ppxscal/embedding_network huggingface dataset:
Learning motor trajectory by iteration for elastic joint industrial robot manipulators
Authors: Tae-Jun Ha, Jesung Yeon, Jong-Hyeon Park, Sanghoon Lee
Venue: Proceedings of the 9th WSEAS International Conference on International Conference on Automation and Information (2008)

This paper proposes a process to design an iterative learning control (ILC) to be applied to flexible joint robot manipulators which repeats identical tasks under the same operation conditions. The proposed ILC updates the desired trajectory not the feedback control signals in order to avoid potentially destabilizing embedded controllers of the robot manipulators. To guarantee the convergence of tracking errors, two types of filters are introduced. One is motor encoder to get real time motor state and the other is an external sensor to collect link state for off line process. System transfer function is required during filter design process to achieve stability in the iteration domain. The estimated system transfer function is used to make sure the stability of the designed filters. Simulation is done with a 2-link flexible joint robot manipulator. The simulation shows that joint errors converge to zero as the learning process proceeds.
Generalized latent semantic analysis for document representation
Authors: Gina-Anne Levow, Partha Niyogi
Venue:  (2008)

Document representation has a large impact on the performance of document retrieval and clustering algorithms. Since content words can be combined into semantic classes there has been a considerable interest in low-dimensional term and document representations. We developed the Generalized Latent Semantic Analysis (GLSA) framework to extend the applicability of the idea of the latent semantic space analysis with spectral embedding. GLSA uses linguistically motivated measures of similarity between words and employs different dimensionality reduction techniques to preserve them. The main advantage of the GLSA representation for words and documents is the metric in the resulting low-dimensional space. Cosine and inner product similarities in the GLSA space are linguistically motivated so that GLSA improves the performance of many approaches which are based on pair-wise similarities. Most language modelling and dimensionality reduction approaches are applied to the full vocabulary. However, semantics and syntax provide a theoretical justification for treating subsets of the vocabulary differently. Moreover, the theoretical justification of GLSA suggests that it is most appropriate to apply it to a subset of the vocabulary comprised of content words. Therefore, we proposed to use GLSA with a hybrid approach to document indexing. Our goal was to keep the notion of latent semantic concepts and at the same time to preserve the specifics of the document collection. We evaluated the GLSA representations on a number of tasks. We evaluated the GLSA representation for words on the synonymy test and the term pairs test; we evaluated the GLSA indexing and hybrid document indexing with GLSA on document classification, document clustering and text segmentation tasks. Our experiments showed that GLSA improves the results of the similarity based document classification and clustering algorithms and text segmentation algorithms. GLSA significantly outperformed the baseline and LSA on all applications that we used in our experiments.
Decentralized resource sharing and multimedia workflow processing
Authors: Gi Sik Kwon
Venue:  (2008)

Peer-to-Peer (P2P) computing has been gaining popularity due to the decentralized organization, high scalability, and the manipulation of abundant resources at the edge machines. Of many P2P applications, file sharing is the most ubiquitous with multiple implementations where users of P2P networks can find the files of interest from other computers on the network and download them locally. Most of P2P file sharing systems search the files using either simple flooding or indexed routing with the complex structures, such as the Distributed Hash Table (DHT). However, the file locating performance in these approaches turns out to be poor without a careful consideration of the machine heterogeneity and the network hierarchy. From this observation preliminary works introduce two asymmetric file sharing systems for large-scale P2P networks. Both systems provide not only efficient and deterministic file location, but also self-organization and load balancing by exploiting underlying network topology. This thesis extends the researches to the dynamic service composition in which multimedia workflow processing systems like ARchitecture for Interactive Arts (ARIA) organize workflow operators in a purely decentralized manner. The traditional approach involves a centralized data warehouse, permitting the extensive resource provisioning and replication to manage server load. Recent works propose to leverage routing paths in the DHT to obtain a set of candidate nodes for service placement. However the DHT is not designed to optimize workflow processing in overlay networks, yielding small candidate set sizes for service placement. This work proposes a noble Decentralized, Autonomous and Network-wide service workflow processing System (DANS), which explores DHT-based P2P substrates to lookup and publish the underlying peer information. This system achieves network adaptivity by using sender-initiated operator mapping, as opposed to the receiver-initiation used in most current systems, eliminating pre-establishment of workflow networks. In addition, DANS supports multi-constrained workflow execution. To this end this thesis solves the nested chain workflow problem based on probabilistic models which select qualified service paths enforcing Quality of Service (QoS) metrics, while requiring low bandwidth consumption and sharing processing workloads.
Feature Subset Selection Using Constructive Neural Nets with Minimal Computation by Measuring Contribution
Authors: Md. Monirul Kabir, Md. Shahjahan, Kazuyuki Murase
Venue: Neural Information Processing: 14th International Conference, ICONIP 2007, Kitakyushu, Japan, November 13-16, 2007, Revised Selected Papers, Part I (2007)

