Reviewerly/exHarmony huggingface dataset:
Semantically Guided Large Deformation Estimation with Deep Networks

Deformable image registration is still a challenge when the considered images have strong variations in appearance and large initial misalignment. A huge performance gap currently remains for fast-moving regions in videos or strong deformations of natural objects. We present a new semantically guided and two-step deep deformation network that is particularly well suited for the estimation of large deformations. We combine a U-Net architecture that is weakly supervised with segmentation information to extract semantically meaningful features with multiple stages of nonrigid spatial transformer networks parameterized with low-dimensional B-spline deformations. Combining alignment loss and semantic loss functions together with a regularization penalty to obtain smooth and plausible deformations, we achieve superior results in terms of alignment quality compared to previous approaches that have only considered a label-driven alignment loss. Our network model advances the state of the art for inter-subject face part alignment and motion tracking in medical cardiac magnetic resonance imaging (MRI) sequences in comparison to the FlowNet and Label-Reg, two recent deep-learning registration frameworks. The models are compact, very fast in inference, and demonstrate clear potential for a variety of challenging tracking and/or alignment tasks in computer vision and medical image analysis.
The effect of user characteristics in time series visualizations

There is increasing evidence that user characteristics can have a significant impact on visualization effectiveness, suggesting that visualizations could be enriched with personalization mechanisms that better fit each user's specific needs and abilities. In this paper, we contribute to this body of work with a study that investigates the impact of six user characteristics on the effectiveness of time series visualizations, which was not previously investigated in relation to personalizing Information visualization. We report on a controlled user study that compare four possible time series visualization techniques. User performance and how it was affected by user characteristics was measured while performing tasks from a formal taxonomy using Twitter data about real-world events. Our results show that both the personality trait of locus of control and the cognitive ability of verbal working memory influence which visualization is more effective when dealing with demanding and complex tasks. These findings extend the need for personalization to visualizations for time series data, and we discuss them in the context of creating systems that can utilize knowledge of the user's specific characteristics in order to present the most suitable visualization for each user.
Statistical distributions and reliability functions with type-2 fuzzy parameters

Type-2 fuzzy sets were initially given by Zadeh as an extension of type-1 fuzzy sets. There is a growing interest in type-2 fuzzy set and its memberships (named secondary memberships) to handle the uncertainty in type-1 fuzzy set and its primary membership values. However, arithmetical operators on type-2 fuzzy sets have computational complexity due to third dimension of these sets. In this study, we present some mathematical operators which can be easily applied to type-2 fuzzy sets and numbers. Also, mathematical functions of type-2 fuzzy numbers are given according to their monotonicity. These functions are adapted to reliability and distribution functions of the random variables with the type-2 fuzzy parameters. These functions are applied to Exponential, Chi-square, Weibull distributions with respect to monotonicity of the parameters of these distributions.
An Evaluation of Four Resampling Methods Used in Machine Learning Classification

This article investigates resampling methods used to evaluate the performance of machine learning classification algorithms. It compares four key resampling methods: 1) Monte Carlo resampling, 2) the Bootstrap Method, 3) k-fold Cross Validation, and 4) Repeated k-fold Cross Validation. Two classification algorithms, Support Vector Machines and Random Forests, applied to three datasets, are used in this article. Nine variations of the four resampling methods are used to tune parameters on the two classification algorithms on each of the three datasets. Performance is defined by how well the resampling method chooses a parameter value that fits the data well. A main finding is that Repeated k-fold Cross Validation, overall, outperforms the other resampling methods in selecting the best-fit parameter value across the three different datasets.
Consensus Monte Carlo for Random Subsets Using Shared Anchors

We present a consensus Monte Carlo algorithm that scales existing Bayesian nonparametric models for clustering and feature allocation to big data. The algorithm is valid for any prior on random subsets such as partitions and latent feature allocation, under essentially any sampling model. Motivated by three case studies, we focus on clustering induced by a Dirichlet process mixture sampling model, inference under an Indian buffet process prior with a binomial sampling model, and with a categorical sampling model. We assess the proposed algorithm with simulation studies and show results for inference with three datasets: an MNIST image dataset, a dataset of pancreatic cancer mutations, and a large set of electronic health records. Supplementary materials for this article are available online.
Differentially Private Distributed Learning

