Reviewerly/exHarmony huggingface dataset:
Predict-Observe-Explain Learning Model Assisted by “Educandy” Games to Improve Students’ Conceptual Understanding

A good understanding of concepts is needed to prevent students from misunderstanding. One effort that can be done is to apply the POE (Predict-Observe-Explain) learning model with the help of the Educandy educational game. This study aims to measure the effect of applying the POE learning model with the help of Educandy educational game application to increase students’ conceptual understanding of temperature and heat subjects. This study was quasi-experimental design research, using the nonequivalent control group design. The population of this study was 128 junior high schools students of SMPN 25 Pekanbaru (Class VII.I-Class VII.4). The first class, VII.1, was used as the experimental class, the third class, VII.3, was used as the control class, while the other class was the generalization classes. Research instrument of pretest and post-test containing 28 questions was used. The result was processed by descriptive and interferential techniques using T-test. The data processing technique used was descriptive and inferential, using the t-test for N-gain as an evaluation method. The results of this study indicated that conceptual understanding in both classes had increased, but the average value of students’ conceptual understanding in the experimental class was higher than that in the control class. It was described on the data N-gain as 0.7 in the experiment and 0.3 in the control class. The results of the t-test show that the POE learning model assisted by educational games based on the Educandy application can significantly improve students’ understanding of concepts.
System Verification and Validation Approach Using the MagicGrid Framework

ABSTRACT The ongoing transformation in the industry from a document‐based systems engineering to a model‐based systems engineering approach reveals a need for new methods of verifying and validating systems. Traditional methods of testing the actual system are getting more and more expensive. A model‐based environment could significantly reduce testing and, most importantly, verification and validation processes costs. It allows testing on the system model by applying various techniques, such as simulation, analysis, review, mock‐ups, etc. There are, however, very few approaches today detailing how verification and validation of the entire system (taking into count its components and subsystems) could be performed. This paper proposes an approach to perform verification and validation of a system using system models developed with Systems Modeling Language (SysML) and in accordance with the MagicGrid (formerly known as MBSE Grid) framework. The approach covers system testing activities beginning with verification of the lowest modeled system elements against system requirements and finishing with validation of the system as a whole, against stakeholder needs.
YOLO Based Real Time Human Detection Using Deep Learning

Abstract Person identification which has been widely used in computer vision and one of the difficult applications employed in a variety of fields such autonomous vehicles, robots, security tracking, aiding visually impaired people, etc. As deep learning quickly developed, numerous algorithms strengthened the link between video analysis and visual comprehension. The goal of all these algorithms, regardless of how their network architectures operate, is to find many people inside a complicated image. The freedom of movement in an unknown environment is restricted by the absence of vision impairment, thus it is crucial to use modern technologies and teach them to assist blind people whenever necessary. We provide a system that will identify all potential daily multiple people and then prompt a voice to inform the user about both nearby and distant people.
A Deep Learning approach to 3D Viewing of MRI/CT images in Augmented Reality

Abstract CT (Computed Tomography) and MRI (Magnetic Resonance Imaging) are both used to diagnose internal damage and deformities of the organs. These are 3D data so doctors diagnose these scans by splitting them into many 2D slices and analyse each slices for detecting disease. This is a hard and time-consuming task to accomplish. Experts are enough trained to analyse this data for interpreting insights. But when they try to explain vulnerable situations to their patients, these scans are not very readable and understandable for them. Thus, it scares them a lot when they are asked to take these scans for diagnosis. Thus, an approach is given to viewing these scans in AR (augmented reality), making them available to view via their smartphones. The idea is to build an application that can take CT and MRI scans as input from “.stl” files and help to view them in AR with plane detection for mesh to be rendered and placed properly. This also reduces the task of storing these scans in bulk. Instead, one can just send data to their patients and store it as records on their phones. Whenever necessary, one can visualise it on the spot.
Determination of the Receiver Operating Characteristics (ROC) Curve of the Logistic Regression Model Accuracy Using Some Breast Measurements in the Presence of Multicollinearity

