Reviewerly/exHarmony huggingface dataset:
Data-Efficient Learning via Minimizing Hyperspherical Energy

Deep learning on large-scale data is currently dominant nowadays. The unprecedented scale of data has been arguably one of the most important driving forces behind its success. However, there still exist scenarios where collecting data or labels could be extremely expensive, e.g., medical imaging and robotics. To fill up this gap, this paper considers the problem of data-efficient learning from scratch using a small amount of representative data. First, we characterize this problem by active learning on homeomorphic tubes of spherical manifolds. This naturally generates feasible hypothesis class. With homologous topological properties, we identify an important connection - finding tube manifolds is equivalent to minimizing hyperspherical energy (MHE) in physical geometry. Inspired by this connection, we propose a MHE-based active learning (MHEAL) algorithm, and provide comprehensive theoretical guarantees for MHEAL, covering convergence and generalization analysis. Finally, we demonstrate the empirical performance of MHEAL in a wide range of applications for data-efficient learning, including deep clustering, distribution matching, version space sampling, and deep active learning.
Advancing Vehicle Classification: A Novel Framework for Type, Model, and Fuel Identification Using Nonvisual Sensor Systems for Seamless Data Sharing

Vehicle classification (VC) down to type and model is exclusively visual-/image-based and struggles with restricted data availability and latency in data-sharing toward an increasing market of public and private mobility service providers. This article presents a framework architecture for VC down to type and model using a microelectromechanical systems (MEMS) intrusive magnetic sensor system combined with advanced machine learning (ML). The framework follows the principles of “privacy-by-design” (PbD) and adds fuel identification as a new attribute. The framework was validated in an experiment on nine predefined passenger car models, confirming the capability of nonvisual sensors to classify vehicle type, model, and fuel. This innovative approach has the potential to significantly impact how transport and infrastructure agencies collect, process, and share intelligent traffic monitoring systems (ITMS) data in the future, providing more accurate and detailed information for improving roadway utilization efficiency, predicting transportation needs, enhancing transportation safety, and reducing environmental impact. The work proposes a system framework, experimental design, and evaluation against automatic number plate recognition. The evaluation involved collaboration among various partners, including academia, public road administration, and private companies, with diverse interests that need to be preserved due to significant development progress.
A Pragma-Rhetorical Study of Argument in Islamic-Christian Debates

A debate is a special form of an argumentative dialogue in which two or more parties take part in attacking and defending certain claims through reasoned discourse. This study has set itself to pragmatically analyze and evaluate selected arguments of religious debates. Islamic-Christian debates are specifically chosen as data for the current study because they have not received due attention in language studies. This study is essentially concerned with investigating the pragma-rhetorical strategies utilized in the context of Deedat-Swaggart’s debate. To fulfill the aims, it is hypothesized that debaters utilize certain rhetorical pragmatics strategies: figures of speech (metaphor, allusion, irony, rhetorical question, and hyperbole), argumentation appeals (logos, pathos, ethos), and politeness strategies; and highlighting the rules that have to be fulfilled to produce a successful speech act of persuasion. The study employs a mixed method (qualitative and quantitative) to analyze the data and to verify or reject the hypotheses. The results of the study reveal that the most frequent pragmatic strategies utilized by debate are: rhetorical questions, logos, and off record politeness strategies, whereas Swaggart highly employs: metaphor, ethos, and off record politeness strategies. The results have also shown that Deedat’s arguments are stronger and more persuasive than Swaggart’s in terms of satisfying the rules of the successful speech act of persuasion.
On the pitfalls of Gaussian likelihood scoring for causal discovery

Abstract We consider likelihood score-based methods for causal discovery in structural causal models. In particular, we focus on Gaussian scoring and analyze the effect of model misspecification in terms of non-Gaussian error distribution. We present a surprising negative result for Gaussian likelihood scoring in combination with nonparametric regression methods.
Internet Adoption, Digital Divide, and Corruption: Evidence from ECOWAS Countries

This paper aims to extend the existing literature on Internet adoption and corruption by analyzing the factors impacting the digital divide and assessing the impact of Internet adoption on corruption reduction in the Economic Community of West African States (Benin, Burkina Faso, Cape Verde, Cote d’Ivoire, Gambia, Ghana, Guinea, Guinea-Bissau, Liberia, Mali, Niger, Nigeria, Senegal, Sierra Leone, and Togo). The study uses fixed and random effect panel data techniques covering 17 years (2003-2019), to exploit the times series nature of the relationship between the digital divide and its determinants. In addition, it aims to assess the impact of internet adoption along with other control variables on corruption. The estimation results show that per capita income, human capital, age, population density, government effectiveness, political stability, and the rule of law significantly affect the digital divide in ECOWAS. The findings reveal also that internet adoption affects positively the level of corruption control; the impact of an increase in internet users of 1% implies an increase in corruption control between 0.05% and 0.06%.
Introduction to Web 3.0

