OS and Networks Commons^™

Multi-Channel Graph Neural Network For Entity Alignment, Yixin Cao, Zhiyuan Liu, Chengjiang Li, Zhiyuan Liu, Juanzi Li, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Entity alignment typically suffers from the issues of structural heterogeneity and limited seed alignments. In this paper, we propose a novel Multi-channel Graph Neural Network model (MuGNN) to learn alignment-oriented knowledge graph (KG) embeddings by robustly encoding two KGs via multiple channels. Each channel encodes KGs via different relation weighting schemes with respect to self-attention towards KG completion and cross-KG attention for pruning exclusive entities respectively, which are further combined via pooling techniques. Moreover, we also infer and transfer rule knowledge for completing two KGs consistently. MuGNN is expected to reconcile the structural differences of two KGs, and thus make …

Weaving The Dark Web: Legitimacy On Freenet, Tor, And I2p, John Schriner

Publications and Research

This is a book review of Robert W. Gehl's Weaving the Dark Web: Legitimacy on Freenet, Tor, and I2P (2018). The book explores these anonymity networks and the concept of legitimacy throughout. Using a multidisciplinary approach and interviews with network-builders and users, Gehl helps to demystify the dark web and critically examine these networks and technologies.

Exploring Object Relation In Mean Teacher For Cross-Domain Detection, Qi Cai, Yingwei Pan, Chong-Wah Ngo, Xinmei Tian, Lingyu Duan, Ting Yao

Research Collection School Of Computing and Information Systems

Rendering synthetic data (e.g., 3D CAD-rendered images) to generate annotations for learning deep models in vision tasks has attracted increasing attention in recent years. However, simply applying the models learnt on synthetic images may lead to high generalization error on real images due to domain shift. To address this issue, recent progress in cross-domain recognition has featured the Mean Teacher, which directly simulates unsupervised domain adaptation as semi-supervised learning. The domain gap is thus naturally bridged with consistency regularization in a teacher-student scheme. In this work, we advance this Mean Teacher paradigm to be applicable for crossdomain detection. Specifically, we …

Transferrable Prototypical Networks For Unsupervised Domain Adaptation, Yingwei Pan, Ting Yao, Yehao Li, Yu Wang, Chong-Wah Ngo, Tao Mei

Research Collection School Of Computing and Information Systems

In this paper, we introduce a new idea for unsupervised domain adaptation via a remold of Prototypical Networks, which learn an embedding space and perform classification via a remold of the distances to the prototype of each class. Specifically, we present Transferrable Prototypical Networks (TPN) for adaptation such that the prototypes for each class in source and target domains are close in the embedding space and the score distributions predicted by prototypes separately on source and target data are similar. Technically, TPN initially matches each target example to the nearest prototype in the source domain and assigns an example a …

R2gan: Cross-Modal Recipe Retrieval With Generative Adversarial Network, Bin Zhu, Chong-Wah Ngo, Jingjing Chen, Yanbin Hao

Research Collection School Of Computing and Information Systems

Representing procedure text such as recipe for crossmodal retrieval is inherently a difficult problem, not mentioning to generate image from recipe for visualization. This paper studies a new version of GAN, named Recipe Retrieval Generative Adversarial Network (R2GAN), to explore the feasibility of generating image from procedure text for retrieval problem. The motivation of using GAN is twofold: learning compatible cross-modal features in an adversarial way, and explanation of search results by showing the images generated from recipes. The novelty of R2GAN comes from architecture design, specifically a GAN with one generator and dual discriminators is used, which makes the …

Learning Spatio-Temporal Representation With Local And Global Diffusion, Zhaofan Qiu, Ting Yao, Chong-Wah Ngo, Xinmei Tian, Tao Mei

