Reviewerly/exHarmony huggingface dataset:
An improved butterfly optimization algorithm for training the feed-forward artificial neural networks

Artificial neural network (ANN) which is an information processing technique developed by modeling the nervous system of the human brain is one of the most powerful learning methods today. One of the factors that make ANN successful is its training algorithm. In this paper, an improved butterfly optimization algorithm (IBOA) based on the butterfly optimization algorithm was proposed for training the feed-forward artificial neural networks. The IBOA algorithm has the chaotic property which helps optimization algorithms to explore the search space more dynamically and globally. In the experiments, ten chaotic maps were used. The success of the IBOA algorithm was tested on 13 benchmark functions which are well known to those working on global optimization and are frequently used for testing and analysis of optimization algorithms. The Tent-mapped IBOA algorithm outperformed the other algorithms in most of the benchmark functions. Moreover, the success of the IBOA-MLP algorithm also has been tested on five classification datasets (xor, balloon, iris, breast cancer, and heart) and the IBOA-MLP algorithm was compared with four algorithms in the literature. According to the statistical performance metrics (sensitivity, specificity, precision, F1-score, and Friedman test), the IBOA-MLP outperformed the other algorithms and proved to be successful in training the feed-forward artificial neural networks.
The early Japanese books reorganization by combining image processing and deep learning

Many early Japanese books record a large amount of information, including historical politics, economics, culture, and so on, which are all valuable legacies. These books are waiting to be reorganized at the moment. However, a large amount of the books are described by Kuzushiji, a type of handwriting cursive script that is no longer in use today and only readable by a few experts. Therefore, researchers are trying to detect and recognise the characters from these books through modern techniques. Unfortunately, the characteristics of the Kuzushiji, such as Connect-Separate-characters and Many-variation, hinder the modern technique assisted re-organisation. Connect-Separate-characters refer to the case of some characters connecting each other or one character being separated into unconnected parts, which makes character detection hard. Many-variation is one of the typical characteristics of Kuzushiji, defined as the case that the same character has several variations even if they are written by the same person in the same book at the same time, which increases the difficulty of character recognition. In this sense, this paper aims to construct an early Japanese book reorganisation system by combining image processing and deep learning techniques. The experimentation has been done by testing two early Japanese books. In terms of character detection, the final Recall, Precision and F-value reaches 79.8%, 80.3%, and 80.0%, respectively. The deep learning based character recognition accuracy of Top3 reaches 69.52%, and the highest recognition rate reaches 82.57%, which verifies the effectiveness of our proposal.
Short-term solar radiation forecasting with a novel image processing-based deep learning approach

— In this study, an image processing-based deep learning approach for short-term forecast of solar radiation has been developed. For this purpose, firstly, cloud movements occurred during the day are tracked and future cloud movements are forecasted, accordingly. Subsequently, using the cloud motion estimation and extraterrestrial solar radiation data, 1-min averaged solar radiation values are estimated for 5-min time horizon. Shi-Tomasi method is employed to determine the feature points to be tracked on the sky images whereas, Lucas-Kanade optical flow method is employed to track the determined feature points on the sequential images. Average cloud velocity and directions are calculated by the help of linear regression method from tracked cloud movements. A hybrid approach including K-means and red/blue ratio is built to classify the pixels of the image whether they are clouds or sky. Finally, short-term solar radiations are estimated using the Long-Short Term Memory (LSTM) deep learning method. The performance of the proposed approach is compared with other methods in the literature. As a result it is concluded that, developed approach outperforms most methods in the literature with RMSE values of 47.576, 53.830, 68.103, and 92.386 for four different days and can be used as an alternative approach.
TEBDS: A Trusted Execution Environment-and-Blockchain-supported IoT data sharing system

Data sharing services based on massive IoT data have been widely used in various fields such as health monitoring and image recognition, providing users with more reliable, efficient, and flexible data services and significantly improving the user service quality. With the popularization of IoT applications, the usage rate of IoT data is getting higher and higher. Due to the dangerous network environment, the security of IoT data faces challenges. On the one hand, attacks such as data tampering can lead to the failure of IoT tasks, and on the other hand, the intrusion of malicious users can lead to the collapse of the entire IoT network. In recent years, many protection schemes used for IoT data security have been proposed. However, security flaws still exist in these schemes. Therefore, to solve the data security and identity security issues in the IoT data sharing process, in this paper, we propose a TEE-and-Blockchain-supported IoT data sharing architecture(TEBDS), which combines on-chain and off-chain methods to meet the security requirements of the IoT data sharing framework. Therein, the consortium blockchain realizes the protection of on-chain IoT data and the access control of IoT users. An Intel SGX-based Distributed Storage System (SDSS) is proposed to secure off-chain data. Besides, an incentive mechanism is developed to facilitate the whole system. Security analysis shows that TEBDS meets the requirements of data security and identity security. Experimental results show that TEBDS has better performance than the centralized method SPDS.
Blockchain technology: Applied to big data in collaborative edges

