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Photonic Neuromorphic Accelerator Combined with an Event-Based Neuromorphic Camera for High-Speed Object Classification

Photonic Neuromorphic Accelerator Combined with an Event-Bas...

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2023 Conference on Lasers and Electro-Optics Europe and European Quantum Electronics Conference, CLEO/Europe-EQEC 2023

作者： Tsirigotis, Aris Tsilikas, Ioannis Sozos, Kostas Bogris, Adonis Mesaritakis, Charis University of the Aegean Dept. Information and Communication Systems Engineering Palama 2 str. Karlovasi Samos83200 Greece National Technical University of Athens Dept. Applied Physics and Mathematics Iroon Polytechniou 9 Zografou Athens15772 Greece University of West Attica Dept. Informatics and Computer Engineering Ag. Spiridonos Egaleo12243 Greece

ISBN: (纸本)9798350345995

In this work, we demonstrate the impact of an unconventional convolutional photonic accelerator, based on an optical spectrum slicing (OSS) [1], on the classification accuracy of objects, generated through a high-frame rate neuromorphic event-based camera [2]. The experimental setup is depicted in Fig.1a. Ιt consists of a 5 mW-632 nm-LED source, two objective lenses with NA=0.65 that concentrate the light beam into a 100 μmX100 μm channel. In our experiments, the targeted objects consist of test spheres, of different diameters (12, 16, and 20 μm), used in aqua solutions. A pump regulated the sphere's speed to 0.8 m/s. The objects were recorded by a 10 kframe/s capable neuromorphic camera, with a temporal resolution of 1 μs. The camera detects pixel's contrast changes (events), similar to biological systems [2]. The recorded events were exported into 1 kframes/sec videos through a synthetic frame generator software, resulting to 2988 images per particle [2]. Images were post-processed using only noise reduction by frame subtraction and image cropping so as to reduce data volume to 100 x 100 pixels. © 2023 IEEE.

关键词： Cameras

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Integrating Computational Thinking Into the Curricula to Bridge the Skill Gap in engineering Education

Integrating Computational Thinking Into the Curricula to Bri...

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Frontiers in Education (FIE) Conference

作者： Nasrin Dehbozorgi Maysam Nezafati Mehdi Roopaei Dept. of Software Engineering Kennesaw State University Marietta GA USA Dept. of Biomedical Engineering Georgia Institute of Technology Atlanta GA USA Dept. of Electrical and Computer Engineering) University of Wisconsin-Platteville Platteville WI USA

ISBN: (数字)9798350351507

ISBN: (纸本)9798350363067

This work-in-progress research-to-practice paper presents an intervention on integrating computational thinking modules into a software engineering course. The national consensus on the significance of computational thinking has prompted the expansion of related educational initiatives over the past decade. Since the definition of computational thinking by Wing in 2006, this concept has gained significant attention within the educational community. Particularly this surge of interest has led to extensive research into its conceptual foundations and subsequent integration into educational curricula since 2013. National initiatives have since emerged to incorporate computational thinking into the educational system. Furthermore, as artificial intelligence and computing systems become increasingly integrated into daily life, there is a growing demand from industries for a workforce and graduates adept.at critical thinking and problem-solving. Aligned with this national movement, our study presents a two-year institutional initiative, aimed at integrating computational thinking into the software engineering program. The software engineering discipline extensively involves design thinking and problem-solving skills. However, we noticed that these higher-level skills are not imparted early in the program to teach students this method of thinking and approaching problems. To bridge this skill gap, we developed a set of computational thinking modules and integrated them into a gateway course in the software engineering program. Over two years, we implemented this intervention in an introductory-level course and evaluated its impact on students' computational thinking skills by analyzing their responses to a standard Computational Thinking Assessment survey. The results showed significant improvement in most components. These early findings underscore the effectiveness of integrating these computational thinking modules into the gateway courses, regardless of the specific co

关键词： Bridges Surveys Industries Logic gates Problem-solving Surges engineering education Artificial intelligence Standards software engineering

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Evaluating the Solutions to Predict the Impact of Lung Cancer with an Advanced Intelligent Computing Method 5

Evaluating the Solutions to Predict the Impact of Lung Cance...

