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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Zhenwei Shao Zhou Yu Meng Wang Jun Yu Key Laboratory of Complex Systems Modeling and Simulation School of Computer Science and Technology Hangzhou Dianzi University China School of Computer Science and Information Engineering Hefei University of Technology China

Knowledge-based visual question answering (VQA) requires external knowledge beyond the image to answer the question. Early studies retrieve required knowledge from explicit knowledge bases (KBs), which often introduces irrelevant information to the question, hence restricting the performance of their models. Recent works have sought to use a large language model (i.e., GPT-3 [3]) as an implicit knowledge engine to acquire the necessary knowledge for answering. Despite the encouraging results achieved by these methods, we argue that they have not fully activated the capacity of GPT-3 as the provided input information is insufficient. In this paper, we present Prophet-a conceptually simple framework designed to $prompt$ GPT-3 with answer heuristics for knowledge-based VQA. Specifically, we first train a vanilla VQA model on a specific knowledge-based VQA dataset without external knowledge. After that, we extract two types of complementary answer heuristics from the model: answer candidates and answer-aware examples. Finally, the two types of answer heuristics are encoded into the prompts to enable GPT-3 to better comprehend the task thus enhancing its capacity. Prophet significantly outperforms all existing state-of-the-art methods on two challenging knowledge-based VQA datasets, OK-VQA and A-OKVQA, delivering 61.1% and 55.7% accuracies on their testing sets, respectively.

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A Synchronous Parallel Method with Parameters Communication Prediction for Distributed Machine Learning 19th

A Synchronous Parallel Method with Parameters Communication...

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19th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2023

作者： Zeng, Yanguo Xue, Meiting Xu, Peiran Shi, Yukun Zeng, Kaisheng Zhang, Jilin Yue, Lupeng School of Computer Science and Technology Hangzhou Dianzi University Hangzhou310018 China Key Laboratory for Modeling and Simulation of Complex Systems Ministry of Education Hangzhou310018 China Data Security Governance Zhejiang Engineering Research Center Hangzhou310018 China National University of Defense Technology Changsha China School of Cyberspace HangZhou Dianzi University Hangzhou China Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (纸本)9783031545306

With the development of machine learning technology in various fields, such as medical care, smart manufacturing, etc., the data has exploded. It is a challenge to train a deep learning model for different application domains with large-scale data and limited resources of a single device. The distributed machine-learning technology, which uses a parameter server and multiple clients to train a model collaboratively, is an excellent method to solve this problem. However, it needs much communication between different devices with limited communication resources. The stale synchronous parallel method is a mainstream communication method to solve this problem, but it always leads to high synchronization delay and low computing efficiency as the inappropriate delay threshold value set by the user based on experience. This paper proposes a synchronous parallel method with parameters communication prediction for distributed machine learning. It predicts the optimal timing for synchronization, which can solve the problem of long synchronization waiting time caused by the inappropriate threshold settings in the stale synchronous parallel method. Moreover, it allows fast nodes to continue local training while performing global synchronization, which can improve the resource utilization of work nodes. Experimental results show that compared with the delayed synchronous parallel method, the training time and quality, and resource usage of our method are both significantly improved. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Forecasting

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Evolutionary Interest Representation Network for Click-Through Rate Prediction

Evolutionary Interest Representation Network for Click-Throu...

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IEEE International Conference on Web Services (ICWS)

作者： Renjie Zhou Zhenming Jin Jian Wan Jilin Zhang Xingyu Guo Qiang Liu Key Laboratory of Complex Systems Modeling and Simulation of the Ministry of Education School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China Key Laboratory of Complex Systems Modeling and Simulation of the Ministry of Education ITMO Joint Institute Hangzhou Dianzi University Hangzhou China School of Information and Electronic Engineering Zhejiang University of Science and Technology Hangzhou China Dept. of Computer and Information Technology Zhejiang Police College Hangzhou China

The rapid development of the Internet has revo-lutionized our lives, providing us with an array of convenient services. However, this revolution has also led to the problem of data overload. Personalized recommendation services have been widely recognized as an effective solution to this problem by both the industrial and academic communities. Accurate prediction of click-through rate (CTR) is a critical task in personalized recommendation systems since it can improve the user’s shopping experience, ultimately increasing revenue for the platform. To achieve accurate CTR prediction, it is crucial to capture user interests. While several methods for interest modeling exist, challenges related to user interest evolution and user behavior noise need further attention. This paper proposes a novel CTR prediction model named the Evolutionary Interest Representation Network (EIRN), which learns the evolving interest representation of users from their behaviors, profiles, and environmental attributes. In the model, we introduce an advertisement correlation graph decomposition layer and a noise filter layer to enhance and purify the user behavior representation. An interest extraction layer is designed to capture sequential correlations and common characteristics between user behaviors, representing their interests. Additionally, as user interests evolve over time and their importance changes, an attention-based interest evolution layer is designed to track the evolution of user interests. Our proposed method is validated using four public datasets and an industrial dataset, showing its superiority over existing CTR prediction methods.

