Reviewerly/exHarmony huggingface dataset:
Defect Data Association Analysis of the Secondary System Based on AFWA-H-Mine

The fault data of the secondary system of smart substations hide some information that the association analysis algorithm can mine. The convergence speed of the Apriori algorithm and FP-growth algorithm is slow, and there is a lack of indicators to evaluate the correlation of association rules and the method to determine the parameter threshold. In this paper, the H-mine algorithm is used to realize the fast mining of fault data. The algorithm can traverse data faster by using the data structure of the H-struct. This paper also sets the lift and CF value to screen the association rules with good correlation. When setting the three key parameters of association analysis, namely, support threshold, confidence threshold, and lift threshold, an objective function composed of weighted average lift, CF value, and data coverage rate was selected, and the adaptive fireworks algorithm was used to optimize the parameters in the association analysis. In particular, the rule screening strategy is introduced in fault cause analysis in this paper. By eliminating rules with high similarity, derived signals in association rules are eliminated to the greatest extent to improve the readability of rules and ensure easy understanding of results.
Arabic Scene Text Recognition in the Deep Learning Era: Analysis on a Novel Dataset

The problem of scene text recognition has recently gained extra attention, being an essential part of scene understanding systems. The broad scope of applications and the unresolved challenges has given this problem its popularity. However, the research focus has long been on languages with Latin characters while leaving behind other languages with different characteristics, such as the Arabic language. In this paper, we focus on Arabic scene text recognition and attempt to fill two main gaps regarding this research task. First, the Arabic language is lacking a publicly available benchmark dataset to compare different proposed methods on the same grounds. Therefore, we introduce a novel Arabic/English dataset: Everyday Arabic-English Scene Text dataset (EvArEST), to fill that need. Second, while deep learning methods have continuously evolved and pushed the state of the art in languages with Latin characters, their use for the Arabic language has been very limited. Therefore, we use our new dataset to evaluate the problem of Arabic scene text recognition from three perspectives: (1) using deep learning techniques and studying their suitability for Arabic scene text recognition, where we identify essential components required for the model to obtain good performance; (2) identifying Arabic text challenges that differ from Latin text and require special attention; (3) investigating a bilingual model that concurrently deals with Arabic and English words, since Arabic text is usually found along with other languages. We determine the best model to handle bidirectional text, its challenges, and possible ways to overcome them. We offer both Arabic and Bilingual text recognition results using EvArEST dataset for upcoming research to build upon and improve. We also point to directions for future research based on the analysis performed on the dataset. The dataset is publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/HGamal11/EvArEST-dataset</uri> .
Formal Verification of Neural Networks for Safety-Critical Tasks in Deep Reinforcement Learning

In the last years, neural networks achieved groundbreaking successes in a wide variety of applications. However, for safety critical tasks, such as robotics and healthcare, it is necessary to provide some specific guarantees before the deployment in a real world context. Even in these scenarios, where high cost equipment and human safety are involved, the evaluation of the models is usually performed with the standard metrics (i.e., cumulative reward or success rate). In this paper, we introduce a novel metric for the evaluation of models in safety critical tasks, the violation rate. We build our work upon the concept of formal verification for neural networks, providing a new formulation for the safety properties that aims to ensure that the agent always makes rational decisions. To perform this evaluation, we present ProVe (Property Verifier), a novel approach based on the interval algebra, designed for the analysis of our novel behavioral properties. We apply our method to different domains (i.e., mapless navigation for mobile robots, trajectory generation for manipulators, and the standard ACAS benchmark). Results show that the violation rate computed by ProVe provides a good evaluation for the safety of trained models.
Sentiment classification of user's reviews on drugs based on global vectors for word representation and bidirectional long short-term memory recurrent neural network

&lt;p&gt;The process of product development in the health sector, especially pharmaceuticals, goes through a series of precise procedures due to its directrelevance to human life. The opinion of patients or users of a particular drugcan be relied upon in this development process, as the patients convey their experience with the drugs through their opinion. The social media field provides many datasets related to drugs through knowing the user's ratingand opinion on a drug after using it. In this work, a dataset is used that includes the user’s rating and review on the drug, for the purpose of classifying the user’s opinions (reviews) whether they are positive ornegative. The proposed method in this article includes two phases. The first phase is to use the Global vectors for word representation model for converting texts into the vector of embedded words. As for the second stage, the deep neural network (Bidirectional longshort-termmemory) is employedin the classification of reviews. The user's rating is used as a ground truth inevaluating the classification results. The proposed method present sencouraging results, as the classification results are evaluated through threecriteria, namely Precision, Recall and F-score, whose obtained values equal(0.9543, 0.9597and0.9558), respectively. The classification results of theproposed method are compared to a number of classifiers, and it was noticed that the results of the proposed method exceed those of the alternative classifiers.&lt;/p&gt;
Use of Digital Image Correlation Method to Measure Bio-Tissue Deformation

