Reviewerly/exHarmony huggingface dataset:
Unsupervised Ontology- and Sentiment-Aware Review Summarization

In this Web 2.0 era, there is an ever increasing number of customer reviews, which must be summarized to help consumers effortlessly make informed decisions. Previous work on reviews summarization has simplified the problem by assuming that aspects (e.g., "display") are independent of each other and that the opinion for each aspect in a review is Boolean: positive or negative. However, in reality aspects may be interrelated – e.g., "display" and "display color" – and the sentiment takes values in a continuous range – e.g., somewhat vs very positive. We present a novel, unsupervised review summarization framework that advances the state-of-the-art by leveraging a domain hierarchy of concepts to handle the semantic overlap among the aspects, and by accounting for different sentiment levels. We show that the problem is NP-hard and present bounded approximate algorithms to compute the most representative set of sentences or reviews, based on a principled opinion coverage framework. We experimentally evaluate the proposed algorithms on real datasets in terms of their efficiency and effectiveness compared to the optimal algorithms. We also show that our methods generate summaries of superior quality than several baselines in short execution times.
ETHICAL VALUES AS A PROTECTIVE SYSTEM OF CULTURE

The article discusses one of the essential aspects of the problem of the value foundations of a society and an individual - the place and role of basic ethical values in the context of dynamic social changes that accompany the development of high technologies. The study aims to justify the protective functions of ethical values in the modern culture. It is shown that ethical values included in the ethos of culture are both universal and culturally specific in nature and perform the following basic functions: 1) a universal regulator of development and application of potentially dangerous high technologies; 2) a selective mechanism for protecting cultural identity of a society and an individual; 3) the most important component of civilization planner.
Defining Graph Signal Distances Using an Optimal Mass Transport Framework

In this work, we propose a novel measure of distance for quantifying dissimilarities between signals observed on a graph. Building on a recently introduced optimal mass transport framework, the distance measure is formed using the second-order statistics of the graph signals, allowing for comparison of graph processes without direct access to the signals themselves, while explicitly taking the dynamics of the underlying graph into account. The behavior of the proposed distance notion is illustrated in a graph signal classification scenario, indicating attractive modeling properties, as compared to the standard Euclidean metric.
Learning Activation Functions by Means of Kernel Based Neural Networks

The neuron activation function plays a fundamental role in the complexity of learning. In particular, it is widely known that in recurrent networks the learning of long-term dependencies is problematic due to vanishing (or exploding) gradient and that such problem is directly related to the structure of the employed activation function. In this paper, we study the problem of learning neuron-specific activation functions through kernel-based neural networks (KBNN) and we make the following contributions. First, we give a representation theorem which indicates that the best activation function is a kernel expansion over the training set, then approximated with an opportune set of points modeling 1-D clusters. Second, we extend the idea to recurrent networks, where the expressiveness of KBNN can be an determinant factor to capture long-term dependencies. We provide experimental results on some key experiments which clearly show the effectiveness of KBNN when compared with RNN and LSTM cells.
Single Image based Fog Information Estimation for Virtual Objects in A Foggy Scene

Synthesizing virtual objects in a real scene is an important technique in augmented reality. Immersing these virtual objects into the scene can be challenging, for instance in environments with the presence of fog. This paper investigates strategies for augmenting virtual objects on foggy scenes. Firstly, we synthesize the foggy image including original virtual objects, then depth map of single image is obtained by adapting Conditional Generative Adversarial Networks, which takes the original foggy image containing virtual objects as input. The estimated depth cues can be utilized for generating the foggy-effect on the object according to the atmospheric light model, making the virtual object exhibit a similar degree of foggy effect on real objects at a similar distance range. Experimental results demonstrate that the proposed technique presents improved visual effect for synthesizing virtual objects on foggy scenes.
Efficient Algorithm for Maximal Biclique Enumeration on Bipartite Graphs

Maximal Biclique Enumeration (MBE) has become a central task to many data mining problems arising in Web mining, shopping recommendation, business and bioinformatics. It is crucial to accelerate the sequential MBE that is the basis of the parallel MBE. In this paper, we present an efficient algorithm for maximal biclique enumeration (EMBE) on bipartite graphs in a depth-first manner and need not to store previously computed maximal bicliques in memory for duplicate detection. Previous studies have shown that reduce the number of checking closure condition and manage the child nodes are huge challenges for generating all maximal bicliques. In this paper, we propose that (1) an efficient implementation for pruning technique based on the stack when checking nodes are closed or not, (2) a new method to manage the expansion child nodes through a global data structure.
Knowledge Matching in Horizontal Collaborative Fuzzy Clustering

