Reviewerly/exHarmony huggingface dataset:
Hybrid Multilevel Explanation: A New Approach for Explaining Regression Models

Regression models are commonly used to model the associations between a set of features and an observed outcome, for purposes such as prediction, finding associations, and determining causal relationships. However, interpreting the outputs of these models can be challenging, especially in complex models with many features and nonlinear interactions. Current methods for explaining regression models include simplification, visual, counterfactual, example-based, and attribute-based approaches. Furthermore, these methods often provide only a global or local explanation. In this paper, we propose a hybrid multilevel explanation (Hybrid Multilevel Explanation - HuMiE) method that enhances example-based explanations for regression models. In addition to a set of instances capable of representing the learned model, the HuMiE method provides a complete understanding of why an output is obtained by explaining the reasons in terms of attribute importance and expected values in similar instances. This approach also provides intermediate explanations between global and local explanations by grouping semantically similar instances during the explanation process. The proposed method offers a new possibility of understanding complex models and proved to be able to find examples statistically equal to or better than the main competing methods and to provide a coherent explanation with the context of the explained model.
Towards Generating P-Contrastive Explanations for Goal Selection in Extended-BDI Agents

As agent-based systems have been growing, more and more the general public has access to them and is influenced by decisions taken by these systems. This increases the necessity for such systems to be capable of explaining themselves to a user. The Beliefs-Desires-Intentions (BDI) is a commonly used agent model that has an two-phase internal goal selection process (desires and intentions) to decide what goals to pursue. Belief-based goal processing (BBGP) model is an extended-BDI model whose selection process consists of four phases. This more-grained behavior may have relevant consequences for the analysis of what an intention is and better explain how an intention becomes what it is. Contrastive explanations are commonly employed by people and can bring benefits to the explanation exchange process. A Property-contrast (P-contrast) explanation is a type of contrastive explanation that compares the properties of an explanation object. Thus, we can take a goal as the object about which we want an explanation and its status in the selection process (that is, how much it has advanced) as a property. This work tackles the problem of generating P-contrast explanations and proposes a method to construct them. The method consists of two phases, which in turn consist of a set of steps. The first step of the second phase returns a set of beliefs that constitute a future explanation. Thus, this work focuses on the first phase and the first step of the second phase. We use a scenario of the cleaner world in order to illustrate the performance of our proposal.
Bete: A Brazilian Portuguese Dataset for Named Entity Recognition and Relation Extraction in the Diabetes Healthcare Domain

The biomedical NLP community has seen great advances in dataset development mostly for the English language, which has hindered progress in the field, as other languages are still underrepresented. This study introduces a dataset of Brazilian Portuguese annotated for named entity recognition and relation extraction in the healthcare domain. We compiled and annotated a corpus of health professionals' responses to frequently asked questions in online healthcare forums on diabetes. We measured inter-annotator agreement and conducted initial experiments using up-to-date methods to recognize entities and extract relations, such as BERT-based ones. Data, models, and results are publicly available at https://github.com/pavalucas/Bete .
A Monte Carlo Algorithm for Time-Constrained General Game Playing

General Game Playing (GGP) is a challenging domain for AI agents, as it requires them to play diverse games without prior knowledge. In this paper, we develop a strategy to improve move suggestions in time-constrained GGP settings. This strategy consists of a hybrid version of UCT that combines Sequential Halving and , favoring information acquisition in the root node, rather than overspend time on the most rewarding actions. Empirical evaluation using a GGP competition scheme from the Ludii framework shows that our strategy improves the average payoff over the entire competition set of games. Moreover, our agent makes better use of extended time budgets, when available.
LegalBert-pt: A Pretrained Language Model for the Brazilian Portuguese Legal Domain

Language models trained with Bidirectional Encoder Representations from Transformers (BERT) have demonstrated remarkable results in various Natural Language Processing (NLP) tasks. However, the legal domain poses specific challenges for NLP due to its highly specialized language, which includes technical vocabulary, formal style, frequent use of law citations and semantics based on vast knowledge. Therefore, pretrained language models on a generic corpus may not be suitable for performing specific legal domain tasks. They lack the necessary expertise to understand the nuances of legal language, leading to inaccuracies and inconsistencies. This work describes the development of a specialized language model, LegalBert-pt, for the legal domain in Portuguese. The model was pretrained on a large and diverse corpus of Brazilian legal texts and is now open-source and customizable for specific tasks. Experiments were conducted to evaluate the pretrained model's effectiveness in the legal domain, both intrinsically and in two specific tasks: named-entity recognition and text classification. The results indicate that using LegalBert-pt outperforms the generic language model in all tasks, emphasizing the importance of specialization in achieving effective results for specific tasks in the legal domain.
Collections: General, Subject, and Special

