Reviewerly/exHarmony huggingface dataset:
SMA-LinR: An Energy and SLA-Aware Autonomous Management of Virtual Machines

Cloud datacenters consume enormous energy and generate heat, which affects the environment. Hence, there must be proper management of resources in the datacenter for optimum usage of energy. Virtualization enabled computing improves the performance of the datacenters in terms of these parameters. Therefore, Virtual Machines (VMs) management is a required activity in the datacenter, which selects the VMs from the overloaded host for migration, VM migration from the underutilized host, and VM placement in the suitable host. In this paper, a method (SMA-LinR) has been developed using the Simple Moving Average (SMA) integrated with Linear Regression (LinR), which predicts the CPU utilization and determines the overloading of the host. Further, this predicted value is used to place the VMs in the appropriate PM. The main aim of this research is to reduce energy consumption (EC) and service level agreement violations (SLAV). Extensive simulations have been performed on real workload data, and simulation results indicate that SMA-LinR provides better EC and service quality improvements.
Noisy Bayesian optimization for variational quantum eigensolvers

The variational quantum eigensolver (VQE) is a hybrid quantum-classical algorithm used to find the ground state of a Hamiltonian using variational methods.In the context of this Lattice symposium, the procedure can be used to study lattice gauge theories (LGTs) in the Hamiltonian formulation.Bayesian optimization (BO) based on Gaussian process regression (GPR) is a powerful algorithm for finding the global minimum of a cost function, e.g. the energy, with a very low number of iterations using data affected by statistical noise.This work proposes an implementation of GPR and BO specifically tailored to perform VQE on quantum computers already available today.
Blockchain Based Architecture for Certificate Authentication

Certificate Verification is the process of verifying a certificate issued by an institution and verifying that it is genuine and authentic Certificate issuing authorities appear to have compromised for student data protection certificates. Data is being disposed of and dishonest peoples can use this for their advantage. In preparation for solving this problem of forge certificates, the digital certificates are based on the BlockChain term. It is one of the recent technology which is able to be affected for data security. To verify the fidelity of those data, we relate a blockchain hash affirmative method. This prominence of blockchain helps to stricken the thing of the fake certificates. Authors have used two platforms of blockchain ehtereum and bitcoin for certificate verification. But those two platforms are permission-less blockchain where anyone can join the network. For making it more secured we can use the Hyper-Ledger platform which is more secured and only the present participants can join the network. This application demonstrates that blockchain-based hash validity has been developed to identify baleful and temporary changes in data. The technique that is discussed is that the main data is stored severally from the blockchain, then it allows a sense accessory and a hash of those data that presented to the blockchain, The original information is going to be verified against the hash in the block-chain at any moment.
A Challenge for Contrastive L1/L2 Corpus Studies: Large Inter- and Intra-Individual Variation Across Morphological, but Not Global Syntactic Categories in Task-Based Corpus Data of a Homogeneous L1 German Group

In this paper, we present corpus data that questions the concept of native speaker homogeneity as it is presumed in many studies using native speakers (L1) as a control group for learner data (L2), especially in corpus contexts. Usage-based research on second and foreign language acquisition often investigates quantitative differences between learners, and usually a group of native speakers serves as a control group, but often without elaborating on differences within this group to the same extent. We examine inter-personal differences using data from two well-controlled German native speaker corpora collected as control groups in the context of second and foreign language research. Our results suggest that certain linguistic aspects vary to an extent in the native speaker data that undermines general statements about quantitative expectations in L1. However, we also find differences between phenomena: while morphological and syntactic sub-classes of verbs and nouns show great variability in their distribution in native speaker writing, other, coarser categories, like parts of speech, or types of syntactic dependencies, behave more predictably and homogeneously. Our results highlight the necessity of accounting for inter-individual variance in native speakers where L1 is used as a target ideal for L2. They also raise theoretical questions concerning a) explanations for the divergence between phenomena, b) the role of frequency distributions of morphosyntactic phenomena in usage-based linguistic frameworks, and c) the notion of the individual adult native speaker as a general representative of the target language in language acquisition studies or language in general.
Simple shapes guide visual attention based on their global outline or global orientation contingent on search goals.

