Reviewerly/exHarmony huggingface dataset:
A 0.186-pJ per Bit Latch-Based True Random Number Generator with Mismatch Compensation and Random Noise Enhancement

A calibration and feedback control-free latch-based true random-number generator (TRNG) is presented. It features a mismatch self-compensation and a random noise enhancement technique to drastically improve the noise-to-mismatch ratio. By employing the XOR function of only 4-bit entropy sources, the proposed TRNG can efficiently operate across a wide voltage (0.3~1.0 V) and temperature (−20~100°C) range. An 8-bit von Neumann with waiting (VN8W) post-processing technique is used to extract full entropy bitstreams, which have been verified by the NIST-SP 800-22 randomness tests. Robustness against supply noise injection attack is also demonstrated. The proposed TRNG is fabricated in 130-nm CMOS technology and achieves the state-of-the-art energy of 0.186 pJ/bit at 0.3 V with a core area of 661 um <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> (0.039 MF <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ).
Convolutional Neural Network Hand Gesture Recognition for American Sign Language

With the advancements in the computer vision technology, learning and using sign languages to communicate with deaf and mute people has become easier. Exciting research is ongoing for providing a global platform for communication in different sign languages. In this paper, we present a Deep Learning based approach to recognize a sign performed in American Sign Language by capturing an image as input. The system can predict the signs of 0 to 9 digits performed by the user. By utilizing image processing to convert RGB data to grayscale images, efficient reduction is achieved in the storage requirements and training time of the Convolutional Neural Network. The objective of the experiment is to find a mix of Image Processing and Deep Learning Architecture with lesser complexity to deploy the system in mobile applications or embedded single board computers. The database is trained from scratch using smaller networks as LeNet-5 and AlexNet as well as deeper network such as Vgg16 and MobileNet v2. The comparison of the recognition accuracies is discussed in the paper. The final selected architecture has only 10 layers including a dropout layer which boosted the training accuracy to 91.37% and testing accuracy to 87.5%.
Is the library open? How the pandemic has changed the provision of virtual reference services

Purpose The purpose of this paper is to investigate the impact of the pandemic on the questions received via chat reference at a Canadian university library. Design/methodology/approach A qualitative analysis using coding of chat transcripts and a quantitative analysis of the length of chat interactions were used in this study. Findings The author determined that the types of questions received changed slightly during the pandemic due to the new library services offered. The complexity level of questions did not change significantly nor did the presence of instruction. The length of individual chat interactions and the total amount of time spent on chat increased, most likely due to the extended hours of the service and the number of patron questions present in one interaction. Originality/value This is the first study to investigate the potential impact of the pandemic on virtual reference services at a university library. The findings could lead to practical implications for libraries who need to close their in-person reference desk or need to respond to building closures.
Machine learning model for diagnostic method prediction in parasitic disease using clinical information

Diagnosing a parasitic disease is a very difficult job in clinical practice. In this study, we constructed a machine learning model for diagnosis prediction using patient information. First, we diagnosed whether a patient has a parasitic disease. Next, we predicted the proper diagnosis method among the six types of diagnostic terms (biopsy, endoscopy, microscopy, molecular, radiology, and serology) if the patient has a parasitic disease. To make the datasets, we extracted patient information from PubMed abstracts from 1956 to 2019. We then used two datasets: the prediction for parasite-infected patient dataset (N = 8748) and the prediction for diagnosis method dataset (N = 3780). We then compared four machine learning models: support vector machine, random forest, multi-layered perceptron, and gradient boosting. To solve the data imbalance problem, the synthetic minority over-sampling technique and TomekLinks were used. In the parasite-infected patient dataset, the random forest, random forest with synthetic minority over-sampling technique, gradient boosting, gradient boosting with synthetic minority over-sampling technique, and gradient boosting with TomekLinks demonstrated the best performances (AUC: 79%). In predicting the diagnosis method dataset, gradient boosting with synthetic minority over-sampling technique was the best model (AUC: 87%). For the class prediction, gradient boosting demonstrated the best performances in biopsy (AUC: 88%). In endoscopy (AUC: 94%), molecular (AUC: 90%), and radiology (AUC: 88%), gradient boosting with synthetic minority over-sampling technique demonstrated the best performance. Random forest demonstrated the best performances in microscopy (AUC: 82%) and serology (AUC: 85%). We calculated feature importance using gradient boosting; age was the highest feature importance. In conclusion, this study demonstrated that gradient boosting with synthetic minority over-sampling technique can predict a parasitic disease and serve as a promising diagnosis tool for binary classification and multi-classification schemes.
Neural Network Based Algorithm for Multi-UAV Coverage Path Planning

