scholarly journals An Active Learning Approach with Uncertainty, Representativeness, and Diversity

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Tianxu He ◽  
Shukui Zhang ◽  
Jie Xin ◽  
Pengpeng Zhao ◽  
Jian Wu ◽  
...  
Keyword(s):  
Active Learning ◽  
Clustering Algorithm ◽  
Ad Hoc ◽  
State Of The Art ◽  
The Internet ◽  
Learning Approach ◽  
Learning Approaches ◽  
Learning Framework ◽  
Query Selection ◽  
The Internet Of Things

Big data from the Internet of Things may create big challenge for data classification. Most active learning approaches select either uncertain or representative unlabeled instances to query their labels. Although several active learning algorithms have been proposed to combine the two criteria for query selection, they are usually ad hoc in finding unlabeled instances that are both informative and representative and fail to take the diversity of instances into account. We address this challenge by presenting a new active learning framework which considers uncertainty, representativeness, and diversity creation. The proposed approach provides a systematic way for measuring and combining the uncertainty, representativeness, and diversity of an instance. Firstly, use instances’ uncertainty and representativeness to constitute the most informative set. Then, use the kernelk-means clustering algorithm to filter the redundant samples and the resulting samples are queried for labels. Extensive experimental results show that the proposed approach outperforms several state-of-the-art active learning approaches.

scholarly journals An Adaptive Deep Learning Framework for Dynamic Image Classification in the Internet of Things Environment

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5811
Author(s):  
Syed Muslim Jameel ◽  
Manzoor Ahmed Hashmani ◽  
Mobashar Rehman ◽  
Arif Budiman
Keyword(s):  
Deep Learning ◽  
Internet Of Things ◽  
Image Classification ◽  
The Internet ◽  
Learning Approaches ◽  
Dynamic Image ◽  
Learning Framework ◽  
Internet Of Things Environment ◽  
Future Work ◽  
The Internet Of Things

In the modern era of digitization, the analysis in the Internet of Things (IoT) environment demands a brisk amalgamation of domains such as high-dimension (images) data sensing technologies, robust internet connection (4 G or 5 G) and dynamic (adaptive) deep learning approaches. This is required for a broad range of indispensable intelligent applications, like intelligent healthcare systems. Dynamic image classification is one of the major areas of concern for researchers, which may take place during analysis under the IoT environment. Dynamic image classification is associated with several temporal data perturbations (such as novel class arrival and class evolution issue) which cause a massive classification deterioration in the deployed classification models and make them in-effective. Therefore, this study addresses such temporal inconsistencies (novel class arrival and class evolution issue) and proposes an adapted deep learning framework (ameliorated adaptive convolutional neural network (CNN) ensemble framework), which handles novel class arrival and class evaluation issue during dynamic image classification. The proposed framework is an improved version of previous adaptive CNN ensemble with an additional online training (OT) and online classifier update (OCU) modules. An OT module is a clustering-based approach which uses the Euclidean distance and silhouette method to determine the potential new classes, whereas, the OCU updates the weights of the existing instances of the ensemble with newly arrived samples. The proposed framework showed the desirable classification improvement under non-stationary scenarios for the benchmark (CIFAR10) and real (ISIC 2019: Skin disease) data streams. Also, the proposed framework outperformed against state-of-art shallow learning and deep learning models. The results have shown the effectiveness and proven the diversity of the proposed framework to adapt the new concept changes during dynamic image classification. In future work, the authors of this study aim to develop an IoT-enabled adaptive intelligent dermoscopy device (for dermatologists). Therefore, further improvements in classification accuracy (for real dataset) is the future concern of this study.

Wikipedia as a Tool for Active Learning. Experience Gained within the Framework of the Wikifabricación Project

2013 ◽  
Vol 759 ◽  
pp. 73-82
Author(s):  
J.V. Abellán-Nebot ◽  
G.M. Bruscas ◽  
J. Serrano ◽  
F. Romero
Keyword(s):  
Active Learning ◽  
Technical Writing ◽  
Learning Experience ◽  
Educational Innovation ◽  
The Internet ◽  
Learning Approach ◽  
Undergraduate Engineering ◽  
Teachers And Students ◽  
Innovation Project ◽  
Second Year

Wikipedia, the Free Encyclopedia, is one of the most visited websites on the Internet and it is a tool which students often use in their assignments, although they do not usually understand the basics underlying it. To overcome this limitation and promote the active learning approach in our courses, last year an educational innovation project was carried out that was aimed mainly at improving students skills in technical writing as well as their ability to review the technical contents of the Wikipedias. Additionally, it sought to explore new opportunities that these tools can offer both teachers and students. This paper describes the experiment carried out in a second-year undergraduate engineering course, the results of which show that introducing activities such as edition and revision within Wikipedia is an interesting way to enhance transversal competencies as well as others related to the main contents of the course.

scholarly journals An Efficient Relayed Broadcasting based on the Duplication Estimation Model for IoT Applications

