Reviewerly/exHarmony huggingface dataset:
Data In-Transit Security and Mitigation Mechanism in Cloud Computing

Cloud computing presents extended model for IT administrations and conveyance and it generally includes over-a-network, on-request, self-administrated access, which is powerfully versatile and flexible, using pools of regularly virtualized assets. Through these highlights, distributed computing can possibly further develop the manner in which organizations and IT work by offering quick beginning up, adaptability, versatility and cost productivity. Despite the fact that distributed computing gives convincing advantages and financially savvy alternatives for IT facilitating and development, new dangers and openings for security misuses are presented. Security principles, arrangements and controls are subsequently of the embodiment to help the executives in ensuring and defending frameworks and information. Cloud computing dangers ought to be dissected and perceived to have the option to secure conditions and stay away from information being uncovered. This work dissects current and future patterns on distributed computing and relief for distributed computing security hazards as a key advance towards guaranteeing secure distributed computing conditions. It is of embodiment to comprehend the abilities and dangers of cloud computing before a choice is made to move to a cloud computing provider.
Computing payoff allocations in the approximate core of linear programming games in a privacy-preserving manner

We investigate privacy-preserving ways of allocating payoffs among players participating in a joint venture, using tools from cooperative game theory and differential privacy. In particular, we examine linear programming games, an important class of cooperative games that model a myriad of payoff sharing problems, including those from logistics and network design. We show that we can compute a payoff allocation in the approximate core of these games in a way that satisfies joint differential privacy.
Research on Markov chain and PageRank algorithm

The rapid growth of the amount of information on the Internet makes web search technology become the main means for people to obtain information. How to measure the importance of web pages efficiently and quickly has become an important topic, and has different degrees of influence on all aspects of the web page, such as web crawling, web page grading and ranking. This paper discusses an important algorithm–PageRank, based on Markov chain model then a discussion of the advantages and disadvantages of PageRank is provided here. Finally, according to the drawbacks, some corrections and improvements are also discussed in this paper.
Automated Analysis of Big Data from Social Networks as a Way to Compile a Psychological Portrait of a Personality

The Internet contains huge volumes of information data, both structured and unstructured, including user data. The general availability of this kind of information makes it possible to use machine learning to determine the psychoemotional state of users. The article describes the main methods of processing and analyzing the data of Internet users, and also discusses possible approaches to compiling a psychological portrait of a person based on the analyzed information. Particular attention is paid to the prospect of using artificial intelligence to prevent the deviation of the mental state of users. A review of studies to identify the relationship between the characteristics of the user&#x0027;s personality and the nature of her activity in the social network is carried out. The problem of automatic extraction of cause-and-effect relationships from tests in natural language is considered.
Single-solution based metaheuristic approach to a novel restricted clustering problem

Clustering problems have been widely studied in the literature. Multiple types of solutions have been devised depending on the specific problem considered, including ad-hoc heuristics and metaheuristic approaches. In this paper, we centre our attention on a particular clustering problem in which the sizes of the individual clusters are fixed a priori and where a membership restriction due to an initial classification is present. A novel formal description of the problem is presented, and an example of how it appears in practice as a post-processing step of non-negative matrix factorisation analysis is described. Adaptations of three classical single-solution based metaheuristic methods are proposed as possible solution strategies for this problem. Experiments are performed using synthetic data and then using a test dataset constructed from repeated NMF extractions of multiple face images. The efficacy of the various approaches is compared and discussed.
Ship Target Tracking Algorithm Based on Adaptive Improvement Unscented Particle Filter

Aiming at the impact of limited network transmission bandwidth and strong clutter interference on ship tracking, an adaptive unscented particle filter fusion method combined with adaptive bit quantization technology for ship tracking is studied. First of all, two clusters were performed using information such as the location of the radiation source and carrier frequency to achieve data compression and clutter suppression; Next, apply adaptive bit quantization technology to obtain random quantized observations of each tracking node to the moving ship. The process is modeled by the first-order Markov model with random noise, and the quantization error is used as the component of the system state estimation to establish a target tracking model with unknown variance of part of the process noise component. Finally, using the adaptive unscented particle filter as the basic filter, the Sage-Husa estimator is used to estimate and correct the statistical characteristics of unknown noise in real time, and a centralized multi-sensor fusion method for offshore ship tracking is derived. The simulation results show that the algorithm can effectively improve the fusion estimation accuracy of the quantization filter.
Parallel Hybrid String Matching Algorithm Using CUDA API Function

