ppxscal/embedding_network huggingface dataset:
Building an Ontology of Hypertension Management
Authors: Olivier Steichen, Christel Daniel-Le Bozec, Marie-Christine Jaulent, Jean Charlet
Venue: Proceedings of the 11th conference on Artificial Intelligence in Medicine (2007)

The analysis of customized decisions during hypertension management in a specialized unit requires a detailed representation of clinical cases. We are building a specific ontology to code medical records and process them with computerized tools. Relevant concepts to describe and justify medical decisions are extracted from three sources: (i) Clinical guidelines; (ii) Items of the semi-structured medical record form used in the clinical unit; (iii) Free-text answers from 5,000 completed record forms. Combining terminological sources is mandatory to cover the whole spectrum of possible justifications for clinical decisions, including contextual specificities and patients' particulars.
Pattern Languages as Tool for Discount Usability Engineering
Authors: Elbert-Jan Hennipman, Evert-Jan Oppelaar, Gerrit Veer
Venue: Interactive Systems. Design, Specification, and Verification: 15th International Workshop, DSV-IS 2008 Kingston, Canada, July 16-18, 2008 Revised Papers (2008)

Despite growing pattern collections in the field of Human-Computer Interaction (HCI), both on the Internet [1-3] and in books [4, 5], these collections have usability problems when being used by those software engineers, who lack expertise in human-centered design. In this paper we report on the development of a tool that is intended to improve accessibility and usability of HCI design patterns for engineers. We aim at a tool that is simple and safe to be used without expert knowledge. Such a tool can be used in what could be labeled "discount usability engineering". A new type of HCI pattern language combined with a supportive tool is intended to overcome some of the gaps these engineers have in HCI-background.
Output sufficient modules for uniform decomposition of synchronous sequential circuits
Authors: C. C. Huang, R. Y. Cain, L. L. Kinney
Venue: Proceedings of the 13th Annual Symposium on Switching and Automata Theory (swat 1972) (1972)

Some results concerning uniform modular decomposition are presented. The primary concern is with the number of inputs which a universal module must have and also with the conditions which a module must satisfy if it is to be output sufficient. It is shown that the number of inputs required by an output sufficient module grows exponentially with the number of inputs to the sequential machines to be realized. If the special class of strongly connected modules which have a minimal number of inputs is considered, then the module is output sufficient if and only if each state of the module is output onto. This condition can be tested by a simple examination of the state table of the module. If the module has more than the minimal number of inputs, this condition is no longer necessary. A test for the output sufficiency of an arbitrary module has not yet been found.
Improving Situated Agents Adaptability Using Interruption Theory of Emotions
Authors: Clément Raïevsky, François Michaud
Venue: Proceedings of the 10th international conference on Simulation of Adaptive Behavior: From Animals to Animats (2008)

Emotions play several important roles in the cognition of human beings and other life forms, and are therefore a legitimate inspiration to provide adaptability and autonomy to situated agents. However, there is no unified theory of emotions and many discoveries are yet to be made in the applicability of emotions to situated agents. This paper investigates the feasibility and utility of an artificial model of anger and fear based on Interruption Theory of Emotions. This model detects and highlights situations for which an agent's decision-making mechanism is no longer pertinent. These situations are detected by analyzing discrepancies between the agent's actions and its intentions, making this model independent from the agent's environment and tasks. Collective foraging simulations are used to characterize the influence of the model. Results show that the model improves the adaptability of a group of agents by simultaneously optimizing multiple performance criterion.
Communication goals and online persuasion: An empirical examination
Authors: E. Vance Wilson, Ying Lu
Venue: Computers in Human Behavior (2008)

