Xtreme-Noc: Extreme Gradient Boosting Based Latency Model For Network-On-Chip Architectures,
2021
Minnesota State University, Mankato
Xtreme-Noc: Extreme Gradient Boosting Based Latency Model For Network-On-Chip Architectures, Ilma Sheriff
All Graduate Theses, Dissertations, and Other Capstone Projects
Multiprocessor System-on-Chip (MPSoC) integrating heterogeneous processing elements (CPU, GPU, Accelerators, memory, I/O modules ,etc.) are the de-facto design choice to meet the ever-increasing performance/Watt requirements from modern computing machines. Although at consumer level the number of processing elements (PE) are limited to 8-16, for high end servers, the number of PEs can scale up to hundreds. A Network-on-Chip (NoC) is a microscale network that facilitates the packetized communication among the PEs in such complex computational systems. Due to the heterogeneous integration of the cores, execution of diverse (serial and parallel) applications on the PEs, application mapping strategies, and many other …
On Studying Distributed Machine Learning,
2021
Liberty University
On Studying Distributed Machine Learning, Simeon Eberz
Senior Honors Theses
The Internet of Things (IoT) is utilizing Deep Learning (DL) for applications such as voice or image recognition. Processing data for DL directly on IoT edge devices reduces latency and increases privacy. To overcome the resource constraints of IoT edge devices, the computation for DL inference is distributed between a cluster of several devices. This paper explores DL, IoT networks, and a novel framework for distributed processing of DL in IoT clusters. The aim is to facilitate and simplify deployment, testing, and study of a distributed DL system, even without physical devices. The contributions of this paper are a deployment …
Technical Q8a Site Answer Recommendation Via Question Boosting,
2021
Singapore Management University
Technical Q8a Site Answer Recommendation Via Question Boosting, Zhipeng Gao, Xin Xia, David Lo, John Grundy
Research Collection School Of Computing and Information Systems
Software developers have heavily used online question and answer platforms to seek help to solve their technical problems. However, a major problem with these technical Q&A sites is "answer hungriness" i.e., a large number of questions remain unanswered or unresolved, and users have to wait for a long time or painstakingly go through the provided answers with various levels of quality. To alleviate this time-consuming problem, we propose a novel DeepAns neural network-based approach to identify the most relevant answer among a set of answer candidates. Our approach follows a three-stage process: question boosting, label establishment, and answer recommendation. Given …
Unsupervised Representation Learning By Predicting Random Distances,
2021
Singapore Management University
Unsupervised Representation Learning By Predicting Random Distances, Hu Wang, Guansong Pang, Chunhua Shen, Congbo Ma
Research Collection School Of Computing and Information Systems
Deep neural networks have gained great success in a broad range of tasks due to its remarkable capability to learn semantically rich features from high-dimensional data. However, they often require large-scale labelled data to successfully learn such features, which significantly hinders their adaption in unsupervised learning tasks, such as anomaly detection and clustering, and limits their applications to critical domains where obtaining massive labelled data is prohibitively expensive. To enable unsupervised learning on those domains, in this work we propose to learn features without using any labelled data by training neural networks to predict data distances in a randomly projected …
Preforming A Vulnerability Assessment On A Secured Network,
2021
The University of Akron
Preforming A Vulnerability Assessment On A Secured Network, Mathias Sovine
Williams Honors College, Honors Research Projects
A computer network will be built using 3 routers, 1 switch, and 4 computers. The network will be used to simulate the connections between an at home office and the internet. The network will be divided into 3 sub-networks. The routers will be secured using methods like access control lists, changing default admin passwords, and network encryption. The switch will be secured using methods like switchport security and setting access passwords. Once the network is secured, three penetration testing techniques and three exploits will be performed on the network. The results of the exploits and penetration testing techniques will be …
Efficient Algorithms For Identifying Loop Formation And Computing Θ Value For Solving Minimum Cost Flow Network Problems,
2021
Old Dominion University
Efficient Algorithms For Identifying Loop Formation And Computing Θ Value For Solving Minimum Cost Flow Network Problems, Timothy Michael Chávez, Duc Thai Nguyen
Computational Modeling & Simulation Engineering Faculty Publications
While the minimum cost flow (MCF) problems have been well documented in many publications, due to its broad applications, little or no effort have been devoted to explaining the algorithms for identifying loop formation and computing the value needed to solve MCF network problems. This paper proposes efficient algorithms, and MATLAB computer implementation, for solving MCF problems. Several academic and real-life network problems have been solved to validate the proposed algorithms; the numerical results obtained by the developed MCF code have been compared and matched with the built-in MATLAB function Linprog() (Simplex algorithm) for further validation.
