Reviewerly/exHarmony huggingface dataset:
VIPS: Real-Time Perception Fusion for Infrastructure-Assisted Autonomous Driving

Infrastructure-assisted autonomous driving is an emerging paradigm that expects to significantly improve the driving safety of autonomous vehicles. The key enabling technology for this vision is to fuse LiDAR results from the roadside infrastructure and the vehicle to improve the vehicle's perception in real time. In this work, we propose VIPS, a novel lightweight system that can achieve decimeter-level and real-time (up to 100ms) perception fusion between driving vehicles and roadside infrastructure. The key idea of VIPS is to exploit highly efficient matching of graph structures that encode objects' lean representations as well as their relationships, such as locations, semantics, sizes, and spatial distribution. Moreover, by leveraging the tracked motion trajectories, VIPS can maintain the spatial and temporal consistency of the scene, which effectively mitigates the impact of asynchronous data frames and unpredictable communication/ compute delays.
Learning Web Programming: Identifying Complex Topics from Student Discussion Forums and Lecture Slides

Designing and delivering web application courses for computing undergraduates is a challenging task. Lack of understanding of web concepts affects the students” interest in web application development. Therefore., faculty employ several traditional strategies including hands on exercises and labs as well as innovative strategies such as videos and discussion forums. However., due to the volume of the posts in the forums., the instructors find it challenging to attend to the students' challenges and focus on more challenging topics across the classroom. In this paper., we propose a text mining based solution to extract the questions and complex topics. We evaluated the solution on the year 2 Web Application course offered to the computing undergraduates. Our experiments show that logistic regression model performs better in classifying question posts and cosine similarity performs better in assigning the topic label to the posts. The findings are visually depicted and are useful to the faculty to identify the topics that requires more attention to improve students' learning.
AWS DeepRacer: A Way to Understand and Apply the Reinforcement Learning Methods

Reinforcement Learning (RL) is an area of Machine Learning (ML) that takes care of what decisions are better to make with no prior information; it creates its own datasets via reward shaping. It has taken importance in the last years in the industrial field since it is a significant tool to make decisions under uncertainty. However, there is a lack of research in RL projects that contribute to develop innovative education in the professional education context. Therefore, in this work, a possible contribution of the AWS DeepRacer models to the autonomous driving for handicapped people is briefly presented in a theoretical way. Besides, through RL, three time-trial models were developed using Amazon Web Services (AWS) (AWS DeepRacer and AWS Console). These three models were developed for the first stage of the study, where there are no obstacles for the car. The resultant performance of each model is presented and discussed. Lastly, for future work three extra stages of the work are proposed: field tests of the presented models, static obstacles reward function design, and dynamic obstacles reward function development.
Utilizing Augmented Reality to Enhance Twenty-First Century Skills in Chemistry Education

Numerous studies have shown that mobile devices like smartphones play a significant role in education these days, and it's clear that the influence and benefits of these devices regarding the potential for pedagogical perspectives are obvious. Mobile learning allows for flexibility by eliminating the need for learning to happen at a particular time and place. Moreover, mobile devices that can support Augmented Reality (AR) are becoming more powerful, less expensive and quite useful in the education process. In this paper, we conduct a study where mobile AR application alongside project-based learning is utilized in a classroom setting, where interviews are administered to understand the K-12 students' attitude towards the educational model based on mobile AR for enhancing the twenty-first century skills in chemistry education. The interviews are conducted with thirty students and their teacher who teaches them chemistry course. Four skills are investigated and discussed, i.e., innovation and creativity, critical skills and problem solving, communication and collaboration, and information culture. Based on the research findings, some recommendations are provided. Furthermore, in the scope of Agenda 2030 for Palestine, we believe these findings could serve as a basis for developing practical policy interventions attributed to enhancing the educational system with emerging technologies.
Augmented Reality for Programming Teaching: An Exploratory Study

The growing adoption of instructional technology in education has been driven by its contribution to society's development. Our study aimed to examine the use of Augmented Reality (AR) in teaching Python programming principles. The study consisted of three phases: a systematic review of AR using the PRISMA statement, the development of a mobile AR application called “EDUpy,” and the evaluation of EDUpy through two experiences with undergraduate students enrolled in different course units. The study found 30 studies in literature focused on the introduction of AR in educational contexts, ranging from kindergarten to higher education. Additionally, 57 students were enrolled in the experiences and the results showed that EDUpy was suitable in terms of user experience and effective in achieving students' learning goals. Further research is necessary to determine the impact of the application on students' soft skills, such as creativity, engagement, motivation, teamwork, and self-learning.
Preliminary Study for Identifying Personnel for Innovative Software Engineering Project

