Reviewerly/exHarmony huggingface dataset:
Augmented Reality in Technology Education for Tourism Promotion

Tourism today is one of the main sources of income for different countries in the world, giving a livelihood to thousands of families who subsist thanks to domestic and foreign tourists. The objective of this research work was to develop a mobile application using augmented reality for use on the different monuments of the city of Ambato, where a virtual presenter is responsible for publicizing the characteristics of the sector and the places to visit and obviously the reason for the presence of the monument. In other words, the application generates an educational process or a teaching-learning process by the virtual presenter, where the user learns through the presenter’s indications. To achieve the objective of the research, a mobile application was developed with the Unity graphics engine that allows the tourist to access relevant information through a virtual guide to support such information during his visit to the main monuments of the city of Ambato using augmented reality through his smartphone to achieve the impact that this technology generates in people, for this research is integrated by the public entities responsible for tourism in the city of Ambato. The project has a quantitative approach for data analysis and qualitative approach that allowed knowing the degree of satisfaction of public entities and users of the mobile application. Users used the Technological Acceptance Model (TAM) and Design Model to determine the degree of acceptance. The results obtained showed a high rate of acceptance of augmented reality and its use for the tourist promotion of the monuments of the city of Ambato.
Security of Digital Images Based on SHA-3 and Multiplication-Rotation-XOR (MRX) Operations over Galois Field

In this paper, we propose a technique for securing digital images of different formats, namely binary, grayscale, index, and the true color images, based on the Secure Hash Algorithm 3 (SHA-3) and Multiplication-Rotation-XOR (MRX) operations over the Galois field. The technique performs encryption and decryption in the Cipher Block Chaining (CBC) mode of operation. SHA-3 is used for generating 256-bit message digest, to be used in constructing Initialization Vector (IV). Each plain image block is flipped horizontally and vertically, and then rows are updated via XOR and rotation (by random bits) operations. Finally, each block is pre-and-post multiplied by secret key over the Galois field. Several performance evaluation metrics are used on the standard images to empirically evaluate the efficiency and robustness of the designed approach. Moreover, a thorough comparison is also made with the recent state-of-the-art techniques.
Ultra-Dense Request Impact on Cluster-Based Web Server Performance

The objective of this study is to evaluate cluster-based performance under ultra-dense HTTP traffic. This paper provides a performance analysis of the existing load balancing algorithms (Round Robin, Least Connection, and IP-Hash/Source) in cluster-based web servers. The performance testing process is operated with Apache-Jmeter 5.1 and distributing technique to realize ultra-dense load (100000-500000 HTTP requests) in a real network. Generally, the results indicated the proposed Nginx-based cluster is more responsive, stable, and consumes fewer resources concerning Response Time, Throughput, Standard Deviation, and CPU Usage measurements. While in terms of Error Rate, the Apache-based cluster is more efficient. Moreover, with the Nginx-based cluster, the Round Robin algorithm provided slightly better performance. In contrast, the IP-Hash algorithm outperformed the other two algorithms for the Apache-based cluster in terms of all the utilized metrics.
Performance Analysis of YOLOv4 and SSD Mobilenet V2 for Foreign Object Debris (FOD) Detection at Airport Runway Using Custom Dataset

Indonesia is a country that has heavy air traffic every day. Therefore, safety is a very important thing to pay attention to, one of them is the runway safety. The runway is an important component in aviation activities because aircraft use it for takeoff and landing. Foreign objects or FOD (Foreign Object Debris) could appear on the runway which can cause damage to the aircraft and may result in an accident. Therefore, we need a security system that can detect foreign objects in real-time. One approach that can be done is to use Computer Vision technology by using a camera. This method utilizes Artificial Intelligence (AI) technology for FOD detection. Various methods or algorithms have been developed for Computer Vision, SSD and YOLO are the most frequently used methods for real-time detection because of their high FPS and accuracy performance. Where in this study it was found that SSD MobileNet V2 can reach up to 12 FPS with mAP 0.5 value of 86.8% and for YOLOv4 can reach up to 31 FPS with mAP 0.5 value of 98.73%.
Data-driven technique for disruption prediction in GOLEM tokamak using stacked ensembles with active learning