Computer-mediated communication (CMC) is frequently applied as a tool for organizational marketing and consumer research. This paper explores the underlying structure of message receivers' communication goals and their impact on persuasiveness in the context of CMC. Extending prior research on the structure of primary and secondary goals, we identified five specific communication goals that are important to receivers. We conducted an online exercise in which subjects respond to a message requesting them to volunteer their time. The results demonstrate all five communication goals are important to one or more indicators of persuasiveness, including attitude toward the issue, source credibility, perceived information quality, and behavioral intention to comply with the request.
Challenges in Chinese Text Similarity Research
Authors: Xiuhong Wang, Shiguang Ju, Shengli Wu
Venue: Proceedings of the 2008 International Symposiums on Information Processing (2008)

There are many opportunities and challenges in Chinese text similarity research, which is one of the most important issues in the information retrieval field. Quite a few models and approaches have been investigated for this. Chinese is one of the most complicated languages on morphology, syntax, semantics and pragmatics. In Chinese, there is not an explicit delimiter between words as in English. The difficulties in Chinese natural language processing, such as segmentation, knock down both effectiveness and efficiency of text similarity computation. This paper addresses some challenges in Chinese text similarity computation, which are undergoing from Chinese linguistics, models and approaches used in information retrieval. We consider Chinese text similarity computing tasks to cover broad topics of word, sentence and document similarity. Our work provides insights into the difficulties and bottleneck in the research, including tradeoffs between effectiveness and efficiency. New directions of the future work are discussed.
Unsupervised Discovery of Compound Entities for Relationship Extraction
Authors: Cartic Ramakrishnan, Pablo N. Mendes, Shaojun Wang, Amit P. Sheth
Venue: Proceedings of the 16th international conference on Knowledge Engineering: Practice and Patterns (2008)

In this paper we investigate unsupervised population of a biomedical ontology via information extraction from biomedical literature. Relationships in text seldom connect simple entities. We therefore focus on identifying compound entities rather than mentions of simple entities. We present a method based on rules over grammatical dependency structures for unsupervised segmentation of sentences into compound entities and relationships. We complement the rule-based approach with a statistical component that prunes structures with low information content, thereby reducing false positives in the prediction of compound entities, their constituents and relationships. The extraction is manually evaluated with respect to the UMLS Semantic Network by analyzing the conformance of the extracted triples with the corresponding UMLS relationship type definitions.
The Challenges of Service Evolution
Authors: Mike P. Papazoglou
Venue: Proceedings of the 20th international conference on Advanced Information Systems Engineering (2008)

Services are subject to constant change and variation. Services can evolve typically due to changes in structure, e.g., attributes and operations; in behavior and policies, e.g., adding new business rules and regulations, in types of business-related events; and in business protocols. This paper introduces two types of service changes: shallow changes - where changes are confined to services or the clients - and deep changes - where cascading effects and side-effects occur. The paper introduces a theoretical approach for dealing with shallow service changes and a change-oriented service lifecycle methodology that addresses the effects of deep service changes.
RZ: A Tool for Bringing Constructive and Computable Mathematics Closer to Programming Practice
Authors: Andrej Bauer, Christopher A. Stone
Venue: Proceedings of the 3rd conference on Computability in Europe: Computation and Logic in the Real World (2007)

Realizability theory can produce interfaces for the data structure corresponding to a mathematical theory. Our tool, called RZ, serves as a bridge between constructive mathematics and programming by translating specifications in constructive logic into annotated interface code in Objective Caml. The system supports a rich input language allowing descriptions of complex mathematical structures. RZ does not extract code from proofs, but allows any implementation method, from handwritten code to code extracted from proofs by other tools.
Statistical Piecewise Assembled Model (SPAM) for the Representation of Highly Deformable Medical Organs
Authors: Jun Feng, Peng Du, Horace H. Ip
Venue: Proceedings of the 4th international workshop on Medical Imaging and Augmented Reality (2008)

