Reviewerly/exHarmony huggingface dataset:
Resource allocation models in/for edge computing

During last decades, cloud computing enabled centralization of computing, storage and network management through cloud data centres, cellular core networks and backbone IP networks. Unlimited computing resources and storage can be used to provide elastic cloud services to a vast range of customers from organizations encompassing hundreds of servers to resource-constrained end users. Despite numerous advantages, Cloud computing is facing increasing limitations such as remarkable communication delay, jitter and network traffic caused due to far located data centres from end users. Edge computing has emerged in order to shift the traditional cloud model towards a decentralized paradigm to deal with strict requirements of latency, mobility and localization of new systems and applications (e.g. Internet of Things (IoT)). Edge computing places the content and resources near to end users to ensure a better user experience. Edge computing is still at the stage of development and encounters many challenges such as network architecture design, fault-tolerance and distributed service management, cloud-edge interoperability and resource allocation. In this chapter, we present an overview on existing resource allocation models proposed for and leveraged in edge computing. Specifically, we explore the key concepts of resource allocation in edge computing, and investigate recent researches have been conducted in this domain along with their optimization models. Finally, we discuss challenges and open issues.
Improving the Tourists Experiences: Application of Firebase and Flutter Technologies in Mobile Applications Development Process

Mobile application development gained special interest from researchers in the last years. Many people have a smartphone and use mobile applications for various purposes such as transport, travel, shopping, medical services, etc. These applications proved their importance also in the tourism domain. Various applications were developed to facilitate the access of the tourists to accommodation options, must-see locations suggestions, restaurants etc., but the main drawback is that many of these are separate applications. For this reason, the users shall switch between all these applications. This paper intends to improve the users&#x2019; experience by providing all the before mentioned functionalities in just one application. Current research highlights the development process of an all-in-one touristic mobile app for Timi&#x015F;oara (Romania) by employing recent technologies such as Firebase and Flutter. These technologies will show their benefits through the proposed application as a case study.
Cryptanalysis of RSA Variants with Primes Sharing Most Significant Bits

We consider four variants of the RSA cryptosystem with an RSA modulus \(N=pq\) where the public exponent e and the private exponent d satisfy an equation of the form \(ed-k\left( p^2-1\right) \left( q^2-1\right) =1\). We show that, if the prime numbers p and q share most significant bits, that is, if the prime difference \(|p-q|\) is sufficiently small, then one can solve the equation for larger values of d, and factor the RSA modulus, which makes the systems insecure.
An automated system for electrical power symbol placement in electrical plan drawing

An electrical plan drawing–sometimes called a wiring diagram or electrical drawing–consists of lines and symbols. Electrical plan drawings are prepared on 2D architectural floor plans using Computer-Aided Design and/or Drafting (CAD) programs. The placement/drawing of electrical power symbols–such as sockets, lights, and switches–is the first step of an electrical plan drawing. For this purpose, a smart system has been developed in this study to automatically draw/place electrical power symbols in appropriate locations. The system is based on the detection and classification/recognition of furnishing (decorative) symbols in the floor plans. We have created a furnishing symbol dataset drawing on dozens of architectural plan drawings that contain symbols of the most commonly used tools in floor plans, such as furniture, appliances, plumbing, doors, and windows. We used a Deep Convolutional Neural Network (D-CNN) with transfer learning–Inception-v3 model– to classify furnishing symbols. We tested the model on 20 real floor plans and achieved a very satisfactory accuracy of 97.05% in furnishing symbol classification. The symbol drawing step, which is the first step of drawing the electrical plan, was automated using the work developed, thus achieving the aim of saving time and labour. Experimental studies show the effectiveness of the proposed automated system.
Towards supporting SPL engineering in low-code platforms using a DSL approach

Low-code application platforms enable citizen developers to autonomously build complete applications, such as web applications or mobile applications. Some of these platforms also offer support for reuse to facilitate the development of similar applications. The offered mechanisms are usually elementary, they allow module reuse or building a new application from a template. However, they are insufficient to achieve the industrial level reuse necessary for software product lines (SPL). In fact, these platforms were conceived to help build standalone applications, not software families and even fewer software product lines. In this paper, we argue that the major limitation is that these platforms seldom provide access to their metamodel, the access to applications' models and code is also limited and, therefore, makes it harder to analyze commonality and variability and construct models based on it. An approach is proposed to surpass these limitations: firstly, a metamodel of the applications built with the platform is obtained, and then, based on the metamodel, a domain-specific language (DSL) that can express the models of the applications, including variability, is constructed. With this DSL, users can combine and reuse models from different applications to explore and build similar applications. The solution is illustrated with an industrial case study. A discussion of the results is presented as well as its limitations and related work. The authors hope that this work provides inspiration and some ideas that the community can explore to facilitate the adoption and implementation of SPLs in the context, and supported by, low-code platforms.
Impact on Biometric Identification Systems of COVID-19

