ppxscal/embedding_network huggingface dataset:
Detecting Word Substitutions in Text
Authors: SW. Fong, D. Roussinov, D. B. Skillicorn
Venue: IEEE Transactions on Knowledge and Data Engineering (2008)

Searching for words on a watchlist is one way in which large-scale surveillance of communication can be done, for example in intelligence and counterterrorism settings. One obvious defense is to replace words that might attract attention to a message with other, more innocuous, words. For example, the sentence the attack will be tomorrow" might be altered to the complex will be tomorrow", since 'complex'is a word whose frequency is close to that of 'attack'. Such substitutions are readily detectable by humans since they do not make sense. We address the problem of detecting such substitutions automatically, by looking for discrepancies between words and their contexts, and using only syntactic information. We define a set of measures, each of which is quite weak, but which together produce per-sentence detection rates around 90% with false positive rates around 10%. Rules for combining persentence detection into per-message detection can reduce the false positive and false negative rates for messages to practical levels. We test the approach using sentences from the Enron email and Brown corpora, representing informal and formal text respectively.
The Challenge of Hardware-Software Co-verification
Authors: Panagiotis Manolios
Venue: Verified Software: Theories, Tools, Experiments: First IFIP TC 2/WG 2.3 Conference, VSTTE 2005, Zurich, Switzerland, October 10-13, 2005, Revised Selected Papers and Discussions (2005)

Building verified computing systems such as a verified compiler or operating system will require both software and hardware verification. How can we decompose such verification efforts into mostly separate tasks, one involving hardware and the other software? What theorems should we prove? What specification languages should we use? What tools should we build? To what extent can the process be automated? We address these issues, using as a running example our recent and on-going work on refinement-based pipelined machine verification.
Small Sample Spaces Cannot Fool Low Degree Polynomials
Authors: Noga Alon, Ido Ben-Eliezer, Michael Krivelevich
Venue: Proceedings of the 11th international workshop, APPROX 2008, and 12th international workshop, RANDOM 2008 on Approximation, Randomization and Combinatorial Optimization: Algorithms and Techniques (2008)

A distribution D on a set $S \subset \mathbb Z_{p}^{N}$ ¿-fools polynomials of degree at most d in N variables over $\mathbb Z_{p}$ if for any such polynomial P, the distribution of P(x) when x is chosen according to D differs from the distribution when x is chosen uniformly by at most ¿ in the ¿1 norm. Distributions of this type generalize the notion of ¿-biased spaces and have been studied in several recent papers. We establish tight bounds on the minimum possible size of the support S of such a distribution, showing that any such S satisfies $$ |S|\geq c_{1} \cdot \left(\frac{(\frac{N}{2d})^{d} \cdot \log{p}}{\epsilon^{2}\log{(\frac{1}{\epsilon})}}+p\right). $$ This is nearly optimal as there is such an S of size at most $$ c_{2} \cdot \frac{(\frac{3N}{d})^{d} \cdot \log{p} + p}{\epsilon^{2}}. $$
Stability of Neural Networks with Parameters Disturbed by White Noises
Authors: Wuyi Zhang, Wudai Liao
Venue: Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks (2008)

Almost sure exponential stability (ASES) of neural networks with parameters disturbed by noises is studied, the basis of which is that the parameters in the implemented neural networks by very large scale integration (VLSI) approaches is well defined by the white-noise stochastic process, and an appropriate way to impose random factors on deterministic neural networks is proposed. By using the theory of stochastic dynamical system and matrix theory, some stability criteria are obtained which ensure the neural networks ASES, and the convergent rate is estimated. Also, the capacity of enduring random factors of the well-designed neural networks is estimated. The results obtained in this paper need only to compute the eigenvalues or verify the negative-definite of some matrices constructed by the parameters of the neural networks. An illustrative example is given to show the effectiveness of the results in the paper.
Towards a Tag-Based User Model: How Can User Model Benefit from Tags?
Authors: Francesca Carmagnola, Federica Cena, Omar Cortassa, Cristina Gena, Ilaria Torre
Venue: Proceedings of the 11th international conference on User Modeling (2007)

Social tagging is a kind of social annotation by which users label resources, typically web objects, by means of keywords with the goal of sharing, discovering and recovering them. In this paper we investigate the possibility of exploiting the user tagging activity in order to infer knowledge about the user. Up to now the relation between tagging and user modeling seems not to have been investigated in depth. Given the widespread diffusion of web tools for collaborative tagging, it is interesting to understand how user modeling can benefit from this feedback.
Leveraging Model Driven Engineering in Software Product Line Architectures
Authors: Bruce Trask, Angel Roman
Venue: Proceedings of the 2008 12th International Software Product Line Conference (2008)

