Reviewerly/exHarmony huggingface dataset:
A Novel Feature Extraction Model to Enhance Underwater Image Classification

Underwater images often suffer from scattering and color distortion because of underwater light transportation characteristics and water impurities. Presence of such factors make underwater image classification task very challenging. We propose a novel classification convolution autoencoder (CCAE), which can classify large size underwater images with promising accuracy. CCAE is designed as a hybrid network, which combines benefits of unsupervised convolution autoencoder to extract non-trivial features and a classifier, for better classification accuracy. In order to evaluate classification accuracy of proposed network, experiments are conducted on Fish4Knowledge dataset and underwater synsets of benchmark ImageNet dataset. Classification accuracy, precision, recall and f1-score results are compared with state-of-the-art deep convolutional neural network (CNN) methods. Results show that proposed system can accurately classify large-size underwater images with promising accuracy and outperforms state-of-the-art deep CNN methods. With the proposed network, we expect to advance underwater image classification research and its applications in many areas like ocean biology, sea exploration and aquatic robotics.
Railway Key Exchange Scheme for Improving Communication Efficiency of RSSP-II Protocol

RSSP-II protocol security function module designs peer-to-peer interaction to establish a secure connection. By analyzing the vulnerability of the original key distribution, for the first time, this paper introduces the idea of key agreement into the field of railway wireless communication, the implementation principle of Diffe-Hellman key agreement algorithm is briefly introduced,and the interactive process of the algorithm is simulated and reproduced. Finally, the algorithm is analyzed in detail from two aspects of time consumption and storage overhead, combining the actual communication scenario of CTCS-3 train control system, the feasibility of transplanting the algorithm to railway communication scenario is proved.
The State-of-the-Art Cryptography Techniques for Secure Data Transmission

Cryptography is a progression where message correspondences are intelligently sent from one abuser to an additional abuser which endows with frequent defense services like privacy, data truthfulness, or verification to the wireless transportation structure. An encryption method keeps exceptional crucial contribution to communication safety measures. Here we mentioned characteristics of various Symmetric and Asymmetric encryption techniques along with inclusion of optimization techniques in cryptography for decrease computation difficulty. Moreover, advanced encryption techniques such as Zero-knowledge, Multi-party, Homomorphism encryptions, and Cognitive cryptography, Blockchain with their associated protocols are described. The present day's extensive research practices on quantum computer machines explain mathematical tribulations which are complicated or stubborn for classical computers. Quantum cryptography, challenges, Goal of Quantum resistant cryptography with associated literature work is described.
A Data-Driven Deep Neural Network Pruning Approach Towards Efficient Digital Signal Modulation Recognition

State-of-the-art digital signal modulation recognition approaches involves more and more deep learning method. However, the tremendous potential exhibited by deep learning is often offset by architectural and computational complexity, making widespread deployment a challenge for edge scenarios such as mobile and other consumer devices. To the best of our knowledge, this is almost the first work to deploy deep neural work in edge equipment for physical signal classification. Inspired by an observation that fully connected layer makes up about 75% of total parameters, we will focus on network pruning in deep neural network fully connected layer. Our experiments show that we could obtain high compression ratio about 17%~18% of parameters without losing over 0.1% in accuracy.
Improved K-Means Algorithm for Finding Public Opinion of Mount Emei Tourism

Due to the rise of Mount Emei Tourism, more and more tourists express their travel thoughts, which will influence potential tourists. In this paper, relying on the data-crawling technology, public opinions of Mount Emei are crawled. After data cleaning, word-segmentation is used to build the word bags. Through contour coefficient, the number of cluster is determined. An improved K-means algorithm is proposed to cluster the public opinions. With the crawled data, the public opinions of Mount Emei is categorized into five cluster. The results show that the tourists are satisfied with the tourism in Mount Emei.
Post-Quantum Security of Messaging Protocols: Analysis of Double Ratcheting Algorithm

Development in the area of quantum technologies led to the appearance of first quantum computers. The threat of using a quantum computer for cryptanalysis requires wide implementing post-quantum security in computing algorithms and communication protocols. We evaluate the computational power of some existing quantum computers to illustrate the relevance of research in post-quantum security. One of the best ways to test post-quantum protocols is to embed them into some non-critical but widely-used sphere. Secure messaging is an excellent example of such an application. In the paper, we analyze the post-quantum security of well-known messaging specification Signal, which is considered to have high-security properties. The core of Signal specification is the Double Ratchet protocol. We notice and explain why it is not a post-quantum secure scheme. After that, we suggest some possible ways to improve the security features of Signal specification.
Multi-Factor Stateful Authentication using NFC, and Mobile Phones

