Reviewerly/exHarmony huggingface dataset:
Community Model - A New Data Fusion Filter Paradigm

Data fusion is an important area in different fields of applications as it provides a detailed analysis of the data store which helps in analytics and decision support system. Among the numerous Data Fusion models available in literature, the six pioneer pieces of work in terms of Data Fusion Models are – Waterfall Model, Multisensor Integration Fusion Model, Behavioral Knowledge-based Data Fusion Model, Omnibus Model, Dasarathy Model and JDL Model. In this paper, we have given a comparative study of the above models based on basic features like signalling overhead, power consumption, fault tolerance, survivability and robustness facilities subsequently. Here, a new improved model, named Community Model has been proposed for better throughput in terms of Data Transmission Time (DTT). This model can be applied to any number of filter levels irrespective of application areas.
Fundamental problems of education in the context of modern social processes (International scientific and practical conference at Novosibirsk State Pedagogic University)

On December 8–10, 2020, the international scientific and practical conference “Fundamental problems of education in the context of modern social processesˮ was held at Novosibirsk State Pedagogical University with the support of the RFBR grant. The conference was organised by the NSPU together with the Ministry of Education of the Russian Federation, the Institute for Education Development Strategy of the Russian Academy of Education, the Ministry of Education of Novosibirsk Region, the Ombudsman for the Rights of the Child in Novosibirsk Region and the municipal autonomous institution for additional education “Palace of Creative Work of Children &amp; Student Youth “Junior”.
Semantics-Guided Representation Learning with Applications to Visual Synthesis

Learning interpretable and interpolatable latent representations has been an emerging research direction, allowing researchers to understand and utilize the derived latent space for further applications such as visual synthesis or recognition. While most existing approaches derive an interpolatable latent space and induces smooth transition in image appearance, it is still not clear how to observe desirable representations which would contain semantic information of interest. In this paper, we aim to learn meaningful representations and simultaneously perform semantic-oriented and visually-smooth interpolation. To this end, we propose an angular triplet-neighbor loss (ATNL) that enables learning a latent representation whose distribution matches the semantic information of interest. With the latent space guided by ATNL, we further utilize spherical semantic interpolation for generating semantic warping of images, allowing synthesis of desirable visual data. Experiments on MNIST and CMU Multi-PIE datasets qualitatively and quantitatively verify the effectiveness of our method.
Reengineering Legacy Systems Towards New Technologies

This chapter presents all the concepts, techniques, and analyses in relation to the problem of reengineering existing (legacy) systems towards new technologies. Reengineering is a sub-problem of software engineering. It is the study and analysis of an existing system for purposes of understanding, maintenance, or migration towards new technologies that arise from day to day, without rewriting the software from scratch. This will save us time and money in the software development process. Author's objective is not to create new terms, but to introduce the terms already in use with new perspectives. So in this chapter, definitions and techniques are introduced, taxonomies and models are proposed, relevant questions are answered, some specialized conferences and journals are listed and compared; all this to highlight the ways to authors who are interested in writing research papers or surveys in the software reengineering field.
Multi-Scale Cascade Disparity Refinement Stereo Network

Stereo matching has attracted much attention in recent years. Traditional methods can quickly generate a disparity result, but the accuracy is low. On the contrary, methods based on neural networks can achieve a high accuracy level, but they are difficult to reach the real-time level. Therefore, this paper presents MCDRNet, which combines traditional methods with neural networks to achieve real-time and accurate stereo matching results. Concretely, our network first generates a rough disparity map based on the traditional ADCensus algorithm. Then we design a novel Multi-Scale Cascade Network to refine the disparity map from coarse to fine. We evaluate our best-trained model on the KITTI official website. The results show that our network is much faster than most current top-performing methods(31×than CSPN, 56×than GANet, etc.). Meanwhile, it is more accurate than traditional stereo methods(SGM, SPS-St) and other fast 2D convolution networks(Fast DS-CS, DispNetC, etc.), demonstrating the rationalities and feasibilities of our method.
Smart Delay Module For In-Vehicle Live IPTV Streaming

Due to the emergence of new trends in the automotive industry, such as autonomous vehicles, there is an increasing emphasis on the driver and passengers' activities as well as on their user experience in such vehicles. One of the main forms of in-vehicle passenger entertainment is infotainment devices that allow them to stream media. Since on-the-go clients do not have a consistent Internet connection quality, streaming live media in such conditions becomes challenging. In this paper, we give an overview of the challenges regarding live media streaming in the no-signal area. We propose a solution for these challenges in the form of extension of the existing architecture of the media subsystem for clients on the move with a smart delay module that provides improvements to Quality of Experience (QoE) and Quality of Service (QoS) for the live media stream while driving.
Feature Reuse for a Randomization Based Neural Network

