ppxscal/embedding_network huggingface dataset:
Case---Based Support to Small---Medium Enterprises: The Symphony Project
Authors: Stefania Bandini, Paolo Mereghetti, Esther Merino, Fabio Sartori
Venue: Proceedings of the 10th Congress of the Italian Association for Artificial Intelligence on AI*IA 2007: Artificial Intelligence and Human-Oriented Computing (2007)

This paper presents Symphony, an IMS (Intelligent Manufacturing System) developed in the context of an interregional project supported by the European Commission. Symphony was a three year project that aimed at the development of an integrated set of tools for management of enterprizes, in order facilitate the continuous creation, exploration and exploitation of business opportunities through strategic networking. In particular, Symphony is devoted to support human resource managers of Small---Medium Enterprises in their decision making process about the looking for newcomers and/or assigning people to jobs. About this topic, the paper focuses on SymMemory, a case---based module of the main system that has been developed to identify what features are necessary to evaluate a person, aggregate them into a suitable case---structure representing a person or job profile and comparing profiles according to a specific similarity algorithm.
Visual Discovery of Network Patterns of Interaction between Attributes
Authors: Simeon J. Simoff, John Galloway
Venue: Visual Data Mining: Theory, Techniques and Tools for Visual Analytics (2008)

Visual discovery of network patterns of interaction between attributes in a data set identifies emergent networks between myriads of individual data items and utilises special algorithms that aid visualisation of `emergent' patterns and trends in the linkage. It complements conventional data mining methods, which assume the independence between the attributes and the independence between the values of these attributes. The approach complements analytical data mining techniques where the rules or definitions of what might constitute an exception are able to be known and specified ahead of time. For example, in the analysis of transaction data there are no known suspicious transactions. This chapter presents a human-centred visual data mining methodology that addresses the issues of depicting implicit relationships between data attributes and/or specific values of these attributes. Different aspects of the approach is demonstrated through the reflection of the analytical process in two cases: one looking at fraudulent activity which will be difficult, if not impossible to detect with conventional exception detection methods, and the other one looking at exploring a large data set of low level communication data. The chapter argues that for many problems, a `discovery' phase in the investigative process based on visualisation and human cognition is a logical precedent to, and complement of, more automated `exception detection' phases.
Getting More from Reputation Systems: A Context-Aware Reputation Framework Based on Trust Centers and Agent Lists
Authors: Rehab AlNemr, Christoph Meinel
Venue: Proceedings of the 2008 The Third International Multi-Conference on Computing in the Global Information Technology (iccgi 2008) (2008)

Reputation is a crucial factor in trust and thus in web communities. Trust strategies may involve investigating user reputation or directly using transitive reputation to form the Web of Trust. We suggest an approach that takes advantage of both strategies without increasing the cost of investigation. Several systems nowadays form what we call “user web communities”. In these communities, reputation related to different contexts needs to be exchanged. The perception, calculation and interpretation of this reputation differ from one community to another. We propose the development of reference models to diminish the distance between these multi-perceptions. We also propose the use of reputation centers to facilitate reputation transfer and highlight the importance of their role in analyzing attacks on reputation.
Bloomier Filters: A Second Look
Authors: Denis Charles, Kumar Chellapilla
Venue: Proceedings of the 16th annual European symposium on Algorithms (2008)

A Bloom filter is a space efficient structure for storing static sets, where the space efficiency is gained at the expense of a small probability of false-positives. A Bloomier filter generalizes a Bloom filter to compactly store a function with a static support. In this article we give a simple construction of a Bloomier filter. The construction is linear in space and requires constant time to evaluate. The creation of our Bloomier filter takes linear time which is faster than the existing construction. We show how one can improve the space utilization further at the cost of increasing the time for creating the data structure.
Analyzing Data Dependence Based on Workflow Net
Authors: Yu Huang, Hanpin Wang, Wen Zhao, Jiaqi Zhu
Venue: Proceedings of the 7th international conference on Computational Science, Part III: ICCS 2007 (2007)

Workflow management systems (WfMSs) frequently use data to coordinate the execution of workflow instances. A WfMS makes routing decisions according to data constraints. This paper presents an extended workflow net which has its business form and portrays data constraints among tasks which act as the decision condition. Data constraint is expressed by pre-condition and post-condition which formalize a task's input and output conditions. After introducing the model, we give an important definition of consistence related to data dependance. Then, we proposes a domain computation method during reduction to verify the consistence. The method considers data operation during reduction. In the end, a case study is analyzed by our method.
Lossless Compression for Space Imagery in a Dynamically Reconfigurable Architecture
Authors: Xiaolin Chen, C. Nishan Canagarajah, Raffaele Vitulli, Jose L. Nunez-Yanez
Venue: Proceedings of the 4th international workshop on Reconfigurable Computing: Architectures, Tools and Applications (2008)

