Reviewerly/exHarmony huggingface dataset:
Survey on UAV Coverage Path Planning Problem

The advanced and highly mobile design of UAVs has led to a wide range of applications in fields such as battlefield monitoring, intelligent agriculture, photogrammetry, and disaster management, all of which are based on the coverage path planning problem (CPP). In recent years, there have been numerous researches on UAV coverage path planning problem. It is very important to plan an effective flight path for UAV to cover the whole target area completely. The purpose of this paper is to summarize and analysis of the existing literature involving different methods to solve the problem of UAV coverage path planning, we will survey the coverage path planning algorithm is mainly divided into two categories: one is geometric algorithm based on polygon region decomposition, and the second is heuristic algorithm for variant TSP and GTSP problems based on grid division and gives out the summary and analysis method for every type of coverage.
Threat Risk Assessment (TRA) for Physical Security

Maintaining the physical security of an organization entails navigating an intricate landscape of threats, adversaries, systems, and policies. As organizations evolve, become more complex and spatially distributed, security risks increase exponentially and become difficult to fully understand. Organizations entrusted with critical missions and ownership of high risk/high value assets realize that physical protection systems and policies are crucial to prevent unacceptable consequences arising from harmful influences, whether deliberate, accidental or natural. The more serious the consequences, the more important it is to have a high degree of confidence that physical protection will be as effective as planned. The highest level of confidence in physical protection is best achieved through the design and implementation of protective measures that are linked to a thorough understanding of the threats and vulnerabilities. This is achieved through comprehensive and up-to-date analysis of the motivations, intentions, and capabilities of potential adversaries against which protection systems are designed and evaluated. This chapter presents the conceptual development of a Threat Risk Assessment (TRA) Methodology for physical security planning and design. The methodology addresses critical knowledge and capability gaps in TRA approaches, and aims to strengthen the transparency, robustness and defensibility of an organisational Security Risk Management program. The chapter concludes with a discussion of lessons learned and recommendations for future work.
Augmented Reality and Intelligent Packaging for Smart Tourism: A Systematic Review and Analysis

The tourism sector is one of the largest and fastest-growing financial industries developed and redefined through technology. Information Communication Technology (ICT) and immersive imaging technologies, such as Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR), develop a digital environment where smart tourism aims at improving travellers’ experience through interactive and adaptive applications. Additionally, intelligent packaging’s emerging trend enters the tourism market dynamically, benefiting manufacturers, and buyers-users. This chapter addresses the systematic review and analysis of AR’s convergence with intelligent packaging, specifying this interaction inside the touristic environment. The interchange of the real with the digital world gives importance to Internet of Things, Fifth Generation Mobile Phone Network (5G), Radio Frequency Identification (RFID), and Near Field Communication (NFC), which elicit the “intelligence” for the smart tourism. The review is focused on the components of tourism (tourist attractions, accommodation, catering, and transportation) that can be enhanced by the simultaneous redefinition of intelligent packaging with AR features and presenting possible perspectives of personalized and multisensorial attributes.
Multi-Agent-Based Framework for Resource Allocation in Cloud Computing

Resource allocation in computing is the first operation carried out in the execution of processes within the cloud computing. This operation consists of allocating the requested resources to the consumer. The resource can be computed and\or data storage (virtual machine, bandwidth, CPU, memory…). Cloud computing requires techniques and methods that adapt to the dynamic behaviors inherent in cloud computing environments. In this paper, we propose a multi-agent-based framework for resource allocation in cloud computing.
Hybrid fuzzy interface model of sports rehabilitation activities

At present, the relevant test data and training indicators of athletes during rehabilitation training lack screening and analysis, so it is impossible to establish a long-term longitudinal tracking research system and evaluation system. In order to improve the practical effect of sports rehabilitation activities, this paper successively introduces the matrix normal mixed model and the fuzzy clustering algorithm based on the K-L information entropy regularization and the matrix normal mixed model. Moreover, this paper uses the expectation maximization algorithm to estimate the parameters of the model, discusses the framework, key technologies and core services of the development platform, and conducts certain research on the related technologies of the three-tier architecture. At the same time, according to the actual needs of sports rehabilitation training, this paper designs the functions required for exercise detection and prescription formulation. In addition, this paper analyzes and designs the database structure involved in each subsystem. Finally, this paper designs experiments to verify the performance of the model constructed in this paper. The research results show that the performance of the model constructed in this paper meets the expectations of model construction, so it can be applied to practice.
Control of network representation efficiency in neurogenesis

The article discusses a method to increase the efficiency of evolutionary genetic search in coding of modular network structures. A method for unfolding a genotype to form a phenotype using production rules generated by applying formal apparatus of L-systems and repair mechanisms is proposed. Forward and reverse coding techniques are considered. The method provides a combination of compactness of genetic code representation and advanced capabilities to increase dimension of resulting networks.
Academic Records: A Feasible Use Case for Blockchain?

