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International Conference on Trends in Electronics and Informatics (ICEI)

作者： M. Vani Pujitha Teja Aravind Kumar Rayalavarapu Lal Basha Shaik Sashank Madhabushi Department of Computer Science and Engineering Velagapudi Ramakrishna Siddhartha Engineering College Andhra Pradesh India Dept. of Artificial Intelligence and Data Science Velagapudi Ramakrishna Siddhartha Engineering College Andhra Pradesh India

ISBN: (数字)9798331544607

ISBN: (纸本)9798331544614

Vehicle emissions significantly exacerbate air pollution, releasing harmful gases such as carbon monoxide (CO), nitrogen dioxide (NO 2 ), and hydrocarbons (HC) from poorly maintained engines and inefficient combustion processes. The study presents an Internet of Things (IoT)-based system to monitor the emissions, which mainly includes key pollutants like CO, NO 2 , and HC in real-time. Strategically positioned gas sensors near the vehicle's exhaust capture emission data, which an Arduino microcontroller processes with high accuracy. The system delivers continuous gas level updates via an LCD display and leverages an IoT framework to ensure seamless integration of sensors, microcontrollers, and mobile devices. This connectivity enables real-time data transmission and remote alerting, enhancing emission management. When pollutant levels surpass predefined safety thresholds, the system triggers immediate alerts through a Global System for Mobile Communications (GSM) module (SIM 900) via SMS to the vehicle owner's mobile, complemented by an audible buzzer. This alert notification encourages timely maintenance, reducing emissions and improving vehicle performance and contributing to cleaner air.

关键词： GSM Microcontrollers Real-time systems Sensor systems Mobile handsets Sensors Safety Nitrogen Gas detectors Monitoring

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SSRN

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SSRN 2023年

作者： Tan, Chang Wei Petitjean, François Keogh, Eamonn Webb, Geoffrey I. Department of Data Science and Artificial Intelligence Monash Data Futures Institute Monash University MelbourneVIC3800 Australia Department of Computer Science and Engineering University of California - Riverside RiversideCA92521 United States

Real-world time series data often have unequal lengths. These differences in length may arise from a number of fundamentally different mechanisms. In this work, we identify and evaluate two classes of such mechanism – variations in sampling rate relative to the relevant signal and variations between the start and end points of one time series relative to one another. We investigate how time series generated by each of these classes of mechanism are best addressed for time series similarity assessment. We perform extensive experiments and provide practical recommendations. © 2023, The Authors. All rights reserved.

关键词： Time series

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Instruction-Driven Game Engine: A Poker Case Study

Instruction-Driven Game Engine: A Poker Case Study

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Wu, Hongqiu Liu, Xingyuan Wang, Yan Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University China Shanghai Key Laboratory of Trusted Data Circulation and Governance in Web3 China Tencent China

ISBN: (纸本)9798891761674

The Instruction-Driven Game Engine (IDGE) project aims to democratize game development by enabling a large language model (LLM) to follow free-form game descriptions and generate game-play processes. The IDGE allows users to create games simply by natural language instructions, which significantly lowers the barrier for game development. We approach the learning process for IDGEs as a Next State Prediction task, wherein the model autoregressively predicts the game states given player actions. The computation of game states must be precise;otherwise, slight errors could corrupt the game-play experience. This is challenging because of the gap between stability and diversity. To address this, we train the IDGE in a curriculum manner that progressively increases its exposure to complex scenarios. Our initial progress lies in developing an IDGE for Poker, which not only supports a wide range of poker variants but also allows for highly individualized new poker games through natural language inputs. This work lays the groundwork for future advancements in transforming how games are created and played. © 2024 Association for Computational Linguistics.

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Plasma proteomics identifies proteins and pathways associated with incident depression in 46,165 adults

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science Bulletin 2025年第4期70卷 573-586页

作者： Jujiao Kang Liu Yang Tianye Jia Wei Zhang Lin-Bo Wang Yu-Jie Zhao Jia You Yue-Ting Deng Yi-Jun Ge Wei-Shi Liu Yi Zhang Yi-Lin Chen Xiao-Yu He Barbara J.Sahakian Yucheng TYang Xing-Ming Zhao Jin-Tai Yu Jianfeng Feng Wei Cheng Institute of Science and Technology for Brain-Inspired Intelligence(ISTBI) Fudan UniversityShanghai 200433China Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan UniversityMinistry of EducationShanghai 200433China Department of Neurology and National Center for Neurological Disorders Huashan HospitalState Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain ScienceShanghai Medical CollegeFudan UniversityShanghai 200433China Department of Psychiatry University of CambridgeCambridge CB20SZUK Department of Computer Science University of WarwickCoventry CV47ALUK School of Data Science Fudan UniversityShanghai 200438China

