Reviewerly/exHarmony huggingface dataset:
A Conceptual Model of Mobile Augmented Reality for Hearing Impaired Museum Visitors’ Engagement

Many studies have begun to consider how to ensure a pleasant experience during visits to cultural heritage sites and museums. Although, when considering the populace of the visitors to these sites, the hearing impaired (HI) visitors which made up of a smaller percentage, have not been in the literature limelight as much as the normal hearing visitors. Thus, the hearing impaired tends to endure certain unpalatable experiences leading to dissatisfaction of their visits. Literature has shown that Mobile Augmented Reality (MAR) can improve the experiences of visitors to the museum in terms of engagement, enjoyment and learning. This is evident in a number of articles tailored towards normal hearing visitors. However, a recent study has taken into consideration the hearing impaired visitors by identifying the engagement elements of MAR for the HI museum visitors. The identified elements include; aesthetics, interaction, interest, usability, satisfaction, motivation, curiosity, enjoyment, perceived control, self-efficacy, and focused attention. This article thus takes a step further by introducing the MAR for the HI museum visitors’ engagement (MARHIME) conceptual model. These elements are derived from a review of literature which has been done comprehensively and are validated by a panel of experts. Altogether eleven elements went through the expert review process and only six elements were validated to be used for the construction of the MARHIME model. This article also further grounds the justification of these selected six elements in relation to engagement. Future work will include the development of the MARHIME prototype which will be used to validate the model among the hearing impaired visitors at a museum.
Direct Writing Activity: A Strategy in Expanding Narrative Writing Skills for Elementary Schools

Narrative writing learning material in the fourth grade thematic books Curriculum 2013 in Indonesia does not have appropriate writing steps.This causes the learning process to write narrative texts is not carried out properly.Therefore a solution is proposed by developing narrative writing teaching materials based on direct writing activity strategy.This type of study is Research and Development using the ADDIE model.The research data consisted of validity data, practicality data, and effectivity data.The result showed that the validity score of teaching material was 3.79 by category highly valid.The learning process of narrative writing had a score of 3.63 by category very good.The student response score for teaching material was 3.81, while the teacher's response score was 3.78.The mean of the experimental class learning outcomes was 85.27, while the mean of the control class learning outcomes was only 72.64.The statistical test proved a significant difference in the teaching material for narrative writing developed.Based on these results, it can be concluded that the teaching material for narrative writing using direct writing activity is valid, practical, and effective to use.
Kernel-Based Analysis of Massive Data

Dealing with massive data is a challenging task for machine learning. An important aspect of machine learning is function approximation. In the context of massive data, some of the commonly used tools for this purpose are sparsity, divide-and-conquer, and distributed learning. In this paper, we develop a very general theory of approximation by networks, which we have called eignets, to achieve local, stratified approximation. The very massive nature of the data allows us to use these eignets to solve inverse problems such as finding a good approximation to the probability law that governs the data, and finding the local smoothness of the target function near different points in the domain. In fact, we develop a wavelet-like representation using our eignets. Our theory is applicable to approximation on a general locally compact metric measure space. Special examples include approximation by periodic basis functions on the torus, zonal function networks on a Euclidean sphere (including smooth ReLU networks), Gaussian networks, and approximation on manifolds. We construct pre-fabricated networks so that no data-based training is required for the approximation.
Implementation of Online Learning during the Covid-19 Epidemic in Indonesia: Assessment of Higher Education Students' Use and Implementation of Online Learning Technology

By the end of 2019, the Covid-19 virus was causing concern around the world.It quickly claimed Indonesian government attention -especially as the government was already anticipating a spread of online education technology around the world that was already underway.The Indonesian Ministry of Education and Culture appealed to educators for help in the creation of online programs.Researchers have tried to assess the ability of Indonesian tertiary level students to adapt to and implement online education technology.Several Indonesian universities participated in this survey.A Likert 5 item questionnaire is using to construct a "TRI" index (Index of technological preparedness).Data analysis employed descriptive statistics.Results suggest that many tertiary-level students happily agree to online lectures.Respondents were in happy agreement that online education removes inhibitions of time and distance.However, there were quite a several students who expressed anxiety at being unable to connect with necessary networks at specific times of the day.This level of insecurity in the students is different from and separate from any other feelings of dissatisfaction with the course of lectures provided.
Reinforcement Learning via Gaussian Processes with Neural Network Dual Kernels

