Machine Learning Enhancing Adaptivity of Multimodal Mobile Systems

2011 ◽  
pp. 121-138
Author(s):  
Floriana Esposito ◽  
Teresa M.A. Basile ◽  
Nicola Di Mauro ◽  
Stefano Ferilli
Keyword(s):  
Machine Learning ◽  
Mobile Device ◽  
Pattern Discovery ◽  
User Interaction ◽  
User Model ◽  
Mobile Systems ◽  
Machine Learning Techniques ◽  
Two Phase ◽  
Learning Techniques ◽  
First Time

One of the most important features of a mobile device concerns its flexibility and capability to adapt the functionality it provides to the users. However, the main problems of the systems present in literature are their incapability to identify user needs and, more importantly, the insufficient mappings of those needs to available resources/services. In this paper, we present a two-phase construction of the user model: firstly, an initial static user model is built for the user connecting to the system the first time. Then, the model is revised/adjusted by considering the information collected in the logs of the user interaction with the device/context in order to make the model more adequate to the evolving user’s interests/ preferences/behaviour. The initial model is built by exploiting the stereotype concept, its adjustment is performed exploiting machine learning techniques and particularly, sequence mining and pattern discovery strategies.

Machine Learning Enhancing Adaptivity of Multimodal Mobile Systems

2012 ◽  
pp. 969-985
Author(s):  
Floriana Esposito ◽  
Teresa M.A. Basile ◽  
Nicola Di Mauro ◽  
Stefano Ferilli
Keyword(s):  
Machine Learning ◽  
Mobile Device ◽  
Pattern Discovery ◽  
User Interaction ◽  
User Model ◽  
Mobile Systems ◽  
Machine Learning Techniques ◽  
Two Phase ◽  
Learning Techniques ◽  
First Time

One of the most important features of a mobile device concerns its flexibility and capability to adapt the functionality it provides to the users. However, the main problems of the systems present in literature are their incapability to identify user needs and, more importantly, the insufficient mappings of those needs to available resources/services. In this paper, we present a two-phase construction of the user model: firstly, an initial static user model is built for the user connecting to the system the first time. Then, the model is revised/adjusted by considering the information collected in the logs of the user interaction with the device/context in order to make the model more adequate to the evolving user’s interests/ preferences/behaviour. The initial model is built by exploiting the stereotype concept, its adjustment is performed exploiting machine learning techniques and particularly, sequence mining and pattern discovery strategies.

scholarly journals Delving into Android Malware Families with a Novel Neural Projection Method

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Rafael Vega Vega ◽  
Héctor Quintián ◽  
Carlos Cambra ◽  
Nuño Basurto ◽  
Álvaro Herrero ◽  
...  
Keyword(s):  
Machine Learning ◽  
Projection Method ◽  
Hebbian Learning ◽  
Real Life ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Android Malware ◽  
Learning Techniques ◽  
First Time

Present research proposes the application of unsupervised and supervised machine-learning techniques to characterize Android malware families. More precisely, a novel unsupervised neural-projection method for dimensionality-reduction, namely, Beta Hebbian Learning (BHL), is applied to visually analyze such malware. Additionally, well-known supervised Decision Trees (DTs) are also applied for the first time in order to improve characterization of such families and compare the original features that are identified as the most important ones. The proposed techniques are validated when facing real-life Android malware data by means of the well-known and publicly available Malgenome dataset. Obtained results support the proposed approach, confirming the validity of BHL and DTs to gain deep knowledge on Android malware.

scholarly journals Tree-based machine learning performed in-memory with memristive analog CAM

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Giacomo Pedretti ◽  
Catherine E. Graves ◽  
Sergey Serebryakov ◽  
Ruibin Mao ◽  
Xia Sheng ◽  
...  
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Von Neumann ◽  
Mapping Algorithm ◽  
Content Addressable Memory ◽  
Model Inference ◽  
Learning Techniques ◽  
Top Performers ◽  
Access Patterns ◽  
First Time

AbstractTree-based machine learning techniques, such as Decision Trees and Random Forests, are top performers in several domains as they do well with limited training datasets and offer improved interpretability compared to Deep Neural Networks (DNN). However, these models are difficult to optimize for fast inference at scale without accuracy loss in von Neumann architectures due to non-uniform memory access patterns. Recently, we proposed a novel analog content addressable memory (CAM) based on emerging memristor devices for fast look-up table operations. Here, we propose for the first time to use the analog CAM as an in-memory computational primitive to accelerate tree-based model inference. We demonstrate an efficient mapping algorithm leveraging the new analog CAM capabilities such that each root to leaf path of a Decision Tree is programmed into a row. This new in-memory compute concept for enables few-cycle model inference, dramatically increasing 103 × the throughput over conventional approaches.

Machine Learning as an Enabler of Continuous and Adaptive Authentication in Multimedia Mobile Devices

2020 ◽  
pp. 21-47
Author(s):  
José María Jorquera Valero ◽  
Pedro Miguel Sánchez Sánchez ◽  
Alberto Huertas Celdran ◽  
Gregorio Martínez Pérez
Keyword(s):  
Machine Learning ◽  
Mobile Device ◽  
Machine Learning Techniques ◽  
Security Level ◽  
Design And Implementation ◽  
Continuous Authentication ◽  
Learning Techniques ◽  
Authentication System ◽  
And Performance ◽  
Selection Of

Continuous authentication systems allow users not to possess or remember something to authenticate themselves. These systems perform a permanent authentication that improves the security level of traditional mechanisms, which just authenticate from time to time. Despite the benefits of continuous authentication, the selection of dimensions and characteristics modelling of user's behaviour, and the creation and management of precise models based on Machine learning, are two important open challenges. This chapter proposes a continuous and adaptive authentication system that uses Machine Learning techniques based on the detection of anomalies. Applications usage and the location of the mobile device are considered to detect abnormal behaviours of users when interacting with the device. The proposed system provides adaptability to behavioural changes through the insertion and elimination of patterns. Finally, a proof of concept and several experiments justify the decisions made during the design and implementation of this work, as well as demonstrates its suitability and performance.

Two-Phase Flow Regime Identification Using Fluctuating Force Signals Under Machine Learning Techniques

2018 ◽  
Author(s):  
Yuta Saito ◽  
Shuhei Torisaki ◽  
Shuichiro Miwa
Keyword(s):  
Machine Learning ◽  
Flow Regime ◽  
Two Phase Flow ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Phase Flow ◽  
Two Phase ◽  
Learning Techniques ◽  
Flow Regime Identification

For rational design of industrial machineries such as nuclear power plants and heat exchanging devices, understanding of the two-phase flow regime is crucial. In this study, a new method of flow regime identification using the two-phase fluctuating force signals is proposed. Unlike the existing methodologies to measure two-phase flow parameters, the advantageous feature of utilizing the fluctuating force signal is that the measurement can be conducted under completely intrusive environment. Experiments were conducted using the tri-axial force transducers installed at the 90 degrees pipe bend of the vertical upward flow. For signal classification, machine learning techniques were utilized to identify flow regime, and four types flow regimes, namely, bubbly, slug, churn-turbulent, and annular flows were considered. From the obtained fluctuating force database, the features that characterize the signal were selected in the time and the frequency domain. In the current study, three types of machine learning algorithms such as the artificial neural network (ANN), support vector machine (SVM), and decision tree were examined and results obtained by each learning technique was compared.

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

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