ppxscal/embedding_network huggingface dataset:
Using unlabeled data to improve classification in the naive bayes approach: Application to web searches
Authors: Stella M. Salvatierra
Venue: Journal of Computational Methods in Sciences and Engineering (2004)

This paper introduces a method to build a classifier based on labelled and unlabelled data. We set up the Expectation-Maximization (EM) algorithm steps for the particular case of the naive Bayes approach and show empirical work for the restricted web page database. Original contributions includes the application of the EM algorithm to simulated data in order to see the behavior of the algorithm for different numbers of labelled and unlabelled data, and to study the effect of the sampling mechanism for the unlabelled data on the results.
Video Semantic Content Analysis Framework Based on Ontology Combined MPEG-7
Authors: Liang Bai, Songyang Lao, Weiming Zhang, Gareth J. Jones, Alan F. Smeaton
Venue: Adaptive Multimedial Retrieval: Retrieval, User, and Semantics: 5th International Workshop, AMR 2007, Paris, France, July 5-6, 2007 Revised Selected Papers (2007)

The rapid increase in the available amount of video data is creating a growing demand for efficient methods for understanding and managing it at the semantic level. New multimedia standard, MPEG-7, provides the rich functionalities to enable the generation of audiovisual descriptions and is expressed solely in XML Schema which provides little support for expressing semantic knowledge. In this paper, a video semantic content analysis framework based on ontology combined MPEG-7 is presented. Domain ontology is used to define high level semantic concepts and their relations in the context of the examined domain. MPEG-7 metadata terms of audiovisual descriptions and video content analysis algorithms are expressed in this ontology to enrich video semantic analysis. OWL is used for the ontology description. Rules in Description Logic are defined to describe how low-level features and algorithms for video analysis should be applied according to different perception content. Temporal Description Logic is used to describe the semantic events, and a reasoning algorithm is proposed for events detection. The proposed framework is demonstrated in sports video domain and shows promising results.
A UML/SPT Model Analysis Methodology for Concurrent Systems Based on Genetic Algorithms
Authors: Marwa Shousha, Lionel Briand, Yvan Labiche
Venue: Proceedings of the 11th international conference on Model Driven Engineering Languages and Systems (2008)

Concurrency problems, such as deadlocks, should be identified early in the design process. This is made increasingly difficult as larger and more complex concurrent systems are being developed. We propose here an approach, based on the analysis of specific models expressed in the Unified Modeling Language (UML) that uses a specifically designed genetic algorithm to detect deadlocks. Our main motivations are (1) to devise practical solutions that are applicable in the context of UML design without requiring additional modeling and (2) to achieve scalable automation. All relevant concurrency information is extracted from systems' UML models that comply with the UML Schedulability, Performance and Time profile, a standardized specialization of UML for real-time, concurrent systems. Our genetic algorithm is then used to search for execution sequences exhibiting deadlocks. Results on three case studies show that our approach can achieve efficient results.
Synthetic Environments for Cooperative Product Design
Authors: Egon L. Broek, Frank Meijer, Jan Miedema, Huaxin Wang, Mascha C. Voort, Joris S. Vergeest
Venue: Proceedings of the 5th international conference on Cooperative Design, Visualization, and Engineering (2008)

Synthetic Environments (SE) facilitate an easy setup of various virtual realities since they are component-based and relatively easy maintainable. Through three stages, involving various types of stakeholders, the feasibility of SEs for cooperative product design (CPD) was explored: 1) Semi-structured group interviews with 19 engineers and designers assessed SE's potential for CPD; 2) Implementation of a SE; 3) Comparison of a SE with its real counterpart, through: a) an experimental task and b) questionnaires to determine the task performance, the mental workload, the experienced spatial presence, involvement, and realness. 16 participants had a similar task performance, mental workload, and involvement but differed in experienced realism (F(1,30)=5.11, p=.03) and spatial presence (F(1,30)=7.02, p=.01). This research emphasizes the benefit of SE for CPD, which can increase the communication, speed and quality of CPD. Hence, a first step is made towards a new era of CPD.
Mobile Code for Coordination
Authors: Alexei Iliasov
Venue: Proceedings of the 2008 The Third International Multi-Conference on Computing in the Global Information Technology (iccgi 2008) (2008)

An effective coordination mechanism is central to building large distributed systems. Overcoming limitations of a coordination language can be a major challenge for developers of distributed systems. This paper presents a novel coordination mechanism based on code mobility. In this mechanism coordination is realised by exchanging pieces of mobile code between coordinated entities. This approach overcomes constrains of a statically defined coordination language by allowing coordinated entities to dynamically redefine coordination rules and use all the power of a programming language to express such rules. This helps to implement sophisticated and efficient data retrieval and processing algorithms and also reduce network traffic. The paper also discusses a non-language based coordination framework specially developed to support coordination based on mobile code and a pragmatic solution to the problem of protecting hosts from potentially harmful mobile code. A proof of concept implementation demonstrates the feasibility and applicability of the proposal.
Robots in Smart Spaces - A Case Study of a u-Object Finder Prototype -
Authors: Tomomi Kawashima, Jianhua Ma, Bernady O. Apduhan, Runhe Huang, Qun Jin
Venue: Proceedings of the 5th international conference on Ubiquitous Intelligence and Computing (2008)

