scholarly journals Classifier for Activities with Variations

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3529 ◽  
Author(s):  
Rabih Younes ◽  
Mark Jones ◽  
Thomas Martin
Keyword(s):  
Activity Recognition ◽  
State Of The Art ◽  
The State ◽  
Daily Activities ◽  
Controlled Environment ◽  
Novel Approach ◽  
Specific Activities ◽  
Application Specific ◽  
Complex Activities ◽  
Better Than

Most activity classifiers focus on recognizing application-specific activities that are mostly performed in a scripted manner, where there is very little room for variation within the activity. These classifiers are mainly good at recognizing short scripted activities that are performed in a specific way. In reality, especially when considering daily activities, humans perform complex activities in a variety of ways. In this work, we aim to make activity recognition more practical by proposing a novel approach to recognize complex heterogeneous activities that could be performed in a wide variety of ways. We collect data from 15 subjects performing eight complex activities and test our approach while analyzing it from different aspects. The results show the validity of our approach. They also show how it performs better than the state-of-the-art approaches that tried to recognize the same activities in a more controlled environment.

scholarly journals Density Guarantee on Finding Multiple Subgraphs and Subtensors

2021 ◽  
Vol 15 (5) ◽  
pp. 1-32
Author(s):  
Quang-huy Duong ◽  
Heri Ramampiaro ◽  
Kjetil Nørvåg ◽  
Thu-lan Dam
Keyword(s):  
Lower Bound ◽  
State Of The Art ◽  
The State ◽  
The Other ◽  
Exact Methods ◽  
Practical Solution ◽  
Novel Approach ◽  
Wide Range ◽  
Real World Datasets ◽  
Tensor Data

Dense subregion (subgraph & subtensor) detection is a well-studied area, with a wide range of applications, and numerous efficient approaches and algorithms have been proposed. Approximation approaches are commonly used for detecting dense subregions due to the complexity of the exact methods. Existing algorithms are generally efficient for dense subtensor and subgraph detection, and can perform well in many applications. However, most of the existing works utilize the state-or-the-art greedy 2-approximation algorithm to capably provide solutions with a loose theoretical density guarantee. The main drawback of most of these algorithms is that they can estimate only one subtensor, or subgraph, at a time, with a low guarantee on its density. While some methods can, on the other hand, estimate multiple subtensors, they can give a guarantee on the density with respect to the input tensor for the first estimated subsensor only. We address these drawbacks by providing both theoretical and practical solution for estimating multiple dense subtensors in tensor data and giving a higher lower bound of the density. In particular, we guarantee and prove a higher bound of the lower-bound density of the estimated subgraph and subtensors. We also propose a novel approach to show that there are multiple dense subtensors with a guarantee on its density that is greater than the lower bound used in the state-of-the-art algorithms. We evaluate our approach with extensive experiments on several real-world datasets, which demonstrates its efficiency and feasibility.

Ultrathin RuRh@(RuRh)O2 core@shell nanosheets as stable oxygen evolution electrocatalysts

2020 ◽  
Vol 8 (31) ◽  
pp. 15746-15751 ◽  
Author(s):  
Kai Wang ◽  
Bolong Huang ◽  
Weiyu Zhang ◽  
Fan Lv ◽  
Yi Xing ◽  
...  
Keyword(s):  
Oxygen Evolution ◽  
State Of The Art ◽  
The State ◽  
Oxide Shell ◽  
Core Shell ◽  
Stable Oxygen ◽  
Better Than

We report a novel architecture of ultrathin RuRh@(RuRh)O2 core/shell nanosheets with a core of ultrathin metallic RuRh nanosheets and a shell of (RuRh)O2 oxides as a superb electrocatalyst toward the oxgen evolution reaction (OER), better than most of the state-of-the-art Ru-based or Ir-based electrocatalysts. Moreover, the RuRh@(RuRh)O2 core/shell nanosheets exhibit good durability because the (RuRh)O2 oxide shell protects the normally labile RuRh NS core against dissolution during the OER process.

scholarly journals Learning to Transfer Relational Representations through Analogy

2019 ◽  
Vol 33 ◽  
pp. 10015-10016
Author(s):  
Gaetano Rossiello ◽  
Alfio Gliozzo ◽  
Michael Glass
Keyword(s):  
State Of The Art ◽  
Relation Extraction ◽  
Knowledge Bases ◽  
The State ◽  
Large Set ◽  
Relational Information ◽  
Siamese Network ◽  
Distant Supervision ◽  
Novel Approach ◽  
Art Methods

We propose a novel approach to learn representations of relations expressed by their textual mentions. In our assumption, if two pairs of entities belong to the same relation, then those two pairs are analogous. We collect a large set of analogous pairs by matching triples in knowledge bases with web-scale corpora through distant supervision. This dataset is adopted to train a hierarchical siamese network in order to learn entity-entity embeddings which encode relational information through the different linguistic paraphrasing expressing the same relation. The model can be used to generate pre-trained embeddings which provide a valuable signal when integrated into an existing neural-based model by outperforming the state-of-the-art methods on a relation extraction task.

