Reviewerly/exHarmony huggingface dataset:
CNN based Auto-Assistance System as a Boon for Directing Visually Impaired Person

World Health Organization (WHO) outlined that there are two eighty five million visually disabled worldwide. Among them thirty nine million people are completely blind. One of the difficult activities that could be conducted by visually impaired is obstacle detection which could be implemented using Machine Learning (ML). It is an approach towards Artificial Intelligence (AI) that provides system the capacity for natural learning and development from experience without specifically programmed. It provides computer vision to the system which make decisions based on training algorithms. The chief goal of this research paper is to develop an object detection system to assist totally blind individual to manage their activities independently. Paper also compares different object detection algorithms like Haar Cascade and Convolutional Neural Network (CNN). Haar Cascade classifier is a basic face detection algorithm which could also be trained to detect different objects whereas convolutional neural network falls under deep learning approach which could be employed for object recognition. The custom dataset is created with 2300 images consisting of 3 different classes. This comparison is being executed to find the CNN as a suitable algorithm for this system from the aspect of accuracy for real time scenario.
Machine learning approach for breast cancer prognosis prediction

Despite the progress made in the past, breast cancer is still the leading cause of death in women worldwide. For this reason, any development that may lead to successful prognosis of the disease and its severity is of great importance. It is essential to provide appropriate patient-specific tool that will help clinicians find hidden links among large number of parameters and make more informed decision on therapy suggestion. In this chapter, we present and discuss machine learning approach for the development of predictive models that could be used for the prediction of survival period after surgery and the prediction of cancer recurrence. The main aim is to explore database composed of numerous prognostic and predictive factors that are already available in clinical centers for every patient that suffer from this disease by using machine learning methods.
Lifted static analysis using a binary decision diagram abstract domain

Many software systems are today variational. They can produce a potentially large variety of related programs (variants) by selecting suitable configuration options (features) at compile time. Specialized variability-aware (lifted, family-based) static analyses allow analyzing all variants of the family, simultaneously, in a single run without generating any of the variants explicitly. In effect, they produce precise analysis results for all individual variants. The elements of the lifted analysis domain represent tuples (i.e. disjunction of properties), which maintain one property from an existing single-program analysis domain per variant. Nevertheless, explicit property enumeration in tuples, one by one for all variants, immediately yields to combinatorial explosion given that the number of variants can grow exponentially with the number of features. Therefore, such lifted analyses may be too costly or even infeasible for families with a large number of variants.
Hierarchical Representation of Information Objects in a Digital Library Environment

This paper studies the problem of building a modern information society as a task of forming a virtual knowledge space. This task can be implemented on the IT platform of a digital library, which ensures the formation and provision of information resources in various areas to general public users. It shows how electronic copies of objects of a library, archive and museum storage represented in the form of texts, graphic images, and audio/video objects, including 3D-models, can be integrated using digital library facilities. The concept of a hierarchical level of electronic objects is introduced. The definitions of objects of various levels are given and the principles of work with objects of each level are formulated. The hierarchical representation of electronic objects in a digital library environment is proposed. A distinction between thematic, theme-specific, and interdisciplinary collections is shown. The information environment for forming subtheme collections is represented as a set of databases created by information fund holders. This environment is distributed and unified in terms of the software and hardware used, as well as in terms of a set of requirements for digital images being formed. The paper formulates the basic principles of forming collections of various levels in a digital library environment and reflects the developed digital solutions that ensure their implementation. For each hierarchical level, it gives examples of objects and collections provided on the portal of the Scientific Heritage of Russia Digital Library (SHR DL) and formed in compliance with the proposed principles and digital solutions. In particular, the paper describes in detail the thematic collection of publications in mathematics being a database formed through LibMeta DBMS; interdisciplinary collection “Garden of Life” created in collaboration with one of the Russian museums, and some other collections.
Improved Deep Fuzzy Clustering for Accurate and Interpretable Classifiers

While deep learning has demonstrated excellent performance in many challenging learning tasks, it has not yet found broad acceptance amongst users. The key deficiency seems to be the uninterpretable nature of a deep neural network; no general, practical method for explaining the predictions or decisions of such a network has been devised. Lacking such, users seem unwilling to entrust deep learning with critical decisions. One approach to generating explanations is to design algorithms that are inherently more interpretable. Neuro-fuzzy systems are an example, which we are extending to deep networks; in particular by designing deep fuzzy clustering algorithms. Deep fuzzy clustering employs a deep learner as an automated feature extractor. A fuzzy clustering is performed in the extracted feature space, and a classifier built from it. The resulting model appears more interpretable, but at the cost of lower accuracy. This paper explores improvements to deep fuzzy clustering leading to a more accurate deep fuzzy classifier that still seems highly interpretable. We evaluate the accuracy and interpretability of the model on the MNIST dataset.
Radar track prediction method based on BP neural network

