ppxscal/embedding_network huggingface dataset:
A Method for Blur and Affine Invariant Object Recognition Using Phase-Only Bispectrum
Authors: Ville Ojansivu, Janne Heikkilä
Venue: Proceedings of the 5th international conference on Image Analysis and Recognition (2008)

In this paper, we propose a novel method for recognizing objects in images in a way that is invariant to blur and affine transformation of the images. The method is based on a set of rotated and log-log sampled phase-only bispectrum slices of the images and their phase correlation. The method is invariant to centrally symmetric blur, such as linear motion or out of focus blur. Because of the normalization of the amplitude information, the method is also invariant to uniform illumination changes. The only known method having similar invariance properties is based on image moments. According to the experiments conducted, the proposed method outperforms the moment based method in the presence of various degradations.
GA-Net: A Genetic Algorithm for Community Detection in Social Networks
Authors: Clara Pizzuti
Venue: Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X (2008)

The problem of community structure detection in complex networks has been intensively investigated in recent years. In this paper we propose a genetic based approach to discover communities in social networks. The algorithm optimizes a simple but efficacious fitness function able to identify densely connected groups of nodes with sparse connections between groups. The method is efficient because the variation operators are modified to take into consideration only the actual correlations among the nodes, thus sensibly reducing the research space of possible solutions. Experiments on synthetic and real life networks show the capability of the method to successfully detect the network structure.
Segmentation of Offline Palmprint
Authors: Yan Zheng, Yuanfang Liu, Guangshun Shi, Jiyi Li, Qingren Wang
Venue: Proceedings of the 2007 Third International IEEE Conference on Signal-Image Technologies and Internet-Based System (2007)

In this paper, an offline palmprint image is divided into four components: blank regions, knuckle-finger regions, high quality palm regions and unrecoverable low quality palm regions. The high quality palm regions are the ROI of offline palmprint images and the other three regions constitute background regions. A block-based segmentation algorithm is proposed to identify the high quality palm regions from background regions. In experiment section, the segmentation algorithm is tested from three aspects: by human inspection, by comparing the segmentation results with an official pattern and by observing the change of Accuracy Rate of minutiae after masking the unrecoverable low quality palm regions. Our palmprint database for testing contains 200 image samples and each image comprises 1600 blocks. Only 2.4% of blocks are misclassified. The Accuracy Rate also increases significantly after masking.
Recognition of Data Records in Semi-structured Web-Pages Using Ontology and Χ2 Statistical Distribution
Authors: Amin Keshavarzi, Amir Masoud Rahmani, Mehran Mohsenzadeh, Reza Keshavarzi
Venue: Proceedings of the 4th international conference on Advanced Data Mining and Applications (2008)

Information extraction (IE) has been emerged as a novel discipline in computer science. In IE, intelligent algorithms are employed to extract the required data, and structure them so that they are appropriate for query. In most IE systems, a web-page structure, e.g. HTML tags are used to recognize the looked-for information. In this article, an algorithm is developed to recognize the main region of web-pages containing the looked-for information, by means of an ontology, a web-page structure and goodness-of-fit &Chi;2 test. After recognizing the main region, the existing records of the region are recognized, and then each record is put in a text file.
Visual ScatterUnit: A Visual Model-Driven Testing Framework of Wireless Sensor Networks Applications
Authors: Mohammad Al Saad, Nicolai Kamenzky, Jochen Schiller
Venue: Proceedings of the 11th international conference on Model Driven Engineering Languages and Systems (2008)

We present a model-driven test environment called Visual ScatterUnit, which optimizes the application testing process of wireless sensor networks. Instead of having to implement the test case completely manually, the model-driven test environment allows the abstract modeling of the test process. At the same time the test case's technical implementation requirements are kept hidden from the user.In the following a code generator is able to create the necessary test case code, which complies with technical requirements from the test case model. The test case is executed with the help of Visual ScatterUnit and the test results are subsequently visualized in the test case model's diagrams. In this way the user can easily comprehend the test run and consequently understand the cause of failure. Furthermore the test case's abstract modeling allows it to be validated, which brings with it a quality assurance for the executable test case.
Pre-processing deconvolution based technique for improving the performances of ECG codecs: Comparison to SPIHT
Authors: Lina El Khansa, Amine Nait-Ali, Mohamad Khalil
Venue: Digital Signal Processing (2008)

