Results 1  10
of
17
Exact Indexing of Dynamic Time Warping
, 2002
"... The problem of indexing time series has attracted much research interest in the database community. Most algorithms used to index time series utilize the Euclidean distance or some variation thereof. However is has been forcefully shown that the Euclidean distance is a very brittle distance me ..."
Abstract

Cited by 326 (33 self)
 Add to MetaCart
The problem of indexing time series has attracted much research interest in the database community. Most algorithms used to index time series utilize the Euclidean distance or some variation thereof. However is has been forcefully shown that the Euclidean distance is a very brittle distance measure. Dynamic Time Warping (DTW) is a much more robust distance measure for time series, allowing similar shapes to match even if they are out of phase in the time axis.
On the Need for Time Series Data Mining Benchmarks: A Survey and Empirical Demonstration
 SIGKDD'02
, 2002
"... ... mining time series data. Literally hundreds of papers have introduced new algorithms to index, classify, cluster and segment time series. In this work we make the following claim. Much of this work has very little utility because the contribution made (speed in the case of indexing, accuracy in ..."
Abstract

Cited by 289 (56 self)
 Add to MetaCart
(Show Context)
... mining time series data. Literally hundreds of papers have introduced new algorithms to index, classify, cluster and segment time series. In this work we make the following claim. Much of this work has very little utility because the contribution made (speed in the case of indexing, accuracy in the case of classification and clustering, model accuracy in the case of segmentation) offer an amount of "improvement" that would have been completely dwarfed by the variance that would have been observed by testing on many real world datasets, or the variance that would have been observed by changing minor (unstated) implementation details. To illustrate our point
Chaosbased random number generatorspart uppercaseI: analysis [cryptography
 IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications
, 2001
"... Abstract—This paper and its companion (Part II) are devoted to the analysis of the application of a chaotic piecewiselinear onedimensional (PL1D) map as random number generator (RNG). Piecewise linearity of the map enables us to mathematically find parameter values for which a generating partition ..."
Abstract

Cited by 18 (0 self)
 Add to MetaCart
(Show Context)
Abstract—This paper and its companion (Part II) are devoted to the analysis of the application of a chaotic piecewiselinear onedimensional (PL1D) map as random number generator (RNG). Piecewise linearity of the map enables us to mathematically find parameter values for which a generating partition is Markov and the RNG behaves as a Markov information source, and then to mathematically analyze the information generation process and the RNG. In the companion paper we discuss practical aspects of our chaosbased RNGs. Index Terms—Chaos, random number generator, symbolic dynamics. I.
Clustering of streaming time series is meaningless
 In Proc. of the SIGMOD workshop in Data Mining and Knowledge Discovery
, 2003
"... Time series data is perhaps the most frequently encountered type of data examined by the data mining community. Clustering is perhaps the most frequently used data mining algorithm, being useful in it’s own right as an exploratory technique, and also as a subroutine in more complex data mining algor ..."
Abstract

Cited by 14 (0 self)
 Add to MetaCart
(Show Context)
Time series data is perhaps the most frequently encountered type of data examined by the data mining community. Clustering is perhaps the most frequently used data mining algorithm, being useful in it’s own right as an exploratory technique, and also as a subroutine in more complex data mining algorithms such as rule discovery, indexing, summarization, anomaly detection, and classification. Given these two facts, it is hardly surprising that time series clustering has attracted much attention. The data to be clustered can be in one of two formats: many individual time series, or a single time series, from which individual time series are extracted with a sliding window. Given the recent explosion of interest in streaming data and online algorithms, the latter case has received much attention. In this work we make a surprising claim. Clustering of streaming time series is completely meaningless. More concretely, clusters extracted from streaming time series are forced to obey a certain constraint that is pathologically unlikely to be satisfied by any dataset, and because of this, the clusters extracted by any clustering algorithm are essentially random. While this constraint can be intuitively demonstrated with a simple illustration and is simple to prove, it has never appeared in the literature. We can justify calling our claim surprising, since it invalidates the contribution of dozens of previously published papers. We will justify our claim with a theorem, illustrative examples, and a comprehensive set of experiments on reimplementations of previous work. Although the primary contribution of our work is to draw attention to the fact that an apparent solution to an important problem is incorrect and should no longer be used, we also introduce a novel method which, based on the concept of time series motifs, is able to meaningfully cluster some streaming time series datasets.
A Simple PLLbased True Random Number Generator for embedded digital systems
 Computing and Informatics
"... Abstract. The paper presents a simple true random number generator (TRNG) which can be embedded in digital Application Specific Integrated Circuits (ASICs) and Field Programmable Logic Devices (FPLDs). As a source of randomness, it uses onchip noise generated in the internal analog phaselocked loop ..."
Abstract

