Open Access. Powered by Scholars. Published by Universities.®
Physical Sciences and Mathematics Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Keyword
-
- Peer-to-peer computing (6)
- Computer science (4)
- Delay (4)
- Graph theory (4)
- P2P networks (4)
-
- Probability (4)
- Resilience (4)
- Analytical models (3)
- Large-scale systems (3)
- Out-of-turn interaction (3)
- Peer to peer computing (3)
- Peer-to-peer (3)
- Random processes (3)
- User churn (3)
- Communications Society (2)
- Create-based method (2)
- Failure analysis (2)
- Gnutella networks (2)
- Hierarchical hypermedia (2)
- Hypermedia (2)
- Information Retrieval (2)
- Information personalization (2)
- Internet (2)
- Lifetime estimation (2)
- Mixed-initiative interaction (2)
- Navigation (2)
- Node-isolation model (2)
- Partial evaluation (2)
- Routing (2)
- Sampling methods (2)
- Publication Year
Articles 1 - 30 of 130
Full-Text Articles in Physical Sciences and Mathematics
Image De‑Photobombing Benchmark, Vatsa S. Patel, Kunal Agrawal, Samah Baraheem, Amira Yousif, Tam Nguyen
Image De‑Photobombing Benchmark, Vatsa S. Patel, Kunal Agrawal, Samah Baraheem, Amira Yousif, Tam Nguyen
Computer Science Faculty Publications
Removing photobombing elements from images is a challenging task that requires sophisticated image inpainting techniques. Despite the availability of various methods, their effectiveness depends on the complexity of the image and the nature of the distracting element. To address this issue, we conducted a benchmark study to evaluate 10 state-of-the-art photobombing removal methods on a dataset of over 300 images. Our study focused on identifying the most effective image inpainting techniques for removing unwanted regions from images. We annotated the photobombed regions that require removal and evaluated the performance of each method using peak signal-to-noise ratio (PSNR), structural similarity index …
Predicting An Optimal Medication/Prescription Regimen For Patient Discordant Chronic Comorbidities Using Multi-Output Models, Ichchha Pradeep Sharma, Tam Nguyen, Shruti Ajay Singh, Tom Ongwere
Predicting An Optimal Medication/Prescription Regimen For Patient Discordant Chronic Comorbidities Using Multi-Output Models, Ichchha Pradeep Sharma, Tam Nguyen, Shruti Ajay Singh, Tom Ongwere
Computer Science Faculty Publications
This paper focuses on addressing the complex healthcare needs of patients struggling with discordant chronic comorbidities (DCCs). Managing these patients within the current healthcare system often proves to be a challenging process, characterized by evolving treatment needs necessitating multiple medical appointments and coordination among different clinical specialists. This makes it difficult for both patients and healthcare providers to set and prioritize medications and understand potential drug interactions. The primary motivation of this research is the need to reduce medication conflict and optimize medication regimens for individuals with DCCs. To achieve this, we allowed patients to specify their health conditions and …
Ai Vs. Ai: Can Ai Detect Ai-Generated Images?, Samah S. Baraheem, Tam Van Nguyen
Ai Vs. Ai: Can Ai Detect Ai-Generated Images?, Samah S. Baraheem, Tam Van Nguyen
Computer Science Faculty Publications
The proliferation of Artificial Intelligence (AI) models such as Generative Adversarial Net- works (GANs) has shown impressive success in image synthesis. Artificial GAN-based synthesized images have been widely spread over the Internet with the advancement in generating naturalistic and photo-realistic images. This might have the ability to improve content and media; however, it also constitutes a threat with regard to legitimacy, authenticity, and security. Moreover, implementing an automated system that is able to detect and recognize GAN-generated images is significant for image synthesis models as an evaluation tool, regardless of the input modality. To this end, we propose a framework …
Model Checking Time Window Temporal Logic For Hyperproperties, Ernest Bonnah, Luan Viet Nguyen, Khaza Anuarul Hoque
