ppxscal/embedding_network huggingface dataset:
SAT-Based Planning with Minimal-#actions Plans and "soft" Goals
Authors: Enrico Giunchiglia, Marco Maratea
Venue: Proceedings of the 10th Congress of the Italian Association for Artificial Intelligence on AI*IA 2007: Artificial Intelligence and Human-Oriented Computing (2007)

Planning as Satisfiability (SAT) is the best approach for optimally solving classical planning problems. The SAT-based planner satplan has been the winner in the deterministic track for optimal planners in the 4th International Planning Competition (IPC-4) and the co-winner in the last 5th IPC (together with another SAT-based planner). Given a planning problem ¿, satplan works by (i) generating a SAT formula ¿ n with a fixed "makespan" n, and (ii) checking ¿ n for satisfiability. The algorithm stops if ¿ n is satisfiable, and thus a plan has been found, otherwise n is increased.Despite its efficiency, and the optimality of the makespan, satplan has significant deficiency related in particular to "plan quality", e.g., the number of actions in the returned plan, and the possibility to express and reason on "soft" goals.In this paper, we present satplan ¿ , a system, modification of satplan, which makes a significant step towards the elimination of satplan's limitations. Given the optimal makespan, satplan ¿ returns plans with minimal number of actions and maximal number of satisfied "soft" goals, with respect to both cardinality and subset inclusions. We selected several benchmarks from different domains from all the IPCs: on these benchmarks we show that the plan quality returned by satplan ¿ is often significantly higher than the one returned by satplan.Quite surprisingly, this is often achieved without sacrificing efficiency while obtaining results that are competitive with the winning system of the "SimplePreferences" domain in the satisfying track of the last IPC.
PROBADO --- A Generic Repository Integration Framework
Authors: Harald Krottmaier, Frank Kurth, Thorsten Steenweg, Hans-Jürgen Appelrath, Dieter Fellner
Venue: Proceedings of the 11th European conference on Research and Advanced Technology for Digital Libraries (2007)

The number of newly generated multimedia documents (e.g. music, e-learning material, or 3D-graphics) increases year by year. Today, the workflow in digital libraries focuses on textual documents only. Hence, considering content-based retrieval tasks, multimedia documents are not analyzed and indexed sufficiently. To facilitate content-based retrieval and browsing, it is necessary to introduce recent techniques for multimedia document processing into the workflow of nowadays digital libraries. In this short paper, we introduce the PROBADO-framework which will (a) integrate different types of content-repositories --- each one specialized for a specific multimedia domain --- into one seamless system, and (b) will add features available in text-based digital libraries (such as automatic annotation, full-text retrieval, or recommender services) to non-textual documents. Existing libraries will benefit from the framework since it extends existing technology for handling textual documents with features for dealing with the non-textual domain.
Improving Social Filtering Techniques Through WordNet-Based User Profiles
Authors: Pasquale Lops, Marco Degemmis, Giovanni Semeraro
Venue: Proceedings of the 11th international conference on User Modeling (2007)

Collaborative filtering algorithms predict the preferences of a user for an item by weighting the contributions of similar users, called neighbors, for that item. Similarity between users is computed by comparing their rating styles, i.e. the set of ratings given on the same items. Unfortunately, similarity between users is computable only if they have common rated items. The main contribution of this paper is a (content-collaborative) hybrid recommender system which overcomes this limitation by computing similarity between users on the ground of their content-based profiles. Traditional keyword-based profiles are unable to capture the semantics of user interests, due to the natural language ambiguity. A distinctive feature of the proposed technique is that a statistical model of the user interests is obtained by machine learning techniques integrated with linguistic knowledge contained in the WordNet lexical database. This model, called the semantic user profile, is exploited by the hybrid recommender in the neighborhood formation process. The results of an experimental session in a movie recommendation scenario demonstrate the effectiveness of the proposed approach.
Application of Simulation Approaches to Creation of Decision Support System for IT Service Management
Authors: Yuri G. Karpov, Rostislav I. Ivanovsky, Kirill A. Sotnikov
Venue: Proceedings of the 9th international conference on Parallel Computing Technologies (2007)

