Reviewerly/exHarmony huggingface dataset:
Hybrid Bayesian network discovery with latent variables by scoring multiple interventions

Abstract In Bayesian Networks (BNs), the direction of edges is crucial for causal reasoning and inference. However, Markov equivalence class considerations mean it is not always possible to establish edge orientations, which is why many BN structure learning algorithms cannot orientate all edges from purely observational data. Moreover, latent confounders can lead to false positive edges. Relatively few methods have been proposed to address these issues. In this work, we present the hybrid mFGS-BS (majority rule and Fast Greedy equivalence Search with Bayesian Scoring) algorithm for structure learning from discrete data that involves an observational data set and one or more interventional data sets. The algorithm assumes causal insufficiency in the presence of latent variables and produces a Partial Ancestral Graph (PAG). Structure learning relies on a hybrid approach and a novel Bayesian scoring paradigm that calculates the posterior probability of each directed edge being added to the learnt graph. Experimental results based on well-known networks of up to 109 variables and 10 k sample size show that mFGS-BS improves structure learning accuracy relative to the state-of-the-art and it is computationally efficient.
Humans of Instagram

Instagram has become a contemporary platform for presenting our digital identity. In this study, the Instagram influencer phenomenon is investigated from an identity, self-presentation, and impression management perspective. By focusing on the language of Instagram that enables this identity, its navigation and manifestations are explored. In-depth interviews were conducted with 8 fashion, beauty and lifestyle influencers, and the obtained data was analysed through the approach of discourse analysis, rooted in discursive psychology. Prominent discourses include a critical discussion on the ideas of aesthetic influencer identity, ‘handle names’, ‘follower’s gaze’, ‘stories’, ‘posts’, and ‘filters’. The findings provide an exploratory and critical perspective on the ways in which Instagram is creating and shaping identities. By offering an understanding of the facets of the influencer identity, this pioneering study highlights the different negotiations, conflicts, and resolutions in the performance of this identity.
Evolutionary convolutional neural network for image classification based on multi-objective genetic programming with leader–follower mechanism

Abstract As a popular research in the field of artificial intelligence in the last 2 years, evolutionary neural architecture search (ENAS) compensates the disadvantage that the construction of convolutional neural network (CNN) relies heavily on the prior knowledge of designers. Since its inception, a great deal of researches have been devoted to improving its associated theories, giving rise to many related algorithms with pretty good results. Considering that there are still some limitations in the existing algorithms, such as the fixed depth or width of the network, the pursuit of accuracy at the expense of computational resources, and the tendency to fall into local optimization. In this article, a multi-objective genetic programming algorithm with a leader–follower evolution mechanism (LF-MOGP) is proposed, where a flexible encoding strategy with variable length and width based on Cartesian genetic programming is designed to represent the topology of CNNs. Furthermore, the leader–follower evolution mechanism is proposed to guide the evolution of the algorithm, with the external archive set composed of non-dominated solutions acting as the leader and an elite population updated followed by the external archive acting as the follower. Which increases the speed of population convergence, guarantees the diversity of individuals, and greatly reduces the computational resources. The proposed LF-MOGP algorithm is evaluated on eight widely used image classification tasks and a real industrial task. Experimental results show that the proposed LF-MOGP is comparative with or even superior to 35 existing algorithms (including some state-of-the-art algorithms) in terms of classification error and number of parameters.
A framework for <scp>IoT</scp> and blockchain based smart food chain management system

Summary The worldwide food supply chain network firms have embraced digitalization and have changed customers' everyday lives in serval viewpoints. This work proposes a framework for IoT and Blockchain based smart Food Chain Management System (IBFS) aiming to regulate the food inventory systems in an organization focusing on the safety and nature of the items conveyed to the end consumers. The proposed IBFS framework is an IoT‐based blockchain with smart contract architecture for trust, reliability, and transparency. A Message Queue Telemetry Transport (MQTT) (Message Queue Telemetry Transport) broker or server is deployed on the broadcast to record all data collected from the IoT sensors. The data provided by IoT‐sensors or devices is pooled and gathered by MQTT based cloud brokers and the shipment's geolocation, movement, and temperature are logged. The work uses two types of smart contracts based on Ethereum‐based blockchain technology (BCT) environment as the operational backbone written in the Solidity language. The first smart contract deals with custody and the second smart contract deals with shipment or transportation to track the produce. The food supply chain process evaluates the collected data from the IoT sensors managed with the help of blockchain and MQTT servers. The quality of food is measured by evaluating by temperature and humidity function, transition time, final item and for validation of smart contracts. Performance evaluation of IBFS in terms of Gas cost under various test conditions prove the effectiveness of the work.
The Global Pandemic, Laboratory of the Cashless Economy?

