ppxscal/embedding_network huggingface dataset:
A Novel Pixel-Level and Feature-Level Combined Multisensor Image Fusion Scheme
Authors: Min Li, Gang Li, Wei Cai, Xiao-Yan Li
Venue: Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks, Part II (2008)

This paper proposes a novel image fusion scheme, which combines the merits of pixel-level and feature-level fusion algorithms. It avoids some of the well-known problems in pixel-level fusion such as blurring effects and high sensitivity to noise and misregistration. The algorithm first segments images into several regions, then extract features from each segmented region to get the fused image. Two typical image segmentation methods, region growing and edge detection based are both presented in this paper. Experimental results have demonstrated that the proposed method has extensive application scope and it outperforms the multiscale decompositions (MSD) based fusion approaches, both in visual effect and objective evaluation criteria.
A Study of Acoustic Correlates of Speaker Age
Authors: Susanne Schötz, Christian Müller
Venue: Speaker Classification II: Selected Projects (2007)

Speaker age is a speaker characteristic which is always present in speech. Previous studies have found numerous acoustic features which correlate with speaker age. However, few attempts have been made to establish their relative importance. This study automatically extracted 161 acoustic features from six words produced by 527 speakers of both genders, and used normalised means to directly compare the features. Segment duration and sound pressure level (SPL) range were identified as the most important acoustic correlates of speaker age.
Performance Comparison of Sleep Mode Operations in IEEE 802.16e Terminals
Authors: Youn-Hee Han, Sung-Gi Min, Dongwon Jeong
Venue: Proceedings of the 7th international conference on Computational Science, Part IV: ICCS 2007 (2007)

IEEE 802.16e Task Group has developed an IEEE 802.16 amendment supporting mobile terminals such as PDAs, phones, and laptops. The energy efficiency design is an important research issue for mobile terminals due to limited battery power of them. In this paper, we study the energy efficiency design of IEEE 802.16e. For the purpose of power efficient operation, there are two sleep mode operations related to normal data traffic in the current IEEE 802.16e specification. We propose analytical models of the two sleep mode operations and conduct their performance comparison in terms of the power consumption and the frame response time.
Hypermedia Discourse: Contesting Networks of Ideas and Arguments
Authors: Simon Buckingham Shum
Venue: Proceedings of the 15th international conference on Conceptual Structures: Knowledge Architectures for Smart Applications (2007)

This invited contribution motivates the Hypermedia Discourse research programme, investigating the reading, writing and contesting of ideas as hypermedia networks grounded in discourse schemes. We are striving for cognitively and computationally tractable conceptual structures: fluid enough to serve as augmentations to group working memory, yet structured enough to support long term memory. I will describe how such networks can be (i) mapped by multiple analysts to visualize and interrogate the claims and arguments in a literature, and (ii) mapped in real time to manage a team's information sources, competing interpretations, arguments and decisions, particularly in time- pressured scenarios where harnessing collective intelligence is a priority. Given the current geo-political and environmental context, the growth in distributed teamwork, and the need for multidisciplinary approaches to wicked problems, there has never been a greater need for sensemaking tools to help diverse stakeholders build common ground.
First experiences using wireless sensor networks for noise pollution monitoring
Authors: Silvia Santini, Benedikt Ostermaier, Andrea Vitaletti
Venue: Proceedings of the workshop on Real-world wireless sensor networks (2008)

The assessment of environmental pollution levels is a complex and expensive task that public administration and often also private entities are willing or forced to take over. Focusing on the assessment of environmental noise pollution in urban areas, we provide qualitative considerations and experimental results to show the feasibility of wireless sensor networks to be used in this context. We present a prototype for the collection and logging of noise pollution data based on the Tmote invent prototyping platform, using which we performed indoor and outdoor noise pollution measurements. We build upon these first experimental results to depict the potentials and limits of currently available wireless sensor networks prototyping platforms to be used as noise pollution sensors. Furthermore, we present tinyLAB, a Matlab-based tool developed in the context of this work, which enables real-time acquisition, processing and visualization of data collected in wireless sensor networks.
Proving Group Protocols Secure Against Eavesdroppers
Authors: Steve Kremer, Antoine Mercier, Ralf Treinen
Venue: Proceedings of the 4th international joint conference on Automated Reasoning (2008)

