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Improved Memory Type Product Estimator for Population Mean in Stratified Random Sampling Under Linear Cost Function

SN Computer science 2023年第3期4卷 1-9页

作者： Yadav, Subhash Kumar Vishwakarma, Gajendra K. Varshney, Rahul Pal, Arun Department of Statistics School of Physical and Decision Sciences Babasaheb Bhimrao Ambedkar University Lucknow 226025 India Department of Mathematics and Computing Indian Institute of Technology Dhanbad Dhanbad 826004 India Institute of Actuarial Science and Data Analytics UCSI University Kuala Lumpur 56000 Malaysia

In this article, we consider the memory type product estimator to estimate the population mean of the study variable in stratified random sampling. The suggested estimators’ bias and mean square error (MSE) expressions for the first order of approximation are derived. The proposed estimator is compared with the competing estimators under consideration and the efficiencies’ conditions are obtained. Further, we obtain the optimum allocation of samples in different strata along with MSEs. To achieve the solution of the problem, we frame our problem as AINLPP and solve through genetic programming technique. To show the efficiency of the estimator, we conduct simulation study. An empirical analysis based on real-life data is also offered to support the findings of the simulation study. The most efficient estimator is recommended for different practical applications. The suggested estimator is proven to be the best among the competing estimators. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： AINLPP Genetic programming technique Linear cost function Product estimator Stratified random sampling

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arXiv

引用

arXiv 2024年

作者： Song, Jianqing Huang, Jianguo Jiang, Wenyu Zhang, Baoming Li, Shuangjie Wang, Chongjun State Key Laboratory of Novel Software Technology Nanjing University China Department of Statistics and Data Science Southern University of Science and Technology China College of Computing and Data Science Nanyang Technological University Singapore

Graph Neural Networks have achieved remarkable accuracy in semi-supervised node classification tasks. However, these results lack reliable uncertainty estimates. Conformal prediction methods provide a theoretical guarantee for node classification tasks, ensuring that the conformal prediction set contains the ground-truth label with a desired probability (e.g., 95%). In this paper, we empirically show that for each node, aggregating the non-conformity scores of nodes with the same label can improve the efficiency of conformal prediction sets while maintaining valid marginal coverage. This observation motivates us to propose a novel algorithm named Similarity-Navigated Adaptive Prediction Sets (SNAPS), which aggregates the non-conformity scores based on feature similarity and structural neighborhood. The key idea behind SNAPS is that nodes with high feature similarity or direct connections tend to have the same label. By incorporating adaptive similar nodes information, SNAPS can generate compact prediction sets and increase the singleton hit ratio (correct prediction sets of size one). Moreover, we theoretically provide a finite-sample coverage guarantee of SNAPS. Extensive experiments demonstrate the superiority of SNAPS, improving the efficiency of prediction sets and singleton hit ratio while maintaining valid coverage. © 2024, CC BY.

关键词： Prediction models

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LARGE LANGUAGE MODELS ASSUME PEOPLE ARE MORE RATIONAL THAN WE REALLY ARE

arXiv

引用

arXiv 2024年

作者： Liu, Ryan Geng, Jiayi Peterson, Joshua C. Sucholutsky, Ilia Griffiths, Thomas L. Department of Computer Science Princeton University United States Computing & Data Sciences Boston University United States Center for Data Science New York University United States Department of Psychology Department of Computer Science Princeton University United States

In order for AI systems to communicate effectively with people, they must understand how we make decisions. However, people’s decisions are not always rational, so the implicit internal models of human decision-making in Large Language Models (LLMs) must account for this. Previous empirical evidence seems to suggest that these implicit models are accurate — LLMs offer believable proxies of human behavior, acting how we expect humans would in everyday interactions. However, by comparing LLM behavior and predictions to a large dataset of human decisions, we find that this is actually not the case: when both simulating and predicting people’s choices, a suite of cutting-edge LLMs (GPT-4o & 4-Turbo, Llama-3-8B & 70B, Claude 3 Opus) assume that people are more rational than we really are. Specifically, these models deviate from human behavior and align more closely with a classic model of rational choice — expected value theory. Interestingly, people also tend to assume that other people are rational when interpreting their behavior. As a consequence, when we compare the inferences that LLMs and people draw from the decisions of others using another psychological dataset, we find that these inferences are highly correlated. Thus, the implicit decision-making models of LLMs appear to be aligned with the human expectation that other people will act rationally, rather than with how people actually act. © 2024, CC BY-NC-SA.

关键词： Human form models

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Hybrid Approach for Electricity Price Forecasting using AlexNet and LSTM

Hybrid Approach for Electricity Price Forecasting using Alex...