The COVID-19 pandemic has had a profound impact on payment systems and preferences around the world, reducing the use of cash in favor of digital payment instruments and accelerating the discussion around the need for a central bank digital currency. This article presents the digital payments and cashless agenda before and after the pandemic, focusing on how the changing payments landscape has influenced the priorities and decisions of regulators, banks and other financial intermediaries, with regards to the future shape of payment systems. It finds that while the pandemic demonstrated the benefits associated with building an advanced, competitive and integrated digital payments eco-system, it has also brought to the forefront more fragmentation than convergence between payment systems in different regions of the world.
Fusing external knowledge resources for natural language understanding techniques: A survey

Knowledge resources, e.g. knowledge graphs, which formally represent essential semantics and information for logic inference and reasoning, can compensate for the unawareness nature of many natural language processing techniques based on deep neural networks. This paper provides a focused review of the emerging but intriguing topic that fuses quality external knowledge resources in improving the performance of natural language processing tasks. Existing methods and techniques are summarised in three main categories: (1) static word embeddings, (2) sentence-level deep learning models, and (3) contextualised language representation models, depending on when, how and where external knowledge is fused into the underlying learning models. We focus on the solutions to mitigate two issues: knowledge inclusion and inconsistency between language and knowledge. Details on the design of each representative method, as well as their strength and limitation, are discussed. We also point out some potential future directions in view of the latest trends in natural language processing research.
RAG-TCGCN: Aspect Sentiment Analysis Based on Residual Attention Gating and Three-Channel Graph Convolutional Networks

Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment analysis task that mainly judges the polarity of a given aspect word in a review. Current methods mainly use graph networks to do aspect-level sentiment classification tasks, most of which use syntactic or semantic graphs, and utilize attention mechanisms to interact and correlate aspect terms and contexts to obtain more useful feature representations. However, these methods may ignore some insignificant syntactic structures and some implicit information in some sentences. The attention mechanism then easily loses the original information, which eventually leads to inaccurate sentiment analysis. In order to solve this problem, this paper proposes a model based on residual attention gating and three-channel graph convolutional network (RAG-TCGCN). Firstly, the model uses a three-channel network composed of syntactic information, semantic information, and public information to simultaneously optimize and fuse through the multi-head attention mechanism to solve the problem of sentences without significant syntactic structure and with implicit information. Through the residual attention gating mechanism the problem of loss of original information is solved. Experimental verification shows that the accuracy and F1 value of the model are improved on the three public datasets.
Multi-class classification of COVID-19 documents using machine learning algorithms

In most biomedical research paper corpus, document classification is a crucial task. Even due to the global epidemic, it is a crucial task for researchers across a variety of fields to figure out the relevant scientific research papers accurately and quickly from a flood of biomedical research papers. It can also assist learners or researchers in assigning a research paper to an appropriate category and also help to find the relevant research paper within a very short time. A biomedical document classifier needs to be designed differently to go beyond a "general" text classifier because it's not dependent only on the text itself (i.e. on titles and abstracts) but can also utilize other information like entities extracted using some medical taxonomies or bibliometric data. The main objective of this research was to find out the type of information or features and representation method creates influence the biomedical document classification task. For this reason, we run several experiments on conventional text classification methods with different kinds of features extracted from the titles, abstracts, and bibliometric data. These procedures include data cleaning, feature engineering, and multi-class classification. Eleven different variants of input data tables were created and analyzed using ten machine learning algorithms. We also evaluate the data efficiency and interpretability of these models as essential features of any biomedical research paper classification system for handling specifically the COVID-19 related health crisis. Our major findings are that TF-IDF representations outperform the entity extraction methods and the abstract itself provides sufficient information for correct classification. Out of the used machine learning algorithms, the best performance over various forms of document representation was achieved by Random Forest and Neural Network (BERT). Our results lead to a concrete guideline for practitioners on biomedical document classification.
Designing a financial quantification model for cyber risk: A case study in a bank

