ppxscal/embedding_network huggingface dataset:
Distributed control of robotic networks
Authors: George J. Pappas
Venue:  (2008)

The field of robotics is evolving from single monolithic robots to teams of small but interconnected robots that achieve global objectives using local coordination. Coordinated missions for such teams of mobile robots include coordinated estimation, surveillance, and coverage, coordinated satellite alignment, as well as distributed placement and assignment in creating desirable team formations. The fundamental challenge in such problems is the design of local rules, such as distributed controllers and estimators, which by local coordination give rise to the desired global objectives.In this thesis, we present the first distributed, scalable, and verifiable algorithm that achieves dynamic robot placement and assignment using local coordination rules. This is achieved using a combination of multi-destination potential fields and assignment coordination protocols, and results in a dynamic network of hybrid robots that is able to reach any desired assignment or formation. We then address the problem of maintaining connectivity in a robotic network, where the robot nodes are mobile. Our solution to this problem is not only the first distributed solution to this problem, but has been both theoretically and experimentally verified. A novel application of the proposed connectivity control algorithm is in multi-robot flocking, where it results in the first flocking algorithm where network connectivity is no longer an assumption but a control objective instead.Finally, we discuss an extension of the above algorithm to the problem of distributed topology control of ad-hoc sensor networks. Such networks typically consist of sensors that due to power constraints can switch between active and sleep operation modes. The control objective investigated in this thesis is how to regulate the switching process so that communication between any pair of active sensors is always guaranteed.Many of the problems addressed in this thesis have recently attracted considerable attention and are interesting in their own right. Our goal is not only to propose efficient solutions to these particular problems, but also to provide guidelines and new ideas, in the interface of engineering and pure sciences, that can result in more systematic approaches in controlling robotic networks.
Isp's traffic engineering and peering strategy
Authors: Dah Ming Chiu
Venue:  (2007)

The Internet has quickly evolved into a vast global network owned and operated by thousands of interconnected Internet Service Providers. Each of these ISPs, as one autonomous system, has its individual economic interests. ISPs can achieve their objectives through peering strategy and interdomain traffic engineering. These two issues are important for ISPs' business and have significant implications on the Internet architecture.Our study on interdomain traffic engineering focuses on AS Path Prepending (ASPP), a popular way for inbound traffic engineering. In order to improve the current situation that ISPs often practise this approach in a trial-and-error basis, we propose a greedy algorithm to help ISPs perform this approach systematically and efficiently. Then we demonstrate two fundamental issues of decentralized selfish traffic engineering, routing instability and global network performance degradation, based on an abstract model where ISPs perform traffic engineering for their individual load balance. We also present a real-world pathologic case of prepending instability from our measurement study. Some simple guidelines are given for ISPs to avoid such routing instability.Over the past several years, numerous types of &ldquo;overlay&rdquo; networks change the interdomain traffic pattern and ISPs lose the routing control of some interdomain traffic flows due to the application layer routing. As a result, some ISPs may provide unintended transit service for other local ISPs. It upsets the traditional business model and makes ISPs' peering strategies more complicated.Our work on peering strategy is to help ISPs understand the economic implications of various traffic patterns and make proper decisions to optimize their business. We first conduct an economic analysis for an overlay streaming network to gain some insights on the free ride phenomenon. We further improve the analysis by taking the response of subscribers into consideration and formulate the dynamic market as a multi-leader-follower game to capture the Nash Equilibrium of the routing tussle among the major players of the Internet marketplace. Based on this framework together with a gravity traffic model, we present some important observations on the implications of overlays on ISPs' peering strategy.
Technical Section: Surface reconstruction using bivariate simplex splines on Delaunay configurations
Authors: Juan Cao, Xin Li, Guozhao Wang, Hong Qin
Venue: Computers and Graphics (2009)

Recently, a new bivariate simplex spline scheme based on Delaunay configuration has been introduced into the geometric computing community, and it defines a complete spline space that retains many attractive theoretic and computational properties. In this paper, we develop a novel shape modeling framework to reconstruct a closed surface of arbitrary topology based on this new spline scheme. Our framework takes a triangulated set of points, and by solving a linear least-square problem and iteratively refining parameter domains with newly added knots, we can finally obtain a continuous spline surface satisfying the requirement of a user-specified error tolerance. Unlike existing surface reconstruction methods based on triangular B-splines (or DMS splines), in which auxiliary knots must be explicitly added in advance to form a knot sequence for construction of each basis function, our new algorithm completely avoids this less-intuitive and labor-intensive knot generating procedure. We demonstrate the efficacy and effectiveness of our algorithm on real-world, scattered datasets for shape representation and computing.
Improving simulated borescope inspection with constrained camera motion and haptic feedback
Authors: Deepak Vembar, Andrew T. Duchowski, Anand K. Gramopadhye, Carl Washburn
Venue: Proceedings of Graphics Interface 2009 (2009)

