倒计时1天丨GPC 2023＆ICPCSEE 2023将于9月22-24日在哈尔滨举办
会议介绍
GPC 2023＆ICPCSEE 2023( The 18th International Conference on Green, Pervasive, and Cloud Computing & The 9th International Conference of pioneering computer scientists, engineers and educators）将于9月22-24日在哈尔滨工程大学举办。
本届大会由GPC学术委员会、ICPCSEE 学术委员会联合主办，华中科技大学、哈尔滨工程大学、中科国鼎数据科学研究院联合承办，哈尔滨工业大学、东北林业大学、哈尔滨理工大学、黑龙江省计算机学会等单位联合协办。大会邀请到三位海外专家作大会特邀报告，同时还将举办顶会顶刊报告会、遥感卫星技术应用研讨会、AIGC时代下的信息安全机遇与挑战论坛、论文宣讲报告会等学术交流活动。
大会内容涵盖多个领域，包括人工智能、机器学习、云计算、物联网、网络安全、大数据分析等，旨在为计算机科学家、工程师和教育者提供一个交流的平台，交流计算机科学和工程领域的最新研究成果、技术创新和教学实践。会议议程
特邀报告嘉宾介绍Invited Report
September 24th
10:30-11:20 Keynote
TOPIC
The Rise of Graph Computation
SPEAKER
Xuemin Lin(Shanghai Jiaotong University, China)
BIO
Xuemin Lin is a Chair Professor at Shanghai Jiaotong University, and the head of department of data and business intelligence. Xuemin is a fellow of IEEE and AAIA. He is also a foreign member of Academia of Europaea. Xuemin’s research interests lie in databases, data mining, algorithms, and complexities. Specifically, he is working in the areas of scalable processing and mining of large scale data, including graph, spatial-temporal, streaming, text and uncertain data.
Xuemin was the editor-in-Chief of IEEE Transactions on Knowledge and Data Engineering (Jan 2017 – now). He was an associate editor of ACM Transactions Database Systems (2008-2014) and IEEE Transactions on Knowledge and Data Engineering (Feb 2013- Jan 2015), and an associate editor-in-Chief of IEEE Transactions on Knowledge and Data Engineering (2015-2016), respectively. He has been regularly serving as a PC member and area chairs/SPC in SIGMOD, VLDB, ICDE, ICDM, KDD, CIKM, and EDBT. He is a PC co-chair of ICDE2019 and VLDB2022.
ABSTRACT
Graph data are key parts of Big Data and widely used for modelling complex structured data with a broad spectrum of applications. Over the last decades, tremendous research efforts have been devoted to many fundamental problems in managing and analysing graph data. In this talk, I will focus on the three key problems, 1) efficiently computing subgraph mappings over large-scale graphs, 2) mining cohesive subgraphs, and 3) determining the resilience of graphs. I will cover applications and recent advantages.
10:30-11:20 Keynote
TOPIC
Towards a Distributed Continuum Computing Platform for ML Based Self Adaptive IoT Applications
SPEAKER
Nabil Abdennadher (University of Applied Sciences, Switzerland)
BIO
Nabil Abdennadher received the Diploma in Engineering (Computer science) from Ecole Nationale des Sciences de l’Informatique (ENSI, Tunisia), and the Ph.D. degrees in Computer Science from University of Valenciennes (France) in 1988 and 1991, respectively. He was an assistant professor at the University of Tunis II from 1992 to 1998 and a research assistant at the Swiss Federal Institute of Technology (EPFL) from 1999 to 2000.
In 2001, he joined the University of Applied Sciences, Western Switzerland (HES-SO, HEPIA) as an assistant professor. In 2008, he became an associate professor and in 2017 he was promoted to full professor.
Nabil Abdennadher was head of the inIT research institute at HEPIA from 2010 to 2022. He is currently head of the LSDS research group, representative of the DataBooster initiative in Swiss Romandie and member of the Editorial Board of the Journal of Reliable Intelligent Environments.
Nabil Abdennadher is currently working on several Swiss and European projects aiming at developing selfadaptive edge-to-cloud digital platforms applied to smart grid and smart city.
