In this work, we tackle the challenging problem of unsupervised video domain adaptation (UVDA) for action recognition. We specifically focus on scenarios with a substantial domain gap, in contrast to existing works primarily deal with small domain gaps between labeled source domains and unlabeled target domains. To establish a more realistic setting, we introduce a novel UVDA scenario, denoted as Kinetics→BABEL, with a more considerable domain gap in terms of both temporal dynamics and background shifts. To tackle the temporal shift, i.e., action duration difference between the source and target domains, we propose a global-local view alignment approach. To mitigate the background shift, we propose to learn temporal order sensitive representations by temporal order learning and background invariant representations by background augmentation. We empirically validate that the proposed method shows significant improvement over the existing methods on the Kinetics→BABEL dataset with a large domain gap. The code is available at https://github.com/KHU-VLL/GLAD.

Most programming languages support foreign language interoperation that allows developers to integrate multiple modules implemented in different languages into a single multilingual program. While utilizing various features from multiple languages expands expressivity, differences in language semantics require developers to understand the semantics of multiple languages and their inter-operation. Because current compilers do not support compile-time checking for interoperation, they do not help developers avoid interoperation bugs. Similarly, active research on static analysis and bug detection has been focusing on programs written in a single language.

This paper presents an effective mobile cloud-based interactive 3D rendering and streaming system with data-service cost constraints over heterogeneous wireless networks. The proposed system contains software-defined networking (SDN)-enabled adaptive cloud resource management module and context-aware 3D rendering and streaming module to enhance the quality-of-service of interactive 3D rendering and streaming services. The first module is designed to efficiently control the limited cloud resources, such as server computing power and available backbone link bandwidth, and the second module is implemented to pursue an effective tradeoff between 3D rendering quality and compressed image quality. The system is implemented using Mininet network emulator with an OpenDay-light SDN controller, and VirtualGL with an online open-source 3D game, PlaneShift. Finally, the system is examined at a mobile device connected to real long-term evolution and WiFi networks.

Application caching is a key feature to enable fast application switches for mobile devices by caching the entire memory pages of applications in the device's physical memory. However, application caching requires a prohibitive amount of memory unless a swap feature is employed to maintain only the working sets of the applications in memory. Unfortunately, mobile devices often disable the invaluable swap feature as it can severely decrease the flash-based local storage device's already marginal lifespan due to the increased writes to the device. As a result, modern mobile devices suffering from the insufficient memory space end up killing memory-hungry applications and keeping only a few applications in the memory. In this paper, we propose CloudSwap, a fast and robust swap mechanism for mobile devices to enable the memory-oblivious application caching. The key idea of CloudSwap is to use the fast local storage as a cache of read-intensive swap pages, while storing prefetch-enabled, write-intensive swap pages in a cloud storage. To preserve the lifespan of the local storage, CloudSwap minimizes the number of writes to the local storage by storing the modified portions of the locally swapped pages in a cloud. To reduce the remote swap-in latency, CloudSwap exploits two cloud-assisted prefetch schemes, the app-aware read-ahead scheme and the access pattern-aware prefetch scheme. Our evaluation shows that the performance of CloudSwap is comparable to a fast, but lifespan-critical local swap system, with only 18% lifespan reduction, compared to the local swap system's 85% lifespan reduction.

Although mobile devices have been evolved enough to support complex mobile programs, performance of the mobile devices is lagging behind performance of servers. To bridge the performance gap, computation offloading allows a mobile device to remotely execute heavy tasks at servers. However, due to architectural differences between mobile devices and servers, most existing computation offloading systems rely on virtual machines, so they cannot offload native applications. Some offloading systems can offload native mobile applications, but their applicability is limited to well-analyzable simple applications. This work presents automatic cross-architecture computation offloading for general-purpose native applications with a prototype framework that is called Native Offloader. At compile-time, Native Offloader automatically finds heavy tasks without any annotation, and generates offloading-enabled native binaries with memory unification for a mobile device and a server. At run-time, Native Offloader efficiently supports seamless migration between the mobile device and the server with a unified virtual address space and communication optimization. Native Offloader automatically offloads 17 native C applications from SPEC CPU2000 and CPU2006 benchmark suites without a virtual machine, and achieves a geomean program speedup of 6.42× and battery saving of 82.0%.

In this work, we propose a Raptor codes-based energy-efficient multipath multimedia transport protocol over heterogeneous wireless networks (HetNets) in order to provide seamless high-quality video streaming service in an energy efficient way. In the proposed protocol, systematic Raptor codes are employed to mitigate the video quality degradation. For energy saving, the proposed protocol not only shapes a video stream into bursts, but also determines the Raptor encoding parameters including code rate, symbol size, and the number of source symbols for energy saving. The proposed protocol is fully implemented on an Android-based smartphone, and examined over real HetNets.