ppxscal/embedding_network huggingface dataset:
Beyond Emoticons: Combining Affect and Cognition in Icon Design
Authors: Siné Mcdougall, Irene Reppa, Gary Smith, David Playfoot
Venue: Proceedings of the 8th International Conference on Engineering Psychology and Cognitive Ergonomics: Held as Part of HCI International 2009 (2009)

Recently there has been a shift in emphasis from interface usability to interface appeal. Very few studies, however, have examined the link between usability and appeal and evidence regarding the direction of the relationship between the two remains equivocal. This paper examines the nature of the relationships between the usability and aesthetic appeal of icons. The findings from three studies presented here show evidence, not only for the symbiotic relationship between aesthetic preference and performance, but also for the possible causal links between the two. The implications of these findings for interface design and theoretical explanations of usability are discussed.
Temporal feature modification for retrospective categorization
Authors: Robert Liebscher, Richard K. Belew
Venue: Proceedings of the ACL Workshop on Feature Engineering for Machine Learning in Natural Language Processing (2005)

We show that the intelligent use of one small piece of contextual information--a document's publication date--can improve the performance of classifiers trained on a text categorization task. We focus on academic research documents, where the date of publication undoubtedly has an effect on an author's choice of words. To exploit this contextual feature, we propose the technique of temporal feature modification, which takes various sources of lexical change into account, including changes in term frequency, associative strength between terms and categories, and dynamic categorization systems. We present results of classification experiments using both full text papers and abstracts of conference proceedings, showing improved classification accuracy across the whole collection, with performance increases of greater than 40% when temporal features are exploited. The technique is fast, classifier-independent, and works well even when making only a few modifications.
Conceptual Flow-Based Modeling of Agents
Authors: Sabah Al-Fedaghi
Venue: Proceedings of the 2009 WRI Global Congress on Intelligent Systems - Volume 01 (2009)

Agent-based systems have emerged as an important next-generation automation paradigm. One of the aims of agent-oriented research is to simplify agent design by introducing a conceptual abstraction layer for interacting agents. This paper provides a new multilevel conceptual foundation for modeling of agents, their relationships, and their interactions based on BDI concepts. The methodology utilizes a flow model of five stages. It incorporates all BDI aspects by assigning agents to information/beliefs, goals, plans, and actions spheres.
Which Is Better for Frequent Pattern Mining: Approximate Counting or Sampling?
Authors: Willie Ng, Manoranjan Dash
Venue: Proceedings of the 11th International Conference on Data Warehousing and Knowledge Discovery (2009)

We investigate the problem of finding frequent patterns in a continuous stream of transactions. In the literature two prominent approaches are often used: (a) perform approximate counting (e.g., lossy counting algorithm (LCA) of Manku and Motwani, VLDB 2002) by using a lower support threshold than the one given by the user, or (b) maintain a running sample (e.g., reservoir sampling (Algo-Z) of Vitter, TOMS 1985) and generate frequent itemsets from the sample on demand. Both approaches have their advantages and disadvantages. For instance, LCA is known to output all frequent itemsets (recall = 1) but it also outputs many false frequent itemsets (low precision). Sampling is fast, but it outputs a large number of false itemsets as frequent itemsets, particularly when sample size is not large. Although both approaches are known to be practically useful, to the best of our knowledge there has been no comparison between the two approaches. In addition, we propose a novel sampling algorithm (DSS). DSS selects transactions to be included in the sample based on histogram of single itemsets. An empirical comparison study between the 3 algorithms is performed using synthetic and benchmark datasets. Results show that DSS is consistently more accurate than LCA and Algo-Z, whereas LCA performs consistently better than Algo-Z. Furthermore, DSS, although requires more time than Algo-Z, is faster than LCA.
Team member reallocation via tree pruning
Authors: Noa Agmon, Gal A. Kaminka, Sarit Kraus
Venue: Proceedings of the 20th national conference on Artificial intelligence - Volume 1 (2005)

