Cybercrime Deterrence And International Legislation: Evidence From Distributed Denial Of Service Attacks, 2017 Singapore Management University
Cybercrime Deterrence And International Legislation: Evidence From Distributed Denial Of Service Attacks, Kai-Lung Hui, Seung Hyun Kim, Qiu-Hong Wang
Research Collection School Of Information Systems
In this paper, we estimate the impact of enforcing the Convention on Cybercrime (COC) on deterring distributed denial of service (DDOS) attacks. Our data set comprises a sample of real, random spoof-source DDOS attacks recorded in 106 countries in 177 days in the period 2004-2008. We find that enforcing the COC decreases DDOS attacks by at least 11.8 percent, but a similar deterrence effect does not exist if the enforcing countries make a reservation on international cooperation. We also find evidence of network and displacement effects in COC enforcement. Our findings imply attackers in cyberspace are rational, motivated by ...
Recommender Response To Diversity And Popularity Bias In User Profiles, 2017 Boise State University
Recommender Response To Diversity And Popularity Bias In User Profiles, Sushma Channamsetty, Michael D. Ekstrand
Michael D. Ekstrand
Automatic Verification Of Finite Precision Implementations Of Linear Controllers, 2017 University of Pennsylvania
Automatic Verification Of Finite Precision Implementations Of Linear Controllers, Junkil Park, Miroslav Pajic, Oleg Sokolsky, Insup Lee
Departmental Papers (CIS)
We consider the problem of verifying finite precision implementation of linear time-invariant controllers against mathematical specifications. A specification may have multiple correct implementations which are different from each other in controller state representation, but equivalent from a perspective of input-output behavior (e.g., due to optimization in a code generator). The implementations may use finite precision computations (e.g. floating-point arithmetic) which cause quantization (i.e., roundoff) errors. To address these challenges, we first extract a controller's mathematical model from the implementation via symbolic execution and floating-point error analysis, and then check approximate input-output equivalence between the extracted model ...
Designing Novel Nanostructured Permanent Magnets, 2017 University of Nebraska at Omaha
Designing Novel Nanostructured Permanent Magnets, Ali Al Kadhim
Student Research and Creative Activity Fair
Rare earth element based alloys have been the source of high performance magnetic alloys, and have played a paramount role in the development of various technologies, including: memory devices (such as credit cards, random-access memory), sensors, and various biomedical applications. However, there is a tremendous need to replace rare earth metals with material with powerful magnetic properties. Our group recently found CrTe-based materials that show very promising magnetic properties in nanostructured form. The magnetic modeling of such material in nanostructured form prior to their fabrication demonstrates their magnetic properties in bulk form. In this project, we investigate the behavior of ...
Vision-Based Mobile Robotic Platform For Autonomous Landing Of Quadcopters, 2017 University of Nebraska at Omaha
Vision-Based Mobile Robotic Platform For Autonomous Landing Of Quadcopters, Timothy R. Joe
Student Research and Creative Activity Fair
This project deals with the development of a vision-based control algorithm to assist quadcopters in the landing process. For demonstration purposes, the approach has been implemented in a mobile robotic platform (turtlebot). In this project, the objective is to use the mobile robot as a landing platform. The camera on-board the mobile robot detects the quadcopter (AprilTag attached to the flying robot) and keeps track of it. Based on this idea, the proposed approach estimates in real-time the landing zone. Once this zone is calculated, the mobile robot moves towards this area, stops under the quadcopter, and acts as a ...
Mixed-Initiative Personal Assistants, 2017 University of Dayton
Mixed-Initiative Personal Assistants, Joshua W. Buck
Computer Science Faculty Publications
Specification and implementation of flexible human-computer dialogs is challenging because of the complexity involved in rendering the dialog responsive to a vast number of varied paths through which users might desire to complete the dialog. To address this problem, we developed a toolkit for modeling and implementing task-based, mixed-initiative dialogs based on metaphors from lambda calculus. Our toolkit can automatically operationalize a dialog that involves multiple prompts and/or sub-dialogs, given a high-level dialog specification of it. Our current research entails incorporating the use of natural language to make the flexibility in communicating user utterances commensurate with that in dialog ...
Protein-Rna Interface Residue Prediction Using Machine Learning: An Assessment Of The State Of The Art, 2017 Iowa State University
Protein-Rna Interface Residue Prediction Using Machine Learning: An Assessment Of The State Of The Art, Rasna R. Walia, Cornelia Caragea, Benjamin A. Lewis, Fadi Towfic, Michael Terribilini, Yasser El-Manzalawy, Drena Dobbs, Vasant Honavar
Background: RNA molecules play diverse functional and structural roles in cells. They function as messengers for transferring genetic information from DNA to proteins, as the primary genetic material in many viruses, as catalysts (ribozymes) important for protein synthesis and RNA processing, and as essential and ubiquitous regulators of gene expression in living organisms. Many of these functions depend on precisely orchestrated interactions between RNA molecules and specific proteins in cells. Understanding the molecular mechanisms by which proteins recognize and bind RNA is essential for comprehending the functional implications of these interactions, but the recognition ‘code’ that mediates interactions between proteins ...
