ppxscal/embedding_network huggingface dataset:
Efficiently determining the appropriate mix of personal interaction and reputation information in partner choice
Authors: Shulamit Reches, Philip Hendrix, Sarit Kraus, Barbara J. Grosz
Venue: Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2 (2008)

Many multi-agent settings require that agents identify appropriate partners or teammates with whom to work on tasks. When selecting potential partners, agents may benefit from obtaining information about alternative possibilities through gossip (i.e., by consulting others) or using a reputation system (a centralized repository of information about past behavior). This paper defines a statistical model, the "Information-Acquisition Source Utility model" (IASU) by which agents operating in an uncertain world can determine the amount of information to collect about potential partners before choosing one and which information sources they should consult (gossip, reputation system, or additional personal interaction with the agent). The IASU model explicitly represents the cost of information, which may vary by information source. To maximize the expected gain from a choice, it estimates the utility of choosing a partner by iteratively estimating the benefit of additional information. The paper reports empirical studies that compare the effectiveness of the IASU model with a baseline in which only prior experience with a potential partner is used as the basis of the decision and with a model that determines in advance both the amount of information and its allocation among the different sources. Two different application domains are used in these empirical studies, the Surrogate Venture Game model, which concerns choosing an optimal partner for a business venture, and a restaurant domain. The results of the experiments show that the use of the model significantly increases the agents' overall utility.
Enhanced Empirical Mode Decomposition
Authors: Denis Donnelly
Venue: Proceedings of the international conference on Computational Science and Its Applications, Part II (2008)

Empirical mode decomposition (EMD) associated with the Hilbert-Huang transform (HHT) deconstructs a time-series signal into a set of monocomponent signals called intrinsic mode functions (IMF). EMD also acts as a filter limiting the frequency range of each IMF. EMD filtering is less than ideal and can lead to misleading results. This difficulty is ameliorated by first subjecting the time-series to bandpass filtration, where the pass-band frequency range is sufficiently narrow that the entire pass-band is captured in a single IMF. A series of such filtrations are required to treat a multicomponent signal. This approach avoids partial representation of those frequencies at the crossover between two successive IMF's. Bandpass enhanced EMD is applied to a bat chirp signal. That the fundamental, the first, second, and part of the third harmonic are expressed, demonstrates the improved sensitivity of this method over the standard HHT approach. The fundamental and harmonics of this chirp have an exponential form with a decay rate proportional to the square root of the time.
An ACO Algorithm with Adaptive Volatility Rate of Pheromone Trail
Authors: Zhifeng Hao, Han Huang, Yong Qin, Ruichu Cai
Venue: Proceedings of the 7th international conference on Computational Science, Part IV: ICCS 2007 (2007)

Ant colony optimization (ACO) has been proved to be one of the best performing algorithms for NP-hard problems as TSP. The volatility rate of pheromone trail is one of the main parameters in ACO algorithms. It is usually set experimentally in the literatures for the application of ACO. The present paper proposes an adaptive strategy for the volatility rate of pheromone trail according to the quality of the solutions found by artificial ants. The strategy is combined with the setting of other parameters to form a new ACO algorithm. Finally, the experimental results of computing traveling salesman problems indicate that the proposed algorithm is more effective than other ant methods.
Semi-supervised Learning of Tree-Structured RBF Networks Using Co-training
Authors: Mohamed F. Abdel Hady, Friedhelm Schwenker, Günther Palm
Venue: Proceedings of the 18th international conference on Artificial Neural Networks, Part I (2008)

