Fine-Grained and Context-Aware Behavioral Biometrics for Pattern Lock on Smartphones

2021 ◽  
Vol 5 (1) ◽  
pp. 1-30
Author(s):  
Dai Shi ◽  
Dan Tao ◽  
Jiangtao Wang ◽  
Muyan Yao ◽  
Zhibo Wang ◽  
...  
Keyword(s):  
State Of The Art ◽  
Context Aware ◽  
Behavioral Biometrics ◽  
Body Postures ◽  
Fine Grained ◽  
Authentication Mechanism ◽  
System A ◽  
Authentication System

Pattern lock has been widely used in smartphones as a simple and effective authentication mechanism, which however is shown to be vulnerable to various attacks. In this paper, we design a novel authentication system for more secure pattern unlocking on smartphones. The basic idea is to utilize various behavior information of the user during pattern unlocking as additional authentication fingerprints, so that even if the pattern password is leaked to an attacker, the system remains safe and protected. To accommodate a variety of user contexts by our system, a context-aware module is proposed to distinguish any of such contexts (e.g., body postures when drawing the pattern) and use it to guide the authentication. Moreover, we design a polyline weighted strategy with overlapping based on the consistency of pattern lock, which analyzes the behavior information of the user during the unlock process in a fine-grained manner and takes an overall consideration the results of different polylines. Based on 14,850 samples collected from 77 participants, we have extensively evaluated the proposed system. The results demonstrate that it outperforms state-of-the-art implicit authentication based pattern lock approaches, and that each key module in our system is effective.

Considering Fine-Grained and Coarse-Grained Information for Context-Aware Recommendations

2020 ◽  
Author(s):  
Yiqin Luo ◽  
Yanpeng Sun ◽  
Liang Chang ◽  
Tianlong Gu ◽  
Chenzhong Bin ◽  
...  
Keyword(s):  
State Of The Art ◽  
Recommendation Systems ◽  
Coarse Grained ◽  
Evaluation Metrics ◽  
Parallel Structure ◽  
Context Aware ◽  
Preference Information ◽  
Fine Grained ◽  
Category Information ◽  
Candidate Item

Abstract In context-aware recommendation systems, most existing methods encode users’ preferences by mapping item and category information into the same space, which is just a stack of information. The item and category information contained in the interaction behaviours is not fully utilized. Moreover, since users’ preferences for a candidate item are influenced by the changes in temporal and historical behaviours, it is unreasonable to predict correlations between users and candidates by using users’ fixed features. A fine-grained and coarse-grained information based framework proposed in our paper which considers multi-granularity information of users’ historical behaviours. First, a parallel structure is provided to mine users’ preference information under different granularities. Then, self-attention and attention mechanisms are used to capture the dynamic preferences. Experiment results on two publicly available datasets show that our framework outperforms state-of-the-art methods across the calculated evaluation metrics.

Behavioral Biometrics

2010 ◽  
pp. 101-120 ◽  
Author(s):  
Kenneth Revett
Keyword(s):  
User Authentication ◽  
Error Rates ◽  
Keystroke Dynamics ◽  
Behavioral Biometrics ◽  
Authentication Mechanism ◽  
Computer Based ◽  
Authentication System ◽  
Novel Approaches ◽  
New Form ◽  
Minimal Effort

Behavioral biometrics is a relatively new form of authentication mechanism which relies on the way a person interacts with an authentication device. Traditional instances of this approach include voice, signature, and keystroke dynamics. Novel approaches to behavioral biometrics include biosignals, such as the electroencephalogram and the electrocardiogram. The biosignal approach to user authentication has been shown to produce equal error rates on par with more traditional behavioral biometric approaches. In addition, through a process similar to biofeedback, users can be trained with minimal effort to produce computer-based input via the manipulations of endogenous biosignal patterns. This chapter discusses the use of biosignal based biometrics, highlighting key studies and how this approach can be integrated into a multibiometric user authentication system.

