Reviewerly/exHarmony huggingface dataset:
A K-means Grasshopper Algorithm Applied to the Knapsack Problem

In engineering and science, there are many combinatorial optimization problems. A lot of these problems are NP-hard and can hardly be addressed by full techniques. Therefore, designing binary algorithms based on swarm intelligence continuous metaheuristics is an area of interest in operational research. In this paper we use a general binarization mechanism based on the k-means technique. We apply the k-means technique to grasshopper algorithm to solve multidimensional knapsack problem (MKP). Experiments are designed to demonstrate the utility of the k-means technique in binarization. Additionally we verify the efficiency of our algorithm through benchmark instances, showing that binary k-means grasshopper algorithm (BKGOA) obtains adequate results when it is evaluated against another state of the art algorithm.
The motion planning of Six-Degree-of-Freedom manipulator based on P-Rob

In order to solve the problem of low efficiency of path planning of robot manipulator in high-dimensional joint space, and to further improve the smoothness of motion of the robotic system during the movement, the trajectory planning algorithm is explored and the path planning algorithm is improved of the six-degree-of-freedom robot manipulators system based on Personal Robotics (P-Rob). The trajectory planning simulation experiments of the robot manipulators system were implemented using joint space and Cartesian space trajectory planning, and the trajectory planning method suitable for the P-Rob system was explored. The path planning of the robot manipulators was optimized using the improved fast-expanding random tree algorithm, and the improved path planning algorithm was confirmed to be more efficient through Matlab simulation analysis. Finally, experiments were conducted on the actual P-Rob model, and the experimental results showed the practicality and efficiency of using the motion planning algorithm designed in this paper for a robot manipulators system of the same structure.
Compact Solution for Low Earth Orbit Surveillance

Low Earth Orbit Objects (LEOs) are objects that circle our planet at a distance of less than 2000 km from the surface. Due to their small orbital radius, they move fast and are sometimes affected by atmospheric drag, meaning that their orbit will change in time. This orbit includes communication satellites, Earth observation satellites, but also space debris such as rocket bodies which will eventually reenter the atmosphere. The fast motion, the changing nature of the orbit, their sheer number, and the periodic reentry events, lead to the need of intense observation of their position. This paper presents a compact, portable system for surveillance of the LEO objects. The system is built with commercially available, low-cost items, and is capable of on-site acquisition and real time processing of images. The acquired images are processed by background subtraction, analysis of the difference between frames, extraction of elongated objects corresponding to the satellite streaks, and forming trajectories (tracklets) from consecutive detections. The emphasis on trajectories instead of individual object properties allows successful detection of faint objects, without a significant increase in false positives.
The virtual teaching process and academic self-efficacy in learning and the intention to drop out university studies in times of Covid-19

The purpose of this study was to test the relationship between academic self-efficacy, learning self-efficacy, the teaching-learning process, student-teacher interaction, mediated by learning self-efficacy and the level of influence on students’ intention to drop out. university students in times ofCovid-19. The study comprises two phases, firstly, an exploratory factor analysis using the IBM-SPSS, to determine the correct adaptation of the factors with their corresponding items, secondly, the confirmatory analysis was carried out, for the validation of the structural model proposed and establish the relationships of the factors proposed in the model, the PLS-SEM methodology, Modeling of Structural Equations with Partial Least Squares, was used. The model was validated with a sample of 230 students in a private university in Peru and is explained by the determination coefficient R2 in 51.9%, so it is inferred that there are other latent factors or variables that would explain the intention dropout in university students by 48.1%.
Forecasting Industrial Water Demand Using Case-Based Reasoning: A Case Study in Zhangye City, China

Forecasting the industrial water demand accurately is crucial for sustainable water resource management. This study investigates industrial water demand forecasting by case-based reasoning (CBR) in an arid area, with a case study of Zhangye City, China. CBR uses past experience to solve new problems. Since CBR is a methodology rather than a technique, this definition makes case-based reasoning system be an open system, which can constantly absorb new technologies and methods, and be more conducive to the development of itself. This research constructed a case base with 420 original cases of 28 cities in China, extracted six attributes of the industrial water demand, and employed a back propagation neural network (BPN) to weight each attribute, as well as the grey incidence analysis (GIA) to calculate the similarities between target case and original cases. The forecasting values were calculated by weighted similarities. The results show that the industrial water demand of Zhangye in 2030, which is the target case, will reach 11.9 million tons. There are ten original cases which have relatively high similarities to the target case. Furthermore, the case of Yinchuan, 2010, has the largest similarity, followed by Yinchuan, 2009, and Urumqi, 2009. This research also made a comparison experiment in which CBR is more accurate than the grey forecast model and back propagation neural network in water demand forecasting. It is expected that the results of this study will provide references to water resources management and planning.
A Resource Allocation Model for Desktop Clouds