ABSTRACT
The proliferation of sensing device technologies, and the growing demand for data-intensive IoT applications, are paving the way to the next wave of transformation in IoT computing systems architecture. The goal today is to design, implement and deploy a seamless interconnection of IoT, edge and cloud resources in one computing system, to form a compute continuum, also referred to as edge-to-cloud or fog-to-cloud.
In this talk, compute continuum refers to the deployment and execution of self-adaptive machine learning-based applications employing IoT sensors. Because of their distributed nature over constrained resources devices, these applications leverage the cloud infrastructure for learning tasks while exploiting edge devices for inference tasks on data coming from local IoT sensors. But the next wave of development is already underway; it will involve designing edge-to-edge platforms where learning takes place locally. A coordination platform is used to exchange intelligence between the edges.
This talk will be organised as follow: (1) why and What is continuum computing? (2) A comparative study of continuum computing solutions and (3) an example of a ML based IoT application deployed on an open source distributed continuum computing solution. This application targets energy market (smart grid).
11:20-12:10 Keynote
TOPIC
Cyber-Physical-Social Intelligence
SPEAKER
Laurence T. Yang (St.Francis Xavier University / Hainan University, Canada/China)
BIO
Laurence T. Yanggot his BE in Computer Science and Technology and BSc in Applied Physics both from Tsinghua University, China and Ph.D in Computer Science from University of Victoria, Canada. He is the Academic Vice-President and Dean of School of Computer Science and Technology, Hainan University, China. His research includes Cyber-Physical-Social Intelligence. He has published 300+ papers in the above area on top IEEE/ACM Transactions with total citations of 36691 and H-index of 96 including 8 and 40 papers as top 0.1% and top 1% highly-cited ESI papers, respectively.
His recent honors and awards include the member of Academia Europaea, the Academy of Europe (2021), the John B. Stirling Medal (2021) from Engineering Institute of Canada, IEEE Sensor Council Technical Achievement Award (2020), IEEE Canada C. C. Gotlieb Computer Medal (2020), Clarivate Analytics (Web of Science Group) Highly Cited Researcher (2019, 2020, 2022), Fellow of Institution of Engineering and Technology (2020), Fellow of Institute of Electrical and Electronics Engineers (2020), Fellow of Engineering Institute of Canada (2019), Fellow of Canadian Academy of Engineering (2017).
ABSTRACT
The booming growth and rapid development in embedded systems, wireless communications, sensing techniques and emerging support for cloud computing and social networks have enabled researchers and practitioners to create a wide variety of Cyber-Physical-Social Systems (CPSS) that reason intelligently, act autonomously, and respond to the users’ needs in a context and situation-aware manner, namely Cyber-Physical-Social Intelligence. It is the integration of computation, communication and control with the physical world, human knowledge and sociocultural elements. It is a novel emerging computing paradigm and has attracted wide concerns from both industry and academia in recent years.
This talk will present our latest research on Cyber-Physical-Social Intelligence. Corresponding case studies in some typical applications will be shown to demonstrate the feasibility and flexibility.
Top Issue / Top Report
September 23rd
09:00-09:40 Keynote
TOPIC
Selective-PRS-Spoofing Attacks and Defence on 5G NR Positioning Systems
SPEAKER
Hongwu Lv (Harbin Engineering University, China)
BIO
Lv Hongwu,professor and doctoral supervisor at the School of Computer Science and Technology, Harbin Engineering University, director of the Heilongjiang Provincial Key Laboratory of New Generation Network Technology and Information Assurance, executive member of the Distributed Computing and Systems Special Committee of the Computer Society, and Internet of Things Special Committee of the Heilongjiang Computer Society committee member. The main research directions are new generation network technology and security, Internet of Things and high-precision positioning. He has presided over more than 10 projects including National Natural Science Foundation of China, National Natural Science Foundation of China Youth Project, National Defense Administration of Science, Technology and Industry, and Chinese Academy of Sciences Aerospace Information Innovation Institute. His research results have been published in journals including IEEE JSAC, IEEE/ACM TON, IEEE TIFS, IEEE He has obtained 14 invention patent authorizations from high-level journals and conferences such as TPDS, IEEE INFOCOM, and Journal of Computer Science, and his related research results have won 2 second prizes of the Heilongjiang Provincial Science and Technology Award.
