ppxscal/embedding_network huggingface dataset:
A New Watermarking Protocol of Copyright Protection
Authors: Ru Zhang, Xiao-gang Yu, Lin-na Zhou, Hong-peng Li
Venue: Proceedings of the 2006 International Conference on Intelligent Information Hiding and Multimedia (2006)

Most existing watermarking protocols deal with the copyright protection as a transaction between the seller and the buyer. However, these protocols don.t appropriately resolve the copyright problem of copyright authority, copyrighter, ICP and customer. To address this issue, a new watermarking protocol proposed in this article uses three watermarks in structure. By introducing the CA, the signature and the timestamp, the protocol sets up a hiberarchy of digital copyright authorities to protect rights, defends ambiguity attacks and collusive attacks, and resolves the non-repudiation problem and CS overload problem. Because ICP and customer cannot remove the watermark from watermarked digital content, and neither the copyrighter nor the ICP can fabricate piracy, the proposed protocol can trace piracy. With the proxy signature, the digital content transaction without the Third party is performed in which there are only two participants, the seller and the buyer.
What's Strange About Recent Events (WSARE): An Algorithm for the Early Detection of Disease Outbreaks
Authors: Weng-Keen Wong, Andrew Moore, Gregory Cooper, Michael Wagner
Venue: The Journal of Machine Learning Research (2005)

Traditional biosurveillance algorithms detect disease outbreaks by looking for peaks in a univariate time series of health-care data. Current health-care surveillance data, however, are no longer simply univariate data streams. Instead, a wealth of spatial, temporal, demographic and symptomatic information is available. We present an early disease outbreak detection algorithm called What's Strange About Recent Events (WSARE), which uses a multivariate approach to improve its timeliness of detection. WSARE employs a rule-based technique that compares recent health-care data against data from a baseline distribution and finds subgroups of the recent data whose proportions have changed the most from the baseline data. In addition, health-care data also pose difficulties for surveillance algorithms because of inherent temporal trends such as seasonal effects and day of week variations. WSARE approaches this problem using a Bayesian network to produce a baseline distribution that accounts for these temporal trends. The algorithm itself incorporates a wide range of ideas, including association rules, Bayesian networks, hypothesis testing and permutation tests to produce a detection algorithm that is careful to evaluate the significance of the alarms that it raises.
A Tree Matching Approach for the Temporal Registration of Retinal Images
Authors: Xinyu Guo, Wynne Hsu, Mong Li Lee, Tien Yin Wong
Venue: Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence (2006)

The temporal registration of retinal images provides an important groundwork for doctors to monitor the progression of diseases. Retinal image registration is challenging because the intensity of the retina and the vascular structure can vary greatly over time. In this paper, we describe a tree matching approach to register retinal images. We model each vessel in a retinal image as a tree, called Vessel Feature Tree (VFT). We design a matching function to compute the similarity of a pair of vessels based on their VFTs. We develop a global alignment algorithm to compute the best match between the vessels in two images. Experiment results on 300 pairs of real-world retina images indicate that the proposed approach is able to achieve an accuracy of 93%.
The force/work differencing of exceptional points in the discrete, compatible formulation of Lagrangian hydrodynamics
Authors: R. Loubère, E. J. Caramana
Venue: Journal of Computational Physics (2006)

This study presents the force and mass discretization of exceptional points in the compatible formulation of Lagrangian hydrodynamics. It concludes a series of papers that develop various aspects of the theoretical exposition and the operational implementation of this numerical algorithm. Exceptional points are grid points at the termination of lines internal to the computational domain, and where boundary conditions are therefore not applied. These points occur naturally in most applications in order to ameliorate spatial grid anisotropy, and the consequent timestep reduction, that will otherwise arise for grids with highly tapered regions or a center of convergence. They have their velocity enslaved to that of neighboring points in order to prevent large excursions of the numerical solution about them. How this problem is treated is given herein for the aforementioned numerical algorithm such that its salient conservation properties are retained. In doing so the subtle aspects of this algorithm that are due to the interleaving of spatial contours that occur with the use of a spatially-staggered-grid mesh are illuminated. These contours are utilized to define both forces and the work done by them, and are the central construct of this type of finite-volume differencing. Additionally, difficulties that occur due to uncertainties in the specification of the artificial viscosity are explored, and point to the need for further research in this area.
Function-approximation-based perfect control variates for pricing American options
Authors: Nomesh Bolia, Sandeep Juneja
Venue: Proceedings of the 37th conference on Winter simulation (2005)

