Reviewerly/exHarmony huggingface dataset:
A conceptual model for ontology quality assessment

With the continuous advancement of methods, tools, and techniques in ontology development, ontologies have emerged in various fields such as machine learning, robotics, biomedical informatics, agricultural informatics, crowdsourcing, database management, and the Internet of Things. Nevertheless, the nonexistence of a universally agreed methodology for specifying and evaluating the quality of an ontology hinders the success of ontology-based systems in such fields as the quality of each component is required for the overall quality of a system and in turn impacts the usability in use. Moreover, a number of anomalies in definitions of ontology quality concepts are visible, and in addition to that, the ontology quality assessment is limited only to a certain set of characteristics in practice even though some other significant characteristics have to be considered for the specified use-case. Thus, in this research, a comprehensive analysis was performed to uncover the existing contributions specifically on ontology quality models, characteristics, and the associated measures of these characteristics. Consequently, the characteristics identified through this review were classified with the associated aspects of the ontology evaluation space. Furthermore, the formalized definitions for each quality characteristic are provided through this study from the ontological perspective based on the accepted theories and standards. Additionally, a thorough analysis of the extent to which the existing works have covered the quality evaluation aspects is presented and the areas further to be investigated are outlined.
MrTF: model refinery for transductive federated learning

We consider a real-world scenario in which a newly-established pilot project needs to make inferences for newly-collected data with the help of other parties under privacy protection policies. Current federated learning (FL) paradigms are devoted to solving the data heterogeneity problem without considering the to-be-inferred data. We propose a novel learning paradigm named transductive federated learning to simultaneously consider the structural information of the to-be-inferred data. On the one hand, the server could use the pre-available test samples to refine the aggregated models for robust model fusion, which tackles the data heterogeneity problem in FL. On the other hand, the refinery process incorporates test samples into training and could generate better predictions in a transductive manner. We propose several techniques including stabilized teachers, rectified distillation, and clustered label refinery to facilitate the model refinery process. Abundant experimental studies verify the superiorities of the proposed Model refinery framework for Transductive Federated learning. The source code is available at https://github.com/lxcnju/MrTF .
Mobile Phone Innovation and Doing Business in Sub-Saharan Africa

This article assesses how knowledge diffusion modulates the effect of the mobile phone on entrepreneurship or doing business in sub-Saharan Africa. The empirical evidence is based on Generalised Method of Moments in which mobile phones are interacted with three knowledge diffusion variables, namely, education, internet penetration and scientific output. Ten variables of entrepreneurship are used. The following three main findings are established. First, the net effects from interacting mobile phones with the internet and scientific publications are negative, whereas the corresponding net impact from the interaction between mobile phones and education is positive on the cost of doing business. Second, the mobile phone interacts with education (the internet) to have a positive (negative) net effect on the time needed to construct a warehouse whereas, the corresponding interaction with the internet yields a net negative effect on the time to enforce a contract. Third, there is a positive net effect from the interaction of mobile phones with education on the time to start a business. Given the construction of the education variable, the positive net effects from education are consistent with corresponding negative net effects from the other knowledge diffusion variables.
Use all tokens method to improve semantic relationship learning

Recently, research on inference methods has been actively conducted to use language models more effectively for studying natural language understanding. Inference in language models that use bidirectional encoder representations from transformers (BERT) is performed using classification tokens that convey information from the input sentences. The use of single-token inference method for inference does not involve the hidden state vector that contains relevant connection information between the words, which in turn limits the ability to infer semantic relationships. This study proposes a use all tokens (UAT) method that combines unused tokens to improve inference methods through a single token. The UAT method effectively combines hidden state vectors and ensembles the global information of sentences with the local information between words. When the Stanford natural language inference (SNLI) corpus was solved using DeBERTaV3large, compared to the existing single token inference method, the UAT method improved the precision of the neutral relationship by 4.3% (87.7% vs. 92.0%) and the recall of the entailment and contradiction relationship by an average of 2% (93.5% vs. 95.5%). The UAT method proposed in this study can be readily implemented in BERT-based language models, and it enhances the accuracy and F1-score, thereby improving the learning of semantic relationships between sentences.
A metaheuristic causal discovery method in directed acyclic graphs space

