Reviewerly/exHarmony huggingface dataset:
Hybrid Biogeography-Based Optimization Techniques for Geo-Spatial Feature Extraction: A Brief Survey

Nature-inspired computational intelligence (CI) techniques take inspiration from the natural phenomenon to provide optimized solutions to the problems. Hybrid variants of biogeography-based optimization with FPAB, geo-science algorithms and ACO2 have been presented which can be used for optimization problems and also for many other applications. These techniques have been successfully applied in varied domains, geo-spatial feature extraction being one of the crucial applications that has been explored. An analytical study of each technique along with the study of feature extraction results on a standard multi-spectral satellite dataset of area of Alwar in state of Rajasthan is carried out by calculating the error matrices for all the features of land cover needed to be obtained from the image. The classified images of different classifiers are compared on the basis of classification matrices kappa coefficient, user’s accuracy, producer’s accuracy and classification error. The results given by the algorithms for land cover features and their characteristics make them suitable for various other application areas also like finding optimal path of submarines, groundwater detection, military camp location, etc. The hybrid algorithms combine the strengths of the algorithms, thus making them suitable for most of the major application areas including parallel computing, face and pattern recognition, job scheduling and terrain specific applications.
Recent Applications of Swarm-Based Algorithms to Color Quantization

Nowadays, images are very important elements in everyday communication. Current devices include high-quality displays that allow showing images with many colors. Nevertheless, the size of these images is a major issue when the speed of transmission and the storage space must be taken into consideration. The color quantization process reduces the number of different colors used to represent an image while trying to make the new image similar to the original. In addition to enabling visualization with low-end devices and efficient image storage and transmission, color reduction is related to other operations applied to images, such as segmentation, compression, texture analysis, watermarking and content-based image retrieval. The color quantization problem is complex, since the selection of the best colors to represent the image is a NP-complete problem. The complexity and interest of the problem have led to several solution approaches over the years. Recently, several interesting solutions have been proposed that apply swarm-based algorithms. Said algorithms are based on a population of individuals that cooperate to solve a problem. This chapter focuses on the swarm-based solutions proposed for the color quantization problem and shows that these novel methods can generate better images than those obtained by classical solution approaches.
Timestamping Metadata Using Blockchain: A Practical Approach

Long-term preservation of digital information requires confidence in the credibility and ability of digital archives and systems to consistently provide accessible and usable content. Ensuring that the provided information has remained unchanged over time is a particular challenge. Trusted timestamping is an effective method that allows anyone to prove without any doubt that specific content existed at a particular date and time. A practical approach for trusted timestamping using the Ethereum blockchain is presented here. A complete metadata record is stored as transaction input data along with a document hash digest. The approach is uncomplicated, human and machine readable, self-explanatory, and modular. It supports metadata preservation and copyright protection of digital documents applying verification without disclosure. The approach aims at extending current digital archives and systems using existing, well-tested technology.
A Validation Method of Self-Adaptive Strategy Based on POMDP

Self-Adaptive Systems (SASs) can dynamically adjust themselves to adapt to the changes, by planning some strategies to guide the adjustments. However, many uncertainties in runtime affect the efficiency of strategies and the ability to attain goals for SASs. So, it is necessary to validate strategies before they are executed. At present, most of the validation methods ignore the fact that less data can be observed at runtime, so it is difficult to describe the state accurately with these data. Most methods do not support uncertain state reasoning, but state transition is uncertain. This will result in the verification method not dealing well with the uncertainties. This paper proposes a strategy validation method based on Partially Observable Markov Decision Process, and makes several key contributions: (1) a uncertain state model that not only support description of states through partial information, but also describe the uncertain transitions of states, (2) a validation method to validate whether the strategies meets the requirements at runtime, (3) a strategy correction method to get effective strategies as quickly as possible.
HoneyGadget: A Deception Based ROP Detection Scheme

Return-Oriented Programming (ROP) is a robust attack which has been proven to be Turing-complete. ROP reuses code segments named gadget in vulnerable applications and modifies control flow to achieve malicious attacks. Existing defense techniques for code reuse attacks attempt to restrict the policy of control flow transfer (e.g. CFI) or make locating gadgets a hard work (e.g. ASLR). However, decades of the arm race proved the ability to detect up-to-date attacks remains the Achille’s heel. In honeypot, a general pattern for operators is spreading honeytokens and hunting spammers by capturing their malicious behavior. In order to capture the attack pattern of code reuse attacks, we present a novel deception based ROP detection model named HoneyGadget. HoneyGadget inserts various types of honey gadgets as tokens to some specific points of binary files where normal control flow would not reach and record their places once the application is loaded. During the execution, HoneyGadget uses Last Branch Record (LBR) to trace execution records. On performing a sensitive function call, HoneyGadget compares LBR records with the maintained address list, and terminates the program immediately if some records match. Since these honey gadgets will not be executed by normal control flow, there must be a ROP attack. We have developed a fully functioning prototype of HoneyGadget. Our evaluation results show that HoneyGadget can (1) capture ROP attacks actively and (2) incurs an acceptable overhead of 7.61%.
A Study on Secure Data Access Scheme Based on CP-ABE in Cloud Environments