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5th International Conference on Smart systems and Inventive Technology, ICSSIT 2023

作者： Sundar, R. Ramadass, Sudhir Meeha, D. Subramanian, Balambigai Shankar, S. Siva Parasa, Gayatri Madanapalle Institute of Technology and Science Dept. of Computer Science and Engineering India Sterck Systems Chennai India Dr. Rangarajan Dr. Sakunthala Engg College Vel Tech Multi Tech Dept. of Information Technology Avadi Chennai India Dept. of Ece Kongu Engineering College Perundurai India Kg Reddy College of Engineering and Technology Dept. of Cse -AIandML Telangana Chilukuru India Koneru Lakshmaiah Education Foundation Dept. of Cse Vaddeswaram Andhra Pradesh Guntur India

ISBN: (纸本)9781665474672

Using symptoms as a basis for diagnosing lung cancer, Lung cancer detection was accomplished using several different machine-learning regression strategies. By comparing the efficacy of several regression algorithms for predicting lung cancer, considering factors including age, gender, chest discomfort, shortness of breath, alcohol intake, chronic illness, trouble swallowing, anxiety, and peer pressure. Lung cancer predictions and evaluations are made using regression methods such as the linear algorithm, polynomial regression, logistic regression, logarithmic regression, and multiple regression. With a predictive accuracy of 96%, multiple regression is superior to other regression techniques when identifying future lung cancer cases. The r-squared value, which can be calculated using several regression machine learning approaches, may also be used to evaluate the association between the various symptoms and lung cancer. Lung cancer is diagnosed using the r-squared value, which is calculated using several algorithms and takes into symptoms, including chronic illness. © 2023 IEEE.

关键词： Forecasting

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Development of a teaching model based on lifelong learning through active methodological strategies 12

Development of a teaching model based on lifelong learning t...

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12th International Conference on Virtual Campus, JICV 2022

作者： Trujillo-Aguilera, F D. Blazquez-Parra, E.B. Almendros, J. Calderon Marti-Campoy, A. Plaza-Garcia, I. University of Malaga Dept. Electronic Technology Malaga Spain University of Malaga Dept. Graphic Engineering Design and Projects Malaga Spain Polytechnic University of Valencia Dept. of Computer and Systems Informatics Valencia Spain University of Zaragoza Dept. of Electronic Engineering and Communications Zaragoza Spain

ISBN: (数字)9781665451062

ISBN: (纸本)9781665451062

Based on one of the fundamental principles of the educational model of the European Higher Education Area concerning the development of the capacity to achieve lifelong learning, this project has been developed in which a set of active teaching activities have been implemented for the acquisition of key competences in the challenge of achieving one of the most important goals that any educational system must provide: lifelong learning, thereby facilitating the incorporation of new university graduates into the world of work. This project has been implemented on the subject of Power Electronics. However, to facilitate the transfer to other subjects, the catalogue of active teaching activities is sufficiently varied to make it easy to extrapolate and use in other subjects, thanks to the fact that teachers have a wide variety of activities to choose from the set. Finally, a survey has been drawn to evaluate this new active methodology's satisfaction, impact and improvement to know the students' perspectives once they have worked with this catalogue of activities. © 2022 IEEE.

关键词： E-learning

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Demonstration of Effectiveness of Nativeness in Stepwise Learning by Performing Sentiment Analysis

Demonstration of Effectiveness of Nativeness in Stepwise Lea...