ABSTRACT
5G positioning systems, as a solution for city-range integrated-sensing-and-communication (ISAC), are flooding into reality. However, the positioning security aspects of such an ISAC system have been overlooked. In this paper, we propose a new threat model for 5G positioning scenarios, namely the selective-PRSspoofing attack (SPS), disabling the latest security enhancement method reported in 3GPP R18. In our attack pattern, the attacker first cracks the broadcast information of a 5G network and then poisons specific resource elements of the channel, which can introduce substantial localization errors at victims or even completely control the positioning results. Worse, such attacks are transparent to both the UE-end and the networkend due to their stealthiness and easily bypass the current 3GPP defense mechanisms. To solve this problem, a DL-based defense method called in-phase quadrature Network (IQ-Net) is proposed, which utilizes the hardware features of base stations to perform identification at the physical level, thereby thwarting SPS attacks on 5G positioning systems. Extensive experiments demonstrate that our method has 98% defense accuracy and good robustness to noise.
09:40-10:20 Keynote
TOPIC
High-Quality Class Center Learning System for Deep Face Recognition
SPEAKER
Xianwei Lv (Huazhong University of Science and Technology, China)
BIO
Xianwei Lvreceived the B.S. and M.S. degree in computer science from Huazhong University of Science and Technology, Wuhan, China, in 2016 and 2019, respectively. He is currently working toward the Ph.D. degree in the National Engineering Research Center for Big Data Technology and System, Services Computing Technology and System Lab, Big Data Technology and System Lab, Cluster and Grid Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China, under the guidance of Pro. Chen Yu. His research interests include ubiquitous computing, edge computing and deep learning.
ABSTRACT
Benefited from the proposals of function losses margin-based, face recognition has achieved significant improvements in recent years. Those losses aim to increase the margin between the different identities to enhance the discriminability. Ideally, the class center of different identities is far from each other, and face samples are compact around the corresponding class center. Hence, it’s very vital to produce a high-quality class center. However, the class center is determined by the distribution of training sets. With low-quality samples being in the majority, the class center would be close to the samples with little identity information. As a result, it would impair the discriminability of the learned model for those unseen samples. This talk will present our latest research about how to improve the class center for deep face recognition and thus improve the performance of face recognition.
10:40-11:20 Keynote
TOPIC
Towards Efficient Self-Supervised Learning on Graphs
SPEAKER
Qiaoyu Tan (New York University Shanghai, China)
BIO
Qiaoyu Tanis a tenure-track assistant professor in the Computer Science Department at New York University Shanghai. He obtained his Ph.D. degree in the Department of Computer Science and Engineering at Texas A&MUniversity in August 2023, supervised by Dr. Xia Hu. His research interests center around machine learning and data mining, with a particular focus on graph machine learning, foundation model, multimodal learning, and applications in bioinformatics and healthcare. He has published more than 20 papers in major data mining and machine learning conferences/journals, such as KDD, WWW, WSDM, ICDM, SIGIR, AAAI, IJCAI, NeurIPS, and TKDE.
ABSTRACT
Graphs have become ubiquitous for describing and analyzing objects with relationships in various machine learning applications, including social science, transportation services, and biomedical informatics. Recently, deep learning on graphs has gained significant attention as the demand for such applications grows. However, most of the research in this area has been focused on supervised learning, leading to several shortcomings, such as heavy label reliance, poor generalization, and weak robustness. To overcome these issues, self-supervised learning (SSL) is emerging as a promising approach for graph data.In this presentation, I will first provide an overview of the current state of SSL research on graphs and then highlight the efficiency challenges faced when working with large-scale graphs and resource-constrained applications like online services. Finally, I will describe some of our most recent works that aim to accelerate the training and inference process of SSL on graphs, which have resulted in up to 7x speedup during training and 100x speedup during inference without sacrificing performance.
