ppxscal/embedding_network huggingface dataset:
A visual interface for multi-person exploration of personal databases
Authors: Chia Shen, Frederic Vernier, Neal Lesh
Venue: Proceedings of the working conference on Advanced visual interfaces (2002)

The Personal Digital Historian (PDH) is an ongoing research project aimed at allowing small groups of people co-present to casually browse, embellish, and explore large collections of their personal data, such as pictures, video, or more business-related items such as spreadsheets or PowerPoint slides. In this interactive poster, we demonstrate our initial prototype system which is designed for a tabletop display. The interface allows people to organize their images along the four questions essential to storytelling: who?, when?, where?, and what? Users are provided with a wide variety of flexible interaction methods, including region of interest query specification with in-place freeform stroke input, image-based book marking, suggestion generation via automatic query relaxation, and output summarization. With this interface, the users can enjoy their conversation while having the photos at their fingertips, rather than being distracted by the effort of formulating queries.
GA-based learning bias selection mechanism for real-time scheduling systems
Authors: Yeou-Ren Shiue, Ruey-Shiang Guh, Tsung-Yuan Tseng
Venue: Expert Systems with Applications: An International Journal (2009)

The use of machine learning technologies in order to develop knowledge bases (KBs) for real-time scheduling (RTS) problems has produced encouraging results in recent researches. However, few researches focus on the manner of selecting proper learning biases in the early developing stage of the RTS system to enhance the generalization ability of the resulting KBs. The selected learning bias usually assumes a set of proper system features that are known in advance. Moreover, the machine learning algorithm for developing scheduling KBs is predetermined. The purpose of this study is to develop a genetic algorithm (GA)-based learning bias selection mechanism to determine an appropriate learning bias that includes the machine learning algorithm, feature subset, and learning parameters. Three machine learning algorithms are considered: the back propagation neural network (BPNN), C4.5 decision tree (DT) learning, and support vector machines (SVMs). The proposed GA-based learning bias selection mechanism can search the best machine learning algorithm and simultaneously determine the optimal subset of features and the learning parameters used to build the RTS system KBs. In terms of the accuracy of prediction of unseen data under various performance criteria, it also offers better generalization ability as compared to the case where the learning bias selection mechanism is not used. Furthermore, the proposed approach to build RTS system KBs can improve the system performance as compared to other classifier KBs under various performance criteria over a long period.
Searching satellite imagery with integrated measures
Authors: Ashok Samal, Sanjiv Bhatia, Prasanth Vadlamani, David Marx
Venue: Pattern Recognition (2009)

Due to the advances in imaging and storage technologies, the number and size of images continue to grow at a rapid pace. This problem is particularly acute in the case of remotely sensed imagery. The continuous stream of sensory data from satellites poses major challenges in storage and retrieval of the satellite imagery. In the mean time, the ubiquity of Internet has resulted into an ever-growing population of users searching for various forms of information. In this paper, we describe the search engine SIMR-Satellite Image Matching and Retrieval system. SIMR provides an efficient means to match remotely sensed imagery. It computes spectral and spatial attributes of the images using a hierarchical representation. A unique aspect of our approach is the coupling of second-level spatial autocorrelation with quad tree structure. The efficiency of the web-based SIMR has been evaluated using a database of images with known characteristics: cities, towns, airports, lakes, and mountains. Results show that the integrated signature can be an effective basis for accurately searching databases of satellite based imagery.
Using timed-release cryptography to mitigate the preservation risk of embargo periods
Authors: Rabia Haq, Michael L. Nelson
Venue: Proceedings of the 9th ACM/IEEE-CS joint conference on Digital libraries (2009)

Due to temporary access restrictions, embargoed data cannot be refreshed to unlimited parties during the embargo time interval. A solution to mitigate the risk of data loss has been developed that uses a data dissemination framework, the Timed-Locked Embargo Framework (TLEF), that allows data refreshing of encrypted instances of embargoed content in an open, unrestricted scholarly community. TLEF exploits implementations of existing technologies to "time-lock" data using timed-release cryptology so that TLEF can be deployed as digital resources encoded in a complex object format suitable for metadata harvesting. The framework successfully demonstrates dynamic record identification, time-lock puzzle encryption, encapsulation and dissemination as XML documents. We implement TLEF and provide a quantitative analysis of its successful data harvest of time-locked embargoed data with minimum time overhead without compromising data security and integrity.
Toward Automatic Expertise Identification of Blogger
Authors: Chia Chun Shih, Jay Stu, Wen-Tai Hsieh, Wei Shen Lai, Shih-Chun Chou, Tse-Ming Tsai
Venue: Proceeding of the 2009 conference on Information Modelling and Knowledge Bases XX (2009)