COVID-19 has had an inevitable impact on the daily life of people in 2020. Changes in behavior such as wearing masks have a considerable impact on biometric systems, especially face recognition systems. When people are aware of this impact, a comprehensive evaluation of this phenomenon is lacking. The purpose of this paper is to qualitatively evaluate the impact of COVID-19 on various biometric systems and to quantitatively evaluate face detection and recognition. The experimental results show that a real-world masked face dataset is essential to build an effective face recognition-based biometric system.
SimMC: Simple Masked Contrastive Learning of Skeleton Representations for Unsupervised Person Re-Identification

Recent advances in skeleton-based person re-identification (re-ID) obtain impressive performance via either hand-crafted skeleton descriptors or skeleton representation learning with deep learning paradigms. However, they typically require skeletal pre-modeling and label information for training, which leads to limited applicability of these methods. In this paper, we focus on unsupervised skeleton-based person re-ID, and present a generic Simple Masked Contrastive learning (SimMC) framework to learn effective representations from unlabeled 3D skeletons for person re-ID. Specifically, to fully exploit skeleton features within each skeleton sequence, we first devise a masked prototype contrastive learning (MPC) scheme to cluster the most typical skeleton features (skeleton prototypes) from different subsequences randomly masked from raw sequences, and contrast the inherent similarity between skeleton features and different prototypes to learn discriminative skeleton representations without using any label. Then, considering that different subsequences within the same sequence usually enjoy strong correlations due to the nature of motion continuity, we propose the masked intra-sequence contrastive learning (MIC) to capture intra-sequence pattern consistency between subsequences, so as to encourage learning more effective skeleton representations for person re-ID. Extensive experiments validate that the proposed SimMC outperforms most state-of-the-art skeleton-based methods. We further show its scalability and efficiency in enhancing the performance of existing models. Our codes are available at https://github.com/Kali-Hac/SimMC.
Epidemiologia da leishmaniose tegumentar e visceral na região Nordeste durante os anos de 2010 a 2019 / Epidemiology of cutaneous and visceral leishmaniasis in the Northeast region during the year 2010 to 2019

Brazil is an extremely susceptible country to diseases caused by parasites, as they are typical of underdeveloped countries, especially those where the predominant climate is humid tropical, fitting these characteristics perfectly.The type of parasitosis that will be highlighted in this scientific work is leishmania, which is transmitted by a hematophagous protozoan of the genus leishmaniasis.Although man is only an intermediate carrier in the life cycle of the parasite, once infected, it tends to develop diseases, which in the case of leishmania, manifests itself in two clinical forms: visceral leishmaniasis (VL) and tegumentary leishmaniasis American (TLA).This article aims to describe the epidemiological profile of LV and TLA leishmaniasis in the years 2010 to 2019.The methodology will use tables and graphs of data obtained from DATASUS with a specific focus on the northeast region of the country, and through these numbers, the nine States in the region will be evaluated, including their capitals and the five most affected municipalities in each state, with an analysis of the impact of the disease on the community.
CloudFindr: A Deep Learning Cloud Artifact Masker for Satellite DEM Data

Artifact removal is an integral component of cinematic scientific visualization, and is especially challenging with big datasets in which artifacts are difficult to define. In this paper, we describe a method for creating cloud artifact masks which can be used to remove artifacts from satellite imagery using a combination of traditional image processing together with deep learning based on U-Net. Compared to previous methods, our approach does not require multi-channel spectral imagery but performs successfully on single-channel Digital Elevation Models (DEMs). DEMs are a representation of the topography of the Earth and have a variety applications including planetary science, geology, flood modeling, and city planning.
Aerodynamic Analysis of Quadrotor UAV Propeller using Computational Fluid Dynamic

