Reviewerly/exHarmony huggingface dataset:
Full-round impossible differential attack on shadow block cipher

Abstract Lightweight block ciphers are the essential encryption algorithm for devices with limited resources. Its goal is to ensure the security of data transmission through resource-constrained devices. Impossible differential cryptanalysis is one of the most effective cryptanalysis on block ciphers, and assessing the ability of resisting this attack is a basic design criterion. Shadow is a lightweight block cipher proposed by Guo et al. (IEEE Internet Things J 8(16):13014–13023, 2021). It utilizes a combination of ARX operations and generalized Feistel structure to overcome the weakness of the traditional Feistel structure that only diffuses half in one round. In this paper, we focus on the differential property of Shadow and its security against impossible differential cryptanalysis. First, we use the SAT method to automatically search for a full-round impossible differential distinguisher of Shadow-32. Then, based on the experimental results, we prove that Shadow has a differential property with probability 1 based on the propagation of the state. Further, we can obtain an impossible differential distinguisher for an arbitrary number of rounds of Shadow. Finally, we perform a full key recovery attack on the full-round Shadow-32 and Shadow-64. Both experimentally and theoretically, our results indicate that Shadow is critically flawed, and regardless of the security strength of the internal components and the number of rounds applied, the overall cipher remains vulnerable to impossible differential cryptanalysis.
Cost-Effective Electronic Voting Machine

Voting System is very much in need to rule democratically. The ballot paper was introduced to ensure the same, but it had its own cons like tampering, and counting of votes took much time and workforce. To overcome that, Electronic Voting machines (EVM) were introduced so that problems like tampering and other related issues can be reduced to some extent. As these EVMs are a hybrid of software and hardware, the inclusion of the former one made them more vulnerable to hacking, and unfair practices were done by interchanging the votes in between the candidates which in turn led to tampering with data. The proposed model in this paper is completely on the digital circuit with security involved in the circuit which makes it harder to tamper, as there is no software involved it is difficult to manipulate the results. The complexity of the machine is less which in turn needs compact space, cost-effective within no time, and setting up of the voting machine can be done with few steps.
Uses of Geographic Information Systems and Remote Sensing to Study the Natural Resource Management of the Red Sea Governorate, Egypt

The Red Sea governorate is rich in natural resources that create challenges for Egypt's future economy and growth. The right ways must be found to manage and discover these resources without wasting them. Remote sensing is one of the best methods used in the development of natural resources. It is based on measuring electromagnetic ray reflections from deep-seated or dispersed natural resources, and these images are processed uniquely for a variety of applications, including the detection of minerals, making land cover maps, shifting rocks, and monitoring of geological distortions. The altered rocks that existed in the study area were identified using recently developed sensors for earth observations, such as the Landsat 8 Operational Land Imager (OLI). The images have been processed using the band ratio technique and false color composite. The band ratios (red/blue, swir2/nir, swir1/nir) and spectral band combinations (Kaufmann ratio, Sabins ratio) have been used to accurately map hydrothermal alteration minerals. The OLI band ratios (6/7, 6/2, 4/5, and 6/5*4/5) have been applied to distinguish between some rocks (metavolcanic, metasedimentary, felsic, and granitic). The OLI (FCC) (6/7, 6/5, 4/2), (4/6, 4/2, 6/5), and (6/7, 4/2, 6/5*4/5) have been used to identify the minerals (Serpentine, talc-carbonate schist, quartz, and granodiorite). The methods used in this study are time, effort, and cost-saving. These methods are applicable to hard-to-reach areas. The results proved that combining spectral bands effectively identifies potential mineralization points through satellite data. Validation of these results using geological maps showed a good relationship with the location of minerals.
Building integrated information practical environment to support practice-oriented model of university training

The author discusses several issues of building integrated digital information practical environment as an element of functional infrastructure to support practice-oriented dual model of training engineers for innovative enterprises (designing innovative products). This model is to be applied for learning general professional knowledge and skills, as well as highly specialized practice-oriented competences. The case study illustrates how the information practical environment is being built within the framework of the university-hi-tech organization partnership of Moscow Institute of Physics and Technology and Dukhov All-Russian Research Institute of Automatics (Rosatom State Corporation’s daughter enterprise).
Concept of Computational Fluid Dynamics Design and Analysis Tool for Food Industry: A Bibliometric

