ppxscal/embedding_network huggingface dataset:
Coordination and adaptation in impromptu teams
Authors: Michael Bowling, Peter McCracken
Venue: Proceedings of the 20th national conference on Artificial intelligence - Volume 1 (2005)

Coordinating a team of autonomous agents is one of the major challenges in building effective multiagcnt systems. Many techniques have been devised for this problem. and coordinated teamwork has been demonstrated even in highly dynamic and adversarial environments. A key assumption of these techniques. though. is that the team members are developed together as a whole. In many multi agent scenarios. this assumption is violated. We study the problem of coordination in impromptu teams, where a team is composed of independent agents each unknown to the others. The team members have their own skills. models. strategies. and coordination mechanisms. and no external organization is imposed upon them. In particular. we propose two techniques. one adaptive and one predictive. for coordinating a single agent that joins an unknown team of existing agents. We experimentally evaluate these mechanisms in the robot soccer domain, while introducing useful baselines for evaluating the performance of impromptu teams. We show some encouraging success while demonstrating this is a very fertile area of research.
Combining multiple heuristics online
Authors: Matthew Streeter, Daniel Golovin, Stephen F. Smith
Venue: Proceedings of the 22nd national conference on Artificial intelligence - Volume 2 (2007)

We present black-box techniques for learning how to interleave the execution of multiple heuristics in order to improve average-case performance. In our model, a user is given a set of heuristics whose only observable behavior is their running time. Each heuristic can compute a solution to any problem instance, but its running time varies across instances. The user solves each instance by interleaving runs of the heuristics according to a task-switching schedule. We present (i) exact and approximation algorithms for computing an optimal task-switching schedule offline, (ii) sample complexity bounds for learning a task-switching schedule from training data, and (iii) a no-regret strategy for selecting task-switching schedules online. We demonstrate the power of our results using data from recent solver competitions. We outline how to extend our results to the case in which the heuristics are randomized, and the user may periodically restart each heuristic with a fresh random seed.
TringIt: Easy Triggering of Web Actions from a Phone
Authors: Vinod Anupam
Venue: Proceedings of the 13th International Conference on Human-Computer Interaction. Part III: Ubiquitous and Intelligent Interaction (2009)

Much information that is of interest to mobile users is available on the Web, yet is difficult to access for most users. We introduce a novel method for users to interact with network-connected computers using their phones, and describe a system called TringIt that implements the method. TringIt enables users to trigger Web actions by simply dialing specific numbers --- an action that we call a Phone Click. TringIt can be used out-of-the-box by any phone user. The Phone Click is most useful from mobile phones that can receive messages in response to the click. TringIt enables users to easily initiate interaction with businesses and content owners by simply dialing numbers discovered in offline media (e.g. print, TV, radio) as well as online media (e.g. Web, SMS, MMS.) It makes every mobile phone a more compelling information, interaction and participation device.
Dynamic Detection of Unknown Malicious Executables Base on API Interception
Authors: Fei Chen, Yan Fu
Venue: Proceedings of the 2009 First International Workshop on Database Technology and Applications (2009)

In this paper, we propose a new approach for the dynamic detection of malicious executables on the platform of Windows. Our approach extracts signatures of malicious executable's behaviors by using API (Application Program Interface) interception technique which makes possible the detection of unknown malicious executables. The dynamic detection of unknown malicious executables is achieved in three major steps: getting the sequence of API function calls of the executable, processing the API sequence to generate a vector, calculating the similarity between the vector and the feature library constructed by security policies to verify if the executable is malicious. The experiment confirms that this approach is effective in detection of unknown malicious executables.
Energy Bucket: A Tool for Power Profiling and Debugging of Sensor Nodes
Authors: Jacob Andersen, Morten Tranberg Hansen
Venue: Proceedings of the 2009 Third International Conference on Sensor Technologies and Applications (2009)

