Reviewerly/exHarmony huggingface dataset:
MLOA: Meta Heuristic Approach for Load Balancing in Cloud Computing

Scheduling performs a vital function in Cloud Computing. The approach the jobs are allotted to the available sources defines the overall performance and usage performance. Traditional scheduling algorithms are easier to implement but doesn't hold good for higher complexity problems. Meta-heuristic algorithms are primarily based upon application of operators together with transition, assessment and resolution for imparting efficient scheduling techniques. The assessment of Meta-heuristic algorithms are carried out based totally on computation time, useful resource utilization, response fee and scheduling fee. In this paper we deliver a quick assess of existent meta-heuristic algorithms. Also we offer a task-scheduling algorithm. The suggested scheduling algorithm is as paralleled with scheduling algorithm primarily based on Particle Swarm Optimization.
Regional University Partnership for Sustainable Development in the Age of Digital Technologies

In the modern world, the sustainable development of higher education institutions is determined by their ability to integrate into the global digital knowledge. The study aimed to find out the needs of faculty in digital technologies for teaching and research. The study also aimed to find out the faculty's opinion about the building of Regional Universities' Partnership for Technical and Vocational Education and Training (TVET) in Information and Communication Technologies (ICT). The practical implementation of the study could be launching a regional universities' partnership and the development of a regional resource center, which will serve for internationalization, intercultural and interpersonal exchange, and provide long-term benefits to partner universities to improve their educational potential. Due to the uneven development of digital technologies in higher education, these conclusions apply to universities not only in Central Asia but throughout the Eurasian region.
Heterogeneous Fusion of an IMM Track with Measurements from Different Sources

The problem addressed in this paper is to track a maneuvering target from multiple sources, namely the source <tex>$\mathbf{S}_{\mathrm{A}}$</tex> and source <tex>$\mathrm{S}_{\mathrm{B}}$</tex>. Source <tex>$\mathbf{S}_{\mathrm{A}}$</tex> sends a target track generated by its local Interacting Multiple Model (IMM) estimator to the Fusion Center (FC), whereas <tex>$\mathrm{S}_{\mathrm{B}}$</tex> provides its measurements to the FC. The objective of the FC is to perform heterogeneous fusion of the <tex>$\mathbf{S}_{\mathrm{A}}$</tex> IMM track and <tex>$\mathrm{S}_{\mathrm{B}}$</tex> measurements. This problem cannot be solved by the existing information decorrelation algorithm [6], which works only when a (single model) Kalman filter is used in the <tex>$\mathrm{S}_{\mathrm{A}}$</tex> local tracker (suitable for tracking a non-maneuvering target). To cope with the commonly used IMM estimator (assumed deployed in <tex>$\mathrm{S}_{\mathrm{A}}$</tex>), this paper will introduce the cumulated measurement information concept, and develop the IMM Cumulated information Fusion (IMM-CFusion) algorithm. It evaluates the cumulated <tex>$\mathbf{S}_{\mathrm{B}}$</tex> measurement in the information space with multiple models, and performs fusion with the <tex>$\mathbf{S}_{\mathrm{A}}$</tex> IMM track state (without knowing <tex>$\mathbf{S}_{\mathrm{A}}$</tex> IMM &#x201C;inside information&#x201D; on model matched state estimates). Simulation tests are conducted to demonstrate the performance of the IMM-CFusion.
The Construction and Research of the Enterprise Financial Ecosystem Model Based on Blockchain

Blockchain technology is applied in many fields and industries such as economy and management. Financial ecosystem is a financial management system model based on the natural ecosystem. It links the internal and external environment of the enterprise development and various departments and institutions, and has a certain promotion effect on improving the financial management level of the enterprise. This paper constructs the financial ecosystem model based on blockchain, analyzes the problems existing in the application of blockchain and puts forward suggestions for its improvement.
Remote Sensing Image Super-Resolution Using Residual Dense Network

Jayanarayan, Abhijith Sowmya, V. Soman, K. P.Image super-resolution (SR) is a wide research topic, as it has found multiple applications in different fields. We implement image super-resolution for satellite images using a residual dense network (RDN). RDN is a CNN-based model, but unlike most CNN-based super-resolution models, it utilizes the hierarchic features from the input low resolution (LR) images and combines both the specific and general features present in the image, therefore resulting in a better performance. The novelty of our work lies in two aspects. First, we apply the residual dense network to remote sensing data to obtain higher structure similarity index metric (SSIM) and peak signal-to-noise ratio (PSNR) values than the existing models. Second, we use transfer learning due to the lack of training samples in remote sensing domain. Our RDN is first trained using an external dataset DIVerse2K (DIV2K). This model is then used to obtain high-resolution(HR) images of the remote sensing U.C Merced dataset, and the corresponding PSNR and SSIM values are computed for different scaling factors such as $$\times $$2, $$\times $$4 and $$\times $$8. The experimental results obtained using the proposed work demonstrates the better performance of RDN for the super-resolution of remote sensing images, when compared to the existing methods like super-resolution generative adversarial network (SRGAN) and transferred generative adversarial network (TGAN).
Educational Model of Innovation and Entrepreneurship Based on the Concepts of OBE and MOOC

