ppxscal/embedding_network huggingface dataset:
Teaching Information Visualization
Authors: Andreas Kerren, John T. Stasko, Jason Dykes
Venue: Information Visualization: Human-Centered Issues and Perspectives (2008)

Teaching InfoVis is a challenge because it is a new and growing field. This paper describes the results of a teaching survey based on the information given by the attendees of Dagstuhl Seminar 07221. It covers several aspects of offered InfoVis courses that range from different kinds of study materials to practical exercises. We have reproduced the discussion during the seminar and added our own experiences. We hope that this paper can serve as an interesting and helpful source for current and future InfoVis teachers.
AT and DfA Standardisation: What Is Currently Going on?
Authors: Jan Engelen
Venue: Proceedings of the 11th international conference on Computers Helping People with Special Needs (2008)

At several occasions in the past details of ongoing standardisation activities in the field of Assistive Technology and Design for All have been reported at the ICCHP conference. In this contribution several recent important changes and additions are brought under the attention of this year's ICCHP delegates too.
Low-Power Design Using Multiple Channel Lengths and Oxide Thicknesses
Authors: Naran Sirisantana, Kaushik Roy
Venue: IEEE Design Test (2004)

Two CMOS design techniques use dual threshold voltages to reduce power consumption while maintaining high performance. Simulation results show power savings of 21% for one technique at low activity, and for the other, 19% at high activity and 38% at low activity.
Logical Hierarchical Hidden Markov Models for Modeling User Activities
Authors: Sriraam Natarajan, Hung H. Bui, Prasad Tadepalli, Kristian Kersting, Weng-Keen Wong
Venue: Proceedings of the 18th international conference on Inductive Logic Programming (2008)

Hidden Markov Models (HMM) have been successfully used in applications such as speech recognition, activity recognition, bioinformatics etc. There have been previous attempts such as Hierarchical HMMs and Abstract HMMs to elegantly extend HMMs at multiple levels of temporal abstraction (for example to represent the user's activities). Similarly, there has been previous work such as Logical HMMs on extending HMMs to domains with relational structure. In this work we develop a representation that naturally combines the power of both relational and hierarchical models in the form of Logical Hierarchical Hidden Markov Models (LoHiHMMs). LoHiHMMs inherit the compactness of representation from Logical HMMs and the tractability of inference from Hierarchical HMMs. We outline two inference algorithms: one based on grounding the LoHiHMM to a propositional HMM and the other based on particle filtering adapted for this setting. We present the results of our experiments with the model in two simulated domains.
Design and Implementation of OpenMPD: An OpenMP-Like Programming Language for Distributed Memory Systems
Authors: Jinpil Lee, Mitsuhisa Sato, Taisuke Boku
Venue: Proceedings of the 3rd international workshop on OpenMP: A Practical Programming Model for the Multi-Core Era (2007)

MPI is a de facto standard for parallel programming on distributed memory system although writing MPI programs is often a time-consuming and complicated work. We propose a simple programming model named OpenMPD for distributed memory system. It provides directives for data parallelization, which allow incremental parallelization for a sequential code as like as OpenMP. The result of evaluation shows that OpenMPD archieves 3 to 8 times of speed-up with a PC cluster with 8 processors with a small modification to the original sequential code.
Optimal Monotone Encodings
Authors: Noga Alon, Rani Hod
Venue: Proceedings of the 35th international colloquium on Automata, Languages and Programming, Part I (2008)

Moran, Naor and Segev have asked what is the minimal r = r(n, k) for which there exists an (n,k)-monotone encoding of length r, i.e., a monotone injective function from subsets of size up to k of {1, 2, &hellip;, n} to r bits. Monotone encodings are relevant to the study of tamper-proof data structures and arise also in the design of broadcast schemes in certain communication networks. To answer this question, we develop a relaxation of k-superimposed families, which we call &alpha;-fraction k-multi-user tracing ((k, &alpha;)-FUT families). We show that r(n, k) = &theta;(k log(n/k)) by proving tight asymptotic lower and upper bounds on the size of (k, &alpha;)-FUT families and by constructing an (n,k)-monotone encoding of length O(k log(n/k)). We also present an explicit construction of an (n, 2)-monotone encoding of length 2logn + O(1), which is optimal up to an additive constant.
Stable cooperation in changing environments
Authors: Colm O'Riordan, Humphrey Sorensen
Venue: Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 3 (2008)