Technological tools exist for the first time in human history to connect the bottom billion unbanked to the global economy, provide digital identity to stateless people, provide direct benefits to women and girls, and accelerate climate action. These tools can transform the lives of the world's population. Web 2.0 gave the world access to information. Web 3.0 will be the next iteration of the internet – “The Internet of Value” (IOV). This chapter outlines the motivation for producing a book on Web 3.0, the change model providing a framework for the book, and a summary of the chapters. The internet of value (IoV) will enable the exchange of any valued asset with another person without the need for an intermediary. This will be transformative.
Code-switching input for machine translation: a case study of Vietnamese–English data

Multilingualism presents both a challenge and an opportunity for Natural Language Processing, with code-switching representing a particularly interesting problem for computational models trained on monolingual datasets. In this paper, we explore how code-switched data affects the task of Machine Translation, a task which only recently has started to tackle the challenge of multilingual data. We test three Machine Translation systems on data from the Canberra Vietnamese–English Codeswitching Natural Speech Corpus (CanVEC) and evaluate translation output using both automatic and human metrics. We find that, perhaps counter-intuitively, Machine Translation performs better on code-switching input than monolingual input. In particular, comparison of human and automatic evaluation suggests that codeswitching input may boost the semantic faithfulness of the translation output, an effect we term lexico-semantic enrichment. We also report two cases where this effect is most and least clear in Vietnamese–English, namely gender-neutral 3SG pronouns and interrogative constructions respectively. Overall, we suggest that Machine Translation, and Natural Language Processing more generally, ought to view multilingualism as an opportunity rather than an obstacle.Abbreviations: 1: First person; 2: Second person; 3: Third person; CLF: Classifier; COP: Copula; DET: Determiner; PL: Plural; POSS: Possessive marker; PRT: Particle; PST: Past tense; Q: Question marker; SG: Singular
Beyond Diverse Datasets: Responsible MIR, Interdisciplinarity, and the Fractured Worlds of Music

Musical worlds, not unlike our lived realities, are fundamentally fragmented and diverse, a fact often seen as a challenge or even a threat to the validity of research in Music Information Research (MIR). In this article, we propose to treat this characteristic of our musical universe(s) as an opportunity to fundamentally enrich and re-orient MIR. We propose that the time has arrived for MIR to reflect on its ethical and cultural turns (if they have been initiated at all) and take them a step further, with the goal of profoundly diversifying the discipline beyond the diversification of datasets. Such diversification, we argue, is likely to remain superficial if it is not accompanied by a simultaneous auto-critique of the discipline&rsquo;s raison d&rsquo;&ecirc;tre. Indeed, this move to diversify touches on the philosophical underpinnings of what MIR is and should become as a field of research: What is music (ontology)? What are the nature and limits of knowledge concerning music (epistemology)? How do we obtain such knowledge (methodology)? And what about music and our own research endeavor do we consider &ldquo;good&rdquo; and &ldquo;valuable&rdquo; (axiology)? This path involves sincere inter- and intra-disciplinary struggles that underlie MIR, and we point to &ldquo;agonistic interdisciplinarity&rdquo; &mdash; that we have practiced ourselves via the writing of this article &mdash; as a future worth reaching for. The two featured case studies, about possible philosophical re-orientations in approaching ethics of music AI and about responsible engineering when AI meets traditional music, indicate a glimpse of what is possible.
CTHP: Selection for adoption of open‐source bioinformatics software based on a customised ISO 25010 quality model, three‐way decision and Delphi hierarchy process

Abstract The ever‐growing open‐source software tools in different domains increase the difficulty of software selection from the end‐users perspective. The process of evaluating, comparing, and selecting open‐source solutions is far from trivial. Especially, when additional requirements need to be considered, the existing methodologies will fail to adapt to the new tasks. The objective of this study is to present a solution for dealing with this issue. A novel approach, CTHP, is presented for the evaluation and selection of open‐source software in the Bioinformatics domain. First, the ISO 25010 quality model is chosen as the basis. This model is customised according to the special characteristics of the Bioinformatics applications. The customisation is done by extracting the quality factors from the Bioinformatics applications, weighting these factors from the viewpoints of both developers and end‐users, and adding them to the model. After that, Three‐way Decision and Delphi Hierarchy Process are integrated to assist in the selection for adoption. Finally, as a case study, the proposed approach is applied to assist the decision‐making process of two popular natural language processing frameworks in the Bioinformatics area. Our study is a valuable contribution since it provides a systematic way to document the decision‐making process and help the researchers and practitioners of Bioinformatics to make better decisions among the alternatives.
Web 3.0 and ESG and Sustainability

