Reviewerly/exHarmony huggingface dataset:
Financial Big Data Based on Internet of Things and Wireless Network Communication

With the advent of the era of big data, Internet of things technology and wireless communication technology have been in a state of rapid development. Opportunities and challenges in all walks of life are being subverted. Financial management, as the foundation of corporate governance, is important for improving economic efficiency and achieving sustainable business development which plays an important role. In order to realize the management and classification of financial big data, better identify the financial data of different enterprises, strengthen the safe storage of financial information, and provide early warning for the security issues involved, this article is based on the Internet of things and wireless communication networks. In the method part, this article introduces the framework of the Internet of things, Bluetooth, and infrared data transmission in wireless network communication and the principles of financial big data. The algorithm introduces a single-user MIMO system, free space propagation, and spectrum and energy efficiency. The analysis part analyzes the spectrum efficiency of different algorithms, social utility, average number of retransmissions, comprehensive scores of competitiveness in various fields of the Internet of things, and the significance of financial indicators. By comparing the data, it can be seen that the algorithm in this paper is superior to the two algorithms of IAN-CoMP and IA-CoMP. When the number of users is 100, the social utility of the algorithm in this paper is 4.45, while IAN-CoMP is 3.43 and IA-CoMP is 3.67. When the number of users increases to 700, the social utility of the algorithm in this paper is 28.34. The other two algorithms are, respectively, 24.45 and 25.99, and we know that the social utility of the algorithm in this paper is the best. Through comprehensive analysis, it is concluded that the financial big data model based on the Internet of things and wireless network communication in this paper can better realize data management and collection, so as to meet the needs of information developers.
The Effect of Blockchain Operation Capabilities on Competitive Performance in Supply Chain Management

Owing to blockchain characteristics such as transparency, traceability, and disintermediation, blockchain technology has been widely employed in sustainable supply chain management. The COVID-19 pandemic has accelerated the use of blockchain technology in the supply chain. Although most companies have realized the importance of blockchain technology, they often lack understanding of how to plan, measure, cultivate, and improve their own blockchain operation capabilities. Academic research has insufficiently explored the connotations and internal structure of blockchain operation capabilities and does not provide a clear understanding of how to transform blockchain operation capabilities to produce effective performance. In this context, we proposed a concept of blockchain operation capabilities for first time. We took the perspectives of the resource-based view and sociomaterialism theory, based on IT capabilities, big data analysis capabilities, and existing blockchain supply chain research, and explored the relationship between blockchain operation capabilities and competitive performance. We then constructed a hierarchical model for blockchain operation capabilities. To test our proposed research model, we used an online survey to collect data from 1206 firm managers with blockchain technology supply chain experience. The results showed that blockchain operation capabilities has a positive impact on supply chain integration and competitive performance, while supply chain integration has a strong mediating effect on the blockchain operation capabilities and competitive performance relationship. Implications for research and practice are discussed.
Diagnosing Malaria Patients with Plasmodium falciparum and vivax Using Deep Learning for Thick Smear Images

We propose a new framework, PlasmodiumVF-Net, to analyze thick smear microscopy images for a malaria diagnosis on both image and patient-level. Our framework detects whether a patient is infected, and in case of a malarial infection, reports whether the patient is infected by Plasmodium falciparum or Plasmodium vivax. PlasmodiumVF-Net first detects candidates for Plasmodium parasites using a Mask Regional-Convolutional Neural Network (Mask R-CNN), filters out false positives using a ResNet50 classifier, and then follows a new approach to recognize parasite species based on a score obtained from the number of detected patches and their aggregated probabilities for all of the patient images. Reporting a patient-level decision is highly challenging, and therefore reported less often in the literature, due to the small size of detected parasites, the similarity to staining artifacts, the similarity of species in different development stages, and illumination or color variations on patient-level. We use a manually annotated dataset consisting of 350 patients, with about 6000 images, which we make publicly available together with this manuscript. Our framework achieves an overall accuracy above 90% on image and patient-level.
ConvAbuse: Data, Analysis, and Benchmarks for Nuanced Detection in Conversational AI

We present the first English corpus study on abusive language towards three conversational AI systems gathered 'in the wild': an opendomain social bot, a rule-based chatbot, and a task-based system.To account for the complexity of the task, we take a more 'nuanced' approach where our ConvAI dataset reflects fine-grained notions of abuse, as well as views from multiple expert annotators.We find that the distribution of abuse is vastly different compared to other commonly used datasets, with more sexually tinted aggression towards the virtual persona of these systems.Finally, we report results from bench-marking existing models against this data.Unsurprisingly, we find that there is substantial room for improvement with F1 scores below 90%.Warning: This paper contains examples of language that some people may find offensive or upsetting.
An Enhanced Slime Mould Algorithm and Its Application for Digital IIR Filter Design

