Augmented Reality is a technology that has been utilized in solutions to reduce the difficulties of human navigation. In this paper we survey a collection of these solutions, focusing on solutions with visualization elements meant to guide users. We propose a holistic framework for categorizing and understanding these solutions inspired by the Model-View-Controller software design pattern. The collected solutions are analyzed with our framework, and trends in the research are identified along with unsolved problems and potential future work.

Smart city projects require data to be transferred from one destination to the next using a number of different network protocols. The data pipelines involved in these smart city projects often have limited bandwidth or compute resources due to the low power nature of most embedded hardware. The data transferred between devices in these types of embedded systems are often structured in non-standard data schemata. Remote procedure calls (RPC) are implemented to transfer data between devices and switching between RPC implementations can be tricky due to the lack of standardization. There is no guarantee that an existing data schema will work with a different RPC implementation. This makes it difficult for a researcher or system developer to benchmark and compare different RPC im-plementations. In this paper, a conceptual infrastructure named SpeciServe is introduced where gRPC is used as a communication backbone due its support for flatbuffers and multiple server modes. Multiple software services are described to allow for dissimilar RPC implementations to be run in parallel. This system is intended to allow for researchers in machine learning, smart cities, and Internet of Things (loT) to be able test different versions of RPCs and provide support for system developers to define the functions of an edge service.

Point cloud data in the form of LiDAR is often utilized for its spatial qualities, especially in smart city projects for tasks involving vehicles and pedestrians. However, the process in which LiDAR data is acquired can be cumbersome to setup and automate. In this paper, we introduce a streaming and an on-demand pipeline for capturing LiDAR data from Velodyne Ultra Pucks placed along northern Nevada intersections known as the Living Lab as part of a smart city project for the city of Reno. The data coming from these intersections consist of the following formats: ROS 2 bag file, PCD, LAZ, Google Draco, and PCAP. A streaming point cloud service with PCD, LAZ, and Draco was implemented to stream any of these formats, as well as to allow the user to capture the current monitored point cloud. Additionally, two on-demand web services were implemented for both the PCAP and ROS 2 bag file to enable a user to start and stop the acquisition of LiDAR data in these formats. Through our analysis, it was discovered that Draco provided the best processing time and had a wider range of options that affected the quality of the point cloud. To evaluate this pipeline, the features of existing software were compared and a discussion was provided with an analysis of the point cloud formats.

Municipal buildings are major energy consumers in urban areas, contributing significantly to greenhouse gas emissions and climate change. Understanding and predicting their energy consumption patterns is crucial for informing energy policy and planning decisions. This study presents an investigation into the energy consumption patterns of municipal buildings in New York City, employing a suite of artificial intelligence (AI) techniques. Utilizing a robust dataset, we apply a range of machine learning models, including linear regression, random forest regressor, gradient boosting regressor, and neural networks, to predict energy consumption patterns. Our findings reveal that the random forest regressor model outperforms other models, achieving a mean squared error of 134.63. This underscores the potential of AI in providing accurate predictions of energy consumption, which can inform energy policy and planning decisions. However, the interpretability of these models remains a significant challenge, highlighting the need for further research into methods for enhancing the transparency and explainability of AI models. This study contributes to the burgeoning field of AI and energy consumption, offering valuable insights for policymakers, researchers, and practitioners. It underscores the potential of AI in transforming our understanding of energy consumption patterns, while also highlighting the challenges that need to be addressed to fully harness the power of AI in this domain.

This paper presents a work-in-progress on a learning system that will provide robotics students with a personalized learning environment. This addresses both the scarcity of skilled robotics instructors, particularly in community colleges and the expensive demand for training equipment. The study of robotics at the college level represents a wide range of interests, experiences, and aims. This project works to provide students the flexibility to adapt their learning to their own goals and prior experience. We are developing a system to enable robotics instruction through a web-based interface that is compatible with less expensive hardware. Therefore, the free distribution of teaching materials will empower educators. This project has the potential to increase the number of robotics courses offered at both two- and four-year schools and universities. The course materials are being designed with small units and a hierarchical dependency tree in mind; students will be able to customize their course of study based on the robotics skills they have already mastered. We present an evaluation of a five module mini-course in robotics. Students indicated that they had a positive experience with the online content. They also scored the experience highly on relatedness, mastery, and autonomy perspectives, demonstrating strong motivation potential for this approach.

