Reviewerly/exHarmony huggingface dataset:
Fast Analytical Rank Estimation

Rank estimation is an important tool for a side-channel evaluations laboratories. It allows estimating the remaining security after an attack has been performed, quantified as the time complexity and the memory consumption required to brute force the key given the leakages as probability distributions over d subkeys (usually key bytes). These estimations are particularly useful when the key is not reachable with exhaustive search. We propose a new framework for rank estimation that is conceptually simple, and more time and memory efficient than previous proposals. Our main idea is to bound each subkey distribution by an analytical function, and estimate the rank by a closed formula. To demonstrate the power of the framework, we instantiate it with Pareto-like functions to create the PRank algorithm. Pareto-like functions have long-tails that model empirical SCA distributions, and they are easily calculable. We evaluated the performance of PRank through extensive simulations based on two real SCA data corpora, and compared it to the currently-best histogram-based algorithm. We show that PRank gives a good rank estimation with much improved time and memory efficiency, especially for large ranks: For ranks between $$2^{80}-2^{100}$$ PRank estimation is at most 10 bits above the histogram rank and for ranks beyond $$2^{100}$$ the PRank estimation is only 4 bits above the histogram rank—yet it runs in milliseconds, and uses negligible memory. One could employ our framework with other classes of functions and possibly achieve even better results.
Investigating the Effects of Popularity Data on Predictive Relevance Judgments in Academic Search Systems

The elements of a surrogate serve as clues to relevance. They may be seen as operationalized relevance criteria by which users judge the relevance of a search result according to their information need. In addition to short textual summaries, today's academic search systems integrate additional data into their search results presentation, for example, the number of citations or the number of downloads. This kind of data can be described as popularity data, serving as factors also incorporated in search engines' ranking algorithms. Past research shows that there are diverse criteria and factors involved in relevance judgements from the user perspective. However, previous empirical studies on relevance criteria and clues examined surrogates that did not include popularity data. The goal of my doctoral research is to gain significant knowledge on the criteria by which users in an academic search situation make relevance judgements based on surrogates that include popularity data. This paper describes the current state of the experimental research design and method of data collection.
Advanced Monitoring System for Daily Activity in Elderly People

The purpose of this paper is to evaluate the use of accelerometers in the detection of daily motion activities as well as falls, applied to an intelligent home system for dependent adults, thus determining the effectiveness of the use of these technological components for Ambient Assisted Living (AAL) environments. The proposed system aims to get the pattern of daily motion activities such as walking, getting up from a chair or suffering a fall in different ways by means of an accelerometer plus a gyroscope in a low energy and low weight device, minimally invasive.
Mining Result Validation in the Ethereum Сryptocurrency

Cryptocurrency mining is a process of calculation of some hard-computational task. Ethereum cryptocurrency mining sets a task of determining a special block header value which is corresponds to requirements of cryptocurrency system. While doing computational jobs there are software computational errors which will lead to wrong mining task solution. To ensure mining correctness, the mining results should be validated. This paper describes reliable mining result validation methodology, which is not affecting mining performance.
Deep detection and classification of mitotic figures

Breast cancer is the second largest cause of cancer death among women after skin cancer. Mitotic count is an important biomarker for predicting the breast cancer prognosis according to Nottingham Grading System. Pathologists look for tumour areas and select 10 HPF(high power field) images and assign a grade based on the number of mitotic counts. Mitosis detection is a tedious task because the pathologist has to inspect a larger area. The pathologist’s views about mitotic cell are also subjective. Because of these problems, an assisting tool for the pathologist will generalize and reduce the time for diagnosis. Due to recent advancements in whole slide imaging, CAD(computer-aided diagnosis) systems are becoming popular. Mitosis detection for scanner images is difficult because of variability in shape, color, texture and its similar appearance to apoptotic nuclei, darkly stained nuclei structures. In this paper, the mitotic detection task is carried out with state of the art object detector (Faster R-CNN) and classifiers (Resnet152, Densenet169, and Densenet201) for ICPR 2012 dataset. The Faster R-CNN is used in two ways. In first, it was treated as an object detector which gave an F1-score of 0.79 while in second, it was treated as a Region Proposal Network followed by an ensemble of classifiers giving an F1-score 0.75.
Emergent synthesis approach for understanding the value of artifacts for individuals and society

