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4th International Conference on Cybernetics and Intelligent System, ICORIS 2022

作者： Bimantio, Mohammad Prasanto Putra, Dian Pratama Suparyanto, Teddy Ferhat, Amallia Nugraha, Nanda Satya Hidayat, Alam Ahmad Pardamean, Bens Institute of Agriculture STIPER Department of Food and Agricultural Technology Yogyakarta Indonesia Institute of Agriculture STIPER Department of Agrotechnology Yogyakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Institute of Agriculture STIPER Department of Agricultural Socio-Economics Yogyakarta Indonesia Institute of Agriculture STIPER Department of Forestry Yogyakarta Indonesia Bina Nusantara University BINUS Graduate Program - Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781665453950

In this era of digital world information, farmers face challenges about information management of huge data and the complexity of processes in precision farming. The data collection carried out so far can cause several difficulties, especially if the data is needed by the government or academics to support decision-making. Agricultural data collection applications only cover 12% of the market share of Android-based agriculture-based applications. In this study, Tani Milenial application is present as an alternative to support agricultural data collection activities in Indonesia. This application encourages farmers to be active and contribute independently to their role as subjects of agricultural development and is accustomed to digitally recording their agricultural activities. In this developed application system, the community, especially farmers and farmer groups, will find it easier to get information about agricultural potential data around where they carry out agricultural activities and can access this information online. Tani Milenial works as a guide to help farmers and transform agriculture but still have some gaps which should be checked and removed. The data collected can be used by various parties for research and decision-making. The app can be further improved to develop new features related to education, market information, and goods. © 2022 IEEE.

关键词： Decision making

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Agile Ontology: A Dynamic Framework for E-Business Evolution

Agile Ontology: A Dynamic Framework for E-Business Evolution

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Emerging Smart Technologies and Applications (eSmarTA), International Conference on

作者： Mohammad Mustafa Taye Rawan Abulail Belal Al-Ifan Software Engineering Department Philadelphia University Amman Jordan Computer Science Department Philadelphia University Amman Jordan Data Science and Artificial Intelligence Department Philadelphia University Amman Jordan

ISBN: (数字)9798350354133

ISBN: (纸本)9798350354140

In this work, a progressed version of Extreme programming (XP) for e-business is provided. In an effort to efficiently address the precise troubles of this dynamic industry, the strategy promotes iterative procedures and more functions. By including new elements that guide growth, independence, and fine manipulation, Enhanced XP for E-business (EXPe) builds upon the fundamental XP concepts of continuous feedback and collaboration. The e-business enterprise faces challenges such as rapidly evolving marketplace needs and the want for scalable, adaptive solutions. EXPe targets to cope with these by using selling iterative development and incorporating more suitable functionalities to help non-stop adaptation and scalability. The layout prioritizes user standards, adds more scalability mechanisms, and employs iterative improvement techniques to help development teams adapt quickly and efficaciously to changing E-enterprise desires. This study indicates that EXPe is a great choice for quickly and efficaciously growing E-business solutions due to its many benefits and capabilities. This technique aims to provide a strong foundation for engaging in non-intrusive studies on agile methods. Enhanced XP for E-Business (EXPe) uniquely integrates ontology-unique requirements and semantic research into the agile framework, extensively enhancing the adaptability and scalability of e-commercial enterprise solutions. Furthermore, listed for this purpose are applicable files and XP artifacts, including user stories.