The rich data used to train learning models increasingly tend to be distributed and private. It is important to efficiently perform learning tasks without compromising individual users’ privacy even considering untrusted learning applications and, furthermore, understand how privacy-preservation mechanisms impact the learning process. To address the problem, we design a differentially private distributed algorithm based on the stochastic variance reduced gradient (SVRG) algorithm, which prevents the learning server from accessing and inferring private training data with a theoretical guarantee. We quantify the impact of the adopted privacy-preservation measure on the learning process in terms of convergence rate, by which it indicates noises added at each gradient update results in a bounded deviation from the optimum. To further evaluate the impact on the trained models, we compare the proposed algorithm with SVRG and stochastic gradient descent using logistic regression and neural nets. The experimental results on benchmark data sets show that the proposed algorithm has minor impact on the accuracy of trained models under a moderate amount of privacy budget.
Robust Facial Landmark Detection via Aggregation on Geometrically Manipulated Faces

In this work, we present a practical approach to the problem of facial landmark detection. The proposed method can deal with large shape and appearance variations under the rich shape deformation. To handle the shape variations we equip our method with the aggregation of manipulated face images. The proposed framework generates different manipulated faces using only one given face image. The approach utilizes the fact that small but carefully crafted geometric manipulation in the input domain can fool deep face recognition models. We propose three different approaches to generate manipulated faces in which two of them perform the manipulations via adversarial attacks and the other one uses known transformations. Aggregating the manipulated faces provides a more robust landmark detection approach which is able to capture more important deformations and variations of the face shapes. Our approach is demonstrated its superiority compared to the state-of-the-art method on benchmark datasets AFLW, 300-W, and COFW.
Biomedical signals interpolation spline models

This article is devoted to the study of the properties of Biomedicine signals and the construction of splayn models, which are considered important in signal processing. The cube spline models signals of biomedicine interpretive high rate of accuracy, If this is the case, treating infected patients can make the right decision as appropriate. As an example, it was taken initial experimental information of ECG signals and based on this information, a local interpolation cubic spline model was constructed. The process of interpolation of ECG signal carried out with the help of this model.
Semantic Segmentation of Multispectral Images using Res-Seg-net Model

Semantic segmentation is pixel-wise labeling of the image. Recently deep convolutional neural network (DCNN) providing progressive results in semantic segmentation. However, in remote sensing multispectral imagery very limited work has been done due to lack of training dataset. In this paper, a Res-Seg-net model is proposed for the semantic segmentation which is motivated by the existing Resnet and Segnet models. This model consists of encoder-decoder parts in which residual mapping is followed. For validation and testing of the proposed model, the RIT-18 dataset of multispectral imagery is used. The comparison results of the experiment on a multispectral imagery dataset have demonstrated the effectiveness of the proposed model.
Spam SMS Filtering using Recurrent Neural Network and Long Short Term Memory

SMS is the abbreviation for Short Messaging Service which uses standard protocols for mobile devices to exchange information via short text messages. Today SMS's are an easy, inexpensive and widely accepted way to communicate rather than phone calls. Spam can be described as random unsolicited messages sent at large without any authorization from the receiver. People still deal with spammer's misusing SMS's to advertise false claims and can gain access to private information of users. Emails, social media sites, review, and even Twitter has seen spammers trying to intrude everywhere with the advent of Internet. Spam appears in many forms like comments, emails, search results and personal messages where spammers tend to gain revenues. Various Machine Learning algorithms like Neural Networks have tried to detect spam messages and normal messages or ham from SMS's. These techniques can learn high level features automatically using raw data unlike traditional ways where features are selected after analysis for classification. In this research paper, we propose a new method utilizing Recurrent Neural Network (RNN) and Long Short Term Memory (LSTM) using Keras models and Tensorflow backend to detect Spam and Ham from `SpamSMSCollection' dataset available at UCI machine learning repository. Crucial preprocessing of dataset included tokenization, TF-IDF Vectorization and removal of stopwords. Overall accuracy of 98% is achieved and shows improvement from other machine learning algorithms for spam detection.
Protection of Sensitive Data in Zero Trust Model

