Reviewerly/exHarmony huggingface dataset:
Effective Handling of COVID-19 Pandemic: Experiences and Lessons from the Perspective of South Korea

The ongoing pandemic (i.e., novel corona virus disease 2019 (COVID-19)) is a major crisis that humanity is facing across the globe. In response to COVID-19, every country has designed and enforced various mechanisms to control its spread, and save their citizens from this deadly disease. Besides the general containment strategies, the use of technology and person-specific data collection and processing also vary from country to country. In this brief case report, we describe the measures that South Korea (SK) has adopted in order to curb the spread of COVID-19, and the success of SK in keeping the cases at a manageable level since the start of this pandemic until present via extensive use of technology. Specifically, we describe the data collected to control this pandemic, technical framework in place to ensure co-ordination with different authorities, containment strategies, and major breakthrough SK achieved when sporadic clusters emerged throughout the nation. With this brief overview, we aim to update the research community about the unprecedented efforts SK made, utilizing previous pandemic experiences, and the successful results they have obtained so far amid this pandemic, leveraging technology.
Secure Mobile Learning

The widespread adoption and employment of hand-held devices is a well-known and academically acknowledged fact as digital pervasiveness and hardware availability has triggered off this massive and convenient trend of accessing and consuming online resources and services from anywhere at any time. In this paper we argue that five areas in the mobile learning platform and the computer networks that support the processes and environments, require particular attention and proper training of the people involved to ensure the soundness of all the security methods and best practices are maintained, along with continual maintenance and updating of systems and procedures in each of these five categories. The paper closes with numerous recommendations regarding security in a mobile learning environment.
Named Entity Recognition Method for Educational Emergency Field Based on BERT

Educational emergencies provide a lot of information. Aiming at the problem that traditional named entity recognition methods in this field cannot represent the ambiguity of a word, this article proposes a named entity recognition method BERT+BiLSTM-CRF in the field of educational emergencies based on the BERT pre-training language model. Firstly, BERT is trained on the corpus of educational emergencies to obtain the vectorized representation of the words, and then the context encoding of the serialized text is obtained using BiLSTM, and then the sequence is decoded and annotated by CRF to obtain the corresponding entities in the educational emergencies. Experiments show that the BERT+BiLSTM-CRF fusion model has achieved an accuracy of 91.62% on the educational emergency data set, which is a significant improvement compared to the traditional named entity recognition model.
Influence of Virtual Human Appearance Fidelity within Building Science Storytelling Educational Applications

Building science employs student–professional interactions to increase the learning and engagement of domain topics. Although these interactions are beneficial to students, labor shortages and automation trends will create barriers to expose students to professionals in the future. One solution is to utilize virtual situated learning contexts for students to experience simulated interactions with virtual professionals. Within these simulations, virtual humans are often used as a vehicle to preserve knowledge and deliver it to students. However, the impact of the fidelity of virtual human appearance on providing knowledge to the students within the building science domain remains unexplored. This paper presents a virtual reality (VR) educational system integrated with digital storytellers and grounded in the building science electrical career path context to explore how virtual human appearance fidelity affects student learning and engagement. A between-subject study was conducted with 42 construction management participants to explore the impact of 3 virtual human appearance fidelity conditions (realistic, modeled, and abstracted virtual human) in terms of student learning, social presence, virtual human persona factors, and topic interest. The findings of this study demonstrated that successful educational outcomes could be achieved with low virtual human appearance fidelity due to the lack of significant differences observed across experimental conditions. Furthermore, the study results did not detect significant differences in students' engagement by increasing the virtual human appearance fidelity. However, students across all experimental conditions reported engagement increments in the topic learned. The increments in student engagement suggest that the virtual human's age and gender are important factors to consider when designing educational interventions. These results contribute to the understanding of how to create virtual humans for building science educational interventions.
Framework for Integrated Communication of Mobile and Cloud service provider via Cloud cluster with Homomorphic Encryption Technique

The selection of cloud service usage and quality assessment of the cloud resource supplier, up to the customer satisfaction enhanced the small organizations to adopt cloud computing. By survey approach it is sum-up that Load time, Reliability and Data Integrity parameters are best for managing services of cloud providers. Through fog we have compute the values of three parameters Load time for services, Integrity for each provider and Reliability to calculate Trust factor. Then selection of the service and uploading of file is done on the basis of cluster which offers the different services to the set of nodes. The security mechanism are applied to maintained the safety, integrity and correctness of data in cloud .The sever stores the encrypted data and at client side it is decrypted. This will assure the user that his data is secured at Cloud server and no one cannot access his data. Anyhow, the data stored on cloud may be altered without the knowledge of client then key generation method proofs the uniqueness of data. This verifies the data is not altered at server and a same as put by the client. This method involves texts, audios, videos, images, and all other type of data etc.
Predictive model for the identification of activities of daily living (ADL) in indoor environments using classification techniques based on Machine Learning

