ppxscal/embedding_network huggingface dataset:
Implementing Fuzzy Reasoning on a Spiking Neural Network
Authors: Cornelius Glackin, Liam Mcdaid, Liam Maguire, Heather Sayers
Venue: Proceedings of the 18th international conference on Artificial Neural Networks, Part II (2008)

This paper presents a supervised training algorithm that implements fuzzy reasoning on a spiking neural network. Neuron selectivity is facilitated using receptive fields that enable individual neurons to be responsive to certain spike train frequencies. The receptive fields behave in a similar manner as fuzzy membership functions. The network is supervised but learning only occurs locally as in the biological case. The connectivity of the hidden and output layers is representative of a fuzzy rule base. The advantages and disadvantages of the network topology for the IRIS classification task are demonstrated and directions of current and future work are discussed.
Paper Retraction: On the Hardness of Embeddings Between Two Finite Metrics
Authors: Matthew Cary, Atri Rudra, Ashish Sabharwal
Venue: Proceedings of the 34th international colloquium on Automata, Languages and Programming (2007)

We regret to report that we have found an error in our paper "On the Hardness of Embeddings Between Two Finite Metrics," which appeared in the Proceedings of the 32nd International Colloquium on Automata, Languages and Programming (ICALP), Lisboa, Portugal, July 2005.
Monitoring Human Resources of a Public Health-Care System Through Intelligent Data Analysis and Visualization
Authors: Aleksander Pur, Marko Bohanec, Nada Lavrač, Bojan Cestnik, Marko Debeljak, Anton Gradišek
Venue: Proceedings of the 11th conference on Artificial Intelligence in Medicine (2007)

A public health-care system (HCS) is a complex system that requires permanent monitoring. This paper focuses on the Slovenian national HCS sub-system consisting of a network of health-care professionals at the primary care level. The challenge addressed in this paper is the development and application of intelligent data analysis, decision support and visualization methods aimed to improve the monitoring of human resources of this network. The main outcome is a set of proposed performance indicators and the developed model for monitoring the network of primary health-care professionals of Slovenia. The model enables improved planning and management through data analysis and visualization modules developed for the monitoring of physicians' qualification, age, workload and dispersion.
Fusion of LSB and DWT Biometric Watermarking for Offline Handwritten Signature
Authors: Cheng-Yaw Low, Andrew Beng-Jin Teoh, Connie Tee
Venue: Proceedings of the 2008 Congress on Image and Signal Processing, Vol. 5 - Volume 05 (2008)

Biometric watermarking refers to the incorporation of biometrics in watermarking technology. In this paper, we present a novel biometric watermarking scheme to embed handwritten signature invisibly in the host as a notice of legitimate ownership. We propose to adaptively fuse Least Significant Bit (LSB) and Discrete Wavelet Transform (DWT)-based approaches into a unison framework, which to be known as LSB-DWT scheme. The performance of LSB-DWT scheme is validated against simulated frequency and geometric attacks, specifically JPG compression, low pass filtering, median filtering, noise addition, scaling, rotation and cropping through visual inspection, Peak Signal to Noise Ratio (PSNR) and watermark distortion rate. Experiment results prove that LSB-DWT scheme is sufficiently robust even in the presence of deliberate distortions.
A Robust Geometrical Attack Resistant Digital Image Watermarking Based On FastICA Algorithm
Authors: Ming Tong, Wei Feng, Hongbing Ji
Venue: Proceedings of the 2008 Congress on Image and Signal Processing, Vol. 5 - Volume 05 (2008)

A blind digital image watermarking scheme based on Singular Values Decomposition and FastICA algorithm is proposed in this paper. According to the favorable stability property of singular value, the watermark is inserted into the singular values of the image’s Discrete Cosine Transform (DCT) coefficients, which are regarded as two sources are mixed through the ICA instantaneous mixing model. Meanwhile, the FastICA algorithm is introduced in extraction procedure, through which the watermark can be derived efficiently even the original image and the mixing procedure being unknown. Experiment results reveal that our algorithm has great resistance over general signal processing, especially has the unique superiority over geometrical attack.
Logic of Plausibility for Discovery in Multi-agent Environment Deciding Algorithms
Authors: Sergey Babenyshev, Vladimir Rybakov
Venue: Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part III (2008)