Model Checking Time Window Temporal Logic For Hyperproperties, Ernest Bonnah, Luan Viet Nguyen, Khaza Anuarul Hoque
Computer Science Faculty Publications
Hyperproperties extend trace properties to express properties of sets of traces, and they are increasingly popular in specifying various security and performance-related properties in domains such as cyber-physical systems, smart grids, and automotive. This paper introduces HyperTWTL, which extends Time Window Temporal Logic (TWTL)-a domain-specific formal specification language for robotics, by allowing explicit and simultaneous quantification over multiple execution traces. We propose two different semantics for HyperTWTL, synchronous and asynchronous, based on the alignment of the timestamps in the traces. Consequently, we demonstrate the application of HyperTWTL in formalizing important information-flow security policies and concurrency for robotics applications. Furthermore, we …
Uit-Adrone: A Novel Drone Dataset For Traffic Anomaly Detection, Tung Minh Tran, Tu N. Vu, Tam Nguyen, Khang Nguyen
Uit-Adrone: A Novel Drone Dataset For Traffic Anomaly Detection, Tung Minh Tran, Tu N. Vu, Tam Nguyen, Khang Nguyen
Computer Science Faculty Publications
Anomaly detection plays an increasingly important role in video surveillance and is one of the issues that have attracted various communities, such as computer vision, machine learning, and data mining in recent years. Moreover, drones equipped with cameras have quickly been deployed to a wide range of applications, starting from border security applications to street monitoring systems. However, there is a notable lack of adequate drone-based datasets available to detect unusual events in the urban traffic environment, especially in roundabouts, due to the density of interaction between road users and vehicles. To promote the development of anomalous event detection with …
Collaborative Consultation Doctors Model: Unifying Cnn And Vit For Covid-19 Diagnostic, Trong-Thuan Nguyen, Tam Nguyen, Minh-Triet Tran
Collaborative Consultation Doctors Model: Unifying Cnn And Vit For Covid-19 Diagnostic, Trong-Thuan Nguyen, Tam Nguyen, Minh-Triet Tran
Computer Science Faculty Publications
The COVID-19 pandemic presents significant challenges due to its high transmissibility and mortality risk. Traditional diagnostic methods, such as RT-PCR, have limitations that hinder timely and accurate screening. In response, AI-powered computer-aided imaging analysis techniques have emerged as a promising alternative for COVID-19 diagnosis. In this paper, we propose a novel approach that combines the strengths of Convolutional Neural Network (CNN) and Vision Transformer (ViT) to enhance the performance of COVID-19 diagnosis models. CNN excels at capturing spatial features in medical images, while ViT leverages self-attention mechanisms inspired by human radiologists. Additionally, our approach draws inspiration from subclinical diagnosis, a …
Disease Recognition In X-Ray Images With Doctor Consultation-Inspired Model, Kim Anh Phung, Thuan Trong Nguyen, Nileshkumar Wangad, Samah Baraheem, Nguyen D. Vo, Khang Nguyen
Disease Recognition In X-Ray Images With Doctor Consultation-Inspired Model, Kim Anh Phung, Thuan Trong Nguyen, Nileshkumar Wangad, Samah Baraheem, Nguyen D. Vo, Khang Nguyen
Computer Science Faculty Publications
The application of chest X-ray imaging for early disease screening is attracting interest from the computer vision and deep learning community. To date, various deep learning models have been applied in X-ray image analysis. However, models perform inconsistently depending on the dataset. In this paper, we consider each individual model as a medical doctor. We then propose a doctor consultation-inspired method that fuses multiple models. In particular, we consider both early and late fusion mechanisms for consultation. The early fusion mechanism combines the deep learned features from multiple models, whereas the late fusion method combines the confidence scores of all …
