26803013
Jikzi: A New Framework for Secure Publishing
We present a new way to deal with security policy on the web. Our aim is to let people build integrated publishing and e-commerce services using appropriate, simple and uniform mechanisms. Our goal is a single framework that applies equally to the publication of catalogues, music, software, public key certificates and even old fashioned books. Our prototype framework, Jikzi, supports multiple security policies – even in the same document – with a single transparent markup language.
42871030
An Efficient Method for Detection of Key Objects in Video Shots with Camera Motions
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9196724
STRG-Index: spatio-temporal region graph indexing for large video databases
In this paper, we propose new graph-based data structure and indexing to organize and retrieve video data. Several researches have shown that a graph can be a better candidate for modeling semantically rich and complicated multimedia data. However, there are few methods that consider the temporal feature of video data, which is a distinguishable and representative characteristic when compared with other multimedia (i.e., images). In order to consider the temporal feature effectively and efficiently, we propose a new graph-based data structure called Spatio-Temporal Region Graph (STRG). Unlike existing graph-based data structures which provide only spatial features, the proposed STRG further provides temporal features, which represent temporal relationships among spatial objects. The STRG is decomposed into its subgraphs in which redundant subgraphs are eliminated to reduce the index size and search time, because the computational complexity of graph matching (subgraph isomorphism) is NP-complete. In addition, a new distance measure, called Extended Graph Edit Distance (EGED), is introduced in both non-metric and metric spaces for matching and indexing respectively. Based on STRG and EGED, we propose a new indexing method STRG-Index, which is faster and more accurate since it uses tree structure and clustering algorithm. We compare the STRG-Index with the M-tree, which is a popular tree-based indexing method for multimedia data. The STRG-Index outperforms the M-tree for various query loads in terms of cost and speed.
17811625
A Graph-Based Approach for Modeling and Indexing Video Data
In this work, we propose new graph-based data model and indexing to organize and manage video data. To consider spatial and temporal characteristics of video, we introduce a new graph-based data model called spatio-temporal region graph (STRG). Unlike existing graph-based data structures which provide only spatial features, the proposed STRG further provides temporal features, which represent temporal relationships among spatial objects. The STRG is decomposed into its subgraphs object graphs (OGs) and background graphs (BGs). In addition, a new distance measure, called extended graph edit distance (EGED), is introduced in metric space for matching and indexing. Based on clustering and EGED, we propose a new indexing method STRG-index, which is faster and more accurate. We compare the STRG-Index with the M-tree, which is a popular tree-based indexing method for multimedia data. The STRG-index outperforms the M-tree in terms of cost and speed
7043785
A new family of authentication protocols
We present a related family of authentication and digital signature protocols based on symmetric cryptographic primitives which perform substantially better than previous constructions. Previously, one-time digital signatures based on hash functions involved hundreds of hash function computations for each signature; we show that given online access to a timestamping service, we can sign messages using only two computations of a hash function. Previously, techniques to sign infinite streams involved one such one-time signature for each message block; we show that in many realistic scenarios a small number of hash function computations is sufficient. Previously, the Diffie Hellman protocol enabled two principals to create a confidentiality key from scratch: we provide an equivalent protocol for integrity, which enables two people who do not share a secret to set up a securely serialised channel into which attackers cannot subsequently intrude. In addition to being of potential use in real applications, our constructions also raise interesting questions about the definition of a digital signature, and the relationship between integrity and authenticity.
12522566
Real Time Video Data Mining for Surveillance Video Streams
We extend our previous work [1] of the general framework for video data mining to further address the issue such as how to mine video data using motions in video streams. To extract and characterize these motions, we use an accumulation of quantized pixel differences among all frames in a video segment. As a result, the accumulated motions of segment are represented as a two dimensional matrix. Further, we develop how to capture the location of motions occurring in a segment using the same matrix generated for the calculation of the amount. We study how to cluster those segmented pieces using the features (the amount and the location of motions) we extract by the matrix above. We investigate an algorithm to find whether a segment has normal or abnormal events by clustering and modeling normal events, which occur mostly. In addition to deciding normal or abnormal, the algorithm computes Degree of Abnormality of a segment, which represents to what extent a segment is distant to the existing segments in relation with normal events. Our experimental studies indicate that the proposed techniques are promising.
