pinecone/core-2020-05-10-deduplication huggingface dataset:
Towards a comprehensive evaluation of ultrasound speckle reduction

Over the last three decades, several despeckling filters have been developed to reduce the speckle noise inherently present in ultrasound images without losing the diagnostic information. In this paper, a new intensity and feature preservation evaluation metric for full speckle reduction evaluation is proposed based contrast and feature similarities. A comparison of the despeckling methods is done, using quality metrics and visual interpretation of images profiles to evaluate their performance and show the benefits each one can contribute to noise reduction and feature preservation. To test the methods, noise-free images and simulated B-mode ultrasound images are used. This way, the despeckling techniques can be compared using numeric metrics, taking the noise-free image as a reference. In this study, a total of seventeen different speckle reduction algorithms have been documented based on adaptive filtering, diffusion filtering and wavelet filtering, with sixteen qualitative metrics estimation.info:eu-repo/semantics/publishedVersio
Automatic Method for Visual Grading of Seed Food Products

International audienceThis paper presents an automatic method for visual grading, designed to solve the industrial problem of evaluation of seed lots. The sample is thrown in bulk onto a tray placed in a chamber for acquiring color image. An image processing method had been developed to separate and characterize each seed. The approach adopted for the segmentation step is based on the use of marked point processes and active contour, leading to tackle the problem by a technique of energy minimization
Multiscale Shape Description with Laplacian Profile and Fourier Transform

International audienceWe propose a new local multiscale image descriptor of vari-able size. The descriptor combines Laplacian of Gaussian values at dif-ferent scales with a Radial Fourier Transform. This descriptor provides a compact description of the appearance of a local neighborhood in a manner that is robust to changes in scale and orientation. We evaluate this descriptor by measuring repeatability and recall against 1-precision with the Affine Covariant Features benchmark dataset and as well as with a set of textureless images from the MIRFLICKR Retrieval Evalu-ation dataset. Experiments reveal performance competitive to the state of the art, while providing a more compact representation
Self-Stabilizing Leader Election in Polynomial Steps

International audienceWe propose a silent self-stabilizing leader election algorithm for bidirectional connected identified networks of arbitrary topology. This algorithm is written in the locally shared memory model. It assumes the distributed unfair daemon, the most general scheduling hypothesis of the model. Our algorithm requires no global knowledge on the network (such as an upper bound on the diameter or the number of processes, for example). We show that its stabilization time is in Θ(n^3) steps in the worst case, where n is the number of processes. Its memory requirement is asymptotically optimal, i.e., Θ(log n) bits per processes. Its round complexity is of the same order of magnitude — i.e., Θ(n) rounds — as the best existing algorithm (Datta et al, 2011) designed with similar settings. To the best of our knowledge, this is the first self-stabilizing leader election algorithm for arbitrary identified networks thatis proven to achieve a stabilization time polynomial in steps. By contrast, we show that the previous best existing algorithm designed with similar settings (Datta et al, 2011) stabilizes in a non polynomial number of steps in the worst case
Frequency independence of ultrasound transit time spectroscopy

Recent studies have shown that ultrasound transit time spectroscopy (UTTS) is an alternative method to describe ultrasound wave propagation through complex samples as an array of parallel sonic rays. This technique has the potential to characterize bone properties including volume fraction and may be implemented in clinical systems to predict osteoporotic fracture risk. In contrast to broadband ultrasound attenuation, which is highly frequency dependent, we hypothesise that UTTS is frequency independent. This study measured 1 MHz and 5 MHz broadband ultrasound signals through a set of acrylic step-wedge samples. Digital deconvolution of the signals through water and each sample was applied to derive a transit time spectrum. The resulting spectra at both 1 MHz and 5 MHz were compared to the predicted transit time values. Linear regression analysis yields agreement (R2) of 99.23% and 99.74% at 1 MHz and 5 MHz respectively indicating frequency independence of transit time spectra
Probabilistic Modelling of Multiresolution Biological Data

