ppxscal/embedding_network huggingface dataset:
Evolving Complex Neural Networks
Authors: Mauro Annunziato, Ilaria Bertini, Matteo Felice, Stefano Pizzuti
Venue: Proceedings of the 10th Congress of the Italian Association for Artificial Intelligence on AI*IA 2007: Artificial Intelligence and Human-Oriented Computing (2007)

Complex networks like the scale-free model proposed by Barabasi-Albert are observed in many biological systems and the application of this topology to artificial neural network leads to interesting considerations. In this paper, we present a preliminary study on how to evolve neural networks with complex topologies. This approach is utilized in the problem of modeling a chemical process with the presence of unknown inputs (disturbance). The evolutionary algorithm we use considers an initial population of individuals with differents scale-free networks in the genotype and at the end of the algorithm we observe and analyze the topology of networks with the best performances. Experimentation on modeling a complex chemical process shows that performances of networks with complex topology are similar to the feed-forward ones but the analysis of the topology of the most performing networks leads to the conclusion that the distribution of input node information affects the network performance (modeling capability).
Application of Classification Methods to Individual Disability Income Insurance Fraud Detection
Authors: Yi Peng, Gang Kou, Alan Sabatka, Jeff Matza, Zhengxin Chen, Deepak Khazanchi, Yong Shi
Venue: Proceedings of the 7th international conference on Computational Science, Part III: ICCS 2007 (2007)

As the number of electronic insurance claims increases each year, it is difficult to detect insurance fraud in a timely manner by manual methods alone. The objective of this study is to use classification modeling techniques to identify suspicious policies to assist manual inspections. The predictive models can label high-risk policies and help investigators to focus on suspicious records and accelerate the claim-handling process.The study uses health insurance data with some known suspicious and normal policies. These known policies are used to train the predictive models. Missing values and irrelevant variables are removed before building predictive models. Three predictive models: Naïve Bayes (NB), decision tree, and Multiple Criteria Linear Programming (MCLP), are trained using the claim data. Experimental study shows that NB outperformed decision tree and MCLP in terms of classification accuracy.
One Learning and Teaching Tool for Preliminary Virtual Instrument Course
Authors: Yongkai Fan, Tianze Sun, Xiaolong Fu, Jun Lin, Yangyi Sui
Venue: Proceedings of the 7th international conference on Computational Science, Part III: ICCS 2007 (2007)

This paper presents one teaching and leaning assistant tool with the purpose of reinforcing both teaching process and learning process for introductory virtual instrument course. This tool has two main ends: from the teacher's viewpoint, the tool gives preliminary introductory information and foundational theory related the course which will help reinforce lecture and laboratory session, therefore, teacher can save time to pay more attention to personalized education for different student; from the students' viewpoint, it providing a leaning environment which equipped with help, collaborative learning group, communication tool, all of those which can facilitate their personal work and make them more motivated to involved in course session.
Gradual Typing for Objects
Authors: Jeremy Siek, Walid Taha
Venue: Proceedings of the 21st European conference on ECOOP 2007: Object-Oriented Programming (2007)

Static and dynamic type systems have well-known strengths and weaknesses. In previous work we developed a gradual type system for a functional calculus named $\lambda^?_\to$. Gradual typing provides the benefits of both static and dynamic checking in a single language by allowing the programmer to control whether a portion of the program is type checked at compile-time or run-time by adding or removing type annotations on variables. Several object-oriented scripting languages are preparing to add static checking. To support that work this paper develops $\mathbf{Ob}^{?}_{, a gradual type system for object-based languages, extending the Ob < : calculus of Abadi and Cardelli. Our primary contribution is to show that gradual typing and subtyping are orthogonal and can be combined in a principled fashion. We also develop a small-step semantics, provide a machine-checked proof of type safety, and improve the space efficiency of higher-order casts.
Multi-level Fusion for Semantic Video Content Indexing and Retrieval
Authors: Rachid Benmokhtar, Benoit Huet
Venue: Adaptive Multimedial Retrieval: Retrieval, User, and Semantics: 5th International Workshop, AMR 2007, Paris, France, July 5-6, 2007 Revised Selected Papers (2007)