The paper presents a simulation-based approach to creation of decision support system for IT Service Management. The presented approach includes monitoring of stochastic data IT Services and calculation of measure of alignment to business goals with its characteristic basing on SLA/SLO. The approach combines the benefits of two kinds of models: analytical and simulation ones. The key idea of the paper is to demonstrate how modern methods of stochastic process analysis may enhance trustworthiness and quality of decision making along business goals within IT Services.
The IRIS Workstation
Authors: Randy Nickel
Venue: IEEE Computer Graphics and Applications (1984)

Custom VLSI chips improve reliability and increase speed in this high-performance engineering workstation, which combines real-time 3-D color graphics with Unix software and Ethernet network communications. The ACM Portal is published by the Association for Computing Machinery. Copyright © 2010 ACM, Inc. Terms of Usage Privacy Policy Code of Ethics Contact Us Useful downloads: Adobe Acrobat QuickTime Windows Media Player Real Player
A Secure IPv6-based Urban Wireless Mesh Network (SUMNv6)
Authors: Ramanarayana Kandikattu, Lillykutty Jacob
Venue: Computer Communications (2008)

Security and fast handover are the two major concerns for the existence of Wireless Mesh Network (WMN). Existing solutions for Internet access by mobile users - Mobile IPv6, and its enhancements such as HMIPv6, F-HMIPv6 and HCF - have been designed to improve the handover latency without any light weight security considerations. The Route Optimization (RO) of MIPv6 introduces new security threats and the Return Routability (RR) test is only a weak solution for MIPv6 supplemented Internet. This paper proposes a secure F-HMIPv6-based framework to WMN that offers secure and fast handover as well as secure RO. The proposed framework addresses the security threats to both MIPv6 and WMN, improves the security of the existing RR test, and uses efficient key management. It is designed to enable secure, fast, and efficient signaling and communications for mesh clients within WMN as well as with outside world. This paper presents detailed cost analysis and numerical results to compare the proposed scheme with HMIPv6.
Improving the cyber incident mission impact assessment (CIMIA) process
Authors: Michael R. Grimaila, Robert F. Mills, Larry W. Fortson
Venue: Proceedings of the 4th annual workshop on Cyber security and information intelligence research: developing strategies to meet the cyber security and information intelligence challenges ahead (2008)

Despite our best efforts to secure our cyberspace; we inevitably experience incidents in the cyber domain which result in the loss of the confidentiality, integrity, or availability of a cyber resource. When a cyber incident occurs, we must quickly and accurately estimate and report the resulting negative impact, not only in terms of the infrastructure damage, but also in terms of the mission impact experienced by all of the affected organizations. Unfortunately, existing methods for mission impact assessment are hindered by the lack of standardization in the way that we identify, value, track, document, and report critical cyber resources. In this presentation, we discuss the importance of accurate and timely damage assessment in military operations; distinguish between damage and mission impact assessment; motivate the need for a change in mission impact assessment; and propose that a paradigm shift is required in the way that view critical cyber resources. The proposed changes are necessary to provide commanders with dominate cyberspace battlespace knowledge and enable accurate predictive situational awareness.
Hybrid intelligent and adaptive sensor systems with improved signal integrity based on dynamically reconfigurable matched universal sensor electronics
Authors: Senthil Kumar Lakshmanan, Andreas König
Venue: International Journal of Hybrid Intelligent Systems (2008)

