Computer Sciences Commons^™

Krahasimi I Javascript Librarive – React Js Me Vue Js, Ylber Verbaj

Theses and Dissertations

Implementimi i teklogjive moderne në zhvillimin e web-it po merr popullaritet global dhe po rezulton të jetë tejet i suksesshëm në këtë industri. Rezultatet e mira dalin të jenë në rrafshin professional dhe atë personal të zhvilluesve. Rritja dhe zhvillimi i këtyre teknologjive në mënyrë eksponenciale ka shtyrë shume kompani dhe shumë zhvillues se cila prej këtyre teknologjive gjen zbatim të duhur në produktet përkatëse të tyre.

Në këtë hulumtim do të shtjelloj elementet e web-it, mënyrën e funksionimit, ndarjet e web-it në anën e logjikës(backend) dhe ndërfaqës së përdoruesit(frontend). Hulumtimi kryesorë është në pjesën e frontend ku krahasohen dy …

- Aplikimi I Njohjes Së Fytyrës Duke Shfrytëzuar Shërbimet-Aws, Fatson Sylejmani

Theses and Dissertations

Sistemi i njohjes së fytyrës është njëri nga proceset kryesore biometrike të informacionit pasi ka gjetë zbatim shumë të madh në industri të ndryshme. Sistemi i tillë është mjaft kompleks por edhe shumë më efikas dhe më i besueshëm krahasuar me proceset tjera biometrike si: skanimi i irisit, nënshkrimi, gjurmët e gishtave.

Zhvillimi i një sistemi të tillë për njohjen e fytyrës i cili mund të përdoret për arsye të ndryshme ka qenë dhe është në interes për shumë zhvillues dhe kërkues në dy dekadat e fundit. Disa nga arsyet kryesore janë nevoja e njohjeve automatike dhe sistemet e mbikqyrjes, …

Dissemination And Visualization Of Hydro-Climate Data In Sub-Saharan Africa For Analysis Of Climatic Parameters, Divyadharshini Karthikeyan, Aparna S. Varde, Clement Alo

School of Computing Faculty Scholarship and Creative Works

Precipitation can have adverse effects on the climate ecosystem. Too much can impose concerns such as flooding and landslides, resulting in damaged property, agricultural losses, and loss of life. Too little, and drought becomes an issue, inducing wildfires, poor air quality, agricultural losses, and health degradation. While much work has been performed on historical and projected analysis of heavy precipitation, few interactive visualizations exist for end-users to better understand local impacts. The goal of this project is to create a visualization tool that easily demonstrates how precipitation extremes have changed and might change in the future for Sub-Saharan Africa. This …

Cats Are Not Fish: Deep Learning Testing Calls For Out-Of-Distribution Awareness, David Berend, Xiaofei Xie, Lei Ma, Lingjun Zhou, Yang Liu, Chi Xu, Jianjun Zhao

Research Collection School Of Computing and Information Systems

As Deep Learning (DL) is continuously adopted in many industrial applications, its quality and reliability start to raise concerns. Similar to the traditional software development process, testing the DL software to uncover its defects at an early stage is an effective way to reduce risks after deployment. According to the fundamental assumption of deep learning, the DL software does not provide statistical guarantee and has limited capability in handling data that falls outside of its learned distribution, i.e., out-of-distribution (OOD) data. Although recent progress has been made in designing novel testing techniques for DL software, which can detect thousands of …

Efficient Fine-Grained Data Sharing Mechanism For Electronic Medical Record Systems With Mobile Devices, Hui Ma, Rui Zhang, Guomin Yang, Zishuai Zong, Kai He, Yuting Xiao

Research Collection School Of Computing and Information Systems

Sharing digital medical records on public cloud storage via mobile devices facilitates patients (doctors) to get (offer) medical treatment of high quality and efficiency. However, challenges such as data privacy protection, flexible data sharing, efficient authority delegation, computation efficiency optimization, are remaining toward achieving practical fine-grained access control in the Electronic Medical Record (EMR) system. In this work, we propose an innovative access control model and a fine-grained data sharing mechanism for EMR, which simultaneously achieves the above-mentioned features and is suitable for resource-constrained mobile devices. In the model, complex computation is outsourced to public cloud servers, leaving almost no …

On Modeling Labor Markets For Fine-Grained Insights, Hendrik Santoso Sugiarto, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

