OS and Networks Commons^™

Iot-Enhanced Human Experience, Amit P. Sheth, Biplav Srivastava, Florian Michahelles

Kno.e.sis Publications

The two articles in this special section represent ongoing Internet of Things applications in the context of Europe trying to make solutions usable to people in daily times.

A Critical Analysis Of Mobility Management Related Issues Of Wireless Sensor Networks In Cyber Physical Systems, Jalal Al-Muhtadi, Ma Qiang, Khan Zeb, Junaid Chaudhry, Kashif Saleem, Abdelouahid Derhab, Mehmet A. Orgun, Rajan Shankaran, Muhammad Imran, Maruf Pasha

ECU Publications Post 2013

Mobility management has been a long-standing issue in mobile wireless sensor networks and especially in the context of cyber physical systems; its implications are immense. This paper presents a critical analysis of the current approaches to mobility management by evaluating them against a set of criteria which are essentially inherent characteristics of such systems on which these approaches are expected to provide acceptable performance. We summarize these characteristics by using a quadruple set of metrics. Additionally, using this set we classify the various approaches to mobility management that are discussed in this paper. Finally, the paper concludes by reviewing the ...

Secrecy Outage Performance Analysis For Energy Harvesting Sensor Networks With A Jammer Using Relay Selection Strategy, Van Nhan Vo, Tri Gia Nguyen, Chakchai So-In, Zubair Ahmed Baig, Surasak Sanguanpong

ECU Publications Post 2013

In this paper, we study radio frequency energy harvesting (EH) in a wireless sensor network in the presence of multiple eavesdroppers (EAVs). Specifically, the sensor source and multiple sensor relays harvest energy from multiple power transfer stations (PTSs), and then, the source uses this harvested energy to transmit information to the base station (BS) with the help of the relays. During the transmission of information, the BS typically faces a risk of losing information due to the EAVs. Thus, to enhance the secrecy of the considered system, one of the relays acts as a jammer, using harvested energy to generate ...

Pip: An Abstract Dataplane And Virtual Machine, Samuel Goodrick

Honors Research Projects

We present an abstract machine and S-expression-based programming language to describe OpenFlow-style software-defined networking. The implemented Pip virtual machine and language provide facilities for packet decoding, safely writing and setting bitfields within packets, and switching based on packet contents. We have outlined an abstract syntax and structural operational semantics for Pip, thus allowing Pip programs to have predictable and provable properties. Pip allows for easy and safe access and writing to packet fields, as well as a programmable packet pipeline that will rarely stall.

Understanding Group Communication In Capacity Building In Cyberinfrastructure (Ci), Raizi Simons, Andrew Schrock, Kerk Kee

Student Research Day Abstracts and Posters

Cyberinfrastructure (CI) is composed of many characteristics and layers, in which individuals working with a network of supercomputers in the technological world, understand how to connect and communicate efficiently. In this poster, we will distinguish how group communication (i.e. education) among Cyberinfrastructure (CI) stakeholders helps build and grow an organization’s capacity, or the ability of an organization to accomplish its mission effectively (Eisinger, 2002, p. 128). “Stakeholders garner more capacity to develop better Cyberinfrastructure (CI) tools, improve complex analysis of big data, grow the size of their team, and produce stronger publication” (Kee & Schrock, 2017, p. 17). This ...

Understanding Inactive Yet Available Assignees In Github, Jing Jiang, David Lo, Xinyu Ma, Fuli Feng, Li Zhang

Research Collection School Of Information Systems

Context In GitHub, an issue or a pull request can be assigned to a specific assignee who is responsible for working on this issue or pull request. Due to the principle of voluntary participation, available assignees may remain inactive in projects. If assignees ever participate in projects, they are active assignees; otherwise, they are inactive yet available assignees (inactive assignees for short). Objective Our objective in this paper is to provide a comprehensive analysis of inactive yet available assignees in GitHub. Method We collect 2,374,474 records of activities in 37 popular projects, and 797,756 records of activities ...

