Reviewerly/exHarmony huggingface dataset:
Nonlinear Dynamics Tools for Offline Signature Verification Using One-class Gaussian Process

One of the major problems in biometrics and in document forensics is the offline mode of signature verification. This study aims to present a novel approach of verifying an individual’s signature through offline images of handwriting. The approach proposed here relies on a global method which considers signature images as waveforms. First, image decompositions are in terms of a series of wavelet sub-bands at some specific levels. Wavelet sub-bands are then extended so as to obtain waveforms. Each waveform is quantized by two Nonlinear Dynamics Tools in order to generate feature vectors. Multi-Resolution Box-Counting (MRBC) fractal dimension algorithm as well as probabilistic finite state automata (PFSA) are applied separately to signature waveforms. In the training and verification phase, we propose the one-class Gaussian process (GP) priors based on writer-independent approach. As one of the main parameters, optimal decision threshold is selected from False Accept Rate (FAR) and False Reject Rate (FRR) curves. The presented system was tested on two Persian databases (PHBC and UTSig) as well as on two Latin databases (MCYT-75 and CEDAR). In fact, the results produced by this method were generally better in terms of the four signature databases than the state-of-the-art results.
Learn-to-Scale: Parallelizing Deep Learning Inference on Chip Multiprocessor Architecture

Accelerating deep neural networks on resource-constrained embedded devices is becoming increasingly important for real-time applications. However, in contrast to the intensive research works on specialized neural network inference architectures, there is a lack of study on the acceleration and parallelization of deep learning inference on embedded chip-multiprocessor architectures, which are favored by many real-time applications for superb energy-efficiency and scalability. In this work, we investigate the strategies of parallelizing single-pass deep neural network inference on embedded on-chip multi-core accelerators. These methods exploit the elasticity and noise-tolerance features of deep learning algorithms to circumvent the bottleneck of on-chip inter-core data moving and reduce the communication overhead aggravated as the core number scales up. The experimental results show that the communication-aware sparsified parallelization method improves the system performance by 1.6×−1.1× and achieves 4×−1.6× better interconnects energy efficiency for different neural networks.
The SenSoMod-Modeler – A Model-Driven Architecture Approach for Mobile Context-Aware Business Applications

The ubiquity and the low prices of mobile devices like smartphones and tablets as well as the availability of radio networks hold the opportunity for companies to reorganize and optimize their business processes. These mobile devices can help users to execute their process steps by showing instructions or by augmenting reality. Moreover, they can improve the efficiency and effectiveness of business processes by adapting the business process execution. This can be achieved by evaluating the user’s context via the many sensor-data from a smart device and adapting the business process to the current context. The data, not only collected from internal sensors but also via networks from other sources, can be aggregated and interpreted to evaluate the context. To use the advantages of context recognition for business processes an simple way to model this data collection and aggregation is needed. This would enable a more structured way to implement supportive mobile (context-aware) applications. Today, there is no modeling language that supports the modeling of data collection and aggregation to context and offers code generation for mobile applications via a suitable tool. Therefore, this paper presents a domain specific modeling language for context and a model-driven architecture (MDA) based approach for mobile context-aware apps. The modeling language and the MDA-approach have been implemented in an Eclipse-based tool.
Observer-based feedback control of two-level open stochastic quantum system

The observer-based feedback control for the two-level bilinear open stochastic quantum system is proposed in this paper. The state of open stochastic quantum system (OSQS) is described in the Cartesian coordinate system. The proposed state observer is designed by using state-dependent differential Riccati equation (SDDRE) and constructed for optimally estimating the state of OSQS from measurement output of the system. The state of observer is continuously updated by the output data of continuous weak measurement (CWM). A Lyapunov Feedback control is designed based on estimated state of the observer for the state transfer of OSQS. An exponential Lyapunov function is chosen to ensure the stability of the system. The observer-based Lyapunov feedback control (OLFC) strategy is developed according to the stochastic Lyapunov stability theorem. The numerical simulation results verify the achievability of the proposed OLFC strategy in terms of state estimation and state transfer of OSQS. Numerical simulations demonstrate that the observer tracks the state of system asymptotically with minimum error of ± 3%. The proposed OLFC has the ability to move the state of OSQS from arbitrary initial state to the final target eigenstate with high fidelity ≥ 90%.
Deep Neural Network Method of Recognizing the Critical Situations for Transport Systems by Video Images