This paper presents a novel dynamically reconfigurable hardware architecture for lossless compression and its optimization for space imagery. The proposed system makes use of reconfiguration to support optimal modeling strategies adaptively for data with different dimensions. The advantage of the proposed system is the efficient combination of different compression functions. For image data, we propose a new multi-mode image model which can detect the local features of the image and use different modes to encode regions with different features. Experimental results show that our system improves compression ratios of space image while maintaining low complexity and high throughput.
The Underlying Formal Model of Algorithmic Lateral Inhibition in Motion Detection
Authors: José Mira, Ana E. Delgado, Antonio Fernández-Caballero, María T. López, Miguel A. Fernández
Venue: Proceedings of the 2nd international work-conference on Nature Inspired Problem-Solving Methods in Knowledge Engineering: Interplay Between Natural and Artificial Computation, Part II (2007)

Many researchers have explored the relationship between recurrent neural networks and finite state machines. Finite state machines constitute the best characterized computational model, whereas artificial neural networks have become a very successful tool for modeling and problem solving. Recently, the neurally-inspired algorithmic lateral inhibition (ALI) method and its application to the motion detection task have been introduced. The article shows how to implement the tasks directly related to ALI in motion detection by means of a formal model described as finite state machines. Automata modeling is the first step towards real-time implementation by FPGAs and programming of "intelligent" camera processors.
A Short Introduction to Computational Social Choice
Authors: Yann Chevaleyre, Ulle Endriss, Jérôme Lang, Nicolas Maudet
Venue: Proceedings of the 33rd conference on Current Trends in Theory and Practice of Computer Science (2007)

Computational social choice is an interdisciplinary field of study at the interface of social choice theory and computer science, promoting an exchange of ideas in both directions. On the one hand, it is concerned with the application of techniques developed in computer science, such as complexity analysis or algorithm design, to the study of social choice mechanisms, such as voting procedures or fair division algorithms. On the other hand, computational social choice is concerned with importing concepts from social choice theory into computing. For instance, the study of preference aggregation mechanisms is also very relevant to multiagent systems. In this short paper we give a general introduction to computational social choice, by proposing a taxonomy of the issues addressed by this discipline, together with some illustrative examples and an (incomplete) bibliography.
Study on Improved Fast Immunized Genetic Algorithm
Authors: Wei Gao
Venue: Proceedings of the 2008 Second International Conference on Genetic and Evolutionary Computing (2008)

As an effective global optimization method, genetic algorithm has been used in real practice very widely. When it is used in real practice, its slow convergence and poor stability have become the main problems. In order to overcome these problems, from the creation of the initial population, immune selection operation, improved genetic operators, et al, an improved fast immunized genetic algorithm is proposed. Through the simulation experiments of some hard-optimization functions, the proposed algorithm shows its faster convergence and better stability than a lot of existing algorithms'. The ACM Portal is published by the Association for Computing Machinery. Copyright © 2010 ACM, Inc. Terms of Usage Privacy Policy Code of Ethics Contact Us Useful downloads: Adobe Acrobat QuickTime Windows Media Player Real Player
Using a Semi-automatic Keyword Dictionary for Improving Violent Web Site Filtering
Authors: Radhouane Guermazi, Mohamed Hammami, Abdelmajid Ben Hamadou
Venue: Proceedings of the 2007 Third International IEEE Conference on Signal-Image Technologies and Internet-Based System (2007)

The development of the Web has been paralleled by the emergence of harmful Web pages content such as pornography, violence, racism,etc.This emergence involved the necessity of providing filtering systems designed to secure the internet access. In this paper, we propose a violent Web content detection and filtering system called "WebAngels filter" which uses textual and structural content-based analysis. These analysis are based on a violent keyword dictionary. We focus our attention on the keyword dictionary preparation, and we demonstrate that a semi-automatic keyword dictionary can be used to improve the filtering efficiency of violent Web pages.
Spectral Characterization of Volcanic Earthquakes at Nevado del Ruiz Volcano Using Spectral Band Selection/Extraction Techniques
Authors: Mauricio Orozco-Alzate, Marina Skurichina, Robert P. Duin
Venue: Proceedings of the 13th Iberoamerican congress on Pattern Recognition: Progress in Pattern Recognition, Image Analysis and Applications (2008)