In many smart city projects, a common choice to capture spatial information is the inclusion of LiDAR data, but this decision will often invoke severe growing pains within the existing infrastructure. In this paper, we introduce a data pipeline that orchestrates Apache NiFi (NiFi), Apache MiNiFi (MiNiFi), and several other tools as an automated solution in order to relay and archive LiDAR data captured by deployed edge devices. The LiDAR sensors utilized within this workflow are Velodyne Ultra Pucks sensors that capture at a rate of 10 frames per second and produces 6-7 GB packet capture (PCAP) files per hour. By both compressing the file after capturing it and compressing the file in real-time, we discovered that gzip produced a file of 5 GB and saved about 5 minutes in transmission time to NiFi, as well as saving considerable CPU time when compressing the file in real-time. Alternatively, we chose XZ as the compression algorithm for the ingestion of LiDAR data onto an institution compute cluster due to its high compression ratio. In order to evaluate the capabilities of our system design, the features of this data pipeline were compared against existing third-party services, namely Globus and RSync.

BACKGROUND: The advance in single-cell RNA sequencing technology has enhanced the analysis of cell development by profiling heterogeneous cells in individual cell resolution. In recent years, many trajectory inference methods have been developed. They have focused on using the graph method to infer the trajectory using single-cell data, and then calculate the geodesic distance as the pseudotime. However, these methods are vulnerable to errors caused by the inferred trajectory. Therefore, the calculated pseudotime suffers from such errors. RESULTS: We proposed a novel framework for trajectory inference called the single-cell data Trajectory inference method using Ensemble Pseudotime inference (scTEP). scTEP utilizes multiple clustering results to infer robust pseudotime and then uses the pseudotime to fine-tune the learned trajectory. We evaluated the scTEP using 41 real scRNA-seq data sets, all of which had the ground truth development trajectory. We compared the scTEP with state-of-the-art methods using the aforementioned data sets. Experiments on real linear and non-linear data sets demonstrate that our scTEP performed superior on more data sets than any other method. The scTEP also achieved a higher average and lower variance on most metrics than other state-of-the-art methods. In terms of trajectory inference capacity, the scTEP outperforms those methods. In addition, the scTEP is more robust to the unavoidable errors resulting from clustering and dimension reduction. CONCLUSION: The scTEP demonstrates that utilizing multiple clustering results for the pseudotime inference procedure enhances its robustness. Furthermore, robust pseudotime strengthens the accuracy of trajectory inference, which is the most crucial component in the pipeline. scTEP is available at https://cran.r-project.org/package=scTEP .

Time series data are significant to scientific, social, economic, and other areas, such as the prediction of weather changes being instrumental for administrative decision-making. In recent years, deep learning methods have achieved great success in time series prediction when compared with classic machine learning methods. However, because time series data can dynamically change and the correlations between the target variable and other features can also vary, making predictions using time series data is often challenging. To further improve existing machine learning and deep learning models for time series prediction, we propose a framework to integrate machine learning models with anomaly detection algorithms. The extreme events are highlighted so the machine learning models can process them appropriately. We conducted extensive experiments on real-world datasets ranging in size from a few hundred to more than ten thousand records. The experimental results demonstrate that our proposed framework significantly improves machine learning model accuracy and mitigates the accuracy descending rate when the predicting horizon (i.e., the number of timestamps ahead) increases.

