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5th International Conference on Computer Science and Management technology, ICCSMT 2024

作者： Yang, Tianguang Zhang, Yu Kang, Chunting Yan, Weisi Data Consulting Business Center Beijing SGITG-Accenture Information Technology Co. Ltd. Beijing China

ISBN: (纸本)9798400709999

In order to implement the relevant requirements in laws, regulations, standards, and specifications of the People's Republic of China, through refering to practical experiences of other industries, power grid enterprises need to explore constructing a set of data classification and grading strategies that are in line with their own business development for satisfying compliance requirements. Refering to the data classification and grading methods in the national standards, based on the comprehensive consideration of business management activities and data storage conditions, this article explores the construction of enterprise data, personal information, and public data classification strategies for power grid enterprises. Based on a combination of quantitative and qualitative statistical analysis techniques, according to the national criteria for determining important and core data, the affected objects and factors are assigned values to calculate the risk value of data items, and the data items are graded based on the risk value, providing a new data grading strategy and reference for conducting data compliance management. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： data Classification Strategy

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Low frequency error analysis and calibration for multiple star sensors system of GaoFen7 satellite

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地球空间信息科学学报（英文版） 2024年第1期27卷 82-94页

作者： Yanli Wang Mi Wang Ying Zhu Xiaoxiang Long The College of Geodesy and Geomatics Shandong University of Science and TechnologyQingdaoChina The State Key Laboratory of Information Engineering in Surveying Mappingand Remote SensingWuhan UniversityWuhanChina School of Electrical and Information Engineering Wuhan Institute of TechnologyWuhanChina Value-added Service Department China Center for Resources Satellite Data and ApplicationBeijingChina

The GaoFen7(GF7)optical satellite is the first Chinese civilian sub-meter high-resolution stereo mapping satellite and is equipped with a double linear array camera and laser altimeter to achieve large-scale topographic *** improve the accuracy of attitude determination,an attitude determination system comprised of four star sensors is *** to the measurement accuracy and steady performance,the star sensors 1a and 1b is usually used together for satellite attitude calculation,which is called the conventional mode of attitude ***,the combination of star sensors 2a and 2b is called the unconventional mode of attitude *** by variations in the incident angle of sunlight and solar radiation,thermal deformation occurs in the body and installation structure of the star sensor,which causes Attitude Low-Frequency Error(ALFE)and seriously influences the con-sistency of attitude determination results of different combination modes for multiple star sensors *** study proposes an ALFE analysis and calibration approach for the multiple star sensors system of GF7 satellite to ensure the consistency of attitude determination results of different combination *** on the statistical characteristics of the angles of the three axes,the installation parameters of the four star sensors are first *** analyzing the characteristics of the optical axis angles within 1420 orbit periods over 135 days,the segmented ALFE compensation model between the unconventional and conven-tional modes is proposed based on the Fourier series model and input parameter of *** on the on-orbit installation parameters and the ALFE model,the precise attitude determination results of the unconventional mode are *** results show that the attitude determination consistency after compensation is better than 2".Moreover,the reliable application time range of the compensation model is 30 days to satisfy the re

关键词： Attitude Low Frequency Error(ALFE) multiple star sensors system GaoFen7 attitude determination consistency Fourier series

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On the Use of Remote Sensing data for Monitoring NDVI Anomalies and Correlations with Rainfall Anomalies in West Java Province, Indonesia: Addressing Sustainable Development Goal 2

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Remote Sensing in Earth Systems Sciences 2025年第2期8卷 815-833页

作者： Roswintiarti, Orbita Indriasari, Novie Rasyidy, Fadilah Halim Sari, Inggit Lolita Kustiyo Muradi, Hengki Purbantoro, Babag Indradjad, Andy Subehi, Mokhamad Research Center for Geoinformatics National Research and Innovation Agency (BRIN) Bogor Indonesia Research Center for Data Science and Information National Research and Innovation Agency (BRIN) Bogor Indonesia Research Center for Satellite Technology National Research and Innovation Agency (BRIN) Bogor Indonesia Center for Agriculture Data and Information System Ministry of Agriculture Jakarta Indonesia