In a tokamak, disruption is defined as losing control over a confined plasma resulting in sudden extinction of the plasma current. Machine learning offers potent solutions to classify plasma discharges into disruptive and non-disruptive classes. Evolving experimental programs reduce the performance of machine learning models, and also, the need for labeling the huge volume of data incurs more labor cost and time. This paper proposes a data-driven based machine learning technique that employs an active learning approach for labeling and classification of plasma discharges. The designed model uses 117 normally terminated shots and 70 disruptive shots with 14 labeled diagnostic signals. The stacking classifier is built over three base learners: logistic regression, reduced error pruning tree, and categorial boost algorithm, and the logistic regression technique is used at the meta-learner. An active learning approach is proposed for labeling the unlabeled dataset using a modified uncertainty sampling technique with minimal queries. The proposed model queries the unlabeled data to an oracle based on a selection strategy with uncertainty sampling using entropy metrics. The new labeled data and the class probabilities of the base classifiers are channeled to the final predictor for classifying the plasma discharge. The proposed model achieves an accuracy of 98.75% in classifying the disruptive vs non-disruptive discharges, with a minimally trained dataset, and also, it is free from aging of predictors.
Text Recognition in UAV Aerial Images

Text recognition in unmanned aerial vehicle (UAV) aerial images is an important branch in the field of machine intelligence, which can provide important discriminative information for subsequent applications. At this stage, text recognition methods have made breakthrough progress, but the recognition of distorted and slanted text is still a challenge. In this case, we construct a text recognition network model with correction module, and propose a new type of UAV aerial image text recognition method. Specifically, the model mainly includes two parts: rectification network and recognition network. The rectification network can be optimized without manual annotation, and it can regularize various distorted and inclined UAV image texts. The recognition network introduces the attention mechanism and improves the decoder to perform bidirectional recognition of the rectified UAV image text. In addition, we verify the effectiveness of the rectification network through a large number of experiments, and prove that the model composed of the rectification network and the recognition network can achieve the optimal recognition performance.
COLF‐GAN: Learning to axial super‐resolve focal stacks

In order to generate a denser focal stack, a cooperative generative adversarial network is proposed to learn the refocusing ability from light field imaging. To keep the axial continuity, the proposed framework is designed to learn features of a focal stack in both axial directions. Different from the classic generative adversarial network, our generative module consists of a forward prediction sub-network and a backward prediction sub-network, taking the forward-and-backward focal stacks as their inputs, respectively. The bi-directional predictions are then fused by a weighting process, which is guided by an adversarial module. The proposed network is trained on light field focal stacks conducted via digital refocusing. Without loss of the refocus continuity, one can axial super-resolve a focal stack by using the trained model. The effectiveness of the proposed algorithm on different types of focal stacks produced by both light fields and traditional camera shootings is validated. The experimental results indicate that the refocus variation of a focal stack can be well learned and predicted without a complete light field. Therefore, the proposed algorithm outperforms the traditional digital refocusing in terms of run-time.
Machine Translation System Combination with Enhanced Alignments Using Word Embeddings

Machine Translation (MT) is a challenging problem and various techniques proposed for MT have their own strengths and weaknesses. Combining various MT systems has shown promising results. Confusion network decoding is one such approach. In this work, we propose using word embeddings for aligning words from different hypotheses during confusion network generation. Our experiments, on English-Hindi language pair, have shown statistically significant improvement in BLEU scores, when compared to the baseline system combination. Four data-driven MT systems are combined, namely, a phrase based MT, hierarchical-phrase based MT, bi-directional recurrent neural network MT and transformer based MT. All of these have been trained on IIT Bombay English-Hindi parallel corpus.
Privacy-Enhanced Mean-Variance Scheme Against Malicious Signature Attacks in Smart Grids

Secure multidimensional data aggregation (SMDA) has been widely investigated to meet the requirement of protecting individual users’ real-time electricity consumption data privacy in smart grid. However, most previous proposals require decryption of encrypted data to obtain the mean and variance, which leads to the inefficient and insecure in the protocols. This article presents an efficient and privacy-enhanced mean-variance scheme (namely PMVS) to provide the privacy-preserving, in which the Paillier cryptosystem is adopted in a fog-based architecture. To achieve efficient authentication functionality, batch verification technology is applied in our scheme. Our scheme provides two new features: Firstly, Electricity Service Provider (ESP) can directly obtain the mean and variance by decrypting the received ciphertext. Secondly, the PMVS can also resist malicious signature attacks. By identifying invalid signatures, the verified signatures can be aggregated, effectively preventing malicious signature attacks from causing batch verification to fail all the time and failing to enter the secure computing stage. The security analysis shows that the proposed scheme is secure and can preserve the meters’ privacy.
Semantic–Structural Graph Convolutional Networks for Whole-Body Human Pose Estimation