This paper proposes a method to tackle the Coverage Path Planning (CPP) problem for a fleet of AI-driven UAVs while accounting for congestion, collision avoidance, and efficiency of the path. The algorithm relies on a mixed-use of decentralized Artificial Neural Networks (ANN) and the A* pathfinder. Each UAV has elementary cognitive skills to sense information about the nearby environment which are then fed as an input to the network. The neural network creates a correlation between the current state of a UAV and the best action to take at each time step. The exploration strategy is stored in a labeled database for the single-UAV case; the neural network learns and replicates it in the multi-UAV case, being able to generalize the acquired skills over new maps not contained in the database. The training session imitates human priors in a multi-class classification, which completely bypasses common drawbacks such as the need for large databases or high computational resources. The case study focuses on complex urban areas, for which the grid resolution of the traditional approaches can't model the problem with sufficient accuracy.
Distilling from professors: Enhancing the knowledge distillation of teachers

Knowledge distillation (KD) is a successful technique for transferring knowledge from one machine learning model to another model. Specifically, the idea of KD has been widely used for various tasks such as model compression and knowledge transfer between different models. However, existing studies in KD have overlooked the possibility that dark knowledge (i.e., soft targets) obtained from a complex and large model (a.k.a., a teacher model) may be either incorrect or insufficient. Such knowledge can hinder the effective learning of another small model (a.k.a., a student model). In this paper, we propose the professor model, which refines the soft target from the teacher model to improve KD. The professor model aims to achieve two goals; 1) improving the prediction accuracy and 2) capturing the inter-class correlation of the soft target from the teacher model. We first design the professor model by reformulating a conditional adversarial autoencoder (CAAE). Then, we devise two KD strategies using both teacher and professor models. Our empirical study demonstrates that the professor model effectively improves KD in three benchmark datasets: CIFAR100, TinyImagenet, and ILSVRC2015. Moreover, our comprehensive analysis shows that the professor model is much more effective than employing the stronger teacher model, in which parameters are greater than the sum of the teacher’s and professor’s parameters. Since the proposed model is model-agnostic, our model can be combined with any KD algorithm and consistently improves various KD techniques.
Approaches to Numerical Solution of Optimal Control Problem Using Evolutionary Computations

Two approaches to the numerical solution of the optimal control problem are studied. The direct approach is based on the reduction of the optimal control problem to a nonlinear programming problem. Another approach is so-called synthesized optimal control, and it includes the solution of the control synthesis problem and stabilization at some point in the state space, followed by the search of stabilization points and movement of the control object along these points. The comparison of these two approaches was carried out as the solution of the optimal control problem as a time function cannot be directly used in the control system, although the obtained discretized control can be embedded. The control object was a group of interacting mobile robots. Dynamic and static constraints were included in the quality criterion. Implemented methods were evolutionary algorithms and a random parameter search of piecewise linear approximation and coordinates of stabilization points, along with a multilayer network operator for control synthesis.
Multi‐exposure image fusion based on feature evaluation with adaptive factor

The authors present a new multi-exposure images fusion method based on feature evaluation with adaptive factor. It is noticed the existing multi-exposure fusion algorithm is not well adapted to the input images, which are overall bright or dark, the fused image quality is not pretty good, and the details are not preserved completely. So an adaptive factor to adapt the intensity of input images is presented here. First, the exposure assessment weight, texture change weight, and colour intensity weight are calculated by a sliding window. Finally, the images are fused by using a pyramid to avoid the seams. Twenty exposure input images of different scenes are selected, the subjective and objective aspects are analysed and compared with several existing multi-exposure image fusion methods. The experimental results show that the proposed method can retain more details and obtain satisfactory visual effects on static scenes.
A Data Driven Review of Board Game Design and Interactions of Their Mechanics