Supervised learning requires a large amount of labeled data but the data labeling process can be expensive and time consuming. Co-training is a semi-supervised learning method that reduces the amount of required labeled data through exploiting the available unlabeled data in supervised learning to boost the accuracy. It assumes that the patterns are described by multiple independent feature sets and each feature set is sufficient for classification. On the other hand, most of the real-world pattern recognition tasks involve a large number of categories which may make the task difficult. The tree-structured approach is a multi-class decomposition strategy where a complex multi-class problem is decomposed into tree structured binary sub-problems. In this paper, we propose a framework that combines the tree-structured approach with Co-training. We show that our framework is especially useful for classification tasks involving a large number of classes and a small amount of labeled data where the tree-structured approach does not perform well by itself and when combined with Co-training, the unlabeled data boosts its accuracy.
Digital Evidence: Dream and Reality
Authors: Rolf Oppliger, Ruedi Rytz
Venue: IEEE Security and Privacy (2003)

Digital evidence is inherently weak. New evidence-gathering technologies-digital black boxes-must be developed and deployed to support investigations of irreproducible events such as digitally signing a document.
Adaptive Neuro-Fuzzy Inference System Based Autonomous Flight Control of Unmanned Air Vehicles
Authors: Sefer Kurnaz, Okyay Kaynak, Ekrem Konakoğlu
Venue: Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks (2007)

This paper proposes ANFIS logic based autonomous flight controller for UAVs (unmanned aerial vehicles). Three fuzzy logic modules are developed for the control of the altitude, the speed, and the roll angle, through which the altitude and the latitude-longitude of the air vehicle is controlled. The implementation framework utilizes MATLAB's standard configuration and the Aerosim Aeronautical Simulation Block Set which provides a complete set of tools for rapid development of detailed 6 degree-of-freedom nonlinear generic manned/unmanned aerial vehicle models. The Aerosonde UAV model is used in the simulations in order to demonstrate the performance and the potential of the controllers. Additionally, Microsoft Flight Simulator and FlightGear Flight Simulator are deployed in order to get visual outputs that aid the designer in the evaluation of the controllers. Despite the simple design procedure, the simulated test flights indicate the capability of the approach in achieving the desired performance.
Recurrent auctions in e-commerce
Authors: Boleslaw K. Szymanski
Venue:  (2007)

Recent developments of information technologies are causing shift from fixed pricing to dynamic pricing mechanisms in electronic marketplaces. The latter can improve revenue and resource utilization. However, the dynamism makes seller&rsquo;s price decision and buyer&rsquo;s budget planning difficult. Auction based dynamic pricing and negotiation mechanism can resolve such difficulties because price emerges from buyer&rsquo;s (i.e., bidder&rsquo;s) willingness to pay. Thanks to these advantages and inherent negotiation nature, the application domain of auction as dynamic pricing and negotiation mechanism covers service oriented electronic short-term contract marketplaces. Auctions in such markets are recurring since the contracts must be offered repeatedly for specific time intervals. In such recurring auction, uneven wealth distribution of bidders causes the least wealthy bidders to persistently lose auction rounds that motivate them to drop out of recurring auction. This bidder drop problem arises also in the traditional combinatorial winner selection strategy that only focuses on revenue maximization. The bidders dropping out of an auction decrease price competition and may cause a collapse of market price. At the same time, all available resources for fulfillment of electronic services must be sold in each auction round to avoid waste of resources. For these reasons, the previously designed traditional basic auction mechanisms may not be efficient in such service oriented electronic marketplaces.To overcome these problems, this thesis proposes and evaluates novel auction mechanisms for selling short-term contracts in service oriented electronic marketplaces. For homogeneous market structure in which bidders&rsquo; requirements are homogeneous, we propose a Participation Incentive Optimal Recurring Auction (PI-ORA) mechanism that is incentive compatible and a Discriminatory Price Optimal Recurring Auction mechanism that is not incentive compatible. Compared to the traditional basic auction mechanisms, the proposed mechanisms (i.e., PI-ORA and DP-ORA) stabilize the market prices and increase the auctioneer&rsquo;s revenue by preventing the bidder drop problem and avoiding the resource waste problem. Additionally, the proposed mechanisms achieve better long-term fairness of resource allocations. For markets in which bidders&rsquo; requirements are heterogeneous, we propose and evaluate a Participation Incentive Generalized Vickery Auction (PI-GVA) mechanism. Compared to the traditional Generalized Vickery Auction mechanism, the proposed PI-GVA mechanism prevents market price collapse and stabilizes the market by a novel combinatorial winner selection strategy in which bidder&rsquo;s participation is directly rewarded.The three proposed mechanisms also achieve other desirable properties for auction mechanism such as light communication overhead needed to reach agreement between auctioneer and bidders, the simple bidder&rsquo;s optimal strategy that is desirable for implementing an auction in electronic environments, and the distributed resources allocation.
A One-Step Network Traffic Prediction
Authors: Xiangyang Mu, Nan Tang, Weixin Gao, Lin Li, Yatong Zhou
Venue: Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence (2008)