Model Driven Engineering (MDE) is a new innovation in the software industry that has proven to work synergistically with Software Product Line Architectures. It can provide the tools necessary to fully harness the power of Software Product Lines.
A Model Predictive Control of a Grain Dryer with Four Stages Based on Recurrent Fuzzy Neural Network
Authors: Chunyu Zhao, Qinglei Chi, Lei Wang, Bangchun Wen
Venue: Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks (2007)

This paper proposes a model predictive control scheme with recurrent fuzzy neural network (RFNN) by using the temperature of the drying process for grain dryers. In this scheme, there are two RFNNs and two PI controllers. One RFNN with feedforeward and feedback connections of grain layer history position states predicts outlet moisture content (MPRFNN), and the other predicts the discharge rate of the dryer (RPRFNN). One PI controller adjusts the objective of the discharge rate by using MPRFNN, and the other adjusts the given frequency of the discharge motor to control the discharge rate of the grain dryer to reach its objective by using RPRFNN. The experiment is carried out by applying the proposed scheme on the control of a gain dryer with four stages to confirm its effectiveness.
Towards a Medical Virtual Reality Environment for Minimally Invasive Cardiac Surgery
Authors: Terry M. Peters, Cristian A. Linte, John Moore, Daniel Bainbridge, Douglas L. Jones, Gérard M. Guiraudon
Venue: Proceedings of the 4th international workshop on Medical Imaging and Augmented Reality (2008)

We have developed a visualization environment to assist surgeons with therapy delivery inside the beating heart, in absence of direct vision. This system employs virtual reality techniques to integrate pre-operative anatomical models, real-time intra-operative imaging, and models of magnetically-tracked surgical tools. Visualization is enhanced via 3D dynamic cardiac models constructed from high-resolution pre-operative MR or CT data and registered within the intra-operative imaging environment. In this paper, we report our experience with a feature-based registration technique to fuse the pre- and intra-operative data during an in vivo intracardiac procedure on a porcine subject. Good alignment of the pre- and intra-operative anatomy within the virtual reality environment is ensured through the registration of easily identifiable landmarks. We present our initial experience in translating this work into the operating room and employing this system to guide typical intracardiac interventions. Given its extensive capabilities in providing surgical guidance in the absence of direct vision, our virtual environment is an ideal candidate for performing off-pump intracardiac interventions.
An Event-Based Model for the Management of Choreographed Services
Authors: Liliana Ardissono, Roberto Furnari, Anna Goy, Giovanna Petrone, Marino Segnan
Venue: Proceedings of the 9th international conference on E-Commerce and Web Technologies (2008)

We propose a mediation framework supporting a flexible management of choreographed services. The framework separates the management of the business logic of a service from the message flow details reported in the choreography specification. This abstraction step is achieved by introducing an event-driven management of Web Services and by exploiting a rich coordination context which includes business data and synchronization information. By separating activity management issues from communication details, our framework facilitates the management of interaction protocol mismatches between Web Services.
Morphisms of ANN and the Computation of Least Fixed Points of Semantic Operators
Authors: Anthony Karel Seda
Venue: Proceedings of the 2nd international work-conference on The Interplay Between Natural and Artificial Computation, Part I: Bio-inspired Modeling of Cognitive Tasks (2007)

We consider a notion of morphism of neural networks and develop its properties. We show how, given any definite logic program P, the least fixed point of the immediate consequence operator T P can be computed by the colimit of a family of neural networks determined by P.
Dynamic Skin Detection in Color Images for Sign Language Recognition
Authors: Michal Kawulok
Venue: Proceedings of the 3rd international conference on Image and Signal Processing (2008)

Skin detection is the first step of processing in many approaches to face and gesture recognition. This paper presents research aimed at detecting skin in digital images for Polish Sign Language recognition. There are many methods for detecting human skin, including parametric skin models defined in various color spaces and statistical approaches which require appropriate training. The presented method is based on statistical model updated dynamically for every image in which human faces can be detected. The detection is performed in luminance channel based on geometric properties of human faces. The experiments proved that effectiveness of this approach is higher than application of general skin detection models. 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
Smartening up to risk in electronic government
Authors: Helen Margetts
Venue: Information Polity (2005)

This article identifies emerging risks in electronic government: the risk of government lagging behind a 'smart' society, where increasing proportions of citizens and businesses are accustomed to conduct their affairs electronically; risk involved in information technology outsourcing; and risk brought about by the integration of technology into governmental activities, particularly policy innovation. All three represent the introduction of risks more commonly associated with the private sector into government. For each of these three risk factors, the article suggests some potential strategies for dealing with them. First, government needs to use the capability of internet-based technologies to understand 'smart society' more fully. Second, the UK government in particular needs to develop a more relational style of contracting and learn from the sharp variations in contracting regimes across the world. Third, leaders of governmental organisations need to accept that digital technologies are now at the core of a wide range of their activities and adapt policy-making processes accordingly.
Software Engineering for Service-Oriented MAS
Authors: Emilia Garcia, Adriana Giret, Vicente Botti
Venue: Proceedings of the 12th international workshop on Cooperative Information Agents XII (2008)

