Miracle-dz/UniFAR-FTUs huggingface dataset:
A HYBRID RANDOM IMAGE GENERATION STRATEGY (HR-IGS) FOR SECURING PLAIN TEXT DATA IN NETWORKS

The rapid proliferation of information highways has resulted in an explosion of issues on the internet. The greater the amount of data transfers, higher is the number of cyber-attacks like man-in-the-middle, hacking, and so on. In such context, cryptography is the study of secure communication mechanisms that encrypt and decode messages using two separate keys, public and private. Our paper focuses on encrypting and decrypting text using a revolutionary hybrid technique that uses Rivest-Shamir-Adleman (RSA) and DH to ensure security in data transmission. Our suggested method uses a random picture generation process to generate largest prime numbers, which improves the security and dependability of data sent from the concerned sender to the receiver. We have used the RSA Algorithm to extract the prime numbers using XOR function from the corresponding two vectors in each row and column of the scanned picture. Moreover, with the help of DH algorithm we are generating secret keys. Then the encryption and decryption phenomenon is carried on. The main goal here is to enhance the execution time, minimize the image generation time, improving key generation time, while conjointly hiking the avalanche effect, that assert the lower the risk of being hacked in case of proposed scheme. The outcomes of the investigation support the efficacy of our proffered approach over other existing approaches.
Sandstone megabeds from the Tertiary of the North Sea

Abstract Three cored sandstone megabeds of Tertiary (late Palaeocene) age from two quadrants in the Central Graben of the North Sea are discussed. The megabeds are upwards-fining units 3–9 m thick, underlain by gravelly mudstones (1–1.4 m thick) which are interpreted as debrites. The megabeds are divided into five units (M1–M5) based on grain size and sedimentary structures, which are organized into a type vertical sequence. The basal unit M1 comprises a clast-supported mudclast conglomerate. Unit M2 comprises very coarse- to medium-grained, occasionally gravelly, sandstone which is horizontally laminated to cross-stratified. Unit M3 is the thickest unit (2–4 m) and is a laminated, upwards-fining, medium- to fine-grained sandstone with occasional cross-lamination. Unit M4 is a fine- to very fine-grained sandstone characterized by disturbed lamination and water-escape structures. Unit M5 is an upwards-fining silty sandstone to siltstone characterized by discontinuous, irregular, and locally overfolded laminae. The megabeds are interpreted as having been deposited by turbidity currents. There are some similarities to Bouma sequences and high-density turbidite sequences, but massive sandstone is notably absent. The megabeds are associated with mudstones and debrites and only occasional thin turbidites, and as such they are not part of an organized sequence of turbidites. The beds are notably much thicker than those above and below, and lithologies are distinct from those of the surrounding sediments. These features are comparable to those of ‘megaturbidites’ as defined by Bouma. The debrite which immediately underlies the megabeds is significant in that this relationship is also a common feature of megaturbidites. The debrite is thought to represent slope failure which immediately preceded megabed deposition. A hypothesis is presented of earthquake-induced catastrophic slope failure, debrite deposition and associated megaturbidite generation. Such sandstone megabeds are thought to be widespread sheet-like deposits. They are exploited as a hydrocarbon reservoir in two of the wells discussed.
1 Development and characterization of a human model of arteriovenous malformation (AVM)-on-a-chip

Brain arteriovenous malformations (AVMs) are a disorder wherein abnormal, enlarged blood vessels connect arteries directly to veins, without an intervening capillary bed. AVMs are one of the leading causes of hemorrhagic stroke in children and young adults. Most human sporadic brain AVMs are associated with genetic activating mutations in the KRAS gene. Our goal was to develop an in vitro model that would allow for simultaneous morphological and functional phenotypic data capture in real time during AVM disease progression. By generating human endothelial cells harboring a clinically relevant mutation found in most human patients (activating mutations within the small GTPase KRAS ) and seeding them in a dynamic microfluidic cell culture system that enables vessel formation and perfusion, we demonstrate that vessels formed by KRAS4A G12V mutant endothelial cells (ECs) were significantly wider and more leaky than vascular beds formed by wild-type ECs, recapitulating key structural and functional hallmarks of human AVM pathogenesis. Immunofluorescence staining revealed a breakdown of adherens junctions in mutant KRAS vessels only, leading to increased vascular permeability, a hallmark of hemorrhagic stroke. Finally, pharmacological blockade of MEK kinase activity, but not PI3K inhibition, improved endothelial barrier function (decreased permeability) without affecting vessel diameter. Collectively, our studies describe the creation of human KRAS-dependent AVM-like vessels in vitro in a self-assembling microvessel platform that is amenable to phenotypic observation and drug delivery.
Productivity and labor costs in the OECD countries – elements of comparing analysis

