Special Session

SPC Special Session I
Urban Air Mobility Systems

Decentralized Computation Offloading with Cooperative UAVs: Multi-Agent Deep Reinforcement Learning Perspective

Prof. Hoon Lee
Assistant Professor, Pukyong National University
Limited computing resources of IoT devices incur prohibitive latency in processing input data. This triggers new research opportunities toward task offloading systems. Deploying computing servers at existing base stations may not be sufficient. This requests mobile edge servers mounted on unmanned aerial vehicles (UAVs) that provide on-demand mobile edge computing (MEC) services. This talk presents an overview of recent deep reinforcement learning (DRL) approaches, in particular, a multi-agent DRL method where multiple intelligent UAVs cooperatively determine their computations and communication policies. Technical challenges and research opportunities in developing multi-UAV MEC networks are discussed.
Hoon Lee received the B.S. and Ph.D. degrees from Korea University, Seoul, Korea, in 2012 and 2017, respectively. Since 2019, he has been with the Department of Information and Communications Engineering, Pukyong National University, Busan, Korea. His research interests include machine learning, signal processing, and optimization for wireless communications.
Ultimate Mobility, UAM

Prof. KyungHi Chang
Professor, Inha University
Speech covers general aspects of UAM Service, Market, Ecosystem, and Experimental Demonstrations. R&D results on VSLAM-based UAV Trajectory Planning using Reinforced Swarm Intelligence are also presented.
KYUNGHI CHANG (SM'98) received the B.S. and M.S. degrees in electronics engineering from Yonsei University, Seoul, South Korea, in 1985 and 1987, respectively, and the Ph.D. degree in electrical engineering from Texas A&M University, College Station, TX, USA, in 1992. From 1989 to 1990, he was with the Samsung Advanced Institute of Technology (SAIT), and from 1992 to 2003, he was with the Electronics and Telecommunications Research Institute (ETRI). He is currently with the Electrical and Computer Engineering Department, Inha University. His research interests include radio transmission technology in 3GPP LTE & 5G NR systems, public safety and mobile ad-hoc networks (especially for UAV), cellular-V2X technology, NTN(Non-Terrestrial Network) & network intelligence for 6G, and applications of AI technologies. He was a recipient of the Haedong Academic Awards, in 2010, MSIT Minister’s Commendation and KICS Fellow in 2020, and Presidential Commendation, in 2021. He is currently a Chairman of 5G Forum, Executive Committee, and Chairman of Technology Committee for National Integrated Public Network. He has served as a Vice President at the KICS from 2017 to 2018, and from 2021. He has also served as an Editor of ITU-R TG8/1 IMT.MOD.
Learning and Communications for Urban Air Mobility (UAM)

Prof. Walid Saad
Professor, Virginia Tech
To meet the growing mobility needs in intra-city transportation, the concept of urban air mobility (UAM) has been proposed in which vertical takeoff and landing (VTOL) aircraft are used to provide a ride-hailing service. UAMs are expected to revolutionize the transportation industry by providing new aerial mobility options that can change the way in which we travel. However, the effective deployment of UAMs is contingent upon the presence of a reliable wireless and machine learning infrastructure that can enable UAM aircraft to communicate and navigate autonomously in dynamic environments. In this talk, we first present a rigorous performance analysis on the connectivity requirements for UAM systems. Then, leveraging these connectivity results, we propose a novel wireless-enabled asynchronous federated learning (AFL) framework that uses a Fourier neural network to tackle the challenging problem of turbulence prediction during UAM operations. We then show various properties of this framework, and we discuss key results related to learning and communications with UAM. We conclude the talk with some open problems in this field as well as in other related non-terrestrial network research areas.
Walid Saad (S'07, M'10, SM’15, F’19) received his Ph.D degree from the University of Oslo in 2010. Currently, he is a Professor at the Department of Electrical and Computer Engineering at Virginia Tech where he leads the Network sciEnce, Wireless, and Security (NEWS) laboratory. His research interests include wireless networks (5G/6G/beyond), machine learning, game theory, cybersecurity, unmanned aerial vehicles, semantic communications, and cyber-physical systems. Dr. Saad was the author/co-author of eleven conference best paper awards and of the 2015 and 2022 IEEE ComSoc Fred W. Ellersick Prize. He was a co-author of the 2019 IEEE Communications Society Young Author Best Paper and of the 2021 IEEE Communications Society Young Author Best Paper. He is a Fellow of the IEEE. He currently serves as an editor for several major IEEE Transactions. He is an Area Editor for the IEEE Transactions on Network Science and Engineering, and the Editor-in-Chief for the IEEE Transactions on Machine Learning in Communications and Networks.

