Reviewerly/exHarmony huggingface dataset:
Evaluation of the Effect of Technology Readiness on the Acceptance of E-learning among Nurses of Rafsanjan University of Medical Sciences Hospitals

Evaluation of the Effect of Technology Readiness on the Acceptance of E-learning among Nurses of Rafsanjan University of Medical Sciences Hospitals
Academic Continuity During the Covid-19 Global Health Emergency: Education 4.0 and the Flexible-Digital Model of Tecnologico de Monterrey University in Mexico Supporting Secondary Education

Abstract Today, new teaching-learning models, methods, and programs are emerging to guarantee academic continuity in response to the current situation caused by the global health emergency (Covid-19). This work presents how the Flexible-Digital Model of Tecnologico de Monterrey University in Mexico was designed and implemented during this emergency in this institution. This work also addresses the relevant role that technology has taken during this situation, and the concept of Education 4.0 is offered as a framework to model the presented study. Finally, two case studies that were applied at the secondary education level are presented as an example of how higher education is supporting academic continuity at the secondary level.
Online Learning Using The Zenius App At Madrasah Ibtidaiyah Negeri 8 (MIN) Central Aceh

Zenius is an application developed for distance learning.This application has several advantages, including the availability of subject matter and learning videos.This study aimed to determine the effectiveness of using the Zenius application in online learning at MIN 8 Central Aceh.The research method used in this study is a level 2 development research method, according to Sugiono, namely researching to test existing products.Data collection is done using observation, interviews, and tests.Observations and interviews were conducted at the literature study stage as the first step in level 2 development research.At the same time, the tests used were pretest and posttest-data analysis techniques using quantitative analysis.The results showed that from the limited Trial, the pretest and posttest scores increased with a high significance of 16.67%, 0%, and low, 83.33%.Then it can be said to be effective.In the usage trial, it is known that there is an increase in the pretest and posttest scores with a high significance level of 30%, moderate 0%, and low 70%; it is stated that the use of the Zenius application in learning is effective.The broad class trial found an increase in the pretest and posttest scores with a high significance of 35.53%, moderate 0%, and low 64, 47%.It is stated that the application used in online learning is practical.
Chefbot: A Novel Framework for the Generation of Commonsense-enhanced Responses for Task-based Dialogue Systems

Conversational systems aim to generate responses that are accurate, relevant and engaging, either through utilising neural end-to-end models or through slot filling. Human-to-human conversations are enhanced by not only the latest utterance of the interlocutor, but also by recalling relevant information about concepts/objects covered in the dialogue and integrating them into their responses. Such information may contain recent referred concepts, commonsense knowledge and more. A concrete scenario of such dialogues is the cooking scenario, i.e. when an artificial agent (personal assistant, robot, chatbot) and a human converse about a recipe. We will demo a novel system for commonsense enhanced response generation in the scenario of cooking, where the conversational system is able to not only provide directions for cooking step-by-step, but also display commonsense capabilities by offering explanations of how objects can be used and provide recommendations for replacing ingredients.
Modular on-Road Self-Guided Automatic Vehicle and Concept of Self-Power Transmission

A self-guided automated robot is basically a robot designed to follow a line or path which is already pre-determined by the user. The main focus of this report is to design a selfguided automated path following robot which is more reliable by eliminating the demerits of the traditionally used robots by the idea of making it a magnetic field line following robot. This paper presents the principle of a magnetic field line following robot and also explains its design and working. Here, the design of a self-guided automated magnetic field line following robot using LC tank circuits and resonant coupling for navigation purposes is explained. Also this paper gives the idea of a path that can be made invisible and can be buried under the ground and also focuses on the various electrical circuitries involved in this self-guided automated magnetic field line following the robot. This paper discusses all the components that are used in this self-guided automated vehicle. The applications and the advantages of this self-guided automated magnetic field line following robot over the traditional path following robots are discussed along with the future upgradations that can be done to refine it further.
Is the Algorithm of Artificial Neural Network a Deduction or Induction? Discussion between Natural Sciences, Mathematics and Philosophy

