Reviewerly/exHarmony huggingface dataset:
Knowledge sharing between elementary teacher education students in online discussion

In online discussion activities, there are knowledge-sharing activities in which the factors are trust, equality, intrinsic motivation, and the use of technology. This type of research is descriptive quantitative. Here the researcher wants to explain the factors that influence knowledge-sharing activities among elementary teacher education students in online discussions. The sample of this study was 221 elementary teacher education students in Sumatra, Java, Kalimantan, and Sulawesi. Data analysis technique applied Multiple Linear Regression analysis techniques by using SPSS software version 21.0 with a significance level of 0.05. The results of this study indicated that similarity and technology use are factors that greatly influence knowledge-sharing activities, while trust and intrinsic motivation factors are factors that do not significantly affect knowledge-sharing activities. Equality factors such as age, background, and gender provide a major role in the success of knowledge-sharing activities. In order to support knowledge-sharing activities among elementary teacher education students to be effective and efficient, it is necessary to use technology with supportive frequency use.
Client-specific Property Inference against Secure Aggregation in Federated Learning

Federated learning has become a widely used paradigm for collaboratively training a common model among different participants with the help of a central server that coordinates the training. Although only the model parameters or other model updates are exchanged during the federated training instead of the participant's data, many attacks have shown that it is still possible to infer sensitive information or to reconstruct participant data. Although differential privacy is considered an effective solution to protect against privacy attacks, it is also criticized for its negative effect on utility. Another possible defense is to use secure aggregation, which allows the server to only access the aggregated update instead of each individual one, and it is often more appealing because it does not degrade the model quality. However, combining only the aggregated updates, which are generated by a different composition of clients in every round, may still allow the inference of some client-specific information.
Gamification and virtual reality immersionin the education of future engineersin the AEC sector

The article deals with issues in the field of increasing the degree of user involvement in the learning process, using the example of the use of virtual reality and gaming tools. The research was conducted on the example of a course for future engineers in the building sector on clash detection in Building Information Modelling. In the course, the educational content was presented through a virtual world, and it was possible to explore by an avatar controlled by students from a third-person perspective, as in computer games. This was met with an exuberant response from the students and consequently led to an increase in their involvement in performing the prepared educational tasks. The collected experience can also be used in commercial presentations of designed buildings.
UNFC for Geothermal Resources Classification in the SICA Region. Case Studies: El Salvador and Costa Rica

Preview this conference paper: UNFC for Geothermal Resources Classification in the SICA Region. Case Studies: El Salvador and Costa Rica, Page 1 of 1 < Previous page | Next page > /docserver/preview/fulltext/2214-4609/2023/second-eage-workshop-on-geothermal-in-latin-america/UNFC_AbstractICELaGeo-24-70-Torio_Henriquez-Elizabeth-1.gif
Deep Reinforcement Learning

Deep Reinforcement Learning (DRL) is a powerful technique for learning policies for complex decision-making tasks. In this paper, we provide an overview of DRL, including its basic components, key algorithms and techniques, and applications in areas s.a. robotics, game playing, and autonomous driving. We also discuss some of the challenges and limitations of DRL, s.a. sample inefficiency and safety concerns, and we identify some of the promising directions for future research in DRL, s.a. meta-learning, hierarchical reinforcement learning, and combining DRL with formal techniques. In the second part of the paper, we discuss several important applications of DRL, including transfer learning, multi-agent reinforcement learning, and explainable reinforcement learning. We also explore the combination of DRL with formal techniques, a promising area of research for ensuring the safety and reliability of DRL applications. Finally, we identify some of the limitations and open issues in DRL, including sample efficiency, safety, and scalability concerns. To help practitioners effectively apply DRL in their work, we provide recommendations for starting with simple problems, choosing appropriate algorithms and architectures, paying attention to safety and ethics, collaborating with experts, and staying up to date with the latest research in the field. We conclude by highlighting the potential impact of DRL in a wide range of applications and emphasizing the need for careful consideration of the ethical and societal implications of DRL.
Function Locking Fused with Hashing Technique to Mitigate Re-entrancy Attacks in Smart Contract