This paper considers the task reallocation problem, where k agents are to be extracted from a coordinated group of N agents in order to perform a new task. The interaction between the team members and the cost associated with this interaction are represented by a weighted graph. Consider a group of N robots organized in a formation, the graph is the monitoring graph which represents the sensorial capabilities of the robots, i.e., which robot can sense the other and at what cost. Following this example, the team member reallocation problem this paper deals with is the extraction of k robots from the group in order to acquire a new target, while minimizing the cost of the interaction of the remaining group. In general, the method proposed here shifts the utility from the team member itself to the interaction between the members, and calculates the reallocation according to this interaction utility. We found that this can be done optimally by a deterministic polynomial time algorithm under several constraints, the first constraint is that k = O(log N). We describe several other domains in which this method is applicable.
Statistical Relational Learning with Formal Ontologies
Authors: Achim Rettinger, Matthias Nickles, Volker Tresp
Venue: Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II (2009)

We propose a learning approach for integrating formal knowledge into statistical inference by exploiting ontologies as a semantically rich and fully formal representation of prior knowledge. The logical constraints deduced from ontologies can be utilized to enhance and control the learning task by enforcing description logic satisfiability in a latent multi-relational graphical model. To demonstrate the feasibility of our approach we provide experiments using real world social network data in form of a $\mathcal{SHOIN}(D)$ ontology. The results illustrate two main practical advancements: First, entities and entity relationships can be analyzed via the latent model structure. Second, enforcing the ontological constraints guarantees that the learned model does not predict inconsistent relations. In our experiments, this leads to an improved predictive performance.
Dshell++: A Component Based, Reusable Space System Simulation Framework
Authors: Christopher S. Lim, Abhinandan Jain
Venue: Proceedings of the Third IEEE International Conference on Space Mission Challenges for Information Technology (2009)

This paper describes the multi-mission Dshell++ simulation framework for high fidelity, physics-based simulation of spacecraft, robotic manipulation and mobility systems. Dshell++ is a C++/Python library which uses modern script-driven object-oriented techniques to allow component reuse and a dynamic run-time interface for complex, high-fidelity simulation of spacecraft and robotic systems. The goal of the Dshell++ architecture is to manage the inherent complexity of physics-based simulations while supporting component model reuse across missions. The framework provides several features that support a large degree of simulation configurability and usability.
Application of Fuzzy Programming with Recourse in Material Requirement Planning Problem
Authors: Tao Li, Peng Lin, Gao-Ji Sun, Hai-Hong Liu
Venue: Proceedings of the 2009 International Conference on Measuring Technology and Mechatronics Automation - Volume 02 (2009)

Material requirement planning (MRP) is a popular approach used in production processes for planning. Many forms of uncertainty in practice affect the production process and different approaches have been presented for MRP with uncertainty. In order to model MRP problem under fuzzy uncertainty, we present a fuzzy programming model with recourse based on credibility theory. We consider fuzzyvariables coefficients related to the market demand and theunit price of material on spot market. The objective value is minimizing the expected value of total material procurement cost. This proposed MRP problem belongs to an infinitedimensional optimization problem that cannot be solved directly and it is 0-1 mix integer program, neither linear nor convex, traditional optimization algorithms cannot be used to solve it. So to solve the presented MRP model, we design a hybrid algorithm which combines approximation approach (AA) and particle swarm optimization (PSO). We also present one numerical example to illustrate the effectiveness of the designed algorithm.
Failure Tolerance Analysis of a Small Scale Underwater Sensor Network with RF Electromagnetic Communications
Authors: Xianhui Che, Ian Wells, Gordon Dickers, Paul Kear, Xiaochun Gong, Mark Rhodes
Venue: Proceedings of the 2009 Third International Conference on Sensor Technologies and Applications (2009)

