Reviewerly/exHarmony huggingface dataset:
The Effects of Early Childhood Science Education Problem-Based Learning (PBL) Classes on Self-Directed Learning Ability, Academic Self-Efficacy and Learning Motivation in Prospective Early Childhood Teachers

This purpose of this study is to investigate the effects of problem-based learning (PBL) classes on self-directed learning ability, academic self-efficacy, and learning motivation in prospective early childhood teachers. To this end, the effectiveness of PBL classes was analyzed by conducting pre- and post-experimental surveys on self-directed learning ability, academic self-efficacy, and learning motivation, involving 38 preliminary early childhood teachers who took a PBL-based early childhood science education course in the department of early childhood education at C University in the K region. To understand the effects of PBL classes on their self-directed learning ability, academic self-efficacy, and learning motivation according to the research questions, a paired t-test on the collected data were performed using the SPSS/PC (Windows 17.0) program. As a result of the study, the positive effectiveness of PBL classes was confirmed with statistically significant increases in all variables of self-directed learning ability, academic self-efficacy, and learning motivation in prospective early childhood teachers. These results can be used as basic data to find a way to be used in the field of teacher training as a teaching-learning method for strengthening the competence of pre-service early childhood teachers.
Functional Link NN based Adaptive Fuzzy Control for Nonlinear Dynamic Systems

Since their inception, fuzzy logic and its variants involving neural networks have witnessed tremendous applications in the area of identification and control of nonlinear dynamic plants. Fuzzy logic being the universal approximator becomes more powerful when combined with inherent learning capability of Neural Networks (NNs). This research presents a novel adaptive fuzzy control based on Functional Link NNs (FLNNs). The Laguerre orthogonal polynomials have been used for functional expansion of FLNNs. The parameter adaptation and thus the shape of the membership functions and weights of the polynomials of FLNNs are adapted online based on gradient descent optimization technique. Finally, the proposed control scheme has been checked for its performance using comparative evaluation with conventional control schemes applied to different nonlinear plants. The nonlinear time domain simulation results and their quantitative analysis validate the superior performance of the proposed adaptive fuzzy FLNN control.
The use of smartphones for online learning interactions by elementary school students

The purpose of this study was to determine the effect of using smartphones for online learning on teachers' and students' interactions in learning that occurred during the COVID-19 pandemic.Respondents were 303 elementary school students, randomly selected by filling out questions through a Google form application. 20 items were provided, 10 items for the OLWS variable and 10 items for the TSI variable, which were adopted from several literature articles and adapted to the problem of this research.The data normality test was previously carried out and continued with testing the level of validity and reliability of the instrument, followed by evaluation of the structural model related to hypothesis testing using SPSS and SmartPL.The result determined by the PLS-SEM algorithm is 0.507, which is greater than the recommended value, indicating that the model has a reasonable level of justification for the variance in user behavior objectives.The only hypothesis (H1) is supported by the t-value of 15.399 (> 1.96) and a P-value of 0.000 (0.05), which indicates that OLWS has a positive and significant effect on TSI.Since the teacher's role in conveying knowledge to pupils demands specific, complex, and modern equipment that is widely available, easy to use, and follows technological changes, teachers and students can communicate appropriately, effectively, efficiently, and encourage quality results.
The Effect of an Art-Based Online Career Program for College Students Majoring in Social Welfare: Career Resilience and Planned Happenstance Skills

The purpose of this study is to verify the effectiveness of an art-based online career programas a strategy to strengthen career resilience and planned happenstance skills of college studentsmajoring in social welfare in the context of COVID-19. To this end, 12 people were randomlyassigned to the experimental group and 11 people to the comparison group, and the careerprogram and major self study were conducted for a total of 5 sessions each. To verify theeffect, an independent samples t test and ANCOVA were performed using SPSS 21. As a result,first, the experimental group showed a significant increase in career resilience compared to thecomparative group. Second, the experimental group showed a significant increase in theplanned happenstance skills. This study is significant in that it verified the usefulness ofart-based online career programs to strengthen career resilience and planned happenstance skillsof college students majoring in social welfare.
Graphene Coated Liquid Metal Droplet‐Enabled Dual‐Axis Integrated Accelerometer (Adv. Mater. Technol. 1/2023)

Inertial Sensors In article number 2201094, Wedyan Babatain, Muhammad Mustafa Hussain, and co-workers present a liquid metal droplet-based robust dual-axis accelerometer. The liquid metal droplet is coated with graphene flake and confined inside a pyramid-shaped cavity patterned with laser-induced graphene (LIG) interdigitated electrode to realize a highly mobile mass and sensitive sensor. The device's utility is demonstrated for real-time- monitoring of hand motion and precise control of a virtual pong game.
Dynamic attitude measurement taken by monocular line scan vision

