Computer Sciences Commons^™

Event-Triggered Adaptive Distributed State Estimation By Using Active-Passive Sensor Networks, Akhilesh Raj, S. Jagannathan, Tansel Yucelen

Electrical and Computer Engineering Faculty Research & Creative Works

This paper proposes a novel event-triggered adaptive observer for each node in the heterogeneous sensor networks (HSNs) in order to estimate state vector of an unknown target or process by using the sensed output when the input to the target/ process is unknown. A subset of nodes in the HSN referred to as active nodes, can sense the target periodically, estimate the target state vector by using their adaptive observer and can communicate the estimated state vector of the target with the neighboring nodes including passive nodes only at event triggered instants. The adaptive observer parameters of active nodes are …

Novel Use Of Neural Networks To Identify And Detect Electrical Infrastructure Performance, Evan Pierre Savaria

Computer Science Theses & Dissertations

Electrical grid maintenance and repairs are crucial services that keep America’s lights on. Electrical service providers make it their priority to uphold minimal interruptions to this service. Electricity is essential for modern technology within the home, such as cooking, refrigeration, and hot water. Organizations, such as schools, hospitals, and military bases, cannot properly function or operate without power. When analyzing the current electrical infrastructure, it is evident that considerable components of the power grid are aging and in need of replacement. Additionally, threats and damage continue to occur. These damages occur not only due to simple, single power line failure …

A Shared-Memory Coupled Architecture To Leverage Big Data Frameworks In Prototyping And In-Situ Analytics For Data Intensive Scientific Workflows, Alexander Michael Lemon

Theses and Dissertations

There is a pressing need for creative new data analysis methods whichcan sift through scientific simulation data and produce meaningfulresults. The types of analyses and the amount of data handled by currentmethods are still quite restricted, and new methods could providescientists with a large productivity boost. New methods could be simpleto develop in big data processing systems such as Apache Spark, which isdesigned to process many input files in parallel while treating themlogically as one large dataset. This distributed model, combined withthe large number of analysis libraries created for the platform, makesSpark ideal for processing simulation output.Unfortunately, the filesystem becomes …

Siguria E Të Dhënave Në Rrjetë, Shkëlqim Krasniqi

Theses and Dissertations

Siguria e të dhënave në rrjetë është berë pjesë e rëndesishme e institucioneve dhe bizneseve pasi në rrjetë bëhet shkembimi i informacioneve nepër vende të ndryshme. Qellimi i këtij studimi është që të bejmë një hulumtim global dhe lokal për sigurinë e të dhënave në rrjetë.

Siguria e rrjetit është çdo aktivitet i dizajnuar për të mbrojtur përdorshmërinë dhe integritetin e rrjetit tuaj dhe të dhënave. Ai përfshin teknologjinë harduerike dhe softuerike. Siguria efektive e rrjetit menaxhon qasjen në rrjet. Ajo synon një shumëllojshmëri të kërcënimeve dhe i ndalon ato të hyjnë ose të përhapen në rrjetin tuaj.

Problemi i …

Smart City Lights, Shyhrete Buzaku

Theses and Dissertations

Aktualisht, në të gjithë botën, energji e madhe elektrike konsumohet nga dritat e rrugës, të cilat automatikisht ndizen kur bëhet errësirë dhe automatikisht fiken kur bëhet dritë. Kjo është humbje e madhe e energjisë në të gjithë botën dhe duhet të ndryshohet. Qëllimi kryesor i sistemeve të dritave të zgjuara të rrugëve është që dritat të ndizen kur është e nevojshme dhe dritat të fiket kur nuk nevojiten. Sjellja ideale e sistemit të dritave të zgjuara të rrugëve është të bëhet ndezja e dritave të rrugës gjatë natës në mënyrë efiçiente dhe efektive. Sa herë që dikush kalon pranë dritave …

Data-Driven Database Education: A Quantitative Study Of Sql Learning In An Introductory Database Course, Andrew C. Von Dollen

Master's Theses

The Structured Query Language (SQL) is widely used and challenging to master. Within the context of lab exercises in an introductory database course, this thesis analyzes the student learning process and seeks to answer the question: ``Which SQL concepts, or concept combinations, trouble students the most?'' We provide comprehensive taxonomies of SQL concepts and errors, identify common areas of student misunderstanding, and investigate the student problem-solving process. We present an interactive web application used by students to complete SQL lab exercises. In addition, we analyze data collected by this application and we offer suggestions for improvement to database lab activities.