Causal discovery plays a vital role in the human understanding of the world. Searching a directed acyclic graph (DAG) from observed data is one of the most widely used methods. However, in most existing approaches, the global search has poor scalability, and the local search is often insufficient to discover a reliable causal graph. In this paper, we propose a generic metaheuristic method to discover the causal relationship in the DAG itself instead in of any equivalent but indirect substitutes. We first propose several novel heuristic factors to expand the search space and maintain acyclicity. Second, using these factors, we propose a metaheuristic algorithm to further search for the optimal solution closer to real causality in the DAG space. Theoretical studies show the correctness of our proposed method. Extensive experiments are conducted to verify its generalization ability, scalability, and effectiveness on real-world and simulated structures for both discrete and continuous models by comparing it with other state-of-the-art causal solvers. We also compare the performance of our method with that of a state-of-the-art approach on well-known medical data.
Privacy Concerns and Antecedents of Building Data Sharing: A Privacy Calculus Perspective

In recent years, machine learning (ML) based building analytics have been developed for diverse building services. Yet, the widespread sharing of building data, which underpins the establishment of ML models, is not a common practice in the buildings industry today. Clearly, there are privacy concerns. There are studies on protecting building data, e.g., to k -anonymize building data; yet these studies are computational methods. The root causes of why building operators are or are not willing to share data are unclear. In this paper, we study the problem of willingness to share building data. First, we justify our study by investigating the field to show that data sharing is indeed limited. Second, we examine the issue of the willingness to share building data from the perspective of a social science study. We observe that the intention to disclose (i.e., decision making on data sharing) is not only based on perceived risks , but also on perceived benefits. We leverage the privacy calculus theory and present a systematic study. We develop hypotheses, design a questionnaire, conduct a survey involving 95 building operators and service providers around the world, and analyze the results, wherein we quantify how various factors influence the willingness to share building data. We further enhance our results by a small scale interview. Third, we use trust, an important factors to the intention to disclose, to develop a trust model with differentiable trust levels. Such model provides building operators a mechanism to share data besides a 0-and-1 choice. We present a case study where we enhance an existing building data anonymization platform, PAD with the trust model. We show that the enhanced PAD has a substantially smaller computation workloads.
Between global and local: translation and localization in Netflix Turkey’s media paratexts

This study analyses Netflix Turkey’s media paratexts in which Netflix’s foreign productions and Turkish originals are promoted by the creative use of local elements and/or culture-specific references. It aims to identify the role of translation in these paratexts in promoting both Netflix and its shows such as Stranger Things among others in Turkey. It also discusses the functions of these paratexts in relation to their socio-political contexts of production and the wider debates around regulation and censorship in Turkey. The analysis shows that translation and localization in the selected paratexts serve to make the foreign shows relatable, evoke a sentiment of nostalgia and create a sense of belonging. Further, the paratexts on Netflix Turkish originals highlight the untranslatability and culture-specificity of Turkish expressions to appeal to the viewers’ national pride, which is arguably informed by a populist approach due to the increasing possibility of restrictions on content.
Unlocking the Black Box: Explainable Artificial Intelligence (XAI) for Trust and Transparency in AI Systems

Explainable Artificial Intelligence (XAI) has emerged as a critical field in AI research, addressing the lack of transparency and interpretability in complex AI models. This conceptual review explores the significance of XAI in promoting trust and transparency in AI systems. The paper analyzes existing literature on XAI, identifies patterns and gaps, and presents a coherent conceptual framework. Various XAI techniques, such as saliency maps, attention mechanisms, rule-based explanations, and model-agnostic approaches, are discussed to enhance interpretability. The paper highlights the challenges posed by black-box AI models, explores the role of XAI in enhancing trust and transparency, and examines the ethical considerations and responsible deployment of XAI. By promoting transparency and interpretability, this review aims to build trust, encourage accountable AI systems, and contribute to the ongoing discourse on XAI.
Korpusomat.eu: A Multilingual Platform for Building and Analysing Linguistic Corpora

The paper presents Korpusomat, a new, free web-based platform for effortless building and analysing linguistic data sets (corpora). The aim of Korpusomat is to bridge the gap between corpus linguistics, which requires tools for corpus analysis based on various linguistic annotations, and modern multilingual machine learning-based approaches to text processing. A special focus is placed on multilinguality: the platform currently serves 29 languages, but more can be easily added per user request. We discuss the use of Korpusomat in multidisciplinary research, and present a case study located at the intersection between discourse analysis and migration studies, based on a corpus generated and queried in the application. This provides a general framework for using the platform for research based on automatically annotated corpora, and demonstrates the usefulness of Korpusomat for supporting domain researchers in using computational science in their fields.
Deep Reinforcement Learning Object Tracking Based on Actor-Double Critic Network

