Towards analytical provenance visualization for criminal intelligence analysis

In criminal intelligence analysis to complement the information entailed and to enhance transparency of the operations, it demands logs of the individual processing activities within an automated processing system. Management and tracing of such security sensitive analytical information flow originated from tightly coupled visualizations into visual analytic system for criminal intelligence that triggers huge amount of analytical information on a single click, involves design and development challenges. To lead to a believable story by using scientific methods, reasoning for getting explicit knowledge of series of events, sequences and time surrounding interrelationships with available relevant information by using human perception, cognition, reasoning with database operations and computational methods, an analytic visual judgmental support is obvious for criminal intelligence. Our research outlines the requirements and development challenges of such system as well as proposes a generic way of capturing different complex visual analytical states and processes known as analytic provenance. The proposed technique has been tested into a large heterogeneous event-driven visual analytic modular analyst’s user interface (AUI) of the project VALCRI (Visual Analytics for Sensemaking in Criminal Intelligence) and evaluated by the police intelligence analysts through it’s visual state capturing and retracing interfaces. We have conducted several prototype evaluation sessions with the groups of end-users (police intelligence analysts) and found very positive feedback. Our approach provides a generic support for visual judgmental process into a large complex event-driven AUI system for criminal intelligence analysi

Understanding 3D mid-air hand gestures with interactive surfaces and displays: a systematic literature review

3D gesture based systems are becoming ubiquitous and there are many mid-air hand gestures that exist for interacting with digital surfaces and displays. There is no well defined gesture set for 3D mid-air hand gestures which makes it difficult to develop applications that have consistent gestures. To understand what gestures exist we conducted the first comprehensive systematic literature review on mid-air hand gestures following existing research methods. The results of the review identified 65 papers where the mid-air hand gestures supported tasks for selection, navigation, and manipulation. We also classified the gestures according to a gesture classification scheme and identified how these gestures have been empirically evaluated. The results of the review provide a richer understanding of what mid-air hand gestures have been designed, implemented, and evaluated in the literature which can help developers design better user experiences for digital interactive surfaces and displays

Multi-person Spatial Interaction in a Large Immersive Display Using Smartphones as Touchpads

In this paper, we present a multi-user interaction interface for a large immersive space that supports simultaneous screen interactions by combining (1) user input via personal smartphones and Bluetooth microphones, (2) spatial tracking via an overhead array of Kinect sensors, and (3) WebSocket interfaces to a webpage running on the large screen. Users are automatically, dynamically assigned personal and shared screen sub-spaces based on their tracked location with respect to the screen, and use a webpage on their personal smartphone for touchpad-type input. We report user experiments using our interaction framework that involve image selection and placement tasks, with the ultimate goal of realizing display-wall environments as viable, interactive workspaces with natural multimodal interfaces.Comment: 8 pages with reference

Voice Interaction for Augmented Reality Navigation Interfaces with Natural Language Understanding

Voice interaction with natural language understanding (NLU) has been extensively explored in desktop computers, handheld devices, and human-robot interaction. However, there is limited research into voice interaction with NLU in augmented reality (AR). There are benefits of using voice interaction in AR, such as high naturalness and being hands-free. In this project, we introduce VOARLA, an NLU-powered AR voice interface, which navigate courier driver delivery a package. A user study was completed to evaluate VOARLA against an AR voice interface without NLU to investigate the effectiveness of NLU in the navigation interface in AR. We evaluated from three aspects: accuracy, productivity, and commands learning curve. Results found that using NLU in AR increases the accuracy of the interface by 15%. However, higher accuracy did not correlate to an increase in productivity. Results suggest that NLU helped users remember the commands on the first run when they were unfamiliar with the system. This suggests that using NLU in an AR hands-free application can make the learning curve easier for new users

Code smells detection and visualization: A systematic literature review

Context: Code smells (CS) tend to compromise software quality and also demand more effort by developers to maintain and evolve the application throughout its life-cycle. They have long been cataloged with corresponding mitigating solutions called refactoring operations. Objective: This SLR has a twofold goal: the first is to identify the main code smells detection techniques and tools discussed in the literature, and the second is to analyze to which extent visual techniques have been applied to support the former. Method: Over 83 primary studies indexed in major scientific repositories were identified by our search string in this SLR. Then, following existing best practices for secondary studies, we applied inclusion/exclusion criteria to select the most relevant works, extract their features and classify them. Results: We found that the most commonly used approaches to code smells detection are search-based (30.1%), and metric-based (24.1%). Most of the studies (83.1%) use open-source software, with the Java language occupying the first position (77.1%). In terms of code smells, God Class (51.8%), Feature Envy (33.7%), and Long Method (26.5%) are the most covered ones. Machine learning techniques are used in 35% of the studies. Around 80% of the studies only detect code smells, without providing visualization techniques. In visualization-based approaches, several methods are used, such as city metaphors, 3D visualization techniques. Conclusions: We confirm that the detection of CS is a non-trivial task, and there is still a lot of work to be done in terms of: reducing the subjectivity associated with the definition and detection of CS; increasing the diversity of detected CS and of supported programming languages; constructing and sharing oracles and datasets to facilitate the replication of CS detection and visualization techniques validation experiments.info:eu-repo/semantics/acceptedVersio

