Reviewerly/exHarmony huggingface dataset:
Multi-level Thresholding and Quantization for Segmentation of Color Images

Image segmentation is a complex problem, in particular to color images. Different mechanisms exist for the gray-level image segmentation, but a very less work exists for the color image segmentation. Thresholding is the technique which is, in general, used for the gray-level image segmentation. This paper presents an approach for the color image segmentation using the thresholding logic. This paper describes the mechanism to find the multi-level thresholds in view of color image segmentation. The presented procedure uses the histogram to find the multi-level thresholds. Weights, mean, variance and within-class variance are used to find the multi-level thresholds. Experimentations are carried out on the BSD color image dataset.
Interactive Traceability Links Visualization using Hierarchical Trace Map

Traceability links between various software artifacts of a system aid software engineers in system comprehension, verification and change impact analysis. Establishing trace links between software artifacts manually is an error-prone and costly task. Recently, studies in automated traceability link recovery area have received broad attention in the software maintenance community aiming to overcome the challenges of manual trace links maintenance process. In these studies, the trace links results generated by an automated trace recovery approach are presented either in a bland textual matrix format (e.g., tabular format) or two-dimensional graphical formats (e.g. tree view, hierarchical leaf node). Therefore, it is challenging for software engineers to explore the inter-relationships between various artifacts at once (e.g., which test cases and source code files/methods are related to a particular requirement). In this position paper, we propose a hierarchical trace map visualization technique to explore inter-relationships between various artifacts at once naturally and intuitively.
New information and communication technologies in education system

An article in the education system, the use of new information and communication technologies in the process of theoretical and practical issues and is described in more detail on the international experience in this field.
Emotion Conversion in Telugu using Constrained Variance GMM and Continuous Wavelet Transform-$F_{0}$

Vocal emotion conversion forms a significant post-processing step in text-to-speech synthesis. The existing models in emotion conversion are data intensive and computationally expensive. This paper puts forth a hybrid emotion conversion framework in Telugu using enhanced global variance GMM for spectral conversion. Fundamental frequency conversion is achieved using multi-layer feed-forward ANN for higher-dimensional CWT decomposed <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$F_{0}$</tex> . The method performs better than baseline least squares GMM in perceptive quality for all 3 emotions considered viz. anger, fear and happiness. Strength of constrained variance (CV)-GMM is utilized for improving quality of synthesized emotional speech. Listening tests indicated that quality is considerably improved with maximum Comparative Mean Opinion Score (CMOS) of 4.42 (anger) in Telugu dataset.
Solving String Constraints with Streaming String Transducers

We present a procedure to solve the satisfiability problem of string constraints consisting of (i) string concatenation and rational transductions of string variables restricted to be in the “straight-line” fragment, (ii) regular constraints to string variables, and (iii) integer constraints involving the length of string variables. We represent each atomic string constraint by a streaming string transducer. By the sequential composition of streaming string transducers, we obtain a single streaming string transducer. The input straight-line constraint is satisfiable if and only if the domain of the composed streaming string transducer is not empty. In addition, by calculating the Parikh image of the composed streaming string transducers, we can represent the constraints among the length of string variables as a semi-linear set. Then the integer constraints together with the Parikh image can be solved by existing SMT solvers. We have implemented this procedure and performed experiments on several string constraints. Our implementation is slower than other solvers for general cases but performs better for some special cases.
The use of modern information technologies in the studies of Chinese students and postgraduates at the Ukrainian universities

The article proves that the studies of foreign students at the universities of Ukraine is currently not only an important factor in improving the competitiveness of the Ukrainian higher education at the international market, but also one of the most profitable and promising sectors of the Ukrainian economy. Therefore, there is an issue of topical interest to improve the quality of educational teaching process for foreigners. Particular attention in this regard should be paid to applicants from China, whose number at the Ukrainian higher educational institutions is constantly increasing. The purpose of the article is to identify a set of modern information technologies that should be applied in the educational process for Chinese students and postgraduates at Ukrainian universities, as well as to publicise the practical implementation results of the set of information technologies. The scientific work also presents the results of the conducted experiment that proved that the application of this set of information technologies provides an effective improvement in the educational process for the specified category of students and postgraduates. The academic achievements of the experimental group members had more positive changes in comparison with the group in which the teaching was carried out in accordance with traditional methods applied by the Ukrainian universities.
Overcoming the Channel Estimation Barrier in Massive MIMO Communication Systems

