Reviewerly/exHarmony huggingface dataset:
Scheduling of Scientific Workflow in Distributed Cloud Environment Using Hybrid PSO Algorithm

Cloud computing is a domain where distinctive administrations are given to clients over the Web. Task scheduling is one of the critical parts of cloud computing which enhances the execution of the cloud framework. Efficient task scheduling algorithm is one of the main challenges in cloud computing. Scheduling algorithm makes a specific task to get finished in conceivable least time. Task scheduling helps in accomplishing productive usage of resources. In this chapter, the authors generate population for Particle Swarm Optimization (PSO) using the Predict Earliest Finish Time algorithm (PEFT). The authors used makespan time and cost as parameters to evaluate the results of the proposed approach. Experimental results in this study demonstrate that the proposed approach outperforms the existing technique of scheduling using Particle Swarm Optimization algorithm. The results in this work proved that this model consumes less cost and execution time than the other existing approaches of scheduling using Particle Swarm Optimization algorithm.
Foresight: Outlook for the Legal Framework of the National Legal System in the Future

Purpose: The purpose of this study is a comprehensive scientific analysis of the possibility of using Foresight methods not only in economics, but also in law. Design/methodology/approach: In this paper the formal legal and comparative legal methods as well as the bibliometric analysis method, the target setting method, and the determination of the legal future of the national innovation system were used. Findings: Building the civil legal basis of the national innovation system is possible using not only the scenario method, but also using the Delphi method, which will smooth out the contradictions in subjective opinions (public and private investors). Using the foresight scenario method, we can state that it is necessary to build a normative vision of all-national- innovation-system-subjects interaction by creating a unified civil law framework that would satisfy all their interests. Following the development of the economy as well as the law obtained as a result of Foresight not only of the economy, but also of the law, modern Russia will be able to build a national innovation system that will compete in the world market and, moreover, protect the interests of subjects. Originality/value: The possibility of applying foresight methods in building the legal foundations of a national innovation system in modern Russia is being studied in this paper. The relevance of this study is that it is a top priority in Russia to develop and establish a competitive innovation system, which includes a system of organizations where activities are aimed at the production and commercialization of technologies. One of essential issues today in Russia is the creation of a set of financial, legal and social nature institutions which will ensure the interconnection of scientific, entrepreneurial, educational and non-profit organizations in all areas of society. Achieving goals is possible with the use of Foresight methods. Foresight stimulates the creation of the right vectors of economic development, law and public relations.
A New Adaptive Fuzzy Neural Networks Control Using Riccati-Like Equation for Controlling of Magnetorheological Fluid Device

A new proposed control based on the modified Riccati-like equation is developed in this study. The interval type 2 fuzzy model is applied and embedded in the controller for control the uncertainty and also support for calculation progress of the proposed control. To design of the control, the first sliding surface based on the conventional model is established and its elements using for combine with the other elements of the Riccati-like equation. The main input control includes equivalent control and robustness control. The equivalent control is found from the conventional analysis with the first sliding surface. However, the equivalence control is not sufficient for control the internal disturbance such as error approximation of the fuzzy model. Then the robustness control is designed including three components of PID control and matrices of the traditional model of Riccati equation. This approach has relation with the H infinity technique. Hence, the stability of the system is proved following the technique through the Lyapunov function. Model of the proposed control is evaluated by applying to the seat suspension system. In the suspension, a magneto-rheological (MR in short) damper with its stiffness following the variation of applied current is used. The main input control is applied to change the value of the applied current. Belong to the magnitude of vibration, the current is also changed. The results of simulation of the proposed control are shown that the proposed control is better for control real system.
Protecting from Lunchtime Attack Using an Uncalibrated Eye Tracker Signal

Eye movement-based biometric has been developed for over 15 years, but for now - to the authors’ knowledge - no commercial applications utilize this modality. There are many reasons for this, starting from still low accuracy and ending with the problematic setup. One of the essential elements of this setup is the calibration, as nearly every eye tracker needs to be calibrated before its first usage. This procedure makes any authentication based on eye movement a cumbersome and lengthy process.
Improving student learning outcomes using research-based think pair and share models

