ppxscal/embedding_network huggingface dataset:
A Robust ICA-Based Adaptive Filter Algorithm for System Identification Using Stochastic Information Gradient
Authors: Jun-Mei Yang, Hideaki Sakai
Venue: Neural Information Processing: 14th International Conference, ICONIP 2007, Kitakyushu, Japan, November 13-16, 2007, Revised Selected Papers, Part II (2008)

This paper proposes a new adaptive filter algorithm for system identification by using an independent component analysis (ICA) technique, which separates the signal from noisy observation under the assumption that the signal and noise are independent. We first introduce an augmented state-space expression of the observed signal, representing the problem in terms of ICA. By using a nonparametric Parzen window density estimator and the stochastic information gradient, we derive an adaptive algorithm to separate the noise from the signal. The computational complexity of the proposed algorithm is compared with that of the standard NLMS algorithm. The local convergence is analyzed. Due to the additive noise is also on-line estimated during the iteration, the proposed algorithm shows excellent robustness. It can directly be applied to an acoustic echo canceller without any double-talk detector. Some simulation results are carried out to show the superiority of our ICA method to the conventional NLMS algorithm.
Optimised Meeting Recording and Annotation Using Real-Time Video Analysis
Authors: Paul Chippendale, Oswald Lanz
Venue: Proceedings of the 5th international workshop on Machine Learning for Multimodal Interaction (2008)

The research detailed in this paper represents the confluence of various vision technologies to provide a powerful, real-time tool for human behavioural analysis. Gesture recognition algorithms are amalgamated with a robust multi-person tracker based on particle filtering to monitor the position and orientation of multiple people, and moreover to understand their focus of attention and gesticular activity. Additionally, an integrated virtual video director is demonstrated that can automatically control active cameras to produce an optimum record of visual events in real-time.
Autonomous Parsing of Behavior in a Multi-agent Setting
Authors: Dieter Vanderelst, Emilia Barakova
Venue: Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing (2006)

Imitation learning is a promising route to instruct robotic multi-agent systems. However, imitating agents should be able to decide autonomously what behavior, observed in others, is interesting to copy. Here we investigate whether a simple recurrent network (Elman Net) can be used to extract meaningful chunks from a continuous sequence of observed actions. Results suggest that, even in spite of the high level of task specific noise, Elman nets can be used for isolating re-occurring action patterns in robots. Limitations and future directions are discussed.
A Probabilistic Framework for Building Privacy-Preserving Synopses of Multi-dimensional Data
Authors: Filippo Furfaro, Giuseppe M. Mazzeo, Domenico Saccà
Venue: Proceedings of the 20th international conference on Scientific and Statistical Database Management (2008)

The problem of summarizing multi-dimensional data into lossy synopses supporting the estimation of aggregate range queries has been deeply investigated in the last three decades. Several summarization techniques have been proposed, based on different approaches, such as histograms, wavelets and sampling. The aim of most of the works in this area was to devise techniques for constructing effective synopses, enabling range queries to be estimated, trading off the efficiency of query evaluation with the accuracy of query estimates. In this paper, the use of summarization is investigated in a more specific context, where privacy issues are taken into account. In particular, we study the problem of constructing privacy-preserving synopses, that is synopses preventing sensitive information from being extracted while supporting `safe' analysis tasks. In this regard, we introduce a probabilistic framework enabling the evaluation of the quality of the estimates which can be obtained by a user owning the summary data. Based on this framework, we devise a technique for constructing histogram-based synopses of multi-dimensional data which provide as much accurate as possible answers for a given workload of `safe' queries, while preventing high-quality estimates of sensitive information from being extracted.
A Security Model for Internet-Based Digital Asset Management Systems
Authors: I. Chatzigiannakis, V. Liagkou, D. Salouros, P. Spirakis
Venue: Proceedings of the 2nd European conference on Software Architecture (2008)

Usage and exploitation of the Internet is a critical requirement for managing and distributing valuable digital assets. This requirement introduces a great number of threats for commercial (or not) organizations that may cause huge data and financial losses, harm their reputation as well as people's trust on them. In this paper we present the research challenges for secure digital asset management over the web by proposing a model that provides data safety and secure user interaction on especially demanding on-line collaboration environments.
Discovering Trends in Collaborative Tagging Systems
Authors: Aaron Sun, Daniel Zeng, Huiqian Li, Xiaolong Zheng
Venue: Proceedings of the IEEE ISI 2008 PAISI, PACCF, and SOCO international workshops on Intelligence and Security Informatics (2008)