Due to the fast growth of information recently, traditional string matching algorithms face a significant speed challenge. A good string matching algorithm is an algorithm with a small number of character comparisons, a small number of attempts, and a short execution time. These three factors measure the efficiency and performance of string matching algorithms. String matching algorithms are essential in many different applications such as search analysis, intrusion detection, internet search engines, editor’s information retrieval, computer security, and computational biology. These applications use string matching algorithms to search for a small string called a pattern in a large dataset. This search takes a long time and high resources to execute. This paper proposed a parallel design of a hybrid string matching called SSTBMQS on a GPU device using CUDA API. The result shows that the parallel version of SSTBMQS algorithm on GPU device using CUDA API function is unappropriated and un-recommended.
Analyzing Tweet Data to Identify the Impact of Demonetarization

In this century, we have witnessed tremendous growth in the application of technology in our day-to-day life. One such life influencing factor is social media. In a manner or other, each individual’s life is influenced by social media. Individuals and organizations rely more on social media to interact with people, acquire knowledge, promote business, gather information, news telecast, and for many other professional and personal purposes. Researchers use social media as one of the primary data sources for studies related to people’s behavior and for various prediction purposes. This empirical study identifies the importance of social media data in recognizing any event’s influence on the public. In October 2016 Indian government had demonetarized some active currency, and it created mixed reactions in public. For this research, tweet data was used to determine the impact of demonetarization on India’s people. The study results demonstrate that tweet data can be relied on identifying or extracting general public opinions on any given topic or event.
High Performance Reconfigurable Elliptic Curve Cipher Processor Implementation

Abstract Elliptic Curve Encryption (ECC) has been widely used in the field of digital signatures in communication security. ECC standards and the diversification of application scenarios put forward higher requirements for the flexibility of ECC processors. Therefore, it is necessary to design a flexible and reconfigurable processor to adapt to changing standards. The cryptographic processor chip designed in this paper supports the choice of prime and binary fields, supports the maximum key length of 576 bits, uses microcode programming to achieve reconfigurable function, and significantly improves the flexibility of the dedicated cryptographic processor. At the same time, the speed of modular multiplication and modular division can be greatly improved under the condition of keeping the low level of hardware resources through a carefully designed modular unit of operation. After using FPGA for hardware implementation, it is configured into a 256-bit key length. The highest clock frequency of this design can reach 55.7MHz, occupying 12425LUTS. Compared with a similar design, the performance is also greatly improved. After MALU module optimization design, modular multiplication module division also has significant advantages in computing time consumption.
Optimal Lossless Data Compression Methodology

Image compression is an extremely important technique given that the need for image storage is significantly increasing nowadays. The image contains data in the visual form; thus, data comprehension can be done easily. Since working on an image in a frequency domain is a lot more feasible, discrete cosine transform is one of the best possible way as it can represent the image in its elementary frequency components. As it is lossless image compression, quality of recovered image after compression is brilliant. This form of compression is much useful in research field, military and navigation as image quality plays a major role. This work emphasizes on optimal lossless image compression using Discrete Cosine Transform on RGB images. From the compression technique, it is shown that image can be stored in minimum amount of space. Image quality metrics such as PSNR, MSE and CR are briefly deliberated.
Dfinder—An efficient differencing algorithm for incremental programming of constrained IoT devices