Attitude measurements are important parameters for condition monitoring of precision machines. We develop an angle measurement method based on monocular line scan vision to dynamically measure the attitude of shafts. The proposed measurement system, which consists of a single line scan camera, a parallel laser light source, and a triangular mirror, does not require fixation of a sensor on the shaft. The measurement system uses the line scan camera to capture the projection of the shaft. The light source is diffused by a cylindrical lens to yield parallel directional light. Then the parallel directional light is reflected by a fixed-angle mirror and projected onto the projection plane. The captured image can be considered a discrete time sequence. The attitude measurement principle based on linear monocular vision is derived. A double light-spot centroid algorithm is developed to obtain the centroid coordinates of the projection points. The proposed method is investigated by a simulation and experiment. The proposed method can also monitor the vibration of the shaft. The experimental results prove that the proposed method is effective and highly accurate.
An Experiment using Software Agents for Dialogue Analysis in Collaborative Distance Learning

Trends in distance education show a growing emphasis in collaborative learning, stimulating students to exchange ideas and information. A collaborative environment, however, will demand a higher effort from the teacher, who will supervise all discussions among learners, so that they do not deviate from the intended topic for a lesson. Moreover, the information proceeding from interactions among students will provide to the teacher features that allow an individual evaluation of each student and his course. In this way, this paper describes a first experience using a multi-agent architecture that is able to monitor communication tools in a distance-learning group.
Proximity and View Awareness to Reduce Referential Ambiguity in a shared 3D Virtual Environment

This contribution investigates the role of virtual space on social interactions during collaborative tasks. We previously observed that MUD users rely on spatial positions to refine the conversational context and thereby facilitate mutual understanding. Supporting mutual understanding is a main challenge of CSCL research. We explore how this may happen in a continuous 3D space (VRML). Our first hypothesis was that the proximity of the emitter to the referred object clarifies the referential context. Our second hypothesis stated that the receiver uses gaze awareness in order to guess which object the emitter refers to. The experiment results confirmed the first hypothesis, surprisingly rejected the second hypothesis and reveal complex interactions between the two.
Interfaces to Support Children's Co-present Collaboration: Multiple Mice and Tangible Technologies

This paper summarises two different approaches using technology to support young children's collaborative interaction in a classroom setting. KidPad, a 2½D drawing package with zooming capabilities, was adapted for use with multiple mice and tangible interfaces. The first section of the paper focuses on a study carried out to evaluate the effect of multiple mice on children's collaborative behaviour at a desktop computer. Positive effects of the use of two mice included symmetry of mouse use amongst pairs and a greater degree of engagement in the task. However a number of usability issues were identified when children attempted to collaborate, particular problems were faced when the shared control was taken away, and one of the users took control, for example, when navigating. Different types of working styles were also evident between the one mouse and two mice conditions. The second section of the paper describes a move away from the desktop computer towards room-based technologies. Tangible interfaces to KidPad were developed in order to facilitate shared control over actions such as navigation where difficulties had been identified in a desktop situation. The visibility of action is highlighted as a fundamental element in the support of collaboration on a larger scale. Finally, future work and the potential of these technologies in encouraging shareable co-present interaction in a real school context are briefly discussed.
Design of Augmented Creative Environments

In this short paper we present the conceptual framework underlying research projects in a newly established research studio concerned with the design of technology-augmented creative environments. The studio investigates design-oriented collaborative environments for inspirational learning with demands on discovery and creative spaces. This is integrated with inquiries into artistic performance as creative practices. We have a collaborative learning research approach and emphasize the importance of place and body (synkinaesthetics) in collaborative learning. Important contexts are performing arts as creative practices and inspirational learning in design education.
MALARIA PARASITE STATUS IN THE STATE OF UTTAR PRADESH BETWEEN 2018 AND 2022

Background: To the best knowledge of the author, there is no report on the current status of Malaria in the state of Uttar Pradesh, India. This study is an attempt in that direction.&#x0D; Materials and Methods: The study design included an analysis of the 2017 and 2018 annual reports as well as the website of the of the National Centre for Vector-Borne Diseases Control (NCVBDC).&#x0D; Results: Bareilly District in Uttar Pradesh had a low Annual Parasite Incidence (API) of Malaria of 0.06 in 2017 which increased to 7.32 in 2018.&#x0D; Conclusions: If interventions like the treatment of asymptomatic carriers take place, it is expected that the API in this district and the entire state will come down quickly.
Attention-based U-Net for image demoiréing

