Reviewerly/exHarmony huggingface dataset:
Situation-cognitive traffic light control based on object detection using YOLO algorithm

Current traffic lights provide the green signal with fixed-time interval without considering the traffic situation. As a result, cars in a long line have to wait long time, which causes traffic jams and makes the drivers be irritated. In order to solve the problem, it is necessary to control the green signal interval according to the analysed traffic volume using the image processing and the machine learning techniques. This paper presents a situation-cognitive traffic light control algorithm that measures the traffic volume using object detection algorithm called you only look once (YOLO) and controls the traffic signal intervals according to the traffic volume. The algorithm expects the smooth traffic flow and the reduction of the driver's stress.
A feature-based approach for digital camera identification using photo-response non-uniformity noise

Source camera identification of an image is an emerging field of digital forensics. To identify the source camera through which the image is captured, photo-response non-uniformity (PRNU) noise is used as a camera fingerprint, as it is a unique characteristic that distinguishes images taken from the similar cameras. This paper presents a feature-based approach to identify the source camera. The input image is denoised using the denoising filter and from this denoised image, PRNU noise pattern is extracted. These PRNU noise patterns are represented by Hu's invariants, which are perpetual under image scaling, translation and rotation. These features are fed to fuzzy min-max neural network (FMNN) for training and classification for digital camera identification. The proposed approach has the ability to identify the cameras capturing the same scene.
Generating Cipher Text using BLOWFISH Algorithm for Secured Data Communications

Cryptography plays a major role in the network security. In order to secure the data one must do encryption of the original message. In this paper, the design and analysis of high speed and high performance BLOWFISH algorithm is implemented in VHDL coding and compared with AES (Advanced Encryption Standard) algorithm. The BLOWFISH algorithm involves the process of giving the data and key as input to the encryption block. BLOWFISH encryption algorithm is designed and programmed in VHDL coding. Then it is implemented in Xilinx 10.1. This research is carried in the following steps: designing of encryption algorithm, writing VHDL code, simulating the code on “ModelSim altera 6.5e”, synthesizing and implementing the code using Xilinx’s ISE 10.1.This research aims in developing flexible and technology independent architectures in the areas of VPN software, file compression, public domain software such as smart cards, etc. Also presents the comparison of BLOWFISH and AES algorithms. Experimental results show that BLOWFISH algorithm runs faster than AES algorithm while both of them consume almost the same
A comprehensive evaluation system of teaching quality based on big data architecture

Aiming at the problems of long response time and high error rate in traditional teaching quality comprehensive evaluation system, a teaching quality comprehensive evaluation system based on big data structure is proposed. Firstly, different evaluation indicators are designed, the principle of teaching quality comprehensive evaluation is designed by using data dictionary, the requirement analysis of teaching quality comprehensive evaluation system is realised by using UML use case model, then the overall framework of the system is designed, and the comprehensive evaluation system of teaching quality is developed and studied by using Asp.net related technology. The experimental results show that the response time of the designed system is shorter than that of the traditional teaching quality comprehensive evaluation system, and the fitting degree between the evaluation results and the actual situation is higher, which fully proves the superiority of the designed system.
Microsoft Azure and Cloud Computing

Cloud computing as a platform does not require a review; however, the hundreds of new services and updated changes to the Microsoft Azure public cloud can be a great investment for your business. Updated Azure core services and new business services are explained in this book to further your knowledge. This book was written for both technical and business readers to gain greater skills through our guidance.
Fuzzy-MCS algorithm-based ontology generation for e-assessment

Ontologies can lead to important improvements in the definition of a course's knowledge domain, in the generation of an adapted learning path, and in the assessment phase. This paper provides an initial discussion of the role of ontologies in the context of e-learning. Generally, automatic assessment is preferred over manual assessment to avoid bias errors, human errors and also conserves teacher's time. Evaluation through objective tests like multiple choice questions has gained a lot of importance in the e-assessment system. Here we have proposed an efficient ontology generation based on soft computing techniques in e-assessment for multiple choice questions. We have employed fuzzy logic incorporated with optimisation algorithm like modified cuckoo search algorithm. Here a set of rules are first designed for creating the ontology. The rules are generated using fuzzy logic and these rules are optimised in order to generate a better ontology structure.
CNN based Image Classification Model