This paper addresses the issue of emergence of robust cooperation among self-interested agents interacting in N-player social dilemma games. A series of graphs are created each exhibiting a different level of community structure; we show the influence that community structure has on the emergence of cooperation. A strategy set that represent a form of generalised tit-for-tat is used. Two forms of uncertainty in the environment are also modelled. The influence of the game length is also explored.
An Improved Model Selection Heuristic for AUC
Authors: Shaomin Wu, Peter Flach, Cèsar Ferri
Venue: Proceedings of the 18th European conference on Machine Learning (2007)

The area under the ROC curve (AUC) has been widely used to measure ranking performance for binary classification tasks. AUC only employs the classifier's scores to rank the test instances; thus, it ignores other valuable information conveyed by the scores, such as sensitivity to small differences in the score values However, as such differences are inevitable across samples, ignoring them may lead to overfitting the validation set when selecting models with high AUC. This problem is tackled in this paper. On the basis of ranks as well as scores, we introduce a new metric called scored AUC (sAUC), which is the area under the sROC curve. The latter measures how quickly AUC deteriorates if positive scores are decreased. We study the interpretation and statistical properties of sAUC. Experimental results on UCI data sets convincingly demonstrate the effectiveness of the new metric for classifier evaluation and selection in the case of limited validation data.
Texts and Graphs Elaboration: The Effect of Graphs' Examination on Recall
Authors: Gisella Paoletti, Sara Rigutti
Venue: Proceedings of the 5th international conference on Diagrammatic Representation and Inference (2008)

We investigated readers' tendency to ignore graphs in textual contents, the effect of graphs' visibility on recall as well as the spatial contiguity effect of texts and graphs. Eighty subjects read on a computer screen a text with graphs that depicted statistics related to the contents and were tested using a recall questionnaire. The recall performance was found to be improved for observers that examined the graphs while neither the spatial contiguity nor the graphs' visibility affected the mnestic performance. Moreover we found that people tend to examine integrated graphs rather then graphs visualized within pop---up windows.
A Non-blocking Multithreaded Architecture with Support for Speculative Threads
Authors: Krishna Kavi, Wentong Li, Ali Hurson
Venue: Proceedings of the 8th international conference on Algorithms and Architectures for Parallel Processing (2008)

In this paper we provide both a qualitative and a quantitative evaluation of a decoupled multithreaded architecture that uses non-blocking threads. Our architecture is based on simple in-order pipelines and complete decoupling of memory accesses from execution pipelines. We extend the architecture to support thread level speculation using snooping cache coherency protocols. We evaluate the performance gains from speculations by varying the number of load/store instructions compared to computational instructions, miss speculation rates and the degree of thread level speculation. Our architecture presents a viable alternative to complex superscalar and super-speculative CPUs.
Selective Sensor Node Selection Method for Making Suitable Cluster in Filtering-Based Sensor Networks
Authors: Byunghee Kim, Tae Hocho
Venue: Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Theoretical and Methodological Issues (2008)

Sensor nodes, that have a limited battery power, are deployed in open and unattended environments in many sensor network applications. An adversary can compromise the deployed sensor nodes and easily inject fabricated reports into the sensor network through the compromised nodes. Filtering-based secure methods have been proposed to detect and drop the injected fabricated reports. In this scheme, the number of key partitions is important, since a correct report should have to an efficient number of different message authentication codes made by each difference key partition. We propose a selective sensor node selection method to create suitable clusters and enhance the detection power of the filtering scheme. We use a fuzzy system to enhance cluster fitness. The proposed method demonstrates sufficient resilience and energy efficiency based on the simulation results.
China's E-Science Knowledge Grid Environment
Authors: Hai Zhuge
Venue: IEEE Intelligent Systems (2004)