Existing whole-body human pose estimation methods mostly segment the parts of the body’s hands and feet for specific processing, which not only splits the overall semantics of the body, but also increases the amount of calculation and the complexity of the model. To address these drawbacks, we designed a novel semantic–structural graph convolutional network (SSGCN) for whole-body human pose estimation tasks, which leverages the whole-body graph structure to analyze the semantics of the whole-body keypoints through a graph convolutional network and improves the accuracy of pose estimation. Firstly, we introduced a novel heat-map-based keypoint embedding, which encodes the position information and feature information of the keypoints of the human body. Secondly, we propose a novel semantic–structural graph convolutional network consisting of several sets of cascaded structure-based graph layers and data-dependent whole-body non-local layers. Specifically, the proposed method extracts groups of keypoints and constructs a high-level abstract body graph to process the high-level semantic information of the whole-body keypoints. The experimental results showed that our method achieved very promising results on the challenging COCO whole-body dataset.
Digital Etiquette in University Students’ Communicative Practice

Complying with the requirements of digital etiquette is becoming an obligatory norm for building effective communication in the Internet space: in messengers, social networks, blogs, and electronic letters. The youth as a part of the society actively using the digital forms of communication demands special attention and special efforts for forming communicative competences in students, including in the sphere of introducing the digital etiquette norms into the practice of everyday communications. The research object is the speech etiquette of students used in the digital environment. The study is aimed at revealing the features of using the digital etiquette in the students’ communicative practice. The research involved analysis, synthesis, summarization of theoretical knowledge on the topic, and polling of 102 university students. The authors regretfully mark that the students’ general level of culture is not sufficiently high, and they often violate the boundaries when communicating both with each other and professors. The respondents assert that during communication in social networks they are most of all annoyed by late messages and violation of their personal boundaries (35.7% each). 86.7% of the respondents realize the need to use respectful forms of address, 34.7% pointed out the importance of observing the working time, and 23.5% is convinced that one may write personal messages, especially to a professor, only on very important and pressing matters. To solve the problems in the sphere of digital etiquette, it is necessary to introduce the digital etiquette as a discipline to be studied at university.
Remote Sensing Image Surface Feature Classification Based on VGG-UNet

With the rapid development of aerospace remote sensing technology, the resolution of remote sensing image is constantly improving and the amount of data is constantly increasing. Therefore the requirements for surface feature classification are rising. However, due to the high resolution remote sensing image has the characteristics of strong complexity and rich scene edge details, the traditional image segmentation algorithm can not meet its requirements gradually. In this paper, we will quote the UNet neural network in deep learning to replace the encoder with VGGNet, build a remote sensing image segmentation trainer combined with data enhancement technology and migration learning theory, and utilize WHDLD and BDCI2017 datasets for training and verification to improve the final classification accuracy of remote sensing surface features.
Generalized pigeon-inspired optimization algorithm for balancing exploration and exploitation

<p indent="0mm">A generalized pigeon-inspired optimization (GPIO) algorithm for balancing the exploration and exploitation abilities is proposed herein. The traditional pigeon-inspired optimization algorithm includes two operators, namely the map and compass operator and the landmark operator. These two operators are implemented only for one round at a single run. In the GPIO algorithm, the search process is divided into multiple stages, and two operators are implemented in each stage. These two operators are implemented for multiple rounds at one single run. The map and compass operator focuses on the exploration ability, while the landmark operator focuses on the exploitation ability. The GPIO algorithm changes the execution order of the two operators without additional objective function evaluation. Moreover, the structure of the solutions and the parameter settings are extended in the GPIO algorithm, which is beneficial to search quality improvement. The simulation results show that the GPIO algorithm improves the search efficiency and the search results of the algorithm.
Lightweight real-time face detection method based on improved YOLOv4

Aiming at the problem that YOLO, a general target detection algorithm, has a large number of network parameters, and it is difficult to get real-time feedback when it is directly embedded in face detection tasks, a lightweight target detection method improved by YOLOv4 is proposed. Changed backbone feature extraction network CSPDarknet53 to pre-select MobileNet and GhostNet as backbone instead. The best backbone feature extraction network was selected from WIDER FACE, a large open data set of human faces. At the same time, considering that the scale direction of the face is different when it is detected, the k-means++ algorithm is used to generate a new face prediction bounding box to accelerate the convergence speed and improve the detection accuracy. The experimental results show that compared with the original algorithm, the network parameters and computational effort are greatly reduced, and the original accuracy is retained to meet the needs of human face detection.
Nearest neighbor representations of Boolean functions

