Identity verification is a critical component of financial data and systems security and privacy preservation, and is required by regulatory guidelines to ensure compliance with regulatory requirements and to aid in fraud prevention. With advances in artificial intelligence (AI), deep learning, and statistical methods, financial institutions are increasingly adopting multifactor authentication (MFA) that incorporates biometric-based approaches to perform authentication of identities for access to accounts, records or data and for verification of decision making and confirmation of actions.This paper presents a systematic review of current methodologies that utilize facial recognition, voice biometrics, and other data for identity verification in financial institutions. We explore the effectiveness and challenges associated with these approaches, highlighting recent developments in AI-driven models, deep learning architectures, and statistical techniques. In addition, we discuss the integration of multimodal biometric data and the decision and access systems that are developed for MFA approaches to improve security and accuracy. This review offers insights into the future of biometric identity verification in the financial sector.Our findings suggest that the integration of multi-modal data in financial applications could serve as a valuable avenue for future research and practical applications. In addition, investigating the role of biometric authentication in back-end systems is an important area worth further exploration.

This paper proposes a hybrid Convolutional Neural Network – Gated Recurrent Unit (CNN-GRU) architecture for producing algorithmic trading signals for the Binance Coin (BNB) cryptocurrency dataset. It uses the standalone models, CNN and GRU, as benchmarks for comparing both classification and trading performance. Results show that classification performance of CNN-GRU is quite subpar comparing to that of the standalone models; however, this model achieves the highest mean trading performance. Although the outcomes are financially promising, the paper has not explored algorithmic trading in its full glory, so results are open to further improvements. Possible future works include employing other methods for improving imbalanced classification, more feature engineering and testing with different timeframes, and a more involved approach with feature explainability.

This paper provides a systematic review of how artificial intelligence (AI) techniques have been applied in the diagnosis of traditional Chinese medicine (TCM). It highlights future directions for integrating the three key diagnostic indicators, tongue morphology, facial complexion, and pulse waveforms, into a unified framework. We found that existing studies rarely address the fundamental tension between TCM’s dialectical reasoning and the data-driven optimization strategies typical of computer vision models. Beyond reviewing current computer vision applications in tongue, facial, and pulse diagnosis, this paper also highlights the deeper epistemological divide between TCM and AI methodologies.

In this paper, we report on a research investigation exploring the application of a wearable, hands-free, low cost, AR audio-visual guidance system (HelpingHands) in the scenario of students learning clinical health procedures. The system enables health science students (nurses, doctors, and others) to learn and practice clinical procedures with real-time virtual guidance provided by a procedural expert (an instructor) located remotely at another site. User studies have been conducted to seek user requirements and evaluate the usability of the system. And the results demonstrated that HelpingHands is well-received by the end-users (students and instructors). This research contributes to the acquisition, development and performance of practical skills by students whilst undertaking work-integrated learning (WIL) placements in hospitals and other health care settings.

Research on remote assistance in real-world industries is sparse, as most research is conducted in the laboratory under controlled conditions. Consequently, little is known about how users tailor remote assistance technologies at work. Therefore, we developed an augmented reality-based remote assistance prototype called Remote Assist Kit (RAK). RAK is a component-based system, allowing us to study tailoring activities and the usefulness of tailorable remote assistance technologies. We conducted a user evaluation with employees from the plastic manufacturing industry. The employees configured the RAK to solve real-world problems in three collaborative scenarios: (1) troubleshooting a running injection molding machine, (2) tool maintenance, (3) solving a trigonometry problem. Our results show that the tailorability of RAK was perceived as useful, and users were able to successfully tailor RAK to the distinct properties of the scenarios. Specific findings and their implications for the design of tailorable remote assistance technologies are presented. Among other findings, requirements specific to remote assistance in the manufacturing industry were discussed, such as the importance of sharing machine sounds between the local operator and the remote helper. • Users tailor remote assistance to problem scenarios and find it useful. • Movable scene cameras dominated the “injection”-scenario for close-up views. • Awareness of a partner’s tailoring work is crucial for remote assistance. • Sharing machine sounds aids remote troubleshooting of running machines. • Multi-camera setups may enhance awareness at manufacturing machines.

