Reviewerly/exHarmony huggingface dataset:
The Effects of AR Learning Environment to Preschool Children’s Numerical Cognition

Preschool children have difficulty learning and comprehending abstract concepts, and the cognition of numbers has always been the key to mathematical enlightenment for young children. Our research aims to help preschool children build their cognition of cardinal and ordinal numbers, comprehend simple logical relationships, and master simple digital addition. We developed an Augmented Reality learning tool based on theories related to number cognition and a theoretical framework of software design for preschool children. We also conducted a teaching experiment in a kindergarten, and interviewed the teachers of the kindergarten to learn about their attitudes towards the application of AR in preschool education. Through data analysis, interviews, and discussions, we conclude that (a) AR application can positively influence children’s cognitive digital skills; (b) children have positive attitudes and positive evaluations toward AR application use, but there are some unavoidable problems in children’s attention allocation; (c) proficiency in operating AR tools has a large impact on children’s learning effects.
Measurement and Comparative Analysis of Evaporation under the Panel of Panji Floating Photovoltaic Power Station

Compared with the traditional land-based photovoltaic power station, the water surface photovoltaic power station not only strengthens the comprehensive utilization of water area, but also has the advantages of inhibiting evaporation and saving water resources. In this paper, taking the Panji 150 MW floating photovoltaic power station in Huainan City as the object, the first large-scale floating photovoltaic power station empirical platform is built, and the continuous evaporation in-situ monitoring experiment is carried out to process and analyze the evaporation observation data. Firstly, diurnal variation characteristics of natural evaporation in the area of floating photovoltaic construction are summarized. Then, the monthly variation trend of evaporation under the module panel is investigated. Finally, the characteristics of evaporation under different modules are compared and discussed, and it is found that the inhibition rate of evaporation under the photovoltaic module panel is between 39.87% and 47.80% during an evaporation monitoring period in the summer of 2021. The experimental results demonstrate that the coverage of floating photovoltaic can effectively restrain the evaporation, and the evaporation under the module panel has a tendency of uneven distribution within a year. There is a slight difference in the amount of evaporation under different modules. In the autumn of 2020, the total evaporation under the panel of APE back sheet photovoltaic module is the largest, followed by that of TPT, KPK, FCC.
ValidatorRep: Blockchain-based Trust Management for Ensuring Accountability in Crowdsourcing

Crowdsourcing as a computing paradigm, has been widely used in industries and services. Accountability in crowdsourcing services enables participants to work honestly and improves the quality of services. The realization of accountability requires trusted evidence, multiparty verification, and fair reward or punishment. Blockchain technology, which is inherently tamper-resistant, traceable, and decentralized, puts forward a direction for realizing the requirements. However, in the process of data management and decentralized verification, it is hard to achieve a better trade-off between efficiency and security. This paper proposes a blockchain-based verification scheme integrated by trust management, ‘validatorRep’, that is suitable to enhance accountability in the crowdsourcing system. In detail, a decoupled blockchain model is proposed for the differentiated storage of business transactions and log transactions during data interaction. Additionally, a fine-grained trust model is proposed, including both the rep-utation of participants and the trust relationship between participants. Based on fine-grained trust, the decentralized verification scheme is designed to guarantee secure data access, trusted verification, and fair reward or punishment. Finally, the proposed framework is deployed on the Ethereum platform to observe its effectiveness and overall performance. Simulation results also reveal that the proposed fine-grained trust model can provide efficient accountability for crowdsourcing.
eShadow+: Mixed Reality Storytelling Inspired by Traditional Shadow Theatre

eShadow is a digital storytelling platform inspired by traditional Shadow Theatre. It enables the creation of digital stories within a project-based approach that may start from scenario development and include the creation of digital puppets and sceneries, the set-up and recording of story scenes and the final assembly of a digital story. This paper presents how eShadow can be enhanced to solve the problem of creating mixed reality installations to offer rich learning experiences in informal learning settings. This enhanced version is eShadow+ and it is evaluated via two installations which are described and compared. The evaluation results demonstrate the effectiveness of the approach thus offering new learning opportunities that are aligned with current trends in the use of mixed reality technologies.
A Real-Time Background Replacement Method Based on Estimated Depth for AR Applications

Recent technological advances in Virtual Reality (VR) and Augmented Reality (AR) enable users to experience a high-quality virtual world. In VR applications, the user's physical movement is generally restricted because the situation around the real world cannot be seen. AR allows users to experience virtual worlds without restrictions on physical movement, but the extent to which they are replaced as virtual worlds is limited. In this research, assuming the use of smartphones and tablet devices, a partial virtual world system is implemented by removing only the background part from the real-time real-world image taken by the camera and replacing it with a virtual background.
Research on Parameter Identification Method of Asynchronous Motor Considering Load Characteristics