Cyberattacks are increasingly costing organizations billions of dollars annually. To protect against them, cybersecurity information sharing and cyberthreat visualization have become crucial research topics. Our platform, CYBersecurity information EXchange with Privacy (CYBEX-P), implements developments in both areas as an approachable collaborative security tool. CYBEX-P's threat-intelligence graph displays indicators of compromise and their crowd-reported threat levels. Intuitive and efficient data interaction is key for adoption of such a contributor-driven system and is the focus of this work. A user study was conducted with participants from cybersecurity backgrounds to test different visualization configurations. Measurements pertaining to dependent variables such as task accuracy and threat-detection time were recorded. Subsequent analysis revealed that relying on localized color to represent threat comes with serious limitations. Likewise, information density must be carefully considered. We conclude that the misuse of simple visual properties can lead to perilous reductions in accuracy and response-time and provide recommendations for avoiding these pitfalls.

In this paper, we propose a new hybrid structure learning method that incorporates case-injected genetic algorithms as a score-and-search method for determining Bayesian network structure from data. In our approach, we first find the probabilistic dependencies among variables to constrain the search space and then employ case-injected genetic algorithms in the score-and-search phase to find a quality structure from the reduced search space. The novelty of our work lies with the introduction of combining case-based reasoning with genetic algorithms to evolve a near-optimal Bayesian network in fewer generations compared to a randomly initialized genetic algorithm. Our case-injected genetic algorithms enhance Bayesian network structure learning performance over a sequence of similar problems by extracting and storing knowledge from previously solved problems and utilizing the accumulated knowledge to solve subsequent similar problems. To evaluate the viability of our proposed approach, we conducted a series of experiments by generating a sequence of similar problems based on using data sets obtained randomly from three well-known benchmark Bayesian networks. We also compared the performance of our proposed approach with the state-of-the-art algorithm. Our preliminary results show that case-injected genetic algorithms provide better performance in learning Bayesian network structure compared to GA and the state-of-the-art algorithm. Our proposed approach has applications in real-world domains such as e-commerce system and health care.

Learning the structure of a Bayesian network from data is complex because the number of possible structures increases super-exponentially with the increase in the number of nodes. To address this problem, we propose a hybrid approach comprised of two phases: the constraint-based phase that identifies dependencies among variables to minimize the search space, followed by a score-and-search phase which employs a genetic algorithm to evolve the Bayesian network from the reduced search space. We evaluate the performance of our approach by comparing it with existing algorithms on a limited amount of data sets generated from three benchmark networks. The results illustrate that the proposed algorithm achieves good performance in learning the structure particularly for medium to large networks. Next, we apply our method to a new data set generated from a hand-designed network - the RoRSS (Rules of the Road Ship Simulator). The preliminary results indicate that our method is also satisfactory for small networks with a limited amount of data. The work presented here is a proof-of-concept for our proposed approach aimed at discovering knowledge from data samples of varying sizes and in the presence of small to high number of nodes. Based on the results obtained so far, we are confident that our method is suitable to efficiently learn the structure of the RoRSS network from a large data set.

Nonverbal interactions are a key component of human communication. Since robots have become significant by trying to get close to human beings, it is important that they follow social rules governing the use of space. Prior research has conceptualized personal space as physical zones which are based on static distances. This work examined how preferred interaction distance can change given different interaction scenarios. We conducted a user study using three different robot heights. We also examined the difference in preferred interaction distance when a robot approaches a human and, conversely, when a human approaches a robot. Factors included in quantitative analysis are the participants' gender, robot's height, and method of approach. Subjective measures included human comfort and perceived safety. The results obtained through this study shows that robot height, participant gender and method of approach were significant factors influencing measured proxemic zones and accordingly participant comfort. Subjective data showed that experiment respondents regarded robots in a more favorable light following their participation in this study. Furthermore, the NAO was perceived most positively by respondents according to various metrics and the PR2 Tall, most negatively.

