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arXiv 2023年

作者： Wang, Xiaohan Zhao, Zhan Zhang, Hongmou Guo, Xiaotong Zhao, Jinhua Department of Urban Planning and Design The University of Hong Kong Hong Kong Musketeers Foundation Institute of Data Science The University of Hong Kong Hong Kong School of Government Peking University Beijing China Department of Civil and Environmental Engineering Massachusetts Institute of Technology MA United States Department of Urban Studies and Planning Massachusetts Institute of Technology MA United States

Ridesharing is recognized as one of the key pathways to sustainable urban mobility. With the emergence of Transportation Network Companies (TNCs) such as Uber and Lyft, the ridesharing market has become increasingly fragmented in many cities around the world, leading to efficiency loss and increased traffic congestion. While an integrated ridesharing market (allowing sharing across TNCs) can improve the overall efficiency, how such benefits may vary across TNCs based on actual market characteristics is still not well understood. In this study, we extend a shareability network framework to quantify and explain the efficiency benefits of ridesharing market integration using available TNC trip records. Through a case study in Manhattan, New York City, the proposed framework is applied to analyze a real-world ridesharing market with 3 TNCs-Uber, Lyft, and Via. It is estimated that a perfectly integrated market in Manhattan would improve ridesharing efficiency by 13.3%, or 5% of daily TNC vehicle hours traveled. Further analysis reveals that (1) the efficiency improvement is negatively correlated with the overall demand density and inter-TNC spatiotemporal unevenness (measured by network modularity), (2) market integration would generate a larger efficiency improvement in a competitive market, and (3) the TNC with a higher intra-TNC demand concentration (measured by clustering coefficient) would benefit less from market integration. As the uneven benefits may deter TNCs from collaboration, we also illustrate how to quantify each TNC's marginal contribution based on the Shapley value, which can be used to ensure equitable profit allocation. These results can help market regulators and business alliances to evaluate and monitor market efficiency and dynamically adjust their strategies, incentives, and profit allocation schemes to promote market integration and collaboration. Copyright © 2023, The Authors. All rights reserved.

关键词： Efficiency

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Phylogenetic context of a deep-sea clam(Bivalvia:Vesicomyidae) revealed by DNA from 1 500-year-old shells

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Zoological Research 2023年第2期44卷 353-356页

作者： Yi-Xuan Li Yanjie Zhang Jack Chi-Ho Ip Jing Liu Chong Chen Crispin TSLittle Yusuke Yokoyama Moriaki Yasuhara Jian-Wen Qiu Department of Biology Hong Kong Baptist UniversityHong Kong SARChina Southern Marine Science and Engineering Guangdong Laboratory(Guangzhou) GuangzhouGuangdong 511458China School of Life Sciences Hainan UniversityHaikouHainan 570228China Laboratory of Marine Organism Taxonomy and Phylogeny Institute of OceanologyChinese Academy of SciencesQingdaoShandong 266071China X-STAR Japan Agency for Marine-Earth Science and Technology(JAMSTEC)2-15 Natsushima-choYokosukaKanagawa 237-0061Japan School of Earth and Environment University of LeedsLeeds LS29JTUK Life Sciences Department Natural History MuseumLondon SW75BDUK Department of Earth and Planetary Science University of TokyoHongo113-0033Japan School of Biological Sciences Area of Ecology and BiodiversitySwire Institute of Marine ScienceInstitute for Climate and Carbon Neutralityand Musketeers Foundation Institute of Data ScienceUniversity of Hong KongHong Kong SARChina State Key Laboratory of Marine Pollution City University of Hong KongHong Kong SARChina

DEAR EDITOR,Ancient DNA(a DNA) from mollusc shells is considered a potential archive of historical biodiversity and ***, such information is currently lacking for mollusc shells from the deep ocean, especially those from acidic chemosynthetic environments theoretically unsuitable for longterm DNA preservation. Here, we report on the recovery of mitochondrial and nuclear gene markers by Illumina sequencing of a DNA from three shells of Archivesica nanshaensis – a hydrocarbon-seep vesicomyid clam previously known only from a pair of empty shells collected at a depth of 2626 m in the South China Sea.

