Reviewerly/exHarmony huggingface dataset:
Exploring 222 years in Space and Time: The User Experience of the Virtual Sydney Rocks

This chapter examines the Virtual Sydney Rocks (VSR) as a proof of concept and case study of a time-based pedagogical virtual heritage city-world. The VSR consists of an interactive digital model of the historic Sydney Rocks district and an associated website. Users are able to travel freely in space and time to explore the built environment of the Sydney Rocks from 1788 to 2010. Each building is linked to an individual webpage with detailed information and accessed by clicking on the building. Users can also change the speed of time and switch to an overhead view to see a time-lapse. The design, creation and testing of the VSR are described and the findings discussed. The chapter concludes by examining opportunities for deepening public engagement with heritage by crowd-sourcing the co-creation of virtual heritage resources.
Cloud Water Path, Precipitation Amount, and Precipitation Efficiency Derived from Multiple Datasets on the Qilian Mountains, Northeastern Tibetan Plateau

Orographic clouds and precipitation are critical to the water cycle and ecology of arid and semiarid mountainous regions. The spatial and seasonal distributions of the cloud water path (CWP), precipitation amount, and precipitation efficiency (PE) of the Qilian Mountains (QM) in the northeastern Tibetan Plateau were investigated. To solve the limitations of rain gauges and radar observations at high elevations of the QM, we used the latest multiple datasets, including the Global Precipitation Measurement (GPM), Advanced Very High Resolution Radiometer, and rain gauge data from 2016 to 2019. The amount of precipitation retrieved from the two GPM products was evaluated using rain gauge data. The results show that the GPM data generally overestimate the precipitation amount on the QM and have a high correlation with rain gauge data in warm seasons and a low correlation in winter seasons, primarily due to the significant influences of snow and ice coverage. The terrain height and water vapor supply below 600 hPa are two critical factors that determine the spatial and seasonal distributions of clouds and precipitation in the QM. The precipitation generally increased with an increase in terrain elevation below 3000 m and decreased above 3000 m on the QM. CWP, precipitation amount, and PE in the mountainous areas are much higher than those in the surroundings and decrease from the southeast to the northwest because of the decrease in low-level water vapor supply. CWP, precipitation amount, and PE were the highest in summer because of the strong moisture transport from the southeast and the lowest in winter because of the weakest water vapor transport. Water vapor fluxes from the eastern, southern, and northern boundaries contributed to the wetness of the eastern and northeastern slopes of the QM. The lack of low-level water vapor in the westerly winds was the main reason for drought in the western QM.
Instance-Based Transfer Learning Approach for Vietnamese Speech Synthesis with Very Low Resource

This paper proposes a method using the Instance-based transfer learning approach to build a Vietnamese speech synthesis system based on a target voice 45 times smaller than source voice. By using the correlation features between data objects, we can reuse the entire model or part of the previously trained weights to retrain with the new data set. Our model consists of two stages: (1) Training a DC-TTS model on the initial voice with a large data set and (2) Apply the Instance-based Transfer Learning approach to the previously trained model to generate new voices with a small amount of recorded data. After training with only 320 sentences (about 1 h), the model will be able to generate new voices with high quality. Our method can help to decrease data voice for training significantly and build a new speech synthesis system quickly. The MOS score of synthesized voice is approximately the same as that of the Tacotron 2 model, but with a much smaller amount of training data. This proves that our method is highly feasible to build a Vietnamese speech synthesis system in the case of limited data.
Learning from Face recognition under occlusion

Occluded face recognition is a key problem to be solved in a reality-oriented face recognition system. From the point of view of subspace regression, this paper reviews the general methods of mainstream linear regression classifiers to deal with occlusion problems: collaborative representation, dictionary representation of occlusion, and learning and compression techniques of occlusion dictionaries. The existing iterative weight error coding methods are reviewed from two aspects of noise suppression and occlusion detection. In this paper, two ideas to solve the occlusion problem in a modern face recognition system are proposed: the restoration of the occlusion region and the location of occlusion based on attention. The application systems for these two ideas are given respectively. This paper emphasizes the importance of feature extraction in solving the problem of occluded face recognition. Finally, it points out the challenges that occluded face recognition brings to computer vision; the main problems existing in existing methods in optimization algorithms and feature extraction, and the key problems to be considered in using convolution neural networks to deal with occlusion problems in the future. It is introduced that solving the problem of face recognition under occlusion brings many benefits to life.
Real-Time Detection of First Stories in Twitter Using a FastText Model