The deep neural network method of recognizing critical situations for transport systems according to video frames from the intelligent vehicles cameras is offered, that is effective in terms of accuracy and high-speed performance. Unlike the known solutions for the objects and normal or critical situations detection and recognition, it uses the classification with the subsequent reinforcement on the basis of several video stream frames and with the automatic annotation algorithm. The adapted architectures of neural networks are offered: the dual network to identify drivers and passengers according to the face image, the network with independent recurrent layers to classify situations according to the video fragment. The scheme of the intellectual distributed city system of transport safety using the cameras and on-board computers united in a single network is offered. Software modules in Python are developed and natural experiments are made. The possibility of the offered algorithms and programs in UGV or in the driver assistant systems implementation is shown with the illustrating examples in real-time.
Trustless Two-Factor Authentication Using Smart Contracts in Blockchains

Two-factor authentication (2FA) is widely prevalent in banking, emails and virtual private networks (VPN) connections or in accessing any secure web service. In 2FA, to get authenticated the users are expected to provide additional secret information along with the password. Typically, this secret information (tokens) is generated by a centralized trusted third party upon receiving an authentication request from users. Thus, this additional layer of security comes at the cost of inherently trusting the third party for their services. The security of such authentication systems is always under the threat of the trusted party is being compromised. In this paper, we propose a novel approach to make server authentication even more secure by building 2FA over the blockchain platform which is distributed in nature. The proposed solution does not require any trusted third party between claimant (user) and the verifier (server) for the authentication purpose. To demonstrate the idea of using blockchain technology for 2FA, we have added an extra layer of security component to the OpenSSH server a widely used application for Secure Shell (SSH) protocol.
ClickGraph: Web Page Embedding using Clickstream Data for Multitask Learning

The rise of big data frameworks has given website administrators the ability to track user clickstream data with more detail than ever before. These clickstreams can represent the user’s intent and purpose in visiting the site. While existing work has explored methods for predicting future user actions, these methods are limited focus solely on one task at a time, ignore graph structure inherent in clickstreams, or model the conversion of the entire clickstream session, ignoring complexities such as multiple conversions in a single session. In this work, we formulate the novel problem of simultaneously predicting future user actions given a user’s clickstream history. We argue that clickstream data contains important signal for predicting future user action. To tackle this new problem, we propose a novel method called ClickGraph, a recurrent neural network that encodes the graph structure of user click trajectories in the learned representations of web pages. We conduct experiments on a real-world dataset and demonstrate that this multitask learning approach is effective at improving the prediction of form fill conversions over strong baselines. In particular, we demonstrate that the ClickGraph model is effective at reducing false positive rates, increasing F1 scores, and improving recall.
Generate use case from the requirements written in a natural language using machine learning

Recently it has become important to focus on the requirements of the system and how to take them and analyze them to determine the system infrastructure through which they will be relied upon in the rest of the system building. Some difficulties have been encountered in the process of understanding and analyzing the data taken from the user to convert it to UML diagrams. In this paper, we create a new approach that focuses mainly on increasing accuracy for this technique, reducing time in the systems of generating the use case of text written in natural language and finding solutions to some problems in current technologies because people need a smart and accurate system to meet their needs and save their time and increase the reliability of the reliance on software.
DHSGAN: An End to End Dehazing Network for Fog and Smoke

In this paper we propose a novel end-to-end convolution dehazing architecture, called De-Haze and Smoke GAN (DHSGAN). The model is trained under a generative adversarial network framework to effectively learn the underlying distribution of clean images for the generation of realistic haze-free images. We train the model on a dataset that is synthesized to include image degradation scenarios from varied conditions of fog, haze, and smoke in both indoor and outdoor settings. Experimental results on both synthetic and natural degraded images demonstrate that our method shows significant robustness over different haze conditions in comparison to the state-of-the-art methods. A group of studies are conducted to evaluate the effectiveness of each module of the proposed method.
Intelligent transportation video tracking technology based on computer and image processing technology

With the continuous improvement of the economic level of various countries and the continuous development of urbanization, the problems of urban traffic congestion and traffic accidents are also increasing. In recent years, the development of computer vision and the emergence of webcam in traffic roads provide a convenient condition for the realization of intelligent traffic video tracking. In order to realize the video tracking technology in intelligent traffic, the target detection and tracking of the moving objects such as vehicles and human beings should be carried out first, and the detection and tracking of the moving target in the foreground map is of great practical significance and practical value. Based on this problem, the open source image processing library of hardware video server and software – Open CV is applied to design and implement intelligent transportation video tracking technology. The intelligent traffic monitoring, target track monitoring, abnormal target occurrence monitoring, target retention monitoring, target oriented mobile monitoring and other intelligent behavior functions are completed to achieve intelligent traffic video tracking. As a result, intelligent traffic management and better maintenance of traffic order is achieved.
Shop your Right Size: A System for Recommending Sizes for Fashion products