A new wave of wireless services, including virtual reality, autonomous driving and internet of things, is driving the design of new generations of wireless systems to deliver ultra-high data rates, massive number of connected devices and ultra low latency. Massive multiple-input multiple-output (MIMO) is one of the critical underlying technologies that allow future wireless networks to meet these service needs. This article discusses the application of deep learning (DL) for massive MIMO channel estimation in wireless networks by integrating the underlying characteristics of channels in future high-speed cellular deployment. We develop important insights derived from the physical radio frequency (RF) channel properties and present a comprehensive overview on the application of DL for accurately estimating channel state information (CSI) with low overhead. We provide examples of successful DL application in CSI estimation for massive MIMO wireless systems and highlight several promising directions for future research.
Investigating GAN and VAE to train DCNN

The Convolutional Neural Network (CNN) is a class of deep artificial neural network and has recently gained special attention after demonstrating breakthrough accuracies in various classification tasks.CNNs have shown remarkable performance in machine vision tasks such as image classification, natural language processing and speech recognition.There is evidence that the depth of a CNN plays an important role in performance of CNNs.However, we investigated the feasibility of improving the performance of shallow networks via fusion of the features computed by a homogenous and heterogeneous set of pre-trained networks.We also explored a recently developed framework called the Generative Adversarial Network (GAN), in which we simultaneously train two models, a Generator and a Discriminator.The Generator attempts to produce data that mirrors the probability distribution of the "true" dataset.The Discriminator is trained to distinguish between the true dataset and the counterfeit data produced by the Generator.Our work involves the application of a GAN for generation and fine tuning of synthetic data to be used to train a deep CNN.Specifically, we investigate the use of a synthetic data generator along with a GAN to create an unlimited quantity of labeled training data, without the need for hand-labeling images.We apply this technique to the detection and localization of various vehicles.We attempt to distinguish between military trucks and other types of vehicles.A successful outcome could lead to improvements in addressing security threats rapidly, and cost-effectively.We also investigate an alternative method for generating synthetic data, the Variational Auto-Encoder (VAE).Variational auto-encoders are trained to encode then decode input vectors and can also be useful for generating new training data.VAEs are capable of dimensionality reduction and synthesizing data.Finally, we evaluate our multiplicative fusion method compared to the fusion methods that we investigated previously.
Sociological Studies Of International Academic Mobility

Academic mobility is a significant part of a scientist’s career both in the world’s leading scientific countries and in peripheral ones. The data of sociological surveys show that the main incentive to change the country of scientific employment in most cases is the desire to gain experience in international teams, to get under the care of eminent scientists, to expand scientific ties. Material incentives such as wages, social benefits or access to research funding tools usually rank middle in the hierarchy of motives to move in countries with a developed scientific and technological complex. In countries that can be considered lagging behind from this point of view, the picture is somewhat different-material goods play a more significant role. Regarding the representatives of the Russian scientific Diaspora, there are also mainly purely economic prerequisites for leaving abroad, and a fairly low proportion of the scientists who left Russia declared their desire to return. At the same time, remote cooperation with Russian researchers for scientists-compatriots is quite an attractive form of interaction.
SiamMan: Siamese Motion-aware Network for Visual Tracking

In this paper, we present a novel siamese motion-aware network (SiamMan) for visual tracking, which consists of the siamese feature extraction subnetwork, followed by the classification, regression, and localization branches in parallel. The classification branch is used to distinguish the foreground from background, and the regression branch is adopt to regress the bounding box of target. To reduce the impact of manually designed anchor boxes to adapt to different target motion patterns, we design the localization branch, which aims to coarsely localize the target to help the regression branch to generate accurate results. Meanwhile, we introduce the global context module into the localization branch to capture long-range dependency for more robustness in large displacement of target. In addition, we design a multi-scale learnable attention module to guide these three branches to exploit discriminative features for better performance. The whole network is trained offline in an end-to-end fashion with large-scale image pairs using the standard SGD algorithm with back-propagation. Extensive experiments on five challenging benchmarks, i.e., VOT2016, VOT2018, OTB100, UAV123 and LTB35, demonstrate that SiamMan achieves leading accuracy with high efficiency. Code can be found at https://isrc.iscas.ac.cn/gitlab/research/siamman.
Another Look at Some Isogeny Hardness Assumptions

The security proofs for isogeny-based undeniable signature schemes have been based primarily on the assumptions that the One-Sided Modified SSCDH problem and the One-More SSCDH problem are intractable. We challenge the validity of these assumptions, showing that both the decisional and computational variants of these problems can be solved in polynomial time. We further demonstrate an attack, applicable to two undeniable signature schemes, one of which was proposed at PQCrypto 2014. The attack allows to forge signatures in $$2^{4\lambda /5}$$ steps on a classical computer. This is an improvement over the expected classical security of $$2^{\lambda }$$, where $$\lambda $$ denotes the chosen security parameter.
A Deep Neural Network's Loss Surface Contains Every Low-dimensional Pattern

