Reviewerly/exHarmony huggingface dataset:
Implementation of Color harmony and Under water fusion utilizing Image Enhancement and Color Restoration strategies

Submerged picture handling is an insight research field that can possibly assist designers with bettering the submerged climate. Submerged picture preparing has been utilized in a wide assortment of fields, like submerged minuscule discovery, territory examining, mine recognition and self-governing submerged vehicles. Nonetheless, submerged symbolism experiences solid assimilation, dissipating, shading bending, and clamor as a result of the phoney light sources, picture obscure, dimness, as well as a pale blue or greenish tone. In this way, the upgrade of submerged symbolism can be partitioned into two techniques: 1) submerged picture dehazing and 2) submerged picture shading rebuilding. This paper presents the justification submerged picture debasement, studies the best in class insight calculations like profound learning techniques in submerged picture dehazing and reclamation. It likewise presents a submerged picture shading assessment metric, and gives an outline of the major submerged picture applications.
A Survey on Privacy-Preserving Data Publishing Models for Big Data

Big data deals with massive amounts of data with various characteristics and intricate structures. The vast amount of data collection in big data has led to lots of security and privacy threats. Big data evolution and the need for security and privacy in big data have been covered in the study. Big data taxonomy framework, the privacy laws, and acts have also been analyzed and studied. Various privacy-preserving data publishing models and their attack models have been thoroughly studied under the categories of 1) record linkage model, 2) attribute linkage model, 3) table linkage model, and 4) probabilistic model. Furthermore, the trade-off between privacy and utility, future directions, and inference from the study have been summarized. The study gives insights into various techniques in privacy-preserving data publishing to address the problems related to privacy in big data.
The Recognition of Microscopic Images of Ceramics Incorporating Blockchain Technology

Having summarized the previous research on ceramic identification and the anti-counterfeiting, the authors propose a ceramic identification system that combines computer vision algorithms with blockchain technology. The system uses irregular pores on microscopic images of ceramic surfaces as image features, and it applies the SIFT(Scale-invariant feature transform) algorithm to extract feature. The images and feature vector sets are then stored by IPFS(Inter-planetary File System). When a consumer needs to authenticate a ceramic product, it is only necessary to take a microscopic image of the specified location, and then the SIFT algorithm will compare the picture with the data stored in the IPFS network, and was previously obtained through the records on a blockchain network, the matching result then determines whether the photographed ceramic is one of those already recorded. Experimental show that the matching results can be used as a strong basis for identifying the origin of ceramic products.
Blind Restoration of Atmospheric Turbulence Degraded Images Based on Curriculum Learning

Atmospheric turbulence degraded images in typical practical application scenarios are always disturbed by severe additive noise. Severe additive noise corrupts the prior assumptions of most baseline deconvolution methods. Existing methods either ignore the additive noise term during optimization or perform denoising and deblurring completely independently. However, their performances are not high because they do not conform to the prior that multiple degradation factors are tightly coupled. This paper proposes a noise suppression-based restoration network (NSTRN) for turbulent degraded images, where the noise suppression module is designed to learn low-rank subspaces from turbulent degraded images, the attention-based asymmetric U-NET module is designed for blurred image deconvolution, and the fine deep back-projection module (FDBP) is used for multi-level feature fusion to reconstruct the sharp image. Furthermore, an improved curriculum learning strategy is proposed, which trains the network gradually to achieve its superior performance through a local-to-global, easy-to-difficult learning method. .
The Vulnerability of Children in Cyberspace

The internet is a tremendous tool. It allows users to chat, read, play, and be entertained while viewing content from around the world. It's vibrant, diverse, and provides instant access to knowledge on any topic. Although the internet has many advantages, it still has some drawbacks. The internet can be a dangerous place for anyone, but children and teenagers are particularly at risk. Online dangers can have severe, expensive, and even tragic consequences, ranging from cyber bullies to social media posts that can come back to haunt them later in life. Children can unintentionally expose their families to cyber attacks by downloading malware that gives cyber criminals access to their parents' bank accounts or other sensitive information. The internet can be a dangerous place for children due to cyberbullying, sexual communication, identity theft, scams, and exposure to adult content. The chapter is an attempt to view in detail the possible menace of cyber crime, its types, and the national and international conventions and legislations on the same.
Refining Mosca’s Theorem: Risk Management Model for the Quantum Threat Applied to IoT Protocol Security

