Reviewerly/exHarmony huggingface dataset:
A Hybrid Linear Iterative Clustering and Bayes Classification-Based GrabCut Segmentation Scheme for Dynamic Detection of Cervical Cancer

Cervical cancer earlier detection remains indispensable for enhancing the survival rate probability among women patients worldwide. The early detection of cervical cancer is done relatively by using the Pap Smear cell Test. This method of detection is challenged by the degradation phenomenon within the image segmentation task that arises when the superpixel count is minimized. This paper introduces a Hybrid Linear Iterative Clustering and Bayes classification-based GrabCut Segmentation Technique (HLC-BC-GCST) for the dynamic detection of Cervical cancer. In this proposed HLC-BC-GCST approach, the Linear Iterative Clustering process is employed to cluster the potential features of the preprocessed image, which is then combined with GrabCut to prevent the issues that arise when the number of superpixels is minimized. In addition, the proposed HLC-BC-GCST scheme benefits of the advantages of the Gaussian mixture model (GMM) on the extracted features from the iterative clustering method, based on which the mapping is performed to describe the energy function. Then, Bayes classification is used for reconstructing the graph cut model from the extracted energy function derived from the GMM model-based Linear Iterative Clustering features for better computation and implementation. Finally, the boundary optimization method is utilized to considerably minimize the roughness of cervical cells, which contains the cytoplasm and nuclei regions, using the GrabCut algorithm to facilitate improved segmentation accuracy. The results of the proposed HLC-BC-GCST scheme are 6% better than the results obtained by other standard detection approaches of cervical cancer using graph cuts.
A Comparative Study of Various Deep Learning Approaches to Shape Encoding of Planar Geospatial Objects

The shape encoding of geospatial objects is a key problem in the fields of cartography and geoscience. Although traditional geometric-based methods have made great progress, deep learning techniques offer a development opportunity for this classical problem. In this study, a shape encoding framework based on a deep encoder–decoder architecture was proposed, and three different methods for encoding planar geospatial shapes, namely GraphNet, SeqNet, and PixelNet methods, were constructed based on raster-based, graph-based, and sequence-based modeling for shape. The three methods were compared with the existing deep learning-based shape encoding method and two traditional geometric methods. Quantitative evaluation and visual inspection led to the following conclusions: (1) The deep encoder–decoder methods can effectively compute shape features and obtain meaningful shape coding to support the shape measure and retrieval task. (2) Compared with the traditional Fourier transform and turning function methods, the deep encoder–decoder methods showed certain advantages. (3) Compared with the SeqNet and PixelNet methods, GraphNet performed better due to the use of a graph to model the topological relations between nodes and efficient graph convolution and pooling operations to process the node features.
Theory and Practice of Integrating Machine Learning and Conventional Statistics in Medical Data Analysis

The practice of medical decision making is changing rapidly with the development of innovative computing technologies. The growing interest of data analysis with improvements in big data computer processing methods raises the question of whether machine learning can be integrated with conventional statistics in health research. To help address this knowledge gap, this paper presents a review on the conceptual integration between conventional statistics and machine learning, focusing on the health research. The similarities and differences between the two are compared using mathematical concepts and algorithms. The comparison between conventional statistics and machine learning methods indicates that conventional statistics are the fundamental basis of machine learning, where the black box algorithms are derived from basic mathematics, but are advanced in terms of automated analysis, handling big data and providing interactive visualizations. While the nature of both these methods are different, they are conceptually similar. Based on our review, we conclude that conventional statistics and machine learning are best to be integrated to develop automated data analysis tools. We also strongly believe that machine learning could be explored by health researchers to enhance conventional statistics in decision making for added reliable validation measures.
Modeling and suppressing the wavefront degeneration in a CGH interferometric null test

The computer-generated hologram (CGH) enables the ultra-high accuracy of surface measurement but causes the wavefront degeneration in the optical system. In this article, we give a high-accuracy analytical simulation of the wavefront degeneration in null test by the elliptical Gaussian model. We propose an analytical expression of instrumental transfer function (ITF) for the CGH null test without knowing the phase distribution of CGH, which gives an efficient instruction to suppress the wavefront degeneration. The ITF of the interferometric null test for a ∅3m aspheric mirror can be optimized from 0 to 0.65 at 0.4 Nyquist frequency.
Speeding-up one-versus-all training for extreme classification via mean-separating initialization