With the number of university student records that are made on an annual basis and the sensitivity of these records, it is necessary to ensure that these records are authentic. Therefore, a solution based on a distributed ledger technology, such as blockchain, is proposed to ensure the reliability and integrity of student data. Since there are multiple blockchain solutions, after a comparative study between Bitcoin, Ethereum and Hyperledger and as well as a step-by-step systematic review of the subject area, we concluded and propose the use of one of the most stable technological platforms in use today by the industry: Hyperledger Fabric. Regarding the question of feasibility of applying this technological solution to the management of academic records, our study shows that despite it is still a growing area of interest, there is already some evidence that it is adequate to propose it as a case study.
Penerapan Algoritma Backpropagation dalam Memprediksi Kebutuhan Blangko Sertipikat Tanah pada Kantor BPN Kota Pematangsiantar

Pematangsiantar City is one of the regions in Indonesia which has a land area of ​​79.97 KM2. the office of the National Land Affairs Agency is a non-ministerial government institution in Indonesia that has the task of carrying out government duties in the land sector in the city of Pematangsiantar and providing land certificates for the community. A system for predicting the need for blank soil certificates using the Artificial Neural Network method is a method that is able to perform a mathematical process to predict the need for blank certificates for soil certificates using the backpropagation algorithm for data processing implemented with matlab. Data were collected through direct observation and grouped based on the annual need for blango factors. The results obtained from the test are the performance and epoch values ​​where each architecture is not the same. The test results are displayed in the form of a graph comparing the target value with the training and testing process.
Implementing big OLAP Cubes using a NoSQL-Based approach: Cube models and aggregation operators

OLAP is a technology that allows answering multidimensional analytical queries in purpose of decision making process, and It is considered as the core concept and a part of a broad trend in business intelligence. OLAP specification is generally platform-independent, although current implementations rely very heavily on relational and multidimensional databases (R-OLAP and M-OLAP implementations). Terms like data warehouse and OLAP cube are related to processing a huge amount of data; Unfortunately, these classical approaches become obsolete with big data phenomenon and cannot scale to meet the need of big data analysis. In this paper, we study the implementation of OLAP cube under key value-oriented NoSQL database, and for this purpose, we proposed two approaches to fetch data from a relational data warehouse and load it into NoSQL-OLAP cube implemented under Oracle NoSQL database following two models: The first one uses the tabular data structure layered on top of key value store, and in the second model, we proposed an aggregation operator to retrieve and load data into the key value storage API provided by Oracle. In order to validate our proposal, we conducted an experiment using Docker Engine and Docker Swarm to deploy Oracle NoSQL as a container in single-node setup and in a cluster mode. Thus, experiment had shown an interesting perspective on using key value store in OLAP engines.
Differentiation of institutions of public domain and orphan works in a cross-border copyright relations

Institutions of the public domain and orphan works in cross-border copyright relationships have drawn particular doctrinal attention because they are mechanisms capable of transcending territoriality. The article sets the task of distinguishing between the action of these institutions based on the study of the history of the issue, the content of sources, legal regulation regimes. Conclusions are made about the differences according to the following criteria: 1) recognition by the world community — the institution of the public domain is universal, while the institution of orphan works is enshrined at the national (regional) level only in a few states; 2) cases of transition of works under the influence of institutions — into the public domain — expiration of the term of protection, legislative prescriptions of a sanctioning nature, voluntary refusal of the authors from exclusive rights; institution of orphan works — the unwillingness of the author to exercise control or loss of information about the author; 3) on the basis of the definitions of the subjects of regulation: public domain — works that are not protected by copyright; orphan works, by contrast, are believed to be protected by copyright; 4) connection with the harmonization process — the institution of the public domain is enshrined in the law of all states of the world participating in the system of international copyright protection, and its action can be considered the result of harmonization, including the choice of lex loci protectionis to resolve cross-border disputes; there is no uniformity in the mechanism for accessing orphan works. At the same time, at the transboundary level, both institutions are united by the problem of the absence of legal regulation for the situation of returning a work under full copyright protection.
Multi-Domain Synchronous Refinement Network for Unsupervised Cross-Domain Person Re-Identification