Hybrid intelligent sensor systems and networks are composed of modules of tightly co-operating software and hardware components. Bio-inspired information processing is embodied in algorithms as well as dedicated electronics for intelligent processing and system adaptation. This paper focuses on the challenges imposed on the small yet irreplaceable analog and mixed signal components in such a sensor system, which are prone to deviation and degradations. Novel architectures combine issues of rapid-prototyping, trimming, fault-tolerance, and self-repair. However, the common reconfiguration approaches cannot deal efficiently with real-world noise problems. This paper adapts effective solution strategies to advanced sensor electronics for hybrid intelligent and adaptive sensor systems in a 0.35 &mu;m CMOS technology and reports on the design of a novel generic chip with flexible dimensions. Furthermore, we will focus on a generic dynamic reconfigurable amplifier cell with flexible topology and dimension called Generic Operational Amplifier (GOPA) and possible extension to an overall generic sensor system.
State Space Partition for Reinforcement Learning Based on Fuzzy Min-Max Neural Network
Authors: Yong Duan, Baoxia Cui, Xinhe Xu
Venue: Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks (2007)

In this paper, a tabular reinforcement learning (RL) method is proposed based on improved fuzzy min-max (FMM) neural network. The method is named FMM-RL. The FMM neural network is used to segment the state space of the RL problem. The aim is to solve the "curse of dimensionality" problem of RL. Furthermore, the speed of convergence is improved evidently. Regions of state space serve as the hyperboxes of FMM. The minimal and maximal points of the hyperbox are used to define the state space partition boundaries. During the training of FMM neural network, the state space is partitioned via operations on hyperbox. Therefore, a favorable generalization performance of state space can be obtained. Finally, the method of this paper is applied to learn behaviors for the reactive robot. The experiment shows that the algorithm can effectively solve the problem of navigation in a complicated unknown environment.
The Strategic Exploitation of Limited Information and Opportunity in Networked Markets
Authors: Dan Ladley, Seth Bullock
Venue: Computational Economics (2008)

This paper studies the effect of constraining interactions within a market. A model is analysed in which boundedly rational agents trade with and gather information from their neighbours within a trade network. It is demonstrated that a trader's ability to profit and to identify the equilibrium price is positively correlated with its degree of connectivity within the market. Where traders differ in their number of potential trading partners, well-connected traders are found to benefit from aggressive trading behaviour. Where information propagation is constrained by the topology of the trade network, connectedness affects the nature of the strategies employed.
A novel correlation pulse echo methodology for transmission line fault identification and location using pseudorandom binary sequences
Authors: Richard A. Guinee
Venue: Proceedings of the 10th WSEAS International Conference on Mathematical Methods and Computational Techniques in Electrical Engineering (2008)

A novel pulse echo test methodology, using pseudorandom binary sequence (PRBS) excitation, is presented in this paper as an alternative to Time Domain Reflectometry (TDR) for transmission line fault location and identification. The essential feature of this scheme is the cross correlation (CCR) of the fault response echo with the PRBS test input stimulus input which results in a unique signature for identification of the fault type, if any, or load termination present as well as its distance from the point of test stimulus injection. This fault identification method can used in a number of key industrial applications incorporating printed circuit boards, overhead transmission lines and underground cables in inaccessible locations which rely on a pathway for power transfer or signal propagation. As an improved method PRBS fault identification can be performed over several cycles at low amplitude levels online to reject normal signal traffic and extraneous noise pickup for the purpose of multiple fault coverage, resolution and identification. In this paper a high frequency co-axial transmission line model is presented for transmission line behavioural simulation with PRBS stimulus injection under known load terminations to mimic fault conditions encountered in practice for proof of concept. Simulation results, for known resistive fault terminations, with measured CCR response demonstrate the effectiveness of the PRBS test method in fault type identification and location. Key experimental test results are also presented for a co-axial cable, under laboratory controlled conditions, which substantiates the accuracy of PRBS diagnostic CCR method of fault recognition and location using a range of resistive fault terminations. The accuracy of the method is further validated through theoretical calculation via known co-axial cable parameters, fault resistance terminations and link distances in transmission line experimental testing.
Cyclic Viterbi Score for Linear Hidden Markov Models
Authors: Vicente Palazón, Andrés Marzal
Venue: Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part II (2007)

