ppxscal/embedding_network huggingface dataset:
A simplified framework for stochastic workflow networks
Authors: Yajuan Li, Chuang Lin, Quan-Lin Li
Venue: Computers Mathematics with Applications (2008)

This paper presents a novel method to simplify stochastic workflow networks for their performance analysis under a unified computable framework. This method is based on two techniques: (1) module simplification, and (2) PH equivalence and PH approximation. In the first technique, simplified procedures for at least four crucial modules: sequential routing, parallel routing, selective routing and iterative routing are given, respectively; while in the second technique, the closure properties and the two-order approximation for the PH distributions are discussed. Using this method, we analyze several examples for the stochastic workflow networks and illustrate that performance evaluation of complicated stochastic workflow networks can be obtained by means of subsystems which are clearly constructed by some of the four structured modules. Numerical examples indicate that the method of this paper can tackle large-scale and complicated stochastic workflow networks with both effective approximation and low computational complexity.
A dialogue mechanism for public argumentation using conversation policies
Authors: Yuqing Tang, Simon Parsons
Venue: Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1 (2008)

In this paper, we propose a flexible dialogue mechanism through which a set of agents can establish a coherent set of public beliefs. Flexibility and coherence are achieved by decomposing the dialogue mechanism into two parts, a backbone protocol and a set of conversation policies. The backbone protocol maintains the set of arguments put forward by the agents, and each agent uses a preagreed argumentation theory to extract the set of public beliefs from the context. The flexibility is achieved by distributing the other functions of the dialogue mechanism among a set of conversation policies, some of which are public and some of which are private to each agent.
An Agent-Based Approach to the Multiple-Objective Selection of Reference Vectors
Authors: Ireneusz Czarnowski, Piotr Jedrzejowicz
Venue: Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition (2007)

The paper proposes an agent-based approach to the multiple-objective selection of reference vectors from original datasets. Effective and dependable selection procedures are of vital importance to machine learning and data mining. The suggested approach is based on the multiple agent paradigm. The authors propose using JABAT middleware as a tool and the original instance reduction procedure as a method for selecting reference vectors under multiple objectives. The paper contains a brief introduction to the multiple objective optimization, followed by the formulation of the multiple-objective, agent-based, reference vectors selection optimization problem. Further sections of the paper provide details on the proposed algorithm generating a non-dominated (or Pareto-optimal) set of reference vector sets. To validate the approach the computational experiment has been planned and carried out. Presentation and discussion of experiment results conclude the paper.
Signature-Based Calibration of Analytical System-Level Performance Models
Authors: Stanley Jaddoe, Andy D. Pimentel
Venue: Proceedings of the 8th international workshop on Embedded Computer Systems: Architectures, Modeling, and Simulation (2008)

The Sesame system-level simulation framework targets efficient design space exploration of embedded multimedia systems. Even despite Sesame's efficiency, it would fail to explore large parts of the design space simply because system-level simulation is too slow for this. Therefore, Sesame uses analytical performance models to provide steering to the system-level simulation, guiding it toward promising system architectures and thus pruning the design space. In this paper, we present a mechanism to calibrate these analytical models with the aim to deliver trustworthy estimates. Moreover, we also present some initial evaluation results with respect to the accuracy of our calibration mechanism using a case study with a Motion-JPEG encoder.
Faster Steiner Tree Computation in Polynomial-Space
Authors: Fedor V. Fomin, Fabrizio Grandoni, Dieter Kratsch
Venue: Proceedings of the 16th annual European symposium on Algorithms (2008)

Given an n-node graph and a subset of k terminal nodes, the NP-hard Steiner tree problem is to compute a minimum-size tree which spans the terminals. All the known algorithms for this problem which improve on trivial O(1.62 n )-time enumeration are based on dynamic programming, and require exponential space.Motivated by the fact that exponential-space algorithms are typically impractical, in this paper we address the problem of designing faster polynomial-space algorithms. Our first contribution is a simple polynomial-space O(6 k n O(logk))-time algorithm, based on a variant of the classical tree-separator theorem. This improves on trivial O(n k + O(1)) enumeration for, roughly, k ≤ n/4.Combining the algorithm above (for small k), with an improved branching strategy (for large k), we obtain an O(1.60 n )-time polynomial-space algorithm. The refined branching is based on a charging mechanism which shows that, for large values of k, convenient local configurations of terminals and non-terminals must exist. The analysis of the algorithm relies on the Measure Conquer approach: the non-standard measure used here is a linear combination of the number of nodes and number of non-terminals.As a byproduct of our work, we also improve the (exponential-space) time complexity of the problem from O(1.42 n ) to O(1.36 n ).
Business mobility: A changing ecosystem
Authors: Mary McDowell
Venue: Information-Knowledge-Systems Management (2008)