This research addresses the public’s understanding of the water cycle, and attempts to discover a new way of presenting the material. In this paper, we introduce “The Water-Cycle Ride,” a theme park-esque educational and entertainment ride. By incorporating high definition graphics, surround sound, a motion simulator, and an educational video from NASA-Ames, we introduce a new level of thrill to a subject most would not be interested in. Two riders, secured with a seatbelt and harness, can experience the ride together, which lasts about 3 minutes. Together they are taken through water’s three states: solid, liquid, and gas, and its hydrogeology on earth (runoff, groundwater, etc). At appropriate intervals we introduce g-forces so riders experience a rising feeling when evaporation is being explained, or a falling feeling when rain droplets are racing back to Earth. This paper outlines our novel methodology and results obtained, highlighting how a little thrill and excitement can be introduced to a normally passive subject.

In this paper, we present a novel and accessible approach to time-series data validation: the Near-Real Time Autonomous Quality Control (NRAQC) system. The design, implementation, and impacts of this software are explored in detail within this paper. This software system, created in close conference with environmental scientists, leverages microservice design patterns employed for high volume web applications to develop a contemporary solution to the problem of data quality control with streaming sensor data. Through a comparative analysis between NRAQC and the GCE Toolbox, we argue that the web based deployment of QC software enhances accessibility to crucial tools required to make a robust and useful data product from raw measurements. Additionally, a key innovation of the NRAQC platform is its positive impact on modern data management practices and quality data dissemination.

We attack the problem of predicting nitrate concentrations in a stream by using a genetic algorithm to minimize the difference between observed and predicted concentrations on hydrologic nitrate concentration model based on a US Geological Survey collected data set. Nitrate plays a significant role in maintaining ecological balance in aquatic ecosystems and any advances in nitrate prediction accuracy will improve our understanding of the non-linear interplay between the factors that impact aquatic ecosystem health. We compare the genetic algorithm tuned model against the LOADEST estimation tool in current use by hydrologists, and against a random forest, generalized linear regression, decision tree, and gradient booted tree and show that the genetic algorithm does statistically significantly better. These results indicate that genetic algorithms are a viable approach to tuning such non-linear, hydrologic models.

In order to simplify current fire simulation models for more wide-spread use, a Real-time Web-based Wildfire Simulation System (RWWSS) was developed. RWWSS is a web-based application that provides free access to exploring wildfire simulations. It was developed as an educational tool for the purpose of helping people understand the mechanism of fire propagation and its key impact factors, as well as for motivating fire prevention efforts. The model was implemented using the geography of the Lehman Creek watershed, Great Basin National Park, Nevada, USA. Through numerical simulation of fire propagation, the features of fire intensity, direction, and duration, based on the key factors of slope, wind, and vegetation type are estimated and presented on a 2D map. The user can change these key factors, making the application interactive. With improvements to the model RWWSS could be used for further research purposes.

This paper compares two approaches for predicting air temperature from historical pressure, humidity, and temperature data gathered from meteorological sensors in Northwestern Nevada. We describe our data and our representation and compare a standard neural network against a deep learning network. Our empirical results indicate that a deep neural network with Stacked Denoising Auto-Encoders (SDAE) outperforms a standard multilayer feed forward network on this noisy time series prediction task. In addition, predicting air temperature from historical air temperature data alone can be improved by employing related weather variables like barometric pressure, humidity and wind speed data in the training process.

The NSF EPSCOR funded Solar Nexus Project is a collaborative effort between scientists, engineers, educators, and technicians to increase the amount of renewable solar energy in Nevada while eliminating its adverse effects on the surrounding environment and wildlife, and minimizing water consumption. The project seeks to research multiple areas, including water usage at power plants, the effect of power plant construction on the surrounding ecology, alternative wastewater methods to maintain solar panels, and interdisciplinary solutions to improve solar energy in Nevada. In order to organize and analyze this data to produce effective change, Nexus needs a centralized database to store collected data. To this end the Nevada Research Data Center is designed to collect, format, and store data for scientists to view and consider. This paper presents a new architecture solution for the NRDC. Based in microservices, the solution aims to ensure scalability, reliability, and maintainability of this data center. Background on NRDC is provided in the paper, together with details on the proposed solution's software specification, design, and prototype implementation. A discussion of the microservice-based architecture's benefits and an outline of planned directions of future work are also included.

