3,724 research outputs found

    Who Wrote This? Detecting Artificial Intelligence–Generated Text from Human-Written Text

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    This article explores the impact of artificial intelligence (AI) on written compositions in education. The study examines participants’ accuracy in distinguishing between texts generated by humans and those produced by generative AI (GenAI). The study challenges the assumption that the listed author of a paper is the one who wrote it, which has implications for formal educational systems. If GenAI text becomes indistinguishable from human-generated text to a human instructor, marker, or grader, it raises concerns about the authenticity of submitted work. This is particularly relevant in post-secondary education, where academic papers are crucial in assessing students’ learning, application, and reflection. The study had 135 participants who were randomly presented with two passages in one session. The passages were on the topic of “How will technology change education?” and were placed into one of three pools based on the source of origin: written by researchers, generated by AI, and searched and copied from the internet. The study found that participants were able to identify human-generated texts with an accuracy rate of 63%. But with an accuracy of only 24% when the composition was AI-generated. However, the study also had limitations, such as limited sample size and an older predecessor of the current GenAI software. Overall, this study highlights the potential impact of AI on education and the need for further research to evaluate comparisons between AI-generated and human-generated text

    A Case Study of Princess Sumaya University for Technology (PSUT) Engineering Students’ Perceptions of Utilizing Simulation Software via Online Learning

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    The primary goal of this research has been to examine the perceptions related to the use of simulation software in the context of e-learning at Engineering PSUT in Jordan, which is acknowledged as one of the leading private universities in the country. The present study and a descriptive study utilized a 25-item survey given to 270 students. The research findings indicate that, according to the students’ subjective viewpoint, the effectiveness of simulation software in the context of online learning was observed to be significantly high. This observation is supported by an average score of 3.89 and a standard deviation of 0.959, indicating a relatively consistent perception among the participants. The study’s results indicate that there were no significant variations observed in terms of academic year, computer skills, student GPA or gender parameters. The research findings underscore the importance of incorporating simulation software in higher educational institutions to improve the teaching and learning experience

    On the Control of Microgrids Against Cyber-Attacks: A Review of Methods and Applications

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    Nowadays, the use of renewable generations, energy storage systems (ESSs) and microgrids (MGs) has been developed due to better controllability of distributed energy resources (DERs) as well as their cost-effective and emission-aware operation. The development of MGs as well as the use of hierarchical control has led to data transmission in the communication platform. As a result, the expansion of communication infrastructure has made MGs as cyber-physical systems (CPSs) vulnerable to cyber-attacks (CAs). Accordingly, prevention, detection and isolation of CAs during proper control of MGs is essential. In this paper, a comprehensive review on the control strategies of microgrids against CAs and its defense mechanisms has been done. The general structure of the paper is as follows: firstly, MGs operational conditions, i.e., the secure or insecure mode of the physical and cyber layers are investigated and the appropriate control to return to a safer mode are presented. Then, the common MGs communication system is described which is generally used for multi-agent systems (MASs). Also, classification of CAs in MGs has been reviewed. Afterwards, a comprehensive survey of available researches in the field of prevention, detection and isolation of CA and MG control against CA are summarized. Finally, future trends in this context are clarified

    Inverse Design of Metamaterials for Tailored Linear and Nonlinear Optical Responses Using Deep Learning

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    The conventional process for developing an optimal design for nonlinear optical responses is based on a trial-and-error approach that is largely inefficient and does not necessarily lead to an ideal result. Deep learning can automate this process and widen the realm of nonlinear geometries and devices. This research illustrates a deep learning framework used to create an optimal plasmonic design for metamaterials with specific desired optical responses, both linear and nonlinear. The algorithm can produce plasmonic patterns that can maximize second-harmonic nonlinear effects of a nonlinear metamaterial. A nanolaminate metamaterial is used as a nonlinear material, and a plasmonic patterns are fabricated on the prepared nanolaminate to demonstrate the validity and efficacy of the deep learning algorithm for second-harmonic generation. Photonic upconversion from the infrared regime to the visible spectrum can occur through sum-frequency generation. The deep learning algorithm was improved to optimize a nonlinear plasmonic metamaterial for sum-frequency generation. The framework was then further expanded using transfer learning to lessen computation resources required to optimize metamaterials for new design parameters. The deep learning architecture applied in this research can be expanded to other optical responses and drive the innovation of novel optical applications.Ph.D

    If a Machine Could Talk, We Would Not Understand It: Canadian Innovation and the Copyright Act’s TPM Interoperability Framework

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    This analysis examines the legal implications of technological protection measures (‘‘TPMs”) under Canada’s Copyright Act. Through embedded computing systems and proprietary interfaces, TPMs are being used by original equipment manufacturers (‘‘OEMs”) of agricultural equipment to preclude reverse engineering and follow-on innovation. This has anti-competitive effects on Canada’s ‘‘shortline” agricultural equipment industry, which produces add-on or peripheral equipment used with OEM machinery. This requires interoperability between the interfaces, data formats, and physical connectors, which are often the subject of TPM control. Exceptions under the Act have provided little assistance to the shortline industry. The research question posed by this analysis is: how does the CanadianCopyright Act’s protection for TPMs and its interoperability exception impact follow-on innovation in secondary markets? Canada’s protection for TPMs and its interoperability exception is inadequate for protecting follow-on innovation in relation to computerized machinery and embedded systems. This is due to the Act’s broad protection for TPMs, yet limited conceptualization of interoperability as a process that exists only between two ‘‘computer programs”. In legally protecting TPMs which safeguard uncopyrightable processes, data formats and interfaces, the Act’s interoperability exception fails to address the need to access subjects of TPM protection that extend beyond computer programs. This results in an asymmetry of protection and renders the interoperability exception inadequate. This article proposes enacting regulations under the Act to provide new exceptions and limitations to TPM protections which would enable shortline innovation. Both the Copyright Act and the Canada-United States-Mexico Agreement envision such additional TPM exceptions where the effect of protection has adverse effects on competition in a secondary market. In exploring a path forward for Canada’s shortline industry, the article then examines approaches taken in the United States and France to illustrate potential avenues for TPM regulation in Canada