AI-based techniques have included countless applications within the engineering field. These range from the automation of important procedures in Industry and companies, to the field of Process Control. Smart Home (SH) technology is designed to help house residents improve their daily activities and therefore enrich the quality of life while preserving their privacy. An SH system is usually equipped with a collection of software interrelated with hardware components to monitor the living space by capturing the behavior of the resident and their occupations. By doing so, the system can report risks, situations, and act on behalf of the resident to their satisfaction. This research article shows the experimentation carried out with the human activity recognition dataset, CASAS Kyoto, through preprocessing and cleaning processes of the data, showing the Vía Regression classifier as an excellent option to process this type of data with an accuracy 99.7% effective
Tiny Reservoir Computing for Extreme Learning of Motor Control

The electromechanical system of an electric machine controller represents a complex non-linear system that needs to adapt itself continuously over time, since factors as component aging and thermal derating tend to modify its behavior and performance. Algorithms based on Neural Networks (NN), and in particular Recurrent NN (RNN), are expected to improve the motor drive, due to their ability to approximate complex non-linear systems, and to cope with the time-varying nature of signals. This paper proposes a novel control technique which extends the Field Oriented Control (FOC) algorithm by means of Extreme Learning Machine (ELM) and Reservoir Computing (RC). In particular, we introduce a specifically developed NN, named Semi Binary Deep Echo State Networks (SB-DESN), to achieve good control accuracy with low complexity. This allows its deployment into cheap micro-controllers characterized by severe memory, computational, and power consumption constraints. The peculiar property of SB-DESN i.e., a fixed and predictable training complexity, makes it well suited for adoption into Reinforcement Learning on micro-controllers to ensure the continuous adaptability of the control over the time. Beyond the use of SB-DESN for speed, torque, and magnetic flux control, we propose a novel complexity optimization based on sparsity properties of inter-reservoir matrices, which reduces the memory footprint up to a factor 3.7 and the computational complexity up to a factor 2. This corresponds to a SB-DESN with only 2.5kB of memory footprint and an inference time of 20µs on STM32H7, which allows its deployment into mass market FOC solution.
Peculiarities and Applications of Stochastic Processes with Fractal Properties

In this paper, the fractal properties of stochastic processes and objects in different areas were specified and investigated. These included: measuring systems and sensors, navigation and motion controls, telecommunication systems and networks, and flaw detection technologies. Additional options that occur through the use of fractality were also indicated and exemplified for each application. Regarding the problems associated with navigation information processing, the following fractal nature processes were identified: errors of inertial sensors based on the microelectromechanical systems called MEMS, in particular gyroscopic drift and accelerometer bias, and; the trajectory movement of mobile objects. With regard to navigation problems specifically, the estimation problem statement and its solution are given by way of the Bayesian approach for processing fractal processes. The modified index of self-similarity for telecommunication series was proposed, and the self-similarity of network traffic based on the R/S method and wavelet analysis was identified. In failure detection, fractality manifested as porosity, wrinkles, surface fractures, and ultrasonic echo signals measured using non-destructive sensors used for rivet compound testing.
Social Network Mining for Predicting Users’ Credibility with Optimal Feature Selection

The distribution of information in social media is very ad hoc in nature because of the increasing social media and the sophistication of social networks. Identifying fraudulent accounts is one of the key security issues. The task of identifying the reputation of social media users is difficult. In order to prevent malicious users from developing fake accounts, a social network mining model is suggested to evaluate and forecast user's trust. The proposed work mines text data and metadata on Twitter accounts to achieve the spammicity classification of the user. To evaluate the optimal characteristics that lead to optimizing the classification accuracy, the recursive Feature Elimination technique is used. The proposed work is evaluated on a real-time Twitter dataset and the empirical findings indicate that the proposed method performs better.
CNN Adaptations for Boat Detection in Aerial Images Tested on Yolo v2