The algorithm of artificial neural network (ANN) has been defined as a supervised learning and heuristic algorithms. In training an ANN model, big data is necessary to use as training data to obtain perfectly accurate predicted data. However, big data really have no clear definition. Therefore, adding new training data to re-train an ANN model, by which can improve the predicted accuracy. This action of re-training this ANN model with added new training data is repeated to approach the laws of physics that is accessed to the principle of induction e.g., empirical formulas. However, accessing the principle of induction is limited. If the deduction is found using an ANN model, then approach of this ANN model with added new training data is also performed repeatedly to access the principle of deduction e.g., theory formulas. However, accessing the principle of deduction is also limited. It means the law cannot be easily deduced for an ANN model. Therefore, the algorithm of an ANN is not the canonical classical methods. On the other hand, the algorithm of an ANN does not belong to mathematical induction and deduction.
Common Language of New Era in Sport Clubs: Emojis

&lt;em&gt;&lt;span&gt;Defined as an easy and automated way of expressing emotions in the digital age, emojis are emerging as a new language in the social media world and sports clubs also. For sports clubs, it is of vital importance to communicate and establish effective relations with fans or followers. Hence, almost all professional sports clubs use social media and shape their social media accounts to interact with fans/followers and effectively maintain marketing communication efforts.&lt;/span&gt;&lt;/em&gt;&lt;span&gt; &lt;em&gt;The aim of this study was to determine the content of emoji usage in tweets of Turkish sports clubs (Besiktas JK, Fenerbahce SK, Galatasaray SK, Istanbul Başakşehir FK, Trabzonspor SK). Since Twitter is one of the most heavily used social media networks of sports clubs, so in this study was preferred&lt;/em&gt;. &lt;em&gt;Content analysis method was used to examine emojis used by sports clubs. The study found that sports clubs use emojis that create positive and neutral connotations. Emojis used are heavily determined to be visuals depicting the colors and symbols of sports clubs. The study is the first to examine sports clubs' emojis used. Hence, the study included important results for the management of communication and marketing strategies of sports clubs on social media.&lt;/em&gt;&lt;/span&gt;
Decoherence in two-dimensional quantum walks with two- and four-state coins

Two-dimensional quantum walks using a two-state coin have simpler experimental implementation than two-dimensional quantum walks using a four-state coin. However, decoherence occurs inevitably during the evolution of quantum walks due to the coupling between the quantum systems and their environment. Thus, it is interesting to investigate the robustness against decoherence for two- and four-state two-dimensional quantum walks. Here, we investigate the effects of the decoherence on two- and four-state two-dimensional quantum walks produced by the broken-link-type noise and compare their robustness against the broken-link-type noise. Specifically, we analyze the quantum correlation between the two spatial dimensions x and y by using measurement-induced disturbance for the two-state quantum walks, i.e. the alternate walk and the Pauli walk, and the four-state quantum walks, i.e. the Grover, Hadamard and Fourier walks, respectively. Our analysis shows that the two-state walks are more robust against the broken-link-type noise than the four-state walks.
A Method to Understand the Intention of Taste Expressions from Word-of-Mouth Data of Cooking Recipe Sites using Text Mining

The purpose of this study is to propose a quantitative evaluation method for clarifying intentions toward taste expressions that cannot be clarified, from word-of-mouth data of cooking recipe websites using text mining.
Document Management System

Record keeping has been done since time immemorial. As processes became complex, the number of documents created increased, and as technology evolved paper documents were replaced with digital documents. It became necessary to have systems that could make it easy for users to store and search documents. Document Management Systems (DMS) were developed to satisfy these requirements. This paper describes a typical DMS, its features, architecture, uses, and new technologies.
Comparative Analysis of Elliptic Curve and Lattice Based Cryptography

In this paper, we provide a mathematical background and analysis of Lattice Based Cryptography for the less mathematically mature reader. Discussing the benefits and constraints of Lattice Based Crypto, we compare and contrast with Elliptic Curve Cryptography to provide an understanding of what the limitations of Lattice Based Cryptography are, and what needs to be achieved for the quantum resistant protocols to be deploy-able to any and all devices.
Research on the Application of YOLOV4 Target Detection Network in Traffic Scenarios by Machine Vision Technology

