scholarly journals TKCA: a timely keystroke-based continuous user authentication with short keystroke sequence in uncontrolled settings

Cybersecurity ◽  
2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Lulu Yang ◽  
Chen Li ◽  
Ruibang You ◽  
Bibo Tu ◽  
Linghui Li
Keyword(s):  
Short Term Memory ◽  
State Of The Art ◽  
User Authentication ◽  
Natural Setting ◽  
Short Text ◽  
Current State ◽  
Continuous User ◽  
Series Of Experiments ◽  
Lstm Network ◽  
The Relationship

AbstractKeystroke-based behavioral biometrics have been proven effective for continuous user authentication. Current state-of-the-art algorithms have achieved outstanding results in long text or short text collected by doing some tasks. It remains a considerable challenge to authenticate users continuously and accurately with short keystroke inputs collected in uncontrolled settings. In this work, we propose a Timely Keystroke-based method for Continuous user Authentication, named TKCA. It integrates the key name and two kinds of timing features through an embedding mechanism. And it captures the relationship between context keystrokes by the Bidirectional Long Short-Term Memory (Bi-LSTM) network. We conduct a series of experiments to validate it on a public dataset - the Clarkson II dataset collected in a completely uncontrolled and natural setting. Experiment results show that the proposed TKCA achieves state-of-the-art performance with 8.28% of EER when using only 30 keystrokes and 2.78% of EER when using 190 keystrokes.

scholarly journals Continuous Mobile User Authentication Using Combined Biometric Traits

2021 ◽  
Vol 11 (24) ◽  
pp. 11756
Author(s):  
Dominik Reichinger ◽  
Erik Sonnleitner ◽  
Marc Kurz
Keyword(s):  
Mobile Devices ◽  
State Of The Art ◽  
User Authentication ◽  
Single Point ◽  
Mobile User ◽  
Accelerometer Data ◽  
Point Of Entry ◽  
Current State ◽  
Continuous User ◽  
Data Points

Current state of the art authentication systems for mobile devices primarily rely on single point of entry authentication which imposes several flaws. For example, an attacker obtaining an unlocked device can potentially use and exploit it until the screen gets locked again. With continuous mobile user authentication, a system is embedded into the mobile devices, which continuously monitors biometric features of the person using the device, to validate if those monitored inputs match and therefore were made by the previously authenticated user. We start by giving an introduction towards the state of the art of currently used authentication systems and address related problems. For our main contribution we then propose, implement and discuss a continuous user authentication system for the Android ecosystem, which continuously monitors and records touch, accelerometer and timestamp data, and run experiments to gather data from multiple subjects. After feature extraction and normalization, a Hidden Markov Model is employed using an unsupervised learning approach as classifier and integrated into the Android application for further system evaluation and experimentation. The final model achieves an Area Under Curve of up to 100% while maintaining an Equal Error Rate of 1.34%. This is done by combining position and accelerometer data using gestures with at least 50 data points and averaging the prediction result of 25 consecutive gestures.

scholarly journals Insight and psychosis: the next 30 years

2019 ◽  
Vol 217 (3) ◽  
pp. 521-523 ◽  
Author(s):  
Anthony S. David
Keyword(s):  
Conceptual Development ◽  
State Of The Art ◽  
Involuntary Treatment ◽  
The Past ◽  
Current State ◽  
Decision Making Capacity ◽  
Cognitive Neurosciences ◽  
Clinical Associations ◽  
The Relationship ◽  
Made In

Academic interest in the concept of insight in psychosis has increased markedly over the past 30 years, prompting this selective appraisal of the current state of the art. Considerable progress has been made in terms of measurement and confirming a number of clinical associations. More recently, the relationship between insight and involuntary treatment has been scrutinised more closely alongside the link between decision-making capacity and insight. Advances in the clinical and cognitive neurosciences have influenced conceptual development, particularly the field of ‘metacognition’. New therapies, including those that are psychologically and neurophysiologically based, are being tested as ways to enhance insight.