SPC Special Session II
Recent Advances in Mobile and Ubiquitous Computing

Listening to Sounds to Introduce a New Class of Smart Mobile Applications

Prof. Hyosu, Kim
Assistant Professor, Chung-Ang University
In our daily lives, various kinds of sounds are produced from people themselves, computing devices, and surroundings. These sounds have plenty of information including when, where and what happened and eventually lead to the emergence of a new class of smart mobile applications. Two interesting examples are introduced through this talk. One is to turn any surface into a touch interface by listening to sounds. When a user taps a surface, an impact sound, called a touchsound, is produced. We can capture it using the built-in microphones of mobile devices and pinpoint the tapping location by analyzing the propagation pattern of the touchsound. Another work is to leverage sounds as a side channel for mobile payment services. When using magnetic induction-based communication technologies, such as NFC (Near-Field Communication) and MST (Magnetic Secure Transmission), some parts of smartphones are slightly deformed and emit sounds. We reveal that these sounds contain sensitive information and can introduce a new security threat for mobile device users.
Hyosu Kim received his B.S. degree in Department of Computer Engineering from Sungkyunkwan University in 2010. He then received his M.S. and Ph.D. degrees in School of Computing from Korean Advanced Institute of Science and Technology (KAIST), Republic of Korea in 2012 and 2018, respectively. He is currently an assistant professor at Chung-Ang University, School of Computer Science and Engineering, Seoul, South Korea. His research interests include IoT, cyber-physical systems, mobile and ubiquitous systems, and privacy.
mmWave Backscatter for Millions of Concurrent IoT

Prof. Song Min Kim
Associate Professor, KAIST
Massive connectivity is a key to the success of the Internet of Things. While mmWave backscatter has great potential, substantial signal attenuation and overwhelming ambient reflections impose significant challenges. We present OmniScatter, a practical mmWave backscatter with an extreme sensitivity of -115 dBm and scalability up to millions of concurrent tags. At the heart of OmniScatter is the new High Definition FMCW (HD-FMCW), which interplays with the tag (FSK) signal to disentangle the ambient reflections from the tag signal in the frequency domain. This essentially offers immunity to ambient reflections and improves the SNR by over five orders of magnitude! OmniScatter offers coordination-free Frequency Division Multiple Access (FDMA) that scales to millions of concurrent tags without coordination, paving a pathway towards the vision of massive IoT.
Song Min Kim is currently an associate professor in the School of Electrical Engineering at KAIST, Korea. Previously, he was with the Department of Computer Science at George Mason University, USA. He received his Ph.D. from the University of Minnesota in 2016 and M.E./B.E. degrees from Korea University in 2009/2007. His research interests include wireless networking and communication, mobile and embedded systems, and the Internet of Things. He currently serves on the editorial board of IEEE/ACM Transactions on Networking. He has received the best paper awards in ACM MobiSys and IEEE ICDCS.
Cross-layer Multipath Networking for High-speed Railway