This study was aimed at determining the Receiver Operating Characteristics Curve of the Logistic Regression Model accuracy using some breast measurements in the presence of Multicollinearity to detect the presence or absence of tumorous cell of cancer patients. A secondary data from the Breast Cancer Wisconsin (Diagnostic) was used for analysis. The data was cleaned for outliers, recoded numerically and tested for multicollinearity. The ROC curve for the logistic regression model also revealed a high sensitivity and high specificity of the presence of tumorous cells in the patients with a percentage of 95% which is extremely high indicating that the logistic regression model is good in predicting better diagnosis of tumor cell of cancer patients accurately when combined with ROC.
Real Time Robotic Arm using Leap Motion Controller

Abstract Robots are playing key role in today’s world. Robots are actively taking part in the industries, hospitals, defense applications as well. This paper describes about how hand gesture movements can control the robotic arm. Hand gesture data is recorded by the leap motion controller. That captured gestures are then processed and sent to the microcontroller. In this paper we are intended to design a robotic arm that is completely useful to the people with disabilities. We are using new kind of sensor called Leap motion controller, which captures the hand gestures in 3D. we develop a robotic arm that is useful for the paralyzed people, industrial applications, medical fields by just simply using the hands, we can guide the leap motion controller so that the robotic arm can perform its operations.
Design of Deep Multi-Scale CNN for Scene Deblurring

Abstract Dynamic Scene deblurring is a difficult low-level vision problem induced by camera shaking, object motion, and other causes. Since the blur movements of moving objects and the backdrop in dynamic scenarios are different, segmentation of the motion blur is necessary for deblurring each blur motion precisely. In contrast to this limiting assumption, we tackle the issue of deblurring broad dynamic images with many moving objects and camera motion in the current study. A multilayer convolutional neural network (CNN) based video image deblurring technique is suggested as a solution to the issue that inter-frame information and spatiotemporal are easily lost during restoration of a digital video/image that has become blurred. We use the multiscale CNN-built adaptive Laplacian regularisation term to the problem of restoring images from videos. First, we provide a new restoration model by mixing several regularises, in particular by combining NLM regularise with the multiscale CNN, to extract redundant information from video images’ self-similarity. We utilised a basic gradient descent technique to solve the model for restoring images from videos. The experimental findings demonstrate the effective deblurring effect and some noise resilience of our approach.
Exploring Best Practices in Mobile App Design Patterns and Tools

Design patterns are reusable solutions to common design problems that provide a consistent user experience across different apps. This article explores the best practices in mobile app design patterns and tools with a focus on the user-centered approach to design. Design patterns such as navigation bars, tab bars, list views, and card views are discussed, along with design tools such as Sketch, Figma, Adobe XD, and InVision. The problem is to ensure that mobile app design is centered around the needs and preferences of the user, rather than the designer or the technology, and that the right design patterns and tools are used to create interfaces that are familiar and easy to use. The chapter emphasizes the importance of conducting user research to understand the needs and preferences of the target audience and using design patterns and tools to create interfaces that are familiar and easy to use. Mobile apps have become an integral part of our lives, and designing a successful mobile app is a challenging task that requires a thorough understanding of user needs and preferences.
Is Bangladesh’s policy response to the growing e-commerce sector compatible with its commitment to fight predicate offences for money laundering?

Purpose This study aims to investigate Bangladesh’s e-commerce regulations in light of the growing criticism that they are insufficient to curb predicate crimes like fraud and money laundering in the online marketplace. Design/methodology/approach This study used the exploratory design to examine the latest ministerial directives and laws governing e-commerce in Bangladesh to determine why they cannot prevent fraudulent activities in this promising sector and identify potential solutions. Findings Bangladesh’s regulatory responses to e-commerce fraud prevention and detection are reactive and inadequate. Regulators are unwilling and unable to enforce available legal provisions for various reasons, including a lack of knowledge and coordination among the agencies. Research limitations/implications This paper focuses solely on the legal and regulatory framework in place to combat e-commerce fraud. Other critical issues, such as consumer rights, privacy and data protection in e-commerce, are not addressed. Practical implications The findings of this study will assist policymakers in revising current regulatory approaches to e-commerce to protect this sector from criminal abuse. Originality/value This study looked into the possibility of using a proactive risk-based approach in the e-commerce sector, similar to what the Bangladesh Financial Intelligence Unit does in the financial sector.
SDLC Phases of a Mobile Application

