Reviewerly/exHarmony huggingface dataset:
Media Pembelajaran Interaktif Mata Kuliah Pemograman Web Berbasis Android

The learning process faces relatively large challenges, which are related to the extraordinary developments in science and technology (IPTEK). The rapid development of science and technology offers a variety of new facilities in learning, especially the variety of learning media that educators can use in delivering learning materials. Interactive learning media is a learning medium that is used in learning and there is good interaction between students and lecturers, students and students. Currently, the teaching and learning process uses the manual method, provided that lecturers and students come face to face in order to carry out the teaching and learning process. When lecturers cannot attend the learning process, the distribution of knowledge will be hampered. Interactive learning media is an effective way to stimulate students. The purpose of this research is to design applications that can attract the attention of students and facilitate understanding of web programming materials. Data collection methods used are observation and interviews. The research stages of data collection are then the design of an Android-based interactive learning media application device. The results of the study can be concluded that the Interactive Learning media for studying web programming courses can attract students' attention and make it easier to understand web programming materials
Answering Ambiguous Questions through Generative Evidence Fusion and Round-Trip Prediction

In open-domain question answering, questions are highly likely to be ambiguous because users may not know the scope of relevant topics when formulating them.Therefore, a system needs to find possible interpretations of the question, and predict one or multiple plausible answers.When multiple plausible answers are found, the system should rewrite the question for each answer to resolve the ambiguity.In this paper, we present a model that aggregates and combines evidence from multiple passages to adaptively predict a single answer or a set of question-answer pairs for ambiguous questions.In addition, we propose a novel round-trip prediction approach to iteratively generate additional interpretations that our model fails to find in the first pass, and then verify and filter out the incorrect questionanswer pairs to arrive at the final disambiguated output.Our model, named REFUEL, achieves a new state-of-the-art performance on the AMBIGQA dataset, and shows competitive performance on NQ-OPEN and Trivi-aQA.The proposed round-trip prediction is a model-agnostic general approach for answering ambiguous open-domain questions, which improves our REFUEL as well as several baseline models.We release source code for our models and experiments at https://github.com/amzn/refuel-open-domain-qa.
Research on zero watermarking technique based on improved Sobel operator

As digital watermarking technology becomes more and more important in the field of information security and socio-economics, various sectors have become active, making digital watermarking technology develop rapidly. Scientific research institutes, universities and commercial groups from all over the world are actively participating or investing in this research and many advanced algorithms have been proposed. In this paper, we read, study and analyze the existing algorithms based on zero watermarking technology and propose a zero watermarking algorithm against geometric attacks. This technology is of great relevance to people's lives, economic development and social progress.
Digital technologies in foreign language learning

This article will examine the digitalization of education, its effectiveness, pros and cons. In addition, digital technologies will be able to affect the performance and motivation of students. Possible educational potential of digital technologies in teaching of foreign languages, recommendations on methods for integrating digital technologies into the educational process for work and independent work organizing of students and pupils are some of their advantages. Digital education offers new learning opportunities as student engagement in an interactive digital environment and educators use hybrid personalized learning courses, innovative, engaging learnng strategies. Looking at student success in the modern conditions they are required to be not only thoughtful digital content users, but also effective digital media creators are able to collaborate their skills and share ideas through dynamic storytelling, data visualization. The article describes the works by both Russian and foreign authors devoted to the advantages and some risks arising in society as a result of the digitalization in education. Advantages and disadvantages of digital education are clearly have been shown in the article. As the latest and most advanced technology is very expensive, at presents digitalization has both defenders and opponents of this phenomenon. It remembers about tremendous expenses on the newest digital tools and software. The final idea of this article is to create a flexible learning environment that encourages innovation.
Heterogeneous Multi-task Learning with Expert Diversity