Online social networks attract millions of users who post and read microblogs on a daily and even hourly basis. The most prominent of these networks is Twitter, which has more than 300 million users ranging from ordinary individuals up to heads of state, as well as news agencies and large organizations. First story detection (FSD) refers to locating the original instance of a tweeted and retweeted information item. The term frequency-inverse document frequency (TF-IDF) algorithm has commonly been used for FSD, as well as various modifications of TF-IDF. The majority of FSD is to classify a document as a first story if the document differs significantly from those published before and thus may describe a new even. FSD is a very challenging, if not impossible, task for human operators, since it requires inspecting millions of documents per day. This chapter compares the real-time FSD performance of original and modified TF-IDF algorithms with FastText, which is an open-source library for text classification developed by Facebook. Both algorithms are implemented on the Apache Storm computing platform, which has the advantages of scalability, efficiency, and robustness. Results showed that FastText provided significant enhancements over the TF-IDF-based algorithms in both detection time and accuracy in similarity between tweets.
SDN, Big Data and AI Technologies Design and Implementation of the Network of Intelligent CloudCampus

The network structure of traditional park network is complex, the service access is limited, and the maintenance workload is large. Enterprises and organizations have higher and higher requirements for the park network. With the impact of the expanding business demand on the existing park network, especially the problems of boundary disappearance, access control and network security brought by the introduction of wireless network. Under the existing conditions, how to quickly transform the park network by using SDN, big data and AI technology, improve the performance of the park network, and meet the increasing requirements of enterprises and organizations for the park network, which is an urgent problem to be solved.
Comparing Quantum Computing Platforms

This research compares and contrasts two commonly available quantum computing platforms available today to academic researchers: the IBM Q-Experience and the University of Maryland’s IonQ. Hands-on testing utilized the implementation of a simple two qubit circuit and tested the Pauli X, Y, and Z single-qubit gates as well as the CNOT 2+ qubit gate and compared the results, as well as the user experience. The user experience and the interface must be straightforward to help the user’s understanding when planning quantum computing training for new knowledge workers in this exciting new field. Additionally, we demonstrate how a quantum computer’s results, when the output is read in the classical computer, loses some of its information, since the quantum computer is operating in more dimensions than the classical computer can interpret. This is demonstrated with the ZX and XZ gates which appear to give the same result; however, using the mathematics of matrix notation, the phase difference between the two answers is revealed in their vectors, which are 180 $$^\circ $$ apart.
Application of Artificial Intelligence in Computer Network Technology in Big Data Era

Computer network technology has become a widely used modern technology in the development of modern society, which has a good role in promoting the development of many industries and fields in the society, so it is necessary to promote the better application and development of computer network technology. In the current era of big data, in the application and development of computer network technology, more and more modern technical means can be integrated, and artificial intelligence is a common one, which plays an irreplaceable role and value in improving the level of computer network technology, so it is necessary to grasp the application of artificial intelligence reasonably.
Analyzing Software Security-related Size and its Relationship with Vulnerabilities in OSS

This paper presents a preliminary and exploratory study of the relationship of software security-related size, in lines of code, and the software security level, expressed as the vulnerability density. We developed a classifier and employed source code analyzers to determine the proportion of security-related code and the security level of a sample of 162 open source software (OSS) projects, developed in five different programming languages. Based on the information collected, we analyzed the proportion of security-related code and investigated its correlation with the static analysis vulnerability density of the selected projects. We found that the proportion of security size in OSS can vary depending on its type. Enterprise OSS present a median of 22 % of its code related to security, whereas volunteer-based OSS have a 9% median. We did not find a correlation between security-related size and vulnerability density. However, by filtering only volunteer-based OSS projects in the sample, we found a moderate positive correlation between vulnerability density and the proportion of security-related size. Our study results do not support the idea that a larger proportion of security-related code is associated with more security in terms of a lower vulnerability density. Nonetheless, the type of OSS, namely volunteer-based or enterprise, seems to have a significant impact on software security. Volunteer-based OSS presented a smaller proportion of security-related code, but as this proportion increases, so does the vulnerabilities, indicating a worsening on the security level. The phenomenon was not identified in enterprise OSS.
An interactive system to improve cognitive abilities using electromyographic signals