In the past few decades, metaheuristic algorithms (MA) have been developed tremendously and have been successfully applied in many fields. In recent years, a large number of new MA have been proposed. Slime mould algorithm (SMA) is a novel swarm-based intelligence optimization algorithm. SMA solves the optimization problem by imitating the foraging and movement behavior of slime mould. It can effectively obtain a promising global optimal solution. However, it still suffers some shortcomings such as the unstable convergence speed, the imprecise search accuracy, and incapability of identifying a local optimal solution when faced with complicated optimization problems. With the purpose of overcoming the shortcomings of SMA, this paper proposed a multistrategy enhanced version of SMA called ESMA. The three enhanced strategies are chaotic initialization strategy (CIS), orthogonal learning strategy (OLS), and boundary reset strategy (BRS). The CIS is used to generate an initial population with diversity in the early stage of ESMA, which can increase the convergence speed of the algorithm and the quality of the final solution. Then, the OLS is used to discover the useful information of the best solutions and offer a potential search direction, which enhances the local search ability and raises the convergence rate. Finally, the BRS is used to correct individual positions, which ensures the population diversity and enhances the overall search capabilities of ESMA. The performance of ESMA was validated on the 30 IEEE CEC2014 functions and three IIR model identification problems, compared with other nine well-regarded and state-of-the-art algorithms. Simulation results and analysis prove that the ESMA has a superior performance. The three strategies involved in ESMA have significantly improved the performance of the basic SMA.
Announcement Effect of COVID-19 on Cryptocurrencies

This study uses a fractional integration method to evaluate the efficiency of cryptocurrencies before and after the period COVID-19 had been announced as being a pandemic. Evidence of long memory is confirmed across all subsamples. Additionally, we find a greater degree of persistence during the COVID-19 pandemic period than in the pre-pandemic period.
A survey of data minimisation techniques in blockchain-based healthcare

The push for digitising personal health records needs to occur with serious consideration of privacy in order to instill public confidence. However, the healthcare sector still experiences leakage of Personally Identifiable Information (PII) due to improper data protection practices and security failures by data custodians. Data minimisation refers to the practice of limiting personal data collection and usage to only what is required to fulfil a specific purpose, and is one of the directives in many privacy regulations and data protection acts. Blockchain technology provides a neutral third-party platform for healthcare applications on which trust can be built to increase confidence in all participating parties to create, store and share sensitive data. However, the aspects of design and implementation of data minimisation techniques within the blockchain context have not been systematically explored and no effort has been made to review and analyse the existing solutions so far. In this paper, we undertake a survey of data minimisation techniques in blockchain-based healthcare systems. We provide a broad definition of data minimisation and classify data minimisation approaches according to the different lifecycle phases of data processing workflows. We also present a comparative analysis on privacy properties achieved by these methods. This study offers a unique view of data minimisation from both data custodians and data owners’ viewpoints, and suggests several areas of future research and development to improve privacy in healthcare through data minimisation.
Fuzzy filter cost-sensitive feature selection with differential evolution

In recent years, a variety of feature selection methods based on evolutionary computation (EC) techniques have been developed for classification due to their robustness and search ability. However, the previous EC-based feature selection research mostly focuses on enhancing the prediction accuracy without taking the costs associated with the learning process into consideration. In other words, the impact of using EC-based feature selection to improve the cost-sensitive classification performance has not been well studied. Further, it is not possible to find a EC-based filter cost-sensitive feature selection method in the literature, to the best of our knowledge. We therefore design a cost-sensitive evaluation criterion using the principles of fuzzy mutual estimator and then adopt the criterion in differential evolution framework. According to a variety of experiments conducted on various benchmarks, the proposed filter method can effectively minimize both the classification error rate and the feature cost by removing irrelevant and distracting features from the dataset in a reasonable time.
Generating the Gottesman-Kitaev-Preskill qubit using a cross-Kerr interaction between squeezed light and Fock states in optics