The Wiener Attack On Rsa Revisited: A Quest For The Exact Bound, Willy Susilo, Joseph Tonien, Guomin Yang

Research Collection School Of Computing and Information Systems

Since Wiener pointed out that the RSA can be broken if the private exponent d is relatively small compared to the modulus N (using the continued fraction technique), it has been a general belief that the Wiener attack works for. On the contrary, in this work, we give an example where the Wiener attack fails with, thus, showing that the bound is not accurate as it has been thought of. By using the classical Legendre Theorem on continued fractions, in 1999 Boneh provided the first rigorous proof which showed that the Wiener attack works for. However, the question remains whether …

How Could Robots Successfully Elicit Help From Humans?, Layla Alarifi

Theses and Dissertations

With the rapid growth of robot usages, more attention has been focused on the robots’ hardware and software to improve functionality. However, the interaction between humans and robots can be key to enhancing robots’ performance. Autonomous mobile robots may more successfully complete their tasks if they effectively solicit help from humans when they reach their limits. Hence, in this thesis, we examine three different manners, friendly, polite, and indirect, combining different interaction modalities, like speech and gestures. We conducted an experiment (involving 45 subjects) where the NAO robot addresses bystanders with the aforementioned three manners to elicit help with opening …

Scalable Parallel Delaunay Image-To-Mesh Conversion For Shared And Distributed Memory Architectures, Daming Feng

Computer Science Theses & Dissertations

Mesh generation is an essential component for many engineering applications. The ability to generate meshes in parallel is critical for the scalability of the entire Finite Element Method (FEM) pipeline. However, parallel mesh generation applications belong to the broader class of adaptive and irregular problems, and are among the most complex, challenging, and labor intensive to develop and maintain. In this thesis, we summarize several years of the progress that we made in a novel framework for highly scalable and guaranteed quality mesh generation for finite element analysis in three dimensions. We studied and developed parallel mesh generation algorithms on …

Sensitive Behavior Analysis Of Android Applications On Unrooted Devices In The Wild, Xiaoxiao Tang

Dissertations and Theses Collection (Open Access)

Dynamic analysis is widely used in malware detection, taint analysis, vulnerability detection, and other areas for enhancing the security of Android. Compared to static analysis, dynamic analysis is immune to common code obfuscation techniques and dynamic code loading. Existing dynamic analysis techniques rely on in-lab running environment (e.g., modified systems, rooted devices, or emulators) and require automatic input generators to execute the target app. However, these techniques could be bypassed by anti-analysis techniques that allow apps to hide sensitive behavior when an in-lab environment is detected through predefined heuristics (e.g., IMEI number of the device is invalid). Meanwhile, current input …

Exploiting Mobility For Predictive Urban Analytics & Operations, Kasthuri Jayarajah

Dissertations and Theses Collection (Open Access)

As cities worldwide invest heavily in smart city infrastructure, it invites opportunities for a next wave of urban analytics. Unlike its predecessors, urban analytics applications and services can now be real-time and proactive -- they can (a) leverage situational data from large deployments of connected sensors, (b) capture attributes of a variety of entities that make up the urban fabric (e.g., people and their social relationships, transport nodes, utilities, etc.), and (c) use predictive insights to both proactively optimize urban operations (e.g., HVAC systems in smart buildings, buses in the transportation network, crowd-workers, etc.) and promote smarter policy decisions (e.g., …

Making Sense Of Crowd-Generated Content In Domain-Specific Settings, Agus Sulistya

Dissertations and Theses Collection (Open Access)

The rapid advances of the Web have changed the ways information is distributed and exchanged among individuals and organizations. Various content from different domains are generated daily and contributed by users' daily activities, such as posting messages in a microblog platform, or collaborating in a question and answer site. To deal with such tremendous volume of user generated content, there is a need for approaches that are able to handle the mass amount of available data and to extract knowledge hidden in the user generated content. This dissertation attempts to make sense of the generated content to help in three …