11:20-12:00 Keynote
TOPIC
Maximizing Range Sum in Trajectory Data
SPEAKER
Kaiqi Zhang (Harbin Institute of Technology, China)
BIO
Kaiqi Zhangis a Lecturer in the School of Computer Science and Technology at Harbin Institute of Technology. He obtained a doctoral degree from Harbin Institute of Technology in January 2020. The main research directions include queries in big spatiotemporal data, approximation algorithms in big data, Skyline queries, etc. Up to now, He has published 11 papers in international conferences and journals such as ICDE, TKDE, CIKM, ICDCS, and DASFAA. In recent years, He has led one Youth Project of National Natural Science Foundation of China and participated in multiple national projects, including Key Projects of National Natural Science Foundation of China and National Key Research and Development Program.
ABSTRACT
In recent years, trajectory data has received widespread attention in academia and industry. MaxRS query is an important operation in the fields of computational geometry and databases. Currently, the existing work mainly focuses on the situation where the data object is associated with a location point, which cannot solve the MaxRS query problem in trajectory data. This report focuses on the accurate and approximate algorithms for MaxRS queries in trajectory data. The accurate algorithm transforms this problem into a rectilinear polygon intersection problem and efficiently solves it using interval tree based on partitioning technique. The approximation algorithm utilizes random sampling technology and grid index to estimate highly accurate approximation results with a small number of samples.分论坛嘉宾介绍（一）
13:45-14:00 Keynote
TOPIC
Application of Remote Sensing Data in Various Industries in Heilongjiang Province
SPEAKER
Peng Wang (Cyberspace Research Center of Heilongjiang Province, China)
BIO
Wang Peng, holds a Master’s degree in Engineering, and is an Associate Senior Engineer. He serves as the Head of the Satellite Data Management Department at the Heilongjiang Province Cyber Space Research Center, specifically at the High-Resolution Earth Observation System Heilongjiang Data and Application Center. His primary research areas include image processing, data mining, land feature recognition, and quantitative analysis applications. He possesses expertise in core technologies such as automatic registration of multi-source satellite data and high-precision geometric correction.
As a key member, he has actively participated in various projects, including the National Defense Science and Technology Bureau’s Major Special Projects on the Application of High-Resolution Earth Observation Systems and the Industrial Demonstration Project of Major Special Projects on High-Resolution Earth Observation Systems. His extensive project experience has contributed significantly to these initiatives.
Wang Peng has published 6 academic papers in domestic academic journals, including 3 high-quality articles in SCI and EI indexed journals. He also holds 1 software copyright. He was part of the “High-Resolution Special Project Industrialization Application Project for Urban Fine Management in Heilongjiang Province,” which received the First Prize for Advancement in Surveying and Geographic Information in Heilongjiang Province in 2019. Additionally, he has been involved in a key research project related to the economic and social development of Heilongjiang Province.
ABSTRACT
As one of the national regional centers for high-resolution imagery, High-Resolution Heilongjiang Center has been promoting the application of domestically developed high-resolution satellite data in various industries within Heilongjiang Province for many years. Collaborating with over 30 local partners, including the Northeast Institute of Geography and Agroecology of the Chinese Academy of Sciences, the National Administration of Surveying, Mapping and Geoinformation’s Heilongjiang Basic Geographic Information Center, Heilongjiang Provincial Meteorological Bureau, Heilongjiang Provincial Academy of Agricultural Sciences, Harbin Institute of Technology, Harbin Engineering University, and Harbin Aerospace Hengxing Data System Technology Co., Ltd., they have undertaken a wide range of applications in fields such as agriculture, forestry, water resources, land management, environmental protection, border security, tourism, urban development, and disaster monitoring. Their achievements have been promoted and applied nationwide and have showcased the beauty of Heilongjiang Province on multiple occasions during Space Day events.