Abstract In this paper, we show that a simple, data dependent way of setting the initial vector can be used to substantially speed up the training of linear one-versus-all classifiers in extreme multi-label classification (XMC). We discuss the problem of choosing the initial weights from the perspective of three goals. We want to start in a region of weight space (a) with low loss value, (b) that is favourable for second-order optimization, and (c) where the conjugate-gradient (CG) calculations can be performed quickly. For margin losses, such an initialization is achieved by selecting the initial vector such that it separates the mean of all positive (relevant for a label) instances from the mean of all negatives – two quantities that can be calculated quickly for the highly imbalanced binary problems occurring in XMC. We demonstrate a training speedup of up to $$5\times$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>5</mml:mn> <mml:mo>×</mml:mo> </mml:mrow> </mml:math> on Amazon-670K dataset with 670,000 labels. This comes in part from the reduced number of iterations that need to be performed due to starting closer to the solution, and in part from an implicit negative-mining effect that allows to ignore easy negatives in the CG step. Because of the convex nature of the optimization problem, the speedup is achieved without any degradation in classification accuracy. The implementation can be found at https://github.com/xmc-aalto/dismecpp .
A Gender Genetic Algorithm and Its Comparison with Conventional Genetic Algorithm

This study presents a new variety of gender genetic algorithm (GGA). Using the example of five test optimization problems, its superiority over conventional genetic algorithm (GA) with an elitism operator is shown. The analysis of the novel GGA on multi-extreme optimization functions confirms the effectiveness of the idea of gender separation of the population. Based on the results obtained from experimental data and on their analysis, a more advanced modification of GGA is proposed, using determination of the offspring's gender based on the combination of parental chromosomes in it.
Postdigital Territoriality: Disentangling from Digital Media as a Return to Place

People adopt geographical strategies to distance themselves from digital sociality. Rather than merely turning off devices, they engage in a broader, more durable project of disentangling. This effort responds to the homogenizing, standardizing forces of connective media and their coercive entanglements: socially normalized routines of personal media use, hybridizations of human agency with communication technologies, and digitally mediated activities that generate predictive and prescriptive products. Geographical attention to disentangling is merited by the fact that it comes in local variants and brings questions of place and human territoriality back onto the agenda in significantly new ways as part of a postdigital territoriality. We offer two vignettes revealing place’s role as protective, with its territoriality drawing a line around the self. We argue that postdigital territoriality inevitably reflects a differentiated terrain of gender, income, profession, and other elements of positionality.
Cross-graph reference structure based pruning and edge context information for graph matching

Graph matching (GM) plays a vital role in solving various computer vision tasks by establishing a node-to-node correspondence between graph-structured data. In this study, we propose a novel method based on affinity matrix for solving GM tasks. Specifically, a pruning strategy is proposed to remove inconsistent edges from an association graph that formulates the GM problem into a node ranking and selection problem. Moreover, an edge context information with respect to a cross-graph reference structure is proposed and combined with the local distance information to compute the affinities among the graphs’ nodes. Then, the re-weighted random walk technique is utilized to simulate random walks on the association graph and compute a quasi-stationary distribution in an iterative manner. Finally, the Hungarian algorithm is used to discretize the distribution and obtain an approximate matching between two graphs. The experimental results indicate that our proposed GM method is able to produce promising results in the presence of outliers and deformation noise as compared with other methods.
Towards deeper match for multi-view oriented multiple kernel learning

Multi-view representation learning aims to exploit the complementary information underlying multiple view data to enhance the expressive power of data representation. Given that kernels in multiple kernel learning naturally correspond to different views, previous shallow similarity learning models cannot fully capture the complex hierarchical information. This work presents an effective deeper match model for multi-view oriented kernel (DMMV) learning which brings a deeper insight into the kernel match for similarity based multi-view representation fusion. Specifically, we propose local deep view-specific self-kernel (LDSvK) by mimicking the deep neural networks to faithfully characterize the local similarity between view-specific samples. Thus, the representation capacity of each view can be saliently analyzed. We build the global deep multi-view fusion kernel (GDMvK) by learning deep fusion of LDSvKs to learn a comprehensive measurement of the cross-view similarity. Notably, the proposed learning framework of the deeper local information extraction and global deep multiple kernel fusion provides a robust way in fitting multi-view data, and yields better learning performance. Experimental results on several multi-view benchmark datasets well demonstrate the effectiveness of our DMMV over other state-of-the-art methods.
Supporting workspace awareness in remote assistance through a flexible multi-camera system and Augmented Reality awareness cues