Workspace awareness is critical for remote assistance with physical tasks, yet it remains difficult to facilitate. For example, if the remote helper is limited to the single viewpoint provided by the worker’s hand-held or head-mounted camera, she lacks the ability to gain an overview of the workspace. This may be addressed by granting the helper view-independence, e.g., through a multi-camera system. However, it can be cumbersome to set up and calibrate multiple cameras, and it can be challenging for the local worker to identify the current viewpoint of the remote helper. We present CueCam, a multi-camera remote assistance system that supports mutual workspace awareness through a flexible ad-hoc camera calibration and various Augmented Reality cues that communicate the helper’s viewpoint and focus. In particular, we propose visual cues presented through a head-mounted Augmented Reality display (Virtual Hand, Color Cue), and sound cues emitted from the cameras’ physical locations (Spatial Sound). Findings from a lab study indicate that all proposed cues effectively support the worker’s awareness of helper’s location and focus, while the Color Cue demonstrated superiority in task performance and preference ratings during a search task.

Finding a shortest path for a given pair of vertices in a graph drawing is one of the fundamental tasks for qualitative evaluation of graph drawings. In this paper, we present the first machine learning approach to predict human shortest path task performance, including accuracy, response time, and mental effort. To predict the shortest path task performance, we utilize correlated quality metrics and the ground truth data from the shortest path experiments. Specifically, we introduce path faithfulness metrics and show strong correlations with the shortest path task performance. Moreover, to mitigate the problem of insufficient ground truth training data, we use the transfer learning method to pre-train our deep model, exploiting the correlated quality metrics. Experimental results using the ground truth human shortest path experiment data show that our models can successfully predict the shortest path task performance. In particular, model MSP achieves an MSE (i.e., test mean square error) of 0.7243 (i.e., data range from −17.27 to 1.81) for prediction.

Recent methods for visualizing graphs have used a map metaphor: vertices are represented as regions in the plane, and proximity between regions represents edges between vertices.In many real world applications, the data changes over time, resulting in a dynamic map. This paper introduces new methods for representing dynamic graphs with map animation. More specifically, we present three different animation methods: MDSV (Multidimensional scaling - Voronoi), TV (Tutte - Voronoi) and TD (Tutte - dual). These methods support operations such as addition and deletion of vertices and edges. Each of our methods uses a kind of matrix interpolation.

Machine Learning (ML) plays a key role in various intelligent systems, and building an effective ML model for a data set is a difficult task involving various steps including data cleaning, feature definition and extraction, ML algorithms development, model training and evaluation as well as others. One of the most important steps in the process is to compare generated substantial amounts of ML models to find the optimal one for the deployment. It is challenging to compare such models with dynamic number of features. This paper proposes a novel visualisation approach based on a radial net to compare ML models trained with a different number of features of a given data set while revealing implicit dependent relations. In the proposed approach, ML models and features are represented by lines and arcs respectively. The dependence of ML models with dynamic number of features is encoded into the structure of visualisation, where ML models and their dependent features are directly revealed from related line connections. ML model performance information is encoded with colour and line width in the innovative visualisation. Together with the structure of visualization, feature importance can be directly discerned to help to understand ML models.

Dynamic networks are networks in which nodes and edges come and go at different times. The dynamic nature of this type of networks has imposed a challenge in network visualisation. Many techniques have been proposed to visualise dynamic networks in an effort of not only capturing the dynamic features of networks over the time but also laying out the network in a way that is fast and makes the visualisation aesthetically pleasing. However, empirical research has shown that the current techniques have limitations in one way or another. In the paper, we present an interactive visualisation called DGViewer which gives users control and flexibility allowing them to decide what, when and how to view visualisations to improve the analysis and comprehension of dynamic networks. A preliminary usability study indicates that users are positive with this user-centred visualisation for better user experience and task performance.