Hidden Markov Models (HMM) have been successfully applied to describe sequences of observable events. In some problems, objects are more appropriately described as cyclic sequences, i.e., sequences with no begin/end point. Conventional HMMs with Viterbi score cannot deal adequately with cyclic sequences. We propose a cyclic Viterbi score that can be efficiently computed for Linear HMMs. Linear HMMs model sequences that can be partitioned into contiguous segments where each state is responsible for emitting all symbols in one of the segments. Experiments show that our proposal outperforms other approaches in an isolated characters handwritten-text recognition task.
EOD - European Network of Libraries for eBooks on Demand
Authors: Zoltán Mez, Sonja Svoljšak, Silvia Gstrein
Venue: Proceedings of the 11th European conference on Research and Advanced Technology for Digital Libraries (2007)

European libraries host millions of books published from 1500 to 1900. Due to age and value, they are often only accessible to users actually present at these libraries. EOD (eBooks on Demand) is a European wide service which gives an answer to this problem by providing eBooks on request from a wide range of European Libraries. The service is currently carried out within the framework of the EU project "Digitisation on Demand". EOD is an open network and every European library is welcome to join.
Programming Challenges for Petascale and Multicore Parallel Systems
Authors: Vivek Sarkar
Venue: Proceedings of the 3rd international conference on High Performance Computing and Communications (2007)

This decade marks a resurgence for parallel computing with high-end systems moving to petascale and mainstream systems moving to multi-core processors. Unlike previous generations of hardware evolution, this shift will have a major impact on existing software. For petascale, it is widely recognized by application experts that past approaches based on domain decomposition will not scale to exploit the parallelism available in future high-end systems. For multicore, it is acknowledged by hardware vendors that enablement of mainstream software for execution on multiple cores is the major open problem that needs to be solved in support of this hardware trend. These software challenges are further compounded by an increased adoption of high performance computing in new application domains that may not fit the patterns of parallelism that have been studied by the community thus far. In this talk, we compare and contrast the software stacks that are being developed for petascale and multicore parallel systems, and the challenges that they pose to the programmer. We discuss ongoing work on high productivity languages and tools that can help address these challenges for petascale applications on high-end systems. We also discuss ongoing work on concurrency in virtual machines (managed runtimes) to support lightweight concurrency for mainstream applications on multicore systems. Examples will be give from research projects under way in these areas including PGAS languages (UPC, CAF), Eclipse Parallel Tools Platform, Java Concurrency Utilities, and the X10 language. Finally, we outline a new long-term research project being initiated at Rice University that aims to unify elements of the petascale and multicore software stacks so as to produce portable software that can run unchanged on petascale systems as well as a range of homogeneous and heterogeneous multicore systems.
Probabilistic Model for Structured Document Mapping
Authors: Guillaume Wisniewski, Francis Maes, Ludovic Denoyer, Patrick Gallinari
Venue: Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition (2007)

We address the problem of learning automatically to map heterogeneous semi-structured documents onto a mediated target XML schema. We adopt a machine learning approach where the mapping between input and target documents is learned from a training corpus of documents. We first introduce a general stochastic model of semi structured documents generation and transformation. This model relies on the concept of meta-document which is a latent variable providing a link between input and target documents. It allows us to learn the correspondences when the input documents are expressed in a large variety of schemas. We then detail an instance of the general model for the particular task of HTML to XML conversion. This instance is tested on three different corpora using two different inference methods: a dynamic programming method and an approximate LaSO-based method.
Automated Maintainability of TTCN-3 Test Suites Based on Guideline Checking
Authors: George Din, Diana Vega, Ina Schieferdecker
Venue: Proceedings of the 6th IFIP WG 10.2 international workshop on Software Technologies for Embedded and Ubiquitous Systems (2008)

