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On the Challenge of Generating Multivariate Time Series data from Distributed Sensors in IoT-enabled Scenarios 24

On the Challenge of Generating Multivariate Time Series Data...

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14th international conference on the Internet of Things, IoT 2024

作者： Bañuelos, Julián Jerónimo Costa-Requena, Jose He, Jiayuan Salim, Flora D. Sigg, Stephan Department of Information and Communications Engineering School of Electrical Engineering Aalto University Espoo Uusimaa Finland School of Computing Technologies RMIT University MelbourneVIC Australia Cumucore Oy Espoo Uusima Finland School of Computer Science and Engineering University of New South Wales Sydney Australia

ISBN: (纸本)9798400712852

Smart sensing environments supported by an Internet of Things (IoT) infrastructure are enabling a wide array of possibilities for data-intensive remote monitoring applications. Although nowadays it is possible to access and deploy complex setups of sophisticated sensors, the telecommunications infrastructures are struggling to deal with the massive amounts of generated data for transmission, processing, and storage from the diverse range of IoT devices. As data corruption and the loss of data are realistic scenarios in current IoT deployments, it is relevant to find an alternative solution that circumvents the limitations of the physical infrastructure for dealing with potential missing data in remote sensing environments. In this paper, we analyze the feasibility of reproducing missing sensor data due to a communication failure from correlated sources in the same experimental context. We rely on generative adversarial models for the prediction of acceleration data and evaluate the usefulness of the synthetic data with a standard human activity recognition (HAR) classifier. © 2024 Copyright held by the owner/author(s).

关键词： Time series

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SHE: A Generic Framework for data Stream Mining over Sliding Windows 51

SHE: A Generic Framework for Data Stream Mining over Sliding...

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51st international conference on Parallel processing (ICPP)

作者： Wu, Yuhan Fan, Zhuochen Shi, Qilong Zhang, Yixin Yang, Tong Chen, Cheng Zhong, Zheng Li, Junnan Shtul, Ariel Tu, Yaofeng Peking Univ Beijing Peoples R China Peng Cheng Lab Shenzhen Peoples R China Natl Univ Def Technol Changsha Peoples R China Redis Labs Mountain View CA USA ZTE Corp Shenzhen Peoples R China

ISBN: (纸本)9781450397339

data stream mining over a sliding window is a fundamental problem in many applications, such as financial data trackers, intrusion detection and QoS. To meet the demand for high throughput of high speed data streams, sliding window algorithms turn to hardware platforms including FPGA/ASIC and programmable switches. These hardware platforms have three constraints for algorithms running on, which are 1) small memory usage 2) single stage memory access and 3) limited concurrent memory access. algorithms perfectly fit in with these constraints will enable a highest utilization of these hardware platforms. However, no existing sliding window algorithm is specifically designed for hardware platforms. In this paper, we propose the Sliding Hardware Estimator (SHE), which is a generic framework that extends existing fixed window algorithms to sliding windows on hardware platforms. The key idea of SHE is that, during insertions we approximately delete outdated information with little time and space overhead, while during queries we design sophisticated techniques to minimize error. We have fully implemented our SHE on FPGA, achieving a throughput of 544 Mips. We apply SHE to four typical data stream mining tasks. Experimental results show that, when compared with the state-of-the-art which cannot be implemented in hardware, SHE reduces the error by up to 100 times in membership queries. All related source codes are released at Github.

关键词： Field programmable gate arrays (FPGA)

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Research on Enhanced Perception and Attention-Driven Person Re-Identification 20

Research on Enhanced Perception and Attention-Driven Person ...

引用

20th international conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2024

作者： Wu, Cancan Li, Dong Zhang, Zhi College of Computer Science and Technology Wuhan University of Science and Technology Hubei Wuhan430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Hubei Wuhan430065 China Big Data Science and Engineering Research Institute Wuhan University of Science and Technology Hubei Wuhan430065 China

ISBN: (纸本)9798350356328

When matching similarity among pedestrians in images, pedestrian re-identification algorithms are often disturbed by occlusions. A typical tactic is to improve the robustness of occlusion features in the model. However, this approach neglects the noise introduced by other pedestrians in the image, leading to limited learning of relevant information between images. To address these issues, this paper introduces a pedestrian re-identification model that leverages perception-enhanced and attention mechanisms. By enhancing the perception of non-pedestrian noise and erasing non-pedestrian features, we then employ a cross-attention mechanism to subtly embed global and local features for finer feature recognition. Finally, through context attention, we learn the interaction between pedestrian images to improve re-identification accuracy. Results from studies performed on the Market-1501, CUHK03-D, and Duke-ReID datasets indicate that the suggested model surpasses leading contemporary models regarding Rank-1 scores and mean Average Precision (mAP), while maintaining good robustness. © 2024 IEEE.

关键词： Reamers

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Land-Cover Classification with Self-Supervised ResNet50 (Integrating Plantation data)

Land-Cover Classification with Self-Supervised ResNet50 (Int...

