Reviewerly/exHarmony huggingface dataset:
An Automated Detection Approach of Protective Equipment Donning for Medical Staff under COVID-19 Using Deep Learning

Personal protective equipment (PPE) donning detection for medical staff is a key link of medical operation safety guarantee and is of great significance to combat COVID-19.However, the lack of dedicated datasets makes the scarce research on intelligence monitoring of workers' PPE use in the field of healthcare.In this paper, we construct a dress codes dataset for medical staff under the epidemic.And based on this, we propose a PPE donning automatic detection approach using deep learning.With the participation of health care personnel, we organize 6 volunteers dressed in different combinations of PPE to simulate more dress situations in the preset structured environment, and an effective and robust dataset is constructed with a total of 5233 preprocessed images.Starting from the task's dual requirements for speed and accuracy, we use the YOLOv4 convolutional neural network as our learning model to judge whether the donning of different PPE classes corresponds to the body parts of the medical staff meets the dress codes to ensure their self-protection safety.Experimental results show that compared with three typical deeplearning-based detection models, our method achieves a relatively optimal balance while ensuring high detection accuracy (84.14%), with faster processing time (42.02ms) after the average analysis of 17 classes of PPE donning situation.Overall, this research focuses on the automatic detection of worker safety protection for the first time in healthcare, which will help to improve its technical level of risk management and the ability to respond to potentially hazardous events.
Use of Cloud Computing Tools on Pedagogical and Educational Contexts

This article proposes a pedagogical methodology that allows the use of cloud computing tools as complementary material in the development of computational and military skills: navigation, understanding and reading of charts. The aims of this study is to evaluate the impact of cloud computing on the learning of the students of the Soldiers Training School “Vencedores del Cenepa” Ecuador, in the subject of Land Navigation. For this reason, here we present and reflect on how to incorporate the map into educational processes, strengthening positioning skills and competencies in orientation exercises. This article is part of the research project Design of a Pedagogical model based on Cloud Computing and Affective Computing for the development of computational skills of Valencia University. In conclusion, the use of Cloud Computing tools allowed generating an environment of greater interactivity between teachers and the soldiers in training likewise, it allowed to generate an adequate educational process.
An Animated GIF Steganography Using Variable Block Partition Scheme

The paper presents a novel Graphics Interchange Format (GIF) Steganography system.The algorithm uses an animated (GIF) file format video to apply on, a secured and variable image partition scheme for data embedding.The secret data could be any character text, any image, an audio file, or a video file; that is converted in the form of bits.The proposed method uses a variable partition scheme structure for data embedding in the (GIF) file format video.The algorithm estimates the capacity of the cover (GIF) image frames to embed data bits.Our method built variable partition blocks in an empty frame separately and incorporate it with randomly selected (GIF) frames.This way the (GIF) frame is divided into variable block same as in the empty frame.Then algorithm embeds secret data on appropriate pixel of the (GIF) frame.Each selected partition block for data embedding, can store a different number of data bits based on block size.Intruders could never come to know exact position of the secrete data in this stego frame.All the (GIF) frames are rebuild to make animated stego (GIF) video.The performance of the proposed (GIF) algorithm has experimented and evaluated based on different input parameters, like Mean Square Error (MSE) and Peak Signal-to-Noise Ratio (PSNR) values.The results are compared with some existing methods and found that our method has promising results.
Smartphone Sensors Based Physical Life-Routine for Health Education

The physical and the mental health of a human being largely depends upon his physical life-routine (PLR) and today's much advanced technological methods make it possible to recognize and keep track of an individual's PLR.With the successful and accurate recognition of PLR, a sublime service of health education can be made copious.In this regard, smartphones can play a vital role as they are ubiquitous and have utilitarian sensors embedded in them.In this paper, we propose a framework that extracts the features from the smartphone sensors data and then uses the sequential feature selection to select the most useful ones.The system employs a novel approach of codebook assignment that uses vector quantization to efficiently manipulate the data coming from the smartphone sensors of different nature and serve as a data compression module at the same time.The proposed system uses a multilayer perceptron classifier to differentiate among different PLRs.The experimentation was performed on the benchmark Real-life HAR dataset.It provides the data of four sensors: accelerometer, gyroscope, magnetometer, and global positioning system (GPS) for the recognition of four activities namely active, inactive, walking, and driving.The performance of the proposed system was validated using 10-fold cross-validation and the confidence of the system was recorded to be 91.80%.
Improving Approaches for Meta-heuristic Algorithms: A Brief Overview