scholarly journals Complex Deep Neural Networks from Large Scale Virtual IMU Data for Effective Human Activity Recognition Using Wearables

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8337
Author(s):  
Hyeokhyen Kwon ◽  
Gregory D. Abowd ◽  
Thomas Plötz
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Large Scale ◽  
State Of The Art ◽  
Human Activity Recognition ◽  
The State ◽  
Model Parameters ◽  
Data Systems ◽  
Performance Improvements ◽  
Measurement Units

Supervised training of human activity recognition (HAR) systems based on body-worn inertial measurement units (IMUs) is often constrained by the typically rather small amounts of labeled sample data. Systems like IMUTube have been introduced that employ cross-modality transfer approaches to convert videos of activities of interest into virtual IMU data. We demonstrate for the first time how such large-scale virtual IMU datasets can be used to train HAR systems that are substantially more complex than the state-of-the-art. Complexity is thereby represented by the number of model parameters that can be trained robustly. Our models contain components that are dedicated to capture the essentials of IMU data as they are of relevance for activity recognition, which increased the number of trainable parameters by a factor of 1100 compared to state-of-the-art model architectures. We evaluate the new model architecture on the challenging task of analyzing free-weight gym exercises, specifically on classifying 13 dumbbell execises. We have collected around 41 h of virtual IMU data using IMUTube from exercise videos available from YouTube. The proposed model is trained with the large amount of virtual IMU data and calibrated with a mere 36 min of real IMU data. The trained model was evaluated on a real IMU dataset and we demonstrate the substantial performance improvements of 20% absolute F1 score compared to the state-of-the-art convolutional models in HAR.

scholarly journals Confidence-Calibrated Human Activity Recognition

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6566
Author(s):  
Debaditya Roy ◽  
Sarunas Girdzijauskas ◽  
Serghei Socolovschi
Keyword(s):  
Neural Network ◽  
Activity Recognition ◽  
State Of The Art ◽  
Wearable Sensors ◽  
Window Size ◽  
Well Being ◽  
The State ◽  
Calibration Error ◽  
Network Architectures ◽  
Neural Network Architectures

Wearable sensors are widely used in activity recognition (AR) tasks with broad applicability in health and well-being, sports, geriatric care, etc. Deep learning (DL) has been at the forefront of progress in activity classification with wearable sensors. However, most state-of-the-art DL models used for AR are trained to discriminate different activity classes at high accuracy, not considering the confidence calibration of predictive output of those models. This results in probabilistic estimates that might not capture the true likelihood and is thus unreliable. In practice, it tends to produce overconfident estimates. In this paper, the problem is addressed by proposing deep time ensembles, a novel ensembling method capable of producing calibrated confidence estimates from neural network architectures. In particular, the method trains an ensemble of network models with temporal sequences extracted by varying the window size over the input time series and averaging the predictive output. The method is evaluated on four different benchmark HAR datasets and three different neural network architectures. Across all the datasets and architectures, our method shows an improvement in calibration by reducing the expected calibration error (ECE)by at least 40%, thereby providing superior likelihood estimates. In addition to providing reliable predictions our method also outperforms the state-of-the-art classification results in the WISDM, UCI HAR, and PAMAP2 datasets and performs as good as the state-of-the-art in the Skoda dataset.

scholarly journals Category Trees - Classifiers that Branch on Category

2021 ◽  
Vol 12 (06) ◽  
pp. 65-76
Author(s):  
Kieran Greer
Keyword(s):  
State Of The Art ◽  
The State ◽  
Biological Analogy ◽  
Category Type ◽  
Benchmark Datasets ◽  
Batch Input ◽  
Distinguishing Features ◽  
Incorrect Data ◽  
Better Than

This paper presents a batch classifier that splits a dataset into tree branches depending on the category type. It has been improved from the earlier version and fixed a mistake in the earlier paper. Two important changes have been made. The first is to represent each category with a separate classifier. Each classifier then classifies its own subset of data rows, using batch input values to create the centroid and also represent the category itself. If the classifier contains data from more than one category however, it needs to create new classifiers for the incorrect data. The second change therefore is to allow the classifier to branch to new layers when there is a split in the data, and create new classifiers there for the data rows that are incorrectly classified. Each layer can therefore branch like a tree - not for distinguishing features, but for distinguishing categories. The paper then suggests a further innovation, which is to represent some data columns with fixed value ranges, or bands. When considering features, it is shown that some of the data can be classified directly through fixed value ranges, while the rest must be classified using a classifier technique and the idea allows the paper to discuss a biological analogy with neurons and neuron links. Tests show that the method can successfully classify a diverse set of benchmark datasets to better than the state-of-the-art.

scholarly journals Transformer-Capsule Model for Intent Detection (Student Abstract)