Results are presented from empirical evaluation of a borescope simulator developed for non-destructive inspection training. Two experiments were conducted, manipulating camera rotation constraint and provision of haptic feedback. Performance of experienced borescope inspectors is measured in terms of speed and accuracy, with accuracy clearly shown to improve by placing constraints on the simulator's camera tip rotation and by providing haptic response. This is important as damage avoidance of a real borescope is a critical criterion of borescope inspection training. These are likely to be the first such experiments to have been conducted with aircraft engine inspectors evaluating the potential of haptics in borescope simulation.
High Assurance Services Computing, 1st edition
Authors: Jing Dong, Raymond Paul, Liang-Jie Zhang
Venue:  (2009)

Service computing is a cutting-edge area, popular in both industry and academia. New challenges have been introduced to develop service-oriented systems with high assurance requirements. High Assurance Services Computing captures and makes accessible the most recent practical developments in service-oriented high-assurance systems. An edited volume contributed by well-established researchers in this field worldwide, this book reports the best current practices and emerging methods in the areas of service-oriented techniques for high assurance systems. Available results from industry and government, R&D laboratories and academia are included, along with unreported results from the hands-on experiences of software professionals in the respective domains. Designed for practitioners and researchers working for industrial organizations and government agencies, High Assurance Services Computing is also suitable for advanced-level students in computer science and engineering.
Security Abstractions and Intruder Models (Extended Abstract)
Authors: Michele Bugliesi, Riccardo Focardi
Venue: Electronic Notes in Theoretical Computer Science (ENTCS) (2009)

Process algebraic specifications of distributed systems are increasingly being targeted at identifying security primitives well-suited as high-level programming abstractions, and at the same time adequate for security analysis and verification. Drawing on our earlier work along these lines [Bugliesi, M. and R. Focardi, Language based secure communication, in: Proceedings of the 21st IEEE Computer Security Foundations Symposium, CSF 2008, Pittsburgh, Pennsylvania, 23-25 June 2008 (2008), pp. 3-16], we investigate the expressive power of a core set of security and network abstractions that provide high-level primitives for the specifications of the honest principals in a network as well as the lower-level adversarial primitives that must be assumed available to an attacker. We analyze various bisimulation equivalences for security, arising from endowing the intruder with (i) different adversarial capabilities and (ii) increasingly powerful control on the interaction among the distributed principals of a network. By comparing the relative strength of the bimimulation equivalences we obtain a direct measure of the discriminating power of the intruders, hence of the expressiveness of the corresponding models.
Adaptive Learning Interface Customization based on Learning Styles and Behaviors
Authors: Hyun Jin Cha, Yong Se Kim, Sun Hee Park, Yun Jung Cho, Michael Pashkin
Venue: Proceeding of the 2005 conference on Towards Sustainable and Scalable Educational Innovations Informed by the Learning Sciences: Sharing Good Practices of Research, Experimentation and Innovation (2005)

There exists an increasing demand for intelligent learning environments that are adaptive to learner's preferences and tasks. This paper presents a study of an intelligent learning environment where the learner's preferences are diagnosed using learner behavior patterns on user interfaces. In this research, a learning-style model by Felder & Silverman has been adopted as an appropriate basis for designing the behavior-based user interface customization.
Integrated information life-cycle, data management and secret key lifecycle management for military applications
Authors: Nikolaos Doukas, Konstantinos Ntaikos, Ntaikos Bardis
Venue: Proceedings of the 10th WSEAS international conference on Mathematical methods, computational techniques and intelligent systems (2008)

One of the major problems that security aware organizations, such as the military, are concerned with when introducing innovative computational systems inside their operations is the fact that such systems will be allowing the sharing of critical information between multiple users that are connected on different terminals and are possibly located in different sites. All these features represent possible compromises in information security and hence demand the use of advanced encryption algorithms. In the case of such large scale systems, the concept of information lifecycle management has been proposed to help rationalise disk usage for distributed data storage and backup. In this paper the need for a standard to offer services for information security by encryption in such systems is explained. Such schemes exist for the case of asymmetric encryption but not for the case of symmetric key encryption. The requirements from a key management scheme are outlined. An extension of the concept of lifecycle management is proposed for this purpose. The secret key management scheme is shown to be suitable for providing an integrated information protection solution for military and critical civil systems.
On the Relative Merits of Software Reuse
Authors: Andres Orrego, Tim Menzies, Oussama El-Rawas
Venue: Proceedings of the International Conference on Software Process: Trustworthy Software Development Processes (2009)