引用

Intelligent data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： Bosubabu Sambana Kotamsetty Geethika Devi Bandi Rajeswara Reddy Galeti Mohammad Hussain Gownivalla Siddartha Department of Computer Science and Engineering (Data Science) School of Computing Mohan Babu University Tirupati India Department of Computer Science and Systems Engineering Sree Vidyanikethan Engineering College Tirupati India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

The recent development of advanced machine learning methods for hybrid models has greatly addressed the need for the correct prediction of electrical prices. This method combines AlexNet and LSTM algorithms, which are used to introduce a new model with higher accuracy in price forecasting. Despite RNN and ANN being effective, they often fail to deal with forex time sequence data. The traditional methods do not accurately forecast the prices. These traditional methods only focus on demand and price which leads to insufficient analysis of data. To address this issue, using the hybrid approach, which focuses on external variables that also effect the predicted prices. Nevertheless, due to AlexNet's excellent feature extraction and LSTM's learning sequential patterns, the prediction accuracy is vastly increased. The model is built on the past data, which has been supplied with the most significant elements like demand, temperature, sunlight, and rain. For example, the model applies methods, such as minimum-maximum scaling and a time window, to predict the electricity prices of the future. The results show that this hybrid model is good than the standalone ones in terms of accuracy. Although we got our accuracy rating of 97.08, it shows higher accompaniments than remaining models RNN and ANN with accuracies of 96.64 and 96.63 respectively.

关键词： Temperature distribution Accuracy Recurrent neural networks Rain Electricity Predictive models Prediction algorithms data models Forecasting Long short term memory

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Design of ISAC Waveform and Multiple-Access Interference Suppression Receiver for Underwater Acoustic Sensor Networks

Design of ISAC Waveform and Multiple-Access Interference Sup...

引用

IEEE Conference on Global Communications (GLOBECOM)

作者： Wei Men Jun Du Jintao Wang Xiangwang Hou Yong Ren Dusit Niyato Department of Electronic Engineering Tsinghua University Beijing China College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Integrated sensing and communication (ISAC) technology is envisioned as a pivotal component for the next-generation communication networks. Similarly, underwater acoustic ISAC (UWA-ISAC) holds promising prospects for enhancing future UWA sensor networks due to its efficient communication and sensing capabilities. However, the design of UWA-ISAC waveforms and receivers suitable for multi-user scenarios faces formidable challenges, primarily arising from the complex UWA channel and multi-access interference (MAI). In this paper, we propose a UWA-ISAC waveform design scheme based on generalized sinusoidal frequency modulation (GSFM). The proposed waveform provides satisfactory communication and sensing performance and exhibits excellent orthogonality. Furthermore, we design a MAI suppression receiver, leveraging successive interference cancellation based on two-factor compensation and turbo equalization to improve interference suppression capabilities and enhance communication performance. Simulation results validate that the proposed UWA-ISAC waveform has an approximate thumbtack ambiguity function and comparable cross-correlation properties with GSFM under the defined parameters. Moreover, the designed receiver with low training sequence overhead is robust against Doppler, and can iteratively improve the MAI suppression performance.

关键词： Interference suppression Training Phase modulation Simulation Receivers Integrated sensing and communication Delays Performance analysis Doppler effect Underwater acoustics

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OM4OV: Leveraging Ontology Matching for Ontology Versioning

arXiv

引用

arXiv 2024年

作者： Qiang, Zhangcheng Taylor, Kerry Wang, Weiqing School of Computing Australian National University Canberra Australia Department of Data Science AI Monash University Melbourne Australia

Due to the dynamic nature of the semantic web, ontology version control is required to capture time-varying information, most importantly for widely-used ontologies. Despite the long-standing recognition of ontology versioning (OV) as a crucial component for efficient ontology management, the growing size of ontologies and accumulating errors caused by manual labour overwhelm current OV approaches. In this paper, we propose yet another approach to performing OV using existing ontology matching (OM) techniques and systems. We introduce a unified OM4OV pipeline. From an OM perspective, we reconstruct a new task formulation, measurement, and testbed for OV tasks. Reusing the prior alignment(s) from OM, we propose a pipeline optimisation method called cross-reference (CR) mechanism to improve overall OV performance. We experimentally validate the OM4OV pipeline and the cross-reference mechanism in modified Ontology Alignment Evaluation Initiative (OAEI) datasets. We also discuss the insights on OM used for OV tasks, where some false mappings detected by OV systems are not actually false. Copyright © 2024, The Authors. All rights reserved.

关键词： Errors

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Event Causality Identification - Shared Task 3, CASE 2023 6

Event Causality Identification - Shared Task 3, CASE 2023

引用

6th Workshop on Challenges and Applications of Automated Extraction of Socio-Political Events from Text, CASE 2023

作者： Tan, Fiona Anting Hettiarachchi, Hansi Hürriyetoğlu, Ali Oostdijk, Nelleke Uca, Onur Thapa, Surendrabikram Liza, Farhana Ferdousi Institute of Data Science National University of Singapore Singapore School of Computing and Digital Technology Birmingham City University United Kingdom KNAW Humanities Cluster DHLab Netherlands Centre for Language Studies Radboud University Netherlands Department of Sociology Mersin University Turkey Department of Computer Science Virginia Tech United States School of Computing Sciences University of East Anglia United Kingdom

ISBN: (纸本)9789544520892

The Event Causality Identification Shared Task of CASE 2023 is the second iteration of a shared task centered around the Causal News Corpus. Two subtasks were involved: In Subtask 1, participants were challenged to predict if a sentence contains a causal relation or not. In Subtask 2, participants were challenged to identify the Cause, Effect, and Signal spans given an input causal sentence. For both subtasks, participants uploaded their predictions for a held-out test set, and ranking was done based on binary F1 and macro F1 scores for Subtask 1 and 2, respectively. This paper includes an overview of the work of the ten teams that submitted their results to our competition and the six system description papers that were received. The highest F1 scores achieved for Subtask 1 and 2 were 84.66% and 72.79%, respectively. © CASE 2023 - Proceedings of the 6th Workshop on Challenges and Applications of Automated Extraction of Socio-Political Events from Text, associated with 14th International Conference on Recent Advances in Natural Language Processing, RANLP 2023.