Image demoiréing is a particular example of a picture restoration problem. Moiré is an interference pattern generated by overlaying similar but slightly offset templates.In this paper, we present a deep learning based algorithm to reduce moiré disruptions. The proposed solution contains an explanation of the cross-sampling procedure – the training dataset management method which was optimized according to limited computing resources.Suggested neural network architecture is based on Attention U-Net structure. It is an exceptionally effective model which was not proposed before in image demoiréing systems. The greatest improvement of this model in comparison to U-Net network is the implementation of attention gates. These additional computing operations make the algorithm more focused on target structures.We also examined three MSE and SSIM based loss functions. The SSIM index is used to predict the perceived quality of digital images and videos. A similar approach was applied in various computer vision areas.The author’s main contributions to the image demoiréing problem contain the use of the novel architecture for this task, innovative two-part loss function, and the untypical use of the cross-sampling training procedure.
MatGraph: An Energy-Efficient and Flexible CGRA Engine for Matrix-Based Graph Analytics

Graph analytics is increasingly important for solving problems in various fields. Matrix-based graph analytics has obtained much attention due to its high performance and ease of optimization. In the general architecture, due to the extremely high sparsity and complex connectedness of graphs, matrix-based graph analytics suffers from the deep and heavy pipeline as well as the low efficiency of the memory subsystem. Meanwhile, lots of accelerators based on application-specific integrated circuits (ASICs) for graph analytics are not flexible enough to support various matrix operations of diverse matrix-based graph algorithms, which have different graph semantics and dataflow. In this paper, we present MatGraph, an energy-efficient and flexible architecture to support matrix-based graph analytics efficiently. MatGraph is based on coarse-grained reconfigurable architectures (CGRAs) which have both high energy efficiency and flexibility. According to the matrix operations on graphs, we conduct an abstract from the operators to define reduced instructions and design a lightweight pipeline to achieve high parallelism of instructions in CGRAs. To eliminate the impact of the highly sparse graph data, we design a bitmap-aware instruction filtering unit to filter out invalid instructions for each PE and increase the on-chip reuse of instructions. Furthermore, we propose a bidirectional data-aware sparsity removing scheme to eliminate the sparsity and redundant off-chip data accesses. Overall, MatGraph achieves 9.35x, 2.28x speedup, and 11.17x, 7.15x energy savings on average compared to state-of-the-art (SOTA) CPU-based and GPGPU-based solutions respectively. Compared to the SOTA graph analytics accelerator, MatGraph also achieves 1.59x speedup and 1.61x less energy.
Identifying the Relationship Between Hypothesis and Premise

Natural language processing is one the most interesting study leading to huge research solutions in the modern era. Multilingual toxic comment classification can be served as a huge benefit to the existing social media life where comments, tweets, etc. can be analyzed when a topic is known to the system. This would help in the prevention of false commenting and better interpretation and analysis of the miscommunication that occurred on different social media platforms over a certain issue. Multilingual toxic comment classification refers to the analysis of a hypothetical sentence proposed given a premise. This classification is divided into three categories are the hypothetical sentence proposed can be either an entailment to the premise, neutral, or contradictory to the known premise statement. Natural language inference is considered as one of the most trending problems under the field of natural language processing which helps to determine how two statements given the premise and hypothesis are related to each other. Thus, the paper proposes different models such as CBOW, ESIM, BiLSTM and fine-tuned XML-RoBERTa model to predict the relationship between two statements. The prediction helps in the determination of whether the given hypothesis is in an entailment, neutral, contradictory relation with the given premise. The paper shows a study over various algorithms that can be used to solve the natural language inference problem. After analysis, the paper also proposes a model that obtained an accuracy of 95.35% with a ROC score of 0.9629 for the entailment relationship, 0.97076 for the neutral relationships, and 0.9797 for the contradictory relationships between the sentence pairs.
Integer Linear Programming Approach for Hiding Sensitive Association Rules

The privacy preserving data mining plays a vital role in statistical agencies and database privacy investigations, which are concerned with preserving secrecy disclosure during data mining. The large-sized dataset contains sensitive association rules, which are required to be hidden from unauthorized users. Thus, association rule hiding is a competent solution that helps enterprises keeps away from the hazards caused by sensitive knowledge leakage when sharing the data in their collaborations. This paper presents a constraint based optimization model for concealing sensitive association rules through the formulation of a well-defined Integer Linear Programming (ILP). The developed method reduces the database sanitization problem to a Constraint Satisfaction Problem, which is solved using ILP. Sanitization algorithm performs hiding of sensitive rules by reducing the support or the confidence of the sensitive rules. The results of the experimental evaluation of the proposed approach on real-life datasets indicate the promising performance of the approach in terms of side effects on the original database.
Deep Learning based Gesture Recognition for Drones