Recently, with the development of cloud computing, it has become possible to store and share data in cloud environments; however, there are various security threats in these environments. Attacker can leak stored data and, above all, service providers cannot be trusted completely. Therefore, security technologies guaranteeing that data remains secure when stored in cloud environments are required. To this end, researchers are studying techniques based on CP-ABE. However, vulnerabilities to various security threats remain and some schemes are inefficient. In this paper, we apply CP-ABE to address some of the security threats associated with cloud environments, by ensuring that only authorized users can access data stored in the cloud. In addition, we propose an access control system that, in comparison to existing schemes, is more computationally efficient when attributes are revoked.
Euler Recurrent Neural Network: Tracking the Input Contribution to Prediction on Sequential Data

Recurrent neural networks (RNNs) achieve promising results on modeling sequential data. When a model produce an effective prediction, we always wonder which inputs are crucial to the specific prediction. Modern RNNs use nonlinear transformations to update their hidden states, which is hard to quantify the contributions for each input to the prediction. Inspired by the Euler Method, we propose a novel framework named Euler Recurrent Neural Network (ERNN) that uses weighted sums instead of nonlinear transformations to update its hidden states. This model can track the contribution of each input to the prediction at each time-step and achieve competitive result with fewer parameters. After quantification of their contributions to the prediction result, we can find the decisive ones among inputs and can also better understand the principle of the models in the prediction process.
Using Ontology and Context-Awareness for Business Process Modelling: An Overview

In recent years, several approaches have been proposed for modeling business process, with the emergence of Context-Awareness and semantic web, especially Ontologies. Researchers and practitioners had attempted to integrate them into Business Process Modeling. In this paper, we present, on the one hand, some classical approaches for BPM, particularly approaches that integrate Context and others that integrate Ontologies into BPM. On the other hand, we analyze their main characteristics through a comparative analysis based on a series of criteria. The purpose of this survey is to provide a support for a new proposal.
Remarks on “Consistent Abstractions of Affine Control Systems”

This article further considers the abstraction problem of affine control systems proposed in the paper “Consistent abstractions of affine control systems” by Pappas et al. We present a general solution for the problem. Then, constructions in the abovementioned paper are treated as a special case of our results. We also clarify the statement given in the abovementioned paper, claiming that the canonical construction on M gives the desired affine distribution on N, which solves the abstraction problem of smooth affine control systems. Additional assumptions are added to make the statement hold.
Mixed Reality User Interface for Astronauts Procedure Viewer

This paper introduces a Proof-of-Concept (PoC) for Mixed Reality (MR) system to support an astronaut's manual work, the system is called MobiPV4Hololens. It has been developed in the European Space Agency's (ESA) project called "MobiPV4Hololens - Prototype a Media Helmet for MobiPV Implemented Using Microsoft (MS) HoloLens". The MS HoloLens mixed reality platform was integrated as the hands-free user interface to the ESA Mobile Procedure Viewer system called MobiPV. Based on the user evaluation most of the users believe that the MobiPV4Hololens is beneficial in supporting procedure execution.
Detail Recovery in Medical Images Denoising

Details in medical images are important for doctors' diagnosis. However, the images processed by current image denoising methods tend to be over-smooth at edges to some extent due to the lack of special attention to details. In order to remove noises and preserve details of medical images, we propose a GAN-based network named SDGAN which focuses on the recovery of details after denoising. The proposed SDGAN contains two subnetworks: one denoises medical images preliminarily, and the other attempts to reconstruct the details missed in the previous subnetwork. Experiments on Wireless Capsule Endoscopic(WCE) images with noises are conducted to evaluate the performance of SDGAN in comparison with other state-of-the-art denoisers. The results show significant gains in terms of quantitative metrics (PSNR and SSIM) and visual effects using SDGAN. SDGAN is able to recover realistic details textures, making it highly attractive for medical image denoising applications.
Hue Signature Auto Update System for Visual Similarity-Based Phishing Detection with Tolerance to Zero-Day Attack