Predicting multiple heterogeneous biological and medical targets is a challenge for traditional deep learning models. In contrast to single-task learning, in which a separate model is trained for each target, multi-task learning (MTL) optimizes a single model to predict multiple related targets simultaneously. To address this challenge, we propose the Multi-gate Mixture-of-Experts with Exclusivity (MMoEEx). Our work aims to tackle the heterogeneous MTL setting, in which the same model optimizes multiple tasks with different characteristics. Such a scenario can overwhelm current MTL approaches due to the challenges in balancing shared and task-specific representations and the need to optimize tasks with competing optimization paths. Our method makes two key contributions: first, we introduce an approach to induce more diversity among experts, thus creating representations more suitable for highly imbalanced and heterogenous MTL learning; second, we adopt a two-step optimization (Finn et al., 2017 and Lee et al., 2020) approach to balancing the tasks at the gradient level. We validate our method on three MTL benchmark datasets, including UCI-Census-income dataset, Medical Information Mart for Intensive Care (MIMIC-III) and PubChem BioAssay (PCBA).
Investigation of Preliminary Motions from a Static State and Their Predictability

Humans observe the actions of others and predict their movements slightly ahead of time in everyday life. Many studies have been conducted to automate such a prediction ability computationally using neural networks; however, they implicitly assumed that preliminary motions occurred before significant movements. In this study, we quantitatively investigate when and how long a preliminary motion appears in motions from static states and what kinds of motion can be predicted in principle. We consider this knowledge fundamental for movement prediction in interaction techniques. We examined preliminary motions of basic movements such as kicking and jumping, and confirmed the presence of preliminary motions by using them as inputs to a neural network. As a result, although we did not find preliminary motion for a hand-moving task, a left-right jumping task had the most preliminary motion, up to 0.4 s before the main movement.
Budget sharing for multi-analyst differential privacy

Large organizations that collect data about populations (like the US Census Bureau) release summary statistics that are used by multiple stakeholders for resource allocation and policy making problems. These organizations are also legally required to protect the privacy of individuals from whom they collect data. Differential Privacy (DP) provides a solution to release useful summary data while preserving privacy. Most DP mechanisms are designed to answer a single set of queries. In reality, there are often multiple stakeholders that use a given data release and have overlapping but not-identical queries. This introduces a novel joint optimization problem in DP where the privacy budget must be shared among different analysts. We initiate study into the problem of DP query answering across multiple analysts. To capture the competing goals and priorities of multiple analysts, we formulate three desiderata that any mechanism should satisfy in this setting - The Sharing Incentive, Non-interference, and Adaptivity - while still optimizing for overall error. We demonstrate how existing DP query answering mechanisms in the multi-analyst settings fail to satisfy at least one of the desiderata. We present novel DP algorithms that provably satisfy all our desiderata and empirically show that they incur low error on realistic tasks.
Adversarial Training Reduces Information and Improves Transferability

Recent results show that features of adversarially trained networks for classification, in addition to being robust, enable desirable properties such as invertibility. The latter property may seem counter-intuitive as it is widely accepted by the community that classification models should only capture the minimal information (features) required for the task. Motivated by this discrepancy, we investigate the dual relationship between Adversarial Training and Information Theory. We show that the Adversarial Training can improve linear transferability to new tasks, from which arises a new trade-off between transferability of representations and accuracy on the source task. We validate our results employing robust networks trained on CIFAR-10, CIFAR-100 and ImageNet on several datasets. Moreover, we show that Adversarial Training reduces Fisher information of representations about the input and of the weights about the task, and we provide a theoretical argument which explains the invertibility of deterministic networks without violating the principle of minimality. Finally, we leverage our theoretical insights to remarkably improve the quality of reconstructed images through inversion.
DVD: A Diagnostic Dataset for Multi-step Reasoning in Video Grounded Dialogue

A video-grounded dialogue system is required to understand both dialogue, which contains semantic dependencies from turn to turn, and video, which contains visual cues of spatial and temporal scene variations.Building such dialogue systems is a challenging problem, involving various reasoning types on both visual and language inputs.Existing benchmarks do not have enough annotations to thoroughly analyze dialogue systems and understand their capabilities and limitations in isolation.These benchmarks are also not explicitly designed to minimise biases that models can exploit without actual reasoning.To address these limitations, in this paper, we present DVD, a Diagnostic Dataset for Videogrounded Dialogues.The dataset is designed to contain minimal biases and has detailed annotations for the different types of reasoning over the spatio-temporal space of video.Dialogues are synthesized over multiple question turns, each of which is injected with a set of cross-turn semantic relationships.We use DVD to analyze existing approaches, providing interesting insights into their abilities and limitations.In total, DVD is built from 11k CATER synthetic videos and contains 10 instances of 10-round dialogues for each video, resulting in more than 100k dialogues and 1M question-answer pairs.Our code and dataset are publicly available 1 .
Generative Models Based on VAE and GAN for New Medical Data Synthesis

