Computer Sciences Commons^™

Transient Analysis Of A Markovian Single Vacation Feedback Queue With An Interrupted Closedown Time And Control Of Admission During Vacation, A. Azhagappan, T. Deepa

Applications and Applied Mathematics: An International Journal (AAM)

This paper analyzes the transient behavior of an M/M/1 queueing model with single vacation, feedback, interrupted closedown time and control of admission during vacation. The time-dependent system size probabilities for the proposed model are obtained using generating function in the closed form. Further, the system performance measures like mean and variance of system size are also obtained for the time-dependent case. Finally, numerical illustrations are presented to understand the effect for various system parameters.

New Approach Of Deterministic Key Pre-Distribution Scheme Using Triangle Free Quasi Symmetric Designs, Debashis Ghosh, Joydeb Pal

Applications and Applied Mathematics: An International Journal (AAM)

A wireless sensor network (WSN) consists of tiny autonomous sensor nodes with some constraints. There are organizations having moderately necessitates of these kind of networks. So, security become an indispensable concern in WSN, due to potential adversaries. To overcome the security problem, keys are pre-loaded to the nodes before deployment. Among all key distribution schemes, deterministic key pre-distribution scheme (KPS) using combinatorial design is efficient regarding security aspect. In this paper, a deterministic approach, based on combinatorial design, for key assignment before the network deployment has been presented. Here the quasi-symmetric design which is of triangle-free is being used to …

Brain Tumor Classification Using Hit-Or-Miss Capsule Layers, Spencer J. Chang

Master's Theses

The job of classifying or annotating brain tumors from MRI images can be time-consuming and difficult, even for radiologists. To increase the survival chances of a patient, medical practitioners desire a means for quick and accurate diagnosis. While datasets like CIFAR, ImageNet, and SVHN have tens of thousands, hundreds of thousands, or millions of samples, an MRI dataset may not have the same luxury of receiving accurate labels for each image containing a tumor. This work covers three models that classify brain tumors using a combination of convolutional neural networks and of the concept of capsule layers. Each network utilizes …

Grammar-Based Procedurally Generated Village Creation Tool, Kevin Matthew Graves

Computer Engineering

This project is a 3D village generator tool for Unity. It consists of three components: a building, mountain, and river generator. All of these generators use grammar-based procedural generation in order to create a unique and logical village and landscape each time the program is run.

Robot Reporter Website Development, Samuel Verkruyse

Computer Engineering

The Robot Reporter Website Development project created a front end interface for interacting with news stories generated by the Robot Reporter script that was originally developed for the Digital Democracy institute at Cal Poly. Various graphs, videos, and other interesting content is displayed to the user in order to create an automated news website focusing on activity in state legislatures.

Radish: A Cross Platform Meal Prepping App For Beginner Weightlifters, Spoorthy S. Vemula, Tanay Gottigundala, Cory Baxes

Computer Science and Software Engineering

With the increasing ease of access and decreasing price of most food, obesity rates in the developing world have risen dramatically in recent years. As of March 23rd, 2019, obesity rates had reached 39.6%, a 6% increase in just 8 years. Research has shown that people with obesity have a significantly increased risk of heart disease, stroke, type 2 diabetes, and certain cancers, among other life-threatening diseases. In addition, 42% of people who begin weightlifting quit because it’s too difficult to follow a diet or workout regimen.

We created Radish in an attempt to tackle these problems. Radish makes it …

Bgp Hijacking Classification, Shinyoung Cho, Romain Fontugne, Kenjiro Cho, Alberto Dainotti, Phillipa Gill

Computer Science: Faculty Publications

Recent reports show that BGP hijacking has increased substantially. BGP hijacking allows malicious ASes to obtain IP prefixes for spamming as well as intercepting or blackholing traffic. While systems to prevent hijacks are hard to deploy and require the cooperation of many other organizations, techniques to detect hijacks have been a popular area of study. In this paper, we classify detected hijack events in order to document BGP detectors output and understand the nature of reported events. We introduce four categories of BGP hijack: typos, prepending mistakes, origin changes, and forged AS paths. We leverage AS hegemony-a measure of dependency …