Images are the fastest growing content, they contribute significantly to the amount of data generated on the internet every day. Image classification is a challenging problem that social media companies work on vigorously to enhance the user’s experience with the interface. The recent advances in the field of machine learning and computer vision enables personalized suggestions and automatic tagging of images. Convolutional neural network is a hot research topic these days in the field of machine learning. With the help of immensely dense labelled data available on the internet the networks can be trained to recognize the differentiating features among images under the same label. New neural network algorithms are developed frequently that outperform the state-of-art machine learning algorithms. Recent algorithms have managed to produce error rates as low as 3.1%. In this paper the architecture of important CNN algorithms that have gained attention are discussed, analyzed and compared and the concept of transfer learning is used to classify different breeds of dogs..
Robust pedestrian detection for driver assistance systems using machine learning

Vision-based pedestrian detection is a challenge task for a variety of applications such as driving assistance systems, especially in case of insufficient illumination. Effective fusion of complementary information acquired by multispectral images (visible and infrared) allows robust pedestrian detection under various lighting conditions (e.g., day and nighttime). In this paper, we propose a multispectral pedestrian detection approach that combines visible and infrared images. Firstly, an Otsu thresholding is applied to infrared images to detect hot spots most likely representing a pedestrian, after applying some morphological operations to enhance the original image and compensate for clothing-based distortions. The significant regions of interest obtained in the infrared image are mapped into corresponding visible image. Secondly, multispectral aggregated channel features are used with a thermal discrete cosine transform, as descriptor combined with a support vector machine (SVM) classifier. Our approach is evaluated on the KAIST multispectral dataset to prove its efficiency.
Two Pass Multidimensional Key Generation and Encryption Algorithm for Data Storage Security in Cloud Computing

Many companies which essentially required cloud computing services are still in ambivalence whether to adopt it or not. In this research paper an attention has been drawn on security of storage as a Service which is an integral part of Infrastructure as a Service which provide famous CSP, AWS. Whenever someone rent the storage from the cloud service provider, chances of its compromising can take place. Therefore it is intelligence to encrypt the data before uploading it to the cloud and more intelligently divide the data into fragments and encrypt these fragments separately and then upload on cloud. Suppose that if this data is divided into n fragments then n algorithm are required to encrypt these fragments or use single one algorithm and change its key n times otherwise if only one key will be used and someone steal this key then all fragments will be decrypted with the same key. Many algorithms like DES, AES, RSA, Elgamal and Blow Fish are there but they all are static in nature and one more weakness of these algorithms is that size of encrypted file increased. While some researchers used these algorithms in combination and some used double encryption, nevertheless nature remains static and decrypted file size increase rapidly. Now, there is a requirement to design a data sensitive algorithm which could sense data and produce different key for different data automatically. In this paper Two Pass Multidimensional Key Generation and Encryption Algorithm has been proposed which will sort the above problem. Another beauty of this algorithm is that it checks the integrity of the documents while decrypting. Out of millions or billions of characters if only one character or word is altered or removed then this algorithm will show that documents integrity has been violated.
Accelerated grey wolf optimiser for continuous optimisation problems

Grey wolf optimiser (GWO) is a relatively simple and efficient nature-inspired optimisation algorithm which has shown its competitive performance compared to other population-based meta-heuristics. This algorithm drives the solutions towards some of the best solutions obtained so far using a unique mathematical model, which is inspired from leadership behaviour of grey wolves in nature. To combat the issue of premature convergence and local optima stagnation, an enhanced version of GWO is proposed in this paper. The proposed algorithm is named accelerated grey wolf optimiser (A-GWO). In A-GWO, novel modified search equations are developed that enhances the exploratory behaviour of wolves at later generations, and the exploitation of search space is also improved in the whole search process. To validate the performance of the proposed algorithm, set of 23 well-known classical benchmark problems are used. The results and comparison through various metrics show the reliability and efficiency of the A-GWO.
A Worldwide Analysis of Cyber Security And Cyber Crime using Twitter

In the era of social media and the Internet, there has been an exponential increase in threats related to the privacy of user accounts and data. The confidentiality of personal data is compromised for various motives. This sudden increase in security threats has led to widespread problems. Our research is focused on analyzing the extent of cyber-attacks in various countries across the globe. We have proposed a novel approach for analyzing the tweets related to cyber-attacks and its surrounding fields. The analysis proves that Asian countries face more cyber security issues as compared to European countries. Further, it is also analyzed that developing countries like India are more prone to these issues as compare to developed countries like the United States or the United Kingdom.
Bolt 3D Point Cloud Segmentation and Measurement Based on DBSCAN Clustering