Prof. Chenren Xu
Associate Professor, Peking University
Modern high-speed railway (HSR) systems offer a speed of more than 250 km/h, making on-board Internet access through track-side cellular base stations extremely challenging. We conduct extensive measurements on commercial HSR trains, and collect a massive 1.79 TB GPS-labeled TCP-LTE dataset covering a total travel distance of 28,800 km. Leveraging the new insights from the measurement, we design, implement, and evaluate POLYCORN, a first-of-its-kind networking system that can significantly boost Internet performance for HSR passengers. The core design of POLYCORN consists of a suite of novel multipath scheduling algorithms that intelligently determine what, when, and how to schedule user traffic over multiple highly fluctuating cellular links between HSR and track-side base stations. POLYCORN is specially designed for HSR environments through a cross-layer and proactive approach. We deploy POLYCORN on the operational LTE gateway of the popular Beijing-Shanghai HSR route at 300 km/h. Real-world experiments demonstrate that POLYCORN outperforms state-of-the-art multipath schedulers by 31% to 242% in goodput, and reduces the delivery time by 45% for instant messaging applications.
Prof. Chenren Xu (http://ceca.pku.edu.cn/chenren) is a Boya Young Fellow Associate Professor (with early tenure) in the School of Computer Science at Peking University (PKU) where he directs Software-hardware Orchestrated ARchitecture (SOAR) Lab. His research interests span wireless, networking and system, with a current focus on backscatter communication for low power IoT connectivity, future mobile Internet for high mobility data networking, and collaborative edge intelligence system for mobile and IoT computing. He earned his Ph.D. from WINLAB, Rutgers University, and worked as postdoctoral fellow in Carnegie Mellon University and visiting scholars in AT&T Shannon Labs and Microsoft Research. He is the General Secretary of ACM SIGBED China, Executive Committee of ACM SIGMOBILE and ACM SIGBED, Associate Editor of ACM IMWUT and Communications of the CCF. He published papers and has been serving as organization committee and/or TPC in top venues including ACM SIGCOMM, MobiCom, SenSys, UbiComp, and IEEE INFOCOM. He is a recipient of NSFC Excellent Young Scientists Fund (2020), Alibaba DAMO Academy Young Fellow (2018), ACM SIGCOMM China Rising Star (2020), CCF-Intel Young Faculty (2017) and CIE Outstanding Scientific and Technological Worker (2021) awards. His work has been featured in MIT Technology Review.

SPC Special Session III
Next-Generation Communications and Services

ATSC 3.0 Technologies and Future Evolution

Mr. Dong-Joon Choi
Executive Director of Media Broadcasting Research Section, ETRI
The next generation terrestrial digital broadcasting standard, known as ATSC 3.0 has a variety of new features which the first generation system (ATSC 1.0) does not support, responding to the changes of TV watching trends and needs for the efficient cooperation with broadband networks.
The physical layer of ATSC 3.0 provides various operation modes for different robustness and spectral efficiency required differently for each of intended services. Also, the standard is developed as all-IP based, and therefore, it realizes convergence of broadcast and broadband networks more efficiently. Due to the excellence of ATSC 3.0 technology, it was successfully commercialized in South Korea and the U.S., and furthermore, other countries such as India and Brazil are considering ATSC 3.0 as their next generation terrestrial broadcasting standard.
In this talk, I will review the ATSC 3.0 technologies and the status of the next generation broadcast systems for other countries.
Then, I will introduce the new emerging technologies based on the ATSC 3.0 standard - MIMO-LDM, diversity antenna for mobile reception, and channel bonding – which are being developed to accommodate the recent trends of media services, such as future immersive media, 8K UHD, and mobile services.
Dong-Joon Choi received the B.S. and M.S. degree in electronics engineering from the Pohang University of science and technology, Korea, in 1991 and 1993, respectively. He joined in ETRI (Electronics and Telecommunications Research Institute) in 1993 and has worked on satellite, cable and terrestrial broadcasting system technologies. He is currently serving as executive director of Media Broadcasting Research Section at ETRI.
Millimeter-Wave Metamaterial Antennas for 5G Today and LEO Satellites Tomorrow

