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9th World Engineering Education Forum, WEEF 2019

作者： Bhat, Shreeranga Bhat, Sathyendra Raju, Ragesh D'Souza, Rio Binu, K.G. Department of Mechanical Engineering St. Joseph Engineering College Mangaluru575028 India Department of Computer Applications St. Joseph Engineering College Mangaluru575028 India Department of Computer Science and Engineering St. Joseph Engineering College Mangaluru575028 India

Background, Motivation, and Objective - Inculcating critical thinking, teamwork, and lifelong learning are the basic tenets of Outcome Based Engineering Education (OBEE). Collaborative learning is one such pedagogy of engagement to achieve the same. In addition, the implementation of OBEE demands the adoption of corporate best practices in deploying new initiatives, which streamlines the process, removes waste in the process, and ensures sustainment of the improved process. Lean Thinking is one such approach adopted by the industry to ensure productivity, performance, and sustainment of new initiatives. The objective of this paper is to study the scope of collaborative learning in OBEE based on the Lean Thinking approach, and to explore its successful deployment in Indian higher education. Statement of Contribution/Methods - The research reported in this paper is based on Action Research methodology carried out using the Lean Thinking PDCA (Plan-Do-Check-Act) approach and its application in improving OBEE through collaborative learning in India. STAD (Student-Teams-Achievement-Division) collaborative learning was deployed through heterogeneous group formation for enhancing the teaching-learning process. The Indo-Universal Collaboration on Engineering Education (IUCEE) consortium knowledge base and assistance has been effectively used to chalk out the strategy for effective deployment. Basic statistics and outlier analysis is used to draw the inferences. Results, Discussion, and Conclusions - The study describes the effective deployment of collaborative learning to enhance the effectiveness of the OBEE ecosystem through students? performance by utilizing the power of Lean Thinking. The study shows that not only Turn-Around-Time (TAT) of the deployment process is enhanced, but also the attainment of the students? performance. Moreover, it improves team spirit and team performance during the process. The paper is based on a single case study executed in an engineering

关键词： Engineering education

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Digital Twin Definition for Detecting Abnormal Perspiration Patterns in High-Stress Professions

Digital Twin Definition for Detecting Abnormal Perspiration ...

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Roedunet International Conference (RoEduNet)

作者： Cristian Vizitiu Ioan Turcin Kevin Alexander Dominey Selver Softic Adrian-Cătălin Dinculescu Vlad I. Bocăneţ Cosmin Dugan Sorin-Aurel Moraru The Space Applications and Technologies Laboratory Institute of Space Science -Subsidiary of INFLPR Magurele Romania Department of Automatics and Information Technology Faculty of Electrical Engineering and Computer Science Transilvania University of Brasov Brasov Romania Department Automation Technology CAMPUS 02 University of Applied Sciences Graz Austria Department IT & Business Informatics CAMPUS 02 University of Applied Sciences Graz Austria Department of Manufacturing Engineering Faculty of Industrial Engineering Robotics and Production Management Technical University of Cluj-Napoca Cluj-Napoca Romania Bucharest University Emergency Hospital Bucharest Romania

ISBN: (数字)9798331540388

ISBN: (纸本)9798331540395

Perspiration is a physiological response in high-stress situations, that also plays a key role in thermoregulation and stress management. Understanding perspiration patterns is used for assessing physiological responses and optimizing human performance especially in professions such as emergency medical services and military operations, as well as for individuals exposed to Isolated Confined Extreme (ICE) environments such as astronauts and space analog mission participants. This paper defines a Digital Twin (DT) model, that will be integrated with an existing physical sweat simulator module (SSM) prototype. Its goal is to simulate human perspiration under high-stress conditions by integrating environmental, physiological, and psychological data. The system architecture is developed via Model-Based Systems Engineering (MBSE). The scope of the designed virtual model is to accurately reflect physical perspiration, detect abnormal perspiration patterns, and generate recommendations regarding stress countermeasures. The goal is to influence the behavioral and/or physiological state of the physical twin, thereby creating a dynamic, responsive system. Machine learning (ML) models will be used to identify patterns in the environmental, physiological, and psychological inputs and predict sweat volume, chemical composition, and regional sweat distributions across the body. ML will also be used to detect abnormal perspiration patterns based on input data. The application of generative AI for creating test data from simulation templates is anticipated to facilitate the comparative analysis of two to three distinct profiles, such as astronauts, soldiers, and paramedics or firefighters, using simulated values. The SSM, with its integrated sensors, will serve the dual purpose of validating the model and providing realistic physical simulation.

