Reviewerly/exHarmony huggingface dataset:
Bahan Ajar Digital Kerjaan Kuningan dengan Model Inquiry untuk Meningkatkan Literasi Sejarah Siswa

History in the twenty-first century, which must be oriented towards digital teaching materials. Historical learning can be drawn from some of the experiences that teachers do in the classroom. This research aims to explain the teaching materials used, explain the procedures for the development of digital teaching materials, test the effectiveness of the development of digital teaching materials. This development research procedure uses Research and Development (RND) with addie development model, data collection is done using questionnaires, observations, interviews and tests. The results of this study showed that the learning model used in Sma Negeri 3 Kuningan is still conventional and only uses one textbook, then the use of power points is less than optimal. The use of ADDIE design development products consists of five stages. The first analysis was conducted to analyze the need for digital teaching materials to improve the historical literacy of students at Sma Negeri 3 Kuningan. Both designs or product design stages consisting of the history of the Kingdom of Kuningan, the kings - kings who first occupied kuningan territory, and the teachings of madrais religion in the Cigugur area of Kuningan regency, syllabus adjustment, achievement of indicators, and RPP. The third development or product development stage is the process of validating product trial experts and conducting trials. And the fourth, implementation or implementation stage is the implementation of the final product or final product; and five evaluation or evaluation stages of effectiveness tests in experimental classes and control classes. The effectiveness of the Kuningan Kingdom teaching materials showed that the results of the historical literacy test with a signification value of 0.001&lt;0.05. The results show that there is a significant influence on the use of digital teaching materials of the Kingdom of Kuningan during the Dutch East Indies through the inquiry model to improve historical literacy.
High-sensitivity magnetometry at room temperature with post-processed optical readout of single NV-centres

Optical readout from nanofabricated single NV centres is enhanced via Bayesian inference techniques, to demonstrate efficient magnetometry at room temperature conditions. We achieve experimentally Heisenberg-limited sensitivities O(100 nT s1/2), thus competing with state-of-art cryogenic set-ups.
A simple transform domain-based low level primitives preserving texture synthesis

In this work, a new patch-based texture synthesis scheme with orthogonal polynomials model coefficients is presented. The proposed scheme has four phases. In the first phase, a block matching technique that identifies a best match, to synthesis in the output image of bigger size is designed in terms of ordered orthogonal polynomials model coefficients. In case of successful match of block, called patch-hit, the proposed scheme finds candidate blocks with triangular search, in the next phase. In the patch selection phase, the proposed scheme considers a subset of orthogonal polynomials model coefficients among the blocks, for the purpose of synthesis which consumes less memory and time. This synthesised output is smoothened in the final phase, by preserving the low level contents between the synthesised patches. The performance of the proposed scheme is measured with energy, contrast, correlation, homogeneity and entropy between the original and synthesised images and is also compared with existing texture synthesis schemes. The results are encouraging.
An exploration of the pattern of managing volunteers in the Construction Jihad

The purpose of this study was to explore the pattern of managing volunteer forces in Construction Jihad. To achieve this, two strategies, namely, the grounded theory and interpretive structural modelling were used. The present research is a developmental one in terms of nature, an idealist and pragmatic in terms of ontology and is based on subjective interpretations and objective knowledge in terms of epistemology. The research population consist two sections of the Construction Jihad organisation managers and employees. The findings of the research led to the identification of 903 primary codes in the form of 82 concepts. In the next step, the concepts were reduced to 17 more abstract categories. Then, the type of relationship between these elements was evaluated in pairs by the experts and based on that, the final pattern of the research was drawn up. In this pattern, the categories of religious values, empathic leadership, and trust are elements that affect other elements; however, they themselves are not affected, which is why they were plotted in the first level.
Complex crystals

In response to Peter Hannaford and Krzysztof Sacha’s feature “Time crystals enter the real world of condensed matter” (March 2020) in which the authors look at how exotic time crystals – which break time-translational symmetry rather than spatial symmetry – could have applications in condensed-matter devices.
Data Visualization

One of the most appealing elements for RStudio users is the rich library of data visualization tools that are available. In this chapter, we are going to highlight major graphic tools that will inform most of your daily work. Of course, in the R ecosystem we have hundreds of additional libraries and specialized packages that may be used for visualization.
Data Mining Prediction Production of Water Needs using Simple Linear Regression Algorithms