Detecting phishing websites is imperative. Among several detection schemes, the promising ones are the visual similarity-based approaches. In those, targeted legitimate website's visual features referred to as signatures are stored in SDB (Signature Database) by the system administrator. They can only detect phishing websites whose signatures are highly similar to SDB's one. Thus, the system administrator has to register multiple signatures to detect various phishing websites and that cost is very high. This incurs the vulnerability of zero-day phishing attack. In order to address this issue, an auto signature update mechanism is needed. The naive way of auto updating SDB is expanding the scope of detection by adding detected phishing website's signature to SDB. However, the previous approaches are not suitable for auto updating since their similarity can be highly different among targeted legitimate website and subspecies of phishing website targeting that legitimate website. Furthermore, the previous signatures can be easily manipulated by attackers. In order to overcome the problems mentioned above, in this paper, we propose a hue signature auto update system for visual similarity-based phishing detection with tolerance to zero-day attack. The phishing websites targeting certain legitimate website tend to use the targeted website's theme color to deceive users. In other words, the users can easily distinguish phishing website if it has highly different hue information from targeted legitimate one (e.g. red colored Facebook is suspicious). Thus, the hue signature has a common feature among the targeted legitimate website and subspecies of phishing websites, and it is difficult for attackers to change it. Based on this notion, we argue that the hue signature fulfills the requirements about auto updating SDB and robustness for attackers' manipulating. This commonness can effectively expand the scope of detection when auto updating is applied to the hue signature. By the computer simulation with a real dataset, we demonstrate that our system achieves high detection performance compared with the previous scheme.
Information technologies in the study of the implementing complex logic functions possibilities

Abstract In the article approaches of acquaintance of students of pedagogical high school with the information technologies, allowing to carry out multifactor research of logic elements of modern digital computers are considered. Description of stages and results of research of possibilities of realization of difficult logic functions by means of the basic logic elements executed on integrated circuits with potential representation of the information is resulted. The course of research on the basis of the Electronic Workbench training electronic laboratory software, which allows to perform schematic modeling of digital and analogue radio electronic devices, is presented.
Design of Establishment System of Satisfaction Index for Tourist Sites According to the Weather Using Deep Neural Network

Text’s sentiment analysis techniques of texts play an important role in many fields including decision system. Sentiment analysis has been developed by combining with various technologies in accordance with this trend. The approach that extracts and uses the qualities in the sentences and the structure using the deep neural network belong to the example. In this paper, we propose a system to classify the vast amount of SNS tourist sites reviews into many factors that can affect tourism such as weather and includes rating by using the text sentiment analysis technology applying deep neural network.
Meta-Data Construction for Selection of Breast Tissue Biopsy Slides Image Classifier to Identify Ductal Carcinoma

Currently there are large amounts of data available, to obtain useful information, multiple methods have been created to fulfill specific tasks, however, identifying the most appropriate method is often a difficult task. Meta-Learning is presented as an option that can recommend for new data the most appropriate method to perform a particular task based on experience, in which the features of the data and the performance of methods are related, this relationship is known as Meta-Data. Given the continuous increase of patients with breast cancer cases and availability of datasets, the images of slides of breast tissue biopsy to identify Ductal Carcinoma were selected as the object of study. The aim of this work is construction of Meta-Data that allows application of Meta-Learning for selection of the best Ductal Carcinoma identification method in the type of images under study. The proposed methodology presents a performance of the 99.6% accuracy, 99.9% AUC and 99.7% F-measure for Meta-Data Validation.
A New Use of Doppler Spectrum for Action Recognition with the Help of Optical Flow

In this work, we present two new procedures for activity recognition that are based on the Fourier frequencies that are generated when the optical flow values of successive frames of video are processed simultaneously. In the first algorithm, we correlate these 2D Doppler Fourier spectra with the mean spectra of each activity class. These correlation vectors, which include only 30 features in number, are categorized using a reduced robust SVM classification model. This first procedure is of low computational cost for action recognition tasks for numerable activity classes. For large numbers of activity classes, we propose a new method of aggregated weighted spectra of optical flow values across the whole video. The above-mentioned Fourier spectra are concatenated with a short vector representing the distributions of the moving edges. These methods are insensitive to the presence of background as well as to the positions of the subjects and their shapes and can encode the information of a part or of the whole of a video into relatively short vectors. The results of the two procedures seem to be competitive to state-of-the-art action recognition methods when tested on the KTH Royal Institute Database and on the UCF101 Database for action recognition tasks.
Copy–Move Attack Detection from Digital Images: An Image Forensic Approach