We propose a feature reuse approach for an existing multi-layer randomization based feedforward neural network. The feature representation is directly linked among all the necessary hidden layers. For the feature reuse at a particular layer, we concatenate features from the previous layers to construct a large-dimensional feature for the layer. The large-dimensional concatenated feature is then efficiently used to learn a limited number of parameters by solving a convex optimization problem. Experiments show that the proposed model improves the performance in comparison with the original neural network without a significant increase in computational complexity.
Bitcoin Under Inflation-Hedging Scrutiny - The Extended Fisher Hypothesis in Crypto

Improved access to the scarce, digital resource justifies analyzing Bitcoin from a macroeconomic perspective. Using ten years of historical monthly returns, this study examines Bitcoinâ€™s hedging capabilities with respect to inflation measures following the Fisher hypothesis. Short-term interest rates are used to split the ex post observed inflation into an ex ante expected and unexpected part by applying autoregressive distributed lag models. Having 40 countries under scrutiny, the results show that Bitcoin has partial hedging capabilites against expected inflation in specific countries. In a cross-sectional view on expected inflation, rolling window regressions reveal a diminishing number of significant inflation betas over time.
COVID-Vision: A Virtual Reality Experience to Encourage Mindfulness of Social Distancing in Public Spaces

Social distancing is currently the most effective known countermeasure against the rapid proliferation of the virus that causes COVID-19. This project aims to encourage mindfulness about maintaining interpersonal distance in shared public spaces through a multi-user virtual reality experience that simulates shopping in a grocery store. The virtual environment is populated with non-player characters that navigate through the store and also supports up to 20 concurrent live users represented as avatars. Real-time feedback is implemented using a dynamic visual effect that reacts to physical proximity, and comparative performance metrics are also provided for users to reflect on after the task is completed.
NEW SOLUTION TO THE DROPLET KERNEL MODEL

The droplet model of the nucleus is revived, for which an exact solution for an incompressible fluid is obtained using the hydrodynamic potential solution obtained from the Schrödinger equation. Moreover, for an incompressible fluid, there are formulas for the pressure or potential. There is the main part of the hydrodynamic potential, which is obtained by replacing the modulus of the inverse difference of vectors by the difference in moduli of the values of the vectors. The bulk of the potential is expressed in a finite formula with singularities. A formula is obtained for the integral containing the modulus of the difference between the exact values of the vectors minus the main part of the potential. This difference defines a continuous correction with the features taken into account. The main part of the potential at the boundary of the nucleus turned out to be infinitely large with an imaginary part, locking particles in the nucleus. In this case, the real part of the main potential decreases with decreasing radius, becomes negative, and determines the bound state. At half the radius of the nucleus, there is a linear term along the radius. At the zero radius, there is an infinite negative potential with an imaginary part. An expression for the quantum of the emitted energy is obtained. Note that the added mass was not used due to the rotational regime of the nucleus. An algorithm for calculating the spectrum of the kernel is proposed, and each state of the action of the kernel sn corresponds to n calculated frequencies, determined by n angles in the configuration space. The main space is n + 1 dimensional, and each dimension of space has its own energy. But without special means, the potential of the nucleus tends to infinity. It is necessary to introduce the imaginary degree of roughness of the corners, in expressions containing singularities, then the infinities disappear.
Observational interpretations of hybrid dynamic logic with binders and silent transitions

We extend hybrid dynamic logic with binders (for state variables) by distinguishing between observable and silent transitions. This differentiation gives rise to two kinds of observational interpretations: The first one relies on observational abstraction from the ordinary model class of a specification Sp by considering its closure under weak bisimulation. The second one uses an observational satisfaction relation for the axioms of the specification Sp, which relaxes the interpretation of state variables and the satisfaction of modal formulæ by abstracting from silent transitions. We establish a formal relationship between both approaches and show that they are equivalent under mild conditions. For the proof we instantiate the previously introduced concept of a behaviour-abstractor framework to the case of dynamic logic with binders and silent transitions. As a particular outcome we provide an invariance theorem and show the Hennessy-Milner property for weakly bisimilar labelled transition systems and observational satisfaction. In the second part of the paper we integrate our results in a development methodology for reactive systems leading to two versions of observational refinement. We provide conditions under which both kinds of refinement are semantically equivalent, involving implementation constructors for relabelling, hiding, and parallel composition.
Influence of keyboard layout and feedback type in eye-typing tasks: a comparative study