In this paper, we present a multi-factor stateful authentication system that uses Near Field Communication (NFC) technology, a mobile phone, known pictures, and state (time sequence) to authenticate users. A user firstly accesses to the mobile phone and then emulates encrypted NFC tags through a mobile app. This mobile app forwards an authentication code to the computer after correctly selecting a preset picture. A custom process bypasses the built-in OS access control letting the user to type a passcode to gain access to the computer. The custom access control process running on the computer is programmed by using an open source credential provider and a database that stores the encrypted authentication codes. Our experiments show that this multi-factor and stateful authentication prevents man-in-the-middle attacks and improves security with a simplified login process.
Rheumatoid Arthritis anticipation using Adaptive Neuro Fuzzy Inference System

A state of discomfort is known as a disease, also termed as illness or sickness. When the tiniest living things like virus enters our body, it reacts with the cells of the body and results an illness. The Arthritis is very problematic to early forecast. It nurtures with the age and related to the large and small joint pain. The Rheumatoid Arthritis (RA) is chronic disease, its long-term auto-immune and inflammatory disease which damages many joints tissues. It occurs when immune system can't distinguish the cells and tissues. The ANFIS model is used for the prediction of the RA in human mortals. A complete process is mentioned in this study, which helps to a technique for the diagnosis of the Rheumatoid Arthritis in human beings with accuracy 93.5%. This diagnosis is made on the bases of 12 symptoms of RA in human lives like age, stiffness, joint deformity, ESR, CRP, WBC, Uric Acid etc. This paper also compares the ANFIS with Naive Bayes, Bagging algorithm and KNN classifiers.
Setiment Analysis of Public Opinion on The Go-Jek Indonesia Through Twitter Using Algorithm Support Vector Machine

Abstract The development of technology and information, especially in Indonesia is very rapid so that social media is the most popular communication tool by the people of Indonesia today. One of these social media is Twitter. This also causes the public to tend to give opinions and assessments in the form of tweets to service companies, one of which is Go-Jek Indonesia. Public opinion and judgment on Twitter can be classified into 3 classes: negative, neutral, and positive. The purpose of this study is to analyze the sentiment of public opinion on Go-jek Indonesia on twitter using the Support Vector Machine (SVM) algorithm. The approach used were Multiclass One Vs Rest SVM with Univariate Chi Square feature selection to classify community tweets on Go-Jek Indonesia’s services. Using testing data of 170 tweets, 31.2% of people with negative opinions were obtained, 24.1% were neutral and 36.5% were positive opinions and 5.9% failed to be classified. The results of sentiment analysis testing conducted provide a classification accuracy of 91.8%.
Low Power Boolean Logic Circuits using Reversible Logic Gates

Low power design is a promising characteristic for the application ranging from the Internet of things (IoTs) to quantum computing. The boolean logic based on the reversible concept is the emerging technology for low power digital logic circuit design for quantum computing application. The reversible logic circuit provides an entirely new way of processing quantum computing. In this paper,we present the transistor level implementation of state-of-the-art reversible gates and proposed boolean logic circuits using reversible logic gates. The various Boolean logic circuits based on reversible logic gates are designed using the SCL180nm library. Furthers, we introduced D-flip flop and 4×1 MUX using the reversible gate, which has an energy efficiency of 14% and 94%, respectively, with reported paper. This proposed logic circuits achieved area efficiency as well as a power-efficiency because of reversible logic gates, which consume less power due to its distinctive input-output mapping techniques.
VoColReg: A Registry for Supporting Distributed Ontology Development using Version Control Systems

The number of ontologies used for different purposes, such as data integration, information retrieval or search optimization, is constantly increasing. Therefore, it is crucial that ontologies can be developed and explored in an easy way by humans, and are accessible by intelligent agents. To this end, we created VoColReg on top of the VoCol platform. VoColReg provides an integrated registry that hosts VoCol instances, allowing the community to access, browse, reuse, and improve ontologies in a collaborative fashion. VoColReg integrates several improved features, such as syntax validation realized by RDFDoctor, which is able to simultaneously identify a comprehensive list of syntax errors and automatically correct a subset of them. Currently, the VoColReg platform hosts more than 21 ontologies from various domains, where nine of them are publicly available. We analyzed those nine ontologies to discover different facts about them such as hosting platforms used, expressivity of the ontologies, number of triples and modules.
An Efficient End-to-End Channel Level Pruning Method for Deep Neural Networks Compression

