ppxscal/embedding_network huggingface dataset:
How to protect privacy in floating car data systems
Authors: Stefan Rass, Simone Fuchs, Martin Schaffer, Kyandoghere Kyamakya
Venue: Proceedings of the fifth ACM international workshop on VehiculAr Inter-NETworking (2008)

Floating Car Data (FCD) is a valuable source of up-to-date traffic information, with a wide range of applications. Active floating car data techniques require drivers to have their vehicles equipped with on-board units regularly transmitting position and velocity information to a central server. Many potential participants are hence reluctant to join FCD projects because of violations of their privacy due to permanent traceability or possible liability in case of speed limit violations. We present a general method for anonymization of floating car data by deriving pseudonyms for trips and samples with the optional ability of relating samples to trips and trips to each other, whilst hiding the identity of a driver, hence protecting his privacy. The resulting concepts are easy to implement and can be used as building blocks for any FCD system with stringent security constraints. The main advantage of our approach is the guaranteed uniqueness of pseudonyms that can be achieved efficiently, i.e. without any communication between vehicles.
Comparative evaluation of support vector machines and probabilistic neural networks in superficial bladder cancer classification
Authors: P. Spyridonos, P. Petalas, D. Glotsos, D. Cavouras, P. Ravazoula, G. Nikiforidis
Venue: Journal of Computational Methods in Sciences and Engineering (2006)

Purpose: In this paper we address the demanding diagnostic problem of classifying tumors according to the degree of their malignancy by investigating the efficiency of Support Vector Machines (SVMs) and Probabilistic neural networks (PNN). Material and methods: 129 cases of urinary bladder carcinomas were diagnosed as high or low-risk according to the WHO grading system. Each case was represented by 36 automatically extracted nuclear features. Two different classification designs based on SVMs and PNNs were tested according to their ability in differentiating superficial urinary bladder carcinomas according to the degree of malignancy. Best feature combination for each classification scheme was obtained performing an exhaustive search in feature space and employing the leave-one-out method. Results: Both classification models (SVM and PNN) resulted in a relatively high overall accuracy of 85.3% and 83.7% respectively. Descriptors of nuclear size and chromatin cluster patterns were participated in both best feature vectors that optimized classification performance of the two classifiers. Conclusion: The good performance and consistency of the SVM and PNN models render these techniques viable alternatives in the diagnostic process of assigning urinary bladder tumors grade.
A new adaptive detection algorithm for power quality improvement
Authors: David A. Cartes
Venue:  (2007)

In this dissertation, a new adaptive harmonic selective detection algorithm is proposed for power quality improvement applications. The adaptive gains of the proposed method can be chosen relatively large to obtain faster convergence. The stability of the proposed method is guaranteed. The adaptive harmonic selective algorithm is analyzed then compared to a popular d-q method. This proposed adaptive method is simple and effective in extracting fundamental and harmonic current information from harmonic load currents. The extracted fundamental or harmonic currents therefore can be used as the reference signals for power quality improvement applications such as harmonic selective cancellation or reactive power compensation. The proposed adaptive algorithm can estimate time varying power system frequency and it also can identify a dc offset in a load current. These are two conditions that the d-q transformation based detection algorithm is incapable of. This adaptive detection method is phase independent and therefore it can be easily applied to three phase systems. Simulation and experimental results verify the good performance of the proposed new adaptive detection method.
Proposal of a Tool of Support to the Evaluation of User in Educative Web Sites
Authors: Maria Elena Alva, Ana Belén Martínez, José Emilio Gayo, Maria Carmen Suárez, Juan Manuel Cueva, Hernán Sagástegui
Venue: Proceedings of the 1st world summit on The Knowledge Society: Emerging Technologies and Information Systems for the Knowledge Society (2008)