Deep learning target detection algorithm is maturing with the development of machine vision technology. This article applies YOLOV4 target detection algorithm to the industrial traffic scene to detect and recognize cars, trucks, buses, pedestrians, two- wheelers, and three-wheelers in traffic scenes. Six types of common goals. The algorithm in this paper cuts the network model structure, uses 12 anchors of different sizes to adapt to different size targets in the image, the accuracy of the trained detection model can reach 94.2%, and the tensorrt acceleration technology is used to deploy the network in NVIDIA Jetson AGX Xavier embedded edge computing device, and the algorithm inference speed can reach real-time 10ms per frame.
SS-IL: Separated Softmax for Incremental Learning

We consider class incremental learning (CIL) problem, in which a learning agent continuously learns new classes from incrementally arriving training data batches and aims to predict well on all the classes learned so far. The main challenge of the problem is the catastrophic forgetting, and for the exemplar-memory based CIL methods, it is generally known that the forgetting is commonly caused by the prediction score bias that is injected due to the data imbalance between the new classes and the old classes (in the exemplar-memory). While several methods have been proposed to correct such score bias by some additional post-processing, e.g., score re-scaling or balanced fine-tuning, no systematic analysis on the root cause of such bias has been done. To that end, we analyze that computing the softmax probabilities by combining the output scores for all old and new classes could be the main source of the bias and propose a new CIL method, Separated Softmax for Incremental Learning (SS-IL). Our SS-IL consists of separated softmax (SS) output layer and ratio-preserving (RP) mini-batches combined with task-wise knowledge distillation (TKD), and through extensive experimental results, we show our SS-IL achieves very strong state-of-the-art accuracy on several large-scale benchmarks. We also show SS-IL makes much more balanced prediction, without any additional post-processing steps as is done in other baselines.
A Practical Side-Channel Based Intrusion Detection System for Additive Manufacturing Systems

We propose NSYNC, a practical framework to compare side-channel signals for real-time intrusion detection in Additive Manufacturing (AM) systems. The motivation to develop NSYNC is that we find AM systems are asynchronous in nature and there is random variation in timing in a printing process. Although this random variation, referred to as time noise, is very small compared with the duration of a printing process, it can cause existing Intrusion Detection Systems (IDSs) to fail. To deal with this problem, NSYNC incorporates a dynamic synchronizer to find the timing relationship between two signals. This timing relationship, referred to as the horizontal displacement, can not only be used to mitigate the adverse effect of time noise on calculating the (vertical) distance between signals, but also be used as indicators for intrusion detection. An existing dynamic synchronizer is Dynamic Time Warping (DTW). However, we found in experiments that DTW not only consumes an excessive amount of computational resources but also has limited accuracy for processing side-channel signals. To solve this problem, we propose a novel dynamic synchronizer, called Dynamic Window Matching (DWM), to replace DTW. To compare NSYNC against existing IDSs, we built a data acquisition system that is capable of collecting six different types of side-channel signals and performed a total of 302 benign printing processes and a total of 200 malicious printing processes with two printers. Our experiment results show that existing IDSs leveraging side-channel signals in AM systems can only achieve an accuracy from 0.50 to 0.88, whereas our proposed NSYNC can reach an accuracy of 0.99.
Improving Sub-Pixel Estimation of Laser Stripe Reflection Center by Autoconvolution on FPGA

Modern laser scanners perform high-speed real-time image processing algorithms while operating in harsh industrial environments. Their performance goal is to extract the central position of the laser line reflection with Gaussian distribution. Traditional algorithms for sub-pixel estimation, such as the Center of Gravity (CG) or Parabolic Fit (PF), show poor performances under low SNR or if the pixels are saturated. Data pre-processing usually has a key role in suppressing the effects of various noise sources and dynamic environment, especially when the images are overexposed and the top of Gaussian pulse is flattened. Both in simulation and in experiment, this study explains a method that improves the accuracy of estimation of the laser stripe reflection center, by using an autoconvolution for extending the bit-width of pixel intensity. Autoconvolution of the image line is an efficient real-time pre-processing filtering method for improving the accuracy of CG calculation. The proposed algorithm is implemented on Field-Programmable Gate Arrays (FPGAs) and experimentally validated at real operational environment. It is shown that this method can reduce the error of CG laser reflection center estimation for more than one pixel in size when the image is highly affected by external noise sources and ambient light.
Recurrent Neural Networks Analysis for Embedded Systems