Technology that is currently in demand is CFD. By simulating fluid flow around a product on a computer, the computational fluid dynamics (CFD) technique allows designers of new products to be tested. This research aims to analyze the bibliometrics of CFD publications as a design tool in the food industry to determine concepts, trends, and contributions of previous research. Computational Fluid Dynamics (CFD) is a technology used to test product designs through computer simulations of fluid flow around the product. The use of technology in various industries, such as the food industry, is essential to create better products or services. This research uses a bibliometric analysis method supported by theoretical analysis. This research consists of 5 steps, namely (i) determining the research theme for formulating keywords, (ii) collecting publication documents, (iii) data processing, (iv) bibliometric analysis, and (v) preparing a report. The keywords used in this research are "Computational Fluid Dynamics Design (CFD) in Food Industry." The results of the publication search found a total of 211 documents from 1992 to 2023. The average number of publications was 6.59. The trend of CFD publications in the food industry was first carried out in 1992. The development of this publication has been fluctuating, and there has been an increase in publications in the last five years (2019 - 2023). Research publications regarding CFD in the food industry are grouped into 20 subject areas. Contributions to this research consisted of 159 authors, 160 affiliates, and 51 countries. Completing this research will likely provide information regarding publication sources and contributions made by several scientists from various affiliates and countries worldwide.
Cloud Web Services Based Fleet Management System

The cloud-based fleet management system is a web service that allows organizations to manage their vehicle fleets more efficiently. This system is hosted on a cloud infrastructure and can be accessed through the internet from any location. The system provides features such as vehicle tracking, route optimization, fuel management, driver management, and maintenance scheduling. The system utilizes GPS and other sensors to collect data about the vehicles and drivers, which is then analyzed to provide insights and recommendations for improving fleet performance. This cloud-based system offers a cost-effective solution for fleet management, as it eliminates the need for organizations to invest in hardware and software infrastructure. The system is scalable, customizable, and can be integrated with other business systems to provide a comprehensive solution for fleet management. Therefore cloud-based fleet management system provides organizations with a range of features to manage their vehicle fleets more efficiently. The system is cost-effective, scalable, customizable, and easy to use. The system offers valuable insights into fleet performance, enabling organizations to identify areas for improvement and optimize their fleet operations. The system has a wide range of applications across various industries and is a valuable tool for organizations looking to manage their fleets more efficiently.
Low-Skilled Liberalizers: Support for Free Trade in Africa

Abstract Despite populist backlash against globalization in advanced industrialized countries, developing countries have recently sought to liberalize trade. To shed light on this phenomenon, we investigate mass attitudes toward free trade in thirty-six African countries. Using two rounds of Afrobarometer data and original survey data from Ghana and Uganda, we find that individuals hold views that are consistent with their economic self-interest. As factor endowment models predict for a sample of skill-scarce countries, low-skilled individuals are more likely than high-skilled individuals to support free trade. Moreover, the strongest negative effects of skill occur for the most skill-scarce countries in the sample and are driven by individuals in the labor force. Our results are robust to measuring variables more precisely in original surveys and controlling for other factors thought to shape attitudes. The findings indicate that previous evidence against factor endowment models may have partially resulted from inadequate data from the developing world.
Generalized Intra-camera Supervised Person Re-identification

Person re-identification (Re-ID) is to match the images of the same person from different camera views, which demands a view-invariant feature embedding. Recently, intra-camera supervised (ICS) Re-ID develops the Re-ID models without cross-view annotated data. Existing ICS methods are developed based on the assumptions, such as assuming each person in the training set appears under multiple cameras. However, there is no guarantee that the assumptions are true without cross-view annotations, and their performance degrades when the assumptions are violated. In this work, we generalize the ICS Re-ID and develop an ICS Re-ID model without the assumptions. The absence of prior assumptions and cross-view annotations poses a challenge in exploiting the discriminative information among cross-view images. To this end, we propose to mine the view-invariant relations between cross-view images for Re-ID model to exploit discriminative information and overcome the cross-view variations. Specifically, we learn composited view-aware features by compositing the identity information with different camera view information in the feature composition module. Then, we exploit the composited features to model various view-aware relations between pairwise images. By mining the common patterns among the view-aware relations, we obtain the view-invariant pairwise relation for learning. Besides, leveraging the composited view-aware features, we develop a view-aware marginal constraint for robust cross-view learning. To facilitate learning the feature composition module, we augment an auxiliary network to exploit the camera view information at the feature level. Extensive experimental results show the effectiveness of our method under different scenarios.
Dissecting Adversarial Attacks: A Comparative Analysis of Adversarial Perturbation Effects on Pre-Trained Deep Learning Models