Unsupervised cross-domain person re-identification (re-ID) is a challenging task, because it is an open-set problem with completely unknown person identities in the target domain. Existing methods attempt to tackle the challenge by transferring image style across domains or generating pseudo labels in the target domain, whereas the valuable information in multiple domains (i.e., source domain, style-transferred data, and target domain) is not taken fully into consideration. To this end, we propose a novel multi-domain synchronous refinement (MDSR) network, where valuable knowledge from multiple domains is sufficiently exploited and refined to enforce the discriminative ability of the model. MDSR network contains two complementary modules dedicated to source-to-target domain adaptation and style-transferred data to the target domain adaptation, respectively. The domain adaptive knowledge from two modules is aggregated in the final stage. Extensive experiments verify our method achieves significant improvements over the state-of-the-art approaches on multiple unsupervised domain adaptative person re-ID tasks.
Hair Identification Extension for the Application of Automatic Face Detection Systems

Hair identification can act as an additional functionality of facial identification systems to increase the security factor as well as provide hair-based solutions to various organizations for ease of operations and versatility like salons, medical research, and criminal identification. The implementation would help in identification of hair in an image so that it could be classified and modified as per the required purpose. The accuracy achieved by the model is 84.998% with a loss of 0.47838 based on the given dataset of 442 images with batch size 4, epoch as 5, and steps per epoch as 110.
MC-GAN: A Reimplementation of GameGAN With a Gaming Perspective

Creating games is a costly process involving tons of vigorous efforts, time, and resources. Different game engines were introduced to facilitate the game-making process, such as Unreal Engine, Unity, and Godot. However, despite the advent of game engines, the vacuum of automatically creating new games by computers was still felt. With artificial intelligence (AI) development and its growing presence in the industry, game developers made a new game development branch using AI. One of the essential steps in this context was the introduction of GameGAN, which inspired us for this article. Here we propose Multi-Class GamgeGan(MC-GAN), a starting point for the next generation of game engines based on GameGAN. In addition, MC-GAN is capable of classifying each desired game element and even changing its class to change the element's nature (e.g., MC-GAN can change static elements to dynamic ones). Moreover, MC-GAN uses only one complex model trained once per game. Once it is trained for that specific game, there will be no need for training to produce a new level of that game, a considerable step in the developing industry. Considering that MC-GAN is only the beginning of this big journey; therefore, it is now the best choice for developing web games since they are compact and straightforward. This article first briefly introduces GamGAN and its features; afterward, we get through MC-GAN features and compare them to other similar works.
LRSS-GAN: Long Residual Paths and Short Skip Connections Generative Adversarial Networks for Domain Adaptation and Image Inpainting

We propose a new architecture Long Residual and Short Skip GAN (LRSS-GAN), which allows semantic gap reduction and a deeper flow of visual concepts for image generation function. Compiling a labeled image database to train modern machine learning algorithms is very costly. Another attractive alternative is to provide synthetic data where real-world annotations are automatically generated. Most of the trained models on translated images generally fail quite often to learn features when compared with the original images. To address these failures, previous work has introduced domain transformation algorithms in unsupervised environment that extract features from the images that are not dependent on the domain. In this work, we present a novel architecture that learns in an unsupervised manner. LRSS-GAN built upon base UNet architecture uses longer residual paths between encoder and decoder branches and also incorporates short skip connections to pass feature information deeper into the model. This architecture improves the model adaptation of features from source domain images to look like they are from a target domain. Specifically, for MNIST and MNIST-M dataset, it was successfully able to adapt the features learned in before than the one in the latter. The mean classification accuracy obtained was 98.56%, which surpasses state-of-the-art accuracy. Also, to analyze the efficacy of the proposed architecture, it was also assessed for image inpainting scenario on the CelebA dataset by masking major regions of the face, where it gave a mean loss of 0.0122 with better practical results. Experiments proved the theoretical concepts proposed and resonate with this research’s aim and effectiveness in its application.
Colorization for Medical Images Based on Patient-Specific Prior Information and GAN Features

Medical images are mostly gray-scale images. Colorization for medical images can make them have better visualization effect and provide more diagnostic information for doctors. This paper proposes a colorization method for medical images based on prior explicit features and Generative Adversarial Networks implicit features. Firstly, patient attribute features are extracted based on the clinical prior knowledge, including age, gender, drug intake, etc. Meanwhile, image domain features are extracted from gray image, like texture, intensity value and others. Then, the feature value of each point is calculated to obtain the prior feature map. Secondly, the prior feature map and the original image are input to Generative Adversarial Networks to complete end-to-end training of the network. In addition, the method introduces a new loss function with prior explicit features and GAN features to constrain the network learning direction. This method has achieved good performance on both e-anatomy data set and our local dataset. The mean square error is reduced by 30.889 and the peak signal-to-noise ratio is increased by 5.116. Experimental results show that our method is superior in colorization for medical images.
Trading On-Chain: How Feasible is Regulators’ Worst-Case Scenario?

