Reviewerly/exHarmony huggingface dataset:
Portable x-ray fluorescence calibrations: Workflow and guidelines for optimizing the analysis of geological samples

The use of portable Energy Dispersive X-Ray Fluorescence (pXRF) spectrometers has exploded within the last decade for e.g., environmental, geological, and archaeological field research. Producing reliable geochemical data using a pXRF is a challenging process that requires the development of dedicated and matrix-matched empirical calibrations. Although a large field of applications, several research papers are still published using unquantified pXRF data, using manufacturers' built-in calibrations unsuitable for targeted rocks, or interpreting chemical concentrations without checking the limit of quantification of the pXRF spectrometer and its calibration model. In this paper, we present a general workflow for building an empirical calibration model for a pXRF spectrometer, from choosing reference materials to validating calibration's efficiency and quantifying its limit of quantification. We also compare the geochemical results on a sedimentary and an igneous rocks suite obtained from thirty-two calibration models that were built using different software available, including a simple homemade spreadsheet, the EasyCal calibration software, a free and open-source calibration app named CloudCal, and the built-in GeoMining and GeoExplorer calibrations provided by Bruker manufacturer on the Tracer 5 pXRF. This decoupled approach allows (i) to explore the pros and cons of each calibration model and determine which empirical calibration model brings the most accurate chemical concentration for a sedimentary and an igneous rock suite, (ii) to weigh the impact of each calibration parameter (type of regression line, spectral interferences, normalization, influence coefficients, multiple conditions of excitation and acquisition) on the accuracy of geochemical concentrations on rock samples and (iii) to bring innovative tools to assess the limits of quantification of a newly built calibration model.
Decentralized Federated Learning Over Slotted ALOHA Wireless Mesh Networking

Federated Learning (FL) presents a mechanism to allow decentralized training for machine learning (ML) models inherently enabling privacy preservation. The classical FL is implemented as a client-server system, which is known as Centralised Federated Learning (CFL). There are challenges inherent in CFL since all participants need to interact with a central server resulting in a potential communication bottleneck and a single point of failure. In addition, it is difficult to have a central server in some scenarios due to the implementation cost and complexity. This study aims to use Decentralised Federated learning (DFL) without a central server through one-hop neighbors. Such collaboration depends on the dynamics of communication networks, e.g., the topology of the network, the MAC protocol, and both large-scale and small-scale fading on links. In this paper, we employ stochastic geometry to model these dynamics explicitly, allowing us to quantify the performance of the DFL. The core objective is to achieve better classification without sacrificing privacy while accommodating for networking dynamics. In this paper, we are interested in how such topologies impact the performance of ML when deployed in practice. The proposed system is trained on a well-known MINST dataset for benchmarking, which contains labelled data samples of 60K images each with a size 28×28 pixels, and 1000 random samples of this MNIST dataset are assigned for each participant’ device. The participants' devices implement a CNN model as a classifier model. To evaluate the performance of the model, a number of participants are randomly selected from the network. Due to randomness in the communication process, these participants interact with the random number of nodes in the neighborhood to exchange model parameters which are subsequently used to update the participants’ individual models. These participants connected successfully with a varying number of neighbours to exchange parameters and update their global models. The results show that the classification prediction system can achieve 95% accuracy using the three different model optimizers in the training settings (i.e., SGD, ADAM, and RMSprop optimizers). Consequently, the DFL over mesh networking shows more flexibility in IoT systems, which reduces the communication cost and increases the convergence speed which can outperform CFL.
Optimization of the memory reset rate of a quantum echo-state network for time sequential tasks

Quantum reservoir computing is a class of quantum machine learning algorithms involving a reservoir of an echo state network based on a register of qubits, but the dependence of its memory capacity on the hyperparameters is still rather unclear. In order to maximize its accuracy in time--series predictive tasks, we investigate the relation between the memory of the network and the reset rate of the evolution of the quantum reservoir. We benchmark the network performance by three non--linear maps with fading memory on IBM quantum hardware. The memory capacity of the quantum reservoir is maximized for central values of the memory reset rate in the interval [0,1]. As expected, the memory capacity increases approximately linearly with the number of qubits. After optimization of the memory reset rate, the mean squared errors of the predicted outputs in the tasks may decrease by a factor ~1/5 with respect to previous implementations.
Historical PV-output characteristic extraction based weather-type classification strategy and its forecasting method for the day-ahead prediction of PV output

