IoT-based WSNs have proved their significance in delivering critical information pertaining to hostile applications such as Wildfire Detection (WD) with the least possible delay. However, the sensor nodes deployed in ...
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IoT-based WSNs have proved their significance in delivering critical information pertaining to hostile applications such as Wildfire Detection (WD) with the least possible delay. However, the sensor nodes deployed in such networks suffer from the perturbing concern of limited energy resources, restricting their potential in the successful detection of wildfire. To extenuate this concern, we propose an intelligent framework, Sleep scheduling-based Energy Optimized Framework (SEOF), that works in two folds. Firstly, we propose an energy-efficient Cluster Head (CH) selection employing a recently developed meta-heuristic method, Tunicate Swarm Algorithm (TSA), that optimizes the five novel fitness parameters by integrating them into its weighted fitness function. Secondly, we perform a sleep scheduling of closely-located sensor nodes based on the distance threshold calculated through a set of experiments. Sleep scheduling methodology plays a pivotal role in abating the number of data transmissions in SEOF. Finally, we simulate SEOF in MATLAB under different scenarios to examine its efficacy for the various performance metrics and scalability features. Our empirical results prove that SEOF has ameliorated the network stability period for two different scenarios of network parameters by 35.3% and 216% vis-a-vis CIRP.
Previous deep learning based approaches to text baseline detection in historical documents usually take it as a semantic segmentation task. These methods adopt a fully convolutional neural network to predict baseline ...
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Previous deep learning based approaches to text baseline detection in historical documents usually take it as a semantic segmentation task. These methods adopt a fully convolutional neural network to predict baseline pixels first and then group them into lines by heuristic post-processing steps, which tends to suffer from a wrongly merged or wrongly split problem owing to limited context information provided by pixels. To address these issues, we introduce the concept of a baseline primitive, which is defined as a virtual bounding box centered at each baseline pixel. After baseline primitive detection, a relation network is used to predict a link relationship for each pair of primitives. Consequently, text baselines are generated by detecting baseline primitives and grouping them with the corresponding link relationships. Owing to the design of baseline primitives, wider context information can be leveraged to improve link prediction accuracy. Therefore, our approach can effectively detect text baselines with small inter-line or large inter-word spacing. Quantitative experimental results demonstrate the effectiveness of the proposed baseline primitive design. Our approach achieves state-of-the-art performance on two public benchmarks, namely cBAD 2017 and cBAD 2019.
A wireless sensor networks (WSN's) has stimulated significant research work among the researchers in monitoring and tracking tasks. It's a quite challenging task that needs to cope up with various conflicting ...
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A wireless sensor networks (WSN's) has stimulated significant research work among the researchers in monitoring and tracking tasks. It's a quite challenging task that needs to cope up with various conflicting issues such as energy efficiency, network lifetime, connectivity, coverage, etc. in WSN's for designing various applications. This paper explores the recent work and efforts done in addressing the various issues in WSN's. This paper focused on basic concepts regarding the WSN's and discusses meta-heuristics and heuristics algorithms for solving these issues with recent investigations. Various optimization algorithms in the context of WSN, routing algorithms, and clustering algorithms were discussed with details of earlier work done. This paper delivers various Multi-Objective Optimization approaches deeply for solving issues and summarizes the recent research work and studies. It provides researchers an understanding of the various issues, trade-offs between them, and meta-heuristics and heuristics approach for solving these issues. A glimpse of open research challenges has also been provided which will be helpful for researchers. This paper also gives an insight into various issues, open challenges that still exist in WSN's with their heuristics and meta-heuristics solutions and also focuses on various conflicting issues as well.
In the last decade, researchers, practitioners and companies struggled for devising mechanisms to detect cyber-security threats. Among others, those efforts originated rule-based, signature-based or supervised Machine...
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In the last decade, researchers, practitioners and companies struggled for devising mechanisms to detect cyber-security threats. Among others, those efforts originated rule-based, signature-based or supervised Machine Learning (ML) algorithms that were proven effective for detecting those intrusions that have already been encountered and characterized. Instead, new unknown threats, often referred to as zero-day attacks or zero-days, likely go undetected as they are often misclassified by those techniques. In recent years, unsupervised anomaly detection algorithms showed potential to detect zero-days. However, dedicated support for quantitative analyses of unsupervised anomaly detection algorithms is still scarce and often does not promote meta-learning, which has potential to improve classification performance. To such extent, this paper introduces the problem of zero-days and reviews unsupervised algorithms for their detection. Then, the paper applies a question-answer approach to identify typical issues in conducting quantitative analyses for zero-days detection, and shows how to setup and exercise unsupervised algorithms with appropriate tooling. Using a very recent attack dataset, we debate on i) the impact of features on the detection performance of unsupervised algorithms, ii) the relevant metrics to evaluate intrusion detectors, iii) means to compare multiple unsupervised algorithms, iv) the application of meta-learning to reduce misclassifications. Ultimately, v) we measure detection performance of unsupervised anomaly detection algorithms with respect to zero-days. Overall, the paper exemplifies how to practically orchestrate and apply an appropriate methodology, process and tool, providing even non-experts with means to select appropriate strategies to deal with zero-days.
Real-world datasets, particularly Electronic Health Records, are routinely found to be mixed (comprised of both categorical and continuous variables) and/or missing in nature. Such datasets present peculiar challenges...