Cloud computing is a new paradigm that promises to move IT a step further towards utility computing, in which computing services are delivered as a utility service. Traditionally, Cloud employs dedicated resources located in one or more data centres in order to provide services to clients. Desktop Cloud computing is a new type of Cloud computing that aims at providing Cloud capabilities at low or no cost. Desktop Clouds harness non dedicated and idle resources in order to provide Cloud services. However, the nature of such resources can be problematic because they are prone to failure at any time without prior notice. This research focuses on the resource allocation mechanism in Desktop Clouds.The contributions of this chapter are threefold. Firstly, it defines and explains Desktop Clouds by comparing them with both Traditional Clouds and Desktop Grids. Secondly, the paper discusses various research issues in Desktop Clouds. Thirdly, it proposes a resource allocation model that is able to handle node failures.
Internal theft controls

This chapter discusses the concept of internal theft and how that affects companies and their security procedures. Understanding the nature of theft provides a basis for its control. The simple most effective measure in reducing employee problems is a thorough background check.
Research Data Practices of Aerospace Engineering Faculty: A Qualitative Study

Abstract The purpose of this study is to examine the research data practices of engineering scholars, in order to identify opportunities where library services and resources can support the data curation and management practices of aerospace engineering faculty as well as civil engineering faculty. As the academic library transforms from a space to house print books into a place that evolves to meet the changing research, teaching, and learning needs of the institute, one area rapidly evolving is in the area of research data management, storage, and sharing. Federal agencies, institutions, and publishers are increasingly encouraging or requiring researchers to adhere to policies for storing and sharing their research data. In recent years, university librarians have seen this as an opportunity to provide expertise to their respective research faculty communities. The authors conducted a series of semi-structured interviews of selected faculty members in the School of Aerospace Engineering. The authors asked interviewees sets of questions relating to their practices in working with their own research data as well as data generated by others, such as: How do the faculty acquire it? What tools do they use to analyze it? How are data stored, and in what formats? Are there any protocols or policies in place? What are the biggest challenges that they see now, as well as in the future? The interviews were recorded and subsequently transcribed and anonymized. Results will be qualitatively analyzed using grounded theory methodology, with coding schemas devised by the authors based on close transcript analysis. The findings from this analysis will be compared to findings from a similar study conducted in 2018 of civil engineering faculty at the institute. This paper explores the needs of aerospace engineering faculty with respect to such academic research data topics as: data generation, use, analysis, management, and curation. Further, this paper will compare these findings with a similar study that was previously conducted with the civil engineering faculty through a project with the Ithaka S+R organization, identifying similarities and differences in practice between aerospace engineers and civil engineers. This study will address such questions as to whether the qualitative findings indicate a changing need for data storage at the institute level, and whether the qualitative findings imply a need for library engagement with faculty to provide and promote specific data expertise and services. Future trends will be summarized as well. The result might be useful for librarians who work with engineering faculty especially regarding research data. A selective literature review on research data resources and services preferred, required, or requested by engineering faculty at institutes of higher learning will be included.
Efficient Identification of k-Closed Strings

A closed string contains a proper factor occurring as both a prefix and a suffix but not elsewhere in the string. Closed strings were introduced by Fici (WORDS 2011) as objects of combinatorial interest. This paper addresses a new problem by extending the closed string problem to the [Formula: see text]-closed string problem, for which a level of approximation is permitted up to a number of Hamming distance errors, set by the parameter [Formula: see text]. We address the problem of deciding whether or not a given string of length [Formula: see text] over an integer alphabet is [Formula: see text]-closed and additionally specifying the border resulting in the string being [Formula: see text]-closed. Specifically, we present an [Formula: see text]-time and [Formula: see text]-space algorithm to achieve this along with the pseudocode of an implementation and proof-of-concept experimental results.
Design and implementation of Virtual characters’ Expression of interactive storytelling

