ppxscal/embedding_network huggingface dataset:
NOSOSIM: an agent-based model of pathogen circulation in a hospital ward
Authors: Laura Temime, Yohan Pannet, Lidia Kardas, Lulla Opatowski, Didier Guillemot, Pierre-Yves Boëlle
Venue: Proceedings of the 2009 Spring Simulation Multiconference (2009)

Over recent years, nosocomial infections have become a major Public Health problem in hospitals worldwide. In this context, it is very important to understand the underlying mechanisms of nosocomial pathogen transmission. This should in turn enable the devising and the assessment of control strategies. Here, we present an agent-based model of pathogen transmission in a hospital ward, NOSOSIM. In this model, the actions of patients and health-care workers are reproduced at all times in a spatially explicit environment, and pathogen colonization and transmission are simulated. NOSOSIM allows users to define simulation parameters such as: ward geography, including -but not limited to- the number of rooms; daily allocation and schedule of all present healthcare workers; patient status; microbiological and epidemiological characteristics of all circulating pathogens. After describing the model and its implementation, we illustrate its potential applications through two examples. In the first example, NOSOSIM is used to gain better understanding of the dynamics of pathogen circulation in a context of antibiotic exposure and between-pathogen competition. In the second example, NOSOSIM is used to assess the effectiveness of a control strategy (systematic hand hygiene).
Reinforcement Learning for Blackjack
Authors: Saqib A. Kakvi
Venue: Proceedings of the 8th International Conference on Entertainment Computing (2009)

This paper explores the development of an Artificial Intelligence system for an already existing framework of card games, called SKCards, and the experimental results obtained from this. The current Artificial intelligence in the SKCards Blackjack is highly flawed. Reinforcement Learning was chosen as the method to be employed. Reinforcement Learning attempts to teach a computer certain actions, given certain states, based on past experience and numerical rewards gained. The agent either assigns values to states, or actions in states. This will initially be developed for Blackjack, with possible extensions to other games. Blackjack is one of the simpler games and the only current game in the SKCards package which needs an Artificial Intelligence agent. All the other games are single player. To test the performance of the Reinforcement Learning agent, several experiments were devised and run. 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
Decision-theoretic bidding based on learned density models in simultaneous, interacting auctions
Authors: Peter Stone, Robert E. Schapire, Michael L. Littman, János A. Csirik, David McAllester
Venue: Journal of Artificial Intelligence Research (2003)

Auctions are becoming an increasingly popular method for transacting business, especially over the Internet. This article presents a general approach to building autonomous bidding agents to bid in multiple simultaneous auctions for interacting goods. A core component of our approach learns a model of the empirical price dynamics based on past data and uses the model to analytically calculate, to the greatest extent possible, optimal bids. We introduce a new and general boosting-based algorithm for conditional density estimation problems of this kind, i.e., supervised learning problems in which the goal is to estimate the entire conditional distribution of the real-valued label. This approach is fully implemented as ATTac-2001, a top-scoring agent in the second Trading Agent Competition (TAC-01). We present experiments demonstrating the effectiveness of our boosting-based price predictor relative to several reasonable alternatives.
MERO: A Statistical Approach for Hardware Trojan Detection
Authors: Rajat Subhra Chakraborty, Francis Wolff, Somnath Paul, Christos Papachristou, Swarup Bhunia
Venue: Proceedings of the 11th International Workshop on Cryptographic Hardware and Embedded Systems (2009)