This article presents an analysis of the necessity to implement a tool of evaluation and measurement of the usability to support the ME-USitE methodology that contributes to the detection of problems and failures of usability from the perspective of the user of educative Web sites in a real work environment. Taking into account that the Web sites are designed and implemented for a domain and audience in particular, it's important to consider factors like these for the implementation of a tool that allows not only to carry out the evaluation process but also to support the analysis of the gathered data. Thus, the obtained results may allow us to establish policies for the improvement of the usability of the site, when providing a score that determines whether the level of usability offered covers the requirements for the proposed audience and the profile of the participant user in the evaluation.
Ordinal Embedding: Approximation Algorithms and Dimensionality Reduction
Authors: Mihai Bădoiu, Erik D. Demaine, Mohammadtaghi Hajiaghayi, Anastasios Sidiropoulos, Morteza Zadimoghaddam
Venue: Proceedings of the 11th international workshop, APPROX 2008, and 12th international workshop, RANDOM 2008 on Approximation, Randomization and Combinatorial Optimization: Algorithms and Techniques (2008)

This paper studies how to optimally embed a general metric, represented by a graph, into a target space while preserving the relative magnitudes of most distances. More precisely, in an ordinal embedding, we must preserve the relative order between pairs of distances (which pairs are larger or smaller), and not necessarily the values of the distances themselves. The relaxation of an ordinal embedding is the maximum ratio between two distances whose relative order is inverted by the embedding. We develop polynomial-time constant-factor approximation algorithms for minimizing the relaxation in an embedding of an unweighted graph into a line metric and into a tree metric. These two basic target metrics are particularly important for representing a graph by a structure that is easy to understand, with applications to visualization, compression, clustering, and nearest-neighbor searching. Along the way, we improve the best known approximation factor for ordinally embedding unweighted trees into the line down to 2. Our results illustrate an important contrast to optimal-distortion metric embeddings, where the best approximation factor for unweighted graphs into the line is O(n 1/2), and even for unweighted trees into the line the best is $\tilde O(n^{1/3})$. We also show that Johnson-Lindenstrauss-type dimensionality reduction is possible with ordinal relaxation and ¿1 metrics (and ¿ p metrics with 1 ≤ p ≤ 2), unlike metric embedding of ¿1 metrics.
Multiplier-Based Double Precision Floating Point Divider According to the IEEE-754 Standard
Authors: Vítor Silva, Rui Duarte, Mário Véstias, Horácio Neto
Venue: Proceedings of the 4th international workshop on Reconfigurable Computing: Architectures, Tools and Applications (2008)

This paper describes the design and implementation of a unit to calculate the significand of a double precision floating point divider according to the IEEE-754 standard. Instead of the usual digit recurrence techniques, such as SRT-2 and SRT-4, it uses an iterative technique based on the Goldsmith algorithm. As multiplication is the main operation of this algorithm, its implementation is able to take advantage of the efficiency of the embedded multipliers available in the FPGAs. The results obtained indicate that the multiplier-based iterative algorithms can achieve better performance than the alternative digit recurrence algorithms, at the cost of some area overhead.
Process Algebra Models of Population Dynamics
Authors: Chris Mccaig, Rachel Norman, Carron Shankland
Venue: Proceedings of the 3rd international conference on Algebraic Biology (2008)

It is well understood that populations cannot grow without bound and that it is competition between individuals for resources which restricts growth. Despite centuries of interest, the question of how best to model density dependent population growth still has no definitive answer. We address this question here through a number of individual based models of populations expressed using the process algebra WSCCS. The advantage of these models is that they can be explicitly based on observations of individual interactions. From our probabilistic models we derive equations expressing overall population dynamics, using a formal and rigorous rewriting based method. These equations are easily compared with the traditionally used deterministic Ordinary Differential Equation models and allow evaluation of those ODE models, challenging their assumptions about system dynamics. Further, the approach is applied to epidemiology, combining population growth with disease spread.
Global Asymptotic Stability of Cellular Neutral Networks With Variable Coefficients and Time-Varying Delays
Authors: Yonggui Kao, Cunchen Gao, Lijing Zhang
Venue: Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks (2007)

