Reviewerly/exHarmony huggingface dataset:
A GLMB filter for unified multitarget multisensor management

The generalized labeled multi-Bernoulli (GLMB) filter of Vo and Vo is an exact closed-form solution of the multitarget Bayes' filter and is, therefore, provably Bayes-optimal. Its recent implementations are extremely fast, with computational order O(n<sup>2</sup>m) where n,m are the current numbers of tracks resp. measurements. This paper generalizes the GLMB filter to fully integrated multitarget tracking and sensor management, in which dynamically moving sensors can appear and disappear and in which the states of these sensors are estimated via measurements collected by internal actuator sensors.
DNN based multi-speaker speech synthesis with temporal auxiliary speaker ID embedding

In this paper, multi speaker speech synthesis using speaker embedding is proposed. The proposed model is based on Tacotron network, but post-processing network of the model is modified with dilated convolution layers, which used in Wavenet architecture, to make it more adaptive to speech. The model can generate multi speaker voice with only one neural network model by giving auxiliary input data, speaker embedding, to the network. This model shows successful result for generating two speaker's voices without significant deterioration of speech quality.
Performance statistics and learning based detection of exploitative speculative attacks

Most of the modern processors perform out-of-order speculative executions to maximise system performance. Spectre and Meltdown exploit these optimisations and execute certain instructions leading to leakage of confidential information of the victim. All the variants of this class of attacks necessarily exploit branch prediction or speculative execution. Using this insight, we develop a two step strategy to effectively detect these attacks using performance counter statistics, correlation coefficient model, deep neural network and fast Fourier transform. Our approach is expected to provide reliable, fast and highly accurate results with no perceivable loss in system performance or system overhead.
Applying Context to Handwritten Character Recognition

Attempts to automate handwritten character recognition date back to the 1960s, but progress over the past two decades shows extremely accurate recognition of printed characters in English. The most common approaches used today apply a form of machine learning such as support vector machines (SVM) or neural networks. While highly accurate, these forms of machine learning do not attempt to apply higher-level knowledge to improve performance. This paper presents research applying SVM-trained recognizers supplemented with domain knowledge to provide top-down guidance in an attempt to improve recognition accuracy.
The Method of Deductive Inference of Consequences with the Scheme Construction

The paper describes the method of inference in first-order predicate calculus, which allows, besides obtaining the result, to build a special structure—the scheme of inference. This structure represents a special kind of graph and it can be used to interpret the solution both visually and in the analytical way. Besides, the scheme can be applied to evaluate the development of situation in dynamic systems research. The method is based on the operation of division of disjuncts, characterized by a high level of AND-, OR- DCDP-parallelisms, thereby it can be effectively realized on modern multiprocessor and multicore platforms in software mode.
Fuzzy Join as a Preparation Step for the Analysis of Training Data

Analysis of training data has become an inseparable part of sports preparation not only for professional athletes but also for sports enthusiasts and sports amateurs. Nowadays, smart wearables and IoT devices allow monitoring of various parameters of our physiology and activity. The intensity and effectiveness of the activity and values of some physiology parameters may depend on weather conditions in particular days. Therefore, for efficient analysis of training data, it is important to align training data to weather sensor data. In this paper, we show how this process can be performed with the use of the fuzzy join technique, which allows to combine data points shifted in time.
Spatio-temporal Super-resolution with Photographic and Depth Data using GANs

LiDAR technology is essential for self-driving cars, which have seen a surge in interest and investments from startups and established automotive corporations alike. However, the task of automated driving requires high resolution and significant depth-range capabilities of the sensor, keeping its cost prohibitive. Super-resolution of depth maps has been explored as a potential circumvention of these problems, with a substantial number of methods being analyzed in the past few years, yielding various levels of success. We propose a super-resolution algorithm trained for depth-map data and LiDAR compatibility using Generative Adversarial Networks (GANs).
New Technique for Binary Morphological Shape-Based Interpolation

In this paper we consider shape-based methods to generate additional slices in 3D binary volumes. The focused interpolation approaches, named SIMOL and BORS, are based on morphological and logical operators. Given two adjacent slices [Formula: see text] and [Formula: see text] of the binary image set, the methods iteratively generate a sequence of new slices showing a gradual transition between the corresponding shapes. First, we analyze the SIMOL and BORS techniques and highlight their problems. Then we present the main contribution of this paper: a new interpolation scheme, called SIMOL-NEW, that combines the iterative scheme of BORS and an interpolation kernel generated through SIMOL framework. Next, we compare SIMOL-NEW and BORS approaches using theoretical elements and computational experiments. The latter are executed using: (a) benchmark shapes; (b) simple volumes defined by sphere and paraboloid; (c) combination of ellipsoids; (d) a fork-like volume; (e) Cylinder Minus Sphere. The conclusion is that SIMOL-NEW performs closer to BORS for the cases (a) and (c) but it is more accurate than BORS in the tests (b) and (d). Besides, we offer comparisons of state-of-the-art approaches in shape-based interpolation and SIMOL-NEW using ground truth volumes (d) and (e). The computational experiment report that SIMOL-NEW gets outstanding results regarding the ability to recover the target volume.
Data Mining Agents as Means of Communicating Users in an E-Learning Environment

