nils-herrmann/clean_scirepeval huggingface dataset:
Image distortion analysis and classification scheme using a neural approach

Generally, IQMs are not able to well predict the image quality for all degradations. Indeed, well performance could be obtained for a given degradation and poor results for others. This is essentially due to the fact that the efficiency of IQMs depends highly on the degradation specificity. To overcome this limitation, we propose to first identify the type of degradation before measuring the quality of the image. The degradation classification is performed using an Artificial Neural Networks (ANN). Then, the most appropriate IQM could be used to estimate the image quality. The obtained results in terms of classification prove the efficiency of the proposed method. This scheme could provide a powerful image distortions recognition and classification that could be used in a image quality assessment system.
An image compression algorithm using reordered wavelet coefficients with compressive sensing

In this paper, we propose a new approach for image compression based on compressive sensing (CS). We introduce a new formulation of sparse vectors for rearranging multilevel 2-D Wavelet coefficients into a structured manner using parent-child relationships. We then use a Gaussian measurement matrix normalized with the weighted average Root Mean Squared (RMS) energies of different wavelet subbands. Compressed sampling is finally performed using this normalized measurement matrix. At the decoding stage, the image is reconstructed using a simple ℓ1-minimization technique. The proposed wavelet-based CS compression results in performance increase compared to other conventional CS-based techniques. Our experimental results show that the proposed algorithm outperforms existing approaches over different natural images.
Image Denoising in the Transformed Domain Using Non Local Neighborhoods

In this paper we address a denoising technique based on calculation of non local means through neighborhoods. Non local neighborhoods are computed in a transformed domain, namely the wavelet domain. A noisy image is transformed using a lifting scheme. The wavelet coefficients in each subband image are modelized by a generalized Gaussian distribution (GGD) whose parameters (scale and shape parameters) are estimated using an appropriate technique. The estimated parameters are used to define a generalized non local mean which allows us to restore the original image. Processing in the wavelet domain is suitable since image are often available in a compressed domain, beside, processing smaller images allows us to reduce the computational cost
A new fingerprint image compression based on wave atoms transform

During the last decade the emergence of many transforms called geometric wavelets have attracted much attention of researchers working on image analysis. These new transforms propose a new representation richer than the classical wavelets multiscale representation. This paper presents a comparative study of these transforms in order to determine what is the best transform dedicated to a particular type of image that is the fingerprint image. While we are not trying to compete with the current standard JPEG2000, which is dedicated to all types of images, we are aware that for a particular type of images, we can do better by choosing for this type of specific image, a more appropriate tool than classical wavelets. The results show that for fingerprint images the wave atom offers better performance than the current transform based compression standard.
Abnormal behavior detection using a multi-modal stochastic learning approach

This paper presents a new approach to trajectory-based abnormal behavior detection (ABD). While existing techniques include position in the feature vector, we propose to estimate the probability distribution locally at each position, hence reducing the dimensionality of the feature vector. Local information derived from accumulated knowledge for a particular position is integrated in the distribution enabling context-based decision for ABD. A stochastic competitive learning algorithm is employed to estimate the local distributions of the feature vector and the location of the distribution modes. The proposed algorithm is tested on the detection of driving under the influence of alcohol. The performance of the new algorithm is evaluated on synthetic data. First the local stochastic learning algorithm is compared to its global variant. Then it is compared to the Kohonen self organizing feature maps. In both cases, the proposed algorithm achieves higher detection rates (at the same false alarm rate) with fewer clusters.
A Grid Computing Approach to Subtraction Radiography

Digital subtraction is a common technique used in radiographic studies of periapical lesions and other dental disorders for which the treatment must be evaluated over time. This paper presents a fast and reliable parallel image registration method for subtracting two digitized radiographs where an unpredicted mismatch is present. An optimal affine transformation is found using an adaptive genetic algorithm (GA) as the optimization strategy and a correlation ratio as the similarity measure. The parallel GA implemented takes advantage of the CPU idle cycles of a computational grid, resulting in an application that exploits an existent infrastructure and with a computational time of about twenty seconds when processing pairs of standard intra-oral radiographs.
Classifying phishing URLs using recurrent neural networks