Monte Carlo simulation techniques that use function approximations have been successfully applied to approximately price multi-dimensional American options. However, for many pricing problems the time required to get accurate estimates can still be prohibitive, and this motivates the development of variance reduction techniques. In this paper, we describe a zero-variance or 'perfect' control variate to price American options. We then discuss how function approximation may be used to approximate this perfect control variate. Empirically, we observe that on simple one dimensional examples, this approximately perfect control variate gives orders of magnitude of variance reduction compared to naive estimation.
AnnoSearch: Image Auto-Annotation by Search
Authors: Xin-Jing Wang, Lei Zhang, Feng Jing, Wei-Ying Ma
Venue: Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (2006)

Although it has been studied for several years by computer vision and machine learning communities, image annotation is still far from practical. In this paper, we present AnnoSearch, a novel way to annotate images using search and data mining technologies. Leveraging the Web-scale images, we solve this problem in two-steps: 1) searching for semantically and visually similar images on the Web, 2) and mining annotations from them. Firstly, at least one accurate keyword is required to enable text-based search for a set of semantically similar images. Then content-based search is performed on this set to retrieve visually similar images. At last, annotations are mined from the descriptions (titles, URLs and surrounding texts) of these images. It worth highlighting that to ensure the efficiency, high dimensional visual features are mapped to hash codes which significantly speed up the content-based search process. Our proposed approach enables annotating with unlimited vocabulary, which is impossible for all existing approaches. Experimental results on real web images show the effectiveness and efficiency of the proposed algorithm.
Adapting for Visual and Verbal Learning Styles in AEH
Authors: Elizabeth Brown, Craig Stewart, Tim Brailsford
Venue: Proceedings of the Sixth IEEE International Conference on Advanced Learning Technologies (2006)

This paper describes how visual and verbal learning styles have been successfully integrated into an adaptive educational environment. User trials of this system were carried out, to determine the effect of the adaptation, and although these user trials do not indicate any statistically significant differences, the qualitative information gleaned from the study indicated that students preferred using this environment over other traditional revision methods, and that they perceived personalized tuition to be better than a 'one size fits all' approach.
Common Neural Substrates for Response Selection across Modalities and Mapping Paradigms
Authors: Yuhong Jiang, Nancy Kanwisher
Venue: Journal of Cognitive Neuroscience (2003)

In many situations, people can only compute one stimulus-to-response mapping at a time, suggesting that response selection constitutes a "central processing bottleneck" in human information processing. Using fMRI, we tested whether common or distinct brain regions were involved in response selection across visual and auditory inputs, and across spatial and nonspatial mapping rules. We isolated brain regions involved in response selection by comparing two conditions that were identical in perceptual input and motor output, but differed in the complexity of the mapping rule. In the visual-manual task of Experiment 1, four vertical lines were positioned from left to right, and subjects pressed one of four keys to report which line was unique in length. In the auditory-manual task of Experiment 2, four tones were presented in succession, and subjects pressed one of four keys to report which tone was unique in duration. For both visual and auditory tasks, the mapping between target position and key position was either spatially compatible or incompatible. In the verbal task of Experiment 3, subjects used nonspatial mappings that were either compatible ("same" if colors matched; "different" if they mismatched) or incompatible (the opposite). Extensive activation overlap was observed across all three experiments for incompatible versus compatible mapping in bilateral parietal and frontal regions. Our results indicate that common neural substrates are involved in response selection across input modalities and across spatial and nonspatial domains of stimulus-to-response mapping, consistent with behavioral evidence that response selection is a central process.
Anecdotes
Authors: Jesse T. Quatse
Venue: IEEE Annals of the History of Computing (2006)

Carnegie Mellon University engaged in a major hardware design project serendipitously as animportant step toward the founding of the Department of Computer Science. Alan Perlis, the first headof the department, needed a research facility adequate for attracting the faculty needed to attractthe funding needed to build the research facility. He resolved the circular dilemma by engaging agraduate student to develop the hardware on the university computer site during the three yearspreceding the Center of Excellence ARPA grant to found the department. The system served its intendedpurpose for the founding of the department and for the following first years of its existence. Thisunusual story is told by that former graduate student.
Multidimensional Vector Regression for Accurate and Low-Cost Location Estimation in Pervasive Computing
Authors: Jeffrey Junfeng Pan James T. Kwok Qiang Yang
Venue: IEEE Transactions on Knowledge and Data Engineering (2006)