This paper proposes an architecture and associated methodology to separate front end UI concerns from back end coding concerns to improve the platform flexibility, shorten the development time, and increase the productivity of developers. Typical UI development is heavily dependent upon the underlying platform, framework, or tool used to create it, which results in a number of problems. We took a separation-based UI architecture and modified it with a domain specific language to support the independence of UI creation thereby resolving some of the aforementioned problems. A methodology incorporating this architecture into the development process is proposed. A climate science application was created to verify the validity of the methodology using modern practices of UX, DSLs, code generation, and model-driven engineering. Analyzing related work provides an overview of other methods similar to our method. Subsequently we evaluate the climate science application, conclude, and detail future work.

Recent human pharmacological fMRI studies suggest that oxytocin (OT) is a centrally-acting neurotransmitter important in the development and expression of trusting relationships in men and women. OT administration in humans was shown to increase trust, acceptance of social risk, memory of faces, and inference of the emotional state of others, in part by directly inhibiting the amygdala. However, the cerebral microcircuitry underlying this mechanism is still unclear. Here, we propose a spiking integrate-and-fire neuronal model of several key interacting brain regions affected by OT neurophysiology during social trust behavior. As a social behavior scenario, we embodied the brain simulator in a behaving virtual humanoid neurorobot, which interacted with a human via a camera. At the physiological level, the amygdala tonic firing was modeled using our recurrent asynchronous irregular nonlinear (RAIN) network architecture. OT cells were modeled with triple apical dendrites characteristic of their structure in the paraventricular nucleus of the hypothalamus. Our architecture demonstrated the success of our system in learning trust by discriminating concordant from discordant movements of a human actor. This led to a cooperative versus protective behavior by the neurorobot after being challenged by a new intent.

Watermarking based DRM implementations insert imperceptible information or watermark in digital media to trace owner of the content and deter the illegal distribution of media. In geometry based 3D watermarking algorithms, a watermark is inserted by modifying the coordinates of vertices in the mesh. It is a requirement of watermarking algorithms that this change in vertex coordinates shouldn't cause perceptible distortion. It has always been a challenge to select vertices in the 3D model which would not cause perceptible distortion on addition of watermark. This paper proposes a novel approach to overcome this challenge using Artificial Neural Networks (ANN). Feature vectors representing the geometry of the vertex and its surrounding vertices are extracted and used to train and simulate ANN. ANN is used as a classifier to determine which vertices should be selected for watermarking. Experimental results simulate various attacks to test the robustness of the algorithm.

As cameras and storage devices have become cheaper, the number of video surveillance systems has also increased. Video surveillance was (and mostly is) done by human operators on a need-to-know basis. The advent of new algorithms from the computer vision community, and increased computational power offered by new CPUs have shown a strong possibility of automating this task. Different approaches have been proposed by computer scientists to solve the difficult problem of content recognition from video data. They use many different videos to prove their usefulness and accuracy. A careful comparison and evaluation needs to be done to find the most suitable method under given conditions. To compare the results given by video surveillance applications, the ground truth needs to be established. In the case of computer vision, the ground truth needs to be provided by humans, making it one of the most time-consuming tasks in the evaluation process. This paper presents a tool (GTVT) that allows the user to establish the ground truth for a given video. GTVT presents a user-friendly interface to perform the cumbersome task of ground truth establishment and verification.

We present a computational model of creative design based on collaborative interactive genetic algorithms. We test our model on floorplanning. We guide the evolution of floorplans based on subjective and objective criteria. The subjective criteria consists of designers picking the floorplan they like the best from a population of floorplans, and the objective criteria consists of coded architectural guidelines. We support collaboration by allowing individual designers to view each others' designs during the evolutionary process and the sharing of designs via case injection. This methodology supports team design, and reflects the view of creativity that collaboration accounts for much of our intelligence and creativity. We present a description of the model and a comparative study of floorplans created individually versus collaboratively. Our results show that floorplans created collaboratively were considered to be more ldquorevolutionaryrdquo and ldquooriginalrdquo than those created individually.