It is still unclear which features of a two-dimensional shape (e.g., triangle, square) can efficiently guide visual attention. Possible guiding features are edge orientations (single oriented shape edges; e.g., verticals during search for squares), global outlines (combination of the target edges; e.g., squares), or global orientations (specific orientations of global outlines; e.g., squares but not diamonds). Using a contingent-capture protocol, we found evidence for task-dependent guidance by the global shape outline and the global shape orientation. First, if participants searched for a shape (an equilateral triangle) independent of its pointing direction, cues with the same global shape outline as the target captured attention, even without sharing any edge orientations with the target. Second, however, if a shape's specific pointing direction was task-relevant, attentional guidance changed to the specific orientation of the global shape. Our results show that the global shape outline and the global shape orientation can both guide visual attention, contingent on the nature of the shape and the current search goals. We discuss differences between shapes (equilateral triangles and isosceles trapezoids) considering models of shape perception and conclude with a critical review of the contingent-capture protocol as a complementary method to visual search protocols. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
Medical students’ satisfaction on online flipped learning by learning styles

Purpose: The purpose of this study was to examine the differences in the medical students’ satisfaction on online flipped learning by the learning style.Methods: A total of 42 second-year medical students were participated in this study. As study tools, Felder and Soloman’s Index of Learning Styles and the Korean Educational Development Institute’s National Assessment of Student Engagement in Learning were utilized. Data analysis was conducted with the Kruskal-Wallis test, a nonparametric statistical method.Results: Results showed that there were statistically significant differences within active–reflective types and sensing–intuitive types. The active–reflective type has been shown to have statistically significant differences for the three effects of learning, such as problem solving and understanding, active participation, and self-directed learning attitudes. For sensing–intuitive type, active participation showed a significant difference from other effects of learning.Conclusion: The learning style is an important concept in understanding the diverse ways in which students process and absorb new information. Hence research is needed to conduct successful small group activities through online. In the future, it is necessary to find the factors that can lead to successful online classes in medical schools.
Biased unconstrained non-negative matrix factorization for clustering

Clustering remains a challenging research hotspot in data mining. Non-negative matrix factorization (NMF) is an effective technique for clustering, which aims to find the product of two non-negative low-dimensional matrices that approximates the original matrix. Since the matrices must satisfy the non-negative constraints, the Karush–Kuhn–Tucker conditions need to be used to obtain the update rules for the matrices, which limits the choice of update methods. Moreover, this method has no learning rate and the updating process is completely dependent on the data itself. In addition, the two low-dimensional matrices in NMF are randomly initialized, and the clustering performance of the model is reduced. To address these problems, this paper proposes a biased unconstrained non-negative matrix factorization (BUNMF) model, which integrates the l2 norm and adds bias. Specifically, BUNMF uses a non-linear activation function to make elements of the matrices to remain non-negative, and converts the constrained problem into an unconstrained problem. The matrices are renewed by sequentially updating the matrices' elements using stochastic gradient descent to obtain an update rule with a learning rate. Furthermore, the BUNMF model is constructed by three different activation functions and their iteration update algorithms are given through detailed reasoning. Finally, experimental results on eight public datasets show the effectiveness of the proposed model.
Entrepreneurship Orientation and Entrepreneurship Culture and the Performance of Small and Medium-scale Enterprises in Calabar Metropolis - Nigeria

The study empirically examined entrepreneurship orientation, entrepreneurship culture and performance of small and medium-scale enterprises in Calabar Metropolis. The major objective of the paper was to explore how strategic entrepreneurship practices affect the accomplishment of SMEs goals in Cross River State. The study was anchored on Resource-based theory (RBT). The study used survey research design. Survey research design was employed, and a population of one thousand, two hundred and ninety-four (1294) employees were involved. The study utilized Taro Yamane formula to determine the sample size of 400 employees. The sampling technique used in this study was purposive sampling method. The paper used multiple linear regression technique in testing the two hypotheses. The study revealed a positive relationship between entrepreneurial orientation and growth of SMEs. Furthermore, it was found that entrepreneurial culture has a negative effect on profitability of SMEs. Also, it was found that strategic resource management has significant effect on the quality of products of SMEs. The researcher supported various findings with suggestions among other things that the managers should continuously incorporate new decision-making strategies, procedures and behaviour that will put the organization into an advantageous position into entering new or established markets with new or existing goods or services. It was also recommended among others that entrepreneurial culture should be strengthened by managers of SMEs in Cross River State in pursuit of firm’s performance as well as yielding to the economic growth of Nigeria.
VR Realism Scale—Revalidation of contemporary VR headsets on a Polish sample