In this paper, we present an algorithm for multidimensional vector regression on data that are highly uncertain and nonlinear, and then apply it to the problem of indoor location estimation in a wireless local area network (WLAN). Our aim is to obtain an accurate mapping between the signal space and the physical space without requiring too much human calibration effort. This location estimation problem has traditionally been tackled through probabilistic models trained on manually labeled data, which are expensive to obtain. In contrast, our algorithm adopts Kernel Canonical Correlation Analysis (KCCA) to build a nonlinear mapping between the signal-vector space and the physical location space by transforming data in both spaces into their canonical features. This allows the pairwise similarity of samples in both spaces to be maximally correlated using kernels. We use a Gaussian kernel to adapt to the noisy characteristics of signal strengths and a Matérn kernel to sense the changes in physical locations. By using real data collected in an 802.11 wireless LAN environment, we achieve accurate location estimation for pervasive computing while requiring a much smaller set of labeled training data than previous methods.
CCCS: a top-down associative classifier for imbalanced class distribution
Authors: Bavani Arunasalam, Sanjay Chawla
Venue: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining (2006)

In this paper we propose CCCS, a new algorithm for classification based on association rule mining. The key innovation in CCCS is the use of a new measure, the "Complement Class Support (CCS)" whose application results in rules which are guaranteed to be positively correlated. Furthermore, the anti-monotonic property that CCS possesses has very different semantics vis-a-vis the traditional support measure. In particular, "good" rules have a low CCS value. This makes CCS an ideal measure to use in conjunction with a top-down algorithm. Finally, the nature of CCS allows the pruning of rules without the setting of any threshold parameter! To the best of our knowledge this is the first threshold-free algorithm in association rule mining for classification.
Organizational self-design in semi-dynamic environments
Authors: Sachin Kamboj, Keith S. Decker
Venue: Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems (2006)

In this paper we propose a run-time approach to organization that is contingent on the task structure of the problem being solved and the environmental conditions under which it is being solved. We use T&AElig;MS as the underlying representation for our problems and describe a framework that uses Organizational Self-Design (OSD) to allocate tasks and resources to the agents and coordinate their activities.
A Method of Calculating Image Saliency and of Optimizing Efficient Distribution of ImageWindows
Authors: Takashi Toriu, Shigeyoshi Nakajima
Venue: Proceedings of the First International Conference on Innovative Computing, Information and Control - Volume 1 (2006)

In this paper we propose a method of calculating the saliency map of an input image. In this method the saliency gets higher in the areas where the image features are different than those in the surrounding areas and where the features are unfamiliar. Based on this method, we also propose a method to apply the idea of calculating the saliency to the problem of determining the most efficient distribution of image windows to acquire the information. This distribution might be considered to correspond to the distribution of attention. We conducted two experiments. The first is the experiment to apply the saliency model to a moving image and shows that in the area where the feature about motion is different than those in other areas the saliency gets higher. In the second experiment it is shown that a definite number of image windows to acquire the information can be efficiently distributed owing to the local image features.
Parallel data cube storage structure for range sum queries and dynamic updates
Authors: Hong Gao, Jian-Zhong Li
Venue: Journal of Computer Science and Technology (2005)

I/O parallelism is considered to be a promising approach to achieving high performance in parallel data warehousing systems where huge amounts of data and complex analytical queries have to be processed. This paper proposes a parallel secondary data cube storage structure (PHC for short) to efficiently support the processing of range sum queries and dynamic updates on data cube using parallel computing systems. Based on PHC, two parallel algorithms for processing range sum queries and updates are proposed also. Both the algorithms have the same time complexity, O(logd n/P). The analytical and experimental results show that PHC and the parallel algorithms have high performance and achieve optimum speedup.
Decomposing Components of Task Preparation with Functional Magnetic Resonance Imaging
Authors: Marcel Brass, D. Yves Von Cramon
Venue: Journal of Cognitive Neuroscience (2004)

It is widely acknowledged that the prefrontal cortex plays a major role in cognitive control processes. One important experimental paradigm for investigating such higher order cognitive control is the task-switching paradigm. This paradigm investigates the ability to switch flexibly between different task situations. In this context, it has been found that participants are able to anticipatorily prepare an upcoming task. This ability has been assumed to reflect endogenous cognitive control. However, it is difficult to isolate task preparation process from task execution using functional magnetic resonance imaging (fMRI). In the present study, we introduce a new experimental manipulation to investigate task preparation with fMRI. By manipulating the number of times a task was prepared, we could demonstrate that the left inferior frontal junction (IFJ) area (near the junction of inferior frontal sulcus and inferior precentral sulcus), the right inferior frontal gyrus, and the right intraparietal sulcus are involved in task preparation. By manipulating the cue-task mapping, we could further show that this activation is not related to cue encoding but to the updating of the relevant task representation. Based on these and previous results, we assume that the IFJ area constitutes a functionally separable division of the lateral prefrontal cortex. Finally, our data suggest that task preparation does not differ for switch and repetition trials in paradigms with a high proportion of switch trials, casting doubt on the assumption that an independent task set reconfiguration process takes place in the preparation interval.
Fast Computation of the Slater Integrals
Authors: L. Gr. Ixaru, N. S. Scott, M. P. Scott
Venue: SIAM Journal on Scientific Computing (2006)