关键词： shells evolution acidic

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Machine Learning Designed identification on cervical cancers in patient

Machine Learning Designed identification on cervical cancers...

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2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent engineering, ICECONF 2023

作者： Sangeetha, M. Sasikala, G. Anitha, K. Ragavendiran, S.D. Prabu Kumar, K.R. Senthil Deivakani, M. Department of Data Science and Business Systems SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India Department of Mathematics Srm Valliammai Engineering College Kattankulathur Tamil Nadu Chennai603202 India Department of Electronics and Communication Engineering Saveetha Engineering College Tamil Nadu Thandalam602105 India Department of Computer Science and Engineering Rvs Technical Campus - Coimbatore Tamil Nadu Coimbatore641402 India Department of Mechanical Engineering R. M. K. Engineering College Tamil Nadu Kavaraipettai601206 India Department of Electronics and Communication Engineering Psna College of Engineering and Technology Tamil Nadu Dindigul624622 India

ISBN: (纸本)9798350334357

In this paper, we develop an automatic diagnosis model that aims to screen and detect the presence of cervical cancer in women patients. The diagnosis model consists of a series of stages that involves pre-processing, feature extraction and classification of cancer using bees swarm optimization (BSO). The BSO helps to classify the instances from the extracted features in an effective way that does not fall into premature convergence. The fully grown solution provides effective classification of images from the pre-defined medical datasets. The simulation is conducted on python in a high-end computing system to test the efficacy of BSO in classifying the cervical cancer. The validation shows an increasing precision of classifying the instances than other state-of-art methods. © 2023 IEEE.

关键词： Python

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Scarce Resource Allocations That Rely On Machine Learning Should Be Randomized

arXiv

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arXiv 2024年

作者： Jain, Shomik Creel, Kathleen Wilson, Ashia Institute for Data Systems and Society MIT United States Department of Philosophy & Religion Khoury College of Computer Sciences Northeastern University United States Department of Electrical Engineering and Computer Science MIT United States

Contrary to traditional deterministic notions of algorithmic fairness, this paper argues that fairly allocating scarce resources using machine learning often requires randomness. We address why, when, and how to randomize by proposing stochastic procedures that more adequately account for all of the claims that individuals have to allocations of social goods or opportunities. © 2024, CC BY.

关键词： Machine learning

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Real-Time IoT Cybersecurity using Machine Learning-based AI Threat Detection System to Train Generative Robots

Real-Time IoT Cybersecurity using Machine Learning-based AI ...

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Trends in Material science and Inventive Materials (ICTMIM), International Conference on

作者： P Srilakshmi Koushik Reddy Chaganti Talachendri Suryam Sweety Julia I D. Chaithanya Jami Kavitha Department of CSE G Pulla Reddy Engineering College Dept. of Computer and Information Systems University of the Cumberlands Department of CSE (DATA SCIENCE) Vignan Institute of Technology and Science Department of AI&ML Malla Reddy University Department of CSE CVR College of Engineering Department of CSE Sanketika Vidya Parishad Engineering College

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

Existing security protocols encounter major cybersecurity difficulties because the online devices' growing popularity continues to spread across networks. These security protocols are ineffective because they do not properly handle current cyber threats. The main goal of this study involves developing enhanced IoT cybersecurity through the development of a threat detection system which brings together adversarial training and deep learning models (CNN-LSTM) and Federated Learning (FL). The system enables distributed Internet of Things devices to work on security model development through Federated Learning while maintaining total privacy of their information. Security procedures controlled by generative artificial intelligence robots alongside real-time attack protection functions decrease security response durations. Through its Federated CNN-LSTM model the system upholds a 1.2% false positive rate alongside a 98.3% accuracy evaluation and 160 milliseconds of exact threat tracking time. The designed system sustains a minimal occurrence of incorrect alarm activations. The developed system provides real-time security for the Internet of Things framework because it enables adaptive protection systems while preserving user privacy in current IoT settings.