Intelligent data analysis is performed on large and unprocessed data sets which obtained as a result of observations or collection by electronic means, it also uses advanced algorithms and methods, which are used for studying and searching of hidden patterns and strategies in order to classify, typify, and make decisions on the adjustment of communications of participants in the educational process. The use of data mining agents at this stage is for determining the communicative strategies of students in the educational environment, which will allow you to quickly gain recommendations for students on the organization of the learning process, teachers and methodologists.
Oppositional Learning Prediction Operator with Jumping Rate for Simulated Kalman Filter

Simulated Kalman filter (SKF) is among the new generation of metaheuristic optimization algorithm established in 2015. In this study, we introduce a prediction operator in SKF to prolong its exploration and to avoid premature convergence. The proposed prediction operator is based on oppositional learning with a jumping rate. The results show that using CEC2014 as benchmark problems, the SKF algorithm with oppositional learning prediction operator with jumping rate outperforms the original SKF algorithm in most cases.
A Method to Determine the Characteristic of Object Based on 2D/3D Correspondance

This paper presents a method to retrieve semantic using combination of images and the corresponding 3D model reconstructed using photogrammetry. Our proposed method for determining the characteristics consists of two major steps. First, recognize characteristics based on the curvature of the point cloud and image processing of input photographies. Second, determine the characteristics of both spaces (2D and 3D) simultaneously. Our proposed method has the advantage of a combination of additional missing information on images and 3D model, to enhance detection and extraction of features. Experimental examples are presented to present the occurrely and the flexitricity of the method that can be applied to a large variety of objects.
Scene classification based on the bag-of-visual-words and Doc2Vec models for high-spatial resolution remote-sensing imagery

A probabilistic topic model (PTM) combined with the bag-of-visual-words model is a common method to bridge the so-called “semantic gap” problem in remote-sensing image classification research. Owing to the inherent shortcomings of PTMs, such as time consumption and failures to consider a spatial arrangement of various objects, we introduce a natural language processing document-to-vector (Doc2Vec) model, to capture the high-level semantic information of the images, instead of a PTM. The model characterizes words and documents as dense, low-dimensional vectors and implements a simplified, shallow neural network to train a language model and word vectors. It is expected to mine semantic information of remote-sensing images from a new perspective. We also improve the low-level feature quality by using feature-specific sampling methods. Two high-spatial resolution remote-sensing image datasets, UC Merced and RSSCN7, are employed to conduct a scene classification experiment to discuss the performance of the Doc2Vec model. The experimental results show that the Doc2Vec model is highly efficient in mining semantic information of the images, compared with the state-of-the-art methods.
Creating Geo-Information Base of an Internet Resource "Ancient Waterways in the North-West of Russia"

Nowadays, all kinds of gadgets open up new possibilities for presenting information online. The geoinformation base of the Internet-site "Ancient waterways in the North-West of Russia" allows a wide range of users to get more information about waterways at any time. This Internet resource is unique in Russia.
Mitigating Overfitting Using Regularization to Defend Networks Against Adversarial Examples

Recent work has shown that neural networks are vulnerable to adversarial examples. There is an discussion if this problem is related to overfitting. While many researcher stress that overfitting is not related to adversarial sensitivity, Galloway et al. [4] showed that mitigating overfitting improves the accuracy on adversarial examples. In this study we add to this view that overfitting is a factor in adversarial sensitivity. To make this argument, we include two directions in our study, the first is to evaluate several standard regularization techniques with adversarial attacks and to the second is to evaluate binarized stochastic neural networks on adversarial examples. We report that strong regularizations including binarized stochastic neural networks do not only improve overfitting but also help the networks in fighting against adversarial attacks. Supplemental materials are available at https://github.com/ykubo82/ovf .
Analysis of ICT education in Azerbaijan: current state, foreign experience, problems and prospects

