Deep semantic encodings for language modeling

2015 ◽  
Author(s):  
Ali Orkan Bayer ◽  
Giuseppe Riccardi
Keyword(s):  
Language Modeling ◽  
Semantic Encodings

On Using Written Language Training Data for Spoken Language Modeling

1994 ◽  
Author(s):  
R. Schwartz ◽  
L. Nguyen ◽  
F. Kubala ◽  
G. CHou ◽  
G. Zavaliagkos ◽  
...  
Keyword(s):  
Spoken Language ◽  
Language Modeling ◽  
Written Language ◽  
Training Data ◽  
Language Training

Character-Aware Sub-Word Level Language Modeling for Uyghur and Turkish ASR

2019 ◽  
Author(s):  
Chang Liu ◽  
Zhen Zhang ◽  
Pengyuan Zhang ◽  
Yonghong Yan
Keyword(s):  
Language Modeling ◽  
Word Level

A Language Modeling Approach to Information Retrieval

2017 ◽  
Vol 51 (2) ◽  
pp. 202-208 ◽  
Author(s):  
Jay M. Ponte ◽  
W. Bruce Croft
Keyword(s):  
Information Retrieval ◽  
Language Modeling ◽  
Modeling Approach

Betalogger: Smartphone Sensor-based Side-channel Attack Detection and Text Inference Using Language Modeling and Dense MultiLayer Neural Network

2021 ◽  
Vol 20 (5) ◽  
pp. 1-17
Author(s):  
Abdul Rehman Javed ◽  
Saif Ur Rehman ◽  
Mohib Ullah Khan ◽  
Mamoun Alazab ◽  
Habib Ullah Khan
Keyword(s):  
Neural Network ◽  
Language Modeling ◽  
Attack Detection ◽  
Machine Learning Algorithms ◽  
Second Phase ◽  
Sensitive Data ◽  
Phase Sequence ◽  
Smartphone Technology ◽  
Conventional Machine ◽  
Sequence Generator

With the recent advancement of smartphone technology in the past few years, smartphone usage has increased on a tremendous scale due to its portability and ability to perform many daily life tasks. As a result, smartphones have become one of the most valuable targets for hackers to perform cyberattacks, since the smartphone can contain individuals’ sensitive data. Smartphones are embedded with highly accurate sensors. This article proposes BetaLogger , an Android-based application that highlights the issue of leaking smartphone users’ privacy using smartphone hardware sensors (accelerometer, magnetometer, and gyroscope). BetaLogger efficiently infers the typed text (long or short) on a smartphone keyboard using Language Modeling and a Dense Multi-layer Neural Network (DMNN). BetaLogger is composed of two major phases: In the first phase, Text Inference Vector is given as input to the DMNN model to predict the target labels comprising the alphabet, and in the second phase, sequence generator module generate the output sequence in the shape of a continuous sentence. The outcomes demonstrate that BetaLogger generates highly accurate short and long sentences, and it effectively enhances the inference rate in comparison with conventional machine learning algorithms and state-of-the-art studies.

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