Size selection is a critical step while purchasing fashion products. Unlike offline, in online fashion shopping, customers don't have the luxury of trying a product and have to rely on the product images and size charts to select a product that fits well. As a result of this gap, online shopping yields a large percentage of returns due to size and fit. Hence providing size recommendation for customers enhances their buying experience and also reduces operational costs incurred during exchanges and returns. In this paper, we present a robust personalized size recommendation system which predicts the most appropriate size for users based on their order history and product data. We embed both users and products in a size and fit space using skip-gram based Word2Vec model and employ GBM classifier to predict the fit likelihood. We describe the architecture of the system and challenges we encountered while developing it. Further we also analyze the performance of our system through extensive offline and online testing, compare our technique with another state-of-art technique and share our findings.
Place and Object Recognition by CNN-Based COSFIRE Filters

COSFIRE filters are an effective means for detecting and localizing visual patterns. In contrast to a convolutional neural network (CNN), such a filter can be configured by presenting a single training example and it can be applied on images of any size. The main limitation of COSFIRE filters so far was the use of only Gabor and DoGs contributing filters for the configuration of a COSFIRE filter. In this paper, we propose to use a much broader class of contributing filters, namely filters defined by intermediate CNN representations. We apply our proposed method on the MNIST data set, on the butterfly data set, and on a garden data set for place recognition, obtaining accuracies of 99.49%, 96.57%, and 89.84%, respectively. Our method outperforms a CNN-baseline method in which the full CNN representation at a certain layer is used as input to an SVM classifier. It also outperforms traditional non-CNN methods for the studied applications. In the case of place recognition, our method outperforms NetVLAD when only one reference image is used per scene and the two methods perform similarly when many reference images are used.
PrivUML: A Privacy Metamodel

As part of our work, we seek to define a modeling language for model-driven design of secure and privacy-friendly systems. Our approach is based on access control strengthened by the criteria of personal data protection. We show how UML can be used to specify consolidated access control information by measures that guarantee privacy in the overall design of an application. These information will be used later to automatically generate complete access control infrastructures. This work presents our choice of the security policy answering the requirements of private life protection, as well as our proposal of the tool which will allow us to model this security policy.
Designing Secure Cryptographic Accelerators with Information Flow Enforcement

Designing a secure cryptographic accelerator is challenging as vulnerabilities may arise from design decisions and implementation flaws. To provide high security assurance, we propose to design and build cryptographic accelerators with hardware-level information flow control so that the security of an implementation can be formally verified. This paper uses an AES accelerator as a case study to demonstrate how to express security requirements of a cryptographic accelerator as information flow policies for security enforcement. Our AES prototype on an FPGA shows that the proposed protection has a marginal impact on area and performance.
A Genre-Based Item-Item Collaborative Filtering

Recommender System is a technique which is used to recommend an item or product to a user based on the user's preference'. Collaborative filtering is an approach that is vastly used in recommender systems. Item-item-based collaborative filtering is a collaborative filtering recommender system technique where the user got the recommendation based on the similarity among the item ratings. Here, we present an approach where we calculate the similarity among the items based on the genre of items. Any item may belong to more than one genre or category. Based on items propensity to a specific genre or category we propose a new item-item-based similarity metric and a little improvement in prediction method that can efficiently compute the ratings and provide more accurate recommendation compare to the state-of-art works. Our model addresses the problem of the cold start since traditional similarity model takes the user ratings into account whereas our model can calculate the similarity based on item genre or category among them. We also show the extensive simulation results based on sparsity and other recommender system evaluation techniques. We also distinguish that our result outperforms than the traditional collaborative filtering recommender systems.
Fully hyperbolic convolutional neural networks

