ppxscal/embedding_network huggingface dataset:
Using Social Commitments to Control the Agents' Freedom of Speech
Authors: Guillaume Muller, Laurent Vercouter
Venue: Agent Communication II: International Workshops on Agent Communication, AC 2005 and AC 2006, Utrecht, Netherlands, July 25, 2005, and Hakodate, Japan, May 9, 2006, Selected and Revised Papers (2006)

Communication is essential in multi-agent systems, since it allows agents to share knowledge and to coordinate. However, in open multi-agent systems, autonomous and heterogeneous agents can dynamically enter or leave the system. It is then important to take into account that some agents may behave badly, i.e. may not respect the rules that make the system function properly. In this article, we focus on communication rules and, especially, on the means necessary to detect when agents lie. Based on a model of the social semantics adapted to decentralised system, we first explicit the limits of the communicative behaviour of an agent, through the definition of obligations. Then, we propose a decentralised mechanism to detect situations where the obligations are violated. This mechanism is used to identify agents that exceed their rights and to build a representation of the honesty of the other agents by the way of a reputation value.
Minimal Resource Allocation on CAN Bus Using Radial Basis Function Networks
Authors: Yan Hao Wei, Mu-Song Chen, Chuan Ku Lin, Chi-Pan Hwang
Venue: Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III (2007)

Optimal message scheduling is one of the key issues in the field of controller area network (CAN) bus system. There are numerous approaches related to this issue. Most of them are essentially based on priority-based strategies. In 1, we utilized Radial Basic Function (RBF) network 2 as a message scheduling controller to dynamically schedule messages. Furthermore, an online Backward-Through-Time (BTT) algorithm is presented for parameter optimization under a priori fixed network structure. Intuitively, an inappropriate RBF network structure leads to performance degradation. In the worst case, the CAN system diverges. In this paper, we extend our previous works by including Minimal Resource Allocation (MRA) algorithm for structure determination. In this way, both problems of parameter optimization and structure determination can be resolved at the same time. Simulation results demonstrated that the proposed BTT with MRA methods outperform our previous results in terms of convergence time, stability, and the number of required hidden neurons (or radial basis functions).
The Partial Augment---Relabel Algorithm for the Maximum Flow Problem
Authors: Andrew V. Goldberg
Venue: Proceedings of the 16th annual European symposium on Algorithms (2008)

The maximum flow problem is a classical optimization problem with many applications. For a long time, HI-PR, an efficient implementation of the highest-label push-relabel algorithm, has been a benchmark due to its robust performance. We propose another variant of the push-relabel method, the partial augment-relabel (PAR) algorithm. Our experiments show that PAR is very robust. It outperforms HI-PR on all problem families tested, asymptotically in some cases.
Perceptual Issues in Haptic Digital Watermarking
Authors: Domenico Prattichizzo, Mauro Barni, Gloria Menegaz, Alessandro Formaglio, Hong Z. Tan, Seungmoon Choi
Venue: IEEE MultiMedia (2007)

The growing interest in haptic applications suggests that haptic digital media will soon become widely available, and the need will arise to protect digital haptic data from misuse. In this article, we present our study and findings on psychophysical experiments regarding human abilities to perceive a digital watermark, or hidden signal, through a haptic interface.
One Modelling Formalism & Simulator Is Not Enough! A Perspective for Computational Biology Based on James II
Authors: Adelinde M. Uhrmacher, Jan Himmelspach, Matthias Jeschke, Mathias John, Stefan Leye, Carsten Maus, Mathias Röhl, Roland Ewald
Venue: Proceedings of the 1st international workshop on Formal Methods in Systems Biology (2008)