A smart space is a physical spatial environment such as a room that can provide automatic responses according to the contextual information occurring in an environment. The context data is usually acquired via various devices which are installed somewhere in the environment and/or embedded into some special physical objects, called u-objects, which are capable of executing computation and/or communication. However, the devices used in current researches on smart space are either fixed or residing in real objects, which are incapable of moving by themselves. Likewise, more and more robots have been produced, but most of them are designed and developed based on the assumption that the space surrounding a robot is an ordinary one, i.e., a non-smart space. Therefore, it is necessary to study what additional services can be offered and what specific technical issues will be faced when adding robots to smart spaces. To identify the potential novel services and technology problems, this research is focused on a case study of a u-object finding service performed by a robot in a smart space. This paper presents the design and development of the system prototype robot which can communicate with other devices and can find a u-object with attached RFID tag in a smart room. Some preliminary experiments were conducted and the results verified the functionalities of the system.
Experience of Enhancing the Space Sensing of Networked Robots Using Atlas Service-Oriented Architecture
Authors: Sumi Helal, Shinyoung Lim, Raja Bose, Hen-I Yang, Hyun Kim, Young-Jo Cho
Venue: Proceedings of the 8th Asia-Pacific conference on Computer-Human Interaction (2008)

In this paper, we describe how we enhance the space sensing capabilities of the Ubiquitous Robotic Companion (URC) developed by ETRI Korea, by utilizing the plug-and-play service-oriented architecture provided by University of Florida's Atlas Platform. Based on our experience in deploying services using the Atlas Platform in smart houses, for the elderly and people with special needs, a requirement emerged for utilizing networked robots to assist elderly people in their activities of daily living. Networked robots consisting of sensors, actuators and smart devices can collaborate as mobile sensing platforms with the other networked robots in a smart space, providing a complex and sophisticated actuator and human interface. This paper describes our experience in designing and implementing system architecture to integrate URC robots into pervasive computing environments using the Atlas Platform. We also show how the integrated system is able to provide better services which enhance the space sensing capabilities of URCs in the smart space.
Induced Subshifts and Cellular Automata
Authors: Silvio Capobianco
Venue: Language and Automata Theory and Applications: Second International Conference, LATA 2008, Tarragona, Spain, March 13-19, 2008. Revised Papers (2008)

Given a shift subspace over a finitely generated group, we define the subshift induced by it on a larger group. Then we do the same with cellular automata and, while observing that the new automaton can model a different abstract dynamics, we remark several properties that are shared with the old one. After that, we simulate the old automaton inside the new one, and discuss some consequences and restrictions.
A Lattice-Based Minimal Partial Realization Algorithm
Authors: Li-Ping Wang
Venue: Proceedings of the 5th international conference on Sequences and Their Applications (2008)

In this paper we extend a minimal partial realization algorithm for vector sequences to matrix sequences by means of a lattice basis reduction algorithm in function fields. The different ways of transforming a given basis into a reduced one lead to different partial realization algorithms and so our technique provides a unified approach to the minimal partial realization problem.
The Consistency Extractor System: Querying Inconsistent Databases Using Answer Set Programs
Authors: Monica Caniupan, Leopoldo Bertossi
Venue: Proceedings of the 1st international conference on Scalable Uncertainty Management (2007)

We present the Consistency Extractor System (ConsEx) that uses answer set programming to compute consistent answers to first-order queries posed to relational databases that may be inconsistent wrt their integrity constraints. Among other features, ConsEx implements a magic sets technique to evaluate queries via disjunctive logic programs with stable model semantics that specify the repair of the original database. We describe the methodology and the system; and also present some experimental results.
Connectivity-Guaranteed and Obstacle-Adaptive Deployment Schemes for Mobile Sensor Networks
Authors: Guang Tan, Stephen A. Jarvis, Anne-Marie Kermarrec
Venue: Proceedings of the 2008 The 28th International Conference on Distributed Computing Systems (2008)