When the measurements from the ever improving measurement technology are accumulated over a period of time, the result is the collection of data in different representations. However, most machine learning and data mining algorithms, in their standard form, are designed to operate on data in single representation.
This thesis proposes machine learning and data mining algorithms to analyze data in different representation with respect to the resolution within a single analysis. The novel algorithms proposed to analyze multiresolution data are in the field of probabilistic modelling and semantic data mining. First, three different deterministic data transformation methods are proposed to transform data across different resolutions. After the data transformation, the resulting data in same resolution are integrated and modeled using mixture models.
Second, similar mixture components in a mixture model are merged one by one repetitively to generate a chain of mixture models. A new fast approximation of the KL-divergence is derived to determine the similarity of the mixture components. The chain of generated mixture models are useful for comparison, for example, in model selection. Third, mixture components in different resolutions are iteratively merged to model multiresolution data generating models in each modeled resolution that incorporate information from data in other resolution.
Fourth, a single multiresolution mixture model with multiresolution mixture components is proposed whose mixture components independently have the capabilities of a Bayesian network. Finally, three--part methodology consisting of clustering using mixture models, rule learning using semantic subgroup discovery, and pattern visualization using banded matrices is developed for comprehensive analysis of multiresolution data.
The multiresolution data analysis methods presented in this thesis improves the performance of the methods in comparison with the their single resolution counterparts. Furthermore, developed methods aims to make the results understandable to the domain experts. Therefore, the developed methods are useful addition in the analysis of chromosomal aberration patterns and the cancer research in general
Incremental learning with social media data to predict near real-time events

International audienceIn this paper, we focus on the problem of predicting some particular user activities in social media. Our challenge is to consider real events such as message posting to friends or forwarding received ones, connecting to new friends, and provide near real-time prediction of new events. Our approach is based on latent factor models which can exploit simultaneously the timestamped interaction information among users and their posted content information. We propose a simple strategy to learn incrementally the latent factors at each time step. Our method takes only recent data to update latent factor models and thus can reduce computational cost. Experiments on a real dataset collected from Twitter show that our method can achieve performances that are comparable with other state-of-the-art non-incremental techniques
Feature Selection in Hierarchical Feature Spaces

Feature selection is an important preprocessing step in data mining, which has an impact on both the runtime and the result quality of the subsequent processing steps. While there are many cases where hierarchic relations between features exist, most existing feature selection approaches are not capable of exploiting those relations. In this paper, we introduce a method for feature selection in hierarchical feature spaces. The method first eliminates redundant features along paths in the hierarchy, and further prunes the resulting feature set based on the features' relevance. We show that our method yields a good trade-off between feature space compression and classification accuracy, and outperforms both standard approaches as well as other approaches which also exploit hierarchies
Algorithm selection on data streams

We explore the possibilities of meta-learning on data streams, in particular algorithm selection. In a first experiment we calculate the characteristics of a small sample of a data stream, and try to predict which classifier performs best on the entire stream. This yields promising results and interesting patterns. In a second experiment, we build a meta-classifier that predicts, based on measurable data characteristics in a window of the data stream, the best classifier for the next window. The results show that this meta-algorithm is very competitive with state of the art ensembles, such as OzaBag, OzaBoost and Leveraged Bagging. The results of all experiments are made publicly available in an online experiment database, for the purpose of verifiability, reproducibility and generalizability
Advances in Analysis and Exploration in Medical Imaging

With an ever increasing life expectancy, we see a concomitant increase in diseases capable of disrupting normal cognitive processes. Their diagnoses are difficult, and occur usually after daily living activities have already been compromised. This dissertation proposes machine learning methods for the study of the neurological implications of brain lesions. It addresses the analysis and exploration of medical imaging data, with particular emphasis to (f)MRI. Two main research directions are proposed. In the first, a brain tissue segmentation approach is detailed. In the second, a document mining framework, applied to reports of neuroscientific studies, is described. Both directions are based on retrieving consistent information from multi-modal data.
A contribution in this dissertation is the application of a semi-supervised method, discriminative clustering, to identify different brain tissues and their partial volume information. The proposed method relies on variations of tissue distributions in multi-spectral MRI, and reduces the need for a priori information. This methodology was successfully applied to the study of multiple sclerosis and age related white matter diseases. It was also showed that early-stage changes of normal-appearing brain tissue can already predict decline in certain cognitive processes.
Another contribution in this dissertation is in neuroscience meta-research. One limitation in neuroimage processing relates to data availability. Through document mining of neuroscientific reports, using images as source of information, one can harvest research results dealing with brain lesions. The context of such results can be extracted from textual information, allowing for an intelligent categorisation of images. This dissertation proposes new principles, and a combination of several techniques to the study of published fMRI reports. These principles are based on a number of distance measures, to compare various brain activity sites. Application to studies of the default mode network validated the proposed approach.
The aforementioned methodologies rely on clustering approaches. When dealing with such strategies, most results depend on the choice of initialisation and parameter settings. By defining distance measures that search for clusters of consistent elements, one can estimate a degree of reliability for each data grouping. In this dissertation, it is shown that such principles can be applied to multiple runs of various clustering algorithms, allowing for a more robust estimation of data agglomeration
Contingent Effects of Humor Type and Cognitive Style on Consumer Attitudes

