Reviewerly/exHarmony huggingface dataset:
Geochemical anomaly mapping using sparse principal component analysis in Jining, Inner Mongolia, China

Mapping geochemical anomalies is one of the most important tasks in mineral exploration. Sparse principal component analysis (SPCA) can be applied to investigate the correlations among geochemical elements and geological variables, which forces the loading values of the partial elements to zero. In this paper, the robust SPCA algorithm was used to process geochemical exploration data on a scale of 1:50,000 for identifying geochemical anomalies related to mineralization. The results show SPC1 and SPC3 indicate the spatial distribution of the Hannuoba basalt and Jining Group formation, respectively. The outcropped Hannuoba formations are located in high-value area of scores map of SPC1. The element association of W-Mo on SPC2 is related to molybdenum (Mo) mineralization. The spatial distribution of SPC2 scores suggests the distribution of Mo mineralization. The discovered Mo polymetallic deposits are located in the anomalous area of SPC2 scores. The SPCs identify localized geochemical anomalies and disambiguate the effects of outliers, which improve interpretability. These results indicate SPCA is a powerful tool for mapping geochemical anomalies.
Certificate-Based Anonymous Authentication With Efficient Aggregation for Wireless Medical Sensor Networks

Wireless medical sensor networks (WMSNs) have aroused widespread attention in recent years with the development of Internet of Things (IoT) technology. WMSNs offer many new opportunities for healthcare professionals to monitor patients and patient self-monitoring. To overcome the resource (such as memory and power) limitations of sensors and attain data security of patients’ private medical information, researchers have designed plenty of work for securing WMSNs. For years, certificate-based aggregate signature (CBAS) schemes have been put forward for WMSNs to prevent patients’ sensitive medical data from being tampered with and damaged. In this work, we analyze the security flaws of a very recent CBAS scheme proposed by Verma <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> (2021) by presenting two types of security attacks. We later propose a CBAS scheme with user anonymity protection for WMSNs and prove its security based on the standard cryptographic assumption. The performance comparison results from theory and experiment illustrate the practicality of our design.
Design and Implementation of a Face Recognition System Based on API mobile vision and Normalized Features of Still Images

Biometric identification is the automated technique of measuring the biological data. The term biometrics is commonly used today to recognize a person by analyzing his/her physical characteristics and comparing these characteristics to a database such as fingerprint recognition, retinal scanning, face recognition etc. Face recognition is one of the biometric techniques that are used for identifying and verifying the identity of a person. This paper presents the design and implementation of a face recognition system for Android mobile phone platform. The design methodology includes two main steps. The first step is the extraction of the image's features and the second one is the recognition according to the classification of patterns. This system includes face detection using face detector available in the Android called object detection (Google's API mobile vision), features extraction (nose detection, mouth detection, eyes detection and cheek detection) using the same algorithm object detection API. From the feature extraction, we have 30 geometrical measurements of each person. The recognition step is done by calculating the Pearson Correlation Coefficients between the test image's geometrical measurements and measurements stored in the training database. The system will accept a facial image of the person as authenticated and allow to access the mobile applications if the percentage of correlation is greater than a chosen threshold otherwise it rejects the image. The recognition rate of the proposed approach has achieved an accuracy more than 95% of other approaches.
Blockchain-Based Crowdfunding: A Trust Building Model

Blockchain-based crowdfunding is one of the new, upcoming alternatives to the traditional centralized approach to crowdfunding. Traditional crowdfunding platforms are vulnerable to data leaks, high transaction and platform fees, and rampant frauds which happens due to the anonymity of user’s identity i.e., users cannot be identified when they commit cybercrimes. As blockchain is immutable and decentralized, it can reduce the possibility of data breaches. This brings in transparency as there is no central authority over the blockchain-based crowdfunding system. This paper attempts to solve these existing issues with the aid of a digital identity management system with an underlying Blockchain system. By implementing blockchain in a digital identity management system, malicious users can be identified and action can be taken against them. This paper explores donation-based crowdfunding using Ethereum as a framework and has been tested on the Rinkeby Test Network. This system can conduct several crowdfunding campaigns simultaneously. This paper explains the smart contract written in Solidity language in detail.
An explanatory analytics framework for early detection of chronic risk factors in pandemics