This paper examines the evolution of the still-nascent business mobility ecosystem and its key drivers, such as consumer behavior, that shape the segment. It also explores the changing roles and relationships of the ecosystem's key players; projections for growth in business mobility; and the value or ROI of business mobility. It offers advice to businesses that are considering business mobility solutions. And it points out a number of changes that members of the business mobility ecosystem will need to make in order for business mobility to evolve to the point of fruition, where companies are willingly ready to purchase solutions as a strategic investment, and where the solutions are as solid but also as flexible and easy to buy and integrate in a heterogeneous, global market. Lastly, the paper takes a look at a few large companies that have made significant steps toward strategic and holistic adoption of business mobility.
Geodesic Gaussian kernels for value function approximation
Authors: Masashi Sugiyama, Hirotaka Hachiya, Christopher Towell, Sethu Vijayakumar
Venue: Autonomous Robots (2008)

The least-squares policy iteration approach works efficiently in value function approximation, given appropriate basis functions. Because of its smoothness, the Gaussian kernel is a popular and useful choice as a basis function. However, it does not allow for discontinuity which typically arises in real-world reinforcement learning tasks. In this paper, we propose a new basis function based on geodesic Gaussian kernels, which exploits the non-linear manifold structure induced by the Markov decision processes. The usefulness of the proposed method is successfully demonstrated in simulated robot arm control and Khepera robot navigation.
Short Proofs of Strong Normalization
Authors: Aleksander Wojdyga
Venue: Proceedings of the 33rd international symposium on Mathematical Foundations of Computer Science (2008)

This paper presents simple, syntactic strong normalization proofs for the simply-typed ¿-calculus and the polymorphic ¿-calculus (system F) with the full set of logical connectives, and all the permutative reductions. The normalization proofs use translations of terms and types of to terms and types of and from to .
Multi-facet Problem Comprehension: Utilizing an Algorithmic Idea in Different Contexts
Authors: Bruria Haberman, Orna Muller, Haim Averbuch
Venue: Proceedings of the 3rd international conference on Informatics in Secondary Schools - Evolution and Perspectives: Informatics Education - Supporting Computational Thinking (2008)

Instructional design has a significant influence on the construction of knowledge, especially for novices. Specifically, the way the instruction of algorithmic problem solving is designed has a significant effect on the development of the student's capabilities to analyze and solve problems. We present a pedagogical approach regarding teaching algorithmic problem solving, which is based on the assimilation of a new concept by demonstrating its different facets through a variety of relevant examples. The approach aims to support multi-facet problem comprehension, as well as to enhance the student's ability to utilize algorithmic ideas in different contexts. The approach was introduced to computer science teachers through a workshop activity aimed at discussing the topic of evaluating the complexity level of problems and their challenging characteristics. We think that an activity of this kind is beneficial for raising teachers' awareness of the way they select problems in order to develop students' problem-solving skills.
Adaptive Spiking Neural Networks for Audiovisual Pattern Recognition
Authors: Simei Gomes Wysoski, Lubica Benuskova, Nikola Kasabov
Venue: Neural Information Processing: 14th International Conference, ICONIP 2007, Kitakyushu, Japan, November 13-16, 2007, Revised Selected Papers, Part II (2008)

The paper describes the integration of brain-inspired systems to perform audiovisual pattern recognition tasks. Individual sensory pathways as well as the integrative modules are implemented using a fast version of spiking neurons grouped in evolving spiking neural network (ESNN) architectures capable of lifelong adaptation. We design a new crossmodal integration system, where individual modalities can influence others before individual decisions are made, fact that resembles some characteristics of the biological brains. The system is applied to the person authentication problem. Preliminary results show that the integrated system can improve the accuracy in many operation points as well as it enables a range of multi-criteria optimizations.
Multi-category Bayesian Decision by Neural Networks
Authors: Yoshifusa Ito, Cidambi Srinivasan, Hiroyuki Izumi
Venue: Proceedings of the 18th international conference on Artificial Neural Networks, Part I (2008)

For neural networks, learning from dichotomous random samples is difficult. An example is learning of a Bayesian discriminant function. However, one-hidden-layer neural networks with fewer inner parameters can learn from such signals better than ordinary ones. We show that such neural networks can be used for approximating multi-category Bayesian discriminant functions when the state-conditional probability distributions are two dimensional normal distributions. Results of a simple simulation are shown as examples.
100% organic: design and implementation of self-sustaining cellular networks
Authors: Nathanael Thompson, Petros Zerfos, Robert Sombrutzki, Jens-Peter Redlich, Haiyun Luo
Venue: Proceedings of the 9th workshop on Mobile computing systems and applications (2008)