Board games have often been recognised as a tool to model complex concepts in abstract environments for entertainment, education, and research in fields such as military and artificial intelligence.With more board games being designed and published, it is timely to draw attention towards board game design strategies and mechanics which capture the attributes that drive game play.The game design and the mechanics used define the structure, functionality and play experience of these games.Towards this end, this paper presents a data driven review of board game mechanics and play-related attributes, their interactions and relationships.The analysis expects to draw insights into how board games can be utilised across diverse domains as a tool to understand and explore complex concepts through abstract models.The investigations focus on identifying the trends and patterns of board games being published and their individual mechanics over time.Moreover, the correlation between mechanics and play-related attributes such as game complexity, rating and duration are explored.The interactions and similarities between individual mechanics based on co-occurrence, mutual information and clustering based approaches are also illustrated.The results show that the level of complexity and engagement of a game is not a simple function of the set of mechanics used, but rather the interactions that exist between mechanics, and the nature of their specific implementation are the critical factors in determining play experience of a board game.
Development of Teacher Digital Competence in the Area of E-Safety through Educational Video Games

There is a clear need to promote motivating and effective training actions for the development of teachers’ digital competence, especially in the area of e-safety. Although educational video game-based learning has proven effective to improve motivation and learning outcomes, the existing evidence about its effectiveness for the development of teachers’ digital competence is very limited. This study examines the use of educational video games in an online course in MOOC format with the aim of developing teachers’ digital competence in the e-safety area. A total of 179 teachers from nonuniversity schools in the region of Castilla y León (Spain) participated in this study. A pre-test and a post-test were used to measure the knowledge acquired by the participants, and a questionnaire was used to measure their perceptions. The obtained results suggest that game-based learning using educational video games is an effective and viable approach to train teachers in the e-safety area of digital competence.
GIFMarking: The robust watermarking for animated GIF based deep learning

Animated GIF has become a key communication tool in contemporary social platforms thanks to highly compatible with affective performance, and it is gradually adopted in commercial applications. Therefore, the copyright protection of the animated GIF requires more attention. Digital watermarking is an effective method to embed invisible data into a digital medium that can identify the creator or authorized users. However, few works have been devoted to robust watermarking for the animated GIF. One of the main challenges is that the animated image also contains time frame dimension information compare with still images. This paper proposes a robust blind watermarking framework based 3D convolutional neural networks for the animated GIF image, which achieves watermark image embedding and extraction for the animated GIF. Also, noise simulation is developed in frame-level to ensure robustness for the attack of the temporal dimension in this framework. Furthermore, the invisibility of the watermarked animated image is optimized by adversarial learning. Experimental results provide the effectiveness of the proposed framework and show advantages over existing works.
Asynchronous Federated Learning over Wireless Communication Networks

Federated learning (FL) has gained considerable attention of wireless communications community owing to its nature of decentralized training and privacy-preserving. However, with limited radio resources and increasing number of user equipments (UEs), it is very hard to realize strictly synchronous model updating among all the involved UEs as required in the traditional FL algorithms. In this paper, we propose a novel asynchronous FL framework, which considers the potential failures in uploading the local models and the resultant varying degrees of staleness among the models for global update. Specifically, we first design two working modes for adapting to systems with different communication environments and tasks of different difficulty. Next, a central model fusion algorithm is designed for carefully determining the fusion weight during the global update. On one hand, it aims to make the most of the fresh information contained in the uploaded local models. On the other hand, it avoids the biased convergence by making the impact of each UE be proportional to its sample share. Numerical experiments validate that the proposed asynchronous FL framework can achieve the fast and smooth convergence and enhance the training efficiency significantly.
Lifelong Mixture of Variational Autoencoders

In this article, we propose an end-to-end lifelong learning mixture of experts. Each expert is implemented by a variational autoencoder (VAE). The experts in the mixture system are jointly trained by maximizing a mixture of individual component evidence lower bounds (MELBO) on the log-likelihood of the given training samples. The mixing coefficients in the mixture model control the contributions of each expert in the global representation. These are sampled from a Dirichlet distribution whose parameters are determined through nonparametric estimation during lifelong learning. The model can learn new tasks fast when these are similar to those previously learned. The proposed lifelong mixture of VAE (L-MVAE) expands its architecture with new components when learning a completely new task. After the training, our model can automatically determine the relevant expert to be used when fed with new data samples. This mechanism benefits both the memory efficiency and the required computational cost as only one expert is used during the inference. The L-MVAE inference model is able to perform interpolations in the joint latent space across the data domains associated with different tasks and is shown to be efficient for disentangled learning representation.
MedicalGuard: U-Net Model Robust against Adversarially Perturbed Images