Cited by 7 (2 self)
 Add to MetaCart
(Show Context)
Abstract. The paper presents a simple true random number generator (TRNG) which can be embedded in digital Application Specific Integrated Circuits (ASICs) and Field Programmable Logic Devices (FPLDs). As a source of randomness, it uses onchip noise generated in the internal analog phaselocked loop (PLL) circuitry. In contrast with traditionally used free running oscillators, it uses a novel method of randomness extraction based on two rationally related synthesized clock signals. The generator has been developed for embedded cryptographic applications, where it significantly increases the system security, but it can be used in a wide range of applications. The quality of TRNG output is confirmed by applying special statistical tests, which pass even for high output bitrates of several hundreds of kilobits per second. 1
Fast and reliable random number generators for scientific computing, Lecture
 Proc. PARA'04 Workshop on the StateoftheArt inScientific Computing
"... Abstract. Fast and reliable pseudorandom number generators are required for simulation and other applications in Scientific Computing. We outline the requirements for good uniform random number generators, and describe a class of generators having very fast vector/parallel implementations with exce ..."
Abstract

Cited by 6 (2 self)
 Add to MetaCart
(Show Context)
Abstract. Fast and reliable pseudorandom number generators are required for simulation and other applications in Scientific Computing. We outline the requirements for good uniform random number generators, and describe a class of generators having very fast vector/parallel implementations with excellent statistical properties. We also discuss the problem of initialising random number generators, and consider how to combine two or more generators to give a better (though usually slower) generator. 1
Extracting Randomness from External Interrupts
 In The IASTED International Conference on Communication, Network, and Information Security
, 2003
"... We present a method for generating random bits on a computer system using interrupts from external sources (e.g., keyboard strokes, harddisk I/O completions, network packet arrivals, etc). We show how a sequence of timestamps of external interrupts can be converted into a uniformly distributed rand ..."
Abstract

Cited by 3 (0 self)
 Add to MetaCart
(Show Context)
We present a method for generating random bits on a computer system using interrupts from external sources (e.g., keyboard strokes, harddisk I/O completions, network packet arrivals, etc). We show how a sequence of timestamps of external interrupts can be converted into a uniformly distributed random sequence of 0's and 1's. We also describe how the random sequence can later be used in blocking (/dev/random) and nonblocking (/dev/urandom) devices for providing a source of random bits in an UNIX environment.
Protecting Web Usage of Credit Cards Using OneTime Pad Cookie Encryption
, 2002
"... The blooming ecommerce is demanding better methods to protect online users' privacy, especially the credit card information that is widely used in online shopping. Holding all these data in a central database of the web sites would attract hackers' attacks, impose unnecessary liability o ..."
Abstract

Cited by 3 (0 self)
 Add to MetaCart
(Show Context)
The blooming ecommerce is demanding better methods to protect online users' privacy, especially the credit card information that is widely used in online shopping. Holding all these data in a central database of the web sites would attract hackers' attacks, impose unnecessary liability on the merchant web sites, and raise the customers' privacy concerns. In this paper we introduce and discuss in details the secure distributed storage of sensitive information using HTTP cookie encryption. We are able to employ OneTime Pads to encrypt the cookies, because encryption and decryption are both done by the server, which is an interesting characteristic overlooked by the existing systems. We implemented this protocol and showed that it is simple, fast and easy to program with.
HArdware Volatile Entropy Gathering and Expansion: generating . . .
, 2002
"... The availability of a random number generator with high cryptographic qualities on a computer is one of the central issues of cryptographic implementations. ..."
Abstract

Cited by 2 (1 self)
 Add to MetaCart
The availability of a random number generator with high cryptographic qualities on a computer is one of the central issues of cryptographic implementations.