Prof. Sungtek Kahng
Professor, Incheon National University
Since the 5G mobile service kicked off, with a view to the wider band and beamforming, millimeter-wave antennas have been employed for the communication system. In order to make up for rapid path-loss in the Ka-band, the antenna is in the form of an array which ends up with inevitable loss from the PCB and wirings. To cope with this drawback, metamaterial surfaces are devised to enhance the antenna in light of directivity and gain, without resorting to excessive use of beamformer chipsets. The metamaterial antenna technology is extended to the wireless link of moving vehicles such as LEO satellites. The IITP-RF RRC/MiEMI presents examples of millimeter-wave beamforming antennas generating radiated far-fields of high directivity ascribed to the lensing effect of the metasurface whose size is way smaller than the conventional array antenna at the same frequency. Plus, some of them including the RIS are tested with TEXWave5G the measurement equipment of our own making.
Prof. Sungtek Kahng received his Ph.D. degree from Hanyang University, Korea in 2000, with a specialty in Radio Science and Engineering. From 2000 to 2004, he worked for the Electronics and Telecommunication Research Institute(briefly, ETRI), and developed Satellite Payloads of GEOs, Computational EM methods and Electromagnetic Field Measurement Techniques.
Currently, in Dept. of Info. & Telecomm. Eng. of Incheon National University, he works on WPT devices, PD sensors, EMI/EMC for IED, RF components for UAVs and satellites, smart antennas for 5G/IoT networking. He in the committee evaluating Korean Satellite Development Programs appointed by NRF has cooperated with LGE, LIGNEX1, ETRI, KARI, ADD, CAMM, Corning, Samsung, AceTechnology, Hyundai, Amotech, Innertron, etc. He served as the General Chair for IEEE APCAP 2019.
6G LEO Satellite Network Optimization with Quantum Computing

Dr. Changjun Kim
Specialist, LG U+
The satellite network is a key element of 6G, and it is expected to overcome the limitations of terrestrial communication and provide communication without spatial restrictions such as deserts or airplanes. Among them, the Low-Earth Orbit (LEO) satellite network, which is actively studied nowadays, is more complex and more time-sensitive than the geostationary orbit (GSO) satellite network. In this talk, I will introduce a solution using a D-wave quantum computer, the first commercialized quantum computer. I will explain how to set the constraints and the objective functions of the LEO satellite network. It will be formulated with Quadratic Unconstrained Binary Optimization (QUBO) so that a quantum computer can solve the problem. Also, I will review the basic quantum properties for a quantum computer to solve QUBO.
Changjun Kim received the B.S., M.S., and Ph.D. (2021) degrees in electrical engineering from KAIST, Daejeon, South Korea. He currently working as a specialist in LG Uplus. Dr. Changjun Kim's research interests are quantum computing and advanced core network including cloud.