A novel model is presented in this paper to provide access control to sensitive data in Zero Trust Model (ZTM). In ZTM, there is no default trust for internal or external parties in the network. In this model, it is considered that threat to sensitive data may arise from inside and outside of the militarized zone. Outside locations include internet and cloud across a corporate network. Users from all locations may request access for the sensitive data. An access control proxy is introduced in this model to protect the sensitive data by controlling the access by analyzing access request, user type, device type, application type and data type. Accordingly new policies are implemented in ZTM to protect sensitive data. This model is found very effective in enhancing the security of sensitive data from unauthorized access and manipulation.
A novel approach to text clustering using genetic algorithm based on the nearest neighbour heuristic

In this paper, we propose a novel clustering algorithm which uses a weighted combination of several criteria as its fitness function. We demonstrate the suitability of the new method in the case of clustering text documents. The proposed algorithm leverages the concept of nearest neighbour separation (NNS) to enhance the separation of the clusters and also outlines a heuristic to compute the NNS. A new parameterized fitness function has been proposed which can be tuned to provide more weightage to the traditional metrics based on inter- and intra-cluster distances of clusters or on the NNS. Genetic Algorithm has been used to perform the actual clustering and the results obtained has been compared with the traditional K-Means algorithm. The performance of the algorithm has been tested on different standard datasets, and the results have been presented.
An Improved Method for the Calibration of a 2-D LiDAR With Respect to a Camera by Using a Checkerboard Target

This paper proposes an improved method for the calibration of a 2D LiDAR or a planar scanning laser rangefinder with respect to a camera, which uses the same checkerboard target as in the camera calibration. To address one of the drawbacks in the LiDAR calibration with a checkerboard, the sensitivity to checkerboard poses, laser points on the checkerboard are obtained from both the originally positioned LiDAR and the same LiDAR vertically rotated from the original position, in the proposed calibration. They construct the additional constraints with the normal vector of the checkerboard, and strengthen the basic point-on-plane constraints in the LiDAR calibration with a checkerboard. However, in practice, the LiDAR rotation can be imprecise. Thus, we further develop the calibration procedure and examine its calibration performance, in the presence of such impreciseness in the LiDAR rotation. A performance analysis is conducted with synthetic noisy data, and it is confirmed that the proposed calibration yields smaller error in the parameter estimation than a conventional baseline calibration method. Subsequently, we examine the calibration performance with real data, where the proposed calibration is carried out by building a calibration system. It is shown that the proposed calibration also has more consistency in the parameter estimation. For further study, we compare the proposed calibration based on point-on-plane constraints with a calibration based on point-to-point constraints, and show that the proposed calibration, with easier data acquisition, yields comparable results.
Fine-grained analysis of language varieties and demographics

Abstract The rise of social media empowers people to interact and communicate with anyone anywhere in the world. The possibility of being anonymous avoids censorship and enables freedom of expression. Nevertheless, this anonymity might lead to cybersecurity issues, such as opinion spam, sexual harassment, incitement to hatred or even terrorism propaganda. In such cases, there is a need to know more about the anonymous users and this could be useful in several domains beyond security and forensics such as marketing, for example. In this paper, we focus on a fine-grained analysis of language varieties while considering also the authors’ demographics. We present a Low-Dimensionality Statistical Embedding method to represent text documents. We compared the performance of this method with the best performing teams in the Author Profiling task at PAN 2017. We obtained an average accuracy of 92.08% versus 91.84% for the best performing team at PAN 2017. We also analyse the relationship of the language variety identification with the authors’ gender. Furthermore, we applied our proposed method to a more fine-grained annotated corpus of Arabic varieties covering 22 Arab countries and obtained an overall accuracy of 88.89%. We have also investigated the effect of the authors’ age and gender on the identification of the different Arabic varieties, as well as the effect of the corpus size on the performance of our method.
Evaluation of Power Insulator Detection Efficiency with the Use of Limited Training Dataset

