Reviewerly/exHarmony huggingface dataset:
Research on image recognition of Wushu action based on remote sensing image and embedded system

The cultural connotation of martial arts inherited the country's culture and played an important role in promoting. Martial arts culture and inspire the national spirit in the strong penetration of the East's strong culture and the collision of Western culture Western. Martial arts education is related to martial arts. Penetration of the spread and martial arts culture in primary and secondary schools has imminent. To foster a national spirit and carry forward has played a very important role. Method of comparing literature, cultural studies, the use of the modern significance of education, and the forward martial arts research. After development for over fifty years, human beings are now the atmosphere. To obtain a large-scale data set accuracy and for marine-based, can use different light and microwave sensors. Frequency and spatial resolution of the data acquisition range per minute at a time from one month, microwave, centimeter-scale from kilometers from the visible light, and the range of the electromagnetic spectrum covers wavelengths Data progress from the point of view of remote sensing sensor ethnic significant space and time of lives to expand understanding of the human nature of the environment, to get the data on a global scale, more and more digital Earth were able to provide the resources. The development and trends of remote sensing satellites around the world have been introduced.
Neural Network Virtual Sensors for Fuel Injection Quantities with Provable Performance Specifications

Recent work has shown that it is possible to learn neural networks with provable guarantees on the output of the model when subject to input perturbations, however these works have focused primarily on defending against adversarial examples for image classifiers. In this paper, we study how these provable guarantees can be naturally applied to other real world settings, namely getting performance specifications for robust virtual sensors measuring fuel injection quantities within an engine. We first demonstrate that, in this setting, even simple neural network models are highly susceptible to reasonable levels of adversarial sensor noise, which are capable of increasing the mean relative error of a standard neural network from 6.6% to 43.8%. We then leverage methods for learning provably robust networks and verifying robustness properties, resulting in a robust model which we can provably guarantee has at most 16.5% mean relative error under any sensor noise. Additionally, we show how specific intervals of fuel injection quantities can be targeted to maximize robustness for certain ranges, allowing us to train a virtual sensor for fuel injection which is provably guaranteed to have at most 10.69% relative error under noise while maintaining 3% relative error on non-adversarial data within normalized fuel injection ranges of 0.6 to 1.0.
Variable Selection in Genetic Algorithm Model with Logistic Regression for Prediction of Progression to Diseases

Earlier risk assessment and identification of different diseases is the most crucial issue for avoiding and lowering their progression. The researchers typically used the statistical comparative analysis or step-by-step methods of feature selection using regression techniques to estimate the risk factors of diseases. The outcomes from these methods emphasized on individual risk factors separately. A combination of factors, however, is likely to affect the development of disease rather than just anyone alone. Genetic algorithms (GA) can be useful and efficient for searching a combination of factors for the fast diagnosis with best accuracies, especially for a large number of complex and poorly understood factors, as in the case in the prediction of disease development. Our proposed model shows the potential for the application of GA in diagnosing disease and predicting accuracy. Our proposed model demonstrated that the amalgamation of a small subset of input features produces the optimum performance than the use of all the single significant features individually. This model not only predicts the best feature sets and accuracy but also overcome the problem of missing values present in the dataset. Variables more frequently selected by LR might be more important for the prediction of disease development and accuracies by GA.
A Scheme of Reversible Data Hiding for the Encryption-Then-Compression System

This paper proposes an encryption-then-compression (EtC) system-friendly data hiding scheme for images, where an EtC system compresses images after they are encrypted. The EtC system divides an image into non-overlapping blocks and applies four block-based processes independently and randomly to the image for visual encryption of the image. The proposed scheme hides data to a plain, i.e., unencrypted image and the scheme can take hidden data out from the image encrypted by the EtC system. Furthermore, the scheme serves reversible data hiding, so it can perfectly recover the unmarked image from the marked image whereas the scheme once distorts unmarked image for hiding data to the image. The proposed scheme copes with the three of four processes in the EtC system, namely, block permutation, rotation/flipping of blocks, and inverting brightness in blocks, whereas the conventional schemes for the system do not cope with the last one. In addition, these conventional schemes have to identify the encrypted image so that image-dependent side information can be used to extract embedded data and to restore the unmarked image, but the proposed scheme does not need such identification. Moreover, whereas the data hiding process must know the block size of encryption in conventional schemes, the proposed scheme needs no prior knowledge of the block size for encryption. Experimental results show the effectiveness of the proposed scheme.
Study on customized knowledge graph of student pilot learning in FITS training