A new model for developing cellular-like networks is emerging in the market in which end users own and install part of the infrastructure and share it with other customers. The network grows "organically" as more end users join and contribute to its expansion. To drastically reduce the cost of these customer-installed networks, self-managed service designs are needed that implement all the services and their management functionality with minimal support from a core network or central planning entity. In this paper, we describe the design and implementation of two basic cellular network services, authentication, and location registration, from which we learn some important insights into the design of future services. We also highlight challenges that need to be solved in order for organic networks to prove a viable alternative to centrally managed cellular infrastructures.
Credal Model Averaging: An Extension of Bayesian Model Averaging to Imprecise Probabilities
Authors: Giorgio Corani, Marco Zaffalon
Venue: Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I (2008)

We deal with the arbitrariness in the choice of the prior over the models in Bayesian model averaging (BMA), by modelling prior knowledge by a set of priors (i.e., a prior credal set). We consider Dash and Cooper's BMA applied to naive Bayesian networks, replacing the single prior over the naive models by a credal set; this models a condition close to prior ignorance about the models, which leads to credal model averaging (CMA). CMA returns an indeterminate classification, i.e., multiple classes, on the instances for which the learning set is not informative enough to smooth the effect of the choice of the prior. We give an algorithm to compute exact credal model averaging for naive networks. Extensive experiments show that indeterminate classifications preserve the reliability of CMA on the instances which are classified in a prior-dependent way by BMA.
A Semantic Analysis of a Logic for Pragmatics with Assertions, Obligations, and Causal Implication
Authors: Kurt Ranalter
Venue: Fundamenta Informaticae (2008)

One of the aims of a logic for pragmatics is to provide a logical framework that formalizes reasoning about speech acts. In this paper we investigate the semantics of a fragment of the logic for pragmatics proposed by Bellin and Dalla Pozza in "A pragmatic interpretation of substructural logics" (Feferman Festschrift, ASL Lecture Notes in Logic 15, 2002). The logic deals with acts of assertion and acts of obligation, and it incorporates a rule that relates acts of obligation to acts of assertion via a notion of causal implication. As our main result we show that the logic is sound and complete with respect to a class of algebraic, Kripke, and categorical models.
The long road to 64 bits
Authors: John Mashey
Venue: Communications of the ACM (2009)

Double, double toil and trouble ---Shakespeare, Macbeth, Act 4, Scene 1
Visual human tracking and group activity analysis: a video mining system for retail marketing
Authors: Mihran Tuceryan
Venue:  (2007)

In this thesis we present a system for automatic human tracking and activity recognition from video sequences. The problem of automated analysis of visual information in order to derive descriptors of high level human activities has intrigued computer vision community for decades and is considered to be largely unsolved. A part of this interest is derived from the vast range of applications in which such a solution may be useful. We attempt to find efficient formulations of these tasks as applied to the extracting customer behavior information in a retail marketing context. Based on these formulations, we present a system that visually tracks customers in a retail store and performs a number of activity analysis tasks based on the output from the tracker. In tracking we introduce new techniques for pedestrian detection, initialization of the body model and a formulation of the temporal tracking as a global trans-dimensional optimization problem. Initial human detection is addressed by a novel method for head detection, which incorporates the knowledge of the camera projection model.The initialization of the human body model is addressed by newly developed shape and appearance descriptors. Temporal tracking of customer trajectories is performed by employing a human body tracking system designed as a Bayesian jump-diffusion filter. This approach demonstrates the ability to overcome model dimensionality ambiguities as people are leaving and entering the scene. Following the tracking, we developed a two-stage group activity formulation based upon the ideas from swarming research. For modeling purposes, all moving actors in the scene are viewed here as simplistic agents in the swarm. This allows to effectively define a set of inter-agent interactions, which combine to derive a distance metric used in further swarm clustering. This way, in the first stage the shoppers that belong to the same group are identified by deterministically clustering bodies to detect short term events and in the second stage events are post-processed to form clusters of group activities with fuzzy memberships. Quantitative analysis of the tracking subsystem shows an improvement over the state of the art methods, if used under similar conditions. Finally, based on the output from the tracker, the activity recognition procedure achieves over 80% correct shopper group detection, as validated by the human generated ground truth results.
Cross-Language Weaving Approach Targeting Software Security Hardening
Authors: Azzam Mourad, Dima Alhadidi, Mourad Debbabi
Venue: Proceedings of the 2008 Sixth Annual Conference on Privacy, Security and Trust (2008)

In this paper, we propose an approach for systematic security hardening of software based on aspect-oriented programming and Gimple language. We also present the first steps towards a formal specification for Gimple weaving together with the implementation methodology of the proposed weaving semantics. The primary contribution of this approach is providing the software architects with the capabilities to perform systematic security hardening by applying well-defined solutions and without the need to have expertise in the security solution domain. We explore the viability of our propositions by realizing the weaving semantics for Gimple by implementing it into the GCC compiler and applying our methodologies for systematic security hardening to develop a case study for securing the connections of client applications together with experimental results.
Static Analysis of Dynamic Communication Systems by Partner Abstraction
Authors: Jörg Bauer, Reinhard Wilhelm
Venue: Proceedings of the 14th international Symposium on Static Analysis (2007)