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2023 international Workshop on Artificial Intelligence and Image processing, IWAIIP 2023

作者： Purba, Azka Perceka, Dimas Wardhana, Wahyu Adisaputro, Didi PT. Koltiva Indonesia Jakarta Indonesia Gadjah Mada University Faculty of Forestry Yogyakarta Indonesia

ISBN: (纸本)9798350382914

In this study, we utilize a self-supervised ResNet50 model for classify land-cover imagery combining learning from both Sentinel-1 and Sentinel-2 images using the SEN12MS dataset. The model is further fine-tuned with the DFC2020 dataset, enriched by our addition of 336 new rubber data patches covering 75 × 90 km2 area. The model achieved 98.1% accuracy in classifying rubber using only a 25% training data split, and 74.9% accuracy in classifying forest using only 5% training data split. This study demonstrates that accurate classification outcomes are achievable with a small number of labeled datasets by fine-tuning self-pretrained models and incorporating it with new labels. This approach not only showcases the model's ability to efficiently learn from limited data, but also highlights the potential of adapting pre-existing models for land-cover classification tasks. © 2023 IEEE.

关键词： Classification (of information)

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Development of a Digital Thread for Orchestrating data Along the Product Lifecycle for Large-Part and High-Precision Manufacturing 32nd

Development of a Digital Thread for Orchestrating Data Along...

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32nd international conference on Flexible Automation and Intelligent Manufacturing

作者： Vidal, Felix Alonso, Lucia de Luca, Patrick Duplessis-Kergomard, Yann Castillo, Roberto AIMEN Technol Ctr O Porrino 36400 Spain ESI Grp 25 Rue Marcel Issartier F-33700 Merignac France ATOS Spain SA Madrid Spain

ISBN: (纸本)9783031382406;9783031382413

Manufacturing of large components is a complex and challenging process characterized by high customization requirements, involving a large variety of subassemblies that must be manufactured and assembled involving multistage manufacturing processes. Moreover, product quality and production efficiency are key elements to ensure. Therefore, an integrated framework capable of ensuring an efficient manufacturing from the initial design phase is needed. The goal is to obtain the continuous evolution of the large component being manufactured by linking Product, Process and Resources data. In this regard, this paper presents a methodology for implementing a data-driven digital infrastructure supporting data exchange in a heterogeneous engineering landscape between engineering, production, quality and management. As a result, a digital platform integrating data models, digital twins and data-driven algorithms have been implemented enabling the application of feedforward control strategies as well as optimization loops to enable a more precise, efficient and sustainable manufacturing.

关键词： Digital Pipeline Control Orchestration Technologies Hierarchical and modular digital thread

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Synerise Monad: A Foundation Model for Behavioral Event data 23

Synerise Monad: A Foundation Model for Behavioral Event Data

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46th international ACM SIGIR conference on Research and Development in Information Retrieval (SIGIR)

作者： Rychalska, Barbara Lukasik, Szymon Dabrowski, Jacek Synerise Warsaw Poland Warsaw Univ Technol Fac Math & Informat Sci Warsaw Poland AGH Univ Sci & Technol Fac Phys & Appl Comp Sci Krakow Poland

ISBN: (纸本)9781450394086

The complexity of industry-grade event-based datalakes grows dynamically each passing hour. Companies actively gather behavioral information on their customers, recording multiple types of events, such as clicks, likes, page views, card transactions, add-to-basket, or purchase events. In response to this, the Synerise Monad platform has been proposed. The primary focus of Monad is to produce Universal Behavioral Representations (UBRs) - large vectors encapsulating the behavioral patterns of each user. UBRs do not lose knowledge about individual events, in contrast to aggregated features or averaged embeddings. They are based on award-winning algorithms developed at Synerise - Cleora and EMDE - and allow to process real-life datasets composed of billions of events in record time. In this paper, we introduce a new aspect of Monad: private foundation models for behavioral data, trained on top of UBRs. The foundation models are trained in purely self-supervised manner and allow to exploit general knowledge about human behavior, which proves especially useful when multiple downstream models must be trained and time constraints are tight, or when labeled data is scarce. Experimental results show that the Monad foundation models can reduce training time by half and require 3x less data to reach optimal results, often achieving state-of-the-art results.

关键词： foundation models big data representation learning graph learning behavioral modeling

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data analysis of relationship between equity concentration and innovation investment

Data analysis of relationship between equity concentration a...

