Reviewerly/exHarmony huggingface dataset:
Mukbang- and Cookbang-watching status and dietary life of university students who are not food and nutrition majors

As watching food-related programs has become very popular among the young generation in Korea, this study sought to compare the Mukbang- and Cookbang-watching status of university students with their dietary life.The participants were 380 students who were not majoring in food and nutrition at a university in Gyeonggi, Korea. Based on self- reports, the participants were grouped according to their frequency of watching Mukbang or Cookbang: frequent-watching (FW) 21.1% and 5.3%, respectively; moderate-watching (MW) 43.9% and 27.9%, respectively; and not-watching (NW) 35.0% and 66.8% respectively.In the FW group, up to 88.8% and 70.0% of participants reported watching Mukbang and Cookbang, respectively, ≥ 3 days/week. Almost all participants in the FW and MW groups reported intention to keep watching these shows. The most frequent watching route was "YouTube" and the most important criterion to select a program was "food". In the case of Mukbang, but not Cookbang, the participants in the FW group scored their diet significantly worse than those in the NW group (P < 0.05). A greater proportion of participants felt that watching Cookbang improved their diets rather than worsened them (14.3% vs. 0.8%, respectively), while more participants said that watching Mukbang worsened their diets rather than improved them (8.1% vs. 2.4%, respectively). In both cases, greater differences were shown in the FW groups compared to the MW groups (P < 0.05 and P < 0.01 for Cookbang and Mukbang, respectively). Moreover, the participants answered that Mukbang-watching prompted them to eat more of less desirable food, such as through eating out and purchasing convenient and delivered foods, whereas Cookbang-watching made them want to cook more of their own food.Our results suggested that Korean university students who frequently watch Mukbang, but not Cookbang, may be a nutritionally vulnerable group that needs attention.
Coalitions’ Weights in a Dispersed System with Pawlak Conflict Model

Abstract The article addresses the issues related to making decisions by an ensemble of classifiers. Classifiers are built based on local tables, the set of local tables is called a dispersed knowledge. The paper discusses a novel application of Pawlak analysis model to examine the relations between classifiers and to create coalitions of classifiers. Each coalition has access to some aggregated knowledge on the basis of which joint decisions are made. Various types of coalitions are formed—a strong coalitions consisting of a large number and significant classifiers, and a weak coalitions consisting of insignificant classifiers. The new contributions of the paper is a systematical investigation of the weights of coalitions that influence the final decision. Four different method of calculating the strength of the coalitions have been applied. Each of these methods consider another aspect of the structure of the coalitions. Generally, it has been experimentally confirmed that, for a method that correctly identifies the relations between base classifiers, the use of coalitions weights improves the quality of classification. More specifically, it has been statistically confirmed that the best results are generated by the weighting method that is based on the size of the coalitions and the method based on the unambiguous of the decisions.
Fuzzy Keyword Matching Using N-Gram and Cryptographic Approach Over Encrypted Data in Cloud

Due to widespread use of cloud computing, there is more and more data is being stored over cloud by the users every day in such case finding a solution for easy, secure and safe way to handle the data is necessary. So I have given the solution for such problem using some algorithms like AES encryption and similarity calculating algorithms. So, instead of searching directly using keyword here in this paper the approach is different. Here, I am using searching over the encrypted data which is much secure than the normal searching. In this project, I will be using AES encryption algorithm for encryption and Jaccard coefficient to find the similarity between the keywords. So after giving keyword, the encrypted keywords can be searched and user will be able to find the file over cloud. In this project, I have implemented and tested the solution to the problem of secure cloud computing. By using the fuzzy keyword searching over encrypted cloud computing and taking care of safety and secrecy of data files. This fuzzy keyword searching significantly increases the efficiency and safety over cloud. This is user friendly and easy to manage and require less resource. And the results are accurate enough to get exact files searched by the user. The efficiency is increased by approximated computing and it works great. In this solution, Jaccard coefficient is being used to calcite similarity and used two advanced techniques to make fuzzy keyword set, which achieves the great results. Where n-gram algorithm is used to generate the set of different length n-grams of any keyword which is going to be encrypted and is being stored over cloud. I have done security analysis and found out that this system is secure and reliable for modern cloud computing operations and file storage. This system achieves its proposed goal efficiently and results are shown for same.
Secure Face Recognition in Edge and Cloud Networks: From the Ensemble Learning Perspective