Workspace awareness is critical for remote assistance with physical tasks, yet it remains difficult to facilitate. For example, if the remote helper is limited to the single viewpoint provided by the worker's hand-held or head-mounted camera, she lacks the ability to gain an overview of the workspace. This may be addressed by granting the helper view-independence, e.g., through a multi-camera system. However, it can be cumbersome to set up and calibrate multiple cameras, and it can be challenging for the local worker to identify the current viewpoint of the remote helper. We present CueCam, a multi-camera remote assistance system that supports mutual workspace awareness through a flexible ad-hoc camera calibration and various Augmented Reality cues that communicate the helper's viewpoint and focus. In particular, we propose visual cues presented through a head-mounted Augmented Reality display (Virtual Hand, Color Cue), and sound cues emitted from the cameras' physical locations (Spatial Sound). Findings from a lab study indicate that all proposed cues effectively support the worker's awareness of helper's location and focus, while the Color Cue demonstrated superiority in task performance and preference ratings during a search task.
The Use of Terrestrial and Maritime Autonomous Vehicles in Nonintrusive Object Inspection

Traditional nonintrusive object inspection methods are complex or extremely expensive to apply in certain cases, such as inspection of enormous objects, underwater or maritime inspection, an unobtrusive inspection of a crowded place, etc. With the latest advances in robotics, autonomous self-driving vehicles could be applied for this task. The present study is devoted to a review of the existing and novel technologies and methods of using autonomous self-driving vehicles for nonintrusive object inspection. Both terrestrial and maritime self-driving vehicles, their typical construction, sets of sensors, and software algorithms used for implementing self-driving motion were analyzed. The standard types of sensors used for nonintrusive object inspection in security checks at the control points, which could be successfully implemented at self-driving vehicles, along with typical areas of implementation of such vehicles, were reviewed, analyzed, and classified.
Visual Relationship Detection with Multimodal Fusion and Reasoning

Visual relationship detection aims to completely understand visual scenes and has recently received increasing attention. However, current methods only use the visual features of images to train the semantic network, which does not match human habits in which we know obvious features of scenes and infer covert states using common sense. Therefore, these methods cannot predict some hidden relationships of object-pairs from complex scenes. To address this problem, we propose unifying vision-language fusion and knowledge graph reasoning to combine visual feature embedding with external common sense knowledge to determine the visual relationships of objects. In addition, before training the relationship detection network, we devise an object-pair proposal module to solve the combination explosion problem. Extensive experiments show that our proposed method outperforms the state-of-the-art methods on the Visual Genome and Visual Relationship Detection datasets.
Using Convolutional Neural Networks for Cloud Detection on VENμS Images over Multiple Land-Cover Types

In most parts of the electromagnetic spectrum, solar radiation cannot penetrate clouds. Therefore, cloud detection and masking are essential in image preprocessing for observing the Earth and analyzing its properties. Because clouds vary in size, shape, and structure, an accurate algorithm is required for removing them from the area of interest. This task is usually more challenging over bright surfaces such as exposed sunny deserts or snow than over water bodies or vegetated surfaces. The overarching goal of the current study is to explore and compare the performance of three Convolutional Neural Network architectures (U-Net, SegNet, and DeepLab) for detecting clouds in the VENμS satellite images. To fulfil this goal, three VENμS tiles in Israel were selected. The tiles represent different land-use and cover categories, including vegetated, urban, agricultural, and arid areas, as well as water bodies, with a special focus on bright desert surfaces. Additionally, the study examines the effect of various channel inputs, exploring possibilities of broader usage of these architectures for different data sources. It was found that among the tested architectures, U-Net performs the best in most settings. Its results on a simple RGB-based dataset indicate its potential value for any satellite system screening, at least in the visible spectrum. It is concluded that all of the tested architectures outperform the current VENμS cloud-masking algorithm by lowering the false positive detection ratio by tens of percents, and should be considered an alternative by any user dealing with cloud-corrupted scenes.
Context-sensitive parsing for programming languages

Parsing programming languages using context-sensitive rather than context-free grammars is being considered here because a stronger formalism might be beneficial for dealing with increasingly complex programming languages and their syntax, or is more appropriate in some applications. A new deterministic non-backtracking algorithm for parsing deterministic context-sensitive languages is described. It is a significant improvement of the algorithm built into WEAVE and CWEAVE tools for literate programming and requires that the language is described by a context-sensitive reduction system, namely a deterministic formalism similar to a context-sensitive grammar but with strict rules about how reductions are to be applied. The new algorithm uses a reduction automaton for finding the position of the next reduction at each step during parsing rather than a hardcoded trie that is build into the original algorithm of WEAVE and CWEAVE. The new algorithm performs at least twice as few operations per input symbol as the original one. Furthermore, it is shown that parsing a language described by a context-sensitive reduction system need not be limited to typesetting purposes as in literate programming but can be used as a general parsing approach.
A label distribution manifold learning algorithm