Large-scale quantum computation (QC) presents a serious threat to many modern cryptographic primitives. This has profound implications to the critical information infrastructure (CII) as well. The main mitigation techniques involve migrating to cryptographic schemes that are postulated to be “quantum resilient”, i.e. there are no known quantum algorithms for them. The solution requires that all the known instances of the vulnerable cryptographic schemes are replaced with quantum resistant schemes. In a lecture at the NIST premises in 2015, Dr. Mosca gave his famous theorem of when different stakeholders should start upgrading their systems. This theorem is, however, very general and assumes the worst possible scenario. Does it apply equally well for all or even the majority of use cases of cryptography? Is the answer same across all domains of CII or all types of networks? Currently the most challenging environments are those with very little computational and networking capabilities, i.e. Internet-of-Things (IoT). IoT is today used in many CI subcategories, such as power grid (including SCADA), water supply, logistics, agriculture and dangerous goods handling. In this text, we develop a more detailed risk management model to prepare for QC and apply this model to survey the status of Internet-of-Things (IoT) protocols. We cover in total 17 different IoT protocols or protocol families.
Evaluating the Effects of Size and Precision of Training Data on ANN Training Performance for the Prediction of Chaotic Time Series Patterns

In this research, artificial neural networks (ANN) with various architectures are trained to generate the chaotic time series patterns of the Lorenz attractor. The ANN training performance is evaluated based on the size and precision of the training data. The nonlinear Auto-Regressive (NAR) model is trained in open loop mode first. The trained model is then used with closed loop feedback to predict the chaotic time series outputs. The research goal is to use the designed NAR ANN model for the simulation and analysis of Electroencephalogram (EEG) signals in order to study brain activities. A simple ANN topology with a single hidden layer of 3 to 16 neurons and 1 to 4 input delays is used. The training performance is measured by averaged mean square error. It is found that the training performance cannot be improved by solely increasing the training data size. However, the training performance can be improved by increasing the precision of the training data. This provides useful knowledge towards reducing the number of EEG data samples and corresponding acquisition time for prediction.
EMPLOYMENT PROSPECTS WHEN OBTAINING PHYSICAL EDUCATION

This article discusses the profitability of obtaining a physical education. As part of the study, monitoring of postgraduate employment after graduation from the Department of Physics was carried out. A marketing research was carried out by questioning former graduates of this department over the past 10 years, which included various questions for a more detailed study of both positive and negative aspects in choosing a specialty, training, employment, obtaining additional education and enrolling in a magistracy. The analysis of the motives of applicants when choosing specialties at the Department of Physics is carried out. Based on the data obtained, an assessment of vocational guidance work in schools of the city and region is given. Also, a study was carried out on the correspondence of education and the position held, the prospects for employment outside the specialty, wages and the impact on its increase, obtaining additional education at one of the largest enterprises in Petropavlovsk. The key points that influence the choice of a specialty by applicants are briefly described.
Orthoepic Competence Descriptors in Japanese Language Education: CEFR Levels B1 to C2

The Council of Europe’s 2001 Common European Framework of Reference for Languages (CEFR) has shown rapid global adoption, and now includes Japanese language education though it primarily aimed at alphabetically transcribed languages. It basically acknowledges that orthoepic competence relates to comprehension of characters yet does not indicate descriptors. Descriptors examining A1 and A2 levels, using altered techniques, have already been set. In this paper, I re-examine descriptors for levels B1 to C2, which have not yet been attempted, and combine them with the results for levels A1 and A2 to present descriptors for levels A1 to C2 in overall.
Software tool for supporting the experiential teaching of selected methods of formal semantics

This article presents a contribution to the modernization of university education in the field of theoretical computer science, namely in the field of formal semantics. The use of software tools enabling interaction and visualization of calculations can be considered as a modern approach in teaching that supports its attractiveness. In this work, we present a visualization software tool that emulates the behavior of an abstract machine for structural operational semantics. The tool supports the visualization of abstract machine calculations in two variants – depending on the used formal representation of the abstract machine memory.
Reduced Reference Quality Assessment of Screen Content Images Rooted in Primitive Based Free-Energy Theory

With the growing popularity of portable electronic devices, such as portable computer and cellular phone, a wide variety of digital screen content images (SCIs) have drastically invaded into our daily lives. Unlike natural scene images, SCIs are typically composed of graphic and textual images, with simpler shapes, and a larger frequency of thin lines, which may lead to different viewing experience. Therefore, an accurate quality metric for SCIs which could take into account its special properties is of particular interest. In this paper, we propose a novel reduced-reference method for assessing the perceptual quality of SCIs. Specifically, the principle of free energy models the perception and understanding of images as an active reasoning process, in which the brain attempts to explain the visual scene with an internal generative model. Sparse primitive cues are explored to model the human perception of the visual scene taking account of the unique properties of SCIs and the structure of primitives (atoms in the dictionary). The difference of the prediction discrepancies between the pristine and distorted images is defined as a measurement of the image quality. Experimental results show the effectiveness of the proposed metric and it performs favorably against state-of-the-arts on the benchmark screen image quality assessment database.
Concatenate and Shuffle Network: A Real-Time Underwater Object Detector for Small and Dense Objects