Writing with a keyboard using only one's gaze, i.e., eye-typing has aroused researchers’ interest in the last decades. Initial efforts aimed to provide a means of communication for people suffering from severe motor disabilities, while recently, the interest has expanded to target non-disabled people. The present paper examined the impact of the keyboard shape and type of feedback on writing performance and usability. More specifically, three keyboard shapes (i.e., QWERTY, ABCDE, and Circular) and three types of feedback (i.e., visual, audio, none) were investigated. Besides confirming the impact of visual feedback for successful performance in an eye-typing task, our results expand previous findings showing the advantages of deploying a circularly-shaped keyboard.
Paraphrase-Sense-Tagged Sentences

Many natural language processing tasks require discriminating the particular meaning of a word in context, but building corpora for developing sense-aware models can be a challenge. We present a large resource of example usages for words having a particular meaning, called Paraphrase-Sense-Tagged Sentences (PSTS). Built upon the premise that a word's paraphrases instantiate its fine-grained meanings -- i.e. 'bug' has different meanings corresponding to its paraphrases 'fly' and 'microbe' -- the resource contains up to 10,000 sentences for each of 3 million target-paraphrase pairs where the target word takes on the meaning of the paraphrase. We describe an automatic method based on bilingual pivoting used to enumerate sentences for PSTS, and present two models for ranking PSTS sentences based on their quality. Finally, we demonstrate the utility of PSTS by using it to build a dataset for the task of hypernym prediction in context. Training a model on this automatically-generated dataset produces accuracy that is competitive with a model trained on smaller datasets crafted with some manual effort.
L’acquisizione imperfetta di una lingua adstrato L’italiano degli analfabeti a Malta dal Settecento al Novecento

The fact that Maltese has adopted over 20,000 Sicilian and Italian words, including 41% of its vocabulary at the Threshold Level, shows that this is not a case of borrowing at the higher social levels. To explain this unusual phenomenon the extraordinary growth in the island’s population and its rapid development under the Knights of St John and the British government are taken into account. Whereas works written in Italian from the 16th to the 20th century are abundant, and their quality is not inferior to those written in Italy, little is known about the efforts made by illiterate persons to speak the high language. The paper presents a few symptomatic comments and samples from the past, and from the present.
A PSO Algorithm Based Web Page Retrieval System

Webpage retrieval is the process of obtaining and presenting more related webpage from the largest collection of webpage resources according to the user’s need. The tremendous growth in information resources on the Internet makes the information retrieval process a tedious and difficult task for users. Due to information overloading, there is a need for better techniques to retrieve most relevant information from web. This paper presents the webpage retrieval system by using the PSO algorithm. In presented system, to extract the text from web documents, all html tags are removed. After that stop words and special characters are removed from extracted text for recovering only meaningful contents. The TF-IDF concept is used for feature selection. Now PSO optimization technique is used for identifying and refining the features set, these selected features are stored in a database which is used for webpage retrieval process. On the other side of the system, in search technique firstly the user query sequences are transformed into multiple query strings and using these query strings the search is performed over the database. The performance of the search system is computed in terms of accuracy and error rate. The results are also compared with the traditional search model which demonstrate the proposed technique is superior then the traditional search system.
Two Stage Steganography on Compressed and Encrypted Message

Security of confidential information in the insecure era of information transmission (Internet) is still one of the most important challenges of the day. The combination of cryptography and steganography increases the security of embedded data to avoid from unauthorized access. Furthermore, compression of secret data reduces the size of transmitted message. In addition to compression and encryption, in this paper, two stage steganography is employed to enhance the security. In the proposed approach, the Huffman coding as lossless compression, the Blowfish, DES, 3DES, AES, and RSA as cryptography algorithms and LSB (Least significant Bit) as steganography technique are employed with enhancement of security by two stage steganography. The results are analyzed through quality parameters including MSE (Mean Square Error) and PSNR (Peak Signal to Noise Ratio), and histogram of images.
Cross-view geo-localization via Salient Feature Partition Network

Abstract Cross-view geo-localization aims to find images containing the same geographic target from images obtained from different platforms. The extreme viewpoint variations bring challenges to this task. Existing methods usually focus on mining the fine-grained features of geographic targets in images, ignoring the potential contextual information around them. In this paper, we consider that the background regions can be used as auxiliary information, which can make the image representation for geo-localization more discriminative. Specifically, we designed a classification network that divides regional features based on saliency, called Salient Feature Partition Network (SFPN), which utilizes background information in an end-to-end manner. Without using additional part estimators, SFPN divides the features into foreground and background based on saliency. It simplifies part matching and realizes the region division learning. The method proposed in this paper has achieved competitive results on the university 1652 dataset.
Softcomputing Approach to Sarcasm Analysis

