Reviewerly/exHarmony huggingface dataset:
Exploration of Applying Lego NXT and Arduino in Situated Engineering Teaching: A Case Study of a Robotics Contest at King Saud University

Saudi students in engineering courses suffer from a lack of science, technology, engineering, and mathematics (STEM) knowledge due to the teaching philosophy and programs offered in secondary and intermediate schools. This weakness naturally impacts their motivation, grades, and their relationship with teachers. In this paper, we introduce a new experimental teaching experience in the Applied Mechanical Department of the Applied Engineering College of King Saud University, based on situated learning theory, which emphasizes that knowledge must be learned in constructed situational context). This experience involved introducing Lego NXT and Arduino to enhance the enthusiasm and interest of students through designing and building robots in agreement with the “Introduction to design” course syllabus. Two experimental challenges were associated with: the line-follower problem and the maze problem. These challenges took the form of an internal completion at the end of each semester. The experience was conducted for two consecutive terms (30 students, the academic year 2019-2020), and the results were compared to those in six previous terms (100 students, academic years from 2016 to 2019). The experimental group demonstrated grades improvement (course mean grade rose from 77.1 to 85.3), the progress of academic achievement, and interest that enables them to actively explore and construct knowledge.
Blood Vessel Detection Algorithm for Tissue Engineering and Quantitative Histology

Immunohistochemistry for vascular network analysis plays a fundamental role in basic science, translational research and clinical practice. However, identifying vascularization in histological tissue images is time consuming and markedly depends on the operator's experience. In this study, we present "blood vessel detection-BVD", an automatic algorithm for quantitative analysis of blood vessels in immunohistochemical images. BVD is based on extraction and analysis of low-level image features and spatial filtering techniques, which do not require a training phase. BVD algorithm performance was comparatively evaluated on histological sections from three different in vivo experiments. Collectively, 173 independent images were analyzed, and the algorithm's results were compared to those obtained by human operators. The developed BVD algorithm proved to be a robust and versatile tool, being able to quantify number, area, and spatial distribution of blood vessels within all three considered histologic datasets. BVD is provided as an open-source application working on different operating systems. BVD is supported by a user-friendly graphical interface designed to facilitate large-scale analysis.
Semantic Web

The semantic web aims at making web content interpretable. It is no less than offering knowledge representation at web scale. The main ingredients used in this context are the representation of assertional knowledge through graphs, the definition of the vocabularies used in graphs through ontologies, and the connection of these representations through the web. Artificial intelligence techniques and, more specifically, knowledge representation techniques, are put to use and to the test by the semantic web. Indeed, they have to face typical problems of the web: scale, heterogeneity, incompleteness, and dynamics. This chapter provides a short presentation of the state of the semantic web and refers to other chapters concerning those techniques at work in the semantic web.
Analysis of Covid 19 Data in Indonesia Using Supervised Emerging Patterns

This research method uses CRISP-DM with emerging pattern supervision modeling and EPM Algorithm. The contribution of the research is to assist the Government in overcoming the problem of the spread of the COVID-19 cluster in several regions in Indonesia. The research aims to implement information on the COVID-19 data mining pattern in the DKI Jakarta area. The problems faced are the difficulty of identifying the pattern of COVID-19 data in one area, it is difficult to dig up data on the http://corona.jakarta.go.id website. It is not easy to decide on the handling of COVID-19. The output of the research results in a cluster of information on COVID-19 in the DKI Jakarta area based on Significance level depends on the Covid Map In terms of Region, Status, Gender, &amp; age And Signification can be the basis for determining covid OTG, DTG, and Positive. The theoretical and practical implications can be stated as follows: The use of supervised emerging pattern methods can affect the processing of COVID-19 data. For 5 Regions in DKI Jakarta and distribution to determine covid OTG, DTG, and Positive. The result of the development of this data mining system is to produce pattern reports to produce Supervised Emerging Patterns technology for decision making at the COVID-19 Task Force in DKI Jakarta.
Keeping the living bond between actors and remote audiences in distributed virtual theater

This paper introduces a new method to achieve remote distributed performances using a virtual shadow theater and real time motion capture. It was used during the lockdowns in France in 2020-21 to reach remote scholar audiences with The Wizard without Shadow performance. After a review of remote distributed virtual experiments in performing arts, the paper details the creation-as-research process that leads to two versions of the same story. It shows how the quality of the theatrical bond between the actor and the audience of the first face-to-face version is maintained in the second remote version combining a video conference platform and live motion capture in a video game engine to bring actors performances to two remote distributed audiences.
3D Convolution Recurrent Neural Networks for Multi-Label Earthquake Magnitude Classification