Research Collection School Of Computing and Information Systems

Convolutional Neural Networks (CNN) have been regarded as a powerful class of models for visual recognition problems. Nevertheless, the convolutional filters in these networks are local operations while ignoring the large-range dependency. Such drawback becomes even worse particularly for video recognition, since video is an information-intensive media with complex temporal variations. In this paper, we present a novel framework to boost the spatio-temporal representation learning by Local and Global Diffusion (LGD). Specifically, we construct a novel neural network architecture that learns the local and global representations in parallel. The architecture is composed of LGD blocks, where each block updates local …

Keeping Anonymity At The Consumer Behavior On The Internet: Proof Of Sacrifice, Sachio Horie

Computer Ethics - Philosophical Enquiry (CEPE) Proceedings

The evolution of the Internet and AI technology has made it possible for the government and the businesses to keep track of their personal lives. GAFA continues to collect information unintended by the individuals. It is a threat that our privacy is violated in this way. In order to solute such problems, it is important to consider a mechanism that enables us to be peaceful lives while protecting privacy in the Internet society.

This paper focuses on the consumption behavior on the Internet and addresses anonymity. We consider some network protocols that enable sustainable consensus by combining anonymity methods such …

Network Alignment In Heterogeneous Social Networks, Priyanka Kasbekar

Master's Projects

Online Social Networks (OSN) have numerous applications and an ever growing user base. This has led to users being a part of multiple social networks at the same time. Identifying a similar user from one social network on another social network will give in- formation about a user’s behavior on different platforms. It further helps in community detection and link prediction tasks. The process of identifying or aligning users in multiple networks is called Network Alignment. More the information we have about the nodes / users better the results of Network Alignment. Unlike other related work in this field that …

Forensic Analysis Of Spy Applications In Android Devices, Shinelle Hutchinson, Umit Karabiyik

Annual ADFSL Conference on Digital Forensics, Security and Law

Smartphones with Google's Android operating system are becoming more and more popular each year, and with this increased user base, comes increased opportunities to collect more of these users' private data. There have been several instances of malware being made available via the Google Play Store, which is one of the predominant means for users to download applications. One effective way of collecting users' private data is by using Android Spyware. In this paper, we conduct a forensic analysis of a malicious Android spyware application and present our findings. We also highlight what information the application accesses and what it …

Predictive Analysis For Cloud Infrastructure Metrics, Paridhi Agrawal

Master's Projects

In a cloud computing environment, enterprises have the flexibility to request resources according to their application demands. This elastic feature of cloud computing makes it an attractive option for enterprises to host their applications on the cloud. Cloud providers usually exploit this elasticity by auto-scaling the application resources for quality assurance. However, there is a setup-time delay that may take minutes between the demand for a new resource and it being prepared for utilization. This causes the static resource provisioning techniques, which request allocation of a new resource only when the application breaches a specific threshold, to be slow and …

Analysis Of Computer Audit Data To Create Indicators Of Compromise For Intrusion Detection, Steven Millett, Michael Toolin, Justin Bates

SMU Data Science Review

Network security systems are designed to identify and, if possible, prevent unauthorized access to computer and network resources. Today most network security systems consist of hardware and software components that work in conjunction with one another to present a layered line of defense against unauthorized intrusions. Software provides user interactive layers such as password authentication, and system level layers for monitoring network activity. This paper examines an application monitoring network traffic that attempts to identify Indicators of Compromise (IOC) by extracting patterns in the network traffic which likely corresponds to unauthorized access. Typical network log data and construct indicators are …

Celltrademap: Delineating Trade Areas For Urban Commercial Districts With Cellular Networks, Yi Zhao, Zimu Zhou, Xu Wang, Tongtong Liu, Yunhao Liu, Zheng Yang

Research Collection School Of Computing and Information Systems

Understanding customer mobility patterns to commercial districts is crucial for urban planning, facility management, and business strategies. Trade areas are a widely applied measure to quantity where the visitors are from. Traditional trade area analysis is limited to small-scale or store-level studies because information such as visits to competitor commercial entities and place of residence is collected by labour-intensive questionnaires or heavily biased location-based social media data. In this paper, we propose CellTradeMap, a novel district-level trade area analysis framework using mobile flow records (MFRs), a type of fine-grained cellular network data. CellTradeMap extracts robust location information from the irregularly …

