OS and Networks Commons^™

A Semantics-Based Measure Of Emoji Similarity, Sanjaya Wijeratne, Lakshika Balasuriya, Amit Sheth, Derek Doran

Amit P. Sheth

Emoji have grown to become one of the most important forms of communication on the web. With its widespread use, measuring the similarity of emoji has become an important problem for contemporary text processing since it lies at the heart of sentiment analysis, search, and interface design tasks. This paper presents a comprehensive analysis of the semantic similarity of emoji through embedding models that are learned over machine-readable emoji meanings in the EmojiNet knowledge base. Using emoji descriptions, emoji sense labels and emoji sense definitions, and with different training corpora obtained from Twitter and Google News, we develop and test ...

What Are People Tweeting About Zika? An Exploratory Study Concerning Its Symptoms, Treatment, Transmission, And Prevention, Michele Miller, Tanvi Banerjee, Roopteja Muppalla, William L. Romine, Amit Sheth

Amit P. Sheth

Background: In order to harness what people are tweeting about Zika, there needs to be a computational framework that leverages machine learning techniques to recognize relevant Zika tweets and, further, categorize these into disease-specific categories to address specific societal concerns related to the prevention, transmission, symptoms, and treatment of Zika virus. Objective: The purpose of this study was to determine the relevancy of the tweets and what people were tweeting about the 4 disease characteristics of Zika: symptoms, transmission, prevention, and treatment. Methods: A combination of natural language processing and machine learning techniques was used to determine what people were ...

A Comparative Study On Machine Learning Algorithms For Network Defense, Abdinur Ali, Yen-Hung Hu, Chung-Chu (George) Hsieh, Mushtaq Khan

Virginia Journal of Science

Network security specialists use machine learning algorithms to detect computer network attacks and prevent unauthorized access to their networks. Traditionally, signature and anomaly detection techniques have been used for network defense. However, detection techniques must adapt to keep pace with continuously changing security attacks. Therefore, machine learning algorithms always learn from experience and are appropriate tools for this adaptation. In this paper, ten machine learning algorithms were trained with the KDD99 dataset with labels, then they were tested with different dataset without labels. The researchers investigate the speed and the efficiency of these machine learning algorithms in terms of several ...

Morphogenesis And Growth Driven By Selection Of Dynamical Properties, Yuri Cantor

All Dissertations, Theses, and Capstone Projects

Organisms are understood to be complex adaptive systems that evolved to thrive in hostile environments. Though widely studied, the phenomena of organism development and growth, and their relationship to organism dynamics is not well understood. Indeed, the large number of components, their interconnectivity, and complex system interactions all obscure our ability to see, describe, and understand the functioning of biological organisms.

Here we take a synthetic and computational approach to the problem, abstracting the organism as a cellular automaton. Such systems are discrete digital models of real-world environments, making them more accessible and easier to study then their physical world ...

Tinyvisor: An Extensible Secure Framework On Android Platforms, Dong Shen, Zhoujun Li, Xiaojing Su, Jinxin Ma, Deng, Robert H.

Research Collection School Of Information Systems

As the utilization of mobile platform keeps growing, the security issue of mobile platform becomes a serious threat to user privacy. The current security measures mainly focus on the application level and the framework level, with little protection on the kernel. Virtualization technologies have been used in x86 platforms to protect the security of the kernel. With a higher privilege than the guest operating system, the hypervisor can effectively detect and defend against the malicious activity inside the guest kernel. In this paper, we build a hypervisor framework called TinyVisor leveraging the ARM virtualization extensions to protect the guest system ...

On Return Oriented Programming Threats In Android Runtime, Akshaya Venkateswara Raja, Jehyun Lee, Debin Gao

Research Collection School Of Information Systems

Android has taken a large share of operating systems forsmart devices including smartphones, and has been an attractive target to theattackers. The arms race between attackers and defenders typically occurs ontwo front lines — the latest attacking technology and the latest updates to theoperating system (including defense mechanisms deployed). In terms of attackingtechnology, Return-Oriented Programming (ROP) is one of the most sophisticatedattack methods on Android devices. In terms of the operating system updates,Android Runtime (ART) was the latest and biggest change to the Android family.In this paper, we investigate the extent to which Android Runtime (ART) makesReturn-Oriented Programming (ROP ...