In this paper we propose a new approach to select feature subset based on contribution of input attributes in a three-layered feedforward neural network (NN). Three techniques: constructive, contribution, and backward elimination are integrated together in this method. Initially, to determine the minimal NN architecture, the number of hidden neurons is determined by a constructive approach. After that, one-by-one removal of input attributes is performed to compute their contribution. Finally, a sequential backward elimination is used to generate relevant feature subset from the original input space. The elimination process is continued depending on a criterion. To evaluate the proposed method, we applied it to four real-world benchmark problems. Experimental results confirmed that, the proposed method significantly reduces the irrelevant features without degrading the network performance and generates the feature subset with good generalization ability.
The Open Three Consortium: An Open-Source Initiative at the Service of Healthcare and Inclusion
Authors: Paolo Inchingolo
Venue: Medical Imaging and Informatics: 2nd International Conference, MIMI 2007, Beijing, China, August 14-16, 2007 Revised Selected Papers (2008)

The Higher Education in Clinical Engineering (HECE) of the University of Trieste constituted in 2005 the Open Three Consortium (O3), an innovative open-source project dealing with the multi-centric integration of hospitals, RHIOs (Regional health information organizations) and citizens (care at home and on the move, and ambient assisted living), based on about 60 HECE bilateral cooperation Agreements with Hospitals, Medical Research Centers, Healthcare Enterprises, Industrial Enterprises and Governmental Agencies and on the International Networks ABIC-BME (Adriatic Balcanic Ionian Cooperation on Biomedical Engineering) and ALADIN (Alpe Adria Initiative Universities' Network). The collaboration with multiple open-source solutions has been extended, starting an international cooperation with the open-source based company Sequence Managers Software, Raleigh, NC, United States. The O3 Consortium proposes e-inclusive citizen-centric solutions to cover the above reported three main aspects of the future of e-health in Europe with open-source strategies joined to full-service maintenance and management models. The Users' and Developers' O3 Consortium Communities are based mainly on the HECE agreements.
New Form of Permutation Bias and Secret Key Leakage in Keystream Bytes of RC4
Authors: Subhamoy Maitra, Goutam Paul
Venue: Fast Software Encryption: 15th International Workshop, FSE 2008, Lausanne, Switzerland, February 10-13, 2008, Revised Selected Papers (2008)

Consider the permutation S in RC4. Roos pointed out in 1995 that after the Key Scheduling Algorithm (KSA) of RC4, each of the initial bytes of the permutation, i.e., S[y] for small values of y, is biased towards some linear combination of the secret key bytes. In this paper, for the first time we show that the bias can be observed in S[S[y]] too. Based on this new form of permutation bias after the KSA and other related results, a complete framework is presented to show that many keystream output bytes of RC4 are significantly biased towards several linear combinations of the secret key bytes. The results do not assume any condition on the secret key. We find new biases in the initial as well as in the 256-th and 257-th keystream output bytes. For the first time biases at such later stages are discovered without any knowledge of the secret key bytes. We also identify that these biases propagate further, once the information for the index j is revealed.
Decimv2
Authors: Côme Berbain, Olivier Billet, Anne Canteaut, Nicolas Courtois, Blandine Debraize, Henri Gilbert, Louis Goubin, Aline Gouget, Louis Granboulan, Cédric Lauradoux, Marine Minier, Thomas Pornin, Hervé Sibert
Venue: New Stream Cipher Designs: The eSTREAM Finalists (2008)