Water is a natural resource that becomes the basic human needs. The city of Lubuklinggau every day provides the need for clean water to the community through PDAM Regional company Tirta Bukit Sulap. Clean water production is based on the analysis of water consumption by the community every month in advance. So, there is often a clean water delivery due to lack of clean water production. Also, the need for clean water will always increase with the increasing customer every month. Therefore, to be able to ensure the availability of clean water in the city of Lubuklinggau then need to be analyzed to predict the need for clean water in the city of each him is by applying data mining for prediction of needs Clean water by using simple linear regression algorithms. By applying data mining, clean water needs can be predicted for the next few years, making it easier for decision-makers to determine the volume of clean water produced. So the public can utilize clean water according to the needs.
Probabilistic Classifiers

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A Picture You Can Handle: Infants Treat Touch-Screen Images More Like Photographs than Objects

Infants actively explore their world in order to determine the different ways in which they can interact with various objects.Although research on infant perception has focused on how infants understand the differences between 2-and 3-dimensional objects, today's infants increasingly encounter 2D images with interactive qualities on smart-phone screens, tablets, and laptops.The purpose of this experiment was to examine the types of manual behaviors infants direct towards tablet images and to compare these actions to those evoked by 2D photographs or 3D when tactile feedback is controlled.Infants between the ages of 7-10 months sat on their parent's lap in front of a table with a built-in well covered by a clear, plastic sheet while the three types of displays (photographs, objects, and screen images on a tablet) were presented for 30 seconds each.Infants saw three examples of each type of display presented in the built-in well so that tactile feedback information from the different displays was controlled.Coders noted the proportion of trials in which infants grasped, scratched, rubbed, or patted the display.Results indicate that infants direct significantly more grasps, scratches, and rubs towards 3D objects than 2D photographs.Infants also direct more grasps to objects compared to screen images.Our data suggests that infants are treating screen images more similarly to 2D photographs than 3D objects.
Robust Modelling to Jointly Address Security Threats, Bandwidth Utilization, and Energy issues in IoT

The advent of Internet-of-Things (IoT) revolutionizes the mechanism of connectivity of massive chains of machine offering better improvement in data communication and analytical services. Review of existing approaches towards IoT performance improvement shows that there are still larger scope of enhancing the present solution. The proposed system introduces a mechanism where 3 different problems has been jointly addressed viz. security issues, channel capacity issues, energy depletion issues. The proposed model uses analytical research methodology where energy problem is offered common focus and linked with other two problem using stochastic method, probability logic, decision-making approach, packet scheduling, etc. The simulation outcome of the study shows that proposed system offers better energy efficiency, better resistivity against threats, and effective optimization of the bandwidth in contrast to existing related mechanism towards IoT ecosystem.
Flooding

Africa Research Bulletin: Economic, Financial and Technical SeriesVolume 56, Issue 11 p. 22788A-22788C Policy and Practice Flooding First published: 02 January 2020 https://doi.org/10.1111/j.1467-6346.2019.09243.xRead the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume56, Issue11January 2020Pages 22788A-22788C RelatedInformation
A Multi Factor Duplicate Image Deduplication System

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Development of Touch Sensor Using Ion Gel

The polymer gel is composed of polymeric chains which are crosslinked in a three-dimensional (3D) network structure and liquid which is widely applied to tissue engineering, medical treatment, and solid state electrochemical devices. The gel contains a large amount of water inside the three-dimensional network structure. The human body is also mostly composed of water, hence, the percentage of water in human body is relatively high similar to hydrogel. However, water contained in the gel evaporates in the air atmosphere. Among various kind of gel, ion gel possessing ionic liquid is beneficial for applications since ionic liquids are liquid salts at room temperature and have useful properties such as non-volatility, non-inflammability, electrochemical stability, thermal stability and high ionic conductivity. In this study, we investigated the piezoelectric properties of ion gels to apply them for touch and pressure sensors in soft-matter robotics. In this study, the used ion gel was made utilizing thiol-ene reaction and was composed of thiol monomer. The thiol-ene reaction is hardly influenced by the surrounding environment, hence, we can easily make ion gel. In addition, it is possible to form any objects using a photopolymerization initiator and a 3D printer. The aim of this research is to develop a tactile sensor using ion gel having non-volatility, non-flammability, thermal stability, high conductivity and softness close to humans. The sensor is composed of ion gel and silicone rubber. It was made by injecting an ion gel into silicone rubber. The changes of impedance is measured by the change of shape of the ion gel due to the pressure. The sensor was tested with a press tester and an LCR meter for pressure sensing. It shows 521 kPa in maximum pressure sensing. Currently, we are working on three-dimensional modeling of the sensor by using an ion gel and an insulating gel instead of using a mold. The technique shown in this study is expected to be applied to soft robot and robot hand.
The impact of different types of service robots usage in hotels on guests’ intention to stay

