ppxscal/embedding_network huggingface dataset:
Joint temporal-spatial rate control for adaptive video transcoding
Authors: Shan Liu, C.-C. J. Kuo
Venue: Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 1 (2003)

A joint temporal-spatial rate control method is proposed for adaptive MPEG video transcoding in this work. The proposed rate control scheme adjusts both the frame rate and the frame quantization step size according to the available channel bandwidth to achieve good temporal-spatial quality tradeoff. In the proposed transcoding scheme, motion vectors are reused to facilitate the transcoding speed. Consequently, I and P frames are not skipped in order to maintain the prediction sequential order while some B frames containing more temporal redundancy may be skipped to save the bit rate for image quality enhancement. Skipped frames can be regenerated at the decoder to ensure the full frame rate playback. The TM7 quadratic Rate-Qtz model is adopted in the proposed scheme to calculate the re-quantization step size from a given target bit rate. Simulation results show that the proposed rate control scheme outperforms the MPEG-4 frame level rate control in the average PSNR value when applied to the context of MPEG-2 to MPEG-4 video transcoding. The complexity of the proposed transcoder remains low so that it can be used in real-time applications.
A psychophysically plausible model for typicality ranking of natural scenes
Authors: Adrian Schwaninger, Julia Vogel, Franziska Hofer, Bernt Schiele
Venue: ACM Transactions on Applied Perception (TAP) (2006)

Natural scenes constitute a very heterogeneous stimulus class. Each semantic category contains exemplars of varying typicality. It is, therefore, an interesting question whether humans can categorize natural scenes consistently into a relatively small number of categories, such as, coasts, rivers/lakes, forests, plains, and mountains. This is particularly important for applications, such as, image retrieval systems. Only if typicality is consistently perceived across different individuals, a general image-retrieval system makes sense. In this study, we use psychophysics and computational modeling to gain a deeper understanding of scene typicality. In the first psychophysical experiment, we used a forced-choice categorization task in which each of 250 natural scenes had to be classified into one of the following five categories: coasts, rivers/lakes, forests, plains, and mountains. In the second experiment, the typicality of each scene had to be rated on a 50-point scale for each of the five categories. The psychophysical results show high consistency between participants not only in the categorization of natural scenes, but also in the typicality ratings. In order to model human perception, we then employ a computational approach that uses an intermediate semantic modeling step by extracting local semantic concepts, such as, rock, water, and sand. Based on the human typicality ratings, we learn a psychophysically plausible distance measure that leads to a high correlation between the computational and the human ranking of natural scenes. Interestingly, model comparisons without a semantic-modeling step correlated much less with human performance, suggesting that our model is psychophysically very plausible.
Hyperclique pattern discovery
Authors: Hui Xiong, Pang-Ning Tan, Vipin Kumar
Venue: Data Mining and Knowledge Discovery (2006)

Existing algorithms for mining association patterns often rely on the support-based pruning strategy to prune a combinatorial search space. However, this strategy is not effective for discovering potentially interesting patterns at low levels of support. Also, it tends to generate too many spurious patterns involving items which are from different support levels and are poorly correlated. In this paper, we present a framework for mining highly-correlated association patterns called hyperclique patterns. In this framework, an objective measure called h-confidence is applied to discover hyperclique patterns. We prove that the items in a hyperclique pattern have a guaranteed level of global pairwise similarity to one another as measured by the cosine similarity (uncentered Pearson's correlation coefficient). Also, we show that the h-confidence measure satisfies a cross-support property which can help efficiently eliminate spurious patterns involving items with substantially different support levels. Indeed, this cross-support property is not limited to h-confidence and can be generalized to some other association measures. In addition, an algorithm called hyperclique miner is proposed to exploit both cross-support and anti-monotone properties of the h-confidence measure for the efficient discovery of hyperclique patterns. Finally, our experimental results show that hyperclique miner can efficiently identify hyperclique patterns, even at extremely low levels of support.
Parallelizing XQuery In a Cluster Environment
Authors: Xiaogang Li, Gagan Agrawal
Venue: Proceedings of the 10th International Database Engineering and Applications Symposium (2006)

In this paper, we report on a parallel implementation of XQuery. As XQuery is being used for processing large datasets, and/or for computeintensive applications, efficiency of XQuery implementations is becoming an important issue. Our work has specifically focused on scienti fic data processing and data mining applications. Parallelization of this class of XQuery queries involves a number of challenges, which include data distribution, parallelization of generalized reductions, and translation to an imperative language like C/C++, so as to invoke effi- cient parallel communication libraries. In this paper, we report our solutions towards the above problems. By implementing the techniques in a compiler and generating code based on a C++ SAX parser and the Message Passing Interface (MPI), we are able to achieve efficient parallel execution on a cluster of machines.
Abox Inference for Large Scale OWL-Lite Data
Authors: Xiaofeng Wang, Jianbo Ou, Xiaofeng Meng, Yan Chen
Venue: Proceedings of the Second International Conference on Semantics, Knowledge, and Grid (2006)