There have been giant leaps in the field of AI storytelling in the last ten years. The AI technology has provided PC gaming more opportunities to improve themselves at their own pace. While most of game virtual engine uses various modules, the sensitive information they collect raises concerns among the player community. As technology seeps into every aspect of PC gaming currently a central problem for AI-generated narrative is the difficulty in balancing the consistency and structure of the plot.This paper aims to discuss Propp’s theory analyzed Russian folklore enabled him to develop a total of 31 "functions" for interactive narrative in role-playing games, deeply discuss its realization, mechanism, and the new experiences of players in cognition and aesthetic. We therefore addressed two primary research questions. Existing architectures in virtual reality game engine system have been listed out. This section reviews the core components of story generation and presentation in AI- and computer-related storytelling. The following section focuses on outlines links between the algorithms deployed in AI-storytelling systems and the formalism of "traditional" narrative analysis and what the similarities and distinctions can tell us about the possibility of developing and enhancing AI-produced interactive stories.
Image Presentation

This chapter deals with image presentationImage presentation, a technique to present images to user after images are retrieved from an image database. Variety of ways of presenting images to users or visualising an image database are demonstrated to readers in this chapter. The chapter begins with simple caption browsingCaption browsing to category browsingCategory browsing which are very common on the web. More powerful and sophisticated content browsingContent browsing techniques are introduced in the next, including content browsingContent browsing in 3D, with focus andForce directed content browsing force directed content browsingContent browsing. The chapter is highlighted and concluded with Google image search which is a sophisticated application of variety of image presentationImage presentation techniques discussed earlier in the chapter.
Detection of Machinery Failure Signs From Big Time-Series Data Obtained by Flow Simulation of Intermediate-Pressure Steam Turbines

Abstract The periodic maintenance, repair, and overhaul (MRO) of turbine blades in thermal power plants are essential to maintain a stable power supply. During MRO, older and less-efficient power plants are put into operation, which results in wastage of additional fuels. Such a situation forces thermal power plants to work under off-design conditions. Moreover, such an operation accelerates blade deterioration, which may lead to sudden failure. Therefore, a method for avoiding unexpected failures needs to be developed. To detect the signs of machinery failures, the analysis of time-series data is required. However, data for various blade conditions must be collected from actual operating steam turbines. Further, obtaining abnormal or failure data is difficult. Thus, this paper proposes a classification approach to analyze big time-series data alternatively collected from numerical results. The time-series data from various normal and abnormal cases of actual intermediate-pressure steam-turbine operation were obtained through numerical simulation. Thereafter, useful features were extracted and classified using K-means clustering to judge whether the turbine is operating normally or abnormally. The experimental results indicate that the status of the blade can be appropriately classified. By checking data from real turbine blades using our classification results, the status of these blades can be estimated. Thus, this approach can help decide on the appropriate timing for MRO.
Paint to Better Describe: Learning Image Caption by Using Text-to-Image Synthesis

Image caption aims to generate accurate and informative description according to given image. Although numerous existing methods have been proposed to generate high-quality and semantically real text description by using Encoder-Decoder architecture, how to guarantee whether the generated description is informative enough remains very challenging. In this paper, we propose a symmetrical structure named “Paint to better describe” (PTBD) by learning text-to-image synthesis to address this problem. Our PTBD approach consists of three modules, an image caption module (ICM), an image regeneration module (IREM), and a visual semantic alignment module (VSAM). ICM uses transformer-based method to generate image caption, leveraging both visual contents and object tags from the image. IREM uses generative adversarial network to regenerate the image according to the generated description, which semantically aligns with it. VSAM keeps network to generate accurate and informative description by measuring whether the regenerated image has enough semantics and visual information. Through experiments on MSCOCO dataset demonstrate the effectiveness of PTBD over other representative methods.
Cryptocurrency Mining

This chapter is mainly concerned with outlining the process of cryptocurrency mining. There are dozens of altcoins which you could mine; however, Bitcoin by far is the most popular choice for cryptocurrency miners. This is primarily due to the advancement in technology concerning the hardware and software used to mine cryptocurrencies. Specialized hardware and software are now being designed for the sole purpose of mining Bitcoin. Miners have a variety of hardware and software to choose from depending on their mining strategy. This chapter introduces the basic concept of mining, its essential functions, the hardware and software required for mining, different methods of mining and the factors influencing mining.
Inducing gaze gestures by static illustrations