Stressmon: Large Scale Detection Of Stress And Depression In Campus Environment Using Passive Coarse-Grained Location Data, Camellia Zakaria

Dissertations and Theses Collection (Open Access)

The rising mental health illnesses of severe stress and depression is of increasing concern worldwide. Often associated by similarities in symptoms, severe stress can take a toll on a person’s productivity and result in depression if the stress is left unmanaged. Unfortunately, depression can occur without any feelings of stress. With depression growing as a leading cause of disability in economic productivity, there has been a sharp rise in mental health initiatives to improve stress and depression management. To offer such services conveniently and discreetly, recent efforts have focused on using mobile technologies. However, these initiatives usually require users to …

A Novel Method For Intelligent Single Fault Detection Of Bearings Using Sae And Improved D–S Evidence Theory, Jianguang Lu, Huan Zhang, Xianghong Tang

Computer Science Faculty Publications

In order to realize single fault detection (SFD) from the multi-fault coupling bearing data and further research on the multi-fault situation of bearings, this paper proposes a method based on features self-extraction of a Sparse Auto-Encoder (SAE) and results fusion of improved Dempster–Shafer evidence theory (D–S). Multi-fault signal compression features of bearings were extracted by SAE on multiple vibration sensors’ data. Data sets were constructed by the extracted compression features to train the Support Vector Machine (SVM) according to the rule of single fault detection (R-SFD) this paper proposed. Fault detection results were obtained by the improved D–S evidence theory, …

Ethical Hacking For Effective Defense (Modules, Labs, And Lectures), Hossain Shahriar

Computer Science and Information Technology Ancillary Materials

Summer 2019 Update: Through a Round Twelve ALG Mini-Grant for Ancillary Materials Creation and Revision, five new modules have been added to this collection:

• Enumeration with Sparta
• Enumeration with Inguma
• Hacking Web Servers with Dirbuster
• Hacking Web Servers with Skipfish
• Hacking Wireless and IoT with Bluehydra

The following set of materials is used in the Textbook Transformation Grants implementation of Ethical Hacking for Effective Defense:

https://oer.galileo.usg.edu/compsci-collections/8/

Topics include:

• TCP/IP Level Attacks
• Port Scanning
• DDoS
• Footprinting and Social Engineering
• Enumeration
• Programming for Security Professionals
• Operating System Vulnerabilities
• Embedded System Security
• Hacking Web Servers
• Hacking Wireless Networks
• Cryptography
• Protecting Networks with …

Message From Program Chairs, Bruce M. Mcmillin, David Lo

Computer Science Faculty Research & Creative Works

No abstract provided.

Success Factors Impacting Artificial Intelligence Adoption --- Perspective From The Telecom Industry In China, Hong Chen

Theses and Dissertations in Business Administration

As the core driving force of the new round of informatization development and the industrial revolution, the disruptive achievements of artificial intelligence (AI) are rapidly and comprehensively infiltrating into various fields of human activities. Although technologies and applications of AI have been widely studied, and factors that affect AI adoption are identified in existing literature, the impact of success factors on AI adoption remains unknown. Accordingly, the main study of this paper proposes a framework to explore the effects of success factors on AI adoption by integrating the technology, organization, and environment (TOE) framework and diffusion of innovation (DOI) theory. …

Early Information Access To Alleviate Emergency Department Congestion, Anjee Gorkhali

Theses and Dissertations in Business Administration

Alleviating Emergency Department (ED) congestion results in shorter hospital stay which not only reduces the cost of medical procedure but also increase the hospital performance. Length of patient stay is used to determine the hospital performance. Organization Information Processing (OIPT) Theory is used to explain the impact of information access and availability on the information processing need and ability of a hospital. Technical devices such as RFID that works as “Auto Identification tags” is suggested to increase the information availability as well as the information processing capability of the hospitals. This study suggests that the OIPT needs to be further …

Applying Case-Based Learning For A Postgraduate Software Architecture Course, Eng Lieh Ouh, Yunghans Irawan