In most popular hybrid logics from the field of Artificial Intelligence, knowledge is usually combined with other logical operations (e.g. awareness). We consider a hybrid logic LPD combining agent's knowledge operations, linear time, operations for discovering information and plausibility operation. The plausibility operation is seemed to be quite new in this framework. The paper introduces LPD via semantical models based on special Kripke/Hintikka frames. We provide formation rules and rules for computing truth values of formulas in the chosen language inside such models, discuss properties of the described logic comparing to some other already known logics. The main problem we are focused on is finding an algorithm for recognition of satisfiable in LPD formulas, we propose such an algorithm (so we show that LPD is decidable w.r.t. satisfiability). The paper is completed with a short discussion on the essence and features of this algorithm.
Bitableau bases for Garsia--Haiman modules of hollow type
Authors: Edward E. Allen, Miranda C. Marion, Gregory S. Warrington
Venue: Journal of Combinatorial Theory Series A (2008)

Garsia-Haiman modules C[X"n,Y"n]/I"@c are quotient rings in the variables X"n={x"1,x"2,...,x"n} and Y"n={y"1,y"2,...,y"n} that generalize the quotient ring C[X"n]/I, where I is the ideal generated by the elementary symmetric polynomials e"j(X"n) for 1=
Reputation Systems for Anonymous Networks
Authors: Elli Androulaki, Seung Geol Choi, Steven M. Bellovin, Tal Malkin
Venue: Proceedings of the 8th international symposium on Privacy Enhancing Technologies (2008)

We present a reputation scheme for a pseudonymous peer-to-peer (P2P) system in an anonymous network. Misbehavior is one of the biggest problems in pseudonymous P2P systems, where there is little incentive for proper behavior. In our scheme, using ecash for reputation points, the reputation of each user is closely related to his real identity rather than to his current pseudonym. Thus, our scheme allows an honest user to switch to a new pseudonym keeping his good reputation, while hindering a malicious user from erasing his trail of evil deeds with a new pseudonym.
SVD Based Feature Selection and Sample Classification of Proteomic Data
Authors: Annarita D'Addabbo, Massimo Papale, Salvatore Paolo, Simona Magaldi, Roberto Colella, Valentina D'Onofrio, Annamaria Palma, Elena Ranieri, Loreto Gesualdo, Nicola Ancona
Venue: Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part III (2008)

Feature selection becomes a central task when 'signature' profiles specific to a pathological status have to be extracted from high dimensional gene expression or proteomic data. In the present paper, we propose a feature selection method based on Singular Value Decomposition (SVD) and apply it to SELDI-TOF/MS proteomic data from a cohort of Type 2 Diabetics affected by Glomerulosclerosis and Membranous Nephropathy. We have selected a profile composed of 24 proteins that seems to be an effective signature for the pathology at hand, allowing to efficiently discriminate between the considered subtype of diabetes.
State of the nation in data integration for bioinformatics
Authors: Carole Goble, Robert Stevens
Venue: Journal of Biomedical Informatics (2008)

Data integration is a perennial issue in bioinformatics, with many systems being developed and many technologies offered as a panacea for its resolution. The fact that it is still a problem indicates a persistence of underlying issues. Progress has been made, but we should ask ''what lessons have been learnt?'', and ''what still needs to be done?'' Semantic Web and Web 2.0 technologies are the latest to find traction within bioinformatics data integration. Now we can ask whether the Semantic Web, mashups, or their combination, have the potential to help. This paper is based on the opening invited talk by Carole Goble given at the Health Care and Life Sciences Data Integration for the Semantic Web Workshop collocated with WWW2007. The paper expands on that talk. We attempt to place some perspective on past efforts, highlight the reasons for success and failure, and indicate some pointers to the future.
Case-Based Reasoning and the Statistical Challenges
Authors: Petra Perner
Venue: Proceedings of the 9th European conference on Advances in Case-Based Reasoning (2008)

