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SSRN 2023年

作者： Hu, Wentao Jiang, Dawei Wu, Sai Chen, Ke Chen, Gang Zhejiang Police College Hangzhou China The Key Laboratory of Big Data Intelligent Computing of Zhejiang Province Zhejiang University China

Domain experts in fraud detection and data Security often face challenges in automatically discovering constraints from relational databases, which is a critical process for identifying anomalous data. Its objective is to discover a set of inherent constraints underlying the database such that tuples violating them are considered anomalous. IceBerg, a novel system, is designed to simultaneously detect anomalous tuples and discover associated human-readable constraints, enabling both efficient anomaly detection and interpretable fraud. The backbone of IceBerg is a novel generative network, namely KD-VAE. By integrating Kernel Density estimation with Variational AutoEncoder, KD-VAE addresses the challenge of capturing complex data distributions, a critical aspect for effective anomaly detection. The KD-VAE, with its unique capability of combining kernel density estimation with variational autoencoders, provides superior performance in capturing the distributions of normal tuples, paving the way for effective anomaly detection. We can perform anomalous data detection by calculating the likelihood that the tuple fits the distributions of normal tuples and abnormality interpretation by comparing the detected anomalous tuples with their generated normal counterparts. We conducted empirical comparisons of IceBerg with several state-of-the-art outlier detection methods, evaluating performance metrics such as F1-score, across 13 real-world datasets. The results show that IceBerg outperforms its competitors in most cases. Its inherent capability to model complex, high-dimensional distributions makes it especially potent when handling datasets with high-dimensional features. © 2023, The Authors. All rights reserved.

关键词： Anomaly detection

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BENCHMARKING AGENTIC WORKFLOW GENERATION

arXiv

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arXiv 2024年

作者： Qiao, Shuofei Fang, Runnan Qiu, Zhisong Wang, Xiaobin Zhang, Ningyu Jiang, Yong Xie, Pengjun Huang, Fei Chen, Huajun Zhejiang University China Alibaba Group China Zhejiang Key Laboratory of Big Data Intelligent Computing China

Large Language Models (LLMs), with their exceptional ability to handle a wide range of tasks, have driven significant advancements in tackling reasoning and planning tasks, wherein decomposing complex problems into executable workflows is a crucial step in this process. Existing workflow evaluation frameworks either focus solely on holistic performance or suffer from limitations such as restricted scenario coverage, simplistic workflow structures, and lax evaluation standards. To this end, we introduce WORFBENCH, a unified workflow generation benchmark with multi-faceted scenarios and intricate graph workflow structures. Additionally, we present WORFEVAL, a systemic evaluation protocol utilizing subsequence and subgraph matching algorithms to accurately quantify the LLM agent’s workflow generation capabilities. Through comprehensive evaluations across different types of LLMs, we discover distinct gaps between the sequence planning capabilities and graph planning capabilities of LLM agents, with even GPT-4 exhibiting a gap of around 15%. We also train two open-source models and evaluate their generalization abilities on held-out tasks. Furthermore, we observe that the generated workflows can enhance downstream tasks, enabling them to achieve superior performance with less time during inference1 Copyright © 2024, The Authors. All rights reserved.

关键词： Benchmarking

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Dynamic Modeling and Sensitivity Analysis for an MEA-Based CO_(2) Capture Absorber

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intelligent Automation & Soft computing 2023年第6期36卷 3535-3550页

作者： Hongwei Guan Lingjian Ye Yurun Wang Feifan Shen Yuchen He Ningbo University of Finance and Economics Ningbo315175China Huzhou University Huzhou313000China Key Laboratory of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang Province China Jiliang UniversityHangzhou310018China Ningbotech University Ningbo315100China

The absorber is the key unit in the post-combustion monoethanolamine(MEA)-based carbon dioxide(CO_(2))capture process.A rate-based dynamic model for the absorber is developed and validated using steady-state experimental data reported in open *** analysis is performed with respect to important model parameters associated with the reaction,mass transport and phy-sical property ***,a singular value decomposition(SVD)-based subspace parameter estimation method is proposed to improve the model ***,dynamic simulations are carried out to investigate the effects of the feed rate of lean MEA solution and the flue inlet *** results indicate that the established dynamic model can reasonably reflect the physical behavior of the *** new insights are gained from the simulation results.

关键词： CO_(2)capture dynamic modeling sensitivity analysis model validation

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WISE: Rethinking the Knowledge Memory for Lifelong Model Editing of Large Language Models

arXiv

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arXiv 2024年

作者： Wang, Peng Li, Zexi Zhang, Ningyu Xu, Ziwen Yao, Yunzhi Jiang, Yong Xie, Pengjun Huang, Fei Chen, Huajun Zhejiang University China Alibaba Group China Zhejiang Key Laboratory of Big Data Intelligent Computing China