Timely decision-making in national and global health emergencies such as pandemics is critically important from various aspects. Especially, early identification of risk factors of contagious viral diseases can lead to efficient management of limited healthcare resources and saving lives by prioritizing at-risk patients. In this study, we propose a hybrid artificial intelligence (AI) framework to identify major chronic risk factors of novel, contagious diseases as early as possible at the time of pandemics. The proposed framework combines evolutionary search algorithms with machine learning and the novel explanatory AI (XAI) methods to detect the most critical risk factors, use them to predict patients at high risk of mortality, and analyze the risk factors at the individual level for each high-risk patient. The proposed framework was validated using data from a repository of electronic health records of early COVID-19 patients in the US. A chronological analysis of the chronic risk factors identified using our proposed approach revealed that those factors could have been identified months before they were determined by clinical studies and/or announced by the United States health officials.
Semi-Supervised Self-Learning for Arabic Hate Speech Detection

One key for improving hate speech detection performance is to have a textual training corpus that is vast and confidently labeled. This paper develops a semi-supervised learning approach with self-training to benefit from the abundant amount of social media content and develop a robust hate speech classifier for future predictions. The classifier is self-trained iteratively using the most confident pseudo labels obtained from a large-scale unlabelled Twitter corpus. We demonstrate our approach’s efficacy and the high quality of the produced supervised hate speech dataset through experiments.
Speculative Design as a Collaborative Practice: Ameliorating the Consequences of Illiteracy through Digital Touch

This article and the design fictions it presents are bound up with an ongoing qualitative-ethnographic study with Imazighen, the native people in remote Morocco. This group of people is marked by textual and digital illiteracy. We are in the process of developing multi-modal design fictions that can be used in workshops as a starting point for the co-development of further design fictions that envision the local population's desired digital futures. The design fictions take the form of storyboards, allowing for a non-textual engagement. The current content seeks to explore challenges, potentials, margins, and limitations for the future design of haptic and touch-sensitive technology as a means for interpersonal communication and information procurement. Design fictions provide a way of exposing the locals to possible digital futures so that they can actively engage with them and explore the bounds and confines of their literacy and the extent to which it matters.
A bibliometric review of research on digital identity: Research streams, influential works and future research paths

In recent years, potentially disruptive identity-related topics emerged, such as digital twin technology for product lifecycle management or self-sovereign identity (SSI) for sovereign data control. In this study, we identify research streams and emerging trends in academic research on digital identity through a bibliometric analysis of 1,395 peer-reviewed articles and their 44,412 references. We derive seven distinct research streams and their interrelations by means of co-citation analysis. We name the seven research streams: i) Digital twin technology for smart manufacturing and industrial health monitoring, ii) identity-based signcryption schemes, iii) distributed networks and user privacy, iv) user authentication in wireless sensor networks, v) attribute-based encryption schemes, vi) secure data exchange in the Internet of Things and vii) blockchain and smart contracts for secure data management. Each stream's high-impact publications and its development over time are reviewed and the interrelation between publications and streams are visualized. In addition, we extract directions for future research from the field's most influential publications. The results offer a comprehensive and systematic overview of publications and discourses in digital identity research.
The influence of fake accounts on sentiment analysis related to COVID-19 in Indonesia

Cases of the spread of COVID-19 that continue to increase in Indonesia have made the level of public satisfaction with the government in dealing with this virus fairly low. One way to measure the level of community satisfaction is by analyzing social media. Sentiment analysis can be used to analyze feedback from the public. Research related to sentiment analysis has been mostly carried out, but so far, it has focused more on opinions contained in sentences and comments and has not considered the subject of the account that posted it. On the other hand, the use of fake accounts or bots on social media is becoming more and more prevalent, so that the credibility of opinion makers is reduced. Based on these problems, this research conducted several experiments related to sentiment analysis using a machine learning approach and fake account categories to see the influence of fake accounts on sentiment analysis. The data used in this research were taken from social media Twitter. The results showed that there was an influence from fake accounts that can reduce the performance of sentiment classification. The experimental results of the two algorithms also prove that the Support Vector Machine algorithm has a better performance than the Naïve-Bayes algorithm for this case with the highest Accuracy value of 80.6%. In addition, the results of the sentiment visualization showed that there was an influence from fake accounts which actually leads to positive sentiment although it is not significant.
Development of AR training systems for Humanitude dementia care

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Progressive Transfer Learning