Knowledge graphs are useful sources for various AI applications, however the basic paradigm to support pilot training is still unclear. In the paper, It is proposed to generate the customized knowledge graph of flight trainings using machine learning method for the flight training program. In order to provide the successful key to the further understanding of the learning problems between the students and the instructors. In this research, we collected data from an aeronautical academic in Taiwan that students were trained for Recreation Pilot License Program. We performed a test on 24 students at the first of each training course, 16 data of collected been used on building the module, 8 of them used to exam the module. There are 12 courses in the training program, and 30 hours total time were suggested by academic. The score which we applied on test were based on LCG method which is the sum of Maneuver and SRM Grades. For the indicators of course component in Learner Centered Grading, namely (a) CCS1: Operation &amp; Effect of Controls; (b) CCS2: Straight &amp; Level; (c) CCS3: Climbing &amp; Descending; (d) CCS4: Turning; (e) CCS5: Stalling; (f) CCS6: Revision; (g) CCS7: Circuits; (h) CCS8: Cross-Wind Training; (i) CCS9: Circuit Emergency; (j) CCS10: Solo Circuit; (k) CCS11: Forced Landing; and (l) CCS12: Precautionary &amp; Searching Landing. Through the method of Knowledge Graph, we deduct and predict the number of hours that need to be added for each student’s learning. Using the dynamic knowledge graph to display the key issues of the course learning continuously, and make follow-up decisions for the students, instructors and airliners.
Playing Carcassonne with Monte Carlo Tree Search

Monte Carlo Tree Search (MCTS) is a relatively new sampling method with multiple variants in the literature. They can be applied to a wide variety of challenging domains including board games, video games, and energy-based problems to mention a few. In this work, we explore the use of the vanilla MCTS and the MCTS with Rapid Action Value Estimation (MCTS-RAVE) in the game of Carcassonne, a stochastic game with a deceptive scoring system where limited research has been conducted. We compare the strengths of the MCTS-based methods with the Star2.5 algorithm, previously reported to yield competitive results in the game of Carcassonne when a domainspecific heuristic is used to evaluate the game states. We analyse the particularities of the strategies adopted by the algorithms when they share a common reward system. The MCTS-based methods consistently outperformed the Star2.5 algorithm given their ability to find and follow long-term strategies, with the vanilla MCTS exhibiting a more robust game-play than the MCTS-RAVE.
HisBERT for Conversational Reading Comprehension

It is still an open problem to efficiently explore BERT for contextual question answering, such as conversational reading comprehension (CRC). Previous work on using BERT for CRC does not deeply integrate conversation history into the architecture of BERT. In order to make better use of BERT for CRC, in this paper, we propose HisBERT (BERT with conversation history) that consists of two parallel units and divides BERT into three blocks to better integrate conversation history. Additionally we use adversarial training to disturb the word embedding layer, so as to increase the robustness of the proposed model. Experiment results show that HisBERT not only achieves strong competitive performance, but also benefits from a good trade-off between performance and training time.
Simulation of quantum key distribution in a secure star topology optimization in quantum channel

The field of quantum cryptography is mostly theoretical therefore in this paper we represent its implementation by means of virtual scenarios. The central issue in cryptography is the secure transmission of the key between nodes. Thus, in this paper we establish a secure channel using Quantum Key Distribution (QKD) for the transfer of the key material between the nodes and help to identify an eavesdropper in the channel. A graphical representation of the quantum channel traffic at the ideal state and also during network disruption has been established. Due to the complex nature of quantum networks and high cost of establishment, a physical implementation of the same is not feasible. Hence a simulation has been implemented via the use of NS-3 (Network Simulator Version 3) which has QKDNetSim module built into it. Finally, our simulation indicates the presence of an intruder by virtue of various network implementations within the quantum channel.
WITHDRAWN: Smart internet of vehicle maintenance system

With an increase in the number of vehicles, the problems related to vehicle maintenance systems are increasing day by day. The problem of monitoring vehicle maintenance is becoming very critical for people in the busy world. This paper discusses the implementation of a two-wheeler maintenance system using IoT. This system continuously monitors the fuel quantity, air pressure, engine oil viscosity, battery health, and pollution. This system predicts faults and failures of parts in the vehicle before it even happens, sending real-time data of vehicle's health to mobile with the usage of sensors through IoT cloud via IoT gateway.
SubICap: Towards Subword-informed Image Captioning