Nowadays, service-oriented architectures (SOA) and multiagent systems (MAS) are two increasingly important technologies. Despite the differences in technology, SOA and MAS have some similar objectives and their integration produces systems with more flexibility, functionality and interoperability. Their integration creates new requirements and special methods and tools are necessary to develop systems that integrate both technologies. This paper analyzes the most important issues for developing Service-oriented MAS. Furthermore, some methods and tools to develop this kind of systems are analyzed to show how current approaches solve the problem of the integration between agents and services.
Rolling Shutter Image Compensation
Authors: Steven P. Nicklin, Robin D. Fisher, Richard H. Middleton
Venue: RoboCup 2006: Robot Soccer World Cup X (2006)

This paper describes corrections to image distortion found on the Sony AIBO ERS-7 robots. When obtaining an image the camera captures each pixel in series, that is there is effectively a 'rolling shutter'. This results in a delay between the capture of the first and last pixel. When combined with movement of the camera the image produced will be distorted. The sensor values from the robot, coupled with knowledge of the camera's timing, are used to calculate the effect of the robots movement on the image. This information can then be used to remove much of the distortion from the image. The correction improves the effectiveness of shape recognition and bearing-to-object accuracy.
Not all agents are equal: scaling up distributed POMDPs for agent networks
Authors: Janusz Marecki, Tapana Gupta, Pradeep Varakantham, Milind Tambe, Makoto Yokoo
Venue: Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1 (2008)

Many applications of networks of agents, including mobile sensor networks, unmanned air vehicles, autonomous underwater vehicles, involve 100s of agents acting collaboratively under uncertainty. Distributed Partially Observable Markov Decision Problems (Distributed POMDPs) are well-suited to address such applications, but so far, only limited scale-ups of up to five agents have been demonstrated. This paper escalates the scale-up, presenting an algorithm called FANS, increasing the number of agents in distributed POMDPs for the first time into double digits. FANS is founded on finite state machines (FSMs) for policy representation and expoits these FSMs to provide three key contributions: (i) Not all agents within an agent network need the same expressivity of policy representation; FANS introduces novel heuristics to automatically vary the FSM size in different agents for scaleup; (ii) FANS illustrates efficient integration of its FSM-based policy search within algorithms that exploit agent network structure; (iii) FANS provides significant speedups in policy evaluation and heuristic computations within the network algorithms by exploiting the FSMs for dynamic programming. Experimental results show not only orders of magnitude improvements over previous best known algorithms for smaller-scale domains (with similar solution quality), but also a scale-up into double digits in terms of numbers of agents.
Model Transformation as an Optimization Problem
Authors: Marouane Kessentini, Houari Sahraoui, Mounir Boukadoum
Venue: Proceedings of the 11th international conference on Model Driven Engineering Languages and Systems (2008)

Most of the available work on model transformation is based on the hypothesis that transformation rules exist and that the important issue is how to express them. But in real life, the rules may be difficult to define; this is often the case when the source and/or target formalisms are not widely used or proprietary. In this paper, we consider the transformation mechanism as a combinatorial optimization problem where the goal is to find a good transformation starting from a small set of available examples. Our approach, named model transformation as optimization by examples (MOTOE), combines transformation blocks extracted from examples to generate a target model. To that end, we use an adapted version of particle swarm optimization (PSO) where transformation solutions are modeled as particles that exchange transformation blocks to converge towards an optimal transformation solution. MOTOE has two main advantages: It proposes a transformation without the need to derive transformation rules first, and it can operate independently from the source and target metamodels.
Visualization of Possibilistic Potentials
Authors: Matthias Steinbrecher, Rudolf Kruse
Venue: Proceedings of the 12th international Fuzzy Systems Association world congress on Foundations of Fuzzy Logic and Soft Computing (2007)

The constantly increasing capabilities of database storage systems leads to an incremental collection of data by business organizations. The research area of Data Mining has become a paramount requirement in order to cope with the acquired information by locating and extracting patterns from these data volumes. Possibilistic networks comprise one prominent Data Mining technique that is capable of encoding dependence and independence relations between variables as well as dealing with imprecision. It will be argued that the learning of the network structure only provides an overview of the qualitative component, yet the more interesting information is contained inside the network parameters, namely the potential tables. In this paper we introduce a new visualization technique that allows for a detailed inspection of the quantitative component of possibilistic networks.
On the Construction and Evaluation of Flexible Plan-Refinement Strategies
Authors: Bernd Schattenberg, Julien Bidot, Susanne Biundo
Venue: Proceedings of the 30th annual German conference on Advances in Artificial Intelligence (2007)