Prominent examples of dynamic communication systems include traffic control systems and ad hoc networks. They are hard to verify due to inherent unboundedness. Unbounded creation and destruction of objects and a dynamically evolving communication topology are characteristic features.Partner graph grammars are presented as an adequate specification formalism for dynamic communication systems. They are based on the single pushout approach to algebraic graph transformation and specifically tailored to dynamic communication systems. We propose a new verification technique based on abstract interpretation of partner graph grammars. It uses a novel two-layered abstraction, partner abstraction, that keeps precise information about objects and their communication partners. We identify statically checkable cases for which the abstract interpretation is even complete. In particular, applicability of transformation rules is preserved precisely. The analysis has been implemented in the hiralysis tool. It is evaluated on a complex case study, car platooning, for which many interesting properties can be proven automatically.
Image Fusion Algorithm Using RBF Neural Networks
Authors: Hong Zhang, Xiao-Nan Sun, Lei Zhao, Lei Liu
Venue: Proceedings of the 3rd international workshop on Hybrid Artificial Intelligence Systems (2008)

This paper presents the image fusion algorithm using Radial Basis Function (RBF) neural networks. The clustering of every original image pixel is obtained by RBF neural networks combined with nearest neighbor clustering method. For each cluster center, the membership of every fused image pixel is adopted as the weighting coefficient of the weighted strategy, which is used to obtain the fusion image. The membership is obtained by maximum rule. The original data set is chosen as the candidate set of nearest neighbor clustering algorithm, and the center set of hidden units are dynamically established. In this experiment, the fusion results of various widths of hidden unit are compared with the results obtained by self-organizing feature map (SOFM) neural networks method. The influence of the various widths is discussed in this paper. The experiment results show that the proposed method can achieve better performance of the fused image.
Benchmarking GPUs to tune dense linear algebra
Authors: Vasily Volkov, James W. Demmel
Venue: Proceedings of the 2008 ACM/IEEE conference on Supercomputing (2008)

We present performance results for dense linear algebra using recent NVIDIA GPUs. Our matrix-matrix multiply routine (GEMM) runs up to 60% faster than the vendor's implementation and approaches the peak of hardware capabilities. Our LU, QR and Cholesky factorizations achieve up to 80--90% of the peak GEMM rate. Our parallel LU running on two GPUs achieves up to ~540 Gflop/s. These results are accomplished by challenging the accepted view of the GPU architecture and programming guidelines. We argue that modern GPUs should be viewed as multithreaded multicore vector units. We exploit blocking similarly to vector computers and heterogeneity of the system by computing both on GPU and CPU. This study includes detailed benchmarking of the GPU memory system that reveals sizes and latencies of caches and TLB. We present a couple of algorithmic optimizations aimed at increasing parallelism and regularity in the problem that provide us with slightly higher performance.
Asynchronous Data Replication: A National Integration Strategy for Databases on Telemedicine Network
Authors: Douglas Dyllon Jeronimo de Macedo, Hilton Ganzo William Perantunes, Rafael Andrade, Aldo von Wangenheim, Mario Antonio Ribeiro Dantas
Venue: Proceedings of the 2008 21st IEEE International Symposium on Computer-Based Medical Systems (2008)

Telemedicine systems currently have increased the volume of information stored in their databases. A centralized telemedicine system project covers datasets that ranges from personal patient's information, physicians and institutions to all images of examinations performed. As they store large amounts of examinations, the medical databases can reach several terabytes of volume. This paper presents a contribution characterized by an asynchronous replication model for medical distributed databases. The model, called PostgresReplication, is an extension to the relational database PostgreSQL. In our experiment, an engine has been created to manage all integration operations and information replication for the medical databases. The early results indicate that the model and its implementation have successfully reached a good performance level and interoperability.
Towards Validating a Platoon of Cristal Vehicles Using CSP||B
Authors: Samuel Colin, Arnaud Lanoix, Olga Kouchnarenko, Jeanine Souquières
Venue: Proceedings of the 12th international conference on Algebraic Methodology and Software Technology (2008)

The so-called platooning problem consists in making autonomous vehicles move in a convoy. It crosses several domains: distributed systems, embedded systems, multi-agent systems and critical systems. We thus propose to use the combination named CSP||B of two well-known formal methods to assess and verify properties of this complex system. To that end we make use of previous theoretical results on CSP||B. We also illustrate how this methodology spans the multiple composition levels of the resulting model.