The Gottesman-Kitaev-Preskill (GKP) qubit is a promising ingredient for fault-tolerant quantum computation (FTQC) in optical continuous variables due to its advantage of noise tolerance and scalability. However, one of the main problems in the preparation of the optical GKP qubit is the difficulty in obtaining the nonlinearity. Cross-Kerr interaction is one of the promising candidates for this nonlinearity. There is no existing scheme to use the cross-Kerr interaction to generate the optical GKP qubit for FTQC. In this work, we propose a generation method of the GKP qubit by using a cross-Kerr interaction between a squeezed light and a superposition of Fock states. We numerically show that the GKP qubit with the 10 dB can be generated with mean fidelities of 99.99 and 99.9% at the success probabilities of 2.7 and 4.8%, respectively. Therefore, our method has the potential to generate the optical GKP qubit with a quality required for FTQC when we obtain the sufficient technologies for the preparation of ancillary Fock states and a cross-Kerr interaction.
An Artificial Intelligent Virtual Reality Interactive Model for Distance Education

Information and communication technologies play an important role in education. This fact was emphasized even more due to the significant upheavals, globally, caused by the pandemic of COVID-19 and because distance education uses tools of modern technology that prove to be significantly important. Particularly, virtual reality immersion systems, which may include 3D spatial representations, multisensory interaction channels, and real-time intuitive physical interaction, can extend the learning process by providing learners with stimuli that represent a real advanced training environment. In the present work, based on the most modern technologies of computational intelligence, virtual and augmented reality, wireless communications, and space-sensitive positioning applications, an Artificial Intelligent Virtual Reality (AI-VR) interactive model for distance education is presented. This is a case study of escape-room-type educational application, wherewith appropriate practices and methodologies promote individual and collective participation, enhance the active role of the learner, personalize the educational experience, and upgrade the process of participation in distance education, strengthening and assisting role of the educator. Specifically, a Newton polynomial is used for max-polynomials arising from the stereoscopic problem of augmented reality used to indirectly indicate the number of linear regions in order to optimize the problem. The proposed application is related to the training course of agricultural education and methods of modernization of crops, including additional sound, 3D graphics, and short film projection. The history-space-objects relationship takes and evolves with various versions such as the parts of the story to be unlocked as the trainee moves from one tool to the next or the story to guide the trainee to explore the space-time continuum, giving a new dimension to the technological development of agricultural education and the methods of modernization of crops. This finding shows that they learn best when they receive information in a style that incorporates visual, auditory, and kinetic stimuli so that learning improves when learners can examine an idea or concept using a multidimensional approach.
How Validation Methodology Influences Human Activity Recognition Mobile Systems

In this article, we introduce explainable methods to understand how Human Activity Recognition (HAR) mobile systems perform based on the chosen validation strategies. Our results introduce a new way to discover potential bias problems that overestimate the prediction accuracy of an algorithm because of the inappropriate choice of validation methodology. We show how the SHAP (Shapley additive explanations) framework, used in literature to explain the predictions of any machine learning model, presents itself as a tool that can provide graphical insights into how human activity recognition models achieve their results. Now it is possible to analyze which features are important to a HAR system in each validation methodology in a simplified way. We not only demonstrate that the validation procedure k-folds cross-validation (k-CV), used in most works to evaluate the expected error in a HAR system, can overestimate by about 13% the prediction accuracy in three public datasets but also choose a different feature set when compared with the universal model. Combining explainable methods with machine learning algorithms has the potential to help new researchers look inside the decisions of the machine learning algorithms, avoiding most times the overestimation of prediction accuracy, understanding relations between features, and finding bias before deploying the system in real-world scenarios.
Coupled online sequential extreme learning machine model with ant colony optimization algorithm for wheat yield prediction

Abstract Inadequate agricultural planning compounded by inaccurate predictions results in an inflated local market rate and prompts higher importation of wheat. To tackle this problem, this research has designed two-phase universal machine learning (ML) model to predict wheat yield (W pred ), utilizing 27 agricultural counties’ data within the Agro-ecological zone. The universal model, online sequential extreme learning machines coupled with ant colony optimization (ACO-OSELM) is developed, by incorporating the significant annual yield data lagged at ( t − 1) as the model’s predictor to generate future yield at 6 test stations. In the first phase, ACO is adopted to search for suitable, statistically relevant data stations for model training, and the corresponding test station by virtue of a feature selection strategy. An annual wheat yield time-series input dataset is constructed utilizing data from each selected training station (1981–2013) and applied against 6 test stations (with each case modelled with 26 station data as the input) to evaluate the hybrid ACO-OSELM model. The partial autocorrelation function is implemented to deduce statistically significant lagged data, and OSELM is applied to generate W pred . The two-phase hybrid ACO-OSELM model is tested within the 6 agricultural districts (represented as stations) of Punjab province, Pakistan and the results are benchmarked with extreme learning machine (ELM) and random forest (RF) integrated with ACO (i.e., hybrid ACO-ELM and hybrid ACO-RF models, respectively). The performance of the ACO-OSELM model was proven to be good in comparison to ACO-ELM and ACO-RF models. The hybrid ACO-OSELM model revealed its potential to be implemented as a decision-making system for crop yield prediction in areas where a significant association with the historical agricultural crop is well-established.
Hate It? Automate It!