Behavioral Genetics: Concepts for Research and Practice in Language Development and Disorders

1995 ◽  
Vol 38 (5) ◽  
pp. 1126-1142 ◽  
Author(s):  
Jeffrey W. Gilger
Keyword(s):  
Language Development ◽  
Behavioral Genetics ◽  
State Of The Art ◽  
Genetic Research ◽  
Great Promise ◽  
Behavioral Genetic ◽  
Fine Grained ◽  
Future Goals ◽  
Current State ◽  
Research Designs

This paper is an introduction to behavioral genetics for researchers and practioners in language development and disorders. The specific aims are to illustrate some essential concepts and to show how behavioral genetic research can be applied to the language sciences. Past genetic research on language-related traits has tended to focus on simple etiology (i.e., the heritability or familiality of language skills). The current state of the art, however, suggests that great promise lies in addressing more complex questions through behavioral genetic paradigms. In terms of future goals it is suggested that: (a) more behavioral genetic work of all types should be done—including replications and expansions of preliminary studies already in print; (b) work should focus on fine-grained, theory-based phenotypes with research designs that can address complex questions in language development; and (c) work in this area should utilize a variety of samples and methods (e.g., twin and family samples, heritability and segregation analyses, linkage and association tests, etc.).

Enhanced context-aware recommendation using topic modeling and particle swarm optimization

2021 ◽  
pp. 1-16
Author(s):  
Ibtissem Gasmi ◽  
Mohamed Walid Azizi ◽  
Hassina Seridi-Bouchelaghem ◽  
Nabiha Azizi ◽  
Samir Brahim Belhaouari
Keyword(s):  
Topic Modeling ◽  
Latent Dirichlet Allocation ◽  
State Of The Art ◽  
Weighting Function ◽  
Contextual Factors ◽  
Pearson Correlation ◽  
Correlation Coefficients ◽  
Pso Algorithm ◽  
Context Aware ◽  
Proposed Model

Context-Aware Recommender System (CARS) suggests more relevant services by adapting them to the user’s specific context situation. Nevertheless, the use of many contextual factors can increase data sparsity while few context parameters fail to introduce the contextual effects in recommendations. Moreover, several CARSs are based on similarity algorithms, such as cosine and Pearson correlation coefficients. These methods are not very effective in the sparse datasets. This paper presents a context-aware model to integrate contextual factors into prediction process when there are insufficient co-rated items. The proposed algorithm uses Latent Dirichlet Allocation (LDA) to learn the latent interests of users from the textual descriptions of items. Then, it integrates both the explicit contextual factors and their degree of importance in the prediction process by introducing a weighting function. Indeed, the PSO algorithm is employed to learn and optimize weights of these features. The results on the Movielens 1 M dataset show that the proposed model can achieve an F-measure of 45.51% with precision as 68.64%. Furthermore, the enhancement in MAE and RMSE can respectively reach 41.63% and 39.69% compared with the state-of-the-art techniques.

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.

scholarly journals Analysis of the Practical Implementation of Flicker Measurement Coprocessor for AMI Meters

Energies ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1589
Author(s):  
Krzysztof Kołek ◽  
Andrzej Firlit ◽  
Krzysztof Piątek ◽  
Krzysztof Chmielowiec
Keyword(s):  
Code Generation ◽  
State Of The Art ◽  
Power Grids ◽  
Practical Implementation ◽  
System A ◽  
Measuring Devices ◽  
Point Representation ◽  
Measurement Algorithm ◽  
On Chip ◽  
Vhdl Code

Monitoring power quality (PQ) indicators is an important part of modern power grids’ maintenance. Among different PQ indicators, flicker severity coefficients Pst and Plt are measures of voltage fluctuations. In state-of-the-art PQ measuring devices, the flicker measurement channel is usually implemented as a dedicated processor subsystem. Implementation of the IEC 61000-4-15 compliant flicker measurement algorithm requires a significant amount of computational power. In typical PQ analysers, the flicker measurement is usually implemented as a part of the meter’s algorithm performed by the main processor. This paper considers the implementation of the flicker measurement as an FPGA module to offload the processor subsystem or operate as an IP core in FPGA-based system-on-chip units. The measurement algorithm is developed and validated as a Simulink diagram, which is then converted to a fixed-point representation. Parts of the diagram are applied for automatic VHDL code generation, and the classifier block is implemented as a local soft-processor system. A simple eight-bit processor operates within the flicker measurement coprocessor and performs statistical operations. Finally, an IP module is created that can be considered as a flicker coprocessor module. When using the coprocessor, the main processor’s only role is to trigger the coprocessor and read the results, while the coprocessor independently calculates the flicker coefficients.