Spectral content of seismic signals contains essential information for discriminating different classes of volcanic earthquakes. Such an information is largely redundant; therefore, a reduce number of spectral regions may provide almost the same description of the original events. By reducing the number of bands considered, the amount of data to be processed is significantly decreased and the interpretability of the characterization results is enhanced as well. We consider several spectral band selection methods in a two-class classification problem of volcanic earthquakes recorded at Nevado del Ruiz Volcano. Selection approaches have been compared to each other in terms of classification accuracy as well as by looking at the resulting spectral divisions. Detailed discussions about the technical considerations of the selection approaches as well as regarding their possible physical interpretations have been conducted. Results show that the sequential selection approach is the most flexible and powerful for classifying and characterizing volcanic earthquakes.
A University Management Analysis System for Qualitative Strategy Planning
Authors: Tokuro Matsuo, Takayuki Fujimoto
Venue: Proceedings of the 2008 19th International Conference on Systems Engineering (2008)

This paper describes the qualitative simulation-based university analyses. In university management, the staff of decision making section generally analyze their school status about the costs, cash flow from operations, investment cash flow, cash flow from financial activities and so on by using numerical analyses. However, such universities are like large business. Most of universities are small and medium-sized enterprise and such universities sometimes do not have a section for risk management and decision making. To help and enhance the strategy making for them, this paper propose a new management analysis method using qualitative simulation. The management states are divided as the cost of advertisement, payment of salary, and several other factors. Each factor has qualitative state value and its changing trend. Our qualitative simulation is carried out through non-landmark process. By using the method and qualitative simulation, university staff who is not engaging strategy planning can understand easily the management states of his/her organization.
The ASRS_RL --- A Research Platform for Spoken Language Recognition and Understanding Experiments
Authors: Inge Gavat, Corneliu Octavian Dumitru
Venue: Proceedings of the international conference on Computational Science and Its Applications, Part II (2008)

In the present paper are described the component stages implemented on the Automatic Speech Recognition System for Romanian Language, ASRS_RL and the speech recognition experiments made with the system. The discussed stages are the feature extraction based on cepstral analysis and linear prediction and the learning strategies implemented on the system for acoustical modelling. The first goal of our research was to develop and experiment a system for continuous speech recognition and understanding in the statistical framework of hidden Markov models (HMM). A second goal was to asses other learning strategies like support vector machines (SVM), artificial neural networks (ANN), and some hybrid structures as alternatives for the HMM. The made experiments in isolated words (digits) and vowels recognition gave good results and proved the competitively of the applied new structures.
A Novel Algorithm for Heart Motion Analysis Based on Geometric Constraints
Authors: Mingxing Hu, Graeme Penney, Daniel Rueckert, Philip Edwards, Michael Figl, Philip Pratt, David Hawkes
Venue: Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I (2008)

Recently, much attention has been focused on heart motion analysis for minimally invasive beating-heart surgery. Unfortunately existing techniques usually require the camera(s) to be fixed during the motion analysis, which can restrict its usefulness during surgery. In this paper we present a novel method for heart motion analysis using geometric constraint, which can estimate the motion from a moving camera and employ multiple image features to improve robustness to noise. Our approach combines the benefits of geometry estimation for obtaining an accurate and robust solution with the proper treatment of respiratory motion. The proposed method can be applied to not only beating heart surgery, but also to other procedures involving periodic organ motion, such as lung and liver.
GrAMoS: A Flexible Service for WS-Agreement Monitoring in Grid Environments
Authors: Glauber Scorsatto, Alba Cristina Melo
Venue: Proceedings of the 14th international Euro-Par conference on Parallel Processing (2008)

In this paper, we propose and evaluate GrAMoS, a low-overhead WS-Agreement monitoring service that runs on Globus 4 and is integrated to JSS. To monitor resources and detect agreement violations, GrAMoS uses the stratified random sampling technique and proposes flexible agreement violation actions. The experimental results collected with a prototype of GrAMoS which monitored CPU reservation WS-Agreements show that the overhead incurred is lower than 2.8%, for grid tasks that take longer than 55s.
Parallel Ant Miner 2
Authors: Omid Roozmand, Kamran Zamanifar
Venue: Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing (2006)

In this paper, we propose a flexible parallel ant colony algorithm for classification rule discovery in the large databases. We call this algorithm Parallel Ant-Miner2. This model relies on the extension of real behavior of ants and data mining concepts. The artificial ants are firstly generated and separated into several groups. Each group is assigned a class label which is the consequent parts of the rules it should discover. Ants try to discover rules in parallel and then communicate with each other to update the pheromones in different paths. The communication methods help ants not to gather irrelevant terms of the rule. The parallel executions of ants reduce the speed of convergence and consequently make it possible to extract more new high quality rules by exploring all search space. Our experimental results show that the proposed model is more accurate than the other versions of Ant-Miner.
Bootstrapping Information Extraction from Semi-structured Web Pages
Authors: Andrew Carlson, Charles Schafer
Venue: Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I (2008)