In horizontal collaborative fuzzy clustering, the collaboration is implemented by adapting the difference between the collaborated partition matrix and the collaborating partition matrixes (called knowledge) in the objective function. Given a partition matrix of a dataset, the matrix obtained by exchanging its rows arbitrarily is also a partition matrix of the dataset. These partition matrices are the same except the orders of the rows, thus they represent the same knowledge. Let the original partition matrix be the collaborated one, the other be the collaborating ones, the final partition matrix given by collaborative fuzzy clustering should be the same as the original one. But the experiments result is different. If we change the collaborating matrixes to the same as the collaborated one, the final partition matrix remains the same as the collaborated one. This tells us the matching of the rows of the collaborated matrix and the collaborating matrixes is necessary. Furthermore, similar problems happened in practice. To deal with this problem, this paper proposes a knowledge matching algorithm based on the Kuhn Munkras (KM) algorithm in bipartite graph matching, then gives an improved horizontal collaborative fuzzy clustering algorithm. Experimental results show that the proposed horizontal collaborative clustering algorithm has superior performance than the existing ones.
Revisiting Multiple Pattern Matching

We consider the classical exact multiple string matching problem. Our solution is based on $q$-grams combined with pattern superimposition, bit-parallelism and alphabet size reduction. We discuss the pros and cons of the various alternatives of how to achieve best combination. Our method is closely related to previous work by (Salmela et al., 2006). The experimental results show that our method performs well on different alphabet sizes and that they scale to large pattern sets.
Floating-point Precision and Deformation Awareness for Scalable and Robust 3D Face Alignment

This paper improves the accuracy of heatmap-based 3D face alignment neural networks. Many current approaches in face alignment are limited by two major problems, quantization and the lack of regularization of heatmaps. The first limitation is caused by the non-differentiable argmax function, which extracts landmark coordinates from heatmaps as integer indices. Heatmaps are generated at low-resolution to reduce the memory and computational costs, which results in heatmaps far lower than the input image’s resolution. We propose a heatmap generator network producing floating-point precision heatmaps that are scalable to higher-resolutions. To resolve the second limitation, we propose a novel deformation constraint on heatmaps. The constraint is based on graph-Laplacian and enables a heatmap generator to regularize overall shape of the output face landmarks using the global face structure. By eliminating quantization and including regularization, our method can vastly improve landmark localization accuracy, and achieves the state-of-the-art performance without adding complex network structures.
Specificity of the Integrative Approach in Teaching Students a Foreign Language

The article deals with the problem of integrativity in teaching foreign language in the system of school education. The aim of the integrative approach in teaching a foreign language is to achieve a synergetic effect on the basis of mastering a curricilum and independent use of the language in an externally specifi ed context. The author describes the specifi cs of the integrative approach in the school foreign language course, which lies in interdisciplinary synthesis in mastering a foreign language; inclusive education of children with disabilities and normal students; cultural-based study of native and second languages and comparative analysis of their native and foreign cultures. The author highlights the pedagogical and methodological importance of the integrative approach as an element of holistic education, emphasizes the need for individualization of education in the context of the humanistic paradigm.
Immersive Analytics using Augmented Reality for Computational Fluid Dynamics Simulations

This project aimed to employ multi-sensory visual analytics to a Computational Fluid Dynamics (CFD) dataset using augmented and mixed reality interfaces. Initial application was developed for a Hololens which allows users to interact with the CFD data using gestures, enabling control over the position, rotation and scale of the data, sampling, as well as voice commands that provide a range of functionalities such as changing a parameter or render a different view. This project leads to a more engaging and immersive experience of data analysis, generalised for CFD simulations. The application is also able to explore CFD datasets in fully collaborative ways, allowing engineers, scientists, and end-users to understand the underlying physics and behaviour of fluid flows together.
To Protect Freedom of Expression, Why Not Steal Victory from the Jaws of Defeat?