Proteomic alterations preceding the onset of depression offer valuable insights into its development and potential *** data from 46,165 UK Biobank participants and 2920 plasma proteins profiled at baseline,we conducted a longitudinal analysis with a median follow-up of 14.5 years to explore the relationship between plasma proteins and incident *** regression was then used to assess associations between depression-related proteins and brain structures,genetic factors,and stress-related *** analysis identified 157 proteins associated with incident depression(P<1.71×10^(-5)),including novel associations with proteins such as GAST,PLAUR,LRRN1,BCAN,and ***,higher expression levels of GDF15(P=6.18×10-26)and PLAUR(P=2.88×10^(-14))were linked to an increased risk of depression,whereas higher levels of LRRN1(P=4.28×10^(-11))and ITGA11(P=3.68×10^(-9))were associated with a decreased *** of the 157 proteins is correlated with brain regions implicated in depression,including the hippocampus and middle temporal ***,these protein alterations were strongly correlated with stress-related events,including self-harm events,adult,and childhood *** pathway enrichment analysis highlighted the critical roles of the immune *** and TNF emerged as key proteins in the protein-protein interaction ***3A2,newly linked to incident depression(P=4.35×10^(-10)),was confirmed as a causal factor through Mendelian randomization *** summary,our research identified the proteomic signatures associated with the onset of depression,highlighting its potential for early intervention and tailored therapeutic avenues.

关键词： Plasma proteins Depression Brain structure

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Learning a Typhoon Bayesian Network Structure from Natural Language Reports 5th

Learning a Typhoon Bayesian Network Structure from Natural...

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5th IFIP TC 12 International Conference on Intelligence science, ICIS 2022

作者： Yao, Zhangrui Chen, Junhan Pan, Yinghui Zeng, Yifeng Ma, Biyang Ming, Zhong Department of Automation and Xiamen Key Laboratory of Big Data Intelligent Analysis and Decision-Making Xiamen University Xiamen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Computer and Information Sciences Northumbria University Newcastle upon Tyne United Kingdom School of Computer Science and Engineering Minnan Normal University Zhangzhou China

ISBN: (纸本)9783031149023

Given the huge toll caused by natural disasters, it is critically important to develop an effective disaster management and emergency response technique. In this article, we investigate relationships between typhoon-related variables and emergency response from natural language (NL) reports. A major challenge is to exploit typhoon state information for typhoon contingency plan generation, especially from unstructured text data based on NL input. To tackle this issue, we propose a novel framework for learning typhoon Bayesian network structures (FLTB), which can extract typhoon state information from unstructured NL, mine inter-information causal relationships and then generate Bayesian networks. We first extract information about typhoon states through NL processing (NLP) techniques, and then analyze typhoon reports by designing heuristic rules to identify causal relationships between states. We leverage these features to improve the learned structures and provide user-interaction mechanisms to finalize Bayesian networks. We evaluate the performance of our framework on real-world typhoon datasets and develop the Bayesian networks based typhoon emergency response systems. © 2022, IFIP International Federation for Information Processing.

关键词： Bayesian networks

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FedTIU: Securing Virtualized PLCs Against DDoS Attacks Using a Federated Learning Enabled Threat Intelligence Unit

FedTIU: Securing Virtualized PLCs Against DDoS Attacks Using...

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International Conference on Smart Computing (SMARTCOMP)

作者： Priyanka Verma Miguel Ponce De Leon John G. Breslin Donna O’Shea Data Science Institute University of Galway Ireland VMware VMware Research Cork Ireland Department of Computer Science Munster Technological University Cork Ireland

Conventional Programmable Logic Controller (PLC) systems are becoming increasingly challenging to manage due to hardware and software dependencies. Moreover, the number and size of conventional PLCs on factory floors continue to increase, and virtualized PLC (vPLC) offers a solution to address these challenges. The utilization of vPLC offers the advantages of streamlining communication between high-level applications and low-level machine operations, enhancing programming ability in process control systems by abstracting control functions from I/O modules, and increasing automation in industrial control networks. Nevertheless, the connection of vPLC to the internet and cloud services presents a considerable cybersecurity risk, and the crucial aspect of information security for vPLCs is ensuring their availability. Distributed Denial of Service (DDoS) attacks can be particularly devastating for vPLCs, as they rely on internet connectivity to function. DDoS attacks on vPLC overwhelm it and causing it to become unavailable. vPLCs manages control systems and if targeted by a DDoS attack, these systems could become unresponsive, leading to significant disruption to industrial processes. Thus, implementing effective DDoS protection measures is crucial for ensuring the availability and reliability of vPLCs in industrial settings. Therefore, this work proposes a Federated learning enabled Threat Intelligence Unit (FedTIU) for detecting DDoS attacks on vPLCs on an Edge Compute Stack near to vPLC. The proposed approach involves collaborative model training using federated learning techniques to gain knowledge of new attack patterns from other industrial sites while maintaining data privacy.