14:00-14:20Keynote
TOPIC
Satellite Remote Sensing Empowers “Digital Longjiang”
SPEAKER
Yiming Gu (Heilongjiang Basic Geographic Information Center, China)
BIO
Gao Yiming, master’s degree, senior engineer, technical director of the Science and Technology Development Department of Heilongjiang Basic Geographic Information Center (Heilongjiang Remote Sensing Information Center) of the Ministry of Natural Resources. Main research directions: remote sensing, geographical information systems, satellite navigation, spatiotemporal big data analysis and other application technologies. As a key member, he participated in the major special industrialization demonstration projects of the high-resolution earth observation system of the State Administration of Science, Technology and Industry for National Defense (two special projects of urban refined management and ecological civilization services), and accumulated rich project experience. Published 11 academic papers in domestic academic journals, 3 local standards, and 12 national and provincial scientific and technological progress awards for surveying, mapping, geographical information and satellite navigation. The “Construction of Heilongjiang Provincial Geographic Information Public Service Platform” he participated in won the second prize of the Heilongjiang Provincial Government Science and Technology Progress Award in 2009, and the “Research on Surveying and Mapping Geographic Information Service System for Antarctic Scientific Expedition” won the second prize of the China Surveying and Mapping Science and Technology Progress Award in 2017. “Key Technologies for Surveying and Mapping Geographic Information Application Services” won the second prize of the 2018 China Surveying and Mapping Science and Technology Progress Award.
ABSTRACT
The Heilongjiang Basic Geographic Information Center supports the development of Heilongjiang province’s smart cities, natural resource management, ecological civilization construction, resource and environmental monitoring, and other aspects of “digital government” construction and social digital governance through the utilization of remote sensing satellites, geographic information, and information infrastructure resources from the Heilongjiang Provincial Geographic Spatial Big Data Center and the Spatiotemporal Big Data Platform.
14:20-14:40Keynote
TOPIC
Time-series High-resolution Satellite Remote Sensing Image Processing Technology
SPEAKER
Guoming Gao (Harbin Institute of Technology, China)
BIO
Guoming Gao, born in 1987, holds a Ph.D. in Engineering and is an Associate Researcher as well as a Master’s thesis advisor. His primary research focuses on satellite time-series (multitemporal/synthetic aperture radar/video) remote sensing image processing and target monitoring, as well as intelligent interpretation of high-resolution multimodal remote sensing images.
He has led 5 research projects, including National Natural Science Foundation projects and Young Scientist Fund projects. Additionally, he has participated in several significant projects, such as the National Natural Science Foundation major scientific instrument development project, key international cooperation projects, outstanding young scientist fund projects, and defense-related projects.
Dr. Gao has published 15 papers in high-impact SCI journals, including 9 in IEEE Transactions, with a cumulative impact factor exceeding 100. He has also applied for 12 patents.
ABSTRACT
Time-series satellite remote sensing can be divided into three types: multi-temporal remote sensing, high-orbit staring satellite remote sensing and low-orbit video imaging remote sensing. Time-series satellite images can effectively monitor the dynamic change information of ground objects/targets, and have great achievements in the fields of ground object change analysis and dynamic target monitoring. important application value. The lecturer and his research group have conducted in-depth analysis and research in this field in recent years: Aiming at the problem of multi-dimensional feature changes between multi-temporal remote sensing images affecting cross-image analysis, a high-scoring multi-temporal remote sensing image alignment technology was developed; To solve the problem of weak and small targets in orbit-gazing satellite target monitoring, we have developed time-series background suppression and multi-frame target monitoring correlation technologies. For satellite video dynamic target monitoring, we have developed satellite video super-resolution, dynamic interest scene detection, video intrinsic decomposition and Satellite video moving target tracking and other technologies.