In this paper, we present Decim v2, a stream cipher hardware-oriented selected for the Phase 3 of the ECRYPT stream cipher project eSTREAM. As required by the initial call for hardware-oriented stream cipher contribution, Decim v2 manages 80-bit secret keys and 64-bit public initialization vectors. The design of Decim v2 combines two filtering mechanisms: a nonlinear Boolean filter over a LFSR, followed by an irregular decimation mechanism called the ABSG. Since designers have been invited to demonstrate flexibility of their design by proposing variants that take 128-bit keys, we also present a 128-bit security version of Decim called Decim-128.
Online chasing problems for regular polygons
Authors: Hiroshi Fujiwara, Kazuo Iwama, Kouki Yonezawa
Venue: Information Processing Letters (2008)

We consider a server location problem with only one server to move. In this paper we assume that a request is given as a region and that the service can be done anywhere inside the region. Namely, for each request an online algorithm chooses an arbitrary point in the region and moves the server there. Note that if every request is a single point and the server must exactly go there in the given order as conventional server problems, there is no choice for the online player and the problem is trivial. Our main result shows that if the region is a regular n-gon, the competitive ratio of the greedy algorithm is 1/sin@p2n for odd n, and 1/sin@pn for even n. In particular for a square region, the greedy algorithm turns out to be optimal.
Pillow: Interactive Flattening of a 3D Model for Plush Toy Design
Authors: Yuki Igarashi, Takeo Igarashi
Venue: Proceedings of the 9th international symposium on Smart Graphics (2008)

Pillow is an interactive pattern-design system for creating plush toys. The user imports a 3D surface model into the system and specifies the segmentation boundaries interactively by drawing seam lines on the model surface. Based on this segmentation, the system generates a 2D pattern by flattening each outlined region, and then visualizes the shape of the resulting plush toy by applying a simple physics simulation. If the result is not satisfactory, the user can make different seam lines. This closed-loop framework allows users to experiment with various seam patterns before actually working on real fabric to obtain the best-looking result. The system also estimates total sewing time based on the total length of the seam lines. We implemented the system to produce plush toys and a balloon.
A Context-Aware Movie Preference Model Using a Bayesian Network for Recommendation and Promotion
Authors: Chihiro Ono, Mori Kurokawa, Yoichi Motomura, Hideki Asoh
Venue: Proceedings of the 11th international conference on User Modeling (2007)

This paper proposes a novel approach for constructing users' movie preference models using Bayesian networks. The advantages of the constructed preference models are 1) consideration of users' context in addition to users' personality, 2) multiple applications, such as recommendation and promotion. Data acquisition process through a WWW questionnaire survey and a Bayesian network model construction process using the data are described. The effectiveness of the constructed model in terms of recommendation and promotion is also demonstrated through experiments.
6DOF Whole-Hand Haptic Interaction System Based on Hybrid Grasping Approach
Authors: Yongwan Kim, Yongseok Jang, Wookho Son, Jinah Park
Venue: Proceedings of the 6th international conference on Haptics: Perception, Devices and Scenarios (2008)

