Reviewerly/exHarmony huggingface dataset:
The Integration of Digital Techniques in Engineering Education: A Case Study to Evaluate Student’s Motivation and Performance

The importance of a paradigm shift in the current traditional teaching strategies is becoming of high importance. Integrating disruptive technologies into undergraduate civil engineering students can improve their performance and motivation levels. Students were assessed on their ability to identify mistakes in a concrete design developed using Autodesk Revit®. The students were divided into three different groups and assessed using three different media: 2 dimensional plans (2D), 3 dimensional models (3D) and virtual reality (VR). The results have shown that 3D and VR students managed to score 54.9% and 62.5% respectively. 2D students scored 20.5%. This shows a substantial difference between VR and 3D relative to the 2D group. From the surveys conducted, it was also perceived that students found 3D and VR methods more motivating than the 2D traditional method. Spearman correlation also indicated a positive and medium to strong correlation between the level of motivation and performance of VR students. The results show that VR is a valid alternative to conventional methods of teaching. This study is part of an ongoing research effort related to utilization of VR in engineering education.
A modified version of the ABC algorithm and evaluation of its performance

The artificial bee colony (ABC) optimization algorithm has been widely used to solve the global optimization problems. Many versions of ABC algorithm exist in the literature intending to achieve optimum solution for problems in different domains. Some modifications of the ABC algorithm are general and can be applied to any problem domain, while some are application dependent. This paper proposes a modified version of the ABC algorithm named as, MABC-SS (modified artificial bee colony algorithm with selection strategy), that can be applied to any problem domain. The algorithm is modified in terms of population initialization and update of a bee position using the old and a new food source equation based on the algorithm's performance in the previous iteration. The selection strategy is measured based on a novel approach called the rate of change. The population initialization in any optimization algorithm plays an important role in achieving the global optimum. The algorithm proposed in the paper uses random and an opposition-based learning technique to initialize the population and updates a bee's position after exceeding a certain number of trial limits. The rate of change is based on the average cost and is calculated for the past two iterations and compared for a method to be used in the current iteration to achieve the best result. The proposed algorithm is experimented with 35 benchmark test functions and 10 real world test functions. The findings indicate that the proposed algorithm is able to achieve the optimal result in most cases. The proposed algorithm is compared with the original ABC algorithm, modified versions of the ABC algorithm, and other algorithms in the literature using the test mentioned above. The parameters such as population size, number of iterations and runs were kept same for comparison with non-variants of ABC. In case of ABC variants, ABC specific parameters such as abandonment limit factor (0.6) and acceleration coefficient (1) were kept same. Results indicate that in 40% of the traditional benchmark test functions, the suggested algorithm works better than other variants of ABC (ABC, GABC, MABC, MEABC, BABC, and KFABC), while 30% of the traditional benchmark test functions are comparable. The proposed algorithm was compared to non-variants of ABC as well. The results show that the proposed algorithm achieved the best mean result in 50% of the CEC2019 benchmark test functions and in 94% of the classical benchmark test functions. The result is confirmed by Wilcoxon sum ranked test which shows that MABC-SS achieved statistically significant result in 48% of the classical and 70% of the CEC2019 benchmark test functions when compared with the original ABC. Overall, based on assessment and comparison in benchmark test functions used in this paper, the suggested algorithm is superior to others.
Detecting entanglement of unknown states by violating the Clauser–Horne–Shimony–Holt inequality

Entangled states play a fundamental role in quantum mechanics and are at the core of many contemporary applications, such as quantum communication and quantum computing. Therefore, determining whether a state is entangled or not is an important task. Here, we propose a method to detect the entanglement of unknown two-qubit quantum states. Our method is based on the violation of the Clauser–Horne–Shimony–Holt inequality. This maximizes the value of the inequality even when it contains an unknown quantum state. The method iteratively generates local measurement settings that lead to increasing values of the inequality. We show by numerical simulations for pure and mixed states that our algorithm exceeds the classical limit of 2 after a few iterations.
Machine Learning Based Risk Prediction for Major Adverse Cardiovascular Events for ELGA-Authorized Clinics1