Predicting Protein-Protein Interface Residues Using Local Surface Structural Similarity, 2017 Iowa State University
Predicting Protein-Protein Interface Residues Using Local Surface Structural Similarity, Rafael R. Jordan, Yasser El-Manzalawy, Drena Dobbs, Vasant Honavar
Identification of the residues in protein-protein interaction sites has a significant impact in problems such as drug discovery. Motivated by the observation that the set of interface residues of a protein tend to be conserved even among remote structural homologs, we introduce PrISE, a family of local structural similarity-based computational methods for predicting protein-protein interface residues.
We present a novel representation of the surface residues of a protein in the form of structural elements. Each structural element consists of a central residue and its surface neighbors. The PrISE family of interface prediction methods uses a representation of structural ...
Scripts For Recommender Response To Diversity And Popularity Bias In User Profiles, 2017 Boise State University
Scripts For Recommender Response To Diversity And Popularity Bias In User Profiles, Sushma Channamsetty, Michael D. Ekstrand
Michael D. Ekstrand
An Application Of High Fidelity Ftds For Ab Initio Pilot Training: The Way Ahead, 2017 Embry-Riddle Aeronautical University - Daytona Beach
An Application Of High Fidelity Ftds For Ab Initio Pilot Training: The Way Ahead, Nickolas D. Macchiarella, Tim Brady, Brandon S. Lyon
"Decreases in simulation costs and increases in aircraft training costs led to the need for further investigation into the application of simulation-based training. Researchers conducted an eighteen-month study using ab initio student pilots as participants. This study applied a Federal Aviation Administration (FAA) approved, Part 142, flight-training curriculum that included 60% flight training device (FTD) use. Researchers identified five causal factors that warranted further investigation. The causal factors identified were visual fidelity, procedural similarity, dynamic flight environment, difficulty of task, and visual scanning and response. These causal factors have the potential to affect transfer of training (ToT) from simulated flight ...
Alternating Direction Method Of Multipliers For Penalized Zero-Variance Discriminant Analysis, 2017 University of Alabama - Tuscaloosa
Alternating Direction Method Of Multipliers For Penalized Zero-Variance Discriminant Analysis, Brendan P.W. Ames, Mingyi Hong
We consider the task of classification in the high dimensional setting where the number of features of the given data is significantly greater than the number of observations. To accomplish this task, we propose a heuristic, called sparse zero-variance discriminant analysis, for simultaneously performing linear discriminant analysis and feature selection on high dimensional data. This method combines classical zero-variance discriminant analysis, where discriminant vectors are identified in the null space of the sample within-class covariance matrix, with penalization applied to induce sparse structures in the resulting vectors. To approximately solve the resulting nonconvex problem, we develop a simple algorithm based ...
Sample Approximation-Based Deflation Approaches For Chance Sinr-Constrained Joint Power And Admission Control, 2017 Chinese Academy of Sciences
Sample Approximation-Based Deflation Approaches For Chance Sinr-Constrained Joint Power And Admission Control, Ya-Feng Liu, Mingyi Hong, Enbin Song
Consider the joint power and admission control (JPAC) problem for a multiuser single-input single-output (SISO) interference channel. Most existing works on JPAC assume the perfect instantaneous channel state information (CSI). In this paper, we consider the JPAC problem with the imperfect CSI, i.e., we assume that only the channel distribution information (CDI) is available. We formulate the JPAC problem into a chance (probabilistic)-constrained program, where each link's SINR outage probability is enforced to be less than or equal to a specified tolerance. To circumvent the computational difficulty of the chance SINR constraints, we propose to use the ...
Asynchronous Distributed Admm For Large-Scale Optimization—Part Ii: Linear Convergence Analysis And Numerical Performance, 2017 The Chinese University of Hong Kong
Asynchronous Distributed Admm For Large-Scale Optimization—Part Ii: Linear Convergence Analysis And Numerical Performance, Tsung-Hui Chang, Wei-Cheng Lao, Mingyi Hong, Xiangfeng Wang
The alternating direction method of multipliers (ADMM) has been recognized as a versatile approach for solving modern large-scale machine learning and signal processing problems efficiently. When the data size and/or the problem dimension is large, a distributed version of ADMM can be used, which is capable of distributing the computation load and the data set to a network of computing nodes. Unfortunately, a direct synchronous implementation of such algorithm does not scale well with the problem size, as the algorithm speed is limited by the slowest computing nodes. To address this issue, in a companion paper, we have proposed ...