Secure Network Access Via Ldap,
2021
The University of Akron
Secure Network Access Via Ldap, Nicholas Valaitis
Williams Honors College, Honors Research Projects
Networks need the ability to be access by secure accounts and users. The goal of this project is to configure and expand on LDAP configurations with considerations for AAA via TACACS+ and Radius for network equipment. This will provide adequate security for any given network in terms of access and prevent lose of access to devices which happens all to often with locally configured accounts on devices.
Using Eye-Gaze To Evaluate Neural Attention,
2020
Indian Statistical Institute
Using Eye-Gaze To Evaluate Neural Attention, Shahansha Salim
Master’s Dissertations
The ability to selectively concentrate on areas of interest while ignoring the rest is termed as attention in human beings. This ability has played a key role in survival as well as information processing. Neural Attention is said to be an effort to bring similar action of selectively concentrating areas of relevance in deep neural networks. This simple yet powerful concept has attracted a lot of research in recent years, yielding breakthrough results in Natural Language Processing (NLP) problems and main stream Computer Vision problems such as Image Caption Generation, Neural Machine Translation (NMT), Visual Question Answering (VQA), Action Recognition, …
Towards Theoretically Understanding Why Sgd Generalizes Better Than Adam In Deep Learning,
2020
Singapore Management University
Towards Theoretically Understanding Why Sgd Generalizes Better Than Adam In Deep Learning, Pan Zhou, Jiashi Feng, Chao Ma, Caiming Xiong, Steven C. H. Hoi, Weinan E
Research Collection School Of Computing and Information Systems
It is not clear yet why ADAM-alike adaptive gradient algorithms suffer from worse generalization performance than SGD despite their faster training speed. This work aims to provide understandings on this generalization gap by analyzing their local convergence behaviors. Specifically, we observe the heavy tails of gradient noise in these algorithms. This motivates us to analyze these algorithms through their Lévy-driven stochastic differential equations (SDEs) because of the similar convergence behaviors of an algorithm and its SDE. Then we establish the escaping time of these SDEs from a local basin. The result shows that (1) the escaping time of both SGD …
Theory-Inspired Path-Regularized Differential Network Architecture Search,
2020
Singapore Management University
Theory-Inspired Path-Regularized Differential Network Architecture Search, Pan Zhou, Caiming Xiong, Richard Socher, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
Despite its high search efficiency, differential architecture search (DARTS) often selects network architectures with dominated skip connections which lead to performance degradation. However, theoretical understandings on this issue remain absent, hindering the development of more advanced methods in a principled way. In this work, we solve this problem by theoretically analyzing the effects of various types of operations, e.g. convolution, skip connection and zero operation, to the network optimization. We prove that the architectures with more skip connections can converge faster than the other candidates, and thus are selected by DARTS. This result, for the first time, theoretically and explicitly …
Watch Out! Motion Is Blurring The Vision Of Your Deep Neural Networks,
2020
Singapore Management University
Watch Out! Motion Is Blurring The Vision Of Your Deep Neural Networks, Qing Guo, Felix Juefei-Xu, Xiaofei Xie, Lei Ma, Jian Wang, Bing Yu, Wei Feng, Yang Liu
Research Collection School Of Computing and Information Systems
The state-of-the-art deep neural networks (DNNs) are vulnerable to adversarial examples with additive random noise-like perturbations. While such examples are hardly found in the physical world, the image blurring effect caused by object motion, on the other hand, commonly occurs in practice, making the study of which greatly important especially for the widely adopted real-time image processing tasks (e.g., object detection, tracking). In this paper, we initiate the first step to comprehensively investigate the potential hazards of blur effect for DNN, caused by object motion. We propose a novel adversarial attack method that can generate visually natural motion-blurred adversarial examples, …
Lightweight Deep Learning For Botnet Ddos Detection On Iot Access Networks,
2020
Missouri State University
Lightweight Deep Learning For Botnet Ddos Detection On Iot Access Networks, Eric A. Mccullough
Graduate Theses/Dissertations