The concept of “emergent synthesis” provides a useful guideline to understand the complexity of actual problems to be solved in a systematic way. However, it is intrinsically difficult to understand the value of artifacts for individuals and society because there are many aspects of value. This paper discusses how previous studies on emergent synthesis in several fields have approached various aspects of value. Then it proposes the strategy for analysis and synthesis of value of products and services in the internet of things era.
An Automated Text Classification Method: Using Improved Fuzzy Set Approach for Feature Selection

A well representing feature set that has enough differentiated power plays an important role in the classification. The existing techniques for feature set selection are mostly statistical. They are not flexible to incorporate the human reasoning and the changing requirements and preferences of the real-life systems. They only make a decision between a feature inclusion or exclusion. The fuzziness of human reasoning and thinking are not considered at all that may improve the feature selection and hence the accuracy of the classifier. Also, the selection of overlapping features in case of Local Feature Selection (LFS) methods is an important issue that negatively impacts classification accuracy. For example, in case of Odd Ratio (OR), the selection may contain overlapping features of multiple classes. In this paper, a Fuzzy Set Theory (FST) based feature selection method has been proposed. The approach aims to tackle both above mentioned issues efficiently. The selected final feature set is used to train the well-known classification algorithms and the results are compared with Global Feature Selection (GFS) and LFS methods. The comparison shows that the proposed method has improved the accuracy of the classifiers and also extract comparatively small feature set that ultimately reduces the time complexity of the system.
An API for Development of User-Defined Scheduling Algorithms in Aneka PaaS Cloud Software

Cloud computing provides resources using multitenant architecture where infrastructure is created from one or more distributed datacenters. Scheduling of applications in cloud infrastructures is one of the main research area in cloud computing. Researchers have developed many scheduling algorithms and evaluated them using simulators such as CloudSim. Their performance needs to be validated in real-time cloud environments to improve their usefulness. Aneka is one of the prominent PaaS software which allows users to develop cloud application using various programming models and underline infrastructure. This chapter presents a scheduling API developed for the Aneka software platform. Users can develop their own scheduling algorithms using this API and integrate it with Aneka to test their scheduling algorithms in real cloud environments. The proposed API provides all the required functionalities to integrate and schedule private, public, or hybrid cloud with the Aneka software.
Improving Text Models with Latent Feature Vector Representations

Probabilistic topic models are widely used to discover potential topics in a collection of documents, while latent feature vector representations have been used to achieve high performance in many NLP tasks. In this paper, we first make document topic vector representations by combining LDA and Topic2Vec, and then we perform document representations based on the topic vectors and the document vectors obtained through Doc2Vec training. Experimental results show that our new model has produced significant improvements in topic consistency and document classification tasks.
Parallel Algorithm for Precise Navigation Using Black-Box Forward Model and Motion Primitives

Reliable robot navigation is an active research topic for many real-world applications, such as the automation of industrial equipment, where machines with arbitrary shapes need to navigate very close to obstacles to perform efficiently. We have developed a new planning architecture that allows wheeled vehicles to navigate safely in cluttered environments. Our method belongs to the Model Predictive Control (MPC) family of local planning algorithms. It works in the space of two-dimensional occupancy grids and plans in motor command space using a black box forward model for state inference. Our method has several properties that make it well-suited for commercial applications: itis deterministic, computationally efficient, runs in constant time, and can be used on platforms of arbitrary shape and drive type. We provide a detailed description of the algorithm, showcase its application on real robots, and compare it with other state-of-the-art planning algorithms.
Applications of wavefront modulation devices in aspheric and freeform measurement

Wavefront modulation devices are of great significance in optical information processing systems. These devices capable of phase modulation are used in a variety of optical applications: wavefront correction, optical metrology, adaptive optics, aberration compensation, etc. The liquid crystal spatial light modulators (LC-SLMs) and deformable mirrors (DMs) have been regarded as the promising device for their flexibility and programmability on wavefront modulations. This paper presents applications of LC-SLMs and DMs when they are used as aberration compensators in testing of aspheric and freeform. Besides, a pixel-wise method based on analysis of the phase maps obtained by a Fizeau interferometer for calibrating the phase modulation characteristics of the LC-SLM is proposed. A PLUTO-VIS-020 LC-SLM produced by the Holoeye Company is employed in the calibrating experiment. A Zygo interferometer based on Fizeau interference theory is also employed. The experimental results demonstrate that the phase modulation characteristics of LC-SLM and a specific lookup table (LUT) for every pixel of the LC-SLM aperture can be obtained by utilizing the proposed method with convenience and high efficiency. The device calibrated in this paper provides a high phase shift up to 6π at 632.8nm wavelength and has a linearized phase distribution. It coincides well with the average modulation curve offered in the manual of the device. This paper provides a simple and accurate method for pixel-wise phase modulation characteristics calibration.
Ensembles of sparse classifiers for osteoporosis characterization in digital radiographs

