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.
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.
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 …
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 …
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 …
Identifying Relationships Of Interest In Complex Environments By Using Channel Theory,
2019
Fraunhofer IPA
Identifying Relationships Of Interest In Complex Environments By Using Channel Theory, Andreas Bildstein, Junkang Feng
Communications of the IIMA
Complex environments show a high degree of dynamics caused by vital interactions between objects within those environments and alterations through which the set of objects and their characteristics within those environments go over time. Within this work, we show that we can tame the level of complexity in dynamic environments by identifying ‘relationships of interest’ between objects in such environments. To this end, we apply the theory of Information Flow, also known as Channel Theory, to the application area of smart manufacturing. We enhance the way how the Channel Theory has been applied so far by using an …
Efficient Distributed Reachability Querying Of Massive Temporal Graphs,
2019
Singapore Management University
Efficient Distributed Reachability Querying Of Massive Temporal Graphs, Tianming Zhang, Yunjun Gao, Chen Lu, Wei Guo, Shiliang Pu, Baihua Zheng, Christian S. Jensen
Research Collection School Of Computing and Information Systems
Reachability computation is a fundamental graph functionality with a wide range of applications. In spite of this, little work has as yet been done on efficient reachability queries over temporal graphs, which are used extensively to model time-varying networks, such as communication networks, social networks, and transportation schedule networks. Moreover, we are faced with increasingly large real-world temporal networks that may be distributed across multiple data centers. This state of affairs motivates the paper's study of efficient reachability queries on distributed temporal graphs. We propose an efficient index, called Temporal Vertex Labeling (TVL), which is a labeling scheme for distributed …
Detecting Toxicity Triggers In Online Discussions,
2019
Singapore Management University
Detecting Toxicity Triggers In Online Discussions, Hamad Bin Khalifa University, Haewoon Kwak
Research Collection School Of Computing and Information Systems
Despite the considerable interest in the detection of toxic comments, there has been little research investigating the causes -- i.e., triggers -- of toxicity. In this work, we first propose a formal definition of triggers of toxicity in online communities. We proceed to build an LSTM neural network model using textual features of comments, and then, based on a comprehensive review of previous literature, we incorporate topical and sentiment shift in interactions as features. Our model achieves an average accuracy of 82.5% of detecting toxicity triggers from diverse Reddit communities.
A Common Approach For Consumer And Provider Fairness In Recommendations,
2019
Singapore Management University
A Common Approach For Consumer And Provider Fairness In Recommendations, Dimitris Sacharidis, Kyriakos Mouratidis, Dimitrios Kleftogiannis
Research Collection School Of Computing and Information Systems
We present a common approach for handling consumer and provider fairness in recommendations. Our solution requires defining two key components, a classification of items and a target distribution, which together define the case of perfect fairness. This formulation allows distinct fairness concepts to be specified in a common framework. We further propose a novel reranking algorithm that optimizes for a desired trade-off between utility and fairness of a recommendation list.
A Machine Learning Model For Clustering Securities,
2019
Southern Methodist University
A Machine Learning Model For Clustering Securities, Vanessa Torres, Travis Deason, Michael Landrum, Nibhrat Lohria
SMU Data Science Review
In this paper, we evaluate the self-declared industry classifications and industry relationships between companies listed on either the Nasdaq or the New York Stock Exchange (NYSE) markets. Large corporations typically operate in multiple industries simultaneously; however, for investment purposes they are classified as belonging to a single industry. This simple classification obscures the actual industries within which a company operates, and, therefore, the investment risks of that company.
By using Natural Language Processing (NLP) techniques on Security and Exchange Commission (SEC) filings, we obtained self-defined industry classifications per company. Using clustering techniques such as Hierarchical Agglomerative and k-means clustering we …
Machine Learning In Support Of Electric Distribution Asset Failure Prediction,
2019
Southern Methodist University
Machine Learning In Support Of Electric Distribution Asset Failure Prediction, Robert D. Flamenbaum, Thomas Pompo, Christopher Havenstein, Jade Thiemsuwan
SMU Data Science Review
In this paper, we present novel approaches to predicting as- set failure in the electric distribution system. Failures in overhead power lines and their associated equipment in particular, pose significant finan- cial and environmental threats to electric utilities. Electric device failure furthermore poses a burden on customers and can pose serious risk to life and livelihood. Working with asset data acquired from an electric utility in Southern California, and incorporating environmental and geospatial data from around the region, we applied a Random Forest methodology to predict which overhead distribution lines are most vulnerable to fail- ure. Our results provide evidence …
Machine Learning Predicts Aperiodic Laboratory Earthquakes,
2019
Southern Methodist University
Machine Learning Predicts Aperiodic Laboratory Earthquakes, Olha Tanyuk, Daniel Davieau, Charles South, Daniel W. Engels
SMU Data Science Review
In this paper we find a pattern of aperiodic seismic signals that precede earthquakes at any time in a laboratory earthquake’s cycle using a small window of time. We use a data set that comes from a classic laboratory experiment having several stick-slip displacements (earthquakes), a type of experiment which has been studied as a simulation of seismologic faults for decades. This data exhibits similar behavior to natural earthquakes, so the same approach may work in predicting the timing of them. Here we show that by applying random forest machine learning technique to the acoustic signal emitted by a laboratory …
Texture-Based Deep Neural Network For Histopathology Cancer Whole Slide Image (Wsi) Classification,
2019
Kennesaw State University
Texture-Based Deep Neural Network For Histopathology Cancer Whole Slide Image (Wsi) Classification, Nelson Zange Tsaku
Master of Science in Computer Science Theses
Automatic histopathological Whole Slide Image (WSI) analysis for cancer classification has been highlighted along with the advancements in microscopic imaging techniques. However, manual examination and diagnosis with WSIs is time-consuming and tiresome. Recently, deep convolutional neural networks have succeeded in histopathological image analysis. In this paper, we propose a novel cancer texture-based deep neural network (CAT-Net) that learns scalable texture features from histopathological WSIs. The innovation of CAT-Net is twofold: (1) capturing invariant spatial patterns by dilated convolutional layers and (2) Reducing model complexity while improving performance. Moreover, CAT-Net can provide discriminative texture patterns formed on cancerous regions of histopathological …
Faster First-Order Methods For Stochastic Non-Convex Optimization On Riemannian Manifolds,
2019
Singapore Management University
Faster First-Order Methods For Stochastic Non-Convex Optimization On Riemannian Manifolds, Pan Zhou, Xiao-Tong Yuan, Shuicheng Yan, Jiashi Feng
Research Collection School Of Computing and Information Systems
First-order non-convex Riemannian optimization algorithms have gained recent popularity in structured machine learning problems including principal component analysis and low-rank matrix completion. The current paper presents an efficient Riemannian Stochastic Path Integrated Differential EstimatoR (R-SPIDER) algorithm to solve the finite-sum and online Riemannian non-convex minimization problems. At the core of R-SPIDER is a recursive semi-stochastic gradient estimator that can accurately estimate Riemannian gradient under not only exponential mapping and parallel transport, but also general retraction and vector transport operations. Compared with prior Riemannian algorithms, such a recursive gradient estimation mechanism endows R-SPIDER with higher computational efficiency in first-order oracle complexity. …