This paper describes a system for the systematic construction and evaluation of planning strategies. It is based on a proper formal account of refinement planning and allows to decouple plan-deficiency detection, refinement computation, and search control. In adopting this methodology, planning strategies can be explicitly described and easily deployed in various system configurations.We introduce novel domain-independent planning strategies that are applicable to a wide range of planning capabilities and methods. These so-called HotSpot strategies are guided by information about current plan defects and solution options. The results of a first empirical performance evaluation are presented in the context of hybrid planning.
Discovering Local Patterns of Co-evolution
Authors: Yifat Felder, Tamir Tuller
Venue: Proceedings of the international workshop on Comparative Genomics (2008)

Co-evolution is the process in which a set of orthologs exhibits a similar or correlative pattern of evolution. Co-evolution is a powerful way to learn about the functional interdependencies between sets of genes and cellular functions, about their complementary and backup relations, and more generally, for answering fundamental questions about the evolution of biological systems.Orthologs that exhibit strong signal of co-evolution in part of the evolutionary tree may show mild signal of co-evolution in other parts of the tree. The major reasons for this phenomenon are noise in the biological input, genes that gain or lose functions, and the fact that some measures of co-evolution relate to rare events such as positive evolution. Previous works in the field dealt with the problem of finding sets of genes that co-evolved along an entire underlying phylogenetic tree, without considering the fact that often co-evolution is local.In this work, we describe a new set of biological problems that are related to finding patterns of local co-evolution. We discuss their computational complexity and design algorithms for solving them. These algorithms outperform other bi-clustering methods as they are designed specifically for solving the set of problems mentioned above. We use our approach to trace the co-evolution of fungal and Eukaryotic genes at a high resolution across the different parts of the corresponding phylogenetic trees. Our analysis shows that local co-evolution is a wide-scale phenomenon.
Counting Complexity of Minimal Cardinality and Minimal Weight Abduction
Authors: Miki Hermann, Reinhard Pichler
Venue: Proceedings of the 11th European conference on Logics in Artificial Intelligence (2008)

Abduction is an important method of non-monotonic reasoning with many applications in artificial intelligence and related topics. In this paper, we concentrate on propositional abduction, where the background knowledge is given by a propositional formula. We have recently started to study the counting complexity of propositional abduction. However, several important cases have been left open, namely, the cases when we restrict ourselves to solutions with minimal cardinality or with minimal weight. These cases --- possibly combined with priorities --- are now settled in this paper. We thus arrive at a complete picture of the counting complexity of propositional abduction.
A Neural Network Based Regression Approach for Recognizing Simultaneous Speech
Authors: Weifeng Li, Kenichi Kumatani, John Dines, Mathew Magimai-Doss, Hervé Bourlard
Venue: Proceedings of the 5th international workshop on Machine Learning for Multimodal Interaction (2008)

This paper presents our approach for automatic speech recognition (ASR) of overlapping speech. Our system consists of two principal components: a speech separation component and a feature estmation component. In the speech separation phase, we first estimated the speaker's position, and then the speaker location information is used in a GSC-configured beamformer with a minimum mutual information (MMI) criterion, followed by a Zelinski and binary-masking post-filter, to separate the speech of different speakers. In the feature estimation phase, the neural networks are trained to learn the mapping from the features extracted from the pre-separated speech to those extracted from the close-talking microphone speech signal. The outputs of the neural networks are then used to generate acoustic features, which are subsequently used in acoustic model adaptation and system evaluation. The proposed approach is evaluated through ASR experiments on the PASCAL Speech Separation Challenge II (SSC2) corpus. We demonstrate that our system provides large improvements in recognition accuracy compared with a single distant microphone case and the performance of ASR system can be significantly improved both through the use of MMI beamforming and feature mapping approaches.
Predictability & criticality metrics for coordination in complex environments
Authors: Rajiv T. Maheswaran, Pedro Szekely
Venue: Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2 (2008)

We address the problem of coordinating the activities of a team of agents in a dynamic, uncertain, nonlinear environment. Bounded rationality, bounded communication, subjectivity and distribution make it extremely challenging to find effective strategies. In these domains it is difficult to accurately predict whether potential policy modifications will lead to an increase in the value of the team reward. Our Predictability and Criticality Metrics (PCM) approach errs on the side of safety, and advocates considering policy modifications that are guaranteed to not harm the current policy, and uses simple metrics to choose from within that set a modification that increases the team reward. In the context of the DARPA Coordinators program, we show how the PCM approach yielded a system that significantly outperformed several competing approaches in an extensive independent evaluation.