Reviewerly/exHarmony huggingface dataset:
Synthetic Energy Data Generation Using Time Variant Generative Adversarial Network

Energy consumption data is being used for improving the energy efficiency and minimizing the cost. However, obtaining energy consumption data has two major challenges: (i) data collection is very expensive, time-consuming, and (ii) security and privacy concern of the users which can be revealed from the actual data. In this research, we have addressed these challenges by using generative adversarial networks for generating energy consumption profile. We have successfully generated synthetic data which is similar to the real energy consumption data. On the basis of the recent research conducted on TimeGAN, we have implemented a framework for synthetic energy consumption data generation that could be useful in research, data analysis and create business solutions. The framework is implemented using the real-world energy dataset, consisting of energy consumption data of the year 2020 for the Australian states of Victoria, New South Wales, South Australia, Queensland and Tasmania. The results of implementation is evaluated using various performance measures and the results are showcased using visualizations along with Principal Component Analysis (PCA) and t-distributed stochastic neighbor embedding (TSNE) plots. Overall, experimental results show that Synthetic data generated using the proposed implementation possess very similar characteristics to the real dataset with high comparison accuracy.
A novel method for anomaly detection using beta Hebbian learning and principal component analysis

Abstract In this research work a novel two-step system for anomaly detection is presented and tested over several real datasets. In the first step the novel Exploratory Projection Pursuit, Beta Hebbian Learning algorithm, is applied over each dataset, either to reduce the dimensionality of the original dataset or to face nonlinear datasets by generating a new subspace of the original dataset with lower, or even higher, dimensionality selecting the right activation function. Finally, in the second step Principal Component Analysis anomaly detection is applied to the new subspace to detect the anomalies and improve its classification capabilities. This new approach has been tested over several different real datasets, in terms of number of variables, number of samples and number of anomalies. In almost all cases, the novel approach obtained better results in terms of area under the curve with similar standard deviation values. In case of computational cost, this improvement is only remarkable when complexity of the dataset in terms of number of variables is high.
Current applications and challenges of artificial intelligence in pathology

Machine learning and artificial intelligence are poised to transform pathology. Technologic advances have continued to develop various pathology subdomains such as surgical pathology, cytology, molecular, and laboratory medicine. Pathology includes substantial imaging and non-imaging data that can be used to develop machine learning tools or provide input data for pre-trained clinical tools and have outputs infer clinical decision making, research, or education. While there are proven technologies, there remain clear and present hindrances to understand and overcome. This review will cover many applications of machine learning in pathology in various subdomains, use cases that are available to support clinical decision making, and discuss challenges related to the feasibility and implementation of such systems.
Channel Attention GAN Trained With Enhanced Dataset for Single-Image Shadow Removal

Even today, where many deep-learning-based methods have been published, single-image shadow removal is a challenging task to achieve high accuracy.This is because the shadow changes depending on various conditions such as the target material or the light source, and it is difficult to estimate all the physical parameters.In this paper, we propose a new single-image shadow removal method (Channel Attention GAN: CANet) using two networks for detecting shadows and removing shadows.Intensity change in shadowed regions has different characteristics depending on the wavelength of light.In addition, the image acquisition system of the camera acquires an image in a state where the RGB values influence each other.Therefore, our method focused on the physical properties of shadows and the camera's image acquisition system.The proposed network has a structure considering the relationship between color channels.When training this network, we modified the color and added some artifacts to the training images in order to make the training dataset more complex.These image processing are based on the shadow model, considering the camera image acquisition system.With these new proposals, our method can remove shadows in all ISTD, ISTD+, SRD, and SRD+ datasets with higher accuracy than the state-of-the-art methods.The code is available on GitHub: https://github.com/ryo-abiko/CANet.
Exploring New Vista of Intelligent Recommendation Framework for Tourism Industries: An Itinerary through Big Data Paradigm