As the technical skills and costs associated with the deployment of phishing attacks decrease, we are witnessing an unprecedented level of scams that push the need for better methods to proactively detect phishing threats. In this work, we explored the use of URLs as input for machine learning models applied for phishing site prediction. In this way, we compared a feature-engineering approach followed by a random forest classifier against a novel method based on recurrent neural networks. We determined that the recurrent neural network approach provides an accuracy rate of 98.7% even without the need of manual feature creation, beating by 5% the random forest method. This means it is a scalable and fast-acting proactive detection system that does not require full content analysis.
Extracting Salient Brain Patterns for Imaging-Based Classification of Neurodegenerative Diseases

Neurodegenerative diseases comprise a wide variety of mental symptoms whose evolution is not directly related to the visual analysis made by radiologists, who can hardly quantify systematic differences. Moreover, automatic brain morphometric analyses, that do perform this quantification, contribute very little to the comprehension of the disease, i.e., many of these methods classify but they do not produce useful anatomo-functional correlations. This paper presents a new fully automatic image analysis method that reveals discriminative brain patterns associated to the presence of neurodegenerative diseases, mining systematic differences and therefore grading objectively any neurological disorder. This is accomplished by a fusion strategy that mixes together bottom-up and top-down information flows. Bottom-up information comes from a multiscale analysis of different image features, while the top-down stage includes learning and fusion strategies formulated as a max-margin multiple-kernel optimization problem. The capacity of finding discriminative anatomic patterns was evaluated using the Alzheimer's disease (AD) as the use case. The classification performance was assessed under different configurations of the proposed approach in two public brain magnetic resonance datasets (OASIS-MIRIAD) with patients diagnosed with AD, showing an improvement varying from 6.2% to 13% in the equal error rate measure, with respect to what has been reported by the feature-based morphometry strategy. In terms of the anatomical analysis, discriminant regions found by the proposed approach highly correlates to what has been reported in clinical studies of AD.
Data driven Vertical Total Electron Content workflow for GNSS positioning for single frequency receivers

Global Navigation Satellite Systems (GNSS) systems are largely affected by ionospheric effects, which are larger in low latitudes. Traditional mitigation approaches rely either on high cost equipment or simplified models that have shown limitations for the complex ionospheric dynamics of the equatorial region. Data availability from increasingly larger GNSS receiver networks, together with scalable machine learning techniques, provides the foundation to approach this problem from a data driven perspective. This paper shows the usage of data driven models to predict the Vertical Total Electron Content (VTEC) of the ionosphere based on pseudoranges measurements and, thus, improve GNSS positioning using single frequency receivers especially in low latitudes. Our results reduce by more than 60% the error in VTEC prediction as compared to the Klobuchar model used by GPS, achieving an average positioning residual error below 1 meter, both in medium and low latitudes and in periods of high and low solar activity.
A necklace sonar with adjustable scope range for assisting the visually impaired

A sonar based device with tactile feedback was developed to improve the mobility and independence of visually impaired individuals. It features a transceiver/receiver, a potentiometer, a microcontroller, a rechargeable polymer lithium ion battery, and a Nokia Cell phone vibrator. All components are commercially available and housed in a custom acrylic package with 86 mm × 34 mm × 12 mm in dimension, and 120 grms in weight. Additionally, the device features an adjustable detection scheme for user customization of distance range, and a tactile feedback system that avoids interference with auditory sensory information. The device was tested for its navigational efficacy in an artificial indoor environment, and in a live outdoor setting. Ten subjects (9 males and 1 female), with a mean age of 35 years-old (range: 17 to 52) were presented with a series of navigational tasks resulting in considerable reduction of head, shoulder, chest, and arms collisions during their locomotion. We conclude that this device greatly improves the mobility and safety of visually impaired individuals.
Multimodal Image Annotation Using Non-negative Matrix Factorization

Visual content has become an important component of the web. In many cases, visual content is mixed with other modalities (e.g. text) that can be exploited to extract information and knowledge. This paper presents a strategy for mining multimodal visual content. The strategy encompasses two main components: a rich representation of the multimodal objects and a model for automatically annotating unannotated images. The proposed method has two distinguishing characteristics: it uses a bag-of-features representation for images and a non-negative matrix factorization algorithm to build a latent representation.
A framework for high performance image analysis pipelines