Optimization problems can be observed in many applications, ranging from engineering applications and decision-making to computer science and finance. Optimization can be described as a process for discovering optimal solution among all available solutions of the defined problem, considering complex and high-dimensional constraints in searching for the optimal solution. Among the numerous approaches for solving optimization issues, optimization algorithms are one of the most well-known methods. Such algorithms possess the ability and efficiency to solve optimization issues in different science and engineering disciplines; however, these algorithms face problems, such as slow convergence speed and falling into local optimum. Various methods can be applied to improve optimization algorithms’ performance. This chapter aims to introduce some of these methods and their theory, along with different combination methods to achieve better performance in finding the global solution.
Learning-Based Adaptive Optimal Control for Flotation Processes Subject to Input Constraints

This article presents a learning-based adaptive optimal control approach for flotation processes subject to input constraints and disturbances using adaptive dynamic programming (ADP) along with double-loop iteration. First, the principle of the operational pattern is adopted to preset reagents’ addition based on the feeding condition. Then, this article leverages a deep learning model, which is composed of multiple neural layers to detect flotation indexes directly from the raw froth images. After that, the tracking error between the detected flotation indexes and the reference values can be minimized by using ADP-based double-loop iteration. Particularly, a policy-iteration (PI) method is utilized for the proposed learning-based ADP algorithm. In the inner loop, the optimal control problem is formulated as a linear quadratic regulator (LQR) problem using the low-gain feedback design method. In the outer loop, the design parameters, i.e., weighting matrices, are tuned automatically to satisfy the input constraints. Finally, the analytical results demonstrate that the proposed scheme can guarantee asymptotic tracking in the presence of actuator saturation and disturbances.
Energy Optimized Dynamics Incorporated A-star Algorithm For A Four-Wheeled Mobile Robot

The navigation of mobile robots has been a keen interest for many researchers. Motion planning has always played a crucial role in navigation. The major portion of navigation involves obstacle avoidance while moving from a start position to a goal position. The basic motion planners don’t include the dynamics of mobile robots which when ignored results in trajectories that can cause difficulty in tracking. This paper presents an approach in minimizing the amount of energy required by the robot while considering the kinematics and dynamics of a mobile robot along an energy-optimized path obtained by updating the cost function in the A-star algorithm, while also considering ground conditions and static obstacle avoidance.
Framework of CNN Architecture for Fashion Image Classification

Nowadays, online shopping is booming due to the pandemic. As stores are not operational during the lockdown, retailers try to sell everything online. Also, competition is increasing between them, allowing retailers to enhance their online application features. The fashion business has recently added many features like clothes search, clothes recommendation, etc. All these features can be implemented with the help of image classification. Clothes have many attributes and hidden features which can be extracted and classified with the help of Deep learning. This paper implements a CNN-based architecture, a deep learning approach, to classify images. This architecture is trained on the Fashion MNIST dataset. Proposed CNN architecture is examined, and substantial results are obtained.
Adversarial Spam Detector With Character Similarity Network

More and more social platforms suffer from the spam messages and trouble in detecting them. Lizhi, one of the most famous audio APPs in China, also suffers from the spam messages. The spam detector in Lizhi faces two major challenges: adversarial actions taken by the spammers and lack of labeled data. In this article, we propose a novel adversarial spam detector based on character similarity network for detecting adversarial spam messages in Lizhi, namely Lizhi adversarial spam detector. The character similarity network is designed to solve the adversarial actions before being taken by the spammers, and the character embedding model is proposed to learn the embeddings of all the characters in the corpus. Then, the model generates the sentence embedding of the messages sent by users. At last the classifier will predict whether the message is a spam. Also, the model utilizes active learning to solve the problem of lack of labeled data. Both of offline and online experiments are conducted to confirm the effectiveness of the proposed method.
Transfer Learning and Sentiment Analysis of Bahraini Dialects Sequential Text Data Using Multilingual Deep Learning Approach

Sentiment analysis is a crucial Natural Language Processing task to analyze the user’s emotions and opinions towards events, entities, services, or products. Arabic NLP faces numerous challenges, some of which include: (1) the scarcity of resources, especially in modern standard Arabic and Arabic dialects, particularly the Bahraini one; (2) lack of multilingual deep learning models; and (3) transfer learning studies on Arabic dialects in general and Bahraini dialects, specifically. This research aims to create a balanced dataset of Bahraini dialects that covers product reviews by translating English Amazon product reviews to modern standard Arabic, which were then converted to Bahraini dialects. Another aim of this research is to provide a reusable multilingual deep learning long short term memory model to analyze the parallel dataset of English, modern standard Arabic, and Bahraini dialects, which differ in linguistic properties. Many experiments were conducted using train-validate-test split and k-fold cross-validation to evaluate the model performance using accuracy metrics. The model runs average accuracy on all datasets ranges from 96.72% to 97.04% when utilizing an augmentation technique. Moreover, a pre-trained Long Short Term Memory model was created to exploit and transfer the knowledge gained from analyzing the Bahraini dialects products reviews to perform sentiment analysis on a small dataset of movie comments in Bahraini dialects. The Pre-trained model performance is 96.04% accuracy.
Lifeblood of Health is Data

