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Geunsik Lim

Publications and source records attributed to Geunsik Lim.

17 recordsLinked to original sources

Prioritization of Risks from Artificial Intelligence: A Delphi Study of 272 International Experts

Artificial intelligence poses many risks, ranging from familiar present-day harms to unprecedented and potentially catastrophic ones. Effective risk management requires prioritization: we must understand which risks are most severe, who is most vulnerable, and who is most responsible for addressing them. We report results from a three-round Delphi study conducted late 2025 with 272 international AI experts. Experts rated 24 AI risks on harm probability and severity, sector and actor vulnerability, actor responsibility, and overall concern. Experts estimated the five most severe harms in the next 5 years were likely to come from dangerous capabilities, competitive dynamics, weapons & cyberattacks (including CBRNE), power centralization, and false information. In a business-as-usual scenario, experts judged 18 of 24 risks as having a more than 10% probability of catastrophic outcomes (e.g., more than 1 million deaths or more than USD 100B in financial loss) in the next 5 years (2025-2030). In a scenario where pragmatic mitigations are implemented, experts still judged five risks as having a more than 10% probability of catastrophic outcomes: dangerous capabilities, weapons & cyberattacks, environmental harm, inequality & unemployment, and power centralization. All 24 risks were judged as being more than 5% likely to cause catastrophic outcomes. AI users and the general public were judged the most vulnerable to these risks, but experts assigned the highest responsibility for addressing them to general-purpose AI developers and governance actors (including governments, regulators, and standards bodies). Across most risks, experts identified information, finance, and national security as the most vulnerable sectors. These findings can guide AI risk prioritization and clarify expert expectations about who should bear responsibility for mitigation.

cs.CY

A Generative AI-Driven Reliability Layer for Action-Oriented Disaster Resilience

As climate-related hazards intensify, conventional early warning systems (EWS) disseminate alerts rapidly but often fail to trigger timely protective actions, leading to preventable losses and inequities. We introduce Climate RADAR (Risk-Aware, Dynamic, and Action Recommendation system), a generative AI-based reliability layer that reframes disaster communication from alerts delivered to actions executed. It integrates meteorological, hydrological, vulnerability, and social data into a composite risk index and employs guardrail-embedded large language models (LLMs) to deliver personalized recommendations across citizen, volunteer, and municipal interfaces. Evaluation through simulations, user studies, and a municipal pilot shows improved outcomes, including higher protective action execution, reduced response latency, and increased usability and trust. By combining predictive analytics, behavioral science, and responsible AI, Climate RADAR advances people-centered, transparent, and equitable early warning systems, offering practical pathways toward compliance-ready disaster resilience infrastructures.

cs.AI

SWAM: Revisiting Swap and OOMK for Improving Application Responsiveness on Mobile Devices

Existing memory reclamation policies on mobile devices may be no longer valid because they have negative effects on the response time of running applications. In this paper, we propose SWAM, a new integrated memory management technique that complements the shortcomings of both the swapping and killing mechanism in mobile devices and improves the application responsiveness. SWAM consists of (1) Adaptive Swap that performs swapping adaptively into memory or storage device while managing the swap space dynamically, (2) OOM Cleaner that reclaims shared object pages in the swap space to secure available memory and storage space, and (3) EOOM Killer that terminates processes in the worst case while prioritizing the lowest initialization cost applications as victim processes first. Experimental results demonstrate that SWAM significantly reduces the number of applications killed by OOMK (6.5x lower), and improves application launch time (36% faster) and response time (41% faster), compared to the conventional schemes.

cs.OS

Non-Stop & Non-Breakable Code Review Services in a Distributed System: Detecting Issues in Real Time

The two most significant bottlenecks in code merging are the build process and the unit tests. However, as the number of items to be checked in a code review increases, that code review becomes a bottleneck for code merging as well. Because of the dependency structure between code review services, an error in one service affects the entire service. As a result, whenever a service error occurs, it is crucial to have methods for determining which code review service has ultimately caused the error. With the goal of achieving a non-stop & non-breakable code review service, this paper describes an early error detection method along with a case study of the service.

cs.SE

Distributed Compilation System for High-Speed Software Build Processes

The idle time of personal computers has increased steadily due to the generalization of computer usage and cloud computing. Clustering research aims at utilizing idle computer resources for processing a variable workload on a large number of computers. The workload is processed continually despite the volatile status of the individual computer resources. This paper proposes a distributed compilation system for improving the processing speed of CPU-intensive software compilations. This significantly reduces the compilation time of mass sources by using the idle resources. We expect gains of up to 65% compared to non-distributed compilation systems.

cs.DC

User-Aware Power Management for Mobile Devices

The power management techniques to extend battery lifespan is becoming increasingly important due to longer user applications' running time in mobile devices. Even when users do not use any applications, battery lifespan decreases continually. It occurs because of service daemons of mobile platform and network-based data synchronization operations. In this paper, we propose a new power management system that recognizes the idle time of the device to reduce the battery consumption of mobile devices.

