ppxscal/embedding_network huggingface dataset:
Symbolic verification of multiagent graphical decision models
Authors: Yifeng Zeng, Kim-leng Poh
Venue: International Journal of Intelligent Systems (2008)

Multiply sectioned influence diagrams (MSID), together with hyper relevance graph (HRG), is a probabilistic graphical decision model representing distributed decision problems involving multiple agents. To ensure a valid model representation, an MSID observes a set of constraints that must be verified before its evaluation. We present a symbolic method to verify some constraints of this graphical decision model in a cooperative fashion. The method makes full use of an algebraic description of factorization joint probability in an MSID instead of focusing on the graphical structure of decision model. It is a cooperative verification that protects agent's privacy in multiagent systems. Furthermore, the issue of verification failure is investigated with some helpful corrections. Finally, a pairwise verification is proposed concerning other constraints to realize a valid model associated with the knowledge domain. &copy; 2008 Wiley Periodicals, Inc.
Automatic Vectorization Technology of Scanning Landuse Map Based on Mathematical Morphology
Authors: Yonghua Sun, Xiaojuan Li, Peipei Gu
Venue: Proceedings of the 2008 Congress on Image and Signal Processing, Vol. 4 - Volume 04 (2008)

In 1st July 2007, China Government formally started a great project named "The Second Survey for the Land and Resources". China plans to investigate the current landuse status, and realize the land and resource informatization during two and a half years. Vectorization of Scanning Landuse Map plays an important role in land informatization and digital land project. Traditionally, it's very laborious and time-consuming to digitize from scanning landuse map by hand. Aiming at these problems, this paper designed a technology flow process for automatic vectorization of scanning landuse map based on mathematical morphology. Many key techniques, such as noise reducing, image thinning; separation of graphic and text were studied in this paper.
An overview on model-based approaches in face recognition
Authors: Omid Khayat, Hamid Reza Shahdoosti, Ahmad Jaberi Motlagh
Venue: Proceedings of the 7th WSEAS International Conference on Artificial intelligence, knowledge engineering and data bases (2008)

In recent years face recognition has received substantial attention from both research communities and the market, but still remained very challenging in real applications. A lot of face recognition algorithms, along with their modifications, have been developed during the past decades. A number of typical algorithms are presented, being categorized into appearance based and model-based schemes. In this paper we expand and describe the Model-Based approaches. The model-based approaches are introduced, including Elastic Bunch Graph matching, Active Appearance Model and 3D Morphable Model methods. A number of face databases available in the public domain and several published performance evaluation results are digested. Future research directions based on the current recognition results are pointed out.
A radio multiplexing architecture for high throughput point to multipoint wireless networks
Authors: Ramakrishna Gummadi, Rabin Patra, Sergiu Nedevschi, Sonesh Surana, Eric Brewer
Venue: Proceedings of the 2008 ACM workshop on Wireless networks and systems for developing regions (2008)

WiFi-based point-to-multipoint systems are a cost-effective solution for providing high-bandwidth connectivity to remote rural regions. However, current point-to-multipoint deployments are hampered by several challenges. Their capacity to support a large number of clients simply by increasing the number of radios at the base station is limited because space restrictions on radio towers bound the number of antennas that can be installed at one physical location. Also the limited availability of wireless channels restricts the number of clients that can be simultaneously supported by a base station at any point of time. In this paper, we explore high-throughput architectures for point-to-multipoint networks. We show how we can increase the number of radios at the base station without increasing the number of antennas simultaneously. We propose a simple yet practical multiplexing design that uses cheap RF combiner/splitter devices for multiplexing several radios onto a single antenna. We also examine a more general design that uses RF switches, and which allows us to allocate radios to antennas dynamically based on client traffic demands. As a proof of concept, we demonstrate and evaluate the simple case of combining up to three radios operating on different channels onto only one antenna, using off-the-shelf combiner/splitters and attenuators. We show that not only is such a design feasible, but also that the achieved link throughput in both directions is as good as the one obtained by using separate antennas for each radio, as long as we provide sufficient RF isolation between the multiplexed radios.
Integrated resource management for lambda-grids: The Distributed Virtual Computer (DVC)
Authors: Andrew A. Chien, Nut Taesombut
Venue: Future Generation Computer Systems (2009)

The Distributed Virtual Computer (DVC) is the key unifying element of the OptIPuter software architecture. It provides a simple, clean abstraction for applications or higher-level middleware, allowing them to use lambda-grids with the same convenience as a VPN. The DVC is successful because it employs integrated network and end-resource selection, achieving high quality results so that there is little incentive for end-users to expose and manage lower-level interfaces. We describe the development of the DVC abstraction, key results, and experience with multiple applications and testbeds.
Fraud Detection and Prevention in Smart Card Based Environments Using Artificial Intelligence
Authors: Wael William Malek, Keith Mayes, Kostas Markantonakis
Venue: Proceedings of the 8th IFIP WG 8.8/11.2 international conference on Smart Card Research and Advanced Applications (2008)