We examine a classification task in which signals of naturally occurring earthquakes are categorized ranging from minor to major, based on their magnitude. Generalized to a single-label classification task, most prior investigations have focused on assessing whether an earthquake’s magnitude falls into the minor or large categories. This procedure is often not practical since the tremor it generates has a wide range of variation in the neighboring regions based on the distance, depth, type of surface, and several other factors. We present an integrated 3-dimensional convolutional recurrent neural network (3D-CNN-RNN) trained to classify the seismic waveforms into multiple categories based on the problem formulation. Recent studies demonstrate using artificial intelligence-based techniques in earthquake detection and location estimation tasks with progress in collecting seismic data. However, less work has been performed in classifying the seismic signals into single or multiple categories. We leverage the use of a benchmark dataset comprising of earthquake waveforms having different magnitude and present 3D-CNN-RNN, a highly scalable neural network for multi-label classification problems. End-to-end learning has become a conventional approach in audio and image-related classification studies. However, for seismic signals classification, it has yet to be established. In this study, we propose to deploy the trained model on personal seismometers to effectively categorize earthquakes and increase the response time by leveraging the data-centric approaches. For this purpose, firstly, we transform the existing benchmark dataset into a series of multi-label examples. Secondly, we develop a novel 3D-CNN-RNN model for multi-label seismic event classification. Finally, we validate and evaluate the learned model with unseen seismic waveforms instances and report whether a specific event is associated with a particular class or not. Experimental results demonstrate the superiority and effectiveness of the proposed approach on unseen data using the multi-label classifier.
Virtual microscopy enhances the reliability and validity in histopathology curriculum: Practical guidelines

This article was migrated. The article was marked as recommended. Digital pathology innovation and application in medical education have paved the path for a significant shift in the advancement of the medical curriculum. The new technology of virtual microscopy is a proven reliable and valid pedagogy method for histopathology learning objectives, and assessments. The current transformation has brought educators around the globe nearer towards the goal of achieving competence in Curriculum Inventory in the medical curriculum. This paper emphasises the practical tips and guidelines for cost-effective implementation and the successful use of Virtual Microscope technology to enhance the histopathology curriculum in a medical school.
Homogeneous Agent Behaviours for the Multi-Agent Simultaneous Searching and Routing Problem

Through the use of autonomy Unmanned Aerial Vehicles (UAVs) can be used to solve a range of of multi-agent problems that exist in the real world, for example search and rescue or surveillance. Within these scenarios the global objective might often be better achieved if aspects of the problem can be optimally shared amongst its agents. However, in uncertain, dynamic and often partially observable environments centralised global-optimisation techniques are not achievable. Instead, agents may have to act on their own belief of the world, making the best decisions independently and potentially myopically. With multiple agents acting in a decentralised manner how can we discourage competitive behaviour and instead facilitate cooperation. This paper focuses on the specific problem of multiple UAVs simultaneously searching for tasks in an environment whilst efficiently routing between them and ultimately visiting them. This paper is motivated by this idea that collaboration can be simple and achieved without the need for a dialogue but instead through the design of the individual agent’s behaviour. By focusing on what is communicated we expand the use of a single agent behaviour. Which through minor modifications can produce distinct agents demonstrating independent, collaborative and competitive behaviour. In particular by investigating the role of sensor and communication ranges this paper will show that increased sensor ranges can be detrimental to system performance, and instead the simple modelling of nearby agents’ intent is a far better approach.
Systematic Literature Review: Instagram Fake Account Detection Based on Machine Learning

The popularity of social media continues to grow, and its dominance of the entire world has become one of the aspects of modern life that cannot be ignored. The rapid growth of social media has resulted in the emergence of ecosystem problems. Hate speech, fraud, fake news, and a slew of other issues are becoming un-stoppable. With over 1.7 billion fake accounts on social media, the losses have al-ready been significant, and removing these accounts will take a long time. Due to the growing number of Instagram users, the need for identifying fake accounts on social media, specifically in Instagram, is increasing. Because this process takes a long time if done manually by humans, we can now use machine learning to identify fake accounts thanks to the rapid development of machine learning. We can detect fake accounts on Instagram using machine learning by implementing the combination of image detection and natural language processing.
Fuzzy Decision Support System for ABC University Student Admission Selection