In gesture-based user interfaces, the effort needed for learning the gestures is a persistent problem that hinders their adoption in products. However, people's natural gaze paths form shapes during viewing. For example, reading creates a recognizable pattern. These gaze patterns can be utilized in human-technology interaction. We experimented with the idea of inducing specific gaze patterns by static drawings. The drawings included visual hints to guide the gaze. By looking at the parts of the drawing, the user's gaze composed a gaze gesture that activated a command. We organized a proof-of-concept trial to see how intuitive the idea is. Most participants understood the idea without specific instructions already on the first round of trials. We argue that with careful design the form of objects and especially their decorative details can serve as a gaze-based user interface in smart homes and other environments of ubiquitous computing.
A Novel Distributed Software Architecture for Managing Customer Behavior Data: A Case Study in Banking Sector

We propose a Lambda-based, distributed, big data analysis architecture explicitly designed for the banking sector within the scope of this study. The proposed architecture is designed to extract useful information from user behavior data. The results derived from user behavior data obtained from many sources aim to increase the performance and efficiency of digital banking applications. The prototype implementation of the proposed architecture is designed, developed, and implemented. Performance and scalability tests were carried out on the prototype application. The results obtained reveal the usability of the proposed architecture in the banking sector.
Research on information security user behavior path of agricultural logistics industrial park under big data environment

With the development of digital and information technology in logistics industry, big data has become one of the most important Means of production in logistics industry. It brings new impetus to the development of the logistics industry, but it also brings some challenges to the information security, especially the logistics industrial park which is supported by the information platform. This paper takes agricultural logistics industrial park as an example to analyze its information demand and information structure. And on this basis, through constructing the supply chain model in the logistics industrial park, TSP algorithm is used to solve the shortest path of the logistics business in the supply chain, its logistics operation must rely on the original supply chain shortest path structure. Taking the user behavior behind the logistics information security management as the carrier object, this paper constructs the variation user behavior entropy TSP model. The information security problem of the network is abstracted as the shortest path that does not repeatedly connect all nodes. Based on this, the user behavior path is simulated and optimized, and relevant conclusions are drawn.
Blockchain-Based Decision Tree Classification in Distributed Networks

In a distributed system such as Internet of things, the data volume from each node may be limited.Such limited data volume may constrain the performance of the machine learning classification model.How to effectively improve the performance of the classification in a distributed system has been a challenging problem in the field of data mining.Sharing data in the distributed network can enlarge the training data volume and improve the machine learning classification model's accuracy.In this work, we take data sharing and the quality of shared data into consideration and propose an efficient Blockchain-based ID3 Decision Tree Classification (BIDTC) framework for distributed networks.The proposed BIDTC takes advantage of three techniques: blockchain-based ID3 decision tree, enhanced homomorphic encryption, and stimulation smart contract to conduct classification while effectively considering the data privacy and the value of user data.BIDTC employs the data federation scheme based on homomorphic encryption and blockchain to achieve more training data sharing without sacrificing data privacy.Meanwhile, smart contracts are integrated into BIDTC to incentivize users to share more high-quality data.Our extensive experiments have demonstrated that the proposed BIDTC significantly outperforms existing schemes in constructed consortium blockchain networks.
Solar Nowcasting

With the increasing ubiquity of solar energy systems, solar nowcasting is needed to redress short-term power system imbalances emerging from solar energy integration and normalize electricity markets in near real time. In this chapter, we provide an overview of the applications, solar resource data, evaluation procedures, modeling methods, and emerging technologies in solar nowcasting.
HOPE-L: A Lossless Database Watermarking Method in Homomorphic Encryption Domain

Cloud computing has been widely adopted in the Internet economy; however, this poses numerous risks including illegal data copying and digital copyright infringement, leading to the challenge on protecting the security and copyright of key data stored in databases. In this paper, We propose a novel method, Lossless Database Watermarking in Homomorphic Encryption domain (HOPE-L), to address this challenge. Our method combines the database encryption (i.e., homomorphic encryption, order-preserving encryption) and information hiding (i.e., lossless databased watermarking) technologies. More specifically, the information hiding algorithm leverages the homomorphic properties of the homomorphic encryption algorithm to embed secret data. No distortion is introduced throughout the data embedding process. Therefore, we can embed the watermark into the encrypted data losslessly. The watermark can be used to authenticate the copyright, and the recipients can directly recover the original database without loss. Analysis and theorem prove that HOPE-L can achieve more embedding space and no distortion. Extensive experiments show that the whole operation process is time-efficient, the embedded watermark is robust, and HOPE-L performs more robust than the existing algorithms and can resist common database attacks.