Similar to software development, the test development must be accompanied with a set of rules specifying how to write tests. They are grouped together into a document called guideline. Guidelines are especially necessary for large test specifications involving many developers and have the goal to reduce the effort of the overall development. So far, no universal guidelines for the TTCN-3 language [1] have been defined. Instead, each company or team defines and follows own development rules for test structuring and development. This paper deals with the problem of how to automate the validation whether a TTCN-3 test specification complies or not with an established guideline, i.e. guideline checking. The results of the validation process are a list of non-consistencies. A follow up step is the refactoring which automatically proposes and applies changes to improve the test suite compliance level, and thus its quality.
An Algorithm for Generating Classification Rules Based on Extended Function Dependency
Authors: Xiaoping Zhang, Fengzhan Tian, Houkuan Huang
Venue: Proceedings of the 7th international conference on Computational Science, Part III: ICCS 2007 (2007)

Classification is an important task in the fields of data mining and pattern recognition. Now there have been many algorithms for this task, while most of them do not focus on the application in databases. In this paper we extend the definition of function dependency, prove the properties of the extended function dependency, and on this basis propose an algorithm for classification. According to the two theorems in the paper, our algorithm is complete that means it can find all the classification rules from the database. At last, we demonstrate our algorithm by an example that shows the validity of our algorithm.
A Meeting Scheduling Problem Respecting Time and Space
Authors: Florian Berger, Rolf Klein, Doron Nussbaum, Jörg-Rüdiger Sack, Jiehua Yi
Venue: Proceedings of the 4th international conference on Algorithmic Aspects in Information and Management (2008)

We consider the problem of determining suitable meeting times and locations for a group of participants wishing to schedule a new meeting subject to already scheduled meetings possibly held at a number of different locations. Each participant must be able to reach the new meeting location, attend for the entire duration, and reach the next meeting location on time. In particular, we give a solution to the problem instance where each participant has two scheduled meetings separated by a free time interval. In [2], we presented an O(n logn) algorithm for n participants obtained by purely geometrical arguments. Our new approach uses the concept of LP-type problems and leads to a randomized algorithm with expected running time O(n).
Simulation of individual spontaneous reactive behavior
Authors: Alejandra García-Rojas, Mario Gutiérrez, Daniel Thalmann
Venue: Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1 (2008)

The context of this work is the search for realism and believability of Virtual Humans. Our contribution to achieve this goal is to enable Virtual Humans (VH) to react to spontaneous events in virtual environments (VE). In order to reflect the individuality of each VH, these reactions have to be expressive and unique. In this paper we present firstly a model of reaction based on personality traits. The model was defined using statistical analysis of real people reacting to unexpected events. We also consider that the emotional state is involved in the modulation of reactions, thus we integrate a model of emotion update. Secondly, we present a semantic-based methodology to compose reactive animation sequences using inverse kinematics (IK) and key frame (KF) interpolation animation techniques. Finally, we present an application that demonstrates how Virtual Humans can produce different movements as reaction to unexpected stimuli, depending on their personality traits and emotional state.
Theoretical analysis of a directional stability-based clustering algorithm for vanets
Authors: Peng Fan, Prasad Sistla, Peter Nelson
Venue: Proceedings of the fifth ACM international workshop on VehiculAr Inter-NETworking (2008)

Clustering has the potential to reduce redundant messaging in MANETs and provide an efficient hierarchical network structure. However, it takes time to form and maintain a cluster structure which also requires additional control overhead. In this paper, we aim to analyze a distributed DIrectional Stability-based Clustering Algorithm (DISCA) designed for VANETs, which takes direction, mobility features, and leadership duration into consideration. We provide insights on the theoretical analysis of DISCA and show that the overhead incurred by DISCA is bound by a constant per node per time step, avoiding expensive reclustering chain reactions.