Operating System Identification By Ipv6 Communication Using Machine Learning Ensembles, Adrian Ordorica

Theses and Dissertations

Operating system (OS) identification tools, sometimes called fingerprinting tools, are essential for the reconnaissance phase of penetration testing. While OS identification is traditionally performed by passive or active tools that use fingerprint databases, very little work has focused on using machine learning techniques. Moreover, significantly more work has focused on IPv4 than IPv6. We introduce a collaborative neural network ensemble that uses a unique voting system and a random forest ensemble to deliver accurate predictions. This approach uses IPv6 features as well as packet metadata features for OS identification. Our experiment shows that our approach is valid and we achieve ...

Transaction Cost Optimization For Online Portfolio Selection, Bin Li, Jialei Wang, Dingjiang Huang, Steven C. H. Hoi

Research Collection School Of Information Systems

To improve existing online portfolio selection strategies in the case of non-zero transaction costs, we propose a novel framework named Transaction Cost Optimization (TCO). The TCO framework incorporates the L1 norm of the difference between two consecutive allocations together with the principles of maximizing expected log return. We further solve the formulation via convex optimization, and obtain two closed-form portfolio update formulas, which follow the same principle as Proportional Portfolio Rebalancing (PPR) in industry. We empirically evaluate the proposed framework using four commonly used data-sets. Although these data-sets do not consider delisted firms and are thus subject to survival bias ...

Discovering Explanatory Models To Identify Relevant Tweets On Zika, Roopteja Muppalla, Michele Miller, Tanvi Banerjee, William L. Romine

Kno.e.sis Publications

Zika virus has caught the worlds attention, and has led people to share their opinions and concerns on social media like Twitter. Using text-based features, extracted with the help of Parts of Speech (POS) taggers and N-gram, a classifier was built to detect Zika related tweets from Twitter. With a simple logistic classifier, the system was successful in detecting Zika related tweets from Twitter with a 92% accuracy. Moreover, key features were identified that provide deeper insights on the content of tweets relevant to Zika. This system can be leveraged by domain experts to perform sentiment analysis, and understand the ...

How Technology Is Reshaping Financial Services: Essays On Consumer Behavior In Card, Channel And Cryptocurrency Services, Dan Geng

Dissertations and Theses Collection

The financial services sector has seen dramatic technological innovations in the last several years associated with the “fintech revolution.” Major changes have taken place in channel management, credit card rewards marketing, cryptocurren-cy, and wealth management, and have influenced consumers’ banking behavior in different ways. As a consequence, there has been a growing demand for banks to rethink their business models and operations to adapt to changing consumer be-havior and counter the competitive pressure from other banks and non-bank play-ers. In this dissertation, I study consumer behavior related to different aspects of financial innovation by addressing research questions that are motivated ...

Automated Android Application Permission Recommendation, Lingfeng Bao, David Lo, Xin Xia

Research Collection School Of Information Systems

The number of Android applications has increased rapidly as Android is becoming the dominant platform in the smartphone market. Security and privacy are key factors for an Android application to be successful. Android provides a permission mechanism to ensure security and privacy. This permission mechanism requires that developers declare the sensitive resources required by their applications. On installation or during runtime, users are required to agree with the permission request. However, in practice, there are numerous popular permission misuses, despite Android introducing official documents stating how to use these permissions properly. Some data mining techniques (e.g., association rule mining ...

A Knowledge Graph Framework For Detecting Traffic Events Using Stationary Cameras, Roopteja Muppalla, Sarasi Lalithsena, Tanvi Banerjee, Amit Sheth

Kno.e.sis Publications

With the rapid increase in urban development, it is critical to utilize dynamic sensor streams for traffic understanding, especially in larger cities where route planning or infrastructure planning is more critical. This creates a strong need to understand traffic patterns using ubiquitous sensors to allow city officials to be better informed when planning urban construction and to provide an understanding of the traffic dynamics in the city. In this study, we propose our framework ITSKG (Imagery-based Traffic Sensing Knowledge Graph) which utilizes the stationary traffic camera information as sensors to understand the traffic patterns. The proposed system extracts image-based features ...

Interestingness Of Discovered Association Rules In Terms Of Neighborhood-Based Unexpectedness, Guozhu Dong, Jinyan Li

Jinyan Li

One of the central problems in knowledge discovery is the development of good measures of interestingness of discovered patterns. With such measures, a user needs to manually examine only the more interesting rules, instead of each of a large number of mined rules. Previous proposals of such measures include rule templates, minimal rule cover, actionability, and unexpectedness in the statistical sense or against user beliefs. In this paper we will introduce neighborhood-based interestingness by considering unexpectedness in terms of neighborhood-based parameters. We first present some novel notions of distance between rules and of neighborhood of rules. The neighborhood-based interestingness of ...