End users are now encircled by an ever-increasing volume of information from edge devices relevant to a range of stakeholders, thanks to the introduction of edge computing in a variety of application domains. However, because of their distrust, these edge devices are unable to communicate significant amounts of data. The non-repudiation and non-tampering features of the block chain are used in this study to provide trust in collaborative edges. To address the limited processing capabilities of edge devices, create a block chain-based huge data sharing architecture in cooperative edges. Then, for high computational reduction, propose a Proof-of-Collaboration consensus technique, in which edge devices participate in block formation by giving their PoC credits. Furthermore, a useless transaction filter technique was proposed for transaction offloading, drastically decreasing the block chain's storage space in edges. Comprehensive tests are carried out to illustrate our proposal's better performance. Using this learning opportunity an environment friendly block chain sustainable infrastructure be created for developing countries.
YOLO-SD: Small Ship Detection in SAR Images by Multi-Scale Convolution and Feature Transformer Module

As an outstanding method for ocean monitoring, synthetic aperture radar (SAR) has received much attention from scholars in recent years. With the rapid advances in the field of SAR technology and image processing, significant progress has also been made in ship detection in SAR images. When dealing with large-scale ships on a wide sea surface, most existing algorithms can achieve great detection results. However, small ships in SAR images contain little feature information. It is difficult to differentiate them from the background clutter, and there is the problem of a low detection rate and high false alarms. To improve the detection accuracy for small ships, we propose an efficient ship detection model based on YOLOX, named YOLO-Ship Detection (YOLO-SD). First, Multi-Scale Convolution (MSC) is proposed to fuse feature information at different scales so as to resolve the problem of unbalanced semantic information in the lower layer and improve the ability of feature extraction. Further, the Feature Transformer Module (FTM) is designed to capture global features and link them to the context for the purpose of optimizing high-layer semantic information and ultimately achieving excellent detection performance. A large number of experiments on the HRSID and LS-SSDD-v1.0 datasets show that YOLO-SD achieves a better detection performance than the baseline YOLOX. Compared with other excellent object detection models, YOLO-SD still has an edge in terms of overall performance.
Exploring the Learnability of Program Synthesizers by Novice Programmers

Modern program synthesizers are increasingly delivering on their promise of lightening the burden of programming by automatically generating code, but little research has addressed how we can make such systems learnable to all. In this work, we ask: What aspects of program synthesizers contribute to and detract from their learnability by novice programmers? We conducted a thematic analysis of 22 observations of novice programmers, during which novices worked with existing program synthesizers, then participated in semi-structured interviews. Our findings shed light on how their specific points in the synthesizer design space affect these tools' learnability by novice programmers, including the type of specification the synthesizer requires, the method of invoking synthesis and receiving feedback, and the size of the specification. We also describe common misconceptions about what constitutes meaningful progress and useful specifications for the synthesizers, as well as participants' common behaviors and strategies for using these tools. From this analysis, we offer a set of design opportunities to inform the design of future program synthesizers that strive to be learnable by novice programmers. This work serves as a first step toward understanding how we can make program synthesizers more learnable by novices, which opens up the possibility of using program synthesizers in educational settings as well as developer tooling oriented toward novice programmers.
AUIT – the Adaptive User Interfaces Toolkit for Designing XR Applications

Adaptive user interfaces can improve experiences in Extended Reality (XR) applications by adapting interface elements according to the user's context. Although extensive work explores different adaptation policies, XR creators often struggle with their implementation, which involves laborious manual scripting. The few available tools are underdeveloped for realistic XR settings where it is often necessary to consider conflicting aspects that affect an adaptation. We fill this gap by presenting AUIT, a toolkit that facilitates the design of optimization-based adaptation policies. AUIT allows creators to flexibly combine policies that address common objectives in XR applications, such as element reachability, visibility, and consistency. Instead of using rules or scripts, specifying adaptation policies via adaptation objectives simplifies the design process and enables creative exploration of adaptations. After creators decide which adaptation objectives to use, a multi-objective solver finds appropriate adaptations in real-time. A study showed that AUIT allowed creators of XR applications to quickly and easily create high-quality adaptations.
Generating Synthetic Images for Healthcare with Novel Deep Pix2Pix GAN