The aim of this study is to present, analyze and assess the level and relations of labor costs and productivity in the OECD countries. The analyses of labor markets in European Union states or in the OECD countries show that these markets are characterized by very similar measures regarding the levels of professional activity, unemployment rate and efficiency of labor market policy programs. However, the difference in the level of wages still remains high. Among the reasons for this phenomenon we can indicate the levels of labor costs and productivity, which determine the levels of wages in particular member states. The thesis stated in the study is that the maintained differences in wages in the OECD countries may be explained by differences in the levels and relations of labor costs and productivity, which are discussed in detail in the study. In the first and in the second part of the elaboration the analysis of productivity was carried out including definition of this subject and special empirical data showing measurement of productivity. In the third part we presented labor costs and tax wedge in the same period of time. The hierarchical clustering algorithm allowed to distinguish many groups of countries with different degree of similarity in terms of compared variables. Finally, the fourth part is devoted to a statistical analysis using measures such as the correlation coefficient between presented variables. The period of analysis are years 2000-2014 and the main source of data is the OECD database available in the Internet.
Conﬁgurable Readout Error Mitigation in Quantum Workﬂows

: Current quantum computers are still error-prone, with measurement errors being one of the factors limiting the scalability of quantum devices. To reduce their impact, a variety of readout error mitigation methods, mostly relying on classical post-processing, have been developed. However, the application of these methods is complicated by their heterogeneity and a lack of information regarding their functionality, conﬁguration, and integration. To facilitate their use, we provide an overview of existing methods, and evaluate general and method-speciﬁc conﬁguration options. Quantum applications comprise many classical pre-and post-processing tasks, including readout error mitigation. Automation can facilitate the execution of these often complex tasks, as their manual execution is time-consuming and error-prone. Workﬂow technology is a promising candidate for the orchestration of heterogeneous tasks, offering advantages such as reliability, robustness, and monitoring capabilities. In this paper, we present an approach to abstractly model quantum work-ﬂows comprising conﬁgurable readout error mitigation tasks. Based on the method conﬁguration, these workﬂows can then be automatically reﬁned into executable workﬂow models. To validate the feasibility of our approach, we provide a prototypical implementation and demonstrate it in a case study from the quantum humanities domain.
Performance Analysis of Decision Tree Extended Classifiers (dtec) on Clinical Dataset of Covid Prediction Using Optimal Feature Selection

In Computer Science Technology, Artificial Intelligence, Data Analytics and Machine Learning plays a predominant role in decision-making strategies. By choosing suitable method and algorithms from any of these fields, a good decision can be taken. In health care industry, at present, prediction of Covid 19, is still a very big challenge, since false positive, false negative metrics are occurring frequently through various covid test. Our main aim, in this proposed work is to increase the prediction of accuracy of Covid, by considering the optimum number of features alone, by taking into consideration the laboratory measurement, and also by clinical test understanding. When target variable is binary, the classification algorithm based on Tree Based Extended Classifiers like Random Forest, AdaBoost, XGBoost can be proposed with necessary features. The results are observed from the proposed algorithms, that gets trained using the training dataset using standard data repository and it is being tested with the testing dataset. By analyzing the performance metrics, the obtained results showed that the prediction accuracy is increased and also false positive and false negative are reduced. In the proposed work, the tree based extended classifiers of Random Forest and Extended Gradient Boosting produces maximum 92% accuracy with 11 features using Gini Index. Apart from accuracy, the metrics such as false positive and false negative are playing the important role. In this proposed work, the false negative is as low as 5 out of 14 by XGB, and false positive with the minimum value of 3 out of 106 using Random Forest. Thus, these methods of covid predictions are useful for health care community, if it is being utilized in an efficient manner. © 2022 Little Lion Scientific.
MACHINE LEARNING MODEL TO IMPROVE CLASSIFICATION PERFORMANCE IN THE PROCESS OF DETECTING PHISHING URLS IN QR CODES

This paper discusses the model of detection of phishing URLs in QR codes using Machine Learning techniques. Phishing URLs are URLs that resemble real websites created by cybercriminals to obtain user information for profit. The dataset is collected from various sources, with some important features based on address bar, domain and HTML. The six Machine Learning algorithms including Decision Trees, Random Forests, SVM, Multilayer Perceptron, Autoencoder Neural Network and XGBoost. The experimental results show that the XGBoostt algorithm provides the highest detection accuracy with 92% accuracy.
HMQV: A High-Performance Secure Diﬃe-Hellman Protocol