Diverse modelling formalisms are applied in Computational Biology. Some describe the biological system in a continuous manner, others focus on discrete-event systems, or on a combination of continuous and discrete descriptions. Similarly, there are many simulators that support different formalisms and execution types (e.g. sequential, parallel-distributed) of one and the same model. The latter is often done to increase efficiency, sometimes at the cost of accuracy and level of detail. James II has been developed to support different modelling formalisms and different simulators and their combinations. It is based on a plug-in concept which enables developers to integrate spatial and non-spatial modelling formalisms (e.g. stochastic ¿ calculus, Beta binders, Devs, space- ¿), simulation algorithms (e.g. variants of Gillespie's algorithms (including Tau Leaping and Next Subvolume Method),space- ¿ simulator, parallel Beta binders simulator) and supporting technologies (e.g. partitioning algorithms, data collection mechanisms, data structures, random number generators) into an existing framework. This eases method development and result evaluation in applied modelling and simulation as well as in modelling and simulation research.
Unsupervised data pruning for clustering of noisy data
Authors: Yi Hong, Sam Kwong, Yuchou Chang, Qingsheng Ren
Venue: Knowledge-Based Systems (2008)

Data pruning works with identifying noisy instances of a data set and removing them from the data set in order to improve the generalization of a learning algorithm. It has been well studied in supervised classification where the identification and removal of noisy instances are guided by available labels of instances. However, to the best knowledge of the authors', very few work has been done on data pruning for unsupervised clustering. This paper deals with the problem of data pruning for unsupervised clustering under the condition that labels of instances are unknown beforehand. We claim that unsupervised data pruning can benefit for the clustering of the data with noise. We propose a feasible approach, termed as unsupervised Data Pruning using Ensembles of multiple Clusterers (DPEC), to identify noisy instances of a data set. DPEC checks all instances of a data set and identifies noisy instances by using ensembles of multiple clustering results provided by different clusterers on the same data set. We test the performance of DPEC on several real data sets with artificial noise. Experimental results demonstrate that DPEC is often able to improve the accuracy and robustness of the clustering algorithm.
Stochastic Functional Annealing as Optimization Technique: Application to the Traveling Salesman Problem with Recurrent Networks
Authors: Domingo López-Rodríguez, Enrique Mérida-Casermeiro, Gloria Galán-Marín, Juan M. Ortiz-De-Lazcano-Lobato
Venue: Proceedings of the 30th annual German conference on Advances in Artificial Intelligence (2007)

In this work, a new stochastic method for optimization problems is developed. Its theoretical bases guaranteeing the convergence of the method to a minimum of the objective function are presented, by using quite general hypotheses. Its application to recurrent discrete neural networks is also developed, focusing in the multivalued MREM model, a generalization of Hopfield's. In order to test the efficiency of this new method, we study the well-known Traveling Salesman Problem. Experimental results will show that this new model outperforms other techniques, achieving better results, even on average, than other methods.
Organic Control of Traffic Lights
Authors: Holger Prothmann, Fabian Rochner, Sven Tomforde, Jürgen Branke, Christian Müller-Schloer, Hartmut Schmeck
Venue: Proceedings of the 5th international conference on Autonomic and Trusted Computing (2008)

In recent years, Autonomic and Organic Computing have become areas of active research in the computer science community. Both initiatives aim at handling the growing complexity in technical systems by creating systems with adaptation and self-optimisation capabilities. One application scenario for such "life-like" systems is the control of road traffic signals in urban areas. This paper presents an organic approach to traffic light control and analyses its performance by an experimental validation of the proposed architecture which demonstrates its benefits compared to classical traffic control.
Human Behavior Classification Using Multiple Views
Authors: Dimitrios I. Kosmopoulos, Panagiota Antonakaki, Konstandinos Valasoulis, Anastasios Kesidis, Stavros Perantonis
Venue: Proceedings of the 5th Hellenic conference on Artificial Intelligence: Theories, Models and Applications (2008)

We present our current results in understanding the behavior of humans moving an a plane using multiple cameras. We exploit the merits of camera registration based on homography estimation to extract position on a 2D map. We use the output of HMM and SVM classifiers to model and extract human behaviour from both the target trajectory and the target short term activity (i.e., walking, running, abrupt motion etc). The proposed approach is verified experimentally in an indoor environment. The current results with a single moving agent are presented.
Textual Modelling Embedded into Graphical Modelling
Authors: Markus Scheidgen
Venue: Proceedings of the 4th European conference on Model Driven Architecture: Foundations and Applications (2008)

