Reviewerly/exHarmony huggingface dataset:
OP-MLB: An Online VM Prediction-Based Multi-Objective Load Balancing Framework for Resource Management at Cloud Data Center

The elasticity of cloud resources allows cloud clients to expand and shrink their demand for resources dynamically over time. However, fluctuations in the resource demands and pre-defined size of virtual machines (VMs) lead to lack of resource utilization, load imbalance, and excessive power consumption. To address these issues and to improve the performance of data center, an efficient resource management framework is proposed, which anticipates resource utilization of the servers and balances the load accordingly. It facilitates power saving, by minimizing the number of active servers, VM migrations, and maximizing the resource utilization. An online resource prediction system, is developed and deployed at each VM to minimize the risk of Service Level Agreement (SLA) violations and performance degradation due to under/overloaded servers. In addition, multi-objective VM placement and migration algorithms are proposed to reduce the network traffic and power consumption within data center. The proposed framework is evaluated by executing experiments on three real world workload datasets namely, Google Cluster dataset, Planet Lab, and Bitbrains VM traces. The comparison of proposed framework with the state-of-the-art approaches reveals its superiority in terms of different performance metrics. The improvement in power saving achieved by OP-MLB framework is upto 85.3 percent over the Best-Fit approach.
A novel robust Kalman filter with unknown non-stationary heavy-tailed noise

In this article, the state estimation with unknown non-stationary heavy-tailed process and measurement noises (HPMN) is considered. The measurement likelihood and the one-step state prediction are modelled as the mixture of two Gaussian (M2G) distributions, respectively. In the proposed M2G, one is a high probability Gaussian distribution with a nominal covariance and the other is a low probability Gaussian distribution with an adaptive large covariance. The adaptive large covariance is formulated as the nominal covariance multiplied by a time varying scale parameter. An indicator is introduced to identify whether it is generated by the nominal covariance or the larger covariance. The unknown nominal covariance is modelled as an inverse Wishart distribution. Using hierarchical priors on the indicator and scale parameter, a robust model is formulated and a variational Bayesian algorithm is developed. Experiments with synthetic data and real intelligent vehicle data show the effectiveness of the proposed filter under unknown non-stationary HPMN.
Using t-distributed Stochastic Neighbor Embedding (t-SNE) for cluster analysis and spatial zone delineation of groundwater geochemistry data

Cluster analysis is a valuable tool for understanding spatial and temporal patterns (e.g., spatial zones) of groundwater geochemistry. To determine cluster numbers and cluster memberships that are unknown in real-world problems, a number of methods have been used to assist cluster analysis, among which graphic approaches are popular and intuitive. This study introduced, for the first time, the t-distributed Stochastic Neighbor Embedding (t-SNE) method as a graphic approach to assist cluster analysis for groundwater geochemistry data. The hierarchical cluster analysis (HCA) was applied to original groundwater geochemistry data, and t-SNE was used to help determine the number of cluster and cluster memberships. Afterward, t-SNE was used to help delineate spatial zones of groundwater geochemistry. The t-SNE-based cluster visualization was compared to the visualization based on principal component analysis (PCA). By applying HCA, PCA, and t-SNE to three geochemical datasets (Oslo transect, Taiyuan karst water, and Jianghan Plain groundwater datasets, which are characterized by different number of samples and features collected across different space and time scales), we found that t-SNE outperformed PCA to assist HCA as a promising tool for helping determine the number of HCA clusters and delineate spatial zones of groundwater geochemistry. It should be noted that t-SNE alone cannot be used for cluster analyses, partly because t-SNE visualization depends on a hyperparameter called perplexity that is a priori unknown for real-world problems. The perplexity values used in this study were determined empirically, and a small value of 0.1 was used for the Taiyuan karst water dataset with 14 samples. For the other two datasets with hundreds of samples, the corresponding perplexity values were 20 and 30, within the range of 5 – 50 commonly used in t-SNE.
DJAYA: A discrete Jaya algorithm for solving traveling salesman problem

