An Exact Single-Agent Task Selection Algorithm For The Crowdsourced Logistics,
2020
Singapore Management University
An Exact Single-Agent Task Selection Algorithm For The Crowdsourced Logistics, Chung-Kyun Han, Shih-Fen Cheng
Research Collection School Of Computing and Information Systems
The trend of moving online in the retail industry has created great pressure for the logistics industry to catch up both in terms of volume and response time. On one hand, volume is fluctuating at greater magnitude, making peaks higher; on the other hand, customers are also expecting shorter response time. As a result, logistics service providers are pressured to expand and keep up with the demands. Expanding fleet capacity, however, is not sustainable as capacity built for the peak seasons would be mostly vacant during ordinary days. One promising solution is to engage crowdsourced workers, who are not employed …
Stochastic Orthogonalization And Its Application To Machine Learning,
2019
Southern Methodist University
Stochastic Orthogonalization And Its Application To Machine Learning, Yu Hong
Electrical Engineering Theses and Dissertations
Orthogonal transformations have driven many great achievements in signal processing. They simplify computation and stabilize convergence during parameter training. Researchers have introduced orthogonality to machine learning recently and have obtained some encouraging results. In this thesis, three new orthogonal constraint algorithms based on a stochastic version of an SVD-based cost are proposed, which are suited to training large-scale matrices in convolutional neural networks. We have observed better performance in comparison with other orthogonal algorithms for convolutional neural networks.
Adversarial Attack On Neural Machine Translation System,
2019
Indian Statistical Institute
Adversarial Attack On Neural Machine Translation System, Abijith K. P.
Master’s Dissertations
Nowadays Deep Neural Network based solutions are deployed to solve numerous tasks. Thus, it has become absolutely important to study the robustness of these systems. Machine Translation is one of the popular applications of Deep Neural Networks. This thesis studies the robustness of Neural Machine Translation systems by generating adversarial examples with the objective to fool the model. Whenever there is a change in the source, i.e. when a word in the input sentence is replaced by an unrelated word, the translation system is supposed to reflect the changes while doing translation. These unwanted invariance learned by the model is …
Developing A Computational Framework For A Construction Scheduling Decision Support Web Based Expert System,
2019
University of Southern Mississippi
Developing A Computational Framework For A Construction Scheduling Decision Support Web Based Expert System, Feroz Ahmed
Dissertations
Decision-making is one of the basic cognitive processes of human behaviors by which a preferred option or a course of action is chosen from among a set of alternatives based on certain criteria. Decision-making is the thought process of selecting a logical choice from the available options. When trying to make a good decision, all the positives and negatives of each option should be evaluated. This decision-making process is particularly challenging during the preparation of a construction schedule, where it is difficult for a human to analyze all possible outcomes of each and every situation because, construction of a project …
Contrasting Geometric Variations Of Mathematical Models Of Self-Assembling Systems,
2019
University of Arkansas, Fayetteville
Contrasting Geometric Variations Of Mathematical Models Of Self-Assembling Systems, Michael Sharp
Graduate Theses and Dissertations
Self-assembly is the process by which complex systems are formed and behave due to the interactions of relatively simple units. In this thesis, we explore multiple augmentations of well known models of self-assembly to gain a better understanding of the roles that geometry and space play in their dynamics. We begin in the abstract Tile Assembly Model (aTAM) with some examples and a brief survey of previous results to provide a foundation. We then introduce the Geometric Thermodynamic Binding Network model, a model that focuses on the thermodynamic stability of its systems while utilizing geometrically rigid components (dissimilar to other …
The Generation Of Operational Policy For Cyber-Physical Systems In Smart Homes,
2019
Missouri State University
The Generation Of Operational Policy For Cyber-Physical Systems In Smart Homes, Jared Wayne Hall
Graduate Theses/Dissertations
The term “Cyber-Physical Systems” (CPS) refers to those systems which seamlessly integrate sensing, computation, control, and networking into physical objects and infrastructure [1]. In these systems, computers and networks of physical entities interact with each other to bring new capabilities to traditional physical systems. Since its introduction, the field of Cyber-Physical Systems (CPS) has evolved with new and interesting advancements concerning its capability, adaptability, scalability, and usability [1]. One such advancement is the unification of the Internet of Things (IoT), a concept that enables real-world everyday objects to connect to the internet and interact with each other, with CPS [1]. …
Iomt Malware Detection Approaches: Analysis And Research Challenges,
2019
Old Dominion University
Iomt Malware Detection Approaches: Analysis And Research Challenges, Mohammad Wazid, Ashok Kumar Das, Joel J.P.C. Rodrigues, Sachin Shetty, Youngho Park
VMASC Publications
