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Secure Intelligent Vehicular Network Including Real-Time Detection of DoS Attacks in IEEE 802.11P Using Fog Computing
No full textVANET (Vehicular ad hoc network) has a main objective to improve driver safety and traffic efficiency. Intermittent exchange of real-time safety message delivery in VANET has become an urgent concern, due to DoS (Denial of service), and smart and normal intrusions (SNI) attacks. Intermittent communication of VANET generates huge amount of data which requires typical storage and intelligence infrastructure. Fog computing (FC) plays an important role in storage, computation, and communication need. In this research, Fog computing (FC) integrates with hybrid optimization algorithms (OAs) including: Cuckoo search algorithm (CSA), Firefly algorithm (FA) and Firefly neural network, in addition to key distribution establishment (KDE), for authenticating both the network level and the node level against all attacks for trustworthiness in VANET. The proposed scheme which is also termed “Secure Intelligent Vehicular Network using fog computing” (SIVNFC) utilizes feedforward back propagation neural network (FFBP-NN). This is also termed the firefly neural, is used as a classifier to distinguish between the attacking vehicles and genuine vehicles. The proposed scheme is initially compared with the Cuckoo and FA, and the Firefly neural network to evaluate the QoS parameters such as jitter and throughput. In addition, VANET is a means whereby Intelligent Transportation System (ITS) has become important for the benefit of daily lives. Therefore, real-time detection of all form attacks including hybrid DoS attacks in IEEE 802.11p, has become an urgent attention for VANET. This is due to sporadic real-time exchange of safety and road emergency message delivery in VANET. Sporadic communication in VANET has the tendency to generate enormous amount of message. This leads to the RSU (roadside unit) or the CPU (central processing unit) overutilization for computation. Therefore, it is required that efficient storage and intelligence VANET infrastructure architecture (VIA), which include trustworthiness is desired. Vehicular Cloud and Fog Computing (VFC) play an important role in efficient storage, computations, and communication need for VANET. This dissertation also utilizes VFC integration with hybrid optimization algorithms (OAs), which also possess swarm intelligence including: Cuckoo/CSA Artificial Bee Colony (ABC) Firefly/Genetic Algorithm (GA), in additionally to provide Real-time Detection of DoS attacks in IEEE 802.11p, using VFC for Intelligent Vehicular network. Vehicles are moving with certain speed and the data is transmitted at 30Mbps. Firefly FFBPNN (Feed forward back propagation neural network) has been used as a classifier to also distinguish between the attacked vehicles and the genuine vehicle. The proposed scheme has also been compared with Cuckoo/CSA ABC and Firefly GA by considering Jitter, Throughput and Prediction accuracy
Oral Health Disparities and How Midlevel Providers can Improve Access to Quality Dental Care
No full textOral disease continues to be a significant issue across the United States and throughout the world. Oral disease is multifactorial and links to general health. Poor oral hygiene is associated with a lack of education and low socioeconomic status. A literature review was conducted to determine the issues related to oral disease, access to quality dental care, and the implementation of a midlevel dental provider being a solution to improve these issues. The research was gathered from peer-reviewed literature. There are no articles used that are older than seven years, except for historical data. The results of the studies support the need for a midlevel dental provider, especially in communities of low socioeconomic status and the uninsured. The limitations include the research completed, populations used in the research studies, statistical testing, and the outcomes of the studies. The populations chosen in the research studies were mainly of the disadvantaged and underserved. These groups of people are associated with low socioeconomic status, lack of education, lack of medical and dental insurance. Based on the findings, this literature review shows the benefit of a midlevel dental provider and how the implementation of this profession across the United States could positively impact oral health care and improve oral health disparities
GNSS-Based Attitude Determination Techniques - A Comprehensive Literature Survey
No full textGNSS-based Attitude Determination (AD) of a mobile object using the readings of the Global Navigation Satellite Systems (GNSS) is an active area of research. Numerous attitude determination methods have been developed lately by making use of various sensors. However, the last two decades have witnessed an accelerated growth in research related to GNSS-based navigational equipment as a reliable and competitive device for determining the attitude of any outdoor moving object using data demodulated from GNSS signals. Because of constantly increasing number of GNSS-based AD methods, algorithms, and techniques, introduced in scientific papers worldwide, the problem of choosing an appropriate approach, that is optimal for the given application, operational environment, and limited financial funding becomes quite a challenging task. The work presents an extensive literature survey of the methods mentioned above which are classified in many different categories. The main aim of this survey is to help researchers and developers in the field of GNSS applications to understand pros and cons of the current state of the art methods and their computational efficiency, the scope of use and accuracy of the angular determination.https://doi.org/10.1109/ACCESS.2020.297008
Recent Advances in Variational Autoencoders With Representation Learning for Biomedical Informatics: A Survey
