The omnipresence of internet technology and the advent of smart devices accumulate varieties of voluminous, viscous real-time data in a network from varieties of sources and also facilitates a way for criminal, intrud...
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In this paper a new optimization technique has been proposed to take the advantage of both the Hungarian Algorithm and Deep Q-learning Neural Network (DQN) to solve the frequency and power resources allocation problem...
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ISBN:
(纸本)9783030336073;9783030336066
In this paper a new optimization technique has been proposed to take the advantage of both the Hungarian Algorithm and Deep Q-learning Neural Network (DQN) to solve the frequency and power resources allocation problem in Vehicle to Vehicle (V2V) future networks. The result shows a better performance for the sum of cellular users throughput, reducing the complexity of the classical optimization methods, overcome the huge State-Action matrix in Q-learning and provides wireless environment features to approximate the Q-values.
Human activity recognition (HAR), driven by large deep learning models, has received a lot of attention in recent years due to its high applicability in diverse application domains, manipulate time-series data to spec...
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ISBN:
(纸本)9781728150116
Human activity recognition (HAR), driven by large deep learning models, has received a lot of attention in recent years due to its high applicability in diverse application domains, manipulate time-series data to speculate on activities. Meanwhile, the cloud term "as-a-service" has essentially revolutionized the information technology industry market over the last ten years. These two trends somehow are incorporating to inspire a new model for the assistive living application: HAR as a service in the cloud. However, with frequently updates deep learning frameworks in open source communities as well as various new hardware features release, which make a significant software management challenge for deep learning model developers. To address this problem, container techniques are widely employed to facilitate the deep learning software development cycle. In addition, models and the available datasets are being larger and more complicated, and so, an expanding amount of computing resources is desired so that these models are trained in a feasible amount of time. This requires an emerging distributed training approach, called data parallelism, to achieve low resource utilization and faster execution in training time. Therefore, in this paper, we apply the data parallelism to build an assistive living HAR application using LSTM model, deploying in containers within a Kubernetes cluster to enable the real-time recognition as well as prediction of changes in human activity patterns. We then systematically measure the influence of this technique on the performance of the HAR application. Firstly, we evaluate our system performance with regard to CPU and GPU when deployed in containers and host environment, then analyze the outcomes to verify the difference in terms of the model learning performance. Through the experiments, we figure out that data parallelism strategy is efficient for improving model learning performance. In addition, this technique helps to increase the sc
We argue that better use of "digital footprints" (data generated from students’ learning activities) could be used to improve the traditional lecture. We point out some potentially important data sources, a...
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In order to effectively solve the discrete optimization problem of cognitive radio network spectrum allocation, a new binary adaptive cuckoo search (BACS) algorithm is proposed, which introduces reverse learning in th...
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ISBN:
(纸本)9781450371728
In order to effectively solve the discrete optimization problem of cognitive radio network spectrum allocation, a new binary adaptive cuckoo search (BACS) algorithm is proposed, which introduces reverse learning in the initialization stage of traditional cuckoo search (CS), it increases the diversity of the population. Second, the adaptive discovery probability balances the ability of global and local optimization. Finally, a cognitive wireless network spectrum allocation method based on binary adaptive cuckoo algorithm is proposed, and compared with the classical spectrum allocation algorithm for network benefit function and fairness. The simulation shows that BACS has better optimization ability and can be used to maximize the network benefits and fairness between users when applied to spectrum allocation.
This paper explores object detection in the small data regime, where only a limited number of annotated bounding boxes are available due to data rarity and annotation expense. This is a common challenge today with mac...
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ISBN:
(纸本)9781728148038
This paper explores object detection in the small data regime, where only a limited number of annotated bounding boxes are available due to data rarity and annotation expense. This is a common challenge today with machinelearning being applied to many new tasks where obtaining training data is more challenging, e.g. in medical images with rare diseases that doctors sometimes only see once in their life-time. In this work we explore this problem from a generative modeling perspective by learning to generate new images with associated bounding boxes, and using these for training an object detector. We show that simply training previously proposed generative models does not yield satisfactory performance due to them optimizing for image realism rather than object detection accuracy. To this end we develop a new model with a novel unrolling mechanism that jointly optimizes the generative model and a detector such that the generated images improve the performance of the detector. We show this method outperforms the state of the art on two challenging datasets, disease detection and small data pedestrian detection, improving the average precision on NIH Chest X-ray by a relative 20% and localization accuracy by a relative 50%.
The current study considers an unconventional framework of unsupervised feature selection for supervised learning. We provide a new unsupervised algorithm which we call LIA, for Label-Independent Algorithm, which comb...
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ISBN:
(纸本)9783030375997;9783030375980
The current study considers an unconventional framework of unsupervised feature selection for supervised learning. We provide a new unsupervised algorithm which we call LIA, for Label-Independent Algorithm, which combines information theory and network science techniques. In addition, we present the results of an empirical study comparing LIA with a standard supervised algorithm (MRMR). The two algorithms are similar in that both minimize redundancy, but MRMR uses the labels of the instances in the input data set to maximize the relevance of the selected features. This is an advantage when such labels are available, but a shortcoming when they are not. We used cross-validation to evaluate the effectiveness of selected features for generating different well-known classifiers for a variety of publicly available data sets. The results show that the performance of classifiers using the features selected by our proposed label-independent algorithm is very close to or in some cases better than their performance when using the features selected by a common label-dependent algorithm (MRMR). Thus, LIA has potential to be useful for a wide variety of applications where dimension reduction is needed and the instances in the data set are unlabeled.
From the newsfeeds, the products and services advertised to us, job screening, risk analysis, facial recognition and to the results we get through search engines, human-curated algorithms sitting behind the scenes, ar...
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Problem based learning model is applicable in applied Mathematics II course in order to obtain better students' problem-solving skill in mathematic. In this research, one can learn the differences of student perfo...
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Problem based learning model is applicable in applied Mathematics II course in order to obtain better students' problem-solving skill in mathematic. In this research, one can learn the differences of student performance to solve problem by problem-based learning compared to traditional learning. Besides, observation has been done to student activities during class session. data analysis shows significant of a = 0.05 gained t(count) = 2.16 and t(Table) = 1.72 in other hand teaaat > ttabie, one can conclude that problem-based learning increase student performance in solving problem compared to those in control group in applied Mathematics II course, Study Program of Industrial Engineering Polytechnic of South Aceh. The student activity was rate at 69.64% average.
In-hospital cardiac arrest (IHCA) diminish the survival rate of patients, despite most of the IHCA cases are preventable. More than 54% IHCA patient had abnormal clinical manifestation before they suffered a cardiac a...
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ISBN:
(纸本)9781538691380
In-hospital cardiac arrest (IHCA) diminish the survival rate of patients, despite most of the IHCA cases are preventable. More than 54% IHCA patient had abnormal clinical manifestation before they suffered a cardiac arrest. If appropriate steps were taken, patients' survival rate would be higher and medical expense would be decreased. This paper proposes a novel approach to detect IHCA before the event occurred. We construct two types of shifting windows (corresponding to two tasks) that allow machinelearning to be applied for our dataset which is severely imbalanced. The results show that our approach can effectively handle the imbalanced dataset for detecting cardiac arrest. As the selection of performance index, we used the area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC). In our experiments, the best classifier is random forest for task 1, with AUROC of 0.88. LSTM is the best for task 2, with AUPRC of 0.71 for the second task.
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