Global social media platforms are grappling with whether to align their corporate speech codes with international human rights law. Facebook’s June 2019 report that summarized worldwide feedback about its proposed independent oversight board for content moderation noted a split in stakeholder opinions on this topic. The UN’s top expert on freedom of expression as well as many civil society members recommended that Facebook anchor its content moderation in the international human rights law regime. Others expressed concern that this legal regime would not be sufficiently protective of speech and contained inconsistencies that create problems for content moderation.Those concerns were linked to a recent scholarly call for updates to the United Nations’ international legal regime regarding freedom of expression, particularly with respect to the International Covenant on Civil and Political Rights and the Convention on the Elimination of All Forms of Racial Discrimination. This Article examines the scholarly call’s analysis to assess whether its conclusions are correct, which would make this body of law less useful for platforms to adopt in content moderation.This Article finds that the state of international law on freedom of expression is more protective of speech (and more coherent) than the scholars assessed and proposes ways to achieve their laudable goal of promoting broad protections for freedom of expression in international law. The Article concludes that the existing international legal regime on freedom of expression remains a useful resource for content moderation by global platforms.
Appropriate Expressiveness of Planning Domain Models

The level of expressiveness of planning domain models determines their accuracy in describing real-world domains as well as their efficiency in terms of performance of planners. More expressive models might be accurate but hard for planners while less expressive models might be easier for planners but not very accurate. In this paper, we consider a problem of efficient routing of vehicles in urban road networks. We propose three domain models with different level of expressiveness to address the problem. We investigate how the models differ in terms of accuracy (quality of plans they produce) and efficiency of plan generation.
Development of Eyeball Movement and Voice Controlled Wheelchair for Physically Challenged People

Many people with disabilities do not have the ability to control the powered wheelchair manually. This is overcomed in this project by the construction of well structured design and intelligent wheelchair for physically handicapped people. This wheelchair is modeled such that it can be run with less effort from the patient by using voice processing module to the microcontroller by giving voice commands for different directions. Another feature provided to patients are that, the wheelchair can also be controlled by eyeball module with sensors. Based on the movement of eyeball, wheelchair can be controlled.
Finitely Memory Strategies in Special Büchi Games with Inductive Measurable Payoffs

Abstract The main concern of this paper is to give simple determination of games and investigate the optimality ( ε -optimal) strategies (i.e., type of strategy) for each player in a new game, called Special Büchi game. We first review on extension version of reachability games, i.e., generalized reachability games. We next observe games with more complex objectives, games with Büchi objectives. We then introduce and study a new type of Büchi game called Special Büchi game. We define a new strategy, namely a finite memory strategy and provide various interesting results.
Mitosis Detection in Multispectral Histopathological Images with Deep Learning

In this study, segmentation of cellular structures in the multispectral histopathological images and possibility of the discrimination within normal and mitotic cells have been investigated. In histopathological images, it is very challenging task to extract the mitotic cells from the histopathological image. In the first stage of the study, `discriminative images' are obtained using linear discriminant analysis. The discriminative images found are used to screen mitotic cell candidates and train them with deep learning networks. Since mitotic cells are usually dark pixels, the discriminating images are first filtered to obtain dark areas. Then, two-clustered k-means algorithm was used to differentiate background and mitotic candidates. With the help of convolutional neural networks, it is aimed to find the mitotic and non-mitotic areas with the classification approach. In experimental studies, ICPR-2012 dataset is used in both training and prediction stages and the training of deep artificial neural network architecture is carried out with samples of mitotic and non-mitotic regions. As a result, the F-measure for the data set is found as 0.760, the recall is 0.723 and the sensitivity is 0.802.
Internal node bagging

When training neural networks, regularization methods are needed to avoid model overfitting. Dropout is a widely used regularization method, but its working principle is inconclusive and it does not work well for small models. This paper introduced a novel view to understand how dropout works as a layer-wise ensemble training method, that each feature in hidden layers is learned by multiple nodes, and next layer integrates the outputs of these nodes. Basing on the novel understanding of dropout, we proposed a new neural network training algorithm named internal node bagging, which explicitly forces a group of nodes to learn the same feature during training phase and combines these nodes into one node during testing phase. This means that more parameters can be used during training phase to improve the fitting ability of models while keeping model remains small during testing phase. After experimenting on three datasets, it is found that this algorithm can significantly improve the test performance of small models.
Application prospects for vector time maps of cognitive images links

Abstract Neural networks have a great advantage in the analysis of implicit dependencies, big data analysis, image recognition and other recognition systems. However, they do not perform the function of memorizing information in an explicit form, they cannot logically think like a person. Neural networks are not managed repositories of information about cognitive images. They are quite difficult to edit or create new structures based on existing trained models. They do not have the necessary flexibility and scalability in the process of learning and in use. We propose to create vector-time communication maps of cognitive images links, containing functions for memorizing individual cognitive images and their links, with the possibility of forming explicit centers and with the possibility of scaling.
Modelling The Recovery of Health Conditions for Pulmonary Tuberculosis Patients at Dr. Soegiri Hospital Lamongan in 2018 Using Binary Logistic Regression