In the information society today computer networks are an indispensable part of people's life. Network traffic prediction is important to network planning, performance evaluation and network management directly. A variety of machine learning models such as artificial neural networks (ANN) and support vector machine (SVM) have been applied in traffic prediction. In this paper, a novel network traffic one-step-ahead prediction technique is proposed based on a state-of-the-art learning model called minimax probability machine (MPM). The predictive performance is tested on traffic data of Ethernet, experimental results show that the predictions of MPM match the actual traffics accurately and the proposed methods can increases the computational efficiency. Furthermore, we compare the MPM-based prediction technique with the SVM-based techniques. The results show that the predictive performance of MPM is competitive with SVM.
Transparency Measurement of Telepresence Systems
Authors: Marwan Radi, Jordi Artigas, Carsten Preusche, Hubert Roth
Venue: Proceedings of the 6th international conference on Haptics: Perception, Devices and Scenarios (2008)

After Stability, transparency is a major goal when designing haptic telepresence schemes. Ideal transparency, where the user feels direct haptic interaction with the remote environment, becomes affected by each of the components of the teleoperation system. In an ideal scenario, the energy observed at the master side is straightforwardly conveyed to the slave side. Conversely, realistic scenarios include sources of energy leaks, such as time delays and discretization blocs, which, if stability is to be preserved, they will normally have a lossy passive nature, which therefore lowers transparency. Only a few studies on how to evaluate transparency quantitatively are reported in the literature. This work investigates and extends two methods which aim at outputting a transparency coefficient: an analytical model-based method which uses Yokokohji's Indexes of Transparency [1], and an empirical approach based on the Z width concept [2], which allows to measure transparency without the need of precise knowledge of the system. The methods are validated using a velocity-force haptic telepresence system testing for different control parameters and different time delays in channel.
Design, fabrication and hydrodynamic analysis of a biomimetic robot fish
Authors: Donya Mohammadshahi, Aghil Yousefi-Koma, Shahnaz Bahmanyar, Hesam Maleki
Venue: Proceedings of the 10th WSEAS International Conference on Automatic Control, Modelling & Simulation (2008)

This paper is a review on design, fabrication and hydrodynamic analysis of a biomimetic robot fish that is made in Advanced Dynamic and Control System Laboratory, ADCSL, at University of Tehran. In order to build a fish-like swimming robot comprehensive hydrodynamic and structural analysis were performed in addition to a lot of try and errors in fabrication. All of these followed by extensive study of biology of the fish especially their maneuverability and propulsion system. Swimming principle is achieved from Carangiform swimming mode. This is the swimming mode of fish that use their tail and peduncle for propulsion. Experiments show feasibility and good execution of ADCSL robot fish.
Institutionalised collaborative tagging as an instrument for managing the maturing learning and knowledge resources
Authors: Ronald Maier, Stefan Thalmann
Venue: International Journal of Technology Enhanced Learning (2008)