Class models are an essential part of mobile game architecture, providing a framework for organizing and structuring the code. These models help developers to understand the relationships and dependencies between different objects in the game, allowing for more efficient and effective code development. The use of class models is likely to continue to evolve and improve in the future, as mobile games become more complex and sophisticated. As AI and ML technologies become more advanced, developers will need to create new classes and models to represent these technologies in mobile games. Additionally, the use of cloud computing and other distributed technologies is likely to become more prevalent, requiring the creation of new classes and models to represent these technologies in the game. Overall, class models will play a critical role in enabling developers to create more engaging and immersive mobile gameplay experiences.
An Approach With Iterative and Incremental Development (IID) for Mobile Applications

There are several studies on the software development life cycle (SDLC) and various approaches that can be utilized in planning and designing a software project. The contemporary norm for the software development process across industries is to build products using iterative or incremental design. Iterative and incremental development (IID) is useful for different types of software. The authors have provided a literature review of the current definitions of iterative and incremental design, and also discuss the most common pitfalls for each approach. The authors also analysed which approach is better for different stages of the development of mobile applications. At the end of this research, it was concluded that the iterative approach is the best approach for front-end development, while the incremental approach is a better fit for back-end development. Finally, the authors provided an analysis of utilizing IID for applications in the banking sector.
Uses of Mobile Apps for Youth in Education Purposes

People nowadays use their phones constantly throughout the day, for a variety of reasons. It seems reasonable that they may be used for educational purposes as well. The process of education has become much easier with mobile applications. Through these mobile apps, students can access all kinds of books and information. They can save a lot of time without the need to go to the library to acquire it. Thanks to these mobile apps, classes and lectures are not confined to physical classrooms only. Students no longer have to wait until class to ask questions; instead, they may get in touch with their professors whenever the need appears. When schools were closed and lessons were done online during Covid-19, mobile applications were also incredibly helpful.
Accelerating Evolution Through Gene Masking and Distributed Search

In building practical applications of evolutionary computation (EC), two optimizations are essential. First, the parameters of the search method need to be tuned to the domain in order to balance exploration and exploitation effectively. Second, the search method needs to be distributed to take advantage of parallel computing resources. This paper presents BLADE (BLAnket Distributed Evolution) as an approach to achieving both goals simultaneously. BLADE uses blankets (i.e., masks on the genetic representation) to tune the evolutionary operators during the search, and implements the search through hub-and-spoke distribution. In the paper, (1) the blanket method is formalized for the (1 + 1)EA case as a Markov chain process. Its effectiveness is then demonstrated by analyzing dominant and subdominant eigenvalues of stochastic matrices, suggesting a generalizable theory; (2) the fitness-level theory is used to analyze the distribution method; and (3) these insights are verified experimentally on three benchmark problems, showing that both blankets and distribution lead to accelerated evolution. Moreover, a surprising synergy emerges between them: When combined with distribution, the blanket approach achieves more than $n$-fold speedup with $n$ clients in some cases. The work thus highlights the importance and potential of optimizing evolutionary computation in practical applications.
PromptFusion: A Low-Cost Prompt-Based Task Composition for Multi-task Learning

Prompt-tuning takes advantage of large-scale pretrained language models and achieves great performance while being more parameter-efficient. However, existing prompt-tuning methods require tuning different pretrained language models for each specific task, and fail to utilize information across different tasks, which limits their applicability in complex situations. To address above issues, we propose PromptFusion, a unique prompt-based multi-task transfer learning approach which learns knowledge from multiple tasks and incorporates for the target task at low cost. The proposed approach first learns task-specific parameters with prompts to extract information individually, then, a fusion module is designed to aggregate information. Our method is interpretable because it can explain which sources of tasks are the crucial factors to influence the model decision on the target task. We also examine a more effective way to encapsulate information by incorporating parallel adapter modules into transformer layers, and this makes a linkage between parameter-efficient transfer learning methods. We empirically evaluate our methods on the GLUE benchmark and a variety of hard NLU tasks. The results show that our approach outperforms full fine-tuning and other parameter-efficient multi-task methods.
MORRFx and Its Framework: Multi-objective Sampling Based Path Planning for Unpredictable Environments

