiv, and BioRxiv.We selected all studies that reported on publicly available adult patient-level intensive care datasets.A total of four publicly available, adult, critical care, patient-level databases were included (Amsterdam University Medical Center data base [AmsterdamUMCdb], eICU Collaborative Research Database eICU CRD], High time-resolution intensive care unit dataset [HiRID], and Medical Information Mart for Intensive Care-IV). Databases were compared using a priori defined categories, including demographics, patient characteristics, and data richness. The study protocol and search strategy were prospectively registered.Four ICU databases fulfilled all criteria for inclusion and were queried using SQL (PostgreSQL version 12; PostgreSQL Global Development Group) and analyzed using R (R Foundation for Statistical Computing, Vienna, Austria). The number of unique patient admissions varied between 23,106 (AmsterdamUMCdb) and 200,859 (eICU-CRD). Frequency of laboratory values and vital signs was highest in HiRID, for example, 5.2 (±3.4) lactate values per day and 29.7 (±10.2) systolic blood pressure values per hour. Treatment intensity varied with vasopressor and ventilatory support in 69.0% and 83.0% of patients in AmsterdamUMCdb versus 12.0% and 21.0% in eICU-CRD, respectively. ICU mortality ranged from 5.5% in eICU-CRD to 9.9% in AmsterdamUMCdb.We identified four publicly available adult clinical ICU datasets. Sample size, severity of illness, treatment intensity, and frequency of reported parameters differ markedly between the databases. This should guide clinicians and researchers which databases to best answer their clinical questions.
Blockchain for the circular economy: Theorizing blockchain's role in the transition to a circular economy through an empirical investigation

Abstract Blockchain is increasingly lauded as an enabler of the transition to a circular economy. While there is considerable conceptual research and some empirical studies on this phenomenon, scholars have yet to develop a theoretical model of blockchain's role in this transition. Grounded in the sustainability transition literature, this paper addresses this gap through the following research question: What role does blockchain play in the transition to a circular economy? Following an abductive approach, we conducted interviews with ground‐level experts implementing blockchain innovations for the circular economy across Europe and the United States. Through a thematic analysis, we derived a theoretical model of the relationships among (1) drivers and barriers of the transition to a circular economy, (2) blockchain innovation for the circular economy, (3) technical challenges of blockchain, and (4) the circular economy. While blockchain plays a moderating role, interviewees considered it only an infrastructural resource rather than a panacea.
Reinforcement Learned Distributed Multi-Robot Navigation With Reciprocal Velocity Obstacle Shaped Rewards

The challenges to solving the collision avoidance problem lie in adaptively choosing optimal robot velocities in complex scenarios full of interactive obstacles. In this letter, we propose a distributed approach for multi-robot navigation which combines the concept of reciprocal velocity obstacle (RVO) and the scheme of deep reinforcement learning (DRL) to solve the reciprocal collision avoidance problem under limited information. The novelty of this work is threefold: (1) using a set of sequential VO and RVO vectors to represent the interactive environmental states of static and dynamic obstacles, respectively; (2) developing a bidirectional recurrent module based neural network, which maps the states of a varying number of surrounding obstacles to the actions directly; (3) developing a RVO area and expected collision time based reward function to encourage reciprocal collision avoidance behaviors and trade off between collision risk and travel time. The proposed policy is trained through simulated scenarios and updated by the actor-critic based DRL algorithm. We validate the policy in complex environments with various numbers of differential drive robots and obstacles. The experiment results demonstrate that our approach outperforms the state-of-art methods and other learning based approaches in terms of the success rate, travel time, and average speed.
The Effectiveness of Centralized Payment Network Advertisements on Digital Branding during the COVID-19 Crisis

Crises are always challenging for banking systems. In the case of COVID-19, centralized payment networks and FinTech companies’ websites have been affected by user behavior globally. As a result, there is ample opportunity for marketing managers and professionals to focus on big data from FinTech websites. This can contribute to a better understanding of the variables impacting their brand name and how to manage risk during crisis periods. This research is divided into three stages. The first stage presents the web analytics and the data retrieved from the FinTech platforms. The second stage illustrates the statistical analysis and the fuzzy cognitive mapping (FCM) performed. In the final stage, an agent-based model is outlined in order to simulate and forecast a company’s brand name visibility and user behavior. The results of this study suggest that, during crises, centralized payment networks (CPNs) and FinTech companies with high organic traffic tend to convert new visitors to actual “customers”.