Pervasive Healthcare Internet Of Things: A Survey, Kim Anh Phung, Cemil Kirbas, Leyla Dereci, Tam Van Nguyen
Pervasive Healthcare Internet Of Things: A Survey, Kim Anh Phung, Cemil Kirbas, Leyla Dereci, Tam Van Nguyen
Computer Science Faculty Publications
Thanks to the proliferation of the Internet of Things (IoT), pervasive healthcare is gaining popularity day by day as it offers health support to patients irrespective of their location. In emergency medical situations, medical aid can be sent quickly. Though not yet standardized, this research direction, healthcare Internet of Things (H-IoT), attracts the attention of the research community, both academia and industry. In this article, we conduct a comprehensive survey of pervasive computing H-IoT. We would like to visit the wide range of applications. We provide a broad vision of key components, their roles, and connections in the big picture. …
Few-Shot Object Detection Via Baby Learning, Anh-Khoa Nguyen Vu, Nhat-Duy Nguyen, Khanh-Duy Nguyen, Vinh-Tiep Nguyen, Thanh Duc Ngo, Thanh-Toan Do, Tam Nguyen
Few-Shot Object Detection Via Baby Learning, Anh-Khoa Nguyen Vu, Nhat-Duy Nguyen, Khanh-Duy Nguyen, Vinh-Tiep Nguyen, Thanh Duc Ngo, Thanh-Toan Do, Tam Nguyen
Computer Science Faculty Publications
Few-shot learning is proposed to overcome the problem of scarce training data in novel classes. Recently, few-shot learning has been well adopted in various computer vision tasks such as object recognition and object detection. However, the state-of-the-art (SOTA) methods have less attention to effectively reuse the information from previous stages. In this paper, we propose a new framework of few-shot learning for object detection. In particular, we adopt Baby Learning mechanism along with the multiple receptive fields to effectively utilize the former knowledge in novel domain. The propoed framework imitates the learning process of a baby through visual cues. The …
Vietnamese Document Analysis: Dataset, Method And Benchmark Suite, Khang Nguyen, An Nguyen, Nguyen D. Vo, Tam Nguyen
Vietnamese Document Analysis: Dataset, Method And Benchmark Suite, Khang Nguyen, An Nguyen, Nguyen D. Vo, Tam Nguyen
Computer Science Faculty Publications
Document image understanding is increasingly useful since the number of digital documents is increasing day-by-day and the need for automation is increasing. Object detection plays a significant role in detecting vital objects and layouts in document images and contributes to providing a clearer understanding of the documents. Nonetheless, previous research mainly focuses on English document images, and studies on Vietnamese document images are limited. In this study, we extensively benchmark state-of-the-art object detectors and analyze the performance of each method on Vietnamese document images. Moreover, we also investigate the effectiveness of four different loss functions on the experimental object detection …
Masked Face Analysis Via Multi-Task Deep Learning, Vatsa S. Patel, Zhongliang Nie, Trung-Nghia Le, Tam Van Nguyen
Masked Face Analysis Via Multi-Task Deep Learning, Vatsa S. Patel, Zhongliang Nie, Trung-Nghia Le, Tam Van Nguyen
Computer Science Faculty Publications
Face recognition with wearable items has been a challenging task in computer vision and involves the problem of identifying humans wearing a face mask. Masked face analysis via multi-task learning could effectively improve performance in many fields of face analysis. In this paper, we propose a unified framework for predicting the age, gender, and emotions of people wearing face masks. We first construct FGNET-MASK, a masked face dataset for the problem. Then, we propose a multi-task deep learning model to tackle the problem. In particular, the multi-task deep learning model takes the data as inputs and shares their weight to …
Verification Of Piecewise Deep Neural Networks: A Star Set Approach With Zonotope Pre-Filter, Hoang-Dung Tran, Neelanjana Pal, Diego Manzanas Lopez, Patrick Musau, Xiaodong Yang, Luan Viet Nguyen, Weiming Xiang, Stanley Bak, Taylor T. Johnson