We propose a novel Statistical Piecewise Assembled Model (SPAM) to address the open problem of small sample size encountered when applying Point Distribution Models (PDM) in 3-D medical data analysis. Specifically, in our SPAM, the Statistical Frame Model (SFM) constructed from the salient landmarks characterizes the global topological variability of the structure. Then the landmarks are employed to partition a complex object surface into piecewise segments. After that, the Statistical deformable Piecewise surface segment Models (SPMs) are established to define the fine details of local surface shape variations. The hierarchical nature of SPAM enables it to generate much more variation modes than conventional statistical models given a very small sample size training set. The experimental results demonstrate that SPAM can achieve more accuracy rates for model representation compared with traditional Active Shape Model (ASM) and Multi-resolution ASM.
Enriching student experience with student driven content while teaching an online data mining class
Authors: Suresh Kalathur
Venue: Proceedings of the 9th ACM SIGITE conference on Information technology education (2008)

An online class provides a unique opportunity for the students to participate in the class at their own convenience. However, the students have to be kept motivated by providing the tools to enable discussions related to the class topics. This paper implements and presents a model coupled with data mining techniques that will predict how the students are faring in the classroom discussions. The model provides feedback on which students are active in their online discussions, what topics they are discussing, the relevance of those discussions, and share the information with all those involved in the class. The techniques presented in this work can be applied to any online class. However, presenting this work in the context of a data mining class is especially rewarding. Students can use their discussions themselves as a case study and apply them in the context of various data mining and text mining algorithms covered in the class. The framework not only aides an instructor in understanding the student interest and participation in an online class, but also helps the students with feedback on their posts with respect to the others.
Predicting Mechanical Properties of Rubber Compounds with Neural Networks and Support Vector Machines
Authors: Mira Trebar, Uroš Lotrič
Venue: Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part II (2007)

The quality of rubber compounds is assessed by rheological and mechanical tests. Since mechanical tests are very time consuming, the main idea of this work is to quest for strong nonlinear relationships between rheological and mechanical tests in order to reduce the latter. The multilayered perceptron and support vector machine combined with data preprocessing were applied to model hardness and density of the vulcanizates from the rheological parameters of the raw compounds. The results outline the advantage of proper data preprocessing.
Parameterized Algorithms for Generalized Domination
Authors: Venkatesh Raman, Saket Saurabh, Sriganesh Srihari
Venue: Proceedings of the 2nd international conference on Combinatorial Optimization and Applications (2008)

We study the parameterized complexity of a generalization of Dominating Set problem, namely, the Vector Dominating Set problem. Here, given an undirected graph G = (V,E), with V = {v 1, ¿ , v n }, a vector $\vec{l}=(l(v_1),\cdots, l(v_n))$ and an integer parameter k, the goal is to determine whether there exists a subset D of at most k vertices such that for every vertex v ¿ V ¿ D, at least l(v) of its neighbors are in D. This problem encompasses the well studied problems --- Vertex Cover (when l(v) = d(v) for all v ¿ V, where d(v) is the degree of vertex v) and Dominating Set (when l(v) = 1 for all v ¿ V). While Vertex Cover is known to be fixed parameter tractable, Dominating Set is known to be W[2]-complete. In this paper, we identify vectors based on several measures for which this generalized problem is fixed parameter tractable and W-hard. We also show that the Vector Dominating Set is fixed parameter tractable for graphs of bounded degeneracy and for graphs excluding cycles of length four.
Portuguese Part-of-Speech Tagging Using Entropy Guided Transformation Learning
Authors: Cícero Nogueira Dos Santos, Ruy L. Milidiú, Raúl P. Rentería
Venue: Proceedings of the 8th international conference on Computational Processing of the Portuguese Language (2008)

Entropy Guided Transformation Learning (ETL) is a new machine learning strategy that combines the advantages of Decision Trees (DT) and Transformation Based Learning (TBL). In this work, we apply the ETL framework to Portuguese Part-of-Speech Taggging. We use two different corpora: Mac-Morpho and Tycho Brahae. ETL achieves the best results reported so far for Machine Learning based POS tagging of both corpora. ETL provides a new training strategy that accelerates transformation learning. For the Mac-Morpho corpus this corresponds to a factor of three speedup. ETL shows accuracies of 96.75% and 96.64% for Mac-Morpho and Tycho Brahae, respectively.
A Stakeholder Model for Interorganizational Information Systems
Authors: Luciana C. Ballejos, Silvio M. Gonnet, Jorge M. Montagna
Venue: Proceedings of the 14th international conference on Requirements Engineering: Foundation for Software Quality (2008)