In order to ensure trusted in---field operation of integrated circuits, it is important to develop efficient low---cost techniques to detect malicious tampering (also referred to as Hardware Trojan) that causes undesired change in functional behavior. Conventional post--- manufacturing testing, test generation algorithms and test coverage metrics cannot be readily extended to hardware Trojan detection. In this paper, we propose a test pattern generation technique based on multiple excitation of rare logic conditions at internal nodes. Such a statistical approach maximizes the probability of inserted Trojans getting triggered and detected by logic testing, while drastically reducing the number of vectors compared to a weighted random pattern based test generation. Moreover, the proposed test generation approach can be effective towards increasing the sensitivity of Trojan detection in existing side---channel approaches that monitor the impact of a Trojan circuit on power or current signature. Simulation results for a set of ISCAS benchmarks show that the proposed test generation approach can achieve comparable or better Trojan detection coverage with about 85% reduction in test length on average over random patterns.
UofL: word sense disambiguation using lexical cohesion
Authors: Yllias Chali, Shafiq R. Joty
Venue: Proceedings of the 4th International Workshop on Semantic Evaluations (2007)

One of the main challenges in the applications (i.e.: text summarization, question answering, information retrieval, etc.) of Natural Language Processing is to determine which of the several senses of a word is used in a given context. The problem is phrased as "Word Sense Disambiguation (WSD)" in the NLP community. This paper presents the dictionary based disambiguation technique that adopts the assumption of one sense per discourse in the context of SemEval-2007 Task 7: "Coarse-grained English all-words".
Viewpoints in complex event processing: industrial experience report
Authors: Ingmar Kellner, Ludger Fiege
Venue: Proceedings of the Third ACM International Conference on Distributed Event-Based Systems (2009)

In the automation industry an increasing need for integration exists, spanning from field level to enterprise resource planning. An important requirement is the adaptability of the involved production processes, because industry must be able to react quickly to new situations and challenges. Complex Event Processing (CEP) is a promising technology to detect and react to such situations. However, current approaches for CEP often mix the derivation of high-level business events with their interpretation and follow a bottom - up development that is not focused on Key Performance Indicators (KPIs). Consequently, maintainability and the required adaptability are hard to achieve. In this paper, we investigate the separation of two viewpoints in a CEP application: i) modeling critical business situations with the reactions to be taken and ii) aggregation of the necessary base data to distill key performance indicators. Additionally, we present a methodology for the engineering of these viewpoints. This gives guidance for the development of the underlying eventing infrastructure and provides a goal/KPI-oriented approach.
Combining a statistical language model with logistic regression to predict the lexical and syntactic difficulty of texts for FFL
Authors: Thomas L. François
Venue: Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop (2009)

Reading is known to be an essential task in language learning, but finding the appropriate text for every learner is far from easy. In this context, automatic procedures can support the teacher's work. Some tools exist for English, but at present there are none for French as a foreign language (FFL). In this paper, we present an original approach to assessing the readability of FFL texts using NLP techniques and extracts from FFL textbooks as our corpus. Two logistic regression models based on lexical and grammatical features are explored and give quite good predictions on new texts. The results shows a slight superiority for multinomial logistic regression over the proportional odds model.
Using a dependency parser to improve SMT for subject-object-verb languages
Authors: Peng Xu, Jaeho Kang, Michael Ringgaard, Franz Och
Venue: Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics (2009)

We introduce a novel precedence reordering approach based on a dependency parser to statistical machine translation systems. Similar to other preprocessing reordering approaches, our method can efficiently incorporate linguistic knowledge into SMT systems without increasing the complexity of decoding. For a set of five subject-object-verb (SOV) order languages, we show significant improvements in BLEU scores when translating from English, compared to other reordering approaches, in state-of-the-art phrase-based SMT systems.
Parse selection with a German HPSG grammar
Authors: Berthold Crysmann
Venue: Proceedings of the Workshop on Parsing German (2008)

We report on some recent parse selection experiments carried out with GG, a large-scale HPSG grammar for German. Using a manually disambiguated treebank derived from the Verbmobil corpus, we achieve over 81% exact match accuracy compared to a 21.4% random baseline, corresponding to an error reduction rate of 3.8.
The Framework of City Informationization Based on Pervasive Computing
Authors: Liu Dan, Zhang Zhonghui, Lv Tingjie
Venue: Proceedings of the 2009 Second International Symposium on Electronic Commerce and Security - Volume 02 (2009)