Abstract The purpose of education is not only the provision of subject matter in the classroom, but can provide an invitation to students to find and build their own knowledge through the experiences gained. Knowledge gained from oneself is easier for students to remember than is obtained from others. This required an effective learning model to improve learning outcomes. Research-based learning trains students to solve problems, and make decisions and develop critical thinking skills by connecting existing theories. The purpose of this study is to examine the effectiveness of research-based Think Pair and Share (TPS) learning on student learning outcomes. The research method uses the correct experimental design with the posttest-only control design. The results showed student learning outcomes using the TPS model based on research had high results compared to normal learning.
Anveshan – A Model for Search

Search and the engines performing them have witnessed significant transformations over time. While the web wants to be semantic, search demands to be customized, and every attached entity wants to learn; the challenges magnify leaving behind intricate woven questions to be addressed and resolved. The concept of machine learning challenges the designer to exploit the full potential of a machine capability. Through this paper, we propose a model – Anveshan, which uncovers the said challenges by inducing a learning model. The model carries hooks to establish the parameters, uses the user-defined initial weights, and the humongous data available on the web. It then prepares a list of results based on the considered parameters. The results learn and rearrange based on user clicks and preferences. The model also produces a bit string for the considered hooks, indices it, and makes it agent readable. The intention to create a universal bit string is to make it machine-understandable and contribute towards the semantic web. The paper discusses the model and then presents a specific case study in results and discussion. Anveshan works towards creating a learnable exploration space as per the user specifications. Anveshan is a fork-able model overlaid with a custom environment. The results are promising to guide towards a better search space.
Developing an AR-Based Ubiquitous Learning System for a Smart Campus

With the fast development of artificial intelligence and IoT technologies, ubiquitous learning has become a promising learning paradigm. In AIoT learning environments, it is desirable that the rich learning content is retrieved and demonstrated according to the learners’ requirements and the environmental contexts, such as learners’ profiles, location and time. To provide an adaptive context-aware ubiquitous learning service, our idea is to combine LBS, sensors and AR technologies with ubiquitous learning applications to provide multi-disciplinary learning. Therefore, the real-life campus is used as a learning stage of a different learning style, so that students can immerse themselves in learning anytime, anywhere. In this research, taking the campus of Asia University as an example, we firstly propose a campus ontology building algorithm to construct a campus ontology. Based upon the ontology and the deployed sensors, teachers can design AR-based learning content for points of interest. Using our system, students can obtain adaptive learning content according to the requirements and their own characteristics. Experimental results show that the proposed approach can increase the learning performance of students.
Towards Wider Adoption of Continuous Authentication on Mobile Devices

Continuous Authentication (CA) is the process of constantly checking for the authorized user’s presence, which brings unique advantages and disadvantages. CA is more secure and facilitates schemes with multiple levels of authentication security; however, it can consume more resources and cause user anxiety about privacy. In this chapter we seek to understand the practical aspects of CA; in particular, user perception and resource consumption. To gauge user perception towards CA, we conducted a survey with roughly 500 respondents. We found that users desire multiple levels of authentication security. Furthermore, users are willing to adopt CA for mobile devices. We then analyzed factors like security awareness, gender, and mobile device OS, to draw statistically significant conclusions regarding their effect on users’ willingness to adopt CA, and user perceptions about CA. We also compare between biometric modalities based on their resource consumption, as measured by their Resource Profile Curve (RPC). This Curve reveals the trade-off between authentication accuracy and resource usage, and is helpful for different usage scenarios in which a CA system needs to operate. In particular, we explain how a CA system can intelligently switch between RPCs to conserve battery power, memory usage, or to maximize authentication accuracy. We argue for the importance of understanding user perceptions and using RPCs to guide the development of practical CA systems.
A Comparative Study of Block-BasedCopy-Move Forgery Detection Techniques

The credence of digital images in terms of their authenticity and integrity especially in medical records, scientific publications, celebrity magazines, news items, political campaigns or when presented as a proof in a courtroom is being questioned as easy to use and cost-effective image editing tools with malicious intend can be used to make copy-move forgery to these images. Consequently, efficient, fast, robust, and computer vision-based image forgery detection technique have become essential to ensure digital image authenticity and integrity. The current work surveys recent progress of copy-move forgery detection technologies to identify factors that impede forgery detection and reviews accuracy of different proposed methods. The work presents a broader classification of different image forgery detection techniques along with their pros and cons. Further, a comprehensive study of block-based copy-move forgery detection methods has been made and discussed with respect to performance evaluation parameters including feature vector length, block size, precision, recall, and F1 score.
Semi-supervised Sparse Representation Classification with Insufficient Samples