cs.AR

TAOS-CI: Lightweight & Modular Continuous Integration System for Edge Computing

With the proliferation of IoT and edge devices, we are observing a lot of consumer electronics becoming yet another IoT and edge devices. Unlike traditional smart devices, such as smart phones, consumer electronics, in general, have significant diversities with fewer number of devices per product model. With such high diversities, the proliferation of edge devices requires frequent and seamless updates of consumer electronics, which makes the manufacturers prone to regressions because the manufacturers have less resource per an instance of software release; i.e., they need to repeat releases by the number of product models times the number of updates. Continuous Integration (CI) systems can help prevent regression bugs from actively developing software packages including the frequently updated device software platforms. The proposed CI system provides a portable and modular software platform automatically inspecting potential issues of incoming changes with the enabled modules: code format and style, performance regressions, static checks on the source code, build and packaging tests, and dynamic checks with the built binary before deploying a platform image on the IoT and edge devices. Besides, our proposed approach is lightweight enough to be hosted in normal desktop computers even for dozens of developers. As a result, it can be easily applied to a lot of various source code repositories. Evaluation results demonstrate that the proposed method drastically improves plug-ins execution time and memory consumption, compared with methods in previous studies.

cs.SE

User-Level Memory Scheduler for Optimizing Application Performance in NUMA-Based Multicore Systems

Multicore CPU architectures have been established as a structure for general-purpose systems for high-performance processing of applications. Recent multicore CPU has evolved as a system architecture based on non-uniform memory architecture. For the technique of using the kernel space that shifts the tasks to the ideal memory node, the characteristics of the applications of the user-space cannot be considered. Therefore, kernel level approaches cannot execute memory scheduling to recognize the importance of user applications. Moreover, users need to run applications after sufficiently understanding the multicore CPU based on non-uniform memory architecture to ensure the high performance of the user's applications. This paper presents a user-space memory scheduler that allocates the ideal memory node for tasks by monitoring the characteristics of non-uniform memory architecture. From our experiment, the proposed system improved the performance of the application by up to 25% compared to the existing system.

cs.DC

Cloud-Based Content Cooperation System to Assist Collaborative Learning Environment

Online educational systems running on smart devices have the advantage of allowing users to learn online regardless of the location of the users. In particular, data synchronization enables users to cooperate on contents in real time anywhere by sharing their files via cloud storage. However, users cannot collaborate by simultaneously modifying files that are shared with each other. In this paper, we propose a content collaboration method and a history management technique that are based on distributed system structure and can synchronize data shared in the cloud for multiple users and multiple devices.

cs.DC

Virtual Memory Partitioning for Enhancing Application Performance in Mobile Platforms

Recently, the amount of running software on smart mobile devices is gradually increasing due to the introduction of application stores. The application store is a type of digital distribution platform for application software, which is provided as a component of an operating system on a smartphone or tablet. Mobile devices have limited memory capacity and, unlike server and desktop systems, due to their mobility they do not have a memory slot that can expand the memory capacity. Low memory killer (LMK) and out-of-memory killer (OOMK) are widely used memory management solutions in mobile systems. They forcibly terminate applications when the available physical memory becomes insufficient. In addition, before the forced termination, the memory shortage incurs thrashing and fragmentation, thus slowing down application performance. Although the existing page reclamation mechanism is designed to secure available memory, it could seriously degrade user responsiveness due to the thrashing. Memory management is therefore still important especially in mobile devices with small memory capacity. This paper presents a new memory partitioning technique that resolves the deterioration of the existing application life cycle induced by LMK and OOMK. It provides a completely isolated virtual memory node at the operating system level. Evaluation results demonstrate that the proposed method improves application execution time under memory shortage, compared with methods in previous studies.

cs.AR

BB: Booting Booster for Consumer Electronics with Modern OS

Unconventional computing platforms have spread widely and rapidly following smart phones and tablets: consumer electronics such as smart TVs and digital cameras. For such devices, fast booting is a critical requirement; waiting tens of seconds for a TV or a camera to boot up is not acceptable, unlike a PC or smart phone. Moreover, the software platforms of these devices have become as rich as conventional computing devices to provide comparable services. As a result, the booting procedure to start every required OS service, hardware component, and application, the quantity of which is ever increasing, may take unbearable time for most consumers. To accelerate booting, this paper introduces \textit{Booting Booster} (BB), which is used in all 2015 Samsung Smart TV models, and which runs the Linux-based Tizen OS. BB addresses the init scheme of Linux, which launches initial user-space OS services and applications and manages the life cycles of all user processes, by identifying and isolating booting-critical tasks, deferring non-critical tasks, and enabling execution of more tasks in parallel. BB has been successfully deployed in Samsung Smart TV 2015 models achieving a cold boot in 3.5 s (compared to 8.1 s with full commercial-grade optimizations without BB) without the need for suspend-to-RAM or hibernation. After this successful deployment, we have released the source code via http://opensource.samsung.com, and BB will be included in the open-source OS, Tizen (http://tizen.org).

cs.DC

LightSys: Lightweight and Efficient CI System for Improving Integration Speed of Software

