Reviewerly/exHarmony huggingface dataset:
Trust and Perceived Risks in High School Students' Online Learning Behaviour During Covid19 Pandemic

This study explores online learning by Indonesian high school students during the Covid19 Pandemic. Many high schools in Indonesia use online learning technology in Google Classroom and Google Meet. The sudden and forced switch from a conventional classroom to a fully online one caught many off guards. This study looks at the behavior of Indonesian high school students in facing sudden changes in study mode from offline or hybrid to full online due to the Covid19 Pandemic. Theory of Planned Behaviour is used and extended by adding Perceived Risks dan Trust to develop a questionnaire. Trust in this study is differentiated between Trust Toward Application and Trust Toward Organization. The survey was distributed to 1986 students from three private high schools in Yogyakarta, Indonesia. As many as 462 responses were received, representing a 23.26% response rate. Data were analyzed using PLS-SEM. The analysis of survey results confirms that TPB, Perceived Risks, and Trust could explain the use of Online Learning by Indonesian high school students. Furthermore, Trust is also influenced, albeit in a small percentage, Perceived Risks.
The Effectiveness of Probing-Prompting Learning Model using Google Meet Platform on Student's Mathematical Communication Ability

The purpose of this study was to determine whether the probing-prompting learning model using the google meet platform was effective in improving students' mathematical communication and to find out whether the mathematical communication of students taught by the probing-prompting learning model is higher than the mathematical communication of students taught by conventional learning. The method used is a quantitative research type of experiment. The population in this study were all seventh grade students of SMP Negeri 1 Socah, while the samples in this study were class VII-A as the experimental class and VII-B as the control class. This research was conducted from April to July 2021. The instrument used is an observation sheet on the teacher's ability to manage learning with a probing-prompting learning model using the google meet platform, observation sheet on students' mathematical communication skills taught using the probing-prompting learning model using the google meet platform, as well as pretest and posttest sheets. The effective aspect is shown by the average observation result of the teacher's ability to manage learning with the probing-prompting learning model using the google meet platform of 3.6 which is in the very good category; the average observation result of students' mathematical communication skills who were taught using the probing-prompting learning model using the google meet platform was 62.47% in the high category; and completeness of student learning by 100%. Based on the results of the independent sample t-test, the mathematical communication of students who were taught using the probing-prompting learning model using the google meet platform was higher than the mathematical communication of students who were taught using conventional learning.&#x0D; Keywords: Probing-Prompting Learning Model, Google Meet, Mathematical Communication.
Evaluating Dynamic Tor Onion Services for Privacy Preserving Distributed Digital Identity Systems

Digital identity documents provide several key benefits over physical ones. They can be created more easily, incur less costs, improve usability and can be updated if necessary. However, the deployment of digital identity systems does come with several challenges regarding both security and privacy of personal information. In this paper, we highlight one challenge that digital identity systems face if they are set up in a distributed fashion: Network Unlinkability. We discuss why network unlinkability is so critical for a distributed digital identity system that wants to protect the privacy of its users and present a specific definition of unlinkability for our use-case. Based on this definition, we propose a scheme that utilizes the Tor network to achieve the required level of unlinkability by dynamically creating onion services and evaluate the feasibility of our approach by measuring the deployment times of onion services.
Crypto-Currency: Trends and Determinants

In this paper, author analyses the role of cryptocurrencies in the economy and showed the trends of prices of Bitcoin and Ethereum in terms of US$ during 2017m1-2021m12 and also showed the trends of the market capitalization of Bitcoin during 2017m1-2021m12. All the trendlines are non-linear with cyclical behavior. Traditional regression model revealed that the market capitalization of Bitcoin is positively related with prices of Bitcoin and inflation rate and negatively related with price of Ethereum significantly from 2019m1 to 2021m12. Cointegration and VEC model suggested that the market capitalization of Bitcoin has long run causality with the prices of Bitcoin and Ethereum and inflation rate but the cointegrating equation has been proved diverging away from equilibrium. Bitcoin price and market capitalization have bi-directional short run causality and the price of Ethereum has short run causality to market capitalization of Bitcoin during the specified period. The volatility of market capitalization of Bitcoin showed a non-stationary process.
Usage of Media Products by Teachers: Pedagogical Potential, Expertise and Implementation

