Reviewerly/exHarmony huggingface dataset:
Definition and Implementation of an Elliptic Curve Cryptosystem using a New Message Mapping Scheme

Elliptic curve cryptography (ECC) is strongly requested for encryption and decryption processes since its guarantees high level of data security, insured by small key sizes that usually lead to fast algorithms. Such processes can only deal with points on elliptic curves on which the message has to be mapped. In this paper, we define a new message mapping algorithm coupled with Elgamal encryption based on an elliptic curve defined over a finite prime field. The established algorithm will be implemented and discussed for a chosen plaintext corresponding to an integer message. Our numerical tests demonstrate that the execution time of the message mapping, encryption, reverse message mapping and decryption, increase linearly versus data sizes. These results are promising for the design of fast and secure cryptosystems desirable for implementations on constrained environments.
Geo-integration bases of services generation for the Multipurpose aerospace system for forecasting monitoring of disasters

Abstract The article describes the principles for development of information services for disaster monitoring and forecasting within the Multipurpose Aerospace System for Forecasting Monitoring (MAKSM) of Natural and Man-Caused Disasters in Russia and the Commonwealth of Independent States in the context of the MAKSM main target tasks at the initial stage of its development and with reference to the status and functions of information services in the system. It defines the target tasks addressed by the services to implement the main information conversion of estimates of disaster precursor parameters into parameters of disaster forecasts. The article explains the geo-integration functional for communication of the services with monitoring data sources and the functional for thematic processing, data storage and presentation of forecast results to consumers. The authors describe implementation patterns of the geo-information functional of the services at the current stage of remote monitoring system development.
Detecting Protuberant Saliency from a Depth Image

The visual attention of a human enables quick perception of noticeable regions in an image. The research on the models of visual attention has been actively studied for decades in the computer vision areas. For example, detecting visual saliency in a scene allows to estimate which details humans find interesting in advance to understand the scene. This also forms the important basis of a variety of latter tasks related to visual detection and tracking. By virtue of increasing diffusion of low-cost 3D sensors, many studies have been proposed to examine how to incorporate 3D information into visual attention models. Despite many advantages of depth data, relatively few studies on the visual attention of a depth image have delved into how to fully exploit the structural information of depth perception based on depth data itself. In this paper, Protuberant saliency is proposed to effectively detect the saliency in a depth image. The proposed approach explores the inherent protuberance information encoded in a depth structure. The fixation of a human eye in a depth scene is directly estimated by Protuberant saliency. It is robust to the isometric deformation and varying orientation of a depth region. The experimental results show that the rotation invariant and flexible architecture of Protuberant saliency produces the effectiveness against those challenging conditions.
Design and development of a web traffic analyser

Abstract This paper focuses on web traffic analysis; we explore the complexity of modern web pages by tracing network packets for the types of objects requested when loading a web page. Furthermore, web pages are not analogous to each other, their design and implementation relies on the purpose of the website. Similarly, web pages that are designed for a specific area, say e-commerce, would have the same development process, which then reflects on the code and objects used to develop such websites. This will lead us to a speculation that web surfers who have similar intention of browsing, they will have similar web traffic in terms of number of different objects and the total size of data transferred through the network. Conversely, inspecting the type, size and number of objects within the browsing session will show us an approximate of what the users’ intention to browse or like to browse next.
Sentence Embedding Model Based on Feature Selection

The method of calculating the word vector using the neural network method provides the motivation for generating the representation model of the sentence. For the smooth inverse frequency sentence vector model, only the word frequency information on the general data set is considered to calculate the word weight, but when it is specific to the task, the problem that the different words contribute differently to the task and its weight correction is not considered. According to the distribution of characteristic words in different categories in the dataset, the task contribution factor (TCF) is proposed by using the improved information gain feature selection method. Based on this factor, a sentence vector representation model (IIG-SIF) based on task contribution is proposed. By testing on the standard text classification dataset 20 Newsgroups and the text similarity calculation dataset SICK, the IIG-SIF model has a greater improvement in the two tasks of text categorization and Text similarity calculation than the original SIF model.
Automatic Identification Method of Pointer Meter under Complex Environment