Internet of Things (IoT) proliferation has been remarkably, interconnecting a vast number of devices for the support of complex data-driven applications in a variety of domains. The ability to remotely update these devices is of paramount importance, as it allows the integration of additional functionality into their firmware, the resolution of code errors, the fixing of security vulnerabilities, or even their complete re-purpose, without physically accessing them. Such Over-the-Air Programming (OTAP) solutions require the reduction of the required transmitted data during a network update, in order to minimise devices’ energy consumption due to the communication overhead. In this paper, we present the design and evaluation of Dfinder, a differencing algorithm that operates at byte-level and is able to generate small patches based on delta encoding that makes feasible the transition from a current firmware version to a new one. The algorithm runs in O(nlogn) time and O(n) space complexity, utilising enhanced suffix arrays and state-of-the-art construction techniques that enable the efficient detection of common segments between two firmware versions. Additionally, we propose an extension of the algorithm, which halves the storage requirements at the IoT device side (compared to other state-of-the-art approaches), so that devices with limited storage can also be efficiently re-programmed over-the-air. Moreover, we evaluate its performance, comparing it with other differencing algorithms, and by integrating it in a complete IoT OTAP system.
Blockchain

Blockchain is distributed ledger technology. Its advancement has been compared to the rise of the internet with debate about the technology's probability to disrupt multiple industries including healthcare, transportation, real estate, public domains, manufacturing, intellectual property, education, and financial services. It is predicted that the blockchain will have a major impact on many trust-based environments due to its nature of recording any digital transaction that is secure, efficient, transparent, auditable, and resistant to the outage, thereby providing the much-needed security in the transfer of assets in cyberspace. This chapter will highlight some of the business processes that can be disrupted by blockchain technology.
Helper Classes

In Chapter 6 we slow things down a little and spend some time reviewing the helper classes that power important, albeit somewhat hidden, functionality in the Hover Racers game. Through the course of this chapter you'll be introduced to helper classes that power the destroyer and water hazard scripts. You'll also see some interesting use cases like the engine whine modulation and the blimp camera scripts.
Knowledge Reuse Evaluation in Software Development: A Case Study on a Startup Company

The software development process often faces some problems. In finding solutions for the problems, software development teams often seek previous knowledge. The main problem from the case study is searching for previous knowledge that fits the context of the problem at hand. In fact, the search process to find previous knowledge took a long time and the results are not appropriate. The methodology used in this research is qualitative research using Systematic Literature Review, data triangulation, and Design Sprint. This research provides a system design for the reuse of knowledge that results from associations between best practices and practices in organizations. The proposed system has several features to support better knowledge reuse processes in PT XYZ. The features of the proposed system are dashboard, search knowledge, knowledge detail, knowledge graph, create knowledge, and profile.
Comparative Study of IoT Technologies for a Geolocalizing Device for People with Early-Stage Dementia

This paper shows the main low power wireless technologies (LPWAN) available in Colombia and identifies the optimal technology for future IoT implementations. Our work focuses on alternatives for the development of an electronic geolocation device for people with early-stage dementia syndrome. As a result of this work, it is possible to highlight that the technology that best suits the objectives of the device development is SigFox.
Research on the Application of Computer Big Data Technology and 3D Simulation Design System in Landscape Architecture

This article develops a new 3D simulation design system for urban planning based on computer big data technology. Build a MongoDB database to safely store the measured data of urban planning and the data generated during the design process. The 3D design module builds a 3D model of the urban scene based on OpenGL modules, and uses digital elevation models and digital orthophotos to solve urban planning problems with large slope terrain. The computer big data technology module calculates the vertex normal, vertex coordinates, and texture coordinates of the urban landscape to present virtual planning effects, and performs model traversal rendering according to code instructions, and interactively controls the rotation and translation of the camera through interactive control instructions to realize interactive roaming of urban planning dynamic landscape simulation. According to the results of the simulation experiment, the system can plan a reasonable number of landscapes according to the slope of the terrain, and the accuracy of processing 3D point cloud data is as high as 99%. It is a landscape planning system with high satisfaction.
A Novel Cluster-Sorting Method for Radar Signals Based on Graph Autoencoder

Aiming at the problems of traditional clustering algorithms that require manual screening of features, only learn the attribute information of the input signal, and have poor results in radar signal sorting. In this paper, a novel cluster-sorting method for radar signals based on Graph Autoencoder (GAE) is proposed. The key of this method is to obtain the adjacency matrix of the radar signal data by using the K nearest neighbor (KNN) algorithm, and then obtain the graph structure representation of the radar signals. The low-dimensional vector embedding is obtained after Graph Autoencoder learns the relevant information of the graph. Next the K-Means method is applied to the clustering of low-dimensional vector embedding, and the cluster-sorting of radar signals are completed. Finally, the proposed algorithm is compared with five typical clustering algorithms. The experimental results show that the proposed algorithm is better than the typical method in radar signal cluster-sorting, and the signal sorting accuracy is higher 12.5%.
An Explainable TV Program Recommendation Model Based on Attention Mechanisms