One of the factors that lead to a successful project is the team dynamics in a software project. A lot of study concerns the method of achieving successful projects but is not specific to innovative ones. Innovative projects relate to new solutions and inventions. The specific skills set of successful team dynamics are not specific for innovative projects. This study aims to identify innovative personnel and recommend team member combinations for successful innovative projects. Design thinking methodology is proposed for software project success and also innovative team building for innovative software projects. This method analyzes an incremental of data to create an interactive model that can identify innovative personnel. The output of this study is integrating innovative skills for the software engineering team. The findings of this study are expected to help evaluate and improve the software engineer's skills., identify the right person of the company's recruitment process and reduce the software projects failure.
Flipped classroom as a learning strategy in reading comprehension: a case study of higher education

This research carries out a study on the inverted class as a learning strategy in the reading comprehension of university students; taking into consideration that the currently inverted class as a didactic strategy promotes significant learning in students. The objective of this research is to analyze the use of the flipped classroom to improve reading comprehension in the English language in university students. The research is of an experimental nature through a quantitative and qualitative information approach, this allows the data obtained to be classified according to the perceptions of the study population and then planned, and then tabulated to measure the trend they had and therefore, the ADDIE methodology is applied for the development of the gamification resources that were used in the development of the teaching-learning process of the English classes; As a technique, the survey was applied both in the pre-test and in the post-test; Likewise, the technology acceptance model was applied in order to know if the resources applied in the flipped classroom were the most appropriate, as well as the investigation by means of testing the hypothesis that the flipped classroom as a learning strategy improves reading comprehension in university students in the area of English. The results obtained in this experimentation with third-level English students were quite positive, where it was possible to show that when applying the flipped classroom, as well as the gamification resources in synchronous and asynchronous classes, creativity, motivation and collaborative work in the reading comprehension of this language. Finally, it is concluded that the flipped classroom as a learning strategy is directly related to the new educational models that can be used in all fields of knowledge; which allows for breaking the digital gap that exists; Likewise, the student can study according to her learning style; at his own pace, mode, and time that he deems necessary for the generation of knowledge.
Flipped classroom methodology as a teaching strategy in education 4.0

The flipped classroom methodology as a learning strategy in education 4.0 is considered one of the learning systems where students must study what corresponds to each subject, all this in advance of the synchronous or face-to-face class through videos in which a wide variety of concepts are exposed. This research is experimental-exploratory where the learning results were evaluated through the resources assigned in the Moodle platform for the students of the third-level Web Application Development career; the resources were created using the PADDIE M+ model, which focuses on the correct application of ICT with pedagogy, and PACIE was applied to the Moodle platform as a development methodology in flipped classroom learning; the approach applied was by parts; starting with the measurement of perceptions regarding the use of digital and technological materials and resources; Finally, a survey is applied where the acceptance of these technologies can be identified through the TAM model; In the same way, the student's qualifications were taken as precedents by not applying the digital resources and finally the advances obtained through the application of the same were evidenced. As result, it was evidenced that the students improved their grades by correctly applying the digital resources within the virtual classroom (Moodle), as well as with the structure through PACIE on the Moodle platform; in the same way, the aspects related to the acceptance of the technology were taken into consideration, both positive and negative aspects; through the instruments applied and the results obtained, improvements were made in the activities for a better understanding of the learning that students must acquire in virtual classes.
PICA-PICA: Exploring a Customisable Smart STEAM Educational Approach via a Smooth Combination of Programming, Engineering and Art

The STEAM approach in education has been gaining increasing popularity over the last decade. This is due to its potential in enhancing students' learning, when teaching arts and scientific disciplines together. This paper introduces the PICA-PICA concept, where we aim to develop a smart customisable environment, combining, in a unique way, teaching programming in conjunction with the engineering of artworks. The PICA-PICA concept was implemented, used and tested in real-life, by upper primary school students in Japan, during a 4-day workshop. Initial results illustrated the quality of the solution proposed by PICA-PICA. We noted that the integration was perceived as smooth, and not contrived: all participants understood how to use the PICA-PICA environment to engineer programmable art objects. Furthermore, the PICA-PICA approach led to high motivation: children did not get bored and were fully engaged. Finally, the quality of their work as a learning outcome was high: by including a programming segment with the other expressive activities in the artwork, the children were able to design the electronics in a more concentrated and meaningful way than their curriculum-structured learning. This study also presents an innovative implementation of the STEAM approach using Micro:bits technology to create exciting artwork whilst using household recyclable items, which also teaches about sustainability. The involvement of parents and their interest in learning is another unique aspect of this study.
What is the optimal blended learning strategy throughout engineering curricula? Lesson learned during Covid-19 pandemic

