Reviewerly/exHarmony huggingface dataset:
An Android Based Flight Simulator using VR Technology

For training young pilots. Flight simulators enhance the safety measures, as the risk factor of severe injuries are eliminated since the training is now synthetic. However, flight simulators also require large space, large budget and does have many high end requirements, which are to be met in order to make the simulator respond and perform identical to that of a real aircraft. Flight simulators does have their hardware constraints and because of these limitations, the simulators are not of generic nature that supports different sort of aircrafts. In this paper, the simulator environment is deployed in virtual reality to simulate the similar controls on a stereoscopic environment. Deployment of the simulator is such, that experience of flying an aircraft from cockpit will give a sensational view to the user. Virtual reality is used in learning purpose, as normal person can experience what it will be like, to fly an aircraft
A Multilingual Thematic Picture Dictionary for Assisting Sixth Grade Elementary School Students’ Literacy

Indonesia is known for having low literacy skills. Students' literacy skills are low because schools do not provide enough media to help students' literacy skills, especially dictionaries. This study aims to develop a multilingual thematic illustrated dictionary to assist sixth-grade elementary school students' literacy. This has an impact on student learning outcomes are low. In developing the dictionary, this study applies the Design and Development (D&amp;D) using four instruments to collect data: observation sheets, interview guides, questionnaires, and expert assessments. The subjects of this study were teachers and students from elementary schools in urban and rural areas in Buleleng Regency, North Bali. The results show that this dictionary is relevant to the eleven criteria of a good multilingual thematic illustrated dictionary. This dictionary is also categorized as an excellent medium of 10 themes with words and sentences in three languages and authentic pictures. Thus, multilingual thematic illustrated dictionaries are a helpful medium for students and teachers to develop literacy skills. This research implies that students in learning can use the developed dictionary.
Diagonal Attention and Style-Based GAN for Content-Style Disentanglement in Image Generation and Translation

One of the important research topics in image generative models is to disentangle the spatial contents and styles for their separate control. Although StyleGAN can generate content feature vectors from random noises, the resulting spatial content control is primarily intended for minor spatial variations, and the disentanglement of global content and styles is by no means complete. Inspired by a mathematical understanding of normalization and attention, here we present a novel hierarchical adaptive Diagonal spatial ATtention (DAT) layers to separately manipulate the spatial contents from styles in a hierarchical manner. Using DAT and AdaIN, our method enables coarse-to-fine level disentanglement of spatial contents and styles. In addition, our generator can be easily integrated into the GAN inversion framework so that the content and style of translated images from multi-domain image translation tasks can be flexibly controlled. By using various datasets, we confirm that the proposed method not only outperforms the existing models in disentanglement scores, but also provides more flexible control over spatial features in the generated images.
TUDA-Reproducibility @ ReproGen: Replicability of Human Evaluation of Text-to-Text and Concept-to-Text Generation

This paper describes our contribution to the Shared Task ReproGen by Belz et al. (2021), which investigates the reproducibility of human evaluations in the context of Natural Language Generation. We selected the paper “Generation of Company descriptions using concept-to-text and text-to-text deep models: data set collection and systems evaluation” (Qader et al., 2018) and aimed to replicate, as closely to the original as possible, the human evaluation and the subsequent comparison between the human judgements and the automatic evaluation metrics. Here, we first outline the text generation task of the paper of Qader et al. (2018). Then, we document how we approached our replication of the paper’s human evaluation. We also discuss the difficulties we encountered and which information was missing. Our replication has medium to strong correlation (0.66 Spearman overall) with the original results of Qader et al. (2018), but due to the missing information about how Qader et al. (2018) compared the human judgements with the metric scores, we have refrained from reproducing this comparison.
Epidemic Exposure, Financial Technology, and the Digital Divide

We ask whether epidemic exposure leads to a shift in financial technology usage and who participates in this shift. We exploit a dataset combining Gallup World Polls and Global Findex surveys for some 250,000 individuals in 140 countries, merging them with information on the incidence of epidemics and local 3G internet infrastructure. Epidemic exposure is associated with an increase in remote-access (online/mobile) banking and substitution from bank branch-based to ATM activity. Heterogeneity in response centers on the age, income and employment of respondents. Young, high-income earners in full-time employment have the greatest tendency to shift to online/mobile transactions in response to epidemics. These effects are larger for individuals with better ex ante 3G signal coverage, highlighting the role of the digital divide in adaption to new technologies necessitated by adverse external shocks.
Integrated Learning Components of Cloud System-Based Office Administration in Vocational High Schools