TV program recommendation can help users find programs from a large amount program information that is often overwhelming and confusing. The recommendation systems based on neural network architectures have been successfully applied to TV program recommendation. However, owing to the “black box” of neural network architectures, it is hard to explain the methodologies behind these recommendations and this often leads to hesitancy of users in using recommendation. Here we propose a label-based model with hierarchical attention mechanisms for TV program recommendations. We introduce the attention in two dimensions of “user” and “program” to measure the importance of labels in TV programs. In the process of training the model using real world data, we can visualize the attention to explain the changes in the model. For the test data, we use the attention to interpret the recommended samples. Experiments demonstrate exceptional interpretability of our model.
Rough Set Theory Approach in Determining Social Assistance Decision

World Digital Charity Organization of Rumah Zakat manages zakat, infaq, sadaqah, and social fund through community empowerment programs. Nowadays, an analysis is essential to determine the proper assistance needed by a community as the assistance recipient, whether it is in the form of education, health, economic, or environment assistance. Currently, the provision of aid funds is always not on target due to several reasons. All this time the method applied is First In, First Service, which gives the assumption that not every condition of the assistance-applicants who register earlier would be treated as the top priority to be processed. Therefore, this study aims to determine how appropriate the grants can be given to applicants. Therefore, this study aims to determine how appropriate grants can be given to applicants. To achieve this goal, we use the rough set method to filter several essential parameters in granting grants. The rough set method is a piece of mathematical equipment utilized to solve uncertainty and certainty. The samples were 80 data of the applicants, 17 equivalent classes were found from it. The number of rules formed from the equivalent classes were 58 rules. The results of the accuracy-test show a value of 80% of the closeness between the predicted value and the actual value and more rules formed in the designed application, it means the results would be more accurate.
Improved RRT path planning algorithm based on growth evaluation

Path planning algorithm is a key research problem in the application of autonomous mobile robots. RRT algorithm is one of the excellent methods for robot path planning. However, RRT algorithm has drawbacks such as high time consumption, high number of samples, low operational efficiency. On the basis of an adaptive resolution Octree Map, this paper presents an improved RRT path planning algorithm through establishing a growth point evaluation function. By adding the restriction through the evaluation function to random growing points of RRT, the random growth can be turned into purpose-oriented, and redundant growth points and routes can be eliminated. Besides, the growth path is reselected and rewired by the parent node and candidate nodes obtained through the growing process. Through reselection and rewiring, the route is continuously optimized and smoothed. Experiments are carried out for performance evaluation. Experiment results indicate that, comparing with the traditional RRT, RRT* and B-RRT*, the improved RRT algorithm can eliminate the redundant bifurcations of the growth tree reduce the number of sampling times, and greatly improves the growth efficiency.
Application of Blockchain in E-Healthcare Systems

The e-healthcare system maintains sensitive and private information about patients. In any e-healthcare system, exchanging health information is often required, making privacy and security a primary concern for e-healthcare systems. Another major issue is that existing e-healthcare systems use centralized servers. These centralized servers require high infrastructure and maintenance costs for day-to-day services. Along with that, server failure may affect the working of e-healthcare systems drastically and may create life-threatening situations for patients. Blockchain technology is a very useful way to provide decentralized, secure storage for healthcare information. A blockchain is a time-stamped series of immutable records of data that is managed by a cluster of computers not owned by any single entity. These blocks create a chain of immutable, tamper-proof blocks in a ledger. This chapter will discuss the different aspects of blockchain and its application in different fields of the e-healthcare system.
Conceptual Issues of Creating a Multifunctional Simulation Complex.

The paper discusses conceptual issues, goals and objectives of creating a multifunctional simulation complex. The idea of creating the multifunctional simulation complex on the basis of the Gagarin CTC’s technical means for cosmonaut training is substantiated. The basic principles of the development of this complex are shown as well.