As for the industrial pointer meter's recognition reading in outdoor scenes is easily affected by interference from complex environments such as light, occlusion, and blurred images, this article combines the theory and algorithm of case segmentation with pointer type in complex environments In meter detection and recognition, an automatic image segmentation and reading system for meter images was designed based on pyqt5. In the method designed in this paper, the de-noised image is firstly pre-processed by Non-Local Dehazing, Criminisi, and Deep Denoiseing Super Resolution, and then the instance segmentation algorithm Mask RCNN is used to segment and locate the pointer, and finally use the angle method for reading operation. Experimental tests show that this method can effectively overcome the interference of the complex environment on the pointer meter recognition, reduce the reading error, and has strong robustness and adaptive ability.
Fixed viewport applications for omnidirectional video content

With omnidirectional videos, the viewer is able to direct her Field-of-View (FoV) to any part of the scene while watching the content. This is achieved by rendering the 360 video content on the inside of a (conceptual) sphere in which the viewer is typically placed at the center. This is in contrast with traditional video that is rendered on a 2D plane and the viewer is watching always through a viewport directed by the content creator. These two approaches create a conflict between user experience and creativity, since omnidirectional video provides the user with viewing freedom, while traditional video allows for greater artistic expression by controlling the viewport. In order to combine these two approaches we propose an immersive setup in which the content changes between free-form viewing of omnidirectional (360 video mode) and directed viewing of traditional videos (director's mode). In this demo paper we present the benefits and reasoning behind this proposal and the means to implement it using the OMAF (MPEG-I - Part 2) standard.
Using strain gages to assess the bearing properties of underlying surface with a robot crawler

Abstract The advent of military robotic systems has raised requirements for control systems, especially independent motion control, in particular, lateral possibility and flotation when moving in partially defined or undefined environment and in the presence of external perturbations. This paper proposes approaches to determine bearing reactions under the dynamic interaction of RV running gear with the bearing surface. It demonstrates a set of gages built into the tracked running gear allowing to evaluate the underlying surface properties. It proposes a computer-aided engineering system based on the finite element method to identify the most efficient locations to install strain gages.
VTutor: A Platform for Improving Searchability and Interactivity of Recorded Lectures

Recorded lectures have gained popularity as a method of delivering lecture content as they give learners a host of distinct advantages such as the ability to follow lectures without time or location constraints and to consume the lectures at their own pace. However, despite such benefits, they have a tendency to be lengthy and tedious to watch. They also prove cumbersome when precise information needs to be extracted from the content. Another drawback is that recorded lecture videos fail to show the connection between the lecture and its support material such as slides and questionnaires. Though many of the existing platforms allow editing lecture videos for more interactivity, the methods employed by these platforms have always been manual, and therefore time intensive. VTutor is a web platform that aims to address these drawbacks by introducing automation into the video enhancement process, eventually combining the lecture material to create an enhanced user experience. Specifically, VTutor allows users to navigate through a lecture video using subtopics, its corresponding slides and code samples. Furthermore, it is equipped with the ability to automatically generate questions by scraping the internet based on provided keywords thus improving the level of engagement that a learner has with the lecture.
Tracking an Object over 200 FPS with the Fusion of Prior Probability and Kalman Filter

Efficient object tracking is a challenge problem as it needs to distinguish the object by learned appearance model as quickly as possible. In this paper, a novel robust approach fusing the prediction information of Kalman filter and prior probability is proposed for tracking arbitrary objects. Firstly, we obtain an image patch based on predicted information by fusing the prior probability and Kalman filter. Secondly, the samples derived from the obtained image patch for our tracker are entered into support vector machine (SVM) to classify the object, where these samples need to be extracted features by Histogram of Oriented Gradients (HOG). Our approach has two advantages: efficient computation, and certain anti-interference ability. The samples obtained from image patch is less than that obtained from image, which makes SVM model more efficient in classification and reduces interference outside the image patch. Experimentally, we evaluate our approach on a standard tracking benchmark that includes 50 video sequences to demonstrate our tracker's nearly state-of-the-art performance compared with 5 trackers. Furthermore, because extracting samples and classifying HOG features is computationally very cheap, our tracker is much faster than these mentioned trackers. It achieves over 200 fps on the Intel i3 CPU for tracking an arbitrary object on benchmark.
Finger Contact in Gesture Interaction Improves Time-domain Input Accuracy in HMD-based Augmented Reality