Large language models (LLMs) need knowledge updates to meet the ever-growing world facts and correct the hallucinated responses, facilitating the methods of lifelong model editing. Where the updated knowledge resides in memories is a fundamental question for model editing. In this paper, we find that editing either long-term memory (direct model parameters) or working memory (nonparametric knowledge of neural network activations/representations by retrieval) will result in an impossible triangle—reliability, generalization, and locality can not be realized together in the lifelong editing settings. For long-term memory, directly editing the parameters will cause conflicts with irrelevant pretrained knowledge or previous edits (poor reliability and locality). For working memory, retrieval-based activations can hardly make the model understand the edits and generalize (poor generalization). Therefore, we propose WISE to bridge the gap between memories. In WISE, we design a dual parametric memory scheme, which consists of the main memory for the pretrained knowledge and a side memory for the edited knowledge. We only edit the knowledge in the side memory and train a router to decide which memory to go through when given a query. For continual editing, we devise a knowledge-sharding mechanism where different sets of edits reside in distinct subspaces of parameters and are subsequently merged into a shared memory without conflicts. Extensive experiments show that WISE can outperform previous model editing methods and overcome the impossible triangle under lifelong model editing of question answering, hallucination, and out-of-distribution settings across trending LLM architectures, e.g., GPT, LLaMA, and Mistral. Copyright © 2024, The Authors. All rights reserved.

关键词： Memory architecture

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Uncertainty-aware complementary label queries for active learning

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Frontiers of Information Technology & Electronic Engineering 2023年第10期24卷 1497-1503页

作者： Shengyuan LIU Ke CHEN Tianlei HU Yunqing MAO Key Lab of Intelligent Computing Based Big Data of Zhejiang Province Zhejiang UniversityHangzhou 310027China State Key Laboratory of Blockchain and Data Security Zhejiang UniversityHangzhou 310027China City Cloud Technology(China)Co. Ltd.Hangzhou 310000China

Many active learning methods assume that a learner can simply ask for the full annotations of some training data from *** methods mainly try to cut the annotation costs by minimizing the number of annotation ***,annotating instances exactly in many realworld classification tasks is still *** reduce the cost of a single annotation action,we try to tackle a novel active learning setting,named active learning with complementary labels(ALCL).ALCL learners ask only yes/no questions in some *** receiving answers from annotators,ALCL learners obtain a few supervised instances and more training instances with complementary labels,which specify only one of the classes to which the pattern does not *** are two challenging issues in ALCL:one is how to sample instances to be queried,and the other is how to learn from these complementary labels and ordinary accurate *** the first issue,we propose an uncertainty-based sampling strategy under this novel setup.

关键词： complementary instance ordinary

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Some Thoughts on the Classification Standard of intelligent Autonomous Level of Measurement and Control System 9

Some Thoughts on the Classification Standard of Intelligent ...

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9th International Conference on big data and Information Analytics, bigDIA 2023

作者： Yuan, Guangfu Yang, Liu Han, Haoran Li, Yong Shao, Cong Zhan, Siyu Gansu Photoelectric Measurement Technology Research Laboratory Gansu735018 China University of Electronic Science and Technology of China Laboratory of Intelligent Collaborative Computing Chengdu China Trusted Cloud Computing and Big Data Key Laboratory of Sichuan Province Chengdu611731 China

ISBN: (纸本)9798350330076

Intelligence is an important trend in the development of measurement and control systems. However, there is still a lack of unified understanding of the intelligent autonomous classification of the measurement and control system. The establishment of intelligent evaluation standards in this field has become an urgent research topic. This paper first gives an overview of the aviation measurement and control system, and then takes the classification standards of autonomy in the fields of automobile driving automation in China and the United states, the autonomy and autonomy level of typical ships in China, and the automation of civil unmanned aircraft system in China as a reference. A 5-level intelligent grading standard based on task adaptability, environmental complexity and human-computer interaction is proposed. The classification standard is practical and easy to understand. It can provide reference for the establishment of intelligent standards for measurement and control systems, improve the application speed of intelligent autonomous technology in the field of measurement and control, and broaden the application prospect of intelligent measurement and control systems. © 2023 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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OmniThink: Expanding Knowledge Boundaries in Machine Writing through Thinking

arXiv

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arXiv 2025年

作者： Xi, Zekun Yin, Wenbiao Fang, Jizhan Wu, Jialong Fang, Runnan Zhang, Ningyu Jiang, Yong Xie, Pengjun Huang, Fei Chen, Huajun Zhejiang University China Tongyi Lab Alibaba Group China Zhejiang Key Laboratory of Big Data Intelligent Computing China

Machine writing with large language models often relies on retrieval-augmented generation. However, these approaches remain confined within the boundaries of the model’s predefined scope, limiting the generation of content with rich information. Specifically, vanilla-retrieved information tends to lack depth, novelty, and suffers from redundancy, which negatively impacts the quality of generated articles, leading to shallow, unoriginal, and repetitive outputs. To address these issues, we propose OmniThink, a slow-thinking machine writing framework that emulates the human-like process of iterative expansion and reflection. The core idea behind OmniThink is to simulate the cognitive behavior of learners as they slowly deepen their knowledge of the topics. Experimental results demonstrate that OmniThink improves the knowledge density of generated articles without compromising metrics such as coherence and depth. Human evaluations and expert feedback further highlight the potential of OmniThink to address real-world challenges in the generation of long-form articles. Copyright © 2025, The Authors. All rights reserved.