14:40-15:00Keynote
TOPIC
Collaborative Target Monitoring of Multi-modal Remote Sensing Images
SPEAKER
Nan Su (Harbin Engineering University, China)
BIO
Nan Su, pre-appointed professor, doctoral supervisor, director of the Institute of Spatial Information Processing and Countermeasures Technology, outstanding youth of Heilongjiang Province. He has presided over vertical projects such as the National Natural Science Foundation of China, Youth Projects, Heilongjiang Provincial Key R&D Projects, and China Postdoctoral Fund. He has presided over horizontal projects at the 23rd and 25th Institutes of the Second Academy of Aerospace Science and Industry, the China Electric Power Research Institute and other scientific research institutes. He has published more than 20 SCI papers, including 1 ESI Highly Cited Paper, 10 top journals of the Chinese Academy of Sciences, 13 JCR Area 1 papers, and 10 authorized invention patents. Guest editor of the special issue of the Top Journal of the Chinese Academy of Sciences, member of the Spatial Information Perception and Decision-making Committee of the Chinese Society of Image and Graphics, and member of the Working Organizing Committee of the China Society of Communications.
ABSTRACT
China’s aerospace remote sensing industry has experienced rapid development. With the operation of multiple satellite constellations in orbit, the acquired data comprehensively cover remote sensing images from various types of sensors, including visible light, Synthetic Aperture Radar (SAR), infrared, and multi/high-spectral imagery. Particularly, the maturation of small satellite technology, the successive launch of satellite constellations, and the rapid development of drone technology all contribute to providing more diverse and flexible multimodal remote sensing data for remote sensing monitoring and military reconnaissance.
Effectively harnessing the strengths of different sensors, enhancing data utilization, and making full use of multimodal information for synergistic complementation are essential for improving the monitoring accuracy and density of key targets. This is of significant importance for the development of remote sensing information processing techniques aimed at target monitoring.
15:00-15:20Keynote
TOPIC
Application of Remote Sensing Big Data in Agriculture
SPEAKER
Zhenqiang Song (Harbin Aerospace Hengxing Data System Technology Co., Ltd.)
BIO
Zhenqiang Song, a Senior Engineer, has successively led or participated in several significant projects, including the implementation of the National Defense Science and Technology Bureau’s High-Resolution Special Project at the provincial and regional level (Phase I and Phase II), the implementation of the nation’s first Beidou-3+ High-Resolution Regional Demonstration Project, and the implementation of the Three-River-Source National Park project in Qinghai. He has also participated as a key technical personnel in the High-Resolution Earth Observation Application Technology Innovation Competition.
Throughout his career, Song Zhenqiang has achieved recognition with multiple awards and certificates, including the “China Aerospace Science and Technology Group Co., Ltd. Scientific and Technological Achievement Appraisal Certificate”, “China Aerospace Science and Technology Group Co., Ltd. Second-Class Scientific and Technological Progress Award”, “First Prize in the 2022 Heilongjiang Province Surveying and Geographic Information Science and Technology Award” and “Third-Class Awards for Product Achievements and Technological Achievements in the High-Resolution Special Project Satellite Application Outstanding Achievements Competition”, He has also published more than 10 papers and patents.
ABSTRACT
Agriculture is the foundational industry that safeguards national security, and stability in agriculture ensures stability for the entire nation. In recent years, with the rapid advancement of satellite remote sensing, there has been significant progress in spatial resolution, temporal resolution, and spectral resolution. The integration and analysis of big data based on time-series satellite imagery data, geographic information infrastructure, and industrial data will further enhance the accuracy and efficiency of monitoring soil and agricultural conditions.
This integration of data will undoubtedly contribute to the rapid development of intelligent and digital agriculture, providing effective technological support for precision agriculture.
15:20-15:40Keynote
TOPIC
“Harbin Nongyao-1” Satellite Data and Agricultural Applications
SPEAKER
Gang Li (Heilongjiang Engineering College， China)
BIO
Li Gang, Ph.D., is a faculty member at the School of Surveying and Mapping Engineering at Heilongjiang University of Engineering. His primary research areas focus on satellite remote sensing application technology and spatiotemporal big data technology.
Dr. Li specializes in the integration and application of comprehensive datasets, including satellite remote sensing data, Internet of Things (IoT) data, and geographic information data. He has made significant contributions by overcoming research limitations in spatiotemporal data model construction. His work involves the practical application of spatiotemporal big data model technology in various fields, such as agriculture, forestry, environmental protection, smart cities, sponge cities, carbon neutrality, and more.