In the spirit of examining humor contextually, we consider a basic question in this essay: do different humor types “play by the same rules,” cognitively speaking? We examine the relationship between humor and cognitive processing style, as operationalized through Need for Cognition. We find that humor based on disparagement processes “breaks through” the cognitive differences inherent in incongruity humor. Rather than exhibiting incongruity-based humor’s affective response transferring to attitude for low-NFC subjects, and more centrally-employing high NFC subjects’ higher tendency to dismiss the humor’s effect on overall attitude, disparagement based humor is processed the same by both low- and high-NFC respondents. Overall, our findings suggest that the relationship between cognitive processing (as operationalized through Need for Cognition) and resultant attitudes is a complicated one, strongly contingent on humor type. Were only incongruity-based humor utilized, our findings would have reinforced Zhang’s (Psychol Mark 13:531–545, 1996b) assertion that a person’s Need for Cognition will play a key role in the evaluation of humor, but our results suggest that a more complex conceptualization of the relationship between humor and attitude formation is required when humor is treated as a potentially more complicated, context-dependent construct
The Efficacy of Sexualized Female Models in Young Adult-Male Oriented Cigarette Advertising

This paper explores the portrayal (efficacy) of female models in male-targeted cigarette print advertisements through a content analysis (experiment). We first describe the presence and portrayal of females through a content analysis on male-targeted cigarette advertisements in magazines; we find that, in aggregate, females are often used as sexually attractive decoration, and the overall female portrayal is fundamentally different (and unequal) to that of the male. We next investigate the efficacy of these portrayals through an experiment among young adult-aged male smokers and nonsmokers. Our experimental findings indicate that these sexually charged advertisements generate superior attitudinal results in non-smoking males than smoking males, a finding that – in conjunction with the portrayal of females in the content analysis – suggests potential societal and public policy implications
A Structural Equation Modeling-Based Examination of the Private Label Brand (PLB) Consumer Evaluation Process

The goal of this paper is to answer the question “What variables influence consumer private label brand (PLB) evaluations?” We employ structural equation modeling (SEM) in order to analyze a hypothesized process by which consumer-based evaluations of retailer and product category, along with perceived risk of the category and PLB all affect the ultimate PLB brand evaluation. PLBs have been heavily researched in academic marketing research journals but SEM-based research is minimal. Research has generally been conducted using model-building techniques which are then empirically tested using real-world data. Given the complexity of the process, an alternate approach is to use experimental techniques to gain first-hand PLB data and then analyze it using appropriate methodology. The current project represents a step in that direction. While the model is subject to limitations that call its utility into question, it does provide guidance for a future, more theoretically-sound a priori model and resulting SEM analysis
Comparison of different cue-based swarm aggregation strategies

In this paper, we compare different aggregation strategies for cue-based aggregation with a mobile robot swarm. We used a sound source as the cue in the environment and performed real robot and simulation based experiments. We compared the performance of two proposed aggregation algorithms we called as the vector averaging and naïve with the state-of-the-art cue-based aggregation strategy BEECLUST. We showed that the proposed strategies outperform BEECLUST method. We also illustrated the feasibility of the method in the presence of noise. The results showed that the vector averaging algorithm is more robust to noise when compared to the naïve method
Detecting anomalous energy consumption in android applications

The use of powerful mobile devices, like smartphones, tablets and laptops, are changing the way programmers develop software. While in the past the primary goal to optimize software was the run time optimization, nowadays there is a growing awareness of the need to reduce energy consumption. This paper presents a technique and a tool to detect anomalous energy
consumption in Android applications, and to relate it directly with the source code of the application. We propose a dynamically calibrated model for energy consumption for the Android ecosystem, and that supports different devices. The model is then used as an API to monitor the application execution: first, we instrument the application source code so that we can relate energy consumption to the application source code; second, we use a statistical approach, based on fault-localization techniques, to localize abnormal energy consumption in the source code
High Performance Relaying of C++11 Objects across Processes and Logic-Labeled Finite-State Machines

