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.

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.

With the introduction of microarray technology to measure gene expression in the late 1990's and the advent of current next-generation whole-genome and whole-transcriptome sequencing technology, fast and robust tools are needed to examine, identify, and study relations between genes and proteins at the systems level. Networks provide effective models to study complex systems, including complex biological systems, such as gene and protein interaction networks. We introduce a novel approach to generate gene co-expression network models based on experimental gene expression measures. This approach includes statistical, mathematical, and biological considerations not highlighted by existing co-expression analysis tools. First, most high-throughput expression data are not normally distributed, yet many available network approaches are based on parametric methods. Here appropriate metrics are provided to generate statistically sound network models. Secondly, most biological networks are known to have approximate scale-free and small-world structure, and the biological networks built here follow both these two properties. Thirdly, to generate these small-world, scale-free network models, user-selected input parameters are not required, thereby leading to reproducible results. Lastly, this approach is designed for high-throughput whole-systems data. This method is implemented in the programming language R. Its application to several whole-genome experimental datasets has generated novel meaningful results useful for further biological investigation.

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.

This paper describes an imperceptible, non-blind, fragile watermarking technique for space curves. The proposed technique employs a wavelet-based approach, and computes a multi-resolution representation of the space curve to embed a watermark so that it has widespread presence in the curve. A variety of wavelet families are exploited and experimental results provide a comparison of the performance of different wavelets in terms of the watermark's imperceptibility and tolerance to attacks. To quantify space curve distortion, a signal-to-noise ratio is used, and a linear correlation measure is employed to determine the resistance of the watermark to modifications.

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.

This paper proposes a new DRM system for 3D graphics that makes use of biometric watermarking technology. The presented solution utilizes an image of a biometric trait e.g. face or fingerprint, and embeds it into the 3D graphics as a watermark. This biometric watermark is then used to authenticate a legitimate user. Details for the components of the DRM framework are presented. Adoption of biometric watermarking allows the DRM system to provide consumers unrestricted access to the graphics along with limiting graphics content access to only legitimate users, thereby protecting artists from large scale online piracy. A detailed survey of existing DRM solutions for 3D graphics is provided to identify the limitations of each implementation which offers either restrictive content usage scenarios or is ineffective in preventing unauthorized usage.

Approximating `real' disease transmission networks through genomic sequence comparisons among pathogenic isolates is increasingly feasible with the current growth in genomic sequence data. Here, we derive a network from over 4,200 globally distributed influenza A virus isolates based on alignment-free sequence comparisons. We then employ network mixing pattern analysis to examine transmission probabilities between isolates from different global regions, host types, subtypes and collection years. While we can not use our results to describe the complete global network of influenza A virus, we present a novel analytical process. In addition, we describe some of the characteristics of this subset of currently available data. Most notable results are the high levels of inter regional links and the important role that avian species seem to play in non human global transmission.

Automated control and management of large-scale physical systems is a challenging problem in a wide variety of applications including: power grids, transportation networks, and telecommunication networks. Such systems require (i) data collection, (ii) secure data transfer to processing centers, (iii) data processing, and (iv) timely decision making and control actions. These tasks are complicated by the vast amount of data, the distributed sources of data, and the need for efficient data communication. In addition, large physical systems are often subdivided into separately owned subsystems. This multi-owner structure imposes physical, economic, market, and political constraints on the data transfer. These divisions make systems vulnerable to potential coordinated attacks. Defending against such attacks requires the infrastructures to be more automated and self-healing. Motivated by the challenge of a more efficient, secure and robust power grid, which is less vulnerable to blackouts due to cascaded events, this paper discusses some of the fundamental problems in designing future cyber-physical systems.

One of the characteristics of the Human Visual System (HVS) is to model the sensitivity of the human eye at each coordinate location in the image. This paper explores the use of fuzzy logic for building a non-linear HVS model for perceptual masking in wavelet domain. The fuzzy input variables corresponding to brightness, edge sensitivity, and texture are computed for each wavelet coefficient at different scales in an image. The output of the fuzzy system is a single value which gives a perceptual value for each corresponding wavelet coefficient. This paper proposes a novel method of constructing perceptual mask in wavelet domain using fuzzy logic.

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.

This paper presents a novel robust watermarking algorithm for 3D skinning mesh animations by embedding the watermark in skin weights in addition to key frames. This method can be used for copyright protection, tamper proofing or content annotation purposes. The proposed watermark is immune to noise attacks on key frames and skin weights, key frame dropping and frame modification and is perceptible invisible as well. Experimental results verified that the proposed algorithm has good robustness against attacks and maintains invisibility by preprocessing the animation data sets by key frame decimation.

The destructive capacity of wildfires and the dangers and limitations associated with observing actual wildfires has led researchers to develop mathematical models in order to better understand their behavior; unfortunately, current two-dimensional visualization techniques make discerning driving forces of a fire difficult and restrict comprehension to the trained eye. VFire is an immersive wildfire visualization application that aims to ameliorate these problems. We discuss several recent enhancements made to VFire in order to not only increase the amount of meaningfully visualized data and visual fidelity but also improve user interaction.

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.

Wildfires are a frequent summer-time concern for land managers and communities neighboring wildlands throughout the world. Computational simulations have been developed to help analyze and predict wildfire behavior, but the primary visualization of these simulations has been limited to 2-dimensional graphics images. We are currently working with wildfire research groups and those responsible for managing the control of fire and mitigation of the wildfire hazard to develop an immersive visualization and simulation application. In our visualization application, the fire spread model will be graphically illustrated on a realistically rendered terrain created from actual DEM data and satellite photography. We are working to improve and benefit tactical and strategic planning, and provide training for firefighter and public safety with our application

A simulation-based method for obtaining numerical estimates of the reliability of N-version, real-time software is proposed. An extended stochastic Petri net is used to represent the synchronization structure of N versions of the software, where dependencies among versions are modeled through correlated sampling of module execution times. The distributions of execution times are derived from automatically generated test cases that are based on mutation testing. Since these test cases are designed to reveal software faults, the associated execution times and reliability estimates are likely to be conservative. Experimental results using specifications for NASA's planetary lander control software suggest that mutation-based testing could hold greater potential for enhancing reliability than the desirable but perhaps unachievable goal of independence among N versions. Nevertheless, some support for N-version enhancement of high-quality, mutation-tested code is also offered. Mutation analysis could also be valuable in the design of fault-tolerant software systems.>