Aiming at the problem that the existing asynchronous motor parameter identification method only identifies the parameters of the motor itself, ignoring the parameters of the asynchronous motor load, this paper proposes a particle swarm optimization algorithm combined with spatial disturbance to realize the integrated parameter identification of the asynchronous motor, machine and pump. On the premise of identifying the parameters of the asynchronous motor itself, the load parameters of the pump are identified, and the improved particle swarm optimization algorithm is used to realize the integrated identification of the asynchronous motor and the load. By combining the particle swarm optimization algorithm (PSO) and the spatial disturbance (SD), the six equivalent parameters of the asynchronous motor and the pump load factor can be accurately and effectively identified. Compared with the traditional PSO algorithm, the global search method is increased. Excellent ability. An example proves the effectiveness of the algorithm.
Method for Quantum Denoisers Using Convolutional Neural Network

In many applications of quantum information science, high-dimensional entanglement is needed. Quantum teleportation is used for transferring information from one place to another using Einstein-Podolsk-Rosen pairs (EPR) and two classical bits of communication in a channel. Since we cannot produce multiple copies of an unknown state for amplification, we will generate multiple EPR pairs. However, after the distribution of the EPR pairs, they will have decreased fidelity with the ideal EPR state. So, to maintain the quantum states and maximize the quantification of the entanglement without losing the strength of the states, we propose to denoise the channel for a few types of noise. We created a random noise source and filtered out the irrelevant information without affecting the relevant information encoded in the quantum states. The proposed model is used for successful denoising of GHZ states from spin flips and bit flip errors. Much of the research work is not carried out by using machine-language-based neural networks for noise-reduction in quantum channels. In this paper, we propose a denoiser called quantum denoiser CNQD, which uses a feedforward convolution neural network model. We tuned our model with highly entangled GHZ states with zero phases and phase between [0, ∏] mixed with different kinds of noise. Finally, the proposed model can be used for optimal quantum communication via noisy quantum channels using GHZ states.
Research on Microblog Sentiment Analysis Based on Multimodal and Multiscale Feature Fusion

The pneumonia epidemic spread by the 2019 new coronavirus(2019-nCoV) has affected people's lives in any aspects, and has aroused widespread concern in global public opinion. In order to better grasp the real public opinion situation on the Internet and ensure the progress of epidemic prevention and public opinion analysis, this paper conducts research on netizen sentiment analysis for epidemic-related topics in the Internet community, and proposes a multimodal feature fusion solution. For the fusion of image and text modalities, Bi-LSTM and Bi-GRU are used to further learn the intrinsic correlation between modalities on the basis of bidirectional transformer feature fusion, and an image-based multi-scale feature fusion method is proposed, which can better solve the problem in this task. Experiments show that the method proposed in this paper is better than the current mainstream multimodal sentiment analysis methods.
Decisions, Decisions: An Analysis of Identity Theft Victims’ Reporting to Police, Financial Institutions, and Credit Bureaus

Identity theft, the theft and misuse of another person's identifying information, impacts approximately one-in-ten American adults annually. Despite its prevalence, low police reporting rates by victims means that the dark figure of identity theft remains substantial, with official statistics representing few cases. Instead, many identity theft victims report to credit card companies and banks, and some report to credit bureaus or other private institutions. Drawing on the 2016 National Crime Victimization Survey – Identity Theft Supplement, this paper investigates identity theft victims' decisions to report to law enforcement, financial institutions, and credit bureaus. It finds that along with situational factors, measures of seriousness impact reporting to these institutions and most strongly predict reporting to law enforcement. Moreover, this paper tests for interaction effects between paying out of pocket for losses and the other measures of seriousness and finds that victims who pay out of pocket have distinct reporting patterns compared to those who are reimbursed. This paper thus contributes by improving our understanding of the nature of victimizations that come to the attention of identity theft's various responding institutions.
On Fast Learning of Cooperative Transport by Multi-robots using DeepDyna-Q

In recent years, there have been many studies on cooperative transport. Reinforcement learning has attracted attention for formation changes. As reinforcement learning requires a large number of learning cycles, fast learning algorithms are needed. Model-based learning has been studied for unknown environments to learn efficiently. Deep Dyna-Q, a model-based learning method, has been proposed. In this paper, reinforcement learning using model-based learning is applied to formation changes of cooperative transportations. This study constructed Deep Dyna-Q using model-based learning to improve learning speed and applied it to robot formation change. The results showed that Deep Dyna-Q improved the number of episodes by about half compared to DQN. The formation change achievement rate in Deep Dyna-Q was 74% in an environment with a transport object and was slightly low. As it was reported that the achievement rate of the literature was 99%, an improvement in the success rate is desired. Future studies should aim to use a multi-agent algorithm MADDPG that uses model-based learning to achieve both achievement rate and learning time.
Development of a New Intelligent Mobile Robot Path Planning Algorithm Based on Deep Reinforcement Learning Considering Pedestrian Traffic Rules

