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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Tagasovska, Nataša Gligorijević, Vladimir Cho, Kyunghyun Loukas, Andreas Prescient/MLDD Genentech Research and Early Development United States Department of Computer Science Center for Data Science New York University United States

Across scientific domains, generating new models or optimizing existing ones while meeting specific criteria is crucial. Traditional machine learning frameworks for guided design use a generative model and a surrogate model (discriminator), requiring large datasets. However, real-world scientific applications often have limited data and complex landscapes, making data-hungry models inefficient or impractical. We propose a new framework, PropEn, inspired by "matching", which enables implicit guidance without training a discriminator. By matching each sample with a similar one that has a better property value, we create a larger training dataset that inherently indicates the direction of improvement. Matching, combined with an encoder-decoder architecture, forms a domain-agnostic generative framework for property enhancement. We show that training with a matched dataset approximates the gradient of the property of interest while remaining within the data distribution, allowing efficient design optimization. Extensive evaluations in toy problems and scientific applications, such as therapeutic protein design and airfoil optimization, demonstrate PropEn's advantages over common baselines. Notably, the protein design results are validated with wet lab experiments, confirming the competitiveness and effectiveness of our approach. Our code is available at https://***/prescient-design/propen. © 2024 Neural information processing systems foundation. All rights reserved.

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Smart Contract Evaluation By Multi-Criteria Analysis: Selection Challenges And Open Issues, a Review 3

Smart Contract Evaluation By Multi-Criteria Analysis: Select...

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3rd International Conference on Emerging Smart Technologies and Applications, eSmarTA 2023

作者： Alshahrani, Norah M. Kiah, M.L. Mat Zaidan, B.B. University of Malaya Faculty of Computer Science & It Department of Computer Systems & Technology Kuala Lumpur Malaysia King Khalid University Department of Computer Science Abha Saudi Arabia University of Malaya Faculty of Computer Science & It Department of Computer System & Technology Kuala Lumpur Malaysia Future Technology Research Center National Yunlin University of Science and Technology Douliou Yunlin Taiwan

ISBN: (纸本)9798350305333

Smart contracts have received increasing attention in recent years for their potential to enable decentralized and automated transactions in various fields, including finance, supply chain management, and real estate. However, selecting and evaluating the appropriate smart contract for a specific application remains challenging due to the large number of available options and various criteria that need to be considered. This paper discusses the challenges and open issues in selecting and evaluating appropriate smart contracts for various applications. It proposes multi-criteria analysis (MCA) to evaluate and compare different options based on multiple criteria. The study aims to provide a comprehensive review of the current state-of-the-art in the field by considering various criteria used in the selection process, such as security, scalability, and performance. The review covers the challenges associated with critical criteria for smart contract selection, including security, privacy, efficiency, scalability, and regulatory concerns. Additionally, the paper presents various criteria used in different domains/groups to carry out various blockchain- related tasks, such as operational function, asset management, mitigation, analysis, communications, planning, and others. The criteria vary from one person to another and from application to application, and the importance level of the criteria is subjective. The paper highlights the need for more research on smart contract platforms' long-term performance and scalability and more comprehensive and objective evaluation methods for MCA of smart contract selection. © 2023 IEEE.

关键词： Smart contract

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Deep Bimodal Fusion Approach for Apparent Personality Analysis

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computers, Materials & Continua 2023年第4期75卷 2301-2312页

作者： Saman Riaz Ali Arshad Shahab S.Band Amir Mosavi Department of Computer Science National University of TechnologyIslamabad44000Pakistan Department of Computer Science Institute of Space TechnologyIslamabad44000Pakistan Future Technology Research Center National Yunlin University of Science and TechnologyDouliu64002YunlinTaiwan Faculty of Civil Engineering Technische Universitat DresdenDresden01069Germany

Personality distinguishes individuals’ patterns of feeling, thinking,and behaving. Predicting personality from small video series is an excitingresearch area in computer vision. The majority of the existing research concludespreliminary results to get immense knowledge from visual and Audio(sound) modality. To overcome the deficiency, we proposed the Deep BimodalFusion (DBF) approach to predict five traits of personality-agreeableness,extraversion, openness, conscientiousness and neuroticism. In the proposedframework, regarding visual modality, the modified convolution neural networks(CNN), more specifically Descriptor Aggregator Model (DAN) areused to attain significant visual modality. The proposed model extracts audiorepresentations for greater efficiency to construct the long short-termmemory(LSTM) for the audio modality. Moreover, employing modality-based neuralnetworks allows this framework to independently determine the traits beforecombining them with weighted fusion to achieve a conclusive prediction of thegiven traits. The proposed approach attains the optimal mean accuracy score,which is 0.9183. It is achieved based on the average of five personality traitsand is thus better than previously proposed frameworks.

关键词： Apparent personality analysis deep bimodal fusion convolutional neural network long short-term memory bimodal information fusion approach

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On the Measurement of the Inductive Bias Impact in Convolutional Neural Networks Training for Single Image Super-Resolution 8

On the Measurement of the Inductive Bias Impact in Convoluti...