关键词： Scalability Layout User centered design Semantics Collaboration Ontologies programming

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Dimensionality and dynamics for next-generation artificial neural networks

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Patterns 2025年

作者： Wang, Ge Fan, Feng-Lei Department of Biomedical Engineering Department of Electrical Computer and Systems Engineering Department of Computer Science Center for Computational Innovations Biomedical Imaging Center Center for Biotechnology and Interdisciplinary Studies Rensselaer Polytechnic Institute TroyNY United States Department of Data Science City University of Hong Kong Kowloon Hong Kong

The recent awarding of the Nobel Prize in Physics to Geoffrey E. Hinton and John J. Hopfield highlights their profound impact on artificial neural networks. In this perspective, we explore how their foundational insights can drive the advancement of next-generation artificial intelligence (AI) models. We propose expanding beyond conventional architectures by introducing dimensionality through intra-layer links and dynamics via feedback loops. Network height and additional dimensions, alongside traditional width and depth, enhance learning capabilities, while entangled loops across scales induce emergent behaviors akin to phase transitions in physics. We discuss how these principles extend beyond transformers, fostering a new paradigm of intelligence inspired by physics-driven models and biological cognition mechanisms. © 2025 The Author(s)

关键词： Neural network models

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A COMPARATIVE ANALYSIS ON VOLATILITY AND SCALABILITY PROPERTIES OF BLOCKCHAIN COMPRESSION PROTOCOLS

arXiv

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arXiv 2023年

作者： Zhang, Gerui Zhao, Xiongfei Si, Yain-Whar Center for Data Science University of Macau Macau China Department of Computer and Information Science University of Macau Macau China

Increasing popularity of trading digital assets can lead to significant delays in Blockchain networks when processing transactions. When transaction fees become miners’ primary revenue, an imbalance in reward may lead to miners adopting deviant mining strategies. Scaling the block capacity is one of the potential approaches to alleviate the problem. To address this issue, this paper reviews and evaluates six state-of-the-art compression protocols for Blockchains. Specifically, we designed a Monte Carlo simulation to simulate two of the six protocols to observe their compression performance under larger block capacities. Furthermore, extensive simulation experiments were conducted to observe the mining behaviour when the block capacity is increased. Experimental results reveal an interesting trade-off between volatility and scalability. When the throughput is higher than a critical point, it worsens the volatility and threatens Blockchain security. In the experiments, we further analyzed the relationship between volatility and scalability properties with respect to the distribution of transaction values. Based on the analysis results, we proposed the recommended maximum block size for each protocol. At last, we discuss the further improvement of the compression protocols. Copyright © 2023, The Authors. All rights reserved.

关键词： Scalability

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Flying Neural Network-Based Optimistic Financial Early Alert System in AI Model 6

Flying Neural Network-Based Optimistic Financial Early Alert...

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Boggavarapu, Saikrishna Ali, S. Shabbir Manikandan, G. Mohanraj, R. Singh, Devesh Pratap Revathi, R. University of the Cumberlands Department of Information Technology Prospect HeightsIL United States KL University Department of Electronics & Communication Engineering Guntur India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai Thandalam India St.Martin's Engineering College Department of Artificial Intelligence and Data Science Telangana Secunderabad India Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttrakhand Dehradun India Karpagam Academy of Higher Education Department of Computer Science Coimbatore India

ISBN: (纸本)9798350304480

An innovative method for early warnings in the area of financial risk management is the Flying Neural Network-based Optimistic Financial Early Alert System (FNN-OFEAS). The FNN-OFEAS outperforms conventional models and rule-based systems because it makes use of the Flying Neural Network architecture and incorporates sentiment analysis. Using a fictitious dataset including past stock prices, financial ratios, economic indicators, and sentiment analysis data, this study details the development, use, and findings of the FNN-OFEAS. The results show that the system is effective at capturing complicated temporal correlations, recognising non-linear patterns, and factoring in market sentiment, all of which contribute to improved risk identification in a timely and accurate manner. Early risk identification, investment decision assistance, regulatory compliance, and financial stability evaluation are just few of the real-world uses for the FNN-OFEAS. Its application is beneficial for financial institutions, investors, regulators, and policymakers since it enhances decision-making, reduces risk, and sheds light on previously hidden information. More work has to be done to improve the model architecture, include other data sources, provide real-time implementations, evaluate the model's resilience, and make it easier to comprehend the results. Addressing financial risks and bolstering financial stability in volatile market settings is one area where the FNN-OFEAS shows promise. © 2023 IEEE.