The purpose of the article is to reveal the pedagogical potential of media products, the existing practice of its implementation by teachers and the procedures of pedagogical expertise.To achieve the goal, the following methods were used: generalization of Russian and foreign studies, a survey of teachers, pedagogical analysis of feature films, classification of tasks and functions that media products can perform in the teacher's activity, design of criteria for selecting media products by the teacher, procedures for their pedagogical expertise.According to the results of the study, the following conclusions were made.The pedagogical potential of media products is revealed in the complex of tasks solved by the teacher with their help (didactic, educational, self-developing) and the functions performed by them in relation to the teacher and to the students.For the full use of the pedagogical potential of media products, the teacher needs to conduct his or her pedagogical expertise.Such an examination includes, firstly, the selection of media products based on the following criteria.Secondly, the choice of the method of their pedagogical application.Thirdly, the analysis of the content of the media product from the standpoint of solving the educational, didactic or self-developing task the teacher faces.
Development of Front Office Digital Modules on Applications on Android for Practical Learning in Tourism Schools

Online learning during this Covid-19 Pandemic faces various obstacles, the lack of supporting internet network facilities in each area, and the lack of teaching materials, especially in digital modules, it gives teachers more assignments than providing learning materials in class. This phenomenon must be addressed immediately by creating an Android-based digital module that was used during classroom learning. The research method used is Research and Development (R&amp;D) using the ADDIE (Analyze – Design – Development – Implementation – Evaluation) model. This Android-Based Interactive E-Module learning media can be used offline, designed and developed using software to convert word files into ePub extension files. This media consists of concept maps, material descriptions, learning videos, and interactive quizzes packed all in one form, hoping that students can be more active in the learning process in class. This discovery is expected to answer the goals of this research, which include creating digital e-modules in the front office learning at tourism schools
The MAMe dataset: on the relevance of high resolution and variable shape image properties

The mostcommon approach in image classification tasks is to resize all images in the dataset to a unique shape, while reducing their resolution to a size that makes experimentation at scale easier. This practice has benefits from a computational perspective, but it entails negative side-effects on performance due to loss of information and image deformation. In this work we introduce the MAMe dataset, an image classification dataset with remarkable properties of high resolution and variable shape. The goal of MAMe is to provide a tool for studying the impact of such properties in image classification, while motivating research in the topic. The MAMe dataset contains thousands of artworks from three different museums, and proposes a classification task consisting on differentiating between 29 mediums (i.e., materials and techniques) supervised by art experts. After analyzing the novelty of MAMe in the context of the current image classification tasks, a thorough description of the task is provided, along with statistics of the dataset. Experiments are conducted to evaluate the impact of using high resolution images, variable shape inputs, as well as both properties at the same time. Results illustrate the positive impact in performance when using high resolution images, while highlighting the lack of solutions to exploit variable shapes. An additional experiment exposes the distinctiveness between the MAMe dataset and the prototypical ImageNet dataset, showing that performance improves due to information gain and resolution gain. Finally, the baselines are inspected using explainability methods and expert knowledge, in order to gain insights about the challenges that remain ahead.
Cloud computing task scheduling algorithm based on dynamic priority

In view of the large-scale and complex development trend of task scheduling in the current cloud computing environment, the traditional task priority scheduling algorithm is difficult to ensure the system load balance. A cloud computing task scheduling algorithm based on dynamic priority and scheduling decision is proposed(TS-DP). The algorithm calculates the task dynamic priority according to the task value and task urgency. At the same time, imitating the behavior of fireflies, the task scheduling decision variables are obtained by attraction (the user's expected earliest completion time), fluorescence brightness (task processing time constraint) and load balance, and then the tasks are scheduled to the executable computing node corresponding to the maximum decision variable value according to the task priority. Experimental results show that the algorithm can effectively improve the resource scheduling performance of cloud computing, shorten the completion time of the total task, and achieve good load balancing, especially when the number of tasks and the scale of nodes are large.
The Study on Awareness, Ability and Policy Suggestions to Meet Job Requirements of the Digital Transformation