Object detection of underwater optical images is of great significance in many underwater missions, such as the salvage of underwater objects, the exploration of marine organisms, etc. However, underwater objects are often small and dense, which are difficult to detect. To tackle above issues, we propose a novel framework of underwater object detection named Concatenate and Shuffle Network (CSNet) based on center points detection, which can not only detect small and dense objects with high accuracy, but also detect in real time. Firstly, a multi-scale fusion strategy called Feature Concatenation Shuffle (FCS) is proposed. The detailed features from shallow layer in Convolutional Neural Network are completely integrated into deep layer, and the capability for extracting features of small objects is enhanced. Moreover, to accelerate our method, we propose a lightweight deconvolution block (DB), which integrates a structure of dual-branch feature fusion and a lightweight deconvolution method. In addition, we study the advantages of detecting dense objects based on center points and introduce it to our detector. Lastly, experiments show that CSNet achieves the best speed-accuracy trade-off on URPC 2018 with 39.7% AP at 58.8 FPS and 42.4% AP with multi-scale testing at 5.7 FPS. Compared with several state-of-the-art detectors, CSNet reaches a competitive accuracy at a breakthrough speed and can run in real time under various computing conditions.
Anomaly Detection of Marine Seismic Airgun Signatures using Semi-Supervised Learning

Marine Seismic sources (i.e., Airguns) play an important role in geophysical prospecting at sea. They produce seismic waves that propagate into the earth&#x0027;s surface, whereby sensitive detectors collect the reflected data for analysis. The reflected signal can be thought of as a convolution of the seismic emitter(Airgun) and the earth response plus noise. As such a thorough understanding of this Airgun signature is critical to the eventual signal processing used towards a representative image of the subsurface from these detectors. Airguns are typically deployed in the field as arrays of clusters varying in volume capacity. Together these Airguns produce a signature with distinct geophysical characteristics that are required to extract meaningful information of the earth&#x0027;s subsurface. Metadata of from these individual Airgun Arrays is used to assess their performance since anomalous signals can significantly impact interpretations of the subsurface. Features can be extracted from an Airgun signature via a hydrophone within close proximity to the individual array. These labelled features were then used to design a model that classifies Airgun clusters by volume capacity on data known to be free of anomalies. By analyzing features from metadata with an unknown anomalous status with this model, accuracy metrics were used to assess the health and performance of individual Airguns.
SoundID: Securing Mobile Two-Factor Authentication via Acoustic Signals

Mobile two-factor authentication (TFA), which uses mobile devices as a second security layer of protection to online accounts, has been widely applied with the proliferation of mobile phones. Currently, many studies propose to use acoustic fingerprints as the second factor. However, these solutions ignore the variations of the extracted static acoustic fingerprints incurred by the acoustic propagation process, which we show can be leveraged to develop an enhanced man-in-the-middle (MITM) attack to compromise the security strength of these systems, while hiding the traces of the attacking devices. To address this newly-uncovered vulnerability, we propose SoundID, a secure and novel authentication system that introduces a dual challenge-response design through the acoustic signals of the enrolled phone and the login device. Specifically, the enrolled phone first evaluates its proximity to the login device by the similarity of their audio recordings, and then the login authentication server compares the calculated dynamic acoustic fingerprint with the one received from the enrolled phone. To the best of our knowledge, SoundID is the first scheme that extracts dynamic acoustic fingerprints and can effectively defend against the enhanced MITM attack. SoundID combines the benefits of unpredictable influencing factors of acoustic propagation processes and the stable frequency response of the acoustic hardware, whose high complexity prevents attackers from predicting or impersonating them. We build a prototype of SoundID with off-the-shelf smartphones to validate its robustness and effectiveness. Our results show that SoundID is user-friendly and achieves over 96.62% accuracy with an equal error rate around 4.27%.
Researches advanced in Generative Adversarial Networks

As a new unsupervised learning algorithm to model complex data in various applications, the Generative Adversarial Networks (GANs) have gained extensive recognition and become a research hot. Inspired by the two-person zero-sum game theory, the GANs methods have more powerful feature learning and feature expression ability than traditional machine learning algorithms, while still suffering from several challenges such as the non-convergence, vanishing, and exploding gradients, modal collapse, and instability problems at the model training stage. In this paper, we firstly introduce the theory and framework of GANs and analyze the causes for the above difficulties in detail. Then we survey a wide range of typical improved GANs models by summarizing their Advantages and limitations. Finally, we discuss the existing problems in the application and possible research areas in the future.
Blockchain-based Practical Multi-agent Secure Comparison and its Application in Auctions