Analysis Of M[X1],M[X2]/G1,G2/1 Retrial Queueing System With Priority Services, Working Breakdown, Collision, Bernoulli Vacation, Immediate Feedback, Starting Failure And Repair, G. Ayyappan, P. Thamizhselvi, B. Somasundaram

Applications and Applied Mathematics: An International Journal (AAM)

This paper considers an M^[X₁] , M^[X₂] /G1,G2/1 general retrial queueing system with priority services. Two types of customers from different classes arrive at the system in different independent compound Poisson processes. The server follows the non-pre-emptive priority rule subject to working breakdown, Bernoulli vacation, starting failure, immediate feedback, collision and repair. After completing each service, the server may go for a vacation or remain idle in the system. The priority customers who find the server busy are queued in the system. If a low-priority customer finds the server busy, he is routed to …

Development And Usability Assessment Of A Connected Resistance Exercise Band Application For Strength-Monitoring, John A. Batsis, George G. Boateng, Lillian M. Seo, Curtis L. Petersen, Karen L. Fortuna, Emily V. Wechsler, Ronald J. Peterson, Summer B. Cook, Dawna Pidgeon, Rachel S. Dokko, Ryan J. Halter, David F. Kotz

Dartmouth Scholarship

Resistance exercise bands are a core component of any physical activity strengthening program. Strength training can mitigate the development of sarcopenia, the loss of muscle mass or strength and function with aging. Yet, the adherence of such behavioral exercise strategies in a home-based setting are fraught with issues of monitoring and compliance. Our group developed a Bluetooth-enabled resistance exercise band capable of transmitting data to an open-source platform. In this work, we developed an application to capture this information in real-time, and conducted three usability studies in two mixed-aged groups of participants (n=6 each) and a group of older adults …

Multi-Human Management Of A Hub-Based Colony: Efficiency And Robustness In The Cooperative Best M-Of-N Task, John Rolfes Grosh

Theses and Dissertations

Swarm robotics is an emerging field that is expected to provide robust solutions to spatially distributed problems. Human operators will often be required to guide a swarm in the fulfillment of a mission. Occasionally, large tasks may require multiple spatial swarms to cooperate in their completion. We hypothesize that when latency, bandwidth, operator dropout, and communication noise are significant factors, human organizations that promote individual initiative perform more effectively and resiliently than hierarchies in the cooperative best-m-of-n task. Simulations automating the behavior of hub-based swarm robotic agents and groups of human operators are used to evaluate this hypothesis. To make …

Fault Detection And Estimation For A Class Of Nonlinear Distributed Parameter Systems, Hasan Ferdowsi, Jia Cai, Sarangapani Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This paper presents a new model-based fault detection and estimation framework for a class of multi-input and multi-output (MIMO) nonlinear distributed parameter systems (DPS) described by partial differential equations (PDE) with actuator and sensor faults. The fault functions cover both abrupt and incipient faults. A Luenberger type observer is used to monitor the health of the DPS as a detection observer on the basis of the nonlinear PDE representation of the system with measured output vector. By taking the difference between measured and estimated outputs from this observer, a residual signal is generated for fault detection. If the detection residual …

Social Recommendation With Optimal Limited Attention, Xin Wang, Wenwu Zhu, Chenghao Liu

Research Collection School Of Computing and Information Systems

Social recommendation has been playing an important role in suggesting items to users through utilizing information from social connections. However, most existing approaches do not consider the attention factor causing the constraint that people can only accept a limited amount of information due to the limited strength of mind, which has been discovered as an intrinsic physiological property of human by social science. We address this issue by resorting to the concept of limited attention in social science and combining it with machine learning techniques in an elegant way. When introducing the idea of limited attention into social recommendation, two …

Matching Passengers And Drivers With Multiple Objectives In Ride Sharing Markets, Guodong Lyu, Chung Piaw Teo, Wangchi Cheung, Hai Wang

Research Collection School Of Computing and Information Systems

In many cities in the world, ride sharing companies, such as Uber, Didi, Grab and Lyft, have been able to leverage on Internet-based platforms to conduct online decision making to connect passengers and drivers. These online platforms facilitate the integration of passengers and drivers’ mobility data on smart phones in real-time, which enables a convenient matching between demand and supply in real time. These clear operational advantages have motivated many similar shared service business models in the public transportation arena, and have been a disruptive force to the traditional taxi industry.

