Research in behavioral biometrics, especially keystroke and mouse behavioral biometrics, has increased in recent years, gaining traction in industry and academia across various fields, including the detection of emotion, age, gender, fatigue, identity theft, and online assessment fraud. These methods are popular because they collect data non-invasively and continuously authenticate users by analyzing unique keystroke or mouse behavior. However, user behavior evolves over time due to several underlying factors. This can affect the performance of current keystroke and mouse behavioral biometric-based user authentication systems. We comprehensively survey current keystroke and mouse behavioral biometric approaches, exploring their use in user authentication and other real-world applications while outlining trends and research gaps. In particular, we investigate whether current approaches compensate for user behavior evolution. We find that current keystroke and mouse behavioral biometrics approaches cannot adapt to user behavior evolution and suffer from limited efficacy. Our survey highlights the need for new and improved keystroke and mouse behavioral biometrics approaches that can adapt to user behavior evolution. This study will assist researchers in improving current research efforts toward developing more secure, effective, sustainable, robust, adaptable, and privacy-preserving keystroke and mouse-behavioral biometric-based authentication systems.

Mouse behavioral biometric-based authentication systems have attracted significant attention as they are considered a more secure alternative to conventional online assessment fraud detection systems. This is attributed to their ability to continuously authenticate users non-intrusively by analyzing their distinctive mouse operating behavior. Most behavioral biometric-based research studies focus on predicting user identity as the primary objective for online assessment fraud detection. However, they do not consider predicting other user-centric parameters like age and gender. Furthermore, there is a need to identify the best segmentation approach and mouse behavior feature set for age and gender classification. We propose the AgeGen Bio track system, a continuous mouse behavioral biometric-based age and gender tracking system for online education platforms. To accomplish this, we first collect novel mouse behavior data with user demographic information. We then evaluate the efficacy of different segmentation approaches, feature sets, and machine learning models for age and gender classification. Experimental results show that the random forest algorithm paired with the three mouse-movement segmentation approach and user characteristic feature set are the best approaches that need to be incorporated into the system, as they achieved promising results.

Devices on the Internet of Things (IoT) often have constrained resources and operate in diverse environments, making them vulnerable to unauthorized access and cyber threats. Electrocardiogram (ECG) signals have emerged as a promising biometric for authenticating users in such settings. However, current ECG-based authentication studies lack a standardized evaluation framework tailored to resource-limited IoT contexts and long-term usage, making it difficult to assess their practical reliability. In this paper, we introduce a new evaluation framework for ECG-based authentication on IoT devices and construct a standardized dataset to facilitate rigorous testing. We categorize performance metrics into four key dimensions: scalability, adaptability, efficiency, and cancelability. Using this framework, we evaluate four representative ECG authentication algorithms for IoT devices. The results show that these algorithms struggle to maintain consistent performance under cross-session authentication scenarios. These findings highlight the critical importance of addressing the temporal variability of ECG signals and the current gap in robust ECG-based authentication for IoT devices. We believe the proposed framework will guide future research toward more resilient and secure ECG authentication systems for the IoT.

Recent developments in mobile computing power and wireless communication speeds have significantly improved the efficiency of medical systems. This paper focuses on passive wearable sensor devices, which are integral to noninvasive monitoring of physiological data in healthcare observation. Beyond data collection, some wearables play an active role in patient treatment, underscoring the critical importance of protecting their security and privacy. Breach in these areas can severely affect patient health. However, the distinctive characteristics of wearable technologies introduce unique security and privacy challenges, including the potential for unauthorized access to sensitive location, medical, and physiological data. This review delves into the security and privacy concerns associated with wearable devices and proposes potential remedies. Its value lies in providing insights for researchers and manufacturers, aiming to advance the development of safer and more effective wearable medical technologies.

Online education systems have gained immense popularity due to their ubiquity, flexibility, openness, and accessibility. This has led many higher education institutions to incorporate online courses as part of blended or fully online learning. However, online assessment fraud remains a critical challenge. Conventional assessment fraud detection methods are often one-time, non-repudiable, invasive, expensive, and susceptible to spoofing. Even some advanced systems based on behavioral biometrics report comparatively lower accuracy, underscoring the ongoing challenge of achieving reliable user authentication. Furthermore, few research studies focus on behavioral biometric-based assessment fraud detection in online education platforms. To address these gaps, we introduce the UserID.AGE.GEN framework, which implements a cross-referencing fusion algorithm that integrates user demographic parameters, including age and gender, with mouse behavioral biometrics for user identity verification for online assessment fraud. Additionally, we collect novel task-specific data for our evaluation. Experimental results demonstrate that our method achieves promising results compared to some existing models, highlighting its strong performance and promising potential for broader application and future enhancement. A notable limitation of the proposed model is that it has not yet been evaluated using significantly larger external datasets, which may affect the generalizability of the results. Our evaluation was conducted using internally collected datasets. Additionally, the model has not been tested in real-world settings such as online education platforms, which may limit insights into its practical deployment.