A kind of framework which is based on pervasive computing has been developed. Firstly, it can utilize all kinds of computation devices as the mean of information access. Secondly, supported by the Telecommunication Network, Broadcast Network and Internet Network which were based on the NGN telecommunication platform, it can realize the transmission and sharing of information. Thirdly, it has been designed in the union of the convergence of different database and the sharing methods of information. Lastly, it realizes the interconnection, intercommunication and the team working of various IT information systems in the city. At the same time, it makes comprehensive and integrated use of all kinds of new technology for city informationization. Therefore, the framework has the important significance to speed up the informationization process of city.
Profiling the Nanoparticles Associated Protein-protein Interactions
Authors: Wei-Chen Liao, Nai-Kuei Huang
Venue: Proceedings of the 2009 Ninth IEEE International Conference on Bioinformatics and Bioengineering (2009)

To explore the nanoparticles-dependent signaling pathways is a rapidly growing research field. Here, we globally elucidate the regulation modules of cellular molecules by using graph-based representation of complex networks. We have packed some useful bioinformatics methods (Matlab Simbiology and Pathway Studio) into an integrated system for graph-based representation and complex networks modeling. Here, this system is applied to the public literature and microarray database of nanoparticles and try to find out nanoparticles related genetic regulatory pathways. By comparing to different model organism system (bacteria, mice, cell line), we will establish a multidimensional (protein cellular location, time course expression pattern and chemicals interacting profiles) secondary database. The value added database will uncover some new relationships between genes and nanoparticles and provide useful information for the modeling of nanoparticles cellular regulatory pathway in an interactive form. The new modeling approaches should serve as a research platform to find protein candidates for nanoparticles associated diseases and provides a possible cue to improve the bio-chemo-informatics analyses on gene expression and molecular pharmacology.
Fast Spectral Clustering with Random Projection and Sampling
Authors: Tomoya Sakai, Atsushi Imiya
Venue: Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition (2009)

This paper proposes a fast spectral clustering method for large-scale data. In the present method, random projection and random sampling techniques are adopted for reducing the data dimensionality and cardinality. The computation time of the present method is quasi-linear with respect to the data cardinality. The clustering result can be updated with a small computational cost when data samples or random samples are appended or removed.
Patterns in Time: Modelling Schedules in Social Networks
Authors: Aleksander Wawer, Radoslaw Nielek, Romuald Kotowski
Venue: Proceedings of the 2009 International Workshop on Social Informatics (2009)

A theoretical account aimed at modeling time usage founded on social network-based multiagent system is proposed. The model introduces mutual, dynamic relationships between the network and schedules: on the one hand, a network is generated by schedules, on the other, schedules are shaped by the network. Time allocation is driven by globally distributed semantic resources. Social network structure and agent time usage have been adjusted by applying evolutionary algorithm to match empirically observed values. The resulting allocation reveals an emergent property which ties the number of agents with activity type priorities. Using the adjusted social network, simulations have been run to investigate time patterns. For that purpose, a time cycle similarity measure has been proposed and computed, unveiling a high amount of regularity, which indirectly supports the view of culture's function as a latent pattern maintenance.
Deterministic shift-reduce parsing for unification-based grammars by using default unification
Authors: Takashi Ninomiya, Nobuyuki Shimizu, Takuya Matsuzaki, Hiroshi Nakagawa
Venue: Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics (2009)