The role of organizations, businesses, and governments in achieving their goals is primarily determined by implementing activities through an office.Office workers in Indonesia who come from Vocational High Schools and Universities were majoring in Office Governance Automation.Along with the development of technological processes, it also impacts more effective office processes through office automation.However, the result of a very dynamic world of technological adaptation still cannot be followed by preparing an appropriate workforce in the office world.Based on this context, the research team initiated the development of an office administration simulation system based on cloud integrated learning which is present as an innovation that directly answers the needs in the field based on simulator applications to improve the quality of office vocational education, especially by using integrated learning technology that can answer the needs of the world of work.
Why NFC is a rising star in digital ID

The Covid-19 pandemic has accelerated digital transformation across all industries, enabling many businesses to carry on trading while streamlining processes. But perhaps not surprisingly, this move to digital has been accompanied by a rise in cyber-crime and fraud. This in turn has accelerated the search for technology that can help improve the security of digital ID verification, while enhancing the experience for users.
Improving the Quality of e-Commerce Service by Implementing Combination Models with Step-by-Step, Bottom-Up Approach

e-Commerce, as a hot industry, plays an important role in people's lives.People visit e-commerce websites, check what they want, then click buy, and finally complete the transaction.The developments taking place at the level of electronic services at the global level and the intensification of competition and the increase in the experiences of electronic shoppers, the awareness and understanding of companies of the distinctive characteristics of the population in the region and their purchasing habits has become the most important for companies of e-commerce and services, where it is imperative Companies should keep pace with these developments and provide electronic services via the internet of high quality and efficiency, by focusing on the most important requirements for customer satisfaction, especially in light of the information and technological revolution.However, customers will have an awful experience if they visit crudely made e-commerce websites.Kunst A. (2019, Dec 20) claimed that around a total of 37.4% of customers complained that they had an awful shopping experience.The reason is that the service quality of e-commerce websites is not up to standard.This research aims to improve the quality of e-commerce service by using the Comprehensive and Referential Combination Model by implementing a Step-by-Step, Bottom-Up approach.Finally, we will recommend improving the quality of e-commerce service in construct and revision ways within parts of this model.
Outcome determinism in measurement-based quantum computation with qudits

Abstract In measurement-based quantum computing (MBQC), computation is carried out by a sequence of measurements and corrections on an entangled state. Flow, and related concepts, are powerful techniques for characterising the dependence of the corrections on previous measurement outcomes. We introduce flow-based methods for MBQC with qudit graph states, which we call <?CDATA $\mathbb{Z}_d$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mi mathvariant="double-struck">Z</mml:mi> </mml:mrow> <mml:mi>d</mml:mi> </mml:msub> </mml:math> -flow, when the local dimension is an odd prime. Our main results are a proof that <?CDATA $\mathbb{Z}_d$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mi mathvariant="double-struck">Z</mml:mi> </mml:mrow> <mml:mi>d</mml:mi> </mml:msub> </mml:math> -flow is a necessary and sufficient condition for a strong form of outcome determinism. Along the way, we find a suitable generalisation of the concept of measurement planes to this setting and characterise the allowed measurements in a qudit MBQC. We also provide a polynomial-time algorithm for finding an optimal <?CDATA $\mathbb{Z}_d$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mi mathvariant="double-struck">Z</mml:mi> </mml:mrow> <mml:mi>d</mml:mi> </mml:msub> </mml:math> -flow whenever one exists.
Trapped Ion Quantum Computing

In trapped ion quantum computing (TIQC), ions are created and trapped to form a qubit register. Ions can be suspended and trapped in free space using electromagnetic fields. The qubits, defined by the electronic states of the ions, are manipulated and read out using lasers.
Application of Smart Ideological Course Resource Sharing System Based on Knowledge Management and Data Pattern Mining

Application of smart ideological course resource sharing system based on the knowledge management and data pattern mining is designed in this paper. Cloud outsourcing refers to the use of cloud platforms, combining numerous service outsourcing resources to form a resource pool, and then using cloud management systems to then provide outsourcing services, making cloud services more flexible and convenient, while reducing costs and improving efficiency. Hence, this paper proposes the novel sharing model with the integration of the data pattern analysis framework. The experimental result compared with the other approaches has shown the satisfactory outcome.
DECENTRALIZED DONATION EXPERT SYSTEM TO BRING DOWN COVID-19

