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A Model-Based Deep Transfer Learning Algorithm for Phenology Forecasting Using Satellite Imagery 1

16th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2021

作者： Molina, M.Á. Jiménez-Navarro, M.J. Martínez-Álvarez, F. Asencio-Cortés, G. Data Science and Big Data Lab Pablo de Olavide University Seville41013 Spain

ISBN: (数字)9783030862718

ISBN: (纸本)9783030862701

A new transfer learning strategy is proposed for classification in this work, based on fully connected neural networks. The transfer learning process consists in a training phase of the neural network on a source dataset. Then, the last two layers are retrained using a different small target dataset. Clustering techniques are also applied in order to determine the most suitable data to be used as target. A preliminary study has been conducted to train and test the transfer learning proposal on the classification problem of phenology forecasting, by using up to sixteen different parcels located in Spain. The results achieved are quite promising and encourage conducting further research in this field, having led to a 7.65 % of improvement with respect to other three different strategies with both transfer and non-transfer learning models. © 2021, Springer Nature Switzerland AG.

关键词： Deep learning

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DarkSAM: Fooling Segment Anything Model to Segment Nothing 38

DarkSAM: Fooling Segment Anything Model to Segment Nothing

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zhou, Ziqi Song, Yufei Li, Minghui Hu, Shengshan Wang, Xianlong Zhang, Leo Yu Yao, Dezhong Jin, Hai National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab China Cluster and Grid Computing Lab China Hubei Engineering Research Center on Big Data Security China Hubei Key Laboratory of Distributed System Security China School of Computer Science and Technology Huazhong University of Science and Technology China School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Software Engineering Huazhong University of Science and Technology China School of Information and Communication Technology Griffith University Australia

Segment Anything Model (SAM) has recently gained much attention for its outstanding generalization to unseen data and tasks. Despite its promising prospect, the vulnerabilities of SAM, especially to universal adversarial perturbation (UAP) have not been thoroughly investigated yet. In this paper, we propose DarkSAM, the first prompt-free universal attack framework against SAM, including a semantic decoupling-based spatial attack and a texture distortion-based frequency attack. We first divide the output of SAM into foreground and background. Then, we design a shadow target strategy to obtain the semantic blueprint of the image as the attack target. DarkSAM is dedicated to fooling SAM by extracting and destroying crucial object features from images in both spatial and frequency domains. In the spatial domain, we disrupt the semantics of both the foreground and background in the image to confuse SAM. In the frequency domain, we further enhance the attack effectiveness by distorting the high-frequency components (i.e., texture information) of the image. Consequently, with a single UAP, DarkSAM renders SAM incapable of segmenting objects across diverse images with varying prompts. Experimental results on four datasets for SAM and its two variant models demonstrate the powerful attack capability and transferability of DarkSAM. Our codes are available at: https://***/CGCL-codes/DarkSAM. © 2024 Neural information processing systems foundation. All rights reserved.

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Electricity Consumption Time Series Forecasting Using Temporal Convolutional Networks 19th

Electricity Consumption Time Series Forecasting Using Tempor...

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19th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2020/2021

作者： Torres, J.F. Jiménez-Navarro, M.J. Martínez-Álvarez, F. Troncoso, A. Data Science and Big Data Lab Pablo de Olavide University Seville41013 Spain

ISBN: (纸本)9783030857127

Forecasting electricity demand is crucial for the management of smart grids to ensure a secure, reliable and sustainable supply. Recently, a variant of convolutional neural networks, called temporal convolutional networks, has emerged for data sequence, competing directly with deep recurrent neural networks in terms of execution time and memory requirements. In this work, we propose a deep temporal convolutional network to predict time series, namely, the electricity consumption with a 4-h forecast horizon. Results using nine and a half years of Spanish electricity load, with a 10-min sampling rate, are reported and discussed. In addition, the performance of the proposed model is compared with linear regression, decision trees, gradient boosted trees, random forests, deep feed forward neural networks that use different techniques to find the optimal hyper-parameters and a deep Long Short-Term Memory network. The proposed model reaches competitive results in terms of accuracy, with the smallest error verging on 1%. © 2021, Springer Nature Switzerland AG.

关键词： Decision trees

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Time Series Forecasting in Agriculture: Explainable Deep Learning with Lagged Feature Selection 19th

Time Series Forecasting in Agriculture: Explainable Deep Le...