This article presents a system for improving people’s cognitive abilities using electromyography (EMG) signals. This system was created under the serious game mode with a new way of interaction with the user. The sensor Myo Armband was used to measure the EMG signals, which sends the signals via Bluetooth to a Hand Gesture Recognition Model based on a Support Vector Machine to detect the gesture made by the player. This Hand-Gesture Recognition Model was developed in Matlab 2020a. The correct commands given by the recognition model are sent to another computer through the TCP / IP communication protocol to visualize the effects on the game character. The game was developed with the Unity 3D graphics engine to generate an application that optimizes resources. The objective of the game is to solve mazes with two levels of difficulty which allow improving cognitive skills such as attention, concentration, observation, abstract thinking, psycho-motor skills and problem solving.
Research on Facial Expression Capture Based on Two-Stage Neural Network

To generate realistic three-dimensional animation of virtual character, capturing real facial expression is the primary task.Due to diverse facial expressions and complex background, facial landmarks recognized by existing strategies have the problem of deviations and low accuracy.Therefore, a method for facial expression capture based on two-stage neural network is proposed in this paper which takes advantage of improved multi-task cascaded convolutional networks (MTCNN) and high-resolution network.Firstly, the convolution operation of traditional MTCNN is improved.The face information in the input image is quickly filtered by feature fusion in the first stage and Octave Convolution instead of the original ones is introduced into in the second stage to enhance the feature extraction ability of the network, which further rejects a large number of false candidates.The model outputs more accurate facial candidate windows for better landmarks recognition and locates the faces.Then the images cropped after face detection are input into high-resolution network.Multi-scale feature fusion is realized by parallel connection of multi-resolution streams, and rich high-resolution heatmaps of facial landmarks are obtained.Finally, the changes of facial landmarks recognized are tracked in real-time.The expression parameters are extracted and transmitted to Unity3D engine to drive the virtual character's face, which can realize facial expression synchronous animation.Extensive experimental results obtained on the WFLW database demonstrate the superiority of the proposed method in terms of accuracy and robustness, especially for diverse expressions and complex background.The method can accurately capture facial expression and generate three-dimensional animation effects, making online entertainment and social interaction more immersive in shared virtual space.
Exploration and Practice of Computer Aided Teaching Reform in Industrial Design Specialty

Since the advent of computer technology(CT), it has been widely used and developed, now in the field of design, the influence of CT is also quite far-reaching. In today's ID (industrial design) industry, CT can be said to be a very important core technology, it is an indispensable part of modern industrial product development. Therefore, computer aided instruction is a very important compulsory course in the ID major of University. Now, with the higher and higher level of industry, the industry is also more and more developed, computer-aided teaching(CAT) should also keep pace with the times, timely reform and innovation, this paper is also based on the investigation and discussion. The experimental results show that the teaching performance of class 1(C1) reformed in CAT is better than that of class 2(C2) reformed in CAI. The best teaching score of C1 is 95, and the lowest is 85, which is very satisfactory.
Implementation of an extended FIDO2 authenticator using Attribute-Based Signatures

Passwords has been used as an authentication method for Web services, but it has some issues in terms of usability and security. FIDO solves these problems and is now being used as an authentication method. It consists of two elements: signature verification using public key cryptography and local authentication using an authenticator such as a user's device. It is a robust authentication method that is resistant to current mainstream attacks. When FIDO authentication is used to grant special access rights to agents other than the user, it is necessary to implement access control as a system. We propose to extend the specification of FIDO authentication to use Attribute- Based Signatures, where the access control is based on the cryptographic used for authentication. We also implement an extended FIDO authenticator for the proposed method.
A Subgraph Isomorphism-based Attack Towards Social Networks

It has been widely recognized that social network analysis of group relationships and behaviors come to thrive, so publicity social networks have gained growing attention from third-party individuals for academic researchers and advertisers. Anonymous versions are generally obtained from the naive anonymization mechanism through identity transformation to fend off attacks on socially sensitive information. The adversaries intend to implement person re-identification in anonymous data, and they generally possess a subset of social interaction information of the target user. In this way, a privacy breach could be achieved by exploiting the neighbourhood of the object's known structural information. Say, if one node's information is breached, other nodes’ private information will be compromised according to the detected structural information. Therefore, all the mentioned above are equivalent to the subgraph isomorphism problem to identify who is who in the social networks. Existing enumeration and indexing-related subgraph isomorphism methods cannot process matching problems with both large target and query graphs. Therefore, subgraph querying is a knotty problem pressing for a solution. In this work, we elaborate on the subgraph of structural attack. Our subgraph isomorphism-based method adopts a 3-stage framework for learning and refining structural correspondences over a large graph. First, we generate a set of candidate matches and compare the query graph with these candidate graphs over the corresponding number of vertex and edge, which can noticeably reduce the number of candidate graphs. Secondly, we employ the permutation theorem to evaluate the row sum of vertex and edge adjacency matrix of query graph and candidate graph. Lastly, our proposed scheme deploys the well-found equinumerosity theorem to verify if the query graph and candidate graph satisfy the isomorphic relationship. Solid evaluation criteria on time complexity verify the proposed attack strategy.
Research on block-chain teaching based on goal oriented and flipped classroom