Remote sensing helps us to understand the interdependence between people, the earth, and prosperity, as pointed out in the Sustainable Development Goals framework. Sustainable agriculture is one of the Sustainable Development Goals 2 of the Zero Hunger, as outlined in Target 2.4, which addresses strengthening the capacity for adaptation to drought, flooding, climate change, and other disasters. This research aimed to address the need for productive and sustainable agriculture by investigating the impact of climate, specifically rainfall, on paddy productivity. The areas of interest selected are the Karawang, Subang, and Indramayu Regencies, the most significant rice production areas in the West Java Province of Indonesia. The Reanalysis Product of Global Satellite Mapping of Precipitation (RNL GSMaP) and the Terra/Aqua Normalized Difference Vegetation Index (NDVI) data (2001–2021) were utilized as rainfall and paddy productivity indicators, respectively. We utilized 8-day NDVI anomaly datasets and rainfall rate anomaly data for analysis via canonical correlation analysis (CCA). The application of CCA showed that, in general, rainfall anomalies and NDVI anomalies had simultaneous positive correlations during the early growth stages of paddy fields. Moreover, these parameters were negatively correlated during the reproductive paddy stage. Our findings suggest that identifying the typical lag times of the relationship between rainfall and paddy productivity in West Java Province is necessary, among others, for agricultural planning and water resource management, climate adaptation and risk management, and enhancing food security. This research supports target 2.4 of SDG 2 by providing advantageous data to enhance sustainable and resilient food production systems, thereby contributing to the broader goals of Sustainable Development Goal 2. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Canonical correlation analysis NDVI Paddy productivity Principal component analysis RNL GSMaP SDG 2

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An Entorhinal-Hippocampal Loop Model Based on Non-negative Sparse Coding

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Journal of The Institution of Engineers (India): Series B 2025年第1期106卷 113-127页

作者： Zhao, Kaixin Ren, Menghui College of Computer Science and Technology Henan Institute of Technology Xinxiang453003 China School of Electrical and Information Engineering Zhengzhou University Zhengzhou450001 China Manufacturing IoT Big Data Engineering Technology Research Center of Henan Province Xinxiang453003 China

In our study, we investigate how the brain maps environmental spaces into understandable maps through hippocampal place cells and entorhinal cortex grid cells. We uncover that the hippocampus and entorhinal cortex are not just directly connected but also engage in a sophisticated feedback loop crucial for spatial encoding during navigation. We developed an innovative network model based on this feedback mechanism, linking entorhinal inputs to hippocampal spatial encoding cells through a non-negative sparse coding, using grid cells and weak spatial cells as inputs. Our findings demonstrate that the model learns and adjusts the characteristics of spatial encoding cells, successfully learning the specific spatial selectivities of place cells even with inputs from cells carrying weaker spatial information. This highlights the critical role of feedback encoding in refining spatial representations, an indispensable component of the brain’s navigation system. Moreover, experiments comparing our model with others validate our model’s exceptional ability in generating precise spatial encodings, underscoring its superiority. This research not only deepens our understanding of the brain’s navigational mechanisms but also offers a robust computational framework for future neuroscience studies, emphasizing the intricate interplay between feedback loops and spatial encoding in the brain’s navigation system. © The Institution of Engineers (India) 2024.

关键词： Brain

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Security Challenges and Potential Solutions in Aerial-Terrestrial Wireless Networking

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IEEE Internet of Things Magazine 2023年第4期6卷 118-123页

作者： Sood, Keshav Nguyen, Dinh Duc Nha Qu, Youyang Cui, Lei Karmakar, Kallol Krishna Yu, Shui School of Information Technology Deakin University Australia Data61 Australia Shandong Computer Science Center National Supercomputer Center in Jinan China The University of Newcastle Australia University of Technology Sydney Australia

Integration of unmanned aerial vehicles (UAVs) with terrestrial networks form an aerial-terrestrial wireless networking concept. Particularly, the UAVs are composed of Internet of Things sensors (IoTs) and they also are typically known as flying IoTs. These are used to collect real-time data for diverse network's applications in order to make optimal network performance and secure management decisions. In this networking domain, cyber-attacks on UAVs sensors are rapidly increasing and the attacks on sensors inevitably raise the data sparsity issue, means the recorded data, by drones or UAVs, could be often incomplete and even sparse enough too. This impacts the performance eval-uation of this networking composed of flying and ground base stations. This paper explores the data sparsity issue in context of the integration of aerial-terrestrial wireless networks. Critical challenges and potential high-level solutions are also proposed to enhance the security and performance of networks. © 2018 IEEE.