The MQV protocol of Law, Menezes, Qu, Solinas and Van-stone is possibly the most eﬃcient of all known authenticated Diﬃe-Hellman protocols that use public-key authentication. In addition to great performance, the protocol has been designed to achieve a remarkable list of security properties. As a result MQV has been widely standardized, and has recently been chosen by the NSA as the key exchange mechanism underlying “the next generation cryptography to protect US government information”. One question that has not been settled so far is whether the protocol can be proven secure in a rigorous model of key-exchange security. In order to provide an answer to this question we analyze the MQV protocol in the Canetti-Krawczyk model of key exchange. Unfortunately, we show that MQV fails to a variety of attacks in this model that invalidate its basic security as well as many of its stated security goals. On the basis of these ﬁndings, we present HMQV, a carefully designed variant of MQV, that provides the same superb performance and functionality of the original protocol but for which all the MQV’s security goals can be formally proved to hold in the random oracle model under the computational Diﬃe-Hellman assumption. We base the design and proof of HMQV on a new form of “challenge-response signatures”, derived from the Schnorr identiﬁcation scheme, that have the property that both the challenger and signer can compute the same signature; the former by having chosen the challenge and the latter by knowing the private signature key.
APPLICATION OF ADAPTIVE ELITISM OPERATOR IN FIREFLY ALGORITHM FOR OPTIMIZATION OF LOCAL AND GLOBAL SEARCH

There are two important components in a meta-heuristic algorithm, namely, Diversification and Intensification. These components can be fine-tuned for the optimized execution of a meta-heuristic search algorithm. The firefly algorithm (FA) is the latest in a series of meta-heuristic algorithms. Although the FA has proven to be efficient in local searches, there are times when it might get trapped in several local optima, as a result of which it is unable to efficiently conduct a complete search. In the pursuit of global space scaling, this algorithm needs to generate different solutions leveraging on diversification. The function of updating the effectiveness of diversification in a search algorithm can be performed by elitism operators. In this study, a strategy was proposed to upgrade the FA concerning static issues. The methodology involved the hybridization of elitism with the standard firefly algorithm, and this modified version was known as the AFA. Also, another distribution was introduced to revamp the entire search process in the FA using a t-way test generation (t referring to the strength of the interaction). The experimental results demonstrated that the Elitism firefly algorithm (eFA) had better performance than the standard FA and also than the other up to date algorithms in terms of robustness and the convergence speed.
Comparison of the effects of ﬁsh oil and olive oil on blood lipids and aortic atherosclerosis in Watanabe heritable hyperlipidaemic rabbits

To compare the effects of ﬁsh oil and olive oil on the development of atherosclerosis in Watanabe heritable hyperlipidaemic (WHHL) rabbits, 6-week-old animals were given a daily dose (1.5ml/kg body weight) of ﬁsh oil ( n 10 Þ or olive oil ( n 10 Þ by oral administration for 16 weeks. Plasma cholesterol and triacylglycerols were measured once monthly, and their concentrations in lipoproteins, together with susceptibility of LDL to oxidation were measured in vitro at the termination of the experiment. Aortic atherosclerosis was quantiﬁed biochemically and micro-scopically. After 4 weeks of treatment, and throughout the study thereafter, blood lipids were signiﬁcantly ð P < 0.05 Þ lower in the ﬁsh-oil group than in the olive-oil group (cholesterol: 17.0 v . 30.3 mmol/l, triacylglycerols 2.97 v . 6.25 mmol/l, at termination). In the ﬁsh-oil group cholesterol was signiﬁcantly lower in intermediate-density lipoproteins (2.69 v . 6.76 mmol/l) and VLDL (3.36 v . 11.51 mmol/l). Triacylglycerol levels of intermediate-density lipoproteins and VLDL in the ﬁsh-oil group were also signiﬁcantly lower when compared with the olive-oil group (0.54 v 1.36 mmol/l and 0.92 v . 2.87 mmol/l respectively). No group differences were recorded for LDL-and HDL-cholesterol or triacylglycerol levels. A signiﬁcantly higher oxidation of LDL was recorded 1 h after exposure to CuSO 4 in the ﬁsh-oil group when compared with the olive-oil group (0.465 v . 0.202, arbitrary units). The following indicators of atherosclerosis development were signiﬁcantly lower in the ﬁsh-oil group than in the olive-oil group: the cholesterol content (mg/g tissue) in the ascending aorta (29.8 v . 48.9), the intima:media value (4.81 v . 18.24) and the area of intima (0.10 v . 0.57 mm 2 ) in the thoracic aorta. It was concluded that ﬁsh-oil treatment decreased blood lipids and the development of aortic atherosclerosis in WHHL rabbits when compared with olive-oil treatment.
3D simulation model for IoD-to-vehicles communication in IoD-assisted VANET