The analysis and characterization of imaging patterns is a significant research area with several applications to biomedicine, remote sensing, homeland security, social networking, and numerous other domains. In this paper we study and develop mathematical methods and algorithms for disease diagnosis and tissue characterization. The central hypothesis is that we can predict the occurrence of diseases with a certain level of confidence using supervised learning techniques that we apply to medical imaging datasets that include healthy and diseased subjects. We develop methods for calculation of sparse representations to classify imaging patterns and we explore the advantages of this technique over traditional texture-based classification. We introduce integrative sparse classifier systems that utilize structural block decomposition to address difficulties caused by high dimensionality. We propose likelihood functions for classification and decision tuning strategies. We performed osteoporosis classification experiments on the TCB challenge dataset. TCB contains digital radiographs of the calcaneus trabecular bone of 87 healthy and 87 osteoporotic subjects. The scans of healthy and diseased subjects show little or no visual differences, and their density histograms have significant overlap. We applied 30-fold crossvalidation to evaluate the classification performances of our methods, and compared them to a texture based classification system. Our results show that ensemble sparse representations of imaging patterns provide very good separation between groups of healthy and diseased subjects and perform better than conventional sparse and texture-based techniques.
Refining background subtraction using consistent motion detection in adverse weather

Most background subtraction algorithms developed to detect moving objects are potentially problematic in that they experience performance degradation when weather conditions are adverse. We solve this problem by proposing a refinement method using a consistent motion detection method, the performance of which is robust to weather related changes in video images captured by a static camera. The proposed algorithm reduces the number of false-positive regions and fills parts that are missing as a result of the nature of the background subtraction methods. We show the extent of the improvement afforded by our algorithm in the handling of moving object detection in adverse weather conditions.
Analysis of mystical concepts in Khaghani’s Divan

Abstract Text analysis provides an exciting approach for extracting knowledge from text data. Recently, text analysis has been applied in many research fields. In this study, it was demonstrated that how text analysis can be applied to literary researches. All the lines of Khaghani’s Divan have been considered using different text analysis methods. Then the accuracy of the applied methods is compared.
Maximum Inter Class Variance Segmentation Algorithm Based on Decision Tree

In image segmentation, there are always some false targets which remain in the segmented image. As the grayscale values of these false targets are quite similar to the grayscale values of the targets of interest, it is very difficult to split them out. And because these false targets exist in the original image, which are not caused by noise or traditional filtering methods, such as median filtering, they cannot be eliminated effectively. It is important to analyze the characteristics of false targets, so the false targets can be removed. In addition, it should be noted that the targets of interest cannot be affected when the false targets are removed. In order to overcome above problems, a maximum inter-class variance segmentation algorithm based on a decision tree is proposed. In this method, the decision tree classification algorithm and the maximum inter-class variance segmentation algorithm are combined. First, the maximum inter-class variance algorithm is used to segment the image, and then a decision tree is constructed according to the attributes of regions in the segmented image. Finally, according to the decision tree, the regions of the segmented image are divided into three categories, including large target regions, small target regions and false target regions, so that the false target regions are removed. The proposed algorithm can eliminate the false targets and improve the segmentation accuracy effectively. In order to demonstrate the effectiveness of the algorithm proposed in this article, the proposed method is compared with some frequently used false target removal approaches. Experimental results show that the proposed algorithm can achieve better results than other algorithms.
Online video object segmentation via LRS representation

Video object segmentation has been extensively investigated in computer vision recently because of its wide range of applications. The key factor of the segmentation is the construction of the spatiotemporal coherence. Inaccurate motion approximation as a measurement of the coherence usually leads to an inaccurate segmentation result. To obtain an accurate segmentation result, a low‐rank sparse (LRS)‐based approach is proposed. Regarding each superpixel as an element, this algorithm has a good segmentation accuracy compared with other pixel‐level algorithms. Each element can be represented by the sparse linear combinations of dictionary templates, and this algorithm capitalises on the inherent low‐rank structure of representations that are learnt jointly. The represented coefficients construct an affinity matrix which measures the elements’ similarity between the current frame and the templates in the dictionary. For video object segmentation, a principled spatiotemporal objective function that uses LRS saliency term to propagate information between frames. Furthermore, an online parameter updating scheme is proposed to enhance the system's robustness. The online model propagates information forward without the need to access future frames. Evaluations on many challenging sequences demonstrate that the authors' approach outperforms the state‐of‐the‐art methods in terms of object segmentation accuracy.
Effective nuclei segmentation with sparse shape prior and dynamic occlusion constraint for glioblastoma pathology images