Reentrancy attacks have been a significant threat to smart contracts on blockchain platforms. This attack allows an attacker to repeatedly call a vulnerable function in a contract, leading to unexpected behavior and loss of funds. In this research paper, we propose a preventive approach to mitigate the reentrancy attack by introducing a locking mechanism. Our proposed method involves the use of a lock that prevents a contract from being called again until the previous execution is completed. We implement and evaluate our proposed approach on the Ethereum blockchain platform using various benchmarks. Our results show that our approach effectively prevents reentrancy attacks while incurring a low overhead cost. Our proposed approach can be integrated into smart contract development to enhance security and protect against reentrancy attacks by implementing the Function locking technique and Verifying by hashing technique.
Sentiment Classification for IMDB Movie Reviews in Benchmark Dataset Using LR, MNB and SGD

A breakdown of all movie reviews can help viewers decide by saving them the time it would take to read all of the reviews. Critics commonly use movie-rating websites to submit comments and rate films, so guiding viewers in determining whether or not to see the film. Sentiment analysis was used to determine reviewers' attitudes based on their opinions. Sentiment analysis of a movie review can help assess how positive or negative a review is, and thus the film's overall rating. In this research, the sentiment classification methods LR, MNB, and SGD are suggested for a big movie review data set. We chose 50K IMDB movie review entries that are entirely written in English, with 25K positive and 25K negative ratings. The results show that the Multinomial Naive Bayes (MNB) algorithm outperforms other classification algorithms in terms of mistake rate and accuracy.
Real-time Multiple object detection Network model based on YOLOV4

Deep learning produced very good results for object detection, however, it has still many problems with photographs from real photography, like noise, blurring, etc. Object detection is significantly impacted by these issues. Using the You Only Look Once (YOLO) strategy, the main goal is to find objects. When compared with the remaining object detection method, the YOLO approach provides a number of benefits. YOLO is a two-stage detector. The algorithms, like C Neural Networks, a n d F-R Convolution Neural Network, do not fully examine the images, but YOLO does so the bounding boxes predicating The image more quickly by using a CNN, determining the probabilities of class for these boxes, and identifying images. By using this algorithm, we were able to identify various types of items. This research proposes a trainable framework used to detect real-time images and videos. We are utilizing a YOLO-based detection method. The resulting system is quick and precise, helping applications that need object detection. The proposed model performs better in object detection than other models.
A QoS Based Resource Allocation and Optimization in a Multi Layer Cloud Computing Environment

Cloud computing is one of the emerging platforms in IT industries, mobile computing applications and marketing. Computing resources like network, servers, applications, storage and services are utilized and charged based on usage rather than purchasing it. Today, the number of cloud users has drastically increased due to its reliability, reduced cost and performance. Quality of Service (QoS) delivery and resource usage optimization has been the most important requirements of the cloud users and the cloud service providers. Increase in the number of cloud users, QoS requirements and resource usage optimization has necessitated the need for efficient resource allocation techniques in the dynamic cloud environment. This paper presents a novel resource allocation approach to enhance QoS delivery and resource usage optimization. Cloud resources are partitioned into three layers: computation intensive layer, data intensive layer and hybrid intensive layer. A central scheduler selects a layer for the execution of incoming task and a layer scheduler selects a resource to serve the task. The number of task allocated to cloud resource is in proportion to the computing power, memory and transmission rate of the resource. Cloud resource with high computing power receives more number of tasks to process and a low computing power resource receives little number of tasks. The proposed approach is evaluated using CloudSim simulation tool and the experimental result proves that the proposed approach is more efficient than the existing Minimum Completion Time (MCT) and Online Potential Finish Time (OPFT) in terms of response time, QoS delivery and resource utilization.
Sarcasm Detection in Telugu and Tamil: An Exploration of Machine Learning and Deep Neural Networks

Sarcasm is a language phenomenon usually employed to express negative sentiments in a humorous or critical manner. Although sarcasm is a widely used communication tool, it is often challenging to comprehend. The existing approaches for detecting sarcastic sentiments are mainly designed for English language text, and the study of low-resource languages has gained significant attention among scholars. Hence, this research aims to identify sarcasm in Twitter posts that include sentences in Tamil and Telugu languages. This paper presents the first effort to create two distinct datasets for sarcasm detection in Telugu and Tamil languages by scraping tweets from Twitter, thereby addressing the lack of such resources in these languages. To analyze sarcasm in Tamil and Telugu tweets, we proposed a variety of methodologies. These included machine learning models like Decision Tree, Naive Bayes, Logistic Regression, Support Vector Machine, and Random Forest as well as sophisticated neural network techniques like Convolutional Neural Networks and Long Short-Term Memory. Our findings show that the Telugu and Tamil datasets achieved an accuracy rate of 95.68% and 95.37%. The results provide a better understanding of the use of sarcasm in low-resource languages and offer a foundation for further research in this field. This research paper contributes to the field of natural language processing by demonstrating the effectiveness of the proposed methodologies in identifying sarcasm in Tamil and Telugu tweets.
Analysis of the smoothing and payload distribution method on reversible audio steganography