This paper introduces the use of pictogram design concept in ancient China for the development of a set of today's "graphic icons" or "infographics" in modern social network visualization systems. These graphic icons should be close to sensory symbols that derive their expressive power from their ability to use the perceptual processing power of the brain without learning. Therefore, with the use of such a set of "sensory symbols" we aim to achieve the identification of corresponding physical objects (or their attributes) to be performed close to the pre-attentive time.

We have proposed a proof of concept of a gaze window interface. A gaze window interface uses the user's gaze point to show the relevant content nearby the current gaze point. A system of a gaze window interface has been implemented that allows users to manage looking at multiple objects of interest on the screen while the system is responsive to the user's gaze points with relevant information shown in the gaze window. We also conducted user studies to investigate the effects of the interface on user performance and behaviors. In this paper, we report on a user study in which the gaze window was compared with a mouse window for supporting single user data entry tasks. We describe details of the study design and conduction and present the results.

Recent empirical works on graph drawing have analyzed users' interpretation ability with user-sketched layouts. However, user-sketched layouts have not been studied with vertex-weighted graphs. We conducted a study that was to extend the previous work to conduct an empirical study with vertex-weighted graphs. In a four-stage experiment, we analyzed characteristics of the final graph drawings, participants' drawing processes and strategies, participants' drawing preferences through questionnaires, and the differences between two groups of participants divided according to different attributes. In this paper, we report on effects of individual difference in terms of user preference, gender and prior drawing experience on user-sketched layouts of vertex-weighted graphs.

Scatterplots and scatterplot matrix methods have been popularly used for showing statistical graphics and for exposing patterns in multivariate data. A recent technique, called Linkable Scatterplots, provides an interesting idea for interactive visual exploration which provides a set of necessary plot panels on demand together with interaction, linking and brushing. This article presents a controlled study with a mixed-model design to evaluate the effectiveness and user experience on the visual exploration when using a Sequential-Scatterplots who a single plot is shown at a time, Multiple-Scatterplots who number of plots can be specified and shown, and Simultaneous-Scatterplots who all plots are shown as a scatterplot matrix. Results from the study demonstrated higher accuracy using the Multiple-Scatterplots visualization, particularly in comparison with the Simultaneous-Scatterplots.​ While the time taken to complete tasks was longer in the Multiple-Scatterplots technique, compared with the simpler Sequential-Scatterplots, Multiple-Scatterplots is inherently more accurate. Moreover, the Multiple-Scatterplots technique is the most highly preferred and positively experienced technique in this study. Overall, results support the strength of Multiple-Scatterplots and highlight its potential as an effective data visualization technique for exploring multivariate data.

We process information in memory and different people have different memory capacity. It is therefore important to understand possible impact of memory capacity when it comes to graph comprehension. In an attempt towards this direction, we conducted a user study investigating the impact of working memory capacity on graph reading task performance. Forty-six university students participated in the study performing a graph reading task with one hundred graph drawings of different complexity levels. Their working memory capacity and task performance (accuracy and time) were measured and recorded. The results of regression analyses indicated that working memory capacity was a significant predictor of performance accuracy, but not for response time. In this paper, we present the details of the study and discuss our findings and limitations of the study. Possible future research directions are also suggested.

Many researchers have studied various visual communication cues (e.g. pointer, sketching, and hand gesture) in Mixed Reality remote collaboration systems for real-world tasks. However, the effect of combining them has not been so well explored. We studied the effect of these cues in four combinations: hand only, hand + pointer, hand + sketch, and hand + pointer + sketch, with three problem tasks: Lego, Tangram, and Origami. The study results showed that the participants completed the task significantly faster and felt a significantly higher level of usability when the sketch cue is added to the hand gesture cue, but not with adding the pointer cue. Participants also preferred the combinations including hand and sketch cues over the other combinations. However, using additional cues (pointer or sketch) increased the perceived mental effort and did not improve the feeling of co-presence. We discuss the implications of these results and future research directions.