Due to recent developments in deep learning and artificial intelligence, the healthcare industry is currently going through a significant upheaval. Despite a considerable advance in medical imaging and diagnostics, the healthcare industry still has a lot of unresolved problems and unexplored applications. The transmission of a huge number of medical images in particular is a difficult and time-consuming problem. In addition, obtaining new medical images is too expensive. To tackle these issues, we propose deep pix2pix generative adversarial networks (GAN) for generating synthetic medical images. For the comparison, we implemented CycleGAN, Pix2Pix GAN and Deep Pix2Pix GAN. The result has shown that our proposed approach can generate a new synthetic medical image from a different image with more accuracy than that of the other models. To provide a robust model, we trained and evaluated our models on a widely used brain image dataset, the IXI Dataset.
Session-based recommendation: Learning multi-dimension interests via a multi-head attention graph neural network

In a recent study, it was shown that, with batch training of a graph neural network, it is possible to recommend suitable items for users. Although the method has obtained item embedding and considered the complex transitions between items, there is no multi-dimensional focus on the users' interests and preferences. In this paper, we propose a multi-head attention graph neural network (MAE-GNN) for session-based recommendation by combining a dual-gated graph neural network and multi-head attention mechanisms. MAE-GNN can select important node information and extract users' interests and preferences from multiple dimensions. Experimental evaluation has been conducted to show that, compared with the state-of-the-art methods, the proposed model has significant improvement in term of P@K and MRR@K for session-based recommendation.
Standards of Teacher Digital Competence in Higher Education: A Systematic Literature Review

In a society marked by continuous technological changes in favor of education, digital competence is an unavoidable feature in the professional profile of the university teaching staff. This systematic literature review aims at answering the following questions: what basic standards are established by the literature for teacher training in digital competence to strengthen their work inside and outside the classroom? What models or frameworks do they use as references? What are university professors’ training needs? What contributions have been made in this line of study and what technologies are recommended for teacher training in digital competence? Based on the four phases of the PRISMA flowchart: identification, selection, eligibility, and inclusion, the articles indexed in the Web of Science, Scopus, and Scielo databases, both in English and Spanish, and published in 2015 until the end of May 2022, were analyzed. The initial search resulted in a total of 187 potentially useful articles, 26 of which met the inclusion and quality criteria. The authors of the selected papers concur in identifying the dimensions of competence and, subsequently, in establishing the standards, direction, and focus of training.
A reinforcement learning model for the reliability of blockchain oracles

Smart contracts struggle with the major limitation of operating on data that is solely residing on the blockchain network. The need of recruiting third parties, known as oracles, to assist smart contracts has been recognized with the emergence of blockchain technology. Oracles could be deviant and commit ill-intentioned behaviors, or be selfish and hide their actual available resources to gain optimal profit. Current research proposals employ oracles as trusted entities with no robust assessment mechanism, which entails a risk of turning them into centralized points of failure. The need for an effective method to select the most economical and rewarding oracles that are self-interested and act independently is somehow neglected. Thus, this paper proposes a Bayesian Bandit Learning Oracles Reliability (BLOR) mechanism to identify trustless and cost-efficient oracles. Within BLOR, we learn the behavior of oracles by formulating a Bayesian cost-dependent reputation model and utilize reinforcement learning (knowledge gradient algorithm) to guide the learning process. BLOR enables all the blockchain validators to verify the obtained results while running the algorithm at the same time by dealing with the randomness issue within the limited blockchain structure. We implement and experiment with BLOR using Python and the Solidity language on Ethereum. BLOR is benchmarked against several models where it proved to be highly efficient in selecting the most reliable and economical oracles with a fair balance.
Graph convolutional network with triplet attention learning for person re-identification

Person re-identification (re-ID) is a method that uses several non-overlapping cameras to identify the same individual. Person re-ID has been employed successfully in a diversity of computer vision applications. This task is made more difficult by occlusions, abrupt illumination, pose changes among camera views, cluttered backgrounds, and inaccurate detections. Therefore, we propose a new graph convolutional network with attention modules. This research reveals a new attention network that encompasses the encoder-decoder and the triplet attention module. The proposed attention module employs the self-attention process to achieve potent and discriminatory features by utilizing temporal, spatial, and channel context information. The triplet attention module is utilized to capture cross-dimension dependencies and pedestrian features, and also reduces the impact of the imperfect pedestrian image to remedy the occlusion issue. The encoder-decoder is used to observe the whole-body shape. Experiments on several publicly available datasets reveal that our method has a high degree of generalization and outperforms existing methods. On Market1501, the proposed method outperformed the recent approaches with an accuracy of 92.98% for rank-1. According to the results, our method ameliorates quantitative and qualitative person re-ID methods.
Detecting distracted students in educational VR environments using machine learning on eye gaze data