Deep neural networks (DNNS) have obtained compelling performance among many visual tasks by a significant increase in the computation and memory consumption, which severely impede their applications on resource-constrained systems like smart mobiles or embedded devices. To solve these problems, recent efforts toward compressing DNNS have received increased focus. In this paper, we proposed an effective end-to-end channel pruning approach to compress DNNS. To this end, firstly, we introduce additional auxiliary classifiers to enhance the discriminative power of shallow and intermediate layers. Secondly, we impose Ll-regularization on the scaling factors and shifting factors in batch normalization (BN) layer, and adopt the fast and iterative shrinkage-thresholding algorithm (FISTA) to effectively prune the redundant channels. Finally, by forcing selected factors to zero, we can prune the corresponding unimportant channels safely, thus obtaining a compact model. We empirically reveal the prominent performance of our approach with several state-of-theart DNNS architectures, including VGGNet, and MobileNet, on different datasets. For instance, on cifar10 dataset, the pruned MobileNet achieves 26. 9x reduction in model parameters and 3. 9x reduction in computational operations with only 0.04% increase of classification error.
Digitization and Current Educational Changes in Switzerland - Inspiration for the Czech Republic?

Ongoing digitization in most European countries is transforming the context in which teaching and learning take place. The anytime, anywhere availability of knowledge is leading to an adjustment of learning processes and changes in the roles of teachers and students. The challenge for schools is to prepare the young generation for lifelong learning, a progressively more complex society and a world of work in constant change. Two important objectives are to ensure that all citizens will be able to use ICT in order to participate competently in political, social, economic and cultural processes, and that they know how to deal with online risks in an informed and independent manner. The administrative and organizational conditions in each of these areas must be designed to exploit available potential and ensure a high level of security. In the text we suggest possible inspiration from the Swiss digitization in education.
A non-commutative viewpoint on graph signal processing

The emerging field of graph signal processing aims to develop analysis and processing techniques for data that is best represented on irregular domains such as graphs. To this end, important notions of classical signal processing, such as smoothness, band-limitedness, and sampling, should be extended to the case of graph signals. One of the most fundamental concepts in classical signal processing is the Fourier transform. Recently, graph Fourier transform was defined as a generalization of the Fourier transform on Abelian groups, and many of its properties were investigated. However, a graph is usually the manifestation of a non-commutative structure; this can be easily seen in the case of the Cayley graph of a non-Abelian group. In this article, we investigate a new approach to develop concepts of Fourier analysis for graphs. Our point of view is inspired by the theory of non-commutative harmonic analysis, and is founded upon the representation theory of non-Abelian groups.
Hybrid Bat and Genetic Algorthim Approach for Cost Effective SaaS Placement in Cloud Environment

The increasing demand of software service in cloud environment needs strategic placement in the cloud infrastructure. Thus, in which the users use the service based on the service model of the provider and pays based on their use of the resources. These resources are storage, memory processing element and bandwidth. Efficient optimal placement is the main issue in order to provide a cost effective service to the user. This research has proposed hybrid approaches to addresses the initial software task placement problem by exploring the advantage of both Bat algorithm (BA) and Genetic algorithm (GA), to make the initial ST placement processes optimum and cost effective. In order to evaluate the performance of the proposed hybrid algorithms, an experimental environment had configured using CloudSim simulation tool. The proposed solution performance has evaluated by compared with those existing placement algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization algorithm (PSO). According to the result the proposed algorithm has reduced the placement cost up to 2 -13% on a cloud environment.
Sentiment Analysis on Predicting Presidential Election: Twitter Used Case

Twitter is a popular tool for social interaction over the Internet. It allows users to share/post opinions, social media events, and interact with other political and ordinary people. According to Statista web site 2019 statistical report, it estimated that the number of users on Twitter had grown dramatically over the past couple of years to research 300 million users. Twitter has become the largest source of news and postings for key presidents and political figures. Referring to the Trackalytics 2019 report, the recent president of the USA had posted 4,000 tweets per year, which indicates an average of 11–12 tweets per day. Our research proposes a technique that extracts and analyzes tweets from blogs and predicts election results based on tweets analysis. It assessed the people’s opinion and studied the impact that might predict the final results for the Turkey 2018 presidential election candidates. The final results were compared with the actual election results and had a high accuracy prediction percentage based on the collected 22,000 tweets.
Trusted execution environment with Intel SGX