In this paper, we present the results of our work on the analysis of an automatic semantic video content indexing and retrieval system based on fusing various low level visual descriptors. Global MPEG-7 features extracted from video shots, are described via IVSM signature (Image Vector Space Model) in order to have a compact description of the content. Both static and dynamic feature fusion are introduced to obtain effective signatures. Support Vector Machines (SVMs) are employed to perform classification (One classifier per feature). The task of the classifiers is to detect the video semantic content. Then, classifier outputs are fused using a neural network based on evidence theory (NNET) in order to provide a decision on the content of each shot. The experimental results are conducted in the framework of the TRECVid feature extraction task.
Individualism and Collectivism in Trade Agents
Authors: Gert Jan Hofstede, Catholijn M. Jonker, Tim Verwaart
Venue: Proceedings of the 21st international conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: New Frontiers in Applied Artificial Intelligence (2008)

Agent-Based Modeling can contribute to the understanding of international trade processes. Models for the effects of culture and cultural differences on agent behavior are required for realistic agent-based simulation of international trade. This paper makes a step toward modeling of culture in agents. The focus is one of the five dimensions of culture according to Hofstede: individualism versus collectivism. The paper presents an analysis based on social science literature about national cultures. For cultural differentiation of agent behavior, rules are formulated for individualist versus collectivist agent behavior with respect to negotiations, cooperation or defection in the delivery phase of transactions, trade partner selection, and trust. Example computations demonstrate the feasibility in multi-agent simulations.
Computational and robotic models of human postural control
Authors: Arash Mahboobin
Venue:  (2007)

Currently, no bipedal robot exhibits fully human-like characteristics in terms of its postural control and movement. Current biped robots move more slowly than humans and are much less stable. Humans utilize a variety of sensory systems to maintain balance, primary among them being the visual, vestibular and proprioceptive systems. A key finding of human postural control experiments has been that the integration of sensory information appears to be dynamically regulated to adapt to changing environmental conditions and the available sensory information, a process referred to as &ldquo;sensory re-weighting.&rdquo; In contrast, in robotics, the emphasis has been on controlling the location of the center of pressure based on proprioception, with little use of vestibular signals (inertial sensing) and no use of vision. Joint-level PD control with only proprioceptive feedback forms the core of robot standing balance control. More advanced schemes have been proposed but not yet implemented. The multiple sensory sources used by humans to maintain balance allow for more complex sensorimotor strategies not seen in biped robots, and arguably contribute to robust human balance function across a variety of environments and perturbations. Our goal is to replicate this robust human balance behavior in robots. In this work, we review results exploring sensory re-weighting in humans, through a series of experimental protocols, and describe implementations of sensory re-weighting in simulation and on a robot.
Design of an Unsupervised Weight Parameter Estimation Method in Ensemble Learning
Authors: Masato Uchida, Yousuke Maehara, Hiroyuki Shioya
Venue: Neural Information Processing: 14th International Conference, ICONIP 2007, Kitakyushu, Japan, November 13-16, 2007, Revised Selected Papers, Part I (2007)

A learning method using an integration of multiple component predictors as an ultimate predictor is generically referred to as ensemble learning. The present paper proposes a weight parameter estimation method for ensemble learning under the constraint that we do not have any information of the desirable (true) output. The proposed method is naturally derived from a mathematical model of ensemble learning, which is based on an exponential mixture type probabilistic model and Kullback divergence. The proposed method provides a legitimate strategy for weight parameter estimation under the abovementioned constraint if it is assumed that the accuracy of all multiple predictors are the same. We verify the effectiveness of the proposed method through numerical experiments.
Optimal Features Subset Selection Using Genetic Algorithms for Iris Recognition
Authors: Kaushik Roy, Prabir Bhattacharya
Venue: Proceedings of the 5th international conference on Image Analysis and Recognition (2008)