A nearest neighbor representation of a Boolean function is a set of positive and negative prototypes in Rn such that the function has value 1 on an input iff the closest prototype is positive. For k-nearest neighbor representation the majority classification of the k closest prototypes is considered. The nearest neighbor complexity of a Boolean function is the minimal number of prototypes needed to represent the function. We give several bounds for this measure. Separations are given between the cases when prototypes can be real or are required to be Boolean. The complexity of parity is determined exactly. An exponential lower bound is given for mod 2 inner product, and a linear lower bound is given for its k-nearest neighbor complexity. The results are proven using connections to other models such as polynomial threshold functions over {1,2}. We also discuss some of the many open problems arising.
An Anchor-free Detector Based on Residual Feature Enhancement Pyramid Network for UAV Vehicle Detection

Vehicle detection in Unmanned Aerial Vehicle (UAV) images is a challenging task because there are many small objects in UAV images, and the scale of objects varies greatly, which brings great difficulty to vehicle detection using existing algorithms. This paper proposes an anchor-free detector called Residual Feature Enhancement Pyramid Network (RFEPNet) for UAV vehicle detection. RFEPNet contains a Cross-Level Context Fusion Network (CLCFNet) and a Residual Feature Enhancement Module (RFEM) based on pyramid convolution. Specifically, CLCFNet utilizes the densely connected structure and Dual Attention Fusion Module (DAFM) to increase the sensitivity of high-resolution feature maps to small objects. Simultaneously, RFEM exploits pyramid convolution and residual connection structure to enhance the semantic information of the feature pyramid. In addition, the anchor-free head is used for classification and bounding box regression. The experimental results on the UAVDT dataset show that the proposed RFEPNet achieves state-of-the-art performance.
Compact model for estimating area-level photovoltaic power generation on facade surface using 3D city model and solar radiation simulation

We propose a compact model to estimate the total power generation amount using all building facade surfaces in the town area. Using GIS software, aviation LiDAR data, basic map information, and past weather data, photovoltaic power generations for some Tokyo areas have been simulated about summer and winter seasons. The linear regression model, which give estimations of photovoltaic power generation for each season, were proposed based on the estimated area-level power generations. They can provide simple estimations of the area-level power generation for each season from only geometric characteristics.
Functionality Recognition on Binary Code with Neural Representation Learning

The functionality recognition of binary code has important application value in malware analysis, software forensics, binary code similarity analysis and other applications. Most of the existing methods are based on source code or machine learning strategies to carry out program similarity analysis, and this similarity analysis is also applied to a pair of programs, there are limitations in detection accuracy and quantity. Inspired by the recent great success of neural networks and representation learning in various program analysis tasks, We propose NPFI to analyze the binary code of the program and identify its functionality from the perspective of assembly instruction sequence. To evaluate the performance of NPFI, we built a large dataset consisting of 39,000 programs from six different categories collected from Google Code Jam. A large number of experiments show that the accuracy of NPFI in binary code function recognition can reach 95.8%, which is much better than the existing methods.
Research on the application of low-polygon design to Chinese chess

In order to promote and protect the Chinese traditional culture of chess, this paper compares the Chinese chess pieces with the Western chess pieces in three modules: the origin of Chinese chess, the contemporary status of the culture, and the philosophical ideas in chess. Using the modern universal design concept, the Chinese chess "knight" is realistically designed by referring to the design of the "Eastern Han Dynasty Bronze Prancing Horse", and adding low-polygonal design elements in MAYA modeling. The support of 3D printing technology and engine technology allows Chinese chess designs to be both physically produced and easily used in the information internet era.
Conversational Design Patterns for a UX Evaluation Instrument Implemented by Voice

In recent years there has been an increase of voice interfaces, driven by developments in Artificial Intelligence and the expansion of commercial devices that use them, such as smart assistants present on phones or smart speakers. One field that could take advantage of the potential of voice interaction is in the self-administered surveys data collection, such as standardized UX evaluation questionnaires. This work proposes a set of conversational design patterns for standardized UX evaluation questionnaires that use semantic difference scales as a means of collecting quantitative information on user experience, as is the case of AttrakDiff and UEQ (User Experience Questionnaire). The presented design patterns seek to establish a natural conversation created in accordance with the user, the conservation of context between subsequent questions, the minimization of statements and with statement repair mechanisms not completely understood by the user or voice agent, as eliciting explanation of a concept or repetition.