The labor market consists of job seekers looking for jobs, and job openings waiting for applications. Classical labor market models assume that salary is the primary factor explaining why job-seekers select certain jobs. In practice, job seeker behavior is much more complex and there are other factors that should be considered. In this paper, we therefore propose the Probabilistic Labor Model (PLM) which considers salary satisfaction, topic preference matching, and accessibility as important criteria for job seekers to decide when they apply for jobs. We also determine the user and job latent variables for each criterion and define a graphical …

Zone Path Construction (Zac) Based Approaches For Effective Real-Time Ridesharing, Meghna Lowalekar, Pradeep Varakantham, Patrick Jaillet

Research Collection School Of Computing and Information Systems

Real-time ridesharing systems such as UberPool, Lyft Line, GrabShare have become hugely popular as they reduce the costs for customers, improve per trip revenue for drivers and reduce traffic on the roads by grouping customers with similar itineraries. The key challenge in these systems is to group the "right" requests to travel together in the "right" available vehicles in real-time, so that the objective (e.g., requests served, revenue or delay) is optimized. This challenge has been addressed in existing work by: (i) generating as many relevant feasible (with respect to the available delay for customers) combinations of requests as possible …

Temporal Heterogeneous Interaction Graph Embedding For Next-Item Recommendation, Yugang Ji, Mingyang Yin, Yuan Fang, Hongxia Yang, Xiangwei Wang, Tianrui Jia, Chuan Shi

Research Collection School Of Computing and Information Systems

In the scenario of next-item recommendation, previous methods attempt to model user preferences by capturing the evolution of sequential interactions. However, their sequential expression is often limited, without modeling complex dynamics that short-term demands can often be influenced by long-term habits. Moreover, few of them take into account the heterogeneous types of interaction between users and items. In this paper, we model such complex data as a Temporal Heterogeneous Interaction Graph (THIG) and learn both user and item embeddings on THIGs to address next-item recommendation. The main challenges involve two aspects: the complex dynamics and rich heterogeneity of interactions. We …

Optimal Control Of Excitable Systems Near Criticality, Kathleen Finlinson, Woodrow L. Shew, Danile B. Larremore, Juan G. Restrepo

Physics Faculty Publications and Presentations

Experiments suggest that the cerebral cortex gains several functional advantages by operating in a dynamical regime near the critical point of a phase transition. However, a long-standing criticism of this hypothesis is that critical dynamics are rather noisy, which might be detrimental to aspects of brain function that require precision. If the cortex does operate near criticality, how might it mitigate the noisy fluctuations? One possibility is that other parts of the brain may act to control the fluctuations and reduce cortical noise. To better understand basic aspects of controlling neural activity fluctuations, here we numerically and analytically study a …

Artificial Intelligence In Pursuit-Evasion Games, Specifically In The Scotland Yard Game, Arif M. Alamri

Theses and Dissertations

This research provides a heuristic algorithm for the detectives, who try to collectively capture a criminal known as Mr. X, in the Scotland Yard pursuer-evasion game. In Scotland Yard, a team of detectives attempts to converge on and capture a criminal known as Mr. X. The heuristic algorithm developed in this thesis is designed to emulate human strategies when playing the game. The algorithm uses the current state of the board at each time step, including the current positions of the detectives as well as the last known position of Mr. X. The heuristic algorithm then analyses all of the …

A Methodology To Identify Alternative Suitable Nosql Data Models Via Observation Of Relational Database Interactions, Paul M. Beach

Theses and Dissertations

The effectiveness and performance of data-intensive applications are influenced by the suitability of the data models upon which they are built. The relational data model has been the de facto data model underlying most database systems since the 1970’s. However, the recent emergence of NoSQL data models have provided users with alternative ways of storing and manipulating data. Previous research has demonstrated the potential value in applying NoSQL data models in non-distributed environments. However, knowing when to apply these data models has generally required inputs from system subject matter experts to make this determination. This research, sponsored by the Air …

Bots And Humans On Social Media, Lale Madahali

Interdisciplinary Informatics Faculty Proceedings & Presentations

Social networks are an important part of today’s life. They are used for entertainment, getting the news, advertisements, and branding for businesses and individuals alike. Research shows that automated accounts, also known as bots, contribute to the content spread on social media allowing the the environment pollution and public opinion manipulation. This research aims at investigating bots’ behavior on Twitter and examine how different and similar they are compared to humans. I will investigate their underlying network, whether it is an information network or social network. In the second step, I attempt to answer whether they follow the structure of …

