Reviewerly/exHarmony huggingface dataset:
Spectral Characterization of Parent Soils From Globally Important Dust Aerosol Entrainment Regions

Abstract We investigated the variation in the mineralogical composition of surface soil samples from a wide range of atmospheric dust aerosol generating regions. These soils likely have mineral compositions similar to dust aerosols. We measured the visible, shortwave infrared (VSWIR) and long wave infrared (LWIR) reflectance spectra, as well as LWIR transmission spectra and carried out linear spectral mixture modeling on the transmission data. The spectroscopically identified minerals were compared with the mineralogy previously obtained using X‐ray diffraction (XRD) and optical microscopy (OM). The results showed that VSWIR is very sensitive to iron‐bearing minerals and better identifies their presence and type compared to XRD and OM. Clay minerals and carbonates have multiple distinct absorptions features in SWIR but are often overlapping, limiting our ability to uniquely assign spectral features. LWIR reflectance easily distinguishes carbonates, even in trace amounts that were not identified with other techniques. LWIR reflectance spectra for silicates were more complicated to interpret. In some samples, we were able to identify Si‐O features diagnostic of common silicates; however, in most cases these features were absent, and an unusual new peak (∼7.4–7.9 μm) was observed. LWIR transmission is characterized by strong absorption that arises from overlapping features from multiple silicate minerals, but carbonates are readily identifiable. Spectroscopy is complementary to XRD and can help identify additional minerals in these soil samples. Our results can help improve global soil atlases and can be used to support interpretation and validation of data acquired by current and future remote sensing instruments.
Searching for chromate replacements using natural language processing and machine learning algorithms

Abstract The past few years have seen the application of machine learning utilised in the exploration of materials. As in many fields of research—the vast majority of knowledge is published as text, which poses challenges in either a consolidated or statistical analysis across studies and reports. To address this issue, the application of natural language processing (NLP) has been explored in several studies to date. In the present work, we have employed the Word2Vec model, previously explored by others, and the BERT model—applying them towards the search for chromate replacements in the field of corrosion protection. From a database of over 80 million records, a down-selection of 5990 papers focused on the topic of corrosion protection were examined using NLP. This study demonstrates it is possible to extract knowledge from the automated interpretation of the scientific literature and achieve expert human-level insights.
Blockchain Secured Dynamic Machine Learning Pipeline for Manufacturing

ML-based applications already play an important role in factories in areas such as visual quality inspection, process optimization, and maintenance prediction and will become even more important in the future. For ML to be used in an industrial setting in a safe and effective way, the different steps needed to use ML must be put together in an ML pipeline. The development of ML pipelines is usually conducted by several and changing external stakeholders because they are very complex constructs, and confidence in their work is not always clear. Thus, end-to-end trust in the ML pipeline is not granted automatically. This is because the components and processes in ML pipelines are not transparent. This can also cause problems with certification in areas where safety is very important, such as the medical field, where procedures and their results must be recorded in detail. In addition, there are security challenges, such as attacks on the model and the ML pipeline, that are difficult to detect. This paper provides an overview of ML security challenges that can arise in production environments and presents a framework on how to address data security and transparency in ML pipelines. The framework is presented using visual quality inspection as an example. The presented framework provides: (a) a tamper-proof data history, which achieves accountability and supports quality audits; (b) an increase in trust by protocol for the used ML pipeline, by rating the experts and entities involved in the ML pipeline and certifying legitimacy for participation; and (c) certification of the pipeline infrastructure, the ML model, data collection, and labelling. After describing the details of the new approach, the mitigation of the previously described security attacks will be demonstrated, and a conclusion will be drawn.
Movement Time for Pointing Tasks in Real and Augmented Reality Environments