Health is the most important aspect of life. However, health is the least developed aspect of modern society that increasingly uses data as a major resource for understanding complexities in a system and relies on predictive control for the overall improvement of systems. Progress in biology, multimodal sensors, mobile devices, computing, and related areas is rapidly establishing that health is a perpetual personal data system rather than current population-based episodic disease-oriented healthcare approach. Lifestyle is more important for controlling health than medicine. Technology now allows us to perpetually measure lifestyle and analyze it to build personal models and estimate health state for developing predictive, preventive, personalized approach for maximizing healthiness for most people in the world. These emerging approaches have demonstrated their early success and are now ready for helping people achieve their health goals. Data is the lifeblood of health; technology has a great opportunity to bring this imminent transformation now.
Performance Analysis of Least Squares of Continuous-Time Model Based on Sampling Data

In this letter, we consider the parameter estimation problem of continuous-time linear stochastic regression models described by stochastic differential equations using the sampling data. An online least squares (LS) algorithm is proposed by minimizing the accumulative prediction error at discrete sampling time instants. By employing both the stochastic Lyapunov function and martingale estimate methods, we establish the convergence analysis of the proposed algorithm under conditions of the excitation of the sampling data and the sampling time interval. We also provide the upper bound of the accumulative regret for the adaptive predictor. A simulation example is given to verify our theoretical results.
Information-Enhanced Hierarchical Self-Attention Network for Multiturn Dialog Generation

Transformer structure has shown promising results in multiturn dialog generation. The self-attention mechanism can learn global dependencies but ignores local information, limiting the model’s ability to model context information. In this article, we propose an information-enhanced hierarchical self-attention network (IEHSA). In the word-level encoder, words in successive windows are automatically encoded as local information, and words with dependent words are automatically encoded as syntactic information, both of which are used to enhance word information and then feed into the self-attention mechanism. In the utterance-level encoder, adjacent utterance representations are automatically encoded as dialog structure information, and the self-attention mechanism is used to update the utterance representations. The context and masked response representation are then updated using the self-attention mechanism in the decoder. Finally, the correlation between context and reply is calculated and used in further decoding. We compared IEHSA with the current popular hierarchical model on several datasets, and the experiments show that the proposed method has substantial improvements in both metric-based and human evaluations.
Hearing loss detection in complex setting by stationary wavelet Renyi entropy and three-segment biogeography-based optimization

Improving the image detection efficiency and accuracy of sensorineural hearing loss under a complex system is the main goal of our research. The current detection methods of hearing loss mostly design for a single pipeline program and ignore the linkage of the setting. The cross-correlation and multibalance problems are considered in this chapter by an improved feature extraction method: stationary wavelet Renyi entropy (SWRE), as well as joint optimization algorithm for model and feature extraction: three-segment biogeography-based optimization. The 600 subject images were fed into the single hidden layer feedforward neural network in terms of SWRE and divided into three classes: the healthy control (HC), the left-side hearing loss (LHL), and the right-side hearing loss (RHL). The experiments selected the optimal value of 10 times ten-fold cross-validation and showed that the results obtained the sensitivities of HC 95.00%, LHL 95.17%, RHL 95.67% and overall measurement 95.28% respectively. In all, the results prove that our method can be well applied to the diagnosis during the intricacy scene.
Arduino UNO-Based Smart Hand Gloves for Physically Challenged People

Smart hand gloves assist impaired individuals with living with typical individuals. This paper aims to aid auditory and speech impaired people by providing them a means to communicate basic lines without having to learn sign language, or as a temporary communication device until they can learn sign language. The glove has three flex sensors that run along the length of any three fingers. The flex sensors detect when a finger has been moved due to a change in their bend angle and produce an accordingly varying output resistance. This change is given as input to Arduino UNO that in turn, activates a pre-determined sentence to be displayed on a 16 × 2 LCD module. The pre-determined sentences can be changed by changing the Arduino source code accordingly, and thus, the device can be easily customized to the user’s needs.
Object Detection in Driving Datasets Using a High-Performance Computing Platform: A Benchmark Study