Offloading the computationally intensive workloads to the edge and cloud not only improves the quality of computation, but also creates an extra degree of diversity by collecting information from devices in service, which, in turn, has raised significant concerns on privacy as the aggregated information could be misused without the permission by the third party. Sparse coding, which has been successful in computer vision, is finding application in this new domain. In this paper, we develop a secure face recognition framework to orchestrate sparse coding in edge and cloud networks. Specifically, 1). To protect the privacy, we develop a low-complexity encrypting algorithm based on random unitary transform, where its influence on dictionary learning and sparse representation is analysed. We further prove that such influence will not affect the accuracy of face recognition. 2). To fully utilize the multi-device diversity, we extract deeper features in an intermediate space, expanded according to the dictionaries from each device, and perform classification in this new feature space to combat the noise and modeling error.
A Model of Double Descent for High-Dimensional Logistic Regression

We consider a model for logistic regression where only a subset of features of size p is used for training a linear classifier over n training samples. The classifier is obtained by running gradient-descent (GD) on the logistic-loss. For this model, we investigate the dependence of the classification error on the overparameterization ratio κ = p/n. First, building on known deterministic results on convergence properties of the GD, we uncover a phase-transition phenomenon for the case of Gaussian features: the classification error of GD is the same as that of the maximum-likelihood (ML) solution when κ <; κ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sub> , and that of the max-margin (SVM) solution when κ > κ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sub> . Next, using the convex Gaussian min-max theorem (CGMT), we sharply characterize the performance of both the ML and SVM solutions. Combining these results, we obtain curves that explicitly characterize the test error of GD for varying values of κ. The numerical results validate the theoretical predictions and unveil “double-descent” phenomena that complement similar recent observations in linear regression settings.
Quick Boot of Trusted Execution Environment With Hardware Accelerators

The Trusted Execution Environment (TEE) offers a software platform for secure applications. The TEE offers a memory isolation scheme and software authentication from a high privilege mode. The procedure uses different algorithms such as hashes and signatures, to authenticate the application to secure. Although the TEE hardware has been defined for memory isolation, the security algorithms often are executed using software implementations. In this paper, a RISC-V system compatible with TEEs featuring security algorithm accelerators is presented. The hardware accelerators are the SHA-3 hash and the Ed25519 elliptic curve algorithms. TileLink is used for the communications between the processor and the register of the accelerators. For the TEE boot, the software procedures are switched with the accelerated counterpart. Comparing to the software approach, a 2.5-decade increment is observed in the throughput of the signature procedure using the SHA-3 acceleration for big chunks of data. The Ed25519 performs 90% better compared to the software counterpart in execution times.
Interpretation of Integrated Aerial Geophysical Surveys by Unmanned Aerial Vehicles in Mining: a Case of Additional Flank Exploration

Abstract Low-attitude remote Earth sensing (RES) performed by unmanned aerial vehicles (UAV) helps run geological surveys more efficiently; aeromagnetic surveys have been on rise recently. UAV surveying can be made significantly more informative and cost-effective by switching from monomethod surveying to integrated efforts that combine multiple complementary methods. However, how to run such surveys efficiently is a question barely covered in special literature. This paper uses evidence from a site in Bodaybinsky District (Eastern Siberia, Russia), a promising black-shale gold mineralization site, to analyze a typical geological exploration case, namely additional exploration of underexplored areas near a known deposit. It considers a method for integrated UAV geophysical surveying and its interpretation; this multi-method approach comprises aeromagnetic surveys, gamma radiometry, and multispectral photography. Emphasis is made on how important correct interpretation of geophysical data is for the latter. The paper shows that such photography is necessary for identifying man-affected segments in the area and for assessing the distribution of vegetation in it; this helps adjust the gamma background charts. Use of integrated low-altitude surveys taken from UAVs helps quickly and cheaply identify potential gold-bearing sites by a set of indirect features. Exploratory drilling has proven the approach effective.
Unsupervised Intra-domain Adaptation for Semantic Segmentation through Self-Supervision

Convolutional neural network-based approaches have achieved remarkable progress in semantic segmentation. However, these approaches heavily rely on annotated data which are labor intensive. To cope with this limitation, automatically annotated data generated from graphic engines are used to train segmentation models. However, the models trained from synthetic data are difficult to transfer to real images. To tackle this issue, previous works have considered directly adapting models from the source data to the unlabeled target data (to reduce the inter-domain gap). Nonetheless, these techniques do not consider the large distribution gap among the target data itself (intra-domain gap). In this work, we propose a two-step self-supervised domain adaptation approach to minimize the inter-domain and intra-domain gap together. First, we conduct the inter-domain adaptation of the model; from this adaptation, we separate the target domain into an easy and hard split using an entropy-based ranking function. Finally, to decrease the intra-domain gap, we propose to employ a self-supervised adaptation technique from the easy to the hard split. Experimental results on numerous benchmark datasets highlight the effectiveness of our method against existing state-of-the-art approaches. The source code is available at https://github.com/feipan664/IntraDA.git.
How much off-the-shelf knowledge is transferable from natural images to pathology images?