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Next-Generation Computing, IoT and Machine Learning (NCIM), International Conference on

作者： Al-Mahmud Kazutaka Shimada Dept. of Computer Science and Systems Engineering Kyushu Institute of Technology Japan

Nowadays, more people and entities express opinions on online platforms. With this increasing trend of online opinion mass information, it is impossible to analyze them manually. For this reason, an automatic approach to opinion and sentiment analysis is essential and useful in the real world. Several approaches based on machine learning techniques in sentiment analysis have been proposed. However, machine learning essentially needs massive data to generate a robust model. On the other hand, collecting the training data is costly, especially in low-resource language. To solve this problem, we introduce stepwise learning. Stepwise learning is a technique that improves the model capacity for a target task (i.e., main task) by exploiting auxiliary information from source tasks. It is effective when the data in the target tasks are inadequate. The target task of this paper is sentiment analysis for reviews in the Bangla language. Our proposed method consists of two stages: source and target tasks. For the source task, we train a transformers-based model, such as BanglaBERT, by a large dataset translated into the Bangla language; the original dataset is written in English. For the target task, we retrain and evaluate the model with a relatively small target dataset. We further investigate the effect of opinion expression style of writers between source and target data in stepwise learning. We call it “nativeness.” The experimental result shows that nativeness has an important role in improving performance.

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Risk Prediction of Opioid Dependency Using Machine Learning Based on Electronic Health Record

Risk Prediction of Opioid Dependency Using Machine Learning ...

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computer Science and Data engineering (CSDE), IEEE Asia-Pacific Conference on

作者： Samaneh Madanian Sherry Dai Priyadarshini Natarajan Hamidreza Rasoulipanah Dept. of Computer Science and Software Engineering Auckland University of Technology Auckland New Zealand

Opioid abuse and dependence have emerged as a pressing global concern, posing significant challenges to public health and society. Early identification and prediction of opioid dependency represent crucial steps in mitigating its abuse impact on individuals and communities. The application of machine learning techniques to analyze medical data has opened new avenues for achieving this goal. While this field and the prediction is still in its infancy, our research explores the potential of several machine learning algorithms including LightGBM for this risk prediction. To tackle the inherent class imbalance in the MIMICIII dataset, we implemented the Synthetic Minority Oversampling Technique (SMOTE). We developed predictive models using four distinct algorithms: decision trees, random forests, support vector machines, and LightGBM. These models were meticulously evaluated to assess their performance. Ultimately, our findings revealed that the LightGBM model outperformed the other algorithms, demonstrating superior accuracy and achieving a higher Area Under the Curve value. This outcome underscores the potential of LightGBM as a valuable algorithm in the early prediction of the risk of opioid dependence, thereby offering substantial benefits to both patients and society at large.

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Recognition of Leukocytes using a Combination of Deep Features and Ensemble Classifier

Recognition of Leukocytes using a Combination of Deep Featur...

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International Conference on Emerging Technologies, ICET

作者： Syeda Tehreem Haider Muhammad Faraz Khushbakht Iqtidar Dept. of Electronics Engineering University of Engg. & Tech. Taxila Taxila Pakistan Dept. of Computer & Software Engineering National University of Sciences & Tech. Islamabad Pakistan

ISBN: (数字)9798350379587

ISBN: (纸本)9798350379594

Detection of the peripheral blood plays a vital role in the field of medical diagnostics and control of major diseases. In this regard, detecting leukocytes or White Blood Cells (WBCs) is important. Leukocytes have five classes: Basophils, Eosinophils, Lymphocytes, Monocytes, and Neutrophils. The dataset used for this study is Raabin-WBC, containing leukocyte images. Deep learning techniques are applied to the dataset for the detection of mentioned leukocyte classes. In this case, to remove class imbalance, oversampling/replication of data is done. Transfer learning is applied in which a pre-trained ResNet18 model gets adapted to new data. Deep features are extracted from the pool5 layer of the network and are fed to the Ensemble Bagged Trees classifier. A remarkable classification accuracy of 99.5% is achieved through 5-fold cross-validation.

关键词： Deep learning Accuracy Transfer learning Feature extraction Mathematical models Robustness Medical diagnosis MATLAB Random forests Diseases

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Exploring the Impact of Robotics in STEM Education Activities and Competitive Challenges

Exploring the Impact of Robotics in STEM Education Activitie...