Verification Of Piecewise Deep Neural Networks: A Star Set Approach With Zonotope Pre-Filter, Hoang-Dung Tran, Neelanjana Pal, Diego Manzanas Lopez, Patrick Musau, Xiaodong Yang, Luan Viet Nguyen, Weiming Xiang, Stanley Bak, Taylor T. Johnson
Computer Science Faculty Publications
Verification has emerged as a means to provide formal guarantees on learning-based systems incorporating neural network before using them in safety-critical applications. This paper proposes a new verification approach for deep neural networks (DNNs) with piecewise linear activation functions using reachability analysis. The core of our approach is a collection of reachability algorithms using star sets (or shortly, stars), an effective symbolic representation of high-dimensional polytopes. The star-based reachability algorithms compute the output reachable sets of a network with a given input set before using them for verification. For a neural network with piecewise linear activation functions, our approach can …
Olympic Games Event Recognition Via Transfer Learning With Photobombing Guided Data Augmentation, Yousef I. Mohamad, Samah S. Baraheem, Tam Van Nguyen
Olympic Games Event Recognition Via Transfer Learning With Photobombing Guided Data Augmentation, Yousef I. Mohamad, Samah S. Baraheem, Tam Van Nguyen
Computer Science Faculty Publications
Automatic event recognition in sports photos is both an interesting and valuable research topic in the field of computer vision and deep learning. With the rapid increase and the explosive spread of data, which is being captured momentarily, the need for fast and precise access to the right information has become a challenging task with considerable importance for multiple practical applications, i.e., sports image and video search, sport data analysis, healthcare monitoring applications, monitoring and surveillance systems for indoor and outdoor activities, and video captioning. In this paper, we evaluate different deep learning models in recognizing and interpreting the sport …
R2u3d: Recurrent Residual 3d U-Net For Lung Segmentation, Dhaval D. Kadia, Md Zahangir Alom, Ranga Burada, Tam Nguyen, Vijayan K. Asari
R2u3d: Recurrent Residual 3d U-Net For Lung Segmentation, Dhaval D. Kadia, Md Zahangir Alom, Ranga Burada, Tam Nguyen, Vijayan K. Asari
Computer Science Faculty Publications
3D Lung segmentation is essential since it processes the volumetric information of the lungs, removes the unnecessary areas of the scan, and segments the actual area of the lungs in a 3D volume. Recently, the deep learning model, such as U-Net outperforms other network architectures for biomedical image segmentation. In this paper, we propose a novel model, namely, Recurrent Residual 3D U-Net (R(2)U3D), for the 3D lung segmentation task. In particular, the proposed model integrates 3D convolution into the Recurrent Residual Neural Network based on U-Net. It helps learn spatial dependencies in 3D and increases the propagation of 3D volumetric …
Divide And Slide: Layer-Wise Refinement For Output Range Analysis Of Deep Neural Networks, Chao Huang, Jiameng Fan, Xin Chen, Wenchao Li, Qi Zhu
Divide And Slide: Layer-Wise Refinement For Output Range Analysis Of Deep Neural Networks, Chao Huang, Jiameng Fan, Xin Chen, Wenchao Li, Qi Zhu
Computer Science Faculty Publications
In this article, we present a layer-wise refinement method for neural network output range analysis. While approaches such as nonlinear programming (NLP) can directly model the high nonlinearity brought by neural networks in output range analysis, they are known to be difficult to solve in general. We propose to use a convex polygonal relaxation (overapproximation) of the activation functions to cope with the nonlinearity. This allows us to encode the relaxed problem into a mixed-integer linear program (MILP), and control the tightness of the relaxation by adjusting the number of segments in the polygon. Starting with a segment number of …
