Reviewerly/exHarmony huggingface dataset:
Geo-visualization of Sarawak COVID-19 Publicly Available Data Employing Open-source Geospatial Software

The state government of Sarawak with the help of the Sarawak Disaster Management Committee (SDMC) has continuously made the updated information on the state COVID-19 situation and its ensuing control measures available to general public in the form of daily press statements.However, these statements are merely providing textual information on daily basis though the data are in fact rich in temporal and spatial properties.Since the onset of COVID-19 pandemic, spatiotemporal analysis becomes the key element to better understand the spread of COVID-19 in various spatial levels worldwide.Hence, there is an urgent need to convert this textual information into more valuable insights by applying geo-visualization techniques and geospatial statistics.The paper demonstrates the prospect of retrieving geospatial data from publicly available document to locate, map and analyze the spread of COVID-19 up to division level of Sarawak.Specifically, map visualization and geospatial statistical analysis are performed for the list of exposed locations, which are indeed locations visited by COVID-19 patients prior to being tested positive in Kuching division, using open-source geospatial software QGIS.It is found that these exposed locations concentrate on the build-up areas in the division and are in south-west to north-east direction of the center of Kuching in September and October 2021.Despite the number of exposed locations published is relatively small compared to the number of confirmed cases reported, both are nearly strongly correlated.The insights gained from such geospatial analysis may assist the local public health authorities to impose applicable disease control interventions at division level.
A Voyage on Neural Machine Translation for Indic Languages

With the invention of deep learning concepts, Machine Translation (MT) migrated towards Neural Machine Translation (NMT) architectures, eventually from Statistical Machine Translation (SMT), which ruled MT for a few decades. Slowly, NMT paved its path into Indian MT research and witnessed many works for various language pairs in this regard. Numerous NMT architectures are floating across the international and national research pool; many claims to be state-of-the-art architectures. Though NMT for Indic languages (ILNMT) is giving better results for majority speaking language pairs, the translation quality is low due to a lack of significant resources. Automated machine translation models are unavailable for some less spoken Indic languages like Kashmiri and Dogri. Hence, there is increasing demand in the research to address the challenges of developing applicable MT models even when minuscule training data is available. Based on the corpus availability, the languages are categorized into High Resource Languages (HRLs), Low Resource Languages (LRLs), and Zero Resource Languages (ZRLs). Many Indic languages are classified into HRLs, LRLs, and ZRLs based on corpus availability. The vision behind this literature survey paper is to make this paper a collective source for all information regarding the predominant ILNMT architectures, the toolkits available for building NMT models, and various pre-trained language models needed by researchers who contribute to the ILNMT research community. In this survey paper, ILNMT architectures for different Indic languages are covered, e.g., Hindi, Tamil (HRLs), Kannada, Marathi (LRLs), Sinhala, and Nepali (ZRLs). There are a few language-specific survey papers on ILNMT, and this is one of the first kinds of survey papers where all the information is gathered under one canopy.
Analyzing and Defending against Membership Inference Attacks in Natural Language Processing Classification

The risk posed by Membership Inference Attack (MIA) to deep learning models for Computer Vision (CV) tasks is well known, but MIA has not been addressed or explored fully in the Natural Language Processing (NLP) domain. In this work, we analyze the security risk posed by MIA to NLP models. We show that NLP models are at great risk to MIA, in some cases even more so than models trained on Computer Vision (CV) datasets. This includes an 8.04% increase in attack success rate on average for NLP models (as compared to CV models and datasets). We determine that there are some unique issues in NLP classification tasks in terms of model overfitting, model complexity, and data diversity that make the privacy leakage severe and very different from CV classification tasks. Based on these findings, we propose a novel defense algorithm - Gap score Regularization Integrated Pruning (GRIP), which can protect NLP models against MIA and achieve competitive testing accuracy. Our experimental results show that GRIP can decrease the MIA success rate by as much as 31.25% when compared to the undefended model. In addition, when compared to differential privacy, GRIP offers 7.81% more robustness to MIA and 13.24% higher testing accuracy. Overall our experimental results span four NLP and two CV datasets, and are tested with a total of five different model architectures.
Sign Spotting via Multi-modal Fusion and Testing Time Transferring

