Reviewerly/exHarmony huggingface dataset:
Preference-driven Control over Incompleteness of Knowledge Graph Query Answers

Entities in today’s knowledge graphs do not only differ in their property values but also in the schematic structures they are represented by. Given their extraction-based foundation, it is quite common that in practical knowledge base instances totally unrelated graph structures describe entities of the same type. Hence, operators for handling such heterogeneity are mandatory when designing a robust query language for knowledge graphs. While SPARQL does offer optional patterns for this purpose, their query answers often suffer from an unintuitive matching behavior. In contrast, preference semantics seem to be a much more intuitive and still robust way of expressing how the optimal query result may look like. While preferences over data value domains are already applied for graph data, we argue for structural preferences to achieve fine-grained control of heterogeneity in the query answers. Therefore, we propose a new operator for SPARQL, enabling the expression of structural as well as some value preferences. Equipped with a Pareto-style semantics, we give examples of how to model preferences with the new operator. Our prototypical implementation allows for evaluating several encodings of the new construct at DBpedia’s SPARQL endpoint.
Double Deep Q Network with In-Parallel Experience Generator

In this paper, an algorithm, for in-parallel, greedy experience generator (briefly IPE, In Parallel Experiences), has been crafted, and added to the Double Deep Q-Learning algorithm. The algorithm aims to perturbs the weights of the online network, and as results, the network, trying to recover from the perturbed weights, escapes from the local minima. DDQN with IPE takes about the double of time of the previous to compute, but even if it slows down the learning rate in terms of wall clock time, the solution converges faster in terms of number of epochs.
A Large Misalignment Angles Initial Alignment Method of SINS based on Feedback Correction and the Fading Multi-innovation Tracking

In order to shorten the initial alignment time of SINS and improve the system's fast response ability, a large misalignment angle initial alignment method is proposed by feedback correction Kalman filter of the fading multi-innovation strong orthogonal tracking. Based on this method, the process of initial alignment does not need coarse alignment, the nonlinear large misalignment angle can be corrected to small misalignment angle and the initial alignment can be completed directly. Also, the employment of the fading multi-innovation improves the feedback tracking ability of SINS, and the system performance is improved.
IFSTOOL - Software for Intuitionistic Fuzzy Sets Necessity, Possibility and Circle Operators

The present paper introduces the augmented functionality of IFSTool software. Several new operators have been implemented in the software, which allows the end user to check validity of certain axioms involving them.
An Edge Detection and Sliding Window Based Approach to Improve Object Localization in YOLOv3

Object detection is considered as a challenging field in computer vision. Once an object has been detected, the next challenge is object localization where a rectangular boundary box is drawn around the location of detected object. The proposed framework addresses the problem of object localization by improving its precision. You only look once or YOLOv3 is one of the well-known object detection algorithm with its state-of-the-art object detection and real time capabilities. Because of this reason, the proposed scheme uses YOLOv3 as the base algorithm. In this work, COCO dataset is used to detect an object, and to improve the precision of boundary box this work make use of edge detection, thresholding and morphological operation. Also, redundant edge removal algorithm is proposed to remove redundant edges and boundary box construction algorithm draws rectangular boundary box around detected object. When compared with YOLOv3, the proposed model produces significantly better results when boundary boxes around detected object is concern.
CoMet: A Meta Learning-Based Approach for Cross-Dataset Labeling Using Co-Training

In many practical domains, applying machine learning is challenging not due to the lack of available data, but because labeled samples are in short supply. A common approach for obtaining additional labeled samples is co-training, a semi-supervised learning setting where two learners (agents), trained on different perspectives of the data, iteratively label additional samples. The rationale of this approach is that the different learner perspectives will produce a more diverse labeled set, resulting in more effective classifiers. While co-training proved effective in multiple cases, the labeling mechanisms used by existing approaches are heuristic and error-prone. We propose CoMet, a meta learning-based co-training algorithm. CoMet utilizes meta-models trained on previously-analyzed datasets to select the samples to be labeled for the current dataset. Our experiments, conducted on 35 datasets, show that CoMet significantly outperforms the standard co-training approach.
Calibration of Availability and Safety of a Video-Based Detection System for Airport APMs