Electrocardiogram(ECG)-based biometric authentication has become a promising method to improve security in wearable devices due to its inherent uniqueness and difficulty to replicate. However, no studies currently demonstrate that ECG authentication can resist modern attack techniques employed against biometric authentication. In this paper, we present a novel dictionary attack against ECG authentication systems, which poses a significant threat. In contrast to conventional targeted attacks, this approach utilizes random pairing to breach a vast number of users, without requiring specific information about their biometric data. Our approach leverages adversarial optimization and clustering to generate synthetic ECG waveforms capable of bypassing authentication mechanisms of various systems, revealing critical vulnerabilities in the current implementation of ECG-based biometrics. We comprehensively evaluate the effectiveness of this attack across different ECG authentication models, demonstrating that despite the intrinsic uniqueness of ECG signals, a substantial number of users are vulnerable. Our attack method can bypass the authentication system of an average of 20% of users even at the most stringent false acceptance rate of 1%. With up to five attack attempts allowed, our method can bypass up to 62% of users’ ECG authentication models.

In recent times, electrocardiogram signals have been leveraged for biometric verification. The efficacy of such authentication is reliant on the feature extraction from the electrocardiogram signals. A number of electrocardiogram feature extraction methods are currently available, but these methods may not be universally applicable in different dataset collection scenarios. To tackle this issue, this paper introduces a clustering-based framework to assess the feature extraction techniques for electrocardiogram biometrics. In this paper, the effectiveness of the framework is validated by using different electrocardiogram feature extraction techniques and different electrocardiogram databases. The framework provides important insights into electrocardiogram signal.

Computational ecoacoustics has seen significant growth in recent decades, facilitated by the reduced costs of digital sound recording devices and data storage. This progress has enabled the continuous monitoring of vocal fauna through Passive Acoustic Monitoring (PAM), a technique used to record and analyse environmental sounds to study animal behaviours and their habitats. While the collection of ecoacoustic data has become more accessible, the effective analysis of this information to understand animal behaviours and monitor populations remains a major challenge. This survey paper presents the state-of-the-art ecoacoustics data analysis approaches, with a focus on their applicability to large-scale PAM. We emphasise the importance of large-scale PAM, as it enables extensive geographical coverage and continuous monitoring, crucial for comprehensive biodiversity assessment and understanding ecological dynamics over wide areas and diverse habitats. This large-scale approach is particularly vital in the face of rapid environmental changes, as it provides crucial insights into the effects of these changes on a broad array of species and ecosystems. As such, we outline the most challenging large-scale ecoacoustics data analysis tasks, including pre-processing, visualisation, data labelling, detection, and classification. Each is evaluated according to its strengths, weaknesses and overall suitability to large-scale PAM, and recommendations are made for future research directions.

Spatio-temporal sequence forecasting is one of the fundamental tasks in spatio-temporal data mining.It facilitates many real world applications such as precipitation nowcasting, citywide crowd flow prediction and air pollution forecasting.Recently, a few Seq2Seq based approaches have been proposed, but one of the drawbacks of Seq2Seq models is that, small errors can accumulate quickly along the generated sequence at the inference stage due to the different distributions of training and inference phase.That is because Seq2Seq models minimise single step errors only during training, however the entire sequence has to be generated during the inference phase which generates a discrepancy between training and inference.In this work, we propose a novel curriculum learning based strategy named Temporal Progressive Growing Sampling to effectively bridge the gap between training and inference for spatio-temporal sequence forecasting, by transforming the training process from a fully-supervised manner which utilises all available previous groundtruth values to a less-supervised manner which replaces some of the ground-truth context with generated predictions.To do that we sample the target sequence from midway outputs from intermediate models trained with bigger timescales through a carefully designed decaying strategy.Experimental results demonstrate that our proposed method better models long term dependencies and outperforms baseline approaches on two competitive datasets.

Spatio-temporal trajectory classification is a fundamental problem for location-based services with many real-world applications such as travel mode classification, animal mobility detection, and location recommendation. In the literature, many approaches have been proposed to solve this classification task including deep learning models like LSTM recently for sequence classification. However, these approaches fail to consider both spatial and temporal interval information simultaneously, but share some common drawbacks: omitting either the spatial information or the temporal interval information out. Some models like Time-LSTM, have been proposed to handle the temporal interval information for spatio-temporal trajectories, but they do not take into account the spatial information. Note that, considering both spatial and temporal interval information is crucial for spatio-temporal data mining in order not to miss any spatio-temporal pattern. In this study, we propose a trajectory classifier called Spatio-Temporal GRU to better model the spatio-temporal correlations and irregular temporal intervals prevalently present in spatio-temporal trajectories. We introduce a novel segmented convolutional weight mechanism to capture short-term local spatial correlations in trajectories and propose an additional temporal gate to control the information flow related to the temporal interval information. Performance evaluation demonstrates that our proposed model outperforms popular deep learning approaches for the travel model classification problem.