In complex railway conditions, the segmentation and height measurement of bolt in RGB image is easily affected by background. Although deep learning based method may complete the task, a large quantity of images for training are required. In this paper, an approach by using the traditional DBSCAN clustering in 3D point cloud is proposed to achieve the segmentation and height measurement of bolts. Specially, with the slice clustering along the height direction and the compression clustering on the certain dimensions implemented on the 3D point cloud while considering the information in the designed drawing, all the bolts can be segmented and then their heights are measured with high precision. The real 3D point of a platform with multiple bolts is obtained by the depth camera and the effectiveness of the proposed approach is demonstrated, showing the stronger anti-interference ability.
Deep learning-based intelligent surveillance model for detection of anomalous activities from videos

For safeguarding and monitoring purposes, public places are equipped with surveillance cameras. Timely and accurate identification of suspicious activities is paramount to securing the public places. Assigning human personnel to keep continuous watch over ongoing activities is error-prone and laborious. To alleviate the need of human personnel for monitoring such videos, automated surveillance systems are required. This paper proposes a deep learning based intelligent surveillance model for detection of anomalous activities. The problem of anomaly detection has been handled as one class classification problem. The proposed approach involves two dimensional convolutional auto-encoder for feature learning, sequence-to-sequence long short term memory model for learning temporal statistical correlation and radial basis function as activation function in fully connected network for one class classification. We experimented on real-world dataset by two variants of proposed approach and achieved significant results at frame-level anomaly detection.
The Dynamic Web Services Adaptation Framework in Context-Aware Mobile Cloud Learning Using Semantic-Based Method

Most of Service-Based Applications (SBAs) have to be changed after their first deployment not solely due to the changing system requirements as well as of continuous change of the environment itself. With the growth of web service paradigm, there is a need for an efficient mechanism in dynamic adaptation process to offer users a better service experience. In particular, context-aware can be adapted in dynamic adaptation by considering user's contexts and device's contexts. Context awareness will be a key area for Mobile Cloud Learning (MCL) environment as it helps in the reasoning process to provide correct education resources for learners' prospect. Contextual information are represented using semantic-based approach for high expressiveness and formal representation for reasoning technique. However, dynamic adaptations frameworks in previous research are still lacking in terms of contextual information of the learner and device, and quality of services (QoS) were not considered. Besides, there is limited support of semantic expressiveness in terms of contextual information and services by using solely semantic-based technique. This paper proposes Dynamic Adaptation in Context Aware Mobile Cloud Learning (DACAMoL) framework to support adaptation process in MCL using semantic-based approach. The framework is applied in MCL mobile application that offers basic learning language.
Extended query model for MOOC education resource metadata based on big data

In traditional education resource metadata extended query methods, data recall is poor. In order to solve this problem, an extended query model for MOOC education resource metadata based on big data information fusion clustering scheduling is proposed. The semantic ontology model is adopted to analyse the storage structure of MOOC education resource metadata and extract binary semantic feature quantity of MOOC education resource metadata to construct a binary semantic decision model for extended query of MOOC education resource metadata; the integrated information processing technology of big data is adopted for extended query and adaptive scheduling of MOOC education resource metadata to improve the ability to retrieve and identify metadata. The simulation results show that the proposed method can provide an average precision ratio of 0.976 in extended query of MOOC education resource metadata and good data recall performance.
Feeble ring

An ideal A of a semiring S is called k-ideal if for any two elements r A and x S such that r A, then x Aʺ. This leads us to introduce the new concept feeble ring as generalization of k-ideal. Several basic properties, example and characterization of this concept are given. Moreover, the study investigate relationship of feeble ring with other classes.&#x0D; &#x0D; http://dx.doi.org/10.25130/tjps.25.2020.020&#x0D;
Detecting Model Clones using Design Metrics

The cloning in software is a frequent phenomenon that leaves a negative impact among the product lines or the version control where the developer is involved in the evolution of software system due to any enhancement or changing requirements that leads to a release of new version. With the advent of MDD, identification of clones shifted from code to models to tackle risks at early stages. Due to the renaming of model elements, some model clones are missed that reports secondary clones instead of primary. So, in order to increase recall of clones in models we have proposed a hybrid approach based on the tree, lexical and metric approaches and validated it using SDMetric tool followed by analysis of detection of exact, renamed clones and modified clones. It provides one to one mapping in the form of corresponding primary clones with maximal matching that helps to reduce the domain for comparison of code at the implementation level. Such clones need to be identified among the versions and the stable part with least changes acts as a pattern and can be reused by the product lines.
Randomized Key-based GMO-BCS Image Encryption for Securing Medical Image