The Sustainable Development Goals (SDGs) are aspirational visionary goals for the future state of humankind. However, they will only be attained by harnessing the potential of technology. Web 3.0, blockchain, and other emerging technologies are crucial tools for social impact. Web 3.0 and blockchain open the possibility for creating transparent collaboration at scale for solving problems of the global commons. Distributed autonomous communities (DAOs) can enable value exchange through token economics to collaborate and invest in global issues like climate change. This chapter will outline how Web 3.0, blockchain, and emerging technologies (artificial intelligence [AI], machine learning, big data, internet of things [IoT], and satellite) can accelerate the achievement of SDG goals.
METROLOGICAL SUPPORT OF MEASURING TECHNOLOGY Of AIRCRAFT REFUELING SERVICES

The article investigates the possibility of carrying out verification of volumetric liquid meters at the place of operation and presents the procedure for carrying out such verification. The issues of metrological support of measuring instruments (MI) of the aircraft refueling service are considered. The data on the verification of measuring instruments of the aircraft refueling service in the EU member states are presented.
Deep embeddings and Graph Neural Networks: using context to improve domain-independent predictions

Abstract Graph neural networks (GNNs) are deep learning architectures that apply graph convolutions through message-passing processes between nodes, represented as embeddings. GNNs have recently become popular because of their ability to obtain a contextual representation of each node taking into account information from its surroundings. However, existing work has focused on the development of GNN architectures, using basic domain-specific information about the nodes to compute embeddings. Meanwhile, in the closely-related area of knowledge graphs, much effort has been put towards developing deep learning techniques to obtain node embeddings that preserve information about relationships and structure without relying on domain-specific data. The potential application of deep embeddings of knowledge graphs in GNNs remains largely unexplored. In this paper, we carry out a number of experiments to answer open research questions about the impact on GNNs performance when combined with deep embeddings. We test 7 different deep embeddings across several attribute prediction tasks in two state-of-art attribute-rich datasets. We conclude that, while there is a significant performance improvement, its magnitude varies heavily depending on the specific task and deep embedding technique considered.
Two-sided Galois duals of multi-twisted codes

Abstract Galois duals of Multi-twisted (MT) codes are considered in this study. We describe a MT code $$\mathcal {C}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>C</mml:mi> </mml:math> as a module over a principal ideal domain. Hence, $$\mathcal {C}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>C</mml:mi> </mml:math> has a generator polynomial matrix (GPM) $$\textbf{G}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>G</mml:mi> </mml:math> that satisfies an identical equation. We prove a GPM formula for the Euclidean dual $$\mathcal {C}^\perp $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow> <mml:mi>C</mml:mi> </mml:mrow> <mml:mo>⊥</mml:mo> </mml:msup> </mml:math> using the identical equation of the Hermite normal form of $$\textbf{G}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>G</mml:mi> </mml:math> . Next, we aim to replace the Euclidean dual with the Galois dual. The Galois inner product is an asymmetric form, so we distinguish between the right and left Galois duals. We show that the right and left Galois duals of a MT code are MT as well but with possibly different shift constants. Some interconnected identities for the right and left Galois duals of a linear code are established and we also introduce the two-sided Galois dual. We use a condition that makes the two-sided Galois dual of a MT code MT, then we describe its GPM. Two special cases are also studied, one when the right and left Galois duals trivially intersect and the other when they coincide. A necessary and sufficient condition is established for the equality of the right and left Galois duals of any linear code.
Extracting and Processing of Russian Unstructured Clinical Texts for a Medical Decision Support System

The rapid growth in the volume of medical data is pushing the development and implementation of artificial intelligence (AI) tools. One of the directions of the application of AI in the field of healthcare is the use of natural language processing methods to build medical decision support systems based on electronic medical record (EMC) data. As a result of this study, a module for the extraction and pretreatment of patients’ EMC was developed. In addition, an approach was implemented to extract features from the unstructured textual information of patient admission protocols, with the formation of an appropriate vector representation of data. Predictive models for the diagnosis of groups of diseases based on the logistic regression model and BERT were developed. The highest efficiency in the experiments was shown by the logistic regression model, with a F1-score of 0.81 and Matthews correlation coefficient of 0.75. The obtained results have been posted for public access based on the django framework and can be used for preliminary assessment of patient health status, as well as integrated into existing medical decision support systems.
Improved baselines for causal structure learning on interventional data

