scholarly journals Beyond spatial pooling: Fine-grained representation learning in multiple domains

2015 ◽  
Author(s):  
Chi Li ◽  
Austin Reiter ◽  
Gregory D. Hager
Keyword(s):  
Representation Learning ◽  
Fine Grained ◽  
Spatial Pooling ◽  
Multiple Domains

scholarly journals Catch me if you can: A participant-level rumor detection framework via fine-grained user representation learning

2021 ◽  
Vol 58 (5) ◽  
pp. 102678
Author(s):  
Xueqin Chen ◽  
Fan Zhou ◽  
Fengli Zhang ◽  
Marcello Bonsangue
Keyword(s):  
Representation Learning ◽  
Fine Grained ◽  
Rumor Detection

scholarly journals Representation Learning for Fine-Grained Change Detection

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4486
Author(s):  
Niall O’Mahony ◽  
Sean Campbell ◽  
Lenka Krpalkova ◽  
Anderson Carvalho ◽  
Joseph Walsh ◽  
...  
Keyword(s):  
Deep Learning ◽  
Change Detection ◽  
Model Calibration ◽  
State Of The Art ◽  
Representation Learning ◽  
Machine Intelligence ◽  
The State ◽  
Sensor Data ◽  
Fine Grained ◽  
Learning Techniques

Fine-grained change detection in sensor data is very challenging for artificial intelligence though it is critically important in practice. It is the process of identifying differences in the state of an object or phenomenon where the differences are class-specific and are difficult to generalise. As a result, many recent technologies that leverage big data and deep learning struggle with this task. This review focuses on the state-of-the-art methods, applications, and challenges of representation learning for fine-grained change detection. Our research focuses on methods of harnessing the latent metric space of representation learning techniques as an interim output for hybrid human-machine intelligence. We review methods for transforming and projecting embedding space such that significant changes can be communicated more effectively and a more comprehensive interpretation of underlying relationships in sensor data is facilitated. We conduct this research in our work towards developing a method for aligning the axes of latent embedding space with meaningful real-world metrics so that the reasoning behind the detection of change in relation to past observations may be revealed and adjusted. This is an important topic in many fields concerned with producing more meaningful and explainable outputs from deep learning and also for providing means for knowledge injection and model calibration in order to maintain user confidence.

Fine-Grained Privacy Detection with Graph-Regularized Hierarchical Attentive Representation Learning

2020 ◽  
Vol 38 (4) ◽  
pp. 1-26
Author(s):  
Xiaolin Chen ◽  
Xuemeng Song ◽  
Ruiyang Ren ◽  
Lei Zhu ◽  
Zhiyong Cheng ◽  
...  
Keyword(s):  
Representation Learning ◽  
Fine Grained

scholarly journals Using global observation protocols to inform research on teaching effectiveness and school improvement: Strengths and emerging limitations

2020 ◽  
Vol 28 ◽  
pp. 62
Author(s):  
Sean Kelly ◽  
Robert Bringe ◽  
Esteban Aucejo ◽  
Jane Cooley Fruehwirth
Keyword(s):  
School Improvement ◽  
Teaching Effectiveness ◽  
Essential Feature ◽  
Study Data ◽  
Fine Grained ◽  
Fundamental Limitations ◽  
Research On Teaching ◽  
Classroom Processes ◽  
Multiple Domains ◽  
Observation Protocols

An essential feature of many modern teacher observation protocols is their “global” approach to measuring instruction. Global protocols provide a summary evaluation of multiple domains of instruction from observers’ overall review of classroom processes.  Although these protocols have demonstrated strengths, including their comprehensiveness and advanced state of development, in this analysis we argue that global protocols also have inherent limitations affecting both research use and applied school improvement efforts.  Analyzing the Measures of Effective Teaching study data, we interrogate a set of five potential limitations of global protocols.  We conclude by discussing fine-grained measures of instruction, including tools that rely on automated methods of observation, as an alternative with the potential to overcome many of the fundamental limitations of global protocols. 

scholarly journals Localization with Transfer Learning Based on Fine-Grained Subcarrier Information for Dynamic Indoor Environments

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 1015
Author(s):  
Yuqing Yin ◽  
Xu Yang ◽  
Peihao Li ◽  
Kaiwen Zhang ◽  
Pengpeng Chen ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Bayesian Model Averaging ◽  
Indoor Localization ◽  
Domain Adaptation ◽  
Model Averaging ◽  
Indoor Environments ◽  
Test Bed ◽  
Localization Accuracy ◽  
Fine Grained ◽  
Multiple Domains