JAZZ MELODY GENERATION USING RECURRENT NETWORKS AND REINFORCEMENT LEARNING

2006 ◽  
Vol 15 (04) ◽  
pp. 623-650
Author(s):  
JUDY A. FRANKLIN
Keyword(s):  
Reinforcement Learning ◽  
Dynamic Systems ◽  
Recurrent Neural Networks ◽  
Short Term Memory ◽  
State Of The Art ◽  
Recurrent Network ◽  
Recurrent Networks ◽  
Short Term ◽  
Long Short Term Memory ◽  
Lstm Network

Recurrent (neural) networks have been deployed as models for learning musical processes, by computational scientists who study processes such as dynamic systems. Over time, more intricate music has been learned as the state of the art in recurrent networks improves. One particular recurrent network, the Long Short-Term Memory (LSTM) network shows promise for learning long songs, and generating new songs. We are experimenting with a module containing two inter-recurrent LSTM networks to cooperatively learn several human melodies, based on the songs' harmonic structures, and on the feedback inherent in the network. We show that these networks can learn to reproduce four human melodies. We then present as input new harmonizations, so as to generate new songs. We describe the reharmonizations, and show the new melodies that result. We also present a hierarchical structure for using reinforcement learning to choose LSTM modules during the course of melody generation.

What Every Therapist Needs to Know About Couple Therapy

2019 ◽  
Vol 36 (3) ◽  
pp. 121-142 ◽  
Author(s):  
W. Kim Halford ◽  
Christopher A. Pepping
Keyword(s):  
Couple Therapy ◽  
State Of The Art ◽  
Couple Relationship ◽  
Couple Relationships ◽  
Art And Science ◽  
Relationship Distress ◽  
Relationship Problems ◽  
Current State ◽  
The Relationship

AbstractThis invited paper is a review of the significance of couple relationships to the practice of all therapists. The article begins with a summary of the evidence on the centrality of committed couple relationships to the lives and wellbeing of adults, and the association of the quality of the parents’ couple relationship on the wellbeing of children. We argue that the well-established reciprocal association between individual problems and couple relationship problems means that all therapists need to pay attention to how a couple relationship might be influencing a client's functioning, even if the relationship is not the presenting problem. There is an outline the evolution of current approaches to behavioural couple therapy, and the current state of the art and science of couple therapy. We present an analysis of the evidence for couple therapy as a treatment for relationship distress, as well as couple-based treatments for individual problems. This is followed by a description of the distinctive challenges in working with couples and how to address those challenges, and recommendations about how to address the needs of diverse couple relationships. Finally, we propose some core therapist competencies needed to work effectively with couples.

A Lightweight Privacy-Aware Continuous Authentication Protocol-PACA

2021 ◽  
Vol 24 (4) ◽  
pp. 1-28
Author(s):  
Abbas Acar ◽  
Shoukat Ali ◽  
Koray Karabina ◽  
Cengiz Kaygusuz ◽  
Hidayet Aksu ◽  
...  
Keyword(s):  
Template Matching ◽  
User Authentication ◽  
Third Party ◽  
Biometric Data ◽  
Concrete System ◽  
Current State ◽  
Continuous Authentication ◽  
User Data ◽  
Continuous User ◽  
Privacy Issues

As many vulnerabilities of one-time authentication systems have already been uncovered, there is a growing need and trend to adopt continuous authentication systems. Biometrics provides an excellent means for periodic verification of the authenticated users without breaking the continuity of a session. Nevertheless, as attacks to computing systems increase, biometric systems demand more user information in their operations, yielding privacy issues for users in biometric-based continuous authentication systems. However, the current state-of-the-art privacy technologies are not viable or costly for the continuous authentication systems, which require periodic real-time verification. In this article, we introduce a novel, lightweight, <underline>p</underline>rivacy-<underline>a</underline>ware, and secure <underline>c</underline>ontinuous <underline>a</underline>uthentication protocol called PACA. PACA is initiated through a password-based key exchange (PAKE) mechanism, and it continuously authenticates users based on their biometrics in a privacy-aware manner. Then, we design an actual continuous user authentication system under the proposed protocol. In this concrete system, we utilize a privacy-aware template matching technique and a wearable-assisted keystroke dynamics-based continuous authentication method. This provides privacy guarantees without relying on any trusted third party while allowing the comparison of noisy user inputs (due to biometric data) and yielding an efficient and lightweight protocol. Finally, we implement our system on an Apple smartwatch and perform experiments with real user data to evaluate the accuracy and resource consumption of our concrete system.