Most modern trains are equipped with onboard CCTV technology, which can be used for a wide variety of applications, such as automatic detection of objects, people, or situations by video analytics. Such as with all detection systems, thorough calibration of the sensors, i.e., cameras in our case, is essential to minimize false alarms and achieve a very high detection rate. From an operational point of view, false alarms should be minimal or not occur at all. From a reliability point of view, no real objects or people shall be missed, especially when it comes to airport security. Unfortunately, operational availability and detection reliability are contradictory parameters. They both depend on the chosen sensitivity of the sensors. Typically, high sensitivity results in a high detection rate (which is desired) but can produce a couple of false alarms. While a low sensitivity will reduce the number of false alarms but some smaller objects may not be detected. This paper presents an approach to analyze the detection performance and choose suitable parameter settings to satisfy both.
Gender Extremism in the Internet Age

The article presents the author’s view on the interpretation of illegal, violent acts motivated by hatred or enmity, based on the biological characteristics of an individual or social group, as well as promoting the superiority or inferiority of a person on the basis of sexual (gender) identification. The authors of the article, based on the analysis of positions, scientific approaches of Russian and some foreign scientists, the practice of applying the norms of criminal and administrative-tort legislation of Russia and foreign countries, the content of sites and pages of social groups in the Internet network, consider the essence and content of gender-oriented (gender or sexual) extremism, formulate its definition, and reveal its features. The article is of theoretical and practical interest for further research and improvement of the mechanism of countering extremism. The results of the research could be used by teachers and students of secondary and higher educational institutions in preparation for classes of anti-extremist orientation.
Improved YOLO model with multi-feature fully convolutional network for object detection

The main task of object detection is to identify and locate interested objects from still images or video sequences. It is one of the key tasks in the field of computer vision. However, the object usually has variable factors in brightness, shape, occlusion and so on, and is interfered by various and complex environmental factors, which makes the research opportunities and challenges of object detection algorithm coexist. In this paper, a main frame of object detection algorithm based on convolutional neural network is studied, which is based on regression. We propose a real-time object detection algorithm based on fully convolution network, which aims to solve the problems of low detection accuracy and poor location accuracy of objects in regression method. The innovation is that the proposed fully convolution network increases the detection flexibility of the model because it is not affected by the input scale. At the same time, we propose a multi feature fusion and multi border prediction strategy, which effectively improves the detection accuracy of small objects. In order to prove the effectiveness of the proposed algorithm, we use PASCAL VOC data set to carry out object detection experiments. In this paper, the accuracy of each object category and the average accuracy of all categories are calculated. Experiments show that the performance of the multi feature fusion algorithm based on the fully convolution network is better than that based on the regression idea such as YOLO, and more than 10% higher than that of the YOLO model.
Evaluation system for user authentication methods on mobile devices

Nowadays, special interest is taken on transparent and reliable user authentication on mobile devices. Variety of proposed authentication methods allows developers to choose flexibly authentication methods according to predefined accuracy, usability and software (hardware) requirements. Nevertheless, implementation of these methods in real devices requires tedious performance analysis of proposed solutions in real situations. The analysis is complicated due to lack of open implementation of proposed authentication methods and results of preliminary testing on different datasets under various metrics.In this work we propose the universal system for evaluation of user authentication methods. The case of authentication on mobile devices, such as smartphones, is considered. The proposed system admits smooth processing of various user features databases without usage of specific importer modules. Imported features are analyzed for reconstruction of multimodal (behavioral) user models that can be used in advanced behavior-based authentication methods. The wide range of feature processing methods and performance indices, such as FAR, FRR, etc., is supported that allows developers to improve authentication method on the fly without re-building of the prototype.
On the Interplay Between Inductive Inference of Recursive Functions, Complexity Theory and Recursive Numberings

The present paper surveys some results from the inductive inference of recursive functions, which are related to the characterization of inferrible function classes in terms of complexity theory, and in terms of recursive numberings. Some new results and open problems are also included.
Information support and promotion of chemistry in the USSR

The effectiveness of the implementation of large-scale socio-economic, technological and scientific programs and projects largely depends on how easily and organically these ideas will be accepted by society. Of course, it will be an exaggeration to say that this is what determines the success or failure of certain socio-economic projects in the USSR. But to neglect and ignore the influence of this factor would be a big mistake.The article examines in detail one of the largest (and one of the last) campaigns of scientific popularization – “chemicalization of the national economy of the USSR”. When analyzing the campaign to popularize and promote chemicalization (late 1950s – early 1980s) in order to understand the logic of its origin, rise and fall, it was necessary to consider the media support of a similar campaign in the USSR in the 1920–1930s. As a result, three waves of this kind of propaganda campaigns have been identified, with those going on with a damped amplitude.The author made an attempt to show how, from the very beginning of the institutionalization of this program, its initiators provided for powerful informational support. Methods and tools have been allocated that the state has primarily resorted to during the chemicalization process throughout the entire period under review. The continuity of these approaches is traced. It is noted that as one of the key moments of the program for the chemicalization of the national economy in the USSR, initially, almost immediately after the end of the Civil War, the Soviet leadership assumed the development of not only applied (primarily related to defense tasks) aspects of chemicalization, but also fundamental chemical science. And all this was accompanied by widespread media support. Moreover, given the scarce resources of the state in the 1920s, the most effective strategy in those conditions was chosen: the “chemicalization” of public education, school and university.Using a wealth of factual material, using the capabilities of computer statistical analysis and rare bibliographic copies of books and periodicals, it was possible to trace the dynamics of the process of popularizing chemical knowledge in Soviet society.
Services Computing: A Brief Overview

