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2023 International Conference on Intelligent Supercomputing and BioPharma, ISBP 2023

作者： Liu, Xiaoyan Xu, Zhiwei Liu, Guangwen Liu, Limin Inner Mongolia University of Technology College of Data Science and Application Hohhot China Chinese Academy of Sciences Institute of Computing Technology Beijing China

ISBN: (纸本)9798350397789

In the compound selection process of anti-cancer drugs, safety properties such as drug activity and pharmacokinetics need to be considered simultaneously. To construct a more complete and precise drug screening mechanism, this paper proposed an intelligent compound selection method for anti-cancer drugs based on multi-objective optimization. The proposed model is executed in the MapReduce environment. Quantitatively analyze the biological activity of the compound, and qualitatively analyze the properties of pharmacokinetics and safety properties (Absorption, Distribution, Metabolism, Excretion, and Toxicity) to build a multi-objective optimization model. Guided by Pareto optimization theory, the set of non-inferior solution values was determined, and the compound combination that satisfies the optimization goal was found by genetic optimization. On this basis, a Monte Carlo hypothesis test was used to determine the equipped range of the compounds. Finally, an example of the compound selection of anti-breast cancer drugs is given, and the experimental evaluation proves that the algorithm can screen compounds limitedly, which provides a basis for anti-cancer drug synthesis. © 2023 IEEE.

关键词： Multiobjective optimization

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Using Intelligent Texts in a Computer science Classroom: Findings from an iTELL Deployment 8

Using Intelligent Texts in a Computer Science Classroom: Fin...

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8th Educational data Mining in Computer science Education Workshop, CSEDM 2024

作者： Crossley, Scott Choi, Joon Suh Morris, Wesley Holmes, Langdon Joyner, David Gupta, Vaibhav Peabody College Vanderbilt University NashvilleTN United States College of Computing Georgia Institute of Tech AtlantaGA United States Data Science Institute Vanderbilt University NashvilleTN United States

This study assesses the efficacy of intelligent texts to help students in a computer science class understand and process information about computational thinking and programming. The intelligent texts used in this study were taken from an introductory programming textbook. The texts were ingested into an intelligent text format using the Intelligent Texts for Enhanced Language Learning (iTELL) framework, which converts any type of machine-readable text into an interactive, intelligent text. iTELL asks students to complete constructed response items and summaries, which are scored automatically by large language models (LLMs) specifically trained to generate scores to inform qualitative feedback to students. Survey results indicated that students responded positively to the constructed response and summary items and felt both items helped them learn. An analysis of delta value gain scores between pre-tests and post-tests for students that used iTELL and those that did not use iTELL indicated that iTELL students showed increased learning gains. Regression analyses showed that delta scores for the iTELL students were predicted by the number of scrolls, word scores on summaries, and pre-test proficiency level (low/high). The results indicate that intelligent texts may help computer science students better learn material than traditional texts. © 2024 Copyright for this paper by its authors.

关键词： Regression analysis

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Revolutionizing lemon grading: an automated CNN-based approach for enhanced quality assessment

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International Journal of Information Technology (Singapore) 2024年第7期16卷 4155-4166页

作者： Sanyal, Samriddha Adhikary, Rahul Choudhury, Suvra Jyoti School of Computing and Data Science FLAME University Maharashtra Pune 412115 India Centurion University of Technology and Management Odisha Parlakhemundi 761211 India Indian Institute of Information Technology Maharashtra Nagpur 441108 India

Lemons, renowned for their versatile applications in the culinary world and various industries, have become a staple commodity worldwide. The demand for high-quality lemons continues to soar, underscoring the need for reliable and efficient grading methods. Traditional lemon grading, which is largely reliant on human expertise, often falls prey to subjectivity, human errors, and time-consuming processes. In this paper, we propose a CNN-based lemon grading model to automate and improve the accuracy of lemon quality assessment. The proposed model utilizes CNNs in a modular and sequential fashion for extracting pertinent features from lemon images and categorizing them into distinct quality grades. A comprehensive dataset of lemon images with corresponding quality labels is used to train and validate the model. The experimental results demonstrate the effectiveness of the CNN-based approach in accurately grading lemons, offering a promising solution for enhancing efficiency and objectivity in the lemon grading process. © Bharati Vidyapeeth's institute of Computer Applications and Management 2024.