scholarly journals ShadingNet: Image Intrinsics by Fine-Grained Shading Decomposition

2021 ◽  
Author(s):  
Anil S. Baslamisli ◽  
Partha Das ◽  
Hoang-An Le ◽  
Sezer Karaoglu ◽  
Theo Gevers
Keyword(s):  
Neural Network ◽  
Large Scale ◽  
State Of The Art ◽  
Image Decomposition ◽  
Natural Environments ◽  
Decomposition Algorithms ◽  
Ambient Light ◽  
Fine Grained ◽  
Large Scale Dataset ◽  
Direct Illumination

AbstractIn general, intrinsic image decomposition algorithms interpret shading as one unified component including all photometric effects. As shading transitions are generally smoother than reflectance (albedo) changes, these methods may fail in distinguishing strong photometric effects from reflectance variations. Therefore, in this paper, we propose to decompose the shading component into direct (illumination) and indirect shading (ambient light and shadows) subcomponents. The aim is to distinguish strong photometric effects from reflectance variations. An end-to-end deep convolutional neural network (ShadingNet) is proposed that operates in a fine-to-coarse manner with a specialized fusion and refinement unit exploiting the fine-grained shading model. It is designed to learn specific reflectance cues separated from specific photometric effects to analyze the disentanglement capability. A large-scale dataset of scene-level synthetic images of outdoor natural environments is provided with fine-grained intrinsic image ground-truths. Large scale experiments show that our approach using fine-grained shading decompositions outperforms state-of-the-art algorithms utilizing unified shading on NED, MPI Sintel, GTA V, IIW, MIT Intrinsic Images, 3DRMS and SRD datasets.

App2Vec: Context-Aware Application Usage Prediction

2021 ◽  
Vol 15 (6) ◽  
pp. 1-21
Author(s):  
Huandong Wang ◽  
Yong Li ◽  
Mu Du ◽  
Zhenhui Li ◽  
Depeng Jin
Keyword(s):  
Dirichlet Process ◽  
Service Providers ◽  
State Of The Art ◽  
Representation Learning ◽  
Context Aware ◽  
Challenging Problem ◽  
Performance Gap ◽  
Bayesian Mixture Model ◽  
Bayesian Mixture ◽  
Spatio Temporal

Both app developers and service providers have strong motivations to understand when and where certain apps are used by users. However, it has been a challenging problem due to the highly skewed and noisy app usage data. Moreover, apps are regarded as independent items in existing studies, which fail to capture the hidden semantics in app usage traces. In this article, we propose App2Vec, a powerful representation learning model to learn the semantic embedding of apps with the consideration of spatio-temporal context. Based on the obtained semantic embeddings, we develop a probabilistic model based on the Bayesian mixture model and Dirichlet process to capture when , where , and what semantics of apps are used to predict the future usage. We evaluate our model using two different app usage datasets, which involve over 1.7 million users and 2,000+ apps. Evaluation results show that our proposed App2Vec algorithm outperforms the state-of-the-art algorithms in app usage prediction with a performance gap of over 17.0%.

scholarly journals A Machine Vision Approach for Bioreactor Foam Sensing

2021 ◽  
pp. 247263032110088
Author(s):  
Jonas Austerjost ◽  
Robert Söldner ◽  
Christoffer Edlund ◽  
Johan Trygg ◽  
David Pollard ◽  
...  
Keyword(s):  
Machine Learning ◽  
Machine Vision ◽  
State Of The Art ◽  
Low Cost ◽  
High Accuracy ◽  
Consumer Electronics ◽  
Learning System ◽  
Automotive Applications ◽  
Fine Grained

Machine vision is a powerful technology that has become increasingly popular and accurate during the last decade due to rapid advances in the field of machine learning. The majority of machine vision applications are currently found in consumer electronics, automotive applications, and quality control, yet the potential for bioprocessing applications is tremendous. For instance, detecting and controlling foam emergence is important for all upstream bioprocesses, but the lack of robust foam sensing often leads to batch failures from foam-outs or overaddition of antifoam agents. Here, we report a new low-cost, flexible, and reliable foam sensor concept for bioreactor applications. The concept applies convolutional neural networks (CNNs), a state-of-the-art machine learning system for image processing. The implemented method shows high accuracy for both binary foam detection (foam/no foam) and fine-grained classification of foam levels.

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