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Cybernetics and Innovations (ICCI), International Conference on

作者： Adil Farooq Syeda Zillay Nain Zukhraf Hafsa Maryam Conrad Attard Mohsin Kamal Dept. of Electrical and Computer Engineering University of Cyprus Cyprus Dept. of Electrical Engineering National University of Computer and Emerging Sciences Pakistan Dept. of Computer Information Systems University of Malta Msida Malta

As robotics and technology become increasingly integrated into education and daily life, it is important to recognize their essential role. Even young children have access to electronic toys and gadgets, which can facilitate their learning and interaction. This study highlights the significance of STEM (science, technology, engineering, and mathematics) education at the elementary school level, emphasizing how university students from a variety of disciplines can design and construct robots capable of carrying out challenging tasks for humanitarian purposes. The study also emphasizes the need to encourage women's participation in robotics to showcase their skills and innovative ideas. Furthermore, the study demonstrates how robotics can effectively be used in both STEM education and competitive challenges, showcasing its effectiveness in teaching and promoting technological advancement. By encouraging early engagement in STEM education and promoting diversity in the field of robotics, we can empower future generations to lead the way in creating innovative solutions to global challenges.

关键词： Technological innovation Education Toy manufacturing industry Task analysis Robots Cybernetics STEM

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Leveraging Language-based Representations for Better Solving Symbol-related Problems with Large Language Models 31

Leveraging Language-based Representations for Better Solving...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Wang, Yile Cheng, Sijie Sun, Zixin Li, Peng Liu, Yang College of Computer Science and Software Engineering Shenzhen University Shenzhen China Dept. of Comp. Sci. & Tech. Institute for AI Tsinghua University Beijing China Tsinghua University Beijing China Jiangsu Collaborative Innovation Center for Language Competence Jiangsu China

ISBN: (纸本)9798891761964

Symbols such as numerical sequences, chemical formulas, and table delimiters exist widely, playing important roles in symbol-related tasks such as abstract reasoning, chemical property prediction, and tabular question-answering. Compared to tasks based on natural language expressions, large language models (LLMs) have limitations in understanding and reasoning on symbol-based representations, making it difficult for them to handle symbol-related problems. In this paper, we propose symbol-to-language (S2L), a method that converts symbol-based representations to language-based representations, providing valuable information for language models during reasoning. We found that, for both closed-source and open-source LLMs, the capability to solve symbol-related problems can be largely enhanced by incorporating such language-based representations. For example, by employing S2L for GPT-4, there can be substantial improvements of +21.9% and +9.5% accuracy for 1D-ARC and Dyck language tasks, respectively. There are also consistent improvements in other six general symbol-related tasks such as table understanding and Tweet analysis. We release the GPT logs in https://***/THUNLP-MT/symbol2language. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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Two-Factor Authentication Protocol with Zero-Knowledge over an Extended Field of Elliptic Curves

Two-Factor Authentication Protocol with Zero-Knowledge over ...

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International Conference on Radio Electronics & Info Communications (UkrMiCo)

作者： Iryna Strelkovska Oleksiy Onatskiy Larysa Yona dept. Cybersecurity Software Engineering and Computer Science International Humanitarian University Odesa Ukraine

The paper proposes a cryptographic protocol two-factor authentication with the zero-knowledge over the extended field GF(2 m ) on elliptic curves using biometric data and private key of the user. The implementation of a cryptographic protocol with zero-knowledge proof based on elliptic curves allows significantly reducing the size of protocol parameters and increasing the cryptographic strength (computational complexity of the breaking). The cryptographic protocol was modeled in the High-Level Protocol Specification Language, the model validation and protocol verification was performed using the Security Protocol Animator tool for Automated Validation of Internet Security Protocols and Applications. The software verification of the cryptographic protocol was performed using the software modules On the Fly Model Checker and Constraint Logic based Attack Searcher.

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