In this article, we focus on boat real-time detection in aerial images taken by UAVs Unmanned Aerial Vehicles. Several methods dealing with this problem are based on convolutional networks. Generally, they start from existing networks that have demonstrated their effectiveness with datasets like COCO Common Object in Context [1] and adapt them to improve their performance on aerial images. The adaptations made should not cause a higher execution time than the initial network, especially if the image processing and detection must be done in real-time. They must also participate in the increase in Recall by detecting even small objects on aerial images which is the case of most objects present in photos taken at high altitude. The purpose of this article is to test the effectiveness of certain adaptations with our boat dataset. We will also propose new adaptations. The tests will be performed using the Yolo v2 neural network [2].
AnnoTag: Concise Content Annotation via LOD Tags derived from Entity-Level Analytics

Digital libraries build on classifying contents by capturing their semantics and (optionally) aligning the description with an underlying categorization scheme. This process is usually based on human intervention, either by the content creator or a curator. As such, this procedure is highly time-consuming and - thus - expensive. In order to support the human in data curation, we introduce an annotation tagging system called “AnnoTag”. AnnoTag aims at providing concise content annotations by employing entity-level analytics in order to derive semantic descriptions in the form of tags. In particular, we are generating “Semantic LOD Tags” (linked open data) that allow an interlinking of the derived tags with the LOD cloud. Based on a qualitative evaluation on Web news articles we prove the viability of our approach and the high-quality of the automatically extracted information.
Entity relation extraction from electronic medical records based on improved annotation rules and BiLSTM-CRF

Extracting entities and their relationships from electronic medical records (EMRs) is an important research direction in the development of medical informatization. Recently, a method was proposed to transform entity relation extraction into entity recognition by using annotation rules, and then solve the problem of relation extraction by an entity recognition model. However, this method cannot deal with one-to-many entity relationship problems.This paper combined the bidirectional long- and short-term memory-conditional random field (BiLSTM-CRF) deep learning model with an improvement of sequence annotation rules, hided relationships between entities in entity labels, then the problem of one-to-many named entity relation extraction in EMRs was transformed into entity recognition based on relation sets, and entity extraction was carried out through the entity recognition model.Entity extraction was achieved through the entity recognition model. The result of entity recognition was transformed into the corresponding entity relationship, thus completing the task of one-to-many entity relation extraction by the improved annotation rules, the accuracy rate of proposed method reaches 83.46%, the recall rate is 81.12%, and the value of comprehensive index F1 is 0.8227.Through the annotation analysis of EMRs, our experimental results show that the improved annotation rules can effectively complete the task of one-to-many medical entity relation extraction from EMRs.
A Provably Convergent Information Bottleneck Solution via ADMM

The Information bottleneck (IB) method enables optimizing over the trade-off between compression of data and prediction accuracy of learned representations, and has successfully and robustly been applied to both supervised and unsupervised representation learning problems. However, IB has several limitations. First, the IB problem is hard to optimize. The IB Lagrangian <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathcal{L}_{IB}: =I(X;Z)-\beta I(Y;Z)$</tex> is non-convex and existing solutions guarantee only local convergence. As a result, the obtained solutions depend on initialization. Second, the evaluation of a solution is also a challenging task. Conventionally, it resorts to characterizing the information plane, that is, plotting <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$I(Y;Z)$</tex> versus <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$I(X;Z)$</tex> for all solutions obtained from different initial points. Furthermore, the IB Lagrangian has phase transitions while varying the multiplier <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\beta$</tex> . At phase transitions, both <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$I(X;Z)$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$I(Y;Z)$</tex> increase abruptly and the rate of convergence becomes significantly slow for existing solutions. Recent works with IB adopt variational surrogate bounds to the IB Lagrangian. Although allowing efficient optimization, how close are these surrogates to the IB Lagrangian is not clear. In this work, we solve the IB Lagrangian using augmented Lagrangian methods. With augmented variables, we show that the IB objective can be solved with the alternating direction method of multipliers (ADMM). Different from prior works, we prove that the proposed algorithm is consistently convergent, regardless of the value of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\beta$</tex> . Empirically, our gradient-descent-based method results in information plane points that are comparable to those obtained through the conventional Blahut-Arimoto-based solvers, and is convergent for a wider range of the penalty coefficient than previous ADMM-based solvers.
An Improvement of Density Peaks Clustering Algorithm Based on KNN and Gravitation