The chapter deals with the construction of generative models using Variational Autoencoder (VAE) and Generative Adversarial Neural Networks to synthesize new medical data. VAE is a synthesis of two complete neural networks: an encoder E and a generator G, as well as the latent space connecting them and enabling them to carry out random transformation and interpolation. Generative Adversarial Nets (GAN) in their turn are built on the principle of interaction between a generative model (generator G) and a discriminating model (discriminator D). When creating generator G (both VAE and GAN), its architecture of a neural network based on convolutional layers, with the application of the new deep learning framework Tensorflow-addons is used. As E and D encoders, respectively, the models of transfer learning, problem domain-image feature vector are used in the work. The comparison between them is made in the chapter and the most optimal model for solving the proposed problem is selected. The chapter presents the results of the research obtained on the basis of VAE and GAN implementation.
Mapping Relational Database to OWL Ontology Based on MDE Settings

Ontology is an important aspect of the semantic web, which is why semantic web developers are interested in constructing ontology in various applications based on domain experts. By transforming an existing application database into ontology, we many construct ontologies without having to hire an expert in the field. Model-driven engineering is the foundation of the suggested strategy (MDE). In a nutshell, the technique is divided into two phases, the first of which attempts to prepare the data needed for the transformation in the form of a model with a database. A compliance relationship between this model and its meta-model is required. Phase (2) applies a set of rules written in the Atlas Transformational Language to change the model produced in the first phase into another model, which is an OWL ontology (ATL). We tested our solution using a set of databases created specifically for this purpose and built it in an eclipse environment using an EMF and ATL transform language. The acquired findings demonstrate the strength and efficacy of the recommended strategy.
Utilizing Big Data analytics and electronic health record data in HIV prevention, treatment, and care research: a literature review

Propelled by the transformative power of modern information and communication technologies, digitalization of data, and the increasing affordability of high-performance computing, Big Data science has brought forth revolutionary advancement in many areas of business, industry, health, and medicine. The HIV research and care service community is no exception to the benefits from the availability and utilization of Big Data analytics. Electronic health record (EHR) data (e.g., administrative and billing data, electronic medical records, or other digital records of information pertinent to individual or population health) are an essential source of health and disease outcome data because of the large amount of real-world, comprehensive, and often longitudinal data, which provide a good opportunity for leveraging advanced Big Data analytics in addressing challenges in HIV prevention, treatment, and care. This review focuses on studies that apply Big Data analytics to EHR data with aims to synthesize the HIV-related issues that EHR data studies can tackle, identify challenges in the utilization of EHR data in HIV research and practice, and discuss future needs and directions that can realize the promising potential role of Big Data in ending the HIV epidemic.
Clustering Longitudinal Life-Course Sequences using Mixtures of Exponential-Distance Models

Sequence analysis is an increasingly popular approach for analysing life courses represented by ordered collections of activities experienced by subjects over time. Here, we analyse a survey data set containing information on the career trajectories of a cohort of Northern Irish youths tracked between the ages of 16 and 22. We propose a novel, model-based clustering approach suited to the analysis of such data from a holistic perspective, with the aims of estimating the number of typical career trajectories, identifying the relevant features of these patterns, and assessing the extent to which such patterns are shaped by background characteristics. Several criteria exist for measuring pairwise dissimilarities among categorical sequences. Typically, dissimilarity matrices are employed as input to heuristic clustering algorithms. The family of methods we develop instead clusters sequences directly using mixtures of exponential-distance models. Basing the models on weighted variants of the Hamming distance metric permits closed-form expressions for parameter estimation. Simultaneously allowing the component membership probabilities to depend on fixed covariates and accommodating sampling weights in the clustering process yields new insights on the Northern Irish data. In particular, we find that school examination performance is the single most important predictor of cluster membership.
Musical-Aesthetic Education of Young Schoolchildren Through Vocal-Choral Performances