Web pages' exponential growth and intrinsic characteristics prevent people from effectively and efficiently sharing information. To overcome the Web's shortcomings, scientists are working toward the next-generation Web, with most research focusing on two approaches: improving the existing Web and creating a new application platform independent of the Web. The Knowledge Grid will exploit research in both of these areas, using it to build a more efficient and effective intelligent application platform.
Anisotropic Continuous-Scale Morphology
Authors: Michael Breuß, Bernhard Burgeth, Joachim Weickert
Venue: Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part II (2007)

We describe a new approach to incorporate adaptivity into the partial differential equations (PDEs) of morphological dilation and erosion. By multiplication of the image gradient with a space-variant matrix, the speed of the evolution is locally adapted to the data. This is used to create anisotropic morphological evolutions that enhance coherent, flow-like image structures. We show that our adaptive method can be implemented by means of a simple modification of the classical Rouy-Tourin finite difference scheme. Numerical experiments confirm that the proposed dilations and erosions are capable of real anisotropic behaviour that can be used for closing interrupted lines.
Symmetric Log-Domain Diffeomorphic Registration: A Demons-Based Approach
Authors: Tom Vercauteren, Xavier Pennec, Aymeric Perchant, Nicholas Ayache
Venue: Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I (2008)

Modern morphometric studies use non-linear image registration to compare anatomies and perform group analysis. Recently, log-Euclidean approaches have contributed to promote the use of such computational anatomy tools by permitting simple computations of statistics on a rather large class of invertible spatial transformations. In this work, we propose a non-linear registration algorithm perfectly fit for log-Euclidean statistics on diffeomorphisms. Our algorithm works completely in the log-domain, i.e. it uses a stationary velocity field. This implies that we guarantee the invertibility of the deformation and have access to the true inverse transformation. This also means that our output can be directly used for log-Euclidean statistics without relying on the heavy computation of the log of the spatial transformation. As it is often desirable, our algorithm is symmetric with respect to the order of the input images. Furthermore, we use an alternate optimization approach related to Thirion's demons algorithm to provide a fast non-linear registration algorithm. First results show that our algorithm outperforms both the demons algorithm and the recently proposed diffeomorphic demons algorithm in terms of accuracy of the transformation while remaining computationally efficient.
Discovery and Investigation of Inherent Scalability in Developmental Genomes
Authors: Gunnar Tufte
Venue: Proceedings of the 8th international conference on Evolvable Systems: From Biology to Hardware (2008)

Evolvable hardware (EHW) struggles with a scalability problem. Even when applied to applications that include known scalable solutions it is hard to achieve equivalent scalability in EHW. The aspect of scalability is one of the main promising features of developmental mappings. The possibility to generate large scale structures together with gene regulation opens for a genome size that may not reflect size or complexity of the developed phenotype. The issue of generating structures include similarities to a traditional modular circuit design concept. Scalability in such designs is inherently achieved by expending the modular circuit. In a developmental system such inherent scaling can be achieved by genomes that can develop to circuits of different sizes whilst scaling parameters of the sought functionality. In this work an experimental approach is taken to find genomes that might include inherent scalability. Further, the candidate genomes are investigated to reveal structural and functional scaling properties. The results show the emergence of genomes that can develop to scalable structures with a scalable sequential functionality.
Multiple Hypernode Hitting Sets and Smallest Two-Cores with Targets
Authors: Peter Damaschke
Venue: Proceedings of the 2nd international conference on Combinatorial Optimization and Applications (2008)