While deep neural networks (DNNs) and Gaussian Processes (GPs) are both popularly utilized to solve problems in reinforcement learning, both approaches feature undesirable drawbacks for challenging problems. DNNs learn complex non-linear embeddings, but do not naturally quantify uncertainty and are often data-inefficient to train. GPs infer posterior distributions over functions, but popular kernels exhibit limited expressivity on complex and high-dimensional data. Fortunately, recently discovered conjugate and neural tangent kernel functions encode the behavior of overparameterized neural networks in the kernel domain. We demonstrate that these kernels can be efficiently applied to regression and reinforcement learning problems by analyzing a baseline case study.We apply GPs with neural network dual kernels to solve reinforcement learning tasks for the first time. We demonstrate, using the well understood mountain-car problem, that GPs empowered with dual kernels perform at least as well as those using the conventional radial basis function kernel. We conjecture that by inheriting the probabilistic rigor of GPs and the powerful embedding properties of DNNs, GPs using NN dual kernels will empower future reinforcement learning models on difficult domains.
An Empirical Examination of the Effect of TQM Practices on Hospital Service Quality: An Assessment Study in UAE Hospitals

Nowadays, the ability to deliver excellent quality to customers is key to an organizations survival and global competitiveness. Essentially, delivering excellent quality increases customer satisfaction and enhances the productivity of institutions. The implementation of quality improvement initiatives, such as Total Quality Management (TQM), is one way that organizations are seeking to deliver quality services to customers. The main aim of this resaerch paper is to examine the impact of TQM elements on hospital service quality in the United Arab Emirates (UAE), focusing on accredited hospitals in Dubai. The empirical data was collected via a self-administered questionnaire using the drop-off technique. Senior hospital employees filled questionnaires examining two variables: TQM and hospital service quality. Of the 480-questionnaire distributed, 292 usable questionnaires were analyzed, yielding a response rate of 60.8%. Descriptive and inferential statistical analysis methods were used to analyse the data using principle component analysis, Pearson correlation coefficient, and multiple regression analyses. The results in this study reinforce the positive effects of organization culture, continuous improvement, customer focus, teamwork and participation, process management, and top management commitment, as independent variables, on improving the quality of services delivered by hospitals as the dependent variable. It was also found that among the eight TQM implementation factors, organization culture expresses the strongest effect on hospital service quality with highest coefficient value of 0.373. The research paper has contributed to the current TQM literature regarding the association between TQM factors and hospital service quality that will deliver significant knowledge to hospital managers, to enhance their existing TQM practices and successively improve service quality.
Photon-added coherent states using the continuous-mode formalism

The addition of a photon into the same mode as a coherent state produces a nonclassical state that has interesting features, including quadrature squeezing and a sub-Poissonian photon-number distribution. The squeezed nature of photon-added coherent (PAC) states potentially offers an advantage in quantum sensing applications. Previous theoretical works have employed a single-mode treatment of PAC states. Here, we use a continuous-mode approach that allows us to model PAC state pulses. We study the properties of a single-photon and coherent-state wave packet superimposed with variable temporal and spectral overlap. We show that, even without perfect overlap, the state exhibits a sub-Poissonian number distribution, second-order quantum correlations, and quadrature squeezing for a weak coherent state. We also include propagation loss in waveguides and study how the fidelity and other properties of PAC state pulses are affected.
Silver Halides in the Hypergene Zone of the Arkharly Gold Deposit as Indicators of their Formation in Dry and Hot Climate (Dzungar Alatau, Kazakhstan)

Oxidation of the ores from the Arkharly deposit was favored under the conditions of the dry and hot climate in the Paleozoic, leading to dissolution of gold and silver, their migration in acid solutions and further deposition on appropriate geochemical barriers, when opportunity arose for sedimention of hardly soluble salts of chlorides, bromides and iodides.Described are interesting findings of silver halide minerals in the oxidation zone of the Arkharly deposit, in a state of accretions with native silver and gold.Native silver, gold and silver-containing sulphides are sources of silver for halide minerals, as witnessed by their zonal accretions.Among silver halide minerals, identified in thin rims were chlorargyrite, brome iodide, embolite, and iodyrite.The hypergene nature of gold is indicated by its close paragenesis with silver and silver halides.
Engineering Purely Nonlinear Coupling between Superconducting Qubits Using a Quarton

Strong nonlinear coupling of superconducting qubits and/or photons is a critical building block for quantum information processing. Because of the perturbative nature of the Josephson nonlinearity, linear coupling is often used in the dispersive regime to approximate nonlinear coupling. However, this dispersive coupling is weak and the underlying linear coupling mixes the local modes, which, for example, distributes unwanted self-Kerr nonlinearity to photon modes. Here, we use the quarton to yield purely nonlinear coupling between two linearly decoupled transmon qubits. The quarton's zero ${\ensuremath{\phi}}^{2}$ potential enables an ultrastrong gigahertz-level cross-Kerr coupling, which is an order of magnitude stronger compared to existing schemes, and the quarton's positive ${\ensuremath{\phi}}^{4}$ potential can cancel the negative self-Kerr nonlinearity of qubits to linearize them into resonators. This ultrastrong cross-Kerr coupling between bare modes of qubit-qubit, qubit-photon, and even photon-photon is ideal for applications such as single microwave photon detection, ultrafast two-qubit gates, and readout.
A Dynamic Searchable Symmetric Encryption Scheme for Multiuser with Forward and Backward Security