Although labelled graphical, many modelling languages represent important model parts as structured text. We benefit from sophisticated text editors when we use programming languages, but we neglect the same technology when we edit the textual parts of graphical models. Recent advances in generative engineering of textual model editors make the development of such sophisticated text editors practical, even for the smallest textual constructs of graphical languages. In this paper, we present techniques to embed textual model editors into graphical model editors and prove our approach for EMF-based textual editors and graphical editors created with GMF.
Polytopic best-mean H∞performance analysis
Authors: Shmuel Boyarski
Venue: Proceedings of the 7th WSEAS International Conference on Application of Electrical Engineering (2008)

In [1], a novel best-mean approach to robust analysis and control over uncertain-parameters boxes was presented. This paper extends the results of [1] to convex uncertainty polytopes of arbitrary shape and arbitrary number of vertices, without an underlying parameters model. The paper addresses robust, polytopic, probabilistic H∞ analysis of linear systems and focuses on the performance distribution over the uncertainty region (rather than just on the performance bound, as is customary in robust control). It is assumed that all system instances over the uncertainty polytope may occur with equal probability; this uniform distribution assumption is common in robust statistical analysis and is known to be conservative. The proposed approach considers different disturbance attenuation levels (DALs) at the vertices of the uncertainty polytope. It is shown that, under the latter assumption, the mean disturbance attenuation level (DAL) over the polytope is the algebraic average of the DALs at the polytope's vertices. Thus, the mean DAL over the polytope can be optimized by minimizing the sum of the DALs at the vertices. The standard deviation of the DAL over the uncertain parameters-box is also addressed, and a method to minimize this standard deviation (in order to enforce uniform performance over the polytope) is shown. The state-feedback design example utilizes a theorem presented in [1]. A Monte-Carlo analysis verifies the statistics of the resulting closed-loop 'pointwise' H∞-norms over the uncertainty region.
Optimization of Boolean Expressions-Historical Developments
Authors: Jack Minker, Rita G. Minker
Venue: IEEE Annals of the History of Computing (1980)

Boolean expressions play an essential role in all aspects of computer science. Interest in them is rooted in compiler design. Clearly, every compiler must be designed to evaluate Boolean expressions. It is therefore essential that the compiled code be as efficient as possible. Historical developments on this subject and related topics are discussed. Given a Boolean expression, techniques are described to optimize its evaluation. The optimum solution to the Boolean expression evaluation is presented for the situation when probabilities are known concerning whether or not the propositional variables evaluate to true (T), and costs are given to determine the evaluation.
An investigation on airflow in pathological nasal airway by PIV
Authors: S. K. Kim, J. R. Haw
Venue: Journal of Visualization (2004)

Understanding of airflow characteristics in nasal cavity is closely related with the physiological functions, like air-conditioning and smelling, and pathological aspects in nasal breathing. Several studies have utilized physical models of the healthy nasal cavity to investigate the relationship between nasal anatomy and airflow. The next step on this topic is naturally studies for disordered nasal airways and this is the main purpose of this article. Airflows in the pathological nasal airways, including nasal cavity and upper pharynx, of Korean adults are investigated experimentally by PIV measurement technique and air resistance measurements. Quantitative data for normal and pathological nasal airway are obtained. Average and RMS velocity distributions are obtained for inspirational and expirational nasal airflows. The CBC PIV algorithm with window offset is used for PIV flow analysis. PIV measurements of nasal airflow for nasal cavities with 50% and 70% adenoid vegetation are conducted for the first time. The asymmetric nasal cavities, due to either congenital deformity or injury, are also investigated. Comparisons in nasal airflows for both normal and abnormal cases are also appreciated and airflow characteristics that are related with the abnormalities in nasal cavity are proposed.
Story Generation to Accelerate Math Problem Authoring for Practice and Assessment
Authors: Yue Cui, Rohit Kumar, Carolyn P. Rosé, Kenneth Koedinger
Venue: Proceedings of the 9th international conference on Intelligent Tutoring Systems (2008)