Design And Implementation Of A Secure Access Layer For A Psd2 Compliant Consent Management Engine, Mentor Elshani

Theses and Dissertations

Online banking is growing fast, and customers are becoming increasing more comfortable with it than with the traditional bank services. Banks have to remain up to date with the latest trends in technology to satisfy customers’ demands for their everyday usage of banking services. Various banks often offer mobile applications with many features so that the user does not have to talk over the phone or chat via the internet with customer support, or even to physically go to the bank branch. When a bank releases its own online banking app for smartphones, the customer is likely to download it …

Deep Learning Techniques For Structural Response Prediction During Strong Ground Motions, Ahmed A. Torky, Susumu Ohno Prof., Toshide Kashima Prof.

Civil Engineering

In this paper, deep learning techniques are applied to predict a building’s structural response to strong ground motions. Data from sensors near and inside structures measure accelerations during strong ground motions. The Building Research Institute (BRI) ANX building, an eight-story structure, has experienced major earthquakes since 1998. Sensors in the building provide and accumulate big data of historic events. Changes of the natural frequency of the ANX building from the big data is initially quantified. The time-series data of the historic events can be used to predict future response to future events using deep learning models rapidly. Although previous literature …

Social Influence Attentive Neural Network For Friend-Enhanced Recommendation, Yuanfu Lu, Ruobing Xie, Chuan Shi, Yuan Fang, Wei Wang, Xu Zhang, Leyu Lin

Research Collection School Of Computing and Information Systems

With the thriving of online social networks, there emerges a new recommendation scenario in many social apps, called FriendEnhanced Recommendation (FER) in this paper. In FER, a user is recommended with items liked/shared by his/her friends (called a friend referral circle). These friend referrals are explicitly shown to users. Different from conventional social recommendation, the unique friend referral circle in FER may significantly change the recommendation paradigm, making users to pay more attention to enhanced social factors. In this paper, we first formulate the FER problem, and propose a novel Social Influence Attentive Neural network (SIAN) solution. In order to …

Large Signal Modeling For Llc Resonant Converter, Zheng Kai, Jianbing Li, Zhou Dongfang, Li Kai, Songzhen Zhang

Journal of System Simulation

Abstract: The large-signal model modeling issue of LLC resonant converter was dealt with by using averaged large-signal modeling method, on the basis of a full-bridge LLC voltage-multiplying resonant converter. The SSOC (self-sustained oscillation controller) was analyzed, and the operation mode of LLC resonant converter under the SSOC mode was investigated. The nonlinear state space model of LLC resonant converter was established by analyzing linear approximation of nonlinear terms and harmonic balance, and the averaged large-signal model of LLC resonant converter was proposed. Two curves of large signal model and nonlinear state space model were proved to be identical …

Construction Of Virtual Atmospheric Environment Based On Mm5 And Sedris, Lianlei Lin, Ding Wei

Journal of System Simulation

Abstract: The new virtual test for virtual atmosphere environment puts forward higher requirements, such as providing complex atmospheric environment which includes a variety of atmospheric parameters and improving usability and reusability of atmospheric environment data. A method to construct virtual atmospheric environment based on MM5 and SEDRIS was proposed which met the above demands. The original data of complex atmospheric environment in any region and scale was generated by using MM5 model. According to the characteristics as well as the dynamic relationship between time and space of grid of atmospheric data, SEDRIS was selected to regulate appropriate DRM class, SRF …

Barriers And Facilitators In Implementing A Pilot, Pragmatic, Telemedicine-Delivered Healthy Lifestyle Program For Obesity Management In A Rural, Academic Obesity Clinic, John A. Batsis, Auden C. Mcclure, Aaron B. Weintraub, Diane Sette, Sivan Rotenberg, Courtney J. Stevens, Diane Gilbert-Diamond, David Kotz, Stephen Bartels, Summer B. Cook, Richard I. Rothstein

Dartmouth Scholarship

Few evidence-based strategies are specifically tailored for disparity populations such as rural adults. Two-way video-conferencing using telemedicine can potentially surmount geographic barriers that impede participation in high-intensity treatment programs offering frequent visits to clinic facilities. We aimed to understand barriers and facilitators of implementing a telemedicine-delivered tertiary-care, rural academic weight-loss program for the management of obesity.

