ppxscal/embedding_network huggingface dataset:
Adaptive mediation in groupware
Authors: Timothy J. Hickey
Venue:  (2008)

Groupware is software that is designed to support groups of people engaged in a task. As with single-user software, groupware mediates the users' interaction with a domain in pursuit of a goal, but when using groupware, the system also mediates the interactions users have with one another. In this dissertation, I will show that this aspect of groupware presents software developers with two opportunities that may be exploited when developing adaptive systems. The first opportunity concerns the knowledge acquisition problem. This opportunity presents itself because information that collaborators need to exchange so they can stay coordinated in their shared task will pass through the system itself. I will show that it is indeed possible to use this information, and that there is a repeatable technique for doing this that can be applied in other systems. The second opportunity concerns how adaptive techniques may be applied once this information is attained. Because the system itself mediates all of the collaborators' interactions, the developer has the ability to &ldquo;reach beyond&rdquo; the software itself, and transform the collaborative process into one that is closer to a notional ideal. I call this adaptive mediation, and this approach represents a relatively novel application of adaptive technology. I will show that it is possible to use knowledge obtained by exploiting the first opportunity in order to transform a group's collaborative process as described. These two opportunities are the focus of this dissertation, and my goal is to show that they exist, and that it is possible to take advantage of them. To do this, I present two case-studies. The first of these illustrates a solution to the knowledge acquisition problem in an adaptive groupware system. The second incorporates this solution, and uses the acquired knowledge to address a well-known problem that afflicts group information processing. Both case-studies successfully exploit the identified opportunities, and the second case study leads to some new techniques for and insights about group decision support.
Alignment-Based Tools for Translation Self-Learning
Authors: J. Gabriel Pereira Lopes, Tiago Ildefonso, Marcelo S. Pimenta
Venue: Proceeding of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology (2005)

Effective use of translation products and services depends on professional and learner translators familiarity with different translation procedures, strategies and tools, currently available for solving specific translation problems. In this poster we will focus on the use of language independent alignment-based technology, applied to parallel corpora, for effectively aiding translation learning and translation quality improvement, both at-work and at-the-school.
Discovering Almost Any Hidden Motif from Multiple Sequences in Polynomial Time with Low Sample Complexity and High Success Probability
Authors: Bin Fu, Ming-Yang Kao, Lusheng Wang
Venue: Proceedings of the 6th Annual Conference on Theory and Applications of Models of Computation (2009)

We study a natural probabilistic model for motif discovery that has been used to experimentally test the effectiveness of motif discovery programs. In this model, there are k background sequences, and each character in a background sequence is a random character from an alphabet Σ. A motif G = g 1 g 2...g m is a string of m characters. Each background sequence is implanted a probabilistically generated approximate copy of G. For a probabilistically generated approximate copy b 1 b 2...b m of G, every character is probabilistically generated such that the probability for b i ¿ g i is at most ¿. It has been conjectured that multiple background sequences can help with finding faint motifs G.In this paper, we develop an efficient algorithm that can discover a hidden motif from a set of sequences for any alphabet Σ with |Σ| ¿ 2 and is applicable to DNA motif discovery. We prove that for $\alpha and any constant x ¿ 8, there exist positive constants c 0, ¿, ¿ 1 and ¿ 2 such that if the length ρ of motif G is at least ¿ 1 logn, and there are k ¿ c 0 logn input sequences, then in O(n 2 + kn) time this algorithm finds the motif with probability at least $1-{1\over 2^x}$ for every $G\in \Sigma^{\rho}-\Psi_{\rho, h,\epsilon}(\Sigma)$, where ρ is the length of the motif, h is a parameter with ρ ¿ 4h ¿ ¿ 2logn, and ¿ ρ, h,¿ (Σ) is a small subset of at most $2^{-\Theta(\epsilon^2 h)}$ fraction of the sequences in Σ ρ . The constants c 0, ¿, ¿ 1 and ¿ 2 do not depend on x when x is a parameter of order O(logn). Our algorithm can take any number k sequences as input.
Decentralized utility maximization in heterogeneous multicell scenarios with interference limited and orthogonal air interfaces
Authors: Ingmar Blau, Gerhard Wunder, Ingo Karla, Rolf Sigle
Venue: EURASIP Journal on Wireless Communications and Networking (2009)