Nowadays, machine learning methods are increasingly used in different parts of autonomous driving and driving assistance systems. Yet, data and computational requirements can be enormous with these methods. Thus, providing several datasets containing many and diverse cases for the target problem and sufficient hardware for training and application of ML methods are too critical for achieving accurate results when applying them. Hence, we present an object detection benchmark study implementing the knowledge graph-based data integration framework to meet the data requirements and run the implementation on a big data and high-performance computing (HPC) platform, namely the EVOLVE. We applied different object detection methods to widely known open datasets, and compared the results on three different hardware setups, including EVOLVE. We also performed a small-scale transfer learning experiment. The results show that EVOLVE allowed the exploitation of much bigger data leading to a more efficient application of the object detection models with the help of the knowledge graph-based data integration framework. EVOLVE significantly improved the execution times compared to running them on a local laptop and a virtual machine and provided the easy-to-use and ready-to-use means to store large datasets and apply different models with its hardware and software stack.
AlphaZero Ideas

Can artificial intelligence (AI) uncover new ideas? As machines are learning fast and becoming increasingly intelligent, can AI not only automate the production function of goods and services, but also of ideas? Economic growth arises from people creating ideas, and thus an affirmative answer to these questions may have drastic implications for a host of important issues. Yet, to date, there is no empirical evidence showing that AI can in fact generate tabula rasa ideas that improve human understanding. Using as an exogenous shock the introduction of AlphaZero, we provide the first causal evidence of the impact of unsupervised AI on the production function of ideas. Specifically, AlphaZero is considered a milestone of scientific progress in AI research. This program rediscovered ideas known in centuries of human chess, and created new ideas as well. We study world experts at the frontier of knowledge and find that at least the player with the highest classical rating in the history of chess learned and adopted new ideas uncovered by AlphaZero. Other players may have also done the same. We contend that obtaining evidence of the impact of AI on the production function of ideas is a necessary first step to think about AI's impact on the innovation and research processes that drive the advancement of knowledge and economic growth.
Simple Linear Iterative Clustering with Efficiency

As an instructive work to generate satisfactory superpixels, simple linear iterative clustering (SLIC), has become fundamental and popular in various computer vision tasks. In this work, the algorithm is reconsidered, and an integrated framework is proposed to further improve the running speed and segmentation performance. In the first stage, a fast convergence strategy for clustering is presented on SLIC to redistribute the seeds efficiently. This is done to initialize a set of clustering centers that is fairly representative of local information on the image plane. Then, a follow-up work of SLIC termed simple non-iterative clustering (SNIC) is utilized to process more accurate segmentation without any post-processing to enforce connectivity. Experimental results show that the framework could generate a synergetic effect and performs better than previous superpixel algorithms in a limited computational time.
Evaluation of Webcam-Based Eye Tracking for a Job Interview Training Platform: Preliminary Results

In job interviews, eye gaze towards the interviewer is an important non-verbal behavior that is considered a trait for hirability of a candidate. Several virtual job interview training platforms include eye trackers to measure eye gaze to provide feedback on performance. Though useful, these eye tracking devices are often pricey and not always accessible. In this article, we explore several camera-based eye tracking methods and implement a webcam-based eye tracking algorithm to determine its suitability for potential integration in virtual job interview simulation platforms. We further use the gaze predictions for interview relevant regions of interest detection. Our study with 12 participants, 7 with eyeglasses and 5 without, shows that during calibration, eyeglasses play no significant role in the differences in mean calibration error. Results from the ROI detection, however, show a limitation that it is important to maintain the same head position and distance during multiple tasks after calibration. Overall, webcam-based eye-tracking has potential, to be integrated in virtual job interview training environments.
Improving Machine Self-Diagnosis with an Instance-Based Selector for Real-Time Anomaly Detection Algorithms

The diffusion of smart sensor technology in production enables real-time monitoring of production conditions. Machine self-diagnosis shall serve the analysis of these conditions by differentiating expected data from anomalies. Several algorithms have been developed in practice and academia to detect anomalies in real-time and to support machine self-diagnosis, so that counteractions can be taken. However, due to the algorithms’ different functionalities, they yield different results when applied to the same data. Our research aims to leverage complementary potentials among these algorithms. To this end, we use a design science research approach to design and prototypically implement a real-time anomaly detection algorithm selector to support decision making regarding machine self-diagnosis. The selector decides in real-time for each sensor-emitted data point, which algorithm yields the most reliable result in terms of anomaly detection. We evaluate functionality and feasibility with two real-world case studies. The evaluation shows that the algorithm selector may outperform single algorithms and that it is applicable in practice.