Increasing geotagged social media data has become a potential repository used to find common trajectory patterns. Various spatial trajectory behaviors have been studied in previous work. In this paper, we extract common trajectory patterns on semantic level. We enrich trajectories with additional contextual semantic annotations and propose a density-based method to find semantic common trajectory patterns with a novel similarity measure method. Real geotagged photo data are used in our experiments. Experimental results demonstrate that our methods are able to generate semantic common trajectory patterns.

Sentiment analysis for social media and online document has been a burgeoning area in text mining for the last decade. However, Email sentiment analysis has not been studied and examined thoroughly even though it is one of the most ubiquitous means of communication. In this research, a hybrid sentiment analysis framework for Email data using term frequency-inverse document frequency term weighting model for feature extraction, and k-means labeling combined with support vector machine classifier for sentiment classification is proposed. Empirical results indicate comparatively better classification results with the proposed framework than other combinations.

We present an extensible, generic, spatio-temporal trajectory simplification framework that modularises trajectory simplification into the stages of normalising, ranking, and reduction. We combine a range of ranking strategies and scoring heuristics – some from the literature and some new – into our framework modules and create a variety of spatio-temporal trajectory simplification methods. These trajectory simplification methods are experimented upon using real world and synthetic datasets, measuring running time, geometric displacement, and region-of-interest visitation. The results indicate that our proposed framework creates a number of efficient and effective spatio-temporal trajectory simplification methods.

Alias-Wavefront OBJ meshes are a common text file type for transferring 3D mesh data between applications made by different vendors. However, as the mesh complexity gets higher and denser, the files become larger and slower to import. This paper explores the use of GPUs to accelerate the importing and parsing of OBJ files by studying file read-time, runtime, and load resistance. We propose a new method of reading and parsing that circumvents GPU architecture limitations and improves performance, seeing the new GPU method outperforms CPU methods with a 6× - 8× speedup. When running on a heavily loaded system, the new method only received an 80% performance hit, compared to the 160% that the CPU methods received. The loaded GPU speedup compared to unloaded CPU methods was 3.5×, and, when compared to loaded CPU methods, 8×. These results demonstrate that the time is right for further research into the use of data-parallel GPU acceleration beyond that of computer graphics and high performance computing.

Segmentation is of particular interest in market area analysis and intelligence. A generalized Voronoi influence model provides a flexible framework for segmenting diverse business cases and market tessellations. In this article, we propose a Voronoi influence model–based computational framework for mining various user-supplied business and market data sets from a GeoWeb model, in particular from Yahoo Local. The market area analysis and intelligence provided by the generalized Voronoi model can be effectively used for various market intelligence and strategy decision support. The advantages of using this computational model from the GeoWeb framework is the access to enormous amounts of participation-driven information on businesses and services available on the Internet. The proposed model supports weighted analysis, order-k analysis (k-nearest neighbor analysis), various metrics modeling different scenarios, complex modeling data types representing complex real-world situations, and obstacles modeling real-world barriers through a series of generalized Voronoi diagrams. We provide a series of cases studies that demonstrate the usefulness and practicability of the proposed model.

Privacy and data explosion issues are major concerns in intelligence and security informatics. An areal data representation is a popular way to overcome these two issues. As data grows at an unprecedented rate, there still needs an improvement in area data representations to be more scalable. This paper determines the potential for an alternative areal representation that can offer performance benefits over traditional methods for use within data-rich environments. From the experiments performed, improvements are promising, especially within time-critical applications that need to consider large amounts of data quickly.

Crime activities are geospatial phenomena and as such are geospatially, thematically and temporally correlated. Thus, crime datasets must be interpreted and analyzed in conjunction with various factors that can contribute to the formulation of crime. Discovering these correlations allows a deeper insight into the complex nature of criminal behavior. We introduce a graph based dataset representation that allows us to mine a set of datasets for correlation. We demonstrate our approach with real crime datasets and provide a comparison with other techniques.

We propose efficient and effective sequential-scan algorithms for intelligent emergency planning, spatial analysis and disaster decision support through the use of Voronoi Tessellations. We propose a modified distance transform algorithm to include complex primitives (point, line and area), Minkowski metrics, different weights, obstacles and higher-order Voronoi diagrams. Illustrated examples demonstrate the usefulness and robustness of our proposed computation model.

In this article, we propose a general-purpose disaster support system based on generalized (multiplicatively-weighted order-k Minkowski-metric) Voronoi diagrams. The proposed system is capable of handling disasters (or emergency units) having different weights in the complete order from 1 to k in the three popular Minkowski metrics (Euclidean, Manhattan and Maximum distance space). The proposed model supports neighboring queries, districting queries, location optimization queries and routing queries.

Understanding the process of categorization is of great importance for building intelligent agents. Formulated categories help agents find information easier and understand the external world better. Instance-based categorization and prototype-based categorization have been two dominant approaches in the AI community. However, they share some drawbacks in common. First, they are crisp boundary-based hard categorizations (similar to classification). Second, they are not well-suited for dynamic category learning and formation. We propose a hybrid soft categorization in the conceptual level that overcomes these drawbacks. The hybrid soft categorization merges the two popular hard categorizations and provides a robust fuzzy boundary-based soft categorization.