Vol. 41, No. 10,
Oct. 2014
Digital Library
Modeling and Analysis of Vehicle Detection Using Roadside Ultrasonic Sensors in Wireless Sensor Networks
To address the problems of existing traffic information acquisition systems such as high cost and low scalability, wireless sensor networks (WSN) based traffic information acquisition systems have been studied. WSN based systems have many benefits including high scalability and low maintenance cost. Recently, various sensors are studied for traffic surveillance based on WSN, such as magnetic, acoustic, and accelerometer sensors. However, ultrasonic sensor based systems have not been studied. There are many issues for WSN based systems, such as battery driven operation and low computing power. Thus, power saving methods and specific algorithms with low complexity are necessary. In this paper, we introduce optimal methodologies for power saving of ultrasonic sensors based on the modeling and analysis in detail. Moreover, a new vehicle detection algorithm for low complexity using ultrasonic data is presented. The proposed methodologies are implemented in a tiny microprocessor. The evaluation results show that our algorithm has high detection accuracy.
Scalable RDFS Reasoning using Logic Programming Approach in a Single Machine
Batselem Jagvaral, Jemin Kim, Wan Gon Lee, Young Tack Park
As the web of data is increasingly producing large RDFS datasets, it becomes essential in building scalable reasoning engines over large triples. There have been many researches used expensive distributed framework, such as Hadoop, to reason over large RDFS triples. However, in many cases we are required to handle millions of triples. In such cases, it is not necessary to deploy expensive distributed systems because logic program based reasoners in a single machine can produce similar reasoning performances with that of distributed reasoner using Hadoop. In this paper, we propose a scalable RDFS reasoner using logical programming methods in a single machine and compare our empirical results with that of distributed systems. We show that our logic programming based reasoner using a single machine performs as similar as expensive distributed reasoner does up to 200 million RDFS triples. In addition, we designed a meta data structure by decomposing the ontology triples into separate sectors. Instead of loading all the triples into a single model, we selected an appropriate subset of the triples for each ontology reasoning rule. Unification makes it easy to handle conjunctive queries for RDFS schema reasoning, therefore, we have designed and implemented RDFS axioms using logic programming unifications and efficient conjunctive query handling mechanisms. The throughputs of our approach reached to 166K Triples/sec over LUBM1500 with 200 million triples. It is comparable to that of WebPIE, distributed reasoner using Hadoop and Map Reduce, which performs 185K Triples/sec. We show that it is unnecessary to use the distributed system up to 200 million triples and the performance of logic programming based reasoner in a single machine becomes comparable with that of expensive distributed reasoner which employs Hadoop framework.
Study of State Machine Diagram Robustness Testing using Casual Relation of Events
Seon Yeol Lee, Heung Seok Chae
Studies of fault injection into state machine diagram have been studied for generating robustness test cases. Conventional studies have, however, tended to inject too many faults into diagrams because they only have considered structural aspects of diagrams. In this paper, we propose a method that aims to reduce the number of injected fault without a decrease in effectivenss of robustness test. A proposed method is demonstrated using a microwave oven sate machine diagram and evaluated using a hash table state machine diagram. The result of the evaluation shows that the number of injected faults is decreased by 43% and the number of test cases is decreased by 63% without a decrease in effectiveness of hash table robustness test.
Spammer Detection using Features based on User Relationships in Twitter
Twitter is one of the most famous SNS(Social Network Service) in the world. Twitter spammer accounts that are created easily by E mail authentication deliver harmful content to twitter users. This paper presents a spammer detection method that utilizes features based on the relationship between users in twitter. Relationship based features include friends relationship that represents user preferences and type relationship that represents similarity between users. We compared the performance of the proposed method and conventional spammer detection method on a dataset with 3% to 30% spammer ratio, and the experimental results show that proposed method outperformed conventional method in Naive Bayesian Classification and Decision Tree Learning.
Fast Hilbert R-tree Bulk-loading Scheme using GPGPU
In spatial databases, R tree is one of the most widely used indexing structures and many variants have been proposed for its performance improvement. Among these variants, Hilbert R tree is a representative method using Hilbert curve to process large amounts of data without high cost split techniques to construct the R tree. This Hilbert R tree, however, is hardly applicable to large scale applications in practice mainly due to high pre processing costs and slow bulk load time. To overcome the limitations of Hilbert R tree, we propose a novel approach for parallelizing Hilbert mapping and thus accelerating bulk loading of Hilbert R tree on GPU memory. Hilbert R tree based on GPU improves bulk loading performance by applying the inversed cell method and exploiting parallelism for packing the R tree structure. Our experimental results show that the proposed scheme is up to 45 times faster compared to the traditional CPU based bulk loading schemes.
Secure Multi-Party Computation of Correlation Coefficients
Sun Kyong Hong, Sang Pil Kim, Hyo Sang Lim, Yang Sae Moon
In this paper, we address the problem of computing Pearson correlation coefficients and Spearman’s rank correlation coefficients in a secure manner while data providers preserve privacy of their own data in distributed environment. For a data mining or data analysis in the distributed environment, data providers(data owners) need to share their original data with each other. However, the original data may often contain very sensitive information, and thus, data providers do not prefer to disclose their original data for preserving privacy. In this paper, we formally define the secure correlation computation, SCC in short, as the problem of computing correlation coefficients in the distributed computing environment while preserving the data privacy (i.e., not disclosing the sensitive data) of multiple data providers. We then present SCC solutions for Pearson and Spearman’s correlation coefficients using secure scalar product. We show the correctness and secure property of the proposed solutions by presenting theorems and proving them formally. We also empirically show that the proposed solutions can be used for practical applications in the performance aspect.