The ability to precisely measure and compare energy consumption and relate this to particular parts of programs is a recurring theme in sensor network research. This paper presents the Energy Bucket, a low-cost tool designed for quick empirical measurements of energy consumptions across 5 decades of current draw. The Energy Bucket provides a light-weight state API for the target system, which facilitates easy scorekeeping of energy consumption between different parts of a target program. We demonstrate how this tool can be used to discover programming errors and debug sensor network applications.Furthermore, we show how this tool, together with the target system API, offers a very detailed analysis of where energy is spent in an application, which proves to be very useful when comparing alternative implementations or validating theoretical energy consumption models.
An Algorithm for Real-Time Visualization of Large-Scale Terrain
Authors: Jin Hailiang, Liu Huijie
Venue: Proceedings of the 2009 WASE International Conference on Information Engineering - Volume 02 (2009)

Large-scale terrain real-time visualization has been widely used in GIS, virtual reality, games, simulation, etc. Based on existing terrain simplification algorithm, this paper proposes an algorithm for real-time visualization of terrain with data block partition, dynamic dispatching and view-dependant. This algorithm can implement the real-time fast walkthrough for large-scale terrain. With view frustum culling, triangle strip optimizing, cracks elimination and geomorphing, the efficiency and effect of the method are greatly increased.
A Novel Tree Graph Data Structure for Point Datasets
Authors: Saeed Behzadi, Ali A. Alesheikh, Mohammad R. Malek
Venue: Proceedings of the International Conference on Computational Science and Its Applications: Part II (2009)

Numerous data structures are developed to organize data and their relations. Point set data in GIS are managed mostly through TIN (Triangulated Irregular Network) or grid structure. Both methods have some disadvantages which will be discussed in this paper. In order to remove these weaknesses, a novel method will be introduced which is based on tree graph data structure. Tree graph data structure is a kind of data structure which shows the relationship between points by using some tree graphs. This paper assesses the commonly used point structures. It then introduces a new algorithm to address the issues of previous structures. The new data structure is inspired by snow falling process in natural environment. In order to evaluate the proposed data structure, a Digital Train Model (DTM) of sample points is constructed and compared with the generated DTM of TIN model. The RMSE of proposed method is 0.585933 while the one which is obtained by TIN method is 0.748113. The details of which are presented in the paper.
Web Services-Based Grid Computing for Traffic Flow Predictive Control
Authors: Yonghua Zhou
Venue: Proceedings of the 2009 WRI Global Congress on Intelligent Systems - Volume 03 (2009)

The vehicles are navigated and assigned to the optimized routes according to the information of real-time and predicted traffic flow so that the optimization of traffic flow system and travelers’ satisfaction degree can be both achieved. The architecture of traffic flow predictive control is presented in this paper including such two parts as traffic flow navigation predictive control or dynamic traffic assignment based on predictive control and traffic signal predictive control. The real-time processing approach of parallel genetic algorithm based on web services in IntraGrid is presented in which distributed-control and central-control parallel genetic algorithms are devised. The simulation results show that the traffic flow navigation predictive control can predict possible congestion and prevent its happening, and thus realize the win-win system-optimal and user-optimal optimization. The IntraGrid computing of parallel genetic algorithm based on web services can meet the requirements of real-time processing for traffic flow predictive control.
Increasing the Gap between Descriptional Complexity and Algorithmic Probability
Authors: Adam R. Day
Venue: Proceedings of the 2009 24th Annual IEEE Conference on Computational Complexity (2009)