The discourse that revolves around the Fourth Industrial Revolution in Vietnam has witnessed participation from policy makers, business circles, and academics.This study sought to explore the future of Vietnamese workforce's awareness and competencies in meeting labour market needs in the Fourth Industrial Revolution.To capture these awareness and competencies, the study used the mixed method.Questionnaires were initially conducted with 800 third-and fourth-year students who were studying in four fields: social sciences and humanities, science and technology, natural sciences, and finance and banking in public and private universities in Ho Chi Minh City.Thirty lecturers and managers from private and state-owned enterprises were then invited to participate in interviews to gain a multidimensional perspective.Analyses showed the large proportion of students had heard of the Fourth Industrial Revolution primarily from the media, which led to a difference in both awareness and behaviour towards the Fourth Industrial Revolution.The survey students had a positive outlook on employment trends, but felt doubtful and diffident about job-seeking after graduation.From a multidimensional perspective, the study also pointed out obsolete knowledge, traditional teaching, and the shortage of connection between universities and enterprises.The present study provided significant implications for higher educational institutions in the development of future training programmes to prepare the future workforce for the digital economy.
A Differentiable Two-stage Alignment Scheme for Burst Image Reconstruction with Large Shift

Denoising and demosaicking are two essential steps to reconstruct a clean full-color image from the raw data. Recently, joint denoising and demosaicking (JDD) for burst images, namely JDD-B, has attracted much attention by using multiple raw images captured in a short time to reconstruct a single high-quality image. One key challenge of JDD-B lies in the robust alignment of image frames. State-of-the-art alignment methods in feature domain cannot effectively utilize the temporal information of burst images, where large shifts commonly exist due to camera and object motion. In addition, the higher resolution (e.g., 4K) of modern imaging devices results in larger displacement between frames. To address these challenges, we design a differentiable two-stage alignment scheme sequentially in patch and pixel level for effective JDD-B. The input burst images are firstly aligned in the patch level by using a differentiable progressive block matching method, which can estimate the offset between distant frames with small computational cost. Then we perform implicit pixel-wise alignment in full-resolution feature domain to refine the alignment results. The two stages are jointly trained in an end-to-end manner. Extensive experiments demonstrate the significant improvement of our method over existing JDD-B methods. Codes are available at https://github.com/GuoShi28/2StageAlign.
ClarET: Pre-training a Correlation-Aware Context-To-Event Transformer for Event-Centric Generation and Classification

Generating new events given context with correlated ones plays a crucial role in many eventcentric reasoning tasks.Existing works either limit their scope to specific scenarios or overlook event-level correlations.In this paper, we propose to pre-train a general Correlationaware context-to-Event Transformer (ClarET) for event-centric reasoning.To achieve this, we propose three novel event-centric objectives, i.e., whole event recovering, contrastive eventcorrelation encoding and prompt-based event locating, which highlight event-level correlations with effective training.The proposed ClarET is applicable to a wide range of eventcentric reasoning scenarios, considering its versatility of (i) event-correlation types (e.g., causal, temporal, contrast), (ii) application formulations (i.e., generation and classification), and (iii) reasoning types (e.g., abductive, counterfactual and ending reasoning).Empirical fine-tuning results, as well as zero-and fewshot learning, on 9 benchmarks (5 generation and 4 classification tasks covering 4 reasoning types with diverse event correlations), verify its effectiveness and generalization ability.
Deletion inference, reconstruction, and compliance in machine (un)learning

Privacy attacks on machine learning models aim to identify the data that is used to train such models. Such attacks, traditionally, are studied on static models that are trained once and are accessible by the adversary. Motivated to meet new legal requirements, many machine learning methods are recently extended to support machine unlearning, i.e., updating models as if certain examples are removed from their training sets, and meet new legal requirements. However, privacy attacks could potentially become more devastating in this new setting, since an attacker could now access both the original model before deletion and the new model after the deletion. In fact, the very act of deletion might make the deleted record more vulnerable to privacy attacks. Inspired by cryptographic definitions and the differential privacy framework, we formally study privacy implications of machine unlearning. We formalize (various forms of) deletion inference and deletion reconstruction attacks, in which the adversary aims to either identify which record is deleted or to reconstruct (perhaps part of) the deleted records. We then present successful deletion inference and reconstruction attacks for a variety of machine learning models and tasks such as classification, regression, and language models. Finally, we show that our attacks would provably be precluded if the schemes satisfy (variants of) deletion compliance (Garg, Goldwasser, and Vasudevan, Eurocrypt’20).
GearNet: Stepwise Dual Learning for Weakly Supervised Domain Adaptation