Stakeholders constitute the principal source of requirements in the development of information systems. They therefore must be considered allover the process. In order to achieve success, they must be also modelled and then integrated with requirements, design and implementation models. Thus, a more complete perspective is added to traditional modelling. This work presents and describes a stakeholder model for interorganizational information systems, in order to incorporate a stakeholders-including approach to traditional modelling, focusing on interorganizational environments.
Word Endpoint Detection Using Dynamic Programming
Authors: Antanas Lipeika, Joana Lipeikienė
Venue: Informatica (2003)

The paper deals with the use of dynamic programming for word endpoint detection in isolated word recognition. Endpoint detection is based on likelihood maximization. Expectation maximization approach is used to deal with the problem of unknown parameters. Speech signal and background noise energy is used as features for making decision. Performance of the proposed approach was evaluated using isolated Lithuanian words speech corpus.
Simulation, visualization, and data mining: a methodology for interactive scientific discovery
Authors: Timothy J. Hickey
Venue:  (2008)

A traditional approach for scientific discovery is to model a natural system mathematically then extrapolate new insights analytically. For models that are analytically intractable this is not possible. One approach to resolve this issue is called Parameter Space Exploration (PSE). The general idea is to perform mass simulations of a model over a uniform distribution of possible parameter inputs, then mine the resulting data to make inferences. Here I propose a methodology that combines PSE simulation with visualization and data mining to support interactive scientific discovery. Tools and techniques are described for supporting the analysis of very large, multidimensional databases. I evaluate and detail the applicability of pixelization, dimensional stacking, query based color maps, and a number of machine learning algorithms that accept human input for interactive analysis. These approaches have been used to investigate model neuron simulation data with the resulting images appearing in the Journal of Neurophysiology. They have also been implemented in a tool called NDVis (N-Dimensional Visualization Tool) which is now being used in graduate neuroscience classes at Emory University. Evaluation of the methods are based on two in-depth long term case studies in different data domains. This includes an attempt to generalize the approaches to non-simulation data sets.
Using CBR Systems for Leukemia Classification
Authors: Juan M. Corchado, Juan F. Paz
Venue: Proceedings of the 3rd international workshop on Hybrid Artificial Intelligence Systems (2008)

The continuous advances in genomics, and specifically in the field of transcriptome, require novel computational solutions capable of dealing with great amounts of data. Each expression analysis needs different techniques to explore the data and extract knowledge which allow patients classification. This paper presents a hybrid systems based on Case-based reasoning (CBR) for automatic classification of leukemia patients from Exon array data. The system incorporates novel algorithms for data mining that allow to filter and classify. The system has been tested and the results obtained are presented in this paper.
Developing a Dialogue System
Authors: Mare Koit, Olga Gerassimenko, Riina Kasterpalu, Andriela Rääbis, Krista Strandson
Venue: Proceedings of the 11th international conference on Text, Speech and Dialogue (2008)

Estonian phone calls to travel agencies are analyzed with the further aim to develop a dialogue system. The analysis is based on the Estonian Dialogue Corpus. Customers' initial requests introduce a topic or check the competencies of the official, and they have to be adjusted in a following information-sharing sub-dialogue. Information is given briefly, using short sentences or phrases. A collaborative travel agent offers substituting information or action if s/he is unable to fulfill the customer's request. A dialogue system is being developed which gives travel information in Estonian. Ready-made sentence patterns are used in the current version for granting the users' requests. Our study will help to make the grants more natural.
Semantic Annotation of Complex Human Scenes for Multimedia Surveillance
Authors: Carles Fernández, Pau Baiget, Xavier Roca, Jordi Gonzàlez
Venue: Proceedings of the 10th Congress of the Italian Association for Artificial Intelligence on AI*IA 2007: Artificial Intelligence and Human-Oriented Computing (2007)