In this paper, a deconvolution preprocessing module for ECG codec performance improvement (DPM-ECPI) is presented. The idea is simple but efficient. Primary, it consists in transforming an ECG signal to an impulsional one using a deconvolution process. The obtained signal is then encoded for either transmission or storage. In this work we take into consideration the well known SPIHT algorithm, used also for efficient comparison purpose. To reconstruct the signal, a simple convolution is however applied to the decoded impulsional signal. For high compression ratios, we show that this new compression scheme is particularly interesting than a direct coding. As it is common, real signals from MIT-BIH arrhythmia database have been used to validate the proposed scheme.
A Tuplespace-Based Middleware for Semantic Web Services Using SWRL
Authors: Liang Li, Bin Li, Junwu Zhu, Xiaowei Zhang
Venue: Proceedings of the 2008 Second International Conference on Genetic and Evolutionary Computing (2008)

Coordination is a vital aspect of any type of interaction between computer agents in open distributed systems such as the Web. As distributed systems become more ubiquitous, autonomous and complex, the need to ground them on common data model grows stronger. In particular, Semantic Web services are based on synchronous message exchange, thus being incompatible with the REST architectural model of the (Semantic) Web. Recent advances in middleware technologies propose semantic aware tuplespaces as an infrastructure for coping with these issues. However, all of the existing related works do not support rule in their extensions of tuplespace. This paper presents a RuleSpaces based on a combination of tuplespace computing and Semantic Web Rule Language. We describe overview of the conceptual model and discuss the necessary extensions to the original tuplespace. An initial version of the RuleSpaces prototype system has been implemented.
Ate Pairing on Hyperelliptic Curves
Authors: R. Granger, F. Hess, R. Oyono, N. Thériault, F. Vercauteren
Venue: Proceedings of the 26th annual international conference on Advances in Cryptology (2007)

In this paper we show that the Ate pairing, originally defined for elliptic curves, generalises to hyperelliptic curves and in fact to arbitrary algebraic curves. It has the following surprising properties: The loop length in Miller's algorithm can be up to g times shorter than for the Tate pairing, with g the genus of the curve, and the pairing is automatically reduced, i.e. no final exponentiation is needed.
Design and Implementation of a General Purpose Neural Network Processor
Authors: Yi Qian, Ang Li, Qin Wang
Venue: Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks (2007)

The general-purpose neural network processor is designed for the most neural networks algorithm and is required for variable bit length data processing ability. This paper proposes a processor that is based on SIMD (Single Instruction Multiple Data) architecture with three data bit mode: 8-bit, 16-bit and 32-bit. It can use the memory and ALU sufficiently when the bit mode changes. The processor is designed basing on 0.25---micron process technology and it can be synthesized at 50MHz with PKS of Cadence Inc. The experiment result shows that the processor can implement the neural network in highly parallel.
Surface matching with salient keypoints in geodesic scale space
Authors: Guangyu Zou Jing Hua
Venue: Computer Animation and Virtual Worlds (2008)

This paper develops a new salient keypoints-based shape description which extracts the salient surface keypoints with detected scales. Salient geometric features can then be defined collectively on all the detected scale normalized local patches to form a shape descriptor for surface matching purpose. The saliency-driven keypoints are computed as local extrema of the difference of Gaussian function defined over a curved surface in geodesic scale space. This method can properly function on either manifold or non-manifold surface without resorting to any surface mapping or parameterization procedures. Therefore, it has a wide utility in many applications such as shape matching, classification, and recognition. Our experiments on 3D shapes demonstrate that the salient keypoints and local feature descriptors are robust and stable to noisy input and insensitive to resolution change. We have applied our technique to the tasks of 3D shape matching, and the experimental results showed good performance and the effectiveness of this new method. Copyright &copy; 2008 John Wiley & Sons, Ltd.
Cognitive Abilities of Functionally Illiterate Persons Relevant to ICT Use
Authors: Sabine Linden, Anita H. Cremers
Venue: Proceedings of the 11th international conference on Computers Helping People with Special Needs (2008)

The present study investigates the proficiency levels of functionally illiterate persons regarding a number of cognitive skills (language processing skills (reading, writing, listening), visual organizational and visual memory skills, mental spatial orientation, speed of cognitive processing, vigilance, divided attention and perceived self-efficacy), which are necessary when using ICT. The results provide insight into how specific (cognitive) limitations can potentially be relieved by specific user interface elements. The findings will serve as guidelines for the design of a new user interface for an Automated Teller Machine (ATM), which is better adapted to the abilities of illiterates.
Privacy homomorphisms for social networks with private relationships
Authors: Josep Domingo-Ferrer, Alexandre Viejo, Francesc Sebé, írsula González-Nicolás
Venue: Computer Networks: The International Journal of Computer and Telecommunications Networking (2008)