Traditionally, strain gauge, extensometer, and reflection tracking markers have been used to measure the deformation of materials under loading. However, the anisotropy and inhomogeneity of most biological materials restricted the accessibility of the real strain field. Compared to the video extensometer, digital image correlation has the advantage of providing full-field displacement as well as strain information. In this study, a digital image correlation method (DIC) measurement system was employed for chicken breast bio-tissue deformation measurement. To increase the contrast for better correlation, a mixture of ground black pepper and white sesame was sprayed on the surface of samples. The first step was to correct the distorted image caused by the lens using the inverse distorted calibration method and then the influence of subset size and correlation criteria, sum of squared differences (SSD), and zero-normalized sum of squared differences (ZNSSD) were investigated experimentally for accurate measurement. Test results of the sample was translated along the horizontal direction from 0 mm to 3 mm, with an increment of 0.1 mm and the measurement result was compared, and the displacement set on the translation stage. The result shows that the error is less than 3%, and accurate measurement can be achieved with proper surface preparation, subset size, correlation criterion, and image correction. Detailed examination of the strain values show that the strain εx is proportional to the displacement of crosshead, but the strain εy indicates the viscoelastic behavior of tested bio-tissue. In addition, the tested bio-tissue’s linear birefringence extracted by a Mueller matrix polarimetry is for comparison and is in good agreement. As noted above, the integration of the optical parameter measurement system and the digital image correlation method is proposed in this paper to analyze the relationship between the strain changes and optical parameters of biological tissue, and thus the relative optic-stress coefficient can be significantly characterized if Young’s modulus of biological tissue is known.
Gain Without Pain: Offsetting DP-Injected Noises Stealthily in Cross-Device Federated Learning

Federated learning (FL) is an emerging paradigm through which decentralized devices can collaboratively train a common model. However, a serious concern is the leakage of privacy from exchanged gradient information between clients and the parameter server (PS) in FL. To protect gradient information, clients can adopt differential privacy (DP) to add additional noises and distort original gradients before they are uploaded to the PS. Nevertheless, the model accuracy will be significantly impaired by DP noises, making DP impracticable in real systems. In this work, we propose a novel noise information secretly sharing (NISS) algorithm to alleviate the disturbance of DP noises by sharing negated noises among clients. We theoretically prove that: 1) if clients are trustworthy, DP noises can be perfectly offset on the PS and 2) clients can easily distort negated DP noises to protect themselves in case that other clients are not totally trustworthy, though the cost lowers model accuracy. NISS is particularly applicable for FL across multiple Internet of Things (IoT) systems, in which all IoT devices need to collaboratively train a model. To verify the effectiveness and the superiority of the NISS algorithm, we conduct experiments with the MNIST and CIFAR-10 data sets. The experimental results verify our analysis and demonstrate that NISS can improve model accuracy by 19% on average and obtain better privacy protection if clients are trustworthy.
Smartphone Sensor Accelerometer Data for Human Activity Recognition Using Spiking Neural Network

Recognizing human actions is a challenging task and actively research in computer vision community. The task of human activity recognition has been widely used in various application such as human monitoring in a hospital or public spaces. This work applied open dataset of smartphones accelerometer data for various type of activities. The analogue input data is encoded into the spike trains using some form of a rate-based method. Spiking neural network is a simplified form of dynamic artificial network. Therefore, this network is expected to model and generate action potential from the leaky integrate-and-fire spike response model. The leaning rule is adaptive and efficient to present synapse exciting and inhibiting firing neuron. The result found that the proposed model presents the state-of-the-art performance at a low computational cost.
Covid 19 and Challenges in Higher Education: An Empirical Analysis