This work aims to locate a query isolated sign in a continuous sign video. In this task, the domain gap between the isolated and continuous sign videos often handicaps the localization performance. To address this issue, we propose a parallel multi-modal sign spotting framework. In a nutshell, our framework firstly takes advantage of multi-modal information (including RGB frames, 2D key-points and 3D key-points) to achieve representative sign features. The multi-modal features are employed to complement each other and thus compensate for the deficiency of a single modality, thus leading to informative representations for sign spotting. Moreover, we introduce a testing time top-k transferring technique into our framework to reduce the aforementioned domain gap. Concretely, we first compare the query sign with extracted sign video clips, and then update the feature of the query sign with the features of the top-k best matching video clips. In this manner, the updated query feature will exhibit a smaller domain gap with respect to continuous signs, facilitating feature matching in the following iterations. Experiments on the challenging OSLWL-Test-Set benchmark demonstrate that our method achieves superior performance (0.559 F1-score) compared to the baseline (0.395 F1-score). Our code is available at https://github.com/bb12346/OpenSLR .
Mediation Effect of Service Quality on the Relationship of Healthcare Service Innovation and Outpatient Experience

Healthcare organizations play an important role in people's well-being all over the world; good healthcare facilities are extremely beneficial in combating many diseases worldwide. Innovation is one of the most important factors influencing service quality in healthcare organizations worldwide, particularly in the UAE. The pandemic, changing human needs and globalization have made organizations more aware of the importance of improving patients' experiences through improved service quality and innovative services, which has become a winning strategy and provides organizations witha comparative advantage over others through customer confidence building. This research assessed mediating effect of service quality on health services innovation and outpatient experience. The model was developed with the help of SmartPLS software package and the data was gathered through questionnaire survey. Analysis of 397 questionnaire forms were used to analyze direct relations between service quality and service innovation with out-patient experience as well as indirect effect of service innovation on out-patient experience through service quality. The developed model was confirmed through convergent validity and discirminant validity. The R-square reveals that service innovation accounted for approximately 47% of the variation in service quality. While service innovation and service quality explained approximately 80% of the change in outpatient experience. This means that a unit change in both service innovation and service quality improves the outpatient experience by 80%.
Interlinking the Brick Schema with Building Domain Ontologies

In the building context, there is a growing requirement for numerous data models and ontologies to coexist as a means to cover different perspectives and communities. While there are some well-known efforts, such as the Brick schema, to create an overall usable ontology, interlinking available ontologies and data models is still a challenge that can provide significant benefits towards interoperability of Building Information Models. To shed light on this matter, we provide a review of some of the most important ontologies in the building context, which we then match against Brick as a means to provide an interlinked data model. For finding matches, we propose TrioNet, an interactive ontology matcher utilizing weak supervision and active learning, which we compare in terms of precision and recall with two well-known state-of-the-art ontology matchers: AgreementMakerLight (AML) and LogMap. TrioNet outperforms them in finding more verified matches with only a few domain expert annotations, making it an ideal tool for the creation of interlinked data models to improve interoperability. With this paper, we contribute the following datasets: (i) the overall Brick data model interlinked to five other ontologies; (ii) the discovered pairwise ontology alignments; and (iii) the manually-annotated matches used for evaluation.
Blockchain-Based Democratic Power Sharing in Building-Scale Energy Communities

The ongoing energy transition pathway from a fossil-based to an environmentally friendly zero-carbon energy sector passes through decarbonization, decentralization, and digitalization. The increasing digitalization empowers several key opportunities for consumers' involvement and engagement in modern peer-to-peer interactive energy communities. Among them, blockchain technology is a timely innovation that is very promising. This paper explores the potential of a recently proposed power-sharing model suitable for building-scale energy communities and blockchain-based management to foster democratic perceptions and social acceptance in non-trusting end-users. Energy communities also benefit distributors in terms of increased demand flexibility, which can result in new methodologies for managing networks and increasing the hosting capacity of networks. The relationship between individual end-users in a community and among them and the distributor are crucial aspects.
Perancangan Sarana Media Informasi Berbasis Web Desa Klambir Lima Menggunakan Metode Waterfall

The development of the current village government, especially the villages that are discussed in this study, namely Klambir Lima Kebun village, Hamparan Perak District, Deli Serdang Regency, is good with the village fund budget from the central government which aims to advance the village, the development of Klambir Lima Kebun village is currently inseparable from the support of information technology, almost every village needs information technology to improve services to the community. One example is the existence of an internet program in the village, the internet is currently not only enjoyed by urban people but has reached the village community. The difficulty of access to existing information makes researchers interested in developing a website-based Klambir Lima Kebun village information system with the aim of improving services to the people of Klambir Lima Kebun village, access to information can be obtained easily, village potential, population, area and so on. In making a web-based exam information system using the waterfall method consisting of analysis, design, coding and testing as well as entity relationship diagrams in designing databases. With this website-based information system, it can improve the services of Klambir Lima Kebun village to the community so that information to the community can be accessed easily and accurately. The information system is equipped with a web-based display so that users can adjust the contents in the system such as a web-based information system as a means of information in achieving development in Klambir Lima Kebun Village
An improved classifier and transliterator of hand-written Palmyrene letters to Latin

