Offloading decision methods for multiple users with structured tasks in edge computing for smart cities

Circular RNAs (circRNAs) are a newly identified type of non-coding RNA (ncRNA) that plays crucial roles in many cellular processes and human diseases, and are potential disease biomarkers and therapeutic targets in human diseases. However, experimentally verified circRNA-disease associations are very rare. Hence, developing an accurate and efficient method to predict the association between circRNA and disease may be beneficial to disease prevention, diagnosis, and treatment. Here, we propose a computational method named KATZCPDA, which is based on the KATZ method and the integrations among circRNAs, proteins, and diseases to predict circRNA-disease associations. KATZCPDA not only verifies existing circRNA-disease associations but also predicts unknown associations. As demonstrated by leave-one-out and 10-fold cross-validation, KATZCPDA achieves AUC values of 0.959 and 0.958, respectively. The performance of KATZCPDA was substantially higher than those of previously developed network-based methods. To further demonstrate the effectiveness of KATZCPDA, we apply KATZCPDA to predict the associated circRNAs of Colorectal cancer, glioma, breast cancer, and Tuberculosis. The results illustrated that the predicted circRNA-disease associations could rank the top 10 of the experimentally verified associations.

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Fusion of multiple heterogeneous networks for predicting circRNA-disease associations.

Medical technological advancements have led to the creation of various large datasets with numerous attributes. The presence of redundant and irrelevant features in datasets negatively influences algorithms and leads to decreases in the performance of the algorithms. Using effective features in data mining and analyzing tasks such as classification can increase the accuracy of the results and relevant decisions made by decision-makers using them. This increase can become more acute when dealing with challenging, large-scale problems in medical applications. Nature-inspired metaheuristics show superior performance in finding optimal feature subsets in the literature. As a seminal attempt, a wrapper feature selection approach is presented on the basis of the newly proposed Aquila optimizer (AO) in this work. In this regard, the wrapper approach uses AO as a search algorithm in order to discover the most effective feature subset. S-shaped binary Aquila optimizer (SBAO) and V-shaped binary Aquila optimizer (VBAO) are two binary algorithms suggested for feature selection in medical datasets. Binary position vectors are generated utilizing S- and V-shaped transfer functions while the search space stays continuous. The suggested algorithms are compared to six recent binary optimization algorithms on seven benchmark medical datasets. In comparison to the comparative algorithms, the gained results demonstrate that using both proposed BAO variants can improve the classification accuracy on these medical datasets. The proposed algorithm is also tested on the real-dataset COVID-19. The findings testified that SBAO outperforms comparative algorithms regarding the least number of selected features with the highest accuracy.

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Binary Aquila Optimizer for Selecting Effective Features from Medical Data: A COVID-19 Case Study

Although intelligent load forecasting is essential for optimal energy management (EM) in smart cities, there is a lack of current research exploring EM in well-regulated Internet-of-Things (IoT) networks. This article develops a new deep learning (DL) model for efficient forecasting of short-term energy consumption while maintaining effective communication between energy providers and users. The proposed Energy-Net stack comprises multiple stacked spatiotemporal modules, where each module consists of a temporal transformer (TT) submodule and a spatial transformer (ST) submodule. The TT models the temporal relationships in load data; and the ST submodule extracts hidden spatial information by integrating convolutional layers and includes an improved self-attention mechanism. The experimental evaluation on IHPEC and independent system operator New England (ISO-NE) data set demonstrates the superiority of Energy-Net over recent cutting-edge DL models with root mean-square error (RMSE) of 0.354 and 0.535, respectively. The computational complexity of Energy-Net is appropriate for dependable resource-constrained IoT devices (i.e., fog nodes or edge nodes) linked to a joint IoT-cloud server that interacts with connected smart grids to handle EM tasks.

Energy-Net: A Deep Learning Approach for Smart Energy Management in IoT-Based Smart Cities

Recent studies mainly focus on high performance or low power consumption for task scheduling on heterogeneous multiprocessor systems (HMSs). Dynamic voltage and frequency scaling (DVFS) is an important energy reduction technique, which adjusts the voltage and frequency of the processor while the task is executing. However, some studies have shown that reducing the voltage of processor increases the transient failure rate, which reduces system reliability. In this paper, we aim at addressing the scheduling problem of optimizing energy under makespan and reliability constraints on HMSs with DVFS. We first propose an improved whale optimization algorithm (WOA) deploying opposition-based learning and individual selection strategy, which can balance the exploration and exploitation ability. To maintain population diversity, we then apply a constrained rank-based method which retains some infeasible individuals in the population. Finally, we reschedule the Critical Path Nodes (CPNs) to further improve the performance of improved WOA. The main difference between our work and most previous works is that we study a new scheduling problem, and utilize an improved WOA algorithm integrating with rescheduling CPNs and a constrained rank-based method. Extensive experiments are conducted to evaluate our proposed algorithm, and the evaluation results show that our proposed algorithm is compelling in comparison with the state-of-the-art algorithms.