The coding theorem is a fundamental result of algorithmic information theory. A well known theorem of Gács shows that the analog of the coding theorem fails for continuous sample spaces. This means that descriptional monotonic complexity does not coincide within an additive constant with the negative logarithm of algorithmic probability. Gács's proof provided a lower bound on the difference between these values. He showed that for infinitely many finite binary strings, this difference was greater than a version of the inverse Ackermann function applied to string length. This paper establishes that this lower bound can be substantially improved. The inverse Ackermann function can be replaced with a function O(log(log(x))). This shows that in continuous sample spaces, descriptional monotonic complexity and algorithmic probability are very different. While this proof builds on the original work by Gács, it does have a number of new features, in particular, the algorithm at the heart of the proof works on sets of strings as opposed to individual strings.
Gappy PCA Classification for Occlusion Tolerant 3D Face Detection
Authors: Alessandro Colombo, Claudio Cusano, Raimondo Schettini
Venue: Journal of Mathematical Imaging and Vision (2009)

This paper presents an innovative approach for the detection of faces in three dimensional scenes. The method is tolerant against partial occlusions produced by the presence of any kind of object. The detection algorithm uses invariant properties of the surfaces to segment salient facial features, namely the eyes and the nose. At least two facial features must be clearly visible in order to perform face detection. Candidate faces are then registered using an ICP (Iterative Correspondent Point) based approach aimed to avoid those samples which belong to the occluding objects. The final face versus non-face discrimination is computed by a Gappy PCA (GPCA) classifier which is able to classify candidate faces using only those regions of the surface which are considered to be non-occluded. The algorithm has been tested using the UND database obtaining 100% of correct detection and only one false alarm. The database has been then processed with an artificial occlusions generator producing realistic acquisitions that emulate unconstrained scenarios. A rate of 89.8% of correct detections shows that 3D data is particularly suited for handling occluding objects. The results have been also verified on a small test set containing real world occlusions obtaining 90.4% of correctly detected faces. The proposed approach can be used to improve the robustness of all those systems requiring a face detection stage in non-controlled scenarios.
Learning Styles Diagnosis Based on Learner Behaviors in Web Based Learning
Authors: Nilüfer Atman, Mustafa Murat Inceoğlu, Burak Galip Aslan
Venue: Proceedings of the International Conference on Computational Science and Its Applications: Part II (2009)

Individuals have different backgrounds, motivation and preferences in their own learning processes. Web-based systems that ignore these differences have difficulty in meeting learners' needs effectively. One of these individual differences is the learning style. For providing adaptively incorporated learning styles, firstly learning styles of learners have to be identified. There are many different learning models in literature. This study is based on Felder and Silverman's Learning Styles Model and investigates only active/reflective and visual/verbal dimensions of this model. Instead of filling out a questionnaire, learner behaviors are analyzed with the help of literature-based approaches so that learning styles of learners can be detected.
A Bayesian Kernel logistic discriminant model: an improvement to the Kernel Fisher's discriminant
Authors: R. Ksantini, D. Ziou, B. Colin, F. Dubeau
Venue: Proceedings of the 23rd national conference on Artificial intelligence - Volume 3 (2008)

The Kernel Fisher's Discriminant (KFD) has proven to be competitive to several state-of-the-art classifiers. However, it is assuming equal covariance structure for all transformed classes, which is not true in many applications. In this paper, we propose a novel Bayesian Kernel Logistic Discriminant model (BKLD) which goes one step further by representing each transformed class by its own covariance matrix. This can perform better than the KFD. An extensive comparison of the BKLD to the KFD and to other state-of-the-art non-linear classifiers is performed.
Interacting with Casework Documents Using Timelines
Authors: Morten Bohøj, Niels Olof Bouvin
Venue: Proceedings of the 12th IFIP TC 13 International Conference on Human-Computer Interaction: Part II (2009)

We present a way of creating an overview of administrative procedures using timelines. Our design also provides for manipulation of the information given on the timeline through interacting directly with the timeline. As an example application, a prototype supporting administrative procedures surrounding parental leave in Denmark, has been developed and evaluated.
Investigating the Effects of Gain and Loss of Esteem on Human-Robot Interaction
Authors: Takanori Komatsu, Hideaki Kaneko, Takashi Komeda
Venue: Proceedings of the FIRA RoboWorld Congress 2009 on Advances in Robotics (2009)