It is well known that the majority of neural networks widely employed today are extremely susceptible to adversarial perturbations which causes the misclassification of the output. This, in turn, can cause severe security concerns. In this paper, we meticulously evaluate the robustness of prominent pre-trained deep learning models against images that are modified with the Fast Gradient Sign Method (FGSM) attack. For this purpose, we have selected the following models: InceptionV3, InceptionResNetV2, ResNet152V2, Xception, DenseNet121, and MobileNetV2. All these models are pre-trained on ImageNet, and hence, we use our custom 10- animals test dataset to produce clean as well as misclassified output. Rather than focusing solely on prediction accuracy, our study uniquely quantifies the perturbation required to alter output labels, shedding light on the models' susceptibility to misclassification. The outcomes underscore varying vulnerabilities among the models to FGSM attacks, providing nuanced insights crucial for fortifying neural networks against adversarial threats. Key Words: Adversarial Perturbations, Deep Learning, ImageNet, FGSM Attack, Neural Networks, Pre-trained Models
Real-Time Object Geopositioning from Monocular Target Detection/Tracking for Aerial Cinematography

In recent years, the field of automated aerial cinematography has seen a significant increase in demand for real-time 3D target geopositioning for motion and shot planning. To this end, many of the existing cinematography plans require the use of complex sensors that need to be equipped on the subject or rely on external motion systems. This work addresses this problem by combining monocular visual target detection and tracking with a simple ground intersection model. Under the assumption that the targets to be filmed typically stand on the ground, 3D target localization is achieved by estimating the direction and the norm of the look-at vector. The proposed algorithm employs an error estimation model that accounts for the error in detecting the bounding box, the height estimation errors, and the uncertainties of the pitch and yaw angles. This algorithm has been fully implemented in a heavy-lifting aerial cinematography hexacopter, and its performance has been evaluated through experimental flights. Results show that typical errors are within 5 meters of absolute distance and 3 degrees of angular error for distances to the target of around 100 meters.
Critical thinking competence as one of the key skills for bachelors of ecology in the era of the fourth industrial revolution

The article is based on a systematic analysis of the rapid changes in the information society associated with the pragmatics of the “leadership” technologies of the fourth industrial revolution. The authors reveal the role and importance of critical thinking as one of the basic skills needed in the 21st century. The article proves that the development of critical thinking is a key trend in modern education and requires not only an in-depth understanding of this phenomenon, but also new technologies for its implementation in pedagogical practice. The authors note that critical thinking includes the ability to analyze information, evaluate its reliability and make informed decisions. Purpose: Describe the importance of critical thinking in the context of a rapidly changing information society and demonstrate how these skills can be used in various areas of life. Scientific hypothesis: the development of critical thinking should become a priority for educational institutions in order to prepare the younger generation for the complexities and challenges of the modern world.
Simulation of the process of plane-radial filtration of fluid in a porous medium considering the migration

Modeling plane-radial fluid filtration in a porous medium is considered in the article, taking into account the condition of fluid flow to a perfect well with a constant flow rate drilled in the center of the formation; the problem of identifying the parameters, entering the mathematical formulation to the problem, is solved. Solving the problem, the values of the following parameters were determined: T - the coefficient of water conductivity, which ranges from 15% to 27%, and μ ∗ − the coefficient of water loss (water filtration), which ranges from 22% to 31% relative to the exact values used in solving the forward problem. In the course of the study, it was revealed that one of the main parameters in the process of ground and underground water filtering in porous media is the identification of evaporation parameters. To identify their values, the following methods were applied in the study: the method of barycentric coordinates; the least squares method; and the gradient method. Numerical experiments were conducted at a time when the critical depth of groundwater was a known value, while q 0 , n were the unknown parameters. The analysis of the studies showed that the “discrepancy” takes the minimum value when the found parameters (taken as zero approximations) are used in the least squares method.
Brand Credibility: Navigating the Pathway to Customer Satisfaction and Loyalty