Mobile devices are being widely and increasingly used in many areas of human activity. Designing applications for mobile devices has introduced several new challenges that are currently being addressed by interested researchers and developers. This paper explores different human-computer interaction challenges in designing an academic researcher's assistant (ARA) software application for mobile devices. ARA is a tool for mobile devices designed to provide academic researchers with a practical portable assistant that helps them organize their daily research-related activities. The paper provides details of ARA's organizing principles, software specification, design, and prototype implementation. Several directions of future work are also presented.

Visual dataflow languages (VDFLs), which include commercial and research systems, have had a substantial impact on end-user programming. Like any other programming languages, whether visual or textual, VDFLs often contain faults. A desire to provide programmers of these languages with some of the benefits of traditional testing methodologies has been the driving force behind our effort in this work. In this article we introduce, in the context of prograph, a testing methodology for VDFLs based on structural test adequacy criteria and coverage. This article also reports on the results of two empirical studies. The first study was conducted to obtain meaningful information about, in particular, the effectiveness of our all-Dus criteria in detecting a reasonable percentage of faults in VDFLs. The second study was conducted to evaluate, under the same criterion, the effectiveness of our methodology in assisting users to visually localize faults by reducing their search space. Both studies were conducted using a testing system that we have implemented in Prograph's IDE.

We attack the problem of user fatigue in using an interactive genetic algorithm to evolve user interfaces in the XUL interface definition language. The interactive genetic algorithm combines computable user interface design metrics with subjective user input to guide evolution. Individuals in our population represent interface specifications and we compute an individual's fitness from a weighted combination of user input and user interface design guidelines. Results from our preliminary study involving three users indicate that users are able to effectively bias evolution towards user interface designs that reflect both user preferences and computed guideline metrics. Furthermore, we can reduce fatigue, defined by the number of choices needing to be made by the human designer, by doing two things. First, asking the user to pick just two (the best and worst) user interfaces from among a subset of nine shown. Second, asking the user to make the choice once every t generations, instead of every single generation. Our goal is to provide interface designers with an interactive tool that can be used to explore innovation and creativity in the design space of user interfaces.

We attack the problem of user fatigue by using an interactive genetic algorithm to evolve user interfaces in the XUL interface definition language. The interactive genetic algorithm combines a set of computable user interface design metrics with subjective user input to guide the evolution of interfaces. Our goal is to provide user interface designers with a tool that can be used to explore innovation and creativity in the design space of user interfaces and make it easier for end-users to further customize their user interface without programming knowledge. User interface specifications are encoded as individuals in an interactive genetic algorithm's population and their fitness is computed from a weighted combination of user interface design guidelines and user input. This paper shows that we can reduce human fatigue in interactive genetic algorithms (the number of choices needing to be made by the designer), by 1) only asking the user to pick two user interfaces from among ten shown on the display and 2) by asking the user to make the choice once every t generations.

We outline our context learning framework that harnesses information from a user's environment to learn user preferences for application actions. Within this framework, we employ XCS in a real world application for personalizing user-interface actions to individual users. Sycophant, our context aware calendaring application and research test-bed, uses XCS to adaptively generate user-preferred alarms for ten users in our study. Our results show that XCS' alarm prediction performance equals or surpasses the performance of One-R and a decision tree algorithm for all the users. XCS' average performance is close to $90$ percent on the alarm prediction task for all ten users. These encouraging results further highlight the feasibility of using XCS for predictive data mining tasks and the promise of a classifier systems based approach to personalize user interfaces.

The development of network-attached devices has ushered in an era of autonomous, multi-function equipment demanding minimal human interaction: the only requirements are data and electricity. Despite these advances, these machines continue underutilized in network environments due to operating system limitations regarding the management of these devices. These limitations force the use of these devices via other network hardware, such as a server, that manage the device access and data. While effective, this results in increased resource consumption and ignores the capabilities presented by network-attached devices. In order to facilitate optimal utilization of these devices, we have designed a new, extensible management architecture for all network-attached devices. This architecture, presented here, supports the central management of network-attached devices while allowing client machines access to the device without intermediate server hardware. Implementation of this paradigm on test networks has decreased resource consumption - especially bandwidth - considerably.