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Bounded Extremum Seeking for Single-Variable Static Map with Large Measurement Delay via Time-Delay Approach to Averaging

Bounded Extremum Seeking for Single-Variable Static Map with...

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IEEE Conference on Decision and Control

作者： Xuefei Yang Emilia Fridman Bowen Zhao Center for Control Theory and Guidance Technology Harbin Institute of Technology Harbin China National Key Laboratory of Modeling and Simulation for Complex Systems School of Electrical Engineering Tel-Aviv University Israel

In this paper, we present a time-delay approach to gradient-based bounded extremum seeking (ES) with large measurement constant delay, for an unknown single-input static quadratic map. We assume that the extremum point and the Hessian $H$ belong to known intervals, whereas the sign of $H$ is known. We apply a time-delay approach to the bounded ES system and arrive at the neutral type system with a nominal linear delayed system. We present the latter system as a retarded one and employ variation of constants formula for practical stability analysis. Explicit conditions in terms of simple scalar inequalities depending on tuning parameters and delay are established to guarantee the practical stability of the bounded ES control systems. Given any delay and neighborhood of the extremum point and through the solution of the constructed inequalities, we find lower bounds on the dither period that ensures the practical stability.

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A Robust Time-Delay Approach to Continuous-Time Extremum Seeking for Multi-Variable Static Map

A Robust Time-Delay Approach to Continuous-Time Extremum See...

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IEEE Conference on Decision and Control

作者： Xuefei Yang Emilia Fridman Center for Control Theory and Guidance Technology Harbin Institute of Technology Harbin China National Key Laboratory of Modeling and Simulation for Complex Systems School of Electrical Engineering Tel-Aviv University Israel

In this article, we introduce a time-delay approach to gradient-based extremum seeking (ES) in the continuous domain for n-dimensional (nD) static quadratic maps. As in the recently introduced (for 2D maps in the continuous domain), we transform the system to the time-delay one (neutral type system). This system is $\mathbf{O}(\varepsilon)$ . ‘perturbation of the averaged linear ODE system. We further explicitly present the neutral system as the linear ODE, where $\mathrm{O}(\varepsilon)$ -terms are considered as disturbances with distributed delays of the length of the small parameter $\varepsilon$ . Quantitative (for uncertain map) and qualitative (for unknown map) practical stability analyses are provided by employing a variation of constants formula that greatly simplifies the results compared to the previously used Lyapunov-Krasovskii (L-K) method. The new approach also simplifies the conditions and improves the results. Examples from the literature illustrate the efficiency of the new approach, allowing essentially large uncertainty of the Hessian matrix with bounds on $\varepsilon$ that are not too small.

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Characterization and evaluation of the treated water quality from the wastewater treatment plant of the Djelfa city

Characterization and evaluation of the treated water quality...

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Renewable Solutions for Ecosystems: Towards a Sustainable Energy Transition (ICRSEtoSET), International Conference on

作者： Salah Karef Mohamed Azlaoui Abdelhadi Hameurlaine Mokhtar Fodili Laboratory of Modeling Optimization and Simulation of Real Complex Systems University of Djelfa Djelfa Algeria Laboratory of Applied Automation & Industrial Diagnostics University of Djelfa Djelfa Algeria Laboratory of Organic Chemistry and Natural Substances University of Djelfa Djelfa Algeria

ISBN: (数字)9798350346336

ISBN: (纸本)9798350346343

In the framework of the protection of the environment, we proceeded during the year 2016 (12 months), to the follow-up of variation of the wastewater pollution parameters at the inlet and at the outlet of the treatment plant of the Djelfa city, which operates by activated sludge, following the medium load *** physicochemical analyzes were carried out on the following parameters: pH, temperature, eclectic conductivity, turbidity, Total suspended solids, COD, BOD5, NH4+, NO3- and total phosphorus. At the end of these analyses, we obtained good elimination yield of 92% to 91% for TSS and 92% to 91% for BOD5. The COD recorded a reduction of 84% to 83%. Total phosphorus achieved a removal efficiency of 55% to 66%. For nitrogen, Ammonia experienced a 61% to 57% reduction from inlet to outlet of the plant. On the other hand, nitrates recorded an increase of 200% to 236%, due to *** quality of the treated water, for the majority of the parameters, complies with the standards required by Algerian regulations and those of the WHO. We can conclude that the treatment system adopted at the level of the WWTP of Djelfa is effective and justified its design and its objective of realization.

关键词： Ammonia Oxygen Urban areas Phosphorus Water pollution Biochemistry Wastewater

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An Anomaly Detection Method Based on Adaptive Log and Dual Feature Fusion Analysis for Distributed systems

An Anomaly Detection Method Based on Adaptive Log and Dual F...

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Information systems and Computing Technology (ISCTech), International Conference on

作者： Yijiang Jia Kefei Li Ping Lu Baodi Xie Huayang Wang Jincai Chen School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan China National Key Laboratory of Modeling and Simulation for Complex Systems Beijing China

In this study, we present a middleware-based approach for detecting anomalies in distributed systems. Our method facilitates the dynamic collection of logs at various levels of detail and incorporates an a priori dictionary-based compression strategy to process and transmit logs, minimizing the performance impact on distributed systems. Additionally, we utilize a dual feature fusion technique to analyze the logs. We evaluate the effectiveness of our approach by performing anomaly detection in a publish/subscribe distributed system and comparing it with existing methods. The results illustrate that our method outperforms other approaches, demonstrating superior performance.