关键词： CNN modular organization Deep learning Incremental learning Lemon health detection

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MLSTM: A Novel Modified Long Short-Term Memory Networks for Melody Synthesis Melody Generation

MLSTM: A Novel Modified Long Short-Term Memory Networks for ...

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International Conference on Advanced Communications and Machine Intelligence, MICA 2023

作者： Ganguly, Tanudeep Sharma, Sushmita Deb, Suman Ezugwu, Absalom El-Shamir Saha, Apu Kumar Department of Computer Science and Engineering National Institute of Technology Agartala Tripura Agartala India Department of Computer Science and Engineering Indian Institute of Information Technology Agartala Tripura Agartala India Unit for Data Science and Computing North-West University Potchefstroom2520 South Africa

ISBN: (纸本)9789819762217

This research elaborates a melody synthesis model utilizing a Modified Long Short-Term Memory (mLSTM) neural network, trained on Kern dataset, preprocessed into Musical Instrument Digital Interface (MIDI) files. After receiving a seed melody as input 01, the model predicts the note that will come next by looking at the notes that have come before it. The model can learn the structures and patterns seen in classical music by using MIDI files as the training dataset. Categorical cross-entropy loss function is used to train the model over several epochs with early halting. To regulate how random the predictions are, the model makes use of a temperature parameter. The assessment demonstrates that the produced tunes have a variety of traits and are aesthetically pleasing to human ears. To justify the performance of the proposed method some criteria were considered and expert evaluation was done. The evaluation shows that the model has generated the melodies that are pleasing to the ear and have a similar structure to the training dataset. The model has several uses, including prediction and music composition. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Long short-term memory

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A generalized software reliability prediction model for module based software incorporating testing effort with cost model

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Life Cycle Reliability and Safety Engineering 2024年第4期13卷 419-436页

作者： Yadav, Akshay Kumar Srivastava, Shilpa Pant, Millie Department of Applied Mathematics and Scientific Computing Indian Institute of Technology Roorkee 247001 India Christ University Karnataka Bengaluru 560029 India Mehta Family School of Data Science and Artificial Intelligence Indian Institute of Technology Roorkee 247667 India

As software innovation has advanced, it has been noted that the testing effort function (TEF) is one of the key factors influencing the improvement of software reliability. This paper presents a simplified model which incorporates the testing effort for the reliability growth of a software. A closed form solution has been derived for the reliability of the software. This study examines the impact of testing efforts on a software reliability model based on NHPP. The sensitivity analysis has been made available to investigate how the created model's system parameters affect the cost function, mean value function, and software’s reliability. The parameters of the model have been estimated using the non-linear least square estimation (MLE) method in MATLAB software. Additionally, a warranty cost model is constructed to assess the optimal release policy for the software. The general form of the reliability expression involves elliptic integrals, which can be computed easily through a software like Mathematica. We have derived analytical solutions for reliability pertaining to several particular cases. Optimal release time for the software product has been calculated for some particular cost-sets. Goodness of fit curves have been plotted to compare the proposed model with some well-known existing SRGMs. Numerical illustrations are provided to bolster the analytical outcomes. © The Author(s), under exclusive licence to Society for Reliability and Safety (SRESA) 2024.

