Reviewerly/exHarmony huggingface dataset:
Calculation the Measure of Expert Opinions Consistency Based on Social Profile Using Inductive Algorithms

The measure of expert opinions consistency is an essential component for building ranking models based on user ratings. The article describes a model building method for calculating the measure of opinions consistency for potential experts relative to the current user in the case when it is impossible to apply methods based on their past activities analysis. In the case when the current user does not have common evaluations with potential experts for some set of web resources, the measure of their opinions consistency can be calculated on the basis of social and personal factors. The article analyzes the results of constructing such models using various inductive modeling algorithms and neural network.
Accurate Bayesian Data Classification Without Hyperparameter Cross-Validation

We extend the standard Bayesian multivariate Gaussian generative data classifier by considering a generalization of the conjugate, normal-Wishart prior distribution, and by deriving the hyperparameters analytically via evidence maximization. The behaviour of the optimal hyperparameters is explored in the high-dimensional data regime. The classification accuracy of the resulting generalized model is competitive with state-of-the art Bayesian discriminant analysis methods, but without the usual computational burden of cross-validation.
Regular Expression Search on Compressed Text

We present an algorithm for searching regular expression matches in compressed text.The algorithm reports the number of matching lines in the uncompressed text in time linear in the size of its compressed version.We define efficient data structures that yield nearly optimal complexity bounds and provide a sequential implementation -zearch-that requires up to 25% less time than the state of the art.
The complexity of AND—decomposition of Boolean functions

We consider the complexity of decomposing a Boolean function into a conjunction of components, which may share a (possibly empty) given set of variables Δ. Boolean functions are given as expressions in CNF, DNF, full DNF, and ANF and it is assumed that decomposition components must be in the same normal form as the input expression. We show that deciding decomposability is in general intractable for CNF and DNF already for the empty set of shared variables between the components. On the other hand, we demonstrate that for positive CNF and DNF and full DNF, the finest decomposition components wrt a given Δ can be computed in polynomial time (in the size of the input expression given as a string). The tractability results employ an efficient transformation of input expressions into series of expressions in ANFs aka Boolean polynomials, for which we provide a polynomial time factorization algorithm. Since the reduction leads to solving series of factorization tasks for Boolean polynomials, we discuss a number of ideas on how to optimize massive factorization and report preliminary experimental results.
Piercing numbers in approval voting

We survey a host of results from discrete geometry that have bearing on the analysis of geometric models of approval voting. Such models view the political spectrum as a geometric space, with geometric constraints on voter preferences. Results on piercing numbers then have a natural interpretation in voting theory, and we survey their implications for various classes of geometric constraints on voter approval sets.
An Optimal Service Composition Algorithm in Multi-Cloud Environment

Major part of internet users are devices which are connected to each other on the internet and are exchanging data with internet brokers to receive requested services. Managing and accounting well to IoT requests needs maximum processing power, speed in data transfer and proper combining services in minimum time. This many devices in IoT, made solving problems in this area to use abilities and facilities of cloud environment. Hence combining services in cloud environment is paid attention recently. In this research we want to give an algorithm with approach of improving factors propounded in the problem combining service composition problem like number of clouds involved in giving services, number of services studied before fulfilling users requests and load balance between clouds. In this paper we use the factor, similarity measure, to find the most suitable cloud and composition plan in each phase which in addition to improving QoS metrics propounded in previous papers, it caused improving QoS metric of load balancing between clouds, prevention of formation of bottleneck in clouds entrance, decreasing the probability of temporarily failing of any of clouds and consequently increasing the users' satisfaction.
Atrial Scar Segmentation via Potential Learning in the Graph-Cut Framework

Late Gadolinium Enhancement Magnetic Resonance Imaging (LGE MRI) emerges as a routine scan for patients with atrial fibrillation (AF). However, due to the low image quality automating the quantification and analysis of the atrial scars is challenging. In this study, we proposed a fully automated method based on the graph-cut framework, where the potential of the graph is learned on a surface mesh of the left atrium (LA), using an equidistant projection and a deep neural network (DNN). For validation, we employed 100 datasets with manual delineation. The results showed that the performance of the proposed method was improved and converged with respect to the increased size of training patches, which provide important features of the structural and texture information learned by the DNN. The segmentation could be further improved when the contribution from the t-link and n-link is balanced, thanks to the inter-relationship learned by the DNN for the graph-cut algorithm. Compared with the existing methods which mostly acquired an initialization from manual delineation of the LA or LA wall, our method is fully automated and has demonstrated great potentials in tackling this task. The accuracy of quantifying the LA scars using the proposed method was 0.822, and the Dice score was 0.566. The results are promising and the method can be useful in diagnosis and prognosis of AF.
Document vectorization method using network information of words