Clustering by fast search and find of Density Peaks (DPC) is a famous clustering algorithm. The main advantages of DPC are that its clustering process is iteration-free and it is independent from the shape and dimensions of a dataset. However, DPC has two main weaknesses: 1) it is subjective to select the cluster centers from the decision graph since they are selected by a user, and thus some selected centers are not necessarily correct; and 2) if a sample point is put into a wrong cluster, then this error can fast be propagated and thus makes many sample points be wrongly put into the cluster. To overcome the above weaknesses, the paper improves DPC (called IDPC). We propose a new density formula combined with the idea of Gravitation and KNN that can make the local densities of sample points in dense and sparse areas have more obvious separability. IDPC does not require users to manually select cluster centers from a decision graph. Experiments on 15 datasets show that our clustering algorithm overall outperforms the state-of-the-art ones including ADPC-KNN, DPC, DPCSA and DPC-KNN.
A novel specimen shape for measurement of linear strain fields by means of digital image correlation

Abstract Strains on the surface of engineering structures or biological tissues are non-homogeneous. These strain fields can be captured by means of Digital Image Correlation (DIC). However, DIC strain field measurements are prone to noise and filtering of these fields influences measured strain gradients. This study aims to design a novel tensile test specimen showing two linear gradients, to measure full-field linear strain measurements on the surface of test specimens, and to investigate the accuracy of DIC strain measurements globally (full-field) and locally (strain gauges’ positions), with and without filtering of the DIC strain fields. Three materials were employed for this study: aluminium, polymer, and bovine bone. Normalized strain gradients were introduced that are load independent and evaluated at two local positions showing 3.6 and 6.9% strain change per mm. Such levels are typically found in human bones. At these two positions, two strain gauges were applied to check the experimental strain magnitudes. A third strain gauge was applied to measure the strain in a neutral position showing no gradient. The accuracy of the DIC field measurement was evaluated at two deformation stages (at $$\approx $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>≈</mml:mo> </mml:math> 500 and 1750 μstrain) using the root mean square error (RMSE). The RMSE over the two linear strain fields was less than 500 μstrain for both deformation stages and all materials. Gaussian low-pass filter (LPF) reduced the DIC noise between 25% and 64% on average. As well, filtering improved the accuracy of the local normalized strain gradients measurements with relative difference less than 20% and 12% for the high- and low-gradient, respectively. In summary, a novel specimen shape and methodological approach are presented which are useful for evaluating and improving the accuracy of the DIC measurement where non-homogeneous strain fields are expected such as on bone tissue due to their hierarchical structure.
SFPD: Simultaneous Face and Person Detection in Real-Time for Human–Robot Interaction

Face and person detection are important tasks in computer vision, as they represent the first component in many recognition systems, such as face recognition, facial expression analysis, body pose estimation, face attribute detection, or human action recognition. Thereby, their detection rate and runtime are crucial for the performance of the overall system. In this paper, we combine both face and person detection in one framework with the goal of reaching a detection performance that is competitive to the state of the art of lightweight object-specific networks while maintaining real-time processing speed for both detection tasks together. In order to combine face and person detection in one network, we applied multi-task learning. The difficulty lies in the fact that no datasets are available that contain both face as well as person annotations. Since we did not have the resources to manually annotate the datasets, as it is very time-consuming and automatic generation of ground truths results in annotations of poor quality, we solve this issue algorithmically by applying a special training procedure and network architecture without the need of creating new labels. Our newly developed method called Simultaneous Face and Person Detection (SFPD) is able to detect persons and faces with 40 frames per second. Because of this good trade-off between detection performance and inference time, SFPD represents a useful and valuable real-time framework especially for a multitude of real-world applications such as, e.g., human–robot interaction.
Facial Structure Guided GAN for Identity-preserved Face Image De-occlusion

In some practical scenarios, such as video surveillance and personal identification, we often have to address the recognition problem of occluded faces, where content replacement by serious occlusion with non-face objects always produces partial appearance and ambiguous representation. Under the circumstances, the performance of face recognition algorithms will often deteriorate to a certain degree. In this paper, we therefore address this problem by removing occlusions on face images and present a new two-stage Facial Structure Guided Generative Adversarial Network (FSG-GAN). In Stage I of the FSG-GAN, the variational auto-encoder is used to predict the facial structure. In Stage II, the predicted facial structure and the occluded image are concatenated and fed into a generative adversarial network (GAN) based model to synthesize the de-occlusion face image. In this way, the facial structure knowledge can be transferred to the synthesis network. Especially, in order to enable the occluded face image to be perceived well, the generator in the GAN based synthesis network utilizes the hybrid dilated convolution modules to extend the receptive field. Furthermore, aiming at further eliminating the appearance ambiguity as well as unnatural texture, a multi-receptive fields discriminator is proposed to utilize the features from different levels. Experiments on the benchmark datasets show the efficacy of the proposed FSG-GAN.