We present a novel authoring infrastructure for accelerating the rate of content creation for coached practice environments. In an initial authoring stage, the tool supports quick authoring of scaffolded problems, drawing upon principles established in prior intelligent tutoring authoring research. However, in contrast to earlier approaches, the result of this stage is a structured template than can be used to generate a multitude of variations on the same problem concept.
A Finite Simulation Method in a Non-deterministic Call-by-Need Lambda-Calculus with Letrec, Constructors, and Case
Authors: Manfred Schmidt-Schauss, Elena Machkasova
Venue: Proceedings of the 19th international conference on Rewriting Techniques and Applications (2008)

The paper proposes a variation of simulation for checking and proving contextual equivalence in a non-deterministic call-by-need lambda-calculus with constructors, case, seq, and a letrec with cyclic dependencies. It also proposes a novel method to prove its correctness. The calculus' semantics is based on a small-step rewrite semantics and on may-convergence. The cyclic nature of letrec bindings, as well as non-determinism, makes known approaches to prove that simulation implies contextual preorder, such as Howe's proof technique, inapplicable in this setting. The basic technique for the simulation as well as the correctness proof is called pre-evaluation, which computes a set of answers for every closed expression. If simulation succeeds in finite computation depth, then it is guaranteed to show contextual preorder of expressions.
Scalability of a Novel Shifting Balance Theory-Based Optimization Algorithm: A Comparative Study on a Cluster-Based Wireless Sensor Network
Authors: Erfu Yang, Nick H. Barton, Tughrul Arslan, Ahmet T. Erdogan
Venue: Proceedings of the 8th international conference on Evolvable Systems: From Biology to Hardware (2008)

Scalability is one of the most important issues for optimization algorithms used in wireless sensor networks (WSNs) since there are often many parameters to be optimized at the same time. In this case it is very hard to ensure that an optimization algorithm can be smoothly scaled up from a low-dimensional optimization problem to the one with a high dimensionality. This paper addresses the scalability issue of a novel optimization algorithm inspired by the Shifting Balance Theory (SBT) of evolution in population genetics. Toward this end, a cluster-based WSN is employed in this paper as a benchmark to perform a comparative study. The total energy consumption is minimized under the required quality of service by jointly optimizing the transmission power and rate for each sensor node. The results obtained by the SBT-based algorithm are compared with the Metropolis algorithm (MA) and currently popular particle swarm optimizer (PSO) to assess the scaling performance of the three algorithms against the same WSN optimization problem.
Automatic Verification of Strongly Dynamic Software Systems
Authors: N. Dor, J. Field, D. Gopan, T. Lev-Ami, A. Loginov, R. Manevich, G. Ramalingam, T. Reps, N. Rinetzky, M. Sagiv, R. Wilhelm, E. Yahav, G. Yorsh
Venue: Verified Software: Theories, Tools, Experiments: First IFIP TC 2/WG 2.3 Conference, VSTTE 2005, Zurich, Switzerland, October 10-13, 2005, Revised Selected Papers and Discussions (2005)

Strongly dynamic software systems are difficult to verify. By strongly dynamic, we mean that the actors in such systems change dynamically, that the resources used by such systems are dynamically allocated and deallocated, and that for both sets, no bounds are statically known. In this position paper, we describe the progress we have made in automated verification of strongly dynamic systems using abstract interpretation with three-valued logical structures. We then enumerate a number of challenges that must be tackled in order for such techniques to be widely adopted.
Cardiac PET Motion Correction Using Materially Constrained Transform Models
Authors: Adrian J. Chung, Paolo G. Camici, Guang-Zhong Yang
Venue: Proceedings of the 4th international workshop on Medical Imaging and Augmented Reality (2008)