Case-based reasoning (CBR) solves problems using the already stored knowledge, and captures new knowledge, making it immediately available for solving the next problem. Therefore, CBR can be seen as a method for problem solving, and also as a method to capture new experience and make it immediately available for problem solving. The CBR paradigm has been originally introduced by the cognitive science community. The CBR community aims to develop computer models that follow this cognitive process. Up to now many successful computer systems have been established on the CBR paradigm for a wide range of real-world problems. We will review in this paper the CBR process and the main topics within the CBR work. Hereby we try bridging between the concepts developed within the CBR community and the statistics community. The CBR topics we describe are: similarity, memory organization, CBR learning, and case-base maintenance. Then we will review based on applications the open problems that need to be solved. The applications we are focusing on are meta-learning for parameter selection, image interpretation, incremental prototype-based classification and novelty detection and handling. Finally, we summarize our concept on CBR.
Zero-Crossing-Based Feature Extraction for Voice Command Systems Using Neck-Microphones
Authors: Sang Kyoon Park, Rhee Man Kil, Young-Giu Jung, Mun-Sung Han
Venue: Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks (2007)

This paper presents zero-crossing-based feature extraction for the speech recognition using neck-microphones. One of the solutions in noise-robust speech recognition is using neck-microphones which are not affected by the environmental noises. However, neck-microphones distort the original voice signals significantly since they only capture the vibrations of vocal tracts. In this context, we consider a new method of enhancing speech features of neck-microphone signals using zero-crossings. Furthermore, for the improvement of zero-crossing features, we consider to use the statistics of two adjacent zero-crossing intervals, that is, the statistics of two samples referred to as the second order statistics. Through the simulation for speech recognition using the neck-microphone voice command system, we have shown that the suggested method provides the better performance than other approaches using conventional speech features.
Centralized Communication in Radio Networks with Strong Interference
Authors: František Galčík
Venue: Proceedings of the 15th international colloquium on Structural Information and Communication Complexity (2008)

We study communication in known topology radio networks with the presence of interference constraints. We consider a real-world situation, when a transmission of a node produces an interference in the area that is larger than the area, where the transmitted message can be received. For each node, there is an area, where a signal of its transmission is too low to be decoded by a receiver, but is strong enough to interfere with other incoming simultaneous transmissions. Such a setting is modelled by a newly proposed interference reachability graph that extends the standard graph model based on reachability graphs. Further, focusing on the information dissemination problem in bipartite interference reachability graphs, we introduce interference ad-hoc selective families as an useful combinatorial tool. They are a natural generalization of ad-hoc selective families. Adopting known algorithms and techniques, we show how to construct small interference ad-hoc selective families in the case when, for each node, the ratio of the only-interfering neighbors to the other neighbors is bounded. Finally, taking into account the maximum degree in an underlying interference reachability graph, we study the broadcasting problem in general radio networks.
A Neighborhood-Based Competitive Network for Video Segmentation and Object Detection
Authors: Rafael Marcos Luque Baena, Enrique Dominguez, Domingo López-Rodríguez, Esteban J. Palomo
Venue: Proceedings of the 18th international conference on Artificial Neural Networks, Part I (2008)

This work proposes an unsupervised competitive neural network based on adaptive neighborhoods for video segmentation and object detection. The designed neural network is proposed to form a background model based on subtraction approach. The synaptic weights and the adaptive neighborhood of the neurons serve as a model of the background and are updated to reflect the statistics of the background. The segmentation performance of the proposed neural network is examined and compared to mixture of Gaussian models. The proposed algorithm is parallelized on a pixel level and designed to enable efficient hardware implementation to achieve real-time processing at great frame rates.
Computing Relaxed Answers on RDF Databases
Authors: Hai Huang, Chengfei Liu, Xiaofang Zhou
Venue: Proceedings of the 9th international conference on Web Information Systems Engineering (2008)