Big Data is changing how organizations conduct operations. Data are assembled from multiple points of view through online quests, investigation of purchaser purchasing conduct, and then some, and industries utilize it to improve their net revenue and give an overall better experience to clients. Each of these organizations must figure out how to improve the general client experience and meet every client’s novel necessities, and big data helps with this cycle. Through the utilization and reviews of Big Data, travel industry organizations can study the inclinations of more modest portions of their intended interest group or even about people in some cases. In this paper, a Crow Search Optimization-based Hybrid Recommendation Model is proposed to get accurate suggestions based on clients’ preferences. The hybrid recommendation is performed by combining collaborative filtering and content-based filtering. As a result, the advantages of collaborative filtering and content-based filtering are utilized. Moreover, the intelligent behavior of Crows’ assists the proper selection of neighbors, rating prediction, and in-depth analysis of the contents. Accordingly, an optimized recommendation is always provided to the target users. Finally, performance of the proposed model is tested using the TripAdvisor dataset. The experimental results reveal that the model provides 58%, 58.5%, 27%, 24.5%, and 25.5% better Mean Absolute Error, Root Mean Square Error, Precision, Recall, and F-Measure, respectively, compared to similar algorithms.
Protected Health Information Recognition by Fine-Tuning a Pre-training Transformer Model

De-identifying protected health information (PHI) in medical documents is important, and a prerequisite to deidentification is the identification of PHI entity names in clinical documents. This study aimed to compare the performance of three pre-training models that have recently attracted significant attention and to determine which model is more suitable for PHI recognition.We compared the PHI recognition performance of deep learning models using the i2b2 2014 dataset. We used the three pre-training models-namely, bidirectional encoder representations from transformers (BERT), robustly optimized BERT pre-training approach (RoBERTa), and XLNet (model built based on Transformer-XL)-to detect PHI. After the dataset was tokenized, it was processed using an inside-outside-beginning tagging scheme and WordPiecetokenized to place it into these models. Further, the PHI recognition performance was investigated using BERT, RoBERTa, and XLNet.Comparing the PHI recognition performance of the three models, it was confirmed that XLNet had a superior F1-score of 96.29%. In addition, when checking PHI entity performance evaluation, RoBERTa and XLNet showed a 30% improvement in performance compared to BERT.Among the pre-training models used in this study, XLNet exhibited superior performance because word embedding was well constructed using the two-stream self-attention method. In addition, compared to BERT, RoBERTa and XLNet showed superior performance, indicating that they were more effective in grasping the context.
CASA: Conversational Aspect Sentiment Analysis for Dialogue Understanding

Dialogue understanding has always been a bottleneck for many conversational tasks, such as dialogue response generation and conversational question answering. To expedite the progress in this area, we introduce the task of conversational aspect sentiment analysis (CASA) that can provide useful fine-grained sentiment information for dialogue understanding and planning. Overall, this task extends the standard aspect-based sentiment analysis to the conversational scenario with several major adaptations. To aid the training and evaluation of data-driven methods, we annotate 3,000 chit-chat dialogues (27,198 sentences) with fine-grained sentiment information, including all sentiment expressions, their polarities and the corresponding target mentions. We also annotate an out-of-domain test set of 200 dialogues for robustness evaluation. Besides, we develop multiple baselines based on either pretrained BERT or self-attention for preliminary study. Experimental results show that our BERT-based model has strong performances for both in-domain and out-of-domain datasets, and thorough analysis indicates several potential directions for further improvements.
Towards a More Socially Sustainable Advanced Pilot Training by Integrating Wearable Augmented Reality Devices

For flying all types of turbine-engine airplanes, a pilot must undergo an intense type rating (TR) course. This study investigated the learning conditions and TR course content, and specifies the most difficult course elements that could be tackled by augmented reality (AR) applications. Because women are underrepresented in the worldwide pilot population, it is important to address gender-specific preferences and needs in the development of AR-based wearable technologies for advanced pilot training. A gender-sensitive survey of the learning conditions and course contents was conducted with 31 pilots and 22 instructors. Despite many similarities, the results confirm that there are gender-specific needs and preferences for the development of future AR-based applications for TR training. In addition, the views of both pilots’ and instructors’ are required to obtain a comprehensive assessment of the learning contents and conditions related to TR. The results also show that time pressure increased the perceived difficulty of the course for some trainees. These results are important because they indicate the directions to be taken in developing future AR-based training applications for a more learner-centered and inclusive TR training. Future directions to foster a socially sustainable development of AR-based training means for TR with special focus on gender diversity are presented.
Confusion matrix and minimum cross-entropy metrics based motion recognition system in the classroom

This research proposes a motion recognition system for early detection of students' physical aggressive behavior in the classroom. The motion recognition system recognizes physical attacks so that teachers can resolve disputes early to reduce other greater injuries. In the beginning, cameras were used in this system to monitor students' classroom activities and to obtain body images by removing background and saliency maps. Two angles from arm to shoulder and shoulder to the center of the body are then measured and the velocity between the two frames from the movement of the body is detected, and use these angle and velocity values as the criterion for judging whether it is a physical attack. In the end, the accuracy of the proposed algorithms is verified by using the confusion matrix based on machine learning and the minimum cross entropy based on neural networks. The simulation proves that the proposed algorithm can correctly detect the attack behavior of the collected videos.
Emergency Digital Teaching during the COVID-19 Lockdown: Students’ Perspectives