The article deals with the necessity and importance of vocal and choir singing in the musical-aesthetic education of young schoolchildren. Musical-aesthetic education is aimed at improving many features of the child’s personality. The formation of artistic and aesthetic culture in the educational process can be successfully implemented in choir classes. The author believes that the process of children’s vocal and choral education occurs in close connection with the formation of the singing apparatus, music and aesthetic education: first, the acquisition of basic vocal and choral skills (breathing, vocalization, diction, intonation). The second is the formation of creative abilities, artistic and aesthetic culture in general. The article also discusses how music teachers not only instill vocal and choral skills in students, but also teach them the basics of spiritual, moral, artistic and aesthetic culture.
Efficient Authentication for Internet of Things Devices in Information Management Systems

With the rapid development of the Internet of Things (IoT) technology, it has been widely used in various fields. IoT device as an information collection unit can be built into an information management system with an information processing and storage unit composed of multiple servers. However, a large amount of sensitive data contained in IoT devices is transmitted in the system under the actual wireless network environment will cause a series of security issues and will become inefficient in the scenario where a large number of devices are concurrently accessed. If each device is individually authenticated, the authentication overhead is huge, and the network burden is excessive. Aiming at these problems, we propose a protocol that is efficient authentication for Internet of Things devices in information management systems. In the proposed scheme, aggregated certificateless signcryption is used to complete mutual authentication and encrypted transmission of data, and a cloud server is introduced to ensure service continuity and stability. This scheme is suitable for scenarios where large-scale IoT terminal devices are simultaneously connected to the information management system. It not only reduces the authentication overhead but also ensures the user privacy and data integrity. Through the experimental results and security analysis, it is indicated that the proposed scheme is suitable for information management systems.
Spatially-Aware Context Neural Networks

A variety of computer vision tasks benefit significantly from increasingly powerful deep convolutional neural networks. However, the inherently local property of convolution operations prevents most existing models from capturing long-range feature interactions for improved performances. In this paper, we propose a novel module, called Spatially-Aware Context (SAC) block, to learn spatially-aware contexts by capturing multi-mode global contextual semantics for sophisticated long-range dependencies modeling. We enable customized non-local feature interactions for each spatial position through re-weighted global context fusion in a non-normalized way. SAC is very lightweight and can be easily plugged into popular backbone models. Extensive experiments on COCO, ImageNet, and HICO-DET benchmarks show that our SAC block achieves significant performance improvements over existing baseline architectures while with a negligible computational burden increase. The results also demonstrate the exceptional effectiveness and scalability of the proposed approach on capturing long-range dependencies for object detection, segmentation, and image classification, outperforming a bank of state-of-the-art attention blocks.
AR Creator: A Mobile Application of Logic Education Based on AR

With the rapid development of augmented reality technology in recent years, the application of mobile Ar has become more and more extensive. This paper introduces an AR mobile application based on AR Foundation which aims to popularize AR life style, let users experience the fun of AR. The project mainly through scanning the real plane, after the recognition is successful, the virtual object can be placed on the real world plane, and then the function operation can be carried out by clicking the corresponding button on the mobile phone screen, so as to realize the logic conversion. The original intention of the project is to let children combine hands-on ability and visual effects, so as to better realize the multidimensional development of intelligence.
Efficient experimental quantum fingerprinting with channel multiplexing and simultaneous detection

Quantum communication complexity studies the efficiency of information communication (that is, the minimum amount of communication required to achieve a certain task) using quantum states. One representative example is quantum fingerprinting, in which the simultaneous message passing model is considered and the minimum amount of communication could be exponentially smaller than the classical fingerprinting protocol. Experimental demonstrations based on a practical quantum fingerprinting protocol where coherent states are used to construct the fingerprints, have successfully shown the superiority of quantum fingerprinting. However, as a consequence of using coherent states, the communication time in this coherent quantum fingerprinting (CQF) protocol is increased. Moreover, the minimum amount of information communicated in these experimental demonstrations is largely limited by the dark counts of the single photon detectors. Here, we propose to enhance the performance of the existing CQF protocol through applying wavelength division multiplexing (WDM) and simultaneous detection of multiple wavelength channels. We show that the new WDM-CQF protocol can potentially reduce the communication time significantly. More importantly, with the same experimental parameters, the amount of communication is much reduced in the new scheme compared with the original CQF protocol. The more wavelength channels are used, the less communication is required by WDM-CQF protocol. We also perform a proof-of-concept experimental demonstration of the new WDM-CQF protocol with 6 wavelength channels. The experimental results further validate that the new scheme with WDM not only beats the classical protocol, but also reduces the amount of communication required by the original CQF protocol by more than half.