Anatomy Of The Edelman: Measuring The World’S Best Analytics Projects, Michael F. Gorman, Lakshminarayana Nittala, Jeffrey M. Aldenb
Anatomy Of The Edelman: Measuring The World’S Best Analytics Projects, Michael F. Gorman, Lakshminarayana Nittala, Jeffrey M. Aldenb
MIS/OM/DS Faculty Publications
Each year, the INFORMS Edelman Award celebrates the best and most impactful implementations of operations research, management science, and analytics. As the Edelman Award approaches its 50-year mark, we provide a history and characterization of the award’s finalists and winners. We provide some basic descriptive analytics about the participating organizations and authors, the impact of their work, and the methods they employed. We also conduct predictive analytics on finalist submissions, gauging contributors to success in establishing winning entries. We find that predicting Edelman winners a priori is extremely difficult; however, given a set of finalists, predictive models based on monetary …
Nnv: The Neural Network Verification Tool For Deep Neural Networks And Learning-Enabled Cyber-Physical Systems, Hoang-Dung Tran, Xiaodong Yang, Diego Manzanas Lopez, Patrick Musau, Luan Viet Nguyen, Weiming Xiang, Stanley Bak, Taylor T. Johnson
Nnv: The Neural Network Verification Tool For Deep Neural Networks And Learning-Enabled Cyber-Physical Systems, Hoang-Dung Tran, Xiaodong Yang, Diego Manzanas Lopez, Patrick Musau, Luan Viet Nguyen, Weiming Xiang, Stanley Bak, Taylor T. Johnson
Computer Science Faculty Publications
This paper presents the Neural Network Verification (NNV) software tool, a set-based verification framework for deep neural networks (DNNs) and learning-enabled cyber-physical systems (CPS). The crux of NNV is a collection of reachability algorithms that make use of a variety of set representations, such as polyhedra, star sets, zonotopes, and abstract-domain representations. NNV supports both exact (sound and complete) and over-approximate (sound) reachability algorithms for verifying safety and robustness properties of feed-forward neural networks (FFNNs) with various activation functions. For learning-enabled CPS, such as closed-loop control systems incorporating neural networks, NNV provides exact and over-approximate reachability analysis schemes for linear …
Reachnn: Reachability Analysis Of Neural-Network Controlled Systems, Chao Huang, Jiameng Fan, Wenchao Li, Xin Chen, Qi Zhu
Reachnn: Reachability Analysis Of Neural-Network Controlled Systems, Chao Huang, Jiameng Fan, Wenchao Li, Xin Chen, Qi Zhu
Computer Science Faculty Publications
Applying neural networks as controllers in dynamical systems has shown great promises. However, it is critical yet challenging to verify the safety of such control systems with neural-network controllers in the loop. Previous methods for verifying neural network controlled systems are limited to a few specific activation functions. In this work, we propose a new reachability analysis approach based on Bernstein polynomials that can verify neural-network controlled systems with a more general form of activation functions, i.e., as long as they ensure that the neural networks are Lipschitz continuous. Specifically, we consider abstracting feedforward neural networks with Bernstein polynomials for …
An Introduction To Declarative Programming In Clips And Prolog, Jack L. Watkin, Adam C. Volk, Saverio Perugini
An Introduction To Declarative Programming In Clips And Prolog, Jack L. Watkin, Adam C. Volk, Saverio Perugini
Computer Science Faculty Publications
We provide a brief introduction to CLIPS—a declarative/logic programming language for implementing expert systems—and PROLOG—a declarative/logic programming language based on first-order, predicate calculus. Unlike imperative languages in which the programmer specifies how to compute a solution to a problem, in a declarative language, the programmer specifies what they what to find, and the system uses a search strategy built into the language. We also briefly discuss applications of CLIPS and PROLOG.