Purpose The purpose of this study is to examine how consumers perceive the importance of using robot technologies for 12 services evaluated under two categories considering the technology acceptance model (TAM). Design/methodology/approach The conceptual model analysis used structural equation modeling with the partial least squares estimation method, considering 638 responses. Findings The results revealed that the perceived importance (PI) of robotic service delivery tasks under “room division” and “food and beverage and secondary services” affect perceived usefulness (PU) and perceived ease of use (PEOU) differently. Besides, PEOU and PU significantly influence attitudes toward using robot-staffed hotels. Research limitations/implications The nonprobability convenience sampling method was used as the data collection method. Future studies that prefer probabilistic methods will open a different perspective for evaluating the results. Practical implications This study’s empirical findings reveal which robot-delivered services are found significant by the customers and contribute to increased customer satisfaction and loyalty. In addition, it guides accurate demand and investment planning for the tourism and hospitality industry in the post-COVID-19 era. Originality/value To the best of the authors’ knowledge, previous literature has not tested or confirmed the effects of PI related to two groups of robotic service delivery tasks on utilitarian variables. This study contributes to the literature by examining how different robotic service delivery tasks are linked to the TAM framework in a hotel setting.
Texture-based Error Analysis for Image Super-Resolution

Evaluation practices for image super-resolution (SR) use a single-value metric, the PSNR or SSIM, to determine model performance. This provides little insight into the source of errors and model behavior. Therefore, it is beneficial to move beyond the conventional approach and reconceptualize evaluation with interpretability as our main priority. We focus on a thorough error analysis from a variety of perspectives. Our key contribution is to leverage a texture classifier, which enables us to assign patches with semantic labels, to identify the source of SR errors both globally and locally. We then use this to determine (a) the semantic alignment of SR datasets, (b) how SR models perform on each label, (c) to what extent high-resolution (HR) and SR patches semantically correspond, and more. Through these different angles, we are able to highlight potential pitfalls and blindspots. Our overall investigation highlights numerous unexpected insights. We hope this work serves as an initial step for debugging blackbox SR networks.
Greedy parameter optimization for diabatic quantum annealing

A shorter processing time is desirable for quantum computation to minimize the effects of noise. We propose a simple procedure to variationally determine a set of parameters in the transverse-field Ising model for quantum annealing (QA) appended with a field along the [Formula: see text]-axis. The method consists of greedy optimization of the signs of coefficients of the [Formula: see text]-field term based on the outputs of short annealing processes. We test the idea in the ferromagnetic system with all-to-all couplings and spin-glass problems, and find that the method outperforms the traditional form of QA and simulated annealing in terms of the success probability and the time to solution, in particular, in the case of shorter annealing times, achieving the goal of improved performance while avoiding noise. The non-stoquastic [Formula: see text] term can be eliminated by a rotation in the spin space, resulting in a non-trivial diabatic control of the coefficients in the stoquastic transverse-field Ising model, which may be feasible for experimental realization. This article is part of the theme issue 'Quantum annealing and computation: challenges and perspectives'.
Factorizing time-heterogeneous Markov transition for temporal recommendation