We present MORRFx, an asymptotically optimal sampling based motion planning algorithm for fast and multi-objective planning in unpredictable dynamic environments. Often in robotics applications, the representation of the environment can change. As a result, over time, the information used to compute the solutions can evolve. The algorithm we propose takes these modifications into account to produce an approximation of the Pareto optimal set of solutions, that are valid at any time, and from which the decision-maker can select a trajectory according to his/her preferences. MORRFx (Multi-Objective Rapidly exploring Random Forest x) is a combination of two algorithms: RRTx (Asymptotically Optimal Single-Query Sampling-Based Motion Planning with Quick Replanning) [1] and MORRF <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> (Sampling-Based Multi-Objective Motion Planning) [2]. In this paper, we present a minimal, multi-objective framework in an efficient implementation. We also use simulations to show the efficiency and capabilities of MORRFx.
Synergistic Perception of the Binocular Imitating Human Eyes

The binocular system of the human eye can accurately focus on the salient areas in the scene, which has great reference significance for the processing volume of simplified data in vision. In response to this method, we propose a method that mimics the attention mechanism of the human eye to complete the bionic machine vision relative to the human eye. According to the principle of the human eye's attention mechanism, the center of multiple cameras can be overlapped with the center of the saliency area while extracting the visual saliency area through the bionic technology that simulates the biological mechanism. The equipment adopts a symmetrical arrangement in structure to reduce the torsional force required by the motor and make the structure more compact. Functionally, the device integrates multiple modules such as image saliency region extraction, visual servings, and multi-motor collaborative control, and has broad application prospects in robotics and intelligent manufacturing.
Identification Technology of Factory Personnel Based on Improved Strong-Baseline

In the factory, in order to feed back some emergencies in time, it is necessary to identify the identity of workers under specific circumstances. Aiming at this problem, this paper proposes a worker identification technology based on improved Strong Baseline. Firstly, the improved feature extraction network L-ResNet101 is used as the feature extractor; Secondly, add attention mechanism modules in different feature layers to make the model pay more attention to key features; Then the extracted features are fused and further classified. The experimental results show that the classification accuracy of the improved Strong Baseline network on Market1501 data set and user-defined AVA-Market data set is more than 90%, which proves that the improved network has better classification accuracy.
Echo State Network based Noise Detection in Energy Internet Orienting Justice Blockchain Data

Energy Internet (EI) is a hot research topic which smartly combines the energy system and information and communication technologies to promote the efficiency and reduce the cost of energy generation, transmitting and utilizing, its related data need to be stored in justice blockchain for possible lawsuit. To ensure the integrate of EI data stored in justice blockchain, noise detection is a necessary function which can detect the amplitude of noise and further discover the cyber attacking. To solve this problem, many noise detecting algorithms are available, and the authors have firstly used three layers artificial neural network (ANN) to estimate the noise level quickly and efficiently. In this paper, based on previous research, the authors manage to use Echo State Network (ESN) to solve this problem, and get better simulation results in most scenes. As the simulation results show, the noise interference or cyber attacking behavior can be detected with probability 99% within 4 continuous samples. Applying this algorithm in justice blockchain will make the system more robust and secure, and its data handling cost can be largely reduced.
Research on Character Recognition in Mechanical Process Card Based on CRNN and Logical Reasoning

The mechanical process card has the function of operation guidance in mechanical manufacturing. The digital reproduction of the mechanical process card with stains in the actual working conditions can help the staff to operate correctly. Aiming at the problem that the characters with stains can not be accurately recognized in the mechanical process card, this paper uses the framework of “machine learning + logical reasoning” to carry out research. Firstly, the preliminary recognition of character elements is completed through CRNN. In this step, the characters with stains may be recognized as wrong results. Secondly, the semantic analysis of the recognition results is carried out according to the special term library of mechanical and electronic industry, and then the semantic combination of various parts is carried out to analyze the syntactic structure of sentences. Finally, in the sentences with grammatical problems, the incorrect characters are corrected according to fixed collocation and other methods to output the correct results. The experimental results that the accuracy of using CRNN only to recognize the characters with stains is low, but combined with the correction of logical reasoning, the accuracy can reach 90%, but it has a good effect only for the Chinese characters, English and special characters with stains, and the recognition effect of numbers with stains is poor.
A Halving Rectangles Sampling High Dimensional Model Representation Algorithm

A High Dimensional Model Representation (HDMR) method based on halving rectangles (HALRECT) sampling strategy is proposed. This method uses the Cut-HDMR to transform the high-dimensional problem into different multiple low-dimensional problems, and the least squares support vector regression (LS-SVR) is designed to deal with these low-dimensional problems, respectively. The simulation results show that this method has higher modeling accuracy and lower modeling cost than traditional approximation methods in high-dimensional nonlinear examples.