关键词： Natural language processing systems

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Enhancing Scientific Knowledge Graph Generation Pipelines with LLMs and Human-in-the-Loop 4

Enhancing Scientific Knowledge Graph Generation Pipelines wi...

引用

4th International Workshop on Scientific Knowledge: Representation, Discovery, and Assessment, Sci-K 2024

作者： Tsaneva, Stefani Dessì, Danilo Osborne, Francesco Sabou, Marta Institute of Data Process and Knowledge Management Vienna University of Economics and Business Austria Computer Science Department College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates Knowledge Media Institute The Open University United Kingdom Department of Business and Law University of Milano Bicocca Italy

Scientific Knowledge Graphs have recently become a powerful tool for exploring the research landscape and assisting scientific inquiry. It is crucial to generate and validate these resources to ensure they offer a comprehensive and accurate representation of specific research fields. However, manual approaches are not scalable, while automated methods often result in lower-quality resources. In this paper, we investigate novel validation techniques to improve the accuracy of automated KG generation methodologies, leveraging both a human-in-the-loop (HiL) and a large language model (LLM)-in-the-loop. Using the automated generation pipeline of the Computer science Knowledge Graph as a case study, we demonstrate that precision can be increased by 12% (from 75% to 87%) using only LLMs. Moreover, a hybrid approach incorporating both LLMs and HiL significantly enhances both precision and recall, resulting in a 4% increase in the F1 score (from 77% to 81%). © 2022 Copyright for this paper by its authors.

关键词： Knowledge graph

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Extended Kalman Filter Algorithm to Detect False data Injection in Wireless Sensor Network 2

Extended Kalman Filter Algorithm to Detect False Data Inject...

引用

2nd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Babuji, R. Ganesan, J. Jeya Lekha, D. Karthikeyan, M. Kalai Selvi, D.M. Rmk Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Sri Sairam Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Rmk College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Srm Institute of Science and Technology School of Computing Faculty of Engineering and Technology Department of Computing Technologies Tamil Nadu Chennai India R.M.D. Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350325799

Wireless Sensor Networks (WSN) contains spatially distributed sensor nodes that collaborate with each other. However, the WSN is susceptible since the wireless medium is unpredictable. Several conventional approaches are used to distinguish the abnormal in the WSN, and all have difficulties. An Extended Kalman Filter Algorithm (EKFA) to detect false inject data in WSN is proposed in this paper. It addresses two issues such as abnormality recognition and obstacle detection. It also distinguishes false insert data. Particularly, it observes neighbors' behaviors and then applying EKFA to forecast their future states. The node behavior is observed by incorporating abnormal node examination and system examination elements. The Path Optimization Method (POM) is a shortest route that is used to discover the obstacle in the WSN. The EKFA mechanism minimizes the forwarder count and delay by applying POM. Simulation results demonstrate that the EKFA approach enhances abnormal node detection and minimizes the false negative ratio. © 2023 IEEE.

关键词： Extended Kalman filters

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Early Fault Detection in Rolling Element Bearings Using Signal Processing Techniques 21

Early Fault Detection in Rolling Element Bearings Using Sign...

引用

21st IEEE International Conference on Smart Communities: Improving Quality of Life using AI, Robotics and IoT, HONET 2024

作者： Saeed, Ali Akram, M. Usman Khan, Muazzam A. Afsar, M. Belal Quaid-i-Azam University ICESCO Big Data Analytics and Edge Computing Department of Computer Science Islamabad Pakistan National University of Sciences and Technology Department of Computer and Software Engineering Islamabad Pakistan Otto-von-Guericke University Faculty of Electrical Engineering and Information Technology Maddeburg Germany

ISBN: (纸本)9798350378078

Many studies show that bearings are the most vulnerable components in low-voltage motors. While advanced bearing diagnostic systems exist, their cost can be a barrier for non-critical machinery due to the potential wait time to see a return on investment. This research explores the feasibility of using basic signal processing techniques on vibration data for bearing condition monitoring. The primary goal is to analyze benchmark data from the Case Western Reserve University (CWRU) dataset and establish a baseline performance for this data-driven approach. The results suggest that, with appropriate signal processing, it's possible to achieve early detection of bearing faults, leading to more efficient time-To-fault identification with higher detection accuracies ranging from 83 % to 100 % for each method. © 2024 IEEE.

关键词： Signal modulation

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