This paper presents validation of the VR Simulation Realism Scale on a Polish sample. The scale enables a self-report measurement of perceived realism of a virtual environment in four main aspects of such realism–scene realism, audience behavior realism, audience appearance realism and sound realism. However, since the development of the original scale, the VR technology significantly changed. We aimed to respond to that change and revalidate the original measure in the contemporary setting. For the purpose of scale validation, data was gathered from six studies with 720 participants in total. Five experiments and one online survey were conducted to examine psychometric properties of the scale in accordance with the Standards for Educational and Psychological Testing. Evidence based on internal structure, relations to other variables and test content was obtained. The factorial structure of the original scale was tested and confirmed. The connections between realism and immersion, presence, aesthetics were verified. A suppressed relationship between realism and positive affect was discovered. Moreover, it was confirmed that scale result is dependent on the quality of VR graphics. Results of the analyses provide the evidence that the VR Simulation Realism Scale is a well-established tool that might be used both in science and in VR development. However, further research needs to be done to increase external validity and predictive power of the scale.
An Effective Deep Learning Framework for Cell Segmentation in Microscopy Images

Cell segmentation is a common step in cell behavior analysis. Reliably and automatically segmenting cells in microscopy images remains challenging, especially in differential inference contrast microscopy images and phase-contrast microscopy images. In this paper, we propose a deep learning solution combining a Mask RCNN architecture with Shape-Aware Loss to produce cell instance segmentation. Our approach outperforms prior works in cell segmentation, achieving an IOU of 91.91% on the DIC-C2DH-HeLa dataset and an IOU of 94.93 % on the PhC-C2DH-U373 dataset. Our framework can calculate cell instance segmentation masks from both types of microscopy images without any additional post-processing.Clinical Relevance — The proposed approach produces accurate instance segmentation in Differential Inference Contrast and Phase-Contrast microscopy images. The segmentation results can be reliably used in cell behavior analysis and cell tracking.
Humans depart from optimal computational models of interactive decision-making during competition under partial information

Abstract Decision making under uncertainty in multiagent settings is of increasing interest in decision science. The degree to which human agents depart from computationally optimal solutions in socially interactive settings is generally unknown. Such understanding provides insight into how social contexts affect human interaction and the underlying contributions of Theory of Mind. In this paper, we adapt the well-known ‘Tiger Problem’ from artificial-agent research to human participants in solo and interactive settings. Compared to computationally optimal solutions, participants gathered less information before outcome-related decisions when competing than cooperating with others. These departures from optimality were not haphazard but showed evidence of improved performance through learning. Costly errors emerged under conditions of competition, yielding both lower rates of rewarding actions and accuracy in predicting others. Taken together, this work provides a novel approach and insights into studying human social interaction when shared information is partial.
Digital technology implementation in students’ proficiency development for english listening

The article proposes a research that shows digital technology for developing students’ proficiency in listening comprehension using the offered Internet resources, the created learning site and well-known on-line apps as it is aimed. The research methods has been used to demonstrate the finding of the paper: the questionnaires in order to analyze students’ attitude to learning with sites; tests for getting students’ progress in developing listening competence; Student’s t-test as a statistical method to estimate the results of pedagogical experiment. In general, 88 universities students took part in this research during 2020-2021. As a conclusion we can state that listening competence is less ready to progress. The present research opens the perspectives for further researches of developing English listening proficiency on-line.
Fault-Tolerant Load Balancing in Cloud Computing: A Systematic Literature Review