Bus Frequency Optimization: When Waiting Time Matters In User Satisfaction, Songsong Mo, Zhifeng Bao, Baihua Zheng, Zhiyong Peng

Research Collection School Of Computing and Information Systems

Reorganizing bus frequency to cater for the actual travel demand can save the cost of the public transport system significantly. Many, if not all, existing studies formulate this as a bus frequency optimization problem which tries to minimize passengers’ average waiting time. However, many investigations have confirmed that the user satisfaction drops faster as the waiting time increases. Consequently, this paper studies the bus frequency optimization problem considering the user satisfaction. Specifically, for the first time to our best knowledge, we study how to schedule the buses such that the total number of passengers who could receive their bus services …

Object Detection In Uav Images Via Global Density Fused Convolutional Network, Ruiqian Zhang, Zhenfeng Shao, Xiao Huang, Jiaming Wang, Deren Li

Geosciences Faculty Publications and Presentations

Object detection in Unmanned Aerial Vehicle (UAV) images plays fundamental roles in a wide variety of applications. As UAVs are maneuverable with high speed, multiple viewpoints, and varying altitudes, objects in UAV images are distributed with great heterogeneity, varying in size, with high density, bringing great difficulty to object detection using existing algorithms. To address the above issues, we propose a novel global density fused convolutional network (GDF-Net) optimized for object detection in UAV images. We test the effectiveness and robustness of the proposed GDF-Nets on the VisDrone dataset and the UAVDT dataset. The designed GDF-Net consists of a Backbone …

Measuring The Perceived Social Intelligence Of Robots, Kimberly A. Barchard, Leiszle Lapping-Carr, R. Shane Westfall, Andrea Fink-Armold, Santosh Balajee Banisetty, David Feil-Seifer

Psychology Faculty Research

Robotic social intelligence is increasingly important. However, measures of human social intelligence omit basic skills, and robot-specific scales do not focus on social intelligence. We combined human robot interaction concepts of beliefs, desires, and intentions with psychology concepts of behaviors, cognitions, and emotions to create 20 Perceived Social Intelligence (PSI) Scales to comprehensively measure perceptions of robots with a wide range of embodiments and behaviors. Participants rated humanoid and non-humanoid robots interacting with people in five videos. Each scale had one factor and high internal consistency, indicating each measures a coherent construct. Scales capturing perceived social information processing skills (appearing …

A Semi-Automated Approach To Medical Image Segmentation Using Conditional Random Field Inference, Yu-Chi Hu

Dissertations, Theses, and Capstone Projects

Medical image segmentation plays a crucial role in delivering effective patient care in various diagnostic and treatment modalities. Manual delineation of target volumes and all critical structures is a very tedious and highly time-consuming process and introduce uncertainties of treatment outcomes of patients. Fully automatic methods holds great promise for reducing cost and time, while at the same time improving accuracy and eliminating expert variability, yet there are still great challenges. Legally and ethically, human oversight must be integrated with ”smart tools” favoring a semi-automatic technique which can leverage the best aspects of both human and computer.

In this work …

Machine Learning Applications For Drug Repurposing, Hansaim Lim

Dissertations, Theses, and Capstone Projects

The cost of bringing a drug to market is astounding and the failure rate is intimidating. Drug discovery has been of limited success under the conventional reductionist model of one-drug-one-gene-one-disease paradigm, where a single disease-associated gene is identified and a molecular binder to the specific target is subsequently designed. Under the simplistic paradigm of drug discovery, a drug molecule is assumed to interact only with the intended on-target. However, small molecular drugs often interact with multiple targets, and those off-target interactions are not considered under the conventional paradigm. As a result, drug-induced side effects and adverse reactions are often neglected …

Unclonable Secret Keys, Marios Georgiou

Dissertations, Theses, and Capstone Projects

We propose a novel concept of securing cryptographic keys which we call “Unclonable Secret Keys,” where any cryptographic object is modified so that its secret key is an unclonable quantum bit-string whereas all other parameters such as messages, public keys, ciphertexts, signatures, etc., remain classical. We study this model in the authentication and encryption setting giving a plethora of definitions and positive results as well as several applications that are impossible in a purely classical setting.