The architecture of participation and sharing that encourage users to add value is one of the fundamental characteristics of a successful Web 2.0 application. Blog, as a personal publish platform on the web, removes the intermediation for channel selection thus everyone can represent himself/herself without any filtering mechanism. In this research, we present a methodology to derive user's degree of expertise from blog data and conduct an experiment using data collected from a enterprise blog system. The result shows that the average precision reaches around 0.8 and which factor is useful in our proposed method.
Bayesian inference for Plackett-Luce ranking models
Authors: John Guiver, Edward Snelson
Venue: Proceedings of the 26th Annual International Conference on Machine Learning (2009)

This paper gives an efficient Bayesian method for inferring the parameters of a Plackett-Luce ranking model. Such models are parameterised distributions over rankings of a finite set of objects, and have typically been studied and applied within the psychometric, sociometric and econometric literature. The inference scheme is an application of Power EP (expectation propagation). The scheme is robust and can be readily applied to large scale data sets. The inference algorithm extends to variations of the basic Plackett-Luce model, including partial rankings. We show a number of advantages of the EP approach over the traditional maximum likelihood method. We apply the method to aggregate rankings of NASCAR racing drivers over the 2002 season, and also to rankings of movie genres.
Redundancy and its applications in wireless sensor networks: a survey
Authors: Daniel-Ioan Curiac, Constantin Volosencu, Dan Pescaru, Lucian Jurca, Alexa Doboli
Venue: WSEAS Transactions on Computers (2009)

In this paper we presented and classified various approaches for redundancy in the area of wireless sensor networks, related to sensing, communication and information processing. Sometimes an ally, sometimes a foe, redundancy is an inherent feature of sensor networks that has to be very carefully examined in order to improve important aspects of their functioning. Moreover, this paper presents two methodologies: one that implies both components of spatial redundancy and one that implies the use of temporal redundancy for achieving the objective of fault-tolerant and safe operation. In the end, the fields in which the redundancy could be applied with significant results are highlighted.
Plenary lecture I: Visualservoing for tentacular robots
Authors: Dorian Cojocaru
Venue: Proceedings of the 4th WSEAS/IASME international conference on Dynamical systems and control (2008)

The lecture deals with the theoretical and experimental research in a domain that is the result of two complex scientific subdomains interconnection: tentacular robotic structures on one hand and, computer vision systems on the other hand. An ideal tentacle manipulator is a non-conventional robotic arm with an infinite mobility. It has the capability to take sophisticated shapes and to achieve any position and orientation in a 3D space. These systems are also known as hyperredundant manipulators and, over the past several years, there has been a rapidly expanding interest in the study and construction of them. The difficulty of the dynamic control is determined by integral-partial-differential models with high nonliniarities that characterise the dynamic of these systems. The subjects of the lecture are set out after the following directions: cinematic and dynamic model design for tentacular robots, visual servoing techniques, methods and algorithms design for tentacular robots, computer vision techniques, methods and algorithms design for tentacular robots control, interactive graphical environment design and implementation for application development, experimental models, results scientific validation and use.
Approximate Optimization Using Computaional Intelligence and its Application to Reinforcement of Cable-stayed Bridges
Authors: Hirotaka Nakayama, Koichi Inoue, Yukihiro Yoshimori
Venue: Proceeding of the 2006 conference on Integrated Intelligent Systems for Engineering Design (2006)

In many practical engineering design problems, the form of objective function is not given explicitly in terms of design variables. Given the value of design variables, under this circumstance, the value of objective function is obtained by some analysis such as structural analysis, fluidmechanic analysis, thermodynamic analysis, and so on. Usually, these analyses are considerably time consuming to obtain a value of objective function. In order to make the number of analyses as few as possible, approximate optimization methods using computational intelligence have been developed. In those methods, optimization is performed in parallel with predicting the form of objective function. In this paper, radial basis function networks (RBFN) are employed in predicting the form of objective function, and genetic algorithms (GA) in searching the optimal value of the predicted objective function. One of the most important tasks in this approach is to allocate sample data moderately in order to make the number of experiments as small as possible. The effectiveness of the suggested method will be shown through some numerical examples along with an application to seismic design in reinforcement of cable-stayed bridges.
A Study on Building of Japanese History Ontology Aiming at Learning Support
Authors: Toshihiro Morioka, Noriyuki Iwane, Yukihiro Matsubara
Venue: Proceeding of the 2007 conference on Supporting Learning Flow through Integrative Technologies (2007)