With photovoltaic (PV) penetration increasing, PV-output prediction has become a research hotspot. Due to the close correlation between PV-output fluctuation and weather conditions, PV-output prediction models often vary different weather types, while the historical/forecast weather types for modeling are mostly obtained from weather-service providers. However, weather-service providers generally have deficiencies in forecast accuracy, spatio-temporal resolution, and investment/operating costs. Based on the above, this paper changes the current acquisition way of the weather types, and proposes a framework of reversely determining weather types from historical PV-output data. First, the symbol-sequence histograms (SSH) are used to describe the PV-output volatility in a coarse-grained manner. Then, the SSHs are partitionally clustered and a classification rule for weather-types is proposed to label the historical weather types. Next, considering the chaotic characteristics of PV output, a prediction method combining phase-space reconstruction with an extremely learning machine based single-layer forward net is developed to predict the SSH. Finally, the day-ahead weather type is forecasted. Simulations were implemented on the weather-type classification and forecasting using a campus PV-system in East China. The PV-output prediction results show that, compared with weather information from a weather-service supplier, 75-day mean errors are significantly reduced by 15.55% (MAPE) and 12.69% (rRMSE), respectively.
Origami plot: a novel multivariate data visualization tool that improves radar chart

We propose the origami plot, which maintains the original functionality of a radar chart and avoids potential misuse of its connected regions, with newly added features to better assist multicriteria decision-making.Built upon a radar chart, the origami plot adds additional auxiliary axes and points such that the area of the connected region of all dots is invariant to the ordering of axes. As such, it enables ranking different individuals by the overall performance for multicriteria decision-making while maintaining the intuitive visual appeal of the radar chart. We develop extensions of the origami plot, including the weighted origami plot, which allows reweighting of each attribute to define the overall performance, and the pairwise origami plot, which highlights comparisons between two individuals.We illustrate the different versions of origami plots using the hospital compare database developed by the Centers for Medicare & Medicaid Services (CMS). The plot shows individual hospital's performance on mortality, readmission, complication, and infection, as well as patient experience and timely and effective care, as well as their overall performance across these metrics. The weighted origami plot allows weighing the attributes differently when some are more important than others. We illustrate the potential use of the pairwise origami plot in electronic health records (EHR) system to monitor five clinical measures (body mass index [BMI]), fasting glucose level, blood pressure, triglycerides, and low-density lipoprotein ([LDL] cholesterol) of a patient across multiple hospital visits.The origami plot is a useful visualization tool to assist multicriteria decision making. It improves radar charts by avoiding potential misuse of the connected regions. It has several new features and allows flexible customization.
SubStrat

Automated machine learning (AutoML) frameworks have become important tools in the data scientist's arsenal, as they dramatically reduce the manual work devoted to the construction of ML pipelines. Such frameworks intelligently search among millions of possible ML pipelines - typically containing feature engineering, model selection, and hyper parameters tuning steps - and finally output an optimal pipeline in terms of predictive accuracy. However, when the dataset is large, each individual configuration takes longer to execute, therefore the overall AutoML running times become increasingly high. To this end, we present SubStrat, an AutoML optimization strategy that tackles the data size, rather than configuration space. It wraps existing AutoML tools, and instead of executing them directly on the entire dataset, SubStrat uses a genetic-based algorithm to find a small yet representative data subset that preserves a particular characteristic of the full data. It then employs the AutoML tool on the small subset, and finally, it refines the resulting pipeline by executing a restricted, much shorter, AutoML process on the large dataset. Our experimental results, performed on three popular AutoML frameworks, Auto-Sklearn, TPOT, and H2O show that SubStrat reduces their running times by 76.3% (on average), with only a 4.15% average decrease in the accuracy of the resulting ML pipeline.
Implementation of blockchain-enabled supply chain finance solutions in the agricultural commodity supply chain: a transaction cost economics perspective