A library collection includes a wide variety of materials that meet the needs of a community and that support a library’s mission, goals, and objectives. Every library has a general collection, which can include general materials for a general community or subject-specific materials for a specialized community. Libraries may also have special collections that contain rare materials and items of historical value. Library collections are continually evolving, and they are actively developed and managed to ensure they stay relevant and in good condition. Current issues include the transition to electronic collections and censorship of library materials.
Tabular-to-Image Transformations for the Classification of Anonymous Network Traffic Using Deep Residual Networks

With the meteoric rise in anonymous network traffic data, there is a considerable need for effective automation in traffic identification tasks. Though many shallow and deep machine learning network traffic classification solutions have been proposed, they often rely on tabular data, making them unable to detect complex spatial relationships. However, recent advancements in computer processing power have increased the viability of transforming tabular data into images for training deep convolutional neural networks, transforming structured data problems into spatial ones. To identify the most effective methods for representing tabular anonymous network traffic data as images, we compared five deep learning classifiers trained on data from different tabular-to-image algorithms–Image Generator for Tabular Data (IGTD), DeepInsight, vector-of-feature wrapping (normalized and non-normalized), and our newly introduced Binary Image Encoding (BIE) technique in the classification of eight network application types. Furthermore, we examine whether deep residual models trained on tabular-to-image data can outperform the top-performing shallow learner, XGBoost, at classifying anonymous network traffic. We found that ResNet-50, a pre-trained instance of deep residual network, trained on image datasets using IGTD and the novel Binary Image Encoding outperformed XGBoost trained on tabular data. Our ResNet-50 models trained using IGTD and BIE achieved F1-scores of 96.0% and 98.49% respectively, improving on the baseline of 95.1% achieved by XGBoost.
Metadata Standards and Models

This entry on metadata standards and models provides a succinct overview of metadata, its essential role in information organisation. It defines the concept of metadata and its various functions, types, models and standards, underscoring its importance in enhancing description, search, retrieval, discovery, access and usage of information resources across different organisations including libraries, archives, e-commerce and research data management. The paper delves into the roles of different stakeholders in metadata creation, examining notable standards such as Dublin Core, MARC21, IFLA LRM, FRBR, RDA and BIBFRAME. It also emphasises the importance of controlled vocabularies and authority headings in maintaining consistency and improving information retrieval. Finally, the paper offers insights into future perspectives and emerging trends in metadata standards and models, including the potential for enrichment, linking, openness, and filtering, driven by collaboration, Semantic Web, open science, big data and AI. The entry highlights that modern metadata standards and models are pivotal in driving resource usage and contributing to an efficient and interconnected information landscape.
Hierarchical Time-Aware Approach for Video Summarization

Video summarization consists of generating a concise video representation that captures all its meaningful information. However, conventional summarization techniques often fall short of capturing all the significant events in a video due to their inability to incorporate the hierarchical structure of the video content. This work proposes an unsupervised method, named Hierarchical Time-aware Summarizer–HieTaSumm, that uses a hierarchical approach for that task. In this regard, hierarchical strategies for video summarization have emerged as a promising solution, in which video content is modeled as a graph to identify keyframes that represent the most relevant information. This approach enables the extraction of the frames that convey the central message of the video, resulting in a more effective and precise summary. Experimental results indicate that the proposed approach has great potential. Specifically, it seems to enhance coherence among different video segments, reducing frame redundancy in the generated summaries, and enhancing the diversity of selected keyframes.
An integrated approach for rapid exploration of carbonatites and related mineral resources