Iris recognition is a flourishing biometrics scheme; however, still there exists some technical difficulties. In this paper, an iris recognition method has been proposed based on genetic algorithms (GA) for the selection of the optimal features subset. The accurate iris patterns classification has become a challenging issue due to the huge number of textural features extracted from an iris image with comparatively a small number of samples per subject. The traditional feature selection schemes like principal component analysis, independent component analysis, singular valued decomposition etc. require sufficient number of samples per subject to select the most representative features sequence; however, it is not always realistic to accumulate a large number of samples due to some security issues. We propose GA to improve the feature subset selection by combining valuable outcomes from multiple feature selection methods. This paper also motivates and introduces the use of Gaussian Mixture Model (GMM) for iris pattern classification. The proposed technique is computationally effective with the recognition rates of 97.90% and 96.30% on the ICE (Iris Challenge Evaluation) and the WVU (West Virginia University) iris datasets respectively.
Speeding Up the NRA Algorithm
Authors: Peter Gurský, Peter Vojtáš
Venue: Proceedings of the 2nd international conference on Scalable Uncertainty Management (2008)

Methods of top-k search with no random access can be used to find k best objects using sorted access to the sources of attribute values. In this paper we present new heuristics over the NRA algorithm that can be used for fast search of top-k objects using wide range of user preferences. NRA algorithm usually needs a periodical scan of a large number of candidates during the computation. In this paper we propose methods of no random access top-k search that optimize the candidate list maintenance during the computation to speed up the search. The proposed methods are compared to a table scan method typically used in databases. We present results of experiments showing speed improvement depending on number of object attributes expressed in a user preferences or selectivity of user preferences.
The IMS T800 Transputer
Authors: Mark Homewood, David May, David Shepherd, Roger Shepherd
Venue: IEEE Micro (1987)

The IMS T800, a complete scientific computer on a single chip, forms the basis of the most powerful supercomputer in Europe.
Ontology Support for Biomedical Information Resources
Authors: Tharam Dillon, Elizabeth Chang, Maja Hadzic
Venue: Proceedings of the 2008 21st IEEE International Symposium on Computer-Based Medical Systems (2008)

The increasing body of distributed and heterogeneous information and the autonomous, heterogeneous and dynamic nature of information resources are important issues hindering effective and efficient data access, retrieval and knowledge sharing. The importance of ontologies has been recognised within the biomedical domain and work has begun on developing and sharing biomedical ontologies. In this paper, we define ontology and ontology commitments and explain the main characteristics and representations of ontology models. Ontologies are highly expressive knowledge models and as such increase expressiveness and intelligence of a system. We highlight the significance of ontologies in a variety of semi-automatic and automatic tasks, and provide an illustrative example of an ontology-based multi-agent system designed to intelligently retrieve information about human diseases from a number of heterogeneous and dispersed information resources.
From Intelligent Agents to Intelligent Beings
Authors: Paul Valckenaers, Hadeli Hadeli, Bart Saint Germain, Paul Verstraete, Jan Belle, Hendrik Brussel
Venue: Proceedings of the 3rd international conference on Industrial Applications of Holonic and Multi-Agent Systems: Holonic and Multi-Agent Systems for Manufacturing (2007)

This paper puts forward a novel concept: the intelligent being. Intelligent agents emphasize action and decision-making, which is strongly reflected in the results of agent research. However, real-world applications require much more than decision-making and the intelligent being addresses a crucial part of this: reflecting reality into the agent world. Importantly, reflecting some part of reality offers significant benefits for software developers. Indeed, properly designed intelligent beings inherit the coherency and consistency of the corresponding reality. This allows the development of systems with unprecedented complexity and size. This paper discusses the novel concept and its implications for software development. Importantly, the intelligent agents themselves may be considered as part of the corresponding reality, resulting in short-term forecasting services.
Stable Relief in feature weighting
Authors: Omid Khayat, Hamid Reza Shahdoosti, Mohammad Hosein Khosravi
Venue: Proceedings of the 7th WSEAS International Conference on Artificial intelligence, knowledge engineering and data bases (2008)