In the near future in urban areas, people and mobile robots will coexist, and several strategies to avoid traffic accidents between human and mobile robot should be investigated. Among variety of research, most research focused on the collision avoidance with the obstacles and few research focused on pedestrians who walk following the traffic rules. If the mobile robot is trained how pedestrians walk under the traffic rule, we can achieve safer traffic for each other. This paper develops a new intelligent path planning algorithm to involve intelligence related to pedestrian traffic rules. We employ deep reinforcement learning for realizing the purpose. We use actual pedestrian walking trajectory data. The mobile robot agent learns pedestrian traffic rules by giving positive rewards for behaviors that conform to the pedestrian traffic rules and negative rewards for behaviors that do not conform. The proposed algorithm outputs translational speed and angular speed, considering the pedestrian’s relative positions, forwarding direction, and speed at the closest distance from the mobile robot. Based on learning results in the simulation environment, we confirmed that the mobile robot run in the manner that it understood the pedestrian traffic rules.
Markov Decision Process-Based Collision Avoidance Method for Multirotor Small Unmanned Aircraft System

The utilization of small unmanned aircraft systems (sUAS), also known as drones, is growing as their performance improves. In particular, multirotor sUAS are expected to have wide-ranging applications, such as delivering goods and aerial surveillance, but the rising number of sUAS flights is also exacerbating the risk of collision between sUAS and manned aircraft. This study develops a collision avoidance method for multirotor sUAS using the Markov decision process (MDP). Recently, the MDP-based collision avoidance method has been developed for various kinds of aircraft including sUAS. However, the collision avoidance method for sUAS is developed without distinguishing multirotor and fixed-wing sUASs from a compatibility perspective. We propose a collision avoidance method that optimally exploits the advantageous flight characteristics of multirotor sUAS, such as their ability to change acceleration dynamically and hover. Numerical simulations are conducted to demonstrate the performance of the proposed method. The results show that the proposed collision avoidance method can avoid collisions while suppressing excess deviation from the original path.
Construction of Chinese Character Semantic Knowledge Graph for Overseas Chinese Learners

Chinese characters have a long history and have been used until now. In recent years, they have attracted more and more foreigners to learn and use. However, traditional methods of Chinese character learning have a long cycle and are difficult. If we can understand the meaning of Chinese characters and form a knowledge group of them with relevant meanings, it can effectively improve learning efficiency. We achieve this goal by building a Chinese character semantic knowledge graph aims at organizing Chinese characters with semantic relevance together to facilitate the learners to understand. In the process of building the knowledge graph, the semantic computing capacity of HowNet was utilized, and 104187 associated edges were finally established for 6752 Chinese characters. Its scale meets the needs of overseas Chinese learners.
A Cloud-Native Online Judge System

Online Judge Systems are designed for the reliable evaluation of source code submitted by users. The system queues the code, compiles and tests them typically in a FIFO manner. However, we found that the cloud-native design of an online judge system is still a pending issue. Many existing open-source online judge systems are hard to deploy and scale, due to the tight coupling with some specific environments and the vague boundaries in their system architectures. In addition, these online judge systems hardly provide the support of resource scheduling as they are usually more concerned about the homogeneity of the resources. In this research, we design and develop a cloud-native online judge system that is able to (1) be built and run stably in dynamic environments (2) scale vertically and horizontally to the workload (3) do resource scheduling over CPUs and GPUs. Furthermore, this research also analyzes the consequence of adopting some modernly advocated design approaches and technologies; these are: microservice architecture design, event-driven architecture, domain-driven design, and containerization technologies such as Docker and Kubernetes.
A Case for Exploration: Exploratory Data Analysis in Neural Networks for Renal Tumor Classification

Exploratory data analysis provides insights into the structure and representation of data. By understanding data, more representative networks can be created without total reliance on implicit processes to match internal representations with expectation. The aim of this paper is to demonstrate how the usage of exploratory data analysis combined with knowledge from subject matter experts can increase the performance of networks and reduce reliance on mechanics. We use manual transformations based on data analysis to represent our data in a way that resembles the abstract processes that experts use to classify renal tumors. Compared to a baseline network, we achieved a significant increase in accuracy, 93.3% to 98.64%, without loss in external validations, while reducing the network size considerably. The reliance on internal mechanics to achieve the expected representation is flawed and exploratory data analysis can mitigate some of the pitfalls of this approach, leading to increased performance and a reduction in required compute power.
Social brokerage and productivity of users in online innovation networks