SPC Special Session IV
Next-Generation AI and Applications

Towards New Challenges on Recommender Systems Using Graph Neural Networks

Prof. Won-Yong Shin
Professor, Yonsei University
Recommender systems have been widely advocated as a way of providing suitable recommendation solutions to customers in various fields such as e-commerce, advertising, and social media sites. In recent years, thanks to the highly expressive capability of graph neural networks (GNNs) for effective representation learning of graphs, GNN-based recommender systems have been developed for improving the recommendation accuracy while exhibiting the state-of-the-art performance. In this talk, I first review existing GNN models for top-K recommendation, which generally were developed using implicit feedback by regarding observed user–item interactions as positive relations. More precisely, I describe state-of-the-art GNN-based recommender systems such as NGCF and LightGCN, which apply convolutions to graph domains, while performing layer aggregation to solve the oversmoothing problem. Then, I address some challenges such that existing GNN-based recommender systems overlook the existence of negative feedback due to their ease of modeling. To tackle this challenge, we discuss how to make use of low rating scores for representing users’ preferences since low ratings can still be informative in designing recommender systems. As a practical solution, I present SiReN, a new Sign-aware Recommender system based on GNN models and its performance superiority over state-of-the-art methods through comprehensive experiments.
Won-Yong Shin received the Ph.D. degree in Electrical Engineering and Computer Science from Korea Advanced Institute of Science and Technology (KAIST), Republic of Korea, in 2008. In May 2009, he joined the School of Engineering and Applied Sciences, Harvard University, MA USA, as a Postdoctoral Fellow and was promoted to a Research Associate in October 2011. From 2012 to 2019, Dr. Shin was a faculty member (with tenure) in the Department of Computer Science and Engineering, Dankook University, Republic of Korea. Since March 2019, he has been with the Department of Computational Science and Engineering, Yonsei University, Republic of Korea, where he is currently an Associate Professor.
From 2014 to 2018, Dr. Shin served as an Associate Editor of the IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences. He also served as an Organizing Committee for the 2015 IEEE Information Theory Workshop and the 2023 IEEE Consumer Communications and Networking Conference. He was a recipient of the Bronze Prize of the Samsung Humantech Paper Contest (2008), the KICS Haedong Young Scholar Award (2016), and the ICT Express Best Guest Editor Award (2021).
Semantics-Native and Knowledge-Driven 6G Semantic Communications

Prof. Jihong Park
Lecturer, Deakin University, Australia
Oftentimes humans can immediately respond to unseen events without instantaneously communicating with others. This is thanks to the ability of reasoning about future events, others’ minds, and their possible reactions to the events based on the background knowledge accumulated from experiences and communication in the past. In this talk I will trace back to the fundamentals of human cognition and artificial intelligence (AI), and introduce the ways to deal with semantics and knowledge in various desciplines including linguistics, semiotics, logic, and category theory. Inspired from this, I will introduce several promising ways towards designing AI and semantics native 6G communication with selected examples using multi-agent reinforcement learning and probabilistic logic programming.
Jihong Park is a Lecturer at the School of IT, Deakin University, Australia. He received the B.S. and Ph.D. degrees from Yonsei University, Seoul, Korea, in 2009 and 2016, respectively. He was a Post-Doctoral Researcher with Aalborg University, Denmark, from 2016 to 2017; the University of Oulu, Finland, from 2018 to 2019. His recent research focus includes distributed machine learning, control, and resource management, as well as their applications to 6G semantic, AI-native, and non-terrestrial communications. Dr. Park served as a Conference/Workshop Program Committee Member for IEEE GLOBECOM, ICC, and INFOCOM, as well as NeurIPS, ICML, and IJCAI. He received the IEEE GLOBECOM Student Travel Grant and the IEEE Seoul Section Student Paper Contest Bronze Prize in 2014, the 6th IDIS-ETNEWS Paper Award sponsored by the Ministry of Science, ICT, and the Future Planning of Korea, and FL-IJCAI Best Student Paper Award in 2022. Currently, he is an Associate Editor of Frontiers in Data Science for Communications and in Signal Processing for Communications. He is a Senior Member of IEEE and a Member of ACM.
Breaking the Hardware Impairment Barrier: from Model-based to AI-based Localization and Sensing

Prof. Henk Wymeersch
Professor, Chalmers University of Technology
Communication systems are moving to increasingly higher carrier frequencies, which brings benefits for high-accuracy localization and sensing, but also poses a significant challenge: at these higher carrier frequencies certain hardware impairments (HWIs) will be more pronounced. In this talk, we cover two HWIs (antenna element perturbations and mutual coupling) and show that (i) for communication these HWIs are relatively mild; (ii) for localization and sensing these HWIs lead to significant performance degradations. To mitigate the effect of these degradations, we propose an AI-based approach, learning both new transmit signals and receiver algorithms, based on data over the impaired channel. We show that under this approach, the performance loss can be recovered. Finally, challenges for future work in this area will be discussed.
Henk Wymeersch is a Professor in Communication Systems with the Department of Electrical Engineering at Chalmers University of Technology, Sweden. He is also a Distinguished Research Associate with Eindhoven University of Technology (TU Eindhoven). Prior to joining Chalmers, he was a Postdoctoral Associate during 2006-2009 with the Laboratory for Information and Decision Systems (LIDS) at the Massachusetts Institute of Technology (MIT). He obtained the Ph.D. degree in Electrical Engineering/Applied Sciences in 2005 from Ghent University, Belgium. He has served as Associate Editor for several IEEE journals and also as General Chair of the 2015 International Conference on Localization and GNSS. Awards include an ERC Starting Grant and a Chalmers supervision award. He currently leads the CROSSNET team at Chalmers.