Dynamic Fusion With Intra-And Inter-Modality Attention Flow For Visual Question Answering, Peng Gao, Zhengkai Jiang, Haoxuan You, Pan Lu, Steven C. H. Hoi, Xiaogang Wang, Hongsheng Li

Research Collection School Of Computing and Information Systems

Learning effective fusion of multi-modality features is at the heart of visual question answering. We propose a novel method of dynamically fusing multi-modal features with intra- and inter-modality information flow, which alternatively pass dynamic information between and across the visual and language modalities. It can robustly capture the high-level interactions between language and vision domains, thus significantly improves the performance of visual question answering. We also show that the proposed dynamic intra-modality attention flow conditioned on the other modality can dynamically modulate the intramodality attention of the target modality, which is vital for multimodality feature fusion. Experimental evaluations on the …

Geometric Top-K Processing: Updates Since Mdm'16 [Advanced Seminar], Kyriakos Mouratidis

Research Collection School Of Computing and Information Systems

The top-k query has been studied extensively, and is considered the norm for multi-criteria decision making in large databases. In recent years, research has considered several complementary operators to the traditional top-k query, drawing inspiration (both in terms of problem formulation and solution design) from the geometric nature of the top-k processing model. In this seminar, we will present advances in that stream of work, focusing on updates since the preliminary seminar on the same topic in MDM'16.

Methodology For Comparison Of Algorithms For Real-World Multi-Objective Optimization Problems: Space Surveillance Network Design, Troy B. Dontigney

Theses and Dissertations

Space Situational Awareness (SSA) is an activity vital to protecting national and commercial satellites from damage or destruction due to collisions. Recent research has demonstrated a methodology using evolutionary algorithms (EAs) which is intended to develop near-optimal Space Surveillance Network (SSN) architectures in the sense of low cost, low latency, and high resolution. That research is extended here by (1) developing and applying a methodology to compare the performance of two or more algorithms against this problem, and (2) analyzing the effects of using reduced data sets in those searches. Computational experiments are presented in which the performance of five …

A Holistic Approach For Enhancing Distributed Education With Multi-Campus Course Delivery Methods, Mahmoud K. Quweider, Ala Qubbaj, Liyu Zhang, Fitratullah Khan, Hansheng Lei

Computer Science Faculty Publications

To create an emerging research institution, a regional university was created that spans multiple campuses within a radius of more than one hundred miles by merging at least three current institutions. The merge allowed the university to pool its human and technical resources. Students can now pursue new degrees that were not available before at one campus or another, take a newly available technical or specialty courses, and even select their own preferred professor when a course is offered by many faculty. In order to serve students at multiple campuses that are geographically far a part, the university instituted policies …

Deep Synthetic Noise Generation For Rgb-D Data Augmentation, Patrick Douglas Hammond

Theses and Dissertations

Considerable effort has been devoted to finding reliable methods of correcting noisy RGB-D images captured with unreliable depth-sensing technologies. Supervised neural networks have been shown to be capable of RGB-D image correction, but require copious amounts of carefully-corrected ground-truth data to train effectively. Data collection is laborious and time-intensive, especially for large datasets, and generation of ground-truth training data tends to be subject to human error. It might be possible to train an effective method on a relatively smaller dataset using synthetically damaged depth-data as input to the network, but this requires some understanding of the latent noise distribution of …

Closetalker: Secure, Short-Range Ad Hoc Wireless Communication, Timothy J. Pierson, Travis Peters, Ronald Peterson, David Kotz

Dartmouth Scholarship

Secure communication is difficult to arrange between devices that have not previously shared a secret. Previous solutions to the problem are susceptible to man-in-the-middle attacks, require additional hardware for out-of-band communication, or require an extensive public-key infrastructure. Furthermore, as the number of wireless devices explodes with the advent of the Internet of Things, it will be impractical to manually configure each device to communicate with its neighbors. Our system, CloseTalker, allows simple, secure, ad hoc communication between devices in close physical proximity, while jamming the signal so it is unintelligible to any receivers more than a few centimeters away. CloseTalker …