This paper presents a qualitative study of the experience of students of the shift from face-to-face learning to online learning during the COVID-19 lockdown in Norway. Detailed inputs were collected from 200 university students enrolled in a bachelor’s degree in information technology in Norway through an online survey. Their responses were categorized into three main themes: the teacher’s role, the life of a student, and digital learning. We found that, surprisingly, the students felt that the shift to digital learning had positive effects on their lives, such as the availability of more time for study, study flexibility through recorded lectures which could be reviewed repeatedly and anytime, and more time to pose questions. However, some students also pointed out negative effects such as more distractions, lack of structure, and a perceived invasion of privacy when required to turn on their cameras. The students valued the use of high-quality technical equipment as well as student engagement during online lectures, but also freedom of choice to participate.
A Hybrid Code Genetic Algorithm for VRP in Public-Private Emergency Collaborations

To minimize the total time for the distribution of relief commodities participated by both private companies and the government, a vehicle routing problem (VRP) model in emergencies was proposed.Considering the differences in the starting points of vehicles, the VRP of general logistics, and departments of vehicles, constraints, such as vehicle capacity limitation and time windows, were introduced into the model, which was close to meeting the practical demands of emergency relief.A hybrid code genetic algorithm (HCGA) was proposed, and it used dynamic mutations to avoid early traps in local optimization and to accelerate convergence.This algorithm was programmed by MATLAB.Furthermore, the vehicle routing optimization plans in an emergency was calculated by a simple genetic algorithm (SGA) and the HCGA, respectively.Results demonstrate that the total time for relief distribution in the HCGA is 11.62 % lower and the calculation time is 14.24 % shorter than that of the SGA.The HCGA is not only convenient in processing the constraints of the model and the natural description of problem solutions, but it is also effective in improving the complexity.
Duolingo: An Arabic Speaking Skills’ Learning Platform for Andragogy Education

Arabic communication skills for non-Arabs are faced with serious problems; therefore, the use of technology-based platforms is considered following students’ wishes in the era of digital technology. Various technological devices have been widely used by educators for multiple levels of education but have not obtained maximum results. The novelty of this research is the use of Duolingo as a platform for teaching Arabic language skills. This study describes that Duolingo is very relevant to be used as a medium for learning Arabic for beginners because the teaching material begins with the new alphabet and vocabulary equipped with pictures. However, Duolingo still needs to make an essential revolution for learning Arabic speaking skills, especially for andragogy learners who have expert Arabic skills. Based on the findings, some implications are put forward.
FMODetect: Robust Detection of Fast Moving Objects

We propose the first learning-based approach for fast moving objects detection. Such objects are highly blurred and move over large distances within one video frame. Fast moving objects are associated with a deblurring and matting problem, also called deblatting. We show that the separation of deblatting into consecutive matting and deblurring allows achieving real-time performance, i.e. an order of magnitude speed-up, and thus enabling new classes of application. The proposed method detects fast moving objects as a truncated distance function to the trajectory by learning from synthetic data. For the sharp appearance estimation and accurate trajectory estimation, we propose a matting and fitting network that estimates the blurred appearance without background, followed by an energy minimization based deblurring. The state-of-the-art methods are outperformed in terms of recall, precision, trajectory estimation, and sharp appearance reconstruction. Compared to other methods, such as deblatting, the inference is of several orders of magnitude faster and allows applications such as real-time fast moving object detection and retrieval in large video collections.
Facial expression recognition based on active region of interest using deep learning and parallelism