Deep neural networks perform well for image recognition, speech recognition, and pattern analysis. This type of neural network has also been used in the medical field, where it has displayed good performance in predicting or classifying patient diagnoses. An example is the U-Net model, which has demonstrated good performance in data segmentation, an important technology in the field of medical imaging. However, deep neural networks are vulnerable to adversarial examples. Adversarial examples are samples created by adding a small amount of noise to an original data sample in such a way that to human perception they appear to be normal data but they will be incorrectly classified by the classification model. Adversarial examples pose a significant threat in the medical field, as they can cause models to misidentify or misclassify patient diagnoses. In this paper, I propose an advanced adversarial training method to defend against such adversarial examples. An advantage of the proposed method is that it creates a wide variety of adversarial examples for use in training, which are generated by the fast gradient sign method (FGSM) for a range of epsilon values. A U-Net model trained on these diverse adversarial examples will be more robust to unknown adversarial examples. Experiments were conducted using the ISBI 2012 dataset, with TensorFlow as the machine learning library. According to the experimental results, the proposed method builds a model that demonstrates segmentation robustness against adversarial examples by reducing the pixel error between the original labels and the adversarial examples to an average of 1.45.
Learning Outcomes of Digital Learning Interventions in Higher Education

Implementing digital technologies has become a policy priority worldwide among all types of education. The COVID-19 pandemic has further accelerated educational institutions' efforts to reorganize their teaching and introduce new digital learning technologies. Although using digital technologies in higher education, including nursing, is considered a modern and innovative way of teaching and learning, uncertainty exists concerning these technologies' actual usefulness in achieving positive learning outcomes. The aim of this scoping review was to examine the current evidence related to the effects of using digital technologies on learning outcomes in higher education. The authors searched five electronic databases for relevant studies and used a scoping review method to analyze and synthesize the evidence. Eighty-six articles from six disciplines met the selection criteria. As a key finding, the outcomes of the interventions were mainly positive. Increased professional knowledge, skills, and attitudes reflect the advancement of professional competence. Academic, collaborative, and study skills, in turn, contribute to general competence development. Our findings suggest that digital technology has the potential to improve learning in various disciplines.
Improving Solar Panel Efficiency by Solar Tracking and Tilt Angle Optimization with Deep Learning

The surge in energy demand over the last few decades has led to the vast depletion of natural sources of energy and calls for active research and development of effective renewable energy systems. Solar energy is the most abundant renewable source of energy available to us and thus, adoption of solar energy over other energy sources can be an efficient way to secure a sustainable future. However, the change in the sun's relative position to earth through the day varies the intensity of sunlight falling on the photo-voltaic cells of a solar panel, leading to a substantial decrease in its overall efficiency. Therefore, development should concentrate on achieving greater efficiencies using solar panel arrays in order to generate maximum solar power. This paper proposes a solution by tracking the sun's relative position to earth continuously and optimizing the tilt angle of the solar panel accordingly with deep learning. The proposed method predicts the tilt and orientation angle of the sun with a 97% accuracy, resulting in the increase of the solar output power by 10.6% when compared to other non-tracking systems.
The Method and Metric of User Experience Evaluation: A Systematic Literature Review

With the growth of User Experience (UX) research field, researchers have developed various ways to implement UX evaluation method. These evaluation methods have different practice, evaluated application, and type of collected aspect. This article provides a systematic literature review on research papers from 2000 to 2019 related to UX evaluation, to better understand UX evaluation method and its implementation, what kind of application its applied to, and what type of collected metric. The result of this paper presents the most frequently used method is self-reported measurement that evaluates self-reported metric and issue-based metric and the least frequently used method is physiological measurement that evaluates emotion and stress metric.
Text Readability and Processing Effort in Second Language Reading: A Computational and Eye‐Tracking Investigation

Abstract Although text readability has traditionally been measured based on simple linguistic features, recent studies have employed natural language processing techniques to develop new readability formulas that better represent theoretical accounts of reading processes. This study evaluated the construct validity of different readability formulas, including both traditional and newer formulas, by examining their ability to predict the processing effort involved during L2 reading as evidenced by eye movements. Two studies (an experimental study and a corpus‐based study) were conducted in which the readability of target texts was calculated using different formulas and then utilized to develop models that predict particular eye movement patterns during reading. These studies revealed that although traditional formulas showed reliable performance in predicting particular eye movement patterns, in many cases, the newer formulas outperformed them. These findings support the newer readability formulas as more theoretically valid and accurate measures of the processing effort involved in L2 reading.