We propose a new method for vectorizing a document using the relational characteristics of the words in the document. For the relational characteristics, we use two types of relational information of a word: 1) the centrality measures of the word and 2) the number of times that the word is used with other words in the document. We propose these methods mainly because information regarding the relations of a word to other words in the document are likely to better represent the unique characteristics of the document than the frequency-based methods (e.g., term frequency and term frequency-inverse document frequency). In experiments using a corpus consisting of 14 documents pertaining to four different topics, the results of clustering analysis using cosine similarities between vectors of relational information for words were comparable to (and more accurate than in some cases) those obtained using vectors of frequency-based methods. The clustering analysis using vectors of tie weights between words yielded the most accurate result. Although the results obtained for the small dataset used in this study can hardly be generalized, they suggest that at least in some cases, vectorization of a document using the relational characteristics of the words can provide more accurate results than the frequency-based vectors.
Sensor fusion for creating a three-dimensional model for mobile robot navigation

This article deals with the design of an automated system for creating a three-dimensional model of the environment with its texture. The method for creating a three-dimensional model of the environment is based on the use of a two-dimensional scanner for which the supporting hardware has been designed and constructed. The whole system extends the use of a two-dimensional scanner that is embedded in a robotic system. Supporting hardware rotates the scanner around the scan axis. This will create a three-dimensional model of the environment using a two-dimensional scanner. Thus, the resulting three-dimensional scan is formed by subsequent two-dimensional scans, each shifted with respect to the previous one. It was necessary to design the appropriate software for hardware management to control the movement of the engine, the scanner, and to process the measured data. The proposed system can be placed on various exploration robotic systems that map the space using the proposed method. Wheeled, band robotic systems or drones can be used to explore hard-to-reach environment.
Photo-Realistic Facial Texture Transfer

Style transfer methods have achieved significant success in recent years with the use of convolutional neural networks. However, many of these methods concentrate on artistic style transfer with few constraints on the output image appearance. We address the challenging problem of transferring face texture from a style face image to a content face image in a photorealistic manner without changing the identity of the original content image. Our framework for face texture transfer (FaceTex) augments the prior work of MRF-CNN with a novel facial semantic regularization that incorporates a face prior regularization smoothly suppressing the changes around facial meso-structures (e.g eyes, nose and mouth) and a facial structure loss function which implicitly preserves the facial structure so that face texture can be transferred without changing the original identity. We demonstrate results on face images and compare our approach with recent state-of-the-art methods. Our results demonstrate superior texture transfer because of the ability to maintain the identity of the original face image.
Fragment shadow

We demonstrate Fragment Shadow a multi-projector system to generate visual transformed optical shadows without any latency. In our system, multiple projectors output different individual images onto the same surface to display one synthesized image. When the human body or objects occlude some of projectors, it reveals images from other projectors. Fragment Shadow enables the image synthesis pipeline in XYZ color space by the bi-directional conversion of RGB and XYZ color space and the correction of un-uniformity of projectors in addition to the geometry calibration between multiple projectors. Our optical shadow can be visually transformed with various expressions, such as geometrically fragments, textured shadow in the completely different visual and even putting our shadows in between the multiple layers of projection images. We envision the visual transformation of own optical shadow can provide unique sensations to our own bodies.
Face recognition system Using Deep Neural Network with Convolutional Neural Networks

Abstract Face recognition has long been a hot topic and challenging research point in areas such as image processing, pattern recognition, and machine vision. The face is a biometric feature with the intrinsic nature of a human. So that the face has self-stability, deep individual differences and can be an ideal basis for verification of an identity. In this research use, Deep Learning Network method uses to perform detection or face recognition. In this study, we present a framework that can be used to detect faces. This research is also able to present a DNN model that is used to study data sources from the data stream in sequence. The most important part of this study is able to adjust the capacity of the model from the simple one. This research uses experimental design method. The first step is a collection of face image data. Then the architecture design starts from the determination of the depth of the network, layout layers, and the selection of layer types that will be used to get the model based on input dataset and label name index.
Handheld combat support tools utilising IoT technologies and data fusion algorithms as reconnaissance and surveillance platforms