Due to the exponential growth in the use of systems with applications of blockchain technology, this paper develops a funding system, with donations and offers of shares, through the Ethereum platform with blockchain technology. Given the benefits that blockchain provides data protection and has high security, this paper offers a decentralized donation expert system using smart contracts that makes fully reliable donation systems to attract more funds to this urgent global health issue. Smart contracts provide faithful donations and meet the characteristics of being versatile, accessible, and sustainable to combat the COVID-19 pandemic. This expert system found the Merkle grid as an optimum method to work efficiently on the blockchain. The expert system proved to be steady and efficient by using an essential test dataset. A reliable donation system expects more donors and investors since a sustainable and reliable approach is always a milestone. The primary purpose of developing this system is to attract donors to bring down the COVID-19 pandemic by providing a faithful donation system.
Learning-Based Safety-Critical Motion Planning with Input-to-State Barrier Certificate

Motion planning in an effective and safe manner is a critical yet challenging task for autonomous driving. Learning-based framework as a new fashion in simulation and optimization has great potentials to develop a time-efficient navigation policy. To design a controller that addresses safety with explicit formulation, we incorporate control barrier function approach to generate a constrained optimization problem. The proposed method with uncertainty analysis helps to deal with disturbance towards motion planning task. Simulation results show that the algorithm produces improvement in the learning process and the adaptation to safety and performance.
Optimized and Improved Methods of Image Style Transfer for Local Reinforcement

Image style transfer, which commonly refers to adding a designated image style to a target content image, is now widely used in the movie industry, animation design, and game rendering, providing strong visual effects and cultural influences.However, there is no common criterion for evaluating the performance of image style transfer.In addition, people are more interested in local regions of images.This paper provides some revised methods to meet customer demand, focusing on an optimized image segmentation method based on DeepLab2, a semantic segmentation method, and fully connected conditional random fields (FCCRFs) for local image style transfer, with experiments demonstrating their usefulness and efficiency.
Integrated Micro-Video Recommender Based on Hadoop and Web-Scrapper

Social-media is the biggest modern platform for sharing and gaining knowledge without geographical restrictions. Videos have became the key of social-media. Short length videos considered here as Micro-videos. Some topics can be effectively elaborated in short time-span. Trending videos are generally short length videos. But every platform like Facebook, YouTube, twitter is focusing on own platform. Cross-platform is the major issue in micro-video recommendation. Users can’t look at every platform for their required videos. It is quite hectic and time-consuming to surf every social-media websites. In this paper an approach is taken to remove cross-platform issue using web-scrapers. Algorithms used for recommendation generation are Content based and Collaborative based algorithms. And the content of the micro-videos are the keywords searched by users. Generally most algorithms work by taking into account the rating given by users. But some platforms don’t have rating options like YouTube. User-based recommendation is a traditional approach. It requires time and space for calculation. Hadoop is the best solution for it. User-based recommendation algorithm based on Map-Reduce without the use of rating has been elaborated in detail. Combination of recommendations generated from different algorithms gives the best result.
Survivable Robotic Control through Guided Bayesian Policy Search with Deep Reinforcement Learning

Many robot manipulation skills can be represented with deterministic characteristics and there exist efficient techniques for learning parameterized motor plans for those skills. However, one of the active research challenge still remains to sustain manipulation capabilities in situation of a mechanical failure. Ideally, like biological creatures, a robotic agent should be able to reconfigure its control policy by adapting to dynamic adversaries. In this paper, we propose a method that allows an agent to survive in a situation of mechanical loss, and adaptively learn manipulation with compromised degrees of freedom-we call our method Survivable Robotic Learning (SRL). Our key idea is to leverage Bayesian policy gradient by encoding knowledge bias in posterior estimation, which in turn alleviates future policy search explorations, in terms of sample efficiency and when compared to random exploration based policy search methods. SRL represents policy priors as Gaussian process, which allows tractable computation of approximate posterior (when true gradient is intractable), by incorporating guided bias as proxy from prior replays. We evaluate our proposed method against off-the-shelf model free learning algorithm (DDPG), testing on a hexapod robot platform which encounters incremental failure emulation, and our experiments show that our method improves largely in terms of sample requirement and quantitative success ratio in all failure modes. A demonstration video of our experiments can be viewed at: https://sites.google.com/view/survivalrl
Dual Projection Generative Adversarial Networks for Conditional Image Generation