With the continuous advancement of information technology and the continuous deepening of education reform, society has set higher requirements for higher education; especially under the role of rapid social and economic development and increasing demand for innovative talents, people’s education for innovative and entrepreneurial education in higher education. The focus of reform is getting higher and higher. Especially in recent years, due to the constant influence of OBE and MOOC concepts, people have begun a new exploration and research on innovative entrepreneurship education models. The purpose of this article is to carry out in-depth exploration and research on the innovative and entrepreneurial education model of Chinese universities under the guidance of the OBE and MOOC concepts, to promote the continuous reform and innovation of this educational model, and to improve students’ comprehensive entrepreneurial ability. This article first outlines the basic connotation of the OBE concept and the MOOC concept, and then conducts an in-depth analysis of the connotation and main characteristics of the innovation and entrepreneurship education model in Chinese universities, and then analyzes the necessity of the innovation and entrepreneurship education model reform based on this; Finally, combined with relevant data, this paper analyzes the main problems of the current innovation and entrepreneurship education model in China, and proposes relevant solutions to these problems. The experiments in this paper show that there are many problems in the innovation and entrepreneurship education model in China, which require long-term efforts.
Performance of Renewable Electricity Generation in the German Power Grid 2015 to 2018

The purpose of this paper is to investigate the performance of renewable energy systems (RES) in the German power grid. Installed capacities and consequences of the intermittency of RES electricity are compared to the demand. Complete grid data for Germany on a 15min time grid are analyzed for the years 2015 to 2018 with SPSS and MATLAB scripts. The capacity factors of RES-systems are determined and found to be in the 10% to 20% range. The probability distributions of electricity demand and available RES-electricity differ qualitatively and the correlation of both quantities is low. To investigate potential back-up capacities for RES-electricity, duration and the frequency of events were determined, when renewables electricity production is below certain thresholds of the total demand. Around 200 such events occur per year, their average duration is around 8h and longest phases observed are around 40h.
Exploiting the Momentary Dependence of Radar Observations for Non-Cooperative Target Recognition

Multiple radar sensors can be used in collaboration to detect targets in an area of surveillance. In this paper, we consider a case, in which a target is detected by a network of radars producing multiple observations of the radar signature of the target during a short time window. Given that this time window is sufficiently narrow, the observations have a dependence between them momentarily related to the change in the orientation of the target. We propose the fusion of these interdependent observations to aid target identification by forming a joint multi-dimensional histogram of the radar cross section (RCS). In addition, we investigate the criteria for windowing the observations to ensure adequate interdependence. We present a case study to demonstrate the ability of the proposed approach to distinguish between different targets using the measured RCS collected by a multi-radar surveillance system. Based on the experiment, we analyze the criteria for the dynamic windowing and discuss the computational requirements of the proposed concept.
Voice Conversion by Dual-Domain Bidirectional Long Short-Term Memory Networks with Temporal Attention

Voice conversion(VC) is a method for seeking to convert one speaker's voice into another person's voice while maintaining the content unchanged. One of the key steps is to construct a mapping of features from the source speaker to the target speaker. Given the strong ability to model contextual information, bidirectional long short-term memory network (BLSTM) is usually taken as the mapping tool. In this paper, an improved version of BLSTM, dual-domain BLSTM, is considered as a baseline where dynamic time warping (DTW) is conventionally taken as a tool to align speech frames. In order to alleviate the negative impacts of temporal misalignment in DTW, a casual attention mechanism is introduced to improve the dual-domain BLSTM. Experiments demonstrated the effectiveness of the proposed approach by yielding lower mel-ceptral distances and higer MOS scores than the baselines.
Distributed Deep Neural Network Training with Important Gradient Filtering, Delayed Update and Static Filtering

With the increasing number of computing nodes in current computer clusters, the performance of large-scale deep neural network training is essentially limited by the communicational cost, especially for transferring gradients among nodes during iteration. In this paper, three methods are proposed to reduce the communicational cost: important gradient filtering, delayed update and static filtering. Important gradient filtering algorithm selects the most important gradients to reduce the size of gradients to be transferred and help convergence. While delayed update algorithm significantly reduces the gradient broadcasting time. Static filtering filters the gradient with very small variance. Results show that a combination of the proposed methods achieves 2.91× to 5.58× communication cost reduction in a cluster with inexpensive commodity Gigabit Ethernet interfaces.
Cost Management Model Based on Multidimensional System