Deep learning has achieved a great success in natural image classification. To overcome data-scarcity in computational pathology, recent studies exploit transfer learning to reuse knowledge gained from natural images in pathology image analysis, aiming to build effective pathology image diagnosis models. Since transferability of knowledge heavily depends on the similarity of the original and target tasks, significant differences in image content and statistics between pathology images and natural images raise the questions: how much knowledge is transferable? Is the transferred information equally contributed by pre-trained layers? If not, is there a sweet spot in transfer learning that balances transferred model's complexity and performance? To answer these questions, this paper proposes a framework to quantify knowledge gain by a particular layer, conducts an empirical investigation in pathology image centered transfer learning, and reports some interesting observations. Particularly, compared to the performance baseline obtained by a random-weight model, though transferability of off-the-shelf representations from deep layers heavily depend on specific pathology image sets, the general representation generated by early layers does convey transferred knowledge in various image classification applications. The trade-off between transferable performance and transferred model's complexity observed in this study encourages further investigation of specific metric and tools to quantify effectiveness of transfer learning in future.
Satellite Broadcasting Enabled Blockchain Protocol: A Preliminary Study

Low throughput has been the biggest obstacle of large-scale blockchain applications. During the past few years, researchers have proposed various schemes to improve the systems' throughput. However, due to the inherent inefficiency and defects of the Internet, especially in data broadcasting tasks, these efforts all rendered unsatisfactory. In this paper, we propose a novel blockchain protocol which utilizes the satellite broadcasting network instead of the traditional Internet for data broadcasting and consensus tasks. An automatic resumption mechanism is also proposed to solve the unique communication problems of satellite broadcasting. Simulation results show that the proposed algorithm has a lower communication cost and can greatly improve the throughput of the blockchain system. Theoretical estimation of a satellite broadcasting enabled blockchain system's throughput is 6,000,000 TPS with a 20 gbps satellite bandwidth.
An Open Source Environment for an Agile Development Model

Tools are of paramount importance in automating software engineering tasks; although the Agile Manifesto prefers "individuals and their interactions over processes and tools", some agile development activities make no exception and can be automated effectively and successfully. In process frameworks like Scrum or similar ones some activities are in fact quite structured and need specific tool support. Hence, it is interesting to study the combination of specific agile practices with OSS tools.
Scaled Vecchia Approximation for Fast Computer-Model Emulation

.Many scientific phenomena are studied using computer experiments consisting of multiple runs of a computer model while varying the input settings. Gaussian processes (GPs) are a popular tool for the analysis of computer experiments, enabling interpolation between input settings, but direct GP inference is computationally infeasible for large datasets. We adapt and extend a powerful class of GP methods from spatial statistics to enable the scalable analysis and emulation of large computer experiments. Specifically, we apply Vecchia’s ordered conditional approximation in a transformed input space, with each input scaled according to how strongly it relates to the computer-model response. The scaling is learned from the data by estimating parameters in the GP covariance function using Fisher scoring. Our methods are highly scalable, enabling estimation, joint prediction, and simulation in near-linear time in the number of model runs. In several numerical examples, our approach substantially outperformed existing methods.Keywordscomputer experimentFisher scoringGaussian processmaximin orderingnearest neighborssparse inverse CholeskyMSC codes62-0862M3062P30
Snapshot 3D reconstruction of liquid surfaces

In contrast to static objects, liquid structures such as drops, blobs, as well as waves and ripples on water surfaces are challenging to image in 3D due to two main reasons: first, the transient nature of those phenomena requires snapshot imaging that is fast enough to freeze the motion of the liquid. Second, the transparency of liquids and the specular reflections from their surfaces induce complex image artefacts. In this article we present a novel imaging approach to reconstruct in 3D the surface of irregular liquid structures that only requires a single snapshot. The technique is named Fringe Projection - Laser Induced Fluorescence (FP-LIF) and uses a high concentration of fluorescent dye in the probed liquid. By exciting this dye with a fringe projection structured laser beam, fluorescence is generated primarily at the liquid surface and imaged at a backward angle. By analysing the deformation of the initial projected fringes using phase-demodulation image post-processing, the 3D coordinates of the liquid surface are deduced. In this article, the approach is first numerically tested by considering a simulated pending drop, in order to analyse its performance. Then, FP-LIF is applied for two experimental cases: a quasi-static pending drop as well as a transient liquid sheet. We demonstrate reconstruction RMS errors of 1.4% and 6.1% for the simulated and experimental cases respectively. The technique presented here demonstrates, for the first time, a fringe projection approach based on LIF detection to reconstruct liquid surfaces in 3D. FP-LIF is promising for the study of more complex liquid structures and is paving the way for high-speed 3D videography of liquid surfaces.
Enhancements in Day-Ahead Forecasts of Solar Irradiation with Machine Learning: A Novel Analysis with the Japanese Mesoscale Model