Drawing on an analysis of the value propositions by two world-leading providers of automation platforms, UiPath and Blue Prism, this chapter shows how Robotic Process Automation (RPA) and more recent forms of intelligent and AI-driven automation are constructed as meaningful through a set of discursive practices through which their ’solutions’ come to make sense. Through an analysis of various forms of promotional and informational materials distributed by the companies behind the platforms, the chapter brings to light how they imagine the future of work, with and without the presence of robots and automated processes. The results show how automation is envisioned and constructed as meaningful, as well as how and to what extent it involves human activity. The chapter shows that the futuristic and often promissory accounts of work automation rely on an understanding of work as flexible, while downplaying the fact that automation innovation and implementation is a rather complex process that transforms work and work tasks in light of dominant discourses and structures. It is argued that implementation of automation platforms involve and require an organisational restructuring through which a digital transformation that fits better with the imaginaries of our times is done. The chapter underscores that the study of automation technologies and their promissory character require a robust research agenda to unmask critical assumptions and potential power relations involved in how the development and implementation of such technologies are thought and done.
Low-Cost Method for 3D Body Measurement Based on Photogrammetry Using Smartphone

This paper is focused on the possibilities of data collection via photogrammetry methods, using smartphone cameras and post-processing. The aim of this paper is to refer to progressive technologies that are part of smartphone devices, which bring more performance and variability of usage year by year. The theoretical part starts with looking to the past, describing problems of measurements and solutions invented by famous mathematicians, which we use nowadays. The following section deals with the background of measuring the human body and photogrammetry. The next section is about measuring and using calibration methods. The results section presents the architecture design of the system and a visual representation of how the application works. The result of processing a 3D person is a data object with measurements in real world metric units with minimum deviation. The conclusion is that we created our own low-cost method for 3D body measurement which partially or completely removes the shortcomings that were identified during the review of similar solutions. Our method is based on the use of open-source libraries, the use of a single smartphone mobile device and the creation of a true 3D human body model.
Measure and Improve Robustness in NLP Models: A Survey

As NLP models achieved state-of-the-art performances over benchmarks and gained wide applications, it has been increasingly important to ensure the safe deployment of these models in the real world, e.g., making sure the models are robust against unseen or challenging scenarios.Despite robustness being an increasingly studied topic, it has been separately explored in applications like vision and NLP, with various definitions, evaluation and mitigation strategies in multiple lines of research.In this paper, we aim to provide a unifying survey of how to define, measure and improve robustness in NLP.We first connect multiple definitions of robustness, then unify various lines of work on identifying robustness failures and evaluating models' robustness.Correspondingly, we present mitigation strategies that are data-driven, model-driven, and inductive-prior-based, with a more systematic view of how to effectively improve robustness in NLP models.Finally, we conclude by outlining open challenges and future directions to motivate further research in this area.
Sentiment Analysis of Reviews in Natural Language: Roman Urdu as a Case Study

Opinion Mining from user reviews is an emerging field. Sentiment Analysis of Natural Language text helps us in finding the opinion of the customers. These reviews can be in any language e.g. English, Chinese, Arabic, Japanese, Urdu, and Hindi. This research presents a model to classify the polarity of the review(s) in Roman Urdu text (reviews). For the purpose, raw data was scraped from the reviews of 20 songs from Indo-Pak Music Industry. In this research a new dataset of 24000 reviews of Roman Urdu text is created. Nine Machine Learning algorithms&#x2014;Na&#x00EF;ve Bayes, Support Vector Machine, Logistic Regression, K-Nearest Neighbors, Artificial Neural Networks, Convolutional Neural Network, Recurrent Neural Networks, ID3 and Gradient Boost Tree, are attempted. Logistic Regression outperformed the rest, based on testing and cross validation accuracies that are 92.25&#x0025; and 91.47&#x0025; respectively.