In this paper, we study the global asymptotic stability properties of cellular neural networks with variable coefficients and time varying delays. We present sufficient conditions for the global asymptotic stability of the neural networks . The proposed conditions, which are applicable to all continuous nonmonotonic neuron activation functions and do not require the interconnection matrices to be symmetric, establish the relationships between network parameters of the neural systems and the delay parameters. Some examples show that our results are new and improve the previous results derived in the literature.
Texture Analysis on Weather Radar Images of Severe Convective Precipitation in the Arid Area of Ningxia
Authors: Wendong Hu, Xiaoyu Huang, Yanchun Li
Venue: Proceedings of the 2008 Congress on Image and Signal Processing, Vol. 2 - Volume 02 (2008)

Using the Yinchuan new generation radar volume scan data of severe convective precipitation in 2004-2005, composite reflectivity images in polar coordinate were projected into the coordinate of x-y. 51 cases were studied with gray level co-occurrence matrix and the parameters of angle second moment, contrast, correlation, entropy and inverse difference moment. Results show that, there are obvious differences of texture between the remote sense images of precipitation in Ningxia and south China. In the radar images of severe convective precipitation in climate arid region in north-mid part of Ningxia, the texture is not as distinct as in south China, and the definition is not very clear, the texture is weaker and thinner, the images are smoother and stable than that of south China, the features indicate that the clouds of severe convective precipitation in the climate arid region in north-mid part of Ningxia are homogeneous.
Push-Poll Recommender System: Supporting Word of Mouth
Authors: Andrew Webster, Julita Vassileva
Venue: Proceedings of the 11th international conference on User Modeling (2007)

Recommender systems produce social networks as a side effect of predicting what users will like. However, the potential for these social networks to aid in recommending items is largely ignored. We propose a recommender system that works directly with these networks to distribute and recommend items: the informal exchange of information (word of mouth communication) is supported rather than replaced. The paper describes the push-poll approach and evaluates its performance at predicting user ratings for movies against a collaborative filtering algorithm. Overall, the push-poll approach performs significantly better while being computationally efficient and suitable for dynamic domains (e.g. recommending items from RSS feeds).
Shadow configuration as a network management primitive
Authors: Richard Alimi, Ye Wang, Y. Richard Yang
Venue: ACM SIGCOMM Computer Communication Review (2008)

Configurations for today's IP networks are becoming increasingly complex. As a result, configuration management is becoming a major cost factor for network providers and configuration errors are becoming a major cause of network disruptions. In this paper, we present and evaluate the novel idea of shadow configurations. Shadow configurations allow configuration evaluation before deployment and thus can reduce potential network disruptions. We demonstrate using real implementation that shadow configurations can be implemented with low overhead.
Automatic Segmentation of Neoplastic Hepatic Disease Symptoms in CT Images
Authors: Marcin Ciecholewski, Marek R. Ogiela
Venue: Proceedings of the 4th international conference on Modeling Decisions for Artificial Intelligence (2007)

In this paper will be described a new method of automatic segmentation of inflammation and neoplastic hepatic disease symptoms, visible in computed-tomography (CT) images. The liver structure will be at first extracted from the image using the ap proximate contour model. Then, the appropriate histogram-based transformations will be proposed to enhance neoplastic focal changes in CT images. For segmentation stage of cancerous symptoms, the analyzed images will be processed using binary morphological filtration with the application of a parameterized mean defining the distribution of pixel gray-levels in the image. Then, the edges of neoplastic lesions situated inside the liver contour are localized. To assess the efficiency of the proposed processing procedures, experiments have been carried out for two types of tumours: haemangiomas and hepatomas. The experiments were conducted on 60 cases of various patients. In this set 30 images showed single and multiple focal hepatic neoplastic lesions, and the remaining 30 images show the healthy organ. Experimental results confirmed that the proposed method is an efficient tool which may be used in the diagnostic support procedures for normal and abnormal liver. The efficiency of proposed algorithm reach the level of over 83% of correct recognition of pathological changes.
The Capacity-C Torch Problem
Authors: Roland Backhouse
Venue: Proceedings of the 9th international conference on Mathematics of Program Construction (2008)