Temporal recommendation which recommends items to users with consideration of time information has been of wide interest in recent years. But huge event space, highly sparse user activities and time-heterogeneous dependency of temporal behaviors make it really challenging to learn the temporal patterns for high-quality recommendation. In this paper, aiming to handle these challenges, especially the time-heterogeneous characteristic of user's temporal behaviors, we proposed the Neural-based Time-heterogenous Markov Transition (NeuralTMT) model. Firstly, users' temporal behaviors are mathematically simplified as the third-order Markov transition tensors. And then a linear co-factorization model which learns the time-evolving user/item factors from these tensors is proposed. Furthermore, the model is extended to the neural-based learning framework (NeuralTMT), which is more flexible and able to capture time-heterogeneous temporal patterns via nonlinear neural network mappings and attention techniques. Extensive experiments on four datasets demonstrate that NeuralTMT performs significantly better than the state-of-the-art baselines. And the proposed method is fundamentally inspired by factorization techniques, which may also provide some interesting ideas on the connection of tensor factorization and neural-based sequential recommendation methods.
Deterministic Generation of Entangled Photonic Cluster States from Quantum Dot Molecules

Successful generation of photonic cluster states is the key step in the realization of measurement-based quantum computation and quantum network protocols. Several proposals for the generation of such entangled states from different solid-state emitters have been put forward. Each of these protocols come with their own challenges in terms of both conception and implementation. In this work we propose deterministic generation of these photonic cluster states from a spin-photon interface based on a hole spin qubit hosted in a quantum dot molecule. Our protocol resolves many of the difficulties of existing proposals and paves the way for an experimentally feasible realization of highly entangled multi-qubit photonic states with a high production rate.
SMPC-Based Federated Learning for 6G-Enabled Internet of Medical Things

Rapidly developing intelligent healthcare systems are underpinned by Sixth Generation (6G) connectivity, ubiquitous Internet of Things (IoT), and Deep Learning (DL) techniques. This portends a future where 6G powers the Internet of Medical Things (IoMT) with seamless, large-scale, and real-time connectivity amongst entities. This article proposes a Convolutional Neural Network (CNN) based Federated Learning framework that combines Secure Multi-Party Computation (SMPC) based aggregation and Encrypted Inference methods, all within the context of 6G and IoMT. We consider multiple hospitals with clusters of mixed IoMT and edge devices that encrypt locally trained models. Subsequently, each hospital sends the encrypted local models for SMPC-based encrypted aggregation in the cloud, which generates the encrypted global model. Ultimately, the encrypted global model is returned to each edge server for more localized training, further improving model accuracy. Moreover, hospitals can perform encrypted inference on their edge servers or the cloud while maintaining data and model privacy. Multiple experiments were conducted with varying CNN models and datasets to evaluate the proposed framework's performance.
Fine-Grained Egocentric Hand-Object Segmentation: Dataset, Model, and Applications

Egocentric videos offer fine-grained information for high-fidelity modeling of human behaviors. Hands and interacting objects are one crucial aspect of understanding a viewer’s behaviors and intentions. We provide a labeled dataset consisting of 11,243 egocentric images with per-pixel segmentation labels of hands and objects being interacted with during a diverse array of daily activities. Our dataset is the first to label detailed hand-object contact boundaries. We introduce a context-aware compositional data augmentation technique to adapt to out-of-distribution YouTube egocentric video. We show that our robust hand-object segmentation model and dataset can serve as a foundational tool to boost or enable several downstream vision applications, including hand state classification, video activity recognition, 3D mesh reconstruction of hand-object interactions, and video inpainting of hand-object foregrounds in egocentric videos. Dataset and code are available at: https://github.com/owenzlz/EgoHOS .
BATMAN: Bilateral Attention Transformer in Motion-Appearance Neighboring Space for Video Object Segmentation

Video Object Segmentation (VOS) is fundamental to video understanding. Transformer-based methods show significant performance improvement on semi-supervised VOS. However, existing work faces challenges segmenting visually similar objects in close proximity of each other. In this paper, we propose a novel Bilateral Attention Transformer in Motion-Appearance Neighboring space (BATMAN) for semi-supervised VOS. It captures object motion in the video via a novel optical flow calibration module that fuses the segmentation mask with optical flow estimation to improve within-object optical flow smoothness and reduce noise at object boundaries. This calibrated optical flow is then employed in our novel bilateral attention, which computes the correspondence between the query and reference frames in the neighboring bilateral space considering both motion and appearance. Extensive experiments validate the effectiveness of BATMAN architecture by outperforming all existing state-of-the-art on all four popular VOS benchmarks: Youtube-VOS 2019 (85.0%), Youtube-VOS 2018 (85.3%), DAVIS 2017Val/Test-dev (86.2%/82.2%), and DAVIS 2016 (92.5%).