The automatic facial expression tracking method has become an emergent topic during the last few decades. It is a challenging problem that impacts many fields such as virtual reality, security surveillance, driver safety, homeland security, human-computer interaction, medical applications. A remarkable cost-efficiency can be achieved by considering some areas of a face. These areas are termed Active Regions of Interest (AROIs). This work proposes a facial expression recognition framework that investigates five types of facial expressions, namely neutral, happiness, fear, surprise, and disgust. Firstly, a pose estimation method is incorporated and to go along with an approach to rotate the face to achieve a normalized pose. Secondly, the whole face-image is segmented into four classes and eight regions. Thirdly, only four AROIs are identified from the segmented regions. The four AROIs are the nose-tip, right eye, left eye, and lips respectively. Fourthly, an info-image-data-mask database is maintained for classification and it is used to store records of images. This database is the mixture of all the images that are gained after introducing a ten-fold cross-validation technique using the Convolutional Neural Network. Correlations of variances and standard deviations are computed based on identified images. To minimize the required processing time in both training and testing the data set, a parallelism technique is introduced, in which each region of the AROIs is classified individually and all of them run in parallel. Fifthly, a decision-tree-level synthesis-based framework is proposed to coordinate the results of parallel classification, which helps to improve the recognition accuracy. Finally, experimentation on both independent and synthesis databases is voted for calculating the performance of the proposed technique. By incorporating the proposed synthesis method, we gain 94.499%, 95.439%, and 98.26% accuracy with the CK+ image sets and 92.463%, 93.318%, and 94.423% with the JAFFE image sets. The overall accuracy is 95.27% in recognition. We gain 2.8% higher accuracy by introducing a decision-level synthesis method. Moreover, with the incorporation of parallelism, processing time speeds up three times faster. This accuracy proves the robustness of the proposed scheme.
Enhancing Detection Quality Rate with a Combined HOG and CNN for Real-Time Multiple Object Tracking across Non-Overlapping Multiple Cameras

Multi-object tracking in video surveillance is subjected to illumination variation, blurring, motion, and similarity variations during the identification process in real-world practice. The previously proposed applications have difficulties in learning the appearances and differentiating the objects from sundry detections. They mostly rely heavily on local features and tend to lose vital global structured features such as contour features. This contributes to their inability to accurately detect, classify or distinguish the fooling images. In this paper, we propose a paradigm aimed at eliminating these tracking difficulties by enhancing the detection quality rate through the combination of a convolutional neural network (CNN) and a histogram of oriented gradient (HOG) descriptor. We trained the algorithm with an input of 120 × 32 images size and cleaned and converted them into binary for reducing the numbers of false positives. In testing, we eliminated the background on frames size and applied morphological operations and Laplacian of Gaussian model (LOG) mixture after blobs. The images further underwent feature extraction and computation with the HOG descriptor to simplify the structural information of the objects in the captured video images. We stored the appearance features in an array and passed them into the network (CNN) for further processing. We have applied and evaluated our algorithm for real-time multiple object tracking on various city streets using EPFL multi-camera pedestrian datasets. The experimental results illustrate that our proposed technique improves the detection rate and data associations. Our algorithm outperformed the online state-of-the-art approach by recording the highest in precisions and specificity rates.
A Blockchain-Based User Authentication Scheme with Access Control for Telehealth Systems

With the development of telecommunication systems and customized monitoring devices, telehealth has been widely used to improve medical quality and reduce overall health costs. However, the convenience of connection between the providers and patients through a public channel also leads to significant security and privacy concerns. Though there have been many authentication schemes designed for secure communications in telecare systems, most of them suffer from malicious attacks or have heavy computation and communication costs. Thus, in this article, we proposed a blockchain-based user authentication scheme integrating with access control and physical unclonable function (PUF). Permissioned blockchain and PUF are used to support secure data sharing across the healthcare service providers and identify the devices, respectively. Security analysis shows that our protocol satisfies the security requirements for telehealth services and is provably secure in the random oracle model. The performance evaluation demonstrates that it has less computation and communication costs compared with three of the latest schemes.
Design of Online Music Education System Based on Artificial Intelligence and Multiuser Detection Algorithm

With the development of information technology, online music education has become a mainstream education method. Especially after the outbreak of COVID-19, music teachers have to teach through online. Therefore, an online music education system that can improve the quality of teaching is particularly important. Multiuser detection algorithms and artificial intelligence have important applications in many fields, and the field of music online education is no exception. This paper takes the music teaching of the music distance teaching unit as the goal and conducts sufficient research on the educational subjects such as teachers, students, and administrators. And with the help of the SCMA system multiuser detection algorithm and artificial intelligence technology, the system analysis and design method is used to analyze and design the music teaching function system. The system module involves basic information management, student music assignments, online courses, and other levels, providing an excellent educational system design example for music online education. The conclusion analysis shows that the music online education system based on SCMA system multiuser detection algorithm and artificial intelligence designed in this paper can significantly improve the audience’s music learning efficiency and has obvious benefits to the student group.