Enabling private relationships in social networks is an important issue recently raised in the literature. We describe in this paper a new protocol which offers private relationships allowing resource access through indirect relationships without requiring a mediating trusted third party (although an optimistic trusted third party is used which only acts in case of conflict). Thanks to homomorphic encryption, our scheme prevents the resource owner from learning the relationships and trust levels between the users who collaborate in the resource access. In this way, the number of users who might refuse collaboration due to privacy concerns is minimized. This results in increased resource availability, as the chances that certain nodes become isolated at a given period of time are reduced. Empirical evidence is provided about the proposed protocol being scalable and deployable in practical social networks.
Probe Selection with Fault Tolerance
Authors: Sheng-Lung Peng, Yu-Wei Tsay, Tai-Chun Wang, Chuan Yi Tang
Venue: Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Theoretical and Methodological Issues (2008)

Microarray techniques play an important role for testing some reactions of diseases which are caused by viruses. Probes in microarray are one kind of the most important materials. Usually, scientists use a unique probe for marking a special target sequence. Thus, for identifying n different viruses, we need n different probes. Recently, some researchers study non-unique probes to identify viruses by using less number of probes. In this case, a virus can be identified by a combination of some probes. In this paper, we study the problem of finding a set of probes that can identify all the given targets. We consider the k-fault tolerance selection of probes. That is, if any k probes fail, then we still can identify each target. We propose a practical algorithm for this k-fault tolerance probe selection problem. Some experiments are studied on SARS, H5N1, and so on.
Multi agent based simulation of transport chains
Authors: Paul Davidsson, Johan Holmgren, Jan A. Persson, Linda Ramstedt
Venue: Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2 (2008)

An agent-based tool for micro-level simulation of transport chains (TAPAS) is described. It is more powerful than traditional approaches as it is able to capture the interactions between individual actors of a transport chain, as well as their heterogeneity and decision making processes. Whereas traditional approaches rely on assumed statistical correlation between different parameters, TAPAS relies on causality, i.e., the decisions and negotiations that lead to the transports being performed. An additional advantage is that TAPAS is able to capture time aspects, such as, the influence of timetables, arrival times, and time-differentiated taxes and fees. TAPAS is composed of two layers, one layer simulating the physical activities taking place in the transport chain, e.g., production, storage, and transports of goods, and another layer simulating the different actors' decision making processes and interaction. The decision layer is implemented as a multi-agent system using the JADE platform, where each agent corresponds to a particular actor. We demonstrate the use of TAPAS by investigating how the actors in a transport chain are expected to act when different types of governmental control policies are applied, such as, fuel taxes, road tolls, and vehicle taxes. By analyzing the costs and environmental effects, TAPAS provides guidance in decision making regarding such control policies. We argue that TAPAS may also complement existing approaches in different ways, for instance by generating input data such as transport demand. Since TAPAS models a larger part of the supply chain, the transport demand is a natural part of the output. Studies may concern operational decisions like choice of consignment size and frequency of deliveries, as well as strategic decisions like where to locate storages, terminals, etc., choice of producer, and adaptation of vehicle fleets.
Accuracy Improvement of Neural Network State Variable Estimator in Induction Motor Drive
Authors: Jerzy Jelonkiewicz, Andrzej Przybył
Venue: Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing (2006)

Some accuracy improvement of the neural network (NN) estimator is proposed in the paper. The estimator approximates stator current components in the rotor flux reference frame. Two approaches are considered: data mining with GMDH algorithm and gradual training of the NN in the desired frequency range. In both cases the accuracy of the estimator is significantly improved. Provided tests confirmed this feature and encourage to implement such an estimator it in a sensorless vector controlled induction motor drive.
Fostering E-Commerce Among Australian SMEs
Authors: Jianming Yong, Yun Yang
Venue: IT Professional (2003)

With government help, small and medium enterprises (SMEs) can become more actively involved in e-commerce.
Recognizing teamwork activity in observations of embodied agents
Authors: Ladislau L. Boloni
Venue:  (2007)

This thesis presents contributions to the theory and practice of team activity recognition. A particular focus of our work was to improve our ability to collect and label representative samples, thus making the team activity recognition more efficient. A second focus of our work is improving the robustness of the recognition process in the presence of noisy and distorted data. The main contributions of this thesis are as follows: We developed a software tool, the Teamwork Scenario Editor (TSE), for the acquisition, segmentation and labeling of teamwork data. Using the TSE we acquired a corpus of labeled team actions both from synthetic and real world sources. We developed an approach through which representations of idealized team actions can be acquired in form of Hidden Markov Models which are trained using a small set of representative examples segmented and labeled with the TSE. We developed set of team-oriented feature functions, which extract discrete features from the high-dimensional continuous data. The features were chosen such that they mimic the features used by humans when recognizing teamwork actions. We developed a technique to recognize the likely roles played by agents in teams even before the team action was recognized. Through experimental studies we show that the feature functions and role recognition module significantly increase the recognition accuracy, while allowing arbitrary shuffled inputs and noisy data.
A New Weighted Support Vector Machine for Regression and Its Parameters Optimization
Authors: Liquan Mei, Shujuan Zhang
Venue: Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence (2008)