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8th International Conference on computer science and Computational Intelligence, ICCSCI 2023

作者： Kwantawijaya, Aldrian Najoan, Daniel Stephanus Tamimmanar Cenggoro, Tjeng Wawan Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia

The performance of SISR models is currently growing at a rapid pace since the adoption of Convolutional Neural Networks (CNN) in the domain. Despite that, the understanding of the underlying factors of this rapid growth is still low. In this study, we proposed a study design to measure the impact of the inductive bias in CNN on its training performance, which is one of the factors of the excellent performance of CNN in SISR. With this study design, we observed that not all CNN has a supportive inductive bias to the training performance. This result can provide guidance to future research in choosing a suitable CNN architecture for optimal SISR performance. © 2023 The Authors. Published by Elsevier B.V.

关键词： Convolutional Neural Networks Deep Learning Inductive Bias Single-Image Super Resolution

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Sentiment Analysis for TikTok Review Using VADER Sentiment and SVM Model 8

Sentiment Analysis for TikTok Review Using VADER Sentiment a...

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8th International Conference on computer science and Computational Intelligence, ICCSCI 2023

作者： Isnan, Mahmud Elwirehardja, Gregorius Natanael Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia

TikTok, a social networking site for uploading short videos, has become one of the most popular. Despite this, not all users are happy with the app;there are criticisms and suggestions, one of which is reviewed via the TikTok app on the Google Play Store. The reviews were extracted and then used for training a sentiment analysis model. The VADER sentiment method was utilized to offer the review's initial labeling (positive, neutral, and negative). The result revealed that most reviews were classified as positive, meaning that the data were imbalanced and challenging to handle in further analysis. Therefore, Random Under-sampling (RUS) and Random Over-sampling (ROS) methods were deployed to deal with that condition. The labeled reviews were subsequently pre-processed using tools such as case folding, noise removal, normalization, and stopwords before being used for training a Support Vector Machine (SVM) model for sentiment classification. The SVM trained without resampling produced the most favorable results, with an F1-score of 0.80. © 2023 The Authors. Published by Elsevier B.V.

关键词： Google Play Store Sentiment Analysis SVM TikTok VADER Sentiment

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Machine Learning Approaches in Detecting Autism Spectrum Disorder 8

Machine Learning Approaches in Detecting Autism Spectrum Dis...

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8th International Conference on computer science and Computational Intelligence, ICCSCI 2023

作者： Daniel Dominic, Nicholas Cenggoro, Tjeng Wawan Pardamean, Bens Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia

Early detection of Autism Spectrum Disorder (ASD) needs to be increased to prevent further adverse impacts. Thus, the classification between ASD and Typically Development (TD) individuals is an intriguing task. This review study has collected 26 related papers to answer four research questions, i.e., what are the most used data inputs, brain atlases, and machine learning models for ASD classification, as also to discover the significant parts of the brain correlated with the ASD. It was eventually found that functional connectivity matrix, Support Vector Machine, and CC200 are the most frequently used data input, model, and brain atlas, respectively. researchers also concluded that the posterior temporal fusiform cortex, intracalcarine cortex, cuneal cortex, subcallosal cortex, occipital pole, and lateral occipital cortex are the brain regions highly correlated with ASD. © 2023 The Authors. Published by Elsevier B.V.

关键词： autism spectrum disorder brain atlas brain region machine learning

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A Reduced State-Space Generation Method for Concurrent Systems Based on CPN-PR Model

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2025年第6期44卷 2328-2342页

作者： Zhong, Wenjie Sun, Tao Zhou, Jian-Tao Wang, Zhuowei Song, Xiaoyu Inner Mongolia University College of Computer Science the Engineering Research Center of Ecological Big Data Ministry of Education the Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software the Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Hohhot010000 China Guangdong University of Technology School of Computer Science and Technology Guangzhou510006 China Portland State University Department of Electrical and Computer Engineering PortlandOR97207 United States

Colored Petri nets (CPNs) provide descriptions of the concurrent behaviors for software and hardware. Model checking based on CPNs is an effective method to simulate and verify the concurrent behavior in system design. However, the model-checking method traverses the full state space, which suffers from the state-space explosion problem. A reduced state-space generation method related to the property of concurrent systems is proposed. Specifically, we extend CPNs to define a property-related model (CPN-PR) and give a property-related analysis method whose results can be used to generate the CPN-PR model. A reduced state-space generation method is developed based on enabled binding element filtering rules. The stutter trace equivalence between the state spaces of CPN and CPN-PR has been proven by showing that the reduced state space may not change the model-checking result. A comparison experiment is conducted to demonstrate the effectiveness of our method. © 1982-2012 IEEE.