Aiming at the problem of poor tracking robustness caused by severe occlusion, deformation, and object rotation of deep learning object tracking algorithm in complex scenes, an improved deep reinforcement learning object tracking algorithm based on actordouble critic network is proposed.In offline training phase, the actor network moves the rectangular box representing the object location according to the input sequence image to obtain the action value, that is, the horizontal, vertical, and scale transformation of the object.Then, the designed double critic network is used to evaluate the action value, and the output double Q value is averaged to guide the actor network to optimize the tracking strategy.The design of double critic network effectively improves the stability and convergence, especially in challenging scenes such as object occlusion, and the tracking performance is significantly improved.In online tracking phase, the well-trained actor network is used to infer the changing action of the bounding box, directly causing the tracker to move the box to the object position in the current frame.Several comparative tracking experiments were conducted on the OTB100 visual tracker benchmark and the experimental results show that more intensive reward settings significantly increase the actor network's output probability of positive actions.This makes the tracking algorithm proposed in this paper outperforms the mainstream deep reinforcement learning tracking algorithms and deep learning tracking algorithms under the challenging attributes such as occlusion, deformation, and rotation.
Improving LocalMaxs Multiword Expression Statistical Extractor

LocalMaxs algorithm extracts relevant Multiword Expressions from text corpora based on a statistical approach. However, statistical extractors face an increased challenge of obtaining good practical results, compared to linguistic approaches which benefit from language-specific, syntactic and/or semantic, knowledge. First, this paper contributes to an improvement to the LocalMaxs algorithm, based on a more selective evaluation of the cohesion of each Multiword Expressions candidate with respect to its neighbourhood, and a filtering criterion guided by the location of stopwords within each candidate. Secondly, a new language-independent method is presented for the automatic self-identification of stopwords in corpora, requiring no external stopwords lists or linguistic tools. The obtained results for LocalMaxs reach Precision values of about $$80\,\%$$ for English, French, German and Portuguese, showing an increase of around $$12-13\,\%$$ compared to the previous LocalMaxs version. The performance of the self-identification of stopwords reaches high Precision for top-ranked stopword candidates.
<i>Debris:</i> Machine learning, archive archaeology, digital audio waste

This article fragments and processes Debris , a project developed to formalise the creative recycling of digital audio byproducts. Debris began as an open call for electronic compositions that take as their point of departure gigabytes of audio material generated through training and calibrating Demiurge , an audio synthesis platform driven by machine learning. The Debris project led us down rabbitholes of structural analysis: what does it mean to work with digital waste, how is it qualified, and what new relationships and methodologies do this foment? To chart the fluid boundaries of Debris and pin down its underlying conceptualisation of sound, this article introduces a framework ranging from archaeomusicology to intertextuality, from actor-network theory to Deleuzian assemblage, from Adornian constellation to swarm intelligence to platform and network topology. This diversity of approaches traces connective frictions that may allow us to understand, from the perspective of Debris , what working with sound means under the regime of machine intelligence. How has machine intelligence fundamentally altered the already shaky diagram connecting humans, creativity and history? We advise the reader to approach the text as a multisensory experience, listening to Debris while navigating the circuitous theoretical alleys below.
Totally Ordered Conditional Independence Models

A subclass of lattice conditional independence models is introduced. The new class of models is called totally ordered independence models. The class is based on an assumption that the index set which orders the random variables is a chain. It is shown that there is a jump in the chain if and only if there is a conditional independence relation. Some comparisons between the lattice conditional independence models and totally ordered independence models are presented.
Tapping the Potential of Coherence and Syntactic Features in Neural Models for Automatic Essay Scoring

In the prompt-specific holistic score prediction task for Automatic Essay Scoring (AES), the general approaches include pre-trained neural model, coherence model, and hybrid model that incorporate syntactic features with neural model. In this paper, we propose a novel approach to extract and represent essay coherence features with NSP that matches the state-of-the-art (SOTA) AES coherence model, and achieves the best performance for long essays. We apply syntactic feature dense embedding to augment BERT-based model and achieve the best performance for hybrid methodology for AES. In addition, we explore various ideas to combine coherence, syntactic information, and semantic embeddings, which no previous study has done. Our combined model also performs better than the SOTA available for combined model, even though it does not outperform our syntactic-enhanced neural model. We further compare with the pure neural models and analyze the strengths and weaknesses of our methodologies.
Strategic Flexibility and Organizational Performance: Mediating Role of Innovation

