Reviewerly/exHarmony huggingface dataset:
A Fast Method for Visual Quality Prediction and Providing in Image Lossy Compression by SPIHT

Wavelet-based image compression techniques, in particular, SPIHT have an obvious advantage of providing a desired compression ratio. However, for a given compression ratio, quality can vary in wide limits and it can be unsatisfactory for a given image, especially if it is highly textural. Then, one needs to choose and set another compression ratio providing an appropriate quality of the compressed image. Often, this adaptation should be done quickly and with providing high accuracy of reaching a desired quality. In this paper, we propose a fast and efficient approach to predict quality of images compressed by SPIHT using two favorable facts. Firstly, a fast procedure of quality prediction has been proposed for the coder AGU based on discrete cosine transform. Secondly, performance characteristics of SPIHT and AGU are quite similar. This allows recalculating predictions obtained for AGU to predictions for SPIHT and decision undertaking concerning parameters of image encoding by SPIHT.
Image Enhancement In Video Analytics Systems

Recently, video analytics systems are rapidly evolving, and the effectiveness of their work depends primarily on the quality of operations at the initial level of the entire processing process, namely the quality of segmentation of objects in the scene and their recognition. Successful performance of these procedures is primarily due to image quality, which depends on many factors: technical parameters of video sensors, low or uneven lighting, changes in lighting levels of the scene due to weather conditions, time changes in illumination, or changes in scenarios in the scene. This paper presents a new, accurate, and practical method for assessing the improvement of image quality in automatic mode. The method is based on the use of nonlinear transformation function, namely, gamma correction, which reflects properties of a human visual system, effectively reduces the negative impact of changes in scene illumination and due to simple adjustment and effective implementation is widely used in practice. The technique of selection in an automatic mode of the optimum value of the gamma parameter at which the corrected image reaches the maximum quality is developed.
An Empirical Study on the Effects of Google-based Activities in the College Level English Learning

As a part of an empirical study in a college level EFL learning environment, a new teaching method using ICT technology has been introduced to increase the participation from groups of university students and its effect was analysed to study how much the method devoted to their achievement scores.To this end, twenty one sets of Google survey-based-quizzes were delivered to the target group of students over the 15 weeks in the fall semester 2019 in an effort to enhance the participation from the learners and see how this approach can have positive effects in learners' achievement results in terms of written test scores.In addition to comparing scores, a feedback survey was administered to learners in the target group.The study shows the Google based quizzes did not have significant impact on achievement scores within the same groups of learners.The feedback survey, however, indicated that the learners preferred the Google based mobile quizzes to the paper based ones.The limits of the study are that the two different types of quizzes should have been administered in different semester for further clarification, and that the achievement scores could have been compared with other teachers who taught the same courses.The new teaching method needs to be studied for further use in the online based course in the future.
A Privacy Protection System in Context-aware Environment The Privacy Controller Module

As context-aware applications are becoming increasingly popular, there are also mounting demands for privacy protection systems. In our work, we propose a context-aware privacy protection system that consists of three modules and aims to recognize the user privacy behavior, classify the context-aware applications and recommend a set of protection action scenarios for the user privacy profile settings. Each module is a challenging problem that needs to be addressed using supervised and unsupervised Machine Learning (ML) algorithms. Part 1 of our work, this paper, consists of deploying hybrid techniques to handle the privacy controller module tasks. Logistic Regression (LR) learning algorithm is integrated with Statistical Method (SM) to recognize user privacy complex activities. The potential of the proposed system is demonstrated using a large-scale real-world dataset provided by institutes from Kuwait, the United States and Belgium. The system demonstration shows promising results with an accuracy of 97.9%.
A Syntactic and Semantic Assessment of a Global Software Engineering Domain Ontology

Globalization has allowed organizations to intensify the search for solutions that minimize challenges, reduce costs and optimize processes. In this way, global software development has emerged as an attempt to use the best resources for its limitations.
Synthetic pseudo-random sequence

Abstract The paper considers the basic information about a new class of pseudo-random sequences — the class of synthetic pseudo-random sequences. The main distinguishing feature of the sequences under consideration is the extended observation window. The proposed synthetic sequences are an extension of the area of existing pseudo-random sequences.
HPAC-sbox- a novel implementation of predictive learning classifier and adaptive chaotic s-box for counterfeiting sidechannel attacks in an IOT networks