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Multi2human: Controllable Human Image Generation with Multimodal Controls

SSRN

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SSRN 2023年

作者： Gu, Xiaoling Xu, Shengwenzhuo Wong, Yongkang Wu, Zizhao Yu, Jun Fan, Jianping Kankanhalli, Mohan S. Key Laboratory of Complex Systems Modeling and Simulation School of Computer Science and Technology Hangzhou Dianzi University China National University of Singapore Singapore School of Digital Media Technology Hangzhou Dianzi University China

Generating high-quality and diverse human images presents a substantial difficulty within the field of computer vision, especially in developing controllable generative models that can utilize input from various modalities. Such models could enable innovative applications like digital human, fashion design, and content creation. In this study, we introduce Multi2Human, a two-stage image synthesis framework for controllable human image generation with multimodal controls. In the first stage, a novel WAvelet-vqVaE (WAVE) architecture is designed to embed human images using a learnable codebook. The WAVE model enhances the conventional Vector Quantized Variational Autoencoder (VQVAE) by integrating wavelets throughout the encoder, thereby enhancing the quality of image reconstruction and synthesis. In the second stage, a new Multimodal Conditioned Diffusion Model (MCDM) is de- signed to estimate the underlying prior distribution within the discrete latent space using a discrete diffusion process, thus allowing for human image generation condi- tioned on multimodal controls. Quantitative and qualitative analysis demonstrates that the proposed method has the ability to create high-quality, lifelike full-body human images while satisfying the specified multimodal controls. Our code is available at https://***/gxl-groups/Multi2Human. © 2023, The Authors. All rights reserved.

关键词： Image reconstruction

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Discovering API Directives from API Specifications with Text Classification

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Journal of Computer Science & Technology 2021年第4期36卷 922-943页

作者： Jing-Xuan Zhang Chuan-Qi Tao Zhi-Qiu Huang Xin Chen College of Compute Science and Technology Nanjing University of Aeronautics and AstronauticsNanjing 210016China Key Laboratory of Safety-Critical Software(Nanjing University of Aeronautics and Astronautics) Ministry of Industry and Information TechnologyNanjing 210016China Key Laboratory of Complex Systems Modeling and Simulation(Hangzhou Dianzi University) Ministry of Education Hangzhou 310018China School of Computer Science and Technology Hangzhou Dianzi UniversityHangzhou 310018China

Application programming interface(API)libraries are extensively used by *** correctly program with APIs and avoid bugs,developers shall pay attention to API directives,which illustrate the constraints of ***,API directives usually have diverse morphologies,making it time-consuming and error-prone for developers to discover all the relevant API *** this paper,we propose an approach leveraging text classification to discover API directives from API ***,given a set of training sentences in API specifications,our approach first characterizes each sentence by three groups of ***,to deal with the unequal distribution between API directives and non-directives,our approach employs an under-sampling strategy to split the imbalanced training set into several subsets and trains several *** a new sentence in an API specification,our approach synthesizes the trained classifiers to predict whether it is an API *** have evaluated our approach over a publicly available annotated API directive *** experimental results reveal that our approach achieves an F-measure value of up to 82.08%.In addition,our approach statistically outperforms the state-of-the-art approach by up to 29.67%in terms of F-measure.

关键词： Application programming interface(API)directive API specification imbalanced learning text classification

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Warming up a Backup Control Barrier Function

Warming up a Backup Control Barrier Function

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Chinese Automation Congress (CAC)

作者： Weijia Wang Tao Meng Xiaofeng Zhao Jiakun Lei Kun Wang Zhejiang University School of Aeronautics and Astronautics Hangzhou Zhejiang China Modeling and Simulation of Complex Systems National Key Laboratory Beijing China Hagnzhou International Innovation Institute of Beihang University Hangzhou Zhejiang China Beihang University Hangzhou Innovation Istitute Hangzhou Zhejiang China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Backup control barrier function (Backup CBF) is a tractable formulation that ensure the feasibility of CBF-based quadratic programming (QP) problems through implicitly defined control invariant set. Backup CBF is based on two fundamental ingredients: a known backup set and a fixed backup policy, and the implicitly defined control invariant set is evaluated through forward integration by backup policy in a finite horizon online. However, there is a lack of systematic methods for providing the backup set and backup policy for a system with input constraints, making it difficult to synthesize a backup CBF. In this paper, we propose a method for second-order systems to obtain backup policy and backup set practically, rather than empirical choice, by constructing a more conservative explicit CBF-QP with auxiliary velocity constraints integrated. The proposed method warms up a backup CBF by an explicit conservative CBF-QP under mild assumptions, while without excessive conservatism, allowing the tasks of safety and feasibility to be guaranteed theoretically. We demonstrate the efficacy of the proposed method in simulation.

关键词： Systematics Automation Control systems Safety Quadratic programming complex systems

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