When consumers trade financial products, they typically use well-identified service providers that operate under government regulation. In theory, decentralized platforms like Ethereum can offer trading services ‘on-chain’ without an obvious entry point for regulators. Fortunately for regulators, most trading volume in blockchain-based assets is still on centralized service providers for performance reasons. However this leaves the following research questions we address in this paper: (i) is secure trading (i.e., resistant to front-running and price manipulation) even feasible as a fully ‘on-chain’ service on a public blockchain, (ii) what is its performance benchmark, and (iii) what is the performance impact of novel techniques (e.g., ‘rollups’) in closing the performance gap? To answer these questions, we ‘learn by doing’ and custom design an Ethereum-based call market (or batch auction) exchange, Lissy, with favorable security properties. We conduct a variety of optimizations and experiments to demonstrate that this technology cannot expect to exceed a few hundred trade executions per block (i.e., 13s window of time). However this can be scaled dramatically with off-chain execution that is not consumer-facing. We also illustrate, with numerous examples throughout the paper, how blockchain deployment is full of nuances that make it quite different from developing in better understood domains (e.g., cloud-based web applications).
Differential Power Analysis Attacks from an Information-Theoretic Perspective

Differential power analysis (DPA) attacks exploit the variance in power measurements of cryptographic devices to recover secret keys. What can an adversary achieve with power measurements? In this work, information-theoretic tools are used to quantity the amount of sensitive information revealed by a power measurement. It is shown that in order to find a secret key, an adversary needs to try a number of different keys. The number is exponential to the key size and the exponent is given by the key's entropy, conditioned on the power measurement.
Modelling the Impact of Threat Intelligence on Advanced Persistent Threat Using Games

System administrator time is not dedicated to just cyber security tasks. With a wide variety of activities that need to be undertaken being able to monitor and respond to cyber security incidents is not always possible. Advanced persistent threats to critical systems make this even harder to manage. The model presented in this paper looks at the Lockheed Martin Cyber Kill Chain as a method of representing advanced persistent threats to a system. The model identifies the impact that using threat intelligence gains over multiple attacks to help better defend a system. Presented as a game between a persistent attacker and a dedicated defender, findings are established by utilising simulations of repeated attacks. Experimental methods are used to identify the impact that threat intelligence has on the capability for the defender to reduce the likelihood of harm to the system.
Optimization of Missile Path Planning Based on APMO-HV Algorithm

In this paper, with considerations of low efficiency of missile path planning (MPP) by traditional aggregation technology, it uses affinity propagation based multi-objective evolutionary algorithm with hypervolume environment selection (APMO-HV) to solve the problem of MPP after establishing the MPP model. The experimental part compares and analyzes APMO-HV with six state-of-the-art algorithms, and applies it to address the MPP problem. The experimental results show that compared with the other six algorithms, APMO-HV has achieved the best solution performance in both the GLT test suite and MPP problem. This not only validates the effect of the proposed algorithm, but also enriches and improves the research results of MPP.
Recommendation of Refactorings for Improving Dependability Attributes

The incidence of design problems (DP) in systems is associated with the quality decay of dependability attributes such as maintainability and robustness. This often results in negative consequences such as rework and error proneness. Thus, there are many techniques for supporting the resolution of DPs through refactoring recommendations. However, in practice, deciding where and when to refactor is still a challenging task. In addition, existing techniques fail to provide effective support. Thus, we aim at advancing the state-of-the-art through a flexible refactoring recommendation technique. Our technique is intended to help practitioners in improving multiple dependability attributes. We hypothesize that our recommendation technique will be able to overcome existing limitations by using empirically validated heuristics. Our heuristics will use information such as the density, diversity, and granularity of symptoms to recommend refactoring opportunities. The effectiveness of our technique will be evaluated through empirical studies involving heterogeneous software projects and experienced software practitioners.