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19th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2024

作者： Troncoso-García, A.R. Jiménez-Navarro, M.J. Lourdes Linares-Barrera, M. Brito, I.S. Martínez-Álvarez, F. Martínez-Ballesteros, M. Department of Computer Languages and Systems University of Seville Seville Spain Data Science & Big Data Lab Pablo de Olavide University Seville41013 Spain Escola Superior de Tecnologia e Gestão Instituto Politécnico de Beja Beja7800-295 Portugal Caparica2829-516 Portugal

ISBN: (纸本)9783031750120

Reference evapotranspiration is a crucial metric in agricultural contexts, characterizing the evapotranspiration rate from a well-hydrated surface and serving as a fundamental benchmark for water management and crop irrigation, especially in arid regions. This study applied neural networks that integrates a Temporal Selection Layer to enhance the prediction of reference evapotranspiration levels through feature selection. This approach not only aims to refine the accuracy of time-series forecasting but also enhances the interpretability and efficiency of the model by identifying the most relevant features and periods. Additionally, the study incorporates the SHAP technique to determine and explain the contribution of individual features to the model outputs. The data for this research was collected from several meteorological stations of the Sistema Agrometeorológico para a Gestão da Rega no Alentejo in Portugal between 2012 and 2022. The results show that the Temporal Selection Layer effectively identifies important features in agricultural data, and by employing the SHAP technique, we enhance the understanding of how these features influence predictions to improve farm management decisions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： agriculture Feature selection neural networks time series forecasting XAI

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ModelLock: Locking Your Model With a Spell 24

ModelLock: Locking Your Model With a Spell

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32nd ACM International Conference on Multimedia, MM 2024

作者： Gao, Yifeng Sun, Yuhua Ma, Xingjun Wu, Zuxuan Jiang, Yu-Gang Shanghai Key Lab of Intell. Info. Processing School of Cs Fudan University Shanghai China Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9798400706868

This paper presents a novel model protection paradigm Model Locking that locks the performance of a finetuned model on private data to make it unusable or unextractable without the right key. Specifically, we proposed a diffusion-based framework dubbed ModelLock that explores text-guided image editing to transform the private finetuning data into unique styles or blend new objects into the background. A model finetuned on this edited dataset will be locked and can only be unlocked by the key prompt, i.e., the same text prompt used to edit the data. We conduct extensive experiments on both image classification and segmentation tasks and show that 1) ModelLock can effectively lock finetuned models without significantly reducing their unlocked performance, and more importantly, 2) the locked model cannot be easily unlocked without knowing both the key prompt and the diffusion model. Our work opens up a new direction for intellectual property protection of private models. © 2024 ACM.

关键词： Keys (for locks)

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An Approach of Digital Twin System Construction for Underground Locomotive Dispatching Management 19

An Approach of Digital Twin System Construction for Undergro...

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19th IEEE International Conference on Networking, Sensing and Control, ICNSC 2022

作者： Wang, Geng Fang, Huan Zhang, Shouzheng College of Mathematics and Big Data Anhui University of Science and Technology Huainan232001 China College of Mathematics and Big Data Anhui Univ. of Sci. and Technol. Anhui Prov. Eng. Lab. for Big Data Anal. and Early Warning Technol. of Coal Mine Safety Huainan232001 China

ISBN: (纸本)9781665472432

For addressing the problems that the existing data cannot be effectively integrated with the process control decision-making in the digital mine system, a digital twin system for underground locomotive dispatching management is proposed in this work. Firstly, the designed digital twin system has a four-tier system architecture, while the part of theoretical prior model is established by Petri nets, and the actual dispatching is constructed by integrating simulation software and a three-dimensional (3D) virtual mapping for underground mines. Then, the effectiveness of dispatching algorithm and the consistency of twin information are systematically evaluated through the setting of simulation parameters and the access of twin data. The designed digital twin system of underground locomotive dispatching can not only visualize the dispatching system in two directions, but also provide decision-making basis for the safety of locomotive dispatching, which deepen and widen the application scope of digital twin system in the field of intelligent mine construction. © 2022 IEEE.

关键词： Mapping

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UniSleepPos: Sleep Posture Identification System Utilizing Millimeter-wave Radar

UniSleepPos: Sleep Posture Identification System Utilizing M...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Liu, Kun Li, Min He, Chu Tian, Xu Mao, Junbin Zeng, Min Liu, Jin Xinjiang University Xinjiang Engineering Research Center of Big Data and Intelligent Software School of Software Urumqi China Central South University Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Changsha China