The advancement in Information and Communications Technology (ICT) has changed the entire paradigm of computing. Because of such advancement, we have new types of computing and communication environments, for example, Internet of Things (IoT) that is a collection of smart IoT devices. The Internet of Medical Things (IoMT) is a specific type of IoT communication environment which deals with communication through the smart healthcare (medical) devices. Though IoT communication environment facilitates and supports our day-to-day activities, but at the same time it has also certain drawbacks as it suffers from several security and privacy issues, such as replay, man-in-the-middle, impersonation, …
Hybrid Recommender Systems Via Spectral Learning And A Random Forest,
2019
East Tennessee State University
Hybrid Recommender Systems Via Spectral Learning And A Random Forest, Alyssa Williams
Electronic Theses and Dissertations
We demonstrate spectral learning can be combined with a random forest classifier to produce a hybrid recommender system capable of incorporating meta information. Spectral learning is supervised learning in which data is in the form of one or more networks. Responses are predicted from features obtained from the eigenvector decomposition of matrix representations of the networks. Spectral learning is based on the highest weight eigenvectors of natural Markov chain representations. A random forest is an ensemble technique for supervised learning whose internal predictive model can be interpreted as a nearest neighbor network. A hybrid recommender can be constructed by first …
Testing Isomorphism Of Graded Algebras,
2019
Colorado State University - Fort Collins
Testing Isomorphism Of Graded Algebras, Peter A. Brooksbank, James B. Wilson, Eamonn A. O'Brien
Faculty Journal Articles
We present a new algorithm to decide isomorphism between finite graded algebras. For a broad class of nilpotent Lie algebras, we demonstrate that it runs in time polynomial in the order of the input algebras. We introduce heuristics that often dramatically improve the performance of the algorithm and report on an implementation in Magma.
Selective Discrete Particle Swarm Optimization For The Team Orienteering Problem With Time Windows And Partial Scores,
2019
Singapore Management University
Selective Discrete Particle Swarm Optimization For The Team Orienteering Problem With Time Windows And Partial Scores, Vincent F. Yu, Perwira A. A. N. Redi, Parida Jewpanya, Aldy Gunawan
Research Collection School Of Computing and Information Systems
This paper introduces the Team Orienteering Problem with Time Windows and Partial Scores (TOPTW-PS),which is an extension of the Team Orienteering Problem with Time Windows (TOPTW). In the context of theTOPTW-PS, each node is associated with a set of scores with respect to a set of attributes. The objective ofTOPTW-PS is to find a set of routes that maximizes the total score collected from a subset of attributes whenvisiting the nodes subject to the time budget and the time window at each visited node. We develop a mathematical model and propose a discrete version of the Particle Swarm Optimization (PSO), …
Harmony Search Algorithm For Time-Dependent Vehicle Routing Problem With Time Windows,
2019
Yuan-Ze University
Harmony Search Algorithm For Time-Dependent Vehicle Routing Problem With Time Windows, Yun-Chia Liang, Vanny Minanda, Aldy Gunawan, Angela Hsiang-Ling Chen
Research Collection School Of Computing and Information Systems
Vehicle Routing Problem (VRP) is a combinatorial problem where a certain set of nodes must be visited within a certain amount of time as well as the vehicle’s capacity. There are numerous variants of VRP such as VRP with time windows, where each node has opening and closing time, therefore, the visiting time must be during that interval. Another variant takes time-dependent constraint into account. This variant fits real-world scenarios, where at different period of time, the speed on the road varies depending on the traffic congestion. In this study, three objectives – total traveling time, total traveling distance, and …
Identifying Regional Trends In Avatar Customization,
2019
Singapore Management University
Identifying Regional Trends In Avatar Customization, Peter Mawhorter, Sercan Sengun, Haewoon Kwak, D. Fox Harrell
Research Collection School Of Computing and Information Systems
Since virtual identities such as social media profiles and avatars have become a common venue for self-expression, it has become important to consider the ways in which existing systems embed the values of their designers. In order to design virtual identity systems that reflect the needs and preferences of diverse users, understanding how the virtual identity construction differs between groups is important. This paper presents a new methodology that leverages deep learning and differential clustering for comparative analysis of profile images, with a case study of almost 100 000 avatars from a large online community using a popular avatar creation …
Agile Earth Observation Satellite Scheduling: An Orienteering Problem With Time-Dependent Profits And Travel Times,
2019
Singapore Management University
Agile Earth Observation Satellite Scheduling: An Orienteering Problem With Time-Dependent Profits And Travel Times, Guansheng Peng, Reginald Dewil, Cédric Verbeeck, Aldy Gunawan, Lining Xing, Pieter Vansteenwegen
Research Collection School Of Computing and Information Systems
The scheduling problem of an Agile Earth Observation Satellite is to schedule a subset of weighted observation tasks with each a specific “profit” in order to maximize the total collected profit, under its operational constraints. The “time-dependent transition time” and the “time-dependent profit” are two crucial features of this problem. The former relates to the fact that each pair of consecutive tasks requires a transition time to maneuver the look angle of the camera from the previous task to the next task. The latter follows from the fact that a different look angle of an observation leads to a different …
Emotion-Aware Chat Machine: Automatic Emotional Response Generation For Human-Like Emotional Interaction,
2019
Huazhong University of Science and Technology