Overlapping coverage of multiple radio access technologies provides new multiple degrees of freedom for tuning the fairness-throughput tradeoff in heterogeneous communication systems through proper resource allocation. This paper treats the problem of resource allocation in terms of optimum air interface and cell selection in cellular multi-air interface scenarios. We find a close to optimum allocation for a given set of voice users with minimum QoS requirements and a set of best-effort users which guarantees service for the voice users and maximizes the sum utility of the best-effort users. Our model applies to arbitrary heterogeneous scenarios where the air interfaces belong to the class of interference limited systems like UMTS or to a class with orthogonal resource assignment such as TDMA-based GSM or WLAN. We present a convex formulation of the problem and by using structural properties thereof deduce two algorithms for static and dynamic scenarios, respectively. Both procedures rely on simple information exchange protocols and can be operated in a completely decentralized way. The performance of the dynamic algorithm is then evaluated for a heterogeneous UMTS/GSM scenario showing high-performance gains in comparison to standard load-balancing solutions.
A decision support system for breast cancer detection in screening programs
Authors: Marina Velikova, Peter J. F. Lucas, Nivea Ferreira, Maurice Samulski, Nico Karssemeijer
Venue: Proceeding of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence (2008)

The goal of breast cancer screening programs is to detect cancers at an early (preclinical) stage, by using periodic mammographic examinations in asymptomatic women. In evaluating cases, mammographers insist on reading multiple images (at least two) of each breast as a cancerous lesion tends to be observed in different breast projections (views). Most computer-aided detection (CAD) systems, on the other hand, only analyze single views independently, and thus fail to account for the interaction between the views. In this paper, we propose a Bayesian framework for exploiting multi-view dependencies between the suspected regions detected by a single-view CAD system. The results from experiments with real-life data show that our approach outperforms the singleview CAD system in distinguishing between normal and abnormal cases. Such a system can support screening radiologists to improve the evaluation of breast cancer cases.
FeatureIDE: A tool framework for feature-oriented software development
Authors: Christian Kastner, Thomas Thum, Gunter Saake, Janet Feigenspan, Thomas Leich, Fabian Wielgorz, Sven Apel
Venue: Proceedings of the 2009 IEEE 31st International Conference on Software Engineering (2009)

Tools support is crucial for the acceptance of a new programming language. However, providing such tool support is a huge investment that can usually not be provided for a research language. With FeatureIDE, we have built an IDE for AHEAD that integrates all phases of feature-oriented software development. To reuse this investment for other tools and languages, we refactored FeatureIDE into an open source framework that encapsulates the common ideas of feature-oriented software development and that can be reused and extended beyond AHEAD. Among others, we implemented extensions for FeatureC++ and FeatureHouse, but in general, FeatureIDE is open for everybody to showcase new research results and make them usable to a wide audience of students, researchers, and practitioners.
Neighborhood-Based Tag Prediction
Authors: Adriana Budura, Sebastian Michel, Philippe Cudré-Mauroux, Karl Aberer
Venue: Proceedings of the 6th European Semantic Web Conference on The Semantic Web: Research and Applications (2009)

We consider the problem of tag prediction in collaborative tagging systems where users share and annotate resources on the Web. We put forward HAMLET, a novel approach to automatically propagate tags along the edges of a graph which relates similar documents. We identify the core principles underlying tag propagation for which we derive suitable scoring models combined in one overall ranking formula. Leveraging these scores, we present an efficient top-k tag selection algorithm that infers additional tags by carefully inspecting neighbors in the document graph. Experiments using real-world data demonstrate the viability of our approach in large-scale environments where tags are scarce.
The road not taken: Estimating path execution frequency statically
Authors: Raymond P. L. Buse, Westley Weimer
Venue: Proceedings of the 2009 IEEE 31st International Conference on Software Engineering (2009)

A variety of compilers, static analyses, and testing frameworks rely heavily on path frequency information. Uses for such information range from optimizing transformations to bug finding. Path frequencies are typically obtained through profiling, but that approach is severely restricted: it requires running programs in an indicative environment, and on indicative test inputs. We present a descriptive statistical model of path frequency based on features that can be readily obtained from a program's source code. Our model is over 90% accurate with respect to several benchmarks, and is sufficient for selecting the 5% of paths that account for over half of a program's total runtime. We demonstrate our technique's robustness by measuring its performance as a static branch predictor, finding it to be more accurate than previous approaches on average. Finally, our qualitative analysis of the model provides insight into which source-level features indicate “hot paths.”
Pattern Discovery in Melanoma Domain Using Partitional Clustering
Authors: David Vernet, Ruben Nicolas, Elisabet Golobardes, Albert Fornells, Carles Garriga, Susana Puig, Josep Malvehy
Venue: Proceeding of the 2008 conference on Artificial Intelligence Research and Development: Proceedings of the 11th International Conference of the Catalan Association for Artificial Intelligence (2008)

Nowadays melanoma is one of the most important cancers to study due to its social impact. This dermatologic cancer has increased its frequency and mortality during last years. In particular, mortality is around twenty percent in non early detected ones. For this reason, the aim of medical researchers is to improve the early diagnosis through a best melanoma characterization using pattern matching. This article presents a new way to create real melanoma patterns in order to improve the future treatment of the patients. The approach is a pattern discovery system based on the K-Means clustering method and validated by means of a Case-Based Classifier System.
VoIP Network Security and Forensic Models Using Patterns
Authors: Juan C. Pelaez
Venue:  (2007)