Model fine-tuning is a widely used transfer learning approach in person Re-identification (ReID) applications, which fine-tuning a pre-trained feature extraction model into the target scenario instead of training a model from scratch. It is challenging due to the significant variations inside the target scenario, e.g., different camera viewpoint, illumination changes, and occlusion. These variations result in a gap between each mini-batch's distribution and the whole dataset's distribution when using mini-batch training. In this paper, we study model fine-tuning from the perspective of the aggregation and utilization of the dataset's global information when using mini-batch training. Specifically, we introduce a novel network structure called Batch-related Convolutional Cell (BConv-Cell), which progressively collects the dataset's global information into a latent state and uses it to rectify the extracted feature. Based on BConv-Cells, we further proposed the Progressive Transfer Learning (PTL) method to facilitate the model fine-tuning process by jointly optimizing BConv-Cells and the pre-trained ReID model. Empirical experiments show that our proposal can greatly improve the ReID model's performance on MSMT17, Market-1501, CUHK03, and DukeMTMC-reID datasets. Moreover, we extend our proposal to the general image classification task. The experiments in several image classification benchmark datasets demonstrate that our proposal can significantly improve baseline models' performance. The code has been released at https://github.com/ZJULearning/PTL.
Mutually testing source-device-independent quantum random number generator

Quantum random numbers have an incomparable advantage over pseudo-random numbers since randomness originates from intrinsic property of quantum mechanics. The generation rate and the security of quantum random numbers are two significant indicators of a quantum random number generator (QRNG) for practical applications. Here we propose a mutually testing source-device-independent QRNG by simultaneously measuring a pair of conjugate quadratures from two separate parts of an untrusted continuous-variable quantum state. The amounts of randomness of the quadratures can be mutually estimated by each other via entropic uncertainty principle. Instead of randomly toggling between the conjugate quadratures of one state for collecting different types of data, two quadratures can generate check data and raw bits simultaneously and continuously in this mutually testing manner, which enhances the equivalent generation rate of private random bits to around 6 Gbit/s with a 7.5 mW laser beam. Moreover, the overall security is also improved by adjusting the conditional min-entropy in real time according to the continually monitored fluctuations of the local oscillator and the randomly measured electronic noise of homodyne detectors.
Dynamic Neural Network for Lossy-to-Lossless Image Coding

Lifting-based wavelet transform has been extensively used for efficient compression of various types of visual data. Generally, the performance of such coding schemes strongly depends on the lifting operators used, namely the prediction and update filters. Unlike conventional schemes based on linear filters, we propose, in this paper, to learn these operators by exploiting neural networks. More precisely, a classical Fully Connected Neural Network (FCNN) architecture is firstly employed to perform the prediction and update. Then, we propose to improve this FCNN-based Lifting Scheme (LS) in order to better take into account the input image to be encoded. Thus, a novel dynamical FCNN model is developed, making the learning process adaptive to the input image contents for which two adaptive learning techniques are proposed. While the first one resorts to an iterative algorithm where the computation of two kinds of variables is performed in an alternating manner, the second learning method aims to learn the model parameters directly through a reformulation of the loss function. Experimental results carried out on various test images show the benefits of the proposed approaches in the context of lossy and lossless image compression.
COVID-19 and mental disorders in healthcare Personnel: A novel framework to develop Personas from an online survey

In this paper we propose a novel framework for the definition of Personas for healthcare workers based on an online survey, with the aim of highlighting different levels of risk of developing mental disorders induced by COVID-19 and tailor psychological support interventions.Data were gathered from Italian healthcare workers between April and May 2020. Information about socio-demographic characteristics, current lifestyle, occupational, COVID-19 infection, and psychological indexes (Maslach Burnout Inventory, Impact of Event Scale and Patient Health Questionnaire) was collected. Respondents were divided in four subgroups based on their health profession: physicians (P), nurses (N), other medical professionals (OMP) and technical-administrative (TA). For each sub-group, collected variables (46) were reduced using Principal Component Analysis and clustered by means of k-medoids clustering. Statistical analysis was then applied to define which variables were able to differentiate among the k clusters, leading to the generation of a Persona card (i.e., a template with textual and graphical information) for each of the obtained clusters.From the 538 respondents (153 P, 175 N, 176 OMP, 344 TA), the highest stress level, workload impact and risk of mental disorders were found in the N subgroup. Two clusters were identified for P, three clusters for N, two for OMP and one for TA.The proposed framework was able to stratify different risk levels of possible development of mental health issues in healthcare workers due to COVID-19. This approach could represent the first step towards the development of mobile health tools to tailor psychological interventions in pandemic situations.
A Comparison of Kubernetes and Kubernetes-Compatible Platforms