Abox inference is an important part in OWL data management. When involving large scale of instance data, it can not be supported by existing inference engines. In this paper, we propose efficient Abox inference algorithms for large scale OWL-Lite data. The algorithms can be divided into two categories: initial inference and incremental inference. Initial inference is used in situation where only raw data exists in storage system, and for this category we propose Rule Static Association Based (RSAB), Rule Dynamic Association Based (RDAB) and Rule Grouped-Sorted Based (RGSB) inference methods. Incremental inference algorithm is used in situation where large volume inference data exists in storage system, and for this category we extend the initial inference algorithm and propose Rule Pattern-Sharing Based(RPSB) method. At last, extensive experiments show that our methods are efficient in practice.
Assessing the viability of an open access policy in an outpatient clinic: a discrete-event and continuous simulation modeling approach
Authors: Ronald E. Giachetti, Edwin A. Centeno, Martha A. Centeno, Ramakrishnan Sundaram
Venue: Proceedings of the 37th conference on Winter simulation (2005)

This paper presents the preliminary results of an ongoing research project investigating the patient appointment scheduling for an outpatient clinic. The outpatient clinic was experiencing three problems of long patient throughput times, a large backlog of appointments, and a high no-show rate. We believe a new scheduling approach called Open Access could address all three problems. To analyze and make recommendations for improvement to patient cycle time we developed a discrete event simulation. To understand the factors leading to a high no-show rate we developed a systems dynamic simulation model. The study identified feasible strategies the clinic management could implement to greatly improve patient throughput time by 50%. Our preliminary results indicate that Open Access is a viable strategy for the clinic. Ongoing work is being conducted to refine the models and determine the best configuration of an Open Access scheduling policy for the outpatient clinic.
Empirical Analysis for Agent System Comprehension and Verification
Authors: Tibor Bosse, Dung N. Lam, K. Suzanne Barber
Venue: Proceedings of the IEEE/WIC/ACM international conference on Intelligent Agent Technology (2006)

Comprehending and verifying agent system behavior is an arduous task when dealing with complex multi-agent systems whose behaviors are sophisticated. This paper presents an approach resulting from collaboration between the Tracer Tool and the TTL Checker, which together automate the analysis and verification of an implemented system's behavior, with the aim of aiding the user in redesigning, debugging, and maintaining the agent system. The Tracer Tool ensures that the user's comprehension of the system behavior is accurate and provides explanations of anomalous behavior, which can be detected as a failed behavioral property by the TTL Checker. The integrated approach has been applied to an agent-based system in an Unmanned Aerial Vehicles domain.
Camera Motion Control from a Java 3D Environment: Virtual Studio Application in Decorative Arts Museum Collections
Authors: Bernardo Uribe Mendoza, German Andres Ramos, Luis Miguel Mendez, William Santamaria, Alexander Pinzon
Venue: Proceedings of the 2006 International Conference on Cyberworlds (2006)

The paper presents an implementation of virtual studio visualization tools in the decorative arts museum collections. Live DV video 2d images are embedded in a Java 3D Environment (a 3D Canvass). The virtual studio implemented includes navigation control of the real camera with a robot support controlled with a joystick. Navigation within the Java 3D virtual environment includes the real camera motion control and the video output. The real object is chroma-keyed and embedded in a virtual setting. Future steps of the work include 1) Development of complementary 3D content and specific navigation tools for the decorative arts collections exhibits. Implementation with the museum public in general and experimentation with the museum research community 2). The virtual studio implementation as part of a larger 3 folded display world : a "Theatre of Memory" applied to virtualized museum collections exhibits .
Coupled Bayesian Framework for Dual Energy Image Registration
Authors: Hao Wu, Yunqiang Chen, Tong Fang
Venue: Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (2006)

Image registration for X-ray dual energy imaging is challenging due to the overlaid transparent layers (i.e., the bone and soft tissue) and the different appearances between the dual images acquired with X-rays at different energy spectra. Moreover, subpixel accuracy is necessary for good reconstruction of the bone and soft-tissue layers. This paper addresses these problems with a novel coupled Bayesian framework, in which the registration and reconstruction can effectively reinforce each other. With the reconstruction results, we can design accurate matching criteria for aligning the dual images, instead of treating them as multi-modality registration. Furthermore, prior knowledge of the bone and soft tissue can be exploited to detect poor reconstruction due to inaccurate registration; and hence correct registration errors in the coupled framework. A multiscale freeform registration algorithm is implemented to achieve subpixel registration accuracy. Promising results are obtained in the experiments.
Variable neighborhood search for the linear ordering problem
Authors: Carlos G. Garcia, Dionisio Pérez-Brito, Vicente Campos, Rafael Martí
Venue: Computers and Operations Research (2006)