Joint Downlink Base Station Association And Power Control For Max-Min Fairness: Computation And Complexity, 2017 Southeast University
Joint Downlink Base Station Association And Power Control For Max-Min Fairness: Computation And Complexity, Qingjiang Shi, Mingyi Hong, Xiqi Gao, Enbin Song, Yunlong Cai, Weiqiang Xu
The performance of full-duplex (FD) relay systems can be greatly impacted by the self-interference (SI) at relays. By exploiting multiple antennas, the spectral efficiency of FD relay systems can be enhanced through spatial SI mitigation. This paper studies joint source transmit beamforming and relay processing to achieve rate maximization for FD multiple-input-multiple-output (MIMO) amplify-and-forward (AF) relay systems with consideration of relay processing delay. The problem is difficult to solve mainly due to the SI constraint induced by the relay processing delay. In this paper, we first present a sufficient condition under which the relay amplification matrix has rank-one structure. Then ...
Decomposition By Successive Convex Approximation: A Unifying Approach For Linear Transceiver Design In Heterogeneous Networks, Mingyi Hong, Qiang Li, Ya-Feng Liu
No abstract provided.
Sinr Constrained Beamforming For A Mimo Multi-User Downlink System: Algorithms And Convergence Analysis, 2017 Zhejiang University
Sinr Constrained Beamforming For A Mimo Multi-User Downlink System: Algorithms And Convergence Analysis, Oingjang Shi, Meisam Razaviyayn, Mingyi Hong, Zhi-Quan Luo
Consider a multiple-input multiple-output (MIMO) downlink multi-user channel. A well-studied problem in such a system is the design of linear beamformers for power minimization with the quality of service (QoS) constraints. The most representative algorithms for solving this class of problems are the so-called minimum mean square error (MMSE)-second-order cone programming (SOCP) algorithm [Visotksy and Madhow, “Optimum Beamforming Using Transmit Antenna Arrays,” Proc. IEEE Veh. Technol. Conf., May 1999, vol. 1, pp. 851-856] , [Wong, Zheng, and Ng, “Convergence Analysis of Downlink MIMO Antenna System Using Second-Order Cone Programming,” Proc. 62nd IEEE Veh. Technol. Conf., Sep. 2005, pp. 492-496] and ...
Asynchronous Distributed Admm For Large-Scale Optimization—Part I: Algorithm And Convergence Analysis, 2017 The Chinese University of Hong Kong
Asynchronous Distributed Admm For Large-Scale Optimization—Part I: Algorithm And Convergence Analysis, Tsung-Hui Chang, Mingyi Hong, Wei-Cheng Liao, Xiangfeng Wang
Aiming at solving large-scale optimization problems, this paper studies distributed optimization methods based on the alternating direction method of multipliers (ADMM). By formulating the optimization problem as a consensus problem, the ADMM can be used to solve the consensus problem in a fully parallel fashion over a computer network with a star topology. However, traditional synchronized computation does not scale well with the problem size, as the speed of the algorithm is limited by the slowest workers. This is particularly true in a heterogeneous network where the computing nodes experience different computation and communication delays. In this paper, we propose ...
A Unified Algorithmic Framework For Block-Structured Optimization Involving Big Data: With Applications In Machine Learning And Signal Processing, Mingyi Hong, Meisam Razaviyayn, Zhi-Quan Luo
This article presents a powerful algorithmic framework for big data optimization, called the block successive upper-bound minimization (BSUM). The BSUM includes as special cases many well-known methods for analyzing massive data sets, such as the block coordinate descent (BCD) method, the convex-concave procedure (CCCP) method, the block coordinate proximal gradient (BCPG) method, the nonnegative matrix factorization (NMF) method, the expectation maximization (EM) method, etc. In this article, various features and properties of the BSUM are discussed from the viewpoint of design flexibility, computational efficiency, parallel/distributed implementation, and the required communication overhead. Illustrative examples from networking, signal processing, and machine ...
Work Integrated Learning In Stem In Australian Universities: Final Report: Submitted To The Office Of The Chief Scientist, Daniel Edwards, Kate Perkins, Jacob Pearce, Jennifer Hong
The Australian Council for Educational Research (ACER) undertook this study for the Office of the Chief Scientist (OCS). It explores the practice and application of Work Integrated Learning (WIL) in STEM, with a particular focus on natural and physical sciences, information technology, and agriculture departments in Australian universities. The project involved a detailed ‘stocktake’ of WIL in practice in these disciplines, with collection of information by interview, survey instruments, consultation with stakeholders and literature reviews. Every university in Australia was visited as part of this project, with interviews and consultation sessions gathering insight from more than 120 academics and support ...
Exploring Algorithms To Recognize Similar Board States In Arimaa, 2017 Rowan University
Exploring Algorithms To Recognize Similar Board States In Arimaa, Malik Khaleeque Ahmed
Theses and Dissertations
The game of Arimaa was invented as a challenge to the field of game-playing artificial intelligence, which had grown somewhat haughty after IBM's supercomputer Deep Blue trounced world champion Kasparov at chess. Although Arimaa is simple enough for a child to learn and can be played with an ordinary chess set, existing game-playing algorithms and techniques have had a difficult time rising up to the challenge of defeating the world's best human Arimaa players, mainly due to the game's impressive branching factor. This thesis introduces and analyzes new algorithms and techniques that attempt to recognize similar board ...