Emotion has been demonstrated to play an important role in the learning process. The capturing of these emotions is increasingly being digitally automated as researchers explore how technology can enhance student learning as well as teaching methodologies. More specifically, what emotions should be captured and how information about the captured emotions should be visualised has not been well researched in the context of teaching and learning. The study presented in this paper aims to produce a proof of concept prototype, called Sens-e-motion, that integrates multiple types of digital emotional monitoring and visualises the results in a meaningful way to the users. The monitoring includes facial expression recognition, blink-rate, text sentiment analysis, and keyword analysis. The prototype was designed and implemented as a web chat application with a RESTful web service backend and a NoSQL database. In this paper, we present the design of our system and describe technical details of it. Possible future research and development is also discussed.

Collaborative learning methods developed with XR technologies are broadly used in different educational fields such as engineering and healthcare. In this survey paper, we discuss different collaborative learning and teaching methods such as trainings and assessment tools from different perspectives. We look at existing research contributions toward collaborative learning systems of healthcare industry and according to the technologies used to develop the collaborative learning systems. The aim of this survey paper is to analyze the available literature, identify the collaborative learning systems which need further developments and discuss the healthcare, categorize which needs more attention, and bring them to the consideration of the future researchers.

Air quality is an important factor in planning activities in our everyday life. The information presented though captured data using Internet of Things (IoT) in smart cities is mostly single-dimensional where citizens do not have much opportunities to directly interact with the system to get personalised insights. Recent years have seen dire reports of extreme air pollution in mega cities around the world, which has led to government authorities grappling with solutions. Taking into account the existing IoT sensor setup in smart cities, it is now very convenient to visually explore the level of pollution of any places in real-time. In this context, this paper presents AqVision, a flexible visualisation tool for future citizens in smart cities that combines personalised awareness with generalised needs and leverages to envisage air pollution hotspots using more interactive manners considering individualised health and safety concerns.

In this paper, we explore the effect of showing a remote partner close to user gaze point in a teleconferencing system. We implemented a gaze following function in a teleconferencing system and investigate if this improves the user's feeling of emotional interdependence. We developed a prototype system that shows a remote partner close to the user's current gaze point and conducted a user study comparing it to a condition displaying the partner fixed in the corner of a screen. Our results showed that showing a partner close to their gaze point helped users feel a higher level of emotional interdependence. In addition, we compared the effect of our method between small and big displays, but there was no significant difference in the users' feeling of emotional interdependence even though the big display was preferred.

Graphs are often drawn into straight-line node-link diagrams for better understanding of the underlying data. However, curves have also been used in graph visualization for various purposes. Orthogonal drawing is one type of graph drawings in which each edge is made up with an alternating sequence of vertical and horizontal line segments. In the past, many algorithms have been developed for orthogonal drawing so that the resulting drawings meet some pre-defined aesthetic criteria and constraints. Experiments have also been conducted to evaluate performance of algorithms and effectiveness of orthogonal drawings. In this paper, we briefly summarize the research that has been done in relation to orthogonal drawing and provide directions for future research.

Curves have long been used for graph visualization with increased popularity in recent years. Curves are mainly used for two purposes: one is to increase readability and the other is to enhance visual aesthetic pleasingness. Although curves can be visually pleasing, the introduction of curves in graph drawing does not increase readability automatically. Attempts have been made to investigate the usability of curved drawings. However, the results on the effect of curves per se on human graph comprehension has not been conclusive. This paper presents a user study that is to examine the effect of curves when they are introduced to remove crossings. Twenty-six participants were recruited to perform typical graph reading tasks. Task performance and user preference data were collected for analysis. The results indicate that curves can be a useful alternative when crossings are to be present in straight-line drawings. The findings of the study are also discussed along with some of our future research activities in this paper.