Over the years the global economy, including India, has undergone a profound change and observed a pioneer role of innovation and technology in every sector. Rapid technological dissemination has seen human or physical touch points being replaced by digital interface. With digitization happening everywhere, it becomes all the more important for the education sector to adapt it in order to keep pace with the challenging world. With the onset of the global pandemic of Covid-19, the only option left with the education sector is to switch to online classes for its smooth and continuous working. This research aims to investigate the challenges that are faced by the students and their level of satisfaction from these classes. The study is based on primary data collected from 700 students of various colleges across Universities in India. The study employs regression models, crosstabs, and various other descriptive statistics for analysis of the data. The findings of the study suggest that the students face a lot of glitches while learning from the online mode of education and would need time to adjust. The research also provides an insight on the scope and the future of online education.
PUF-based Smart Tags for Supply Chain Management

Counterfeiting represents one of the most widespread phenomena at a global level that indiscriminately affects all product sectors, from fashion to food, from medicines to digital media. The fight against counterfeiting remains a significant challenge for industries. Most of the current supply chains rely on centralized authorities or intermediaries that are not sufficient robust to guarantee anti-counterfeiting and traceability of goods.
Visual Servoing of a Moving Target by an Unmanned Aerial Vehicle

To track moving targets undergoing unknown translational and rotational motions, a tracking controller is developed for unmanned aerial vehicles (UAVs). The main challenges are to control both the relative position and orientation between the target and the UAV to within desired values, and to guarantee that the generated control input to the UAV is feasible (i.e., below its motion capability). Moreover, the UAV is controlled to ensure that the target always remains within the field of view of the onboard camera. These control objectives were achieved by developing a nonlinear-model predictive controller, in which the future motion of the target is predicted by quadratic programming (QP). Since constraints of the feature vector and the control input are considered when solving the optimal control problem, the control inputs can be bounded and the target can remain inside the image. Three simulations were performed to compare the efficacy and performance of the developed controller with a traditional image-based visual servoing controller.
Exploring multiuser virtual teaching simulation as an alternative learning environment for student instructors

In this mixed-method study, we investigated the impact and design of a multiuser, virtual reality (VR) supported teaching simulation, in comparison with live classroom teaching simulation, on the participatory training of teaching and the teaching knowledge development of student instructors. A total of 40 university teaching assistants participated in a 4-h training session in which they were randomly assigned to a VR simulation or a live classroom simulation condition. The study indicated that the VR simulation better promoted the lab-teaching knowledge development than the live simulation, whereas the latter better fostered class-teaching knowledge development. All participants reported higher teaching self-efficacy after the training. The qualitative data indicated that domain-specific challenges and authentic environmental prompting in the VR simulation fostered both experiential and vicarious learning of teaching. However, VR participants lacked mutual engagement in collaborative role-playing. The study findings suggest that VR-based simulation can supplement and work as an alternative to the live classroom simulation to host participatory teaching development.
Multimodal Human Recognition in Significantly Low Illumination Environment Using Modified EnlightenGAN

Human recognition in indoor environments occurs both during the day and at night. During the day, human recognition encounters performance degradation owing to a blur generated when a camera captures a person’s image. However, when images are captured at night with a camera, it is difficult to obtain perfect images of a person without light, and the input images are very noisy owing to the properties of camera sensors in low-illumination environments. Studies have been conducted in the past on face recognition in low-illumination environments; however, there is lack of research on face- and body-based human recognition in very low illumination environments. To solve these problems, this study proposes a modified enlighten generative adversarial network (modified EnlightenGAN) in which a very low illumination image is converted to a normal illumination image, and the matching scores of deep convolutional neural network (CNN) features of the face and body in the converted image are combined with a score-level fusion for recognition. The two types of databases used in this study are the Dongguk face and body database version 3 (DFB-DB3) and the ChokePoint open dataset. The results of the experiment conducted using the two databases show that the human verification accuracy (equal error rate (ERR)) and identification accuracy (rank 1 genuine acceptance rate (GAR)) of the proposed method were 7.291% and 92.67% for DFB-DB3 and 10.59% and 87.78% for the ChokePoint dataset, respectively. Accordingly, the performance of the proposed method was better than the previous methods.
Cut points and contexts