This article presents the problem of improving the classifier of handwritten letters from historical alphabets, using letter classification algorithms and transliterating them to Latin. We apply it on Palmyrene alphabet, which is a complex alphabet with letters, some of which are very similar to each other. We created a mobile application for Palmyrene alphabet that is able to transliterate hand-written letters or letters that are given as photograph images. At first, the core of the application was based on MobileNet, but the classification results were not suitable enough. In this article, we suggest an improved, better performing convolutional neural network architecture for hand-written letter classifier used in our mobile application. Our suggested new convolutional neural network architecture shows an improvement in accuracy from 0.6893 to 0.9821 by 142% for hand-written model in comparison with the original MobileNet. Future plans are to improve the photographic model as well.
A BIBLIOMETRIC REVIEW OF CHATBOTS IN THE CONTEXT OF CUSTOMER SUPPORT

With the birth of new technologies and their progressive incorporation into everyday life, human beings have managed to perform tasks much more efficiently that previously required a large number of personnel and time.Nowadays, these tasks, which could be a significant added burden for companies, are considerably reduced thanks to technology and Artificial Intelligence (AI).It is clear that technological globalization is present on a daily basis and is increasingly incorporated progressively in almost all known sectors.These rapid technological advances have allowed the development of various tools that facilitate some of the tasks of different companies, one of which is the Chatbots.This article identifies the important issues on which scholars have focused, and so can serve as a reference for the future research and discussions of chatbots.The first goal we set was to understand what a chatbot is and how they work.The second objective was to know their characteristics, advantages and disadvantages.
The idea of the multiverse: An interdisciplinary perspective

The article discusses modern trends in the development of the idea of the multiverse (plurality of worlds) on the material of natural science. In physics, this is a multi-world interpretation of H. Everett’s quantum mechanics, in biology, the teachings about Umwelt by J. von Uexkul, in cognitive science, the notion of cognitive isolation, subjectively personal coloring, phenomenological certainty of the worlds of cognition and creativity of individuals. It is shown what some conceptual foundations can be offered for finding ways to develop an integrative vision, for building bridges from physics to biology and from biology to social sciences and humanities. Evolutionary epistemology, the conception of autopoiesis, theory of complex systems, and biosemiotics are considered as possible bearing conceptual nodes for interdisciplinary synthesis. These theoretical conceptions make it possible to tentatively explain why there are many worlds and why they are separated from each other, cognitively closed in the living nature. Leibniz’s ideas about possible worlds, a wealth of potentialities, subtle connections and eventual interweaving of worlds and their compossibility and self-reference turn out to be very relevant today. Various realizations of the world that arise as a result of measuring particles states in quantum mechanics, the worlds (umwelts) of living organisms, semantic worlds in human creative activity are just options in which nature expresses itself, reads itself, calculates itself, correlates with itself, and there are countless options.
Building Shared Understanding with THUNDERS

Collaborative work focuses on the interaction between a group of participants and the corresponding search to achieve a better-shared understanding of a problem, while they perform common tasks cooperating towards the achievement of the same objective. In this search for shared understanding, the differences in the experiences, knowledge, and other characteristics of the participants, make it complex to consider all opinions, points of view and also, that everyone understands and agrees with the topic of the activity and with the central idea of what is going to be solved. While trying to tackle these problematics, we defined THUNDERS, a process for building shared understanding in problem-solving activities. This work presents THUNDERS process as well as the quality validation of its process model SPEM 2.0, and an experiment with which its viability and usefulness was validated. The THUNDERS process enables to design, execute, and verify collaborative activities by using a set of phases, activities, tasks, work products, roles, and workflows in a planned way. The multi-cycle action-research methodology with bifurcation was used in the construction of the process. According to the results of the validations, it can be said that THUNDERS is feasible and useful for the building of shared understanding. However, some aspects to be improved were identified, such as the cognitive overload generated by its use and some elements of its formalization in SPEM; likewise, the need to incorporate mechanisms to monitor and assist the permanence of understanding throughout the execution of the activity was identified.
An improved hybrid semi-stacked autoencoder for item-features of recommendation system (iHSARS)