关键词： Adaptation models Generative AI Psychology Human factors Machine learning Predictive models Physiology Digital twins Modeling Biomedical monitoring

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A GPT-4 Reticular Chemist for Guiding MOF Discovery

arXiv

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arXiv 2023年

作者： Zheng, Zhiling Rong, Zichao Rampal, Nakul Borgs, Christian Chayes, Jennifer T. Yaghi, Omar M. Department of Chemistry Kavli Energy Nanoscience Institute Bakar Institute of Digital Materials for the Planet College of Computing Data Science and Society University of California Berkeley BerkeleyCA94720 United States KACST—UC Berkeley Center of Excellence for Nanomaterials for Clean Energy Applications King Abdulaziz City for Science and Technology Riyadh11442 Saudi Arabia Department of Electrical Engineering and Computer Sciences Bakar Institute of Digital Materials for the Planet College of Computing Data Science and Society University of California Berkeley BerkeleyCA94720 United States Department of Electrical Engineering and Computer Sciences Department of Statistics Department of Mathematics School of Information Bakar Institute of Digital Materials for the Planet College of Computing Data Science and Society University of California Berkeley BerkeleyCA94720 United States

We present a new framework integrating the AI model GPT-4 into the iterative process of reticular chemistry experimentation, leveraging a cooperative workflow of interaction between AI and a human researcher. This GPT-4 Reticular Chemist is an integrated system composed of three phases. Each of these utilizes GPT-4 in various capacities, wherein GPT-4 provides detailed instructions for chemical experimentation and the human provides feedback on the experimental outcomes, including both success and failures, for the in-context learning of AI in the next iteration. This iterative human-AI interaction enabled GPT-4 to learn from the outcomes, much like an experienced chemist, by a prompt-learning strategy. Importantly, the system is based on natural language for both development and operation, eliminating the need for coding skills, and thus, make it accessible to all chemists. Our collaboration with GPT-4 Reticular Chemist guided the discovery of an isoreticular series of MOFs, with each synthesis fine-tuned through iterative feedback and expert suggestions. This workflow presents a potential for broader applications in scientific research by harnessing the capability of large language models like GPT-4 to enhance the feasibility and efficiency of research activities. Copyright © 2023, The Authors. All rights reserved.

关键词： Synthesis (chemical)

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A New 32-Bit Color Image Encryption Method Based on Multiple 1D Chaotic Maps and the Four Square Cipher

SSRN

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SSRN 2022年

作者： Es-Sabry, Mohammed El Akkad, Nabil Merras, Mostafa Satori, Khalid LISAC Computer Science Signals Automation and Cognitivism Laboratory Faculty of Sciences Dhar-Mahraz Sidi Mohamed Ben Abdellah University B.P 1796 Atlas Fez Morocco LESA Laboratory of Engineering Systems and Applications ENSA Sidi Mohamed Ben Abdellah University Fez Morocco Department of Computer Science High School of Technology Moulay Ismail University Meknes Morocco

In this paper, we introduce an improved 32-bit color image encryption method using Four 1D chaotic maps that are Logistic map, Chebyshev map, Tent map, and Sine map. these maps were used to fill the four matrices of our encryption system with unique integers that are between 0 and 255. Each matrix has a size of arranged in a square. The four channels of a 32-bit color image are represented by these matrices (red, green, blue, and alpha). The upper-left and lower-right matrices are used to encrypt respectively the red and the alpha channels. The upper-right and lower-left matrices are used to encrypt respectively the green and the blue channels. For the confusion section, we apply the Arnold Cat Map transformation to shuffle all the pixels and change their position. Several safety criteria were used to evaluate the performance of our algorithm against common attacks and have shown excellent results. © 2022, The Authors. All rights reserved.

关键词： Cryptography

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Cloud-Based Data Collaboration: Balanced Anonymity and Monitoring

Cloud-Based Data Collaboration: Balanced Anonymity and Monit...