He has been involved in over 400 projects, including national projects like the 863 Program, Ministry of Science and Technology projects, water-related projects, and smart agriculture projects, as well as provincial and municipal projects. In June 2023, he successfully led the design and development of the digital agriculture satellite “HaCeNongYao-1,” which has been launched and is gradually being used for digital agriculture applications in Heilongjiang Province.
Dr. Li has published 12 papers, including 6 in SCI and EI journals, holds 5 patents, and has 6 software copyrights.
ABSTRACT
The “Harbin Nongyao-1” satellite is China’s first dedicated satellite for digital agriculture. It has been in orbit for 100 days. The shooting of remote sensing image data for the whole province of Heilongjiang Province in August should be completed on September 10. Gradually develop agricultural data services for Heilongjiang Province. There are multiple data application platforms that provide application services in the fields of government digital agriculture, disaster assessment, agricultural insurance, and bank innovation property rights. The next step is to establish data service centers in various regions of Heilongjiang Province, establish Internet of Things links and data receiving platforms, drone duty systems and other ground facilities, supporting different regions, land moisture, crop information, accumulated temperature zones and other elements to build crops in Heilongjiang Province Spectral database. Achieve the constellation construction goals and development plan of automatic acquisition of satellite data, high-accuracy automatic interpretation, automatic release, independent knowledge learning, and real-time agricultural situation assisted decision-making.
15:40-16:00Keynote
TOPIC
Artificial Intelligence and Enterprise-level Remote Sensing Applications
SPEAKER
Zhiqiang Wang (GeoScene Information Technology Co., Ltd.)
BIO
Wang Zhiqiangholds a Master’s degree in Software Engineering and possesses several years of experience in the development and system architecture design of spatial geographic information system software platforms. He is well-versed in various technologies, including digital twins, spatial 3D modeling, big data, deep learning, knowledge graphs, and WebGIS development. He is also known as a blogger on the Youku ArcGIS technology channel ( http://i.youku.com/arcgiswzq).
Mr. Wang has actively participated in and contributed to the design of projects related to provincial and municipal-level City Information Modeling (CIM) platforms, digital twin platforms, spatiotemporal sharing platforms, and big data spatiotemporal cloud platforms.
ABSTRACT
The presentation provides an in-depth exploration of the integration of Earth observation data, remote sensing image processing technology, and information technology (IT) in the field of remote sensing. With the advancement in the performance of Earth observation satellites, there is a growing need for robust remote sensing software to support the handling of vast amounts of remote sensing data. Remote sensing software has made significant progress in handling diverse remote sensing data, functional algorithms, application scenarios, and deep learning techniques. Moreover, software architecture has evolved beyond traditional desktop systems, gradually transitioning towards enterprise-level and cloud computing platforms.
The ENVI remote sensing image processing platform, developed by EasyView Corporation, offers comprehensive support for domestic satellite data and provides tools for constructing streamlined processing workflows. This platform has been instrumental in creating enterprise-level solutions for the production of remote sensing image products. The ENVI remote sensing image information extraction solution boasts various capabilities, including manual interpretation, spectral automatic classification, machine learning, spectral analysis, change monitoring, and deep learning.
The presentation will also showcase the capabilities and advantages of the ENVI platform in the realm of deep learning using examples such as “winter wheat sowing area in Henan Province” and “identification of pine wilt disease-infected trees.”
分论坛嘉宾介绍（二）
13:10-13:35Keynote
TOPIC
Exploring the Issues, Challenges, and Solutions in AIGC
SPEAKER
Likun Liu (Harbin Institute of Technology, China)
BIO
Liu Likun, an Assistant Researcher at the School of Cyberspace Security, Harbin Institute of Technology. His primary research areas encompass artificial intelligence security, network traffic monitoring, browser fingerprinting collection, and countermeasures. He is currently leading three sub-projects under the National Key Research and Development Program and a national information security project. He has been awarded the First Prize for Technological Progress by the China Institute of Communications.