Abstract Causal structure learning (CSL) refers to the estimation of causal graphs from data. Causal versions of tools such as ROC curves play a prominent role in empirical assessment of CSL methods and performance is often compared with “random” baselines (such as the diagonal in an ROC analysis). However, such baselines do not take account of constraints arising from the graph context and hence may represent a “low bar”. In this paper, motivated by examples in systems biology, we focus on assessment of CSL methods for multivariate data where part of the graph structure is known via interventional experiments. For this setting, we put forward a new class of baselines called graph-based predictors (GBPs). In contrast to the “random” baseline, GBPs leverage the known graph structure, exploiting simple graph properties to provide improved baselines against which to compare CSL methods. We discuss GBPs in general and provide a detailed study in the context of transitively closed graphs, introducing two conceptually simple baselines for this setting, the observed in-degree predictor (OIP) and the transitivity assuming predictor (TAP). While the former is straightforward to compute, for the latter we propose several simulation strategies. Moreover, we study and compare the proposed predictors theoretically, including a result showing that the OIP outperforms in expectation the “random” baseline on a subclass of latent network models featuring positive correlation among edge probabilities. Using both simulated and real biological data, we show that the proposed GBPs outperform random baselines in practice, often substantially. Some GBPs even outperform standard CSL methods (whilst being computationally cheap in practice). Our results provide a new way to assess CSL methods for interventional data.
Digital Assets and the Tokenization of Everything

Underpinning Web 3.0 in all its forms is the token economics that will determine the nature of the resources, their uses, their allocation and distribution, and ways to increase and decrease the resources within a Web 3.0 economy. Tokenization can democratize access, unlock the value of illiquid assets, enable faster transactions and settlement, improve traceability and transparency, and improve interoperability. This chapter will discuss different types of tokenized assets, including green digital asset solutions, tokenized health data, tokenized real estate, and other potential future tokenized assets. It will also discuss the regulatory challenges of a tokenized world. It will also provide a brief introduction to virtual assets as investments. There are many types and uses of tokens, and this chapter aims to provide an overview of this growing trend, which will change how people exchange value in a Web 3.0 future.
HBWO-JS: jellyfish search boosted hybrid beluga whale optimization algorithm for engineering applications

Abstract Beluga whale optimization (BWO) algorithm is a recently proposed population intelligence algorithm. Inspired by the swimming, foraging, and whale falling behaviors of beluga whale populations, it shows good competitive performance compared to other state-of-the-art algorithms. However, the original BWO faces the challenges of unbalanced exploration and exploitation, premature stagnation of iterations, and low convergence accuracy in high-dimensional complex applications. Aiming at these challenges, a hybrid BWO based on the jellyfish search optimizer (HBWO-JS), which combines the vertical crossover operator and Gaussian variation strategy with a fusion of jellyfish search (JS) optimizer, is developed for solving global optimization in this paper. First, the BWO algorithm is fused with the JS optimizer to improve the problem that BWO tends to fall into the best local solution and low convergence accuracy in the exploitation stage through multi-stage exploration and collaborative exploitation. Then, the introduced vertical cross operator solves the problem of unbalanced exploration and exploitation processes by normalizing the upper and lower bounds of two stochastic dimensions of the search agent, thus further improving the overall optimization capability. In addition, the introduced Gaussian variation strategy forces the agent to explore the minimum neighborhood, extending the entire iterative search process and thus alleviating the problem of premature stagnation of the algorithm. Finally, the superiority of the proposed HBWO-JS is verified in detail by comparing it with basic BWO and eight state-of-the-art algorithms on the CEC2019 and CEC2020 test suites, respectively. Also, the scalability of HBWO-JS is evaluated in three dimensions (10D, 30D, 50D), and the results show the stable performance of the proposed algorithm in terms of dimensional scalability. In addition, three practical engineering designs and two Truss topology optimization problems demonstrate the practicality of HBWO-JS. The optimization results show that HBWO-JS has a strong competitive ability and broad application prospects.
Gearbox Fault Diagnosis Based on Optimized Stacked Denoising Auto Encoder and Kernel Extreme Learning Machine

The gearbox is one of the key components of many large mechanical transmission devices. Due to the complex working environment, the vibration signal stability of the gear box is poor, the fault feature extraction is difficult, and the fault diagnosis accuracy makes it difficult to meet the expected requirements. To solve this problem, this paper proposes a gearbox fault diagnosis method based on an optimized stacked denoising auto encoder (SDAE) and kernel extreme learning machine (KELM). Firstly, the particle swarm optimization algorithm in adaptive weight (SAPSO) was adopted to optimize the SDAE network structure, and the number of hidden layer nodes, learning rate, noise addition ratio and iteration times were adaptively obtained to make SDAE obtain the best network structure. Then, the best SDAE network structure was used to extract the deep feature information of weak faults in the original signal. Finally, the extracted fault features are fed into KELM for fault classification. Experimental results show that the classification accuracy of the proposed method can reach 97.2% under the condition of low signal-to-noise ratio, which shows the effectiveness and robustness of the proposed method compared with other diagnostic methods.