18,908 research outputs found
Lim Han Joo
No full text학위논문(석사)--아주대학교 정보통신대학원 :정보보호,2009. 2인터넷에서 가장 많이 사용하고 있는 웹 서비스에 대해서 적절한 필터링이 요구됨에도 현재 사용하고 있는 필터링은 IP 기반에 단순한 필터링을 적용하고 있어 공격에 대한 효율적인 필터링을 하기 힘들다. 본 논문에서는 필터링 방법을 다양화 하기 위해 Cookie 나 사용자 정보를 필터링 할 수 있는 구성 요소로 적용함으로 새로운 형태의 사용자 기반의 필터링 기법을 제안 하였다. 기존 필터링 기법보다 필터링 규칙을 다양함으로 공격요청에 대해서 보다 빠르게 탐지 및 서비스를 보호할 수 있다제 1장 서론 ---------- 1
1.1 연구 배경 및 목적 ---------- 1
1.2 연구의 범위와 방법---------- 3
제 2장 웹 필터링 ---------- 4
2.1 개요 ---------- 4
2.2 기존 시스템에 문제점 도출 ---------- 5
제 3 장 사용자 기반의 웹 필터링 연구 ---------- 7
3.1 웹 필터링 ---------- 7
3.2 사용자 기반의 웹 필터링 구성요소 ---------- 7
3.2.1 사용자 Browser Cookie. ---------- 7
3.2.2 Most Recently Used Algorithm ---------- 9
3.2.3 Filtering Table 연구 ---------- 10
3.2.4 Event 에 대한 응답처리 ---------- 12
3.2.5 Cookie 의 다양성 연구 ---------- 13
3.3 시스템 Architecture ---------- 14
3.4 필터링 구현 ---------- 16
3.4.1 URI Key설정 ---------- 16
3.4.2 사용자 Token ---------- 17
3.4.3 추가 Argument ---------- 19
3.4.4 Filter 설정 연구 ---------- 20
3.5 사용자 기반의 웹 필터링 적용 효과 ---------- 22
제 4장 결론 ---------- 23Maste
Lim (Doctor), Han Hoe, [No Service Number]
No full textThis record was harvested from a previous catalogue system and will be withdrawn in 2025. Information in this record may be superseded or incomplete. Visit this record in UMA's new catalogue at: https://archives.library.unimelb.edu.au/nodes/view/399460Surname: LIM (DOCTOR). Given Name(s) or Initials: HAN HOE. Military Service Number or Last Known Location: [No Registration Number]. Missing, Wounded and Prisoner of War Enquiry Card Index Number: 14799.217244
Item: [2016.0049.31753] "Lim (Doctor), Han Hoe, [No Service Number]
Four and a half LIM protein 1C (FHL1C)
No full textFour-and-a-half LIM domain protein 1 isoform A (FHL1A) is predominantly expressed in skeletal and cardiac muscle. Mutations in the FHL1 gene are causative for several types of hereditary myopathies including X-linked myopathy with postural muscle atrophy (XMPMA). We here studied myoblasts from XMPMA patients. We found that functional FHL1A protein is completely absent in patient myoblasts. In parallel, expression of FHL1C is either unaffected or increased. Furthermore, a decreased proliferation rate of XMPMA myoblasts compared to controls was observed but an increased number of XMPMA myoblasts was found in the G(0)/G(1) phase. Furthermore, low expression of K(v1.5), a voltage-gated potassium channel known to alter myoblast proliferation during the G(1) phase and to control repolarization of action potential, was detected. In order to substantiate a possible relation between K(v1.5) and FHL1C, a pull-down assay was performed. A physical and direct interaction of both proteins was observed in vitro. In addition, confocal microscopy revealed substantial colocalization of FHL1C and K(v1.5) within atrial cells, supporting a possible interaction between both proteins in vivo. Two-electrode voltage clamp experiments demonstrated that coexpression of K(v1.5) with FHL1C in Xenopus laevis oocytes markedly reduced K(+) currents when compared to oocytes expressing K(v1.5) only. We here present the first evidence on a biological relevance of FHL1C
Fear conditioning occludes late-phase long-term potentiation at thalamic input synapses onto the lateral amygdala in rat brain slices
No full textLate-phase long-term potentiation (L-LTP) of excitatory synaptic transmission at thalamic input synapses onto the lateral amygdala (T-LA synapses) has been proposed as a cellular substrate for long-term fear memory. This notion is evidenced primarily by previous reports in which the same pharmacological treatments block both T-LA L-LTP and the consolidation of fear memory. In this study, we report that fear conditioning occludes L-LTP at T-LA synapses in brain slices prepared after fear memory consolidation. L-LTP was restored either when synaptic depotentiation was induced prior to L-LTP induction in brain slices prepared from conditioned rats or when brain slices were prepared from conditioned rats that had been exposed to subsequent fear extinction, which is a behavior paradigm known to induce in vivo synaptic depotentiation at T-LA synapses. These results suggest that fear conditioning recruits L-LTP-like mechanisms that are reversible and saturable at T-LA synapses. (C) 2011 Elsevier Ireland Ltd. All rights reserved.