In the authentication setting, we define the notion of one-shot signatures, a fundamental element in building unclonable keys, where the signing key not only …

Set Operators, Xiaojin Ye

Dissertations, Theses, and Capstone Projects

My research is centered on set operators. These are universally applicable regardless of the internal structure (numeric or non-numeric) of each individual observed datum. In our research, we have developed the theory of set operators to fill holes and gaps in observed data and eliminate paper shred garbage, thereby changing the observed symbolic data set into one whose pattern is closer to the pattern in the underlying population from which the observed data set was sampled with perturbations.

We describe different set operators including increasing operators, decreasing operators, ex- pansive operators, contractive operators, union preserving operators, intersection preserving op- erators, …

Role Of Influence In Complex Networks, Nur Dean

Dissertations, Theses, and Capstone Projects

Game theory is a wide ranging research area; that has attracted researchers from various fields. Scientists have been using game theory to understand the evolution of cooperation in complex networks. However, there is limited research that considers the structure and connectivity patterns in networks, which create heterogeneity among nodes. For example, due to the complex ways most networks are formed, it is common to have some highly “social” nodes, while others are highly isolated. This heterogeneity is measured through metrics referred to as “centrality” of nodes. Thus, the more “social” nodes tend to also have higher centrality.

In this thesis, …

Wait For It: Identifying 'On-Hold' Self-Admitted Technical Debt, Rungroj Maipradit, Christoph Treude, Hideaki Hata, Kenichi Matsumoto

Research Collection School Of Computing and Information Systems

Self-admitted technical debt refers to situations where a software developer knows that their current implementation is not optimal and indicates this using a source code comment. In this work, we hypothesize that it is possible to develop automated techniques to understand a subset of these comments in more detail, and to propose tool support that can help developers manage self-admitted technical debt more effectively. Based on a qualitative study of 333 comments indicating self-admitted technical debt, we first identify one particular class of debt amenable to automated management: on-hold self-admitted technical debt (on-hold SATD), i.e., debt which contains a condition …

Marble: Model-Based Robustness Analysis Of Stateful Deep Learning Systems, Xiaoning Du, Yi Li, Xiaofei Xie, Lei Ma, Yang Liu, Jianjun Zhao

Research Collection School Of Computing and Information Systems

State-of-the-art deep learning (DL) systems are vulnerable to adversarial examples, which hinders their potential adoption in safetyand security-critical scenarios. While some recent progress has been made in analyzing the robustness of feed-forward neural networks, the robustness analysis for stateful DL systems, such as recurrent neural networks (RNNs), still remains largely uncharted. In this paper, we propose Marble, a model-based approach for quantitative robustness analysis of real-world RNN-based DL systems. Marble builds a probabilistic model to compactly characterize the robustness of RNNs through abstraction. Furthermore, we propose an iterative refinement algorithm to derive a precise abstraction, which enables accurate quantification of …

Dct: An Scalable Multi-Objective Module Clustering Tool, Ana Paula M. Tarchetti, Luis Henrique Vieira Amaral, Marcos C. Oliveira, Rodrigo Bonifacio, Gustavo Pinto, David Lo

Research Collection School Of Computing and Information Systems

Maintaining complex software systems is a timeconsuming and challenging task. Practitioners must have a general understanding of the system’s decomposition and how the system’s developers have implemented the software features (probably cutting across different modules). Re-engineering practices are imperative to tackle these challenges. Previous research has shown the benefits of using software module clustering (SMC) to aid developers during re-engineering tasks (e.g., revealing the architecture of the systems, identifying how the concerns are spread among the modules of the systems, recommending refactorings, and so on). Nonetheless, although the literature on software module clustering has substantially evolved in the last 20 …

The Gap Of Semantic Parsing: A Survey On Automatic Math Word Problem Solvers, Dongxiang Zhang, Lei Wang, Luming Zhang, Bing Tian Dai, Heng Tao Shen

Research Collection School Of Computing and Information Systems

Solving mathematical word problems (MWPs) automatically is challenging, primarily due to the semantic gap between human-readable words and machine-understandable logics. Despite the long history dated back to the 1960s, MWPs have regained intensive attention in the past few years with the advancement of Artificial Intelligence (AI). Solving MWPs successfully is considered as a milestone towards general AI. Many systems have claimed promising results in self-crafted and small-scale datasets. However, when applied on large and diverse datasets, none of the proposed methods in the literature achieves high precision, revealing that current MWP solvers still have much room for improvement. This motivated …