Virtual Reality (VR) has been found useful to improve engagement and retention level of students, for some topics, compared to traditional learning tools such as books, and videos. However, a student could still get distracted and disengaged due to a variety of factors including stress, mind-wandering, unwanted noise, and external alerts. Student eye gaze data could be useful for detecting these distracted students. Gaze data-based visualizations have been proposed in the past to help a teacher monitor distracted students. However, it is not practical for a teacher to monitor a large number of student indicators while teaching. To help filter students based on distraction level, we propose an automated system based on machine learning to classify students based on their distraction level. The key aspects are: (1) we created a labeled eye gaze dataset from an educational VR environment, (2) we propose an automatic system to gauge a student’s distraction level from gaze data, and (3) we apply and compare several classifiers for this purpose. Each classifier classifies distraction, per educational activity section, into one of three levels (low, mid or high). Our results show that Random Forest (RF) classifier had the best accuracy (98.88%) compared to the other models we tested. Additionally, a personalized machine learning model using either RF, kNN, or Extreme Gradient Boosting (XGBoost) model was found to improve the classification accuracy significantly.
Motion sickness and cybersickness – Sensory mismatch

The use of virtual reality (VR) with head-mounted displays (HMD) may cause side effects called cybersickness with symptoms comparable to those of motion sickness. In this study, we explored whether individual balance characteristics and self-reported tendency to motion sickness could be related to cybersickness vulnerability. Healthy young people (N = 45) were exposed to a VR application with HMD for four minutes, standing with no support. Balance characteristics were measured before (Sensory orientation test) and during (balance platform) the VR exposure. Symptoms of cybersickness were recorded by the Simulator sickness questionnaire (SSQ). Data were analyzed for subgroups with and without a tendency to motion sickness. The participants were negatively affected by the VR exposure: SSQ-before: 21.3 (19.5); SSQ-after: 31.8 (25.2); p<0.01, and 73% experienced increased discomfort. The SSQ sub-scores Nausea and Disorientation were affected, but not the sub-score for Oculomotor disturbance. Surprisingly, the participants described discomfort already after the initial balance assessment (Sensory orientation test). Participants with a self-reported tendency to motion sickness were relatively more affected by this challenge to their sensory integration. Increased postural instability was evident during the VR exposure, but there was a sizeable individual variance in the postural response. The study identified no individual balance characteristics which could be associated with the cybersickness vulnerability. The adverse effect of the Sensory orientation test is a novel finding and it became a bias that diminished subgroup differences in cybersickness vulnerability.
Strategies for developing digital competencies in teachers: Towards a multidimensional Synthesis of Qualitative Data (SQD) survey instrument

Data gathered from 357 undergraduate and graduate teacher education students were analyzed to assess the ability of the Synthesis of Qualitative Data (SQD) instrument to measure six teaching strategies (role models, reflection, collaboration, feedback, instructional design and authentic experiences) important for preparing future teachers to use technology in their future teaching. Factor analysis, multidimensional scaling, and analysis of variance techniques were applied to the 24 SQD items developed through qualitative assessment of effective strategies needed to prepare teachers, in order to assess reliability and validity for six unique four-item scales. Findings were that all scales exhibited acceptable performance characteristics as independent measures, implying that future studies could use subsets of the items rather than the full 24-item instrument. Recommended areas of future research and practical implications for teacher preparation programs are discussed.
Evaluating the Performance of Deep Learning Frameworks for Malaria Parasite Detection Using Microscopic Images of Peripheral Blood Smears

Malaria is a significant health concern in many third-world countries, especially for pregnant women and young children. It accounted for about 229 million cases and 600,000 mortality globally in 2019. Hence, rapid and accurate detection is vital. This study is focused on achieving three goals. The first is to develop a deep learning framework capable of automating and accurately classifying malaria parasites using microscopic images of thin and thick peripheral blood smears. The second is to report which of the two peripheral blood smears is the most appropriate for use in accurately detecting malaria parasites in peripheral blood smears. Finally, we evaluate the performance of our proposed model with commonly used transfer learning models. We proposed a convolutional neural network capable of accurately predicting the presence of malaria parasites using microscopic images of thin and thick peripheral blood smears. Model evaluation was carried out using commonly used evaluation metrics, and the outcome proved satisfactory. The proposed model performed better when thick peripheral smears were used with accuracy, precision, and sensitivity of 96.97%, 97.00%, and 97.00%. Identifying the most appropriate peripheral blood smear is vital for improved accuracy, rapid smear preparation, and rapid diagnosis of patients, especially in regions where malaria is endemic.