This paper discusses the development and research for the detection of fraud in Smart-Card environments by using artificial intelligence. The current research deals with behaviour based detection engine, which will detect abnormalities by learning the usual behaviour of the user and detecting new unusual behaviours. The behaviour-based detection engines is based on `Neural Networks'. This work considers the feasibility of implementing `Neural Network' fraud engine on a Smart card platforms.
On Notions of Security for Deterministic Encryption, and Efficient Constructions without Random Oracles
Authors: Alexandra Boldyreva, Serge Fehr, Adam O'Neill
Venue: Proceedings of the 28th Annual conference on Cryptology: Advances in Cryptology (2008)

The study of deterministic public-key encryption was initiated by Bellare et al. (CRYPTO '07), who provided the "strongest possible" notion of security for this primitive (called PRIV) and constructions in the random oracle (RO) model. We focus on constructing efficient deterministic encryption schemes without random oracles. To do so, we propose a slightly weaker notion of security, saying that no partial information about encrypted messages should be leaked as long as each message is a-priori hard-to-guess given the others (while PRIV did not have the latter restriction). Nevertheless, we argue that this version seems adequate for many practical applications. We show equivalence of this definition to single-message and indistinguishability-based ones, which are easier to work with. Then we give general constructions of both chosen-plaintext (CPA) and chosen-ciphertext-attack (CCA) secure deterministic encryption schemes, as well as efficient instantiations of them under standard number-theoretic assumptions. Our constructions build on the recently-introduced framework of Peikert and Waters (STOC '08) for constructing CCA-secure probabilistic encryption schemes, extending it to the deterministic-encryption setting as well.
Managing out-of-grammar utterances by topic estimation with domain extensibility in multi-domain spoken dialogue systems
Authors: Kazunori Komatani, Satoshi Ikeda, Tetsuya Ogata, Hiroshi G. Okuno
Venue: Speech Communication (2008)

Spoken dialogue systems must inevitably deal with out-of-grammar utterances. We address this problem in multi-domain spoken dialogue systems, which deal with more tasks than a single-domain system. We defined a topic by augmenting a domain about which users want to find more information, and we developed a method of recovering out-of-grammar utterances based on topic estimation, i.e., by providing a help message in the estimated domain. Moreover, domain extensibility, that is, the ability to add new domains to the system, should be inherently retained in multi-domain systems. To estimate domains without sacrificing extensibility, we collected documents from the Web as training data. Since the data contained a certain amount of noise, we used latent semantic mapping (LSM), which enables robust topic estimation by removing the effects of noise from the data. Experimental results showed that our method improved topic estimation accuracy by 23.2 points for data including out-of-grammar utterances.
Consumer adoption of internet banking in Nigeria
Authors: Ping Gao, Olayinka Owolabi
Venue: International Journal of Electronic Finance (2008)

Internet banking as a key component of e-finance has gained research attention. However, the research on developing countries is lacking. This paper investigates the factors that influence the consumer adoption of internet banking in Nigeria. The results show that the currently relevant factors determining the adoption of internet banking in Nigeria include the level of awareness or attention, the accessibility to computers and the internet, convenience, privacy, costs, and the availability of knowledge and support concerning internet banking. This paper argues that the banks should embark on initiatives that would drastically increase the level of consumer awareness of internet banking details, especially its benefits.
A Hybrid Automated Detection System Based on Least Square Support Vector Machine Classifier and k-NN Based Weighted Pre-processing for Diagnosing of Macular Disease
Authors: Kemal Polat, Sadık Kara, Ayşegül Güven, Salih Güneş
Venue: Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part II (2007)

In this paper, we proposed a hybrid automated detection system based least square support vector machine (LSSVM) and k-NN based weighted pre-processing for diagnosing of macular disease from the pattern electroretinography (PERG) signals. k-NN based weighted pre-processing is pre-processing method, which is firstly proposed by us. The proposed system consists of two parts: k-NN based weighted pre-processing used to weight the PERG signals and LSSVM classifier used to distinguish between healthy eye and diseased eye (macula diseases). The performance and efficiency of proposed system was conducted using classification accuracy and 10-fold cross validation. The results confirmed that a hybrid automated detection system based on the LSSVM and k-NN based weighted pre-processing has potential in detecting macular disease. The stated results show that proposed method could point out the ability of design of a new intelligent assistance diagnosis system.
Analysis and placement of storage capacity in large distributed video servers
Authors: Vicent Cholvi, Juan Segarra
Venue: Computer Communications (2008)