Customers are a very valuable asset for any company or brand. Brand credibility is playing a key role in developing customers’ behavioral patterns. The credibility and integrity of a company play an essential role in making its customers contended with the services and also in maintaining their customers for a prolonged time. Considering the importance of customers and brands in marketing any product(s) or service(s), this study aims to analyze the influence of brand credibility on customer satisfaction and loyalty as well as to explicate the association between customer satisfaction or gratification and customer loyalty or devotion. The study was quantitative in nature and a close-ended questionnaire was distributed amongst the drawn sample to assess the influence of credible brands on clients’ satisfaction, devotion, and loyalty towards the brand. A convenient sampling procedure was adopted for acquiring data from the target population of Islamabad/Rawalpindi and the sample size was 160 respondents using the banking services of any public or private sector bank of Pakistan and residing in Islamabad and Rawalpindi. It was found that brand credibility is positively associated with customer loyalty and satisfaction. Besides, the findings also acknowledged a positive association of customer satisfaction with customer devotion and loyalty. This means that the more a brand is credible, the more customer satisfaction is achieved, which consequently influences customer loyalty trust, and devotion toward the brand in a positive manner. Keywords: Brand, Customers, Brand Credibility, Customer Satisfaction, Customer Loyalty.
Federated Learning with Differential Privacy on Personal Opinions: A Privacy-Preserving Approach

Data sharing poses a number of privacy risks, including reconstruction attacks, model inversion attacks, and membership inference attacks. Federated learning is a distributed paradigm that aims to improve privacy by sharing parameter gradients rather than the original data. The purpose of this study was to use federated learning and differential privacy techniques to train a model on sensitive personal opinion data while protecting the privacy of individual participants. The experiments carried out evaluate the effect of various noise levels on the model's performance. As the number of training rounds increases, the model's performance improves continuously, with noise multipliers and clients per round adjusted to achieve a balance between privacy and accuracy. This study adds to the literature on data analysis in scenarios with limited data sharing protocols and illustrates the ability to solve data privacy difficulties and restrictions efficiently using federated learning and differential privacy techniques.
An integrated approach to the analysis of argumentative relationships in scientific communication texts

The problem of automatic analysis of argumentation in scientific communication texts is considered. Argumentation is understood as an ordered set of arguments used to support a certain thesis. An argument includes at least one premise and one conclusion, connected by an argumentative relation. The purpose of the work is an experimental study of neu-ral network approaches to solving the problem of searching and extracting argumentative relations between statements located closely in the text. The study was conducted on a corpus of texts with argumentative markup created using the previously developed web platform. The corpus included texts of scientific news, analytical articles from the Habr web-site, scientific articles and reviews. Datasets for machine learning were built based on these texts. To improve the quali-ty of neural network models training, these sets were supplemented with new data by using automatic paraphrasing and double translation methods. Two approaches to training models were considered: the first one with labeling of indi-cators in texts and the second one with preliminary training of a language model on the task of predicting indicators. To evaluate the models performance, an approach was proposed based on estimates of agreement between experts, usu-ally used to compare markups of manually created texts. A comparison of agreement coefficients between experts and trained models showed that the quality threshold for extracting argumentative relations was almost reached on the model with labeled indicators. A manual analysis of model errors was carried out by visualizing the obtained results. Thus, the novelty of the work lies in the application of an integrated approach to creating data sets, training models and evaluating the results obtained from the automatic extraction of argumentative relations.
Development Model of Higher Education Cluster in Chengdu and Chongqing

This study endeavors to investigate the influencing factors behind the development of higher education clusters in the Chengdu-Chongqing region and establish a model aimed at enhancing their effectiveness. To achieve this objective, a Likert Scale was employed, comprising four primary dimensions, 14 secondary dimensions, and 39 items, initially validated through a pre-survey and SPSS software analysis. Subsequently, 405 valid questionnaires were collected from government officials, university administrators, and business leaders in the Chengdu-Chongqing region through an online platform and email. SmartPLS software was then used for analysis, confirming strong reliability and discriminant validity of the survey data. Further structural analysis revealed no issues of collinearity and identified government, universities, and enterprises as positively influencing factors on the Chengdu-Chongqing higher education cluster’s effectiveness. Eight secondary dimensions and 17 items were identified as positively impacting cluster effectiveness, leading to the formulation of a government-university-enterprise model for higher education cluster development in the Chengdu-Chongqing region.
MAD: Memory-Aware Design Techniques for Accelerating Fully Homomorphic Encryption

Cloud computing has made it easier for individuals and companies to get access to large compute and memory resources. However, it has also raised privacy concerns about the data that users share with the remote cloud servers. Fully homomorphic encryption (FHE) offers a solution to this problem by enabling computations over encrypted data. Unfortunately, all known constructions of FHE require a noise term for security, and this noise grows during computation. To perform unlimited computations on the encrypted data, we need to perform a periodic noise reduction step known as bootstrapping. This bootstrapping operation is memory-bound as it requires several GBs of data. This leads to orders of magnitude increase in the time required for operating on encrypted data as compared to unencrypted data.