Emotion-Aware Chat Machine: Automatic Emotional Response Generation For Human-Like Emotional Interaction, Wei Wei, Jiayi Liu, Xianling Mao, Guibing Guo, Feida Zhu, Pan Zhou, Yuchong Hu
Research Collection School Of Computing and Information Systems
The consistency of a response to a given post at semantic-level and emotional-level is essential for a dialogue system to deliver human-like interactions. However, this challenge is not well addressed in the literature, since most of the approaches neglect the emotional information conveyed by a post while generating responses. This article addresses this problem by proposing a unified end-to-end neural architecture, which is capable of simultaneously encoding the semantics and the emotions in a post for generating more intelligent responses with appropriately expressed emotions. Extensive experiments on real-world data demonstrate that the proposed method outperforms the state-of-the-art methods in terms …
On Finding Two Posets That Cover Given Linear Orders,
2019
University of the Philippines Diliman
On Finding Two Posets That Cover Given Linear Orders, Ivy Ordanel, Proceso L. Fernandez Jr, Henry Adorna
Department of Information Systems & Computer Science Faculty Publications
The Poset Cover Problem is an optimization problem where the goal is to determine a minimum set of posets that covers a given set of linear orders. This problem is relevant in the field of data mining, specifically in determining directed networks or models that explain the ordering of objects in a large sequential dataset. It is already known that the decision version of the problem is NP-Hard while its variation where the goal is to determine only a single poset that covers the input is in P. In this study, we investigate the variation, which we call the 2-Poset …
Protein Inter-Residue Distance Prediction Using Residual And Capsule Networks,
2019
University of Missouri-St. Louis
Protein Inter-Residue Distance Prediction Using Residual And Capsule Networks, Andrew Dillon
Theses
The protein folding problem, also known as protein structure prediction, is the task of building three-dimensional protein models given their one-dimensional amino acid sequence. New methods that have been successfully used in the most recent CASP challenge have demonstrated that predicting a protein's inter-residue distances is key to solving this problem. Various deep learning algorithms including fully convolutional neural networks and residual networks have been developed to solve the distance prediction problem. In this work, we develop a hybrid method based on residual networks and capsule networks. We demonstrate that our method can predict distances more accurately than the algorithms …
Adaptive Randomized Rounding In The Big Parsimony Problem,
2019
Northwestern University
Adaptive Randomized Rounding In The Big Parsimony Problem, Sangho Shim, Sunil Chopra, Eunseok Kim
Annual Symposium on Biomathematics and Ecology Education and Research
No abstract provided.
Collaborative Online Ranking Algorithms For Multitask Learning,
2019
Singapore Management University
Collaborative Online Ranking Algorithms For Multitask Learning, Guangxia Li, Peilin Zhao, Tao Mei, Peng Yang, Yulong Shen, Julian K. Y. Chang, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
There are many applications in which it is desirable to rank or order instances that belong to several different but related problems or tasks. Although unique, the individual ranking problem often shares characteristics with other problems in the group. Conventional ranking methods treat each task independently without considering the latent commonalities. In this paper, we study the problem of learning to rank instances that belong to multiple related tasks from the multitask learning perspective. We consider a case in which the information that is learned for a task can be used to enhance the learning of other tasks and propose …
Fractals As Basis For Design And Critique,
2019
Portland State University
Fractals As Basis For Design And Critique, John Charles Driscoll
Dissertations and Theses
The design profession is responding to the complex systems represented by architecture and planning by increasingly incorporating the power of computer technology into the design process. This represents a paradigm shift, and requires that designers rise to the challenge of both embracing modern technologies to perform increasingly sophisticated tasks without compromising their objective to create meaningful and environmentally sensitive architecture. This dissertation investigated computer-based fractal tools applied within a traditional architectural charette towards a design process with the potential to address the complex issues architects and planners face today. We developed and presented an algorithm that draws heavily from fractal …
Detecting Cyberattacks In Industrial Control Systems Using Online Learning Algorithms,
2019
Singapore Management University
Detecting Cyberattacks In Industrial Control Systems Using Online Learning Algorithms, Guangxia Li, Yulong Shen, Peilin Zhao, Xiao Lu, Jia Liu, Yangyang Liu, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
Industrial control systems are critical to the operation of industrial facilities, especially for critical infrastructures, such as refineries, power grids, and transportation systems. Similar to other information systems, a significant threat to industrial control systems is the attack from cyberspace-the offensive maneuvers launched by "anonymous" in the digital world that target computer-based assets with the goal of compromising a system's functions or probing for information. Owing to the importance of industrial control systems, and the possibly devastating consequences of being attacked, significant endeavors have been attempted to secure industrial control systems from cyberattacks. Among them are intrusion detection systems that …