This paper documents the idea and construction details of mobile support tools used for research in constructing innovative tools supporting selected range of reconnaissance and surveillance activities performed using handhelds and integrated or built-in sensors. In this research a mobile application such as SAME or mCOP serves as a communication and decision support node, exchanging and fusing data gathered from sensors (other handhelds), perimeter sensors, UAVs. The data gathering process is supplemented by the operator-soldier with additional information increasing data reliability. The task of recon data recognition, aggregation and deconfliction, in presented research, is performed by developed algorithms and operators. Introducing human-in-the-loop serves as complex-case solution and mechanisms for extending reconnaissance recognition features in the system, where an operator annotates gathered data and is able to aggregate, supplement, describe battlespace elements such as equipment, personnel, activities (tasks) and in result engaged units. The research contains also a mobile system architecture description, demonstrating proof-of-concept and implementation assumptions which led to deployment of highly specialised, service-based tactical support mobile application mCOP and its upgraded version SAME. Such applications serve as operational picture managers fusing data from command centre and available scenario participants. Both prototypes serve also as technology demonstrators for testing GIS-based decision support and augmented reality features indirectly supporting research on the efficiency of mobile tools introduction to support soldiers and low level commanders.
Splitting quaternion algebras over quadratic number fields

We propose an algorithm for finding zero divisors in quaternion algebras over quadratic number fields, or equivalently, solving homogeneous quadratic equations in three variables over Q(d) where d is a square-free integer. The algorithm is randomized and runs in polynomial time if one is allowed to call oracles for factoring integers.
A Learner Model for Adaptive e-Leaning Based on Learning Theories

Adaptive e-learning promises to deal with the problems of not considering learner characteristics experienced in e-learning systems. Research has shown that adaptive e-learning improves the effectiveness of e-learning systems and removes the loneliness experienced by learners when they are online. Though this is the case, adaptive e-learning systems face the challenge of not only utilizing one learner characteristic to provide adaptation but also the problem of deciding which learner characteristic to model. This study was carried out in order to develop a learner model based on learning theories since learning theories do spell out on how learning occurs and therefore useful in determining which learner characteristic to model. Systematic literature review on articles dealing with adaptive e-learning was carried out. Articles were extracted from various databases and were selected using a three step process. 16 articles were selected for the final analysis. The results of the analysis indicate that, there is evidence of basing adaptive e-learning systems on learning theories and the most utilized theory is cognitivism. The learner characteristic that is majorly modelled is learning style. In order to capture different learner characteristics we propose a learner model based on the three major learning theories; constructivism, cognitivism and behaviriousim.
Enabling Quality Education in Namibia Through Mobile Learning Technologies

Mobile devices have undergone transformation from being used as a channel of communication to tools for teaching and learning. This study ascertained how mobile learning technology can enable quality education in Namibia. It assessed teachers’ perception and the usage pattern of mobile technology in three regions of Namibia (Erongo, Khomas and Otjozondjupa regions). 72 teachers were carefully selected from nine (9) high schools in the 3 regions. Data collected through a questionnaire was analysed using the statistical package for social sciences (SPSS). The findings revealed that teachers are already using mobile devices for academic purposes. It also revealed that majority of the teachers use mobile devices for educational purposes and teachers are of the belief that mobile devices can enhance quality education.
Person Re-Identification Based on DropEasy Method

Currently, majority of person re-identification (reID) technologies are network-constrained by Dropout regularization, which relies on the random zeroing out of some features to make these features more independent. However, such random zeroing regularization methods are not very effective for improving network performance, because they neglect the unique contribution of different features to the network performance. To improve the value of indiscriminative features in network training, a DropEasy-based person reID method is proposed in this paper. Features are classified into discriminative and indiscriminative ones, according to the distance between the feature vectors of positive or negative sample pairs wherein the discriminative features are zeroed out, while the indiscriminative features are reserved, and the network only learns through indiscriminative features. Furthermore, because networks are always inclined to make up for incomplete information by drawing on the surrounding features in the feature maps, Dropout loses its effectiveness for the network-constraints. To solve this challenge, the DropEasy2d method that can be effectively applied to convolutional layers is further proposed in this paper. DropEasy2d searches discriminative feature areas in the feature maps by sliding and zeroing windows while reserving the indiscriminative features areas to constrain network learning. The effectiveness of the proposed method is demonstrated using the Market-1501, DuckMTMC-reID, and CUHK03 datasets. For example, in the Market-1501 dataset, DropEasy can improve the mean average precision (mAP) and Rank-1 accuracy of the ID-discriminative embedding (IDE) to 72.7%(+8.8%) and 90.5%(+6.8%), respectively, while DropEasy2d can raise them to 68.5%(+4.6%) and 88.7%(+5.0%), respectively. Based on the results, the proposed method can improve the network performance during the extraction and generalization of the discriminative features.
Deep Natural Language Processing for Search Systems