Jaya algorithm is a newly proposed stochastic population-based metaheuristic optimization algorithm to solve constrained and unconstrained continuous optimization problems. The main difference of this algorithm from the similar approaches, it uses best and worst solution in the population in order improve the intensification and diversification of the population, and this provides discovering potential solutions on the search space of the optimization problem. In this study, we propose discrete versions of the Jaya by using two major modifications in the algorithm. First is to generate initial solutions by using random permutations and nearest neighborhood approach to create population. Second is the update rule of the basic Jaya algorithm rearranged to solve discrete optimization problems. Due to characteristics of the discrete optimization problem, eight transformation operators are used for the discrete variants of the proposed algorithm. Based on these modifications, the discrete Jaya algorithm, called DJAYA, has been applied to solve fourteen different symmetric traveling salesman problem, which is one of the famous discrete problems in the discrete optimization. In order to improve the obtained best solution from DJAYA, 2-opt heuristic is also applied to the best solution of DJAYA. Once population size, search tendency and the other parameters of the proposed algorithm have been analyzed, it has been compared with the state-of-art algorithms and their variants, such as Simulated Annealing (SA), Tree-Seed Algorithm (TSA), State Transition Algorithm (STA) Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), Genetic Algorithm (GA) and Black Hole (BH). The experimental results and comparisons show that the proposed DJAYA is highly competitive and robust optimizer for the problem dealt with the study.
Deep Neural Networks for Predicting Solar Radiation at Hail Region, Saudi Arabia

Forecasted global horizontal irradiation (GHI) can help for designing, sizing and performances analysis of photovoltaic (PV) systems including water PV pumping systems used for irrigation applications.In this paper, various deep neural networks (DNN) models for one day-ahead prediction of GHI at Hail city (Saudi Arabia) are developed and investigated.The considered DNN models include long-shortterm memory (LSTM), bidirectional-LSTM (BiLSTM), gated recurrent unit (GRU), bidirectional-GRU (Bi-GRU), one-dimensional convolutional neural network (CNN 1D ) and other hybrid configurations such as CNN-LSTM and CNN-BiLSTM.A dataset of daily GHI recordings collected during January 1, 2000 to June 30, 2020 from National Aeronautics and Space Administration (NASA) at an arid location (Hail, Saudi Arabia) is used to develop and compare the above DNN-based models.The parameters affecting the accuracy of the models have been also deeply analyzed.Only historical values of daily GHI have been used to build the DNN-based models whereas additional weather parameters such as air temperature, wind speed, wind direction, atmospheric pressure and relative humidity are not considered in this work.Keras library and Python language have been used to develop and compare the GHI forecasting models.The evaluation metrics such as correlation coefficient (r), Mean Absolute Percent Error (MAPE), Mean Absolute Error (MAE), cumulative distribution function (CDF) and standard deviation (σ ) are opted to evaluate the performance of the prediction models.The obtained results showed that the DNN models have provided globally good performances with a maximum reached value of r = 96%, for daily GHI forecasting.
GIKT: A Graph-Based Interaction Model for Knowledge Tracing

With the rapid development in online education, knowledge tracing (KT) has become a fundamental problem which traces students’ knowledge status and predicts their performance on new questions. Questions are often numerous in online education systems, and are always associated with much fewer skills. However, the previous literature fails to involve question information together with high-order question-skill correlations, which is mostly limited by data sparsity and multi-skill problems. From the model perspective, previous models can hardly capture the long-term dependency of student exercise history, and cannot model the interactions between student-questions, and student-skills in a consistent way. In this paper, we propose a Graph-based Interaction model for Knowledge Tracing (GIKT) to tackle the above problems. More specifically, GIKT utilizes graph convolutional network (GCN) to substantially incorporate question-skill correlations via embedding propagation. Besides, considering that relevant questions are usually scattered throughout the exercise history, and that question and skill are just different instantiations of knowledge, GIKT generalizes the degree of students’ master of the question to the interactions between the student’s current state, the student’s history states, the target question, and related skills. Experiments on three datasets demonstrate that GIKT achieves the new state-of-the-art performance, with 2%–6% absolute AUC improvement.
Refined Global Word Embeddings Based on Sentiment Concept for Sentiment Analysis

Sentiment Analysis is an important research direction of natural language processing, and it is widely used in politics, news and other fields.Word embeddings play a significant role in sentiment analysis.The existing sentiment embeddings methods directly embed the sentiment lexicons into traditional word representation.This sentiment representation methods can only differentiate the sentiment information of different words, not the same word in different contexts, so it cannot provide accurate sentiment information for word in different contexts.This paper proposes sentiment concept to solve the problem.First, we found the optimal sentiment concept of words in Microsoft Concept Graph according to the context of words.Then we obtained the sentiment information of words under optimal sentiment concept from the multi-semantics sentiment intensity lexicon which we constructed in this paper to achieve accurate embedding of sentiment information and provide more accurate semantics and sentiment representation for words.Finally, we combined two refined word embeddings methods to achieve a more comprehensive word representation.Compared with traditional and sentiment embeddings methods on six representative datasets, the validity of the word embeddings method based on sentiment concept proposed in this paper is verified.
Countering Malicious DeepFakes: Survey, Battleground, and Horizon