Many parsing techniques including parameter estimation assume the use of a packed parse forest for efficient and accurate parsing. However, they have several inherent problems deriving from the restriction of locality in the packed parse forest. Deterministic parsing is one of solutions that can achieve simple and fast parsing without the mechanisms of the packed parse forest by accurately choosing search paths. We propose (i) deterministic shift-reduce parsing for unification-based grammars, and (ii) best-first shift-reduce parsing with beam thresholding for unification-based grammars. Deterministic parsing cannot simply be applied to unification-based grammar parsing, which often fails because of its hard constraints. Therefore, it is developed by using default unification, which almost always succeeds in unification by overwriting inconsistent constraints in grammars.
Study on Sensitivity to ECA Behavior Parameters
Authors: Ladislav Kunc, Pavel Slavík
Venue: Proceedings of the 9th International Conference on Intelligent Virtual Agents (2009)

Embodied conversational agents (ECAs) provide one possible way of incorporating nonverbal portion of speech into voice based user interfaces. Part of agent's visual behavior and its appearance is often solved "statically." There is another possibility to change appearance - dynamically (for example head or eye movements, mouth opening, etc.). Our hypothesis is that some "dynamic" parameters are more important for the user than the other ones. In this paper we present the pilot user study and statistical evaluation of four parameters. Eye blinking is one of the parameter we investigate. The importance of agent's gaze (blinking as part of it) was discussed several times for example by Raidt [1].
Designing energy efficient automatic repeat request protocol in wireless sensor networks
Authors: Cheng Guo, R. Venkatesha Prasad, Przemyslaw Pawelczak, Ramin Hekmat
Venue: Proceedings of the 4th ACM workshop on Challenged networks (2009)

Energy constrained Wireless Sensor Networks (WSNs) challenge traditional protocols in many aspects. One such challenge is the requirement of energy efficiency in the Automatic Repeat Request (ARQ) protocols. Although traditional ARQ protocols are optimized for a better throughput, they fail to minimize the energy consumption. In some applications in sensor networks, delay can be tolerated. Using this aspect, we propose a link quality aware ARQ (LQ-ARQ) protocol which only retransmits packets from a buffer when the link from the sender to the receiver is relatively good. The link quality is judged by checking the Received Signal Strength (RSS) of the data packets, thus no extra overhead is introduced. To avoid using an inflexible threshold of RSS to judge a good or bad link, which is hard to decide in practise, we propose a machine learning mechanism to decide the threshold softly depending on link quality. Our protocol is able to deliver packets reliably with least amount of retransmissions. Thus energy consumption is reduced. Moreover, usage of limited resources such as memory is minimized in the protocol. We compared our protocol with traditional ARQ protocols with real field experimental data. Results show significant improvements.
Goal-Oriented Control with Brain-Computer Interface
Authors: Günter Edlinger, Clemens Holzner, Christoph Groenegress, Christoph Guger, Mel Slater
Venue: Proceedings of the 5th International Conference on Foundations of Augmented Cognition. Neuroergonomics and Operational Neuroscience: Held as Part of HCI International 2009 (2009)

A brain-computer interface (BCI) is a new communication channel between the human brain and a digital computer. Such systems have been designed to support disabled people for communication and environmental control. In more recent research also BCI control in combination with Virtual Environments (VE) gains more and more interest. Within this study we present experiments combining BCI systems and VE for navigation and control purposes just by thoughts. Results show that the new P300 based BCI system allows a very reliable control of the VR system. Of special importance is the possibility to select very rapidly the specific command out of many different choices. The study suggests that more than 80% of the population could use such a BCI within 5 minutes of training only. This eliminates the usage of decision trees as previously done with BCI systems.
An Evidence-Theoretic k-Nearest Neighbor Rule for Multi-label Classification
Authors: Zoulficar Younes, Fahed Abdallah, Thierry Denœux
Venue: Proceedings of the 3rd International Conference on Scalable Uncertainty Management (2009)