The goal of the paper is to create a system for sarcasm recognition in texts. The system effectiveness is significantly better than a random guess and it is functional in chosen types of sarcasm. The system is tested and created using datasets based on input from Twitter users but also on headlines of online news magazines. Used datasets contain different type of sarcasm in form appropriate for neural network to train on. The limitation of the system is recognition of specialized sarcasm which requires a unique knowledge to understand it and consider it to be an instance of sarcasm. The system determines sarcasm only through the website and seek the proper context, so the system determines sarcasm only in the given sentence. Two basic solutions had been developed: a neural network with different configurations of layers and a convolutional neural network. The implemented solutions give very satisfactory results.
Measurements of fundamental frequency shifts in the air cavity mode of a violin originating from changes in molecular weight and temperature

Abstract In the violin, the fundamental air cavity mode (the A0 mode) is associated with the sound radiated by the f-holes. The dependences of the sound velocity of the A0 frequency on gaseous temperature and mass were investigated. It was found that the frequency of the A0 mode changed, depending on the molecular weight and the temperature of the gas filling the violin’s body. From these results, it was deduced that a temperature change in the room in which musical instruments are played may be reflected in their tones. The measurement of the A0 modal frequency of a violin, focusing on the molecular weight and temperature of the gas, would be useful to students of physics to help in the understanding of sound velocity.
The Influence of Clustering Population on Estimation Accuracy of Population Totals Vector

Abstract The estimation accuracy of a population totals vector based on a simple cluster sample is considered. The variance-covariance matrix of estimators depends on the intra-cluster spread of variables under study. The spread depends on the partition of the population into clusters. The variance-covariance matrix is evaluated under several variants of clustering algorithm. This lets us find the clustering algorithm providing the most accurate estimation of population vector totals.
Gray System Prediction in the Alpine–Himalayan Earthquake Zone

Abstract Increasingly frequent earthquakes pose a serious threat to human lives and properties. However, the prediction of seismic activity is a major problem in seismic research. In this paper, we use the gray model to study seismic activity patterns. The Alpine–Himalayan seismic zone is selected as a study area for this work. Using seismic activity in the region, we analyze characteristics of time series related to seismic activity. Results show that the gray model for predicting seismic activity is available. The timing of the seismic activity of the Alpine–Himalayan seismic zone displays certain regularity. On the basis of computational analysis on July 20, 2015, a strong earthquake will occur in 26 years. In this paper, we use the application of a gray system that provides a new modeling approach in the field of disaster prevention to predict seismic activity, which is important in disaster prevention, disaster management, and rescue.
How Communication Breakdowns Affect Emotional Responses of End-Users on Progressive Web Apps

Progressive Web App (PWA) is a recent approach made of a set of techniques from both web and native apps. End-User Development (EUD) is an approach from which end-users are allowed to express themselves. The impacts of associating EUD and PWAs have been little exploited. With this in mind, we proposed the PWA-EU approach in previous work. In this paper, we compare the communication breakdowns and the users’ emotional responses with the aim of finding if both aspects are correlated. We conducted a study with 18 participants that interacted with Calendar, a mobile app based on the PWA-EU approach. We carried out a qualitative analysis based on the communication breakdowns and emotional responses of the participants’ interaction. Our findings point out that common issues occurred and affected both the emotional response and breakdowns of the participants. Still, how these common issues affected the participants were different between individuals.
Garbled Circuit for 4 Valued Logic

Garbled circuit is the main tool to realize 2 party computation. Garbled circuit computes the output C (x, y) of the circuit C while hiding the inputs x, y. For 2 valued logic, there were many studies about the garbled circuit for Boolean circuit. Recently, Lindel et al. proposed the first garbled circuit construction for 3 valued logic. In this paper, we propose the first garbled circuit construction for 4 valued logic. The proposed construction is a natural extension of the Lindell et al.'s garbled circuit construction. Intuitively, in the proposed construction, we represent 4 valued logic circuit by Boolean circuit, and then apply Boolean garbled circuit construction to obtain 4 valued garbled circuit. The proposed construction reduces the number of ciphertexts to 4 from 16 of naive Yao's construction. In this paper, we propose the first garbled circuit construction for 4 valued logic. The proposed construction is a natural extension of the Lindell et al.'s garbled circuit construction. Intuitively, in the proposed construction, we represent 4 valued logic circuit by Boolean circuit, and then apply Boolean garbled circuit construction to obtain 4 valued garbled circuit. The proposed construction reduces the number of ciphertexts to 4 from 16 of naive Yao's construction.