ABSTRACT
AIGC (Artificial Intelligence for Generative Content) utilizes large-scale generative AI algorithms to assist or replace human creators in generating large-scale, high-quality, human-like content faster and at a lower cost, based on prompts provided by users. First, let’s delve into the enabling technologies and the general architecture of AIGC, followed by a discussion of its operational mode and key features. Subsequently, we will examine the categorization of security and privacy threats associated with AIGC. Finally, we will identify future challenges and open research directions related to AIGC.
13:35-13:55Keynote
TOPIC
SecXOps Empowering the Implementation of Large-scale Models in the Security Industry
SPEAKER
Xingkai Wang (Nsfocus Technologies Group Co.,ltd., China)
BIO
Xingkai Wang holds a Ph.D. degree from the University of the Chinese Academy of Sciences and completed postdoctoral research through a joint program between Venustech and Tsinghua University. Currently, he is employed at the Venustech Tian Shu Laboratory as a Principal Researcher and leads the Security Intelligent Analysis Team. His primary research areas include security intelligent analysis, artificial intelligence security, and security knowledge graphs. He has actively participated in numerous provincial and ministerial-level projects, filed for over 10 patents with 5 granted, contributed to more than 10 high-quality academic papers, co-authored 4 whitepapers, and delivered keynote speeches at the 2023 INSEC WORLD World Information Security Conference and the XCon2023 Summit. Additionally, he has been involved in the drafting of various standards and industry reports.
ABSTRACT
SecXOps is based on the integration of XOps and security scenarios, aiming to ensure security while reducing the redundancy of technology and processes, thus facilitating the large-scale implementation of the security industry.
15:10-15:30Keynote
TOPIC
Empowering Computational Power – Fueling Research and Innovation in HigherEducation
SPEAKER
Yuhua Wang (Harbin Engineering University, China)
BIO
Yuhua Wang holds a Ph.D. in Engineering and serves as an Associate Professor at the Computer School of Harbin Engineering University. He is also the Director of the High-Performance Computing Research Center at the university. His primary research interests include high-performance computing, artificial intelligence, distributed computing, and simulation.
He is a senior member of the China Computer Society and serves as a council member of the Heilongjiang Province Computer Society. Additionally, he acts as a special envoy for the Heilongjiang Province in the National Olympiad in Informatics in Provinces (NOIP) for high school students.
From 2014 to 2015, he conducted a one-year academic visit at Temple University in the United States, specifically in the Department of Computer Science and Information Systems.
In recent years, he has published over ten high-quality research papers in both domestic and international journals and conferences, including SCI-indexed and Chinese core journals.
Wang Yuhua has also played a crucial role as a key technical member in various significant research projects, including the “China Numerical Wave Tank” project for the Ministry of Industry and Information Technology (MIIT), where he was responsible for the development of virtual experiment scheduling systems and parallel computing optimization. He has participated in more than ten major research tasks, including the “Complex Power Device Integration Simulation Platform Technology.”
ABSTRACT
Computing power empowers research innovation, and computing power drives the future of technology. Computing power is emerging as a new form of productivity, injecting fresh momentum into university research and industry digital transformation.
15:30-15:50Keynote
TOPIC
The Future Network for AIGC
SPEAKER
Jinguang Li ( Inspur Cisco Network Technology Co., Ltd)
BIO
Jinguang Li, the Manager of the Market Promotion Department at Inspur Networks and a senior system architect. Focused on industry trends, emerging technologies, and network architecture design, among other responsibilities
ABSTRACT
Computing power is an indispensable resource engine for the development of AIGC (Artificial Intelligence for Generative Content). With the emergence of OpenAI’s GPT models, the AI industry has rapidly entered the era of AIGC, which relies heavily on large models. The enormous demand for training and inference computing power has placed additional strain on the already imbalanced supply and demand structure of the computing industry.
To better harness the power of cluster computing, the network plays a crucial role. This topic aims to share insights into the technological trends in AIGC networks and facilitate mutual learning among peers.
来源：GPC 2023＆ICPCSEE 2023组委会