Voice over Internet Protocol (VoIP) networks is becoming the most popular telephony system in the world. However, studies of the security of VoIP networks are still in their infancy. VoIP devices and networks are commonly attacked, and it is therefore necessary to analyze the threats against the converged network and the techniques that exist today to stop or mitigate these attacks. We also need to understand what evidence can be obtained from the VoIP system after an attack has occurred. Many of these attacks occur in similar ways in different contexts or environments. Generic solutions to these issues can be expressed as patterns. A pattern can be used to guide the design or simulation of VoIP systems as an abstract solution to a problem in this environment. Patterns have shown their value in developing good quality software and we expect that their application to VoIP will also prove valuable to build secure systems. This book presents a variety of patterns (architectural, attack, forensic and security patterns). These patterns will help forensic analysts as well as secure systems developers because they provide a systematic approach to structure the required information and help understand system weaknesses. The patterns will also allow us to specify, analyze and implement network security investigations for different architectures. The pattern system uses object-oriented modeling (Unified Modeling Language) as a way to formalize the information and dynamics of attacks and systems.
A hybrid Grey & ANFIS approach to bullwhip effect in supply chain networks
Authors: Hakan Tozan, Ozalp Vayvay
Venue: WSEAS TRANSACTIONS on SYSTEMS (2009)

Demand forecasting and decision making processes are among the key activities which directly affect the performance of successful supply chain networks. The variability of the demand information between the stages of the supply chains and the increase in this variability as the demand data moves upstream from the customer to consequent stages of the supply chain networks is called Bullwhip Effect. As demand pattern varies due to the field of activity and architecture of supply chains networks, determining the appropriate forecasting and order decision model for system interested in is complicated. This paper analyzes the response of bullwhip effect to a hybrid grey GM (1, 1) forecasting and ANFIS based order decision model under demand with relatively medium variation in a two stage supply chain network simulation.
Implicit Curves and Surfaces: Mathematics, Data Structures and Algorithms, 1st edition
Authors: Abel Gomes, Irina Voiculescu, Joaquim Jorge, Brian Wyvill, Callum Galbraith
Venue:  (2009)

Implicit objects have gained increasing importance in geometric modeling, visualisation, animation, and computer graphics, because their geometric properties provide a good alternative to traditional parametric objects. This book presents the mathematics, computational methods and data structures, as well as the algorithms needed to render implicit curves and surfaces, and shows how implicit objects can easily describe smooth, intricate, and articulatable shapes, and hence why they are being increasingly used in graphical applications. Divided into two parts, the first introduces the mathematics of implicit curves and surfaces, as well as the data structures suited to store their sampled or discrete approximations, and the second deals with different computational methods for sampling implicit curves and surfaces, with particular reference to how these are applied to functions in 2D and 3D spaces.
Analysis and Design of an Object Tracking Service for Intelligent Environments
Authors: Ignacio Recio, José M. Moya, Álvaro Araujo, Juan Carlos Vallejo, Pedro Malagón
Venue: Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part II: Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living (2009)

This paper describes the design and implementation of an object tracking service for indoor environments. First, the wireless indoor location estimation technology is overviewed presenting advantages and disadvantages. Second, the methodology of the study is presented. To estimate the position we use clues inserted by location clue injectors of the system. In our architecture one of these injectors is a ZigBee sensor network. As location algorithm we have developed a method combining statistical techniques (particle filter) and proximity sensing (nearest neighbour) to get better efficiency. The results obtained show that a good precision and reliability can be achieved with a low-cost solution.
Theoretical Foundations for Discrete Forward-and-Backward Diffusion Filtering
Authors: Martin Welk, Guy Gilboa, Joachim Weickert
Venue: Proceedings of the Second International Conference on Scale Space and Variational Methods in Computer Vision (2009)

Forward-and-backward (FAB) diffusion is a method for sharpening blurry images (Gilboa et al. 2002). It combines forward diffusion with a positive diffusivity and backward diffusion where negative diffusivities are used. The well-posedness properties of FAB diffusion are unknown, and it has been observed that standard discretisations can violate a maximum-minimum principle. We show that for a novel nonstandard space discretisation which pays specific attention to image extrema, one can apply a modification of the space-discrete well-posedness and scale-space framework of Weickert (1998). This allows to establish well-posedness and a maximum-minimum principle for the resulting dynamical system. In the fully discrete 1-D case with an explicit time discretisation, a maximum-minimum principle and total variation reduction are proven in spite of the fact that negative diffusivities may appear. This provides a theoretical justification for applying FAB diffusion to digital images.
Granular Models for Vague Predicates
Authors: Silvia Gaio
Venue: Proceeding of the 2008 conference on Formal Ontology in Information Systems: Proceedings of the Fifth International Conference (FOIS 2008) (2008)