An essential security requirement while transmitting and receiving medical images is to maintain confidentiality and authorization of these medical images. This paper contains a proposal of an enhanced lossless image encryption algorithm that provides security to Digital Imaging and Communications in Medicine (DICOM) images by producing a random key with using enhanced group modulo based bit circular shift (GMO-BCS) technique. Random key production is the backbone of this technique to provide robust security of medical images that transfer over a network. In the encryption process, we randomly generate a key for each and every pixel of the DICOM image. Group theory is used in this process to create circular shifting in 8-bit pixel values while the security enhancement employs the random key for encryption. This technique is more suitable for medical image encryption either by direct transmission or multimedia app-based transmission under telemedicine and others.
Evaluation of an augmented reality based image guidance system for laparoscopic liver surgery

Background: Use of augmented reality (AR) based image guidance systems (IGS) has been proposed as a method to improve anatomical orientation during laparoscopic liver surgery (LLS) with potential benefits for patients. In this study, an AR based IGS (SmartLiver) was evaluated intraoperatively for the first time. In addition enhanced visualisation methods with the potential to improve mental integration between surgeons and AR environment will be presented. Methods: Patients scheduled for staging laparoscopy or LLS were recruited. During surgery a 3D liver model was manually overlayed onto the laparoscopic screen (registration) to create an AR environment. Surgeons scored IGS performance and accuracy on a scale (1-10). Objective accuracy was calculated by measuring distances of anatomical landmarks between 3D model and in-situ liver (mm RMS). Results: Ten patients were recruited to the study. Ease of use, mental integration and accuracy were rated positively (8 each). Length of setup time was rated negatively (2). Objectively measured accuracy was 137±39. A consensus was reached to integrate depth fogging, outlining and plane clipping as enhanced visualisation features into SmartLiver (video). Conclusion: The findings of this study demonstrate that SmartLiver is easy to use and capable of creating an AR environment that integrates well into the surgical workflow. Enhanced visualisation methods may improve the interpretation of AR data. An automatic registration method is needed to improve accuracy and setup time.
Image Feature Selection using Ant Colony Optimization

With the enhancement in imaging technology, huge amount of information is being created whose classification is a challenging task among researchers. The classification performance is solely dependent on the quality of the features defining the respective images. Hence, feature extraction from images and feature selection from this huge data is required. Image Feature Selection is a vital step which can significantly affect the performance of image classification system. This paper proposes an efficient combination of image feature extraction technique and feature selection strategy using Ant Colony Optimization (ACO). ACO utilizes overall subsets performance and local feature importance to fetch problem domain finding prime solutions. For ACO based feature selection in images most of the researchers use priori information of features. However, in this work, the features are extracted separately through the detailed analytical process which carries the best suited features for different classes of images. The performance in terms of classification accuracy has been enhanced through the optimization of features in the dataset. For experimentation and evaluating the performance of proposed work, Corel and Caltech datasets are used. Satisfactory results have been obtained with the proposed approach.
MySQL Workbench

MySQL Workbench is Oracle’s graphical user interface for querying and managing MySQL Server. Together with MySQL Shell, it can be seen as one of the two Swiss army knives for working with MySQL.
Readability and lexical sophistication of colon cancer websites – a corpus-assisted assessment of online educational materials for patients

The aim of this paper is to check whether the information for colon cancer patients available on top websites devoted to this disease is comprehensible for the readers or whether, due to high saturation with special medical terms, it is beyond the recommended readability level of an average internet user. Two main criteria of analysis were involved in the study: readability and lexical sophistication. The methods used in the study include 8 readability tests (ARI, Colemen-Liau, New Dale-Chall, Flesch-Kincid, Fry, Gunning Fog, Raygor Estimate, and SMOG), TAALES software and Lexical Complexity Analyser used to examine syntactic and lexical parameters of texts, and a corpus-assisted web-based tool used for lexical sophistication called VocabProfile. The study has shown that none of the 30 websites under scrutiny meets the demand of the recommended readability level, and that higher lexical sophistication involves a lower readability level.