Services computing provides techniques for the construction, operation and management of large-scale internet service systems. It represents the frontier development direction of software engineering and distributed computing; thus, it has always been a research hotspot and attracted increasing attention from both industry and academia. This chapter introduces the concepts and perspectives, research framework and evolution history of services computing, and gives a preliminary introduction for mobile services computing.
A novel graph matching method based on the local and global distance information of the graph nodes

Graph matching is a classical NP-hard problem, and it plays an important role in many applications in computer science. In this paper, we propose an approximate graph matching method. For two graphs to be matched, our method first constructs an association graph with nodes representing the candidate correspondences between the two original graphs. It then constructs an affinity matrix based on the local and global distance information between the original graphs’ nodes. Each element of the matrix represents the mutual consistency of a pair of nodes of the association graph. After simulating random walks on the association graph, a stable quasi-stationary distribution is obtained. With the Hungarian algorithm, our method finally discretizes the distribution to achieve an approximate matching between the two original graphs. Experiments on two commonly used datasets demonstrate the effectiveness of our method on graph matching.
SWitCH: A Reskilling Program in Information Technology

There has been an increasing demand for Information and Communication Technology (ICT) specialists, namely software engineers. The two-year SWitCH program was created to lower current unemployment rates, help on fulfilling the existing demand for software engineers from ICT companies and promote the reskilling of existing graduate students to the ICT area. This program consists of a one-year academic post-graduation program followed by a one-year paid internship. This paper introduces the SWitCH program, the current approach and post-graduation methodology, its comparison with similar approaches and its results so far.
Foundation Courses’ Soft Skills Evaluation using Fuzzy Cognitive Maps

Soft skills are often the foundation of any curricula. Although soft skills are used as a basis in all our courses, there are gaps in our understanding regarding the best ways to promote and assess soft skills learning in classrooms. Our approach to assess whether those soft skills are introduced, reinforced or emphasized by our engineering curricula is based on the fuzzy cognitive mapping technique which is a semi-quantitative method that can represent complex non-linear causal relationships between different concepts and allows reasoning about them. We built fuzzy cognitive models that reflect the views of two groups of stakeholders, namely academicians and students. The main insights gained from the built models is that most soft skills are reinforced by our foundation courses, and can be emphasized by the same set of courses.
An Application of InterCriteria Analysis Over Intuitionistic Fuzzy Data

An application of InterCriteria Analysis (ICA) over intuitionistic fuzzy data is presented. The proposed approach contains two steps: definition of intuitionistic fuzzy evaluations and an application of InterCriteria Analysis over intuitionistic fuzzy data. The investigation uses datasets for emissions of pollutants in the air from industrial, combustion and production processes. The datasets are extracted from the website of the National Statistical Institute of the Republic of Bulgaria.
Formation Control of Non-holonomic Mobile Robots - Tuning the Algorithm

This paper presents the tuning methodology for the system of multiple two-wheeled mobile robots moving in formation. The procedure was applied to the trajectory tracking algorithm combined with collision avoidance based on the Artificial Potential Functions (APFs). Robots mimic motion of the virtual leader with a certain displacement avoiding collisions with each other and with circular shaped, static obstacles present in the environment. The results obtained during the computations are visualized to enable evaluation of the sensitivity of the closed-loop system to parameter selection. Then, the results of the simulation for the set of best parameters are discussed.
Mining Composite Fuzzy Association Rules Among Nutrients in Food Recipe