New Student Admission (PMB) is a pattern for selecting prospective students.Every year, every University conducts PMB selection, and new student admissions are divided into numerous tracks: the Independent Path.The Basic Competency Exam, TOEFL Prediction, and interviews are commonly used in this selection process.So far, the registrant data has been manually sorted in the selection process.A system that can help determine prospective new students will be required since many student data will be picked for PMB.Because of the system's assistance, universities can make the selection process more effective.The method used in this study to implement a PMB decision system is Fuzzy Mamdani.The method in this study has a sequence of stages: first doing fuzzification, second using inference evaluation rules that have been determined, and finally, defuzzification to get the final results of the calculations.This system was tested by comparing the system's results to the actual results obtained according to the design.The system testing results received a 96 percent approval rating.As a result, the University's new student admissions system can be used to help streamline the formerly manual selection process.
Borrow from Source Models: Efficient Infrared Object Detection with Limited Examples

Recent deep models trained on large-scale RGB datasets lead to considerable achievements in visual detection tasks. However, the training examples are often limited for an infrared detection task, which may deteriorate the performance of deep detectors. In this paper, we propose a transfer approach, Source Model Guidance (SMG), where we leverage a high-capacity RGB detection model as the guidance to supervise the training process of an infrared detection network. In SMG, the foreground soft label generated from the RGB model is introduced as source knowledge to provide guidance for cross-domain transfer. Additionally, we design a Background Suppression Module in the infrared network to receive the knowledge and enhance the foreground features. SMG is easily plugged into any modern detection framework, and we show two explicit instantiations of it, SMG-C and SMG-Y, based on CenterNet and YOLOv3, respectively. Extensive experiments on different benchmarks show that both SMG-C and SMG-Y achieve remarkable performance even if the training set is scarce. Compared to advanced detectors on public FLIR, SMG-Y with 77.0% mAP outperforms others in accuracy, and SMG-C achieves real-time detection at a speed of 107 FPS. More importantly, SMG-Y trained on a quarter of the thermal dataset obtains 74.5% mAP, surpassing most state-of-the-art detectors with full FLIR as training data.
A Hybrid Mobile Robot Path Planning Scheme Based on Modified Gray Wolf Optimization and Situation Assessment

To better solve the problems associated with optimal pathfinding and dynamic obstacle avoidance in the path planning of mobile robots, a hybrid path planning scheme combining modified gray wolf optimization (MGWO) and situation assessment mechanism is proposed. Firstly, a MGWO algorithm is proposed to plan a global path. Secondly, different situational factors for robots in different regions are extracted from the fusion results of 2D laser measurements and image data, and a Bayesian network model of robot action selection is established. Then, the situational factors of the robot are used as evidence for reasoning. Based on the posterior probability value in the inference result, the grid to be moved is selected and the traveling direction of the robot is adjusted in order to take advantage of both global path planning and local dynamic obstacle avoidance. The simulation results show that the proposed MGWO has better optimization performance. When combined with a situation assessment mechanism, it realizes dynamic obstacle avoidance while keeping the path length as short as possible.
The Effects of Writing Strategy Instruction on EFL Learners’ Writing Development

This study examined the effects of integrating writing strategy training into EFL writing instruction on learners&amp;rsquo; strategy use and writing performance. Two classes of EFL adult learners participated in this study. The experimental group were instructed with the writing strategy training in their EFL writing class for 14 weeks, whereas the control group received the same EFL writing program without any explicit strategy training.&amp;nbsp;&#x0D; Mixed methods were applied in collecting data. The quantitative instruments included the writing strategy questionnaires and writing performance tests, which were both pre-tested and post-tested in the both groups. The qualitative instruments of reflective journals were also conducted in the experimental group to probe deeper insights into learners&amp;rsquo; strategy changes. The findings showed that there were significantly positive differences in learners&amp;rsquo; using writing strategies and in writing proficiency favoring the experimental group.&amp;nbsp;&#x0D; The findings of this study indicate that writing strategy training can be integrated in the EFL writing instruction, and can bring to positive impacts for learners&amp;rsquo; strategic awareness and writing strategy use as well as their writing performance. The paper suggested the strong need to the process-based writing instruction, and writing strategy training holds promise in this regard.
A new design of multimedia big data retrieval enabled by deep feature learning and Adaptive Semantic Similarity Function