9935268
Case and Constraint-Based Apartment Construction Project Planning System: FASTrak-APT
To effectively generate a fast and consistent apartment construction project network, Hyundai and KAIST have developed a case and constraint based project planning expert system for apartment domain, FASTrak-APT, inspired by the fact that human expert project planner uses previous cases for planning a new project and modifies them using her/his knowledge on domain constraints. This largescale, case based and mixed-initiative planning system integrated with intensive constraint-based adaptation utilizes semantic level meta-constraints and human decisions for compensating incomplete cases imbedding specific planning knowledge. The case and constraint based architecture inherently supports cross-checking cases with constraints during the system development and maintenance. This system has drastically reduced the time and effort required for initial project planning, improved the quality and completeness of the generated plans, and is expected to give the company the competitive advantage in contract bids for new contracts.
16485757
Reverse Engineering MAC
This paper reverse engineers backoff-based random-access MAC protocols in ad-hoc networks. We show that the contention resolution algorithm in such protocols is implicitly participating in a non-cooperative game. Each link attempts to maximize a selfish local utility function, whose exact shape is reverse engineered from the protocol description, through a stochastic subgradient method in which the link updates its persistence probability based on its transmission success or failure. We prove that existence of a Nash equilibrium is guaranteed in general. The minimum amount of backoff aggressiveness needed for uniqueness of Nash equilibrium and convergence of the best response strategy are established as a function of user density. Convergence properties and connection with the best response strategy are also proved for variants of the stochastic-subgradient-based dynamics of the game. Together with known results in reverse engineering TCP and BGP, this paper completes the recent efforts in reverse engineering the main protocols in layers 2-4.
18004950
Trajectory clustering: a partition-and-group framework
Existing trajectory clustering algorithms group similar trajectories as a whole, thus discovering common trajectories. Our key observation is that clustering trajectories as a whole could miss common sub-trajectories. Discovering common sub-trajectories is very useful in many applications, especially if we have regions of special interest for analysis. In this paper, we propose a new partition-and-group framework for clustering trajectories, which partitions a trajectory into a set of line segments, and then, groups similar line segments together into a cluster. The primary advantage of this framework is to discover common sub-trajectories from a trajectory database. Based on this partition-and-group framework, we develop a trajectory clustering algorithm TRACLUS. Our algorithm consists of two phases: partitioning and grouping. For the first phase, we present a formal trajectory partitioning algorithm using the minimum description length(MDL) principle. For the second phase, we present a density-based line-segment clustering algorithm. Experimental results demonstrate that TRACLUS correctly discovers common sub-trajectories from real trajectory data.
13362734
FINDIT: a fast and intelligent subspace clustering algorithm using dimension voting
Abstract The aim of this paper is to present a novel subspace clustering method named FINDIT. Clustering is the process of finding interesting patterns residing in the dataset by grouping similar data objects from dissimilar ones based on their dimensional values. Subspace clustering is a new area of clustering which achieves the clustering goal in high dimension by allowing clusters to be formed with their own correlated dimensions. In subspace clustering, selecting correct dimensions is very important because the distance between points is easily changed according to the selected dimensions. However, to select dimensions correctly is difficult, because data grouping and dimension selecting should be performed simultaneously. FINDIT determines the correlated dimensions for each cluster based on two key ideas: dimension-oriented distance measure which fully utilizes dimensional difference information, and dimension voting policy which determines important dimensions in a probabilistic way based on V nearest neighbors' information. Through various experiments on synthetic data, FINDIT is shown to be very successful in the high dimensional clustering problem. FINDIT satisfies most requirements for good clustering methods such as accuracy of results, robustness to the noise and the cluster density, and scalability to the dataset size and the dimensionality. Moreover, it is gracefully scalable to full dimension without any modification to algorithm.
20717190
Opportunistic power scheduling for multi-server wireless systems with minimum performance constraints
We present and power scheduling scheme, i.e., a joint time-slot and power allocation method for wireless cellular systems. We allow multiple transmissions in a time-slot that can interfere with each other. Hence, it is important to not only select the mobiles to he scheduled in a time-slot, hut also important to allocate an appropriate power level for transmission to these scheduled mobiles in order to achieve high system performance and quality of service. We model the time-varying wireless channel as a stochastic process and formulate a stochastic programming problem that attempts to maximize the expected total system utility, with constraints on the minimum expected utility for each mobile. The power scheduling algorithm is obtained by using stochastic duality and implemented via stochastic subgradient techniques.