Abstract Carbonatites are proven significant repositories of several critical and strategic elements such as rare earth elements, niobium, thorium, and uranium. Owing to their economic significance, mapping of carbonatites and associated mineral deposits has occupied prominent place in mineral resource exploration programs. In this study an integrated approach was developed to map carbonatite and related mineral deposits in the Loe‐Shilman, Northwest Himalaya of Pakistan, using remotely sensed advance space‐borne thermal emission and reflection radiometer (ASTER) multispectral data and visible near infrared and short‐wave infrared (VNIR‐SWIR) spectral characteristics of minerals in these deposits. Several image enhancement techniques, including band ratio (i.e., B4/B3), principal component and minimum noise fraction transformation (PC6 and MNF5, respectively) helped in highlighting the targeted rocks. The results demonstrate the suitability of ASTER data for discriminating carbonatite related mineral deposits from other surrounding lithologies. Results obtained from these methods were validated through field observations in the area and further confirmed through petrographic and chemical analyses of collected specimens. Field data have also served as training data to perform a supervised classification, allowing further improvement of the mapping results. Moreover, the obtained results from the techniques used for exploring carbonatites and related mineral deposits were stacked together for comparison with each other, to check their sensitivities, and assess their efficiency and accuracy. Generally, all these methods successfully highlighted carbonatites and related mineral deposits; however, when used integratively they exhibit higher degree of accuracy, and has proven to be relatively rapid and cost‐effective.
Optimizing the Cost-Performance Ratio of FaaS Deployments

Autoscaling serverless architectures utilizing Function as a Service (FaaS) is an established model. While there is virtually no limit to scalability in theory, in practice, a trade-off between price and performance determines the cost-efficient scalability of cloud deployments. Finding the correct specifications becomes even harder when the computational demands depend highly on the functions’ inputs. Consequently, a single configuration is often not cost-efficient enough. To solve this problem, our paper proposes a deployment model for multiple specifications to cover inputs with differing computational demands. By defining categories for the functions’ inputs, requests can be routed to particular deployments to increase the overall cost-performance ratio. Applied filters to the functions’ triggers alleviate the complexity of multiple deployments, and deployments can actively select inputs within their assigned category. We evaluated our approach with multiple use cases and programming languages on Amazon Web Services (AWS) and Azure. Multiple deployments can generally be justified, if cost is higher for shorter duration. The efficiency of our approach depends on (i) the assignment of correct categories, (ii) the number of requests in each category, and (iii) the configuration granularity of the cloud service provider. While different languages do not influence the effectiveness of this approach, it is hindered by limited configuration possibilities on Azure. Thus, it is easier to find the best cost-performance ratio on AWS.
Horizontal Scaling of Transaction-Creating Machines for Blockchains

The increasing popularity of blockchains raises the question of how to improve their scalability. While researchers are exploring ways to scale the on-chain processing of transactions, the scalability of the off-chain creation of transactions has not been investigated yet. This is relevant for organizations wishing to send a high volume of transactions in a short time frame, or continuously. Especially for blockchain implementations such as Ethereum which require transactions to include so-called nonces, horizontally scaling transaction creation is non-trivial. In this paper, we propose four different approaches for horizontal scaling of transaction creation in Ethereum. Our experimental evaluation examines the performance of the different approaches in terms of scalability and latency and finds two of the four proposed approaches feasible to scale transaction creation horizontally.
Adaptive Controller to Identify Misconfigurations and Optimize the Performance of Kubernetes Clusters and IoT Edge Devices

Kubernetes default configurations do not always provide optimal security and performance for all clusters and IoT edge devices deployed, affecting the scalability of a given workload and making them vulnerable to security breaches and information leakage if misconfigured. We present an adaptive controller to identify the type of misconfiguration and its consequence threat to optimize the system behavior. Our work differs from existing approaches as it is fully automated and can diagnose various errors on the fly. The controller is evaluated in terms of quality and accuracy of identification. The results show that the controller can identify around 90% of the total number of configuration values with a reasonable average identification overhead.
A Taxonomy for Workload Deployment Orchestration in the Edge-Cloud Continuum

As compute resources continue to proliferate from static large-scale enterprise-grade cloud environments to various types of more dynamic and resource-constrained edge environments, the need increases to orchestrate the deployment of workloads of data and processing applications across the emerging edge-cloud continuum. For many use cases in various application domains and contexts, similar workload deployment orchestration challenges arise. We present a taxonomy for workload deployment orchestration in the edge-cloud continuum that captures the various views and perspectives on workload deployment orchestration. We evaluate and valorise the proposed taxonomy by means of three illustrative and representative types of use cases from different domains and present opportunities for future research.
Time-Aware QoS Web Service Selection Using Collaborative Filtering: A Literature Review

