Virtual Mouse using OpenCV

This research introduces a novel method for controlling mouse movement with a real-time camera. Adding more buttons or repositioning the mouse's tracking ball are two common ways. Instead, we recommend that the hardware be redesigned. Our idea is to employ a camera and computer vision technologies to manage mouse tasks (clicking and scrolling), and we demonstrate how it can do all that existing mouse devices can. This project demonstrates how to construct a mouse control system.

Design and Implementation of Continuous Authentication Mechanism Based on Multimodal Fusion Mechanism

Most of the current authentication mechanisms adopt the “one-time authentication,” which authenticate users for initial access. Once users have been authenticated, they can access network services without further verifications. In this case, after an illegal user completes authentication through identity forgery or a malicious user completes authentication by hijacking a legitimate user, his or her behaviour will become uncontrollable and may result in unknown risks to the network. These kinds of insider attacks have been increasingly threatening lots of organizations, and have boosted the emergence of zero trust architecture. In this paper, we propose a Multimodal Fusion-based Continuous Authentication (MFCA) scheme, which collects multidimensional behaviour characteristics during the online process, verifies their identities continuously, and locks out the users once abnormal behaviours are detected to protect data privacy and prevent the risk of potential attack. More specifically, MFCA integrates the behaviours of keystroke, mouse movement, and application usage and presents a multimodal fusion mechanism and trust model to effectively figure out user behaviours. To evaluate the performance of the MFCA, we designed and implemented the MFCA system and the experimental results show that the MFCA can detect illegal users in quick time with high accuracy.

Mouse movements reflect personality traits and task attentiveness in online experiments

In this rapidly digitizing world, it is becoming ever more important to understand people’s online behaviors in both scientific and consumer research settings. A cost-effective way to gain a deeper understanding of these behaviors is to examine mouse movement patterns. This research explores the feasibility of inferring personality traits from these mouse movement features (i.e., pauses, fixations, cursor speed, clicks) on a simple image choice task. We compare the results of standard univariate (OLS regression, bivariate correlations) and three forms of multivariate partial least squares (PLS) analyses. This work also examines whether mouse movements can predict task attentiveness, and how these might be related to personality traits. Results of the PLS analyses showed significant associations between a linear combination of personality traits (high Conscientiousness, Agreeableness, and Openness, and low Neuroticism) and several mouse movements associated with slower, more deliberate responding (less unnecessary clicks, more fixations). Additionally, several click-related mouse features were associated with attentiveness to the task. Importantly, as the image choice task itself is not intended to assess personality in any way, our results validate the feasibility of using mouse movements to infer internal traits across experimental contexts, particularly when examined using multivariate analyses and a multiverse approach.

Predicting Question Difficulty in Web Surveys: A Machine Learning Approach Based on Mouse Movement Features

Survey research aims to collect robust and reliable data from respondents. However, despite researchers’ efforts in designing questionnaires, survey instruments may be imperfect, and question structure not as clear as could be, thus creating a burden for respondents. If it were possible to detect such problems, this knowledge could be used to predict problems in a questionnaire during pretesting, inform real-time interventions through responsive questionnaire design, or to indicate and correct measurement error after the fact. Previous research has used paradata, specifically response times, to detect difficulties and help improve user experience and data quality. Today, richer data sources are available, for example, movements respondents make with their mouse, as an additional detailed indicator for the respondent–survey interaction. This article uses machine learning techniques to explore the predictive value of mouse-tracking data regarding a question’s difficulty. We use data from a survey on respondents’ employment history and demographic information, in which we experimentally manipulate the difficulty of several questions. Using measures derived from mouse movements, we predict whether respondents have answered the easy or difficult version of a question, using and comparing several state-of-the-art supervised learning methods. We have also developed a personalization method that adjusts for respondents’ baseline mouse behavior and evaluate its performance. For all three manipulated survey questions, we find that including the full set of mouse movement measures and accounting for individual differences in these measures improve prediction performance over response-time-only models.

An Empirical Evaluation of Online Continuous Authentication and Anomaly Detection Using Mouse Clickstream Data Analysis

While the password-based authentication used in social networks, e-mail, e-commerce, and online banking is vulnerable to hackings, biometric-based continuous authentication systems have been used successfully to handle the rise in unauthorized accesses. In this study, an empirical evaluation of online continuous authentication (CA) and anomaly detection (AD) based on mouse clickstream data analysis is presented. This research started by gathering a set of online mouse-dynamics information from 20 participants by using software developed for collecting mouse information, extracting approximately 87 features from the raw dataset. In contrast to previous work, the efficiency of CA and AD was studied using different machine learning (ML) and deep learning (DL) algorithms, namely, decision tree classifier (DT), k-nearest neighbor classifier (KNN), random forest classifier (RF), and convolutional neural network classifier (CNN). User identification was determined by using three scenarios: Scenario A, a single mouse movement action; Scenario B, a single point-and-click action; and Scenario C, a set of mouse movement and point-and-click actions. The results show that each classifier is capable of distinguishing between an authentic user and a fraudulent user with a comparatively high degree of accuracy.

IGF-1 inhibits MPTP/MPP+-induced autophagy on dopaminergic neurons through the IGF-1R/PI3K-Akt-mTOR pathway and GPER

Autophagy dysfunctions are involved in the pathogenesis of Parkinson’s disease (PD). In the present study, we aimed to evaluate the involvement of G protein-coupled estrogen receptor (GPER) in the inhibitory effect of insulin-like growth factor-1 (IGF-1) against excessive autophagy in PD animal and cellular models. 1-Methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) treatment significantly induced mouse movement disorder and decreased the protein level of tyrosine hydroxylase (TH) in the substantia nigra (SN) and dopamine (DA) content in striatum. Along with the dopamine neuron injury, we observed significant upregulations of microtubule-associated light chain-3 II (LC3-II) and α-synuclein as well as a downregulation of P62 in MPTP-treated mice. These changes could be restored by IGF-1 pretreatment. Cotreatment with IGF-1R antagonist JB-1 or GPER antagonist G15 could block the neuroprotective effects of IGF-1. 1-Methy-4-phenylpyridinium (MPP+) treatment could also excessively activate autophagy along with the reduction of cell viability in SH-SY5Y cells. IGF-1 could inhibit the neurotoxicity through promoting the phosphorylation of Akt and mammalian target of rapamycin (mTOR), which could also be antagonized by JB-1 or G15. These data suggest that IGF-1 inhibits MPTP/MPP+-induced autophagy on dopaminergic neurons through the IGF-1R/PI3K-Akt-mTOR pathway and GPER.