Abstract In this work, we investigate the impact of social brokerage on the innovation productivity of users in online innovation networks. Innovation in online networks is leveraged by coordinated interactions between the participating users. We leverage the data of users contributing on posts between 2017 and 2019 in two community question answering (CQA) forums: Stack Overflow and Math Stack Exchange. The innovation productivity of users in such online innovation networks, as valued by other participating users is quantified by the total score they received on their contributing posts. Users occupying brokerage positions are exposed to better ideas that boost the value of their contributing posts, thus improving their total score. Again, existing users with low social brokerage are also likely to gain from resource sharing owing to their denser networks. Our econometric analysis reveals that users in online innovation networks with very low or very high values of social brokerage receive higher scores as valued by other community users. Overall, we access the data of 537,938 users in Stack Overflow contributing on 6,002,996 questions, and 7,903,416 answers, and 20,393 users in the Math Stack Exchange contributing on 488,389 questions, and 638,110 answers. We observe a U ‐shaped effect of the user's social brokerage on the user's innovation productivity. Our empirical findings have various implications for firms hosting online CQA forums.
A comprehensive scheme for tattoo text detection

Tattoo text detection provides a vital clue for person and crime identification. Due to the freestyle and unconstrained nature of handwritten tattoo text over skin regions, accurate tattoo text detection is very challenging. This paper proposes a comprehensive scheme for tattoo text detection which comprises (a) adaptive Deformable Convolutional Neural Network (DCNN) for skin region detection to reduce text detection complexity (b) a Decoupled Gradient Text Detector (DGTD) for tattoo text detection from skin region (c) a Deep Q-Network (DQN) to refine the bounding boxes detected by DGTD, and (d) a Term-Frequency-Inverse-Document-Frequency (TF-IDF) model to group the words into text lines based on semantic information to fix the bounding box for the line. To test the effectiveness, the proposed method is evaluated on different datasets, namely, (i) a newly developed tattoo text dataset, (ii) benchmark bib number dataset of the marathon, and (iii) person re-identification dataset. The proposed method achieves 91.2, 87.5, and 88.8 F-scores from these three respective datasets. To demonstrate its superior performance, the text detection module (without skin detection) is also compared with state-of-the-art scene text detection methods on benchmark datasets, namely, ICDAR 2019 ArT, Total-Text, and DAST1500 and the proposed method achieves 90.3, 88.5 and 89.8 F-score from these respective datasets.
Empirical prior based probabilistic inference neural network for policy learning

Reinforcement learning is very much democratized for autonomous control of an unknown dynamics system. However, low data efficiency is a practical concern in physical systems. The underlying reason is that, for the current state-of-the-art probabilistic models, it is required to evaluate multiple forecasts before uncertainty estimation, leading to computationally intensive, if not intractable, uncertainty inference. To this gap, probabilistic dynamics is introduced in the current study. A self-consistent condition in terms of empirical prior is formulated to measure uncertainty via inferring parametric distribution, and the resulted neural network is termed as probabilistic inference neural network (PINN). This approach is remarkably effective for data regression and policy learning frameworks. Experiments are performed on UCI datasets. The results demonstrate that PINN performance is more competitive than those of the currently prevailing methods. Finally, a PINN-based policy learning framework is proposed and applied to benchmark control tasks and physical control task of an in-house self-developed 14-DOF robot featuring high dimensional state/action spaces. The results demonstrate highly competitive data efficiency and policy performance to outperform the most prevailing policy learning framework.
Artificial Intelligence-Based English Self-Learning Effect Evaluation and Adaptive Influencing Factors Analysis

Under the background of continuous development in our country, traditional English education can no longer meet the needs of modern times. Through the evaluation of English autonomous learning effect of artificial intelligence and the analysis of the influencing factors of adaptability, the teaching effect of English class is improved and the students’ awareness of autonomous learning is cultivated. In the pilot study, students now have an overall level of adaptation (3 &lt; M &lt; 4) supported by English proficiency. That is, the standard deviation is 1. The overall level of self-study in English is higher than that of boys (M = 3.40 for females, M = 3.32 for males, and M = 3.32 for males). Compared with non-English majors, English majors are more suitable for self-study of artificial intelligence in English (M = 3.59 for English majors), (M = 3.36 for non-English majors), and students can improve their adaptive ability to learn AI by creating models. Transfer learning is the key to improving learners’ English proficiency, and adaptive learning is the key to achieving this goal. Self-adaptive learning ensures the quality of students’ autonomous English homework. Improper English learning not only affects students’ learning outcomes but also affects their ability to learn English.