Nowadays, cloud computing is growing daily and has been developed as an effective and flexible paradigm in solving large-scale problems. It has been known as an Internet-based computing model in which computing and virtual resources, such as services, applications, storage, servers, and networks, are shared among numerous cloud users. Since the number of cloud users and their requests are increasing rapidly, the loads on the cloud systems may be underloaded or overloaded. These situations cause different problems, such as high response time and power consumption. To handle the mentioned problems and improve the performance of cloud servers, load balancing methods have a significant impact. Generally, a load balancing method aims to identify under-loaded and overloaded nodes and balance the load among them. In the recent decade, this problem has attracted a lot of interest among researchers, and several solutions have been proposed. Considering the important role of fault-tolerant in load balancing algorithms, there is a lack of an organized and in-depth study in this field yet. This gap prompted us to provide the current study aimed to collect and review the available papers in the field of fault tolerance load balancing methods in cloud computing. The existing algorithms are divided into two categories, namely, centralized and distributed, and reviewed based on vital qualitative parameters, such as scalability, makespan, reliability, resource utilization, throughput, and overhead. In this regard, other criteria such as the type of detected faults and adopted simulation tools are taken into account.
Understanding Memories of the Past in the Context of Different Complex Neural Network Architectures

Deep learning (primarily using backpropagation) and neuroevolution are the preeminent methods of optimizing artificial neural networks. However, they often create black boxes that are as hard to understand as the natural brains they seek to mimic. Previous work has identified an information-theoretic tool, referred to as R, which allows us to quantify and identify mental representations in artificial cognitive systems. The use of such measures has allowed us to make previous black boxes more transparent. Here we extend R to not only identify where complex computational systems store memory about their environment but also to differentiate between different time points in the past. We show how this extended measure can identify the location of memory related to past experiences in neural networks optimized by deep learning as well as a genetic algorithm.
LDPCD: A Novel Method for Locally Differentially Private Community Detection

As one of the cores of data analysis in large social networks, community detection has become a hot research topic in recent years. However, user's real social relationship may be at risk of privacy leakage and threatened by inference attacks because of the semitrusted server. As a result, community detection in social graphs under local differential privacy has gradually aroused the interest of industry and academia. On the one hand, the distortion of user's real data caused by existing privacy-preserving mechanisms can have a serious impact on the mining process of densely connected local graph structure, resulting in low utility of the final community division. On the other hand, private community detection requires to use the results of multiple user-server interactions to adjust user's partition, which inevitably leads to excessive allocation of privacy budget and large error of perturbed data. For these reasons, a new community detection method based on the local differential privacy model (named LDPCD) is proposed in this paper. Due to the introduction of truncated Laplace mechanism, the accuracy of user perturbation data is improved. In addition, the community divisive algorithm based on extremal optimization (EO) is also reﬁned to reduce the number of interactions between users and the server. Thus, the total privacy overhead is reduced and strong privacy protection is guaranteed. Finally, LDPCD is applied in two commonly used real-world datasets, and its advantage is experimentally validated compared with two state-of-the-art methods.
Project Debater APIs: Decomposing the AI Grand Challenge

Project Debater was revealed in 2019 as the first AI system that can debate human experts on complex topics.Engaging in a live debate requires a diverse set of skills, and Project Debater has been developed accordingly as a collection of components, each designed to perform a specific subtask.Project Debater APIs provide access to many of these capabilities, as well as to more recently developed ones.This diverse set of web services, publicly available for academic use, includes core NLP services, argument mining and analysis capabilities, and higher-level services for content summarization.We describe these APIs and their performance, and demonstrate how they can be used for building practical solutions.In particular, we will focus on Key Point Analysis, a novel technology that identifies the main points and their prevalence in a collection of texts such as survey responses and user reviews.
A Formal Assessment Paper on EDUSET-MBA Universities Custom Search

Smartphones and tablets are steadily changing the way we look after our education. Today penetrating further deep into the market study of mobiles require application related to educare. EDUSET team wants to develop a mobile application on MBA university guidance which provides the user with basic university information. A person’s usage of this application helps him/her to begin a good career. Firebase is used as database and android studio as a platform to develop the app. There are many apps regarding MBA learning but there will be no app like EDUSET. This application of this Android Platform is capable of guiding and suggesting the user with university information based on his/her probe. This application also enables individuals to become aware of different universities and its activities. The user will be provided with search option where he/she can probe according to university name or any of the MBA courses. The main vision of this paper is to give a new user-friendly app which is nowhere present in the mobile application development history.