Human–virtual target interactions are becoming more and more common due to the emergence and application of augmented reality (AR) devices. They are different from interacting with real objects. Quantification of movement time (MT) for human–virtual target interactions is essential for AR-based interface/environment design. This study aims to investigate the motion time when people interact with virtual targets and to compare the differences in motion time between real and AR environments. An experiment was conducted to measure the MT of pointing tasks on the basis of both a physical and a virtual calculator panel. A total of 30 healthy adults, 15 male and 15 female, joined. Each participant performed pointing tasks on both physical and virtual panels with an inclined angle of the panel, hand movement direction, target key, and handedness conditions. The participants wore an AR head piece (Microsoft Hololens 2) when they pointed on the virtual panel. When pointing on the physical panel, the participants pointed on a panel drawn on board. The results showed that the type of panel, inclined angle, gender, and handedness had significant (p &lt; 0.0001) effects on the MT. A new finding of this study was that the MT of the pointing task on the virtual panel was significantly (p &lt; 0.0001) higher than that of the physical one. Users using a Hololens 2 AR device had inferior performance in pointing tasks than on a physical panel. A revised Fitts’s model was proposed to incorporate both the physical–virtual component and inclined angle of the panel in estimating the MT. This model is novel. The index of difficulty and throughput of the pointing tasks between using the physical and virtual panels were compared and discussed. The information in this paper is beneficial to AR designers in promoting the usability of their designs so as to improve the user experience of their products.
A mutual embedded self-attention network model for code search

To improve the efficiency of program implementation, developers can selectively reuse the previously written code by searching the open-source codebase. To date, many code search methods have been proposed to actively push the limit of code search accuracy, where the methods designed using Self-Attention mechanism are particularly promising. However, while existing methods can improve the efficiency to capture textual semantics by attending significant words in the code component unit, they typically fail to capture the structural dependencies between the code components which may produce suboptimal search accuracy. In this paper, we propose a novel Self-Attention model termed MESN-CS which considers both word-level attention and code unit-level attention for code search. MESN-CS not only the attention weight of each word in the code component unit is calculated, but also the weight of the embedding between the code combination units is calculated. To verify the effectiveness of the proposed model, three benchmark models were compared on a large-scale code data and CodesearchNet. The experimental results show that the MESN-CS has better [email protected], NDCG and MRR performance than baseline methods. the experiments also show that the semantic syntactic information between sequences can be effectively characterized in MESN-CS.
Online training data acquisition for federated learning in cloud–edge networks

Federated learning (FL) is an effective approach to exploiting different data sources for collaborative model training while maintaining the privacy of users. Adequate data is necessary to improve the accuracy of the federated learning. However, it is difficult for all data sources to acquire sufficient data for each iteration of federated training. In this paper, we study the data acquisition problem in cloud–edge networks, where edges acquire data from users and conduct the local training while the cloud aggregates the local models. Since data acquired by users with uncertainty, it is not easy to make the data acquisition decision for the FL online. Even worse, due to the unknown cost of data, it is difficult to make a macro-timescale decision. We propose a two-timescale online scheduling for federated learning to confront the uncertainty of the data acquisition. By learning empirical state information of the system with a carefully designed Lyapunov virtual queue and coordinating the data acquisition in different timescales in an online manner, the proposed approach minimizes the data acquisition cost of federated learning, reduces the data transmission delay and accelerates the convergence speed of federated learning. Rigorous theoretical analysis shows strong performance guarantees of the proposed two-timescale Lyapunov optimization algorithm and extensive trace-driven experimental results suggests that the algorithm achieves outstanding performance gains over existing benchmarks.
Digital Learning Ecosystem: Current State, Prospects, and Hurdles

Abstract The article is an analysis of the current topic – digitalization of the educational process. In the course of collecting and interpreting the known data, the authors cite the pedagogical foundations of digital literacy, the current state of digital learning and problems, and the prospects for the development of this direction in the future are also considered. In addition, the situations of creating a digital ecosystem in individual countries of the world were analyzed.
AI in Human-computer Gaming: Techniques, Challenges and Opportunities

Abstract With the breakthrough of AlphaGo, human-computer gaming AI has ushered in a big explosion, attracting more and more researchers all over the world. As a recognized standard for testing artificial intelligence, various human-computer gaming AI systems (AIs) have been developed, such as Libratus, OpenAI Five, and AlphaStar, which beat professional human players. The rapid development of human-computer gaming AIs indicates a big step for decision-making intelligence, and it seems that current techniques can handle very complex human-computer games. So, one natural question arises: What are the possible challenges of current techniques in human-computer gaming and what are the future trends? To answer the above question, in this paper, we survey recent successful game AIs, covering board game AIs, card game AIs, first-person shooting game AIs, and real-time strategy game AIs. Through this survey, we 1) compare the main difficulties among different kinds of games and the corresponding techniques utilized for achieving professional human-level AIs; 2) summarize the mainstream frameworks and techniques that can be properly relied on for developing AIs for complex human-computer games; 3) raise the challenges or drawbacks of current techniques in the successful AIs; and 4) try to point out future trends in human-computer gaming AIs. Finally, we hope that this brief review can provide an introduction for beginners and inspire insight for researchers in the field of AI in human-computer gaming.
Online Harassment and Hate Among Media Professionals: Reactions to One’s Own and Others’ Victimization