关键词： Maximum likelihood estimation (MLE) Non-homogeneous Poisson process (NHPP) Optimal release time (ORT) Software reliability growth model (SRGM) Testing effort function (TEF) Warranty cost model

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TransFeat-TPP: An Interpretable Deep Covariate Temporal Point Processes 27

TransFeat-TPP: An Interpretable Deep Covariate Temporal Poin...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Meng, Zizhuo Li, Boyu Fan, Xuhui Li, Zhidong Wang, Yang Chen, Fang Zhou, Feng Data Science Institute University of Technology Sydney Australia School of Computing Macquarie University Australia Center for Applied Statistics School of Statistics Renmin University of China China Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing China

ISBN: (纸本)9781643685489

The classical temporal point process (TPP) constructs an intensity function by taking the occurrence times into account. Nevertheless, occurrence time may not be the only relevant factor, other contextual data, termed covariates, may also impact the event evolution. Incorporating such covariates into the model is beneficial, while distinguishing their relevance to the event dynamics is of great practical significance. In this work, we propose a Transformer-based covariate temporal point process (TransFeat-TPP) model to improve the interpretability of deep covariate-TPPs while maintaining powerful expressiveness. TransFeat-TPP can effectively model complex relationships between events and covariates, and provide enhanced interpretability by discerning the importance of various covariates. Experimental results on synthetic and real datasets demonstrate improved prediction accuracy and consistently interpretable feature importance when compared to existing deep covariate-TPPs. Our code is available at https://***/waystogetthere/***. © 2024 The Authors.

关键词： Spatio-temporal data

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Showcasing Native Wildflowers in Virtual Reality

Showcasing Native Wildflowers in Virtual Reality

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2022 International Conference on Computational science and Computational Intelligence, CSCI 2022

作者： Wagner, Andy Deligiannidis, Leonidas School of Computing and Data Science Wentworth Institute of Technology BostonMA02115 United States

ISBN: (纸本)9798350320282

The goal of this project is to develop a virtual reality program on the Oculus Quest 2 to showcase and educate users on native wildflowers that can be found in North America, specifically in the United States. We have created a virtual environment with framed images of some of America's native flowers and, using interactive menus, we provide educational information on each plant. In addition to the 2D images, a 360-image viewer was implemented to show some of the native habitats of these plants to create a more immersive learning experience. Using the Oculus Quest's experimental hand tracking feature, users can switch between using the Oculus controller and using their actual hands as the controller, allowing the users to choose how they interact with the virtual environment. The United States is home to many beautiful native flowers and by showcasing them in virtual reality anyone can experience these flowers and learn about them. © 2022 IEEE.

关键词： Virtual reality

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LanEvil: Benchmarking the Robustness of Lane Detection to Environmental Illusions 24

LanEvil: Benchmarking the Robustness of Lane Detection to En...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Zhang, Tianyuan Wang, Lu Li, Hainan Xiao, Yisong Liang, Siyuan Liu, Aishan Liu, Xianglong Tao, Dacheng SKLCCSE Beihang University China Shen Yuan Honors College Beihang University China Institute of Dataspace China School of Computing National University of Singapore Singapore Singapore College of Computing & Data Science Nanyang Technological University Singapore Singapore

ISBN: (纸本)9798400706868

Lane detection (LD) is an essential component of autonomous driving systems, providing fundamental functionalities like adaptive cruise control and automated lane centering. Existing LD benchmarks primarily focus on evaluating common cases, neglecting the robustness of LD models against environmental illusions such as shadows and tire marks on the road. This research gap poses significant safety challenges since these illusions exist naturally in real-world traffic situations. For the first time, this paper studies the potential threats caused by these environmental illusions to LD and establishes the first comprehensive benchmark LanEvil for evaluating the robustness of LD against this natural corruption. We systematically design 14 prevalent yet critical types of environmental illusions (e.g., shadow, reflection) that cover a wide spectrum of real-world influencing factors in LD tasks. Based on real-world environments, we create 94 realistic and customizable 3D cases using the widely used CARLA simulator, resulting in a dataset comprising 90,292 sampled images. Through extensive experiments, we benchmark the robustness of popular LD methods using LanEvil, revealing substantial performance degradation (-5.37% Accuracy and -10.70% F1-Score on average), with shadow effects posing the greatest risk (-7.39% Accuracy). Additionally, we assess the performance of commercial auto-driving systems OpenPilot and Apollo through collaborative simulations, demonstrating that proposed environmental illusions can lead to incorrect decisions and potential traffic accidents. To defend against environmental illusions, we propose the Attention Area Mixing (AAM) approach using hard examples, which witness significant robustness improvement (+3.76%) under illumination effects. We hope our paper can contribute to advancing more robust auto-driving systems in the future. Part of our dataset and demos can be found at the https://***/. © 2024 ACM.