Mobile sensors can move and self-deploy into a network. While focusing on the problems of coverage, existing deployment schemes mostly over-simplify the conditions for network connectivity: they either assume that the communication range is large enough for sensors in geometric neighborhoods to obtain each other's locationby local communications, or assume a dense network that remains connected. At the same time, an obstacle-free field or full knowledge of the field layout is often assumed. We present new schemes that are not restricted by these assumptions, and thus adapt to a much wider range of application scenarios. While maximizing sensing coverage, our schemes can achieve connectivity for a network with arbitrary sensor communication/sensing ranges or node densities, at the cost of a small moving distance; the schemes do not need any knowledge of the field layout, which can be irregular and have obstacles/holes of arbitrary shape. Simulations results show that the proposed schemes achieve the targeted properties.
Gene Selection Using Wilcoxon Rank Sum Test and Support Vector Machine for Cancer Classification
Authors: Chen Liao, Shutao Li, Zhiyuan Luo
Venue: Computational Intelligence and Security: International Conference, CIS 2006, Guangzhou, China, November 3-6, 2006, Revised Selected Papers (2007)

Gene selection is an important problem in microarray data processing. A new gene selection method based on Wilcoxon rank sum test and Support Vector Machine (SVM) is proposed in this paper. First, Wilcoxon rank sum test is used to select a subset. Then each selected gene is trained and tested using SVM classifier with linear kernel separately, and genes with high testing accuracy rates are chosen to form the final reduced gene subset. Leave-one-out cross validation (LOOCV) classification results on two datasets: Breast Cancer and ALL/AML leukemia, demonstrate the proposed method can get 100% success rate with the final reduced subset. The selected genes are listed and their expression levels are sketched, which show that the selected genes can make clear separation between two classes.
Why do programmers avoid metrics?
Authors: Medha Umarji, Carolyn Seaman
Venue: Proceedings of the Second ACM-IEEE international symposium on Empirical software engineering and measurement (2008)

Software process improvement initiatives such as metrics programs have a high failure rate during their assimilation in a software organization. Social and organizational issues are some of the factors affecting the adoption and acceptance of metrics, and these issues have not been discussed in detail in existing metrics literature. We undertook an interview-based study with the purpose of studying factors that influence the buy-in of metrics. We interviewed 12 members of the metrics team of a large multi-national corporation, with a thriving metrics program. We found that there was some resistance to standardization of corporate metrics processes introduced by the metrics team. This resistance centered on the metrics data collection and reporting processes. One cause of resistance was the presence of sub-cultures and native data collection and reporting processes within organizational units that were independent businesses before they were acquired. Some of the pushback manifested itself through begrudging compliance, and avoidance activities like scripting and gaming of metrics. In this paper, we present the perspectives of developers, managers and upper-level management to emphasize that each stakeholder in the metrics initiative has a valid viewpoint that should be taken into account while implementing a metrics program and that each metrics effort is inextricably enmeshed with the organizational context. We provide actionable recommendations to understand the different perspectives and to adapt the metrics effort accordingly.
Fibrational Semantics for Many-Valued Logic Programs: Grounds for Non-Groundness
Authors: Ekaterina Komendantskaya, John Power
Venue: Proceedings of the 11th European conference on Logics in Artificial Intelligence (2008)

We introduce a fibrational semantics for many-valued logic programming, use it to define an SLD-resolution for annotation-free many valued logic programs as defined by Fitting, and prove a soundness and completeness result relating the two. We show that fibrational semantics corresponds with the traditional declarative (ground) semantics and deduce a soundness and completeness result for our SLD-resolution algorithm with respect to the ground semantics.
Neural Control Applied to the Problem of Trajectory Tracking of Mobile Robots with Uncertainties
Authors: Nardênio A. Martins, Douglas Bertol, Warody C. Lombardi, Edson R. Pieri, Maria M. Dias
Venue: Proceedings of the 2008 10th Brazilian Symposium on Neural Networks (2008)

In this paper, a trajectory tracking control for a nonholonomic mobile robot by the integration of a kinematic neural controller (KNC) and a torque neural controller (TNC) is proposed, where both the kinematic and dynamic models contains parametric and nonparametrics uncertainties. The proposed neural controller (PNC) is constituted of the KNC and the TNC, and were designed by use of a modeling technique of Gaussian radial basis function neural networks (RBFNNs). The KNC is applied to compensate the uncertainties of the mobile robot. The TNC, based on the computed torque control, is applied to compensate the mobile robot dynamics, significant uncertainties, bounded unknown disturbances, neural networks modeling errors, influence of payload, and unknown kinematic parameters. Also, the PNC are not dependent of the mobile robot kinematics and dynamics neither require the off-line training process. Stability analysis and convergence of tracking errors to zero, as well as the learning algorithms for weights, centers, and variances (becoming nonlinearly parameterized RBFNNs) are guaranteed with basis on Lyapunov theory. In addition, the simulations results are provided to show the efficiency of the PNC.
Tomorrow and All our Yesterdays: MTL Satisfiability over the Integers
Authors: Carlo A. Furia, Paola Spoletini
Venue: Proceedings of the 5th international colloquium on Theoretical Aspects of Computing (2008)