Indoor localization provides robust solutions in many applications, and Wi-Fi-based methods are considered some of the most promising means for optimizing indoor fingerprinting localization accuracy. However, Wi-Fi signals are vulnerable to environmental variations, resulting in data across different times being subjected to different distributions. To solve this problem, this paper proposes an across-time indoor localization solution based on channel state information (CSI) fingerprinting via multi-domain representations and transfer component analysis (TCA). We represent the format of CSI readings in multiple domains, extending the characterization of fine-grained information. TCA, a domain adaptation method in transfer learning, is applied to shorten the distribution distances among several CSI readings, which overcomes various CSI distribution problems at different time periods. Finally, we present a modified Bayesian model averaging approach to integrate the multi-domain outcomes and give the estimated positions. We conducted test-bed experiments in three scenarios on both personal computer (PC) and smartphone platforms in which the source and target fingerprinting data were collected across different days. The experimental results showed that our method outperforms state-of-the-art methods in localization accuracy.

scholarly journals Future Spaces: Reinventing the Home Network for Better Security and Automation in the IoT Era

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2986 ◽  
Author(s):  
Mathieu Boussard ◽  
Dinh Thai Bui ◽  
Richard Douville ◽  
Pascal Justen ◽  
Nicolas Le Sauze ◽  
...  
Keyword(s):  
Data Privacy ◽  
Smart Homes ◽  
Home Network ◽  
Home Networks ◽  
Privacy And Security ◽  
Fine Grained ◽  
Definition Of ◽  
Multiple Domains ◽  
Networking Security ◽  
The Internet Of Things

Cyber-Physical Systems (CPSs) are complex systems comprising computation, physical, and networking assets. Used in various domains such as manufacturing, agriculture, vehicles, etc., they blend the control of the virtual and physical worlds. Smart homes are a peculiar type of CPS where the local networking fundamentals have seen little evolution in the past decades, while the context in which home networks operate has drastically evolved. With the advent of the Internet of Things (IoT), the number and diversity of devices connected to our home networks are exploding. Some of those devices are poorly secured and put users’ data privacy and security at risk. At the same time, administrating a home network has remained a tedious chore, requiring skills from un-savvy users. We present Future Spaces, an end-to-end hardware-software prototype providing fine-grained control over IoT connectivity to enable easy and secure management of smart homes. Relying on Software-Defined Networking-enabled home gateways and the virtualization of network functions in the cloud, we achieve advanced networking security and automation through the definition of isolated, usage-oriented slices. This disrupts how users discover, control and share their connected assets across multiple domains, smoothly adapting to various usage contexts.

scholarly journals Embedding Projection for Targeted Cross-lingual Sentiment: Model Comparisons and a Real-World Study

2019 ◽  
Vol 66 ◽  
Author(s):  
Jeremy Barnes ◽  
Roman Klinger
Keyword(s):  
Sentiment Analysis ◽  
State Of The Art ◽  
Target Language ◽  
Test Machine ◽  
Fine Grained ◽  
Sentence Level ◽  
Level Information ◽  
Cross Lingual ◽  
Multiple Domains ◽  
Embedding Methods

Sentiment analysis benefits from large, hand-annotated resources in order to train and test machine learning models, which are often data hungry. While some languages, e.g., English, have a vast arrayof these resources, most under-resourced languages do not, especially for fine-grained sentiment tasks, such as aspect-level or targeted sentiment analysis. To improve this situation, we propose a cross-lingual approach to sentiment analysis that is applicable to under-resourced languages and takes into account target-level information. This model incorporates sentiment information into bilingual distributional representations, byjointly optimizing them for semantics and sentiment, showing state-of-the-art performance at sentence-level when combined with machine translation. The adaptation to targeted sentiment analysis on multiple domains shows that our model outperforms other projection-based bilingual embedding methods on binary targetedsentiment tasks. Our analysis on ten languages demonstrates that the amount of unlabeled monolingual data has surprisingly little effect on the sentiment results. As expected, the choice of a annotated source language for projection to a target leads to better results for source-target language pairs which are similar. Therefore, our results suggest that more efforts should be spent on the creation of resources for less similar languages tothose which are resource-rich already. Finally, a domain mismatch leads to a decreased performance. This suggests resources in any language should ideally cover varieties of domains.

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