In this paper, we propose a novel label distribution manifold learning (LDML) method for solving the multilabel distribution learning problem. First, using manifold learning, we extract the accurate and reduced-dimension features of the training data. Second, we estimate the unknown label distributions associated with the extracted reduced-dimension features based on multi-output kernel regression. Third, we use the extracted reduced-dimension features and their associated estimated label distributions to form an enhanced maximum entropy model, which enables us to accurately and efficiently estimate the unknown true label distributions for the training data. We refer to this algorithm as the LDML. We also propose to apply the tangent space alignment regression in the second stage, and the resulting algorithm is called the LDML-R. The LDML-R has better label distribution learning performance than the LDML but imposes higher complexity than the latter. We evaluate the proposed LDML and LDML-R algorithms on 15 real-world data sets with ground-truth label distributions, and the experimental results obtained show that our method has advantages in terms of learning accuracy compared to the latest multi-label distribution learning approaches. We also use another 10 real-world multi-class data sets, which do not have the ground-truth label distributions, to demonstrate the superior multilabel classification performance of our LDML-R algorithm over the existing state-of-the-art multi-label classification algorithms.
TraduBem

This work was motivated by the need to adopt an altruistic language, i.e., a language where all people feel represented and respected. The article describes the results of a prototyping process towards a text verification tool, TraduBem, to support people adopting an altruistic language. In addition to surveying this tool's requirements, we conducted a study to investigate people's perceptions about using words in different contexts and how to guide people to use/not use certain words/expressions in different interactive scenarios. The study involved five professionals from different areas, such as Language, Communication, Computing, and Gender. The analysis of the collected data was carried out according to Grounded Theory procedures, aiming to identify emerging topics among the views exposed by each participant. This step identified the following topics: (1) Perceptions related to the origin of the word, (2) Perceptions related to the use of words, (3) Perceptions related to vocabulary change, and (4) Ways of orientation and approach. Additionally, this work shows results from an empirical evaluation of TraduBem.
Master’s Student 3++: Portrait and New Requests

The exclusion of Russia from the Bologna process led to large-scale discussions in society about the future of the Russian magistracy. The implementation of master’s education raised questions throughout 29 years of its existence. Society has developed a perception of magistracy as a problem area of higher education. Thus, when the question arose about the advisability of developing a master’s programs after Russia’s exit from the European Higher Education Area, the majority formed their opinion: no magistracy – no problems. However, objective data allow us to see the situation in the different perspective. The paper presents the result of a study of the qualitative and quantitative composition of students of the domestic master’s programs from 2012 to 2020. Based on the analysis, the main trends in changing the profile of Russian master’s students have been identified. The tasks relevant to the development of the institute of the Russian magistracy are outlined with a focus on a portrait of today’s master’s student. The analysis shows that the magistracy has firmly occupied the niche formed in recent years for the training and retraining of specialists. It has become an independent institution of higher education with its own goals, objectives, resources and, most importantly, with its own contingent. The magistracy has a small inertia in response to the emerging challenges of society. Because of that the magistracy increases its demand every year. At the same time, its self-sufficiency rises. The magistracy has been long and hard embedded into the Russian traditions of education. Finally, it has found its place. The factors considered in the study allow us to speak about the high potential for the Russian magistracy development. The data and conclusions presented in the study can serve as the basis for a scientific discussion about the future of domestic education.
Towards Reliable Baselines for Document-Level Sentiment Analysis in the Czech and Slovak Languages

This article helps establish reliable baselines for document-level sentiment analysis in highly inflected languages like Czech and Slovak. We revisit an earlier study representing the first comprehensive formulation of such baselines in Czech and show that some of its reported results need to be significantly revised. More specifically, we show that its online product review dataset contained more than 18% of non-trivial duplicates, which incorrectly inflated its macro F1-measure results by more than 19 percentage points. We also establish that part-of-speech-related features have no damaging effect on machine learning algorithms (contrary to the claim made in the study) and rehabilitate the Chi-squared metric for feature selection as being on par with the best performing metrics such as Information Gain. We demonstrate that in feature selection experiments with Information Gain and Chi-squared metrics, the top 10% of ranked unigram and bigram features suffice for the best results regarding online product and movie reviews, while the top 5% of ranked unigram and bigram features are optimal for the Facebook dataset. Finally, we reiterate an important but often ignored warning by George Forman and Martin Scholz that different possible ways of averaging the F1-measure in cross-validation studies of highly unbalanced datasets can lead to results differing by more than 10 percentage points. This can invalidate the comparisons of F1-measure results across different studies if incompatible ways of averaging F1 are used.