Given banks’ role in global financial markets, there is growing concern about cyber incidents' impact on broader financial stability. Recent research has focused predominantly on the banking industry as a whole and investigated the aggregate impact of cyber incidents on deposit balances, capital ratios, and operational losses. Little is understood about how risk managers in banks assess the financial impact of a potential cyber incident. Even in banks that feature a high degree of cybersecurity process maturity, there is a continued perception that the impact of a cyber incident remains financially unquantifiable. This study explores how executives in banks currently manage their cyber risk exposure and how a financial quantification model can be designed to capture the impact of a cyber incident. An Australian bank formed the setting for our exploratory case study. Interviews revealed that bank managers consider maintaining customer trust and reputation a key priority following a cyber incident. We developed, tested and validated a conceptual model aimed to guide cybersecurity experts and risk managers in the financial quantification of the impact of a cyber incident in banks.
Replicable semi-supervised approaches to state-of-the-art stance detection of tweets

Stance is defined as the expression of a speaker's standpoint towards a given target or entity. To date, the most reliable method for measuring stance is opinion surveys. However, people's increased reliance on social media makes these online platforms an essential source of complementary information about public opinion. Our study contributes to the discussion surrounding replicable methods through which to conduct reliable stance detection by establishing a rule-based model, which we replicated for several targets independently. To test our model, we relied on a widely used dataset of annotated tweets - the SemEval Task 6A dataset, which contains 5 targets with 4,163 manually labelled tweets. We relied on “off-the-shelf” sentiment lexica to expand the scope of our custom dictionaries, while also integrating linguistic markers and using word-pairs dependency information to conduct stance classification. While positive and negative evaluative words are the clearest markers of expression of stance, we demonstrate the added value of linguistic markers to identify the direction of the stance more precisely. Our model achieves an average classification accuracy of 75% (ranging from 67% to 89% across targets). This study is concluded by discussing practical implications and outlooks for future research, while highlighting that each target poses specific challenges to stance detection.
Hyperparameter optimization through context-based meta-reinforcement learning with task-aware representation

In this paper, we combine context-based Meta-Reinforcement Learning with task-aware representation to efficiently overcome data-inefficiency and limited generalization in the hyperparameter optimization problem. First, we propose a new context-based meta-RL model that disentangles task inference and control, which improves the meta-training efficiency and accelerates the learning process for unseen tasks. Second, the task properties are inferred on-line, which includes not only the dataset representation but also the task-solving experience, thus encouraging the agent to explore in a much smarter fashion. Third, we employ amortized meta-learning to meta-train the agent, which is simple and runs faster than the gradient-based meta-training method. Experimental results suggest that our method can search for the optimal hyperparameter configuration with limited computational cost in a reasonable time.
Towards digital diagnosis of malaria: How far have we reached?

The need for precise and early diagnosis of malaria and its distinction from other febrile illnesses is no doubt a prerequisite, primarily when standard rapid diagnostic tests (RDTs) cannot be totally relied upon. At the time of disease outbreaks, the pressure on hospital staff remains high and the chances of human error increase. Therefore, in the era of digitalisation of medicine as well as diagnostic approaches, various technologies such as artificial intelligence (AI) and machine learning (ML) should be deployed to further aid the diagnosis, especially in endemic and epidemic situations. Computational techniques are now more at the forefront than ever, and the interest in developing such efficient technologies is continuously increasing. A comprehensive understanding of these digital technologies is needed to maintain the scientific rigour in these attempts. This would enhance the implementation of these novel technologies for malaria diagnosis. This review highlights the progression, strengths, and limitations of various computing techniques so far employed to diagnose malaria.
Forecasting of solar radiation for a cleaner environment using robust machine learning techniques

Intensified research is going on worldwide to increase renewable energy sources like solar and wind to reduce emissions and achieve worldwide targets and also to address the depleting fossil fuels resources and meet the increasing energy demand of the population. Solar radiation (SR) is intermittent, so forecasting solar radiation is a must. The objective of this research is to use modern machine techniques for different climatic conditions to forecast SR with higher accuracy. The required dataset is collected from National Solar Radiation Database having features such as temperature, pressure, relative humidity, dew point, solar zenith angle, wind speed, and direction, concerning the y-parameter Global Horizontal Irradiance (GHI) (W/m2). The collected data is first split based on different types of climatic conditions. Each climatic model was trained on various machine learning (ML) algorithms like multiple linear regression (MLR), support vector regression (SVR), decision tree regression (DTR), random forest regression (RFR), gradient boosting regression (GBR), lasso and ridge regression, and deep learning algorithm especially long-short-term memory (LSTM) using Google Colab Platform. From the analysis, LSTM has the least error approximation of 0.0040 loss at the 100th epoch and of all ML models, gradient boosting and RFR top high, when it comes to the Hot weather season—gradient boosting leads 2% than RFR, and similarly for cold weather, autumn and monsoon climate—RFR has 1% higher accuracy than gradient boosting. This high-accuracy model is deployed in a user interface (UI) that will be more useful for real-time solar prediction, load operators for maintenance scheduling, stock commitment, and load dispatch centres for engineers to decide on setting up solar panels, for household clients and future researchers.
Center consistency guided multi-view embedding anchor learning for large-scale graph clustering