We present gusimplewhiteboard, a software architecture analogous to ROS:services and ROS: messages, that enables the construction and extremely efficient inter-process relaying of message-types as C++11 objects, All gusimplewhiteboard objects reside in shared memory. Moreover, our principle is to use idempotent message communication, in direct contrast to previously released platforms for robotic-module communication, that are based on an event-driven subscriber model that queues and multi-threads. We combine this with compiled, time-triggered, logic-labeled finite state machines (llfsms) the are executed concurrently, but scheduled sequentially, in an extremely efficient manner, removing all race conditions and requirements for explicit synchronisation. Together, these tools enable effective robotic behaviour design, where arrangements of llfsms can be organised as hierarchies of machines and submachines, enabling composition of very complex systems. They have proven to be very powerful for Model-Driven Development, capable of simulation, validation, and formal verification.Griffith Sciences, School of Information and Communication TechnologyFull Tex
Strategies for executing federated queries in\ud
SPARQL1.1

A common way for exposing RDF data on the Web is by means of\ud
SPARQL endpoints which allow end users and applications to query just the\ud
RDF data they want. However, servers hosting SPARQL endpoints often restrict\ud
access to the data by limiting the amount of results returned per query or the\ud
amount of queries per time that a client may issue. As this may affect query\ud
completeness when using SPARQL1.1's federated query extension, we analysed\ud
different strategies to implement federated queries with the goal to circumvent\ud
endpoint limits. We show that some seemingly intuitive methods for decomposing\ud
federated queries provide unsound results in the general case, and provide\ud
fixes or discuss under which restrictions these recipes are still applicable. Finally,\ud
we evaluate the proposed strategies for checking their feasibility in practice
Expressive and Scalable Query-Based Faceted Search over SPARQL Endpoints

International audienceLinked data is increasingly available through SPARQL endpoints, but exploration and question answering by regular Web users largely remain an open challenge. Users have to choose between the expressivity of formal languages such as SPARQL, and the usability of tools based on navigation and visualization. In a previous work, we have proposed Query-based Faceted Search (QFS) as a way to reconcile the expressivity of formal languages and the usability of faceted search. In this paper, we further reconcile QFS with scalability and portability by building QFS over SPARQL endpoints. We also improve expressivity and readability. Many SPARQL features are now covered: multidimensional queries, union, negation, optional, filters, aggregations, ordering. Queries are now verbalized in English, so that no knowledge of SPARQL is ever necessary. All of this is implemented in a portable Web application, Sparklis 1.1 , and has been evaluated on many endpoints and questions
Enriching Ontologies with Encyclopedic Background Knowledge for Document
  Indexing

The rapidly increasing number of scientific documents available publicly on
the Internet creates the challenge of efficiently organizing and indexing these
documents. Due to the time consuming and tedious nature of manual
classification and indexing, there is a need for better methods to automate
this process. This thesis proposes an approach which leverages encyclopedic
background knowledge for enriching domain-specific ontologies with textual and
structural information about the semantic vicinity of the ontologies' concepts.
The proposed approach aims to exploit this information for improving both
ontology-based methods for classifying and indexing documents and methods based
on supervised machine learning
Improved Filters for the Approximate Suffix-Prefix Overlap Problem

International audienceComputing suffix-prefix overlaps for a large collection of strings is a fundamental building block for the analysis of genomic next-generation sequencing data. The approximate suffix-prefix overlap problem is to find all pairs of strings from a given set such that a prefix of one string is similar to a suffix of the other. Välimäki et al. (Information and Com-putation, 2012) gave a solution to this problem based on suffix filters. In this work, we propose two improvements to the method of Välimäki et al. that reduce the running time of the computation
The Context-Freeness Problem Is coNP-Complete for Flat Counter Systems

International audienceBounded languages have recently proved to be an important class of languages for the analysis of Turing-powerful models. For instance, bounded context-free languages are used to under-approximate the behav-iors of recursive programs. Ginsburg and Spanier have shown in 1966 that a bounded language L ⊆ a * 1 · · · a * d is context-free if, and only if, its Parikh image is a stratifiable semilinear set. However, the question whether a semilinear set is stratifiable, hereafter called the stratifiability problem, was left open, and remains so. In this paper, we give a partial answer to this problem. We focus on semilinear sets that are given as finite systems of linear inequalities, and we show that stratifiability is coNP-complete in this case. Then, we apply our techniques to the context-freeness problem for flat counter systems, that asks whether the trace language of a counter system intersected with a bounded regular language is context-free. As main result of the paper, we show that this problem is coNP-complete
A bounded model checker for SPARK programs

This paper discusses the design and implementation of a bounded model checker for SPARK code, and provides a proof of concept of the utility and practicality of bounded verification for SPARK.This work is funded by ERDF - European Regional Development Fund through the COMPETE Programme (operational programme for competitiveness) and by National Funds through the FCT - Fundacão para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) within project FCOMP-01-0124-FEDER-020486