In this paper, we study how to distribute storage capacity along a hierarchical system with cache-servers located at each node. This system is intended to deliver stored video streams in a video-on-demand way, ensuring that, once started, a transmission will be completed without any delay or quality loss. We use off-line smoothing for videos, dividing them into CBR video parts. Also, our request rates are distributed following a 24h audience curve. In this system, when a request is received, the server reserves the required bandwidth at the required time slots, trying to serve the video as soon as possible. We perform a detailed analysis by means of simulations of the start-up time delay for some storage distributions. It shows that an adequate storage distribution can increase performance about 25% with respect to a uniform distribution and about 47% with respect to one in which all the storage is attached to the gateway routers that connect the final users. We also analyze bandwidth usage, comparing the behavior of these storage distributions. Finally, we present a method which allows dynamic and transparent video reallocations when their popularity changes.
Extending Services Delivery with Lightweight Composition
Authors: Christian Janiesch, Kathrin Fleischmann, Alexander Dreiling
Venue: Proceedings of the 2008 international workshops on Web Information Systems Engineering (2008)

The tertiary sector is an important employer and its growth is well above average. The Texo project's aim is to support this development by making services tradable. The composition of new or value-added services is a cornerstone of the proposed architecture. It is, however, intended to cater for build-time. Yet, at run-time unforseen exceptions may occur and user's requirements may change. Varying circumstances require immediate sensemaking of the situation's context and call for prompt extensions of existing services. Lightweight composition technology provided by the RoofTop project enables domain experts to create simple widget-like applications, also termed enterprise mashups, without extensive methodological skills. In this way RoofTop can assist and extend the idea of service delivery through the Texo platform and is a further step towards a next generation internet of services.
An architecture framework for composite services with process-personalization
Authors: Murat M. Tanik
Venue:  (2007)

The integration of large systems remains problematic in spite of advances in composite services approaches. Current composite services approaches can effectively address only the integration of mechanistic processes. Processes with human interaction, abundant in large systems, are integrated in an ad hoc manner. As a result, a process gap occurs between user needs and implemented systems. The lack of support for human interaction with processes in large systems is referred to as the composite process-personalization (CPP) challenge. The CPP problems can occur at two stages. First, they occur at the modeling stage because of the difficulties in capturing the semantics of user needs. The lack of understanding of the problem domain and scope of integration of user needs results in a loss of semantics at this stage. Second, the CPP problems occur at the composition stage because of the lack of technology support for configuring and managing the semantics of user needs captured during the modeling stage. The composite P2FRAMEWORK, a composition, integration, and personalization framework, addresses the CPP problems in two ways. First, systematic guidance is provided to alleviate the problems of loss of semantics at the modeling stage. The systematic guidance is based on the dimensions of CPP, which identify the semantic and syntactic needs of large systems integration. The semantic aspects of the dimension provide guidance for the capturing of user needs. The syntactic aspects specify the need for flexible integration and automation of large systems processes. Second, the framework uses the service-agent model, a modeling approach for configuring and managing the semantics of user needs and developing composite services with CPP. The service-agent model supports both the semantic and syntactic dimensions of CPP. The guidance of the composite P2FRAMEWORK for large systems integration with CPP is demonstrated and validated by using two case studies. The first case study is a weather composite service process with a human interaction task. The second case study is the gene linkage identification part of the composite epidemiology research process for identifying candidate genes for obesity research.
The Benefits of Surprise in Dynamic Environments: From Theory to Practice
Authors: Emiliano Lorini, Michele Piunti
Venue: Proceedings of the 2nd international conference on Affective Computing and Intelligent Interaction (2007)

Artificial agents engaged in real world applications require accurate resource allocation strategies. For instance, open systems may require artificial agents with the capability to filter out all information which are irrelevant with respect to the actual intentions and goals. In this work we develop a model of surprise-driven belief update. We formally define a strategy for epistemic reasoning of a BDI -inspired agent, where surprise is the causal precursor of a belief update process. According to this strategy, an agent should update his beliefs only with inputs which are surprising and relevant with respect to his current intentions. We also compare in practice the performances of agents using a surprise-driven strategy of belief update and agents using traditional reasoning processes.
The median function on graphs with bounded profiles
Authors: Kannan Balakrishnan, Manoj Changat, Sandi Klavar
Venue: Discrete Applied Mathematics (2008)