Existing Image Captioning (IC) systems model words as atomic units in captions and are unable to exploit the structural information in the words.This makes representation of rare words very difficult and out-of-vocabulary words impossible.Moreover, to avoid computational complexity, existing IC models operate over a modest sized vocabulary of frequent words, such that the identity of rare words is lost.In this work we address this common limitation of IC systems in dealing with rare words in the corpora.We decompose words into smaller constituent units 'subwords' and represent captions as a sequence of subwords instead of words.This helps represent all words in the corpora using a significantly lower subword vocabulary, leading to better parameter learning.Using subword language modeling, our captioning system improves various metric scores, with a training vocabulary size approximately 90% less than the baseline and various state-of-the-art word-level models.Our quantitative and qualitative results and analysis signify the efficacy of our proposed approach.
A Comparison of Reification and Cokriging for Sequential Multi-Information Source Fusion

Many engineering tasks, such as optimization, analysis, model development, model calibration, and others, can potentially exploit information from many sources. These sources include numerical models, expert opinion, and experimental data. Information fusion over these sources of information has the potential to provide a more complete quantitative picture of the current state of knowledge of a given ground truth quantity of interest. This state of knowledge can be updated as new information from any given source is acquired. In this work, we compare two information fusion approaches that both seek to combine all available information to form a surrogate model of the ground truth. These are model reification and cokriging. The comparison considers several test functions as well as a real world NACA 0012 analysis. A quantity of interest is considered for each test case, as well as the derivative of the quantity of interest in some cases. Each fusion approach performs well generally, with each being superior to the other under certain conditions.
Effects of Different Dynamics in an Ant Colony Optimization Algorithm

Finding shortest path in a labyrinth, made up of roads, crosses and dead ends, and where entrance and exit dynamically change during the time, is an important and challenging optimization task especially in emergency scenarios, such as earthquakes, volcanic eruptions, and/or hurricanes. In this research work we present a study on the effects of cooperative and competitive strategies in an agent-based model using an Ant Colony Optimization (ACO) algorithm for the solution of labyrinth problem. Two different ants' search strategies in the colony have been designed: those that acts competitively and selfishly, damaging some crossings (i.e. nodes) on the path, and cooperative ones, which instead attempt to repair them. The purpose of both strategies is finding a path from the entrance to the exit in order to gain the highest number of some resources positioned appropriately at the exit and bound to be collected if and only if both types of ants reach it via the shortest path. This research work has a two fold aim, that is, finding obviously the shortest path in the labyrinth (then maximize the resources gained), as well as analyzing the effects of both strategies on the overall ACO performances, and inspecting how one strategy affects the other by motivating it to improve its performances and its efficiency. From the overall outcomes, indeed, it emerges that the existence of the competitive ants is a strong incentive for cooperative ones to improve themselves.
A Novel Ontology Design and Comparative Analysis of Various Retrieval Schemes on Education Domain in Protégé

The goal of Semantic Web ensures that the computer is able to recognize the Web data and thus can offer people with a variety of intelligence service. Linked data is the data of today's web. As time passes, the world is changing and the technologies are developing in the same way. The computers are developing, and from the isolated version, they have been entered in the field of networks of information exchange. As the data available on the Web is increasing, it is becoming difficult to handle it and retrieve it relevantly, so Semantic Web helps to search semantically as data on it is available in form of linked data (ontology).In this paper, we have proposed an ontology on education domain with the help of Protégé tool.
Towards a Scalable Censorship-Resistant Overlay Network based on WebRTC Covert Channels

In many regions of the world, nation-states enforce Internet censorship policies that prevent unrestricted access to information and services by their citizens. Over the years, many censorship circumvention tools have been proposed which, however, require either the deployment of a dedicated infrastructure within trusted ISPs, or are vulnerable to state-of-the-art traffic analysis techniques. To fill this gap, we propose to build a practical censorship-circumvention service that exhibits strong resistance against traffic analysis attacks. By relying on a recent proposal for creating covert channels through WebRTC streams, we discuss the design of a distributed system named Censorship-Resistant Overlay Network (CRON). CRON aims at offering to the users located in censored regions a set of services that allow them to locate proxies positioned in the free Internet region, and set up secure covert tunnels for accessing arbitrary sites on the Internet. We present the key challenges and explore the solutions in making CRON robust against state-level attacks.
Competencies, Culture, and Change: A Model for Digital Transformation in K-12 Educational Contexts