The complexity and size increase of software has extended the delay for developers as they wait for code analysis and code merge. With the larger and more complex software, more developers nowadays are developing software with large source code repositories. The tendency for software platforms to immediately update software packages with feature updates and bug-fixes is a significant obstacle. Continuous integration systems may help prevent software flaws during the active development of software packages, even when they are deployed and updated frequently. Herein, we present a portable and modular code review automation system that inspects incoming code changes such as code format and style, performance regression, static analysis, build and deployment tests, and dynamic analysis before merging and changing code. The proposed mechanisms are sufficiently lightweight to be hosted on a regular desktop computer even for numerous developers. The resulting reduced costs allow developers to apply the proposed mechanism to many source code repositories. Experimental results demonstrate that the proposed mechanism drastically reduces overheads and improves usability compared with conventional mechanisms: execution time (6x faster), CPU usage (40% lower), memory consumption (1/180), and no out-of-memory occurrence.

cs.SE

Load-Balancing for Improving User Responsiveness on Multicore Embedded Systems

Most commercial embedded devices have been deployed with a single processor architecture. The code size and complexity of applications running on embedded devices are rapidly increasing due to the emergence of application business models such as Google Play Store and Apple App Store. As a result, a high-performance multicore CPUs have become a major trend in the embedded market as well as in the personal computer market. Due to this trend, many device manufacturers have been able to adopt more attractive user interfaces and high-performance applications for better user experiences on the multicore systems. In this paper, we describe how to improve the real-time performance by reducing the user waiting time on multicore systems that use a partitioned per-CPU run queue scheduling technique. Rather than focusing on naive load-balancing scheme for equally balanced CPU usage, our approach tries to minimize the cost of task migration by considering the importance level of running tasks and to optimize per-CPU utilization on multicore embedded systems. Consequently, our approach improves the real-time characteristics such as cache efficiency, user responsiveness, and latency. Experimental results under heavy background stress show that our approach reduces the average scheduling latency of an urgent task by 2.3 times.

cs.DC

Thread Evolution Kit for Optimizing Thread Operations on CE/IoT Devices

Most modern operating systems have adopted the one-to-one thread model to support fast execution of threads in both multi-core and single-core systems. This thread model, which maps the kernel-space and user-space threads in a one-to-one manner, supports quick thread creation and termination in high-performance server environments. However, the performance of time-critical threads is degraded when multiple threads are being run in low-end CE devices with limited system resources. When a CE device runs many threads to support diverse application functionalities, low-level hardware specifications often lead to significant resource contention among the threads trying to obtain system resources. As a result, the operating system encounters challenges, such as excessive thread context switching overhead, execution delay of time-critical threads, and a lack of virtual memory for thread stacks. This paper proposes a state-of-the-art Thread Evolution Kit (TEK) that consists of three primary components: a CPU Mediator, Stack Tuner, and Enhanced Thread Identifier. From the experiment, we can see that the proposed scheme significantly improves user responsiveness (7x faster) under high CPU contention compared to the traditional thread model. Also, TEK solves the segmentation fault problem that frequently occurs when a CE application increases the number of threads during its execution.

cs.OS

NNStreamer: Efficient and Agile Development of On-Device AI Systems

We propose NNStreamer, a software system that handles neural networks as filters of stream pipelines, applying the stream processing paradigm to deep neural network applications. A new trend with the wide-spread of deep neural network applications is on-device AI. It is to process neural networks on mobile devices or edge/IoT devices instead of cloud servers. Emerging privacy issues, data transmission costs, and operational costs signify the need for on-device AI, especially if we deploy a massive number of devices. NNStreamer efficiently handles neural networks with complex data stream pipelines on devices, significantly improving the overall performance with minimal efforts. Besides, NNStreamer simplifies implementations and allows reusing off-the-shelf media filters directly, which reduces developmental costs significantly. We are already deploying NNStreamer for a wide range of products and platforms, including the Galaxy series and various consumer electronic devices. The experimental results suggest a reduction in developmental costs and enhanced performance of pipeline architectures and NNStreamer. It is an open-source project incubated by Linux Foundation AI, available to the public and applicable to various hardware and software platforms.

cs.LG

NNStreamer: Stream Processing Paradigm for Neural Networks, Toward Efficient Development and Execution of On-Device AI Applications

We propose nnstreamer, a software system that handles neural networks as filters of stream pipelines, applying the stream processing paradigm to neural network applications. A new trend with the wide-spread of deep neural network applications is on-device AI; i.e., processing neural networks directly on mobile devices or edge/IoT devices instead of cloud servers. Emerging privacy issues, data transmission costs, and operational costs signifies the need for on-device AI especially when a huge number of devices with real-time data processing are deployed. Nnstreamer efficiently handles neural networks with complex data stream pipelines on devices, improving the overall performance significantly with minimal efforts. Besides, nnstreamer simplifies the neural network pipeline implementations and allows reusing off-shelf multimedia stream filters directly; thus it reduces the developmental costs significantly. Nnstreamer is already being deployed with a product releasing soon and is open source software applicable to a wide range of hardware architectures and software platforms.

cs.DC