Cybersecurity Education: The Need For A Top-Driven, Multidisciplinary, School-Wide Approach, Lucy Tsado

Journal of Cybersecurity Education, Research and Practice

The human resource skills gap in cybersecurity has created an opportunity for educational institutions interested in cybersecurity education. The current number of schools designated by the Department of Homeland Security (DHS) and National Security Agency (NSA) as Centers of Academic Excellence (CAE) to train cybersecurity experts are not sufficient to meet the shortfall in the industry. The DHS has clearly mapped out knowledge areas for cybersecurity education for both technical and non-technical disciplines; it is therefore possible for institutions not yet designated CAEs to generate cybersecurity experts, with the long-term goal of attaining the CAE designation. The purpose of this …

Transferrable Prototypical Networks For Unsupervised Domain Adaptation, Yingwei Pan, Ting Yao, Yehao Li, Yu Wang, Chong-Wah Ngo, Tao Mei

Research Collection School Of Computing and Information Systems

In this paper, we introduce a new idea for unsupervised domain adaptation via a remold of Prototypical Networks, which learn an embedding space and perform classification via a remold of the distances to the prototype of each class. Specifically, we present Transferrable Prototypical Networks (TPN) for adaptation such that the prototypes for each class in source and target domains are close in the embedding space and the score distributions predicted by prototypes separately on source and target data are similar. Technically, TPN initially matches each target example to the nearest prototype in the source domain and assigns an example a …

Metagraph-Based Learning On Heterogeneous Graphs, Yuan Fang, Wenqing Lin, Vincent W. Zheng, Min Wu, Jiaqi Shi, Kevin Chang, Xiao-Li Li

Research Collection School Of Computing and Information Systems

Data in the form of graphs are prevalent, ranging from biological and social networks to citation graphs and the Web. Inparticular, most real-world graphs are heterogeneous, containing objects of multiple types, which present new opportunities for manyproblems on graphs. Consider a typical proximity search problem on graphs, which boils down to measuring the proximity between twogiven nodes. Most earlier studies on homogeneous or bipartite graphs only measure a generic form of proximity, without accounting fordifferent “semantic classes”—for instance, on a social network two users can be close for different reasons, such as being classmates orfamily members, which represent two distinct …

A Probabilistic Model Of The Bitcoin Blockchain, Marc Jourdan, Sebastien Blandin, Laura Wynter, Pralhad Deshpande

Research Collection School Of Computing and Information Systems

The Bitcoin transaction graph is a public data structure organized as transactions between addresses, each associated with a logical entity. In this work, we introduce a complete probabilistic model of the Bitcoin Blockchain, setting the basis for follow-up AI applications on Bitcoin transactions. We first formulate a set of conditional dependencies induced by the Bitcoin protocol at the block level and derive a corresponding fully observed graphical model of a Bitcoin block. We then extend the model to include hidden entity attributes such as the functional category of the associated logical agent and derive asymptotic bounds on the privacy properties …

Stabilized Svrg: Simple Variance Reduction For Nonconvex Optimization, Rong Ge, Zhize Li, Weiyao Wang, Xiang Wang

Research Collection School Of Computing and Information Systems

Variance reduction techniques like SVRG provide simple and fast algorithms for optimizing a convex finite-sum objective. For nonconvex objectives, these techniques can also find a first-order stationary point (with small gradient). However, in nonconvex optimization it is often crucial to find a second-order stationary point (with small gradient and almost PSD hessian). In this paper, we show that Stabilized SVRG (a simple variant of SVRG) can find an $\epsilon$-second-order stationary point using only $\tilde{O}(n^{2/3}/\epsilon^2 + n/\epsilon^{1.5})$ stochastic gradients. To our best knowledge, this is the first second-order guarantee for a simple variant of SVRG. The running time almost matches the …

Sliced Wasserstein Generative Models, Jiqing Wu, Zhiwu Huang, Dinesh Acharya, Wen Li, Janine Thoma, Danda Pani Paudel, Luc Van Gool