Dynamic Shifting Of Virtual Network Topologies For Network Attack Prevention, Lenoy Avidan

Master's Theses

Computer networks were not designed with security in mind, making research into the subject of network security vital. Virtual Networks are similar to computer networks, except the components of a Virtual Network are in software rather than hardware. With the constant threat of attacks on networks, security is always a big concern, and Virtual Networks are no different. Virtual Networks have many potential attack vectors similar to physical networks, making research into Virtual Network security of great importance. Virtual Networks, since they are composed of virtualized network components, have the ability to dynamically change topologies. In this paper, we explore …

Seeing Eye To Eye: A Machine Learning Approach To Automated Saccade Analysis, Maigh Attre

Honors Scholar Theses

Abnormal ocular motility is a common manifestation of many underlying pathologies particularly those that are neurological. Dynamics of saccades, when the eye rapidly changes its point of fixation, have been characterized for many neurological disorders including concussions, traumatic brain injuries (TBI), and Parkinson’s disease. However, widespread saccade analysis for diagnostic and research purposes requires the recognition of certain eye movement parameters. Key information such as velocity and duration must be determined from data based on a wide set of patients’ characteristics that may range in eye shapes and iris, hair and skin pigmentation [36]. Previous work on saccade analysis has …

A Mobile Cyber-Physical System Framework For Aiding People With Visual Impairment, Martin Goldberg

Dissertations, Theses, and Capstone Projects

It is a challenging problem for researchers and engineers in the assistive technology (AT) community to provide suitable solutions for visually impaired people (VIPs) through AT to meet orientation, navigation and mobility (ONM) needs. Given the spectrum of assistive technologies currently available for the purposes of aiding VIPs with ONM, our literature review and survey have shown that there is a reluctance to adopt these technological solutions in the VIP community.

Motivated by these findings, we think it critical to re-examine and rethink the approaches that have been taken. It is our belief that we need to take a different …

Data Gathering In Cognitive Radio Ad Hoc And Sensor Wireless Networks, Kimberly A. Brown

Theses and Dissertations

Data gathering is a network communication task in which all of the network’s nodes send their individual messages to a distinguished sink node. In cognitive radio ad hoc and sensor wireless networks (CR-AHSWNs), unlicensed secondary users (SUs) opportunistically use channels when the licensed primary users are not using them. Therefore, the channels available to each SU vary with time and location, which makes the development of data gathering algorithms for CR-AHSWNs challenging.

In this thesis, a data gathering protocol for CR-AHSWNs is proposed. The protocol consists of several distributed SU action selection and channel selection algorithms. An algorithm that can …

Model Development And Assessment Of The Gate Network In A High-Performance Sic Power Module, William Austin Curbow

Graduate Theses and Dissertations

The main objective of this effort is to determine points of weakness in the gate network of a high-performance SiC power module and to offer remedies to these issues to increase the overall performance, robustness, and reliability of the technology. In order to accomplish this goal, a highly accurate model of the gate network is developed through three methods of parameter extraction: calculation, simulation, and measurement. A SPICE model of the gate network is developed to analyze four electrical issues in a high-speed, SiC-based power module including the necessary internal gate resistance for damping under-voltage and over-voltage transients, the disparity …

Learning Two-Layer Neural Networks With Symmetric Inputs, Rong Ge, Rohith Kuditipudi, Zhize Li, Xiang Wang

Research Collection School Of Computing and Information Systems

We give a new algorithm for learning a two-layer neural network under a very general class of input distributions. Assuming there is a ground-truth two-layer network $y = A \sigma(Wx) + \xi$, where A, W are weight matrices, $\xi$ represents noise, and the number of neurons in the hidden layer is no larger than the input or output, our algorithm is guaranteed to recover the parameters A, W of the ground-truth network. The only requirement on the input x is that it is symmetric, which still allows highly complicated and structured input. Our algorithm is based on the method-of-moments framework …