Purpose of the study. The aim of the study is to develop recommendations for improving educational curricula for ICT specialties taught to students in universities of the Azerbaijan Republic. The urgency of the problems outlined in this article is determined by the urgency of the transition to the modernization of the national education system and its integration into the global and world educational space, the requirements of creating an information society in Azerbaijan. Modern society needs qualified personnel who own new information technologies and are able to apply them in various fields of activity. Therefore, the process of modernization of the ICT education system is necessary; curricula should comply with international standards and specialization profile. Materials and research methods. The article provides a comparative analysis of higher professional ICT education in the developed countries of the world, shows the similarities and differences of the educational systems of the United States, European countries, Russia and Azerbaijan, analyzes the trends and features of the educational systems of developed countries, provides a list of IT specialties in demand today and in the near future. . The article also outlines the problems facing higher education institutions that train IT profile specialists. As research materials are used: policy documents of the governing bodies of the Republic of Azerbaijan in the field of higher vocational education; training programs of ICT specialties of universities of the Azerbaijan Republic; study programs of ICT specialties of leading foreign universities; scientific works of domestic and foreign authors in the field of ICT education; recommendations of international organizations ACM (The Association for Computing Machinery) and IEEE (The Institute of Electrical and Electronics Engineers) on the development of curricula for various areas of IT profile. Results. Taking into account the analysis of the curriculum of the IT profile of foreign universities, recommendations of international organizations of standardization, monitoring of the labor market, methodical recommendations were developed to improve ICT education in Azerbaijan. The necessity of modernization of the education system and bringing it into line with modern requirements is shown. Conclusion. In modern conditions of the formation of the information society in Azerbaijan, the educational program of the university should take into account the results of monitoring the labor market and the dynamics of macroeconomic changes, the disappearance of old and the emergence of new activities related to the use of ICT. Therefore, an important task is to prepare highly professional specialists in the country who are able to use and implement the advanced development of information technologies in practice. The recommendations outlined in the article can be used by university professors to improve the educational programs of ICT specialties in Azerbaijan.
Research on Multi-Dimensional Analysis Method of Power Equipment Condition Monitoring Based on OLAP

Large data analysis of power equipment condition monitoring is a hot research topic, which is of great significance to ensure the safe and stable operation of power equipment. OLAP can quickly access and analyze data from multiple angles. It is an important technical means to realize large data analysis of power equipment condition monitoring. Aiming at the problems of high cost of connection operation and slow query speed in distributed relational OLAP data model, a state monitoring data model for power equipment based on connectionless hierarchical coding is proposed. It codes the hierarchical information of dimension table and stores into the fact table to reduce connection operation and optimize performance. The experimental results show that this proposed method outperforms the conventional data model in data loading speed, roll-up execution time and storage overhead.
Partial Scoring of Reordering Tasks : Maximal Relative Sequence by Excel

An Excel form was developed by the author for calculating partial scoring of item reordering questions. It is a transplant of the algorithm realised in an application also devised by the author for the same purpose of calculation. Whereas the computer application is quick and efficient, the internal mechanism is a blackbox to general users unless the coding is understood. The present tool shows explicitly how each step of calculation is combined to reach the final score, together with possible combinations of partially correct elements within a target sequence. Given a correct answer A-B-C-D-E, a test-taker answer C-D-A-B-E, for instance, will be evaluated for two partial sequences C-D-E or A-B-E. In these partial sequences (Maximal Relative Sequences), elements run in an ascending order while allowing gaps, and they are the longest possible sequences. The partial score is 2, counting the number of transitions of elements, out of the full score of 4. Any MRS is a result of dislocating elements from the correct answer, which is measurable by `Minimal Edit Distance' (MED). Unlike MED, MRS is locally calculable and therefore eligible for Excel transplantation. This paper explains how each step of forming an MRS is conducted. Despite limitations of memory, the present Excel form is a popular tool which helps users to understand the rationale behind MRS, and of more practical significance it is a convenient tool to get partial scores by simply copying and pasting test-taker answers.
An Overview of Learning Algorithms and Inference Techniques on Restricted Boltzmann Machines (RBMs)

Restricted Boltzmann Machines (RBMs) is one of machine learning's methods which within past decades, the development of RBMs has quite increase. Researches of RBMs focused on theories and applications of RBMs. The application of RBMs has proofed that RBMs good at finishing many tasks, such as feature extraction method, document modeling, representation learning, classification and others. The RBMs' theories also have great movements, such as the development of the learning algorithm and inference techniques of RBMs. The key factors making the RBM success on finishing task are the learning algorithm and inference techniques. They motivated the development of inference techniques which successfully improved the deep neural network (DNN) performance. The aim of this research is reviewing the various types of RBMs as the application side, and the development of learning algorithm and inference techniques as theoretical side. Hopefully, it could motivate more development on the RBMs in order to contribute on overcoming implementation tasks especially on image processing tasks.
Influence of Social Coefficient on Swarm Motion