    IoT-based Secure Data Transmission Prediction using Deep Learning Model in Cloud Computing

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    The security of Internet of Things (IoT) networks has become highly significant due to the growing number of IoT devices and the rise in data transfer across cloud networks. Here, we propose Generative Adversarial Networks (GANs) method for predicting secure data transmission in IoT-based systems using cloud computing. We evaluated our model’s attainment on the UNSW-NB15 dataset and contrasted it with other machine-learning (ML) methods, comprising decision trees (DT), random forests, and support vector machines (SVM). The outcomes demonstrate that our suggested GANs model performed better than expected in terms of precision, recall, F1 score, and area under the receiver operating characteristic curve (AUC-ROC). The GANs model generates a 98.07% accuracy rate for the testing dataset with a precision score of 98.45%, a recall score of 98.19%, an F1 score of 98.32%, and an AUC-ROC value of 0.998. These outcomes show how well our suggested GANs model predicts secure data transmission in cloud-based IoT-based systems, which is a crucial step in guaranteeing the confidentiality of IoT networks

    Privacy-preserving artificial intelligence in healthcare: Techniques and applications

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    There has been an increasing interest in translating artificial intelligence (AI) research into clinically-validated applications to improve the performance, capacity, and efficacy of healthcare services. Despite substantial research worldwide, very few AI-based applications have successfully made it to clinics. Key barriers to the widespread adoption of clinically validated AI applications include non-standardized medical records, limited availability of curated datasets, and stringent legal/ethical requirements to preserve patients' privacy. Therefore, there is a pressing need to improvise new data-sharing methods in the age of AI that preserve patient privacy while developing AI-based healthcare applications. In the literature, significant attention has been devoted to developing privacy-preserving techniques and overcoming the issues hampering AI adoption in an actual clinical environment. To this end, this study summarizes the state-of-the-art approaches for preserving privacy in AI-based healthcare applications. Prominent privacy-preserving techniques such as Federated Learning and Hybrid Techniques are elaborated along with potential privacy attacks, security challenges, and future directions. [Abstract copyright: Copyright © 2023 The Author(s). Published by Elsevier Ltd.. All rights reserved.

    Consensus Algorithms of Distributed Ledger Technology -- A Comprehensive Analysis

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    The most essential component of every Distributed Ledger Technology (DLT) is the Consensus Algorithm (CA), which enables users to reach a consensus in a decentralized and distributed manner. Numerous CA exist, but their viability for particular applications varies, making their trade-offs a crucial factor to consider when implementing DLT in a specific field. This article provided a comprehensive analysis of the various consensus algorithms used in distributed ledger technologies (DLT) and blockchain networks. We cover an extensive array of thirty consensus algorithms. Eleven attributes including hardware requirements, pre-trust level, tolerance level, and more, were used to generate a series of comparison tables evaluating these consensus algorithms. In addition, we discuss DLT classifications, the categories of certain consensus algorithms, and provide examples of authentication-focused and data-storage-focused DLTs. In addition, we analyze the pros and cons of particular consensus algorithms, such as Nominated Proof of Stake (NPoS), Bonded Proof of Stake (BPoS), and Avalanche. In conclusion, we discuss the applicability of these consensus algorithms to various Cyber Physical System (CPS) use cases, including supply chain management, intelligent transportation systems, and smart healthcare.Comment: 50 pages, 20 figure

    Doing Research. Wissenschaftspraktiken zwischen Positionierung und Suchanfrage

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    Forschung wird zunehmend aus Sicht ihrer Ergebnisse gedacht - nicht zuletzt aufgrund der UmwĂ€lzungen im System Wissensschaft. Der Band lenkt den Fokus jedoch auf diejenigen Prozesse, die Forschungsergebnisse erst ermöglichen und Wissenschaft konturieren. Dabei ist der Titel Doing Research als Verweis darauf zu verstehen, dass forschendes Handeln von spezifischen Positionierungen, partiellen Perspektiven und Suchbewegungen geformt ist. So knĂŒpfen alle Beitragenden auf reflexive Weise an ihre jeweiligen Forschungspraktiken an. Ausgangspunkt sind AbkĂŒrzungen - die vermeintlich kleinsten Einheiten wissenschaftlicher Aushandlung und VerstĂ€ndigung. Der in den Erziehungs-, Sozial-, Medien- und Kunstwissenschaften verankerte Band zeichnet ein vieldimensionales Bild gegenwĂ€rtigen Forschens mit transdisziplinĂ€ren AnknĂŒpfungspunkten zwischen DigitalitĂ€t und Bildung. (DIPF/Orig.

    Cloud Computing Services and Security Challenges: A Review

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    An architecture of computing that provides services over the internet on the demand and desires of users that pay for the accessible resources that are shared is refer as the cloud computing. These resources are shared over the cloud and users do not have to acquire them physically. Some of the shared resources are: software, hardware, networks, services, applications and servers. Almost every industry from hospitals to education is moving towards the cloud for storage of data because of managing the effective cost and time of organizing the resources physically on their space. Storage of data over the data centers provided in the form of clouds is the key service of the cloud computing. Users store their desired data on clouds that are publicly available over the internet and away from their boundaries in cost effective manner.  Therefore, techniques like encryption is used for obscuring the user’s information before uploading or storing to the shared cloud devices. The main aim of the techniques is to provide security to the data of users from unauthorized and malicious intrusions
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