Collaborative tagging systems (CTS) offer an interesting social computing application context for topic detection and tracking research. In this paper, we apply a statistical approach for discovering topic-specific bursts from a popular CTS - del.icio.us. This approach allows trend discovery from different components of the system such as users, tags, and resources. Based on the detected topic bursts, we perform a preliminary analysis of related burst formation patterns. Our findings indicate that users and resources contributing to the bursts can be classified into two categories: old and new, based on their past usage histories. This classification scheme leads to interesting empirical findings.
Modeling how humans reason about others with partial information
Authors: Sevan G. Ficici, Avi Pfeffer
Venue: Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1 (2008)

Computer agents participate in many collaborative and competitive multiagent domains in which humans make decisions. For computer agents to interact successfully with people in such environments, an understanding of human reasoning is beneficial. In this paper, we investigate the question of how people reason strategically about others under uncertainty and the implications of this question for the design of computer agents. Using a situated partial-information negotiation game, we conduct human-subjects trials to obtain data on human play. We then construct a hierarchy of models that explores questions about human reasoning: Do people explicitly reason about other players in the game? If so, do people also consider the possible states of other players for which only partial information is known? Is it worth trying to capture such reasoning with computer models and subsequently utilize them in computer agents? We compare our models on their fit to collected data. We then construct computer agents that use our models in one of two ways: emulating human behavior and playing best response to the model. After building our agents, we deploy them in further human-subjects trials for evaluation. Our results indicate that people do reason about other players in our game and also reason under uncertainty. Better models are shown to yield more successful computer agents.
Ontology-Driven Approximate Duplicate Elimination of Postal Addresses
Authors: Matteo Cristani, Alessio Gugole
Venue: Proceedings of the 21st international conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: New Frontiers in Applied Artificial Intelligence (2008)

In several common real-life cases of usage of postal address databases an important problem that is often necessary to solve is the one of duplicate elimination. This may occur because a database of addresses is merged to another one, for instance during a joint-venture or a fusion between two companies, so that two or more than two addresses are the same.Though a trivial approach based upon identification can be used in principle, this attempt would indeed fail in any concrete case, in particular for postal addresses, because the same address can be written in several different ways so that an approximate approach can be adopted successfully, under the condition that the duplicate elimination is correctly performed. We identify an ontology-driven approach for postal addresses which solves the problem in an approximate fashion. The algorithm is based upon a modification of the Levenshtein distance, obtained by introducing the notion of admissible abbreviation, and has a threefold outcome: eliminate duplicates, do not eliminate duplicates, undecided.
Exploiting Ontology Structure and Patterns of Annotation to Mine Significant Associations between Pairs of Controlled Vocabulary Terms
Authors: Woei-Jyh Lee, Louiqa Raschid, Hassan Sayyadi, Padmini Srinivasan
Venue: Proceedings of the 5th international workshop on Data Integration in the Life Sciences (2008)

There is significant knowledge captured through annotations on the life sciences Web. In past research, we developed a methodology of support and confidence metrics from association rule mining, to mine the association bridge (of termlinks) between pairs of controlled vocabulary (CV) terms across two ontologies. Our (naive) approach did not exploit the following: implicit knowledge captured via the hierarchical is-a structure of ontologies, and patterns of annotation in datasets that may impact the distribution of parent/child or sibling CV terms. In this research, we consider this knowledge. We aggregate termlinks over the siblings of a parent CV term and use them as additional evidence to boost support and confidence scores in the associations of the parent CV term. A weight factor (¿) reflects the contribution from the child CV terms; its value can be varied to reflect a variance of confidence values among the sibling CV terms of some parent CV term. We illustrate the benefits of exploiting this knowledge through experimental evaluation.
Clustering Uncertain Data Via K-Medoids
Authors: Francesco Gullo, Giovanni Ponti, Andrea Tagarelli
Venue: Proceedings of the 2nd international conference on Scalable Uncertainty Management (2008)

Uncertain data are usually represented in terms of an uncertainty region over which a probability density function (pdf) is defined. In the context of uncertain data management, there has been a growing interest in clustering uncertain data. In particular, the classic K-means clustering algorithm has been recently adapted to handle uncertain data. However, the centroid-based partitional clustering approach used in the adapted K-means presents two major weaknesses that are related to: (i) an accuracy issue, since cluster centroids are computed as deterministic objects using the expected values of the pdfs of the clustered objects; and, (ii) an efficiency issue, since the expected distance between uncertain objects and cluster centroids is computationally expensive.In this paper, we address the problem of clustering uncertain data by proposing a K-medoids-based algorithm, called UK-medoids, which is designed to overcome the above issues. In particular, our UK-medoids algorithm employs distance functions properly defined for uncertain objects, and exploits a K-medoids scheme. Experiments have shown that UK-medoids outperforms existing algorithms from an accuracy viewpoint while achieving reasonably good efficiency.
Robust Image Watermarking Scheme with General Regression Neural Network and FCM Algorithm
Authors: Li Jing, Fenlin Liu, Bin Liu
Venue: Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Theoretical and Methodological Issues (2008)