Slater integrals are two-dimensional radial integrals whose integrand is constructed from normalized eigenfunctions of the Schrödinger equation. These integrals occur in many atomic structure and scattering computations. However, in two-dimensional R-matrix propagation they represent a significant computational bottleneck. The problem involves two steps, that is, the numerical solution of the Schrödinger equation and the computation of the integrals, respectively. By exploiting the characteristic features of the problem we seek to devise a two-stage computational strategy, where the second stage is influenced and informed by the first. In particular, we focus on the development of extended frequency-dependent quadrature rules (EFDQRs) that improve the accuracy and significantly reduce the computation time of the integrals. The performance of these ad hoc rules is examined and compared to the currently used Newton--Cotes method in the construction of Hamiltonian matrices involving up to 300 x 106 integrals. A gain of two orders of magnitude in the CPU time is obtained.
Memory accesses reduction for MIME algorithm
Authors: S. Goel, M. Shaaban, T. Darwish, H. Mahmoud, M. Bayoumi
Venue: Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 1 (2003)

Power consumption of digital systems has become a critical design parameter. An important class of digital systems includes applications such as video image processing and speech recognition, which are extremely memory dominant. In such systems, a significant amount of power is consumed during memory accesses. Reducing the number of memory accesses can considerably impact the power dissipation in the rest of the design. Therefore, optimizing an application for reduced memory access can greatly effect the overall power consumption in the entire system. This paper presents an architectural enhancement multi-stage interval-based motion estimation (MIME) algorithm that not only saves power by reducing the number of memory accesses but also significantly increases the speedup.
Reference-based indexing of sequence databases
Authors: Jayendra Venkateswaran, Deepak Lachwani, Tamer Kahveci, Christopher Jermaine
Venue: Proceedings of the 32nd international conference on Very large data bases (2006)

We consider the problem of similarity search in a very large sequence database with edit distance as the similarity measure. Given limited main memory, our goal is to develop a reference-based index that reduces the number of costly edit distance computations in order to answer a query. The idea in reference-based indexing is to select a small set of reference sequences that serve as a surrogate for the other sequences in the database. We consider two novel strategies for selecting references as well as a new strategy for assigning references to database sequences. Our experimental results show that our selection and assignment methods far outperform competitive methods. For example, our methods prune up to 20 times as many sequences as the Omni method, and as many as 30 times as many sequences as frequency vectors. Our methods also scale nicely for databases containing many and/or very long sequences.
Bogor: A Flexible Framework for Creating Software Model Checkers
Authors: Mr. Robby, Matthew B. Dwyer, John Hatcliff
Venue: Proceedings of the Testing: Academic & Industrial Conference on Practice And Research Techniques (2006)

Model checking has proven to be an effective technology for verification and debugging in hardware and more recently in software domains. With the proliferation of multicore architectures and a greater emphasis on distributed computing, model checking is an increasingly important software quality assurance technique that can complement existing testing and inspection methods. We believe that recent trends in both the requirements for software systems and the processes by which systems are developed suggests that domain-specific model checking engines may be more effective than general purpose model checking tools. To overcome limitations of existing tools which tend to be monolithic and non-extensible, we have developed an extensible and customizable model checking framework called Bogor. In this article, we summarize how Bogor provides direct support for modeling object-oriented designs and implementations, how its modeling language and algorithms can be extended and customized to create domain-specific model checking engines, and how Bogor can be deployed in broader software development context contexts in conjunction with complementary quality assurance techniques.
The Inherent Queuing Delay of Parallel Packet Switches
Authors: Hagit Attiya, David Hay
Venue: IEEE Transactions on Parallel and Distributed Systems (2006)

The parallel packet switch (PPS) extends the inverse multiplexing architecture and is widely used as the core of contemporary commercial switches. This paper investigates the inherent queuing delay introduced by the PPS's demultiplexing algorithm, responsible for dispatching cells to the middle-stage switches, relative to an optimal work-conserving switch. We first consider an N\times N PPS without buffers in its input ports, operating at external rate R, internal rate r
Fifteen Years after TX: A Look Back at High Assurance Multi-Level Secure Windowing
Authors: Jeremy Epstein
Venue: Proceedings of the 22nd Annual Computer Security Applications Conference (2006)

Research in the late 1980s and early 1990s produced a prototype high assurance multi-level secure windowing system that allowed users to see information of multiple classifications on the same screen, performing cut & paste from low to high windows. This retrospective discusses the motivations for the project, reviews the architecture and implementation of the prototype, discusses developments in the intervening years, and concludes with lessons learned.