The median of a profile @p=(u"1,...,u"k) of vertices of a graph G is the set of vertices x that minimize the sum of distances from x to the vertices of @p. It is shown that for profiles @p with diameter @q the median set can be computed within an isometric subgraph of G that contains a vertex x of @p and the r-ball around x, where r>2@q-1-2@q/|@p|. The median index of a graph and r-joins of graphs are introduced and it is shown that r-joins preserve the property of having a large median index. Consensus strategies are also briefly discussed on a graph with bounded profiles.
Direct Reckoning Reverberation Time from the Scene Images Based on Rough Fuzzy Neural Network
Authors: Wei Gong, Na Qi
Venue: Proceedings of the 2008 Congress on Image and Signal Processing, Vol. 4 - Volume 04 (2008)

As the most important objective parameter, reverberation time plays a significant role in acoustic field characteristics evaluation of the hall. We can get it by measuring an actual room. In this paper, a new method is proposed based on rough fuzzy neural network to reckon the reverberation time from a scene image, which is obviously distinguished from the conventional methods. Finally the validity of the network is proved through the experiment results of network training on the test data. It provides a brand-new idea for virtual reality technique and sound quality evaluation of virtual environment with using this method.
A Soft Constraint of Equality: Complexity and Approximability
Authors: Emmanuel Hebrard, Barry O'Sullivan, Igor Razgon
Venue: Proceedings of the 14th international conference on Principles and Practice of Constraint Programming (2008)

We introduce the SoftAllEqual global constraint, which maximizes the number of equalities holding between pairs of assignments to a set of variables. We study the computational complexity of propagating this constraint, showing that it is intractable in general, since maximizing the number of pairs of equally assigned variables in a set is NP-hard. We propose three ways of coping with NP-hardness. Firstly, we develop a greedy linear-time algorithm to approximate the maximum number of equalities within a factor of 2. Secondly, we identify a tractable (polynomial) class for this constraint. Thirdly, we identify a parameter based on this class and show that the SoftAllEqual constraint is fixed-parameter tractable with respect to this parameter.
Haptic Perception of Real and Virtual Curvature
Authors: Maarten W. Wijntjes, Akihiro Sato, Astrid M. Kappers, Vincent Hayward
Venue: Proceedings of the 6th international conference on Haptics: Perception, Devices and Scenarios (2008)

In this study we compared human discrimination performance for real and virtual curved shapes. To simulate a curved shape we used a device that could independently orient and elevate a moving surface that was in contact with an exploring finger. Thus, the geometry was preserved up to the first order in the virtual shape. In our experiment we found that this preservation was indeed sufficient: discrimination thresholds were similar for the real and virtual conditions. Our results were also in line with previous curvature studies performed with real stimuli.
Dynamic Cardiac Mapping on Patient-Specific Cardiac Models
Authors: Kevin Wilson, Gerard Guiraudon, Doug Jones, Cristian A. Linte, Chris Wedlake, John Moore, Terry M. Peters
Venue: Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I (2008)

Minimally invasive techniques for electrophysiological cardiac data mapping and catheter ablation therapy have been driven through advancements in computer-aided technologies, including magnetic tracking systems, and virtual and augmented-reality environments. The objective of this work is to extend current cardiac mapping techniques to collect and display data in the temporal domain, while mapping on patient-specific cardiac models. This paper details novel approaches to collecting spatially tracked cardiac electrograms, registering the data with a patient-specific cardiac model, and interpreting the data directly on the model surface, with the goal of giving a more comprehensive cardiac mapping system in comparison to current systems. To validate the system, laboratory studies were conducted to assess the accuracy of navigating to both physical and virtual landmarks. Subsequent to the laboratory studies, an in-vivo porcine experiment was conducted to assess the systems overall ability to collect spatial tracked electrophysiological data, and map directly onto a cardiac model. The results from these experiments show the new dynamic cardiac mapping system was able to maintain high accuracy of locating physical and virtual landmarks, while creating a dynamic cardiac map displayed on a dynamic cardiac surface model.
Principal directions for local independent components analysis
Authors: Doru Constantin
Venue: Proceedings of the 9th WSEAS International Conference on Neural Networks (2008)

As it is well known, in nonlinear independent component analysis, usually a series of difficulties related the parameter estimation arise. The reported work proposes an estimation method based on the use of the principal components and on the use of the algorithms of the linear ICA pattern. The estimation of the nonlinear ICA involves a local analysis pattern using the principal directions for the classification of input data and the application of the linear ICA pattern to the level of each class of elements. We use a classification part that corresponds to the nonlinear representation of the mixed data as well as a part of local application of the linear ICA pattern in order to describe the independent characteristics of the data. The purpose is to obtain better data representation as compared to the methods corresponding to the linear ICA pattern at global level. The proposed algorithm was tested in several signals separation applications and the obtained results prove good recognition performances.