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 2038
Author(s):  
Youngboo Kim ◽  
Eun-Chan Park
Keyword(s):  
Wireless Ad Hoc Networks ◽  
Ad Hoc ◽  
The Internet ◽  
Estimation Model ◽  
Simple Method ◽  
Broadcast Storm ◽  
Iot Applications ◽  
Transmission Opportunity ◽  
Hoc Networks ◽  
The Internet Of Things

In this paper, we consider relay-based broadcasting in wireless ad hoc networks, which can enable various emerging services in the Internet of Things (IoT). In this kind of traffic dissemination scheme, also known as flooding, all the nodes not only receive frames but also rebroadcast them. However, without an appropriate relay suppression, a broadcast storm problem arises, i.e., the transmission may fail due to severe collisions and/or interference, many duplicate frames are unnecessarily transmitted, and the traffic dissemination time increases. To mitigate the broadcast storm problem, we propose a reasonable criterion to restrict the rebroadcasting named the duplication ratio. Based on this, we propose an efficient mechanism consisting of duplication suppression and re-queuing schemes. The former discards duplicate frames proactively in a probabilistic manner to decrease the redundancy whereas the latter provides a secondary transmission opportunity reactively to compensate for the delivery failure. Moreover, to apply the duplication ratio practically, we propose a simple method to approximate it based on the number of adjacent nodes. The simulation study confirms that the proposed mechanism tightly ensured the reliability and decreased the traffic dissemination time by up to 6-fold compared to conventional mechanisms.

scholarly journals On the Impact of Labeled Sample Selection in Semisupervised Learning for Complex Visual Recognition Tasks

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Eftychios Protopapadakis ◽  
Athanasios Voulodimos ◽  
Anastasios Doulamis
Keyword(s):  
Visual Recognition ◽  
Clustering Algorithm ◽  
Sample Selection ◽  
Semisupervised Learning ◽  
Learning Approaches ◽  
Training Set ◽  
Learning Framework ◽  
Workflow Monitoring ◽  
Defect Recognition ◽  
The Impact

One of the most important aspects in semisupervised learning is training set creation among a limited amount of labeled data in such a way as to maximize the representational capability and efficacy of the learning framework. In this paper, we scrutinize the effectiveness of different labeled sample selection approaches for training set creation, to be used in semisupervised learning approaches for complex visual pattern recognition problems. We propose and explore a variety of combinatory sampling approaches that are based on sparse representative instances selection (SMRS), OPTICS algorithm, k-means clustering algorithm, and random selection. These approaches are explored in the context of four semisupervised learning techniques, i.e., graph-based approaches (harmonic functions and anchor graph), low-density separation, and smoothness-based multiple regressors, and evaluated in two real-world challenging computer vision applications: image-based concrete defect recognition on tunnel surfaces and video-based activity recognition for industrial workflow monitoring.

Mixed Approaches to Learning in the Flipped Classroom: How Students Approach the Learning Environment

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Margrét Sigrún Sigurðardóttir ◽  
Thamar Melanie Heijstra
Keyword(s):  
Deep Learning ◽  
Active Learning ◽  
Learning Environment ◽  
Learning Process ◽  
Flipped Classroom ◽  
Approaches To Learning ◽  
Male Students ◽  
Learning Approach ◽  
Learning Approaches ◽  
Surface Approach

Flipped teaching is a trend within higher education. Through flipped teaching the learning environment can be altered by moving the lecture out of the classroom through online recordings, while in-classroom sessions focus on active learning and engaging students in their own learning process. In this paper, we used focus groups comprised of male students in a qualitative research course with the aim of understanding the ways in which we might improve active student engagement and motivation within the flipped classroom. The findings indicated that, within the flipped classroom, students mix surface and deep-learning approaches. The online recordings, which students interact with through a surface approach, can function as a stepping stone toward a deep-learning approach to in-class activities, but only if students come to class prepared. The findings therefore suggest that students must be made aware of the importance of preparation prior to flipped classroom in-class activities to ensure the active learning process is successful. By not listening to the recordings (e.g., due to technological failure, as was the case in this study), students can result in only employing a surface approach.

A Novel Multihop Range-Free Localization Based on Kernel Learning Approach for the Internet of Things

2015 ◽  
Vol 87 (1) ◽  
pp. 269-292 ◽  
Author(s):  
Xiaoyong Yan ◽  
Zhong Yang ◽  
Aiguo Song ◽  
Wankou Yang ◽  
Yu Liu ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Kernel Learning ◽  
The Internet ◽  
Learning Approach ◽  
The Internet Of Things ◽  
Range Free

scholarly journals Cross-layer model design in wireless ad hoc networks for the Internet of Things

PLoS ONE ◽  
2018 ◽  
Vol 13 (5) ◽  
pp. e0196818 ◽  
Author(s):  
Xin Yang ◽  
Ling Wang ◽  
Jian Xie ◽  
Zhaolin Zhang
Keyword(s):  
Internet Of Things ◽  
Ad Hoc Networks ◽  
Wireless Ad Hoc Networks ◽  
Ad Hoc ◽  
The Internet ◽  
Cross Layer ◽  
Model Design ◽  
Layer Model ◽  
Hoc Networks ◽  
The Internet Of Things
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