No full textVariational autoencoders (VAEs) are deep latent space generative models that have been immensely successful in multiple exciting applications in biomedical informatics such as molecular design, protein design, medical image classification and segmentation, integrated multi-omics data analyses, and large-scale biological sequence analyses, among others. The fundamental idea in VAEs is to learn the distribution of data in such a way that new meaningful data with more intra-class variations can be generated from the encoded distribution. The ability of VAEs to synthesize new data with more representation variance at state-of-art levels provides hope that the chronic scarcity of labeled data in the biomedical field can be resolved. Furthermore, VAEs have made nonlinear latent variable models tractable for modeling complex distributions. This has allowed for efficient extraction of relevant biomedical information from learned features for biological data sets, referred to as unsupervised feature representation learning. In this article, we review the various recent advancements in the development and application of VAEs for biomedical informatics. We discuss challenges and future opportunities for biomedical research with respect to VAEs.https://doi.org/10.1109/ACCESS.2020.304830
Un modelo basado en agentes para la gestión del diseño en el desarrollo de nuevos productos
No full textOrganizations develop different methodologies for product creation, customization, and improvement. Over time the best-known methods and practices are established and standardized for new product development. An operational planning methodology for projected outcomes of multiple scenarios is Agent-Based Modeling and Simulation (ABMS). Knowledge-based process support methods like ABMS are key in terms of tactic and strategic resource allocation, staffing, policy creation, and management of technology. An agent-based model (ABM) is developed representing the design management organizational function consisting of four autonomous agent types, their actions, and interactions. Types of agents included: designers, design managers, customers, and sales and marketing staff. The objective is to assess the aggregate emergent behavior of the design management organization. Agent characteristics data was acquired from service and manufacturing firms of multiple industry sectors with research questionnaires. A model of design-related agents was developed and a NetLogo simulation produced units sold by the organization over time as a measure of performance. Workflow for three simulation loops was validated with seasoned design managers with a Delphi-based process stage ranking methodology. Simulation approach included an empirical pre-validation and sensitivity analysis for two variables, based on scenario building: designer experience, and number of simulation cycles between new product development committee meetings. Time scale of the simulation, number of units sold levels, and overall behavior were calibrated using sales data from an electronics manufacturing firm. The simulations indicated responsiveness in organizational behavior when customer satisfaction drops due to lack of fitness to specifications. This model provides guidance on how to configure new product development teams, modify designer hiring processes, schedule product development committee meetings, and plan customer feedback collection. The results on the responsiveness of the organizational model to product signals and customer insights are promising for prediction analysis of product update frequency planning, resource allocation, and life-cycle stage analysis. This work also extends design management methodologies into modeling and simulation approaches based on frameworks developed from empirical case-studies, providing decision-makers a straightforward way of presenting practical scenarios for the design management function, the product, and the organization.Las organizaciones desarrollan diferentes metodologías en la creación, mejora y adaptación de productos. Con el tiempo un conjunto de métodos y mejores prácticas, se consolidan y estandarizan para el desarrollo de nuevos productos. Una metodología de planeación operacional cuando se tienen diferentes resultados de diferentes escenarios es la Modelación y Simulación Basada en Agentes (MSBA, o ABM en inglés). Los métodos soportados en proceso basados en conocimiento como la MSBA son claves en el despliegue táctico y estratégico de recursos, personal, establecimiento de políticas y gestión de tecnologías. Un Modelo Basado en Agentes (MBA, o ABM en inglés) fue desarrollado y representa la función organizacional de gestión del diseño, comprendida por cuatro tipos de agentes autónomos, sus acciones e interacciones. Los tipos de agentes incluyen: diseñadores, gestores de diseño, clientes y personal de ventas / mercadeo. El objetivo es evaluar el comportamiento emergente y agregado de la organización que gestiona diseño. Datos sobre las características de los agentes fue recolectada en empresas de manufactura y servicios, en múltiples sectores industriales por medio de encuestas de investigación. Un modelo para los agentes relacionados con el proceso de diseño fue desarrollado y una simulación en NetLogo tuvo como resultado una serie de datos correspondiente a la simulación de las unidades vendidas como medida de la eficiencia organizacional. El flujo de tres bucles de simulación fue validad con gerentes de diseño experimentados usando un proceso de priorización de etapas basado en el método Delphi. El camino de simulación incluyó una pre-validación empírica y un análisis de sensibilidad para dos variables, basados en construcción de escenarios: la experiencia del diseñador interno y el número de ciclos de simulación que hay entre comités de desarrollo de nuevos productos. La escala de tiempo de la simulación, los niveles del número unidades vendidas y el comportamiento general de la satisfacción de los clientes, fueron calibradas