关键词： Training Privacy Protocols Federated learning Real-time systems Threat assessment Internet of Things Computer security Protection Robots

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Contrastive meta learning with behavior multiplicity for recommendation

arXiv

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arXiv 2022年

作者： Wei, Wei Huang, Chao Xia, Lianghao Xu, Yong Zhao, Jiashu Yin, Dawei Department Of Computer Science China Musketeers Foundation Institute Of Data Science University Of Hong Kong Hong Kong South China University Of Technology China Wilfrid Laurier University Canada Baidu Inc China

A well-informed recommendation framework could not only help users identify their interested items, but also benefit the revenue of various online platforms (e.g., e-commerce, social media). Traditional recommendation models usually assume that only a single type of interaction exists between user and item, and fail to model the multiplex user-item relationships from multi-typed user behavior data, such as page view, add-to-favourite and purchase. While some recent studies propose to capture the dependencies across different types of behaviors, two important challenges have been less explored: i) Dealing with the sparse supervision signal under target behaviors (e.g., purchase). ii) Capturing the personalized multi-behavior patterns with customized dependency modeling. To tackle the above challenges, we devise a new model CML, Contrastive Meta Learning (CML), to maintain dedicated crosstype behavior dependency for different users. In particular, we propose a multi-behavior contrastive learning framework to distill transferable knowledge across different types of behaviors via the constructed contrastive loss. In addition, to capture the diverse multi-behavior patterns, we design a contrastive meta network to encode the customized behavior heterogeneity for different users. Extensive experiments on three real-world datasets indicate that our method consistently outperforms various state-of-the-art recommendation methods. Our empirical studies further suggest that the contrastive meta learning paradigm offers great potential for capturing the behavior multiplicity in recommendation. We release our model implementation at: https://***/weiwei1206/***. Copyright © 2022, The Authors. All rights reserved.

关键词： Collaborative filtering

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An Efficient Brain tumor classification using CNN and transfer learning

An Efficient Brain tumor classification using CNN and transf...

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Advances in Computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： P. Sasikumar Srikanth Cherukuvada P Balmurugan P Vijay Anand S. Brindasri R. Nareshkumar Department of Artificial Intelligence and Machine Learning Sphoorthy Engineering College Nadergul Hyderabad Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Bowrampet Hyderabad Telangana Department of Computer Science and Engineering MLR Institute of Technology Hyderabad Department of Artificial Intelligence and Data Science CMR Institute of Technology Bengaluru India Department of Computer Science Hindustan Institute of Technology and Science Padur Chennai India Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

The proper detection of brain tumors as quickly as possible and the administration of efficient therapy are both necessary steps in the process of curing patients. The complexities of tumor morphology, such as size, location, and texture, as well as heteromorphic appearance in medical pictures, make tumor analysis a difficult *** three most frequent types of brain cancer are brain tumors, meningiomas, and pituitary gland tumors. The purpose of this study is to investigate a classification issue known as a triple-class problem in order to correctly identify these *** a pre-trained version of the vgg16 network, the proposed classification method pulls characteristics from brain MRI scans. Classifier models that have shown their worth in practice are given to help put the retrieved attributes into meaningful groups. The presented method is carried out on the Python platform, and its efficacy is evaluated with the use of performance *** to the results, the conventional neural network (CNN) technique was outperformed by the Transfer Learning method, which resulted in the model having a higher overall performance. This can be observed by demonstrating that the model had a stronger overall performance.

关键词： Accuracy Magnetic resonance imaging Transfer learning Brain cancer Brain modeling Feature extraction Pituitary gland

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Detection and Classification of Skin Cancer by Using a Parallel Deconvolutional Network Model 1

Detection and Classification of Skin Cancer by Using a Paral...

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1st IEEE International Interdisciplinary Humanitarian Conference for Sustainability, IIHC 2022

作者： Sangeetha, M. Karthikeyini, C. Vasundhara, S. Saravanan, D. Arunkumar, G. School of Computing SRMIST Department of Data Science and Business Systems Kattankulathur C India Excel Engineering College Department of ECE Tamil Nadu Komarapalayam India G.Narayanamma Institute of Technology &science Department of Humanities & Mathematics JNTUH Hyderabad India IFET College of Engineering Department of CSE Tamil Nadu Villupuram India Madanapalle Institute of Technology & Science Department of CSE Andhra Pradesh Madanapalle India