Today, embedded systems are augmented with the Internet of things and more with the artificial intelligence to make world even connected with aliens. With an IoT networks are getting its insight since it deals with large number of data information, security has considered to be more important and needs to be a diagnosis for every minute. To enhance the security in the network, a mathematically secure algorithms were formulated and runs on the cryptographic embedded chips to counterfeit the risks which are caused by the different attacks such as side channel attacks (SCA) on the networks. Even though many cryptographic encryption algorithms such as AES, DES, RC4 algorithms were gaining its importance, fixed encryption keys, non-intelligent detection of attacks, cognitive countermeasures are some of the real-time challenges in an existing system of encryption. Following the limitations of existing systems, this research article focuses on design of new AES with HPAC-SBOX (Hybrid Prediction and Adaptive Chaos) which integrates powerful predictive learning algorithms and adaptive chaotic logistic S-Box. The following contributions of this research articles are: a) Preparation of Data Sets from the Power consumption traces captured from Multi Core Embedded boards while running the Advanced Encryption Systems(AES) on it b) Implementation of High Speed and High Accurate Prediction learning machines for the prediction of side-channel attacks c) Design of Adaptive Chaotic S-Box using 3-Dlogistic Hyperbolic maps for attacked bits. To evaluate the proposed architecture, experimentation in carried out in an IoT networks and various performance parameters were calculated and analyzed. The results show that the proposed architecture outperforms the other existing algorithms in terms of prediction and performance.
Comparative Analysis of Different Symmetric Encryption Techniques Based on Computation Time

Lately the trend of the internet is taking a front seat for different applications. Organizations are collecting and processing and then sharing the data using the internet. Sharing using public network will invite various security lapses in the data. Security will remain the maj or thrust in the area for providing enough level of security for the data. Encryption is the best way to provide security for the data. There are two different types of approaches for ensuring data security. These techniques are symmetric and asymmetric. The symmetric technique includes different approaches with variation in the time and space complexity. In this research paper five different techniques of the symmetric approaches are compared for three different length strings. AES is the best performing in all the three cases. The time comparison for the AES with different techniques is comparatively better than the other four techniques like IDEA, RC6, Two Fish, MARS.
Verifiable Decentralized Access Control for Distributed Databases

Ahstract- The traditional centralized database management mode brings security and privacy issues to user data. In this paper, we propose a verifiable distributed database access control (VDAC) scheme based on the ciphertext policy attribute-based encryption (CP-ABE) and decentralized ledger technology (DLT). In the VDAC scheme, CP-ABE is used to achieve fine-grained access authorization control on user data, and DLT is employed to publish authorization parameters and guarantee their immutability. Particularly, the decentralized authorization mechanism for distributed database is realized by using Hyperledger fabric platform. Comparison analysis shows that the VDAC scheme not only provides verifiable database access control, but also protects user privacy and system security. Experimental results demonstrate the efficiency and practicality of the VDAC scheme.
Role of the teacher training and the new technologies in the design of innovative educational projects in Greece*

Current research concerns the views of elementary school teachers in Greece about both the necessity of their training in new teaching approaches and the use of new technologies in the implementation of innovative educational projects. Quantitative (questionnaire) and qualitative (focus group) methods were used to collect data. It is noted that teachers were well informed about innovative educational projects and they wanted support by their principal and their colleagues. Experiential seminars by specialized scientists were also considered as necessary. Applications of new technologies were considered to particularly contribute to the implementation of innovative projects. Data analysis did not reveal significant gender differences in teachers’ views. Results are of particular value for the design of training programs and the integration strategies of new technologies in the design of innovative educational projects.
Business Dynamics, Knowledge Economy, and the Economic Performance of African Countries

This paper develops a framework (a) to examine whether or not the African business environment hinders or promotes the knowledge economy (KE), (b) to determine how the KE affects economic performance, and (c) how economic performance relates to the inequality-adjusted human socioeconomic development (IHDI) of 53 African countries during the 1996-2010 time period. We estimate the linkages with three related equations. The results support a strong correlation between the dynamics of starting and doing business and variations in KE. The results also show that there exists a weak link between KE and economic performance. Nonetheless, KE-influenced performance plays a more important role in socioeconomic development than some of the conventional control variables like foreign direct investment (FDI), foreign aid, and even private investment.
Privacy Laws in the Blockchain Environment

The compatibility between emerging technology such as Blockchain and the new data protection obligations has been a matter of discussion since the European General Data Protection Regulation (“GDPR”) came into force back in 2018. The main reason is that GDPR is considered the Regulation that has incorporated the strongest obligations and enforcement consequences. There are some areas of tension on how to comply with these obligations, some of which are still a matter of discussion that have not been concluded by regulators or in court. In this chapter, I set out an overview of the main obligations, relevant areas of tension and set out an opinion on what I have seen that in practice is working the best, and how I recommend dealing with these issues in practice, in order to do an assessment applied to a particular context in which a Blockchain network is used.
A New Method for the Simultaneous Creation of Live HDR and SDR Video