The work "Loss Landscape Sightseeing with Multi-Point Optimization" (Skorokhodov and Burtsev, 2019) demonstrated that one can empirically find arbitrary 2D binary patterns inside loss surfaces of popular neural networks. In this paper we prove that: (i) this is a general property of deep universal approximators; and (ii) this property holds for arbitrary smooth patterns, for other dimensionalities, for every dataset, and any neural network that is sufficiently deep and wide. Our analysis predicts not only the existence of all such low-dimensional patterns, but also two other properties that were observed empirically: (i) that it is easy to find these patterns; and (ii) that they transfer to other data-sets (e.g. a test-set).
What You See is What You Get: Exploiting Visibility for 3D Object Detection

Recent advances in 3D sensing have created unique challenges for computer vision. One fundamental challenge is finding a good representation for 3D sensor data. Most popular representations (such as PointNet) are proposed in the context of processing truly 3D data (e.g. points sampled from mesh models), ignoring the fact that 3D sensored data such as a LiDAR sweep is in fact 2.5D. We argue that representing 2.5D data as collections of (x, y, z) points fundamentally destroys hidden information about freespace. In this paper, we demonstrate such knowledge can be efficiently recovered through 3D raycasting and readily incorporated into batch-based gradient learning. We describe a simple approach to augmenting voxel-based networks with visibility: we add a voxelized visibility map as an additional input stream. In addition, we show that visibility can be combined with two crucial modifications common to state-of-the-art 3D detectors: synthetic data augmentation of virtual objects and temporal aggregation of LiDAR sweeps over multiple time frames. On the NuScenes 3D detection benchmark, we show that, by adding an additional stream for visibility input, we can significantly improve the overall detection accuracy of a state-of-the-art 3D detector.
MetaFusion: Controlled False-Negative Reduction of Minority Classes in Semantic Segmentation

In semantic segmentation datasets, classes of high importance are oftentimes underrepresented, e.g., humans in street scenes. Neural networks are usually trained to reduce the overall number of errors, attaching identical loss to errors of all kinds. However, this is not necessarily aligned with human intuition. For instance, an overlooked pedestrian seems more severe than an incorrectly detected one. One possible remedy is to deploy different decision rules by introducing class priors which assigns larger weight to underrepresented classes. While reducing the false-negatives of the underrepresented class, at the same time this leads to a considerable increase of false-positive indications. In this work, we combine decision rules with methods for false-positive detection. We therefore fuse false-negative detection with uncertainty based false-positive meta classification. We present proof-of-concept results for CIFAR-10, and prove the efficiency of our method for the semantic segmentation of street scenes on the Cityscapes dataset based on predicted instances of the 'human' class. In the latter we employ an advanced false-positive detection method using uncertainty measures aggregated over instances. We thereby achieve improved trade-offs between false-negative and false-positive samples of the underrepresented classes.
ONTOLOGY DRIVEN SOFTWARE ENGINEERING: MODELS, METHODS, IMPLEMENTATIONS

Ontology-driven software engineering is the scope of the paper. A retrospective analysis of the approaches, models and methods is carried out. In the first section, a brief overview of "classical" technologies, approaches and methods for software engineering is presented. It is shown that the emphasis of research and development is currently shifting from the field of technologies of programming towards the modeling of the software design processes, as well as towards the specifications and testing of the developed systems. It is outlined that the general trend in the domain is the use of methods and tools of ontology modeling of both the design processes per se and the specifications of the developed systems. The second section is devoted to a review of publications in the field of ontology-based software engineering. Such paradigms for modeling software development processes as Software Product Lines, plug-and-play software development - Component-Based Development, model-driven software engineering - Model-Driven Engineering, etc. are discussed. It is shown that "programming through generation", i.e. Generative Programming, is a general approach supporting the considered paradigms. It is shown that R&amp;D in the domain of the semantic models of software engineering is currently focused on a new approach - Ontology-Based (-Driven) Software Engineering. Within the framework of this area, ontology engineering tools are developed along with modeling the software development processes. The scientific and technical perspectives of ontology driven software engineering are specified and discussed in the final part of the paper. In particular, it is outlined that the theoretical foundations of ontology-based modeling of software design processes form the solid basis for shifting the focus from the models of compiling the modules to their pattern-based generation from internal specifications. It is noted that the next trend in the field of OBSE-ODSE will be the usage of machine learning methods and tools to generate ontological models of software design.
Differentiable programming and its applications to dynamical systems