This paper presents an account for vague predicates based on granular partitions, alternative to Bittner and Smith's one (e.g. T. Bittner and B. Smith [1], [2], [3].). I consider one kind of vague linguistic expressions: adjectives like tall, short, big, small, etc. They are used in problematic and unproblematic cases. Consider two examples. (a) Marc is 160 cm tall and John 190 cm tall. English speakers do not hesitate to assign the adjective short to Marc and tall to John. (b) A hundred men differ for 0.5 cm with respect to their height. That is a borderline case: speakers can be indecisive about what men are tall and what short. This paper presents a model that accounts for the computational operations that underlie speakers' application of vague adjectives both to problematic and unproblematic cases. The model can then be used in a theory of formal ontology based on the notion of granularity as the one developed by Bittner and Smith. The model is built on two basic ingredients: (i) comparison classes and (ii) granular partitions. (i) Comparison classes are introduced to account for the context-sensitivity of vague adjectives. The extension of the predicate being tall in the comparison class of men is different from its extension in the comparison class of children. (ii) We can look at the elements of a context under different standards of precision, each of them corresponding to a granular level of observation. Finer the level is, more differences between the individuals are detected. Granular partitions as equivalence classes are used to represent indistinguishability relations between objects with respect to the properties expressed by vague adjectives. The elements of each comparison class turn out to be weakly ordered with respect to each vague predicate. Such an algebraic treatment makes the model computational.
Fast track article: EEDTC: Energy-efficient dominating tree construction in multi-hop wireless networks
Authors: Ruiyun Yu, Xingwei Wang, Sajal K. Das
Venue: Pervasive and Mobile Computing (2009)

Motivated by reducing communication overhead and prolonging network lifetime in multi-hop wireless networks, we propose an energy-efficient dominating tree construction (EEDTC) algorithm to construct a dominating tree that can serve as a communication backbone in wireless infrastructures. The algorithm uses k-hop neighbors' information (k@?[1,D"G], where D"G is the diameter of graph G). EEDTC has a theoretical approximation factor of at most 9. When k is equal to 1, it has O(n) message complexity and O(@?n^1^-^1^/^D^"^G@?-1) time complexity. Due to the low message complexity, EEDTC performs well on energy consumption. The energy-aware ranking technique that is introduced can also balance energy consumption in the network, and hence reduce the probability of network failures caused by energy depletion of backbone nodes.
Imitation of intentional behaviour
Authors: Bart Jansen
Venue: Proceeding of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy (2006)

In this paper a computational simulation of the imitation of intentional behaviour is presented. Important assumptions which make the problem computationally tractable are introduced and motivated. It is shown that horizontal, iterated learning, and vertical transmission schemes can be used to learn from imitation using the proposed framework.
Hilbert space embeddings of conditional distributions with applications to dynamical systems
Authors: Le Song, Jonathan Huang, Alex Smola, Kenji Fukumizu
Venue: Proceedings of the 26th Annual International Conference on Machine Learning (2009)

In this paper, we extend the Hilbert space embedding approach to handle conditional distributions. We derive a kernel estimate for the conditional embedding, and show its connection to ordinary embeddings. Conditional embeddings largely extend our ability to manipulate distributions in Hilbert spaces, and as an example, we derive a nonparametric method for modeling dynamical systems where the belief state of the system is maintained as a conditional embedding. Our method is very general in terms of both the domains and the types of distributions that it can handle, and we demonstrate the effectiveness of our method in various dynamical systems. We expect that conditional embeddings will have wider applications beyond modeling dynamical systems.
Develop a novel methodology for strategic product portfolio management considering multi-objectives and operational constraints
Authors: Amy J. C. Trappey, Tzu-An Chiang, Charles V. Trappey, Jen-Yau Kuo
Venue: Proceeding of the 2006 conference on Leading the Web in Concurrent Engineering: Next Generation Concurrent Engineering (2006)

This paper applies the concept of concurrent engineering to develop a product portfolio management (PPM) methodology, which systematically analyzes product portfolio strategy at the beginning of the product lifecycle. This research adopts the approach of multi-attribution decision analysis to estimate the synthesized-performance value for each R&D project. Thus, the most plausible product portfolio can be concurrently derived during the early design stage to avoid investing and executing less ideal product R&D projects. We develop a new methodology for evaluating the resource requirements and the potential risks of product portfolios through applying the activity-group concept. In order to accurately estimate the revenue of a product portfolio, this research considers the levels of substitution among products in a given product portfolio. Finally, a case study of mobile phone PPM helps us demonstrate the efficacy of the methodology presented in the paper.