关键词： 'Flying Neural Network Optimistic Financial Early Alert System (OFEAS)' conventional models early alerts economic indicators Financial anomalies financial ratios financial risk management financial stability historical stock prices hypothetical dataset investment decision support market sentiment practical applications risk detection rule-based systems sentiment analysis

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Meta-Posterior Consistency for the Bayesian Inference of Metastable Systems

arXiv

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arXiv 2024年

作者： Adams, Zachary P. Mukherjee, Sayan Freie Universität Berlin Department of Mathematics and Computer Science Center for Scalable Data Analytics and Artificial Intelligence Universität Liepzig Germany Center for Scalable Data Analytics and Artificial Intelligence Department of Computer Science Universität Liepzig Germany Max Planck Institute for Mathematics in the Natural Sciences Duke University Departments of Statistical Science Mathematics Computer Science and Biostatistics & Bioinformatics United States

The vast majority of the literature on learning dynamical systems or stochastic processes from time series has focused on stable or ergodic systems, for both Bayesian and frequentist inference procedures. However, most real-world systems are only metastable, that is, the dynamics appear to be stable on some time scale, but are in fact unstable over longer time scales. Consistency of inference for metastable systems may not be possible, but one can ask about metaconsistency: Do inference procedures converge when observations are taken over a large but finite time interval, but diverge on longer time scales? In this paper we introduce, discuss, and quantify metaconsistency in a Bayesian framework. We discuss how metaconsistency can be exploited to efficiently infer a model for a sub-system of a larger system, where inference on the global behavior may require much more data. We also discuss the relation between metaconsistency and the spectral properties of the model dynamical system in the case of uniformly ergodic *** Codes 62F15, 60J70 © 2024, CC BY.

关键词： Stochastic systems

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Empirical Analysis of EIP-1559: Transaction Fees, Waiting Times, and Consensus Security 22

Empirical Analysis of EIP-1559: Transaction Fees, Waiting Ti...

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28th ACM SIGSAC Conference on computer and Communications Security, CCS 2022

作者： Liu, Yulin Lu, Yuxuan Nayak, Kartik Zhang, Fan Zhang, Luyao Zhao, Yinhong SciEcon Cic London United Kingdom Center on Frontiers of Computing Studies Peking University Beijing China Department of Computer Science Duke University DurhamNC United States Department of Computer Science Yale University New HavenCT United States Data Science Research Center and Social Science Division Duke Kunshan University Suzhou China

ISBN: (纸本)9781450394505

A transaction fee mechanism (TFM) is an essential component of a blockchain protocol. However, a systematic evaluation of the real-world impact of TFMs is still absent. Using rich data from the Ethereum blockchain, the mempool, and exchanges, we study the effect of EIP-1559, one of the earliest-deployed TFMs that depart from the traditional first-price auction paradigm. We conduct a rigorous and comprehensive empirical study to examine its causal effect on blockchain transaction fee dynamics, transaction waiting times, and consensus security. Our results show that EIP-1559 improves the user experience by mitigating intrablock differences in the gas price paid and reducing users' waiting times. However, EIP-1559 has only a small effect on gas fee levels and consensus security. In addition, we find that when Ether's price is more volatile, the waiting time is significantly higher. We also verify that a larger block size increases the presence of siblings. These findings suggest new directions for improving TFMs. © 2022 Owner/Author.

关键词： Blockchain

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Evolutionary Multiobjective Feature Selection Assisted by Unselected Features 13

Evolutionary Multiobjective Feature Selection Assisted by Un...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Duan, Xuan Liu, Songbai Ji, Junkai Li, Lingjie Lin, Qiuzhen Tan, Kay Chen College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen China Shenzhen China The Hong Kong Polytechnic University Department of Computing Hong Kong

ISBN: (纸本)9798350308365

To enhance the generalization of multi-objective feature selection (MOFS) in classification, this paper proposes an evolutionary multitasking algorithm, diverging from previous approaches that exclusively target selected features. The algorithm integrates information from both selected and unselected features, introducing a novel objective to minimize the accuracy of unselected features. This objective, combined with the goal of minimizing classification errors for selected features, forms an auxiliary MOFS task. The paper presents a dual-population evolutionary multitasking framework that synergizes the main MOFS task with the auxiliary task. A knowledge transfer mechanism, based on accuracy preferences, seamlessly shares insights from the auxiliary to the main task, aiming to identify improved Pareto feature subsets. Empirical results demonstrate the superior performance of several state-of-the-art multi-objective algorithms within this framework, highlighting significant improvements across diverse datasets. © 2024 IEEE.