关键词： Model checking

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A New Secure Channel Free Public Key Encryption with Keyword Search Scheme Based on ElGamal Cryptosystems

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International Journal of Network Security 2023年第6期25卷 1070-1076页

作者： Hwang, Min-Shiang Hsu, Shih-Ting Yang, Cheng-Ying Department of Computer Science and Information Engineering Asia University Taiwan Fintech and Blockchain Research Center Asia University Taichung 354 Wufeng Taiwan Department of Management Information Systems National Chung Hsing University Taiwan Department of Computer Science University of Taipei Taipei Taiwan

Public key encryption with keyword search (PEKS) scheme enables one to send the trapdoor, which involves the encrypted keyword in querying data without revealing the keyword. The trapdoor should transfer into the secret channel, which is costly and inefficient. Many articles proposed efficient, secure, channel-free public key encryption with keyword search (SCF-PEKS) schemes. After that, many schemes focus on "against the offline keyword guessing attack (OKGA)" by enhancing the model. However, most PEKS/SCF-PEKS schemes are constructed with bilinear pairing. However, the PEKS/SCF-PEKS based on bilinear pairing is susceptible to offline keyword-guessing attacks. In this article, we will propose a new SCF-PEKS based on ElGamal cryptography. The scheme is secure against offline keyword guessing attacks and improves efficiency. © (2023), (Femto Technique Co.). All Rights Reserved.

关键词： Search engines

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Machine learning models for modeling the biosorption of Fe(III) ions by activated carbon from olive stone

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Neural Computing and Applications 2024年第21期36卷 13357-13372页

作者： Massaoudi, Ayman Echouchene, Fraj Ben Ayed, Mossaad Berguiga, Abdelwahed Harchay, Ahlem Al-Ghamdi, Sara Belmabrouk, Hafedh Department of Computer Science College of Science and Arts in Gurayat Jouf University Sakakah Saudi Arabia Olive Research Center Jouf University Sakakah Saudi Arabia Laboratory of Electronics and Microelectronics Faculty of Science of Monastir University of Monastir Environment Boulevard Monastir5019 Tunisia Higher Institute of Applied Sciences and Technology of Sousse University of Sousse Sousse Tunisia Department of Industrial Electronics ENISo University of Sousse Sousse Tunisia Department of Computer Science College of Computer and Information Sciences Jouf University Sakakah Saudi Arabia Department of Physics College of Science Majmaah University Al Majma’ah11952 Saudi Arabia

Using experimental results related to the biosorption of Fe(III) by activated carbon derived from olive pit waste, we developed and evaluated four artificial neural network (ANN) models in this study, namely MLP-ANN, RBF-ANN, GR-ANN, and PSO-ANN, to predict the removal efficiency of Fe(III) during the adsorption process. The purpose of these models was to forecast the effect of the following five important operational variables: the initial concentration, time, stirring speed, temperature, and biosorbent dose. We conducted a thorough assessment of the performance of these models and compared their ability to predict the removal capacity. Several statistical metrics have been used to quantify the quality of the different models. The results calculated by the machine learning models were analyzed and compared with the experimental results. The obtained values of the coefficient of determination were 0.9997 for the PSO-ANN model, 0.991 for the GR-ANN model, 0.983 for the RBF-ANN model, and 0.837 for the MLP-ANN model concerning the removal efficiency. All the studied models are able to accurately predict the adsorbed quantity. ANOVA analysis was used to evaluate the effect of the parameters inherent to the PSO-ANN model. The PSO-ANN model proves to be a powerful tool for estimating the efficiency of Fe(III) ion removal. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024. corrected publication 2024.

关键词： Biosorption

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Enhancing YOLOPv2 Lane Detection to Gain Curvature Radius 1

Enhancing YOLOPv2 Lane Detection to Gain Curvature Radius

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1st International Conference on Smart Mechatronics, ICSMech 2024

作者： Noer Kartiman, Fachri Najm Rasim Hasanah, Nurul Salim, Taufik Ibnu Rizqyawan, Muhammad Ilham Basuki, Akbari Indra Pratama, Rian Putra Prabowo, Dwiko Unggul Universitas Pendidikan Indonesia Department of Computer Science Education Bandung Indonesia Research Center for Smart Mechatronics National Research and Innovation Agency Bandung Indonesia

ISBN: (纸本)9798350368918

Lane detection is essential for Advanced Driver Assistance Systems (ADAS) and autonomous driving, enabling vehicles to perceive lane boundaries, and accurately ensuring safe and reliable navigation. Recent research on lane detection on ADAS has more comprehensive data output which is critical for effective lane-keeping and vehicle control. This paper explores the enhancement of YOLOPv2 with Bird's Eye View (BEV) post-processing for lane detection tasks and compares its performance with PolyLaneNet. The study is conducted on a Jetson Orin NX platform, Mini PC RTX 2060, and PC with RTX 4060, comparing the FPS performance of both models. YOLOPv2, enhanced with BEV postprocessing, achieves 3.43 FPS in Orin NX, 11.22 FPS in RTX 2060, 18.34 FPS in RTX 4060 on custom video data while providing detailed lane information, including lane curvature, curvature radius, and lane deviation. In contrast, PolyLaneNet, which provides higher FPS, only offers lane curvature without additional detailed metrics. This study highlights the benefits of BEV post-processing on YOLOPv2's performance and its comprehensive data output, which is critical for effective lane-keeping and vehicle control. © 2024 IEEE.

关键词： Advanced driver assistance systems

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