Conditional Generative Adversarial Networks (cGANs) extend the standard unconditional GAN framework to learning joint data-label distributions from samples, and have been established as powerful generative models capable of generating high-fidelity imagery. A challenge of training such a model lies in properly infusing class information into its generator and discriminator. For the discriminator, class conditioning can be achieved by either (1) directly incorporating labels as input or (2) involving labels in an auxiliary classification loss. In this paper, we show that the former directly aligns the class-conditioned fake-and-real data distributions $P(\text{image}|\text{class})$ ({\em data matching}), while the latter aligns data-conditioned class distributions $P(\text{class}|\text{image})$ ({\em label matching}). Although class separability does not directly translate to sample quality and becomes a burden if classification itself is intrinsically difficult, the discriminator cannot provide useful guidance for the generator if features of distinct classes are mapped to the same point and thus become inseparable. Motivated by this intuition, we propose a Dual Projection GAN (P2GAN) model that learns to balance between {\em data matching} and {\em label matching}. We then propose an improved cGAN model with Auxiliary Classification that directly aligns the fake and real conditionals $P(\text{class}|\text{image})$ by minimizing their $f$-divergence. Experiments on a synthetic Mixture of Gaussian (MoG) dataset and a variety of real-world datasets including CIFAR100, ImageNet, and VGGFace2 demonstrate the efficacy of our proposed models.
A Lightweight Model Based on Co-segmentation Attention for Occluded Person Re-identification

Occlusion generally exists in application scenarios of person re-identification, especially in crowded situations. It is challenging to solve the occlusion problem due to the differences in the size, shape and color of the occlusion. Although pose information is helpful in solving the occlusion problem, it usually takes much time and computing resources, and its accuracy is still limited at present. To address this issue, we propose a lightweight model that uses co-segmentation attention and local features to solve the occlusion problem by attention mechanism. Experimental results on three reported datasets show that the performance of our proposed model surpasses most existing methods and is competitive with the most state-of-the-art method.
Fabric sensor modified by Bi2Te3 nanomaterials in the field of sports rehabilitation

The current research based on Bi2Te3 nanomaterials applied in the field of sports rehabilitation is not deep enough. Therefore, the research purpose of this paper is based on the application of fabric sensors modified with Bi2Te3 nanomaterials in the field of sports rehabilitation. First, learn about the basic structure of Bi2Te3 nanomaterials and prepare for the synthesis of fabric sensors based on nano-Bi2Te3 materials with uniform morphology and higher purity. Secondly, fabric sensors are required to improve the response speed and sensitivity of the dynamic response process and be used in sports. Research on rehabilitation is to further improve the practicality of fabric sensors. Finally, the thermoelectric properties of the performance of Bi2Te3 nanomaterials are used, which shows that the material's function of converting thermal energy into electrical energy can help the wide application of fabric sensors. Experimental data shows that the pressure on the sole of the foot using the fabric sensor as a tool is 150 Newtons, and the effect of accelerating the rehabilitation of each joint is 1.5 times. The experimental results show that the fabric sensor modified based on Bi2Te3 nanomaterials can accelerate the rehabilitation process of patients in the field of sports rehabilitation.
[Comparison and correlation analysis of sleep parameters between watch-type sleep monitor (Actiwatch) and polysomnography].

OBJECTIVE With the rapid development of sleep medicine, there are various methods for detecting sleep diseases. This study compared the correlation between the lightweight watch-type sleep monitor (Actiwatch) and the gold standard polysomnography (PSG) in the Chinese population, in order to provide a basis for clinical application. METHODS From August 2018 to December 2019, 121 subjects who simultaneously performed sleep breathing monitoring (PSG) and wearing a watch-type sleep monitor (Actiwatch) in the Sleep Center of Peking University People's Hospital were enrolled. All subjects received PSG and Actiwatch at the same time, and filled out the sleep diary next morning. Monitoring indicators were collected for linear correlation analysis and paired t test to compare the differences. RESULTS Under low sensitivity conditions, the correlation coefficient of total sleep time (TST) between PSG and Actiwatch was 0.53 (P < 0.05). Paired t test analysis showed that there was no significant difference between the TSTs of Actiwatch and PSG (t=-0.890, P=0.36). According to age stratification, the smaller the age, the stronger the correlation between the TSTs of Actiwatch and PSG, and the coefficient could be up to 0.92 (P < 0.05). Paired t test showed that there was no significant difference between them (t=-1.057, P=0.35). According to the stratification by diagnosis, the correlation coefficient between the TSTs of Actiwatch and PSG in normal PSG group could be as high as 0.79 (P < 0.05), the results of paired t test showed that there was no significant difference between the TSTs of Actiwatch and PSG in normal PSG group (t=-0.784, P=0.44). CONCLUSION As a wearable home recorder, when the analysis parameters of Actiwatch were set as low sensitivity, PSG and Actiwatch had the highest TST correlation. The younger the age, the stronger correlation between the TSTs of Actiwatch and PSG. The PSG and Actiwatch subjects with normal PSG presentation had a higher TST correlation.