This paper describes the software framework being developed to enable the execution of large-scale image analysis pipelines. Images are analyzed through algorithms (feature extraction, annotation, classification, etc.) assembled into processing pipelines and managed by the framework to be run onto the available computing resources, whether cloud, opportunistic or dedicated clusters. Underneath, we use Google's big table storage model for image sources and metadata, showing both flexibility and performance. Additionally, our architecture provides a clear separation between framework developers, providers of algorithms and experimenters, enabling the organization of teams and software repositories to ensure its organizational sustainability in the long term. Altogether, we integrate best practices in pattern recognition, software engineering and high performance computing to enable large scale experiments in image analysis. We herewith describe the framework and present preliminary results demonstrating its scalability and ease of use.
Combining negative selection and classification techniques for anomaly detection

This paper presents a novel approach inspired by the immune system that allows the application of conventional classification algorithms to perform anomaly detection. This approach appears to be very useful where only positive samples are available to train an anomaly detection system. The proposed approach uses the positive samples to generate negative samples that are used as training data for a classification algorithm. In particular, the algorithm produces fuzzy characterization of the normal (or abnormal) space. This allows it to assign a degree of normalcy, represented by membership value, to elements of the space.
ZETEMA: A web service for automatic short-answer questions grading

ZETEMA is a prototype of a web service that allows a user to train an automated system for scoring short-answer questions, and use it for scoring new student answers. In addition, the service provides already-trained models based on standard benchmarks such as SciEntsBank and Beetle, used in SemEval 2013 Student Response Analysis challenge. The service is based on the system that obtained the best results in that competition (soft cardinality) and includes some improvements in time, simplifications in the model preserving performance. ZETEMA was developed in Python and can be used via a web interface using XML format for sending, training and testing texts, and also for retrieving result.
Page clustering using a distance based algorithm

This paper presents an application of a clustering algorithm based on gravitational forces to the problem of Web page clustering in a dynamic environment. The proposed algorithm uses a modification of the gravitational algorithm proposed by Gomez et al. but using only the distance measures (a notion of space is not required). This approach is useful when similarities (and/or then distances) between pages can be defined and compute quickly, but the definition of a space is computationally expensive. Experiments with data representing real URL's and sessions are performed, and a comparison with the incremental connected components algorithm, which has been previously used to solve this problem, is done.
Multimodal image collection summarization using non-negative matrix factorization

The huge amount of biomedical images that are produced every day require of suitable methods to access them in an efficient and effective way. Although there has been an important development in methods to search large information repositories, these methods have been mainly focused on text data, and less work has been devoted to non-text data such as images and video. This paper presents a new method that combines text and visual information in the same latent representation space in which images and text are jointly represented. We also investigate how to select the most representative elements of the collection to build an image collection summary. The proposed method was applied to a collection of histological images and the results where evaluated both qualitatively and quantitatively by an expert. The initial results show that the proposed method is able to build a meaningful summary that can be integrated in an image collection exploration system.
An immunity-based technique to characterize intrusions in computer networks

This paper presents a technique inspired by the negative selection mechanism of the immune system that can detect foreign patterns in the complement (nonself) space. In particular, the novel pattern detectors (in the complement space) are evolved using a genetic search, which could differentiate varying degrees of abnormality in network traffic. The paper demonstrates the usefulness of such a technique to detect a wide variety of intrusive activities on networked computers. We also used a positive characterization method based on a nearest-neighbor classification. Experiments are performed using intrusion detection data sets and tested for validation. Some results are reported along with analysis and concluding remarks.
An evaluation of NMF algorithm on human action video retrieval

Human action video retrieval is a useful tool for video surveillance and sports video analysis, among other applications. Previous work on image retrieval tasks has shown that latent semantic methods are an effective way to build a high-level representation of data to discover implicit relations between visual patterns, achieving a significant improvement on these tasks. The current paper evaluates the applicability of Non-Negative Matrix Factorization (NMF), a latent semantic method, on human action video retrieval. Experiments are carried out on common human action recognition datasets using state-of-the-art descriptors. We focus on evaluating the query by example approach i.e. only videos are used as queries. The performance of the method is compared against classic direct matching between video features.
Convolutional neural networks for mammography mass lesion classification

Feature extraction is a fundamental step when mammography image analysis is addressed using learning based approaches. Traditionally, problem dependent handcrafted features are used to represent the content of images. An alternative approach successfully applied in other domains is the use of neural networks to automatically discover good features. This work presents an evaluation of convolutional neural networks to learn features for mammography mass lesions before feeding them to a classification stage. Experimental results showed that this approach is a suitable strategy outperforming the state-of-the-art representation from 79.9% to 86% in terms of area under the ROC curve.
Self-Regulated Learning in a Computer Programming Course