Sparse representation based classification (SRC) has gained great success in image recognition. In this paper, we propose a generalized CRC method, essentially a SRC using Euler sparse representation, for image classification. To be specific, our CRC first maps the image into a complex space by Euler representation with a negligible effect for outliers and illumination, and then performs a complex SRC method. We also present a fast and efficient algorithm to solve Euler SRC subproblems. Extensive experimental results illustrate that our method outperforms traditional SRC methods and achieves better performance for image classification.
Design and implementation of a system that preserves the confidentiality of stream cipher in non-linear flow coding

In this paper, implementation of stream cipher algorithm based on nonlinear combination generator for any given sequences is introduced. The main contribution of this work is to enhance the security of this generator and level of complexity is added using logic functions. The proposed scheme was measured through a series of randomness tests.
Implementation of Accelerating Video Preprocessing based on ZYNQ Platform Resource Management

Abstract Embedded AI rised in recent years, AI algorithm deployment platforms need strong computing power, but embedded systems focus on the balance between performance and cost. The contradiction has become a great challenge for the development of embedded AI. This paper analyzes the “ARM+FPGA” architecture and NEON register resources of the ZYNQ series chips introduced by Xilinx, and proposes a method to reasonably schedule the above resources to accelerate video preprocessing when it is used as an edge processing platform for intelligent monitoring terminals. This method makes the video intelligent analysis process of the monitoring terminal achieve real-time.
Online approach for Diabetes Diagnosis and Classification with Expert System Modules using Fuzzy Logic

Fuzzy Logic is profoundly reasonable and relevant in structuring frameworks in the field of medical; particularly in the various disease diagnosis procedures and in the preparation of treatment strategies. Fuzzy Expert Systems can deal with imprecise, incomplete and uncertain information which comes in the procedure of disease diagnosis and disease treatments. In the proposed work, where a web-based fuzzy expert system modules were designed and developed which will assist to diabetics, diabetologists, practitioners and experts for diabetes diagnosis and its classification. The System's knowledge base was designed by consulting diabetologists and diabetes patient's data was collected from the hospital. It is an online rule-based fuzzy expert system, which was designed with the aim to reach people with easy access through the internet. The proposed work exhibits architecture, design, and development of a fuzzy logic based expert system for diabetes disease diagnosis and classification. An open source programming environment and libraries were considered and utilized in the proposed system development.
Robust particle filter algorithms for one‐step randomly delayed measurements and heavy‐tailed noise

The development of particle filters for emerging data transmission applications is essential to address network-related issues. In this Letter, a robust particle filter algorithm for non-linear systems with one-step randomly delayed measurements and outlier corrupted measurement noise is investigated. To address the issue of measurement outliers, a Student's-t distribution is utilised to model the measurement noise. For the random delay uncertainties, a Bernoulli random variable is employed, based on which the likelihood function is transformed into an exponential multiplication form. The filter state estimation is obtained by Bayes's theorem and using variational Bayesian approach, the latent random variables and Bernoulli random variable are estimated together. The superior performance of the proposed method is verified through a typical example by comparison with existing particle filters that consider random measurement delay or heavy-tailed measurement noise.
Analysis of the Innovative Application of Virtual Reality Technology in Physical Training of Colleges

In order to realize the effectiveness and scientificity of college physical training, we can integrate modern technology into college physical training, and promote the intellectualization and standardization of college physical training. In view of many problems that need to be solved in college physical education training, this paper designs a physical education training virtual simulation system by combining the application advantages of computer virtual reality technology, introduces the system functions and overall framework in detail, designs the corresponding functional modules, and expounds the advantages of computer virtual reality technology in college physical education training in detail. The research results show that it is necessary to apply computer virtual reality technology in college physical training, which can effectively promote the development of college physical training teaching, and promote college students to strengthen physical training.
Implementation of Image Encryption based on Chaos-IWT– An Image security

Nowadays, digital image communication through internet has become essential in public network as images can give a clear vision than describing something through text. Data such as medical images, military images, etc. which are highly prone to intruders are mandatory to be maintained confidential to transmit and receive data safely without being intruded by unauthorised persons. This technique can be provided by encrypting these digital images through cryptographic techniques. The proposed image encryption is performed in the transform domain, where Integer Wavelet Transform is adopted to separate the image into approximation coefficients and detailed coefficients. In IWT, most of the significant pixels are in the low-frequency band where confusion and diffusion is performed with chaotic logistic keys. The experimental results achieved entropy of 7.97 and near-zero correlation to verify it is able to resist any statistical attacks.
Domain Generalization via Optical Flow: Training a CNN in a Low-Quality Simulation to Detect Obstacles in the Real World