Deep learning models have been very successful in many natural language processing tasks. Search engine works with rich natural language data, e.g., queries and documents, which implies great potential of applying deep natural language processing on such data to improve search performance. Furthermore, it opens an unprecedented opportunity to explore more advanced search experience, such as conversational search and chatbot. This tutorial offers an overview on deep learning based natural language processing for search systems from an industry perspective. We focus on how deep natural language processing powers search systems in practice. The tutorial introduces basic concepts, elaborates associated challenges, reviews the state-of-the-art approaches, covers end-to-end tasks in search systems with examples, and discusses the future trend.
An optimization framework for inverse tone mapping using a single low dynamic range image

Conventional inverse tone-mapping (ITM) methods tend to produce contrast distortions such as contrast loss and contrast reversal in reconstructed high dynamic range (HDR) images. This paper proposes a novel ITM optimization framework based on the assumption that the input low dynamic range (LDR) image is similar to the LDR image obtained by tone mapping a true HDR image. In the proposed framework, an HDR image is initially reconstructed by applying a conventional tone-mapping function in a reverse manner, and then the reconstructed HDR image is iteratively modified toward the optimum HDR image by minimizing the difference between the input LDR image and a tone-mapped LDR image obtained from the reconstructed HDR image. The experimental results demonstrate that the proposed framework effectively reconstructs a high-quality HDR image and outperforms other conventional methods in terms of objective quality.
On error linear complexity of new generalized cyclotomic binary sequences of period p2

We consider the k-error linear complexity of a new generalized cyclotomic binary sequence of period p2 for an odd prime p. The new sequences were introduced recently by Z. Xiao, X. Zeng, C. Li and T. Helleseth by defining a new kind of generalized cyclotomic classes modulo p2. They proved that the sequences had large linear complexity. In this work, we determine the values of the k-error linear complexity in terms of the theory of Fermat quotients. The results indicate that such sequences have good stability, that is, the linear complexity does not significantly decrease by changing a few terms.
Predicting How to Distribute Work Between Algorithms and Humans to Segment an Image Batch

Foreground object segmentation is a critical step for many image analysis tasks. While automated methods can produce high-quality results, their failures disappoint users in need of practical solutions. We propose a resource allocation framework for predicting how best to allocate a fixed budget of human annotation effort in order to collect higher quality segmentations for a given batch of images and automated methods. The framework is based on a prediction module that estimates the quality of given algorithm-drawn segmentations. We demonstrate the value of the framework for two novel tasks related to predicting how to distribute annotation efforts between algorithms and humans. Specifically, we develop two systems that automatically decide, for a batch of images, when to recruit humans versus computers to create (1) coarse segmentations required to initialize segmentation tools and (2) final, fine-grained segmentations. Experiments demonstrate the advantage of relying on a mix of human and computer efforts over relying on either resource alone for segmenting objects in images coming from three diverse modalities (visible, phase contrast microscopy, and fluorescence microscopy).
Marginalized Particle Filtering and Related Filtering Techniques as Message Passing

In this paper, a factor graph approach is employed to investigate the recursive filtering problem for conditionally linear Gaussian state-space models. First, we derive a new factor graph for the considered filtering problem; then, we show that applying the sum-product rule to our graphical model results in both known and novel filtering techniques. In particular, we prove that: 1) marginalized particle filtering can be interpreted as a form of forward only message passing over the devised graph; 2) novel filtering methods can be easily developed by exploiting the graph structure and/or simplifying probabilistic messages.
Hierarchical Classification Using Binary Data

In classification problems, especially those that categorize data into a large number of classes, the classes often naturally follow a hierarchical structure. That is, some classes are likely to share similar structures and features. Those characteristics can be captured by considering a hierarchical relationship among the class labels. Motivated by a recent simple classification approach on binary data, we propose a variant that is tailored to efficient classification of hierarchical data. In certain settings, specifically, when some classes are significantly easier to identify than others, we showcase computational and accuracy advantages.