This paper contains an exploratory study on self-regulation by a group of students during a second-year computer programming course (data structures). Self-regulation was measured through the implementation of an instrument that evaluates motivation and learning strategies. From the results of the implementation, the relation between self-regulation and academic performance among the students was studied through a correlation analysis and other multivariate analyses. The results show new empirical pieces of evidence on the role played by self-regulation within the educational context of computer programming, and suggest which combination of factors allows to discriminate the population subgroups between high and low academic performances, according to self-regulated learning variables.
Multimodal visualization based on latent topic analysis

Image collection visualization is an important component of exploration-based image retrieval systems. In this paper we address the problem of generating an image collection visualization in which images and text can be projected together. Given a collection of images with attached text annotations, our aim is to find a common representation for both information sources to model latent correlations among the collection. Using the proposed latent representation, an image collection visualization is built, in which both data modalities (images and text) can be projected simultaneously. The resulting collection visualization allows to identify the relationships between images and text terms, enabling a better understanding of the collection semantic structure. The resulting visualization scheme can be used as the core metaphor in an interactive image exploration system.
A kernel-based strategy for exploratory image collection search

This paper proposes a strategy to interactively explore large collections of images. The strategy is based on kernel methods, which offer a mathematically strong framework to address each stage of an exploratory image collection system: image representation, similarity function calculation, summarization, visualization and exploration. This work also proposes a dual form of the well-known Rocchio's algorithm in order to learn from user's feedback. Experiments were performed with real users in order to verify the effectiveness and efficiency of the proposed strategy.
OCT-NET: A convolutional network for automatic classification of normal and diabetic macular edema using sd-oct volumes

Diabetic macular edema (DME) is one of the most common eye complication caused by diabetes mellitus, resulting in partial or total loss of vision. Optical Coherence Tomography (OCT) volumes have been widely used to diagnose different eye diseases, thanks to their sensitivity to represent small amounts of fluid, thickness between layers and swelling. However, the lack of tools for automatic image analysis for supporting disease diagnosis is still a problem. Convolutional neural networks (CNNs) have shown outstanding performance when applied to several medical images analysis tasks. This paper presents a model, OCT-NET, based on a CNN for the automatic classification of OCT volumes. The model was evaluated on a dataset of OCT volumes for DME diagnosis using a leave-one-out cross-validation strategy obtaining an accuracy, sensitivity, and specificity of 93.75%.
A framework for semantic analysis of histopathological images using nonnegative matrix factorization

This paper presents a novel and general framework for histopathology image analysis using nonnegative matrix factorization. The proposed method uses a collection-based image representation called Bag of Features (BOF) to represents the visual information of a histopathology image collection. Convex Nonnegative Matrix Factorization (CNMF) is applied to a training set of images to find a compact representation in a latent topic space. The latent representation has two important characteristics: first, CNMF is able to find representative clusters of images in the collection, second, clusters are represented by convex linear combinations of images in the training set. This latent representation is exploited in different ways by the proposed framework: concept labels can be assigned to clusters using the labels of the constituting images, representative images and visual words can be identified for each cluster, and new unlabeled images can be labeled by mapping them to the latent space. The proposed annotation model has an interesting property, it is easily interpretable since it is possible to trace those visual words present in the image which contribute the most to a given annotation. This implies that annotations in an image may be explained by identifying the regions that contributed to them. An exploratory experimentation was performed in a histopathology dataset used to diagnose a type of skin cancer called basal cell carcinoma. The preliminary results show that the combination of BOF and NMF is an interesting alternative for biomedical image collection analysis with a high level of interpretability.
MICS: Multimodal image collection summarization by optimal reconstruction subset selection

This paper presents a new method to automatically select a set of representative images from a larger set of retrieved images for a given query. We define an image collection summary as a subset of images from the collection, which are visually and semantically representative. To build such a summary we propose MICS, a method that fuses two modalities, textual and visual, in a common latent space, and use it to find a subset of images from which the collection visual content could be reconstructed. We conducted experiments on a collection of tagged images and demonstrate the ability of our approach to build summaries with representative visual and semantic content. The initial results show that the proposed method is able to build a meaningful summary that can be integrated in an image collection exploration system.