This paper proposes a system that utilizes YOLO and OpenPose for detecting instances of dumping and evaluating the legality of the garbage. By extracting key joints from the human body and calculating distances, dumping activities can be determined, and the effectiveness of this approach has been established through experimental validation. Additionally, the system utilizes deep learning for video analysis to classify the color of dumped garbage. Information regarding illegal dumping is stored in a NAS (Network Attached Storage) system. The proposed system enables simultaneous detection of dumping and assessment of its legality, allowing for the identification of dumpers through stored video. This system offers significant potential for preventing and monitoring illegal garbage dumping

This paper analyzes the impact of surrounding data on predicting air and water pollution levels by incorporating relevant features and examining their influence.By doing so, we can confirm the relationship between air and water pollution.A hybrid deep learning-based model is trained and various datasets and models are compared and analyzed.The proposed GCN-GRU model achieved the best results not only for P M2.5 but also for Dissolved Oxygen.The hybrid model takes into account the spatial and temporal effects of data characteristics and provides more accurate environmental prediction information through correlation analysis.

A media streaming server can be implemented on a virtual machine for the ease of resource management. However, simply running a media streaming server on a virtual machine has two problems, the duplicate data in file caches of virtual machines and the performance degradation caused by the virtualization overhead. In order to resolve these problems, this paper proposes a high-performance media streaming architecture based on KVM. First, we implement a shared cache among virtual machines in order to eliminate the duplicate cached data. Second, the send file operation is offloaded to the hypervisor to reduce the virtualization overhead in I/O operations. Our evaluations with D-DASH datasets show that the performance of a media streaming server in the proposed architecture is increased by up to 30% as compared to that of the conventional media streaming server that simply runs on a virtual machine.

This paper presents a novel technique, called Zombie Chasing, for efficient flash management in solid state drives (SSDs). Due to the unique characteristics of NAND flash memory, SSDs need to accurately understand the liveness of the data stored in themselves. Recently, the TRIM command has been introduced to notify SSDs of dead data caused by file deletions, which otherwise could not be tracked by SSDs. This paper goes one step further and proposes a new liveness state, called the zombie state, to denote live data that will be dead shortly due to the corresponding dirty data in the buffer cache. We also devise new zombie-aware garbage collection algorithms which utilize the information about such zombie data inside SSDs. To evaluate Zombie Chasing, we implement zombie-aware garbage collection algorithms in the prototype SSD and modify the Linux kernel and the Oracle DBMS to deliver the information on the zombie data to the prototype SSD. Through comprehensive evaluations using our in-house micro-benchmark and the TPC-C benchmark, we observe that Zombie Chasing improves SSD performance effectively by reducing garbage collection overhead. Especially, our evaluation with the TPC-C benchmark on the Oracle DBMS shows that Zombie Chasing enhances the Transactions Per Second (TPS) value by up to 22% with negligible overhead.

The concept of Remote Procedure Call (RPC) was proposed more than 30 years ago. Although various RPC systems have been studied and implemented, the existing RPC systems lack many crucial features and flexibility required for building modern cluster file systems .This paper presents FlexRPC, a flexible user-level RPC system that enables to develop high-performance cluster file systems easily. FlexRPC ensures client-side thread-safeness and fully supports multithreaded RPC servers. Parallel and serial multicasting mechanisms allow for implementing sophisticated replication in modern cluster file systems. The remote procedure can be invoked using both UDP and TCP transports with at-most-once semantics. The concurrent call requests are handled by a set of worker threads on the client and server side where the number of workers varies dynamically according to the request rate. In addition, the semantics and the specification of remote procedures are designed to be as close as possible to SunRPC. The experimental results show that FlexRPC improves both latency and bandwidth significantly in spite of added functionalities. We also demonstrate the performance and the flexibility provided by FlexRPC by building working prototype of cluster file system called Kadoop on top of FlexRPC.