Using process simulation and AI search methods, we compare software reuse against other possible changes to a project. such as reducing functionality or improving the skills of the programmer population. In one case, two generations of reuse were as good or better than any other project change (but a third and fourth generation of reuse was not useful). In another case, applying reuse to a project was demonstrable worse than several other possible changes to a project.Our conclusion is that the general claims regarding the benefits of software reuse do not hold for specific projects. We argue that the merits of software reuse need to be evaluated in a project by project basis. AI search over process models is useful for such an assessment, particularly when there is not sufficient data for precisely tuning a simulation model.
Corrigendum to: "On ordering free groups" [J. Symbolic Comput. 40 (2005) 1285-1290]
Authors: Adam Clay, Lawrence H. Smith
Venue: Journal of Symbolic Computation (2009)

The proof of the main result of the paper is corrected.
Biomedical term recognition with the perceptron HMM algorithm
Authors: Sittichai Jiampojamarn, Grzegorz Kondrak, Colin Cherry
Venue: Proceedings of the Workshop on Linking Natural Language Processing and Biology: Towards Deeper Biological Literature Analysis (2006)

We propose a novel approach to the identification of biomedical terms in research publications using the Perceptron HMM algorithm. Each important term is identified and classified into a biomedical concept class. Our proposed system achieves a 68.6% F-measure based on 2,000 training Medline abstracts and 404 unseen testing Medline abstracts. The system achieves performance that is close to the state-of-the-art using only a small feature set. The Perceptron HMM algorithm provides an easy way to incorporate many potentially interdependent features.
Revealing the Unknown ADSL Traffic Using Statistical Methods
Authors: Marcin Pietrzyk, Guillaume Urvoy-Keller, Jean-Laurent Costeux
Venue: Proceedings of the First International Workshop on Traffic Monitoring and Analysis (2009)

Traffic classification is one of the most significant issues for ISPs and network administrators. Recent research on the subject resulted in a large variety of algorithms and methods applicable to the problem. In this work, we focus on several issues that have not received enough attention so far. First, the establishment of an accurate reference point. We use an ISP internal Deep Packet Inspection (DPI) tool and confront its results with state of the art, freely available classification tools, finding significant differences. We relate those differences to the weakness of some signatures and to the heuristics and design choices made by DPI tools. Second, we highlight methodological issues behind the choices of the traffic classes and the way of analyzing the results of a statistical classifier. Last, we focus on the often overlooked problem of mining the unknown traffic, i.e., traffic not classified by the DPI tool used to establish the reference point. We present a method, relying on the level of confidence of the statistical classification, to reveal the unknown traffic. We further discuss the result of the classifier using a variety of heuristics.
Fuzzy Neural Network Based on Improved T-S Model and Its Application
Authors: Zhiwei Huang, Jianzhong Zhou, Chaoshun Li, Fengpan Li, Yongchuan Zhang
Venue: Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II (2009)

Traditional fuzzy neural networks have many problems like long duration of training learning algorithm, slow convergence and possibility of getting into local dinky value in the network. In view of all these above, this paper proposes an improved training algorithm in fuzzy neural network which takes vantage of the excellent learning and expression ability of fuzzy neural networks based on T-S (Takagi-Sugeno) model. Improved T-S model introduces adaptive learning rate ¿ and momentum factor ¿ into the learning algorithm which helps stabilize the network and makes acceleration weights of three adjusting parameters, including network membership function's central value C ij and width ¿ ij and connection weight of output layer y kj , decrease along the reductive direction. Results of a simulation test with a two-dimensional nonlinear function show that improved network has high convergence rate and good generalization capability. Finally, T-S fuzzy model of hydro-turbine governing system based on the fuzzy neural network is presented, of which the results has indicated that the model has/its higher precision and good generalization ability.
Fair Class-Based Downlink Scheduling with Revenue Considerations in Next Generation Broadband Wireless Access Systems
Authors: Bader Al-Manthari, Hossam Hassanein, Najah Abu Ali, Nidal Nasser
Venue: IEEE Transactions on Mobile Computing (2009)

The success of emerging Broadband Wireless Access Systems (BWASs) will depend, among other factors, on their ability to manage their shared wireless resources in the most efficient way. This is a complex task due to the heterogeneous nature, and hence, diverse Quality of Service (QoS) requirements of different applications that these systems support. Therefore, QoS provisioning is crucial for the success of such wireless access systems. In this paper, we propose a novel downlink packet scheduling scheme for QoS provisioning in BWASs. The proposed scheme employs practical economic models through the use of novel utility and opportunity cost functions to simultaneously satisfy the diverse QoS requirements of mobile users and maximize the revenues of network operators. Unlike existing schemes, the proposed scheme is general and can support multiple QoS classes with users having different QoS and traffic demands. To demonstrate its generality, we show how the utility function can be used to support three different types of traffic, namely best-effort traffic, traffic with minimum data rate requirements, and traffic with maximum packet delay requirements. Extensive performance analysis is carried out to show the effectiveness and strengths of the proposed packet scheduling scheme.
Securing RFID Systems by Detecting Tag Cloning
Authors: Mikko Lehtonen, Daniel Ostojic, Alexander Ilic, Florian Michahelles
Venue: Proceedings of the 7th International Conference on Pervasive Computing (2009)