We propose a novel calculation method of personality based on the Chinese physiognomy. The proposed solution combines the ancient and the modem physiognomy to summarize the corresponding relation between the personality and facial feature and model the baseline to shape the face feature. We compute histogram of image by searching for the threshold values to create a binary image in an adaptive way. The two-pass connected component method indicates the feature region. We encode the binary image to remove the noise point, so that the new connected image can provide a better result. The method was tested on ORL face database.

The aesthetics for user-sketched layouts of clustered graphs with known clustering information is analyzed empirically. In our experiments, given the adjacency list of a clustered graph and its predefined clustering information, each participant was asked to manually sketch clustered graphs "nicely" from scratch on a tablet system using a stylus. The main feature of this work different from the previous work is to investigate if the user's complex task of sketching clustered graphs "nicely" can be alleviated by providing the cluster partition information. Results show that most participants can draw graphs with clear presence of bridge edges and clustering cohesiveness.

How we visualize graph data is important for us to make sense of it. A number of aesthetic criteria have been used in practice to guide the visualization process and judge the quality of graph drawings. These aesthetics are limited since they often conflict with each other. It is generally agreed that in order to make visualizations effective, well-grounded perception and cognitive theories and design principles are needed. Some attempts have been made to develop visualization theories. In this paper, we present a preliminary study which we conducted with a cognitive approach to add to this growing body of research. More specifically, we propose a graph visualization model, which is further conceptualized into a two-stage assessment cycle. Examples of potentially useful methodologies and theories are introduced and their implications for producing user-friendly visualizations are discussed.

Modern visual analytics tools provide mechanism for users to gain unknown knowledge through effective visual interactions for user to quickly understand the progress of algorithms and adjust the input parameters on intermediate visualizations that towards the production of most satisfied outcome. This requires the quick production of a sequence of graph visualizations. However, the traditional force-directed graph drawing algorithms are very slow to reach an equilibrium configuration of forces. They usually spend tens of seconds producing the layout of a graph converge. Thus, they do not satisfy the requirement of rapid drawing of graphs. This paper proposes a fast convergence method for drawing force-directed graphs. We essentially pre-calculate the geometrical position of all vertices before applying a force-directed layout algorithm to reach the energy minimization of the graph layout. The experimental results have shown that this approach could not only reduce the convergence time but also the number of edge crossings that approves the quality of layout significantly.

Recent advance in the design of near-eye displays means that the technology is mature for industrial use. A near eye display has been used as part of a remote mobile guiding system for maintenance operators in mines. In this system, a near eye display is mounted on a helmet worn by a mobile operator to display visual aids sent from a remote helper. The near eye display is partially occluding the view of the operator. In this paper we are interested in exploring the extent to which the partial occlusion of the operator's view is affecting his spatial understanding. In particular, we present an exploratory study investigating the potential impact of the near-eye display on spatial awareness of the operator. Usability was also studied. The study found no evidence showing that near-eye displays had negative impact on spatial awareness and users were also positive about the usability of the near-eye display for remote maintenance.

Aesthetics are often used to judge how good a graph drawing is in terms of specific drawing rules. A direct measurement of overall quality is missing. In this paper, we propose to evaluate quality based on aggregation of individual aesthetics. This measure can be used by visualization designers to quickly compare the quality of drawings at hand at the design stage and make decisions accordingly. We present a user study that validates this measure. The implications of the proposed measure for future research are discussed.