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MLOps: A Review

arXiv

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arXiv 2023年

作者： Wazir, Samar Kashyap, Gautam Siddharth Saxena, Parag Department of Computer Science and Engineering SEST Jamia Hamdard New Delhi India School of Data Science University of North Carolina Charlotte United States

Recently, Machine Learning (ML) has become a widely accepted method for significant progress that is rapidly evolving. Since it employs computational methods to teach machines and produce acceptable answers. The significance of the Machine Learning Operations (MLOps) methods, which can provide acceptable answers for such problems, is examined in this study. To assist in the creation of software that is simple to use, the authors research MLOps methods. To choose the best tool structure for certain projects, the authors also assess the features and operability of various MLOps methods. A total of 22 papers were assessed that attempted to apply the MLOps idea. Finally, the authors admit the scarcity of fully effective MLOps methods based on which advancements can self-regulate by limiting human engagement. © 2023, CC BY.

关键词： Deep learning

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Why AI still doesn't have consciousness?

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CAAI Transactions on Intelligence Technology 2021年第2期6卷 175-179页

作者： Deyi Li Wen He Yike Guo Department of Computer Science and Technology Tsinghua UniversityBeijingChina Instltute of Systems Engineering Academy of Military SciencesPLABeijingChina Data Science Institute Imperial College LondonUK Hongkong Baptist University Hong KongChina

Consciousness is one of the unique features of creatures,and is also the root of biological *** to now,all machines and robots havenJt had ***,will the artificial intelligence(AI)be conscious?Will robots have real intelligence without consciousness?The most primitive consciousness is the perception and expression of *** order to perceive the existence of the concept of‘Ij,a creature must first have a perceivable boundary such as skin to separate‘I’from‘non-1’.For robots,to have the self-awareness,they also need to be wrapped by a similar sensory ***,as intelligent tools,AI systems should also be regarded as the external extension of human *** tools are *** development of AI shows that intelligence can exist without *** human beings enter into the era of life intelligence from AI,it is not the AI became conscious,but that conscious lives will have strong ***,it becomes more necessary to be careful on applying AI to living creatures,even to those lower-level animals with only *** subversive revolution of such application may produce more careful thinking.

关键词： intelligence consciousness conscious

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Evaluation of Logistic Regression and Decision Tree Models for Lung Cancer Prediction Using Survey data

Evaluation of Logistic Regression and Decision Tree Models f...

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Electronic Systems and Intelligent Computing (ICESIC), International Conference on

作者： K E Narayana Jayashree K ThavaVinu A M.Sindhuja Jaeyalakshmi M Department of Information Technology Rajalakshmi Engineering College Chennai Tamilnadu India Department of Artificial Intelligence and Data Science Panimalar Engineering College Chennai Tamilnadu India Department of Information Technology Prathyusha Engineering College Chennai Department of Computer Science and Engineering Acharya University Karakul Uzbekistan Department Of Computer Science And Engineering Rajalakshmi Engineering College Chennai India

ISBN: (数字)9798331522988

ISBN: (纸本)9798331522995

Lung cancer is one of the most deadly and ubiquitous forms of cancer globally. Early detection can make a significant difference in survival rates, prognosis, etc. Background The present study compares the performance of logistic regression and decision trees using a survey-based dataset for predicting lung cancer. This dataset has a total of 284 cases with 16 features; behavioral patterns, symptomatic data, and demographic details. Logistic regression is a binary classification algorithm that uses input features to model the probability of lung cancer presence given these input features using for example a sigmoid function. Using Decision Trees can identify significant fields in a dataset while reducing Gini Impurity for models that categorize patients based on feature values. The performance of both models is evaluated according to accuracy, F1-score, precision, and recall measures. From the preliminary results, it looks like the Decision Tree works a little better than the logistic regression model which gets an accuracy of 94%, but this one does with more accuracy (97%). This result emphasizes the inclusion of logistic regression within this context, as it may be able to predict lung cancer with greater accuracy. Additionally, to ensure the generalization and robustness of the model, further investigation and trials will focus on optimizing performance through ensemble approaches, hyperparameter tuning, and cross-validation. This comparative analysis demonstrates the effectiveness of machine learning techniques in developing prediction models for early lung cancer detection, an important step toward a quick and appropriate intervention.

关键词： Surveys Logistic regression Accuracy Computational modeling Lung cancer Predictive models Robustness Decision trees Prognostics and health management Tuning

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Allocation Multiplicity: Evaluating the Promises of the Rashomon Set

arXiv

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arXiv 2025年

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

The Rashomon set of equally-good models promises less discriminatory algorithms, reduced outcome homogenization, and fairer decisions through model ensembles or reconciliation. However, we argue from the perspective of allocation multiplicity that these promises may remain unfulfilled. When there are more qualified candidates than resources available, many different allocations of scarce resources can achieve the same utility. This space of equal-utility allocations may not be faithfully reflected by the Rashomon set, as we show in a case study of healthcare allocations. We attribute these unfulfilled promises to several factors: limitations in empirical methods for sampling from the Rashomon set, the standard practice of deterministically selecting individuals with the lowest risk, and structural biases that cause all equally-good models to view some qualified individuals as inherently risky. © 2025, CC BY.

关键词： Consensus algorithm

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