An approach to spam comment detection through domain-independent features

Gestures constitute an important form of nonverbal communication where bodily actions are used for delivering messages alone or in parallel with spoken words. Recently, there exists an emerging trend of WiFi sensing enabled gesture recognition due to its inherent merits like device-free, non-line-of-sight covering, and privacy-friendly. However, current WiFi-based approaches mainly reply on domain-specific training since they don't know ``\emph{where to look}'' and ``\emph{when to look}''. To this end, we propose WiGRUNT, a WiFi-enabled gesture recognition system using dual-attention network, to mimic how a keen human being intercepting a gesture regardless of the environment variations. The key insight is to train the network to dynamically focus on the domain-independent features of a gesture on the WiFi Channel State Information (CSI) via a spatial-temporal dual-attention mechanism. WiGRUNT roots in a Deep Residual Network (ResNet) backbone to evaluate the importance of spatial-temporal clues and exploit their inbuilt sequential correlations for fine-grained gesture recognition. We evaluate WiGRUNT on the open Widar3 dataset and show that it significantly outperforms its state-of-the-art rivals by achieving the best-ever performance in-domain or cross-domain.

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WiGRUNT: WiFi-enabled Gesture Recognition Using Dual-attention Network

10.36227/techrxiv.13846052.v1 ◽

2021 ◽

Author(s):

Yu Gu ◽

Xiang Zhang ◽

Yantong Wang ◽

Meng Wang ◽

Zhi Liu ◽

...

Keyword(s):

Gesture Recognition ◽

Recognition System ◽

Channel State ◽

Attention Network ◽

Fine Grained ◽

Domain Specific ◽

Cross Domain ◽

Independent Features ◽

Device Free ◽

Domain Independent

Gestures constitute an important form of nonverbal communication where bodily actions are used for delivering messages alone or in parallel with spoken words. Recently, there exists an emerging trend of WiFi sensing enabled gesture recognition due to its inherent merits like device-free, non-line-of-sight covering, and privacy-friendly. However, current WiFi-based approaches mainly reply on domain-specific training since they don't know ``\emph{where to look}'' and ``\emph{when to look}''. To this end, we propose WiGRUNT, a WiFi-enabled gesture recognition system using dual-attention network, to mimic how a keen human being intercepting a gesture regardless of the environment variations. The key insight is to train the network to dynamically focus on the domain-independent features of a gesture on the WiFi Channel State Information (CSI) via a spatial-temporal dual-attention mechanism. WiGRUNT roots in a Deep Residual Network (ResNet) backbone to evaluate the importance of spatial-temporal clues and exploit their inbuilt sequential correlations for fine-grained gesture recognition. We evaluate WiGRUNT on the open Widar3 dataset and show that it significantly outperforms its state-of-the-art rivals by achieving the best-ever performance in-domain or cross-domain.

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Sentence Level Domain Independent Opinion and Targets Identification in Unstructured Reviews

Computers ◽

10.3390/computers7040070 ◽

2018 ◽

Vol 7 (4) ◽

pp. 70

Author(s):

Kahirullah Khan ◽

Wahab Khan

Keyword(s):

Language Processing ◽

Opinion Mining ◽

Second Phase ◽

Opinion Extraction ◽

Connection Machine ◽

Sentence Level ◽

Two Phases ◽

Independent Features ◽

Processing Techniques ◽

Domain Independent

User reviews, blogs, and social media data are widely used for various types of decision-making. In this connection, Machine Learning and Natural Language Processing techniques are employed to automate the process of opinion extraction and summarization. We have studied different techniques of opinion mining and found that the extraction of opinion target and opinion words and the relation identification between them are the main tasks of state-of-the-art techniques. Furthermore, domain-independent features extraction is still a challenging task, since it is costly to manually create an extensive list of features for every domain. In this study, we tested different syntactic patterns and semantic rules for the identification of evaluative expressions containing relevant target features and opinion. We have proposed a domain-independent framework that consists of two phases. First, we extract Best Fit Examples (BFE) consisting of short sentences and candidate phrases and in the second phase, pruning is employed to filter the candidate opinion targets and opinion words. The results of the proposed model are significant.

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Identification of new long-lived particles using deep neural networks

EPJ Web of Conferences ◽

10.1051/epjconf/202024506013 ◽

2020 ◽

Vol 245 ◽

pp. 06013

Author(s):

Matthias Komm

Keyword(s):

Neural Network ◽

Domain Adaptation ◽

Reference Analysis ◽

The Neural Network ◽

Independent Features ◽

High Level ◽

Fully Connected ◽

Backward Propagation ◽

Domain Independent ◽

Selection Of

We present the development of a deep neural network for identifying generic displaced jets arising from the decays of exotic long-lived particles in data recorded by the CMS detector at the CERN LHC. Various jet features including detailed information about each clustered particle candidate as well as reconstructed secondary vertices are refined through the use of 1-dimensional convolution layers before being combined with high-level engineered features and passed through a series of fully-connected layers. The proper lifetime of the long-lived particle, cτ0, is treated as a parameter of the neural network model, which allows for hypothesis testing over several orders of magnitude ranging from cτ0 = 1 µm to 10 m. Domain adaptation by backward propagation is performed to construct domain-independent features at an intermediate layer of the network to mitiage difference between simulation and data. The training is performed by streaming ROOT trees containing O(100M) jets directly into the TensorFlow queue system, which allows for a flexible selection of input features and asynchronous preprocessing. The application of the tagger is showcased in a search for long-lived gluinos as predicted by split supersymmetric models demonstrating significant gains in sensitivity over a reference analysis.

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Aspect-Based Sentiment Analysis Using Hybrid CNN-SVM with Particle Swarm Optimization for Domain Independent Datasets

International Journal of Emerging Trends in Engineering Research ◽

10.30534/ijeter/2020/628102020 ◽

2020 ◽

Vol 8 (10) ◽

pp. 7014-7023

Keyword(s):

Particle Swarm Optimization ◽

Sentiment Analysis ◽

Particle Swarm ◽

Swarm Optimization ◽

Domain Independent

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Efficient Domain-Independent Experimentation

10.21236/ada269530 ◽

1993 ◽

Cited By ~ 1

Author(s):

Yolanda Gil

Keyword(s):

Domain Independent

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Syntactic representation of null arguments

10.1093/oso/9780198815853.003.0010 ◽

2018 ◽

Author(s):

Marta Ruda

Keyword(s):

Linguistic Variation ◽

Null Arguments ◽

Syntactic Representation ◽

Independent Features ◽

Argument Omission ◽

Type Shifting

Focusing on definite-argument drop, this chapter puts forward the hypothesis that null arguments are minimally represented as [nPn] and maximally as a fully-fledged pronoun ([DP D [PersP Pers [NumP Num [nPn]]]] or [PersP Pers [NumP Num [nPn]]]). The (un)availability of such arguments in a language is a consequence of independent features of its grammar: the lexical specification of its nominalizing n heads (esp. their association with phonetic material) and the avaialbility of post-syntactic type-shifting operations (esp. ι‎). The working of this approach is illustrated mostly with data from English, Polish, and Kashubian. The two latter languages are argued here to differ from English with respect to the inflectional properties of their nouns, as well as with respect to the mechanisms of NP interpretation. The chapter discusses the predictions thehypothesis makes about the identity of null arguments with respect to cross-linguistic variation in the patterns of argument omission.

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