Abstract On the surface, adopting technology presents itself as a technical issue. Yet, the real challenge of digital transformation in educational contexts necessitates a second-order change to disrupt and realign interconnected systems. A significant component of digital transformation in K-12 schools is an understanding of the unique affordances of digital tools and technologies and how these can be leveraged to align with learning goals and targets to impact teaching and learning in new ways. While there are several models for innovation diffusion and technology adoption in K-12 contexts, they fall short, particularly in describing the nature and interactions of these interconnected systems. These aspects of technology implementation remain a mystery. As a result, efforts to enact change in K-12 organizations often fall short due to a lack of understanding of context, inadequate goal-setting, insufficient professional development and personalized supports to build capacity, and a failure to evaluate progress. In K-12 educational settings, the people, the competencies, and the culture, alongside the strong leadership, resources, and organizational context, are all essential to effect sustainable change. We propose a model for digital transformation that considers all of these factors and interconnected systems.
A Brief Review of 3D Face Reconstruction Methods for Face-Related Product Design

3D face reconstruction is highly important in the ergonomics study of 3D face, especially in terms of designing face-related products. With the development of machine vision and deep learning, it becomes feasible to reconstruct the 3D face from a single image, which can make it practical to obtain a large scale data of 3D face shape instead of using the 3D scanning technology. The 3D face reconstruction methods, to recover the 3D facial geometry under unconstrained situations from 2D images, are roughly classified into two categories, namely (1) 3D Morphable Model (3DMM) fitting based method and (2) End-to-end deep convolutional neural network (CNN) based method. The 3DMM as a general face representation is introduced emphatically and two kinds of 3DMM fitting based methods are introduced when improving the 3DMM modeling mechanism. Four representative CNN based methods are compared when regressing from pixels of face image to the 3D face coordinates in different gird-like data structures. Finally, six common face datasets largely used in the training and testing are listed.
Multi-Level Generative Chaotic Recurrent Network for Image Inpainting

This paper presents a novel multi-level generative chaotic Recurrent Neural Network (RNN) for image inpainting. This technique utilizes a general framework with multiple chaotic RNN that makes learning the image prior from a single corrupted image more robust and efficient. The proposed network utilizes a randomly-initialized process for parameterization, along with a unique quad-directional encoder structure, chaotic state transition, and adaptive importance for multi-level RNN updating. The efficacy of the approach has been validated through multiple experiments. In spite of a much lower computational load, quantitative comparisons reveal that the proposed approach exceeds the performance of several image-restoration benchmarks.
Children as food designers: The potential of co-creation to make the healthy choice the preferred one

According to the WHO, childhood obesity is one of the most serious public health challenges of the twenty-first century. In this context, finding ways to make the healthier food choices the preferred ones can be a valuable contribution to solving this multifaceted problem. Sensory and consumer science offers a wide range of tools that can support the development of healthy and well-accepted food alternatives. In traditional sensory and consumer science, children would be involved in the product development process either as testers or informants. However, in our opinion, it would be valuable to extend their role to co-creators or co-designers, an approach already more established in the field of innovation and design, where children actively participate in the idea generation and development of healthy food that they will like and choose. Our own experience has shown that involving children in the idea-generation step for healthy food can be highly motivating and stimulating for them. In this opinion article, we discuss why it is important to include children actively as a relevant consumer segment in product development and suggest a process and methods that could be valuable for brainstorming about food ideas with children.
The research and design of Smart mobile robotic arm based on gesture controlled

In this paper, a smart mobile robotic arm based on gesture control is designed, which is constituted of a remote-control smart car with a robotic arm equipped on the car. The gesture recognition sensor is the Leap Motion module and uses the Processing API function on the PC to locate the position information of both hands in 3D space and convert it into various control data. It uses Bluetooth and the car system to communicate. The car system uses the STM32's NUCLEO-F411RE development board as the controller to analyze and process the gesture data received by Bluetooth accordingly. The left-hand gesture signal was implemented for the movement control of the car, and the right-hand gesture signal is converted into 5 steering gear PWM signals to control the robot arm Elbow and wrist motion and grab. The experimental results show that the car and robot arm can complete the response accurately and quickly, and implement various remote control according to gestures.