With the proliferation of the Internet of Things (IoT), computer networks have rapidly expanded in size. While Internet of Things Devices (IoTDs) benefit many aspects of life, these devices also introduce security risks in the form of vulnerabilities which give hackers billions of promising new targets. For example, botnets have exploited the security flaws common with IoTDs to gain unauthorized control of hundreds of thousands of hosts, which they then utilize to carry out massively disruptive distributed denial of service (DDoS) attacks. Traditional DDoS defense mechanisms rely on detecting attacks at their target and deploying mitigation strategies toward the attacker …
Sadt: Syntax-Aware Differential Testing Of Certificate Validation In Ssl/Tls Implementations,
2020
Singapore Management University
Sadt: Syntax-Aware Differential Testing Of Certificate Validation In Ssl/Tls Implementations, Lili Quan, Qianyu Guo, Hongxu Chen, Xiaofei Xie, Xiaohong Li, Yang Liu, Jing Hu
Research Collection School Of Computing and Information Systems
The security assurance of SSL/TLS critically depends on the correct validation of X.509 certificates. Therefore, it is important to check whether a certificate is correctly validated by the SSL/TLS implementations. Although differential testing has been proven to be effective in finding semantic bugs, it still suffers from the following limitations: (1) The syntax of test cases cannot be correctly guaranteed. (2) Current test cases are not diverse enough to cover more implementation behaviours. This paper tackles these problems by introducing SADT, a novel syntax-aware differential testing framework for evaluating the certificate validation process in SSL/TLS implementations. We first propose a …
Audee: Automated Testing For Deep Learning Frameworks,
2020
Singapore Management University
Audee: Automated Testing For Deep Learning Frameworks, Qianyu Guo, Xiaofei Xie, Yi Li, Xiaoyu Zhang, Yang Liu, Xiaohong Li, Chao Shen
Research Collection School Of Computing and Information Systems
Deep learning (DL) has been applied widely, and the quality of DL system becomes crucial, especially for safety-critical applications. Existing work mainly focuses on the quality analysis of DL models, but lacks attention to the underlying frameworks on which all DL models depend. In this work, we propose Audee, a novel approach for testing DL frameworks and localizing bugs. Audee adopts a search-based approach and implements three different mutation strategies to generate diverse test cases by exploring combinations of model structures, parameters, weights and inputs. Audee is able to detect three types of bugs: logical bugs, crashes and Not-a-Number (NaN) …
Towards Locality-Aware Meta-Learning Of Tail Node Embeddings On Networks,
2020
Singapore Management University
Towards Locality-Aware Meta-Learning Of Tail Node Embeddings On Networks, Zemin Liu, Wentao Zhang, Yuan Fang, Xinming Zhang, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
Network embedding is an active research area due to the prevalence of network-structured data. While the state of the art often learns high-quality embedding vectors for high-degree nodes with abundant structural connectivity, the quality of the embedding vectors for low-degree or tail nodes is often suboptimal due to their limited structural connectivity. While many real-world networks are long-tailed, to date little effort has been devoted to tail node embedding. In this paper, we formulate the goal of learning tail node embeddings as a few-shot regression problem, given the few links on each tail node. In particular, since each node resides …
Cisc 4331 – Systems And Network Administration - Week 2,
2020
CUNY Brooklyn College
Cisc 4331 – Systems And Network Administration - Week 2, Jimmy Richford, Nyc Tech-In-Residence Corps
Open Educational Resources
Lecture 2 for CISC 4331: Systems and Network Administration
Systems And Network Administration - Introduction,
2020
CUNY Brooklyn College
Systems And Network Administration - Introduction, Jimmy Richford, Nyc Tech-In-Residence Corps
Open Educational Resources
Lecture for CISC 4311: Systems and Network Administration (Fall 2020)
Cisc 4331 – Systems And Network Administration - Week 5,
2020
CUNY Brooklyn College
Cisc 4331 – Systems And Network Administration - Week 5, Jimmy Richford, Nyc Tech-In-Residence Corps
Open Educational Resources
Lecture 5 for CISC 4331 - Systems and Network Administration
Cisc 4331 – Systems And Network Administration - Week 4,
2020
CUNY Brooklyn College
Cisc 4331 – Systems And Network Administration - Week 4, Jimmy Richford, Nyc Tech-In-Residence Corps
Open Educational Resources
Lecture 4 for CISC 4331 - Systems and Network Administration
Cisc 4331 – Systems And Network Administration - Week 3,
2020
CUNY Brooklyn College
Cisc 4331 – Systems And Network Administration - Week 3, Jimmy Richford, Nyc Tech-In-Residence Corps
Open Educational Resources
Lecture 3 for CISC 4331: Systems and Network Administration