This study investigated the experiences of Finnish media professionals with online harassment. Participants ( N = 695) answered a survey including questions concerning their experiences with online harassment and a survey experiment involving a death threat received by someone else. Results showed that closeness to the victim was associated with increased anxiety levels, but it did not affect countermeasures recommendations for the victim. Victims’ reactions depended on their visibility in the public sphere and on the frequency and severity of the harassment. The results demonstrate that online harassment is prevalent among media professionals and that prevention and intervention are crucial.
Classifying social media bots as malicious or benign using semi-supervised machine learning

Abstract Users of online social network (OSN) platforms, e.g. Twitter, are not always humans, and social bots (referred to as bots) are highly prevalent. State-of-the-art research demonstrates that bots can be broadly categorized as either malicious or benign. From a cybersecurity perspective, the behaviors of malicious and benign bots differ. Malicious bots are often controlled by a botmaster who monitors their activities and can perform social engineering and web scraping attacks to collect user information. Consequently, it is imperative to classify bots as either malicious or benign on the basis of features found on OSNs. Most scholars have focused on identifying features that assist in distinguishing between humans and malicious bots; the research on differentiating malicious and benign bots is inadequate. In this study, we focus on identifying meaningful features indicative of anomalous behavior between benign and malicious bots. The effectiveness of our approach is demonstrated by evaluating various semi-supervised machine learning models on Twitter datasets. Among them, a semi-supervised support vector machine achieved the best results in classifying malicious and benign bots.
Artificial intelligence-assisted diagnosis of hematologic diseases based on bone marrow smears using deep neural networks

The morphological examination of bone marrow (BM) cells is essential in both diagnosing and treating various hematologic diseases. However, it is still done manually with a heavy workload. An artificial intelligence-assisted diagnosis support system of BM cells is highly required to reduce the workloads of examiners and improve the reproducibility of the results.In this paper, we proposed an artificial intelligence-assisted diagnosis support system of morphological examination based on bone marrow smears including cells detection, classification and prediction of leukemia types. For cell detection, we trained the novel YOLOX-s model to locate cells precisely and obtain single cell images. For cell classification, we regarded it as a fine- grained classification task and proposed a novel architecture called MLFL-Net utilizing multi-level features. Furthermore, we predicted the leukemia types on a dataset including 40 normal people (BM transplantation donors) and 40 patients of different kinds of acute leukemia according to the World Health Organization (WHO) standard.We constructed a large-scale data set of 11,788 fully-annotated micrographs from 728 smears and 131,300 expert-annotated single cell images. With the data set, the detection model achieved 0.9797 AUC and 4.33% box placement error. For cell classification, the total accuracy of our proposed MLFL-Net reached 89.53% which outperformed all the other related models in identifying cell categories. In the meantime, we took acute leukemia as an example to explore the leukemia types prediction procedure of hematological disease. It generated the same diagnostic prediction as the experts gave for 92.5 percent of the cohort.This Artificial Intelligence-assisted system can be implemented to aid in clinical decision making and accelerate diagnosis. The method will contribute to promote the intelligence and modernization of BM cytomorphology, which has vital significance of the development of the medical career.
Cauchy mutation boosted Harris hawk algorithm: optimal performance design and engineering applications

Abstract Harris hawks optimization (HHO) has been accepted as one of the well-established swarm-based methods in the community of optimization and machine learning that primarily works based on multiple dynamic features and various exploratory and exploitative traits. Compared with other optimization algorithms, it has been observed that HHO can obtain high-quality solutions for continuous and constrained complex and real-world problems. While there is a wide variety of strategies in the HHO for dealing with diverse situations, there are chances for sluggish performance, where the convergence rate can gradually slow with time, and the HHO may stay stuck in the current relatively better place and may be unable to explore other better areas. To mitigate this concern, this paper combines the Cauchy mutation mechanism into the HHO algorithm named CMHHO. This idea can boost performance and provide a promising optimizer for solving complex optimization problems. The Cauchy mutation mechanism can speed up the convergence of the solution and help HHO explore more promising regions compared to its basic release. On 30 IEEE CEC2017 benchmark functions, the study compared the proposed CMHHO with various conventional and advanced metaheuristics to validate its performance and quality of solutions. It has been found through experiments that the overall optimization performance of CMHHO is far superior to all competitors. The CMHHO method is applied to four engineering challenges to investigate the capabilities of the proposed algorithm in solving real-world problems, and experimental results show that the suggested algorithm is more successful than existing algorithms.
Quantum Control Modelling, Methods, and Applications