A Novel Representation And Compression For Queries On Trajectories In Road Networks, Xiaochun Yang, Bin Wang, Kai Yang, Chengfei Liu, Baihua Zheng

Research Collection School Of Information Systems

Recording and querying time-stamped trajectories incurs high cost of data storage and computing. In this paper, we explore several characteristics of the trajectories in road mbox{networks}, which have motivated the idea of coding trajectories by associating timestamps with relative spatial path and locations. Such a representation contains large number of duplicate information to achieve a lower entropy compared with the existing representations, thereby drastically cutting the storage cost. We propose several techniques to compress spatial path and locations separately, which can support fast positioning and achieve better compression ratio. For locations, we propose two novel encoding schemes such that ...

Intent Recognition In Smart Living Through Deep Recurrent Neural Networks, Xiang Zhang, Lina Yao, Chaoran Huang, Quan Z. Sheng, Xianzhi Wang

Research Collection School Of Information Systems

Electroencephalography (EEG) signal based intent recognition has recently attracted much attention in both academia and industries, due to helping the elderly or motor-disabled people controlling smart devices to communicate with outer world. However, the utilization of EEG signals is challenged by low accuracy, arduous and time-consuming feature extraction. This paper proposes a 7-layer deep learning model to classify raw EEG signals with the aim of recognizing subjects’ intents, to avoid the time consumed in pre-processing and feature extraction. The hyper-parameters are selected by an Orthogonal Array experiment method for efficiency. Our model is applied to an open EEG dataset provided ...

Improving Hpc Communication Library Performance On Modern Architectures, Matthew G. F. Dosanjh

Computer Science ETDs

As high-performance computing (HPC) systems advance towards exascale (10^18 operations per second), they must leverage increasing levels of parallelism to achieve their performance goals. In addition to increased parallelism, machines of that scale will have strict power limitations placed on them. One direction currently being explored to alleviate those issues are many-core processors such as Intel’s Xeon Phi line. Many-core processors sacrifice clock speed and core complexity, such as out of order pipelining, to increase the number of cores on a die. While this increases floating point throughput, it can reduce the performance of serialized, synchronized, and latency ...

Spring­11: Pdc In Cs1/2 And A Mobile/Cloud Intermediate Mobile/Cloud Intermediate Software Design Course, Joseph P. Kaylor, Konstantin Läufer, Chandra N. Sekharan, George K. Thiruvathukal

Konstantin Läufer

Recent changes in the environment of Loyola University Chicago’s Department of Computer Science include a better differentiation of our four undergraduate majors, growing interest in computing among science majors, and an increased demand for graduates with mobile and cloud skills. In our continued effort to incorporate parallel and distributed computing topics into the undergraduate curriculum, we are focusing on these three existing courses: CS1: In response to a request from the physics department, we started to offer a CS1 section aimed at majors in physics and other hard sciences this spring semester. This section includes some material on numerical ...

Network Technologies Used To Aggregate Environmental Data, Paul Stasiuk, Konstantin Läufer, George K. Thiruvathukal

Konstantin Läufer

The goal of the Loyola Weather Service (lws) project is to design and build a system of functioning environmental monitoring widgets that can intelligently and autonomously control the environment around them based on set thresholds and triggers. The widgets will also have the ability to aggregate their data and easily display this data in various ways: through a user interface in the room that the widget is placed, via a web application, and programmatically via a RESTful web service.

Building Capable, Energy-Efficient, Flexible Visualization And Sensing Clusters From Commodity Tablets, Thomas Delgado Dias, Xian Yan, Konstantin Läufer, George K. Thiruvathukal

Konstantin Läufer

We explore the application of clusters of commodity tablet devices to problems spanning a “trilogy” of concerns: visualization, sensing, and computation. We conjecture that such clusters provide a low-cost, energy-efficient, flexible, and ultimately effective platform to tackle a wide range of problems within this trilogy. This is a work in progress, and we now elaborate our position and give a preliminary status report. A wide range of Android tablet devices are available in terms of price and capabilities. “You get what you pay for” w.r.t. display resolution, sensors, and chipset---corresponding to the trilogy. $200 gets one a 1280x800-pixel ...

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

Tanvi Banerjee

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 ...

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 ...

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 ...