This paper reports that the time-domain accuracy of bare-hand interactions in HMD-based Augmented Reality can be improved by using finger contact: touching a finger with another or tapping one's own hand. The activation of input can be precisely defined by the moment of finger contact, allowing the user to perform the input precisely at the desired moment. Finger contact is better suited to the user's mental model, and natural tactile feedback from the fingertip also benefits the user with the self-perception of the input. The experimental results revealed that using finger contact is the preferred method of input that increases the time-domain accuracy and enables the user to be aware of the moment the input is activated.
Parallel Solution to LIS Using Divide-and-Conquer Approach

This paper presents parallel solution to the LIS problem. The presented approach works by dividing the problem into smaller sub problems and then combining their solutions. The individual sub problems are handled in parallel by multiple processors. The solution produces optimal result in O(n log n) time using n/2 processors. The time taken by a single processor to implement this approach is O(n/2 (log2 n)2).
Recidivism forecasting

Across the world, there are several factors attributed to high crime rates. The prevention of and the fight against crimes is a major concern of all countries. In the era of globalization and new information and communication technologies, reducing these crimes rate by using conventional methods (law enforcement, social interventions...) are not enough. In fact, they have many limits.
Artificial Intelligence Based on Biological Neurons

The chapter describes the new approach in artificial intelligence based on simulated biological neurons and creation of the neural circuits for the sphere of IoT which represent the next generation of artificial intelligence and IoT. Unlike existing technical devices for implementing a neuron based on classical nodes oriented to binary processing, the proposed path is based on simulation of biological neurons, creation of biologically close neural circuits where every device will implement the function of either a sensor or a “muscle” in the frame of the home-based live AI and IoT. The research demonstrates the developed nervous circuit constructor and its usage in building of the AI (neural circuit) for IoT.
A Convolutional Neural Network Model of Image Denoising in Real Scenes

For dealing with the noise images in real scenes, this paper proposed a deep convolutional neural network (CNN) based on real noise scene, which is closer to real life. By taking advantage of CNN multiple convolutional layers, the model learns the data characteristics of noised images in real scenes, and further constantly adjusts its own parameters. The experiment results show that the CNN network model proposed in this paper has better denoising performance for noised images in real scenes. The image processed by the method proposed in this paper is clearer, with better visual effect. At the same time, the edge details in the image are better maintained.
An approach for improving searching algorithm in big data using hybrid technique

Abstract Search algorithms play an essential role in wide variety of applications in computer science. Different search algorithms can be applied to different data structures. Each data structure has its own advantages and limitations which in turn affect the search algorithm complexity and performance. In the context of Big Data such limitations can be more obvious and noticeable in reducing the performance offered by the data structure and search algorithms applied to it because of big data characteristics such as its huge volume. Also, string data sets can be more challenging. They can cause more overhead in preprocessing. Some string datasets such as personal names may have some skew that has negative effect on hash indexes by increasing the ratio of collisions produced. The work in this paper offers a technique for improving the famous Hash table Data Structure by reducing the collision ratio in hashing in the average case, thus reducing the total number of comparisons made by search algorithm. The advantage of reducing the collision ratio was achieved by building a hybrid approach making use of three common data structures, namely Hash Table, B-Tree and Linked List. The results obtained showed noticeable improvement for time complexity of data searching algorithm over the traditional data structures that were used for comparisons.
Environmental Sensor Assessment for a Light Quadcopter Drone

Abstract This paper presented drone design and environmental sensor assessment. The assessment was conducted for temperature and pressure. The measurements were performed offline and online. Offline measurement was by locating drone inside a controlled chamber, while online measurement was when drone is flying. Assessment shows that there were significant error differences in temperature, where offline drone exerted only 0.75% error, but online drone produced 6.23% error. Error increases as temperature increases. Pressure assessment exerted a slight error different between offline and online drone, 0.16% and 0.17%.
Multiple-Hypothesis Vision-Based Landing Autonomy

This paper presents a novel state estimation system for unmanned aerial vehicle landing. A novel vision algorithm that detects a portion of the marker is developed, and this algorithm extends the detectable range of the vision system for any known marker. A vision-aided navigation algorithm is derived within extended Kalman particle filter and Rao–Blackwellized particle filter frameworks in addition to a standard extended Kalman filter framework. These multihypothesis approaches not only deal well with a highly nonlinear and non-Gaussian distribution of the measurement errors of vision but also result in numerically stable filters. The computational costs are reduced compared to a naive implementation of particle filter, and these algorithms run in real time. This system is validated through numerical simulation, image-in-the-loop simulation, and flight tests.
Preliminary sandstone reservoir depth prediction with pre-processing data using principle component analysis (PCA) and partial least square (PLS) based on well logging data attribute