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Deploying separate high dynamic range (HDR) and standard dynamic range (SDR) production facilities to cover the same event simultaneously can be economically prohibitive. In this case study, we present a new method for creating both ultrahigh-definition (UHD) HDR and HD SDR streams from a single UHD HDR production workflow, developed through a series of real-world UHD HDR production trials in 2018 and 2019, concluding with the 2019 Football Association Challenge Cup (FA Cup), a major U.K. sporting event. We describe the findings from those trials and the lessons learned and include descriptions of what worked well and what worked less well. The workflow solution presented allows for the integration of both HDR camera sources and SDR camera sources (e.g., super slow-motion and specialist cameras), prerecorded or archive SDR content and SDR graphics into a single HDR production environment. It can exploit the backward-compatible nature of the hybrid log-gamma (HLG) HDR solution and recent advances in color volume management. We show how a production’s two outputs, HDR and SDR, can be made using current SDR shading/racking monitoring, with only a single HDR vision guarantee monitor</i> .
Novel Bounds on the Probability of Misclassification in Majority Voting: Leveraging the Majority Size

Majority voting is often employed as a tool to increase the robustness of data-driven decisions and control policies, a fact which calls for rigorous, quantitative evaluations of the limits and the potentials of majority voting schemes. This letter focuses on the case where the voting agents are binary classifiers and introduces novel bounds on the probability of misclassification conditioned on the size of the majority. We show that these bounds can be much smaller than the traditional upper bounds on the probability of misclassification. These bounds can be used in a `Probably Approximately Correct' (PAC) setting, which allows for a practical implementation.
One Bad Turn Deserves Another: How Terrorism Sustains the Addiction to Capital Flight in Africa

This inquiry assesses if terrorism sustains the capital flight trap and whether the relationship is affected by varying the levels of governance and globalisation. The empirical evidence is based on interactive Generalised Method of Moments with data from 37 African countries for the period 1996-2010. The followings are established. (1) Evidence of a capital flight trap is apparent because past values of capital flight have a positive effect on future values of capital flight. (2) Terrorism sustains the positive effect of the capital flight trap on capital flight. (3) For the most part (especially with regard to political governance), terrorism sustains the addiction to capital flight in above-median governance sub-samples. Policy implications are discussed.
Student perception of online education supported in the Covid-19 period

The coronavirus pandemic has brought many changes, requiring the identification of new ways of working, interaction and lifestyle. This created a context conducive to questions about the social order, the need for investment and the areas where such investment is needed in the future. Strengths and weaknesses were visible, both in a global context and in a European context; new challenges have arisen that society as a whole has to face. Digital technologies are a key component in collective efforts to address the virus and support new ways of living and doing business in this exceptional time. The European Commission has taken various measures to address the various aspects of the pandemic, and digital technologies play a vital role in this. Ensuring infrastructure, connectivity and rules of use are an important goal to enable the European (but also global) population to remain active and safe. Through this paper, we aimed to measure students' perception of online courses, the technology used to conduct online courses and the quality of teaching by teachers. The study involved, between October 1-15, 2020, a number of 120 students of the Faculty of Law and Administrative Sciences ("Ovidius" University of Constanta)
Impact of the fMRI environment on eye-tracking measures in a linguistic prediction task

The present study investigated the impact of the MRI environment on eye-movement measures in the visual-world paradigm. 24 neurotypical young adults performed a linguistic prediction task in a typical lab setting (Lab) and 22 did so during MRI scanning (Scanner). Data analyses focused on eye-tracking data quality and the time course and magnitude of prediction effects. Data quality was reduced in the Scanner as compared to the Lab, as indicated by a higher rate of track loss and saccades/fixations of atypical duration. Predictive eye movement patterns were generally similar in timing and magnitude between the Lab and Scanner, although there was modest evidence for increased prediction effects in the Scanner. In the Scanner environment only, predictive eye movements were linked to better task performance. Evidently, the MRI environment can enhance prediction effects and their relationship to task performance, possibly due to increased deployment of cognitive control mechanisms.
Segmentation of Images with Tubular Features based on Tight-Frame Technique