A robust image watermarking method based on general regression neural network (GRNN) and fuzzy c-mean clustering algorithm (FCM) is proposed. In order to keep the balance between robustness and imperceptible, it uses FCM to adaptively identify watermarking embedding locations and strength based on four characteristic parameters of human visual system. For good learning ability and fast train speed of GRNN, it trains a GRNN with the feature vector based on the local correlation of digital image. Then embed and extracted watermark signal with the help of the trained GRNN. In watermark extracting it does not need original image. Experimental results show that the proposed method has better performance than the similar method in countering common image process, such as Jpeg compression, noise adding, filtering and so on.
Channel Time Allocation and Routing Algorithm for Multi-hop Communications in IEEE 802.15.3 High-Rate WPAN Mesh Networks
Authors: Ssang-Bong Jung, Hyun-Ki Kim, Soon-Bin Yim, Tae-Jin Lee
Venue: Proceedings of the 7th international conference on Computational Science, Part IV: ICCS 2007 (2007)

IEEE 802.15.3 High-rate Wireless Personal Area Networks (WPANs) have been developed to communicate with devices within 10m at high speed. A mesh network made up of a parent piconet and several child piconets can support multi-hop communications. In this paper, we propose an efficient Channel Time Allocation (CTA) method and routing algorithm for multi-hop communications in IEEE 802.15.3 high-rate WPAN mesh networks. The proposed CTA allocation method allows sufficient CTA allocation for the relay PNCs. The proposed routing algorithm is tree-based, and the routing tables of parent PNCs and child PNCs are created by efficient device discovery process to find the shortest paths via parent PNCs or subordinate child PNCs. We evaluate the performance of the proposed algorithms via simulations. The proposed algorithms are shown to extend the communication range by multi-hop exchange of packets and to provide sufficient Channel Time Allocations (CTAs) for parent and child PNCs.
Planning the Behaviour of a Social Robot Acting as a Majordomo in Public Environments
Authors: Berardina Carolis, Giovanni Cozzolongo
Venue: Proceedings of the 10th Congress of the Italian Association for Artificial Intelligence on AI*IA 2007: Artificial Intelligence and Human-Oriented Computing (2007)

In this paper we propose the use of a social robot as a majordomo interface between users and Smart Environments. We focus, in particular, on the need for the robot to plan its behaviour by taking into account factors that are relevant in public environments in which the robot has a "social" role. In this context, once the situation has been recognized, the robot uses a probabilistic model to trigger social attitudes towards the user that influence, as a consequence, the activation of its high-level goals. According to the most probable goals, the behaviour plan is computed by applying a utility-based approach that allows selecting the most convenient actions in that situation.
Congenital Heart Disease: An Ontology-Based Approach for the Examination of the Cardiovascular System
Authors: M. Esposito
Venue: Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part I (2008)

Congenital Heart Disease (CHD) represents the most common group of congenital malformations of the heart and of its blood vessels. In this paper, we present an ontology-based approach to detect abnormalities and malformations due to CHD. In particular, we propose a formal and well-defined model to represent the anatomy of the cardiovascular system, based on the SNOMED vocabulary. The model defines either the anatomy of the cardiovascular system in normal patients or the anatomy characterized by malformations and abnormalities in CHD patients. We have formalized this model in OWL ontologies and SWRL rules and, then, we have used a logic reasoner to identify either CHD patients or the heart abnormalities and malformations they are affected by.
The nature of words in human protolanguages: It's not a holophrastic-atomic meanings dichotomy
Authors: Mike Dowman
Venue: Artificial Life (2008)

There is an ongoing debate as to whether the words in early presyntactic forms of human language had simple atomic meanings like modern words, or whether they were holophrastic. Simulations were conducted using an iterated learning model in which the agents were able to associate words with meanings, but in which they were not able to use syntactic rules to combine words into phrases or sentences. In some of these simulations words emerged that had neither holophrastic nor atomic meanings, demonstrating the possibility of protolanguages intermediate between these two extremes. Further simulations show how increases in cognitive or articulatory capacity would have produced changes in the type of words that was dominant in protolanguages. It is likely that at some point in time humans spoke a protolanguage in which most words had neither holophrastic nor atomic meanings.
Snow, buses, and mobile data services in the information age
Authors: Vladislav V. Fomin
Venue: The Journal of Strategic Information Systems (2008)