Taking into account the complex electromagnetic environment in which the radar target is located, it is difficult for the traditional track prediction method to adapt for the high complexity, randomness, and uncertainty of the manoeuvring target track. This study proposes a radar track prediction method based on backpropagation (BP) neural network. According to the historical track of the radar target, this method uses BP neural network to model its movement law and obtain the target's predicted track. Finally, the track prediction experiment was conducted with measured data and compared with the Kalman filter track. The results show that the proposed method has higher track prediction accuracy and can be used for radar track prediction.
Robust drone detection with static VIS and SWIR cameras for day and night counter-UAV

Considerable progress with unmanned aerial vehicles (UAVs) has led to an increasing need for counter-UAV systems to detect present, potentially threatening or misused drones. Therefore, a UAV detection algorithm has been developped recently for day and night operation. Whereas high resolution VIS cameras enable to detect UAVs in daylight in further distances, surveillance at night is performed with a short wave infrared (SWIR) camera. The proposed method is based on temporal median computation, structural adaptive image differencing, codebook-based background learning, local density computation, and shape analysis of foreground structures to perform an improved near range change detection for UAVs. Areas with moving scene parts, like leaves in the wind or driving cars on a street, are recognized to minimize false alarms. This paper presents a significant improvement with respect to some of the most challenging tasks in this field, e.g., increasing the UAV detection sensitivity in front of trees with waving leaves, false alarm minimization, and avoiding the background model update problem. The provided results illustrate the reached performance in a variety of different situations.
Cycle-Consistent Training for Reducing Negative Jacobian Determinant in Deep Registration Networks

Image registration is a fundamental step in medical image analysis. Ideally, the transformation that registers one image to another should be a diffeomorphism that is both invertible and smooth. Traditional methods like geodesic shooting study the problem via differential geometry, with theoretical guarantees that the resulting transformation will be smooth and invertible. Most previous research using unsupervised deep neural networks for registration address the smoothness issue directly either by using a local smoothness constraint (typically, a spatial variation loss), or by designing network architectures enhancing spatial smoothness. In this paper, we examine this problem from a different angle by investigating possible training mechanisms/tasks that will help the network avoid predicting transformations with negative Jacobians and produce smoother deformations. The proposed cycle consistent idea reduces the number of folding locations in predicted deformations without making changes to the hyperparameters or the architecture used in the existing backbone registration network. Code for the paper is available at https://github.com/dykuang/Medical-image-registration .
Sequence Diagram Similarity Measurement: A Different Approach

Unified Modified Language (UML) is a modeling language standard for identifying, recording and designing a software. Reusing UML diagrams can help to accelerate the software development process. Reusing UML diagram requires a method of calculating similarities between artifacts in UML diagram. The proposed method contented of two elements of sequence diagrams, i.e. property of class and message sequence. The result of experiment showed that the property class and message sequence could be a suitable parameter in evaluating the UML sequence diagram similarity.
Layup

Although GPUs have emerged as the mainstream for the acceleration of convolutional neural network (CNN) training processes, they usually have limited physical memory, meaning that it is hard to train large-scale CNN models. Many methods for memory optimization have been proposed to decrease the memory consumption of CNNs and to mitigate the increasing scale of these networks; however, this optimization comes at the cost of an obvious drop in time performance. We propose a new memory optimization strategy named Layup that realizes both better memory efficiency and better time performance. First, a fast layer-type-specific method for memory optimization is presented, based on the new finding that a single memory optimization often shows dramatic differences in time performance for different types of layers. Second, a new memory reuse method is presented in which greater attention is paid to multi-type intermediate data such as convolutional workspaces and cuDNN handle data. Experiments show that Layup can significantly increase the scale of extra-deep network models on a single GPU with lower performance loss. It even can train ResNet with 2,504 layers using 12GB memory, outperforming the state-of-the-art work of SuperNeurons with 1,920 layers (batch size = 16).
Interest Level Estimation Based on Tensor Completion via Feature Integration for Partially Paired User’s Behavior and Videos