Artificial Neural Networks (ANNs) are biologically inspired algorithms especially efficient for pattern recog-nition and data classification. In particular, Recurrent Neural Networks (RNN) are a specific type of ANNswhich model and process sequences of data that have temporal relationship. Thus, it introduces interestingbehavior for embedded systems applications such as autopilot systems. However, RNNs (and ANNs in gen-eral) are computationally intensive algorithms, especially to allow the network to learn. This implies a wiseintegration and proper analysis on the embedded systems that we gather these functionalities. We presentin this paper an analysis of two types of Recurrent Neural Networks, Long-Short Term Memory (LSTM)and Gated-Recurrent Unit (GRU), explain their architectures and characteristics. We propose our dedicatedimplementation which is tested and validated on embedded system devices with a dedicated dataset.
Vision and EMG Information Fusion Based on DS Evidence Theory for Gesture Recognition

Aimed at the problem of variety and accuracy of gesture recognition in human-robot interaction, this paper proposes a gesture recognition method based on DS evidence theory for vision and electromyography information fusion. Through the feature analysis of visual images and electromyography signals, human gestures are correctly recognized. The histogram of oriented gradient features of the visual images and the time-domain features of the electromyography signals are extracted respectively to describe the gestures information. Support vector machine is used as classification algorithm. The two description methods are fused at the decision-level by DS evidence theory, which significantly improves the accuracy of gesture recognition. Experiments demonstrate the proposed method has an average accuracy of 93.8% for 36 gestures.
Leggett–Garg inequality in Markovian quantum dynamics: role of temporal sequencing of coupling to bath

Abstract We study Leggett–Garg inequalities (LGIs) for a two level system (TLS) undergoing Markovian dynamics described by unital maps. We first find the analytic expression of LG parameter K 3 (simplest variant of LGIs) in terms of the parameters of two distinct unital maps representing time evolution for intervals: t 1 to t 2 and t 2 to t 3 . We then show that the maximum violation of LGI for all possible unital maps can never exceed well known Lüders bound of <?CDATA $K_{3}^{L\ddot{u}ders} = 3/2$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msubsup> <mml:mi>K</mml:mi> <mml:mrow> <mml:mn>3</mml:mn> </mml:mrow> <mml:mrow> <mml:mi>L</mml:mi> <mml:mover> <mml:mi>u</mml:mi> <mml:mo>¨</mml:mo> </mml:mover> <mml:mi>d</mml:mi> <mml:mi>e</mml:mi> <mml:mi>r</mml:mi> <mml:mi>s</mml:mi> </mml:mrow> </mml:msubsup> <mml:mo>=</mml:mo> <mml:mn>3</mml:mn> <mml:mrow> <mml:mo>/</mml:mo> </mml:mrow> <mml:mn>2</mml:mn> </mml:math> over the full parameter space. We further show that if the map for the time interval t 1 to t 2 is non-unitary unital then irrespective of the choice of the map for interval t 2 to t 3 we can never reach Lüders bound. On the other hand, if the measurement operator eigenstates remain pure upon evolution from t 1 to t 2 , then depending on the degree of decoherence induced by the unital map for the interval t 2 to t 3 we may or may not obtain Lüders bound. Specifically, we find that if the unital map for interval t 2 to t 3 leads to the shrinking of the Bloch vector beyond half of its unit length, then achieving the bound <?CDATA $K_{3}^{{L}\ddot{u}ders}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msubsup> <mml:mi>K</mml:mi> <mml:mrow> <mml:mn>3</mml:mn> </mml:mrow> <mml:mrow> <mml:mrow> <mml:mi>L</mml:mi> </mml:mrow> <mml:mover> <mml:mi>u</mml:mi> <mml:mo>¨</mml:mo> </mml:mover> <mml:mi>d</mml:mi> <mml:mi>e</mml:mi> <mml:mi>r</mml:mi> <mml:mi>s</mml:mi> </mml:mrow> </mml:msubsup> </mml:math> is not possible. Hence our findings not only establish a threshold for decoherence which will allow for <?CDATA $K_{3}\,=\, K_{3}^{L\ddot{u}ders}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mi>K</mml:mi> <mml:mrow> <mml:mn>3</mml:mn> </mml:mrow> </mml:msub> <mml:mo>=</mml:mo> <mml:msubsup> <mml:mi>K</mml:mi> <mml:mrow> <mml:mn>3</mml:mn> </mml:mrow> <mml:mrow> <mml:mi>L</mml:mi> <mml:mover> <mml:mi>u</mml:mi> <mml:mo>¨</mml:mo> </mml:mover> <mml:mi>d</mml:mi> <mml:mi>e</mml:mi> <mml:mi>r</mml:mi> <mml:mi>s</mml:mi> </mml:mrow> </mml:msubsup> </mml:math> , but also demonstrate the importance of temporal sequencing of the exposure of a TLS to Markovian baths in obtaining Lüders bound.
Top Programming Languages: Our Eighth Annual Probe into What's Hot and Not