We propose a segmentation method for nuclei in glioblastoma histopathologic images based on a sparse shape prior guided variational level set framework. By spectral clustering and sparse coding, a set of shape priors is exploited to accommodate complicated shape variations. We automate the object contour initialization by a seed detection algorithm and deform contours by minimizing an energy functional that incorporates a shape term in a sparse shape prior representation, an adaptive contour occlusion penalty term, and a boundary term encouraging contours to converge to strong edges. As a result, our approach is able to deal with mutual occlusions and detect contours of multiple intersected nuclei simultaneously. Our method is applied to several whole-slide histopathologic image datasets for nuclei segmentation. The proposed method is compared with other state-of-the-art methods and demonstrates good accuracy for nuclei detection and segmentation, suggesting its promise to support biomedical image-based investigations.
Learning imbalanced semantic segmentation through cross-domain relations of multi-agent generative adversarial networks

Inspired by the recent success of generative adversarial networks (GANs), we propose a multi-agent GANs, named 3DJoinGANs, for handling imbalanced training data for the task of semantic segmentation. Our proposed method comprises two conditional GANs with four agents: a couple segmentors and a couple discriminators. The proposed framework learns a joint distribution of magnetic resonance images (MRI) and computed tomography images (CT) from different brain diseases by enforcing a weight-sharing constraint. While the first segmentor is trained on 3D multi-model MRI to learn semantic segmentation of a brain tumor(s), the first discriminator classifies whether predicted output by segmentor is real or fake. On the other hand, the second segmentor takes 3D multi modal CT images to learn segmentation of brain stroke lesions, and the second discriminator classifies between a segmented output by segmentor and a ground truth data annotated by an expert. We investigate, the 3DJoinGANs is able to mitigate imbalanced data problems and improve segmentation results due to oversampling and training through a joint distribution of cross-domain images.The proposed architecture has shown promising performance on the ISLES-2018 benchmark for segmentation of 3D multi modal ischemic stroke lesions and semantic segmentation of 3D multi modal brain tumors from the BraTS-2018 challenge.
Malarial Parasite Detection and Recognition using Microscopic Images

Malaria has been a serious infectious disease since 18th century. Manual diagnosis of malaria is most widely used method but it is a time consuming process, and it involves the risk of error due to the subjective assessment of the sample. In this paper, an automatic method involving image processing techniques is presented which is capable of detecting and recognizing the infection in the microscopic images. The images are generated by using a microscope with 800× zooming capacity. Giemsa staining is used before acquiring the images. Five different categories of malarial parasites are defined and used for classification. Image processing techniques are employed to initially detect a parasite and later to classify it as one of the target categories. Images belonging to three categories were classified perfectly, while one of the category received lower recognition rate. As a result, the proposed method produced 100% classification accuracy for four classes, and 60% for the remaining class. The algorithm developed for classification in hierarchical manner showed good results overall, considering the fact that no such research is available for local data.
Multi-structure elements morpholoy for improved anti-noise edge detection

High precision occupies an extremely important position in the field of mechanical processing and laser measurement. Under these high precision requirements, image processing is widely applied and is especially demanding. As the essential pre-processing step in the process of image processing, the quality of image edge extraction directly affects the processing precision of the whole image, and then affects the final measurement or machining precision. The traditional edge detection method has the defects of no noise immunity, and cannot achieve high-activity processing of sophisticated image edge problems. Through the in-depth research and analysis of various knowledges concerning edge extraction, a novel anti-noise edge detector based on multi-structure elements morphology of different directions for binary, gray scale and color images is proposed in this paper. We get the final edge information by using eight morphological operations respectively and synthetic weighted method. It can remove the noise effectively while detecting complete edge information. Experimental results show that, comparing with conventional edge detection operations, the proposed method attains the outcome of eliminating the image noise and maintaining good edge effectively for simulated image.
Cloud-Based Electronic Signature Authentication Issues

One of the main directions in today's sphere of information technologies is user convenience. In this regard, and not far behind the area of information security measures are taken to reduce the size of the equipment, to reduce the size of the memory application, increasing the speed of the application, while not lowering the level of security and without reducing the functionality of applications. In this paper, we consider a way to improve the user experience when working with the public key infrastructure by transferring some functions to a secure remote server.