Cloning of RFID tags can lead to financil losses in many commercial RFID applications. There are two general strategies to provide security: prevention and detection. The security community and the RFID chip manufacturers are currently focused on the former by making tags hard to clone. This paper focuses on the latter by investigating a method to pinpoint tags with the same ID. This method is suitable for low-cost tags since it makes use of writing a new random number on the tag's memory every time the tag is scanned. A back-end that issues these numbers detects tag cloning attacks as soon as both the genuine and the cloned tag are scanned. This paper describes the method and presents a mathematical model of the level of security and an implementation based on EPC tags. The results suggest that the method provides a potentially effective way to secure RFID systems against tag cloning.
Fighting Knowledge Acquisition Bottleneck with Argument Based Machine Learning
Authors: Martin Možina, Matej Guid, Jana Krivec, Aleksander Sadikov, Ivan Bratko
Venue: Proceeding of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence (2008)

Knowledge elicitation is known to be a difficult task and thus a major bottleneck in building a knowledge base. Machine learning has long ago been proposed as a way to alleviate this problem. Machine learning usually helps the domain expert to uncover some of the more tacit concepts. However, the learned concepts are often hard to understand and hard to extend. A common view is that a combination of a domain expert and machine learning would yield the best results. Recently, argument based machine learning (ABML) has been introduced as a combination of argumentation and machine learning. Through argumentation, ABML enables the expert to articulate his knowledge easily and in a very natural way. ABML was shown to significantly improve the comprehensibility and accuracy of the learned concepts. This makes ABML a most natural tool for constructing a knowledge base. The present paper shows how this is accomplished through a case study of building a knowledge base of an expert system used in a chess tutoring application.
Bayesian networks based rare event prediction with sensor data
Authors: Seong-Pyo Cheon, Sungshin Kim, So-Young Lee, Chong-Bum Lee
Venue: Knowledge-Based Systems (2009)

A Bayesian network is a powerful graphical model. It is advantageous for real-world data analysis and finding relations among variables. Knowledge presentation and rule generation, based on a Bayesian approach, have been studied and reported in many research papers across various fields. Since a Bayesian network has both causal and probabilistic semantics, it is regarded as an ideal representation to combine background knowledge and real data. Rare event predictions have been performed using several methods, but remain a challenge. We design and implement a Bayesian network model to forecast daily ozone states. We evaluate the proposed Bayesian network model, comparing it to traditional decision tree models, to examine its utility.
UbiPhone: Human-Centered Ubiquitous Phone System
Authors: Ren-Hung Hwang, Shang-Yi Tsai, Chiung-Ying Wang
Venue: IEEE Pervasive Computing (2009)

Emerging rich wireless networking modalities facilitate the development of new intelligent, innovative services on smart phones. The authors propose a ubiquitous phone (UbiPhone) system that demonstrates innovative context-aware human-centric phone services, which could become available on smart phones. UbiPhone's features include UbiCall, AnyCall, and an emergency contact service. The authors have implemented a UbiPhone prototype on a smart phone with real context sources to demonstrate the system's feasibility.
Efficient processing of underspecified discourse representations
Authors: Michaela Regneri, Markus Egg, Alexander Koller
Venue: Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers (2008)

Underspecification-based algorithms for processing partially disambiguated discourse structure must cope with extremely high numbers of readings. Based on previous work on dominance graphs and weighted tree grammars, we provide the first possibility for computing an underspecified discourse description and a best discourse representation efficiently enough to process even the longest discourses in the RST Discourse Treebank.
An Integrated Approach Towards Intelligent Educational Content Adaptation
Authors: Phivos Mylonas, Paraskevi Tzouveli, Stefanos Kollias
Venue: Proceeding of the 2007 conference on Emerging Artificial Intelligence Applications in Computer Engineering: Real Word AI Systems with Applications in eHealth, HCI, Information Retrieval and Pervasive Technologies (2007)

One of the major shortcomings of modern e-learning schemes is the fact that they significantly lack on user personalization and educational content representation issues. Semi-or fully automated extraction of user profiles based on users' usage history records forms a challenging problem, especially when used under the e-learning perspective. In this chapter we present the design and implementation of such a user profile-based framework, where educational content is matched against its environmental context, in order to be adapted to the end users' needs and qualifications. Our effort applies clustering techniques on an integrated e-learning system to provide efficient user profile extraction and results are promising.