A small scale wireless sensor network was designed using electromagnetic technology in order to provide an underwater coastal monitoring service. The sensor nodes were deployed manually, which formed a multi-hop static topology for the network. Data delivery was scheduled via daily cycles of sleeping and waking-up. Due to the unique features of the network, Ad-hoc On-demand Distance Vector was chosen as the routing protocol. Modeling and simulations were conducted to evaluate network performance in terms of failure recovery and network re-convergence. The results demonstrated a certain level of failure tolerance of the designated network for this and similar scenarios.
WCET-Guided Optimal Data Allocation to Scratchpad Memory
Authors: Xiangtao Jiang, Zhigang Hu, Jianbiao He
Venue: Proceedings of the 2009 WASE International Conference on Information Engineering - Volume 02 (2009)

Scratchpad memory is a popular choice for on-chiptorage in real-time embedded systems. The allocation ofprogram elements to scratchpad memory is performed atompile time leading to predictable memory access la-encies. Current scratchpad memory allocation techniquesmainlyimprovetheaverage-caseexecutiontimeoftasks.Forhard real-time systems, however, worst case execution timeWCET) is a more important key metric. In this paper, wepropose scratchpad allocation techniques for data memoryhat aim to minimize a tasks WCET. We first analyze mainproblem of optimal allocation process and current worksof allocation algorithms. Then, based on common degree ofariables, we design a new heuristic searches algorithm thatachieves better optimal performance for all our benchmarks.
Lead Scoring Client's Web Service
Authors: Yun Ling, Chongyi Ye
Venue: Proceedings of the 9th International Conference on Algorithms and Architectures for Parallel Processing (2009)

What lead scoring does it assign a point value to who prospects are, how they interact with your company and what their need is for your product, the assumption being that the higher their score, the further they are along in the buy process. In this paper we describe our experiences and findings in applying lead scoring Web service. In particular, we focus on how to deploy Web service. We apply Axis framework for constructing Simple Object Access Protocol (SOAP) processors such as clients, which introduce practical approaches for Web service. We develop client's Web service to invoke the lead scoring Web service using Java, which is an open source. At the same time we develop website to display scoring results to the user. We show that Web service make conversation easy between applications and applications as well as Web browser and server's conversation.
A Riemannian Self-Organizing Map
Authors: Dongjun Yu, Edwin R. Hancock, William A. Smith
Venue: Proceedings of the 15th International Conference on Image Analysis and Processing (2009)

We generalize the classic self-organizing map (SOM) in flat Euclidean space (linear manifold) onto a Riemannian manifold. Both sequential and batch learning algorithms for the generalized SOM are presented. Compared with the classical SOM, the most novel feature of the generalized SOM is that it can learn the intrinsic topological neighborhood structure of the underlying Riemannian manifold that fits to the input data. We here compared the performance of the generalized SOM and the classical SOM using real 3-Dimensional facial surface normals data. Experimental results show that the generalized SOM outperforms the classical SOM when the data lie on a curved Riemannian manifold.
Interval Polyhedra: An Abstract Domain to Infer Interval Linear Relationships
Authors: Liqian Chen, Antoine Miné, Ji Wang, Patrick Cousot
Venue: Proceedings of the 16th International Symposium on Static Analysis (2009)

We introduce a new numerical abstract domain, so-called interval polyhedra (itvPol), to infer and propagate interval linear constraints over program variables. itvPol, which allows to represent constraints of the form ¿ k [a k ,b k ]x k ≤ c, is more expressive than the classic convex polyhedra domain and allows to express certain non-convex (even unconnected) properties. The implementation of itvPol can be constructed based on interval linear programming and an interval variant of Fourier-Motzkin elimination. The preliminary experimental results of our prototype are encouraging, especially for programs affected by interval uncertainty, e.g., due to uncertain input data or interval-based abstractions of disjunctive, non-linear, or floating-point expressions. To our knowledge, this is the first application of interval linear algebra to static analysis.
Pumping test interpretation by combination of Latin hypercube parameter sampling and analytical models
Authors: Marc Van Camp, Kristine Walraevens
Venue: Computers Geosciences (2009)