Research Collection School Of Computing and Information Systems

Software architecture remains a difficult subject for learners to grasp and for educators to teach given its level of abstraction. On the other hand, case-based learning (CBL) is a popular teaching approach used across disciplines especially in business, medicine and law where students work in groups apply their knowledge to solve real-world case studies, or scenarios using their reasoning skills and existing theoretical knowledge. In this paper, we provide how we apply case-based learning to address the challenge in teaching a postgraduate software architecture course. Our learners are postgraduate students taking a master’s program in software engineering. We first describe …

Decentralizing Air Traffic Flow Management With Blockchain Based Reinforcement Learning, Nguyen Binh Duong Ta, Umang Chaudhary, Hong-Linh Truong

Research Collection School Of Computing and Information Systems

We propose and implement a decentralized, intelligent air traffic flow management (ATFM) solution to improve the efficiency of air transportation in the ASEAN region as a whole. Our system, named BlockAgent, leverages the inherent synergy between multi-agent reinforcement learning (RL) for air traffic flow optimization; and the rising blockchain technology for a secure, transparent and decentralized coordination platform. As a result, BlockAgent does not require a centralized authority for effective ATFM operations. We have implemented several novel distributed coordination approaches for RL in BlockAgent. Empirical experiments with real air traffic data concerning regional airports have demonstrated the feasibility and effectiveness …

Splitsecond: Flexible Privilege Separation Of Android Apps, Jehyun Lee, Akshaya Venkateswara Venkateswara Raja, Debin Gao

Research Collection School Of Computing and Information Systems

Android applications have been attractive targets to attackers due to the large number of users and the sensitive information they possess. After the success of the first step of an attack exploiting a software vulnerability, the consequential damage is primarily determined by the criticality and the amount of Android permissions that a victim application has. As a countermeasure, process separation techniques that isolate potentially vulnerable components — usually native libraries — from the critical data and permissions, have been proposed. However, existing techniques offer little flexibility in the separation, e.g., with all native code being placed into one process without …

Modeling Intra-Relation In Math Word Problems With Different Functional Multi-Head Attentions, Jierui Li, Lei Wang, Jipeng Zhang, Yan Wang, Bing Tian Dai, Dongxiang Zhang

Research Collection School Of Computing and Information Systems

Several deep learning models have been proposed for solving math word problems (MWPs) automatically. Although these models have the ability to capture features without manual efforts, their approaches to capturing features are not specifically designed for MWPs. To utilize the merits of deep learning models with simultaneous consideration of MWPs’ specific features, we propose a group attention mechanism to extract global features, quantity-related features, quantity-pair features and question-related features in MWPs respectively. The experimental results show that the proposed approach performs significantly better than previous state-of-the-art methods, and boost performance from 66.9% to 69.5% on Math23K with training-test split, from …

Resource Constrained Deep Reinforcement Learning, Abhinav Bhatia, Pradeep Varakantham, Akshat Kumar

Research Collection School Of Computing and Information Systems

In urban environments, resources have to be constantly matched to the “right” locations where customer demand is present. For instance, ambulances have to be matched to base stations regularly so as to reduce response time for emergency incidents in ERS (Emergency Response Systems); vehicles (cars, bikes among others) have to be matched to docking stations to reduce lost demand in shared mobility systems. Such problems are challenging owing to the demand uncertainty, combinatorial action spaces and constraints on allocation of resources (e.g., total resources, minimum and maximum number of resources at locations and regions). Existing systems typically employ myopic and …

Compositional Coding For Collaborative Filtering, Chenghao Liu, Tao Lu, Xin Wang, Zhiyong Cheng, Jianling Sun, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

Efficiency is crucial to the online recommender systems, especially for the ones which needs to deal with tens of millions of users and items. Because representing users and items as binary vectors for Collaborative Filtering (CF) can achieve fast user-item affinity computation in the Hamming space, in recent years, we have witnessed an emerging research effort in exploiting binary hashing techniques for CF methods. However, CF with binary codes naturally suffers from low accuracy due to limited representation capability in each bit, which impedes it from modeling complex structure of the data. In this work, we attempt to improve the …