Security protocols are small programs designed to ensure properties such as secrecy of messages or authentication of parties in a hostile environment. In this paper we investigate automated verification of a particular type of security protocols, called group protocols, in the presence of an eavesdropper, i.e., a passive attacker. The specificity of group protocols is that the number of participants is not bounded. Our approach consists in representing an infinite set of messages exchanged during an unbounded number of sessions, one session for each possible number of participants, as well as the infinite set of associated secrets. We use so-called visibly tree automata with memory and structural constraints (introduced recently by Comon-Lundh et al.) to represent over-approximations of these two sets. We identify restrictions on the specification of protocols which allow us to reduce the attacker capabilities guaranteeing that the above mentioned class of automata is closed under the application of the remaining attacker rules. The class of protocols respecting these restrictions is large enough to cover several existing protocols, such as the GDH family, GKE, and others.
Alternative Aggregates in Mizar
Authors: Gilbert Lee, Piotr Rudnicki
Venue: Proceedings of the 14th symposium on Towards Mechanized Mathematical Assistants: 6th International Conference (2007)

Mizar provides built-in support for defining structures (aggregates) like the familiar algebraic systems of groups or vector spaces. When trying to employ these structures for formalizing graph algorithms we ran into substantial problems stemming from the fact that fields in Mizar structures are not first class objects. We decided that a different approach would be more suitable for the task at hand. Starting from scratch, we modeled structures as functions. In our approach, fields in structures are first class objects and just this one factor made working with graph algorithms much more convenient. We report on our experience and argue that our approach to aggregates is more suitable for a proof assistant like Mizar.
Study of Punch Die Condition Discrimination Based on Wavelet Packet and Genetic Neural Network
Authors: Zhigao Luo, Xiang Wang, Ju Li, Binbin Fan, Xiaodong Guo
Venue: Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks, Part II (2008)

According to the characteristics of the acoustic emission signal which was induced by punch die when It fails, the characteristic parameters of failure signal is determined. The energy eigenvector of signal failure die is extracted by wavelet packet analysis technology, and the comparison between the energy in different frequency bands and total energy is taken as the characteristic parameters. Then a BP neural network is established in which the time factor is considered based on genetic algorithm. The characteristic parameters are used as input specimen, learning and training the network to complete the pattern recognition of model working state. Experiments show that the method can quickly and reliably discriminate the conditions of the punch die and has strong practicability.
Scheduling jobs on a single machine to maximize the total revenue of jobs
Authors: Wen-Hua Yang
Venue: Computers and Operations Research (2009)

In today's hyper-competitive marketplace, many products like memory chips and computers are characterized by short life cycles and rapidly declining sales prices. This implies that the amount of revenue generated as a result of completing a product (job) may be decreasing as its completion time is delayed. In such an environment, there is a decided preference for the maximization of product revenues as an important objective. Based on the assumption that the decreasing rate of revenue is dependent on their product types, we intend to develop a searching algorithm and some heuristic algorithms to locate optimal and near-optimal job sequences, respectively, and thereby maximize total earned revenue.
Challenges, design and analysis of a large-scale p2p-vod system
Authors: Yan Huang, Tom Z.J. Fu, Dah-Ming Chiu, John C.S. Lui, Cheng Huang
Venue: ACM SIGCOMM Computer Communication Review (2008)

P2P file downloading and streaming have already become very popular Internet applications. These systems dramatically reduce the server loading, and provide a platform for scalable content distribution, as long as there is interest for the content. P2P-based video-on-demand (P2P-VoD) is a new challenge for the P2P technology. Unlike streaming live content, P2P-VoD has less synchrony in the users sharing video content, therefore it is much more difficult to alleviate the server loading and at the same time maintaining the streaming performance. To compensate, a small storage is contributed by every peer, and new mechanisms for coordinating content replication, content discovery, and peer scheduling are carefully designed. In this paper, we describe and discuss the challenges and the architectural design issues of a large-scale P2P-VoD system based on the experiences of a real system deployed by PPLive. The system is also designed and instrumented with monitoring capability to measure both system and component specific performance metrics (for design improvements) as well as user satisfaction. After analyzing a large amount of collected data, we present a number of results on user behavior, various system performance metrics, including user satisfaction, and discuss what we observe based on the system design. The study of a real life system provides valuable insights for the future development of P2P-VoD technology.
BMML: A Mark-Up Language for Braille Music
Authors: Enrico Bortolazzi, Nadine Baptiste-Jessel, Giovanni Bertoni
Venue: Proceedings of the 11th international conference on Computers Helping People with Special Needs (2008)

The need of an interchange format for music notation led to several XML encoding initiatives and among them to MusicXML, a de facto standard format supported by market leaders. The result is that music scores can be shared between applications and between users, delivered through the web and/or archived independently by the application that generated them. Braille music is a specific music notation for blind users, written in linear format, character by character. Traditionally Braille scores in electronic format are archived or shared as text files, with only character information dependent on some translation table. The available XML formats for music notation are not suitable to cover this specific notation. We present our approach to the problem and the key design concepts behind Braille Music Mark-up Language, developed as an open format by the EU funded CONTRAPUNCTUS[1] project.
A Spatio-temporal Extension of the SUSAN-Filter
Authors: Benedikt Kaiser, Gunther Heidemann
Venue: Proceedings of the 18th international conference on Artificial Neural Networks, Part I (2008)