SPC Special Session V
AI-Driven Digital Transformation

VisionScaling: Learning and Resource Co-Optimization for Mobile Vision Applications

Prof. Jeongho Kwak
Assistant Professor, DGIST
As deep learning technology becomes advanced, mobile vision applications such as AR/VR are prevalent. Although there exist many studies on the optimization of mobile resource allocation and learning model independently, they cannot reflect realistic mobile environments due to the assumption of fixed distributions for wireless and service request. In this talk, we will discuss the joint optimization of learning model and process/network resources adapting to system dynamics, and development of corresponding algorithm using state-of-the-art Online Convex Optimization (OCO) learning technique. Finally, we show the performance evaluation of the algorithm using recent AI embedded devices.
Jeongho Kwak received the B.S. degree in electrical and computer engineering from Ajou University, Suwon, Korea, in 2008 and the M.S. and Ph.D. degrees in electrical engineering from the KAIST, Daejeon, Korea, in 2011 and 2015, respectively. Prior to joining DGIST, he was with INRS-EMT, Montreal, Canada and Trinity College Dublin, Dublin, Ireland, as a Post-doctoral Researcher and a Marie Sklodowska-Curie Fellow, respectively. His current research interests lie on learning model & resource allocation in hybrid cloud/edge network architecture, code/data offloading and service caching systems, and satellite edge computing architecture.
Intermittent Learning on Harvested Energy

Prof. Shahriar Nirjon
Associate Professor, University of North Carolina
Years of technological advancements have made it possible for small, portable, electronic devices of today to last for years on battery power, and last forever - when powered by harvesting energy from their surrounding environment. Unfortunately, the prolonged life of these ultra-low-power systems poses a fundamentally new problem. While the devices last for years, programs that run on them become obsolete when the nature of sensory input or the operating conditions change. The effect of continued execution of such an obsolete program can be catastrophic. For example, if a cardiac pacemaker fails to recognize an impending cardiac arrest because the patient has aged or their physiology has changed, these devices will cause more harm than any good. Hence, being able to react, adapt, and evolve is necessary for these systems to guarantee their accuracy and response time. We aimed at devising algorithms, tools, systems, and applications that will enable ultra-low-power, sensor-enabled, computing devices capable of executing complex machine learning algorithms while being powered solely by harvesting energy. Unlike common practices where a fixed classifier runs on a device, we take a fundamentally different approach where a classifier is constructed in a manner that it can adapt and evolve as the sensory input to the system, or the application-specific requirements, such as the time, energy, and memory constraints of the system, change during the extended lifetime of the system.
Dr. Shahriar Nirjon is an Associate Professor of Computer Science at the University of North Carolina at Chapel Hill, NC. He is interested in Embedded Intelligence – the general idea of which is to make resource constrained real-time and embedded sensing systems capable of learning, adapting, and evolving. Dr. Nirjon builds practical cyber-physical systems that involve embedded sensors and mobile devices, mobility and connectivity, and mobile data analytics. His work has applications in the area of remote health and wellness monitoring, and mobile health. Dr. Nirjon received his Ph.D. from the University of Virginia, Charlottesville, and has won a number of awards including four Best Paper Awards at Mobile Systems, Applications and Services (MOBISYS 2014), the Real-Time and Embedded Technology and Applications Symposium (RTAS 2012), Distributed Computing in Sensor Systems (DCOSS '19), and Challenges in AI and Machine Learning for IoT (AIChallengeIoT '20). Dr. Nirjon is a recipient of NSF CAREER Award in 2021. Prior to UNC, Dr. Nirjon has worked as a Research Scientist in the Networking and Mobility Lab at the Hewlett-Packard Labs in Palo Alto, CA.
AI/ML in Radio Access Networks