China’s economy has maintained medium and high speed development, and market competition is very fierce. Enterprises must change their management thinking. From managing products to operating customers, the customer relationship management system is a tool that helps SMEs optimize customer resources, improve customer management capabilities, and improve SME management Horizontal information system. As the current multi-dimensional system management effect evaluation fails to adopt a scientific evaluation method, this article explains the concept of multi-dimensional management of distribution network construction projects based on traditional project management theory. Based on the existing multi-dimensional management of distribution network construction projects in China, Analysis of the problem, put forward the idea of systematically establishing a multidimensional management system. The system chose the mature Microsoft VS.NET integrated development environment to develop the system. The MVC theory was integrated into the system in the design, which realized the layering of the program, which made the program portable and reduced the difficulty and cost of maintenance. By using the UML unified modeling language to design the system’s business flowchart, use case diagram, etc., the business and technical specifications of the system design are clearly presented. In addition to the basic daily management functions of small and medium-sized enterprises, the system also uses the open source KETTLE tool to mine the data, complete the data transfer, conversion, and loading, build a new multidimensional analysis model, and implement business intelligence data analysis functions for business management. Provide a highly reliable source of data. Provides support for system data storage by using SQLSERVER 2005 data. This article mainly uses the analytic hierarchy process to analyze the cost of multi-dimensional systems to obtain weight values to determine each importance. Through experiments, it is found that in multi-dimensional system cost management, management costs are prominently important, and the management cost situation in all indicators of multi-dimensional systems Management has the greatest impact, followed by engineering management fees, physical costs, and R & D expenses. Therefore, in multidimensional system cost management, while focusing on engineering management fees, physical costs, and R & D expenses, each organizational unit should focus on strict management costs, check. Project management fees, physical costs, and R & D expense management fees.
Robust state estimation with trajectory shape constraints

In some tracking applications, target states are subjected to a trajectory shape constraint, where only the trajectory shape is known a priori. By incorporating the trajectory shape constraint, the tracking performance can be improved. However, the existing state estimation methods with trajectory shape constraints are either of high computational load or of poor robustness due to the nature of the existing constraint models. In this study, in order to improve the robustness of the state estimation method with trajectory shape constraints, a generalised model for the trajectory shape constraint imposed by a straight trajectory is proposed. Three unknown general trajectory parameters of the trajectory are treated as states to be estimated along with the target state. Then, pseudo-measurements are constructed to incorporate the constraint information into estimators. For non-manoeuvring targets, a robust trajectory shape constraint filter is developed. The sequential measurement processing scheme is employed to reduce the computational load. A corresponding filter initialisation method is derived. While for manoeuvring targets, the proposed constraint filters are embedded into a conventional interacting multiple model estimator as sub-filters to handle the manoeuvring target tracking problem with a trajectory shape constraint. Monte-Carlo simulation results illustrate the effectiveness and robustness of the proposed algorithms.
Architecture design in private Cloud Computing

The Department of Information Networks at the Faculty of Management Science and Informatics University of Žilina operates a private Cloud Computing - CC Open Stack system that provides the type of service Infrastructure as a Service, the category Computing as a Service. IaaS is used for teaching and research purposes. The interest of the service using has gradually increased for all user groups, such as teachers, researchers and students. System operation, including user requirements, is provided by the administrator. Increasing user requests increase the demands on the cloud administration and management, including the required Virtual Machines configurations creating. This has led to the search of a new solution for the administration, management and use of CC in the teaching and research processes. The approach to the solution was inspired by Alan Kay's quoting “A successful technology creates problems that only it can solve.” The problem brought by the cloud is solved by additional CC functionalities. An architectural approach to the development of new functionalities and their implementation in the Cloud Administration System CAS is the content of this contribution.
Sequential Multi-Kernel Convolutional Recurrent Network for Sentiment Classification