Abstract Sandstones containing 60% of oil or gas because of porosity and permeability. The conventional method required sandstones predicted with complete data. This study used neural networks to determine the depth of sandstones and predict incomplete variables well site at the Sunda Strait-south area. Data pre-processing used PCA-PLS to find the most important variables that affect the output thus improving the prediction results. Multicollinearity analysis is used to determine the data compression needed. Raw data multicollinearity result showed that the multicollinearity occurs indicated with VIF over 10 and tolerance under 0.1 at CALI and SP variables. The PCA-PLS analysis uses to reduce data from 13 variables into six important variables namely DEPT GR RHOB NPHI ILD Peff, these results do not experience multicollinearity. The variable that predicted is ILD with the best ANN multilayer perceptron showed small standard deviation and standard error results 2.85 and 0.03. The best ANN model to predict the depth of radial basis sandstone is due to produce a regression test of 0.8 based on the results of the validation of the log image.
FPGA Implementation Of Dynamic Power, Area Optimized Reversible ALU For Various DSP Applications

Today electronic products that are using chip as a component are facing severe problems in power dissipation. So most efficient power reducing architectures has to be developed to reduce the costs for maintenance. This is a most important task for embedded based devices, graphical based processor and similar DSP processor which suffers with low power. The core of each and every embedded device and processor uses ALU as the dependable module. It points out that ALU should consume less power and area and must provide high speed of operation with high accuracy. This paper describes design and implementation of ALU using reversible logic gates. Reversible logics are used to reduces power consumption. The aim of this paper is to compare the power consumption and area of conventional ALU and ALU which uses reversible logic gates. Both the architectures have been developed by using Verilog HDL in Xilinx ISE design suite 14.7.
An ASP-Based Approach for Phase Balancing in Power Electrical Systems

Unbalanced electrical loads on feeders of power electrical systems can cause serious problems, including power losses, significantly lower power quality, damaging of electrical equipment, and tripping of protective devices. Nevertheless, the problem of balancing such systems—which essentially is equivalent to the problem of integer partitioning—has proven to be NP-complete. Against this background, in this article, an algorithm based on the powerful, declarative framework of Answer Set Programming (ASP) is provided, that efficiently attacks practical instances of the phase-balancing problem. To the best of our knowledge, this is the first attempt of approaching this significant engineering problem by means of the ASP paradigm. The whole study indicates that the examined problem is of great interest from an algorithmic viewpoint, as well as an engineering application that highlights ASP’s modelling methodology.
More realistic analysis of mass surveillance – security in multi‐surveillant settings

The PRISM made the research of cryptography against subversion attacks flourish these years. In a subversion attack, surveillants can compromise the security of users’ systems by subverting implementations of cryptographic algorithms. While the scenario of a single-surveillant has been researched by several works, the multi-surveillant setting attracted less consideration. The authors have initialised this notion in previous work but assumed the surveillants to be completely isolated. In this study, the authors follow this idea and consider more realistic scenarios of the multi-surveillant subversion, where surveillants are able to have limited communications. They propose the notions of queryable adversaries and conversational adversaries. In the first setting, adversaries can verify whether output is produced by a subverted implementation from others; in the latter setting, adversaries can have arbitrary conversations with each other without leaking their backdoors. Under the framework of ‘amalgamation and decomposition’, they design randomness generators that are secure against queryable adversaries and conversational adversaries, respectively, by adopting implementations from different sources intentionally. Based on the secure randomness generators, they construct symmetric encryption schemes that match the corresponding security definitions.
Algorithm for determining the coefficients of the interpolation polynomial of Newton with separated differences

Abstract The article considers the problem of determining the coefficients of the Newton interpolation polynomial with separated differences. To solve this problem, a scheme, a tabular method and an algorithm for determining the coefficients without simplification and disclosure of brackets that arise when constructing polynomials by the Newton method are proposed. Based on this tabular method, approximation and interpolation problems can also be solved.
Fast Algorithms for Function Decomposition Based on n-Separate Periodic Wavelets

In this paperPleshcheva, E.A. we give the definition and construction of a theory of periodic n-separate MRA and wavelets on the base of several scaling functions. We give effective numerical algorithms for decomposition of the function applying constructed periodic wavelets and scaling functions.