The multiple weighted hitting set problem is to find a subset of nodes in a hypergraph that hits every hyperedge in at least m nodes. We extend the problem to a notion of hypergraphs with so-called hypernodes and show that it remains fixed-parameter tractable (FPT) for m = 2, with the number of hyperedges as the parameter. This is accomplished by a nontrivial extension of the known dynamic programming algorithm for usual hypergraphs. The result might be of independent interest for assignment problems, but here we need it as an auxiliary result to solve a different problem motivated by network analysis: We give an FPT algorithm that computes a smallest 2-core including a given set of target vertices in a graph, with the number of targets as the parameter. (A d-core is a subgraph where every vertex has degree at least d within the subgraph.) This FPT result is best possible, in the sense that an FPT algorithm for 3-cores cannot exist, for simple reasons.
Power-Aware Software Prefetching
Authors: Juan Chen, Yong Dong, Huizhan Yi, Xuejun Yang
Venue: Proceedings of the 3rd international conference on Embedded Software and Systems (2007)

Some traditional optimizations improve the performance of pro-cessors, but consume the higher power dissipation. We study this trade-off using software prefetching as performance-oriented optimization technique. We first demonstrate that software prefetching provides a significant performance boost with the higher power on several memory-intensive benchmarks. However, when we combine software prefetching with dynamic voltage/frequency scaling (DVFS), the performance gain can be achieved without power increase, which is called a power-aware approach. Besides reducing power dissipation through DVFS, we also improve the performance through adjusting the prefetch distance. A modified SimpleScalar/Wattch is used to evaluate our power-aware software prefetching. Experimental results show this optimization approach is effective to guarantee no power increase due to prefetching and improve the performance of software prefetching.
Theta Phase Coding and Acetylcholine Modulation in a Spiking Neural Network
Authors: Daniel Bush, Andrew Philippides, Phil Husbands, Michael O'Shea
Venue: Proceedings of the 10th international conference on Simulation of Adaptive Behavior: From Animals to Animats (2008)

Theta frequency oscillations are a prominent feature of the hippocampal EEG during active locomotion and learning. It has also been observed that the relative timing of place cell firing recedes as its place field is traversed --- a phenomena known as phase precession. This has led to the development of a theory of theta phase coding, whereby spatial sequences being encountered on a behavioural timescale are compressed into a firing sequence of place cells which is repeated in each theta cycle and stored in an auto-associative network using spike-timing dependent plasticity. This paper provides an abstract, descriptive model of theta phase coding in a spiking neural network, and aims to investigate how learning and recall functions may be separated by the neuromodulatory action of Acetylcholine (ACh). It is demonstrated that ACh is not essential for concurrent learning and recall without interference in this case, thanks to the robust nature of the theta phase coding implementation. However, the neuromodulation of synaptic plasticity offers other advantages, and may be essential to avoid continually consolidating false predictions when learning new routes.
Palmprint Recognition Using a Novel Sparse Coding Technique
Authors: Li Shang, Fenwen Cao, Zhiqiang Zhao, Jie Chen, Yu Zhang
Venue: Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks (2007)

This paper proposes a novel recognition method for palmprints using a new sparse coding (SC) algorithm proposed by us. This algorithm exploited the maximum Kurtosis as the sparse measure criterion, at one time, a fixed variance term of sparse coefficients is used to yield a fixed information capacity. Experimental results show that the feature basis vectors of palmprint images can be successfully extracted by using our SC algorithm. Using the radial basis probabilistic neural network (RBPNN), the classification task can be implemented easily. Finally, compared with methods of principal component analysis (PCA) and the classical SC, simulation results show that our algorithm is indeed efficient and effective in performing palmprint recognition task.
Classification of Artifacts in Clinical Digital Mammography
Authors: Chantal Ongeval, Jurgen Jacobs, André Steen, Federica Zanca, Hilde Bosmans, Guy Marchal
Venue: Proceedings of the 9th international workshop on Digital Mammography (2008)