Agricultural commodity supply chains are characterised by the involvement of multiple intermediaries, lack of access to finance and poor financial conditions of farmers. Additionally, there exist numerous inefficiencies and a lack of transparency in the trading processes. Blockchain-enabled supply chain finance (SCF) solutions can potentially help to overcome these problems. However, there is limited research on the process of developing and implementing such solutions and the potential consequences of their implementation. In this paper, we apply the Context-Intervention-Mechanism-Outcome (CIMO) framework to systematically analyse case studies of four firms that have developed blockchain-enabled SCF solutions in agricultural commodity supply chains. The findings show that blockchain-enabled SCF solutions can reduce different types of transaction costs such as costs associated with information search, negotiation and contracting costs, and costs of accessing finance. The solutions designed with the core objective of improving the financial conditions of farmers will differ from those with the core objective of reducing process inefficiencies. The findings of the study will benefit companies planning to develop and implement blockchain-enabled SCF solutions, by highlighting operational challenges and offering concrete solutions on how they can be overcome.
SwimmerNET: Underwater 2D Swimmer Pose Estimation Exploiting Fully Convolutional Neural Networks

Professional swimming coaches make use of videos to evaluate their athletes' performances. Specifically, the videos are manually analyzed in order to observe the movements of all parts of the swimmer's body during the exercise and to give indications for improving swimming technique. This operation is time-consuming, laborious and error prone. In recent years, alternative technologies have been introduced in the literature, but they still have severe limitations that make their correct and effective use impossible. In fact, the currently available techniques based on image analysis only apply to certain swimming styles; moreover, they are strongly influenced by disturbing elements (i.e., the presence of bubbles, splashes and reflections), resulting in poor measurement accuracy. The use of wearable sensors (accelerometers or photoplethysmographic sensors) or optical markers, although they can guarantee high reliability and accuracy, disturb the performance of the athletes, who tend to dislike these solutions. In this work we introduce swimmerNET, a new marker-less 2D swimmer pose estimation approach based on the combined use of computer vision algorithms and fully convolutional neural networks. By using a single 8 Mpixel wide-angle camera, the proposed system is able to estimate the pose of a swimmer during exercise while guaranteeing adequate measurement accuracy. The method has been successfully tested on several athletes (i.e., different physical characteristics and different swimming technique), obtaining an average error and a standard deviation (worst case scenario for the dataset analyzed) of approximately 1 mm and 10 mm, respectively.
Hybrid phishing detection using joint visual and textual identity

In recent years, phishing attacks have evolved considerably, causing existing adversarial features that were widely utilised for detecting phishing websites to become less discriminative. These developments have fuelled growing interests among security researchers towards an anti-phishing strategy known as the identity-based detection technique. Identity-based detection techniques have consistently achieved high true positive rates in a rapidly changing phishing landscape, owing to its capitalisation on fundamental brand identity relations that are inherent in most legitimate webpages. However, existing identity-based techniques often suffer higher false positive rates due to complexities and challenges in establishing the webpage’s brand identity. To close the existing performance gap, this paper proposes a new hybrid identity-based phishing detection technique that leverages webpage visual and textual identity. Extending earlier anti-phishing work based on the website logo as visual identity, our method incorporates novel image features that mimic human vision to enhance the logo detection accuracy. The proposed hybrid technique integrates the visual identity with a textual identity, namely, brand-specific keywords derived from the webpage content using textual analysis methods. We empirically demonstrated on multiple benchmark datasets that this joint visual-textual identity detection approach significantly improves phishing detection performance with an overall accuracy of 98.6%. Benchmarking results against an existing technique showed comparable true positive rates and a reduction of up to 3.4% in false positive rates, thus affirming our objective of reducing the misclassification of legitimate webpages without sacrificing the phishing detection performance. The proposed hybrid identity-based technique is proven to be a significant and practical contribution that will enrich the anti-phishing community with improved defence strategies against rapidly evolving phishing schemes.
Multi-Intention Oriented Contrastive Learning for Sequential Recommendation

Sequential recommendation aims to capture users' dynamic preferences, in which data sparsity is a key problem. Most contrastive learning models leverage data augmentation to address this problem, but they amplify noises in original sequences. Contrastive learning has the assumption that two views (positive pairs) obtained from the same user behavior sequence must be similar. However, noises typically disturb the user's main intention, which results in the dissimilarity of two views.
Ranking researchers: Evidence from Indonesia