In this study, we systematize the vocabularies learned in Japanese history subject by using ontology and call it Japanese history ontology. By using ontology, the content and relation between vocabularies become clear, and it can be expected that efficient study is realizable. We build Japanese history ontology, and develop an experimental system which offers learning support. In this paper, we propose a system framework for learning support of Japanese history by using ontology.
On study of design and implementation of virtual fixtures
Authors: Rodolfo Prada, Shahram Payandeh
Venue: Virtual Reality (2009)

Virtual fixtures (VFs) can be defined as guiding constraints designed to enhance or assist human performance in a computer-controlled system by providing cues of haptic or audiovisual nature. In this paper we present a new characterization of VFs based on mechanics, and provide a set practical guidelines for the designers of such fixtures from a software architecture point of view. We propose an event-driven approach that facilitates the integration of these guiding constraints in a scene graphed-based environment. In this context some novel implementation of VFs are presented, where users may interact with a single or an assembled set of fixtures. We present two types of force attributes for VF and present their implications in a trajectory-following problem.
Joint detection and estimation of noisy sinusoids using Bayesian inference with reversible jump MCMC algorithm
Authors: D. Üstündaǧ
Venue: Proceedings of the 3rd WSEAS international symposium on Wavelets theory and applications in applied mathematics, signal processing & modern science (2009)

In this paper, we consider a problem of detecting and estimating of sinusoids corrupted by random noise within a Bayesian framework. Unfortunately, all Bayesian inference drawn from posterior probability distributions of parameters requires evaluation of some complicated high-dimensional integrals. Therefore, an attempt for performing the Bayesian computation is made to improve an efficient stochastic algorithm based on reversible jump Markov chain Monte Carlo (RJMCMC) methods. This algorithm, coded in Mathematica programming language is evaluated in simulation studies on synthetic data sets. All the simulations results support the effectiveness of the method.
Reconstructive Dispersers and Hitting Set Generators
Authors: Christopher Umans
Venue: Algorithmica (2009)

We give a generic construction of an optimal hitting set generator (HSG) from any good &#x201c;reconstructive&#x201d; disperser. Past constructions of optimal HSGs have been based on such disperser constructions, but have had to modify the construction in a complicated way to meet the stringent efficiency requirements of HSGs. The construction in this paper uses existing disperser constructions with the &#x201c;easiest&#x201d; parameter setting in a black-box fashion to give new constructions of optimal HSGs without any additional complications. Our results show that a straightforward composition of the Nisan-Wigderson pseudorandom generator that is similar to the composition in works by Impagliazzo, Shaltiel and Wigderson in fact yields optimal HSGs (in contrast to the &#x201c;near-optimal&#x201d; HSGs constructed in those works). Our results also give optimal HSGs that do not use any form of hardness amplification or implicit list-decoding&#x2014;like Trevisan&#x2019;s extractor, the only ingredients are combinatorial designs and any good list-decodable error-correcting code.
Jane's Simulation and Training Systems 2009-2010, 22nd edition
Authors: Ian Strachan
Venue:  (2009)

"Jane's Simulation and Training Systems" enables you to evaluate effectively different land, sea, air and multirole training systems for civil and military personnel. This comprehensive reference product provides details of image generation, visual display including head-mounted (VR) displays, simulator components including trackers and touch-sensitive devices, and motion systems, as well as details of the training systems in use or available for all operational domains. Explanations of the key technologies are also provided to ensure you fully understand the operational strengths and capabilities of each package on the market. Diagrams and photographs, together with a glossary and directory of over 700 simulation and training suppliers, make this a vital resource for procurement and market intelligence professionals.
A pervasive assistant for nursing and doctoral staff
Authors: Alexiei Dingli, Charlie Abela
Venue: Proceeding of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence (2008)

The goal of health-care institutions is to provide patient-centric health care services. Unfortunately, this goal is frequently undermined due to human-related aspects. The PervasIve Nursing And docToral Assistant (PINATA) provides a patient-centric system powered with Ambience Intelligence techniques and Semantic Web technologies. Through PINATA, the movement of patients and medical staff is tracked via RFID sensors while an automated camera system monitors the interaction of people within their environment. The system reacts to particular situations autonomously by directing medical staff towards emergencies in a timely manner and providing them with just the information they require on their handheld devices. This ensures that patients are given the best care possible on a 24/7 basis especially when the medical staff is not around.
Learning Dynamic Obstacle Avoidance for a Robot Arm Using Neuroevolution
Authors: Thomas D'Silva, Risto Miikkulainen
Venue: Neural Processing Letters (2009)