In multi-label learning, each instance in the training set is associated with a set of labels, and the task is to output a label set for each unseen instance. This paper describes a new method for multi-label classification based on the Dempster-Shafer theory of belief functions to classify an unseen instance on the basis of its k nearest neighbors. The proposed method generalizes an existing single-label evidence-theoretic learning method to the multi-label case. In multi-label case, the frame of discernment is not the set of all possible classes, but it is the powerset of this set. That requires an extension of evidence theory to manipulate multi-labelled data. Using evidence theory makes us able to handle ambiguity and imperfect knowledge regarding the label sets of training patterns. Experiments on benchmark datasets show the efficiency of the proposed approach as compared to other existing methods.
A Common Acquaintance Immunization Strategy for Complex Network
Authors: Pan Liu, Huaikou Miao, Qing Li
Venue: Proceedings of the 2009 Eigth IEEE/ACIS International Conference on Computer and Information Science (2009)

The paper analyzes the successful and unsuccessful probability of the acquaintance immunization strategy by utilizing a simple example, and points out the possible reasons of invalidation of this method. To enhance the successful probability of the acquaintance immunization strategy for complex network, a common acquaintance immunization strategy which looks for the common neighbors of randomly chosen nodes in the complex network is presented, and then the related algorithm is also designed. To compare the performance of two methods, a program written in Java is developed. By a series of experiments, some important conclusions are obtained. Compared with the acquaintance immunization strategy, the common acquaintance immunization strategy not only gains higher stability and reliability for protecting the complex network, but also can be taken as a novel method to detect the structure of the unknown network.
Brief paper: Adaptive hinging hyperplanes and its applications in dynamic system identification
Authors: Jun Xu, Xiaolin Huang, Shuning Wang
Venue: Automatica (Journal of IFAC) (2009)

The model of adaptive hinging hyperplanes (AHH) is proposed in this paper. It is based on multivariate adaptive regression splines (MARS) and generalized hinging hyperplanes (GHH) and shares attractive properties of the two. By making a modification to the basis function of MARS, AHH shows linear property in each subregion. The AHH model is actually a special case of the GHH model, which has a universal representation capability for continuous piecewise linear functions. The approximation ability of the AHH model is proved. The AHH algorithm is developed similar to the MARS algorithm. It is adaptive and can be executed efficiently, hence has power and flexibility to model unknown relationships. The AHH procedure is applied to identifying two dynamic systems and its potential is illustrated.
Herding Agents - JIAC TNG in Multi-Agent Programming Contest 2008
Authors: Axel Hessler, Jan Keiser, Tobias Küster, Marcel Patzlaff, Alexander Thiele, Erdene-Ochir Tuguldur
Venue: Programming Multi-Agent Systems: 6th International Workshop, ProMAS 2008, Estoril, Portugal, May 13, 2008. Revised Invited and Selected Papers (2009)

Another essential problem of mankind must be solved in this year's agent contest: cow capturing. We present the JIAC approach to this problem by applying the iterative and incremental JIAC methodology and JIAC tools. The solution will be designed and implemented using the next generation of the JIAC agent framework that provides easier way of agent construction, but that is in early beta state. We admire this contest as an evaluation platform for our developments (like our last year's MicroJIAC team).
Depth Reduction for Circuits with a Single Layer of Modular Counting Gates
Authors: Kristoffer Arnsfelt Hansen
Venue: Proceedings of the Fourth International Computer Science Symposium in Russia on Computer Science - Theory and Applications (2009)

We consider the class of constant depth AND/OR circuits augmented with a layer of modular counting gates at the bottom layer, i.e ${\bf AC}^{0} \circ {\bf MOD}_m$ circuits. We show that the following holds for several types of gates $\mathcal{G}$: by adding a gate of type $\mathcal{G}$ at the output, it is possible to obtain an equivalent probabilistic depth 2 circuit of quasipolynomial size consisting of a gate of type $\mathcal{G}$ at the output and a layer of modular counting gates, i.e $\mathcal{G} \circ {\bf MOD}_m$ circuits. The types of gates $\mathcal{G}$ we consider are modular counting gates and threshold-style gates. For all of these, strong lower bounds are known for (deterministic) $\mathcal{G} \circ {\bf MOD}_m$ circuits. 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