scholarly journals Verification and Parameter Synthesis for Real-Time Programs using Refinement of Trace Abstraction*

2021 ◽  
Vol 178 (1-2) ◽  
pp. 31-57
Author(s):  
Franck Cassez ◽  
Peter Gjøl Jensen ◽  
Kim Guldstrand Larsen
Keyword(s):  
Real Time ◽  
State Of The Art ◽  
Timed Automata ◽  
Robustness Analysis ◽  
Hybrid Automata ◽  
Timed Systems ◽  
Current State ◽  
Parameter Synthesis ◽  
Series Of Experiments ◽  
Time Systems

We address the safety verification and synthesis problems for real-time systems. We introduce real-time programs that are made of instructions that can perform assignments to discrete and real-valued variables. They are general enough to capture interesting classes of timed systems such as timed automata, stopwatch automata, time(d) Petri nets and hybrid automata. We propose a semi-algorithm using refinement of trace abstractions to solve both the reachability verification problem and the parameter synthesis problem for real-time programs. All of the algorithms proposed have been implemented and we have conducted a series of experiments, comparing the performance of our new approach to state-of-the-art tools in classical reachability, robustness analysis and parameter synthesis for timed systems. We show that our new method provides solutions to problems which are unsolvable by the current state-of-the-art tools.

scholarly journals Sentiment Analysis of Review Data Using Blockchain and LSTM to Improve Regulation for a Sustainable Market

2021 ◽  
Vol 17 (1) ◽  
pp. 1-19
Author(s):  
Zhihua Zhao ◽  
Zhihao Hao ◽  
Guancheng Wang ◽  
Dianhui Mao ◽  
Bob Zhang ◽  
...  
Keyword(s):  
Sentiment Analysis ◽  
Data Storage ◽  
Short Term Memory ◽  
Text Data ◽  
Specific Product ◽  
Short Text ◽  
Research Areas ◽  
Regulation Strategies ◽  
Lstm Network ◽  
Regulatory Decisions

E-commerce has developed greatly in recent years, as such, its regulations have become one of the most important research areas in order to implement a sustainable market. The analysis of a large amount of reviews data generated in the shopping process can be used to facilitate regulation: since the review data is short text and it is easy to extract the features through deep learning methods. Through these features, the sentiment analysis of the review data can be carried out to obtain the users’ emotional tendency for a specific product. Regulators can formulate reasonable regulation strategies based on the analysis results. However, the data has many issues such as poor reliability and easy tampering at present, which greatly affects the outcome and can lead regulators to make some unreasonable regulatory decisions according to these results. Blockchain provides the possibility of solving these problems due to its trustfulness, transparency and unmodifiable features. Based on these, the blockchain can be applied for data storage, and the Long short-term memory (LSTM) network can be employed to mine reviews data for emotional tendencies analysis. In order to improve the accuracy of the results, we designed a method to make LSTM better understand text data such as reviews containing idioms. In order to prove the effectiveness of the proposed method, different experiments were used for verification, with all results showing that the proposed method can achieve a good outcome in the sentiment analysis leading to regulators making better decisions.

scholarly journals SWIM: A Simple Word Interaction Model for Implicit Discourse Relation Recognition