Neural networks can be evolved to control robot manipulators in tasks like target tracking and obstacle avoidance in complex environments. Neurocontrollers are robust to noise and can be adapted to different environments and robot configurations. In this paper, neurocontrollers were evolved to position the end effector of a robot arm close to a target in three different environments: environments without obstacles, environments with stationary obstacles, and environments with moving obstacles. The evolved neurocontrollers perform qualitatively like inverse kinematic controllers in environments with no obstacles and like path-planning controllers based on Rapidly-exploring random trees in environments with obstacles. Unlike inverse kinematic controllers and path planners, the approach reliably generalizes to environments with moving obstacles, making it possible to use it in natural environments.
Multilingual ontological analysis of European directives
Authors: Gianmaria Ajani, Guido Boella, Leonardo Lesmo, Alessandro Mazzei, Piercarlo Rossi
Venue: Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions (2007)

This paper describes the main features of our tool called "Legal Taxonomy Syllabus". The system is an ontology based tool designed to annotate and recover multi-lingua legal information and build conceptual dictionaries on European Directives.
DIANE, a diagnosis system for arithmetical problem solving
Authors: Khider Hakem, Emmanuel Sander, Jean-Marc Labat, Jean-François Richard
Venue: Proceeding of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology (2005)

We hereby describe DIANE an environment that aims at performing an automatic diagnosis on arithmetic problems depending on the productions of the learners. This work relies on results from cognitive psychology studies that insist on the fact that problem solving depends to a great extent on the construction of an adequate representation of the problem, which is highly constrained. DIANE allows large-scale experimentations and has the specificity of providing diagnosis at a very detailed level of precision, whether it concerns adequate or erroneous strategies, allowing one to analyze cognitive mechanisms involved in the solving process. The quality of the diagnosis module has been assessed and, concerning non verbal cues, 93.4% of the protocols were diagnosed in the same way as with manual analysis.
A unified and flexible solution for integrating CRL and OCSP into PKI applications
Authors: Diana Berbecaru, Amarkumar Desai, Antonio Lioy
Venue: Software&mdash;Practice Experience (2009)

Public key certificates (PKCs) are used nowadays in several security protocols and applications, so as to secure data exchange via transport layer security channels, or to protect data at the application level by means of digital signatures. However, many security applications often fail to manage properly the PKCs, in particular when checking their validity status. These failures are partly due to the lack of experience (or training) of the users who configure these applications or protocols, and partly due to the scarce support offered by some common cryptographic libraries to the application developers. This paper describes the design and implementation of a light middleware dealing with certificate validation in a unified way. Our middleware exploits on one side the libraries that have already been defined or implemented for certificate validation, and it constructs a thin layer, which provides flexibility and security features to the upper layer applications. In our current approach, this layer boasts an integrated approach to support various certificate revocation mechanisms, it protects the applications from some common security attacks, and offers several configuration and performance options to the programmers and to the end users. We describe the architecture of this approach as well as its practical implementation in the form of a library based on the famous OpenSSL security library, and that can be easily integrated with other certificate-aware security applications. Copyright &copy; 2009 John Wiley & Sons, Ltd.
Top-k typicality queries and efficient query answering methods on large databases
Authors: Ming Hua, Jian Pei, Ada W. Fu, Xuemin Lin, Ho-Fung Leung
Venue: The VLDB Journal &mdash; The International Journal on Very Large Data Bases (2009)

Finding typical instances is an effective approach to understand and analyze large data sets. In this paper, we apply the idea of typicality analysis from psychology and cognitive science to database query answering, and study the novel problem of answering top-k typicality queries. We model typicality in large data sets systematically. Three types of top-k typicality queries are formulated. To answer questions like "Who are the top-k most typical NBA players?", the measure of simple typicality is developed. To answer questions like "Who are the top-k most typical guards distinguishing guards from other players?", the notion of discriminative typicality is proposed. Moreover, to answer questions like "Who are the best k typical guards in whole representing different types of guards?", the notion of representative typicality is used. Computing the exact answer to a top-k typicality query requires quadratic time which is often too costly for online query answering on large databases. We develop a series of approximation methods for various situations: (1) the randomized tournament algorithm has linear complexity though it does not provide a theoretical guarantee on the quality of the answers; (2) the direct local typicality approximation using VP-trees provides an approximation quality guarantee; (3) a local typicality tree data structure can be exploited to index a large set of objects. Then, typicality queries can be answered efficiently with quality guarantees by a tournament method based on a Local Typicality Tree. An extensive performance study using two real data sets and a series of synthetic data sets clearly shows that top-k typicality queries are meaningful and our methods are practical.