When visualizing graphs into node-link diagrams, angular resolution is often used as one of the aesthetic criteria measuring the diagram quality in terms of human comprehension. However, angular resolution has not been empirically validated for its relevance to humans. In addition, although many force-directed algorithms have been proposed for automatic graph drawing, performance evaluation of these algorithms has not been conclusive due to the lack of proper methods. To shorten these gaps, this paper 1) validates the aesthetic based on human experimental data and identifies the best of angular resolution measures used in the literature; 2) introduces a force-directed algorithm, forceAR, for improving angular resolution; 3) proposes a new framework for more reliable and thorough evaluation of force-directed algorithms. Finally as a case study, our forceAR algorithm is evaluated using this framework.

Recent research has indicated that human graph reading performance can be affected by the size of crossing angle. Crossing angle is closely related to another aesthetic criterion: number of edge crossings. Although crossing number has been previously identified as the most important aesthetic, its relative impact on performance of human graph reading is unknown, compared to crossing angle. In this paper, we present an exploratory user study investigating the relative importance between crossing number and crossing angle. This study also aims to further examine the effects of crossing number and crossing angle not only on task performance measured as response time and accuracy, but also on cognitive load and visualization efficiency. The experimental results reinforce the previous findings of the effects of the two aesthetics on graph comprehension. The study demonstrates that on average these two closely related aesthetics together explain 33% of variance in the four usability measures: time, accuracy, mental effort and visualization efficiency, with about 38% of the explained variance being attributed to the crossing angle.

Many automatic graph drawing algorithms implement only one or two aesthetic criteria since most aesthetics conflict with each other. Empirical research has shown that although those algorithms are based on different aesthetics, drawings produced by them have comparable effectiveness. The comparable effectiveness raises a question about necessity of choosing one algorithm against another for drawing graphs when human performance is a main concern. In this paper, we argue that effectiveness can be improved when algorithms are designed by making compromises between aesthetics, rather than trying to satisfy one or two of them to the fullest. In particular, this paper presents a user study. The study compares effectiveness of drawings produced by two different force-directed methods, Classical spring algorithm and BIGANGLE. BIGANGLE produces drawings with a few aesthetics being improved at the same time. The experimental results indicate that BIGANGLE induces significantly better performance of humans in perceiving shortest paths between two nodes.

The end result of graph visualization is that people read the graph and understand the data. To make this effective, it is essential to construct visualizations based on how people read graphs. Despite the popularity and importance of graph usage in a variety of application domains, little is known about how people read graphs. The lack of this knowledge has severely limited the effectiveness of graph visualizations. In attempts to understand how people read graphs, we previously observed that people have geodesic-path tendency based on subjective eye tracking data. This paper presents two controlled experiments. One is to approve the existence of the geodesic-path tendency. The other is to examine the effects of this tendency on people in reading graphs. The results show that in performing path search tasks, when eyes encounter a node that has more than one link, links that go toward the target node are more likely to be searched first. The results also indicate that when graphs are drawn with branch links on the path leading away from the target node, graph reading performance can be significantly improved.

In visualizing graphs as node-link diagrams, it is commonly accepted and employed as a general rule that the number of link crossings should be minimized whenever possible. However, little attention has been paid to how to handle the remaining crossings in the visualization. The study presented in this paper examines the effects of crossing angles on performance of path tracing tasks. It was found that the effect varied with the size of crossing angles. In particular, task response time decreased as the crossing angle increased. However, the rate of the decrease tended to level off when the angle was close to 90 degrees. One of the implications of this study in graph visualization is that just minimizing the crossing number is not sufficient to reduce the negative impact to the minimum. The angles of remaining crossings should be maximized as well.

Many of the complex networks that occur both in nature and in technology are built up from frequently recurring patterns of basic structural elements. These structural patterns known as motifs play a significant role in the function of the network. Visualisation is a useful tool for understanding the structure in a network. The quality of a visualisation can be significantly improved if it effectively displays these motifs. In this paper we present visualisations designed to highlight motifs detected through analysis. We argue that these visualisations designed to show functionally important subgraphs give a greater insight into the function of the network.