An Interactive, Graphical Simulator For Teaching Operating Systems, Joshua W. Buck, Saverio Perugini
An Interactive, Graphical Simulator For Teaching Operating Systems, Joshua W. Buck, Saverio Perugini
Computer Science Faculty Publications
We demonstrate a graphical simulation tool for visually and interactively exploring the processing of a variety of events handled by an operating system when running a program. Our graphical simulator is available for use on the web by both instructors and students for purposes of pedagogy. Instructors can use it for live demonstrations of course concepts in class, while students can use it outside of class to explore the concepts. The graphical simulation tool is implemented using the React library for the fancy ui elements of the Node.js framework and is available as a web application at https://cpudemo.azurewebsites.net. The goals …
Developing A Contemporary And Innovative Operating Systems Course, Saverio Perugini, David J. Wright
Developing A Contemporary And Innovative Operating Systems Course, Saverio Perugini, David J. Wright
Computer Science Faculty Publications
This birds-of-a-feather provides a discussion forum to foster innovation in teaching operating systems (os) at the undergraduate level. This birds-of-a-feather seeks to generate discussion and ideas around pedagogy for os and, in particular, how we might develop a contemporary and innovative model, in both content and delivery, for an os course—that plays a central role in a cs curriculum—and addresses significant issues of misalignment between existing os courses and employee professional skills and knowledge requirements. We would like to exchange ideas regarding a re-conceptualized course model of os curriculum and related pedagogy, especially in the areas of mobile OSs and …
A New Way To Detect Cyberattacks Extracting Changes In Register Values From Radio-Frequency Side Channels, Ronald A. Riley, James T. Graham, Ryan M. Fuller, Rusty O. Baldwin, Ashwin Fisher
A New Way To Detect Cyberattacks Extracting Changes In Register Values From Radio-Frequency Side Channels, Ronald A. Riley, James T. Graham, Ryan M. Fuller, Rusty O. Baldwin, Ashwin Fisher
Computer Science Faculty Publications
The Internet of Things (IoT) and the Internet of Everything (IoE) have driven processors into nearly every powered de- vice, from thermostats to refrigerators to light bulbs. From a security perspective, the IoT and IoE create a new layer of sig- nals and systems that can provide insight into the internal opera- tions of a device via analog side channels. Our research focuses on leveraging these analog side channels in IoT/IoE processors to detect intrusions. Our goal is to defend against cyberattacks that insert malware into IoT devices by detecting deviations in the code running on their processors from known …
Predicting Public Opinion On Drug Legalization: Social Media Analysis And Consumption Trends, Farahnaz Golrooy Motlagh, Saeedeh Shekarpour, Amit Sheth, Krishnaprasad Thirunarayan, Michael L. Raymer
Predicting Public Opinion On Drug Legalization: Social Media Analysis And Consumption Trends, Farahnaz Golrooy Motlagh, Saeedeh Shekarpour, Amit Sheth, Krishnaprasad Thirunarayan, Michael L. Raymer
Computer Science Faculty Publications
In this paper, we focus on the collection and analysis of relevant Twitter data on a state-by-state basis for (i) measuring public opinion on marijuana legalization by mining sentiment in Twitter data and (ii) determining the usage trends for six distinct types of marijuana. We overcome the challenges posed by the informal and ungrammatical nature of tweets to analyze a corpus of 306,835 relevant tweets collected over the four-month period, preceding the November 2015 Ohio Marijuana Legalization ballot and the four months after the election for all states in the US. Our analysis revealed two key insights: (i) the people …
Reachability Analysis For Neural Feedback Systems Using Regressive Polynomial Rule Inference, Souradeep Dutta, Xin Chen, Sriram Sankaranarayanan
Reachability Analysis For Neural Feedback Systems Using Regressive Polynomial Rule Inference, Souradeep Dutta, Xin Chen, Sriram Sankaranarayanan
Computer Science Faculty Publications
We present an approach to construct reachable set overapproxi- mations for continuous-time dynamical systems controlled using neural network feedback systems. Feedforward deep neural net- works are now widely used as a means for learning control laws through techniques such as reinforcement learning and data-driven predictive control. However, the learning algorithms for these net- works do not guarantee correctness properties on the resulting closed-loop systems. Our approach seeks to construct overapproxi- mate reachable sets by integrating a Taylor model-based flowpipe construction scheme for continuous differential equations with an approach that replaces the neural network feedback law for a small subset of …
Developing A Contemporary Operating Systems Course, Saverio Perugini, David J. Wright
Developing A Contemporary Operating Systems Course, Saverio Perugini, David J. Wright
Computer Science Faculty Publications
The objective of this tutorial presentation is to foster innovation in the teaching of operating systems (os) at the undergraduate level as part of a three-year NSF-funded IUSE (Improving Undergraduate STEM Education) project titled “Engaged Student Learning: Reconceptualizing and Evaluating a Core Computer Science Course for Active Learning and STEM Student Success” (2017–2020).