Pumping tests in groundwater reservoirs are a much used and recommended method to derive hydraulic properties of aquifers and aquitards at the field scale. Interpretation of pumping tests can be done by fitting analytical or numerical models to the obtained field data using optimisation procedures. In this paper, an interpretation methodology is presented and implemented in the form of a computer code that combines analytical solutions for confined, semi-confined and unconfined single and multiple layer schematisations, with random parameter generator routines to find best fitting parameter sets to measured pumping test data. As this requires a large number of Monte Carlo (MC) runs, the number of required simulations is restricted by using a stratified instead of a pure random sampling technique. Latin hypercube sampling (LHS) is used as the stratified random sampler. The analytical solutions are defined in the Laplace domain and inverted numerically using the well-known Stehfest algorithm. The program uses a number of iteration cycles during which parameters sets are generated within predefined limits using the LHS technique. Parameters sets which satisfy a chosen maximum value for a selected objective function (root mean square (RMSE) or mean relative deviation (MRD)) are retained and used to update parameter limits for the next cycle. The objective function criterion is decreased during subsequent cycles while the limits of the sampling intervals are adapted. The number of determinable parameters is dependent on the aquifer schematisation and conceptual model that is chosen. After each cycle, statistics of the parameter values of the realisations which satisfy the objective function criterion are calculated. The method is demonstrated with examples including synthetic datasets and field data. The synthetic data examples show that the program is able to retrieve the parameter values used for generating drawdown sets very well. Typical runtimes on a PC are no more than a few minutes. The program can easily be extended with additional analytical solutions for other schematisations.
RainWatch Project: Location-Awared Realtime Detection and Notification of Rain on Internet-Based Sensor Network
Authors: Kaveevivitchai Sathita, Hideya Ochiai, Hiroshi Esaki
Venue: Proceedings of the 2009 Ninth Annual International Symposium on Applications and the Internet (2009)

This paper introduces RainWatch project which enable rain-sensitive users to monitor real time rain information in area of users’ interest. Real-time detection and notification of rain situation will allow users to take appropriate preparation (e.g., evacuation from the area in case of disaster prevention), which can prevent loss of lives and damage on our properties. The RainWatch system stands on top of Live E! data platform, which is a wide-area sensor application testbed operated by multiple-organizations distributedly with about 100 Internet weather stations. The RainWatch system monitors the rain state around a certain geographical point and makes notifications or warnings (e.g., the rain is approaching) in real-time to the users that have interest in the point. Strong needs for this application will influence the design of the sensor networking infrastructure regard to location-awareness and real-time-ness
From whence does your authority come?: utilizing community relevance in ranking
Authors: Lan Nie, Brian D. Davison, Baoning Wu
Venue: Proceedings of the 22nd national conference on Artificial intelligence - Volume 2 (2007)

A web page may be relevant to multiple topics; even when nominally on a single topic, the page may attract attention (and thus links) from multiple communities. Instead of indiscriminately summing the authority provided by all pages, we decompose a web page into separate subnodes with respect to each community pointing to it. Utilizing the relevance of such communities allows us to better model the semantic structure of the Web, leading to better estimates of authority for a given query. We apply a total of eighty queries over two real-world datasets to demonstrate that the use of community decomposition can consistently and significantly improve upon Page-Rank's top-ten results.
Transcription of Catalan Broadcast Conversation
Authors: Henrik Schulz, José A. Fonollosa, David Rybach
Venue: Proceedings of the 12th International Conference on Text, Speech and Dialogue (2009)

The paper describes aspects, methods and results of the development of an automatic transcription system for Catalan broadcast conversation by means of speech recognition. Emphasis is given to Catalan language, acoustic and language modelling methods and recognition. Results are discussed in context of phenomena and challenges in spontaneous speech, in particular regarding phoneme duration and feature space reduction.
Analysis of INS Aided Signal Acquisition Based on Navigation Satellites Software Receivers
Authors: Xiaofeng He, Xiaoping Hu, Kanghua Tang, Liangqing Lu, Meiping Wu
Venue: Proceedings of the 2009 International Conference on Measuring Technology and Mechatronics Automation - Volume 01 (2009)