Urban Underground Infrastructure Monitoring Iot: The Path Loss Analysis, Abdul Salam, Syed Shah

Faculty Publications

The extra quantities of wastewater entering the pipes can cause backups that result in sanitary sewer overflows. Urban underground infrastructure monitoring is important for controlling the flow of extraneous water into the pipelines. By combining the wireless underground communications and sensor solutions, the urban underground IoT applications such as real time wastewater and storm water overflow monitoring can be developed. In this paper, the path loss analysis of wireless underground communications in urban underground IoT for wastewater monitoring has been presented. It has been shown that the communication range of up to 4 kilometers can be achieved from an underground …

Community Detection Via Neighborhood Overlap And Spanning Tree Computations, Ketki Kulkarni, Aris Pagourtzis, Katerina Potika, Petros Potikas, Dora Souliou

Faculty Publications, Computer Science

Most social networks of today are populated with several millions of active users, while the most popular of them accommodate way more than one billion. Analyzing such huge complex networks has become particularly demanding in computational terms. A task of paramount importance for understanding the structure of social networks as well as of many other real-world systems is to identify communities, that is, sets of nodes that are more densely connected to each other than to other nodes of the network. In this paper we propose two algorithms for community detection in networks, by employing the neighborhood overlap metric …

Interaction-Aware Arrangement For Event-Based Social Networks, Feifei Kou, Zimu Zhou, Hao Cheg, Junping Du, Yexuan Shi, Pan Xu

Research Collection School Of Computing and Information Systems

No abstract provided.

Underground Environment Aware Mimo Design Using Transmit And Receive Beamforming In Internet Of Underground Things, Abdul Salam

Faculty Publications

In underground (UG) multiple-input and multiple-output (MIMO), the transmit beamforming is used to focus energy in the desired direction. There are three different paths in the underground soil medium through which the waves propagates to reach at the receiver. When the UG receiver receives a desired data stream only from the desired path, then the UG MIMO channel becomes three path (lateral, direct, and reflected) interference channel. Accordingly, the capacity region of the UG MIMO three path interference channel and degrees of freedom (multiplexing gain of this MIMO channel requires careful modeling). Therefore, expressions are required derived the degrees of …

Dynamic Student Classification On Memory Networks For Knowledge Tracing, Sein Minn, Michel C. Desmarais, Feida Zhu, Jing Xiao, Jianzong Wang

Research Collection School Of Computing and Information Systems

Knowledge Tracing (KT) is the assessment of student’s knowledge state and predicting whether that student may or may not answer the next problem correctly based on a number of previous practices and outcomes in their learning process. KT leverages machine learning and data mining techniques to provide better assessment, supportive learning feedback and adaptive instructions. In this paper, we propose a novel model called Dynamic Student Classification on Memory Networks (DSCMN) for knowledge tracing that enhances existing KT approaches by capturing temporal learning ability at each time interval in student’s long-term learning process. Experimental results confirm that the proposed model …

Perception Coordination Network: A Neuro Framework For Multimodal Concept Acquisition And Binding, You-Lu Xing, Xiao-Feng Shi, Fu-Rao Shen, Jin-Xi Zhao, Jing-Xin Pan, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

To simulate the concept acquisition and binding of different senses in the brain, a biologically inspired neural network model named perception coordination network (PCN) is proposed. It is a hierarchical structure, which is functionally divided into the primary sensory area (PSA), the primary sensory association area (SAA), and the higher order association area (HAA). The PSA contains feature neurons which respond to many elementary features, e.g., colors, shapes, syllables, and basic flavors. The SAA contains primary concept neurons which combine the elementary features in the PSA to represent unimodal concept of objects, e.g., the image of an apple, the Chinese …

Maximizing Multifaceted Network Influence, Yuchen Li, Ju Fan, George V. Ovchinnikov, Panagiotis Karras