Along with the rampant theft of confidential data, technological security systems are the most important thing in the digitalization era. Data cannot be said to be safe from third parties, even though it is private or confidential when transmitted through the public network. Several studies have proposed methods of hiding data using steganography to improve data confidentiality. However, it still has quality and capacity results that are relatively inversely proportional. Therefore, it is necessary to analyze several methods to determine which can produce the best capacity and quality. The focus of this research is to seek an understanding of some recently proposed methods concerned with the smoothing and payload distribution on reversible audio steganography and provide an overview of methods suitable for use in steganography with maximum capacity and quality results.
Two-way Communication: An Integrated System for American Sign Language Recognition and Speech-to-Text Translation

Sign language serves as a means of communication for individuals who are deaf or have hearing impairments, enabling them to interact within their own community and with people from other backgrounds. It is a visual language that expresses ideas using body language, hand gestures, and facial expressions to convey meaning. There are over 300 different sign languages used throughout the world, each with its own distinct characteristics and regional variations. American Sign Language (ASL) has been used in this project. Many individuals do not know sign language. Hence, real-time sign language detection is one of the potential applications for overcoming the communication barrier between such people and the deaf community. Computer vision-based detection of sign language ranges from recognition of sign gesture to text and speech generation. In this paper, we attempt to create a system that establishes two-way communication between someone who signs using ASL, a deaf or hard-of-hearing individual (the signer) and someone who cannot sign (the non-signer) using computer vision. The system consists of gesture recognition using computer vision, text-to-speech conversion, and speech-to-text conversion. The system is an application of human-computer interaction which consists of gesture recognition.
Blockchain-based Patient-Centric Healthcare Architecture: A Secure and Efficient Approach for Medical Data Sharing

Healthcare data is increasing in amount and complexity with the advent of technological advancements like wearable devices are capturing continuous health data to track chronic diseases such as diabetes. Trust-less centralized servers manage and control such data, raising concerns on privacy and security. A decentralized, permissioned blockchain-based approach can potentially give patients more control over medical data. In this paper, a blockchain-based patient-centric healthcare architecture is proposed to address the concerns of privacy, interoperability, data fragmentation, data ownership by centralized servers, data integrity, data access and data sharing. Patients’ diabetic data can only be accessed as per their agreed-upon access policy given their consent which is implemented using Ethereum smart contracts to ensure controlled exchange of sensitive data among various stakeholders. The smart contracts were tested in Truffle to validate the accuracy of their logic. A hybrid model is employed for data storage, that combines MongoDB, as the off-chain secure database and IPFS technologies for on-chain storage. The architecture is designed to be integrated with existing Electronic Health Records (EHRs) systems, promoting efficient and secure data sharing in the healthcare industry.
SafeSteps: A mobile app for monitoring and tracking the elderly with Alzheimer's using an IoT device

A considerable population of older adults in Peru suffers from Alzheimer's disease, which causes progressive degradation of cognitive abilities, high dependency, and other symptoms that result in reduced quality of life. Additionally, memory loss and disorientation account for half of the cases of disappearance of older adults, especially when family members do not have the time or resources to provide care for the elderly. Therefore, an IoT architecture and a real-time monitoring and control system for older adults with Alzheimer's disease is proposed, using an IoT device connected to the mobile devices of family members or caregivers. The proposed system was implemented in five layers: capture, communication, business logic, storage, and presentation. It consists of two applications: a smartwatch and a smartphone. Validation tests were conducted with caregivers and health experts in various districts of Lima Metropolitana, Peru. The results demonstrate that the proposal has high accuracy (100%) and a fast response time (5.6 s) for notifying the caregiver of exceeded limits. The survey results from health experts and caregivers showed an average rating of 4.6 (Completely agree), 4.4 (Agree), and 4.6 (Completely agree) in terms of "functionality," "efficiency," and "usability," respectively.
Human Detection in crowd using One-stage and Two-stage object detection models