With the development of UAV, more and more people use the lens of drones for image recognition. Among them, the application of gesture recognition does not systematically sort out relevant information, and for gesture recognition, its accuracy will be a problem. In addition, during the recognition process, different scenes will also affect the judgment result of the gesture, so how to achieve the reduction of the interference brought by the venue is also a challenge. This paper summarizes recent papers on the application of gesture recognition on drones and applies it to gesture recognition. For the training module, the RNN and CNN architecture will be used for training. For the interference caused by the environment, we add more environment maps for the training. This paper summarizes the recent gesture recognition solutions and applies them to gesture recognition to improve the accuracy of gesture recognition, which will provide an additional option for gesture recognition control.
Private Information Retrieval Schemes Using Cyclic Codes

A Private Information Retrieval (PIR) scheme allows users to retrieve data from a database without disclosing to the server information about the identity of the data retrieved. A coded storage in a distributed storage system with colluding servers is considered in this work, namely the approach in [6] which considers a storage and retrieval code with a transitive group and provides binary PIR schemes with the highest possible rate. Reed-Muller codes were considered in [6]. In this work we consider cyclic codes and we show that binary PIR schemes using cyclic codes provide a larger constellation of PIR parameters and they may outperform the ones coming from Reed-Muller codes in some cases.
Interaction Repertoire in a Distance Education Community

This paper presents results from a study focusing on text-based electronic interaction in a distance educational setting. The analysis of the interaction identifies three typified genres, labeled Query, Feedback and Smalltalk. Together they constitute a shared interaction repertoire with marks of a new social landscape for education with changes in roles and behaviours that are important to grasp for designers as well as teachers.
Presumptive Literacies in Technology-Integrated Science Curriculum

In this paper we explore the multiple literacies presumed in the design of inquiry curriculum created at the Center for Learning Technologies in Urban Schools (LeTUS). The current design of LeTUS inquiry-based, technology embedded science curriculum presumes a facility with, and strategic use of literacies on the part of students who enact the curriculum. Here literacy means two things: deriving meaning from patterns in knowledge domains like science and facility with different information forms. We use teacher interviews and on-line discussions to expose presumptive literacies in design and learners' literacy challenges and offer suggestions that teacher modifications to these units can inform future design to support literacy in science.
Semi-supervised Multi-domain Learning for Medical Image Classification

The limitations of domain dependence in neural networks and data scarcity are addressed in this paper by analyzing the problem of semi-supervised medical image classification across multiple visual domains using a single integrated framework. Under this premise, we learn a universal parametric family of neural networks, which share a majority of their weights across domains by learning a few adaptive domain-specific parameters. We train these universal networks on a suitable pretext task that captures a meaningful representation for image classification and further finetune the networks using a small fraction of training data. We perform our experiments on five medical datasets spanning breast, cervical, and colorectal cancer. Extensive experiments on architectures of domain-adaptive parameters demonstrate that our data-deficient universal model performs equivalently to a fully supervised setup, rendering a semi-supervised multi-domain setting with lower numbers of training samples for medical data extremely feasible in the real world.
A Novel Approach to Enhance Effectiveness of Image Segmentation Techniques on Extremely Noisy Medical Images

Through this study, I contribute towards segmentation of liver areas and have proposed additional improvements, which positively influence image segmentation. In this study, I have subjected medical images from LiTS - Liver Tumour Segmentation Challenge, which are extremely noisy, to various image segmentation techniques belonging to fully automatic and semi-automatic categories. These varied techniques implement different approaches towards image segmentation problem. All the techniques had initially failed to segment the images with very poor results. Commonly used filters for pre-processing, such as median filter, top hat filter, wiener filter, etc., were ineffective in reducing the noise effectively. Through this study, I have introduced a new combinatorial approach which not only is easier to implement but also much faster as well and resulted in much more enhanced input image quality that significantly improved the segmentation outcomes. Our approach has reduced noise, sharpened the edges, “localized” the segmentation problem before subjecting to various segmentation techniques. The techniques which had failed previously now could segment the images with improved speed of execution, efficiency and accuracy. I have studied our approach on 10 well known image segmentation techniques. Accuracy of these segmentation techniques was determined by computing Jaccard Index, Dice Coefficient and Hausdorff Distance.