Convolutional neural networks (CNN) have recently seen tremendous success in various computer vision tasks. However, their application to problems with high dimensional input and output, such as high-resolution image and video segmentation or 3D medical imaging, has been limited by various factors. Primarily, in the training stage, it is necessary to store network activations for back-propagation. In these settings, the memory requirements associated with storing activations can exceed what is feasible with current hardware, especially for problems in 3D. Motivated by the propagation of signals over physical networks, that are governed by the hyperbolic Telegraph equation, in this work we introduce a fully conservative hyperbolic network for problems with high-dimensional input and output. We introduce a coarsening operation that allows completely reversible CNNs by using a learnable discrete wavelet transform and its inverse to both coarsen and interpolate the network state and change the number of channels. We show that fully reversible networks are able to achieve results comparable to the state of the art in 4D time-lapse hyper-spectral image segmentation and full 3D video segmentation, with a much lower memory footprint that is a constant independent of the network depth. We also extend the use of such networks to variational auto-encoders, where optimization begins from an exact recovery and we discover the level of compression through optimization.
Efficient Circuits for Quantum Search over 2D Square Lattice Architecture

Quantum computing has increasingly drawn interest and investments from the academic, industrial, and governmental research communities worldwide. Among quantum algorithms, Quantum Search is important for its quadratic speedup over its classical-computing counterpart. A key ingredient in its implementation is the Multi-Control Toffoli (MCT) gate, which creates a Boolean product of control variables and XORs it into the target. On an idealized quantum computer, all-to-all connectivity would eliminate the need to use SWAP gates to communicate information. This is, however, not affordable in the current Noisy Intermediate-Scale Quantum (NISQ) computing era. In this work, we discuss how to efficiently implement MCT gates on 2D Square Lattices (2DSL), suitable for superconducting circuits, by taking advantage of relative-phase Toffoli gates and H-tree layouts to drastically reduce resulting circuits' depths and the amount of SWAPping required.
Digital Competencies of University Teachers

The article analyzes the ways of improving the quality of higher education in Ukraine in the context of European standards of quality to the educational space of the University. The influence of the level of digital competence of university teachers on the quality of the educational environment of a modern higher educational institution is described. Also the model of corporate standard of digital competence of teachers, developed at the Borys Grinchenko Kyiv University, is described. The tools of measuring the level of formation of digital competence of teachers and an open index of formation level – e-portfolio – are analyzed. The developed e-portfolio system displays a coherent picture of the teacher’s performance with determined quantitative and qualitative performance indicators and their impact on the university’s assessment of the world and state ratings. There have been develop a program of training in order to increase the level of IC’s competence of teachers, it is carried out in the format of 30*70 (in-person / remote). According to the program, an electronic training course “Increase of the teacher’s ICT competence” was created. The result of training is the development and protection of the project by module (subject) of the discipline using combinations of innovative pedagogical and ICT technologies, carrying out the appropriate assessment of the project and identifying strengths and weaknesses. The results of the questionnaire presented indicate the need to introduce such a program for in-service training of teachers.
Trace Element Geochemistry in Quartz in the Jinqingding Gold Deposit, Jiaodong Peninsula, China: Implications for the Gold Precipitation Mechanism

Lots of studies on gold precipitation mechanisms have focused on fluid inclusions within quartz. However, the trace elements in quartz reflect the properties of the ore fluid, and a comparison of the trace element content in different types of quartz can reveal the precipitation mechanism. The Jinqingding gold deposit is the largest gold deposit in the Muping–Rushan gold belt and contains the largest single sulfide–quartz vein type orebody in the gold belt. This study distinguished four types of quartz in this orebody through field work and investigations of the mineralogy and cathodoluminescence (CL) of the quartz and crosscutting relationships as seen under a microscope. In situ studies via electron probe micro-analyzer (EPMA) and laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) were used to determine the trace element content of the different quartz types. Type Qa displayed a comb structure in the field and zoning under the microscope and in CL. Milky white and smoke grey Qb was the most common quartz type and hosted the most sulfide and gold. Qc was Qa and Qb quartz that recrystallized around pyrite or overgrew and appeared different from Qa and Qb in CL images. Qd occurred within fractures in pyrite. Qa formed prior to the mineralization of gold, and Qd formed post-mineralization. Qb and Qc provided information regarding the ore fluid during mineralization. Sericites occurred with pyrite in fractures in the quartz, and some, along with free gold, filled in fractures in pyrite. Free gold occurred within Qa, Qb, Qc, and in brittle fractures in pyrite. Qc had the lowest Al content of all of the quartz types. As Al content is related to the acidity of the ore fluid in previous study, this indicated an acidity decrease during mineralization, which could be attributed to the sericitization. Sericitization could indicate a potential gold occurrence. The Ti content decreased from Qb to Qc, indicating a decrease in temperature during quartz overgrowth formation. Change in acidity and cooling can therefore be identified as possible causes of gold precipitation in the sulfide–quartz vein type in the Jinqingding gold deposit.