Background: Artificial Intelligence (AI) has had an important impact on many industries as well as the field of medical diagnostics. In healthcare, AI techniques such as case-based reasoning and data driven machine learning (ML) algorithms have been used to support decision-making processes for complex tasks. This is used to assist medical professionals in making clinical decisions. A way of supporting clinicians is providing predicted prognoses of various ML models. Objectives: Training an ML model based on the data of a hospital and using it on another hospital have some challenges. Methods: In this research, we applied data analysis to discover required data filters on a hospital’s EHR data for training a model for another hospital. Results: We applied experiments on real-world data of ELGA (Austrian health record system) and KAGes (a public healthcare provider of 20+ hospitals in Austria). In this scenario, we train the prediction model for ELGA- authorized health service providers using the KAGes data since we do not have access to the complete ELGA data. Conclusion: Finally, we observed that filtering the data with both feature and value selection increases the classification performance of the prediction model, which is trained for another system.
Multimedia-Based Learning Media Development to Improve Early Childhood Expressive Language Ability

Currently, rapid technological development has led to children growing up as a more intelligent generation with the use of multimedia. However, on the other hand, it has not yet been able to provide a positive effect on educators and young children. The purpose of this study is to develop multimedia-based learning media and to produce valid, practical, and effective learning media products. The type of research conducted is research and development (RD). Data collection techniques used in this study are interviews and questionnaires. The development procedure refers to the ADDIE model. Based on the results of the validity test by 2 experts was categorized as valid, the practicality test by 2 educators was categorized as very practical, technical and economical, while the effectiveness test was conducted by 21 children aged 5-6 years with an effective category. The conclusion obtained is that multimedia-based learning media developed have been proven effective in improving the expressive language abilities of early childhood aged 5-6 years. The implication of this research is the need to produce multimedia-based learning media widely as an alternative form for teachers in using learning media to facilitate the delivery of material that can improve the expressive language abilities of early childhood aged 5-6 years.
Learning an Invariant and Equivariant Network for Weakly Supervised Object Detection

Weakly Supervised Object Detection (WSOD) is of increasing importance in the community of computer vision as its extensive applications and low manual cost. Most of the advanced WSOD approaches build upon an indefinite and quality-agnostic framework, leading to unstable and incomplete object detectors. This paper attributes these issues to the process of inconsistent learning for object variations and the unawareness of localization quality and constructs a novel end-to-end Invariant and Equivariant Network (IENet). It is implemented with a flexible multi-branch online refinement, to be naturally more comprehensive-perceptive against various objects. Specifically, IENet first performs label propagation from the predicted instances to their transformed ones in a progressive manner, achieving affine-invariant learning. Meanwhile, IENet also naturally utilizes rotation-equivariant learning as a pretext task and derives an instance-level rotation-equivariant branch to be aware of the localization quality. With affine-invariance learning and rotation-equivariant learning, IENet urges consistent and holistic feature learning for WSOD without additional annotations. On the challenging datasets of both natural scenes and aerial scenes, we substantially boost WSOD to new state-of-the-art performance. The codes have been released at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/XiaoxFeng/IENet</uri> .
A Theoretical Framework for a Mathematical Cognitive Model for Adaptive Learning Systems

The emergence of artificial intelligence has made adaptive learning possible, but building an adaptive system requires a comprehensive understanding of students’ cognition. The cognitive model provides a crucial theoretical framework to explore students’ cognitive attributes, making it vital for learning assessment and adaptive learning. This study investigates 52 experts, including primary and secondary school teachers, mathematics education experts, and graduate students, based on the 16 cognitive attributes in the TIMSS 2015 assessment framework. Through an analysis of their attribute questionnaires, the Interpretive structural modeling (ISM) method is used to construct a five-level mathematical cognitive model. The model is then revised through oral reports and expert interviews, resulting in a final cognitive model ranging from “memorize” to “justify”. The cognitive model describes the relationship between different attributes in detail, enabling the development of adaptive systems and aiding in the diagnosis of students’ cognitive development and learning paths in mathematics.
Multilayer Positioning Strategy for Tubesheet Welding Robot Based on Point Cloud Model

Weld seam positioning has been of great importance in enhancing welding automation levels. In this article, a multilayer positioning strategy was proposed for automatic weld seam positioning based on the point cloud model of the tubesheet. The method consists of coarse positioning and fine positioning. In the process of coarse positioning, a feature-based maximum likelihood estimation (MLE) was proposed, whose solution was obtained by the proposed likelihood calculation method between the point cloud model and the tubesheet image. Considering that several different coordinate systems—robot coordinate system (RCS), tubesheet coordinate system (TCS), and point cloud coordinate system (PCS)—exist in the tubesheet welding process, through the coordinates of multiple sets of MLE, the conversion relationships of these coordinate systems were obtained, so as to complete the coarse positioning process. In the process of fine positioning, the image of the weld seam was processed to solve the problem of low positioning accuracy in the coarse positioning process. Experiments showed that the positioning accuracy of the proposed multilayer positioning strategy can meet the welding requirements. Compared with the existing methods, the method in this article has a higher level of welding automation.
The Effect Of Team Quiz Method On Students’ Reading Comprehension Skill In Narrative Text At SMP Swasta Al-Hikmah