Contents Routing in the OpenFlow-based Wireless Mesh Network Environment
Won Suk Kim, Sang Hwa Chung, Hyun suk Choi, Mi Rim Do
The wireless mesh network based on IEEE 802.11s provides a routing based on a destination address as it inherits legacy internet architecture. However, this architecture interested in not ‘what’ which is originally the users goal but ‘where’. Futhermore, because of the rapid increase of the number of mobile devices recently, the mobile traffic increases geometrically. It reduces the network effectiveness as increasing many packets which have same payload in the situation of many users access to the same contents. In this paper, we propose an OpenFlow based contents routing for the wireless mesh network(WMN) to solve this problem. We implement contents layer to the legacy network layer which mesh network uses and the routing technique based on contents identifier for efficient contents routing. In addition we provide flexibility as we use OpenFlow. By using this, we implement caching technique to improve effectiveness of network as decreasing the packet which has same payload in WSN. We measure the network usage to compare the flooding technique, we measure the delay to compare environment using caching and non caching. As a result of delay measure it shows 20% of performance improve, and controller message decrease maximum 89%.
A Buffer-based Video Quality Control Scheme for HTTP Adaptive Streaming in Long-Delay Networks
Jiwoo Park, Dongchil Kim, Kwangsue Chung
HTTP (Hypertext Transfer Protocol) Adaptive Streaming is gaining attention because it changes bitrates to adapt changing network conditions. Since HAS (HTTP Adaptive Streaming) client downloads the video data based on TCP (Transmission Control Protocol), it estimates incorrectly the available bandwidth and leads to an unnecessary video quality change in long delay networks. In this paper, we propose a buffer based quality control scheme in order to improve the service quality and smooth playback in the HAS. The proposed scheme estimates accurately the available bandwidth based on a modified streaming model that considers network delay. It also calculates the sustainability of the video quality to prevent an unnecessary quality change and determines the inter request time on the basis of the buffer status. Through the simulation, we prove that our scheme improves the QoS (Quality of Service) of the HAS service and controls the video quality smoothly in long delay networks.
Prefetching Framework for General Workloads Using Breakpoint
Kwangjin Ko, Junhee Ryu, Kyungtae Kang, Heonshik Shin
Application loading speed can be improved by timely prefetching disk blocks likely to be needed by an application. However, existing prefetchers if they are not specialized to a particular application incur high overheads and are poor at identifying the blocks that will actually be required. There are many sequences in which blocks may be needed and, even if two access sequences are identical, block tracing and access timings can be affected significantly by the state of the buffer cache. We propose a new application independent software based prefetching technique, in which breakpoints are inserted at appropriate places in an application to collect the information on correlations between the blocks and to prefetch the potential blocks ahead of their schedule based on it. Experiments on an HDD based desktop PC demonstrated an average 30% reduction in application launch time and 15% in general I/O, while reducing the wasted overhead.
Web-based 3D Object Retrieval from User-drawn Sketch Query
Jonghun Song, Jae Ho Ju, Sang Min Yoon
Three dimensional (3D) object retrieval from user drawn sketch queries is one of the important research issues in the areas of pattern recognition and computer graphics for simulation, visualization, and Computer Aided Design. The performance of content based 3D object retrieval system depends on the availability of effective descriptors and similarity measures for this kind of data. In this paper, we present a sketch based 3D object retrieval system by extracting a hybrid edge descriptor which is robust against rotation and translation. The experimental results which are based on HTML5 and WebGL show that proposed sketch based 3D object retrieval method is very efficient to search and order the 3D objects according to user’s intention.
Structural Similarity Index for Image Assessment Using Pixel Difference and Saturation Awareness
Until now, a lot of image quality assessment techniques or tools for optimal human visual system(HVS) awareness have been researched and SSIM(Structural SIMilarity) and its improved techniques are representative examples. However, they often cannot cope with various images and different distortion types robustly, and thus this can cause a large gap between their index values and HVS awareness. In this paper, we conduct image quality assessment on SSIM and its variants intensively and analyze the causes of each component function"s observed anomalies. Then, we propose a novel image quality assessment technique to compensate and improve such anomalies. Additionally, through extensive image assessment simulations, we show that the proposed technique can indicate HVS awareness more robustly and consistently than SSIM and its variants for various images and different distortion types.
A Dynamic Approach to Extract the Original Semantics and Structure of VM-based Obfuscated Binary Executables
In recent years, the obfuscation techniques are commonly exploited to protect malwares, so obfuscated malwares have become a big threat. Especially, it is extremely hard to analyze virtualization obfuscated malwares based on unusual virtual machines, because the original program is hidden by the virtual machine as well as its semantics is mixed with the semantics of the virtual machine. To confront this threat, we suggest a framework to analyze virtualization obfuscated programs based on the dynamic analysis. First, we extract the dynamic execution trace of the virtualization obfuscated executables. Second, we analyze the traces by translating machine instruction sequences into the intermediate representation and extract the virtual machine architecture by constructing dynamic context flow graphs. Finally, we extract abstract semantics of the original program using the extracted virtual machine architecture. In this paper, we propose a method to extract the information of the original program from a virtualization obfuscated program by some commercial obfuscation tools. We expect that our tool can be used to understand virtualization obfuscated programs and integrate other program analysis techniques so that it can be applied to analysis of the semantics of original programs using the abstract semantics.
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