Although haptic interaction which provides direct touch sensation is the most natural way of interacting with VR environment, existing haptic solutions are not yet fully satisfying due to the interaction problems such as the hand penetration, frictionlessness, lack of visual feedback, and so on. Among these problems, the hand penetration problem which is an effect sinking into a virtual object is the most visually distracting artifact in grasping, and it is mainly caused by the incomplete physical capabilities of haptic devices and poor grasping manipulation. To address this problem, we have developed a realistic haptic interaction system, which consists of the new 6DOF whole-hand hardware combined with a glove-type device, and propose a hybrid grasping method which intelligently uses both heuristic and physics-based approach.
Toward Recommendation Based on Ontology-Powered Web-Usage Mining
Authors: Rokia Missaoui, Petko Valtchev, Chabane Djeraba, Mehdi Adda
Venue: IEEE Internet Computing (2007)

Content adaptation on the Web reduces available information to a subset that matches a user's anticipated needs. Recommender systems rely on relevance scores for individual content items; in particular, pattern-based recommendation exploits co-occurrences of items in user sessions to ground any guesses about relevancy. To enhance the discovered patterns' quality, the authors propose using metadata about the content that they assume is stored in a domain ontology. Their approach comprises a dedicated pattern space built on top of the ontology, navigation primitives, mining methods, and recommendation techniques.
A generalized data meta-model for production companies
Authors: César Bravo, Jose Aguilar, Addison Ríos-Bolivar, Joseph Aguilar-Martin, Francklin Rivas-Echeverría
Venue: Proceedings of the American Conference on Applied Mathematics (2008)

This work presents a data Meta-model which defines, in a generic way, the representation of production companies' business objects. The model aims to define an ontology framework for facilitating the interoperability between industrial applications and systems. This proposal is based on PRODML and PROSA, which are well know specifications; the first one at oil production applications integration, and the other one at manufacturing automation architecture.
Approximately Counting Embeddings into Random Graphs
Authors: Martin Fürer, Shiva Prasad Kasiviswanathan
Venue: Proceedings of the 11th international workshop, APPROX 2008, and 12th international workshop, RANDOM 2008 on Approximation, Randomization and Combinatorial Optimization: Algorithms and Techniques (2008)

Let H be a graph, and let C H (G) be the number of (subgraph isomorphic) copies of H contained in a graph G. We investigate the fundamental problem of estimating C H (G). Previous results cover only a few specific instances of this general problem, for example, the case when H has degree at most one (monomer-dimer problem). In this paper, we present the first general subcase of the subgraph isomorphism counting problem which is almost always efficiently approximable. The results rely on a new graph decomposition technique. Informally, the new decomposition is a labeling of the vertices generating a sequence of bipartite graphs. The decomposition permits us to break the problem of counting embeddings of large subgraphs into that of counting embeddings of small subgraphs. Using this, we present a simple randomized algorithm for the counting problem. For all decomposable graphs H and all graphs G, the algorithm is an unbiased estimator. Furthermore, for all graphs H having a decomposition where each of the bipartite graphs generated is small and almost all graphs G, the algorithm is a fully polynomial randomized approximation scheme.We show that the graph classes of H for which we obtain a fully polynomial randomized approximation scheme for almost all G includes graphs of degree at most two, bounded-degree forests, bounded-width grid graphs, subdivision of bounded-degree graphs, and major subclasses of outerplanar graphs, series-parallel graphs and planar graphs, whereas unbounded-width grid graphs are excluded.
Supporting the Construction of Spanish Legal Ontologies with Text2Onto
Authors: Johanna Völker, Sergi Fernandez Langa, York Sure
Venue: Computable Models of the Law: Languages, Dialogues, Games, Ontologies (2008)