This paper presents an improved effective particle swarm optimization algorithm named SCPSO. In SCPSO, in order to overcome disadvantages connected with premature convergence, a new approach associated with the social coefficient is included. Instead of random selected social coefficients, the author has proposed dynamically changing coefficients affected by experience of particles. The presented method was tested on a set of benchmark functions and the results were compared with those obtained through MPSO-TVAC, standard PSO (SPSO) and DPSO. The simulation results indicate that SCPSO is an effective optimization method.
Quantification under class-conditional dataset shift

Quantification is the estimation of class proportions in a dataset. It is important in a wide range of fields, such as the proportion of positive reviews in sentiment analysis or the age and gender distribution of respondents in market research.
Asymmetric Laplace Mixture Modelling of Incomplete Power-Law Distributions: Application to ‘Seismicity Vision’

Data used in statistical analyses are often limited to a narrow range over which the quantity of interest is observed to be reliable. The power-law behavior of many natural processes is only observed above a threshold below which information is discarded due to detection limitations. These incomplete data can however also be described by a power law, and the distribution over the full quantity range reformulated as an asymmetric Laplace (AL) distribution. With the detection process heterogeneous in space and time in realistic conditions, the data can be modelled by a mixture of AL components. Using seismicity as example, we describe an asymmetric Laplace mixture model (ALMM), which considers ambiguous overlapping components - as observed in Nature - based on a semi-supervised hard Expectation-Maximization algorithm. We show that the ALMM fits reasonably well incomplete data and that the number of AL components can be related to the seismic network density. We conclude that the full range of data can be used in statistical analyses, including in computer vision. In the case of seismicity, a ten-fold increase in sample size is possible which provides, for example, a better spatial pattern resolution to improve the correlation between fault features and earthquake labels.
Stereo Vision Based Object Detection Using V-Disparity and 3D Density-Based Clustering

In recent years, autonomous driving has inexorably progressed from the domain of science fiction to reality. For a self-driving car, it is of utmost importance that it knows its surroundings. Several sensors like RADARs, LiDARs, and Cameras have been primarily used to sense the environment and make a judgment on the next course of action. Object detection is of a great significance in Autonomous Driving wherein the self-driving car needs to identify the objects around it and must take necessary actions to avoid a collision. Several perception-based methods like classical Computer Vision techniques and Convolutional Neural Networks (CNN) exist today which detects and classifies an object. This paper discusses an object detection technique based on Stereo Vision. One challenge in this process though is to eliminate regions of the image which are insignificant for the detection, like unoccupied road and buildings far ahead. This paper proposes a method to first get rid of such regions using V-Disparity and then detect objects using 3D density-based clustering. Results given in this paper show that the proposed system can detect objects on the road very accurately and robustly.
Practical classroom strategies for effective differentiation

SecEdVol. 2019, No. 11 Differentiation in PracticePractical classroom strategies for effective differentiationMatt BromleyMatt BromleyEducation journalist and author with 20 years' experience in teaching and leadership. He works as a consultant, speaker and trainer and is the author of numerous books for teachers including Making Key Stage 3 Count and How to Learn. Visit www.bromleyeducation.co.uk and follow him on Twitter @mj_bromley. To read his previous best practice articles for SecEd, visit http://bit.ly/1UobmslMatt BromleyPublished Online:14 May 2019https://doi.org/10.12968/sece.2019.11.25AboutSectionsView articleView Full TextPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareShare onFacebookTwitterLinked InEmail View article FiguresReferencesRelatedDetailsCited byReimagining the concept of differentiation in languages classrooms14 October 2022 | The Language Learning Journal, Vol. 10 2 May 2019Volume 2019Issue 11ISSN (print): 1479-7704ISSN (online): 2755-290X Metrics History Published online 14 May 2019 Published in print 2 May 2019 Information© MA Education LimitedPDF download
Learning Curve as a Knowledge-Based Dynamic Fuzzy Set: A Markov Process Model

In the fuzzy set theory introduced by Zadeh [15], membership degree of a fuzzy set is determined by a static membership function, i.e., it does not change over time. To improve this condition, then Wang introduced the dynamic fuzzy logic. In this concept, the membership degree of a fuzzy set is changing over time. Intan and Mukaido [5] introduced the knowledge-based fuzzy set, by means that the membership degree of a set is dependent on the knowledge of a person. Since the knowledge of a person is not static, the knowledge-based fuzzy set can be measured dynamically over time, so that we have the knowledge-based dynamic fuzzy set. In this paper, we approximate the learning process as a knowledge-based dynamic fuzzy set. We consider that the process of learning is dependent on the knowledge of a person from time to time so that we can model the learning process is a Markov process of dynamic knowledge. Additionally, using the triangular fuzzy number, we follow Yabuuchi et al. (2014), for modeling the time difference in the dynamic knowledge fuzzy set as an autoregressive model of order one.