Learn Python. That's the biggest takeaway we can give you from its continued dominance of IEEE Spectrum's annual online interactive rankings of the top programming languages. You don't have to become a dyed-in-the-wool Pythonista, but learning the language well enough to use one of the vast number of libraries written for it is probably worth your time.
Development of an Automatic Lawnmower with Real-Time Computer Vision for Obstacle Avoidance

This work develops an automatic lawnmower (Auto-Lawnmower) using computer vision technology for obstacle avoidance. Several critical issues have been overcome in this development work, including the development of a simplified convolutional neural network (CNN) for decision making, and sufficiently large datasets needed to train the Auto-Lawnmower. The following key strategies are adapted to ensure necessary functionality and efficacy with minimum cost, considering possible mass production of Auto-Lawnmowers. First, we use at-time avoidance strategy: meaning the Auto-Lawnmower makes turns when it encounters an obstacle. This is possible because of the fact that a lawn mower usually can move at a very low speed (walking speed of a man). Second, we decided to use minimum necessary sensors, so as to make the system of our Auto-Lawnmower as simple as possible and hence can be very practical. A single monocular video camera is, therefore, used as the single sensor for both: (1) to generate real-time movie images of a large number of situations that the Auto-Lawnmower may encounter, which is labelled and builds up the training datasets; (2) to capture the real-time image when the trained Auto-Lawnmower is in action, to decide on either to avoid the obstacle or move forward while cutting the grass. A Law dataset with labels, called LawNet, has been established for training of CNNs. It collects a total of 168,542 labelled images taken from the perspective of our Auto-Lawnmower for lawns at the university campus. A concise CNN is created for high efficiency and trained with our LawNet to drive the Auto-Lawnmower. A prototype of Auto-Lawnmower is finally designed, built and tested for lawn mowing in real situations. It is found that it works well as designed, with minimum sensors (a single camera), and hence it has a good potential for mass production.
Temporal-Consistency-Aware Video Color Transfer

This paper proposes a new temporal-consistency-aware color transfer method based on quaternion distance metric. Compared with the state-of-the-art methods, our method can keep the temporal consistency and better reduce the artifacts. Firstly, keyframes are extracted from the source video and transfer the color from the reference image through soft segmentation based on Gaussian Mixture Models (GMM). Then a quaternion-based method is proposed to transfer color from keyframes to the other frames iteratively. Specifically, this method analyses the color information of each pixel along five directions to detect its best matching pixel through a quaternion-based distance metric. Additionally, considering the accumulating errors in frame sequences, an effective abnormal color correction mechanism is designed to improve the color transfer quality. A quantitative evaluation metric is further proposed to measure the temporal consistency in the output video. Various experimental results validate the effectiveness of our method.