The purpose of this study was to investigate the relationship between the robot's static and dynamic design experimentally, and to compare the results of this experiment with the findings of the gain and loss of esteem. Here, the static design is about the appearance or configuration of the robot, and the dynamic design is about the trajectories, behaviors of the robot and achievement level of the given tasks. The results showed that the static design did not have effects on the participants' impressions, but the dynamic design did have strong effects on their impressions.
Answer validation by keyword association
Authors: Masatsugu Tonoike, Takehito Utsuro, Satoshi Sato
Venue: Proceedings of the 3rd Workshop on RObust Methods in Analysis of Natural Language Data (2004)

Answer validation is a component of question answering system, which selects reliable answer from answer candidates extracted by certain methods. In this paper, we propose an approach of answer validation based on the strengths of lexical association between the keywords extracted from a question sentence and each answer candidate. The proposed answer validation process is decomposed into two steps: the first is to extract appropriate keywords from a question sentence using word features and the strength of lexical association, while the second is to estimate the strength of the association between the keywords and an answer candidate based on the hits of search engines. In the result of experimental evaluation, we show that a good proportion (79%) of a multiple-choice quiz "Who wants to be a millionaire" can be solved by the proposed method.
A Non-Local Fuzzy Segmentation Method: Application to Brain MRI
Authors: Benoît Caldairou, François Rousseau, Nicolas Passat, Piotr Habas, Colin Studholme, Christian Heinrich
Venue: Proceedings of the 13th International Conference on Computer Analysis of Images and Patterns (2009)

The Fuzzy C-Means algorithm is a widely used and flexible approach for brain tissue segmentation from 3D MRI. Despite its recent enrichment by addition of a spatial dependency to its formulation, it remains quite sensitive to noise. In order to improve its reliability in noisy contexts, we propose a way to select the most suitable example regions for regularisation. This approach inspired by the Non-Local Mean strategy used in image restoration is based on the computation of weights modelling the grey-level similarity between the neighbourhoods being compared. Experiments were performed on MRI data and results illustrate the usefulness of the approach in the context of brain tissue classification.
Visual String of Reformulation
Authors: Arne Berger, Jens Kürsten, Maximilian Eibl
Venue: Proceedings of the Symposium on Human Interface 2009 on Human Interface and the Management of Information. Information and Interaction. Part II: Held as part of HCI International 2009 (2009)

An interface for query reformulation based on multimedial search widgets is proposed. It allows the co-existence of widgets for unambiguous intellectual metadata and vague, automatically annotated metadata.
Study on the Performance Improvement of Concrete Based on Protective Agent in Harsh Environment
Authors: Shuxia Ren, Furong Yuan, Ming Yang, Dongmei Liu
Venue: Proceedings of the 2009 International Conference on Environmental Science and Information Application Technology - Volume 03 (2009)

The damage of structural safety of concrete is very large in harsh environment. Various properties of concrete can be improved by daubing protective agent on the surface of concrete. Influence of the protective agent on the impermeability and frost resistance and compressive strength of concrete is analyzed and determined byestablishing experiment model and selecting protective agent. It is shown that protective agent can improve significantly the properties of concrete and the penetration depthreduces by 70％; cycle time of freeze-thaw raise by more than 60％; the strength increases from 6％to15％.
An Automatic Detecting System Based on Sound Detection and Image Analysis
Authors: Zheng Zhen-zhen, Feng Hua-jun, Shen Chang-yu, Ding Chi-zhu, Li Qi, Xu Zhi-hai
Venue: Proceedings of the 2009 WRI Global Congress on Intelligent Systems - Volume 02 (2009)