2017 ◽  
Author(s):  
Wenqiang Lei ◽  
Xuancong Wang ◽  
Meichun Liu ◽  
Ilija Ilievski ◽  
Xiangnan He ◽  
...  
Keyword(s):  
Short Term Memory ◽  
State Of The Art ◽  
Interaction Model ◽  
Weighted Averaging ◽  
Short Term ◽  
Current State ◽  
Proposed Model ◽  
Order Of Magnitude ◽  
Long Short Term Memory ◽  
Semantic Interaction

Capturing the semantic interaction of pairs of words across arguments and proper argument representation are both crucial issues in implicit discourse relation recognition. The current state-of-the-art represents arguments as distributional vectors that are computed via bi-directional Long Short-Term Memory networks (BiLSTMs), known to have significant model complexity.In contrast, we demonstrate that word-weighted averaging can encode argument representation which can incorporate word pair information efficiently. By saving an order of magnitude in parameters, our proposed model achieves equivalent performance, but trains seven times faster.

scholarly journals Learning Object Context for Dense Captioning

2019 ◽  
Vol 33 ◽  
pp. 8650-8657 ◽  
Author(s):  
Xiangyang Li ◽  
Shuqiang Jiang ◽  
Jungong Han
Keyword(s):  
Short Term Memory ◽  
State Of The Art ◽  
Short Term ◽  
Visual Elements ◽  
Context Learning ◽  
Learning Procedure ◽  
Benchmark Datasets ◽  
Long Short Term Memory ◽  
Lstm Network ◽  
Context Features

Dense captioning is a challenging task which not only detects visual elements in images but also generates natural language sentences to describe them. Previous approaches do not leverage object information in images for this task. However, objects provide valuable cues to help predict the locations of caption regions as caption regions often highly overlap with objects (i.e. caption regions are usually parts of objects or combinations of them). Meanwhile, objects also provide important information for describing a target caption region as the corresponding description not only depicts its properties, but also involves its interactions with objects in the image. In this work, we propose a novel scheme with an object context encoding Long Short-Term Memory (LSTM) network to automatically learn complementary object context for each caption region, transferring knowledge from objects to caption regions. All contextual objects are arranged as a sequence and progressively fed into the context encoding module to obtain context features. Then both the learned object context features and region features are used to predict the bounding box offsets and generate the descriptions. The context learning procedure is in conjunction with the optimization of both location prediction and caption generation, thus enabling the object context encoding LSTM to capture and aggregate useful object context. Experiments on benchmark datasets demonstrate the superiority of our proposed approach over the state-of-the-art methods.

scholarly journals ALBERT over Match-LSTM Network for Intelligent Questions Classification in Chinese

Agronomy ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1530
Author(s):  
Xiaomin Wang ◽  
Haoriqin Wang ◽  
Guocheng Zhao ◽  
Zhichao Liu ◽  
Huarui Wu
Keyword(s):  
Question Answering ◽  
Information Service ◽  
Classification Performance ◽  
Short Text ◽  
Svm Model ◽  
Series Of Experiments ◽  
Lstm Network ◽  
Sparse Features ◽  
Gated Recurrent Unit

This paper introduces a series of experiments with an ALBERT over match-LSTM network on the top of pre-trained word vectors, for accurate classification of intelligent question answering and thus the guarantee of precise information service. To improve the performance of data classification, a short text classification method based on an ALBERT and match-LSTM model was proposed to overcome the limitations of the classification process, such as few vocabularies, sparse features, large amount of data, lots of noise and poor normalization. In the model, Jieba word segmentation tools and agricultural dictionary were selected to text segmentation, GloVe algorithm was then adopted to expand the text characteristic and weighted word vector according to the text of key vector, bi-directional gated recurrent unit was applied to catch the context feature information and multi-convolutional neural networks were finally established to gain local multidimensional characteristics of text. Batch normalization, Dropout, Global Average Pooling and Global Max Pooling were utilized to solve overfitting problem. The results showed that the model could classify questions accurately, with a precision of 96.8%. Compared with other classification models, such as multi-SVM model and CNN model, ALBERT+match-LSTM had obvious advantages in classification performance in intelligent Agri-tech information service.

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