usando datos de ventas de una empresa de manufactura de productos electrónicos. Las simulaciones mostraron un comportamiento adaptativo y reactivo de la organización, cuando la satisfacción de los clientes cayó por desajustes del producto a las especificaciones. Este modelo brinda orientación sobre como configurar los equipos de desarrollo de nuevos productos, modificar los procesos de contratación de diseñadores, programar las reuniones del comité de desarrollo de nuevos productos, y planear la recolección de retroalimentación de los clientes. Los resultados correspondientes a la capacidad de reacción del modelo organizacional a las señales del comportamiento de los productos y a los datos de los clientes, son prometedoras para el análisis predictivo de la frecuencia de actualización de productos, asignación de recursos y análisis del ciclo de vida. Este trabajo también expande las metodologías de gestión de diseño para incluir la modelación y la simulación como herramientas basadas en los marcos conceptuales creados a partir de estudios de caso empíricos, posibilitando a los tomadores de decisiones una mejor forma de presentar los escenarios factibles para la función de diseño, el producto y la organización
Embedding Mentoring Practices
No full textEvery year teachers enter the workforce with a wide range of educational experiences through alternative routes to certification and traditional teacher preparation programs. These teachers bring a pedagogical skillset that varies in terms of their practicum experiences and school of education course preparation. Much of the intricacies of student teaching are learned within the placement school and classroom but current statistics indicate that novice teachers leave the profession at rates of between 19% and 30% over their first five years of teaching with just 3% of novice teachers receiving comprehensive mentoring support. Teachers who do receive mentoring and collaboration cut the first-year turnover rate by more than half. This qualitative interpretive research explored the perceptions of six participants and addressed the gap in literature supporting mentoring being embedded earlier within teacher preparation programs. These perceptions were explored and guided the identification of core mentoring practices. Intentional, or purposeful, mentoring practices are essential components of a pre-service teachers’ repertoire and need to be embedded within pre-service teacher’s coursework. The research findings supported the significance of embedding mentoring practices to enhance the level of pre-service and novice teaching experiences. By connecting the methodological and theoretical contents of teacher preparation through embedded mentoring, the practice of critical reflection with authentic experiences would be established to reduce novice teacher attrition. Through the lens of mentoring, the results supported embedding such practices earlier into teacher preparation programs, specifically methods of teaching. Education has become an accessible global learning forum and embedding mentoring practices cannot be excluded from this conversation
Enhanced Grey Risk Assessment Model for Support of Cloud Service Provider
No full textThe cloud computing environment provides easy-to-access service for private and confidential data. However, there are many threats to the leakage of private data. This paper focuses on investigating the vulnerabilities of cloud service providers (CSPs) from three risk aspects: management risks, law risks, and technology risks. Additionally, this paper presents a risk assessment model that is based on grey system theory (GST), defines indicators for assessment, and fully utilizes the analytic hierarchy process (AHP). Furthermore, we use the GST to predict the risk values by using the MATLAB platform. The GST determines the bottom evaluation sequence, while the AHP calculates the index weights. Based on the GST and the AHP, layer-based assessment values are determined for the bottom evaluation sequence and the index weights. The combination of AHP and GST aims to obtain systematic and structured well-defined procedures that are based on step-by-step processes. The AHP and GST methods are applied successfully to handle any risk assessment problem of the CSP. Furthermore, substantial challenges are encountered in determining the CSP’s response time and identifying the most suitable solution out of a specified series of solutions. This issue has been handled using two additive features: the response time and the grey incidence. The final risk values are calculated and can be used for prediction by utilizing the enhanced grey model (EGM) (1,1), which reduces the prediction error by providing direct forecast to avoid the iterative prediction shortcoming of standard GM (1,1). Thus, EGM (1,1) helps maintain the reliability on a larger scale despite utilizing more prediction periods. Based on the experimental results, we evaluate the validity, accuracy, and response time of the proposed approach. The simulation experiments were conducted to validate the suitability of the proposed model. The simulation results demonstrate that our risk assessment model contributes to reducing deviation to support CSPs with the three adopted models.https://doi.org/10.1109/ACCESS.2020.298773
African-American High-Tech Enterprises: Agent-Based Modeling and Simulation for Innovation