Recent improvements in the resolution of Positron Emission Tomography (PET) imaging have not translated into equivalent advances in diagnostic accuracy. Due to long acquisition times involved, the functional imaging modality is hampered by motion artefacts due to respiratory motion. In this paper, two methods for correcting reconstructed PET images through a list-mode re-binning process are investigated. The first method rebins the list-mode data according to a globally defined 3D affine transformation. The second is a novel approach that combines multiple independent 2D affine transforms in order to exploit the specific properties of 2D tomographic reconstruction. Each affine transformation method is applied to the respiratory gated sequence of line-of-response events prior to image reconstruction, thus compensating for any respiratory motion. The deformation models are derived from a non-rigid 3D/3D registration model applied to retrospectively gated MRI acquired during free-breathing. The motion correction schemes are validated using a simulation framework with respiratory gated MRI scans of 10 subjects to generate the required activity maps for estimating emission sinograms. This allows the ground truth solution to be derived so that the motion corrected reconstruction can be compared quantitatively. It is shown that the higher degrees of freedom of the 2D affine motion compensation model is superior to the 3D affine transform provided one incorporates weak material constraints to avoid ill conditioning and can tolerate the lower SNR that 2D reconstruction implies.
Fault-tolerant techniques for high performance computing and a bioinformatics application
Authors: Vipin Chaudhary
Venue:  (2007)

Computational clusters have long provided a mechanism for the acceleration of high performance computing (HPC) applications. As today&rsquo;s supercomputers approach the petaflop scale, however, they are also exhibiting an increase in heterogeneity. This heterogeneity spans a range of technologies, from multiple operating systems to hardware accelerators and novel architectures. Because of the exceptional acceleration some of these heterogeneous architectures provide, they are being embraced as viable tools for HPC applications, particularly in the area of biological sequence analysis. In this dissertation we study two of these challenges in detail. We begin with the HMMER sequence analysis suite. It uses a readily parallelizable algorithm based on profile hidden Markov models. However, to date HMMER has seen only limited use in the HPC setting due to its reliance on PVM for parallelization. We develop a more scalable distributed implementation of HMMER, called MPI-HMMER and extend it to include the use of multiple FPGAs for greater acceleration. The heterogeneous aspect of the acceleration brings to the forefront the second challenge studied in this dissertation: fault-tolerance and checkpointing for HPC systems. To address the challenges of HPC checkpointing, we develop a fault-tolerant MPI based on LAM/MPI with asynchronous replication along with checkpoint migration, eliminating the need for central or network storage and allowing for reconfigurable MPI topologies in the event of node failure. We evaluate centralized storage, SAN-based solutions, and a commercial parallel file system-based solution and show that they are not scalable. As a result, we show that our replication-based checkpointing/migration system is uniquely capable of handling the large amount of data generated by a supercomputing application&rsquo;s checkpoint. As a first step towards supporting the checkpointing of heterogeneous systems, we then explore the idea of using virtualization for high performance computing. Using OpenVZ, we demonstrate that the checkpointing of virtualized computational clusters is indeed feasible with relatively low overhead. By adapting the idea of checkpoint replication to the virtual environment, we eliminate any need for network storage or centralized servers, and reduce the impact of checkpointing on non-participating cluster nodes and users.
On Saaty's and Koczkodaj's inconsistencies of pairwise comparison matrices
Authors: Sándor Bozóki, Tamás Rapcsák
Venue: Journal of Global Optimization (2008)

The aim of the paper is to obtain some theoretical and numerical properties of Saaty's and Koczkodaj's inconsistencies of pairwise comparison matrices (PRM). In the case of 3 × 3 PRM, a differentiable one-to-one correspondence is given between Saaty's inconsistency ratio and Koczkodaj's inconsistency index based on the elements of PRM. In order to make a comparison of Saaty's and Koczkodaj's inconsistencies for 4 × 4 pairwise comparison matrices, the average value of the maximal eigenvalues of randomly generated n × n PRM is formulated, the elements a ij (i < j) of which were randomly chosen from the ratio scale $$\dfrac{1}{M}, \dfrac{1}{M-1}, \ldots , \dfrac{1}{2}, 1, 2, \ldots , M - 1, M,$$ with equal probability 1/(2M ¿ 1) and a ji is defined as 1/a ij . By statistical analysis, the empirical distributions of the maximal eigenvalues of the PRM depending on the dimension number are obtained. As the dimension number increases, the shape of distributions gets similar to that of the normal ones. Finally, the inconsistency of asymmetry is dealt with, showing a different type of inconsistency.