Nowadays, multimedia big data have grown exponentially in diverse applications like social networks, transportation, health, and e-commerce, etc. Accessing preferred data in large-scale datasets needs efficient and sophisticated retrieval approaches. Multimedia big data consists of the most significant features with different types of data. Even though the multimedia supports various data formats with corresponding storage frameworks, similar semantic information is expressed by the multimedia. The overlap of semantic features is most efficient for theory and research related to semantic memory. Correspondingly, in recent years, deep multimodal hashing gets more attention owing to the efficient performance of huge-scale multimedia retrieval applications. On the other hand, the deep multimodal hashing has limited efforts for exploring the complex multilevel semantic structure. The main intention of this proposal is to develop enhanced deep multimedia big data retrieval with the Adaptive Semantic Similarity Function (A-SSF). The proposed model of this research covers several phases "(a) Data collection, (b) deep feature extraction, (c) semantic feature selection and (d) adaptive similarity function for retrieval. The two main processes of multimedia big data retrieval are training and testing. Once after collecting the dataset involved with video, text, images, and audio, the training phase starts. Here, the deep semantic feature extraction is performed by the Convolutional Neural Network (CNN), which is again subjected to the semantic feature selection process by the new hybrid algorithm termed Spider Monkey-Deer Hunting Optimization Algorithm (SM-DHOA). The final optimal semantic features are stored in the feature library. During testing, selected semantic features are added to the map-reduce framework in the Hadoop environment for handling the big data, thus ensuring the proper big data distribution. Here, the main contribution termed A-SSF is introduced to compute the correlation between the multimedia semantics of the testing data and training data, thus retrieving the data with minimum similarity. Extensive experiments on benchmark multimodal datasets demonstrate that the proposed method can outperform the state-of-the-art performance for all types of data.
Analysis on the Penetration of Emotional Education in College Physical Education Based on Emotional Feature Clustering

Physical education is a highly skilled education offered in colleges and universities. Teachers do not appear in front of inanimate machines as laborers, and they are not the same as gardeners who grow colorful trees, according to their essential characteristics. Their work is aimed at flesh-and-blood students who are sentimental, thoughtful, and engaged in critical thinking. As a result, schools should prioritize physical education and place a premium on emotional infiltration education, improve learning interest, improve teacher-student relationships, create a harmonious teaching environment, and improve teaching quality; it has a significant impact on an individual’s entire life. The modern educational process places a premium on the transmission of rational knowledge while overlooking the accumulation of emotional experience. The cultivation and development of emotional feeling ability, emotional expression, and expression ability receive less attention than the training and improvement of language, concept, logic, and reasoning abilities. Emotion feature clustering is used to propose an emotion recognition method in this study. This method generates extended features for classification by constructing a co-occurrence matrix based on the co-occurrence relationship of emotion features and then by applying the spectral clustering method. The binary value of whether emotional features of emotional education in college physical education appear in a particular cluster is then expressed as a feature and extended to the original training feature set, alleviating the problem of sparse features.
KeyPIn – mitigating the free rider problem in the distributed cloud based on Key, Participation, and Incentive

Abstract In a distributed cloud, unlike centralized resource management, users provide and share resources. However, this allows for the existence of free riders who do not provide resources to others, but at the same time use resources that others provide. In a distributed cloud, resource providers share resources in a P2P fashion. In this paper, we propose a 3-pronged solution KeyPIn—a Key-based, Participation-based, and Incentive-based scheme to mitigate the free rider problem in a distributed cloud environment. We propose an incentive-based scheme based on game theory for providers to participate in the cloud by providing resources. This participation will be low for free riders thereby limiting their access to resources. A secure time instant key is generated based on a key management scheme that enables good users’ more time to access resources as their participation is high, whereas free riders are given limited or no time as their participation is low. Simulation results show that our scheme is effective in mitigating the free rider problem in the distributed cloud.
Research on Multi Unmanned Aerial Vehicles Emergency Task Planning Method Based on Discrete Multi-Objective TLBO Algorithm

The outbreak of unexpected events such as floods and geological disasters often produces a large number of emergency material requirements, and when common logistics methods are often ineffective, emergency logistics unmanned aerial vehicles (UAVs) become an important means. How to rationally plan multiple UAVs to quickly complete the emergency logistics tasks in many disaster-stricken areas has become an urgent problem to be solved. In this paper, an optimization model is established with the goal of minimizing the task completion time and the penalty cost of advance/delay, and a discrete multi-objective teaching–learning-based optimization (DMOTLBO) algorithm is proposed. The Pareto frontier approximation problem is transformed into a set of single objective sub-problems by the decomposition mechanism of the algorithm, and each sub-problem is solved by the improved discrete TLBO algorithm. According to the characteristics of the problem, TLBO algorithm is improved by discretization, and an individual update method is constructed based on probability fusion of various mutation evolution operators. At the same time, variable neighborhood descent search is introduced to enhance the local search ability. Based on the multi-level comparative experiment, the improvement measures and effectiveness of DMOTLBO are verified. Finally, combined with specific case analysis, the practicability and efficiency of the DMOTLBO algorithm in solving the multi-objective emergency logistics task planning problem of multiple UAVs are further verified.