No full textAfrican-American high-tech enterprises and innovation are underrepresented in the economy and industrial sector, in comparison to other types of ownership. Understanding the causal relationship underlying an enterprise’s interactions with external entities may increase and sustain the innovation capabilities of a high-tech enterprise. Agent-based modeling provides a computational methodology to visualize the iterative processes that lead to innovation output and the aggregate behavior of the simulated entrepreneurial system consisting of multiple interacting entities and factors. It is not possible even with long-term social longitudinal studies. Furthermore, the model can be readily enhanced with newly discovered interactions. This research focused on creating a socio-technological agent-based model (ABM) for assessing the probability of successful innovation by African-American owned businesses in high-tech dominated industries. A set of autonomous agents characterized the ABM: African-American owned high-tech enterprises, funding institutions, government research and development (R&D) services, research universities, and other enterprises. The framework was created using data from interviews with African-American entrepreneurs and implemented as a NetLogo simulation. A genetic algorithm in R was used for the simulation’s evolutionary behavior. By combining variations of agent attributes, the study created seven real-world simulation scenarios to evaluate the impact of initial capital, university R&D collaboration, government policy support, funding institution support, firm networking, and a combination of these factors on an African-American enterprise (AAE). The simulation results were validated with an analytic hierarchy process (AHP) decision model using expert judgments. The key findings of this study confirmed: initial funding is significant for AAE firms, but it can be offset by higher socio-economic status; collaboration assists entrepreneurs strengthen their core competencies and obtain funding support for both starting up and innovation; university collaboration is essential for innovation and start-ups; government support assists in improving the ratio of AAE to non-African-American enterprise (non-AAE) firms; and easy access to bank loans or venture funding assist in creating equal opportunity for firms with different backgrounds, although this does not necessarily lead to higher success rates
Enhanced Deep Learning Architectures for Face Liveness Detection for Static and Video Sequences
No full textFace liveness detection is a critical preprocessing step in face recognition for avoiding face spoofing attacks, where an impostor can impersonate a valid user for authentication. While considerable research has been recently done in improving the accuracy of face liveness detection, the best current approaches use a two-step process of first applying non-linear anisotropic diffusion to the incoming image and then using a deep network for final liveness decision. Such an approach is not viable for real-time face liveness detection. We develop two end-to-end real-time solutions where nonlinear anisotropic diffusion based on an additive operator splitting scheme is first applied to an incoming static image, which enhances the edges and surface texture, and preserves the boundary locations in the real image. The diffused image is then forwarded to a pre-trained Specialized Convolutional Neural Network (SCNN) and the Inception network version 4, which identify the complex and deep features for face liveness classification. We evaluate the performance of our integrated approach using the SCNN and Inception v4 on the Replay-Attack dataset and Replay-Mobile dataset. The entire architecture is created in such a manner that, once trained, the face liveness detection can be accomplished in real-time. We achieve promising results of 96.03% and 96.21% face liveness detection accuracy with the SCNN, and 94.77% and 95.53% accuracy with the Inception v4, on the Replay-Attack, and Replay-Mobile datasets, respectively. We also develop a novel deep architecture for face liveness detection on video frames that uses the diffusion of images followed by a deep Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM) to classify the video sequence as real or fake. Even though the use of CNN followed by LSTM is not new, combining it with diffusion (that has proven to be the best approach for single image liveness detection) is novel. Performance evaluation of our architecture on the REPLAY-ATTACK dataset gave 98.71% test accuracy and 2.77% Half Total Error Rate (HTER), and on the REPLAY-MOBILE dataset gave 95.41% accuracy and 5.28% HTER.https://doi.org/10.3390/e2210118
Smartphone-Based Self-Testing of COVID-19 Using Breathing Sounds
No full textTelemedicine could be a key to control the world-wide disruptive and spreading novel coronavirus disease (COVID-19) pandemic. The COVID-19 virus directly targets the lungs, leading to pneumonia-like symptoms and shortness of breath with life-threatening consequences. Despite the fact that self-quarantine and social distancing are indispensable during the pandemic, the procedure for testing COVID-19 contraction is conventionally available through nasal swabs, saliva test kits, and blood work at healthcare settings. Therefore, devising personalized self-testing kits for COVID-19 virus and other similar viruses is heavily admired. Many e-health initiatives have been made possible by the advent of smartphones with embedded software, hardware, high-performance computing, and connectivity capabilities. A careful review of breathing sounds and their implications in identifying breathing complications suggests that the breathing sounds of COVID-19 contracted users may reveal certain acoustic signal patterns, which is worth investigating. To this end, acquiring respiratory data solely from breathing sounds fed to the smartphone's microphone strikes as a very appealing resolution. The acquired breathing sounds can be analyzed using advanced signal processing and analysis in tandem with new deep/machine learning and pattern recognition techniques to separate the breathing phases, estimate the lung volume, oxygenation, and to further classify the breathing data input into healthy or unhealthy cases. The ideas presented have the potential to be deployed as self-test breathing monitoring apps for the ongoing global COVID-19 pandemic, where users can check their breathing sound pattern frequently through the app.http://doi.org.libproxy.bridgeport.edu/10.1089/tmj.2020.011