We investigate the trade off between investing effort in improving the features of a research environment that increases productivity and investing such effort in actually conducting the research experiments using a less elaborated, albeit sufficiently operational environment. The study case presented is an interactive genetic algorithm environment we created to evolve user interfaces designs. We present three productivity improvements integrated in our environment and examine whether on the long run the research productivity can be in fact increased by spending development time on enhancing the research tools rather than on performing the research itself. The three improvements are the integration of the entire system interface into a main wxPython window, the addition of a runs manager for setting up multiple experiments, and the creation of a data manager for effective exploration and visualization of data produced in the experiment runs. We also discuss several guidelines for transitioning a research environment such as ours from a researcher's tool to an end-user's tool.

Research in context-aware user interfaces aims to improve human-computer interaction by providing more effective, smarter and user-friendlier solutions for computer applications. Currently, software available for performing such research and developing context-aware interfaces is very limited both in scope and possibilities of extension. Sycophant was designed with two objectives in mind: first, to allow easy insertion of new features and capabilities needed for conducting research and, second, to provide a reusable, readily available programming resource for developing new context-aware interactive software applications. Available as open source software, Sycophant's API and the calendaring application we created using it are presented in this paper in terms of functional capabilities, high level architecture, detailed design, and results of use. Procedural steps for developing new context-aware user interfaces using our API are also described in the paper.

We present results from an empirical user-study with ten users which investigates if information from a user's environment helps a user interface to personalize itself to individual users to better meet usability goals and improve user-experience. In our research we use a microphone and a web-camera to collect this information (user-context) from the vicinity of a subject's desktop computer. Sycophant, our context-aware calendaring application and research test-bed uses machine learning techniques to successfully predict a user-preferred alarm type. Discounting user identity and motion information significantly degrades Sycophant's performance on the alarm prediction task. Our user study emphasizes the need for user-context for personalizable user interfaces which can better meet effectiveness and utility usability goals. Results from our study further demonstrate that contextual information helps adaptive interfaces to improve user-experience.

In this article we present a computational approach to developing effective training systems for virtual simulation environments. In particular, we focus on a Naval simulation system, used for training of conning officers. The currently existing training solutions require multiple expert personnel to control each vessel in a training scenario, or are cumbersome to use by a single instructor. The inability of current technology to provide an automated mechanism for competitive realistic boat behaviors thus compromises the goal of flexible, anytime, anywhere training. In this article we propose an approach that reduces the time and effort required for training of conning officers, by integrating novel approaches to autonomous control within a simulation environment. Our solution is to developintelligent, autonomous controllersthat drive the behavior of each boat. To increase the system's efficiency we provide a mechanism for creating such controllers, from the demonstration of a navigation expert, using a simple programming interface. In addition, our approach deals with two significant and related challenges: therealism of behaviorexhibited by the automated boats and theirreal‐time response to changesin the environment. In this article, we describe the control architecture we developed that enables the real‐time response of boats and the repertoire of realistic behaviors we designed for this application. We also present our approach for facilitating the automatic authoring of training scenarios and we demonstrate the capabilities of our system with experimental results.

In today software industry defect tracking tools either help to improve an organization's software development process or an individual's software development process. No defect tracking tool currently exists that help both processes. In this paper we present DuoTracker, a tool that makes possible to track and analyze software defects for organizational and individual software process decision making. To accomplish this, DuoTracker has capabilities to classify defects in a manner that makes analysis at both organizational and individual software processes meaningful. The benefit of this approach is that software engineers are able to see how their personal software process improvement impacts their organization and vice versa. This paper shows why software engineers need to keep track of their program defects, how this is currently done, and how DuoTracker offers a new way of keeping track of software errors. Furthermore, DuoTracker is compared to other tracking tools that enable software developers to record program defects that occur during their individual software processes.