Vehicle ad hoc networks (VANETs) have gradually emerged to enhance transportation information, entertainment, safety, and other services. However, such infrastructures have certain limitations, causing intermittent network disconnection. Further, in urban areas, terrain heights act as obstacles and hinder or attenuate transmitted signals. In this study, we propose a dynamic 3D internet of drones collaborative communication approach for efficient VANET-assistance (3DIoDAV) by integrating the IoD network and VANET to support terrestrial communication. We model IoD locations as an optimization problem to optimize the IoD nodes in three-dimensional terrain. Improved particle swarm optimization is used to optimally deploy IoD nodes in 3D terrain for minimizing the number of isolated vehicles. The proposed approach considers the terrain profile influence on communication. Therefore, we propose a 3D propagation model for efficient IoD-to-vehicle (IoD2V) communication in 3D space. Experiments are performed based on the received signal from ground vehicles to examine the performance of the proposed model and the 3DIoDAV approach. Simulation results show different behaviors of IoD nodes in two-dimensional (2D) and 3D scenarios. Comparison with 2D VANET-assisted and IoDAV approaches demonstrates the proposed 3DIoDAV approach’s ability to detect terrain obstacles, which guarantees the dispatching of IoD nodes into the most appropriate locations in 3D space, thereby minimizing the impact of terrain obstacles on communication.
An examination of impression management associated with visible political party affiliation on Facebook

Unlike traditional media, our interactions with political parties via social media are generally public, subject to scrutiny by others and, consequently, a self-presentation concern. This paper contributes to theory on impression management within social network sites (SNSs) by providing an understanding of the effect of visible affiliation on page ‘Liking’ behavior in the context of political parties; specifically, the possible association with social anxiety and the use of protective impression management. We predict that while users may be motivated to ‘Like’ a political party, some may feel socially anxious about the impressions their friends may derive from this action, and so ultimately choose to refrain from ‘Liking’ the party. Furthermore, we propose a new function of ‘Secret Likes’ (i.e. ‘Likes’ that others cannot see) as a means to increase gateway interactions. A survey of eligible voters ( n =225) was conducted in the month prior to the 2015 UK general election, examining behavior associated with the Facebook pages of the two largest political parties. Results support that conspicuous affiliation with political parties indeed hinders intention to ‘Like’ political pages and is associated with social anxiety. ‘Secret Likes’ were found to be a successful method to increase gateway interactions. In addition to the theoretical contribution, implications for political party communications and site designers are considered.
mimicking unique hand-drawn pencil lines

In applications such as architecture, early design sketches containing accurate line drawings often mislead the target audience. Approximate human-drawn sketches are typically accepted as a better way of demonstrating fundamental design concepts. To this end we have designed an algorithm that creates lines that perceptually resemble human-drawn lines. Our algorithm works directly with input point data and a physically based mathematical model of human arm movement. Our algorithm generates unique lines of arbitrary length given the end points of a line, without relying on a database of human-drawn lines. We found that an observational analysis obtained through various user studies of human lines made a bigger impact on the algorithm than a statistical analysis. Additional studies have shown that the algorithm produces lines that are perceptually indistinguishable from that of a hand-drawn straight pencil line. A further expansion to the system resulted in mimicked dashed lines.
A COMPARISON STUDY OF DOCUMENT CLUSTERING USING DOC2VEC VERSUS TFIDF COMBINED WITH LSA FOR SMALL CORPORA

The selection of a suitable word vector representation is one of the essential parameters in document clustering because it affects the performance of clustering. The excellent word vector representation will generate a good clustering result, even only using the simple clustering algorithm like K-Means. Doc2Vec, as one of word vector representations, has been extensively studied in large text datasets and proven outperforms the performance of traditional word vector representation in document categorization. However, only a few studies analyze word vector representations of small corpora. As appropriate, learning observation in a small corpus is also crucial because, in some cases, a large corpus was not always available, particularly in some low-resources languages like Bahasa Indonesia. Moreover, the clustering of the small datasets also plays essential roles in pattern recognition and can be an initial step to implement the analysis result in a more significant corpus. This study is an experimental study that aims to explore more in-depth exploration to compare document clustering using Doc2Vec versus TFIDF-LSA for small corpora in Bahasa Indonesia. In this study, the quality of word vector representation is measure by the cluster performance using intrinsic and extrinsic measurements. The study also considers measuring word representation based on time and memory consumption. This study also concerns with getting an optimal word vector representation by tuned appropriate hyper-parameter. The word vector representations were tested to various sizes of the small corpora using the K-Means algorithm. The result of this study, a TFIDF-LSA gets a better cluster performance; meanwhile, the Doc2Vec model gets a better time and memory usage efficiency.