Reliability-aware task scheduling for energy efficiency on heterogeneous multiprocessor systems

A metaheuristic method for joint task scheduling and virtual machine placement in cloud data centers

With the development of open source community, through the interaction of developers, the collaborative development of software, and the sharing of software tools, the formation of open source software ecosystem has matured. Natural ecosystems provide ecological services on which human beings depend. Maintaining a healthy natural ecosystem is a necessity for the sustainable development of mankind. Similarly, maintaining a healthy ecosystem of open source software is also a prerequisite for the sustainable development of open source communities, such as GitHub. This paper takes GitHub as an example to analyze the health condition of open source ecosystem and, also, it is a research area in Symmetry. Firstly, the paper presents the healthy definition of GitHub open source ecosystem health and, then, according to the main components of natural ecosystem health, the paper proposes the health indicators and health indicators evaluation method. Based on the above, the GitHub ecosystem health prediction method is proposed. By analyzing the projects and data collected in GitHub, it is found that, using the proposed evaluation indicators and method, we can analyze the healthy development trend of the GitHub ecosystem and contribute to the stability of ecosystem development.

Healthy or Not: A Way to Predict Ecosystem Health in GitHub

Although the accuracy of load forecasting has been studied by many works, the actual deployability of a model is rarely considered. In this work, we consider the actual deployability of a model from four aspects: 1) the prediction performance of the model; 2) the robustness of the model; 3) the dependence of the model on external data; and 4) the storage size of the model. From these four aspects, we propose a multiple wavelet convolutional neural network (MWCNN) for load forecasting. On two public data sets, we verified the performance and robustness of the MWCNN. The MWCNN only uses load data, and the storage size of the model is only 497 kB, which shows that MWCNN has good deployability. In addition, our MWCNN prediction results are interpretable. The experimental results show that the MWCNN can effectively capture the periodic characteristics of load data.

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Multiple Wavelet Convolutional Neural Network for Short-Term Load Forecasting

A lot of image processing research works focus on natural images, such as in classification, clustering, and the research on the recognition of artworks (such as oil paintings), from feature extraction to classifier design, is relatively few. This paper focuses on oil painter recognition and tries to find the mobile application to recognize the painter. This paper proposes a cluster multiple kernel learning algorithm, which extracts oil painting features from three aspects: color, texture, and spatial layout, and generates multiple candidate kernels with different kernel functions. With the results of clustering numerous candidate kernels, we selected the sub-kernels with better classification performance, and use the traditional multiple kernel learning algorithm to carry out the multi-feature fusion classification. The algorithm achieves a better result on the Painting91 than using traditional multiple kernel learning directly.

An Oil Painters Recognition Method Based on Cluster Multiple Kernel Learning Algorithm

Crowdsourcing has the advantages of being cost-effective and saving time, which is a typical embodiment of collective wisdom and community workers’ collaborative development. However, this development paradigm of software crowdsourcing has not been used widely. A very important reason is that requesters have limited knowledge about crowd workers’ professional skills and qualities. Another reason is that the crowd workers in the competition cannot get the appropriate reward, which affects their motivation. To solve this problem, this paper proposes a method of maximizing reward based on the crowdsourcing ability of workers, they can choose tasks according to their own abilities to obtain appropriate bonuses. Our method includes two steps: Firstly, it puts forward a method to evaluate the crowd workers’ ability, then it analyzes the intensity of competition for tasks at Topcoder.com—an open community crowdsourcing platform—on the basis of the workers’ crowdsourcing ability; secondly, it follows dynamic programming ideas and builds game models under complete information in different cases, offering a strategy of reward maximization for workers by solving a mixed-strategy Nash equilibrium. This paper employs crowdsourcing data from Topcoder.com to carry out experiments. The experimental results show that the distribution of workers’ crowdsourcing ability is uneven, and to some extent it can show the activity degree of crowdsourcing tasks. Meanwhile, according to the strategy of reward maximization, a crowd worker can get the theoretically maximum reward.

Yan Zhang

Papers

A metaheuristic method for joint task scheduling and virtual machine placement in cloud data centers

Healthy or Not: A Way to Predict Ecosystem Health in GitHub

Multiple Wavelet Convolutional Neural Network for Short-Term Load Forecasting

An Oil Painters Recognition Method Based on Cluster Multiple Kernel Learning Algorithm

A Data-Driven Game Theoretic Strategy for Developers in Software Crowdsourcing: A Case Study