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Real-world datasets, particularly Electronic Health Records, are routinely found to be mixed (comprised of both categorical and continuous variables) and/or missing in nature. Such datasets present peculiar challenges related both to their clustering and the evaluation of the clusterings obtained. In this paper, we discuss these challenges in detail, as well as the solution approaches applied to them in the literature. We then apply some of these approaches to a multi-racial Chronic Kidney Disease (CKD) dataset comprising of 20 continuous and 12 categorical variables with an over 30% missingness ratio, evaluating our results through external and internal validation as well as cluster stability testing. From the results of our study, the Ahmad-Dey distance measure consistently outperformed Gower's distance on our mixed and missing dataset. In addition, our results show that advanced imputation methods like multiple imputation, which take into consideration the uncertainty inherent in imputation, should be explored when clustering missing datasets. Three clusters were identified from our dataset which were significantly differentiated by age, sex, estimated Glomerular Filtration Rate (eGFR), creatinine, urea, and hemoglobin, but not by race or blood pressure. The fact that, through proper cluster analysis, we were unable to identify five clusters corresponding to the five CKD stages usually used to classify CKD patients indicates that datasets with more than the usual four/six variables used for computing eGFR may contain a latent structure different from this five-group structure, the identification of which will provide valuable insights peculiar to each cohort for medical practitioners.
We present a Bayesian approach to predict the clustering of opinions for a system of interacting agents from partial observations. The Bayesian formulation overcomes the unobservability of the system and quantifies th...
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We present a Bayesian approach to predict the clustering of opinions for a system of interacting agents from partial observations. The Bayesian formulation overcomes the unobservability of the system and quantifies the uncertainty in the prediction. We characterize the clustering by the posterior of the clusters' sizes and centers, and we represent the posterior by samples. To overcome the challenge in sampling the high-dimensional posterior, we introduce an auxiliary implicit sampling (AIS) algorithm using two-step observations. Numerical results show that the AIS algorithm leads to accurate predictions of the sizes and centers for the leading clusters, in both cases of noiseless and noisy observations. In particular, the centers are predicted with high success rates, but the sizes exhibit a considerable uncertainty that is sensitive to observation noise and the observation ratio.
Multiplex graph clustering with network embedding technique has received considerable attention recently. The multiplex graph, a special multilayer network, can model the various interactions among similar entities, a...
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Successful portfolio management requires, in addition to advanced optimization strategies, effective recruitment of specialized financial advisors. Hiring the wrong ones can be detrimental to investors' financial ...
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Successful portfolio management requires, in addition to advanced optimization strategies, effective recruitment of specialized financial advisors. Hiring the wrong ones can be detrimental to investors' financial goals. In this article, we propose an automated crowdsourcing system to organize cooperation between financial advisors and investors. Without interfering with their private portfolio optimization techniques, we design a recruitment framework that matches financial advisors to investors based on their profiles and features, as well as the previous activities of their peers. Using the database of the crowdsourcing platform, we employ an unsupervised technique to regroup advisors with a high degree of similarities into clusters and, hence, shrink the search space. Afterward, we train a machine learning regression model to predict the matching score that can be achieved if an investor hires a particular advisor. These scores are converted to weights of bipartite graphs to which we apply a double-phased many-to-many maximum weight matching algorithm to determine a suitable investor-advisor combination. In the simulations, we investigate the performance of the proposed recruitment approach and show that, compared with other traditional approaches, higher returns can be reached for both investors and financial advisors.
Generating low-rank approximations of kernel matrices that arise in nonlinear machine learning techniques holds the potential to significantly alleviate the memory and computational burdens. A compelling approach cent...
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Generating low-rank approximations of kernel matrices that arise in nonlinear machine learning techniques holds the potential to significantly alleviate the memory and computational burdens. A compelling approach centers on finding a concise set of exemplars or landmarks to reduce the number of similarity measure evaluations from quadratic to linear concerning the data size. However, a key challenge is to regulate tradeoffs between the quality of landmarks and resource consumption. Despite the volume of research in this area, current understanding is limited regarding the performance of landmark selection techniques in the presence of class-imbalanced data sets that are becoming increasingly prevalent in many applications. Hence, this paper provides a comprehensive empirical investigation using several real-world imbalanced data sets, including scientific data, by evaluating the quality of approximate low-rank decompositions and examining their influence on the accuracy of downstream tasks. Furthermore, we present a new landmark selection technique called Distance-based Importance Sampling and clustering (DISC), in which the relative importance scores are computed for improving accuracy-efficiency tradeoffs compared to existing works that range from probabilistic sampling to clustering methods. The proposed landmark selection method follows a coarse-to-fine strategy to capture the intrinsic structure of complex data sets, allowing us to substantially reduce the computational complexity and memory footprint with minimal loss in accuracy.
Heterogeneous multi-core processors (HMP) with the same instruction set architecture (ISA) integrate complex high performance big cores with power efficient small cores on the same chip. In comparison with homogeneous...
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Heterogeneous multi-core processors (HMP) with the same instruction set architecture (ISA) integrate complex high performance big cores with power efficient small cores on the same chip. In comparison with homogeneous architectures, HMPs have been shown to significantly increase energy efficiency. However, current techniques to exploit the energy efficiency of HMPs do not consider fair usage of resources that leads to reduced performance predictability, a longer makespan, starvation, and QoS degradation. The effect of different cluster voltage and frequency levels on fairness is another issue neglected by previous task scheduling algorithms. The present study investigates both the fairness problem and energy efficiency in HMPs. This article proposes a heterogeneous fairness-aware energy efficient framework (HFEE) that employs DVFS to meet fairness constraints and provide energy efficient scheduling. The proposed framework is implemented and evaluated on a real heterogeneous multi-core processor. The experimental results indicate that the introduced technique can significantly improve energy efficiency and fairness when compared to Linux standard scheduler and two energy efficient and fairness-aware schedulers.
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