Multi-view clustering has attracted extensive attention since it can integrate the complementary information of different views. Nonetheless, most existing methods suffer from cubic time computational complexity, so they have to face the challenge of maladjustment to medium and large-scale datasets. Whereupon, researchers usually employ anchor selection strategies to select a proportion of data points as landmarks to simulate the original data, and then build a bipartite graph to bridge the relationship between the original data points and anchors. However, for multi-view data, the existing strategies have three shortcomings, including: (1) on account of redundancy and corruption, the quality of anchors may be greatly affected; (2) these strategies are static ones or deficiency of views consistency; and (3) due to the differences in source, latent distribution, and the dimension of each view, the consensus bipartite graph cannot exploit the comprehensive information of all views precisely. In order to address these problems, this paper first proposes a novel method for scalable clustering, dubbed center consistency guided multi-view embedding anchor learning (2C-MEAL). Specifically, considering the redundancy and corruption, embedding learning is adopted to filter out the adverse information, as well as unify the dimensions of all views. Then, dynamic center consistency guided anchor learning is proposed to adaptively learn a set of high-quality anchors in the clean embedding space. Meanwhile, the refined bipartite graph is obtained for clustering purposes. Overall, 2C-MEAL has promising clustering performance and linear complexity. Experimentally, clustering experiments on benchmark datasets demonstrate that 2C-MEAL possesses effectiveness and efficiency over state-of-the-art methods.
Different voices, different bodies: presence–absence in the digital university

The move to digital, and now hybrid, education has defamiliarised teaching practices and unsettled experiences of what it means to be and to engage at university. In this article, I examine what new questions evolving teaching and learning practices provoke with regards rethinking notions of the body, and concepts of presence and absence. Interweaving theories of performativity, of strangeness, and of agency as a cutting together/apart, I explore a specific example of online course design, a Small Private Online Course. I consider the dispersed and fragmented subjects and bodies that evade enclosure within the course, and the ontological implications for thinking about how subjects, bodies and materialities are cut, performed and made strange: what it means to ‘be online’. I suggest that such a rethinking is necessary if we are to meaningfully conceptualise engagement, and to move beyond limited, but durable, assumptions that pervade contemporary discussions around digital learning.
LSQ 2.0: A linked dataset of SPARQL query logs

We present the Linked SPARQL Queries (LSQ) dataset, which currently describes 43.95 million executions of 11.56 million unique SPARQL queries extracted from the logs of 27 different endpoints. The LSQ dataset provides RDF descriptions of each such query, which are indexed in a public LSQ endpoint, allowing interested parties to find queries with the characteristics they require. We begin by describing the use cases envisaged for the LSQ dataset, which include applications for research on common features of queries, for building custom benchmarks, and for designing user interfaces. We then discuss how LSQ has been used in practice since the release of four initial SPARQL logs in 2015. We discuss the model and vocabulary that we use to represent these queries in RDF. We then provide a brief overview of the 27 endpoints from which we extracted queries in terms of the domain to which they pertain and the data they contain. We provide statistics on the queries included from each log, including the number of query executions, unique queries, as well as distributions of queries for a variety of selected characteristics. We finally discuss how the LSQ dataset is hosted and how it can be accessed and leveraged by interested parties for their use cases.
Collision Avoidance Among Dense Heterogeneous Agents Using Deep Reinforcement Learning

Navigating in a complex congested social environment without collision is a crucial and challenging task. Recent studies have demonstrated the considerable success of Deep Reinforcement Learning (DRL) in multi-agent collision avoidance. However, the assumption of these studies that agents are homogeneous circles deviates from reality, leading to performance deterioration in congested scenarios. The current work extends the DRL-based approaches to develop a collision avoidance method for congested scenarios wherein the heterogeneity of agents can no longer be disregarded. Considering shape heterogeneity, we use the Orientated Bounding Capsule (OBC) to model the agents and transform the interactive state space of Robot-Obstacle agent pair. For speed heterogeneity, we design a velocity-related collision risk function to shape the behavior of the robot. Experimental results demonstrate that our proposed method outperforms state-of-the-art DRL-based approaches in terms of success rate and safety. It also exhibits desired collision avoidance behavior.