Dynamic Searchable Symmetric Encryption for Multiuser (M-DSSE) is an advanced form of symmetric encryption. It extends the traditional symmetric encryption to support the operations of adding and deleting the encrypted data and allow an authenticated group of data users to retrieve their respective desired encrypted data in the dynamic database. However, M-DSSE would suffer from the privacy concerns regarding forward and backward security. The former allows an attacker to identify the keywords contained in the added data by lunching file-injection attacks, while the latter allows to utilize the search results and the deleted data to learn the content. To our knowledge, these privacy concerns for M-DSSE have not been fully considered in the existing literatures. Taking account of this fact, we focus on the dynamic searchable symmetric encryption for multiuser meeting the needs of forward and backward security. In order to propose a concrete scheme, the primitives of Pseudorandom Functions (PRF) and the Homomorphic Message Authenticator (HMAC) are employed to construct the inverted index and update the search token. The proposed scheme is proven secure in the random model. And the performance analysis shows that the proposed scheme achieves the enhanced security guarantees at the reasonable price of efficiency.
A Survey of Time Series Data Visualization Methods

In the era of big data, the general public is more likely to access big data, but they wouldn't like to analyze the data.Therefore, the traditional data visualization with certain professionalism is not easy to be accepted by the general public living in the fast pace.Under this background, a new general visualization method for dynamic time series data emerges as the times require.Time series data visualization organizes abstract and hard-to-understand data into a form that is easily understood by the public.This method integrates data visualization into short videos, which is more in line with the way people get information in modern fast-paced lifestyles.The modular approach also facilitates public participation in production.This paper summarizes the dynamic visualization methods of time series data ranking, studies the relevant literature, shows its value and existing problems, and gives corresponding suggestions and future research prospects.
Mask R-CNN for Geospatial Object Detection

Geospatial imaging technique has opened a door for researchers to implement multiple beneficial applications in many fields, including military investigation, disaster relief, and urban traffic control. As the resolution of geospatial images has increased in recent years, the detection of geospatial objects has attracted a lot of researchers. Mask R-CNN had been designed to identify an object outlines at the pixel level (instance segmentation), and for object detection in natural images. This study describes the Mask R-CNN model and uses it to detect objects in geospatial images. This experiment was prepared an existing dataset to be suitable with object segmentation, and it shows that Mask R-CNN also has the ability to be used in geospatial object detection and it introduces good results to extract the ten classes dataset of Seg-VHR-10.
TLBO Optimization Algorithm Based-Type2 Fuzzy Adaptive Filter for ECG Signals Denoising

A novel type2-fuzzy adaptive filter is presented, which uses the concepts of type2-fuzzy logic, for electrocardiogram signals denoising.Type2-fuzzy adaptive filter is an information processor where both numerical and linguistic information are used as input-output pairs and fuzzy if-then rules, respectively.The proposed approach is based on an iterative procedure to achieve acceptable information extraction in the case where the statistical characteristics of the input-output signals are unknown.The proposed filter is presented as a dual-layered feedback system.Each layer has different function, the first layer being the type2-fuzzy autoregressive filter model.The second layer being responsible for training the membership function parameters.The second layer adjusts the type2-fuzzy adaptive filter parameters by using a teaching learning-based optimization algorithm (TLBO), which will allow the reaching of the required signal reconstruction by decreasing the criterion function.The proposed filter is validated and evaluated through some experimentations using the MIT-BIH ECGs databases where various artifacts were added to the ECGs signals; these included real and artificial noise.For comparison purposes, both model and non-modelbased methods recently published are used.Furthermore, the effect of the proposed filter on the malformation of diagnostic features of the ECG was studied and compared with several benchmark schemes.The results show that the proposed method performs better denoising for all noise power levels and for a different criteria viewpoint.
Performance Analysis of Ml Techniques for Spam Filtering

The rise in the volume of unwanted spam emails has made the development of a lot more necessary more reliable and robust filters for antispam.Current machine learning approaches are used to excel Spam emails can be detected and filtered.Filtering solutions to text spam.The analysis discusses core principles, actions, efficacy, and Spam filtering trend for research.The first topic in the research study aims at the requests Machine learning approaches for the operation of filters of spam by the leading providers of internet infrastructure (ISPs) The increasing quantity of unnecessary bulk email (also called spam) has generated a secure need Filters for anti-spam.Then the review compares the strengths and disadvantages of existing methods of machine learning and open research Spam handling problems.As future strategies suggested extreme leaning and strongly opposed schooling that can handle the danger of spam emails effectively.
Towards Knowledge Uncertainty Estimation for Open Set Recognition