Distributed human computation framework for linked data co-reference resolution
No full textDistributed Human Computation (DHC) is a technique used to solve computational problems by incorporating the collaborative effort of a large number of humans. It is also a solution to AI-complete problems such as natural language processing. The Semantic Web with its root in AI is envisioned to be a decentralised world-wide information space for sharing machine-readable data with minimal integration costs. There are many research problems in the Semantic Web that are considered as AI-complete problems. An example is co-reference resolution, which involves determining whether different URIs refer to the same entity. This is considered to be a significant hurdle to overcome in the realisation of large-scale Semantic Web applications. In this paper, we propose a framework for building a DHC system on top of the Linked Data Cloud to solve various computational problems. To demonstrate the concept, we are focusing on handling the co-reference resolution in the Semantic Web when integrating distributed datasets. The traditional way to solve this problem is to design machine-learning algorithms. However, they are often computationally expensive, error-prone and do not scale. We designed a DHC system named iamResearcher, which solves the scientific publication author identity co-reference problem when integrating distributed bibliographic datasets. In our system, we aggregated 6 million bibliographic data from various publication repositories. Users can sign up to the system to audit and align their own publications, thus solving the co-reference problem in a distributed manner. The aggregated results are published to the Linked Data Cloud
Improving Computational Efficiency in Crowded Task Allocation Games with Coupled Constraints
No full textMulti-agent task allocation is a well-studied field with many proven algorithms. In real-world applications, many tasks have complicated coupled relationships that affect the feasibility of some algorithms. In this paper, we leverage on the properties of potential games and introduce a scheduling algorithm to provide feasible solutions in allocation scenarios with complicated spatial and temporal dependence. Additionally, we propose the use of random sampling in a Distributed Stochastic Algorithm to enhance speed of convergence. We demonstrate the feasibility of such an approach in a simulated disaster relief operation and show that feasibly good results can be obtained when the confirmation and sample size requirements are properly selected
A formula for the arc length of a superhelix
No full textA superhelix is a helix that is coiled around another helix. Despite its importance for the deformation modeling of various shapes, superhelix has been considerably overlooked, in part because of its complexity and in part for the lack of an analytical formula for its arc length. In this study, we present an exact analytical formula for the arc length of a superhelix. The final expression is given in the form of an infinite sum
Super Resolution based on Deep Learning Technique for constructing Digital Elevation Model
No full textIn this paper, the additional learning method on the pre-trained convolutional neural network (CNN) for image super-resolution (SR) and its usage for lunar image postprocessing is proposed. Transfer learning is a popular method in convolutional network (ConvNet) research because training a ConvNet to learn basic features for classification and detection is prohibitively time consuming. Transfer learning enables the re-training of the latter layer of a ConvNet to perform a different task. In SR, the overall ConvNet structure is much different from a ConvNet structure used for classification and detection, as the size of the input and output data must be identical. Inspired by the transfer learning method, an additional CNN structure is added to the base CNN for SR, and the additional ConvNet structure is newly trained. Results show a small improvement in performance over the base ConvNet structure in some example images. The CNN for SR algorithm outperforms the Bicubic interpolation method in restoring a sample lunar image to its original resolution. Possible applications include enhancing the resolution of lunar images to perform shape from shading, de-noising, and template matching the lunar surface image to a given DEM
Analyzing Stack Flows to Compare Java Programs
No full textThis paper presents a method for comparing and detecting clones of Java programs by analyzing program stack flows. A stack flow denotes an operational behavior of a program by describing individual instructions and stack movements for performing specific operations. We analyze stack flows by simulating the operand stack movements during execution of a Java program. Two programs for detection of clones of Java programs are compared by matching similar pairs of stack flows in the programs. Experiments were performed on the proposed method and compared with the earlier approaches of comparing Java programs, the Tamada, k-gram, and stack pattern based methods. Their performance was evaluated with real-world Java programs in several categories collected from the Internet. The experimental results show that the proposed method is more effective than earlier methods of comparing and detecting clones of Java programs
A potential game approach for distributed cooperative sensing for maximum mutual information
No full text- …