A propeller is a device with a rotating hub and blades that form a helical spiral at a specific pitch angle. It acts on a moving fluid, such as air, to convert rotational power into linear thrust. Designers of drones typically rely on vendors and layman recommendations while selecting propellers parameters for their UAVs. Several factors should be considered when designing a new drone propeller blade. However, not much research has been published on the effect of rotor configuration on propulsion system efficiency in hover. Thus, additional influence of rotor configuration such as the coaxial propeller configuration investigated in this work to supplement findings in factors that affect a drone system's propulsion efficiency compares along with the single propulsion system. Using SolidWorks and Ansys Fluent, this project will design and analyse the aerodynamic performance of a quadrotor drone propeller blade. For the computational fluid dynamics (CFD) investigation, 3D CAD models were produced in SOLIDWORKS and imported into Ansys Fluent. The shape of the model is inspired by the original APC drone propeller. The use of design tools like SolidWorks and a Computational Fluid Dynamics (CFD) solution can generate the necessary analysis to estimate the propeller blade's aerodynamic efficiency, allowing for the creation of a nearly optimal design. Several simulation examples are used in the research to find propeller shapes and configurations that improve aerodynamic and propulsion efficiency.
Capitalizing on RGB-FIR Hybrid Imaging for Road Detection

Traditionally, road detection approaches mostly capitalize on RGB images, 3D LiDAR point cloud or their fusion. However, RGB camera is sensitive to light conditions, while LiDAR point cloud is sparse compared with dense image pixels. In this work, a new hybrid image dataset is provided for the task of road detection based on cameras. In this dataset, the hybrid images are acquired by an optically aligned hybrid imaging device, consisting of a far-infrared (FIR) imager and an RGB camera to output pixel-wise registration of thermal and RGB frames. Then we investigate on three methods based on fully convolutional neural network (F-CNN) to demonstrate the advantages by fusing RGB-FIR images in road detection. First, a middle-fusion based model is built, where the output feature maps of encoder branches from RGB and FIR images are directly concatenated into a single-fusion branch as the decoder. Next, the originally discarded layers after fusion operation for both RGB and FIR branches are recovered as the mimic branches to imitate the distributions of the fusion outputs, which constitutes an extended cross model (ECM). Moreover, the outputs of mimic branches at different scales are also used to imitate the corresponding outputs in the fusion branch, called a hierarchical cross model (HCM). The experimental results demonstrate the effectiveness and efficiency of our fusion strategies.
Formation of ecological culture of students – the basis of a healthy lifestyle

The methodological and practical importance of determining the characteristics of the formation of the ecological culture of students is studied in this article based on the provisions and requirements of the main documents of the state as well as the works of scientists. From the conclusions on environmental upbringing, it is clearly formulated that a person should serve consciously to protect the natural environment in the search for effective ways to maximize the reproduction of natural resources, eliminate the negative changes that occur in the natural environment and improve the system of environmental education. The possibility of environmental upbringing in the main areas of the formation of a person’s love for nature on the basis of national customs, proverbs, sayings, riddles, prohibitions, ceremonies is provided. Generally, the universal ecological system as a continuous system of education and upbringing, based on the mastery of skills and knowledge related to the environment, the formation of a cultural environment as a result of influencing the consciousness and worldview of a person including mercy, charity and natural beauty has a huge impact on the development of a spiritual quality personality.
Dynamic Effects of Foreign Aid, Trade Openness and Fdi on Economic Growth for West African Countries

Several arguments have been raised about the tripartite relationship between capital inflows, trade openness and economic growth; and most especially on their role as economic stimulators. Despite the above, the role of capital inflows in the development process cannot be overemphasized since they ensure enhancement of technology transfer, efficiency and improvement in the quality of factor inputs. As alternative to aids and FDI, many West African countries embraced trade liberalisation policy with the belief that trade openness has the potential of enhancing economic growth by increasing the variety of intermediate inputs as well as the size of the domestic market. It is upon this premise that the current paper examines the individual, interactive and threshold effects of aid, FDI and trade openness on economic growth by employing panel autoregressive distributed lag (PARDL) and mean group (MG) estimation techniques on 14 West African countries using datasets from 1980 through 2018. The PARDL is adopted because of its dynamic nature and ability to obtain both short and long-term effects, while mean group technique records the uniqueness of individual West Africa countries and examine their degrees of sensitivity to regional characteristics. The results revealed that long run relationship was observed and aid, FDI and trade openness positively enhanced output growth. Second, the interactive effect of aid, trade openness and FDI was negative, but strengthens the individual effects in the long run period. Third, an average financial flows threshold of 8.3 percent is required to spur output growth to equilibrium. Fourth, MG estimation affirms that it is only in Senegal that the coefficients of these financial flow variables were sensitive to regional characteristics. These are the major contributions to knowledge. Thus, the paper recommends the need to embrace medium-and long-term policy framework which focuses on channelling funds from aid towards infrastructural and human development in order to accelerate future output growth. More so, regional representatives should concentrate efforts towards shifting their exports from primary to secondary and tertiary products so as to increase the value of trade transactions to members. Lastly, regional macroeconomic policies aimed at improving economic integration and regional sensitivity among members should be considered.
Research on Multiscene Vehicle Dataset Based on Improved FCOS Detection Algorithms