ISBN: (纸本)9781665456876

Skin Cancer is one of the most common cancer forms in many countries, it is considered to be one of the dangerous types in the sense that it is lethal and its occurrence over time has been dramatically high. It is one of the deadliest cancers among all diseases and has a large rate of mortality. The efficiency of the earlier approaches to assess one of the most hazardous melanoma diagnosis in dermoscopic criteria are not up to the mark. Therefore, in this research, the work has been carried out in three stages in order to detect melanoma in an efficient manner. In the first stage, prior to the implementation of the image segmentation technique, noise elimination and pre-processing steps are carried out to remove the noise and to achieve better execution results. This segmentation model focuses on the separation of the interesting portions from the background and collects the necessary information from the neighboring pixels of the same category. Gaussian analytical patterns are used to handle the heterogeneous regions/sections of dermoscopy images whose mean and variance can be dynamic. In addition, the third stage of work is based on the texture classification, which is proposed as the CSTC-Mel Identification Model. © 2022 IEEE.

关键词： Machine learning

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Multi-armed Bandit Experimental Design: Online Decision-Making and Adaptive Inference

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Management science 2025年第6期71卷 4828-4846页

作者： Simchi-Levi, David Wang, Chonghuan Institute for Data Systems and Society Massachusetts Institute of Technology CambridgeMA02139 United States Department of Civil and Environmental Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Operations Research Center Massachusetts Institute of Technology CambridgeMA02139 United States Center for Computational Science and Engineering Massachusetts Institute of Technology CambridgeMA02139 United States

Multi-armed bandit has been well known for its efficiency in online decision-making in terms of minimizing the loss of the participants’ welfare during experiments (i.e., the regret). In clinical trials and many other scenarios, the statistical power of inferring the treatment effects (i.e., the gaps between the mean outcomes of different arms) is also crucial. Nevertheless, minimizing the regret entails harming the statistical power of estimating the treatment effect because the observations from some arms can be limited. In this paper, we investigate the trade-off between efficiency and statistical power by casting the multi-armed bandit experimental design into a minimax multi-objective optimization problem. We introduce the concept of Pareto optimality to mathematically characterize the situation in which neither the statistical power nor the efficiency can be improved without degrading the other. We derive a useful sufficient and necessary condition for the Pareto optimal solutions to the minimax multi-objective optimization problem. Additionally, we design an effective Pareto optimal multi-armed bandit experiment that can be tailored to different levels of the trade-off between the two objectives. Moreover, we extend the design and analysis to the setting where the outcome of each arm consists of an adversarial baseline reward and a stochastic treatment effect, demonstrating the robustness of our design. Finally, motivated by clinical trials, we examine the setting where the employed experiment must split the experimental units into a small number of batches, and we propose a flexible Pareto optimal design. © 2024 INFORMS.

关键词： Multiobjective optimization

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Ethereum smart contract security research:survey and future research opportunities

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Frontiers of Computer science 2021年第2期15卷 143-160页

作者： Zeli WANG Hai JIN Weiqi DAI Kim-Kwang Raymond CHOO Deqing ZOU National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabClusters and Grid Computing LabHubei Engineering Research Center on Big Data SecurityWuhan430074China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan430074China School of Cyber Science and Engineering Huazhong University of Science and TechnologyWuhan430074China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen518057China Department of Information Systems and Cyber Security University of Texas at San AntonioSan AntonioTX78249-0631USA

Blockchain has recently emerged as a research trend,with potential applications in a broad range of industries and *** particular successful Blockchain technology is smart contract,which is widely used in commercial settings(e.g.,high value financial transactions).This,however,has security implications due to the potential to financially benefit from a security incident(e.g.,identification and exploitation of a vulnerability in the smart contract or its implementation).Among,Ethereum is the most active and ***,in this paper,we systematically review existing research efforts on Ethereum smart contract security,published between 2015 and ***,we focus on how smart contracts can be maliciously exploited and targeted,such as security issues of contract program model,vulnerabilities in the program and safety consideration introduced by program execution *** also identify potential research opportunities and future research agenda.

关键词： smart contract security blockchain vulnerability unreliable data

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