The Space Of Jumping Emerging Patterns And Its Incremental Maintenance, Jinyan Li, Kotagiri Ramamohanarao, Guozhu Dong

Jinyan Li

The concept of jumping emerging patterns (JEPs) has been proposed to describe those discriminating features which only occur in the positive training instances but do not occur in the negative class at all; JEPs have been used to construct classifiers which generally provide better accuracy than the state-of-the-art classifiers such as C4.5. The algorithms for maintaining the space of jumping emerging patterns (JEP space) are presented in this paper. We prove that JEP spaces satisfy the property of convexity. Therefore JEP spaces can be concisely represented by two bounds: consisting respectively of the most general elements and the most ...

Making Use Of The Most Expressive Jumping Emerging Patterns For Classification, Jinyan Li, Guozhu Dong, Kotagiri Ramamohanarao

Jinyan Li

Classification aims to discover a model from training data that can be used to predict the class of test instances. In this paper, we propose the use of jumping emerging patterns (JEPs) as the basis for a new classifier called the JEP-Classifier. Each JEP can capture some crucial difference between a pair of datasets. Then, aggregating all JEPs of large supports can produce a more potent classification power. Procedurally, the JEP-Classifier learns the pair-wise features (sets of JEPs) contained in the training data, and uses the collective impacts contributed by the most expressive pair-wise features to determine the class labels ...

Evolution And Maintenance Of Frequent Pattern Space When Transactions Are Removed, Mengling Feng, Guozhu Dong, Jinyan Li, Yap-Peng Tan, Limsoon Wong

Jinyan Li

This paper addresses the maintenance of discovered frequent patterns when a batch of transactions are removed from the original dataset. We conduct an in-depth investigation on how the frequent pattern space evolves under transaction removal updates using the concept of equivalence classes. Inspired by the evolution analysis, an effective and exact algorithm TRUM is proposed to maintain frequent patterns. Experimental results demonstrate that our algorithm outperforms representative state-of-the-art algorithms.

Quo Vadis-A Framework For Intelligent Routing In Large Communication Networks., Armin Mikler, Johnny S. Wong, Vasant Honavar

Johnny Wong

This paper presents Quo Vadis, an evolving framework for intelligent traffic management in very large communication networks. Quo Vadis is designed to exploit topological properties of large networks as well as their spatio-temporal dynamics to optimize multiple performance criteria through cooperation among nodes in the network. It employs a distributed representation of network state information using local load measurements supplemented by a less precise global summary. Routing decisions in Quo Vadis are based on parameterized heuristics designed to optimize various performance metrics in an anticipatory or pro-active as well as compensatory or reactive mode and to minimize the overhead associated ...

An Object Oriented Approach To Modeling And Simulation Of Routing In Large Communication Networks, Armin Mikler, Johnny S. Wong, Vasant Honavar

Johnny Wong

The complexity (number of entities, interactions between entities, and resulting emergent dynamic behavior) of large communication environments which contain hundreds of nodes and links make simulation an important tool for the study of such systems. Given the difficulties associated with complete analytical treatment of complex dynamical systems, it is often the only practical tool that is available. This paper presents an example of a flexible, modular, object-oriented toolbox designed to support modeling and experimental analysis of a large family of heuristic knowledge representation and decision functions for adaptive self-managing communication networks with particular emphasis on routing strategies. It discusses in ...

Feature Selection In Intrusion Detection System Over Mobile Ad-Hoc Network, Xia Wang, Tu-Liang Lin, Johnny S. Wong

Johnny Wong

As Mobile ad-hoc network (MANET) has become a very important technology the security problem, especially, intrusion detection technique research has attracted many people�s effort. MANET is more vulnerable than wired network and suffers intrusion like wired network. This paper investigated some intrusion detection techniques using machine learning and proposed a profile based neighbor monitoring intrusion detection method. Further analysis shows that the features collected by each node are too many for wireless devices with limited capacity. We apply Markov Blanket algorithm [1] to the feature selection of the intrusion detection method. Experimental studies have shown that Markov Blanket algorithm ...

Quo Vadis - Adaptive Heuristics For Routing In Large Communication Networks, Armin Mikler, Johnny S. Wong, Vasant Honavar

Johnny Wong

This paper presents Quo Vadis, an evolving framework for intelligent traffic management in very large communication networks. Quo Vadis is designed to exploit topological properties of large networks as well as their spatio-temporal dynamics to optimize multiple performance criteria through cooperation among nodes in the network. It employs a distributed representation of network state information using local load measurements supplemented by a less precise global summary. Routing decisions in Quo Vadis are based on parameterized heuristics designed to optimize various performance metrics in an anticipatory or pro-active as well as compensatory or reactive mode and to minimize the overhead associated ...