Research Collection School Of Computing and Information Systems

An information dissemination campaign is often multifaceted, involving several facets or pieces of information disseminating from different sources. The question then arises, how should we assign such pieces to eligible sources so as to achieve the best viral dissemination results? Past research has studied the problem of Influence Maximization (IM), which is to select a set of k promoters that maximizes the expected reach of a message over a network. However, in this classical IM problem, each promoter spreads out the same unitary piece of information. In this paper, we propose the Optimal Influential Pieces Assignment (OIPA) problem, which is …

Malgazer: An Automated Malware Classifier With Running Window Entropy And Machine Learning, Keith Jones

Masters Theses & Doctoral Dissertations

This dissertation explores functional malware classification using running window entropy and machine learning classifiers. This topic was under researched in the prior literature, but the implications are important for malware defense. This dissertation will present six new design science artifacts. The first artifact was a generalized machine learning based malware classifier model. This model was used to categorize and explain the gaps in the prior literature. This artifact was also used to compare the prior literature to the classifiers created in this dissertation, herein referred to as “Malgazer” classifiers.

Running window entropy data was required, but the algorithm was too …

A Theoretical Model Of Underground Dipole Antennas For Communications In Internet Of Underground Things, Abdul Salam, Mehmet C. Vuran, Xin Dong, Christos Argyropoulos, Suat Irmak

Faculty Publications

The realization of Internet of Underground Things (IOUT) relies on the establishment of reliable communication links, where the antenna becomes a major design component due to the significant impacts of soil. In this paper, a theoretical model is developed to capture the impacts of change of soil moisture on the return loss, resonant frequency, and bandwidth of a buried dipole antenna. Experiments are conducted in silty clay loam, sandy, and silt loam soil, to characterize the effects of soil, in an indoor testbed and field testbeds. It is shown that at subsurface burial depths (0.1-0.4m), change in soil moisture impacts …

Question Answering For Suicide Risk Assessment Using Reddit, Amanuel Alambo, Usha Lokala, Ugur Kursuncu, Krishnaprasad Thirunarayan, Amelia Gyrard, Randon S. Welton, Jyotishman Pathak, Amit P. Sheth

Kno.e.sis Publications

Mental Health America designed ten questionnaires that are used to determine the risk of mental disorders. They are also commonly used by Mental Health Professionals (MHPs) to assess suicidality. Specifically, the Columbia Suicide Severity Rating Scale (C-SSRS), a widely used suicide assessment questionnaire, helps MHPs determine the severity of suicide risk and offer an appropriate treatment. A major challenge in suicide treatment is the social stigma wherein the patient feels reluctance in discussing his/her conditions with an MHP, which leads to inaccurate assessment and treatment of patients. On the other hand, the same patient is comfortable freely discussing his/her mental …

Manifold-Valued Image Generation With Wasserstein Generative Adversarial Nets, Zhiwu Huang, Wu J., G. L. Van

Research Collection School Of Computing and Information Systems

Generative modeling over natural images is one of the most fundamental machine learning problems. However, few modern generative models, including Wasserstein Generative Adversarial Nets (WGANs), are studied on manifold-valued images that are frequently encountered in real-world applications. To fill the gap, this paper first formulates the problem of generating manifold-valued images and exploits three typical instances: hue-saturation-value (HSV) color image generation, chromaticity-brightness (CB) color image generation, and diffusion-tensor (DT) image generation. For the proposed generative modeling problem, we then introduce a theorem of optimal transport to derive a new Wasserstein distance of data distributions on complete manifolds, enabling us to …

A Coordination Framework For Multi-Agent Persuasion And Adviser Systems, Budhitama Subagdja, Ah-Hwee Tan, Yilin Kang

Research Collection School Of Computing and Information Systems

Assistive agents have been used to give advices to the users regarding activities in daily lives. Although adviser bots are getting smarter and gaining more popularity these days they are usually developed and deployed independent from each other. When several agents operate together in the same context, their advices may no longer be effective since they may instead overwhelm or confuse the user if not properly arranged. Only little attentions have been paid to coordinating different agents to give different advices to a user within the same environment. However, aligning the advices on-the-fly with the appropriate presentation timing at the …