The creation or manipulation of facial appearance through deep generative approaches, known as DeepFake, have achieved significant progress and promoted a wide range of benign and malicious applications, e.g., visual effect assistance in movie and misinformation generation by faking famous persons. The evil side of this new technique poses another popular study, i.e., DeepFake detection aiming to identify the fake faces from the real ones. With the rapid development of the DeepFake-related studies in the community, both sides (i.e., DeepFake generation and detection) have formed the relationship of battleground, pushing the improvements of each other and inspiring new directions, e.g., the evasion of DeepFake detection. Nevertheless, the overview of such battleground and the new direction is unclear and neglected by recent surveys due to the rapid increase of related publications, limiting the in-depth understanding of the tendency and future works. To fill this gap, in this paper, we provide a comprehensive overview and detailed analysis of the research work on the topic of DeepFake generation, DeepFake detection as well as evasion of DeepFake detection, with more than 318 research papers carefully surveyed. We present the taxonomy of various DeepFake generation methods and the categorization of various DeepFake detection methods, and more importantly, we showcase the battleground between the two parties with detailed interactions between the adversaries (DeepFake generation) and the defenders (DeepFake detection). The battleground allows fresh perspective into the latest landscape of the DeepFake research and can provide valuable analysis towards the research challenges and opportunities as well as research trends and future directions. We also elaborately design interactive diagrams (http://www.xujuefei.com/dfsurvey) to allow researchers to explore their own interests on popular DeepFake generators or detectors.
Multi-attention Based Ultra Lightweight Image Super-Resolution

Lightweight image super-resolution (SR) networks have the utmost significance for real-world applications. There are several deep learning based SR methods with remarkable performance, but their memory and computational cost are hindrances in practical usage. To tackle this problem, we propose a Multi-Attentive Feature Fusion Super-Resolution Network (MAFFSRN). MAFFSRN consists of proposed feature fusion groups (FFGs) that serve as a feature extraction block. Each FFG contains a stack of proposed multi-attention blocks (MAB) that are combined in a novel feature fusion structure. Further, the MAB with a cost-efficient attention mechanism (CEA) helps us to refine and extract the features using multiple attention mechanisms. The comprehensive experiments show the superiority of our model over the existing state-of-the-art. We participated in AIM 2020 efficient SR challenge with our MAFFSRN model and won 1st, 3rd, and 4th places in memory usage, floating-point operations (FLOPs) and number of parameters, respectively.
Problems and prospects of distance learning in teaching fundamental subjects to future Mathematics teachers

Abstract The research analyzes experience of implementing the courses Mathematical Analysis and History of Mathematics for future Mathematics teachers in the system of managing electronic academic courses (SMEAC) at Kryvyi Rih State Pedagogical University (KSPU). To create the courses, there is a block-modular approach enabling not only structuring the process of studying fundamental mathematical subjects in the distance form of training, but also controlling the rate and depth of students’ mastering the material. There are examples of laboratory works on Mathematical Analysis performed by students independently in the computer mathematics system CoCalc.
Federated Learning with Local Differential Privacy: Trade-Offs Between Privacy, Utility, and Communication

Federated learning (FL) allows to train a massive amount of data privately due to its decentralized structure. Stochastic gradient descent (SGD) is commonly used for FL due to its good empirical performance, but sensitive user information can still be inferred from weight updates shared during FL iterations. We consider Gaussian mechanisms to preserve local differential privacy (LDP) of user data in the FL model with SGD. The trade-offs between user privacy, global utility, and transmission rate are proved by defining appropriate metrics for FL with LDP. Compared to existing results, the query sensitivity used in LDP is defined as a variable, and a tighter privacy accounting method is applied. The proposed utility bound allows heterogeneous parameters over all users. Our bounds characterize how much utility decreases and transmission rate increases if a stronger privacy regime is targeted. Furthermore, given a target privacy level, our results guarantee a significantly larger utility and a smaller transmission rate as compared to existing privacy accounting methods.
Knowledge spillover, knowledge management capabilities, and innovation among returnee entrepreneurial firms in emerging markets: Does entrepreneurial ecosystem matter?

The paper brings an entrepreneurial ecosystem approach to examine the conditions under which returnee entrepreneurs can overcome their liabilities and promote innovation in emerging markets. The existing literature has focused on how returnee entrepreneurial firms can transit knowledge to local firms with the assumption that the former are more innovative than the latter. However, returnee entrepreneurial firms themselves experience difficulties in achieving superior innovation performance as they face liabilities when returning to their home countries. In this paper, we argue that such firms can take advantage of the knowledge spillover in the entrepreneurial ecosystem to promote innovation performance by enhancing their own knowledge management capability. The empirical evidence supports this proposition. Although we proposed that this relationship is likely contingent upon the attributes of entrepreneurial ecosystem, empirical evidence does not support it. Theoretical and practical implications are discussed.
Customers Online Engagement with Social Media Influencers’ Content Related to COVID 19