This paper proposes a detector for spatio-temporal interest points. Interest point detection is a common technique in computer vision to extract salient regions and represent them by a single point for further processing. But while many algorithms exist for static images, there is hardly any method to obtain interest points from image sequences for the representation of salient motion. Here we introduce SUSANinTime, an extension of the well known SUSAN algorithm from 2D to 2D+1D, where the third dimension is time. While SUSAN-2D extracts edge- and corner points, SUSANinTime detects basic events such as turning points of object trajectories. To find out the type and saliency of the detected events, we analyze the second order statistics of the spatio-temporal volume surrounding the interest points in real world image sequences.
Artificial Immunity-Based Discovery for Popular Information in WEB Pages
Authors: Caiming Liu, Xiaojie Liu, Tao Li, Lingxi Peng, Jinquan Zeng, Hui Zhao
Venue: Proceedings of the 7th international conference on Computational Science, Part IV: ICCS 2007 (2007)

An artificial immunity-based discovery method for popular information is proposed. Principles of evolution and concentration of antibodies in artificial immune system are simulated. Key words in web pages are extracted and simulated as antibody and antigen. Antibodies are evolved and excreted dynamically. Concentration of antibodies is computed to attain accurately the degree of popular measurement in quantity. The proposed method improves the intelligent degree of information discovery and provides a new way to discover WEB information.
Progressive High-Dimensional Similarity Join
Authors: Wee Hyong Tok, Stéphane Bressan, Mong-Li Lee
Venue: Proceedings of the 18th International Conference on Database and Expert Systems Applications (2007)

The Rate-Based Progressive Join (RPJ) is a non-blocking relational equijoin algorithm. It is an equijoin that can deliver results progressively. In this paper, we first present a naive extension, called neRPJ, to the progressive computation of the similarity join of high-dimensional data. We argue that this naive extension is not suitable. We therefore propose an adequate solution in the form of a Result-Rate Progressive Join (RRPJ) for high-dimensional distance similarity joins. Using both synthetic and real-life datasets, we empirically show that RRPJ is effective and efficient, and outperforms the naive extension.
Knowledge Representation with Ontologies: The Present and Future
Authors: Christopher Brewster, Kieron O'Hara, Steve Fuller, Yorick Wilks, Enrico Franconi, Mark A. Musen, Jeremy Ellman, Simon Buckingham Shum
Venue: IEEE Intelligent Systems (2004)

Recently, there's been an explosion of interest in ontologies as artifacts to represent human knowledge and as critical components in knowledge management, the Semantic Web, business-to-business applications, and several other application areas. Various research communities commonly assume that ontologies are the appropriate modeling structure for representing knowledge. However, little discussion has occurred regarding the actual range of knowledge an ontology can successfully represent. This installment of Trends and Controversies brings together several practitioners to debate this issue.
SD-Core: Generic Semantic Middleware Components for the Semantic Web
Authors: Ismael Navas-Delgado, José F. Aldana-Montes
Venue: Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part II (2008)

This paper describes a middleware for building Semantic Web applications. The main idea behind this proposal is to help developers build Semantic Web applications by providing them with the main components for this task. This set of components has been implemented and made available through a Web tool (http://khaos.uma.es/SD-Core), and as an installable tool for a Tomcat Web Server. In addition, it has been applied to develop a semantic integration system for biological data sources (http://asp.uma.es/WebMediator).
Atlas-Based Segmentation of the Germinal Matrix from in Utero Clinical MRI of the Fetal Brain
Authors: Piotr A. Habas, Kio Kim, Francois Rousseau, Orit A. Glenn, A. James Barkovich, Colin Studholme
Venue: Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I (2008)

Recently developed techniques for reconstruction of high-resolution 3D images from fetal MR scans allows us to study the morphometry of developing brain tissues in utero. However, existing adult brain analysis methods cannot be directly applied as the anatomy of the fetal brain is significantly different in terms of geometry and tissue morphology. We describe an approach to atlas-based segmentation of the fetal brain with particular focus on the delineation of the germinal matrix, a transient structure related to brain growth. We segment 3D images reconstructed from in utero clinical MR scans and measure volumes of different brain tissue classes for a group of fetal subjects at gestational age 20.5---22.5 weeks. We also include a partial validation of the approach using manual tracing of the germinal matrix at different gestational ages.
A neural network approach to FIR filter design using frequency-response masking technique
Authors: Xiao-Hua Wang, Yi-Gang He
Venue: Signal Processing (2008)