关键词： Adversarial machine learning

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Plasmonic rhenium trioxide self-assembled microtubes for highly sensitive, stable and reproducible surface-enhanced Raman spectroscopy detection

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Chinese Chemical Letters 2023年第3期34卷 218-221页

作者： Jingbin Li Zhiwei Jiao Junfang Li Hua Bai Guangcheng Xi College of Sciences China Jiliang UniversityHangzhou 310018China Institute of Industrial and Consumer Product Safety Chinese Academy of Inspection and QuarantineBeijing 100176China Key Laboratory of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang Province China Jiliang UniversityHangzhou 310018China

Compared with noble metals, improving the sensitivity of semiconducting surface-enhanced Raman scattering(SERS) substrates is of great significance to their fundamental research and practical application of Raman spectroscopy. Herein, a simple chemical method is developed to synthesize a rhenium trioxide(ReO_(3)) microtubes assembled with highly crystalline nanoparticles. The ReO_(3) microtubes show a strong and well-defined surface plasmon resonance(SPR) behavior in visible region, which is rare for non-noble metals. As a low-cost SERS substrate, the plasmonic ReO_(3) microtubes exhibit a Raman enhancement factor of 8.9×10^(5) and a lowest detection limit of 1.0×10^(-9) mol/L for phenolic pollutants. Moreover, these ReO_(3) microtubule SERS substrates show excellent chemical stability and can resist the corrosion of strong acids and bases.

关键词： SERS ReO_(3) Surface plasmon resonance Microtubes Self assembly

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Design and Force Control Analysis of a Novel Waist Massage Instrument

引用

Recent Patents on Mechanical Engineering 2023年第4期16卷 296-307页

作者： Xie, Shenglong Liu, Zijing Zhang, Liangan Wan, Yanjian Key Laboratory of Intelligent Manufacturing Quality Big Data Tracking and Analysis of Zhejiang Province China Jiliang University Zhejiang Hangzhou310018 China Anhui Engineering Research Center for Intelligent Applications and Security of Industrial Internet Anhui University of Technology Anhui Ma’anshan243032 China School of Mechanical Engineering Anhui University of Technology Anhui Ma’anshan243000 China

Background: There is a shortage of rehabilitation doctors and treatment equipment for patients with lumbar spondylosis. Therefore, a waist massage instrument (WMI) is designed to help patients alle-viate lumbar pain. Objective: The objective of this study is to design a new WMI with the advantages of simple structure, lightweight, and function of effective force control, and to verify its function of palmar push, palmar press, and palmar vibration based on the on-off control, PID control, and fuzzy-PID control. Methods: Firstly, the movement characteristics of the Chinese massage manipulation are analyzed to clarify the design principle of the massage instrument. Then, a new WMI composed of massage head, vibration exciter, and actuator is designed to simulate the three massage manipulations. Next, the electric control system and control methods of on-off, PID, and fuzzy-PID controllers are introduced to realize the force control of WMI. Finally, the force control experiments of WMI are carried out to verify the function of the palmar press, palmar push, and palmar vibration based on the on-off control, PID control, and fuzzy-PID control, respectively. Results: The experimental results indicate that the proposed WMI can implement the massage manipulations of palmar push, palmar press, and palmar vibration well, and has good force control ability after using fuzzy-PID control. Conclusion: The newly designed WMI is effective in providing palmar push, palmar press, and palmar vibration massage manipulations for patients with lumbar spondylosis, and the fuzzy-PID controller im-proves the force control ability of the instrument. The WMI has the potential to be used as an effective treatment for lumbar pain. © 2023 Bentham Science Publishers.

关键词： Three term control systems

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IceBerg: Deep Generative Modeling for Constraint Discovery and Anomaly Detection

IceBerg: Deep Generative Modeling for Constraint Discovery a...

引用

IEEE International Conference on big data and Cloud computing (BdCloud)

作者： Wentao Hu Dawei Jiang Sai Wu Ke Chen Gang Chen The Key Laboratory of Big Data Intelligent Computing Zhejiang Province Zhejiang University

Automatic constraint discovery from a relational database is beneficial for domain experts in fraud detection and intelligent auditing. Its objective is to discover a set of inherent constraints underlying the database such that tuples violating them are considered anomalous. In this paper, we propose Ice-Berg as the first system to simultaneously detect anomalous tuples and discover the associated human-readable constraints. The backbone of IceBerg is a novel generative network, namely KD-VAE, that integrates Kernel Density estimation with Variational AutoEncoder. KD-VAE is expected to learn the distributions of normal tuples. We can perform anomalous data detection by calculating the likelihood that the tuple fits the distributions of normal tuples and abnormality interpretation by comparing the detected anomalous tuples with their generated normal counterparts. We empirically compare the proposed method with several state-of-the-art outlier detection methods on 13 real-world datasets. The results show that IceBerg outperforms its competitors in most cases, especially for complex datasets with high-dimensional features.

关键词： Estimation Relational databases Probability distribution Fraud Kernel Task analysis Anomaly detection

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