Recently, social software and collaborative tagging have received high levels of attention in Internet communities and have also been discussed as interesting approaches to annotate resources and distribute the cumbersome task of designing ontologies from few domain experts to large numbers of users of digital resources. This paper discusses the suitability of collaborative tagging for annotating knowledge and learning resources in the institutionalised setting of businesses and organisations. Specifically, the paper discusses commitment, convergence and coordination issues and presents the results of a multi-round experiment involving 174 Bachelor students at the Innsbruck University School of Management.
The Reactive-Causal Architecture: Towards Development of Believable Agents
Authors: Ali Orhan Aydın, Mehmet Ali Orgun, Abhaya Nayak
Venue: Proceedings of the 8th international conference on Intelligent Virtual Agents (2008)

To develop believable agents, agents should be highly autonomous, situated, flexible, and emotional. By this study, our aim is to present an agent architecture called Reactive-Causal Architecture that supports development of believable agents.
An Approximate Model of the WCDMA Interface Servicing a Mixture of Multi-rate Traffic Streams with Priorities
Authors: Damian Parniewicz, Maciej Stasiak, Janusz Wiewióra, Piotr Zwierzykowski
Venue: Proceedings of the 5th European Performance Engineering Workshop on Computer Performance Engineering (2008)

The paper presents an approximate method for blocking probability determination in the WCDMA interface of the UMTS network with priorities. In our considerations we use a new model of the full-availability group servicing multi-rate traffic with priorities. In the proposed model we assume that a new call with a higher priority can terminate connections already in service if they are characterized by a lower priority than a new call. The proposed scheme is applicable for cost-effective WCDMA resource management in 3G mobile networks and can be easily applied to network capacity calculations.
Adaptive Divisible Load Model for Scheduling Data-Intensive Grid Applications
Authors: M. Othman, M. Abdullah, H. Ibrahim, S. Subramaniam
Venue: Proceedings of the 7th international conference on Computational Science, Part I: ICCS 2007 (2007)

In many data grid applications, data can be decomposed into multiple independent sub datasets and schedule for parallel execution and analysis. Divisible Load Theory (DLT) is a powerful tool for modelling data-intensive grid problems where both communication and computation load is partitionable. This paper presents an Adaptive DLT (ADLT) model for scheduling data-intensive grid applications. This model reduces the expected processing time approximately 80% for communication intensive applications and 60% for computation intensive applications compared to the previous DLT model. Experimental results show that this model can balance the loads efficiently.
Two-Level ACO for Haplotype Inference Under Pure Parsimony
Authors: Stefano Benedettini, Andrea Roli, Luca Gaspero
Venue: Proceedings of the 6th international conference on Ant Colony Optimization and Swarm Intelligence (2008)

Haplotype Inference is a challenging problem in bioinformatics that consists in inferring the basic genetic constitution of diploid organisms on the basis of their genotype. This information enables researchers to perform association studies for the genetic variants involved in diseases and the individual responses to therapeutic agents.A notable approach to the problem is to encode it as a combinatorial problem under certain hypotheses (such as the pure parsimony criterion) and to solve it using off-the-shelf combinatorial optimization techniques. At present, the main methods applied to Haplotype Inference are either simple greedy heuristic or exact methods, which are adequate only for moderate size instances.In this paper, we present an iterative constructive approach to Haplotype Inference based on ACO and we compare it against a state-of-the-art exact method.
On Evolutionary Synthesis of Linear Transforms in FPGA
Authors: Zdenĕk Vašíček, Martin Žádník, Lukáš Sekanina, Jiří Tobola
Venue: Proceedings of the 8th international conference on Evolvable Systems: From Biology to Hardware (2008)

In this paper, an evolutionary approach is used to design multiple constant multipliers (MCMs). As these circuits can be composed of adders, subtractors and shifters, they perform a linear transform. An important consequence is that only a single input value is sufficient to completely evaluate a candidate circuit independently of its size and the bit width of the datapath. Proposed method is able to compete with well-optimized heuristics in particular problem instances. This paper also deals with a hardware acceleration of the method in FPGA which provides the speedup of two orders of magnitude in comparison with a conventional PC.
Waveguide Synthesis by Genetic Algorithms with Multiple Crossover
Authors: Jana Jilková, Zbyněk Raida
Venue: Proceedings of the 8th international conference on Evolvable Systems: From Biology to Hardware (2008)