Differentiable programming is the combination of classical neural networks modules with algorithmic ones in an end-to-end differentiable model. These new models, that use automatic differentiation to calculate gradients, have new learning capabilities (reasoning, attention and memory). In this tutorial, aimed at researchers in nonlinear systems with prior knowledge of deep learning, we present this new programming paradigm, describe some of its new features such as attention mechanisms, and highlight the benefits they bring. Then, we analyse the uses and limitations of traditional deep learning models in the modeling and prediction of dynamical systems. Here, a dynamical system is meant to be a set of state variables that evolve in time under general internal and external interactions. Finally, we review the advantages and applications of differentiable programming to dynamical systems.
Investigating bound entangled two-qutrit states via the best separable approximation

We use the linear programming algorithm introduced by Akulin et al. [V. M. Akulin, G. A. Kabatiansky, and A. Mandilara, Phys. Rev. A 92, 042322 (2015)] to perform best separable approximation on two-qutrit random density matrices. We combine the numerical results with theoretical methods in order to generate random representative families of positive partial transposed bound entangled (BE) states and analyze their properties. Our results are disclosing that for the two-qutrit system the BE states have negligible volume and that these form tiny `islands' sporadically distributed over the surface of the polytope of separable states. %We devise a method for estimating numerically the average thickness of these formations and their frequency of occurrence. The detected families of BE states are found to be located under a layer of pseudo one-copy undistillable negative partial transposed states with the latter covering the vast majority of the surface of the separable polytope.
A Web Deployed Multi-Agent Based Approach for Student-Lecturer Appointment Scheduling in Institutions of Higher Learning

Abstract Institutions of higher learning such as universities have been positively influenced by the internet which has facilitated learning and teaching. Likewise, this same internet can also support student lecturer appointments, but currently students and lecturers are faced with issues such as finding free timeslot, difficulty of students to meet supervisor and issues related to managing appointment operations. Therefore, this paper develops a multi-agent architecture and a web-based agent appointment scheduling system to support students and lecturers in managing appointment scheduling in universities. The agent-based appointment scheduling system was implemented as a web system integrated by multi-agents to facilitate students in finding available timeslot, resolving difficulty of students to meet supervisor and addressing difficult of managing scheduled appointment records by lecturers. The usefulness of the agent-based appointment scheduling system was evaluated by collecting data using questionnaire from randomly selected respondents. Furthermore, Statistical Package for the Social Science (SPSS) was employed to analyse the questionnaire data using descriptive and exploratory factor analysis. Findings from the survey reveal that the developed agent-based appointment scheduling system practically supports students and lecturers in scheduling appointments.
Survey on SAP and its application in public-key cryptography

Abstract The concept of the semigroup action problem (SAP) was first introduced by Monico in 2002. Monico explained in his paper that the discrete logarithm problem (DLP) can be generalized to SAP. After defining the action problem in a semigroup, the concept was extended using different mathematical structures. In this paper, we discuss the concept of SAP and present a detailed survey of the work which has been done using it in public-key cryptography.
Web-Based Population Cencus Design in Neighborhood Building

The population census at the neighborhood level is usually still processed using a manual system. However, the obstacles that arise due to manual processing are quite troublesome, especially when searching for information because the majority of the population works so it is difficult to process data. This study aims to produce an information system for population census management that is faster, more effective, effective and efficient in the neighborhood. The method used in compounding and making this application is by utilizing software development methods, namely the waterfall method which includes analysis, design, coding and testing. The results of this study are the creation of a web to make it easier for residents and heads to manage population censuses, assist in the process of inputting data, searching data, and reporting residents.&#x0D;
Mixture model for image segmentation using Gaussian, Student’s t, and Laplacian distribution with spatial dependence

MRI grayscale image data pattern, in reality, is not always symmetrical. Sometimes, it has a skewed pattern, leptokurtic, mesokurtic, platykurtic and even fat-tail in distribution. The Gaussian approach is not always able to explain this kind of data pattern. Therefore, other approaches to employ other distributions are used to overcome this problem. This study tries to compare the Gaussian, Student’s t, and Laplacian mixture model in the modeling of MRI brain tumor image segmentation. In addition, we used the Markov Random Field as the prior to the spatial dependence. The cluster validation is done by calculating the Silhouette Index (SI) and Misclassification Ratio (MCR). The results demonstrate that for the skewed and leptokurtic data pattern, the Laplacian mixture model shows the best representation, while the Student’s t mixture model has a great performance in fat-tail data pattern and also more robust against noise.