This article presents an analysis of the effectiveness of object detection in digital images with the application of a limited quantity of input. The possibility of using a limited set of learning data was achieved by developing a detailed scenario of the task, which strictly defined the conditions of detector operation in the considered case of a convolutional neural network. The described solution utilizes known architectures of deep neural networks in the process of learning and object detection. The article presents comparisons of results from detecting the most popular deep neural networks while maintaining a limited training set composed of a specific number of selected images from diagnostic video. The analyzed input material was recorded during an inspection flight conducted along high-voltage lines. The object detector was built for a power insulator. The main contribution of the presented papier is the evidence that a limited training set (in our case, just 60 training frames) could be used for object detection, assuming an outdoor scenario with low variability of environmental conditions. The decision of which network will generate the best result for such a limited training set is not a trivial task. Conducted research suggests that the deep neural networks will achieve different levels of effectiveness depending on the amount of training data. The most beneficial results were obtained for two convolutional neural networks: the faster region-convolutional neural network (faster R-CNN) and the region-based fully convolutional network (R-FCN). Faster R-CNN reached the highest AP (average precision) at a level of 0.8 for 60 frames. The R-FCN model gained a worse AP result; however, it can be noted that the relationship between the number of input samples and the obtained results has a significantly lower influence than in the case of other CNN models, which, in the authors’ assessment, is a desired feature in the case of a limited training set.
Ensemble Learning on Deep Neural Networks for Image Caption Generation

Capturing the information in an image into a natural language sentence is considered a difficult problem to be solved by computers. Image captioning involves not just detecting objects from images but understanding the interactions between the objects to be translated into relevant captions. So, expertise in the field of computer vision paired with natural language processing is crucial for this purpose. The sequence to sequence modelling strategy of deep neural networks is the traditional approach to generate a sequential list of words that are combined to represent the image. But these models suffer from the problem of high variance by not being able to generalize well on the training data. The main focus of this paper is to reduce the variance factor that will help in generating better captions. To achieve this, Ensemble Learning techniques have been explored, which have the reputation of solving the high variance problem that occurs in machine learning algorithms. Three different ensemble techniques namely, k-fold ensemble, bootstrap aggregation ensemble and boosting ensemble have been evaluated in our work. For each of these techniques, three output combination approaches have been analyzed. Extensive experiments have been conducted on the Flickr8k dataset which has a collection of 8000 images and 5 different captions for every image. The bleu score performance metric, which is considered to be the standard for evaluating natural language processing (NLP) problems, is used to evaluate the predictions. Based on this metric, the analysis shows that ensemble learning performs significantly better and generates more meaningful captions compared to any of the individual models used.
A Novel Hybridized Strategy for Machine Translation of Indian Languages

Only a few translation systems exist for translations between an Aryan language and a Dravidian language, and moreover, the existing system does not perform quite efficiently. In this paper, the cognitive gap between two Indian languages namely Hindi and Malayalam is bridged. This paper proposes a Hindi to Malayalam machine translation system using hybridized strategies such as phrase-based translation, word alignment, and a language model with the emphasis on transition probability computation. Phrase-based translation breaks down a sentence into phrases and translates each phrase independently. This technique is applied on a Hindi–Malayalam parallel corpus. The proposed model interoperates between Hindi and Malayalam Languages. Although standard natural language computing techniques are encompassed, the arrangement of the techniques to suit a pair of Indian languages that are semantically incompliant and achieving a high accuracy is definitely a challenge and is achieved in this paper. The proposed hybridized approach outperforms all the other existing strategies and yields an average precision of 90.725% with a low word error rate of 9.125.
DNS Tunneling Detection with Supervised Learning

This paper presents an advanced analytic capable of detecting general DNS tunneling behavior with high precision and recall. It explores the application of supervised machine learning to a recently introduced technique for analyzing DNS traffic: classifying primary domains instead of queries. This approach is enabled by a partially synthetic dataset generated with a structurally configurable DNS tunneling tool.
dislib: Large Scale High Performance Machine Learning in Python

In recent years, machine learning has proven to be an extremely useful tool for extracting knowledge from data. This can be leveraged in numerous research areas, such as genomics, earth sciences, and astrophysics, to gain valuable insight. At the same time, Python has become one of the most popular programming languages among researchers due to its high productivity and rich ecosystem. Unfortunately, existing machine learning libraries for Python do not scale to large data sets, are hard to use by non-experts, and are difficult to set up in high performance computing clusters. These limitations have prevented scientists to exploit the full potential of machine learning in their research. In this paper, we present and evaluate dislib, a distributed machine learning library on top of PyCOMPSs programming model that addresses the issues of other existing libraries. In our evaluation, we show that dislib can be up to 9 times faster, and can process data sets up to 16 times larger than other popular distributed machine learning libraries, such as MLlib. In addition to this, we also show how dislib can be used to reduce the computation time of a real scientific application from 18 hours to 17 minutes.