Feature weighting algorithms assign weights to features according to their relevance to a particular task. Unfortunately, the best-known feature weighting algorithm, ReliefF, is biased. It decreases the relevance of some features and increases the relevance of others when irrelevant attributes are added to the data set. This paper presents an improved version of the algorithm, Stable Relief, for classification tasks by extracting relevant information. This method, using Stable Relief, is applied to feature weighting for the nearest neighbor classifier and is tested on real-world classification tasks. The results show that it can improve the nearest neighbor classifier on the tested tasks, and also outperforms the Relief. This paper shows that Stable Relief outperforms ReliefF on the task of cat and dog discrimination, using real images.
Using Satisfiability Modulo Theories to Analyze Abstract State Machines (Abstract)
Authors: Margus Veanes, Ando Saabas
Venue: Proceedings of the 1st international conference on Abstract State Machines, B and Z (2008)

We look at a fragment of ASMs used to model protocol-like aspects of software systems. Such models are used industrially as part of documentation and oracles in model-based testing of application-level network protocols. Correctness assumptions about the model are often expressed through state invariants. An important problem is to validate the model prior to its use as an oracle. We discuss a technique of using Satisfiability Modulo Theories or SMT to perform bounded reachability analysis of such models. We use the Z3 solver for our implementation and we use AsmL as the modeling language.
Bandwidth of the Ultrasound Doppler Signal with Applications in Blood/Tissue Segmentation in the Left Ventricle
Authors: Sigve Hovda, Håvard Rue, Bjørn Olstad
Venue: Medical Imaging and Informatics: 2nd International Conference, MIMI 2007, Beijing, China, August 14-16, 2007 Revised Selected Papers (2008)

A new estimator, Bandwidth Imaging, related to the bandwidth of the ultrasound Doppler signal is proposed as a classification function of blood and tissue signal in transthoracial echocardiography of the left ventricle. An in vivo experiment is presented, where the apparent error rate of Bandwidth Imaging is compared with the apparent error rate of Second-Harmonic Imaging on 15 healthy men. The apparent error rates are calculated from the 16 myocardial wall segments defined in [1]. A hypothesis test of Bandwidth Imaging having lower apparent error rate than Second-Harmonic Imaging is proved for a p-value of 0.94 in 3 segments in end diastole and in 1 segment in end systole. When data was averaged by a structural element of 5 radial, 3 lateral and 4 temporal samples the numbers of segments increased to 9 in end diastole and to 6 in end systole. This experiment indicates that Bandwidth Imaging can supply additional information for automatic border detection routines on endocardium.
A High Throughput FPGA-Based Floating Point Conjugate Gradient Implementation
Authors: Antonio Roldao Lopes, George A. Constantinides
Venue: Proceedings of the 4th international workshop on Reconfigurable Computing: Architectures, Tools and Applications (2008)

As Field Programmable Gate Arrays (FPGAs) have reached capacities beyond millions of equivalent gates, it becomes possible to accelerate floating-point scientific computing applications. One type of calculation that is commonplace in scientific computation is the solution of systems of linear equations. A method that has proven in software to be very efficient and robust for finding such solutions is the Conjugate Gradient algorithm. In this paper we present a parallel hardware Conjugate Gradient implementation. The implementation is particularly suited for accelerating multiple small to medium sized dense systems of linear equations. Through parallelization it is possible to convert the computation time per iteration for an order n matrix from ¿(n 2) cycles for a software implementation to ¿(n). I/O requirements are scalable and converge to a constant value with the increase of matrix order. Results on a VirtexII-6000 demonstrate sustained performance of 5 GFLOPS and projected results on a Virtex5-330 indicate sustained performance of 35 GFLOPS. The former result is comparable to high-end CPUs, whereas the latter represents a significant speedup.
Identifying the Boundary of a Wireless Sensor Network with a Mobile Sink
Authors: Majid I. Khan, Wilfried N. Gansterer, Günter Haring
Venue: Proceedings of the 7th international conference on Ad-hoc, Mobile and Wireless Networks (2008)