In this paper, the waveguide design exploiting genetic algorithms as a global optimization technique is described. The influence of the multipoint crossover and mutation operators on the convergence is experimentally examined and discussed. The cross section shape of the waveguide is optimized in order to maximize the bandwidth of the single-mode operation. The influence of the single-point crossover and multipoint crossover operators on the waveguide synthesis was demonstrated on the H-shaped waveguide simulation (2 degrees of freedom) and on the comb-like waveguide (2, 3 and 4 degrees of freedom). The waveguide models were simulated in Comsol Multiphysics 3.3. The optimization algorithm was implemented in MATLAB R2007a.
Dynamic Binary Instrumentation-Based Framework for Malware Defense
Authors: Najwa Aaraj, Anand Raghunathan, Niraj K. Jha
Venue: Proceedings of the 5th international conference on Detection of Intrusions and Malware, and Vulnerability Assessment (2008)

Malware is at the root of a large number of information security breaches. Despite widespread effort devoted to combating malware, current techniques have proven to be insufficient in stemming the incessant growth in malware attacks. In this paper, we describe a tool that exploits a combination of virtualized (isolated) execution environments and dynamic binary instrumentation (DBI) to detect malicious software and prevent its execution. We define two isolated environments: (i) a Testing environment, wherein an untrusted program is traced during execution using DBI and subjected to rigorous checks against extensive security policies that express behavioral patterns of malicious software, and (ii) a Real environment, wherein a program is subjected to run-time monitoring using a behavioral model (in place of the security policies), along with a continuous learning process, in order to prevent non-permissible behavior.We have evaluated the proposed methodology on both Linux and Windows XP operating systems, using several virus benchmarks as well as obfuscated versions thereof. Experiments demonstrate that our approach achieves almost complete coverage for original and obfuscated viruses. Average execution times go up to 28.57X and 1.23X in the Testing and Real environments, respectively. The high overhead imposed in the Testing environment does not create a severe impediment since it occurs only once and is transparent to the user. Users are only affected by the overhead imposed in the Real environment. We believe that our approach has the potential to improve on the state-of-the-art in malware detection, offering improved accuracy with low performance penalty.
Complexity measures for software engineering
Authors: M. Burgin, N. Debnath
Venue: Journal of Computational Methods in Sciences and Engineering (2005)

The aim of this paper is further development of the mathematical theory of algorithmic complexity measures for evaluation of the computer software design and utilization. We consider some basic concepts and constructions from the theory of algorithmic complexity and develop a system structure in the theory of algorithmic complexity. The paper contains main concepts from the axiomatic complexity theory, which are fundamental for the whole theory of algorithmic complexity. Important classes of dual complexity measures are studied. The research is oriented at the development of software engineering and, in particular, creation of more efficient software metrics.
Improved Fuzzy Clustering Method Based on Entropy Coefficient and Its Application
Authors: Li Liu, Jianzhong Zhou, Xueli An, Yinghai Li, Qiang Liu
Venue: Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks, Part II (2008)

Based on the principle of fuzzy clustering analysis and the theory of entropy, an improved fuzzy clustering method is given by improving the method of establishing the membership function, combining the clustering weight with the entropy coefficient, and replacing the Zadeh operator M($\bigvee,\bigwedge$) with the weight average operator M(±, ¿). With the improved method, the zeroweight problem is addressed effectively, the weights of each factor are modified properly and the phenomenon of Major Factor Dominating is also alleviated appropriately. Finally, an illustrative example is given to clarify the method, which shows that the improved fuzzy clustering method is reasonable, feasible, simple and practical.