Database users may be frustrated by no answers returned when they pose a query on the database. In this paper, we study the problem of relaxing queries on RDF databases in order to acquire approximate answers. We address two problems for efficient query relaxation. First, to ensure the quality of answers, we compute the similarities of relaxed queries with regard to the original query and use them to score the potential relevant answers. We also propose the algorithm to get most relevant answers as soon as possible. Second, to optimise query relaxation process, we characterize a type of unnecessary relaxed queries which do not contribute to the final results and propose the method to prune them from the query relaxation graph. At last, we implement and experimentally evaluate our approach.
A CDMAM Image Phantom Software Improvement for Human Observer Assessment
Authors: Gabriel Prieto, Margarita Chevalier, Eduardo Guibelalde
Venue: Proceedings of the 9th international workshop on Digital Mammography (2008)

A software tool is presented to improve the features of CDMAM image phantom by University Hospital Nijmegen. This software tool ensures that the 4-alternative forced choice method of CDMAM is actually kept, even when is being scored by highly expertise observers familiar on the test object pattern. For digital images, the developed software tool automatically changes the image position of the four corners. It can be selected a fixed rotation angle or a random one, so making impossible that any observer is able to remember the exact corner position of the target disc inside any cell. Two alternative successful algorithms have been tested. ROC curve analysis obtained by 36 observers shows that both original and computer-modified images are indistinguishable. The ROC area was 0.507±0.024 for first algorithm and 0.522±0.026 for the second one, indicating that there was no statistical difference between real and computer-modified images for both of them.
The Perplexus Programming Framework: Combining Bio-inspiration and Agent-Oriented Programming for the Simulation of Large Scale Complex Systems
Authors: Olivier Brousse, Gilles Sassatelli, Thierry Gil, Michel Robert, François Grize, Eduardo Sanchez, Andrés Upegui, Yann Thoma
Venue: Proceedings of the 8th international conference on Evolvable Systems: From Biology to Hardware (2008)

This paper presents a flexible agent-oriented programming framework that provides native support for bio-inspired mechanisms. This solution, developed within the Perplexus IST European project [IST-034632] aims at providing a means for the specification of applications running on a distributed and pervasive network of mobile nodes. In such applications, the deployed systems may face time-changing environments and bio-inspiration may prove useful bringing self-adaptability to the system. The presented framework features are demonstrated on a proof-of-concept application made of simple robots that autonomously improve their behaviour over time.
Software Architecture for Extensible Context-Aware Systems
Authors: Jongmyung Choi
Venue: Proceedings of the 2008 International Conference on Convergence and Hybrid Information Technology (2008)

This paper is about a software architecture, named WCAM (Watcher, Controller, Action, and Model), which reduces the complexity of context-aware systems by decoupling of concerns. We first introduce general requirements of context-aware systems. Next, we present our architecture pattern, WCAM. Watcher perceives external environment, and Controller manages the cooperation between Watcher and Model. Model gathers contextual information, interpret the information, and generate contexts for system. Action manages services that are related with contexts.
Automatic Generation of Frequent Case Forms of Query Keywords in Text Retrieval
Authors: Kimmo Kettunen
Venue: Proceedings of the 6th international conference on Advances in Natural Language Processing (2008)

This paper presents implementations of generative management method for morphological variation of query keywords. The method is called FCG, Frequent Case Generation. It is based on the skewed distributions of word forms in natural languages and is suitable for languages that either have fair amount of morphological variation or are morphologically very rich. The paper reports implementation and evaluation of automatic procedures of variant query keyword form generation with short and long queries of CLEF collections for English, Finnish, German and Swedish. The evaluated languages show varying degrees of morphological complexity.
Music Wall: A Tangible User Interface Using Tapping as an Interactive Technique
Authors: Catherine Hu, Kinsun Tung, Lawrence Lau
Venue: Proceedings of the 8th Asia-Pacific conference on Computer-Human Interaction (2008)