We investigate the satisfiability problem for metric temporal logic (MTL) with both past and future operators over linear discrete bi-infinite time models isomorphic to the integer numbers, where time is unbounded both in the future and in the past. We provide a technique to reduce satisfiability over the integers to satisfiability over the well-known mono-infinite time model of natural numbers, and we show how to implement the technique through an automata-theoretic approach. We also prove that MTL satisfiability over the integers is EXPSPACE-complete, hence the given algorithm is optimal in the worst case.
Face Image Annotation Based on Latent Semantic Space and Rules
Authors: Hideaki Ito, Yuji Kawai, Hiroyasu Koshimizu
Venue: Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part II (2008)

This paper presents a face image annotation system based on latent semantic indexing and rules. To achieve annotation, visual and symbolic features are integrated. Two features are corresponding to lengths and/or widths of face parts and keywords, respectively. In order to develop annotation mechanism, it is required to vary the dimensions of the spaces which are constructed by the latent semantic indexing, and to represent direct relationships among features. Associated symbolic features to visual features are represented in rules based on decision trees. Co-occurrence relationships among keywords are represented in association rules.
Event Detection and Tracking Based on Improved Incremental K-Means and Transductive SVM
Authors: Zhen Lei, Jianfeng Liao, Dong Li, Lingda Wu
Venue: Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence (2008)

This paper describes a new method for detecting events. The method utilizes density function to initialize cluster centers so as to get center points objectively, it can be used in both on-line detection and retrospective detection, and the quantity of clusters is affected little by the order in which the news stories are processed. This paper deals with event tracking with transductive support vector machine (TSVM). TSVM takes into account a particular test set and tries to minimize misclassifications of just those particular examples. The problem of effective density radius selection is discussed, and the performance differences between the event detection and tracking methods proposed in this paper and other methods are compared. The experimental results indicate that the methods proposed by this paper are effective and advanced.
Ensemble clustering with voting active clusters
Authors: Kagan Tumer, Adrian K. Agogino
Venue: Pattern Recognition Letters (2008)

Clustering is an integral part of pattern recognition problems and is connected to both the data reduction and the data understanding steps. Combining multiple clusterings into an ensemble clustering is critical in many real world applications, particularly for domains with large data sets, high-dimensional feature sets and proprietary data. This paper presents voting active clusters (VACs), a method for combining multiple ''base'' clusterings into a single unified ''ensemble'' clustering that is robust against missing data and does not require all the data to be collected in one central location. In this approach, separate processing centers produce many base clusterings based on some portion of the data. The clusterings of such separate processing centers are then pooled to produce a unified ensemble clustering through a voting mechanism. The major contribution of this work is in providing an adaptive voting method by which the clusterings (e.g., spatially distributed processing centers) update their votes in order to maximize an overall quality measure. Our results show that this method achieves comparable or better performance than traditional cluster ensemble methods in noise-free conditions, and remains effective in noisy scenarios where many traditional methods are inapplicable.
Gem #18: warnings in GNAT
Authors: Bob Duff
Venue: ACM SIGAda Ada Letters (2008)

Ada Gem #18 -- This "gem" is not about the Ada language, but about a set of GNAT-specific features related to warnings. We discuss how to make the best use of GNAT's warnings.
A Local Discriminative Model for Background Subtraction
Authors: Adrian Ulges, Thomas M. Breuel
Venue: Proceedings of the 30th DAGM symposium on Pattern Recognition (2008)

Conventional background subtraction techniques that update a background model online have difficulties with correctly segmenting foreground objects if sudden brightness changes occur. Other methods that learn a global scene model offline suffer from projection errors. To overcome these problems, we present a different approach that is local and discriminative, i.e. for each pixel a classifier is trained to decide whether the pixel belongs to the background or foreground. Such a model requires significantly less tuning effort and shows a better robustness, as we will demonstrate in quantitative experiments on self-created and standard benchmarks. Finally, segmentation is improved significantly by integrating the probabilistic evidence provided by the local classifiers with a graph cut segmentation algorithm.
Conditional Dempster-Shafer Theory for Uncertain Knowledge Updating
Authors: Hexin Lv, Bin Zhu, Yongchuan Tang
Venue: Proceedings of the 12th international Fuzzy Systems Association world congress on Foundations of Fuzzy Logic and Soft Computing (2007)

This paper presents a theory called conditional Dempster-Shafer theory (CDS) for uncertain knowledge updating. In this theory, a priori knowledge about the value attained by an uncertain variable is modeled by a fuzzy measure and the evidence about the underlying uncertain variable is modeled by the Dempster-Shafer belief measure. Two operations in CDS are discussed in this paper, the conditioned combination rule and conditioning rule, which deal with evidence combining and knowledge updating, respectively. We show that conditioned combination rule and conditioning rule in CDS satisfy the property of Bayesian parallel combination.