Trusted execution environments (TEE) are emerging as a new form of computing on today's hardware. TEEs ensure that code outside of the TEE, including the operating system and hypervisor, cannot compromise the execution integrity and confidentiality of programs run inside the TEE. Based on hardware-rooted trust, TEEs additionally allow to prove the integrity of such execution even to remote third parties (remote attestation). By using TEEs that protect not only against software attackers but also hardware attackers, even the cloud provider is moved out of the trust domain. By leveraging such hardware-based TEEs, there is an alternate approach on building secure multiparty computation toolkits. One such TEE is from Intel Software Guard Extension (SGX) technology available in current hardware. This chapter describes the abstractions and properties offered by TEEs, explains the realization of the TEE abstraction in Intel SGX, explores the deployment of SGX in the Cloud to realize secure multiparty applications, and finishes with an Outlook on challenges and opportunities ahead.
Trends on Fitness Landscape Analysis in Evolutionary Computation and Meta-Heuristics

We present some new trends in fitness landscape analysis in evolutionary computation and meta-heuristic study. The fitness landscape concept is brought from that of evolutionary biology that attempts to present and visualize the relationship between genotype and its success. Evolutionary computation algorithm utilizes this concept to describe the success of an optimized variable. It takes a bridge between the evolutionary computation algorithm and its optimizing problem. We present three study subjects to analyse and model fitness landscape for evolutionary computation and meta-heuristic. They are fitness landscape approximation using functional regression in original space and projected lower-dimensional space, frequency information of fitness landscape using Fourier transform, and estimation of a convergence point from moving vectors in parameter space. The approximation and analysis of fitness landscape can be applied to enhance the search of evolutionary computation, to solve the multimodal optimization problem, and to accelerate the single-objective and multi-objective evolutionary search, etc. Some open topics, future opportunities, and works of new trends on fitness landscape are analysed and discussed as well.
A True Random Number Generator based on a Chaotic Jerk System

We present a scheme for true random number generation by directly sampling a chaotic jerk system. A method for determination of the maximum Lyapunov exponent, and thus the maximum bit rate for true random number generation, of the jerk system of interest is shown. The system is tested over a wide range of sampling parameters in order to simulate possible hardware configurations. This system is readily implemented in high speed electronics and is well suited for random number generation due to its high dynamic complexity, long term aperiodicity, and extreme sensitivity to initial conditions.
Visual Object Tracking by Moving Horizon Estimation with Probabilistic Data Association

Vision sensors are widely used not only for detection and recognition, but also measurement of the pose of the moving objects. But in the crowded environment, occlusion often disrupts the measurement, and wrong data association due to misrecognition deteriorates the tracking performance. Probabilistic data association filter (PDAF) is known as useful to address such issues, in which observed features are weighted by probability to deal with multiple observations as well as to cope with occlusion and false recognition. This paper presents a novel object tracking method in which PDAF is incorporated into moving horizon estimation (MHE) framework to deal with multiple frame tracking and physical constraints. The performance of the proposed method is evaluated by comparing with the PDAF.
Multi-column Deep Neural Network Based On Particle Swarm Optimization

Many architectures for computer vision use of Convolutional Neural Networks (CNN) are biologically inspired by the receptive fields in the visual cortex. A modified deep architecture Thresholding Convolution Neural Network (ThCNN) progresses in this paper. This method is simple and effective to; regularize features map in the early layers of Convolution Neural Network; Compute the optimal global threshold to determine the features that are passed to the next layers; improve the speed performance of the suggested structure. The present paper proposed that each feature map in the convolution layer is connected to half feature maps in the previous layer. Another tendency in current applications is to combine the results of multiple networks, the particle swarm optimization method (PSO) is used to determine the influence of each network in the combining model. The proposed method was evaluated using the MNIST datasets. The experimental results showed that the error rate has decreased (up to 26.98 percent). Furthermore, combining model has decreased the number of the networks with the percent of 20%, and decreased error rate to 78.26 % of error rate of the best single model and to 88.89% of error of the averaging model.
A Low-Complexity Error-and-Erasure Decoding Algorithm for t=2 RS Codes

Reed-Solomon (RS) codes are widely adopted in numerous digital communication systems to handle the possibly occurred errors and/or erasures during the data transmission. This paper focuses on the t=2 RS codes and proposes a low-complexity error-and-erasure decoding algorithm for them. The proposed algorithm directly computes the errata location polynomial instead of performing the iterative Berlekmap-Massey (BM) algorithm which is usually adopted in the conventional RS decoding algorithm. Moreover, a method to directly compute the errata locations and errata magnitudes is also presented. For a (255,251) RS code, the proposed error-and-erasure decoding algorithm can save over 90% multiplications and additions of the conventional decoding algorithm. In addition, the complexity reduction becomes more significant as code length increases.