关键词： Adaptive cruise control

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A survey for light field super-resolution

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High-Confidence computing 2024年第1期4卷 118-129页

作者： Mingyuan Zhao Hao Sheng Da Yang Sizhe Wang Ruixuan Cong Zhenglong Cui Rongshan Chen Tun Wang Shuai Wang Yang Huang Jiahao Shen State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100191China Key Laboratory of Data Science and Intelligent Computing International Innovation InstituteBeihang UniversityHangzhou 311115China State Key Laboratory of Software Development Environment School of Computer Science and EngineeringBeihang UniversityBeijing 100191China

Compared to 2D imaging data,the 4D light field(LF)data retains richer scene’s structure information,which can significantly improve the computer’s perception capability,including depth estimation,semantic segmentation,and LF ***,there is a contradiction between spatial and angular resolution during the LF image acquisition *** overcome the above problem,researchers have gradually focused on the light field super-resolution(LFSR).In the traditional solutions,researchers achieved the LFSR based on various optimization frameworks,such as Bayesian and Gaussian *** learning-based methods are more popular than conventional methods because they have better performance and more robust generalization *** this paper,the present approach can mainly divided into conventional methods and deep learning-based *** discuss these two branches in light field spatial super-resolution(LFSSR),light field angular super-resolution(LFASR),and light field spatial and angular super-resolution(LFSASR),***,this paper also introduces the primary public datasets and analyzes the performance of the prevalent approaches on these ***,we discuss the potential innovations of the LFSR to propose the progress of our research field.

关键词： Light field super-resolution Convolutional neural network Transformer Sub-aperture image Epipolar-plane image

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Towards Efficient Discrete Controller Synthesis: Semantics-Aware Stepwise Policy Design via LLM

Towards Efficient Discrete Controller Synthesis: Semantics-A...

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2024 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2024

作者： Ishimizu, Yusei Li, Jialong Yamauchi, Takuto Chen, Sinan Cai, Jinyu Hirano, Takanori Tei, Kenji School of Computing Tokyo Institute of Technology Tokyo Japan Waseda University Department of Computer Science and Engineering Tokyo Japan Kobe University Center of Mathematical and Data Sciences Kobe Japan

ISBN: (纸本)9798331530839

Discrete Controller Synthesis (DCS) is an automated generation technique to generate controllers from environment models and requirements. The time and space complexities of DCS are critical because they directly govern the feasibility of using DCS for real-time generation of controllers to cope with environmental changes. Stepwise DCS is considered a promising approach to prevent the construction of ineffective state spaces in the subsequent analysis of the full controller by analyzing sub-controllers that meet part of the requirements at an early stage. However, constructing a stepwise policy is a challenge, as the current methods often require domain experts to manually build it by understanding and reasoning the semantics of each sub-environment model and sub-requirement. To reduce the effort of manual construction, this paper introduces a method that leverages large language models (LLMs) to automate the generation of stepwise policies by utilizing the LLM's ability to understand and reason about the semantics of state machines. The evaluation using GPT-4 on a drone control scenario demonstrates the effectiveness of our method, with an average reduction of 82.2% in states and 91.2% in transitions. © 2024 IEEE.

关键词： Control system synthesis

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