In our country, decentralized screening using full field digital mammography (FFDM) is allowed since 2005. Next to physical technical constancy checks, other QA activities in digital mammography aim to ensure the proper functioning of the complete imaging chain, from positioning of the patient until the reading of the images. Radiologists in the center for second reading were asked to carefully control the quality of the individual mammograms acquired in different locations. The artifacts seen in images from 3 Fuji CR (FCR Profect, Japan) systems, 1 Agfa CR (Agfa-HealthCare, Belgium) system, 2 Hologic Lorad Selenia (Hologic, Inc., Danbury, CT) systems, 1 Novation (Siemens, Germany) system and 1 DM 1000 Agfa DR (Agfa-HealthCare, Belgium) system were registered, the origin of the artifacts was determined, the artifacts were then categorized and solutions were discussed. Our data underline the need to control the daily quality of digital mammograms. A digital registration system of artifacts in clinical screening images is suggested.
Matching Integer Intervals by Minimal Sets of Binary Words with don't cares
Authors: Wojciech Fraczak, Wojciech Rytter, Mohammadreza Yazdani
Venue: Proceedings of the 19th annual symposium on Combinatorial Pattern Matching (2008)

An interval [p,q], where 0 ≤ p ≤ q < 2 n , can be considered as the set X of n-bit binary strings corresponding to encodings of all integers in [p,q]. A word w with don't care symbols is matching the set L(w) of all words of the length |w| which can differ only on positions containing a don't care. A set Y of words with don't cares is matching X iff X = ¿ w ¿ Y L(w). For a set X of codes of integers in [p,q] we ask for a minimal size set Y of words with don't cares matching X. Such a problem appears in the context of network processing engines using Ternary Content Addressable Memory (TCAM) as a lookup table for IP packet header fields. The set Y is called a template in this paper, and it corresponds to a TCAM representation of an interval. It has been traditionally calculated by a heuristic called "prefix match", which can produce a result of the size approximately twice larger than the minimal one. In this paper we present two fast (linear time in the size of the input and the output) algorithms for finding minimal solutions for two natural encodings of integers: the usual binary representation (lexicographic encoding) and the reflected Gray code.
Joint Registration and Segmentation of Serial Lung CT Images in Microendoscopy Molecular Image-Guided Therapy
Authors: Zhong Xue, Kelvin Wong, Stephen Wong
Venue: Proceedings of the 4th international workshop on Medical Imaging and Augmented Reality (2008)

In lung cancer image-guided therapy, a real-time electromagnetic tracked microendoscopic optical imaging probe is guided to the small lung lesion of interest. The alignment of the pre-operative lung CT images as well as the intra-operative serial images is often an important step to accurately guide and monitor the interventional procedure in the diagnosis and treatment of these small lung lesions. Registering the serial images often relies on correct segmentation of the images and on the other hand, the segmentation results can be further improved by temporal alignment of the serial images. This paper presents a joint serial image registration and segmentation algorithm. In this algorithm, serial images are segmented based on the current deformations, and the deformations among the serial images are iteratively refined based on the updated segmentation results. No temporal smoothness about the deformation fields is enforced so that the algorithm can tolerate larger or discontinuous temporal changes that often appear during image-guided therapy. Physical procedure models could also be incorporated to our algorithm to better handle the temporal changes of the serial images during intervention. In experiments, we apply the proposed algorithm to align serial lung CT images. Results using both simulated and clinical images show that the new algorithm is more robust compared to the method that only uses deformable registration.
False Positive Reduction in Breast Mass Detection Using Two-Dimensional PCA
Authors: Arnau Oliver, Xavier Lladó, Joan Martí, Robert Martí, Jordi Freixenet
Venue: Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part II (2007)

In this paper we present a novel method for reducing false positives in breast mass detection. Our approach is based on using the Two-Dimensional Principal Component Analysis (2DPCA) algorithm, recently proposed in the field of face recognition, in order to extract breast mass image features. In mammography, it is well known that the breast density measure is highly related to the risk of breast cancer development. Hence, we also propose to take advantage of a previous breast density classification in order to increase the overall breast mass detection performance. We test our approach using a set of 1792 RoIs manually extracted from the DDSM database. Moreover, we compare our results with several existing methods. The obtained results demonstrate the validity of our approach, not only in terms of improving the performance but being a generalizable, simple, and cost-effective approach.