In recent years, information overload has become a phenomenon where it makes people difficult to filter relevant information. To address issues such as high-dimensional data, cold start, and data sparsity, semi-autoencoder is one of the unsupervised deep learning methods used in the recommendation systems. It is particularly useful for reducing data dimensions, capturing latent representations, and flexibly reconstructing various parts of input data. In this article, we propose an improved hybrid semi-stacked autoencoder for item-features of recommendation system (iHSARS) framework. This method aims to show better performance of the hybrid collaborative recommendation via semi-autoencoder (HRSA) technique. Two novel elements for iHSARS’s architecture have been introduced. The first element is an increase sources of side information of the input layer, while the second element is the number of hidden layers has been expanded. To verify the improvement of the model, MovieLens-100K and MovieLens-1M datasets have been applied to the model. The comparison between the proposed model and different state-of-the-art methods has been carried using mean absolute error (MAE) and root mean square error (RMSE) metrics. The experiments demonstrate that our framework improved the efficiency of the recommendation system better than others.
MST-GNN: A Multi-scale Temporal-Enhanced Graph Neural Network for Anomaly Detection in Multivariate Time Series

Anomaly detection in time is an important task in many applications. Sensors are deployed in the industrial site to monitor the condition of different attributes or different places in real time, which generate multivariate time series. Recently, many methods were proposed to detect anomalies with multivariate time series, but they focused on the sequence attributes or spatial and temporal correlation, ignoring the characteristic of single sensor time series. In this paper, we propose a novel model MST-GNN that builds each sensor representation from Multi-Scale Temporal (MST) view, and use Graph Neural Network to mine their latent correlation to improve the performance of anomaly detection. In the MST representation, shapelets learning is introduced to extract its distinguishing features, a recurrent-skip neural network is used to extract the local temporal dependence relationship, and the raw data retains the original features of time series. These three features are fused to form the multi-scale temporal-enhanced features. Subsequently, the graph neural network is adopted to capture the potential interdependencies between multivariate time series and obtain the optimal representation of time series. Finally, bias assessment and anomaly detection are carried out. Extensive experiments on real-world datasets show that MST-GNN outperforms other state-of-the-art methods consistently, which provides an effective solution for anomaly detection in multivariate time series.
Relationship between promotion and employees' performance: Evidence from the university of Abuja

This study investigated the relationship between promotion and employees' performance at the University of Abuja. The study utilized a descriptive research design. The multiple regression methodology and descriptive statistics were employed for the analysis of data. The data was derived through structured questionnaires from 337 workers at the University of Abuja. The findings indicated that the university treats you fairly and equitably with regard to promotion, the university provides opportunities for career development and the university provides promotional opportunities had a positive influence on employees’ performance at the University of Abuja. Conversely, the university is fair and equitable in its treatment of management had a negative impact on employees’ performance at the University of Abuja. However, the university provides clear and consistent requirements for promotion, the university supports ongoing professional development, the university acknowledges the long hours you devote to work and the university rewards excellence in performance through a promotion system did not exert a significant effect on employees’ performance at the University of Abuja. The University treats you fairly and equitably with regard to promotion, the university provides opportunities for career development and the university provides promotional opportunities had a positive influence on employees’ performance at the University of Abuja. However, the university is fair and equitable in its treatment of management had a negative impact on employees’ performance at the University of Abuja. The results suggest that the management of the University of Abuja should continue treating their employees fairly concerning promotion to increase performance. In addition, the university management should sustain their policies on promotions and career development. Furthermore, the management of the University of Abuja needs to review and reinforce the way it handles its management.
PSO Adaptive Fading Memory Kalman Filter Based State Estimation of Indoor Thermal Model with Unknown Inputs

An adaptive filtering approach is proposed in this paper to address the thermal state estimation methodology along with the model parameters jointly for an indoor thermodynamic resistance capacitance model with uncertain stochastic heating inputs. The adaptive dynamics of the state of the model is combined with a particle swarm optimization (PSO) based metaheuristic approach to feed the knowledge of measurement noise statistics and the initial estimation error covariance along with forgetting factor for implementation of fading memory Kalman filter (FMKF). This study has been carried out with the variation of uncertain influential input information to enhance the estimation efficiency with the proposed PSO adaptive FMKF (PSO-AdFMKF) strategy for a real life the test thermodynamic environment scenario inside the building space. Potential observations demonstrate that the proposed estimation algorithm performs encouragingly, with a satisfactory improvement of estimation performance in terms of evaluating error metrics.