The purpose of the current research is to investigate the impact of social media influencers on customer engagement in Jordan with content related to COVID19. Built on a literature review a conceptual framework has been developed in order to guide the research. A quantitative approach was used to collect data through Google Forms. The research questionnaire completed by a convenience sampling technique. A structured online questionnaire was distributed to a sample of 278 respondents. Smart PLS 2.0 was used to test the study's hypotheses. The main findings revealed that the reason for a positive relationship between customers trust in social media influencers and customer participation is when customers find an influencer to be trustworthy they tend to participate more. Practically, the findings of this research revealed that the impact of social media influencers on customers' participation with content related to COVID 19 on social media will give governments and companies a chance to better understand the importance of social media influencers in contributing to the success of social media-based marketing campaigns through encouraging customers' engagement. The current study provided a significant theoretical contribution to our knowledge due to it is originality.
Characterization of the Teaching Profile within the Framework of Education 4.0

The authors of the Education 4.0 concept postulated a flexible combination of digital literacy, critical thinking, and problem-solving in educational environments linked to real-world scenarios. Therefore, teachers have been challenged to develop new methods and resources to integrate into their planning in order to help students develop these desirable and necessary skills; hence, the question: What are the characteristics of a teacher to consider within the framework of Education 4.0? This study was conducted in a higher education institution in Ecuador, with the aim to identify the teaching profile required in new undergraduate programs within the framework of Education 4.0 in order to contribute to decision-making about teacher recruitment, professional training and evaluation, human talent management, and institutional policies interested in connecting competencies with the needs of society. Descriptive and exploratory approaches, where we applied quantitative and qualitative instruments (surveys) to 337 undergraduate students in education programs and 313 graduates, were used. We also included interviews with 20 experts in the educational field and five focus groups with 32 chancellors, school principals, university professors, and specialists in the educational area. The data were triangulated, and the results were organized into the categories of (a) processes as facilitators (b), soft skills, (c) human sense, and (d) the use of technologies. The results outlined the profile of a professor as a specialized professional with competencies for innovation, complex problem solving, entrepreneurship, collaboration, international perspective, leadership, and connection with the needs of society. This research study may be of value to administrators, educational and social entrepreneurs, trainers, and policy-makers interested in implementing innovative training programs and in supporting management and policy decisions.
Measuring Diversity in Graph Learning: A Unified Framework for Structured Multi-View Clustering

Graph learning has emerged as a promising technique for multi-view clustering due to its efficiency of learning a unified graph from multiple views. Previous multi-view graph learning methods mainly try to exploit the multi-view consistency to boost learning performance. However, these methods ignore the prevalent multi-view diversity which may be induced by noise, corruptions, or even view-specific attributes. In this paper, we propose to simultaneously and explicitly leverage the multi-view consistency and the multi-view diversity in a unified framework. The consistent parts are further fused to our target graph with a clear clustering structure, on which the cluster label to each instance can be directly allocated without any postprocessing such as <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -means in classical spectral clustering. In addition, our model can automatically assign suitable weight for each view based on its clustering capacity. By leveraging the subtasks of measuring the diversity of graphs, integrating the consistent parts with automatically learned weights, and allocating cluster label to each instance in a joint framework, each subtask can be alternately boosted by utilizing the results of the others towards an overall optimal solution. Extensive experimental results on several benchmark multi-view datasets demonstrate the effectiveness of our model in comparison to several state-of-the-art algorithms.
Deep Spatial-Spectral Global Reasoning Network for Hyperspectral Image Denoising

Although deep neural networks (DNNs) have been widely applied to hyperspectral image (HSI) denoising, most DNN-based HSI denoising methods are designed by stacking convolution layer, which can only model and reason local relations, and thus ignore the global contextual information. To address this issue, we propose a deep spatial-spectral global reasoning network to consider both the local and global information for HSI noise removal. Specifically, two novel modules are proposed to model and reason global relational information. The first one aims to model global spatial relations between pixels in feature maps, and the second one models the global relations across the channels. Compared to traditional convolution operations, the two proposed modules enable the network to extract representations from new dimensions. For the HSI denoising task, the two modules, as well as the densely connected structures, are embedded into the U-Net architecture. Thus, the new-designed global reasoning network can help tackle complex noise by exploiting multiple representations, e.g., hierarchical local feature, global spatial coherence, cross-channel correlation, and multi-scale abstract representation. Experiments on both synthetic and real HSI data demonstrate that our proposed network can obtain comparable or even better denoising results than other state-of-the-art methods.
Empowering News Recommendation with Pre-trained Language Models

Personalized news recommendation is an essential technique for online news services. News articles usually contain rich textual content, and accurate news modeling is important for personalized news recommendation.