A Multimedia Surveillance System (MSS) is considered for automatically retrieving semantic content from complex outdoor scenes, involving both human behavior and traffic domains. To characterize the dynamic information attached to detected objects, we consider a deterministic modeling of spatio-temporal features based on abstraction processes towards fuzzy logic formalism. A situational analysis over conceptualized information will not only allow us to describe human actions within a scene, but also to suggest possible interpretations of the behaviors perceived, such as situations involving thefts or dangers of running over. Towards this end, the different levels of semantic knowledge implied throughout the process are also classified into a proposed taxonomy.
A Dialogue-Based NLIDB System in a Schedule Management Domain
Authors: Harksoo Kim
Venue: Proceedings of the 33rd conference on Current Trends in Theory and Practice of Computer Science (2007)

To reduce the complexities of SQL query generation and increase the flexibilities of user interface, we propose a dialogue-based NLIDB system. The system classifies users' intentions into domain actions (pairs of a speech act and a concept sequence) using a maximum entropy model. Based on the classification results, the system fills up predefined SQL templates and generates proper SQL queries for natural language interface. In the experiment, the system showed the success rate of 83.4% for SQL generation.
An Inventory Decision Model with Variable Lead Time and Continuous Price Decrease
Authors: Chuanxu Wang
Venue: Proceedings of the 2008 International Symposiums on Information Processing (2008)

This paper develops an inventory model with continuous product price decrease and variable lead time for minimizing the sum of the ordering cost, purchasing cost, holding cost, shortage cost and the lead time crashing cost, in which the number of equal length cycles, the order quantity and lead time are decision variables. An accurate approximate solution algorithm for the model is developed for finding the optimal values of decision variables. The effectiveness of the model is illustrated and the effects of changes in model parameters are analyzed with a numerical example.
A New Text Detection Approach Based on BP Neural Network for Vehicle License Plate Detection in Complex Background
Authors: Yanwen Li, Meng Li, Yinghua Lu, Ming Yang, Chunguang Zhou
Venue: Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks (2007)

With the development of Intelligent Transport Systems (ITS), automatic license plate recognition (LPR) plays an important role in numerous applications in reality. In this paper, a coarse to fine algorithm to detect license plates in images and video frames with complex background is proposed. First, the method based on Component Connect (CC) is used to detect the possible license plate regions in the coarse detection. Second, the method based on texture analysis is applied in the fine detection. Finally, a BP Neural Network is adopted as classifier, parts of the features is selected based on statistic diagram to make the network efficient. The average accuracy of detection is 95.3% from the images with different angles and different lighting conditions.
Achieving Fast BGP Reroute with Traffic Engineering Using Multiple Routing Planes
Authors: Yu Guo, Ning Wang, Kin-Hon Ho, Michael Howarth, George Pavlou
Venue: Proceedings of the 8th IEEE international workshop on IP Operations and Management (2008)

In today's BGP routing architecture, traffic delivery is in general based on single path selection paradigms. The lack of path diversity hinders the support for resilience, traffic engineering and QoS provisioning across the Internet. Some recently proposed multi-plane extensions to BGP offer a promising mechanism to enable diverse inter-domain routes towards destination prefixes. Based on these enhanced BGP protocols, we propose in this paper a novel technique to enable controlled fast egress router switching for handling network failures. In order to minimize the disruptions to real-time services caused by the failures, backup egress routers can be immediately activated through locally remarking affected traffic towards alternative routing planes without waiting for IGP routing re-convergence. According to our evaluation results, the proposed multi-plane based egress router selection algorithm is able to provide both high path diversity and balanced load distribution across inter-domain links with a small number of planes.