Credential hardening by using touchstroke dynamics

Today, reliance on digital devices for daily routines has been shifted towards portable mobile devices. Therefore, the need for security enhancements within this platform is imminent. Numerous research works have been performed on strengthening password authentication by using keystroke dynamics biometrics, which involve computer keyboards and cellular phones as input devices. Nevertheless, experiments performed specifically on touch screen devices are relatively lacking. This paper describes a novel technique to strengthen security authentication systems on touch screen devices via a new sub variant behavioural biometrics called touchstroke dynamics. We capitalize on the high resolution timing latency and the pressure information on touch screen panel as feature data. Following this a light weight algorithm is introduced to calculate the similarity between feature vectors. In addition, a fusion approach is proposed to enhance the overall performance of the system to an equal error rate of 7.71% (short input) and 6.27% (long input)

A review of abnormal behavior detection in activities of daily living

Abnormal behavior detection (ABD) systems are built to automatically identify and recognize abnormal behavior from various input data types, such as sensor-based and vision-based input. As much as the attention received for ABD systems, the number of studies on ABD in activities of daily living (ADL) is limited. Owing to the increasing rate of elderly accidents in the home compound, ABD in ADL research should be given as much attention to preventing accidents by sending out signals when abnormal behavior such as falling is detected. In this study, we compare and contrast the formation of the ABD system in ADL from input data types (sensor-based input and vision-based input) to modeling techniques (conventional and deep learning approaches). We scrutinize the public datasets available and provide solutions for one of the significant issues: the lack of datasets in ABD in ADL. This work aims to guide new research to understand the field of ABD in ADL better and serve as a reference for future study of better Ambient Assisted Living with the growing smart home trend

AGI-P: A Gender Identification Framework for Authorship Analysis Using Customized Fine-Tuning of Multilingual Language Model

In this investigation, we propose a solution for the author’s gender identification task called AGI-P. This task has several real-world applications across different fields, such as marketing and advertising, forensic linguistics, sociology, recommendation systems, language processing, historical analysis, education, and language learning. We created a new dataset to evaluate our proposed method. The dataset is balanced in terms of gender using a random sampling method and consists of 1944 samples in total. We use accuracy as an evaluation measure and compare the performance of the proposed solution (AGI-P) against state-of-the-art machine learning classifiers and fine-tuned pre-trained multilingual language models such as DistilBERT, mBERT, XLM-RoBERTa, and Multilingual DEBERTa. In this regard, we also propose a customized fine-tuning strategy that improves the accuracy of the pre-trained language models for the author gender identification task. Our extensive experimental studies reveal that our solution (AGI-P) outperforms the well-known machine learning classifiers and fine-tuned pre-trained multilingual language models with an accuracy level of 92.03%. Moreover, the pre-trained multilingual language models, fine-tuned with the proposed customized strategy, outperform the fine-tuned pre-trained language models using an out-of-the-box fine-tuning strategy. The codebase and corpus can be accessed on our GitHub page at: https://github.com/mumairhassan/AGI-

Non-invasive health prediction from visually observable features [version 2; peer review: 1 approved, 1 approved with reservations]

Background: The unprecedented development of Artificial Intelligence has revolutionised the healthcare industry. In the next generation of healthcare systems, self-diagnosis will be pivotal to personalised healthcare services. During the COVID-19 pandemic, new screening and diagnostic approaches like mobile health are well-positioned to reduce disease spread and overcome geographical barriers. This paper presents a non-invasive screening approach to predict the health of a person from visually observable features using machine learning techniques. Images like face and skin surface of the patients are acquired using camera or mobile devices and analysed to derive clinical reasoning and prediction of the person’s health. Methods: In specific, a two-level classification approach is presented. The proposed hierarchical model chooses a class by training a binary classifier at the node of the hierarchy. Prediction is then made using a set of class-specific reduced feature set. Results: Testing accuracies of 86.87% and 76.84% are reported for the first and second-level classification. Empirical results demonstrate that the proposed approach yields favourable prediction results while greatly reduces the computational time. Conclusions: The study suggests that it is possible to predict the health condition of a person based on his/her face appearance using cost-effective machine learning approaches