In the span of three years, Indonesia went from being the second worst to the top producer of scientific journal articles in Southeast Asia. We investigate whether a transparent system of ranking every single researcher in the country based on publications and citations (SINTA) contributed to this turnaround. Using panel data from over 200,000 Indonesian researchers (and comparing to researchers from Thailand and the Philippines), we show that the implementation of SINTA coincides with changes in the production of publications by Indonesian researchers consistent with the weights used in the ranking formula. Although we see modest improvements in publication rates in top-ranked journals, 62 % of the observed increase in total publications is from conference proceedings. Because SINTA was launched around the same time as other policies that focused on increasing publications, isolating the precise impact of SINTA remains challenging. Nevertheless, after accounting for such policies, our results imply that a ranking and evaluation system for researchers can contribute to overall improvements in scientific capacity in low- and middle-income countries.
Specific Emitter Identification via Contrastive Learning

Specific emitter identification (SEI) methods via deep learning have shown significant progress in accuracy recently. However, these methods require a large amount of the labeled data. In this letter, contrastive learning is introduced to cope with the problem of the lack of the labeled data, which the network modules include the feature extractor, the projection head, and the classifier. We use a two-stage semi-supervised training scheme. In the first stage, the feature extractor extracts the features of the received signal, and the projection head deepens the network to reserve more information in the features. They are trained with the unlabeled samples via the self-supervised contrastive learning loss. In the second stage, the overall network is fine-tuned over a small amount of the labeled data using an alternative loss. This loss combines the original cross-entropy loss with the supervised contrastive learning loss. Numerical results on the FIT/CorteXlab dataset show that even with tens of the labeled samples, the accuracy of the proposed identifier attains around 90%, which outperforms the conventional supervised and semi-supervised identifiers.
Single-Photon-Memory Measurement-Device-Independent Quantum Secure Direct Communication—Part I: Its Fundamentals and Evolution

Quantum secure direct communication (QSDC) has attracted a lot of attention, which exploits deep-rooted quantum physical principles to guarantee unconditional security of communication in the face of eavesdropping. We first briefly review the fundamentals of QSDC, and then present its evolution, including its security proof, its performance improvement techniques, and practical implementation. Finally, we discuss the future directions of QSDC.
Compilation and scaling strategies for a silicon quantum processor with sparse two-dimensional connectivity

Abstract Inspired by the challenge of scaling-up existing silicon quantum hardware, we propose a 2d spin-qubit architecture with low compilation overhead. The architecture is based on silicon nanowire split-gate transistors which form 1d chains of spin-qubits and allow the execution of two-qubit operations among neighbors. We introduce a silicon junction which can couple four nanowires into 2d arrangements via spin shuttling and Swap operations. We then propose a modular sparse 2d spin-qubit architecture with unit cells of diagonally-oriented squares with nanowires along the edges and junctions on the corners. Targeting noisy intermediate-scale quantum (NISQ) demonstrators, we show that the proposed architecture allows for compilation strategies which outperform methods for 1d chains, and exhibits favorable scaling properties which enable trading-off compilation overhead and colocation of control electronics within each square by adjusting the nanowire length. An appealing feature of the proposed architecture is its manufacturability using complementary-metal-oxide-semiconductor (CMOS) fabrication processes.
A survey of human-computer interaction (HCI) &amp; natural habits-based behavioural biometric modalities for user recognition schemes

The proliferation of Internet of Things (IoT) systems is having a profound impact across all aspects of life. Recognising and identifying particular users is central to delivering the personalised experience that citizens want to experience, and that organisations wish to deliver. This article presents a survey of human-computer interaction-based (HCI-based) and natural habits-based behavioural biometrics that can be acquired unobtrusively through smart devices or IoT sensors for user recognition purposes. Robust and usable user recognition is also a security requirement for emerging IoT ecosystems to protect them from adversaries. Typically, it can be specified as a fundamental building block for most types of human-to-things accountability principles and access-control methods. However, end-users are facing numerous security and usability challenges in using currently available knowledge- and token-based recognition (i.e., authentication and identification) schemes. To address the limitations of conventional recognition schemes, biometrics, naturally come as a first choice to supporting sophisticated user recognition solutions. We perform a comprehensive review of touch-stroke, swipe, touch signature, hand-movements, voice, gait and footstep behavioural biometrics modalities. This survey analyzes the recent state-of-the-art research of these behavioural biometrics with a goal to identify their attributes and features for generating unique identification signatures. Finally, we present security, privacy, and usability evaluations that can strengthen the designing of robust and usable user recognition schemes for IoT applications.