This paper studies a weakly supervised domain adaptation (WSDA) problem, where we only have access to the source domain with noisy labels, from which we need to transfer useful information to the unlabeled target domain. Although there have been a few studies on this problem, most of them only exploit unidirectional relationships from the source domain to the target domain. In this paper, we propose a universal paradigm called GearNet to exploit bilateral relationships between the two domains. Specifically, we take the two domains as different inputs to train two models alternately, and a symmetrical Kullback-Leibler loss is used for selectively matching the predictions of the two models in the same domain. This interactive learning schema enables implicit label noise canceling and exploit correlations between the source and target domains. Therefore, our GearNet has the great potential to boost the performance of a wide range of existing WSDA methods. Comprehensive experimental results show that the performance of existing methods can be significantly improved by equipping with our GearNet.
Learnable Nonlinear Compression for Robust Speaker Verification

In this study, we focus on nonlinear compression methods in spectral features for speaker verification based on deep neural network. We consider different kinds of channel-dependent (CD) nonlinear compression methods optimized in a data-driven manner. Our methods are based on power nonlinearities and dynamic range compression (DRC). We also propose multi-regime (MR) design on the nonlinearities, at improving robustness. Results on VoxCeleb1 and VoxMovies data demonstrate improvements brought by proposed compression methods over both the commonly-used logarithm and their static counterparts, especially for ones based on power function. While CD generalization improves performance on VoxCeleb1, MR provides more robustness on VoxMovies, with a maximum relative equal error rate reduction of 21.6%.
Pyramid-BERT: Reducing Complexity via Successive Core-set based Token Selection

Transformer-based language models such as BERT (Devlin et al., 2018) have achieved the state-of-the-art performance on various NLP tasks, but are computationally prohibitive.A recent line of works use various heuristics to successively shorten sequence length while transforming tokens through encoders, in tasks such as classification and ranking that require a single token embedding for prediction.We present a novel solution to this problem, called Pyramid-BERT where we replace previously used heuristics with a coreset based token selection method justified by theoretical results.The core-set based token selection technique allows us to avoid expensive pre-training, gives a space-efficient fine tuning, and thus makes it suitable to handle longer sequence lengths.We provide extensive experiments establishing advantages of pyramid BERT over several baselines and existing works on the GLUE benchmarks and Long Range Arena (Tay et al., 2020) datasets.
Searching for fingerspelled content in American Sign Language

Natural language processing for sign language video-including tasks like recognition, translation, and search-is crucial for making artificial intelligence technologies accessible to deaf individuals, and is gaining research interest in recent years.In this paper, we address the problem of searching for fingerspelled keywords or key phrases in raw sign language videos.This is an important task since significant content in sign language is often conveyed via fingerspelling, and to our knowledge the task has not been studied before.We propose an end-to-end model for this task, FSS-Net, that jointly detects fingerspelling and matches it to a text sequence.Our experiments, done on a large public dataset of ASL fingerspelling in the wild, show the importance of fingerspelling detection as a component of a search and retrieval model.Our model significantly outperforms baseline methods adapted from prior work on related tasks.
Sim2Real Object-Centric Keypoint Detection and Description

Keypoint detection and description play a central role in computer vision. Most existing methods are in the form of scene-level prediction, without returning the object classes of different keypoints. In this paper, we propose the object-centric formulation, which, beyond the conventional setting, requires further identifying which object each interest point belongs to. With such fine-grained information, our framework enables more downstream potentials, such as object-level matching and pose estimation in a clustered environment. To get around the difficulty of label collection in the real world, we develop a sim2real contrastive learning mechanism that can generalize the model trained in simulation to real-world applications. The novelties of our training method are three-fold: (i) we integrate the uncertainty into the learning framework to improve feature description of hard cases, e.g., less-textured or symmetric patches; (ii) we decouple the object descriptor into two independent branches, intra-object salience and inter-object distinctness, resulting in a better pixel-wise description; (iii) we enforce cross-view semantic consistency for enhanced robustness in representation learning. Comprehensive experiments on image matching and 6D pose estimation verify the encouraging generalization ability of our method. Particularly for 6D pose estimation, our method significantly outperforms typical unsupervised/sim2real methods, achieving a closer gap with the fully supervised counterpart.