Object detection has various uses for recognition, detection, and tracking. Detecting humans in crowded low-quality images is an emerging topic and is a difficult task. In this paper, different object detection are compared for our purpose. Here we use the two models - YOLO and Faster R-CNN and test their working for human detection on low-quality images. We take a low-quality image collection and utilize pre-processing techniques to improve its quality. Using the same experimental setting, we build YOLO and Faster R-CNN and evaluate their results based on accuracy and speed. After comparing the results it shows that in terms of detection speed, YOLO outperforms Faster R-CNN. And YOLO lags than Faster R-CNN when it comes to accuracy. Further we also discuss the advantages and disadvantages of using these models and which is best suited for the scenario we intend to work on. Our findings give useful insights for the development of low-quality image object detection systems for accurate human detection in crowded images, with practical implications for a variety of applications.
Ensemble machine-learning model for solar radiation prediction using explainable AI

Solar radiation prediction is crucial in many contexts, including solar energy production, climate and weather research, and agricultural planning. Accurate and timely solar radiation estimation opens up opportunities for effective solar energy planning and optimization. Even though a number of machine learning algorithms have been put forth in the literature to predict solar radiation, explainable AI (XAI) has not yet been thoroughly investigated. In this study, we are focusing on filling some research gaps in the performance analysis of ensemble methods for predicting solar radiation based on explainable AI. The research aims to achieve a balance between model interpretability and prediction accuracy by exploring ensemble techniques that go beyond conventional approaches. Additionally, by exploring ensemble-based methods and incorporating explainable AI techniques, we intend to improve prediction accuracy while providing interpretable insights for decision-making in solar energy applications. The results have been reported in terms of R2 Score, mean absolute error (MAE), and root mean squared error (RMSE).
SatelliteSkill5—An Augmented Reality Educational Experience Teaching Remote Sensing through the UN Sustainable Development Goals

Advances in visualisation techniques provide new ways for us to explore how we introduce complex topics like remote sensing to non-specialist audiences. Taking inspiration from the popularity of augmented reality (AR) apps, a free, mobile digital AR app titled SatelliteSkill5, has been developed for both Androids and iPhones in Unity AR. SatelliteSkill5 helps users conceptualise remote sensing (RS) theory and technology by showcasing the potential of datasets such as multispectral images, SAR backscatter, drone orthophotography, and bathymetric LIDAR for tackling real-world challenges, with examples tackling many of the United Nations’ Sustainable Development Goals (SDGs) as the focus. Leveraging tried and tested pedagogic practices such as active learning, game-based learning, and targeting cross-curricular topics, SatelliteSkill5 introduces users to many of the fundamental geospatial data themes identified by the UN as essential for meeting the SDGs, imparting users with a familiarity of concepts such as land cover, elevation, land parcels, bathymetry, and soil. The SatelliteSkill5 app was piloted in 12 Irish schools during 2021 and 2022 and with 861 students ranging from 12 to 18 years old. This research shows that both students and teachers value learning in an easy-to-use AR environment and that SDGs help users to better understand complex remote sensing theory.
A Multi‐Class Quantum Kernel‐Based Classifier

Abstract Multi‐class classification problems are fundamental in many varied domains in research and industry. A popular strategy for solving multi‐class classification problems involves first transforming the problem into many binary classification problems. However, this requires the number of binary classification models that need to be developed to grow with the number of classes. Recent work in quantum machine learning has seen the development of multi‐class quantum classifiers that circumvent this growth by learning a mapping between the data and a set of label states. This work presents the first multi‐class SWAP‐Test classifier inspired by its binary predecessor and the use of label states in recent work. With this classifier, the cost of developing multiple models is avoided. In contrast to previous work, the number of qubits required, the measurement strategy, and the topology of the circuits used is invariant to the number of classes. In addition, unlike other architectures for multi‐class quantum classifiers, the state reconstruction of a single qubit yields sufficient information for multi‐class classification tasks. Both analytical results and numerical simulations show that this classifier is not only effective when applied to diverse classification problems but also robust to certain conditions of noise.
Quantification of Entanglement

Quantum entanglement, a fundamental concept in quantum physics, has been elucidated through the development of Bell’s inequalities. Recent advancements have enabled controlled manipulation and measurement of entangled quantum states. This chapter provides a concise overview of entanglement’s mathematical underpinnings, its manipulation, and quantification. Special attention is given to quantification methods and their implications in quantum information. Key principles and measures are presented to facilitate a foundational understanding. Readers are encouraged to supplement this overview with comprehensive review articles and primary literature for further insights.