Abstract The objective of this study is to propose and evaluate a set of modifications to enhance a machine-learning-based method for forecasting day-ahead solar irradiation. To assess the proposed modifications, they were implemented in an initial forecast method, and their effectiveness was analyzed using two years of data on a national scale in Japan. In addition, the accuracy of the modified method was compared with one of the forecast methods for solar irradiation used by the Japan Meteorological Agency (JMA), namely, the mesoscale model (MSM). Such forecasts were made publicly available only recently, which makes this study one of the first ones to compare them with machine-learning-based forecasts. The annual root-mean-square error (RMSE) of local forecasts of the JMA-MSM varied from 0.1 to 0.14 kW h m −2 ; the regional equivalent varied from 0.062 to 0.091 kW h m −2 . In comparison with these results, the modified model achieved an average RMSE reduction of 7.5% on the local scale and 16% on the regional scale. The modified model also had a skill score that was 23% higher than that of the JMA model. Furthermore, the performance of the JMA model had strong spatial and seasonal dependencies, which were reduced in the machine-learning-based forecasts. The results show that the proposed modifications are effective in reducing large forecasts errors, but they cannot compensate for situations in which the input data used to make the forecasts are highly inaccurate.
Establishing Baselines for Text Classification in Low-Resource Languages

While transformer-based finetuning techniques have proven effective in tasks that involve low-resource, low-data environments, a lack of properly established baselines and benchmark datasets make it hard to compare different approaches that are aimed at tackling the low-resource setting. In this work, we provide three contributions. First, we introduce two previously unreleased datasets as benchmark datasets for text classification and low-resource multilabel text classification for the low-resource language Filipino. Second, we pretrain better BERT and DistilBERT models for use within the Filipino setting. Third, we introduce a simple degradation test that benchmarks a model's resistance to performance degradation as the number of training samples are reduced. We analyze our pretrained model's degradation speeds and look towards the use of this method for comparing models aimed at operating within the low-resource setting. We release all our models and datasets for the research community to use.
What Happens To BERT Embeddings During Fine-tuning?

While there has been much recent work studying how linguistic information is encoded in pre-trained sentence representations, comparatively little is understood about how these models change when adapted to solve downstream tasks. Using a suite of analysis techniques (probing classifiers, Representational Similarity Analysis, and model ablations), we investigate how fine-tuning affects the representations of the BERT model. We find that while fine-tuning necessarily makes significant changes, it does not lead to catastrophic forgetting of linguistic phenomena. We instead find that fine-tuning primarily affects the top layers of BERT, but with noteworthy variation across tasks. In particular, dependency parsing reconfigures most of the model, whereas SQuAD and MNLI appear to involve much shallower processing. Finally, we also find that fine-tuning has a weaker effect on representations of out-of-domain sentences, suggesting room for improvement in model generalization.
ORGANIZATION OF STEAM LESSONS IN THE INNOVATIVE CLASSROOM

The article outlines the problem of future teachers preparation for implementation of STEAM education with the help of innovative learning models and new approaches to organization of educational process. The authors carried out the analysis of innovative classroom utilization peculiarities for organization of STEAM training of future teachers on the basis of innovative pedagogical technologies utilization and innovative forms of students’ learning activities organization that are based on the rotation model. In particular, objects of STEAM learning ecosystem were defined as well as the place and the role of learning environments there exemplified by innovative classroom (ICR). The article represents the results of the research on educational trends, innovative pedagogical technologies and methods of innovative classroom organization which will satisfy the needs of the learning process participants. These results are based on the survey where 198 students of Borys Grinchenko Kyiv University took part. Characteristics of innovative classroom, approaches to organization of ICR depending on the number of rotation stations, rotation models, their advantages and disadvantages are described. There is a description of innovative classroom in Borys Grinchenko Kyiv University which is designed as an educational centre for preparation of future primary school teachers, training teachers in the sphere of innovative technologies, in particular, to implement tasks of STEAM education. The practical part of the research is in description of STEAM lesson organization in the innovative classroom using the example of the training carried out using innovative classroom equipment, mentioned innovative technologies and models. The participants of the training had to perform a learning project using inquiry-based method where each stage of the project had to be fulfilled on a definite rotation station of the innovative classroom. The research leading to these results received, within the framework of the Modernization of Pedagogical Higher Education by Innovative Teaching Instruments. MoPED – KA2 CBHE – 586098-EPP-1-2017-1-UA-EPPKA2-CBHE-JP.