Entropy Based Independent Learning In Anonymous Multi-Agent Settings, Tanvi Verma, Pradeep Varakantham, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

Efficient sequential matching of supply and demand is a problem of interest in many online to offline services. For instance, Uber, Lyft, Grab for matching taxis to customers; Ubereats, Deliveroo, FoodPanda etc for matching restaurants to customers. In these online to offline service problems, individuals who are responsible for supply (e.g., taxi drivers, delivery bikes or delivery van drivers) earn more by being at the ”right” place at the ”right” time. We are interested in developing approaches that learn to guide individuals to be in the ”right” place at the ”right” time (to maximize revenue) in the presence of other …

An Intelligent Platform With Automatic Assessment And Engagement Features For Active Online Discussions, Michelle L. F. Cheong, Yun-Chen Chen, Bing Tian Dai

Research Collection School Of Computing and Information Systems

In a universitycontext, discussion forums are mostly available in Learning and ManagementSystems (LMS) but are often ineffective in encouraging participation due topoorly designed user interface and the lack of motivating factors toparticipate. Our integrated platform with the Telegram mobile app and aweb-based forum, is capable of automatic thoughtfulness assessment of questionsand answers posted, using text mining and Natural Language Processing (NLP)methodologies. We trained and applied the Random Forest algorithm to provideinstant thoughtfulness score prediction for the new posts contributed by thestudents, and prompted the students to improve on their posts, thereby invokingdeeper thinking resulting in better quality contributions. In addition, …

Zac: A Zone Path Construction Approach 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 in available vehicles in real-time, so that the objective (e.g., requests served, revenue or delay) is optimized. The most relevant existing work has focussed on generating as many relevant feasible (with respect to available delay for customers) combinations of requests (referred to as trips) as possible in real-time. …

Correlated Learning For Aggregation Systems, Tanvi Verma, Pradeep Varakantham

Research Collection School Of Computing and Information Systems

Aggregation systems (e.g., Uber, Lyft, FoodPanda, Deliveroo) have been increasingly used to improve efficiency in numerous environments, including in transportation, logistics, food and grocery delivery. In these systems, a centralized entity (e.g., Uber) aggregates supply and assigns them to demand so as to optimize a central metric such as profit, number of requests, delay etc. Due to optimizing a metric of importance to the centralized entity, the interests of individuals (e.g., drivers, delivery boys) can be sacrificed. Therefore, in this paper, we focus on the problem of serving individual interests, i.e., learning revenue maximizing policies for individuals in the presence …

Pruneable Sharding-Based Blockchain Protocol, Xiaoqin Feng, Jianfeng Ma, Yinbin Miao, Qian Meng, Ximeng Liu, Qi Jiang, Hui Li

Research Collection School Of Computing and Information Systems

As a distributed ledger technology, the block-chain has gained much attention from both the industrical and academical fields, but most of the existing blockchain protocols still have the cubical dilatation problem. Although the latest Rollerchain has mitigated this issue by changing the blockheader's contents, the low efficiency, severe capacity expansion and non-scalability problems still hinder the adoption of Rollerchain in practice. To this end, we present the pruneable sharding-based blockchain protocol by utilizing the sharding technique and PBFT(Practical Byzantine Fault Tolerance) algorithm in the improved Rollerchain, which has high efficiency, slow cubical dilatation, small capacity expansion and high scalability. Moreover, …

Redpc: A Residual Error-Based Density Peak Clustering Algorithm, Milan Parmar, Di Wang, Xiaofeng Zhang, Ah-Hwee Tan, Chunyan Miao, You Zhou

Research Collection School Of Computing and Information Systems

The density peak clustering (DPC) algorithm was designed to identify arbitrary-shaped clusters by finding density peaks in the underlying dataset. Due to its aptitudes of relatively low computational complexity and a small number of control parameters in use, DPC soon became widely adopted. However, because DPC takes the entire data space into consideration during the computation of local density, which is then used to generate a decision graph for the identification of cluster centroids, DPC may face difficulty in differentiating overlapping clusters and in dealing with low-density data points. In this paper, we propose a residual error-based density peak clustering …