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2020 international conference on data processing algorithms and models, icdpam 2020

作者： Xu, Qian Lin, Hongzhen Wuhan University of Science and Technology Wuhan430034 China

Based on the relevant data of A-share listed high-tech enterprises in Shanghai and Shenzhen in 2014-2018, the relationship between ownership structure and innovation input is empirically studied according to the principal-agent theory and Information asymmetry theory, and combined with the intermediary effect model, further analysis of internal control in the relationship between the intermediary role. It is found that the degree of equity concentration has a negative correlation with innovation input, but has a positive correlation with internal control. The degree of equity concentration can weaken the negative influence on innovation input through the positive influence on internal control, it can be seen that internal control plays an intermediary role in the relationship between ownership concentration and innovation input. The research results can provide enlightenment for high-tech enterprises to optimize the ownership structure, improve the effectiveness of internal control, enhance innovation input and enhance the competitive advantage of science and technology. © Published under licence by IOP Publishing Ltd.

关键词： Competition

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Approximate Continuous Skyline Queries over Memory Limitation-Based Streaming data 1

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7th Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM) international Joint conference on Web and Big data

作者： An, Yunzhe Zhen, Zhu Zhu, Rui Qiu, Tao Xia, Xiufeng Shenyang Aerosp Univ Shenyang Peoples R China

ISBN: (数字)9789819723874

ISBN: (纸本)9789819723867;9789819723874

Continuous skyline query over sliding window is an important problem over streaming data. The query returns all skyline objects to the system whenever the window slides. Existing efforts include exact-based algorithms and approximate-based algorithms. Their key idea is to find objects that cannot become skyline objects before they expire from the window, delete them, and use reminders to support query processing. However, the space cost of all existing efforts is high, and cannot work under memory limitation-based streaming data, i.e., a general environment in real applications. In this paper, we define a novel query named.-approximate continuous skyline query(rho-ACSQ), which returns error-bounded answers to the system. Here, rho is a threshold, which can bind the error ratio between approximate and exact results. In order to support rho-ACSQ, we propose a novel framework named rho-SEAK(short for rho-Self-adaptive Error-based Approximate Skyline). It can self-adaptively adjust. based on the distribution of streaming data, and achieve the goal of supporting rho-ACSQ over memory limitation-based streaming data. Theoretical analysis indicates that even in the worst case, both the running cost and space cost of rho-SEAK are all unrelated with data scale.

关键词： Streaming data Continuous Skyline query Dominance Memory Limitation

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Evaluating the Performance to Detecting Topic Shift of Large Language models: A Study 9

Evaluating the Performance to Detecting Topic Shift of Large...

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9th international conference on Intelligent Computing and Signal processing, ICSP 2024

作者： Tian, Pingfang Liu, Deting Department of Computer Science and Technology Wuhan University of Science and Technology Wuhan430065 China Institute of Scientific and Technical Information of China The Key Laboratory of the Rich Media Knowledge Organisation and Service of Digital Publishing Content Beijing100038 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China Big Data Science and Engineering Research Institute Wuhan University of Science and Technology Wuhan430065 China

ISBN: (纸本)9798350376548

Topic shift detection aims to identify whether there is a change in the current topic of conversation or if a change is needed. The study found previous work did not evaluate the performance of large language models like ChatGPT on the task of topic shift. Therefore, this paper's main task and innovation lie in analyzing ChatGPT's performance on topic shift detection. To provide a more comprehensive evaluation, we conducted topic shift detection tasks on ChatGPT from three aspects: single utterances, adjacent utterances, and contextual levels. Additionally, to gauge the performance of large language models, we conducted experiments on multiple small-scale models and compared the results of the two models. Experimental results on the publicly available English TIAGE dataset showed that small-scale models exhibited lower recall in all three aspects, while ChatGPT performed better in the recall. This suggests that compared to small-scale models, large models are more capable of accurately detecting topic shifts. However, large models also exhibited lower precision, indicating that while ChatGPT can recognize content differences in utterances, its judgment on whether these different contents belong to the same topic is poor. © 2024 IEEE.

关键词： adjacent utterances level ChatGPT contextual level single utterances level topic shift detection

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Extending Actor models in data Spaces

Extending Actor Models in Data Spaces

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32nd World Wide Web conference (WWW)

作者： zum Felde, Hendrik Meyer Bellebaum, Thomas Brost, Gerd Kollenstart, Maarten Dalmolen, Simon Fraunhofer AISEC Garching Germany TNO Groningen Netherlands

ISBN: (纸本)9781450394161

In today's internet almost any party can share sets of data with each other. However, creating frameworks and regulated realms for the sharing of data is very complex when multiple parties are involved and complicated regulation comes into play. As solution data spaces were introduced to enable participating parties to share data among themselves in an organized, regulated and standardized way. However, contract data processors, acting as data space participants, are currently unable to execute data requests on behalf of their contract partners. Here we show that an on-behalf-of actor model can be easily added to existing data spaces. We demonstrate how this extension can be realized using verifable credentials. We provide a sample use case, a detailed sequence diagram and discuss necessary architectural adaptations and additions to established protocols. Using the extensions explained in this work numerous real life use cases which previously could technically not be realized can now be covered. This enables future data spaces to provide more dynamic and complex real world use cases.

关键词： data Spaces Contract data processing Actor models Self-Sovereign Identities international data Spaces Gaia-X On-behalf-of Model

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