Given a matrix of weights, the linear ordering problem (LOP) consists of finding a permutation of the columns and rows in order to maximize the sum of the weights in the upper triangle. This NP-complete problem can also be formulated in terms of graphs, as finding an acyclic tournament with a maximal sum of arc weights in a complete weighted graph. In this paper, we first review the previous methods for the LOP and then propose a heuristic algorithm based on the variable neighborhood search (VNS) methodology. The method combines different neighborhoods for an efficient exploration of the search space. We explore different search strategies and propose a hybrid method in which the VNS is coupled with a short-term tabu search for improved outcomes. Our extensive experimentation with both real and random instances shows that the proposed procedure competes with the best-known algorithms in terms of solution quality, and has reasonable computing-time requirements.Variable neighborhood search (VNS) is a metaheuristic method that has recently been shown to yield promising outcomes for solving combinatorial optimization problems. Based on a systematic change of neighborhood in a local search procedure, VNS uses both deterministic and random strategies in search for the global optimum.In this paper, we present a VNS implementation designed to find high quality solutions for the NP-hard LOP, which has a significant number of applications in practice. The LOP, for example, is equivalent to the so-called triangulation problem for input-output tables in economics. Our implementation incorporates innovative mechanisms to include memory structures within the VNS methodology. Moreover we study the hybridization with other methodologies such as tabu search.
The Method, the Tools and Rationales for Assessing Dynamic Memory Efficiency in Embedded Real-Time Systems in Practice
Authors: Christian Del Rosso
Venue: Proceedings of the International Conference on Software Engineering Advances (2006)

A dynamic memory management system has to take care of the allocation and deallocation of memory blocks in a software system. Real-time embedded systems add some more constraints to the design and the implementation of dynamic memory management systems if compared with the PC world. An increasing number of features are added to embedded mobile devices, however, resources like dynamic memory are limited. In addition, in real-time systems, real-time deadlines must be respected and allocations and deallocations must be done in due time. In this paper we present a case study on evaluating dynamic memory management in embedded real-time systems. We have used a scenario-based approach and used a simulation environment to evaluate the performance of different dynamic memory management systems. Our contribution is to present a practical approach, the tools and the rationales to evaluate dynamic memory management in embedded real-time systems.
Human versus automated facilitation in the GSS context
Authors: Moez Limayem
Venue: ACM SIGMIS Database (2006)

Many group support systems (GSS) are currently being used with the help of a human facilitator who guides the group members through their use of the decision models. Another way to guide the use of these systems is to embed some facilitation mechanisms into the GSS itself (i.e., automated facilitation). Using the Adaptive Structuration Theory, this study investigated the tradeoffs associated with human facilitation and automated facilitation. Groups using a multicriteria decision model in the context of a resource allocation task were exposed to one of two experimental conditions: (1) human facilitation, or (2) automated facilitation. Results indicated that automated facilitation, embedded in the GSS, was as effective as human facilitation in enhancing the faithfulness of appropriation of the technology.
An efficient implementation of a nonlinear predictor using a zero-memory nonlinearity followed by a second order Volterra filter
Authors: M. A. Rahman, Kai-Bor Yu
Venue: Proceedings of the Acoustics, Speech, and Signal Processing,1994. on IEEE International Conference - Volume 04 (1994)

This paper is concerned with the development and analysis of a nonlinear predictor for a non-Gaussian process using a zero-memory nonlinearity (ZMNL) followed by a second order Volterra filter (SVF). The processor exploits partial statistical information such as marginal probability density function (PDF) and the covariance structure. The ZMNL transforms the process into a Gaussian process. The SVF can be implemented as a parallel combination of linear and quadratic filters. Another equivalent structure is to carry out a linear prediction in the transformed Gaussian domain and pass the linear predicted samples through a second order polynomial.
Using XML for Publishing on Demand in Different Output Formats
Authors: Reinhard Ruemer, Klaus Miesenberger
Venue: Proceedings of the Second International Conference on Automated Production of Cross Media Content for Multi-Channel Distribution (2006)

XML^1 as a standard for data exchange between applications is accepted for quite a while now. The character of XML is to simply describe data. There are no aspects of presentation of the data considered. The so called media-neutral storage of the data makes it possible by the usage of transforming algorithms to present the data in different ways. This paper describes the work done in an austrian research project to make educational material accessible for students with disabilities using an XML standard and transformational algorithms.
Neural&ndash;Cardiac Coupling in Threat-Evoked Anxiety
Authors: Kim M. Dalton, Ned H. Kalin, Thomas M. Grist, Richard J. Davidson
Venue: Journal of Cognitive Neuroscience (2005)