2020 ◽  
Vol 34 (10) ◽  
pp. 13885-13886
Author(s):  
Aleksander Obuchowski ◽  
Michał Lew
Keyword(s):  
State Of The Art ◽  
Intent Recognition ◽  
Novel Approach ◽  
Better Than

Intent recognition is one of the most crucial tasks in NLU systems, which are nowadays especially important for designing intelligent conversation. We propose a novel approach to intent recognition which involves combining transformer architecture with capsule networks. Our results show that such architecture performs better than original capsule-NLU network implementations and achieves state-of-the-art results on datasets such as ATIS, AskUbuntu ,and WebApp.

scholarly journals Unsupervised Chunking Based on Graph Propagation from Bilingual Corpus

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Ling Zhu ◽  
Derek F. Wong ◽  
Lidia S. Chao
Keyword(s):  
Knowledge Transfer ◽  
Chinese Text ◽  
State Of The Art ◽  
The State ◽  
Label Propagation ◽  
Novel Approach ◽  
Shallow Parsing ◽  
Graph Propagation ◽  
Experimental Comparisons

This paper presents a novel approach for unsupervised shallow parsing model trained on the unannotated Chinese text of parallel Chinese-English corpus. In this approach, no information of the Chinese side is applied. The exploitation of graph-based label propagation for bilingual knowledge transfer, along with an application of using the projected labels as features in unsupervised model, contributes to a better performance. The experimental comparisons with the state-of-the-art algorithms show that the proposed approach is able to achieve impressive higher accuracy in terms ofF-score.

scholarly journals Multi-Labeled Relation Extraction with Attentive Capsule Network

2019 ◽  
Vol 33 ◽  
pp. 7484-7491 ◽  
Author(s):  
Xinsong Zhang ◽  
Pengshuai Li ◽  
Weijia Jia ◽  
Hai Zhao
Keyword(s):  
Performance Improvement ◽  
Loss Function ◽  
State Of The Art ◽  
Routing Algorithm ◽  
Relation Extraction ◽  
Neural Models ◽  
Novel Approach ◽  
Art Works ◽  
Significant Performance ◽  
Better Than

To disclose overlapped multiple relations from a sentence still keeps challenging. Most current works in terms of neural models inconveniently assuming that each sentence is explicitly mapped to a relation label, cannot handle multiple relations properly as the overlapped features of the relations are either ignored or very difficult to identify. To tackle with the new issue, we propose a novel approach for multi-labeled relation extraction with capsule network which acts considerably better than current convolutional or recurrent net in identifying the highly overlapped relations within an individual sentence. To better cluster the features and precisely extract the relations, we further devise attention-based routing algorithm and sliding-margin loss function, and embed them into our capsule network. The experimental results show that the proposed approach can indeed extract the highly overlapped features and achieve significant performance improvement for relation extraction comparing to the state-of-the-art works.

scholarly journals ULoc

2021 ◽  
Vol 5 (3) ◽  
pp. 1-31
Author(s):  
Minghui Zhao ◽  
Tyler Chang ◽  
Aditya Arun ◽  
Roshan Ayyalasomayajula ◽  
Chi Zhang ◽  
...  
Keyword(s):  
Power Consumption ◽  
State Of The Art ◽  
Tracking System ◽  
The State ◽  
Location Estimation ◽  
High Bandwidth ◽  
Time Of Travel ◽  
Localization And Tracking ◽  
State Of Art ◽  
Better Than

A myriad of IoT applications, ranging from tracking assets in hospitals, logistics, and construction industries to indoor tracking in large indoor spaces, demand centimeter-accurate localization that is robust to blockages from hands, furniture, or other occlusions in the environment. With this need, in the recent past, Ultra Wide Band (UWB) based localization and tracking has become popular. Its popularity is driven by its proposed high bandwidth and protocol specifically designed for localization of specialized "tags". This high bandwidth of UWB provides a fine resolution of the time-of-travel of the signal that can be translated to the location of the tag with centimeter-grade accuracy in a controlled environment. Unfortunately, we find that high latency and high-power consumption of these time-of-travel methods are the major culprits which prevent such a system from deploying multiple tags in the environment. Thus, we developed ULoc, a scalable, low-power, and cm-accurate UWB localization and tracking system. In ULoc, we custom build a multi-antenna UWB anchor that enables azimuth and polar angle of arrival (henceforth shortened to '3D-AoA') measurements, with just the reception of a single packet from the tag. By combining multiple UWB anchors, ULoc can localize the tag in 3D space. The single-packet location estimation reduces the latency of the entire system by at least 3×, as compared with state of art multi-packet UWB localization protocols, making UWB based localization scalable. ULoc's design also reduces the power consumption per location estimate at the tag by 9×, as compared to state-of-art time-of-travel algorithms. We further develop a novel 3D-AoA based 3D localization that shows a stationary localization accuracy of 3.6 cm which is 1.8× better than the state-of-the-art two-way ranging (TWR) systems. We further developed a temporal tracking system that achieves a tracking accuracy of 10 cm in mobile conditions which is 4.3× better than the state-of-the-art TWR systems.

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