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Communication, Computing and Energy Efficient Technologies (I3CEET), International Conference on

作者： Govinda Rajulu G S. Saroja Devi Vinodh Gunnam Charvi Vij Monika Labana Radhakrishna Dodmane Department of CSE Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Avadi Chennai Tamil Nadu India J.J. College of Engineering and Technology Applications Systems Administrator U.S.Bank USA Department: CSE SGT University Parul Institute of Technology Parul University Vadodara Gujarat India Department of Computer and Communication Engineering NMAM Institute of Technology NITTE (Deemed to be University) Nitte

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

Over the last few years there has been an increased interest in group data transmission inside cloud systems. The rise in the use of computing via the cloud has made more visible an obvious fact: sharing data among computations running concurrently or on different dates but that are trying to solve part of a single scientific challenge is essential. Another problem in the reporting of cloud-based data exhibits is how to ensure both anonymity and traceability. We aim to provide a robust method for secure sharing and storage of potentially sensitive anonymous cloud data with some target entity by no more than one individual or legal corporation. The proposed solution in this regard is group signature along with key arrangement to introduce an effective traceable approach for sharing data among a group of users, their cooperation can remain anonymous on public clouds. Members can communicate anonymously with respect to the group signature when necessary, and optionally reveal their true identity. In addition, a shared teleconference key is created by Key Agreement to allow the group members secure data interchange and storage. Importantly, the grunt work to find a common conference key among participants is greatly reduced when an asymmetric-balancing unfinished block design approach for generating keys. Theoretical and experimental studies show that the method is safe and effective in group data sharing for cloud computing.

关键词： Cloud computing Costs Computational modeling Operating systems Information sharing Random access memory Workstations Telecommunication computing Performance analysis Secure storage

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Extensive survey on smart classroom management system using object recognition systems

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AIP Conference Proceedings 2022年第1期2393卷

作者： Manit Malhotra Indu Chhabra Department of Computer Science & Applications Punjab University Chandigarh India

The technological advancements made in the Information Communication and Technologies (ICT) system has impressed the smart classroom management applications. The communication networks play a vital role in connecting the different entities of a smart classroom system. Detecting (or) predicting cheating behavior during online examination at an earlier stage is a never-ending challenging task. This paper is an extensive survey on cheating behavior detection in smart classroom management using object recognition models. The conducted survey covers the significance of cheating behavior detection by means of object recognition approach and the image processing techniques. The survey analysis states that the research challenges such as representation of objects, constancy in scenes, classification of features, searching complexity and the presence of objects and their occlusion needs to be addressed while developing an intelligent cheating behavior detection process.

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A hybrid method to select morphometric features using tensor completion and F-score rank for gifted children identification

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science China(Technological sciences) 2021年第9期64卷 1863-1871页

作者： ZHANG Jin FENG Fan HAN TianYi DUAN Feng SUN Zhe CAIAFA Cesar F. SOLÉ-CASALS Jordi College of Computer Science Nankai UniversityTianjin 300350China Department of Artificial Intelligence Nankai UniversityTianjin 300350China Computational Engineering Applications Unit Head Office for Information Systems and CybersecurityRIKEN351-0198 SaitamaJapan Instituto Argentino de Radioastronomía-CCT La Plata CONICET/CIC-PBA/UNLP1894 V.ElisaArgentina Department of Psychiatry University of CambridgeCambridge CB20SZUnited Kingdom Data and Signal Processing Group University of Vic-Central University of Catalonia08500 VicCataloniaSpain

Gifted children are able to learn in a more advanced way than others, probably due to neurophysiological differences in the communication efficiency in neural pathways. Topological features contribute to understanding the correlation between the brain structure and intelligence. Despite decades of neuroscience research using MRI, methods based on brain region connectivity patterns are limited by MRI artifacts, which therefore leads to revisiting MRI morphometric features, with the aim of using them to directly identify gifted children instead of using brain connectivity. However, the small, high-dimensional morphometric feature dataset with outliers makes the task of finding good classification models challenging. To this end, a hybrid method is proposed that combines tensor completion and feature selection methods to handle outliers and then select the discriminative features. The proposed method can achieve a classification accuracy of 93.1%, higher than other existing algorithms, which is thus suitable for the small MRI datasets with outliers in supervised classification scenarios.