Whether in intelligent transportation or autonomous driving, vehicle detection is an important part. Vehicle detection still faces many problems, such as inaccurate vehicle detection positioning and low detection accuracy in complex scenes. FCOS as a representative of anchor-free detection algorithms was once a sensation, but now it seems to be slightly insufficient. Based on this situation, we propose an improved FCOS algorithm. The improvements are as follows: (1) we introduce a deformable convolution into the backbone to solve the problem that the receptive field cannot cover the overall goal; (2) we add a bottom-up information path after the FPN of the neck module to reduce the loss of information in the propagation process; (3) we introduce the balance module according to the balance principle, which reduces inconsistent detection of the bbox head caused by the mismatch of variance of different feature maps. To enhance the comparative experiment, we have extracted some of the most recent datasets from UA-DETRAC, COCO, and Pascal VOC. The experimental results show that our method has achieved good results on its dataset.
Interventional Fairness with Indirect Knowledge of Unobserved Protected Attributes

The deployment of machine learning (ML) systems in applications with societal impact has motivated the study of fairness for marginalized groups. Often, the protected attribute is absent from the training dataset for legal reasons. However, datasets still contain proxy attributes that capture protected information and can inject unfairness in the ML model. Some deployed systems allow auditors, decision makers, or affected users to report issues or seek recourse by flagging individual samples. In this work, we examine such systems and consider a feedback-based framework where the protected attribute is unavailable and the flagged samples are indirect knowledge. The reported samples are used as guidance to identify the proxy attributes that are causally dependent on the (unknown) protected attribute. We work under the causal interventional fairness paradigm. Without requiring the underlying structural causal model a priori, we propose an approach that performs conditional independence tests on observed data to identify such proxy attributes. We theoretically prove the optimality of our algorithm, bound its complexity, and complement it with an empirical evaluation demonstrating its efficacy on various real-world and synthetic datasets.
PROJECTS FOR ORGANIZATIONAL ADVICE AND GUIDANCE ACTIVITY OF STATE PUBLIC SCIENTIFIC AND TECHNICAL LIBRARY OF THE SIBERIAN BRANCH OF THE RUSSIAN ACADEMY OF SCIENCES (NOVOSIBIRSK, RUSSIA)

The paper describes a role of the State Public Scientific and Technical Library of the Siberian Branch of the Russian Academy of Sciences (Novosibirsk, Russia) in arranging a system of advice and guidance activity in support of work run by libraries of scientific institutions located in Siberian macroregion of the country. The author analyses factors caused decentralization processes in library system of the Siberian Branch of the Russian Academy of Science, presents projects offered by the State Public Scientific and Technical Library of the Siberian Branch of the Russian Academy of Sciences for downsizing proportion of printed documents in libraries' collections and developing workforce capacity in libraries of scientific institutions in conditions of decentralization and digitalization of science.
Learning To Diversify for Single Domain Generalization

Domain generalization (DG) aims to generalize a model trained on multiple source (i.e., training) domains to a distributionally different target (i.e., test) domain. In contrast to the conventional DG that strictly requires the availability of multiple source domains, this paper considers a more realistic yet challenging scenario, namely Single Domain Generalization (Single-DG), where only one source domain is available for training. In this scenario, the limited diversity may jeopardize the model generalization on unseen target domains. To tackle this problem, we propose a style-complement module to enhance the generalization power of the model by synthesizing images from diverse distributions that are complementary to the source ones. More specifically, we adopt a tractable upper bound of mutual information (MI) between the generated and source samples and perform a two-step optimization iteratively: (1) by minimizing the MI upper bound approximation for each sample pair, the generated images are forced to be diversified from the source samples; (2) subsequently, we maximize the MI between the samples from the same semantic category, which assists the network to learn discriminative features from diverse-styled images. Extensive experiments on three benchmark datasets demonstrate the superiority of our approach, which surpasses the state-of-the-art single-DG methods by up to 25.14%.