关键词： Feature Selection

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Towards Fair and Privacy Preserving Federated Learning for the Healthcare Domain

arXiv

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arXiv 2023年

作者： Annapareddy, Navya Liu, Yingzheng Fox, Judy Computer Science Department University of Virginia United States School of Data Science University of Virginia Charlottesville United States

Federated learning (FL) enables data sharing in healthcare contexts where it might otherwise be difficult due to data-use-ordinances or security and communication constraints. Distributed and shared data models allow models to become generalizable and learn from heterogeneous clients. While addressing data security, privacy, and vulnerability considerations, data itself is not shared across nodes in a given learning network. On the other hand, FL models often struggle with variable client data distributions and operate on an assumption of independent and identically distributed(IID) data. As the field has grown, the notion of fairness-aware federated learning (FAFL) mechanisms has also been introduced and is of distinct significance to the healthcare domain where many sensitive groups and protected classes exist. In this paper, we create a benchmark methodology for FAFL mechanisms under various heterogeneous conditions on datasets in the healthcare domain typically outside the scope of current federated learning benchmarks, such as medical imaging and waveform data formats. Our results indicate considerable variation in how various FAFL schemes respond to high levels of data heterogeneity. Additionally, doing so under privacy-preserving conditions can create significant increases in network communication cost and latency compared to the typical federated learning scheme. Copyright © 2023, The Authors. All rights reserved.

关键词： Medical imaging

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Pseudo Triplet Guided Few-shot Composed Image Retrieval

arXiv

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arXiv 2024年

作者： Hou, Bohan Lin, Haoqiang Wen, Haokun Liu, Meng Xu, Mingzhu Song, Xuemeng The Department of Computer Science and Technology Shandong University Qingdao China Shenzhen China The Department of Data Science City University of Hong Kong Hong Kong The Department of Computer Science Shandong Jianzhu University Jinan China The School of Software Shandong University Jinan China

Composed Image Retrieval (CIR) is a challenging task that aims to retrieve the target image with a multimodal query, i.e., a reference image, and its complementary modification text. As previous supervised or zero-shot learning paradigms all fail to strike a good trade-off between the model’s generalization ability and retrieval performance, recent researchers have introduced the task of few-shot CIR (FS-CIR) and proposed a textual inversion-based network based on pretrained CLIP model to realize it. Despite its promising performance, the approach encounters two key limitations: simply relying on the few annotated samples for CIR model training and indiscriminately selecting training triplets for CIR model fine-tuning. To address these two limitations, we propose a novel two-stage pseudo triplet guided few-shot CIR scheme, dubbed PTG-FSCIR. In the first stage, we propose an attentive masking and captioning-based pseudo triplet generation method, to construct pseudo triplets from pure image data and use them to fulfill the CIR-task specific pretraining. In the second stage, we propose a challenging triplet-based CIR fine-tuning method, where we design a pseudo modification text-based sample challenging score estimation strategy and a robust top range-based random sampling strategy for sampling robust challenging triplets to promote the model fine-tuning. Notably, our scheme is plug-and-play and compatible with any existing supervised CIR models. We test our scheme across two backbones on three public datasets (i.e., FashionIQ, CIRR, and Birds-to-Words), achieving maximum improvements of 13.3%, 22.2%, and 17.4% respectively, demonstrating our scheme’s efficacy. © 2024, CC BY.

关键词： Zero-shot learning

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