The IST project SEKT (Semantically Enabled Knowledge Technologies) aims at developing semantic technologies by integrating knowledge management, text mining, and human language technology. Tools and methodologies implemented in the SEKT project are employed and optimized in three case studies, one of them being concerned with intelligent integrated decision support for legal professionals. The main goal of this case study is to offer decision support to newly appointed judges in Spain by means of iFAQ, an intelligent Frequently Asked Questions system based on a complex ontology of the legal domain. Building this ontology is a tedious and time-consuming task requiring profound knowledge of legal documents and language. Therefore, any kind of automatic support can significantly increase the efficiency of the knowledge acquisition process. In this paper we present Text2Onto, an open-source tool for ontology learning, and our experiments with legal case study data. The previously existing English version of Text2Onto has been adapted to support the linguistic analysis of Spanish texts, including language-specific algorithms for the extraction of ontological concepts, instances and relations. Text2Onto greatly facilitated the automatic generation of the initial version of the Spanish legal ontology from a given collection of Spanish documents. In further iterative steps which included a mixture of learning and manual effort the ontology has been refined and applied to the real-world case study.
NN-Based Near Real Time Load Prediction for Optimal Generation Control
Authors: Dingguo Chen
Venue: Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks, Part II (2008)

In the environment of ongoing deregulated power industry, traditional automatic generation control (AGC) has become a set of ancillary services traded in separate markets which are different than the energy market. The performance of AGC is mandated to meet the NERC control performance standards (CPS). The new CPS criteria allow the over-compliant power utilities to loosen control of their generating units. The competition introduced by the deregulation process provides the opportunities for the over-compliant power utilities to sell their excess regulating capabilities. In addition, load following service is often priced lower than regulation service. All these lead generation companies to optimizing the portfolio of their generating assets to achieve better economy. The optimization process involves economic allocation of generation over a consecutive set of time intervals, which requires the load profile to be predicted for the dispatch period of minute level. This paper addresses the importance of very short term load prediction in this context, and proposes a new approach to make load predictions. Procedures involved in this approach are presented. Case studies are presented to demonstrate the effectiveness of the proposed approach.
Computational Humour: Utilizing Cross-Reference Ambiguity for Conversational Jokes
Authors: Hans Wim Tinholt, Anton Nijholt
Venue: Proceedings of the 7th international workshop on Fuzzy Logic and Applications: Applications of Fuzzy Sets Theory (2007)

This paper presents a computer implementation that utilizes cross-reference ambiguity in utterances for simple conversational jokes. The approach is based on the SSTH. Using a simple script representation, it is shown that cross-reference ambiguities always satisfy the SSTH requirement for script overlap. To determine whether script opposition is present, we introduce a method that compares the concepts involved based on their semantic properties. When a given cross-reference ambiguity results in script opposition it is possible to generate a punchline based on this ambiguity. As a result of the low performance of the anaphora resolution algorithm and the data sparseness in ConceptNet the application performs moderately, but it does provide future prospects in generating conversational humour.
New bounds on binary identifying codes
Authors: Geoffrey Exoo, Tero Laihonen, Sanna Ranto
Venue: Discrete Applied Mathematics (2008)

The original motivation for identifying codes comes from fault diagnosis in multiprocessor systems. Currently, the subject forms a topic of its own with several possible applications, for example, to sensor networks. In this paper, we concentrate on identification in binary Hamming spaces. We give a new lower bound on the cardinality of r-identifying codes when r>=2. Moreover, by a computational method, we show that M"1(6)=19. It is also shown, using a non-constructive approach, that there exist asymptotically good (r,@?@?)-identifying codes for fixed @?>=2. In order to construct (r,@?@?)-identifying codes, we prove that a direct sum of r codes that are (1,@?@?)-identifying is an (r,@?@?)-identifying code for @?>=2.
Analyzing the Behavior of the SOM through Wavelet Decomposition of Time Series Generated during Its Execution
Authors: Víctor Mireles, Antonio Neme
Venue: Proceedings of the 18th international conference on Artificial Neural Networks, Part I (2008)

Cluster analysis applications of the SOM require it to be sensible to features, or groupings, of different sizes in the input data. On the other hand, the SOM's behavior while the organization process is taking place also exhibits regularities of different scales, such as periodic behaviors of different frequencies, or changes of different magnitudes in the weight vectors. A method based on the discrete wavelet transform is proposed for measuring the diversity of the scales of regularities, and this diversity is compared to the performance of the SOM. We argue that if this diversity of scales is high then the algorithm is more likely to detect differently sized features of data.