This paper examines the situation of non-use of mobile data and voice services. The observed behavior is at odds with positivist socio-economic theories and commonsensical understanding of the possibilities brought about by the ubiquitous information and communications technology environment. Using a multi-method and theoretical framework designed to combine positivist and interpretative approaches, we attempt at developing new insights into the inter-related roles of ICT and consumer behavior in real-life situations. The findings challenge the commonsensical rhetoric on the advancement of Information Society and provide insights for the design of useful information and communication services.
Two-Stage Robust Network Design with Exponential Scenarios
Authors: Rohit Khandekar, Guy Kortsarz, Vahab Mirrokni, Mohammad R. Salavatipour
Venue: Proceedings of the 16th annual European symposium on Algorithms (2008)

We study two-stage robust variants of combinatorial optimization problems like Steiner tree, Steiner forest, and uncapacitated facility location. The robust optimization problems, previously studied by Dhamdhere et al. [1], Golovin et al. [6], and Feige et al. [4], are two-stage planning problems in which the requirements are revealed after some decisions are taken in stage one. One has to then complete the solution, at a higher cost, to meet the given requirements. In the robust Steiner tree problem, for example, one buys some edges in stage one after which some terminals are revealed. In the second stage, one has to buy more edges, at a higher cost, to complete the stage one solution to build a Steiner tree on these terminals. The objective is to minimize the total cost under the worst-case scenario. In this paper, we focus on the case of exponentially many scenarios given implicitly. A scenario consists of any subset of k terminals (for Steiner tree), or any subset of k terminal-pairs (for Steiner forest), or any subset of k clients (for facility location). We present the first constant-factor approximation algorithms for the robust Steiner tree and robust uncapacitated facility location problems. For the robust Steiner forest problem with uniform inflation, we present an O(logn)-approximation and show that the problem with two inflation factors is impossible to approximate within O(log1/2 ¿ ¿ n) factor, for any constant ¿> 0, unless NP has randomized quasi-polynomial time algorithms. Finally, we show APX-hardness of the robust min-cut problem (even with singleton-set scenarios), resolving an open question by [1] and [6].
A lower bound for the length of a partial transversal in a Latin square
Authors: Pooya Hatami, Peter W. Shor
Venue: Journal of Combinatorial Theory Series A (2008)

It is proved that every nxn Latin square has a partial transversal of length at least n-O(log^2n). The previous papers proving these results (including one by the second author) not only contained an error, but were sloppily written and quite difficult to understand. We have corrected the error and improved the clarity.
Application and development of new learning methodologies for fmri data analysis
Authors: Vladimir Cherkassky
Venue:  (2007)

Statistical and data-driven methodologies have been of growing interest in medical and biomedical applications. In fMRI data analysis, such techniques are helpful for improved understanding of the brain function. Due to finite sample size, future success in this area is limited by two fundamental factors: (a) high dimensionality of the input data and (b) heterogeneous nature of the input data.The main practical motivation for this project is to incorporate various heterogeneous types of information into intelligent data-driven systems for several common fMRI data-analytic problems, such as activation detection and cognitive state classification. Activation detection aims to locate brain regions that are related to a specific cognitive task; cognitive state classification means to infer cognitive states from fMRI images. We aim to improve understanding of different predictive learning approaches appropriate for these problems. To this end, we investigate traditional learning approaches (based on standard inductive learning setting), as well as new emerging learning approaches such as SVM+ and multi-task learning (MTL). For an activation problem, we propose spatial SVM which incorporates two unique characteristics of fMRI data: spatial contiguity and BOLD effect. For the problem of cognitive state classification, we investigate new learning methodology called SVM+ (Vapnik, 2006). SVM+ is a promising approach for heterogeneous fMRI data. However, current understanding of SVM+ is very limited as there are no empirical studies comparing this approach with other existing standard learning methods. In addition, our work extends SVM+ approach to Multi-Task Learning (MTL) setting. This extension leads to a new learning algorithm dubbed svm+MTL. Both SVM+ and svm+MTL have been empirically validated using a real-life fMRI data from a cognitive state classification experiment.
Evaluation of Measurement Techniques for the Validation of Agent-Based Simulations Against Streaming Data
Authors: Timothy W. Schoenharl, Greg Madey
Venue: Proceedings of the 8th international conference on Computational Science, Part III (2008)

This paper presents a study evaluating the applicability of several different measures to the validation of Agent-Based Modeling simulations against streaming data. We evaluate the various measurements and validation techniques using pedestrian movement simulations used in the WIPER system. The Wireless Integrated Phone-based Emergency Response (WIPER) system is a Dynamic Data-Driven Application System (DDDAS) that uses a stream of cellular network activity to detect, classify and predict crisis events. The WIPER simulation is essential to classification and prediction tasks, as the simulations model human activity, both in movement and cell phone activity, in an attempt to better understand crisis events.