The recent Covid-19 pandemic has forced HEI to quickly adjust their approach to teaching and learning by massively adopting, basically overnight, approaches based on digital learning. Covid-19 gave a huge impulse to the spread of digital tools and the consequent literature is rich in details and information about more and less successful experiences with digital learning. This unexpected “experiment” exposed the effectiveness and efficiency of well-conceived learning strategies that blend digital and traditional learning approach. While blended learning per se is an established discipline, a unified framework to go from theory to practice is still elusive. Given the above, the knowledge developed and acquired during the pandemic has a huge potential to reveal what are the features of a good blended learning strategy in different educational situation. This work aims at investigating what would be an optimal blended learning strategy for program design in the context of engineering education. The contribution is based on a focus group discussion involving teacher from 6 different European HEI and results indicates that the level of understanding, as presented in the Bloom taxonomy, of the focal activity and the correct balance between the educational and social dimensions of student's university life are the two critical drivers for blended learning design. The study confirms also the main findings in literature, related to the use of digital tools before and during the pandemic and suggests that the Covid-19 had a positive impact on the readiness to adopt digital tool of HEI. Finally a few problems experienced in the post covid phase are presented along with the identified future directions of research.
Improving the learning effectiveness of educational videos

This paper presents a tool that improves the learning effectiveness of educational/instructional videos by automatically generating an hyperlinked multimedia structure created by 1) analyzing the audio stream to extract a textual description; 2) carrying out semantic analysis to identify the main subjects addressed; 3) identifying keywords and keyframes, and creating a navigational structure that allows the viewers to consume the content in a non-linear and multimodal manner and jump directly into those parts they are mostly interested to watch. The tool performs transformation and automatic curation of any video, allowing the viewers to explore its content in a non-linear form. It can be applied to any type of videos, but it is of particular value in the educational sector, where videos are frequently viewed in parts, and revisited frequently to recall specific aspects of a lecture/topic.
MyOracle: A Question-Answering application to improve access to information for low-literacy users

To bridge the information gap in low-income countries, a critical issue is of being able to provide easily accessible information to low-literacy users. In particular, facilitating access to Science, Technology, Engineering and Mathematics (STEM) related information is crucial to bolster curiosity and accompany the democratization of engineering education. In order to tackle this challenge, we introduce MyOracle, a question-answering application focusing on STEM related questions and which is tailored to low-literacy users. MyOracle achieves this using three steps: extracting answers from the web, curating these answers and then summarizing and translating the resulting text. Finally we study and compare the disparities in content availability between nine languages with varying number of speakers and discuss the trade-offs between a translation-based approach and a native approach for lower-resource languages.
Towards unsupervised keyphrase extraction via an autoregressive approach

Keyphrase extraction is a technique used to capture the core information of documents and is an upstream task for advanced information retrieval systems, particularly in the academic realm. Current unsupervised methods are primarily built on a score-and-rank framework with a consistent inability to acquire mutual information between extracted keyphrases, especially with graph-based models. Utilizing the autoregressive structure that is typically used in sequence-to-sequence text generation models, we propose a plug-and-play optimizer named C-Decay that can be integrated into any graph-based unsupervised keyphrase extraction model for a stable performance boost, and that mitigates the bias of certain semantically or lexically dominant tokens by optimizing the origin score distribution output by graph-based models directly. The architecture of C-Decay includes the keyphrase pool, the gain vector and the decay factor, where the keyphrase pool is designed to realize an autoregressive structure and the gain vector and the decay factor are the optimization operator. Herein, we examine three graph-based models integrated with C-Decay, and the experiment is conducted on four datasets KDD, Semeval, Nguyen, and Krapivin. Moreover, we prove that C-Decay can improve accuracy and F-Measure by an average of approximately 50% and 20%, respectively.
A Novel Framework for Securing ECDH Encrypted DICOM Pixel Data Stored Over Cloud Using IPFS