Anxiety is a debilitating symptom of many psychiatric disorders including generalized anxiety disorder, mood disorders, schizophrenia, and autism. Anxiety involves changes in both central and peripheral biology, yet extant functional imaging studies have focused exclusively on the brain. Here we show, using functional brain and cardiac imaging in sequential brain and cardiac magnetic resonance imaging (MRI) sessions in response to cues that predict either threat (a possible shock) or safety (no possibility of shock), that MR signal change in the amygdala and the prefrontal and insula cortices predicts cardiac contractility to the threat of shock. Participants with greater MR signal change in these regions show increased cardiac contractility to the threat versus safety condition, a measure of the sympathetic nervous system contribution to the myocardium. These findings demonstrate robust neural&ndash;cardiac coupling during induced anxiety and indicate that individuals with greater activation in brain regions identified with aversive emotion show larger magnitude cardiac contractility increases to threat.
Refinement of Correspondences in EXSMAL for XML Document Transformation
Authors: Herzi Khaled, Aicha-Nabila Benharkat, Youssef Amghar
Venue: Proceedings of the 17th International Conference on Database and Expert Systems Applications (2006)

Schema matching is an important prerequisite to the transformation of XML documents with different schemas. In this work, we are interested in the process of matching between data schemes in order to transform documents XML. After explaining related works in the domain, we choose the EXSMAL Algorithm to generate a set of correspondences. Then we try to filter this set in order to obtain 1-1 correspondences. In this purpose, two calculations of similarity are applied: Path similarity and internal similarity. This refinement helps to facilitate the transformation of the documents XML. We also base on a dynamic ontology updated by a user feedback which describes the semantic relation between nodes like IsA, PartOf, Similar, etc. These semantic relations are then expressed in LIMXS data model. The transformation will use operations such as: Connect and Rename for the simple matching, Merge and Split for the complex ones.
Distributed Event Detection in Sensor Networks
Authors: Fernando Martincic, Loren Schwiebert
Venue: Proceedings of the International Conference on Systems and Networks Communication (2006)

This paper introduces a distributed event detection scheme for wireless sensor networks (WSN). Event signatures are disseminated throughout the WSN and, using a localized push-based strategy, nodes match their sensor readings against stored event signatures. The technique proposed is well-suited to large networks by reducing and load-balancing the network traffic generated.
The max-min hill-climbing Bayesian network structure learning algorithm
Authors: Ioannis Tsamardinos, Laura E. Brown, Constantin F. Aliferis
Venue: Machine Learning (2006)

We present a new algorithm for Bayesian network structure learning, called Max-Min Hill-Climbing (MMHC). The algorithm combines ideas from local learning, constraint-based, and search-and-score techniques in a principled and effective way. It first reconstructs the skeleton of a Bayesian network and then performs a Bayesian-scoring greedy hill-climbing search to orient the edges. In our extensive empirical evaluation MMHC outperforms on average and in terms of various metrics several prototypical and state-of-the-art algorithms, namely the PC, Sparse Candidate, Three Phase Dependency Analysis, Optimal Reinsertion, Greedy Equivalence Search, and Greedy Search. These are the first empirical results simultaneously comparing most of the major Bayesian network algorithms against each other. MMHC offers certain theoretical advantages, specifically over the Sparse Candidate algorithm, corroborated by our experiments. MMHC and detailed results of our study are publicly available at http://www.dsl-lab.org/supplements/mmhc_paper/mmhc_index.html.
Cluster Ranking with an Application to Mining Mailbox Networks
Authors: Ziv Bar-Yossef, Ido Guy, Ronny Lempel, Yoelle S. Maarek, Vladimir Soroka
Venue: Proceedings of the Sixth International Conference on Data Mining (2006)

We initiate the study of a new clustering framework, called cluster ranking. Rather than simply partitioning a network into clusters, a cluster ranking algorithm also orders the clusters by their strength. To this end, we introduce a novel strength measure for clusters--the integrated cohesion--which is applicable to arbitrary weighted networks. We then present C-Rank: a new cluster ranking algorithm. Given a network with arbitrary pairwise similarity weights, C-Rank creates a list of overlapping clusters and ranks them by their integrated cohesion. We provide extensive theoretical and empirical analysis of C-Rank and show that it is likely to have high precision and recall. Our experiments focus on mining mailbox networks. A mailbox network is an egocentric social network, consisting of contacts with whom an individual exchanges email. Ties among contacts are represented by the frequency of their co-occurrence on message headers. C-Rank is well suited to mine such networks, since they are abundant with overlapping communities of highly variable strengths. We demonstrate the effectiveness of C-Rank on the Enron data set, consisting of 130 mailbox networks.