The emergence of deep learning as a commanding technique for learning heterogeneous layers of feature representations have consequently substituted traditional machine learning algorithms which are generally poor in analyzing compound sentences. Additionally, convolutional and recurrent neural networks have auspiciously yielded state-of-the-art results in sentiment classification and Natural Language Processing (NLP). In this paper, a deep sentiment representation model through the combination of multiple Convolutional Neural Networks (CNN) kernels with Long Short-Term Memory (LSTM) is proposed for sentiment classification. Our model gains word vector representation using pre-trained Global Vectors for Word Representation (GloVe) embeddings, thereafter used as input to the CNN layer which extracts higher local text representations. Finally, Bidirectional LSTM (biLSTM) generates sentiment classification of sentence representation based on context dependent features. Our combined approach of CNN and biLSTM was experimented using the Stanford Large Movie Review Dataset (IMDB) and Stanford Sentiment Treebank Dataset (SSTB) for binary classification. The evaluation achieves outstanding results in outperforming several existing approaches with 90.4% accuracy on the Stanford Sentiment Treebank dataset and 94.8% accuracy on the Stanford Large Movie Review dataset. These results are achieved with a drastic reduction of model parameters and without a pooling layer in the CNN architecture, helping to retain local and structural information in comparison to other existing deep neural network frameworks.
Towards a Face Recognition Model Analyzer

Machine learning allows computers to learn a model for a given task, such as face recognition, with a high degree of accuracy, using data. However, after these models are generated, they are often treated as black boxes by developers and the limitations of a model are often unknown to end-users. To address these issues, this paper introduces the Face Recognition Model Analyzer (FRMA) ontology and a semantically enabled Resultset viewer. Together these resources describe image features relevant to face recognition and allow users to explore how well a face recognition model does at classifying images that contain an image feature. We evaluated the ontology and Resultset viewer by loading in the Labeled Faces in the Wild [1] dataset, enriching the images with image tags [2], and exploring two popular face recognition models, Facenet [3] and DLib [4]. Using the FRMA ontology and the Result-set viewer, we discovered several classic face recognition model limitations, such as trouble classifying images with occlusions. This evaluation shows that these resources can discover model limitations which can make face recognition model reuse easier for future users.
Defining admissible rewards for high-confidence policy evaluation in batch reinforcement learning

A key impediment to reinforcement learning (RL) in real applications with limited, batch data is in defining a reward function that reflects what we implicitly know about reasonable behaviour for a task and allows for robust off-policy evaluation. In this work, we develop a method to identify an admissible set of reward functions for policies that (a) do not deviate too far in performance from prior behaviour, and (b) can be evaluated with high confidence, given only a collection of past trajectories. Together, these ensure that we avoid proposing unreasonable policies in high-risk settings. We demonstrate our approach to reward design on synthetic domains as well as in a critical care context, to guide the design of a reward function that consolidates clinical objectives to learn a policy for weaning patients from mechanical ventilation.
Application of Blockchain Technology in Pet Medical Industry

Abstract According to the development status of the pet diagnosis and treatment industry, it can be found that the industry is still on the rise at present, but the island phenomenon of information in the traditional medical industry information system also exists. The authenticity and timeliness of information interaction cannot be guaranteed, and Problems such as difficulties about industry chaos and data access also limit the development of the industry. This paper puts forward a model of pet medical data sharing based on block chain, and it has many characteristics, such as safety reliable, decentralized applies to resolve such problems as pet medical industry data sharing. The existing consensus mechanism has been improved, and then the solutions of the model and the possible impacts are discussed and analyzed by comparing the problems of the industry.
Design of Power Efficient Bit Serial Finite Field GF(2<sup>m</sup>) Multiplier

This paper presents the VLSI implementation of a Bit serial multiplier for multiplication in binary Finite Field GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> ) and is based on Shift and Add algorithm. The polynomial base representation is used. The multiplier generates the result by shifting one of the multiplicand. Form bit inputs; it produces output after m-1 clock cycles. The value of m can vary up to 283 bits. Multiplier is designed using Verilog HDL. The designed Multiplier is simulated using NG Spice. The proposed design is synthesized in CADENCE Encounter. Using the low power VLSI methodology power consumption is reduced. Proposed multiplier is implemented using CADENCE virtuoso tool set using GPDK45nm technology.
Random Signal Estimation by Ergodicity associated with Linear Canonical Transform

The linear canonical transform (LCT) provides a general mathematical tool for solving problems in optical and quantum mechanics. For random signals, which are bandlimited in the LCT domain, the linear canonical correlation function and the linear canonical power spectral density can form a LCT pair. The linear canonical translation operator, which is used to define the convolution and correlation functions, also plays a significant role in the analysis of the random signal estimation. Firstly, the eigenfunctions which are invariant under the linear canonical translation and the unitarity property of it are discussed. Secondly, it shows that all of these connect the LCT sampling theorem and the von Neumann ergodic theorem in the sense of distribution, which will develop an estimation method for the power spectral density of a chirp stationary random signal from one sampling signal in the LCT domain. Finally, the potential applications and future work are discussed.