This paper presents a new optimization method for the design of various frequency-response-masking (FRM)-based linear-phase finite-impulse response (FIR) digital filters. The method is based on a batch back-propagation neural network algorithm (NNA), which is taken as a variable learning rate mode. In order to reduce the complexity, the following two-step optimization technique is proposed. At the first step, an initial FRM filter is designed by alternately optimizing the sub-filters. This solution is then used as a start-up solution for further optimization. At the second step, the coefficients of overall sub-filters are optimized simultaneously by the NNA. Algorithm details for the design of basic and multistage FRM filters are presented to show that the proposed approach offers a unified design framework for a variety of FRM filters. Some examples taken from the literatures are included and the results show that the proposed algorithm can design better FRM filters than several existing methods.
Using SPIN to Detect Vulnerabilities in the AACS Drive-Host Authentication Protocol
Authors: Wei Wang, Dongyao Ji
Venue: Proceedings of the 28th IFIP WG 6.1 international conference on Formal Techniques for Networked and Distributed Systems (2008)

In this paper, we use SPIN, a model checker for LTL, to detect vulnerabilities in the AACS drive-host authentication protocol. Before the detection, we propose a variant of the Dolev-Yao attacker model [4] and incorporate the synthesis and analysis rules [7] to formalize the protocol and the intruder capabilities. During the detection, we check the authenticity of the protocol and identify a few weaknesses. Besides, we propose a novel collusion attack that seriously threaten the security of the protocol, and build a corresponding LTL formula. Based on the formula, SPIN detects a few relevant attack instances in the original scheme of the authentication protocol and a modified scheme advanced in [5].
Optimal Nesting of Species for Exact Cover: Many against Many
Authors: Jeffrey Horn
Venue: Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X (2008)

Experiments with resource-defined fitness sharing (RFS) applied to shape nesting problems indicate a remarkable ability to discover exact covers of resources [1, 2]. These exact covers are represented by a maximally sized set of cooperating (non-competing) species. Recent papers by Horn [3, 4] introduce the first formal analyses of this empirical phenomenon. In [3], a minimal case of two species, a and b, against a third, c, is considered: the two-against-one scenario. It is shown that if the team of a and b form an exact cover, then c will be extinct at niching equilibrium. In [4], this result is generalized to the case of two-against-many: if a and b form an exact cover against an arbitrary number of competing species, under very general assumptions, a and b will be the only survivors at niching equilibirum. In the current paper, we extend these results to the most general scenario: many-against-many. We prove that, under certain very general assumptions, any size team of species forming an exact cover will dominate a population with any number of competing species: at niching equilibirum, all such competitors will be extinct. The results are more general than shape-nesting problems, applying as well to the NP-complete problem exact cover by k-sets.
Display of 3-D Digital Images: Computational Foundations and Medical Applications
Authors: Gabor Herman, Jayaram Udupa
Venue: IEEE Computer Graphics and Applications (1983)

These new approaches to 3-D medical imaging promise better and more cost-effective object identification, representation, and manipulation in space and time.
Network Structure and Knowledge Transfer
Authors: Fangcheng Tang
Venue: Proceedings of the 7th international conference on Computational Science, Part IV: ICCS 2007 (2007)

This study employs single layer perceptron model (SLPM) to explore how the topological structure of intra-organization networks affects knowledge transfer. The results demonstrate that in the process of knowledge transfer, both the disseminative capacity of knowledge senders and the absorptive capacity of knowledge receivers should be taken into consideration. While hierarchical networks can enable greater numbers of organizational units to acquire knowledge, they reduce the speed and efficiency of knowledge transfer, whereas scale-free networks can accelerate transfer of knowledge among units.
Automatically Extracting Personal Name Aliases from the Web
Authors: Danushka Bollegala, Taiki Honma, Yutaka Matsuo, Mitsuru Ishizuka
Venue: Proceedings of the 6th international conference on Advances in Natural Language Processing (2008)

Extracting aliases of an entity is important for various tasks such as identification of relations among entities, web search and entity disambiguation. To extract relations among entities properly, one must first identify those entities. We propose a novel approach to find aliases of a given name using automatically extracted lexical patterns. We exploit a set of known names and their aliases as training data and extract lexical patterns that convey information related to aliases of names from text snippets returned by a web search engine. The patterns are then used to find candidate aliases of a given name. We use anchor texts to design a word co-occurrence model and use it to define various ranking scores to measure the association between a name and a candidate alias. The ranking scores are integrated with page-count-based association measures using support vector machines to leverage a robust alias detection method. The proposed method outperforms numerous baselines and previous work on alias extraction on a dataset of personal names, achieving a statistically significant mean reciprocal rank of 0.6718. Experiments carried out using a dataset of location names and Japanese personal names suggest the possibility of extending the proposed method to extract aliases for different types of named entities and for other languages. Moreover, the aliases extracted using the proposed method improve recall by 20% in a relation-detection task.