Different methods of automatic detecting are challenging problems in many fields. Most of them are simply based on detection of image or sound, and have some limitations caused by the camera’s field of view or the frequency of the sound. Our system is based on sound detection and image analysis of object’s motion. Sound detection adopts an algorithm of hyperbolic locating according to signal’s time difference which is sensed by a comparator from two microphones. Image analysis integrates two methods into the system which are based on motion and complexion matching. The proposed system utilizes both sound and image for signal processing, so it can work without serious deterioration performance in case of low resolution. Experiments on sound detection and image analysis are presented and the results are discussed. The design of the system will be applied well in intelligence systems.
Evaluation of Structure Recognition Using Labelled Facade Images
Authors: Nora Ripperda, Claus Brenner
Venue: Proceedings of the 31st DAGM Symposium on Pattern Recognition (2009)

In 3d city modelling the request for details is permanently growing. Because of that automatic facade reconstruction methods are needed. Structure information plays an important role in facade reconstruction since facade elements like windows are arranged in structural patterns. In this paper we present a facade grammar which models these structures. This grammar is used in an rjMCMC based facade reconstruction method. We evaluate the reconstruction method using the eTRIMS [1] image database.
Formalizing Safety Requirements Using Controlling Automata
Authors: Zhe Chen, Gilles Motet
Venue: Proceedings of the 2009 Second International Conference on Dependability (2009)

Safety is an important element of dependability. It is defined as the absence of accidents. Most accidents involving software-intensive systems have been system accidents, which are caused by unsafe inter-system or inter-component interactions. To validate the absence of system hazards concerning dysfunctional interactions, industrials call for approaches of modeling system safety requirements and interaction constraints among components. This paper proposes such a formalism, namely interface control systems (or shortly C-Systems). An interface C-System is composed of an interface automaton and a controlling automaton, which formalizes safe interactions and restricts system behavior at the meta level. This framework differs from the framework of traditional model checking. It explicitly separates the tasks of product engineers and safety engineers, and provides a top-down technique for modeling a system with safety constraints, and for automatically composing a safe system that conforms to safety requirements. The contributions of this work include formalizing safety requirements and a way of automatically ensuring system safety.
Post Recommendation in Social Web Site
Authors: Long Wang, Justus Bross, Christoph Meinel
Venue: Proceedings of the 8th International Conference on Electronic Government (2009)

Web 2.0 applications attract more and more people to express their opinions on the Web in various ways. However, the explosively increasing information in social web sites requires an effective mechanism to timely filter and summarize social common interest, and the moderator needs this mechanism as well to recommend the proper posts and guide public discussions. In this paper, we discuss the problem of recommending post in online communities: we firstly cluster the posts in groups based on their semantic relations, then filter the potential clusters by computing the cluster's support, and finally select the recommended posts as content representatives considering global and local support from each clusters. We compare different feature selections between tags, keywords and topics on cluster formation, and discuss their differences. The human judgement in our experiment shows that the recommendation based on marked tags is much more effective and concise than those on keywords and hidden topics.
The Study on Non-cable Telemetry Seismograph for Metal Mineral Exploration
Authors: Hongyuan Yang, Liguo Han, Jun Lin, Zubin Chen
Venue: Proceedings of the 2009 International Conference on Measuring Technology and Mechatronics Automation - Volume 03 (2009)

So far, there has been no seismograph dedicated for metal mineral exploring in the world. A type of non-cable telemetry seismograph and its network composed of hundreds of Seismic Data Collecting Stations(SDCS) are presented for deep metal ore exploring. Without cable, there are several key problems need to be resolved: the synchronization in network, mass data storage, performance monitoring in field and power consumption management. The technology of Global Positioning System(GPS) timing in conjunction with a high precise Real Time Clock(RTC) is employed to achieve network synchronization with accuracy of ±3.2μs. Instead of data transmission on cables during data acquisiton, a Compact Flash(CF) card of 8G bytes is adopted in embedded linux system based on Advanced RISC Machine(ARM) to store seismic acquisition data. A built-in self testing module is constructed to monitor the health status of each measuring unit. At last, the working voltage and frequency of each SDCS are brought down on premise of normal work and Dynamic Power Management(DPM) is used to reduce the average power dissipation to make the whole system feasible.