Dr. Heesoo Lee
Director of Intelligent Wireless Access Research Section, ETRI
Artificial intelligence/Machine Learning (AI/ML) is known to be effective in reducing network installation cost, automating network operation, and improving network performance by solving nonlinear problems or complex optimization problems in the mobile communication field.
Recently, there are a lot of researches using AI/ML to design air interface and replacing some existing algorithms which was based on mathematical models in RAN. AI/ML is expected to play a defining role in future mobile communication. This presentation introduces some AI/ML use cases, research results, the current status of 3GPP standardization of AI/ML in the RAN domain.
HEESOO LEE received the B.S., M.S., and Ph.D. degrees in industrial engineering from the Korea Advanced Institute of Science and Technology (KAIST) in 1993, 1995, and 2001, respectively. In 2001, he joined the ETRI where he is currently the Director of Intelligent Wireless Access research section.
He is working on core technologies for the future wireless cellular communication, especially in the area of artificial intelligence, millimeter wave, OFDM, SC-FDMA, multiuser MIMO, interference management, relay, etc.

SPC Special Session VI
Emerging Systems and Security

Cyber Meets Physical: Cross-Domain Fuzzing for Autonomous Vehicle Security

Prof. Chung Hwan Kim
Assistant Professor, University of Texas at Dallas
Autonomous vehicles(AVs),such as drones and self-driving cars, are a type of cyber-physical systemsfor automatedtransportation and missions. With their increasing adoption, AVs are facing threats of cyber and cyber-physical attacks that exploit their attack surfaces. Although many AVs are critical to human safety and the environment, it is difficult to make them secure against such attacks due to new challengesthat are not addressable by traditional approaches. Many of these challenges originate from asemantic gap between cyber and physical domainsin which AVsystems operate.
In this talk, Iwill introducemy recent work that bridgesthegap todiscover securityvulnerabilities in AV systemseffectively.Specifically, I willintroducetwofuzzing toolsthatautomatically explore the input spaces of(1) drone control systemsand (2) self-driving car systems, respectively,and detect hidden vulnerabilities that attackers may exploit tocause critical accidents. I will show how ourfuzzing tools (in the cyber domain)generate sensorinputs and monitor vehicle operations(in the physical domain), and discuss uniquechallenges that we addressed while buildingthesemechanisms.Using our tools, wehave discoveredover 100 new bugs inpopular drone control and self-driving car systems and contributedto the eliminationof the bugsfor the security and safety of the AV systems.
Chung Hwan Kim is an Assistant Professor of Computer Science at the University of Texas at Dallas (UT Dallas). Before joining UT Dallas in 2020, he received his Ph.D. in Computer Science from Purdue University in 2017 and worked at NEC Labs as a Researcherfor three years. His research interest lies in solving securityproblems inmodern computing systems, recently with more focus on cyber-physical systemssafety and security. His research seeks to achieve this by developing new toolsusing program analysis, software testing, and operating/embeddedsystemstechniques. He received theUT Dallas New Faculty Research Symposium Grant Award in 2021. His work has been nominated as a Top10 Finalist for the CSAW Best Applied Research Paper Award in 2018.
Improving Cross-Platform Binary Analysis using Representation Learning via Graph Alignment