Many applications in robotics and autonomous systems benefit from machine learning applied to computer vision, but often the acquisition and preparation of data for training is complex and time-consuming. Simulation can significantly reduce the effort and potential risk of data collection, thereby allowing faster prototyping. However, the ability of a data-driven system to generalize from simulated data to the real world is far from obvious and often leading to inconsistent real-world results. This paper demonstrates that some properties of optical flow can be exploited to address this generalization problem. In this work, we train a neural network to detect collisions with simulated optical flow data. Our network, FlowDroNet, is able to correctly predict up to 89 percent of the collisions of a realworld dataset and easily achieves a higher detection accuracy when compared to a network trained on a similar dataset of realworld collisions. We release our code, models and a real-world dataset for collision avoidance as open-source. We also explore the relationship between the complexity of the input information and the ability to generalize to unseen environments, and show that in some situations, optical flow is an interesting tool to bridge the reality gap.
Proofs and surfaces

A formal sequent system dealing with Menelaus' configurations is introduced in this paper. The axiomatic sequents of the system stem from 2-cycles of Δ-complexes. The Euclidean and projective interpretations of the sequents are defined and a soundness result is proved. This system is decidable and its provable sequents deliver incidence results. A cyclic operad structure tied to this system is presented by generators and relations.
Unifying Metadata-Based Storage Reconstruction and Carving with LAYR

Storage resources are usually organized in abstraction layers in computing systems where higher level storage (e.g. files or file systems) is constructed from lower level storage (e.g. disk volumes). Many forensic storage reconstruction techniques exist that gather data at lower layers and interpret this data to reconstruct higher layers. On the one hand, there are metadata-based reconstruction techniques that interpret metadata structures to precisely reconstruct upper layer content. On the other hand, there are pattern-based techniques (carving) that focus mainly on deleted files that cannot be reconstructed by other methods. Instances resembling the former approach are Carrier's The Sleuth Kit (TSK) as well as many commercial tools, while the latter approach is used by file carvers like Foremost and Scalpel. Based on a formalization of storage abstraction layers, we show that all these techniques can be unified within a modular reconstruction framework. We define composition operators that allow to precisely express complex reconstruction tasks that involve both metadata-based and pattern-based techniques and allow to combine their respective strengths seamlessly in forensic analysis. We present LAYR, an implementation of our approach and show that it can automatically and reliably combine different reconstruction approaches.
Modeling and verification of solar panel mesurement for Household scale

Abstract This research aims to build a modeling of solar panels that will be verified with the results of experiment. The measurements of the implementation of solar panels that have been installed at the household scale. Solar photovoltaic (PV) produces power that is affected by solar irradiation and ambient temperature. These two parameters have been the input values in solar panel modeling. Modeling PV cell simulationwas using Simulink Matlab. This modelingused two solar panels with a total capacity of 200 W which are installed in parallel and share the same voltage. This model is able to simulate the I-V characteristic curve and the P-V characteristic curve. Based on the results of measurements of the implementation of solar panels that have been installed in Sukoharjo, Central Java at the longitude location point 10.870902 and latitude -7.603960 with the capacity and electrical characteristics data of the solar panels that were the same as the modeling, the Root Mean Square (RMSE) results of 0.13784 and 0.31305.
Derivation of an Agile Method Construction Set to Optimize the Software Development Process

Today, IT and especially the development of customer-focused software, has become one of the most important elements for a company to remain competitive. The primary purpose of this article is to determine whether it is possible to improve the agile development of a company by adjusting its software development process in terms of cultural, technical, and managerial dimensions. Based on a literature analysis and practical experience in the company, different influencing factors and parameters, which affect the value creation throughout the software development process of a company were derived and clustered. Out of these clusters, a framework (Agile Method Construction Set) based on a Microsoft Excel questionnaire was created in order to analyze and identify optimization potential in the development process of a company. This construction set was adapted to meet the requirements of the development department of a large Swiss insurance company in order to validate and test the framework with real data.