The large increase in the number of available Web services makes the selection of suitable services a big challenge. Several methods have been developed to predict the Quality of Service (QoS) values in order to solve the service selection problem. However, these methods face many limitations that hinder their prediction accuracy. A particular issue is the dynamic nature of the service environment, which causes variations in QoS values (due to network load, hardware problems, etc.). To overcome, QoS selection methods have utilized contextual information, of the surrounding environments, such as service invocation time and/or user and service locations. Amongst these methods are Collaborative Filtering(CF). In the last few years, several CF methods have augmented service invocation time in their prediction process, forming, what is popularly known as, time-aware CF methods. However, current research lacks a dedicated and comprehensive literature review on time-aware CF prediction methods. To this end, this paper analysed the literature and reviewed forty (40) most prominent studies in this field. It provides a thematic categorization of these studies and an insightful analysis detailing their objectives, benefits, and limitations. It identifies the main research gaps and possible research directions for future work. The literature review provides a state-of-the-art update for researchers pursuing research in service oriented computing.
Enhanced Time-Aware Collaborative Filtering for QoS Web Service Prediction

Predicting Quality of Service (QoS) is an essential task in Service Oriented Computing (SOC). In service selection, choosing the right services is a crucial step to achieve high system stability and user satisfaction. Considerable research has been conducted in the last decade to develop accurate prediction methods. Among these are the time-aware Collaborative Filtering (CF) methods, which utilize the QoS values recorded across multiple time periods (slices). However, they suffer from low accuracy due to adopting inaccurate measures, such as averaging old collected QoS or averaging user (or service) similarity values. In this paper, we propose a time-aware method (ETACF) that uses an exponential time-decay function for quantifying the effectiveness of time slices according to their temporal recency. Experiments were conducted in order to evaluate the accuracy of the proposed method. Results show that the developed method achieves a significant improvement in prediction accuracy (decreased NMAE by 9.8%) when compared with the state-of-art methods.
The promotion of self-regulation of children's behaviour, an unresolved issue in initial teacher education in Catalonia (Spain)

The growing concern in classrooms over the increase in disruptive behaviour and its management poses the need to investigate the teaching competences that can help teachers to promote self-regulation of behaviour (SRB) among children. This requires an inclusive approach that understands behaviour in a holistic way, taking into account the whole context. Therefore, different competences need to be activated that together enable teachers to promote children's SRB. This article identifies which specific competences of 923 teaching plans of the five public Catalan universities would contribute to preparing student teachers to foster children's SRB. The perceptions of 929 final-year student teachers on the training they had received were collected using a questionnaire. The most present competences in the teaching plans are pedagogical, inclusive, teacher reflection and collaboration. Among the least present are communication and classroom management, although they are the most essential for promoting children's SRB. Participants reported that the contribution of university subjects to promoting children's SRB is scarce and is largely limited to external regulation strategies instead of supporting children's SRB. In terms of behaviour management as a fundamental and incipient aspect of fostering children's SRB, student teachers do not feel that they even have the key competences for managing behaviour in the classroom, although there are differences between universities and degrees. The active role of student teachers in children's SRB should be enhanced during initial teacher education, even in the attention to diversity specialisation and in the dual system programme. There is an urgent need for universities to equip student teachers with competences to promote children's SRB. The competence profile should be clarified and unified, so that this set of competences can be worked on in a complementary, cross-curricular and practical way in different subjects, regardless of teaching specialisation.
Factors influencing Cryptocurrencies investment intention: A study on the customer perception in Sultanate of Oman

E-governance which is a recent technology plays in a significant role around the world which includes blockchain approach. The topic of cryptocurrency is discussed by numerous research scholars and analysed accordingly to identify the profitable cryptos as it comprises of lots of risk factors. Implementation of block chain in a country governs rules and administration in bureaucratic country. This study would help to understand e-governance and related literature to explore the probable value, risks components and its own restrictions in Sultanate of Oman. Early years in the country regarding the blockchain technology bear cost elements, accuracy matters and reliability aspects, transparent issues and accountable services. Centralization of delegated authority is obligatory to set new guidelines in the Sultanate to resolve the issues relating to block chain methodologies using manpower who are dynamic to frame tax laws. The objective of this article is to understand the concept of cryptocurrency around the world in general and in particular in Sultanate of Oman. The study also would analyze the regulation of cryptocurrencies in Oman and also to comprehend the perception of the general public and investors around the Muscat City. Cryptocurrencies have not been explored much in gulf region and this study would also help us to discover more of cryptocurrencies through a questionnaire which has been collected from investors and general public. The findings of the study have been interpreted with the help latest research statistics.