关键词： Antennas

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Fine extraction of multi-crop planting area based on deep learning with Sentinel- 2 time-series data

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Environmental Science and Pollution Research 2025年第19期32卷 11931-11949页

作者： Jiang, Jingmin Zhang, Jiahua Wang, Xue Zhang, Shichao Kong, Delong Wang, Xiaopeng Ali, Shawkat Ullah, Hidayat Space Information and Big Earth Data Research Center School of Computer Science and Technology Qingdao University Qingdao266071 China Key Laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China

Accurate and timely access to the spatial distribution of crops is crucial for sustainable agricultural development and food security. However, extracting multi-crop areas based on high-resolution time-series data and deep learning still faces challenges. Therefore, this study aims to provide an effective model for multi-crop classification using high-resolution remote sensing time-series data. We designed two deep learning models based on convolutional neural network-long short-term memory (CNN-LSTM) and bidirectional long short-term memory (Bi-LSTM). The monthly synthetic time series of the normalized difference vegetation index (NDVI) from Sentinel-2 data will be used as input features to extract the multi-crop planting area in Shandong province’s northwestern, southwestern, and eastern regions. The results showed that deep learning models achieved higher accuracy compared to the random forest (RF) and extreme gradient boosting (XGBoost) models, with CNN-LSTM achieving the highest overall accuracy of 96.48%. At the county level, the coefficients of determination (R2) for the CNN-LSTM model were 0.91 for wheat, 0.88 for maize, and 0.73 for spring cotton. This study demonstrates that the CNN-LSTM model combined with monthly synthetic time-series NDVI provides a feasible approach for accurately mapping high-resolution multi-crop planting areas and also contributes significantly to decision support and resource management in agricultural production. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Deep learning High-resolution Monthly synthetic time-series NDVI Multi-crop classification Remote sensing Sentinel- 2

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Leveraging Knowledge Graphs for CheapFakes Detection: Beyond dataset Evaluation

Leveraging Knowledge Graphs for CheapFakes Detection: Beyond...

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2023 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2023

作者： Dao, Minh-Son Zettsu, Koji National Institute of Information and Communications Technology Big Data Integration Center Tokyo Japan

ISBN: (纸本)9798350313154

The proliferation of the internet and the availability of vast amounts of information have given rise to a critical and pressing issue of fake news. Among the various forms of fake news, cheapfakes are particularly prominent in deceiving people. Existing research on cheapfakes detection has primarily focused on analyzing the context and correlation between textual and visual information, but has largely overlooked the significance of external knowledge. As a result, most previous approaches, apart from the baseline of ICME'23 Grand Challenge on Detecting Cheapfakes, have heavily relied on evaluating the dataset itself to improve performance. However, despite achieving impressive results on public test datasets, these approaches often suffer from poor performance in real-world scenarios due to their overreliance on the given dataset. In this study, we propose a novel approach that utilizes knowledge graphs to address the issue of insufficient information from external knowledge. Unlike previous approaches, our proposal does not directly alter or participate in the public test dataset to enhance performance, which can potentially result in significant overfitting. Our proposed approach achieved an accuracy score of 83.52% on Task 1, surpassing the baseline by 1.7%, and an accuracy score of 84% on Task 2, outperforming the best result from the previous challenge by 8%. © 2023 IEEE.