关键词： gifted children identification morphometric features tensor completion feature selection

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Self-mirrored reference approach for suppressing ground clutter in the Doppler power spectrum of wind profiler radar

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Remote Sensing applications: Society and Environment 2025年 38卷

作者： Baazil P. Thampy Ajil Kottayil Judy M.V. Department of Computer Applications Cochin University of Science and Technology Kalamassery Cochin 682022 India Satellite Remote Sensing and Applications Advanced Centre for Atmospheric Radar Research Cochin University of Science and Technology Kalamassery Cochin 682022 India

Wind profiler radar networks installed globally offer a holistic understanding of Earth’s atmosphere and dynamics. Under ideal conditions, precise wind parameters can be derived from the Doppler power spectrum obtained by atmospheric probing. However, these derived parameters are prone to error due to various contaminations. Ground clutter is a major contaminant that presents a significant barrier to conventional parameter estimation techniques in deriving reliable wind estimates. This paper presents a self-mirrored reference method (SMR) for suppressing ground clutter and ensuring effective extraction of ambient air motion from the observed wind profiles. We have evaluated the effectiveness of the proposed method using the Doppler power spectrum obtained from the 205 MHz radar facility in Cochin, revealing promising results in efficient ground clutter suppression. The SMR effectively removes ground clutter from the Doppler power spectrum and has been found to be superior to conventionally used methods for ground clutter suppression. Additionally, this paper presents an efficient normalisation method using modified Gaussian, which is well suited for preprocessing the wind profilers’ Doppler power spectrum.

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A Web Traffic Investigation for Unequivocally Large Data using WAL based Data Mining Approach

A Web Traffic Investigation for Unequivocally Large Data usi...

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International Conference on Inventive Systems and Control (ICISC)

作者： J. Lekha A. Abarna Department of Computer Science & Applications Sri Krishna Arts and Science College

ISBN: (数字)9781728128139

ISBN: (纸本)9781728128146

With the quick expanding prominence of the WWW, Websites are assuming a pivotal job to pass on learning and data to the end clients. Finding covered up and significant data about web client's utilization designs is to decide successful showcasing methodologies to advance the Web server used for pleasing future development. A large portion of the as of now accessible Web server traffic investigation instruments unequivocally give measurable data. The server of web traffic investigation apparatuses makes the utilization of a web access list that is produced while the server of that client is getting into the site. A web gets to log includes different passages like the client name, his IP address; byte number exchanged timestamp and so on. The undertaking of traffic in web examination apparatuses turns out to be all the more difficult when the volume of the web traffic is gigantic and continues developing. In this paper, propose a different standard to find and break down valuable information from the accessible Web log information and furthermore gives a similar investigation of an assortment of Log Tester apparatuses exist that helps in dissecting the server system's traffic.

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Competitive Online Quantile Regression 18th

Competitive Online Quantile Regression

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18th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2020

作者： Dzhamtyrova, Raisa Kalnishkan, Yuri Computer Science Department Royal Holloway University of London Egham United Kingdom Laboratory of Advanced Combinatorics and Network Applications Moscow Institute of Physics and Technology Moscow Russia

ISBN: (纸本)9783030501457

Interval prediction often provides more useful information compared to a simple point forecast. For example, in renewable energy forecasting, while the initial focus has been on deterministic predictions, the uncertainty observed in energy generation raises an interest in producing probabilistic forecasts. One aims to provide prediction intervals so that outcomes lie in the interval with a given probability. Therefore, the problem of estimating the quantiles of a variable arises. The contribution of our paper is two-fold. First, we propose to apply the framework of prediction with expert advice for the prediction of quantiles. Second, we propose a new competitive online algorithm Weak Aggregating Algorithm for Quantile Regression (WAAQR) and prove a theoretical bound on the cumulative loss of the proposed strategy. The theoretical bound ensures that WAAQR is asymptotically as good as any quantile regression. In addition, we provide an empirical survey where we apply both methods to the problem of probability forecasting of wind and solar powers and show that they provide good results compared to other predictive models. © 2020, Springer Nature Switzerland AG.

关键词： Forecasting

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