We consider the problem of extracting structured records from semi-structured web pages with no human supervision required for each target web site. Previous work on this problem has either required significant human effort for each target site or used brittle heuristics to identify semantic data types. Our method only requires annotation for a few pages from a few sites in the target domain. Thus, after a tiny investment of human effort, our method allows automatic extraction from potentially thousands of other sites within the same domain. Our approach extends previous methods for detecting data fields in semi-structured web pages by matching those fields to domain schema columns using robust models of data values and contexts. Annotating 2---5 pages for 4---6 web sites yields an extraction accuracy of 83.8% on job offer sites and 91.1% on vacation rental sites. These results significantly outperform a baseline approach.
Spinning Sensors: A Middleware for Robotic Sensor Nodes with Spatiotemporal Models
Authors: Soko Aoki, Jin Nakazawa, Hideyuki Tokuda
Venue: Proceedings of the 2008 14th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications (2008)

This paper proposes Spinning Sensors middleware that realizes a robotic sensor node mechanism comprised of a sensor node and a robotic actuator node such as a motor or a mobile robot. We can increase sensing space, time, and accuracy of a sensor node by attaching them onto robotic actuators. To realize a robotic sensor node, we need to achieve collaborative utilization of arbitrary sensors and actuators, and automated calculation of sensing area and time. We stated these problems as spontaneous coordination problem and sensing area calculation problem. The Spinning Sensors middleware provides the mechanism of device coordination, data processing, and management of spatiotemporal model of robotic sensor nodes. In this paper, we discuss a robotic sensor node model, and design and implementation of the middleware. We introduce three kinds of applications using the middleware such as environment monitoring, sensor controlled robot, and context-aware service. The experiments using the robotic sensor node and the middleware are conducted to evaluate and measure the possibility, performance, and practicality of a robotic sensor node mechanism.
Retrieval of context-aware applications on mobile devices: how to evaluate?
Authors: Stefano Mizzaro, Elena Nazzi, Luca Vassena
Venue: Proceedings of the second international symposium on Information interaction in context (2008)

This paper discusses the issue of evaluation of context-aware retrieval applications. We begin by describing MoBe, a specific architecture that allows automatic download and execution of context-aware applications on mobile devices. In MoBe, the most relevant applications are selected by matching context and application descriptors. Since several alternatives for descriptors implementation exist, it is important to compare their effectiveness. To this aim, we develop a TREC-like benchmark, in which the collection is made up by a set of application descriptors and the topics are made up by context descriptors. We then use the benchmark to evaluate the effectiveness of different descriptors components, and of structured and unstructured (i.e., free text) data, finding results that are somehow useful for future MoBe development. We also discuss the issue of the evaluation methodology for highly interactive and novel applications like context-aware retrieval systems, and MoBe in particular.
An Empirical Study of Flexible Business Process Based on Modularity System Theory
Authors: Fang Ding, Liu Jie
Venue: Proceedings of the 2008 The Third International Multi-Conference on Computing in the Global Information Technology (iccgi 2008) (2008)

This paper introduced concept of modularity business process. High modular business processes have properties of encapsulation and plug-and-play, which make business process more flexible and responsible to environment change. We also proposed a mechanism model of business process modularity and hypothesizes based on modular system theory and transaction cost economics. Diversity of customer demand and Supplier specialization will lead to modular design of business process. Physical assets specificity, human assets specificity and procedure assets specificity of business process will decrease the attractiveness of modularity business process. Empirical results support these hypothesizes.
Techniques to Produce Optimal Web Service Compositions
Authors: Eduardo Blanco, Yudith Cardinale, Maria-Esther Vidal, Jesús Graterol
Venue: Proceedings of the 2008 IEEE Congress on Services - Part I (2008)

As Web Services proliferate, it becomes more difficult to find a service that can perform a given task, and a coordination of several services may be required. We present two algorithms to identify orderings of Web Service compositions. These algorithms follow different strategies to prune the space of possibilities while minimizing the evaluation cost. The first one, DP-BF, combines a best first strategy with a dynamic-programming technique and produces good Web Service compositions by exploring a small portion of the search space. The second one, PT-SAM, adapts a petri-net unfolding algorithm and tries to find a desired marking from an initial state. We conducted an experimental study to evaluate the behavior of DP-BF and PT-SAM comparedto SAM and to the exhaustive solution. Our experimentsshow that the quality of the compositions identified by ouralgorithms are close to the optimal solution produced by theexhaustive algorithm, while the optimization time is close to the time required by SAM to identify a solution.