In this paper what we study is twofold. Firstly a new weighted support vector machine(WSVM) is introduced, in which different roles of samples are considered. It better processes the singularity in the sample than SVM. Secondly based on grid search and the solution path algorithm, a new algorithm is given to quickly find the optimum parameters. Numerical results show the effectiveness and the stability of the algorithm.
Boosting over Groups and Its Application to Acronym-Expansion Extraction
Authors: Weijian Ni, Yalou Huang, Dong Li, Yang Wang
Venue: Proceedings of the 4th international conference on Advanced Data Mining and Applications (2008)

In many real-world classification applications, instances are generated from different `groups'. Take webpage classification as an example, the webpages for training and testing can be naturally grouped by network domains, which often vary a lot from one to another in domain size or webpage template. The differences between `groups' would result that the distribution of instances from different `groups' also vary. Thus, it is not so reasonable to equally treat the instances as the independent elements during training and testing as in conventional classification algorithms. This paper addresses the classification problem where all the instances can be naturally grouped. Specifically, we give a formulation to this kind of problem and propose a simple but effective boosting approach, which is called AdaBoost.Group. The problem is demonstrated by the task of recognizing acronyms and their expansions from text, where all the instances are grouped by sentences. The experimental results show that our approach is more appropriate to this kind of problems than conventional classification approaches.
Comparing the Robustness of Expansion Techniques and Retrieval Measures
Authors: Stephen Tomlinson
Venue: Evaluation of Multilingual and Multi-modal Information Retrieval: 7th Workshop of the Cross-Language Evaluation Forum, CLEF 2006, Alicante, Spain, September 20-22, 2006, Revised Selected Papers (2007)

We investigate which retrieval measures successfully discern robustness gains in the monolingual (Bulgarian, French, Hungarian, Portuguese and English) information retrieval tasks of the Ad-Hoc Track of the Cross-Language Evaluation Forum (CLEF) 2006. In all 5 of our experiments with blind feedback (a technique known to impair robustness across topics), the mean scores of the Average Precision, Geometric MAP and Precision@10 measures increased (and most of these increases were statistically significant), implying that these measures are not suitable as robust retrieval measures. In contrast, we found that measures based on just the first relevant item, such as a Generalized Success@10 measure, successfully discerned some robustness gains, particularly the robustness advantage of expanding Title queries by using the Description field instead of blind feedback.
Reliable Probabilistic Classification and Its Application to Internet Traffic
Authors: Mikhail Dashevskiy, Zhiyuan Luo
Venue: Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Theoretical and Methodological Issues (2008)

Many machine learning algorithms have been used to classify network traffic flows with good performance, but without information about the reliability in classifications. In this paper, we present a recently developed algorithmic framework, namely the Venn Probability Machine, for making reliable decisions under uncertainty. Experiments on publicly available real traffic datasets show the algorithmic framework works well. Comparison is also made to the published results.
Accessible Online Shops for the Older Generation and People with Disabilities
Authors: Michael Stenitzer, Maria Putzhuber, Sascha Nemecek, Fabian Büchler
Venue: Proceedings of the 11th international conference on Computers Helping People with Special Needs (2008)

Because of their limited mobility elderly and disabled people can benefit from online shopping to a great extent. At the same time they suffer from poorly built online shops that are focused on internet affine users. User testing helped to identify major barriers and to deduce design guidelines for accessible web shops.
Induction of Partial Orders to Predict Patient Evolutions in Medicine
Authors: John A. Bohada, David Riaño, Francis Real
Venue: Proceedings of the 11th conference on Artificial Intelligence in Medicine (2007)

In medicine, prognosis is the task of predicting the probable course and outcome of a disease. Questions like, is a patient going to improve?, what is his/her chance of recovery?, and how likely a relapse is? are common and they rely on the concept of state. The feasible states of a disease define a partial order structure with extreme states those of 'cure' and 'death'; improving, recovering, and survival meaning particular transitions between states of the partial order. In spite of this, it is not usual in medicine to find an explicit representation either of the states or of the states partial order for many diseases. On the contrary, the variables (e.g. signs and symptoms) related to a disease and their normality and abnormality values are broadly agreed. Here, an inductive algorithm is introduced that generates partial orders from a data matrix containing information about the patient-professional encounters, and the normality functions of each one of these disease variables.
Computing machines in aircraft engineering
Authors: Charles R. Strang
Venue: Papers and discussions presented at the Dec. 10-12, 1951, joint AIEE-IRE computer conference: Review of electronic digital computers (1951)

I Have been specifically asked to present a user's critical view of computing machinery with emphasis on its limitations.