The future holds the possibility of hospitals sharing medical images obtained through non-invasive systems to patients remotely. The advent of cloud and the storage and deployment of medical healthcare images in the cloud has resulted in the increased need for application of Cryptographic techniques to protect them from unauthorized access and malicious attacks. The Digital Imaging and Communication in Medicine (DICOM) standard is more compatible across medical imaging instruments globally. The pixel data of DICOM images requires more privacy and security. A novel ECDS based cryptographic approach is suggested to encrypt the original DICOM image as well as the ROI pixel data extracted from DICOM images. Results computed experimentally have proved that medical image encryption via ECDH is more robust, efficient and faster than existing medical image encryption schemes.
Does Digital Transformation Accelerate the Acceptance of Social CRM on the Small and Medium Enterprises?

Due to the rapid spread of COVID-19 worldwide, businesses that have been impacted by the pandemic are relying more on digital technologies to adapt to the current situation, where contactless interactions are becoming more common. With the decrease in face-to-face interactions, customers are using social media more frequently, and companies are utilizing social CRM(sCRM) to maintain relationships with customers through social media. Thus, This study aims to examine the intention of employees to use sCRM in a pandemic situation among small and medium-sized enterprises(SMEs) by utilizing the Technology Acceptance Model(TAM), which includes perceived ease of use and perceived usefulness, as well as four additional factors: cost reduction, compatibility, awareness of COVID-19, and digital transformation. Data were collected from employees who had experience in sCRM, and analyzed using structural equation models. As a result of the study, the four factors positively impacted the perceived usefulness and ease of use of sCRM, which significantly influenced employees’ intention to use sCRM. This finding is particularly relevant in the current COVID-19 era, where there is a preference for non-face-to-face communication and digital transformation is rapidly accelerating.
Metastatic Breast Cancer Detection Using Deep Learning Algorithms: A Systematic Review

Breast cancer (BC) is a pervasive issue that leads to countless fatalities among women worldwide, and metastatic breast cancer is responsible for most of these deaths. Early detection of metastatic BC is essential for improving patient outcomes and increasing survival rates. There have been a lot of earlier Machine Learning (ML)-based investigations. Decision trees (DT), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Naive Bayes (NB), and other ML procedures achieve better in their corresponding fields. However, current methods for detecting metastatic BC can be time-consuming, invasive, and costly. Recently, deep learning (DL) algorithms have shown great potential in improving the accuracy and efficiency of BC detection. This paper delivers an inclusive systematic review (SR) of the existing research on using DL algorithms for metastatic BC detection. The article highlights the potential of DL algorithms in improving BC detection and the challenges associated with their use. Future research should address these challenges to improve the clinical utility of DL algorithms for metastatic BC detection.
Comparing Core Consensus mechanisms for Education Blockchains

Blockchain being a decentralized system relies on verifying transactions via a majority of nodes on the network. This verification is what validates the creation of blocks on the blockchain. The verification is done via a concept called consensus. This paper discusses the core consensus mechanisms used in different blockchain networks that apply to education. They include Proof of Work (PoW), Proof of Stake (PoS) and Delegated Proof of Stake (DPoS). The paper outlines the concepts involved, the challenges that the mechanisms pose to the network and the overall efficiency of the system when used in educational activities. This paper notes the consensus challenges that directly affect academic activities and proposes possible solutions that could be imbibed for a more efficient system. It endorses DPoS as the consensus mechanism of choice for blockchains in the educational system.
Development of a Machine Learning Model For Big Data Analytics

The aim of the study was to utilize machine learning approaches in the development of a big data analytics model that can effectively discover patterns and aid in making intelligent decisions. The impact of Big Data on scientific discoveries and technology in Big Data analytics cannot be overstated. In this regard, the study focused on the development of a system to control incoming illegal messages from intruders/unknown users, in order to alleviate illicit contextual communication channels. Unstructured data sets were collected from online blogs and labeled to suit the needs of the machine learning algorithm. The data was preprocessed using Weka machine learning libraries and converted into attribute related file form (arff). The resampling technique was used to partition the data into 80% training set and 20% testing set, which was used to develop the big data model and identify illicit message content to prevent people from being victimized. The Na ï ve Bayes machine learning algorithm was utilized to predict the categorized labels as binary values. The results of the study showed that Naïve Bayes had an accuracy of 96.76% for metrics evaluation of true precision model. It is recommended that the application of other machine learning techniques to the classification model would improve its performance.