This study aims to determine the effect of the quiz team method on students' reading comprehension skill in narrative texts in the eighth grade of SMP Swasta Al-Hikmah. The method used in this study is a quantitative method. This research design is experimental research. It was carried out into two classes namely the experimental class (VIII-1) which consisted of 30 students was taught using the quiz team method while the control class (VIII-2) consisted of 30 students taught without the team quiz method. The instrument of this research is a multiple choice test consisting of 10 questions. The tests consist of two types, namely pre-test and post-test to collect data. After collecting data, the writer then analyzed the test results using the t-test formula. Based on the analysis of the data obtained, it shows that the t-test is 4.59 and the t-table is 1.67 for ? = 0.05. Because the t-test is higher than the t-table (4.59 &gt; 1.67), this means that Ho is rejected and Ha is accepted. Based on the results, it was concluded that using the quiz team method was effective in improving students' reading comprehension skill in narrative text at eight grade of SMP Swasta Al-Hikmah
Meta-Heuristic Parameter Optimization for ANN and Real-Time Applications of ANN

Artificial neural networks (ANN) are often more suitable for classification problems. Even then, training of ANN is a surviving challenge task for large and high dimensional natured search space problems. These hitches are more for applications that involves process of fine tuning of ANN control parameters: weights and bias. There is no single search and optimization method that suits the weights and bias of ANN for all the problems. The traditional heuristic approach fails because of their poorer convergence speed and chances of ending up with local optima. In this connection, the meta-heuristic algorithms prove to provide consistent solution for optimizing ANN training parameters. This chapter will provide critics on both heuristics and meta-heuristic existing literature for training neural networks algorithms, applicability, and reliability on parameter optimization. In addition, the real-time applications of ANN will be presented. Finally, future directions to be explored in the field of ANN are presented which will of potential interest for upcoming researchers.
The use of indicators in the different stages of the cycle of blood: use of selection tool

Introduction: Hemotherapy is a medical specialty structured with several actions aimed at the quality of the blood component produced and the safety for both the donor and the patient, besides being directly integrated into the health care process.The first indicators for hemotherapy were created to guide the direction of the Blood Quality Program, but, in the hemotherapy scenario, we must consider not only the quality of the blood but also patient and donor safety and hemovigilance actions.Objective: Based on the analysis of data available in the various reports and records published by the Brazilian National Health Surveillance Agency [Agência Nacional de Vigilância Sanitária (Anvisa)], the Brazilian Ministry of Health (MH), and the General Coordination of Blood and Blood Products [Coordenação Geral de Sangue e Hemoderivados (CGSH)] in the last five years, we aimed to identify some weaknesses and risks in the transfusion process and propose the use of a tool called the indicators matrix, to prioritize indicators with a focus on transfusion safety.Methodology: The selection of indicators was based on the creation of a prioritization matrix, using the strategic axes defined by the WHO, whose objective is to control the entire cycle of blood, including the transfusion process.Results: The selection of indicators aims to improve the processes involved throughout the cycle of blood, and the use of the tool aims to facilitate the choice of these indicators according to the scenario of each institution, from a hemotherapy service to a transfusion agency.Conclusion: These indicators can improve blood transfusion practices and processes, as well as optimize the manageability of each service, regardless of their complexity.Furthermore, they serve to continuously improve the quality, safety, and efficiency of hemotherapy centers.
Local Feature Matters: Cascade Multi-Scale MLP for Edge Segmentation of Medical Images