Association Rule Mining is a data mining technique to discover associations among attributes of data in the form of if.. then rules in large databases of transactions. Fuzzy Association Rule Mining (FARM) emerged as a significant research area and is an extension of classical association rule mining which applies Fuzzy set theory to address the uncertainty in the case of categorical data. Different algorithms are proposed for Fuzzy Association Rule Mining and applied in different domains. Composite Fuzzy Association Rule Mining (CFARM) is one of the algorithms based on the concept of composite data items. In this paper Composite Fuzzy association rule mining technique is applied on different recipes containing their nutrient values. The recipes considered are prepared from green leafy vegetables, other vegetables and recipes of Fish and Meat. The recipes are combination of nutrient attributes like moisture, protein, fat carbohydrates etc. and micronutrient combination of some nutrient attribute like calcium, iron, vitamin and moisture etc. The Composite Fuzzy Association Rule Mining algorithm is applied to discover association among the nutrients values in the recipes. This paper contains an overview of CFARM algorithm and a composite dataset is prepared to generate rules. Experimental results are presented and analyzed with different measures of interestingness along with scope of future works.
On the Perceptron’s Compression

We study and provide exposition to several phenomena that are related to the perceptron’s compression. One theme concerns modifications of the perceptron algorithm that yield better guarantees on the margin of the hyperplane it outputs. These modifications can be useful in training neural networks as well, and we demonstrate them with some experimental data. In a second theme, we deduce conclusions from the perceptron’s compression in various contexts.
Secure Communication in the Quantum Era: (Group) Key Establishment

This paper gives a brief overview of NATO SPS project G5448. This project, which involves partners from four countries, focuses on the task of establishing a cryptographic key among a group of users over a (classic) public communication network. It is assumed that the adversary may eventually obtain access to quantum computers, i. e., standard cryptographic hardness assumptions like factoring integers being asymptotically hard may be invalidated. Next to laying out the basic project structure, we describe some of the work that has already been done since the beginning of the project, including in particular some of the work done by students.
Heterogeneous tracking system of kernel correlation filtering based on PYNQ framework

Aiming at the complex structure and weak real-time performance of traditional visual tracking system, a scheme based on PYNQ (Python productivity for Zynq) framework is designed and deployed in Zynq heterogeneous platform. Firstly, according to the real-time requirements, the kernel-related filter tracking algorithm is selected. Then, according to the characteristics of the Zynq computing platform, the software and hardware collaborative design method is used to divide the system tasks, and the algorithm is converted into RTL (Register Transfer Level) by HLS (High Level Synthesis) development tools, and the calculation is performed. The process is optimized and exported as an IP core. Then, Python is used to import the IP core as a hardware coprocessor at the top level to implement the underlying to top-level data interaction. Finally, the system result is asynchronously updated in the Jupyter notebook. Experiments show that the system has good real-time performance, and the tracking speed averages 27.9FPS. At the same time, it has superior execution efficiency, is easy to develop and transplant, and has certain engineering reference value.
Dynamic Virtual Machine Provisioning in Cloud Computing Using Knowledge-Based Reduction Method

Cloud infrastructure performance extremely depends ahead on the task scheduling and load balancing. The recent growth of cloud computing and service provider’s key challenge is scheming proficient mechanism for managing the restricted resources shared by different applications. Resource administration method has to do efficient assignment of resources for virtual machines by ensuring optimal resource exploitation of available physical machines. This paper proposes the application of rough-set model for provisioning of virtual machines. The proposed method uses knowledge-based reduction technique, and it generates the rules to reduce unnecessary attributes of the virtual machines. These rules help virtual machine managers for making effective administration of restricted resources.
MANOVA Test Statistics

The main purpose of this chapter is to give a method for obtaining error bounds for asymptotic expansions of the null distributions of Hotelling’s $$T^2$$ (or Lawley–Hotelling criterion, $$T_{LH}$$ ), the likelihood-ratio criterion $$T_{LR}$$ and the Bartlett–Nanda–Pillai criterion $$T_{BNP}$$ in the MANOVA model when the sample size is large. The results for $$T_{LH}$$ and $$T_{LR}$$ are obtained by expressing these statistics in terms of a multivariate scale mixture, and using error bounds evaluated in $$L_1$$ -norm. The error bound is given for the limiting distribution of $$T_{BNP}$$ by using a relationship between $$T_{BNP}$$ and $$T_{LH}$$ . Further, we give error bounds for these criteria when the sample size and the dimension are large.
Player Ready? A Model for Evaluating VR Game Interfaces

We report on a study investigating the experience and usability of 3D interfaces in virtual reality games. To aid analysis of player behaviour performing simple tasks, a model is proposed which categorises player actions as discover, interact or move. For certain interfaces, movement caused unnecessary overhead as players must travel to the interface before discovery or interaction can take place. Additionally, menu items outside the players field of view caused task failures. The analysis converges with five heuristics for menu design in virtual reality games which we believe will aid developers in the field.