Nowadays, Kubernetes is an advanced container orchestration tool due to its high reliability, scalability and fault tolerance. However, Kubernetes requires a significant number of resources for its work. Therefore, to ensure the operation of Kubernetes in conditions of limited resources, lightweight analogues such as MicroKubernetes and K3S were created. These platforms provide easier deployment and support. In this paper, the authors analyze performance metrics for orchestration actions such as adding/removing nodes and starting/stopping deployments in terms of resource utilization, cluster startup speed, and consumed time for lightweight platforms and original Kubernetes. The results show that the original Kubernetes outperforms MicroKubernetes and K3S in many tests, but K3S demonstrates better disk utilization. On the other hand, MicroKubernetes demonstrates worst results in the performed tests.
Agile Teams Working from Home During the Covid-19 Pandemic: A Literature Review on New Advantages and Challenges

Whilst co-location is the common and preferred kind and key standard for self-organizing agile teams, this option is not always possible for some organizations that have to lead to the distribution of teams and/or individuals in one or another form, especially because of Sars-Cov-2 pandemic (Covid-19) today. The pandemic has forced a shift to virtual working for many organizations, which makes it necessary to investigate its possible effects on the self-organizing agile teams. In this manner, this study aims to investigate emergent challenges and advantages arising from working at home for self-organizing agile teams where every team member works from home with the impact of the Covid-19 pandemic by systemically reviewing the literature. Finally, all the findings, derived from the literature, were discussed from coordination, collaboration and communication, agile practices, agility, emotions and feelings, leadership, productivity, and quality aspects. The results demonstrate that along with some specific challenges for the agile teams during the pandemic, there are several advantages of working at home for them.
An Isogeny-Based ID Protocol Using Structured Public Keys

Isogeny-based cryptography is known as one of the promising approaches to the emerging post-quantum public key cryptography. In cryptography, an IDentification (ID) protocol is a primitive that allows someone's identity to be confirmed. We present an efficient variation of the isogeny-based interactive ID scheme used in the base form of the CSI-FiSh signature [], which was initially proposed by Couveignes-Rostovtsev-Stolbunov [, ], to support a larger challenge space, and consequently achieve a better soundness error rate in each execution. To this end, we prolong the public key of the basic ID protocol with some well-formed elements that are generated by particular factors of the secret key. Due to the need for a well-formed (or structured) public key, the (secret and public) keys are generated by a trusted authority. Our analysis shows that, for a particular security parameter, by extending a public key of size 64B to 2.1 MB, the prover and verifier of our ID protocol can be more than 14\(\times \) faster than the basic ID protocol which has a binary challenge space, and moreover, the proof in our case will be about 13.5\(\times \) shorter. Using standard techniques, we also turn the presented ID protocol into a signature scheme that is as efficient as the state-of-the-art CSI-FiSh signature, and is existentially unforgeable under chosen message attacks in the (quantum) random oracle model. However, in our signature scheme, a verifier should get the public key of a signer from a trusted authority, which is standard in a wide range of current uses of signatures. Finally, we show how to eliminate the need for a trusted authority in our proposed ID protocol.
Upon Improving the Performance of Localized Healthcare Virtual Assistants

Virtual assistants are becoming popular in a variety of domains, responsible for automating repetitive tasks or allowing users to seamlessly access useful information. With the advances in Machine Learning and Natural Language Processing, there has been an increasing interest in applying such assistants in new areas and with new capabilities. In particular, their application in e-healthcare is becoming attractive and is driven by the need to access medically-related knowledge, as well as providing first-level assistance in an efficient manner. In such types of virtual assistants, localization is of utmost importance, since the general population (especially the aging population) is not familiar with the needed "healthcare vocabulary" to communicate facts properly; and state-of-practice proves relatively poor in performance when it comes to specialized virtual assistants for less frequently spoken languages. In this context, we present a Greek ML-based virtual assistant specifically designed to address some commonly occurring tasks in the healthcare domain, such as doctor's appointments or distress (panic situations) management. We build on top of an existing open-source framework, discuss the necessary modifications needed to address the language-specific characteristics and evaluate various combinations of word embeddings and machine learning models to enhance the assistant's behaviour. Results show that we are able to build an efficient Greek-speaking virtual assistant to support e-healthcare, while the NLP pipeline proposed can be applied in other (less frequently spoken) languages, without loss of generality.