This paper discusses the INS aided signal acquisition method based on GNSS software receivers. In order to overcome low dynamics and sensitivity to RF interference which are the two vulnerabilities of GNSS receivers, INS velocity information is used to assist the fast signal search. The research aims at analyzing the acquisition performance varying with dynamics and INS accuracy. The relation between acquisition ability, dynamics and INS velocity errors is described in theory. With simulation data, an approach of INS aided signal acquisition using FFT is demonstrated. The results show that INS aided acquisition improves dynamics and acquisition effectiveness compared to unaided methods.
Supporting Local Aliases as Usable Presentation of Secure Pseudonyms
Authors: Elke Franz, Katja Liesebach
Venue: Proceedings of the 6th International Conference on Trust, Privacy and Security in Digital Business (2009)

Privacy-Enhancing Identity Management (PIM) enables users to control which personal data they provide to whom by partitioning this information into subsets called partial identities. Since these partial identities should not be linkable except by their owner, randomly generated pseudonyms that are unique are used as their identifiers instead of real names. Randomly generated pseudonyms do not leak any information about the corresponding user, but their handling is not easy for human beings. Users should therefore be enabled to assign local aliases according to their individual preferences to such pseudonyms to allow for a better recognizability in interaction scenarios. However, the use of local aliases requires a reasonable support to ensure both privacy and usability.This paper introduces an architecture that enables users to manage local aliases in a reasonable and usable way. Possible solutions for alias assignment, alias improvement, and replacement between aliases and pseudonyms are discussed. The suggested approach was realized within a collaborative eLearning environment but is also applicable for other collaborative applications.
High-Fidelity Prototyping of Interactive Systems Can Be Formal Too
Authors: Philippe Palanque, Jean-François Ladry, David Navarre, Eric Barboni
Venue: Proceedings of the 13th International Conference on Human-Computer Interaction. Part I: New Trends (2009)

The design of safety critical systems calls for advanced software engineering models, methods and tools in order to meet the safety requirements that will avoid putting human life at stake. When the safety critical system encompasses a substantial interactive component, the same level of confidence is required towards the human-computer interface. Conventional empirical or semi-formal techniques, although very fruitful, do not provide sufficient insight on the reliability of the human-system cooperation, and offer no easy way to, for example, quantitatively compare two design options. The aim of this paper is to present a method, with supporting tools and techniques, for engineering the design and development of usable user interfaces for safety-critical applications. More precisely we present the Petshop environment which is a Petri net based tool for the design specification, prototyping and validation of interactive software. In this environment models of the interactive application can be interactively modified and executed. This is used to support prototyping phases (when the models and the interactive application evolve significantly to meet late user requirements for instance) as well as in the operation phase (after the system is deployed). The use of the description technique (the ICO formalism) supported by PetShop is presented on a multimodal ground segment application for satellite control and more precisely how prototyping can be performed at the various levels of the architecture of interactive systems.
The FF planning system: fast plan generation through heuristic search
Authors: Jörg Hoffmann, Bernhard Nebel
Venue: Journal of Artificial Intelligence Research (2001)

We describe and evaluate the algorithmic techniques that are used in the FF planning system. Like the HSP system, FF relies on forward state space search, using a heuristic that estimates goal distances by ignoring delete lists. Unlike HSP's heuristic, our method does not assume facts to be independent. We introduce a novel search strategy that combines hill-climbing with systematic search, and we show how other powerful heuristic information can be extracted and used to prune the search space. FF was the most successful automatic planner at the recent AIPS-2000 planning competition. We review the results of the competition, give data for other benchmark domains, and investigate the reasons for the runtime performance of FF compared to HSP.