Convolution-based methods are increasingly being used in medical image segmentation tasks and have shown good performance, but there are always problems in segmenting edge parts. These methods all have the following challenges: 1) Previous methods do not highlight the relationship between foreground and background in segmented regions, which is helpful for complex segmentation edges, 2) inductive bias of the convolutional layer leads to the fact that the extracted information is mainly the main part of the segmented area, and cannot effectively perceive complex edge changes and the aggregation of small and many segmented areas,3) different regions around the segmentation edge have different reference values for segmentation, and the ordering of these values is more important when the segmentation task is more complex. To address these challenges, we propose the CM-MLP framework on Multi-scale Feature Interaction (MFI) block and Axial Context Relation Encoder (ACRE) block for accurate segmentation of the edge of medical image. In the MFI block, we propose the Cascade Multi-scale MLP (Cascade MLP) to process all local information from the deeper layers of the network simultaneously, using Squeeze and Excitation in Space(SES) to process and redistribute the weights of all windows in Cascade MLP and utilize a cascade multi-scale mechanism to fuse discrete local information gradually. Then, multiple ACRE blocks cooperate with the deep supervision mechanism to gradually explore the boundary relationship between the foreground and the background, and gradually fine-tune the edges of the medical image. The segmentation accuracy (Dice) of our proposed CM-MLP framework reaches 96.98%, 96.67%, and 83.83% on three benchmark datasets: CVC-ClinicDB dataset, sub-Kvasir dataset, and our in-house dataset, respectively, which significantly outperform the state-of-the-art method. The source code and trained models will be available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/ProgrammerHyy/CM-MLP</uri> .
A System for Traceability of Agricultural Products Based on Two-Dimensional Barcoding

With the developments of mobile Internet and agricultural product e-commerce, agricultural product traceability systems have been applied to supervise and trace the quality of agricultural products. In the cloud environment, an agricultural product traceability system is designed and developed based on the two-dimensional barcode. The system uses two-dimensional barcode and RFID tag to trace the production, transportation, sales and other records of agricultural products. Combined with MySQL database and Tomcat server, the Spring SpringMVC MyBatis (SSM) framework is used to build the traceability data center. Consumers can easily obtain the traceability of agricultural products stored in the QR code.
Coarse-to-Fine Task-Driven Inpainting for Geoscience Images

The processing and recognition of geoscience images have wide applications. Most of existing researches focus on understanding the high-quality geoscience images by assuming that all the images are clear. However, in many real-world cases, the geoscience images might contain occlusions during the image acquisition. This problem actually implies the image inpainting problem in computer vision and multimedia. As far as we know, all the existing image inpainting algorithms learn to repair the occluded regions for a better visualization quality, they are excellent for natural images but not good enough for geoscience images, and they never consider the following gescience task when developing inpainting methods. This paper aims to repair the occluded regions for a better geoscience task performance and advanced visualization quality simultaneously, without changing the current deployed deep learning based geoscience models. Because of the complex context of geoscience images, we propose a coarse-to-fine encoder-decoder network with the help of designed coarse-to-fine adversarial context discriminators to reconstruct the occluded image regions. Due to the limited data of geoscience images, we propose a MaskMix based data augmentation method, which augments inpainting masks instead of augmenting original images, to exploit the limited geoscience image data. The experimental results on three public geoscience datasets for remote sensing scene recognition, cross-view geolocation and semantic segmentation tasks respectively show the effectiveness and accuracy of the proposed method. The code is available at: https://github.com/HMS97/Task-driven-Inpainting.
Efficient variational synthesis of quantum circuits with coherent multi-start optimization

We consider the problem of the variational quantum circuit synthesis into a gate set consisting of the CNOT gate and arbitrary single-qubit (1q) gates with the primary target being the minimization of the CNOT count. First we note that along with the discrete architecture search suffering from the combinatorial explosion of complexity, optimization over 1q gates can also be a crucial roadblock due to the omnipresence of local minimums (well known in the context of variational quantum algorithms but apparently underappreciated in the context of the variational compiling). Taking the issue seriously, we make an extensive search over the initial conditions an essential part of our approach. Another key idea we propose is to use parametrized two-qubit (2q) controlled phase gates, which can interpolate between the identity gate and the CNOT gate, and allow a continuous relaxation of the discrete architecture search, which can be executed jointly with the optimization over 1q gates. This coherent optimization of the architecture together with 1q gates appears to work surprisingly well in practice, sometimes even outperforming optimization over 1q gates alone (for fixed optimal architectures). As illustrative examples and applications we derive 8 CNOT and T depth 3 decomposition of the 3q Toffoli gate on the nearest-neighbor topology, rediscover known best decompositions of the 4q Toffoli gate on all 4q topologies including a 1 CNOT gate improvement on the star-shaped topology, and propose decomposition of the 5q Toffoli gate on the nearest-neighbor topology with 48 CNOT gates. We also benchmark the performance of our approach on a number of 5q quantum circuits from the ibm_qx_mapping database showing that it is highly competitive with the existing software. The algorithm developed in this work is available as a Python package CPFlow.