The torch problem (also known as the bridge problem or the flashlight problem) is about getting a number of people across a bridge as quickly as possible under certain constraints. Although a very simply stated problem, the solution is surprisingly non-trivial. The case in which there are just four people and the capacity of the bridge is two is a well-known puzzle, widely publicised on the internet. We consider the general problem where the number of people, their individual crossing times and the capacity of the bridge are all input parameters. We present an algorithm that determines the shortest total crossing time; the number of primitive computations executed by the algorithm (i.e. the worst-case time complexity of the algorithm) is proportional to the square of the number of people.
Ultra Fast Grey Scale Face Detection Using Vector SIMD Programming
Authors: O. Vermeulen, A. Manzanera, L. Lacassagne
Venue: Proceedings of the 2007 Third International IEEE Conference on Signal-Image Technologies and Internet-Based System (2007)

This paper presents an ultra-fast detection algorithm for locating faces in grey scale videos. We first use motion detection to reduce the working area and find the approximate position of the head. Then a morphology-based technique is applied in this area to detect eye-analogue and lips-analogue regions. Next, the resulting components are used to search for potential facial features. Finally we select from the candidate triplets, the one that best represents a real face, calculating a fitness which takes into account things such as the symmetry and the proximity with the extrapolated position of the face. In order to achieve the maximal speed-up, we use the vector parallelism provided by the SIMD (Simple Instruction Multiple Data) extensions, available on most mainstream processors. The final program runs 65 times faster than the real-time. Experiments demonstrate that the success rate for single face videos reaches 85% in good conditions and can go down to 60% in harder cases. This approach can be useful in many applications, where the detection rate is not as important as the computation time, such as video face identification, or human-computer visual interfaces.
Quantification of Segmentation and F0 Errors and Their Effect on Emotion Recognition
Authors: Stefan Steidl, Anton Batliner, Elmar Nöth, Joachim Hornegger
Venue: Proceedings of the 11th international conference on Text, Speech and Dialogue (2008)

Prosodic features modelling pitch, energy, and duration play a major role in speech emotion recognition. Our word level features, especially duration and pitch features, rely on correct word segmentation and F0 extraction. For the FAU Aibo Emotion Corpus, the automatic segmentation of a forced alignment of the spoken word sequence and the automatically extracted F0 values have been manually corrected. Frequencies of different types of segmentation and F0errors are given and their influence on emotion recognition using different groups of prosodic features is evaluated. The classification results show that the impact of these errors on emotion recognition is small.
Separation of ERP and Noise Subspaces in EEG Data without Whitening
Authors: Andriy Ivannikov, Tommi Kärkkäinen, Tapani Ristaniemi, Heikki Lyytinen
Venue: Proceedings of the 2008 21st IEEE International Symposium on Computer-Based Medical Systems (2008)

In this article, a method for separating linear subspaces of time-locked brain responses and other noise sources in multichannel electroencephalography data is proposed. The components related to time-locked and noise subspaces are distinguished by method based on different behavior they experience after traditional averaging. The actual separation of the two subspaces is performed without whitening by maximizing/minimizing the same criterion. The detailed derivation of the method is given, and the results of the method's application to simulated and real EEG datasets are studied. The possibilities of improving the results are also discussed.
Searching for Prototypical Facial Feedback Signals
Authors: Dirk Heylen, Elisabetta Bevacqua, Marion Tellier, Catherine Pelachaud
Venue: Proceedings of the 7th international conference on Intelligent Virtual Agents (2007)