ISBN: (纸本)9798350386226

Sleep posture identification is crucial for accurately assessing sleep quality and diagnosing related diseases. In the realm of non-intrusive sleep monitoring, non-contact technologies are becoming increasingly mainstream. Millimeter-wave radar is frequently utilized in sleep posture identification due to its high resolution, strong penetration, and excellent sensitivity. However, traditional radar-based methods for sleep posture identification often struggle with reliability when dealing with diverse individuals and complex sleep environments. To address these challenges, we propose UniSleepPos, which designs a novel dual-view fusion mechanism to integrate depression and elevation angle signals obtained from radar, thus accurately capturing the posture information of the monitored subject in three-dimensional space. Furthermore, we combine sleep posture identification with individual characteristics, utilizing existing individual labels as prior knowledge to assist in sleep posture identification. The integration of prior knowledge provides a valuable information source for the model, helping to enhance its understanding of the data and improve its performance. We collected sleep posture data from eight volunteers using millimeter-wave radar devices under various environmental conditions. Leave-one-subject-out experiments were conducted to validate the effectiveness of UniSleepPos. The results indicated that UniSleepPos significantly outperforms existing methods, demonstrating its potential for practical applications. © 2024 IEEE.

关键词： Sleep research

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Beyond Numbers: Creating Analogies to Enhance data Comprehension and Communication with Generative AI

arXiv

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arXiv 2024年

作者： Chen, Qing Shuai, Wei Zhang, Jiyao Sun, Zhida Cao, Nan Intelligent Big Data Visualization Lab Tongji University China College of Computer Science & Software Engineering Shenzhen University China Intelligent Big Data Visualization Lab Tongji College of Design and Innovation China

Unfamiliar measurements usually hinder readers from grasping the scale of the numerical data, understanding the content, and feeling engaged with the context. To enhance data comprehension and communication, we leverage analogies to bridge the gap between abstract data and familiar measurements. In this work, we first conduct semi-structured interviews with design experts to identify design problems and summarize design considerations. Then, we collect an analogy dataset of 138 cases from various online sources. Based on the collected dataset, we characterize a design space for creating data analogies. Next, we build a prototype system, AnalogyMate, that automatically suggests data analogies, their corresponding design solutions, and generated visual representations powered by generative AI. The study results show the usefulness of AnalogyMate in aiding the creation process of data analogies and the effectiveness of data analogy in enhancing data comprehension and communication. © 2024, CC BY.

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Optimizing the Copy-on-Write Mechanism of Docker by Dynamic Prefetching

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Tsinghua science and Technology 2021年第3期26卷 266-274页

作者： Yan Jiang Wei Liu Xuanhua Shi Weizhong Qiang National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabHuazhong University of Science and TechnologyWuhan 430074China

Docker,as a mainstream container solution,adopts the Copy-on-Write(CoW)mechanism in its storage *** mechanism satisfies the need of different containers to share the same ***,when a single container performs operations such as modification of an image file,a duplicate is created in the upper readwrite layer,which contributes to the runtime *** the accessed image file is fairly large,this additional overhead becomes *** we present the concept of Dynamic Prefetching Strategy Optimization(DPSO),which optimizes the Co W mechanism for a Docker container on the basis of the dynamic prefetching *** the beginning of the container life cycle,DPSO pre-copies up the image files that are most likely to be copied up later to eliminate the overhead caused by performing this operation during application *** experimental results show that DPSO has an average prefetch accuracy of greater than 78%in complex scenarios and could effectively eliminate the overhead caused by the CoW mechanism.

关键词： Docker container Copy-on-Write(CoW) storage driver prefetch strategy

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MatchPrompt: Prompt-based Open Relation Extraction with Semantic Consistency Guided Clustering

MatchPrompt: Prompt-based Open Relation Extraction with Sema...

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2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022

作者： Wang, Jiaxin Zhang, Lingling Liu, Jun Liang, Xi Zhong, Yujie Wu, Yaqiang Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering School of Computer Science and Technology Xi'an Jiaotong University China National Engineering Lab for Big Data Analytics Xi'an Jiaotong University China Lenovo Research Beijing China

Relation clustering is a general approach for open relation extraction (OpenRE). Current methods have two major problems. One is that their good performance relies on large amounts of labeled and pre-defined relational instances for pre-training, which are costly to acquire in reality. The other is that they only focus on learning a high-dimensional metric space to measure the similarity of novel relations and ignore the specific relational representations of clusters. In this work, we propose a new prompt-based framework named MatchPrompt, which can realize OpenRE with efficient knowledge transfer from only a few predefined relational instances as well as mine the specific meanings for cluster interpretability. To our best knowledge, we are the first to introduce a prompt-based framework for unlabeled clustering. Experimental results on different datasets show that MatchPrompt achieves the new SOTA results for OpenRE. © 2022 Association for Computational Linguistics.

关键词： Extraction

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