This paper describes a tangible user interface that uses taps on a hard surface as the mode of interaction between the user and the information device. The surface is constructed by a matrix of small acoustic sensing modules that work together to track both the location of taps on the surface and the rhythm of tap sequences. Each module is also embedded with LEDs to provide visual feedback and novel lighting effects. The system is a generic input/output system and could be used to interface with digital devices. The present prototype of a music wall is one embodiment of the system, implemented as a digital music player. A function set using position-based tap sequence recognition has also been designed to work with music enjoyment on the system.
iOBS3: An iSCSI-Based Object Storage Security System
Authors: Huang Jianzhong, Xie Changsheng, Li Xu
Venue: Computational Intelligence and Security: International Conference, CIS 2006, Guangzhou, China, November 3-6, 2006, Revised Selected Papers (2007)

With the growing importance of data, networked storage of high performance and strong security has become a hotspot of research. Aiming at the goals, an iSCSI-based object storage security system (iOBS3) adopting OSD command sets has been proposed in this paper. Firstly, incurring to the block I/O channel of iSCSI and the security method within OSD, iOBS3 can achieve high-speed bandwidth of block I/O and access control capability of object I/O; secondly, iOBS3 enhances its transfer security by employing IPSec at IP layer or SSL above TCP layer; thirdly, iOBS3 greatly improves the I/O speed using zero-copy TCP at initiator-side. The experimental results indicate iOBS3 can achieve high throughput, in the meantime guaranteeing both the transmission and storage security.
Artificial Creativity in Linguistics Using Evolvable Fuzzy Neural Networks
Authors: Keith Douglas Stuart, Maciej Majewski
Venue: Proceedings of the 8th international conference on Evolvable Systems: From Biology to Hardware (2008)

The paper describes an application of evolvable fuzzy neural networks for artificial creativity in linguistics. The task of the creation of an English vocabulary was resolved with neural networks which have an evolvable architecture with learning capabilities as well as a fuzzy connectionist structure. The paper features a form of artificial creativity which creates words on its own using genetic algorithms, fuzzy logic methods, and multiple layer neural networks. Tests of the new method are also described.
Translated Poisson Mixture Model for Stratification Learning
Authors: Gloria Haro, Gregory Randall, Guillermo Sapiro
Venue: International Journal of Computer Vision (2008)

A framework for the regularized and robust estimation of non-uniform dimensionality and density in high dimensional noisy data is introduced in this work. This leads to learning stratifications, that is, mixture of manifolds representing different characteristics and complexities in the data set. The basic idea relies on modeling the high dimensional sample points as a process of translated Poisson mixtures, with regularizing restrictions, leading to a model which includes the presence of noise. The translated Poisson distribution is useful to model a noisy counting process, and it is derived from the noise-induced translation of a regular Poisson distribution. By maximizing the log-likelihood of the process counting the points falling into a local ball, we estimate the local dimension and density. We show that the sequence of all possible local countings in a point cloud formed by samples of a stratification can be modeled by a mixture of different translated Poisson distributions, thus allowing the presence of mixed dimensionality and densities in the same data set. With this statistical model, the parameters which best describe the data, estimated via expectation maximization, divide the points in different classes according to both dimensionality and density, together with an estimation of these quantities for each class. Theoretical asymptotic results for the model are presented as well. The presentation of the theoretical framework is complemented with artificial and real examples showing the importance of regularized stratification learning in high dimensional data analysis in general and computer vision and image analysis in particular.
An Analytical Study of the Resource Diffusion in Non-homogeneous P2P Networks
Authors: Daniele Manini, Marco Gribaudo
Venue: Proceedings of the 15th international conference on Analytical and Stochastic Modeling Techniques and Applications (2008)

One of the most popular P2P application is file sharing. Its purpose is to spread out different contents, ranging from multimedia to data and software. In order to understand the dynamics of such application, it is important to study the movement of shared resources across the network. We consider a non-homogeneous distribution of the resources, by grouping peers into Autonomous Systems. We propose a probabilistic model that takes into account the peers behavior, and we exploit it to study the resource diffusion and the impact of freeloaders.