Abstract East Java province occupied the second place with pulmonary tuberculosis cases in Indonesia. Lamongan is one of the regencies of East Java has a number of pulmonary tuberculosis treatment success that has not met the target, this means that there are still many pulmonary tuberculosis patients who do not recover after undergoing treatment or even died. Therefore conducted a study using a binary logistic regression method to find out the factors that affect recovery pulmonary tuberculosis sufferer's health condition in the Provincial Hospital Dr. Soegiri Lamongan so that people can avoid the factors that significantly affect the recovery of the health condition of pulmonary tuberculosis disease sufferers. The results showed that nutritional status, presence of complications, when influential significant and long against the recovery of pulmonary tuberculosis patients condition.
A Survey on Various Integrity Verification Schemes on the Data Outsourced to Cloud Storage

The cloud storage is available on-demand, flexible resource provisioning with pay-as-you-go pricing methodologies powered by data centers and virtualization technologies. One of the major applications from cloud service providers is that of the cloud storage. The service providers are ensuring reliability in such a way that, if one server crashes or currently down, a backup server is some other location facilitates continued service-provisioning for the customers. In such an environment, the customers data are stored in a single cloud server or multiple cloud servers. In the current trend, the data has become more valued than the hardware. Hence, verification of data uploaded to the cloud servers at regular intervals is important. This research work strives to provide a wide range of methods which offer this integrity verification service with their merits and demerits.
Peer Learning Project for Teachers

The paper deals with the issue of the faculty development at ITMO University by means of informal education in the context of implementation of new educational technologies in Russian institutions of higher education. The project team of the University designed and implemented a peer learning project which enables educators to share their experiences with technology and train each other to use new tools, methods, and techniques in their professional practice. The article describes the first results of the ITMO.EXPERT project, provides the analysis of its current state, and proposes measures for improving the efficiency of the project. The special focus in the paper is made on the analysis of such aspects of the ITMO.EXPERT project as the competencies that the participants are supposed to develop; the key characteristics of the target audience of the project that should be taken into consideration when planning improvements; the structure of a single workshop and the structure of the whole project.
The power of random counterexamples

Learning a target concept from a finite n×m concept space requires Ω(n) proper equivalence queries in the worst case. We propose a variation of the usual equivalence query in which the teacher is constrained to choose counterexamples randomly from a known probability distribution on examples. We present and analyze the Max-Min learning algorithm, which identifies an arbitrary target concept in an arbitrary finite n×m concept space using at most an expected log2⁡n proper equivalence queries with random counterexamples.
Force vs. Nudge

Android's 3X3 graphical pattern lock scheme is one of the widely used authentication method on smartphone devices. However, users choose 3X3 patterns from a small subspace of all possible 389,112 patterns. The two recently proposed interfaces, SysPal by Cho et al. and TinPal by the authors, demonstrate that it is possible to influence users 3X3 pattern choices by making small modifications in the existing interface. While SysPal forces users to include one, two or three system-assigned random dots in their pattern, TinPal employs highlighting mechanism to inform users about the set of reachable dots from the current selected dot. Both interfaces improved the security of 3X3 patterns without affecting usability, but no comparison between SysPal and TinPal was presented. To address this gap, we conduct a new user study with 147 participants and collect patterns on three SysPal interfaces, 1-dot, 2-dot and 3-dot. We compare SysPal and TinPal patterns using a range of security and usability metrics including pattern length, stroke length, guessability, recall time and login attempts. Overall, we found that patterns created on TinPal were significantly longer and offered more resistance to guessing attacks.
Supporting Self-Directed Vocabulary Study: Towards Effective Design of OERs for Mobile Learning Environments

Although open educational resources (OERs) can help bridge high-school – university vocabulary gaps, optimal utilization of OERs requires effective self-directed learning. This bi-national two-year study adopted a design-based research paradigm to collect Chinese university students' (n = 358) perceptions regarding design factors that support self-directed vocabulary learning in mobile learning environments (MLEs). Results showed that additional scaffolding after Year One in the form of L1 translations of high-frequency academic words significantly correlated with improved performance on basic level vocabulary exercises and pop quizzes. However, results suggest that moving mobile learning into the mainstream will require (1) design features that provide additional scaffolding for more complex vocabulary learning, (2) clearer guidelines for students on integrating desktop and mobile devices and (3) ongoing guidance from teachers to develop students' capacity for self-directed learning to ensure optimal benefit from independent study of vocabulary OERs.