Embodied conversational agents should be able to provide feedback on what a human interlocutor is saying. We are compiling a list of facial feedback expressions that signal attention and interest, grounding and attitude. As expressions need to serve many functions at the same time and most of the component signals are ambiguous, it is important to get a better idea of the many to many mappings between displays and functions. We asked people to label several dynamic expressions as a probe into this semantic space. We compare simple signals and combined signals in order to find out whether a combination of signals can have a meaning on its own or not, i. e. the meaning of single signals is different from the meaning attached to the combination of these signals. Results show that in some cases a combination of signals alters the perceived meaning of the backchannel.
Enriching Reverse Engineering with Annotations
Authors: Andrea Brühlmann, Tudor Gîrba, Orla Greevy, Oscar Nierstrasz
Venue: Proceedings of the 11th international conference on Model Driven Engineering Languages and Systems (2008)

Much of the knowledge about software systems is implicit, and therefore difficult to recover by purely automated techniques. Architectural layers and the externally visible features of software systems are two examples of information that can be difficult to detect from source code alone, and that would benefit from additional human knowledge. Typical approaches to reasoning about data involve encoding an explicit meta-model and expressing analyses at that level. Due to its informal nature, however, human knowledge can be difficult to characterize up-front and integrate into such a meta-model. We propose a generic, annotation-based approach to capture such knowledge during the reverse engineering process. Annotation types can be iteratively defined, refined and transformed, without requiring a fixed meta-model to be defined in advance. We show how our approach supports reverse engineering by implementing it in a tool called Metanool and by applying it to (i) analyzing architectural layering, (ii) tracking reengineering tasks, (iii) detecting design flaws, and (iv) analyzing features.
Topics Identification Based on Event Sequence Using Co-occurrence Words
Authors: Kei Wakabayashi, Takao Miura
Venue: Proceedings of the 13th international conference on Natural Language and Information Systems: Applications of Natural Language to Information Systems (2008)

In this paper, we propose a sophisticated technique for topic identification of documents based on event sequences using co-occurrence words. Here we consider each document as an event sequence, each event as a verb and some words correlated with the verb. We propose a new method for topic classification of documents by using Markov stochastic model. We show some experimental results to examine the method.
Interpreting the perceptions of self-regulating the internet by online consumers in seattle, washington
Authors: Jeffrey R. Snider
Venue:  (2008)

The implication of increases in Internet fraud each year is that government agencies and private corporations are not successfully controlling online fraudulent behavior. Several researchers support the belief that a strategy requiring online consumers to self-regulate offers the greatest probability of limiting fraudulent behavior. However, researchers have not answered the question of whether online consumers are prepared to accept the responsibility of self-regulation. The scope of this study was to examine the perceptions of self-regulation by online consumers in Seattle, Washington using a qualitative phenomenological approach. Common themes interpreted from participants' responses to open ended questions revealed that self-regulation is an acceptable approach to preventing online fraud; however the online consumers in Seattle, Washington do not believe that they have the skills and resources to self-regulate. The ACM Portal is published by the Association for Computing Machinery. Copyright © 2010 ACM, Inc. Terms of Usage Privacy Policy Code of Ethics Contact Us Useful downloads: Adobe Acrobat QuickTime Windows Media Player Real Player
An Eclipse-Based Framework for AIS Service Configurations
Authors: András Kövi, Dániel Varró
Venue: Proceedings of the 4th international symposium on Service Availability (2007)

In the paper, we propose an Eclipse-based model-driven framework to support an integrated development, analysis and deployment of Application Interface Specification (AIS) service configurations. Service configurations are first captured by platform-independent models (PIM), which directly correspond to the AIS standard itself, and abstract from vendor-specific details. Specificities of vendor-specific AIS middleware are incorporated into platform-specific models (PSM), which are derived from PIMs by automatic model transformations. Model analysis can be carried out either on the PIM-level to ensure standard compliance of a given service configuration, or on the PSM-level to detect availability bottlenecks by formal analysis early in the service configuration design. Finally, deployment descriptors of the selected AIS platform are generated from verified service configurations by automatic code generation techniques.