AI applications find widespread use in a variety of domains. For further acceptance, mostly when multiple agents interact with the system, we must aim to preserve the privacy of participants information in such applications. Towards this, the Yao’s Millionaires’ problem (YMP), i.e., to determine the richer among two millionaires’ privately, finds relevance. This work presents a novel, practical, and verifiable solution to YMP, namely, Secure Comparison Protocol (SCP). We show that SCP achieves this comparison in a constant number of rounds, without using encryption and not requiring the participants’ continuous involvement. SCP uses semi-trusted third parties - which we refer to as privacy accountants - for the comparison, who do not learn any information about the values. That is, the probability of information leak is negligible in the problem size. In SCP, we also leverage the Ethereum network for pseudo-anonymous communication, unlike computationally expensive secure channels such as Tor. We present a Secure Truthful cOmbinatorial aUction Protocol (STOUP) for single-minded bidders to demonstrate SCP’s significance. We show that STOUP, unlike previous works, preserves the privacies relevant to an auction even from the auctioneer. We demonstrate the practicality of STOUP through simulations.
Comparative Analysis of Digital Signature and Elliptic Curve Digital Signature Algorithms for the Validation of QR Code Vulnerabilities

Quick response (QR) codes are currently used ubiq-uitously. Their interaction protocol design is initially unsecured. It forces users to scan QR codes, which makes it harder to differentiate a genuine code from a malicious one. Intruders can change the original QR code and make it fake, which can lead to phishing websites that collect sensitive data. The interaction model can be improved and made more secure by adding some modifications to the backend side of the application. This paper addresses the vulnerabilities of QR codes and recommends improvements in security design. Furthermore, two state-of-the-art algorithms, Digital Signature (DS) and Elliptic Curve Digital Signature (ECDS), are analytically compared to determine their strengths in QR code security.
Sistem Informasi Administrasi Kepegawaian pada Dinas Tenaga Kerja di Kabupaten Mandailing Natal Berbasis Web

The development of technology and information is currently accelerating, this development has made many conveniences ranging from the use of computer technology to automated management handling. In this study, we will try to create a Web-Based Information System for Personnel Administration at the Office of the Manpower Office in Mandailing Natal Regency which currently uses computers for operation, but the data processing process is still manual. So we need a system that can accelerate performance and be developed more modern in order to operate using a computerized system. The method used in designing and making this web application is the Native Web Method, the programming language used is PHP, and MySQL as data storage. For display design using the Bootstrap AdminLTE Framework which provides basic HTML and CSS templates for the application of typography, forms, buttons, navigation, and many other interface components. This research will produce a Web-Based Personnel Administration Information System at the Office in Mandailing Natal.&#x0D; Keywords: sysstem, administration, web
Compressibility of Distributed Document Representations

Contemporary natural language processing (NLP) revolves around learning from latent document representations, generated either implicitly by neural language models or explicitly by methods such as doc2vec or similar. One of the key properties of the obtained representations is their dimension. Whilst the commonly adopted dimensions of 256 and 768 offer sufficient performance on many tasks, it is many times unclear whether the default dimension is the most suitable choice for the subsequent downstream learning tasks. Furthermore, representation dimensions are seldom subject to hyperparameter tunning due to computational constraints. The purpose of this paper is to demonstrate that a surprisingly simple and efficient recursive compression procedure can be sufficient to both significantly compress the initial representation, but also potentially improve its performance when considering the task of text classification. Having smaller and less noisy representations is the desired property during deployment, as orders of magnitude smaller models can significantly reduce the computational overload and with it the deployment costs. We propose CORE, a straightforward, compression-agnostic framework suitable for representation compression. The CORE’S performance is showcased and studied on a collection of 17 real-life corpora from biomedical, news, social media, and literary domains. We explored CORE’S behavior when considering contextual and non-contextual document representations, different compression levels, and 9 different compression algorithms. Current results based on more than 100,000 compression experiments indicate that recursive Singular Value Decomposition offers a very good trade-off between the compression efficiency and performance, making CORE useful in many existing, representation-dependent NLP pipelines.
Mutual Deep Deterministic Policy Gradient Learning

In deep reinforcement learning (DRL), policy gradient (PG) and actor-critic (AC) based methods are among the most populous and effective methods for training DRL agents. One such method is the state-of-the-art deep deterministic policy gradient (DDPG). In this research, we employ the framework of mutual learning with DDPG to present a novel, Mutual DDPG (MuDDPG) agent with the aim to improve the performance and robustness of conventional DDPG. We also propose an additional simple innovation of adaptive reward-based exploration to further improve the rate of learning. We demonstrate that by employing these schemes, MuDDPG can converge faster and perform better than vanilla DDPG in two simple simulated tasks while adding significant robustness to the learning process.