In research, policy, and practice, continuous variables are often categorized. Statisticians have generally advised against categorization for many reasons, such as loss of information and precision as well as distortion of estimated statistics. Here, a different kind of problem with categorization is considered: the idea that, for a given continuous variable, there is a unique set of cut points that is the objectively correct or best categorization. It is shown that this is unlikely to be the case because categorized variables typically exist in webs of statistical relationships with other variables. The choice of cut points for a categorized variable can influence the values of many statistics relating that variable to others. This essay explores the substantive trade‐offs that can arise between different possible cut points to categorize a continuous variable, making it difficult to say that any particular categorization is objectively best. Limitations of different approaches to selecting cut points are discussed. Contextual trade‐offs may often be an argument against categorization. At the very least, such trade‐offs mean that research inferences, or decisions about policy or practice, that involve categorized variables should be framed and acted upon with flexibility and humility. Lay Summary In research, policy, and practice, continuous variables are often turned into categorical variables with cut points that define the boundaries between categories. This involves choices about how many categories to create and what cut‐point values to use. This commentary shows that different choices about which cut points to use can lead to different sets of trade‐offs across multiple statistical relationships between the categorized variable and other variables. These trade‐offs mean that no single categorization is objectively best or correct. This context is critical when one is deciding whether and how to categorize a continuous variable.
Producing virtual reality (VR) field trips – a concept for a sense-based and mindful geographic education

Abstract. Virtual reality (VR) enables users to have an alternate reality experience. The virtual world surrounds users and can be perceived atmospherically. However, VR triggers human perception with far-reaching consequences. VR worlds are hardly questioned by the consumers. The effects of virtual experiences could have a lasting impact on learning or acting. Therefore, it is necessary to reflect on the use of VR in educational contexts. In our research and teaching project students produced VR field trips on topics of urban society–environment research. Based on the project findings we explain how we implement VR as a practice of sense-based and content-based acquirement of space and a (visual) practice of mediation. A key element is the critical reflection on the meaning of body and emotion. This can be a way to mindfulness – both in the context of teaching and in the context of the transformation of society–environment relations in general.
Uncertainty-Aware Balancing for Multilingual and Multi-Domain Neural Machine Translation Training

Learning multilingual and multi-domain translation model is challenging as the heterogeneous and imbalanced data make the model converge inconsistently over different corpora in real world.One common practice is to adjust the share of each corpus in the training, so that the learning process is balanced and low-resource cases can benefit from the highresource ones.However, automatic balancing methods usually depend on the intra-and interdataset characteristics, which is usually agnostic or requires human priors.In this work, we propose an approach, MULTIUAT, that dynamically adjusts the training data usage based on the model's uncertainty on a small set of trusted clean data for multi-corpus machine translation.We experiment with two classes of uncertainty measures on multilingual (16 languages with 4 settings) and multi-domain settings (4 for in-domain and 2 for out-of-domain on English-German translation) and demonstrate our approach MULTIUAT substantially outperforms its baselines, including both static and dynamic strategies.We analyze the crossdomain transfer and show the deficiency of static and similarity based methods. 1
Lessons in the Use of Technology for Science Education during COVID-19 Age under a Teachers’ Collaboration Cluster

The COVID-19 confinement has represented both opportunities and losses for education. Rarely before has any other period moved the human spirit into such discipline or submission—depending on one’s personal and emotional points of view. Both extremes have been widely influenced by external factors on each individual’s life path. Education in the sciences and engineering has encountered more issues than other disciplines due to specialized mathematical handwriting, experimental demonstrations, abstract complexity, and lab practices. This work analyses three aspects of science education courses taught by university professors in a collaborative teacher cluster, sharing technology applications and education methodologies in science over three semesters when the COVID-19 lockdown was in effect. The first aspect was a didactic design coming from several educational frameworks through adoption or sharing. The second one was an analysis by discipline of multiple factors affecting student engagement during the health contingency. The third analysis examined the gains and losses in our students caused by the university closure and the pandemic’s intrusions. The report explores the correlations of the exiting student perceptions with their academic performance in the courses and survey results about the impact of decisions or happenings during the crisis. This work’s value lies in the lessons for the future of education concerning the teacher dominions of didactic design, support, and collaboration in a broader sense than only for teaching.
Secondary constructions of (non)weakly regular plateaued functions over finite fields

Plateaued (vectorial) functions over finite fields have diverse applications in symmetric cryptography, coding theory, and sequence theory.Constructing these functions is an attractive research topic in the literature.We can distinguish two kinds of constructions of plateaued functions: secondary constructions and primary constructions.The first method uses already known functions to obtain new functions while the latter do not need to use previously constructed functions to obtain new functions.In this work, the first secondary constructions of (non)weakly regular plateaued (vectorial) functions are presented over the finite fields of odd characteristics.We also introduce some recursive constructions of (non)weakly regular plateaued p -ary functions by using already known such functions.We obtain nontrivial plateaued functions from the previously known trivial plateaued (partially bent) functions in the proposed construction methods.