Strategic flexibility is deemed to be an important organizational capability for enhancing innovation and improving organizational performance (OP) in uncertain, turbulent, and ever-changing business environments. Many studies have investigated the impact of strategic flexibility on innovation and OP in various contexts and found that strategic flexibility enhances innovation and improves OP. Other studies have found negative association or no association between strategic flexibility and OP. Therefore, it is not much clear whether strategic flexibility improves OP directly or through innovation. Especially, the mediating role of innovation in the association between strategic flexibility and OP in engineering project-based organizations has not been fully grabbed in prior literature and Pakistan remained unexplored. This study has strived to examine the mediating role of innovation in the association between strategic flexibility and OP in this context. Drawing upon resource based and dynamic capability views, an explanatory model was developed and tested by applying PLS-SEM higher order component modeling approach using sample data from 184 organizations in Pakistan. The results revealed that strategic flexibility is positively associated with innovation and OP. Innovation is positively associated with OP. The association between strategic flexibility and OP is partially mediated by innovation. The study promotes academic rigor and provides a new theoretical model. Mangers, decision-makers, and policy-makers can utilize the results to update their plans to achieve sustainable OP. The other organizations in various countries operating under similar conditions can also take the advantage of this study.
Estimating the Amount of Sparsity in Two-Point Mixture Models

We consider the problem of estimating the fraction of nonzero means in a sparse normal mixture model in the region where variable selection is possible. The focus is on the situation in which the proportion of nonzero means is very small. The proposed estimator is shown to be nearly rate optimal in the asymptotically minimax sense. Using this estimator, one can also consistently estimate the sparsity parameter in sparse normal mixtures, whose knowledge, in particular, is required to carry out the so-called almost full variable selection procedure. The advantage of using the new estimator is illustrated analytically and numerically. The obtained results can be extended to some nonnormal mixtures.
Exploding the android: encounters with social robotics in a science centre

ABSTRACTABSTRACTThis paper examines science centre displays incorporating robotic technologies to provoke reflection on social robotics. Drawing on research in post-phenomenology and science and technology studies, it considers how exhibits in Born or Built? about robotic emotion undermine its framing narrative of human-robot convergence. Comparing these science centre exhibits to social robotics demonstrations draws attention to how the exhibits move away from convincing audiences about the truth or merits of these technologies to instead convey their difference and open-endedness. While there are limited popular narratives and images to invoke in exploring these ideas, relationships can be drawn between the displays and historical automata and contemporary robotic artwork, practices that frequently seek to promote reflection on robotic otherness, appearance and relationality. Representing robots with robotics points towards the relevance of style to conceptualising robotic diversity and materiality. Style in this sense is not simply an embellishment or surface addition to content, but a material expression inseparable from what is communicated and relative to technological, cultural and social milieus.KEYWORDS: Social roboticsrobotsanthropomorphismscience centreemotion Disclosure statementNo potential conflict of interest was reported by the author(s).AcknowledgmentsThank you to the staff at Questacon for making this research possible by providing access to documentation on the development of Born or Built?.Additional informationNotes on contributorsErika KerruishErika Kerruish is a researcher and senior lecturer in the Faculty of Arts, Business and Law at Southern Cross University. Her research is at the intersection of continental philosophy and computational technologies and digital media and examines questions of affect and perception in digital culture.
Striving for Authentic and Sustained Technology Use in the Classroom: Lessons Learned from a Longitudinal Evaluation of a Sensor-Based Science Education Platform

Technology integration in educational settings has led to the development of novel sensor-based tools that enable students to measure and interact with their environment. Although reports from using such tools can be positive, evaluations are often conducted under controlled conditions and short timeframes. There is a need for longitudinal data collected in realistic classroom settings. However, sustained and authentic classroom use requires technology platforms to be seen by teachers as both easy to use and of value. We describe our development of a sensor-based platform to support science teaching that followed a 14-month design process. We share insights from this design and development approach, and report findings from a six-month large-scale evaluation involving 35 schools and 1245 students. We share lessons learnt, including that technology integration is not an educational goal per se and that technology should be a transparent tool to enable students to achieve their learning goals.