Prof. Dokyung Song
Assistant Professor, Yonsei University
Cross-platform binary analysis requires a common representation of binaries across platforms, on which a specific analysis can be performed. Recent work proposed to learn low-dimensional, numeric vector representations (i.e., embeddings) of disassembled binary code, and perform binary analysis in the embedding space. Unfortunately, however, existing techniques fall short in that they are either (i) specific to a single platform producing embeddings not aligned across platforms, or (ii) not designed to capture the rich contextual information available in a disassembled binary.
In this talk, I will present a deep learning-based method, XBA, which addresses the aforementioned problems. To this end, binaries are first represented as typed graphs, dubbed binary disassembly graphs (BDGs), which encode control-flow and other rich contextual information of different entities found in a disassembled binary, including basic blocks, external functions called, and string literals referenced. Binary code representation learning is then formulated as a graph alignment problem, i.e., finding the node correspondences between BDGs extracted from two binaries compiled for different platforms. XBA uses graph convolutional networks to learn the semantics of each node, (i) using its rich contextual information encoded in the BDG, and (ii) aligning its embeddings across platforms. This formulation allows XBA to learn semantic alignments between two BDGs in a semi-supervised manner, requiring only a limited number of node pairs be aligned across platforms for training. The evaluation results show that XBA can learn semantically-rich embeddings of binaries aligned across platforms without apriori platform-specific knowledge.
Dokyung Song is an assistant professor in the Department Computer Science at Yonsei University and the director of Yonsei University's Cyber Security Lab. He received his B.S. degree in Electrical and Computer Engineering from Seoul National University in 2014, and his M.S. and Ph.D. degree in Computer Science from UC Irvine in 2019 and 2020, respectively. During his Ph.D. studies, he worked as an intern in the C++ dynamic analysis team and the Fuchsia OS security team at Google, and in the product security team at Qualcomm. He also worked in the server technologies group at Oracle as a senior member of technical staff. His research interest lies in the broad area of systems security, and his current focus is on developing new systems, compiler and machine learning techniques that can better analyze the security of OS kernels as well as binary-only software.
Sense for Less: Physical-Informed Adaptation for Vibration-Based Internet of Things

Prof. Shijia Pan
Assistant Professor, University of California Merced
The number of everyday smart devices is projected to grow to the billions in the coming decade, which enables various smart building applications. These applications, especially in-home long-term occupant monitoring, rely on the emerging Internet-of-Things sensing techniques. We introduce ‘Structures as Sensors’, where we leverage ambient vibration induced by people to infer their information indirectly and non-intrusively. From the system perspective, general problems faced by sensing technologies, especially for indirect sensing, are the tradeoff between the sensing data efficiency/quality and the system deployment constraints. We address this issue by repurposing human sensing data to learn the information about the deployment. Then we optimize the sensing system to acquire high-quality data for the application accordingly. However, high-quality sensing data may still lead to low prediction accuracy, due to the complexity of the physical world -- sensing data distributions can change significantly under different sensing conditions. Therefore, from the data/learning perspective, accurate information learning through pure data-driven approaches requires a large amount of labeled data, which is costly and difficult to obtain in real-world applications. We address these challenges by combining physical and data-driven knowledge to reduce label data needed via physical knowledge-guided model transfer.
Dr. Shijia Pan is an Assistant Professor at the University of California Merced. She received her bachelor’s degree in Computer Science and Technology from the University of Science and Technology of China and her Ph.D. degree in Electrical and Computer Engineering from Carnegie Mellon University. Her research interests include cyber-physical sensing systems (CPS), multimodal learning for CPS/IoT, and ubiquitous computing. She worked in multiple disciplines and focused on indoor human information acquisition through ambient sensing. She has published in both top-tier Computer Science ACM/IEEE conferences and high-impact Civil Engineering journals. She received Rising Stars in EECS, Nick G. Vlahakis Graduate Fellowship, Google Anita Borg Scholarship, Best Paper Awards (IoTDI, ASME SHM/NDE, HASCA), Best Poster Awards (SenSys, IPSN), Best Demo Award (Ubicomp, BuildSys), Best Presentation Award (SenSys Doctoral Colloquium), and Audience Choice Award (BuildSys) from ACM/IEEE conferences.