Due to the newSoni, Badal development of image handling tool or software, copy–moveDas, Pradip K. attack is increasingly becoming a common practiceThounaojam, Dalton Meitei and on the other hand, the detection of such type ofBiswas, Debalina attack from digital images has become the challenging and active research area. This paper presents the recent block and keypoints-based Copy–Move Forgery Detection (CMFD) techniques. In this paper, we cover the critical discussions of different blocks and keypoints-based CMFD techniques with their pros and cons. The paper also describes the different publicly available databases and performance evaluation measures. Some unsolved research issues in the field of copy–move forgery detection is identified and present in this paper.
A Suggestion of Image’s Position Analysis System Using Beacon Positioning

When a 360° image is produced from images taken by a plurality of general cameras, an image stitching technique for finding the same area between a plurality of images and correcting the geometry is required. The image stitching technique compares the similarities of feature points extracted from several images, and connects the images together to form a large image. In this paper, assuming a theater with a beacon attached, the position of the photographer is roughly grasped through the beacon, and the position of the photographer captured by the user through the application, the azimuth angle of the image and the field of view (FOV). We propose a method of filtering images with stitching object images.
Digital Projection Speckle Technique for Fringe Generation

Shadow moiré technique has been a widely utilized method in industry to determine surface flatness, out-of-plane displacement, and 3D metrology. In this paper, we present a digitized speckle method analogous to the shadow moiré method. A randomly generated speckle pattern is first projected onto a screen and digitally recorded. The same pattern is projected onto a specimen and digitally recorded as well. The two images are then converted into TIFF files to be superimposed and processed using a Fourier transform based algorithm to generate fringes that are similar to shadow moiré fringes. Additionally, a Digital Image Correlation (DIC) software was used to generate fringes that are similar to shadow moiré fringes. The technique is simple and straightforward. We apply the technique to a variety of specimens to demonstrate its applicability.
PROFESSIONAL-VERBAL FORMAL EDUCATION AND FORMATION THEREOF IN PEDAGOGICAL HIGHER SCHOOL BACHELORS

Purpose of the study: The relevance of the issue under study is conditioned by the necessity to form professional-verbal formal education in the bachelors of pedagogical universities. The goal of this article is to determine qualitative characteristics of professional-verbal formal education in bachelors of pedagogical higher school, substantiation of use of forms and methods for generation of professional-verbal formal education.&#x0D; Methodology: The empirical method is a leading method for studying of this problem, as it allows to observe the process of formation of professional-verbal formal education of bachelors, to measure and compare indicators of the level of the professional-verbal formal education by means of performing specialized tasks; methods of mathematical statistics and graphical imaging of the study results are also used.&#x0D; Results: The concept of formal education is presented by the authors as a totality of personalistic and professional-verbal properties of a teacher and is regarded as a property which determines living standards of the man as an indicator of the human and intellectual capital, as an indicator of the efficiency of the social and professional activity.&#x0D; Applications of this study: The materials of this article can be of use in practical work for teachers of linguistic disciplines of not only universities but also secondary schools and colleges; they also can be of interest to teachers and psychologists of the education system and for all related to vocational-pedagogic activity.&#x0D; Novelty/Originality of this study: A set of measures has been developed and approbated which is necessary and sufficient for the generation of the professional-verbal formal education in bachelors; use of game situations at academic classes and conducting of various events in out-of-classes time, etc. were proposed.
Dataset Model to Predict Bound Error via Supervised Machine Learning for Wireless Sensor Network Systems

We consider the problem how to build the dataset model which supports to track the target in wireless sensor network (WSN) for online tranining phase. Our current works introduced particle filter (PF) based on Kullback-Leibler Distance (KLD) with adjusted bound error method to ameliorate the negative Received Signal Strength (RSS) variations by creating a sample set within the high-likelihood region. The bound error of this method is one of three parameters that decide to enhance the estimation accuracy and convergence rate of declining number of particle used. Therefore, in this paper, we propose the dataset model and apply a typical supervised machine learning such as K-Nearest Neighbor (KNN) to predict the bound error. The first iteration, using the observation information via KLD resampling optimal bound error to conduct a resampling on the basis of the initial bound error. From the second to the end iteration, we propose the KNN technique to search the predicted bound error value that fulfills the minimum of mean number of particle used between at the current and the next iteration. Our experiments show that our dataset model applied KNN-KLD method imptoves the estimation accuracy and the efficient number of particles compared to the traditional methods.