Image segmentation is an important topic in the field of computer vision and image processing. It is also of major significance in the area of medical imaging. Segmenting medical images with thin tube-like structures is a major challenge. The model proposed in this paper can be used to obtain a desired segmented result from an image having vessel or tubular features. This model uses the concept of solving Mumford-Shah model to obtain a smooth image. This smoothened image is segmented using the idea of Tight-Frame-based Algorithm with Eigenvector (TFAE). This hybrid method gives better segmented results than that of TFAE method alone. The given model also works well with noised images. The proposed method has been implemented on images with and without noise. The result obtained by this method has been further compared with existing models.
Text document representation and classification using Convolution Neural Network

Understanding Actual meaning of a natural written language document is easy for a human but to enable a machine to do the same task require an accurate document representation as a machine do not have the same common sense as human have. For the task of document classification, it is required that text must be converted to numerical vector and recently, word embedding approaches are giving acceptable results in terms of word representation at global context level. In this study author has experimented with news dataset of multiple domain and compared the classification performance obtained from traditional bag of word model to word2vec model and found that word2vec is giving promising results in case of large vocabulary with low dimensionality which will help to classify the data dynamically as demonstrated in section experimental result.
Face Aging Through Uniqueness Conserving by cGAN with Separable Convolution

Inspired by the transition of style from Gatys and Goodfellow and the generative adversarial network (GAN), the use of conditional GAN (cGAN) is to achieve human age progression. cGAN is good at generating fake images, where labels are used as conditions which is why it is better than other GANs. For face aging, pre-processing of the original dataset is highly required. This makes it more computational. In terms of performance, “identity-preserving” technique is used to yield better results. Also, in this paper, a novel age order approach is presented by arranging reproduction misfortune and present the boundary λ which is even between huge age highlights and unobtrusive surface highlights. The trial results exhibited here, proposed cGAN, furnishes better maturing faces with personality protecting over other best in class age movement techniques.
DeepDynamic Clustering of Spam Reviewers using Behavior-Anomaly-based Graph Embedding

Online reviews have become an increasingly important factor in the purchase decision of a customer. However, many spammers write deceptive reviews to alter the credibility of a product/service. Often than not, these spammers exhibit group behavior, which can be exploited to differentiate them from authentic reviewers. Such behaviors are found in spammers working together as well as with crowdsourced review manipulators. The existing graph-based spammer detection approaches do not capture the dynamic and nonlinear relationship between the users. This paper aims to address this issue by introducing a method to use a deep structure embedding approach that preserves highly nonlinear structural information along with the dynamic aspects of user reviews to identify and cluster the spam users. It is worth mentioning that, in the experiment with real datasets, our method captures about 92% of all spam reviewers using an unsupervised learning approach.
Longest previous overlapping factor array

• The paper presents an algorithm to compute the Longest Previous Overlapping Factor Array (LPoF) for an input string. • The algorithm runs in linear time and space in the length of the input string. • The LPoF is a variant of the Longest Previous Factor Array (LPF). • The LPoF Array can be seen as a complement of the Longest Previous Non-overlapping Array (LPnF). • The LPF array can be trivially computed for the LPnF and LPoF Arrays together. In this paper, we introduce the longest previous overlapping factor array of a string – a variant of the longest previous factor array. We show that it can be computed in linear time in the length of the input string, via a reduction to the Max-variant of the Manhattan skyline problem Crochemore et al. (2014) [5] .
Single Trace Electromagnetic Side-Channel Attacks on FPGA Implementation of Elliptic Curve Cryptography

Elliptic curve cryptography (ECC) is one of the commonly used public key cryptosystems. In this paper, we propose a generic single trace attack on ECC implementations which exploits the EM leakage of secret scalar bits. The proposed attack works in a black-box setting. Practical EM side channel attacks are demonstrated on unprotected and protected (point re-randomisation, scalar blinding) FPGA of the scalar multiplication over Twisted Edwards curve with success rate of about 99%. The attack setting directly applies to ECC based key agreement (ECDHE) and signatures (ECDSA) schemes with ephemeral secrets.
cJAYA: Cellular JAYA Algorithm

In this paper, the concepts of cellular automata have been utilized in the optimization process of JAYA algorithm for global optimization problems. JAYA algorithm is a recent swarm intelligence algorithm inspired by the survival of the fittest principle. Cellular automata is a successful structured population mechanism used to reorganize the swarm (or population) as a tripodal mesh. Each swarm member can pass the knowledge into his neighboring swarm during the search. Through this process, the diversity of JAYA algorithm is strictly controlled thus the equilibrium state of the convergence rate is spanned. The proposed method is called cellular JAYA algorithm (cJAYA algorithm) which is evaluated using 15 mathematical benchmark functions. Initially, cellular automata concepts are tested. Thereafter, a comparative evaluation of other cellular-based optimization methods is conducted. The results prove the efficacy of the proposed cJAYA algorithm.