关键词： Computer vision

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Ihoya: knowledge management system based on community using User-centered Design (UCD) method

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International Journal of information technology (Singapore) 2025年第4期17卷 2067-2074页

作者： Rafi, Muhammad Puspitodjati, Sulistyo Apriani, Niken Fitria Hakim, Shidiq Al Siagian, Al Hafiz Akbar Maulana Riyanto, Slamet Rodiah, Rodiah Kushadiani, Siti Kania Faculty of Computer Science and Information Technology Universitas Gunadarma West Java Depok Indonesia Research Center for Data and Information Sciences National Research and Innovation Agency West Java Bogor Indonesia

Hoya represents a significant component of Indonesia’s biodiversity with considerable potential;however, its utilization remains suboptimal. There exists a keen interest among researchers, collectors, and horticultural enthusiasts to delve into this fascinating plant. The wide variety of Hoya species in Indonesia substantially challenges their identification. Consequently, there is a pressing need for a system that facilitates the identification of the Hoya plant and its various species. In this study, we propose developing a knowledge management system (KMS) to identify Hoya and its species. This proposal is anchored in the user-friendly nature of mobile-based applications, which offer accessibility at any time. To ensure the creation of a high-quality application that meets user satisfaction, a User Interface and User Experience (UI/UX) design process is implemented, employing the User centered Design (UCD) methodology that concentrates on the preferences and requirements of users. Subsequently, the System Usability Scale (SUS) method is adopted to assess the design and verify its alignment with user needs. This research culminates in our developed KMS prototype, namely iHoya, encompassing mockup pages tailored to users’ preferences, requirements, and navigational flows. An evaluation of the prototype design using the SUS method, conducted through a survey of targeted users, categorizes the design as acceptable while indicating the necessity for certain enhancements. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2024.

关键词： Hoya Knowledge management system SUS UCD UI/UX

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Triadic interaction in the background of a pairwise spin-glass

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Physical Review E 2024年第6期109卷 064105-064105页

作者： M. Bagherikalhor B. Askari G. R. Jafari Department of Physics Institute of Information Technology and Data Science Center for Communications Technology

Developing an equilibrium solution for a pairwise spin-glass with a quenched random infinite range shows a continuous phase transition. Models with p-spin interactions have been studied and the exact solution was provided that shows a continuous phase transition for p=2 and a first-order one for p>2. Although the p-spin interactions were studied individually without considering lower-order interactions, is it always feasible to ignore the lower ones? Here, we are interested in finding an analytical solution for considering a triadic interaction as a perturbation in the background of a pairwise interaction in the Sherrington-Kirkpatrick spin-glass model. Our results indicate a sudden phase transition as a consequence of considering triadic interactions that signal a switch from a continuous to an explosive phase transition.

关键词： Order parameters Spin glasses

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Controlling update distance and enhancing fair trainable prototypes in federated learning under data and model heterogeneity

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Defence technology(防务技术) 2025年第5期47卷 15-31页

作者： Kangning Yin Zhen Ding Xinhui Ji Zhiguo Wang School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu 610054China Institute of Public Security Kash Institute of Electronics and Information IndustryKash 844000China Shanghai New Energy Vehicle Public Data Collection and Monitoring Research Center Shanghai 200241China

Heterogeneous federated learning(HtFL)has gained significant attention due to its ability to accommodate diverse models and data from distributed combat *** prototype-based HtFL methods were proposed to reduce the high communication cost of transmitting model *** methods allow for the sharing of only class representatives between heterogeneous clients while maintaining ***,existing prototype learning approaches fail to take the data distribution of clients into consideration,which results in suboptimal global prototype learning and insufficient client model personalization *** address these issues,we propose a fair trainable prototype federated learning(FedFTP)algorithm,which employs a fair sampling training prototype(FSTP)mechanism and a hyperbolic space constraints(HSC)mechanism to enhance the fairness and effectiveness of prototype learning on the server in heterogeneous ***,a local prototype stable update(LPSU)mechanism is proposed as a means of maintaining personalization while promoting global consistency,based on contrastive *** experimental results demonstrate that FedFTP achieves state-of-the-art performance in HtFL scenarios.

关键词： Heterogeneous federated learning Model heterogeneity data heterogeneity Contrastive learning

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