Research Collection School Of Computing and Information Systems

In generative modeling, the Wasserstein distance (WD) has emerged as a useful metric to measure the discrepancy between generated and real data distributions. Unfortunately, it is challenging to approximate the WD of high-dimensional distributions. In contrast, the sliced Wasserstein distance (SWD) factorizes high-dimensional distributions into their multiple one-dimensional marginal distributions and is thus easier to approximate. In this paper, we introduce novel approximations of the primal and dual SWD. Instead of using a large number of random projections, as it is done by conventional SWD approximation methods, we propose to approximate SWDs with a small number of parameterized orthogonal projections …

Learning Spatio-Temporal Representation With Local And Global Diffusion, Zhaofan Qiu, Ting Yao, Chong-Wah Ngo, Xinmei Tian, Tao Mei

Research Collection School Of Computing and Information Systems

Convolutional Neural Networks (CNN) have been regarded as a powerful class of models for visual recognition problems. Nevertheless, the convolutional filters in these networks are local operations while ignoring the large-range dependency. Such drawback becomes even worse particularly for video recognition, since video is an information-intensive media with complex temporal variations. In this paper, we present a novel framework to boost the spatio-temporal representation learning by Local and Global Diffusion (LGD). Specifically, we construct a novel neural network architecture that learns the local and global representations in parallel. The architecture is composed of LGD blocks, where each block updates local …

Time-Based Payout Ratio For Coordinating Supply And Demand On An On-Demand Service Platform, Jiaru Bai, Kut C. So, Christopher S. Tang, Xiqun Chen, Hai Wang

Research Collection School Of Computing and Information Systems

Many on-demand service platforms use a fixed payout ratio (i.e., the percentage of the platform’s revenue that is paid to the providers) regardless of the customer demand and the number of participating providers that tend to vary over time. In this chapter, we examine the implications of time-based payout ratios. To do so, we first present a queueing model with endogenous supply (number of participating providers) and endogenous demand (customer request rate) to model this on-demand service platform. In our model, earnings-sensitive independent providers have heterogeneous reservation price (for work participation) to serve wait-time and price-sensitive customers with heterogeneous valuation …

Automatic Loop Summarization Via Path Dependency Analysis, Xiaofei Xie, Bihuan Chen, Liang Zou, Yang Liu, Wei Le, Xiaohong Li

Research Collection School Of Computing and Information Systems

Analyzing loops is very important for various software engineering tasks such as bug detection, test case generation and program optimization. However, loops are very challenging structures for program analysis, especially when (nested) loops contain multiple paths that have complex interleaving relationships. In this paper, we propose the path dependency automaton (PDA) to capture the dependencies among the multiple paths in a loop. Based on the PDA, we first propose a loop classification to understand the complexity of loop summarization. Then, we propose a loop analysis framework, named Proteus, which takes a loop program and a set of variables of interest …

Learning Cross-Modal Embeddings With Adversarial Networks For Cooking Recipes And Food Images, Hao Wang, Doyen Sahoo, Chenghao Liu, Ee-Peng Lim, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

Food computing is playing an increasingly important role in human daily life, and has found tremendous applications in guiding human behavior towards smart food consumption and healthy lifestyle. An important task under the food-computing umbrella is retrieval, which is particularly helpful for health related applications, where we are interested in retrieving important information about food (e.g., ingredients, nutrition, etc.). In this paper, we investigate an open research task of cross-modal retrieval between cooking recipes and food images, and propose a novel framework Adversarial Cross-Modal Embedding (ACME) to resolve the cross-modal retrieval task in food domains. Specifically, the goal is to …

Corrn: Cooperative Reflection Removal Network, Renjie Wen, Boxin Shi, Haoliang Li, Ling-Yu Duan, Ah-Hwee Tan, Alex C. Kot

Research Collection School Of Computing and Information Systems

Removing the undesired reflections from images taken through the glass is of broad application to various computer vision tasks. Non-learning based methods utilize different handcrafted priors such as the separable sparse gradients caused by different levels of blurs, which often fail due to their limited description capability to the properties of real-world reflections. In this paper, we propose a network with the feature-sharing strategy to tackle this problem in a cooperative and unified framework, by integrating image context information and the multi-scale gradient information. To remove the strong reflections existed in some local regions, we propose a statistic loss by …