A novel method for interest level estimation based on tensor completion via feature integration for partially paired users' behavior and videos is presented in this paper. The proposed method defines a novel canonical correlation analysis (CCA) framework that is suitable for interest level estimation, which is a hybrid version of semi-supervised CCA (SemiCCA) and supervised locality preserving CCA (SLPCCA) called semi-supervised locality preserving CCA (S2LPCCA). For partially paired users' behavior and videos in actual shops and on the Internet, new integrated features that maximize the correlation between partially paired samples by the principal component analysis (PCA)-mixed CCA framework are calculated. Then videos that users have not watched can be used for the estimation of users' interest levels. Furthermore, local structures of partially paired samples in the same class are preserved for accurate estimation of interest levels. Tensor completion, which can be applied to three contexts, videos, users and “canonical features and interest levels,” is used for estimation of interest levels. Consequently, the proposed method realizes accurate estimation of users' interest levels based on S2LPCCA and the tensor completion from partially paired training features of users' behavior and videos. Experimental results obtained by applying the proposed method to actual data show the effectiveness of the proposed method.
Fraud prediction in bank loan administration using decision tree

Abstract The rate at which banks looses funds to loan beneficiaries due to loan default is alarming. This trend has led to the closure of many banks, potential beneficiaries deprived of access to loan; and many workers losing their jobs in the banks and other sectors. This work uses past loan records based on the employment of machine learning to predict fraud in bank loan administration and subsequently avoid loan default that manual scrutiny by a credit officer would not have discovered. However, such hidden patterns are revealed by machine learning. Statistical and conventional approaches in this direction are restricted in their accuracy capabilities. With a large volume and variety of data, credit history judgement by man is inefficient; case-based, analogy-based reasoning and statistical approaches have been employed but the 21 st century fraudulent attempts cannot be discovered by these approaches, hence; the machine learning approach using the decision tree method to predict fraud and it delivers an accuracy of 75.9 percent.
An Ontology for SNORT Rule

Intrusion Detection System (IDS) is an important component to provide security for an organization. Its main function is to detect harmful traffic. The detection is done based on rules. SNORT is a widely used open-source IDS system. To have efficient detection, SNORT rule specification allows very expressive way to create a rule. This is not a trivial task especially for self-defined rule. Due to this complexity and huge amount of rules, there can be problems about both syntax and semantics which in turn effects on the IDS system performance. We thus propose an ontology for SNORT rules which is designed to support SNORT rule verification using OWL ontology.
Mapping graph state orbits under local complementation

Graph states, and the entanglement they posses, are central to modern quantum computing and communications architectures. Local complementation---the graph operation that links all local-Clifford equivalent graph states---allows us to classify all stabiliser states by their entanglement. Here, we study the structure of the orbits generated by local complementation, mapping them up to 9 qubits and revealing a rich hidden structure. We provide programs to compute these orbits, along with our data for each of the 587 orbits up to 9 qubits and a means to visualise them. We find direct links between the connectivity of certain orbits with the entanglement properties of their component graph states. Furthermore, we observe the correlations between graph-theoretical orbit properties, such as diameter and colourability, with Schmidt measure and preparation complexity and suggest potential applications. It is well known that graph theory and quantum entanglement have strong interplay---our exploration deepens this relationship, providing new tools with which to probe the nature of entanglement.
Wavelet packets: Uniform approximation and numerical integration

In this paper, it is proved that under some conditions, wavelet packet basis of [Formula: see text] can be used as a tool for the uniform approximation of an [Formula: see text]-times ([Formula: see text]) continuously differentiable and square integrable function [Formula: see text]. Sufficient conditions which establish that the approximations of wavelet packet sequences of square integrable function [Formula: see text] at lower levels are uniformly reliable and they uniformly approach zero as [Formula: see text] are given. Finally, a method based on wavelet packet expansion to find the definite integral of a function in [Formula: see text] is given and its error analysis has been discussed.
An optimization model for temporal video lecture segmentation using word2vec and acoustic features