Chameleon: A Customizable Language For Teaching Programming Languages, Saverio Perugini, Jack L. Watkin
Chameleon: A Customizable Language For Teaching Programming Languages, Saverio Perugini, Jack L. Watkin
Computer Science Faculty Publications
ChAmElEoN is a programming language for teaching students the concepts and implementation of computer languages. We describe its syntax and semantics, the educational aspects involved in the implementation of a variety of interpreters for it, its malleability, and student feedback to inspire its use for teaching languages.
An Application Of The Actor Model Of Concurrency In Python: A Euclidean Rhythm Music Sequencer, Daniel P. Prince, Saverio Perugini
An Application Of The Actor Model Of Concurrency In Python: A Euclidean Rhythm Music Sequencer, Daniel P. Prince, Saverio Perugini
Computer Science Faculty Publications
We present a real-time sequencer, implementing the Euclidean rhythm algorithm, for creative generation of drum sequences by musicians or producers. We use the Actor model of concurrency to simplify the communication required for interactivity and musical timing, and generator comprehensions and higher-order functions to simplify the implementation of the Euclidean rhythm algorithm. The resulting application sends Musical Instrument Digital Interface (MIDI) data interactively to another application for sound generation.
The Design Of An Emerging/Multi-Paradigm Programming Languages Course, Saverio Perugini
The Design Of An Emerging/Multi-Paradigm Programming Languages Course, Saverio Perugini
Computer Science Faculty Publications
We present the design of a new special topics course, Emerging/Multi-paradigm Languages, on the recent trend toward more dynamic, multi-paradigm languages. To foster course adoption, we discuss the design of the course, which includes language presentations/papers and culminating, inal projects/papers. The goal of this article is to inspire and facilitate course adoption.
Natural Language, Mixed-Initiative Personal Assistant Agents, Joshua W. Buck, Saverio Perugini, Tam W. Nguyen
Natural Language, Mixed-Initiative Personal Assistant Agents, Joshua W. Buck, Saverio Perugini, Tam W. Nguyen
Computer Science Faculty Publications
The increasing popularity and use of personal voice assistant technologies, such as Siri and Google Now, is driving and expanding progress toward the long-term and lofty goal of using artificial intelligence to build human-computer dialog systems capable of understanding natural language. While dialog-based systems such as Siri support utterances communicated through natural language, they are limited in the flexibility they afford to the user in interacting with the system and, thus, support primarily action-requesting and information-seeking tasks. Mixed-initiative interaction, on the other hand, is a flexible interaction technique where the user and the system act as equal participants in an …
A Reliable And Efficient Wireless Sensor Network System For Water Quality Monitoring, Dung Nguyen, Phu Huu Phung
A Reliable And Efficient Wireless Sensor Network System For Water Quality Monitoring, Dung Nguyen, Phu Huu Phung
Computer Science Faculty Publications
Wireless sensor networks (WSNs) are strongly useful to monitor physical and environmental conditions to provide realtime information for improving environment quality. However, deploying a WSN in a physical environment faces several critical challenges such as high energy consumption, and data loss.In this work, we have proposed a reliable and efficient environmental monitoring system in ponds using wireless sensor network and cellular communication technologies. We have designed a hardware and software ecosystem that can limit the data loss yet save the energy consumption of nodes. A lightweight protocol acknowledges data transmission among the nodes. Data are transmitted to the cloud using …