Reasoning about privacy in mobile application install decisions: Risk perception and framing

© 2020 Elsevier Ltd Data sharing has become prevalent with the rapid growth of mobile technologies. A lack of awareness and understanding of privacy practices often results in the installation of privacy-invasive applications (apps) which could potentially put users' personal data at risk. This study aimed to explore how users’ risk perception could be shifted towards more privacy-aware decisions through generation fluency and framing manipulations. It is an online study composed of three components, an experiment and two questionnaires. We manipulated the availability of privacy worries, by asking participants to generate either 2 or 10 privacy worries. Generating 10 worries was experienced as difficult, whereas generating 2 was easy. The difficult experience led to downgraded perception of risk, and consequently increased likelihood of installing a low privacy rated fictional app. Therefore, we suggest that improving generation fluency of privacy concerns could encourage users’ adoption of a more conservative judgment strategy when installing an app, safeguarding them against privacy-invasive apps

Discutindo a educação ambiental no cotidiano escolar: desenvolvimento de projetos na escola formação inicial e continuada de professores

A presente pesquisa buscou discutir como a Educação Ambiental (EA) vem sendo trabalhada, no Ensino Fundamental e como os docentes desta escola compreendem e vem inserindo a EA no cotidiano escolar., em uma escola estadual do município de Tangará da Serra/MT, Brasil. Para tanto, realizou-se entrevistas com os professores que fazem parte de um projeto interdisciplinar de EA na escola pesquisada. Verificou-se que o projeto da escola não vem conseguindo alcançar os objetivos propostos por: desconhecimento do mesmo, pelos professores; formação deficiente dos professores, não entendimento da EA como processo de ensino-aprendizagem, falta de recursos didáticos, planejamento inadequado das atividades. A partir dessa constatação, procurou-se debater a impossibilidade de tratar do tema fora do trabalho interdisciplinar, bem como, e principalmente, a importância de um estudo mais aprofundado de EA, vinculando teoria e prática, tanto na formação docente, como em projetos escolares, a fim de fugir do tradicional vínculo “EA e ecologia, lixo e horta”.Facultad de Humanidades y Ciencias de la Educació

Password Strengthening by Using Keystroke Dynamics

In this thesis,we have proposed a promising method to strengthen existing password authentication system based on individuals\' typing pattern. Using keystroke dynamics as an additional biometric security entity is reliable, fast, non invasive and cost-effective. This additional security mechanism is invisible to users as they only need to type on a keyboard as usual and the security mechanism will be responsible to capture and process the users\' typing timing pattern without their knowledge

A survey of keystroke dynamics biometrics

Research on keystroke dynamics biometrics has been increasing, especially in the last decade. The main motivation behind this effort is due to the fact that keystroke dynamics biometrics is economical and can be easily integrated into the existing computer security systems with minimal alteration and user intervention. Numerous studies have been conducted in terms of data acquisition devices, feature representations, classification methods, experimental protocols, and evaluations. However, an up-to-date extensive survey and evaluation is not yet available. The objective of this paper is to provide an insightful survey and comparison on keystroke dynamics biometrics research performed throughout the last three decades, as well as offering suggestions and possible future research directions

Improving keystroke dynamics authentication system via multiple feature fusion scheme

This paper reports the performance and effect of diverse keystroke features combination on keystroke dynamic authentication system by using fusion scheme. First of all, four types of keystroke features are acquired from our collected dataset, later then transformed into similarity scores by the use of Gaussian Probability Density Function (GPD) and Direction Similarity Measure (DSM). Next, three fusion schemes are introduced to merge the scores pairing with six fusion rules. Result shows that the finest performance is obtained by the combination of both dwell time and flight time collectively. Finally, this experiment also investigates the effect of using larger dataset on performance, which turns out to be rather consistent. © 2012 IEEE