This review concerns quantum control results and methods that, over the years, have been used in the various operations involving quantum systems. Most of these methods have been originally developed outside the context of quantum physics, and, then, adapted to take into account the specificities of the various quantum physical platforms. Quantum control consists in designing adequate control signals required to efficiently manipulate systems conforming the laws of quantum mechanics in order to ensure the associated desired behaviours and performances. This work attempts to provide a thorough and self-contained introduction and review of the various quantum control theories and their applications. It encompasses issues spanning quantum control modelling, problem formulation, concepts of controllability, as well as a selection of the main control theories. Given the vastness of the field, we tried our best to be as concise as possible, and, for the details, the reader is pointed out to a profusion of references. The contents of the review are organized in the three major classes of control problems - open-loop control, closed-loop learning control, and feedback control - and, for each one of them, we present the main developments in quantum control theory. Finally, concerning the importance of attaining robustness and reliability due to inherent fragility of quantum systems, methods for quantum robust control are also surveyed.
Dual Attention and Patient Similarity Network for drug recommendation

Artificially making clinical decisions for patients with multi-morbidity has long been considered a thorny problem due to the complexity of the disease. Drug recommendations can assist doctors in automatically providing effective and safe drug combinations conducive to treatment and reducing adverse reactions. However, the existing drug recommendation works ignored two critical information. (i) Different types of medical information and their interrelationships in the patient's visit history can be used to construct a comprehensive patient representation. (ii) Patients with similar disease characteristics and their corresponding medication information can be used as a reference for predicting drug combinations.To address these limitations, we propose DAPSNet, which encodes multi-type medical codes into patient representations through code- and visit-level attention mechanisms, while integrating drug information corresponding to similar patient states to improve the performance of drug recommendation. Specifically, our DAPSNet is enlightened by the decision-making process of human doctors. Given a patient, DAPSNet first learns the importance of patient history records between diagnosis, procedure and drug in different visits, then retrieves the drug information corresponding to similar patient disease states for assisting drug combination prediction. Moreover, in the training stage, we introduce a novel information constraint loss function based on the information bottleneck principle to constrain the learned representation and enhance the robustness of DAPSNet. We evaluate the proposed DAPSNet on the public MIMIC-III dataset, our model achieves relative improvements of 1.33%, 1.20% and 2.03% in Jaccard, F1 and PR-AUC scores, respectively, compared to state-of-the-art methods.The source code is available at the github repository: https://github.com/andylun96/DAPSNet.
Dependences and risk spillover effects between Bitcoin, crude oil and other traditional financial markets during the COVID-19 outbreak

This study examines the relationship and risk spillover between Bitcoin, crude oil, and six traditional markets (the US stock, Chinese stock, gold, bond, currency, and real estate markets) from 2019 to 2020, during which the coronavirus disease 2019 (COVID-19) outbreak occurred as well. We first discuss the static relationship between Bitcoin and these markets using a quantile-on-quantile model and examine the dynamic relationship using a time-varying copula model. A conditional value-at-risk model is subsequently used to estimate the risk spillover between the markets studied. The empirical results reveal that the relationship between these markets is always time-varying, and the COVID-19 outbreak has revealed such changes in the relationship between Bitcoin and other traditional financial markets. The risk of all single markets has enhanced because of the pandemic. Further, the risk spillover of these markets has also changed dramatically since the COVID-19 outbreak during which the Bitcoin market has played an important role and exerted a significant impact on the crude oil market, and the four other markets (US stock, gold, Chinese stock, and real estate markets). Overall, our findings indicate that investors and policymakers need to be made aware of the risk spillover between Bitcoin, crude oil, and other traditional markets and that flexible hedge strategies and policies should be implemented in response to the challenges and economic recession observed following the COVID-19 outbreak.