Taking the teaching of block-chain principle and technology as an example, an innovative hybrid teaching method based on goal-oriented and flipped classroom is proposed. The traditional teaching methods is reformed in the proposed method, which combines two new teaching modes for goal orientation and flipped classroom, and applies them to the curriculum teaching practice of block-chain principle and technology. This method first groups the students according to the teaching content of the block-chain principle and technology and the number of students in the class, assigns the corresponding learning tasks, and puts forward the learning priorities and difficulties corresponding to the curriculum learning tasks, as well as the curriculum knowledge points to be understood. The group members study and discuss according to the course learning tasks, and display the learning results in the classroom. The instructor evaluates the courseware and content explanation made by the students, points out the shortcomings of the students in knowledge understanding and explanation, and explains in detail the deficiencies and places that need to be further emphasized or added content, then discuss and communicate with the whole class to make the students fully understand and master the knowledge. Compared with the traditional teaching methods, the method provided in this paper can play better teaching effects in cultivation and improvement of students' abilities such as teamwork, independent thinking, autonomous learning and self-expression.
Modern Learning Strategies Used by Teachers to Increase Students’ Motivation Toward E-learning in Many Societies

This study examines the modern learning strategies that teachers employ to increase students’ motivation for e-learning in six societies (Palestine, Bahrain, Turkey, the United Kingdom, Canada, and the United States), as well as the impact of these strategies on increasing students’ participation in the e-learning process, the learning strategies employed by teachers, and the challenges they face. By performing separate open semi-structured interviews with a group of ten teachers, the study used the qualitative Methodology of (multiple) case study method. The findings show that teachers use learning strategies differently in the e-learning process and that these strategies are critical in raising students’ interest, enhancing their attention, and encouraging them to engage in e-learning. The findings also revealed the most significant challenges that teachers faced when implementing these educational strategies in e-learning, which were broken down into material, technological, technical, educational, and skills-related challenges, and ways to improve them. In order to resolve these obstacles, the participants recommended providing students and parents with materials and technical assistance. The current study suggests that teachers be trained and qualified in order to efficiently use e-learning strategies.
Robust Motion Mapping Between Human and Humanoids Using CycleAutoencoder

Teleoperation needs accurate and robust motion mapping between human and humanoid motion to generate intuitive robot control with human-like motion. Data-driven methods are often deployed as it can result in intuitive, real time motion mapping. When using these methods, the common focus is on the accuracy of the motion mapping model. However, effort needs to be put into making the mapping model robust in face of noisy or incomplete dataset. In other words, the model needs to learn the generalizable mapping rules, not just be accurate in predicting the training data. To create a robust and accurate model for motion mapping, we developed the novel CycleAutoencoder method. This method simultaneously trains two autoencoders using traditional losses, mixed losses, and cycle losses. These losses allow the autoencoders to reconstruct the motion mutually between humans and humanoids. This allows the method to learn the mapping with improved accuracy and robustness compared to training a traditional autoencoder. The results of human subject involved experiments demonstrated that the CycleAutoencoder method can achieve both accuracy and robustness for the mapping compared with other autoencoder-based mapping methods.
Self-supervised Attention Learning for Robot Control

Inspired by how humans solve a task: only paying attention to the useful part in the vision while neglecting the irrelevant information, we propose an attention mechanism, which learns to focus on the motion part (such as the robot arm and the manipulating target) in an image, while neglecting the noisy background. The model takes RGB images as input, using CNN to extract intermediate spatial features first; then by transporting learned features between two different images and minimizing the reconstruction loss, the attention module is forced to learn a pattern of attention. The output of the model is soft attention map, highlighting the useful part while suppressing background clutter in the image, which can be used as the combined weights for downstream control. Our method is trained in a fully self-supervised way, no manual labeling data is used during training, which increases its ease of use in robot tasks.