This paper summarizes an effort to evaluate the usability of a mobile sink for identifying the boundary of a wireless sensor network. In order to achieve the desired task we transform the problem of boundary identification into one of edge node identification. The algorithm designed is based on a mobile sink equipped with a directional antenna, which identifies the edge nodes and connects them to complete the boundary of the sensor field. The proposed scheme has following distinct features. Firstly, it is independent of the sensor node deployment, and therefore can be used for fields having very low node density. Secondly, it does not require sensor field flooding which helps preserving the nodes' energy. Thirdly, it works with low cost sensor nodes, i.e., it does not impose any special requirements on the hardware of individual sensor nodes (no GPS, no special antennas, etc.), which makes it cost effective.
Does Body Orientation Matter When Reasoning about Depicted or Described Scenes?
Authors: Marios N. Avraamides, Stephanie Pantelidou
Venue: Proceedings of the international conference on Spatial Cognition VI: Learning, Reasoning, and Talking about Space (2008)

Two experiments were conducted to assess whether the orientation of the body at the time of test affects the efficiency with which people reason about spatial relations that are encoded in memory through symbolic media. Experiment 1 used depicted spatial layouts while Experiment 2 used described environments. In contrast to previous studies with directly-experienced spatial layouts, the present experiments revealed no sensorimotor influences on performance. Differences in reasoning about immediate and non-immediate environments are thus discussed. Furthermore, the same patterns of findings (i.e., normal alignment effects) were observed in the two experiments supporting the idea of functional equivalence of spatial representations derived from different modalities.
Categories, Software and Meaning
Authors: Robin Milner
Venue: Concurrency, Graphs and Models: Essays Dedicated to Ugo Montanari on the Occasion of His 65th Birthday (2008)

I am delighted to be able to share in celebrating Ugo's birthday, if not with a formal paper then at least with some loosely-knit philosophical ideas.I have worked on ideas similar to Ugo's for most of our careers. Ugo had much to do with the stream of expert Italians, many now well-known, who travelled from the warmth of Pisa to the romantic but cooler climate of Edinburgh, often to launch their careers with a PhD there. Going further back, I remember with excitement the meeting on parallel processes at Pisa in 1973, organised I believe by Ugo, the first concurrency conference I ever attended.
IrisNet: An Architecture for a Worldwide Sensor Web
Authors: Phillip B. Gibbons, Brad Karp, Yan Ke, Suman Nath, Srinivasan Seshan
Venue: IEEE Pervasive Computing (2003)

Today's common computing hardware Internet-connected desktop PCs and inexpensive, commodity off-the-shelf sensors such as Webcams is an ideal platform for a worldwide sensor web. In a worldwide sensor web, users can query, as a single unit, vast quantities of data from thousands or even millions of widely distributed, heterogeneous sensors. Users can also actuate sensors and adapt sensor-feed processing in reaction to sensed data in real time.IrisNet is a software infrastructure that aims to provide the missing software components for realizing a worldwide sensor web. It supports many features that simplify the task of creating sensor web services that are feature-rich, widely-distributed, high performance, highly scalable, adapt well to usage patterns, and robust against failures. Three services from different application domains currently use IrisNet.
Linguistic Summarization of Time Series Using Fuzzy Logic with Linguistic Quantifiers: A Truth and Specificity Based Approach
Authors: Janusz Kacprzyk, Anna Wilbik
Venue: Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing (2006)

We reformulate and extend our previous works (cf. Kacprzyk, Wilbik and Zadrożny [7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17]), mainly towards a more complex and realistic evaluation of results on the linguistic summarization of time series which is meant as the derivation of an linguistic quantifier driven aggregation of partial trends with respect to the dynamics of change, duration and variability. We use Zadeh's calculus of linguistically quantified propositions but, in addition to the basic criterion of a degree of truth (validity), we also use a degree of specificity to make it possible to account for a frequent case that though the degree of truth of a very general (not specific) summary is high, its usefulness may be low due to its low specificity. We show an application to the absolute performance type analysis of daily quotations of an investment fund.