Video lectures are part of our daily lives. Whether to learn something new, review content for exams or just out of curiosity. People are increasingly looking for video lectures that address what they are looking for. Unfortunately, finding specific content in this type of video is not an easy task. Many video lectures are extensive and cover several topics, and not all of these topics are relevant to the user who has found the video. The result is that the user spends so much time trying to find topic of interest in the middle of content irrelevant to him. The temporal segmentation of video lectures in topics can solve this problem allowing users to navigate of a non-linear way through all topics of a video lecture. However, temporal video lecture segmentation is not an easy task and needs to be automatized. For this reason, in this paper we propose an optimization model for the temporal video lecture segmentation problem. This model uses as features the Word2Vec representation of video lecture's audio transcripts and low-level acoustic characteristics. To find the best video partition, an genetic algorithm with local search is used. We have performed experiments in two data sets and results showed that our proposal is able to overcome state-of-the-art methods and achieve good results for different kinds of video lectures.
Complex Query Augmentation for Question Answering over Knowledge Graphs

Question answering systems have often a pipeline architecture that consists of multiple components. A key component in the pipeline is the query generator, which aims to generate a formal query that corresponds to the input natural language question. Even if the linked entities and relations to an underlying knowledge graph are given, finding the corresponding query that captures the true intention of the input question still remains a challenging task, due to the complexity of sentence structure or the features that need to be extracted. In this work, we focus on the query generation component and introduce techniques to support a wider range of questions that are currently less represented in the community of question answering.
Digital Educational Environment, Health Protecting Aspects

The priority project “Modern digital educational environment in the Russian Federation” focuses on widespread introduction of modern ICT into the educational system. At the same time, there is a notable imbalance between the number of students using modern technologies and the number of teachers willing to conduct such training. Only 16 % of teachers are prepared to create and use digital resources for educational purposes. Moreover, only digital environment of educational organization is evaluated, although in digital education actual training also occurs outside of it. We have attempted to assess the current situation based on learning process in high-tech information educational environment, including the educational organization and students’ place of residence or stay, since such environment is likely to impact students’ health but is also the only environment that enables educational process accounting for health conditions of the students. However, availability of digital educational resources and teachers able to utilize them does not guarantee success. Parents’ ability to use technology to oversee educational process and maintain information security / protection of personal data of the student outside the educational institution is essential. Results of the study can serve as basis for development of a program for formation of students’ digital educational environment, accounting for contents of the educational organization, students’ place of residence and level of information culture of all participants of the learning process
Student Monitoring System for School Bus Using Facial Recognition

Recent reports confirm the fact that school students are the most vulnerable to social crimes happening across the globe and our country too. Many of these cases happen during their ply from their residence to school and vice versa. In multiple cases these social crimes including sexual harassment happened in their school bus itself. Considering this serious situation, we are proposing a real time monitoring system using image processing techniques. - Identifying a student with an image has been popularized through the mass media like camera. This system monitors the images inside the vehicle and identifies the students and their movements inside the bus. The system recognizes the student faces and their count are also monitored. The system will also raise an alarm to get the attention of the public if it is so essential. Technologies are available in the Open-Computer-Vision (OpenCV) library and implement those using Python. For face detection, Haar-Cascades classifier was used and for face recognition Eigenfaces, and Local binary pattern histograms were used. each stage of the system described by some flowcharts. And also face recognition used in automation attendance system which eliminates most of the drawbacks that the manual attendance systems pose, easy manipulation of attendance records, proxy-attendances, and insecure system.
$(t,n)$ -Threshold Quantum Secret Sharing Based on One-Way Local Distinguishability

We propose a quantum secret sharing protocol with a set of orthogonal generalized Bell states and one-way local distinguishability. In the proposed protocol, the participants use local projective measurements and one-way classical communication to distinguish among four Bell states, and we combine with the Shamir (t,n) -threshold scheme to realize (t,n) -threshold access structure. Compared to the existing schemes, the practical communication costs are dramatically improved as there is need for one-way classic communication between the two participants in our protocol. In addition, the efficiency in the process of information transmission will also be improved, as the number of communications will be reduced. At last, we demonstrate our protocol to be secure against both eavesdropping and dishonest participants.
Fancy Fruits - An Augmented Reality Application for Special Needs Education

Augmented Reality (AR) allows for a connection between real and virtual worlds, thus providing a high potential for Special Needs Education (SNE). We developed an educational application called Fancy Fruits to teach disabled children the components of regional fruits and vegetables. The app includes marker-based AR elements connecting the real situation with virtual information. To evaluate the application, a field study was conducted. Eleven children with mental disabilities took part in the study. The results show a high enjoyment of the participants. The study also validated the app's child-friendly design.