Uncertainty is ubiquitous and happens in every single prediction of Machine Learning models. The ability to estimate and quantify the uncertainty of individual predictions is arguably relevant, all the more in safety-critical applications. Real-world recognition poses multiple challenges since a model’s knowledge about physical phenomenon is not complete, and observations are incomplete by definition. However, Machine Learning algorithms often assume that train and test data distributions are the same and that all testing classes are present during training. A more realistic scenario is the Open Set Recognition, where unknown classes can be submitted to an algorithm during testing. In this paper, we propose a Knowledge Uncertainty Estimation (KUE) method to quantify knowledge uncertainty and reject out-of-distribution inputs. Additionally, we quantify and distinguish aleatoric and epistemic uncertainty with the classical information-theoretical measures of entropy by means of ensemble techniques. We performed experiments on four datasets with different data modalities and compared our results with distance-based classifiers, SVM-based approaches and ensemble techniques using entropy measures. Overall, the effectiveness of KUE in distinguishing in- and out-distribution inputs obtained better results in most cases and was at least comparable in others. Furthermore, a classification with rejection option based on a proposed combination strategy between different measures of uncertainty is an application of uncertainty with proven results.
Effective Unsupervised Domain Adaptation with Adversarially Trained Language Models

Recent work has shown the importance of adaptation of broad-coverage contextualised embedding models on the domain of the target task of interest.Current self-supervised adaptation methods are simplistic, as the training signal comes from a small percentage of randomly masked-out tokens.In this paper, we show that careful masking strategies can bridge the knowledge gap of masked language models (MLMs) about the domains more effectively by allocating self-supervision where it is needed.Furthermore, we propose an effective training strategy by adversarially masking out those tokens which are harder to reconstruct by the underlying MLM.The adversarial objective leads to a challenging combinatorial optimisation problem over subsets of tokens, which we tackle efficiently through relaxation to a variational lower-bound and dynamic programming.On six unsupervised domain adaptation tasks involving named entity recognition, our method strongly outperforms the random masking strategy and achieves up to +1.64 F1 score improvements.
Criteria for Site Selection of Solar Parks in Bangladesh: A Delphi-AHP Analysis

The demand for electricity is increasing fast in Bangladesh to sustain rapid economic growth. The largest share of electricity in Bangladesh is produced by fossil fuel powered electricity generation plants. Though solar energy has high potential in Bangladesh due to favorable geographical location, very little initiative has been taken to develop the solar energy sector. At present, the Bangladesh government’s approval criteria for site selection of solar energy parks does not have any scientific basis. Consequently, the present study was undertaken to develop the site selection criteria for solar parks in Bangladesh. Delphi method with AHP was used to determine the criteria and its weightage for site selection of solar energy park. Two rounds of questionnaire for the Delphi method were conducted: in the first, the criteria list was formulated, and in the second, the weightage of the criteria was finalized. The finalized criteria were: land cover (i.e., vegetation, water bodies, build up area, bare land), land slope, surface solar irradiance, land surface temperature, and location of power sub-stations. Among these, land cover and location of power sub-stations were found to have the highest significance for determining suitable locations for solar energy parks in regard to Bangladesh. DOWNLOAD HISTORY | This article has been downloaded 43 times in Digital Commons before migrating into this platform.
Topological Photonics for Optical Communications and Quantum Computing

The ongoing digital transformation is bringing a pervasive diffusion of ultra-broadband, fixed-mobile connectivity, the deployment of cloud-native Fifth Generation (5G) infrastructures, edge and fog computing and a wide adoption of artificial intelligence. This transformation will have far-reaching techno-economic impacts on our society and industry. Nevertheless, this transformation is still laying its foundation in electronics and the impending end of Moore’s law. Therefore, looking at the future, a rethinking of the ways of doing computations and communications has already started. An extended adoption of quantum technologies is one possible direction of innovation. As a matter of fact, a first quantum revolution, started decades ago, has already brought quantum technologies into our daily lives. Indeed, today, a second revolution seems to be underway, exploiting advancements in the ability to detect and manipulate single quantum objects (e.g., photons, electrons, atoms and molecules). Among the different technological approaches, topological photonics is a rapidly growing field of innovation. Drawing inspiration from the discovery of the quantum Hall effect and topological insulators in condensed matter, recent advances in topological photonics hold a promising opportunity for optical networking and quantum computing applications.