Promoting reality awareness in virtual reality through proxemics

Head-Mounted Virtual reality (VR) systems provide full-immersive experiences to users and completely isolate them from the outside world, placing them in unsafe situations. Existing research proposed different alert-based solutions to address this. Our work builds on these studies on notification systems for VR environments from a different perspective. We focus on: (i) exploring alert systems to notify VR users about non-immersed bystanders' in socially related, non-critical interaction contexts; (ii) understanding how best to provide awareness of non-immersed bystanders while maintaining presence and immersion within the Virtual Environment(VE). To this end, we developed single and combined alert cues - leveraging proxemics, perception channels, and push/pull approaches and evaluated those via two user studies. Our findings indicate a strong preference towards maintaining immersion and combining audio and visual cues, push and pull notification techniques that evolve dynamically based on proximity

Spring warming in Yukon mountains is not amplified by the snow albedo feedback

Decreasing spring snow cover may amplify Arctic warming through the snow albedo feedback. To examine the impact of snowmelt on increasing temperature we used a 5,000 m elevation gradient in Yukon, Canada, extending from valley-bottom conifer forests, through middle elevation tundra, to high elevation icefields, to compare validated downscaled reanalysis air temperature patterns across elevational bands characterized by different patterns of spring snowmelt. From 2000 to 2014 we observed surface warming of 0.01 °C/a·1,000 m in May (0.14 °C/a at 1,000 m to 0.19 °C/a at 5,000 m), and uniform cooling of 0.09 °C/a in June at all elevations. May temperature trends across elevationally dependent land cover types were highly correlated with each other despite large variations in albedo and snow cover trends. Furthermore, a clear dependency of infrared skin temperature on snow cover mediated albedo decline was observed in tundra, but this was insufficient to influence average diurnal air temperature. We observed negative June temperature trends which we attribute to increasing daytime cloud cover because albedo and snow cover trends were unchanging. We conclude that 8-day and monthly averaged Spring air temperature trends are responding to a synoptic external forcing that is much stronger than the snow albedo feedback in sub-Arctic mountains

Hacking with Multi-touch for Java (MT4j)

Developing applications for touch devices is hard. Developing touch based applications for multi-user input is harder. The Multi-Touch for Java (MT4j) toolkit supports developing touch based applications for multiple users. In this paper, we outline our experience using MT4j for developing a number of software applications to support developers working in co-located teams. Our experience using the toolkit will help developers to understand the nuances of the toolkit and design issues that can be applied to other toolkits for developing multi-user touch based applications

Promoting Reality Awareness in Virtual Reality through Proxemics

Head-Mounted Virtual reality (VR) systems provide full-immersive experiences to users and completely isolate them from the outside world, placing them in unsafe situations. Existing research proposed different alert-based solutions to address this. Our work builds on these studies on notification systems for VR environments from a different perspective. We focus on: (i) exploring alert systems to notify VR users about non-immersed bystanders' in socially related, non-critical interaction contexts; (ii) understanding how best to provide awareness of non-immersed bystanders while maintaining presence and immersion within the Virtual Environment(VE). To this end, we developed single and combined alert cues - leveraging proxemics, perception channels, and push/pull approaches and evaluated those via two user studies. Our findings indicate a strong preference towards maintaining immersion and combining audio and visual cues, push and pull notification techniques that evolve dynamically based on proximity

Insect temperature-body size trends common to laboratory, latitudinal and seasonal gradients are not found across altitudes

Body size affects rates of most biological and ecological processes, from individual performance to ecosystem function, and is fundamentally linked to organism fitness. Within species, size at maturity can vary systematically with environmental temperature in the laboratory and across seasons, as well as over latitudinal gradients. Recent meta‐analyses have revealed a close match in the magnitude and direction of these size gradients in various arthropod orders, suggesting that these size responses share common drivers. As with increasing latitude, temperature also decreases with increasing altitude. Although the general direction of body size clines along altitudinal gradients has been examined previously, to our knowledge altitude–body size (A–S) clines have never been synthesised quantitatively, nor compared with temperature–size (T–S) responses measured under controlled laboratory conditions. Here we quantitatively examine variation in intraspecific A–S clines among 121 insect species from 50 different global locations, representing 12 taxonomic orders. While some taxa were better represented in the literature than others, our analysis reveals extensive variation in the magnitude and direction of A–S clines. Following the assumption that temperature on average declines by 1°C per 150 m increase in altitude, order‐specific A–S clines in the field appear to deviate from laboratory T–S responses. Specifically, the magnitude of A–S clines and T–S responses are more closely matched in some taxonomic orders (e.g. Diptera) than others (e.g. Orthoptera). These findings contrast with the strong co‐variation observed between latitude‐size clines and T–S responses, and between laboratory and seasonal T–S responses. The lack of clear size relationships with elevation, and hence temperature, is likely due to the counteracting effects of other major drivers with altitude, including season length and oxygen partial pressure. Switches in voltinism within species across altitude, and the dispersal of individuals across different elevations, may also obscure trends. A plain language summary is available for this article