OS and Networks Commons^™

Memory Dynamics In Attractor Networks, Guoqi Li, Kiruthika Ramanathan, Ning Ning, Luping Shi, Changyun Wen

Research Collection School Of Computing and Information Systems

As can be represented by neurons and their synaptic connections, attractor networks are widely believed to underlie biological memory systems and have been used extensively in recent years to model the storage and retrieval process of memory. In this paper, we propose a new energy function, which is nonnegative and attains zero values only at the desired memory patterns. An attractor network is designed based on the proposed energy function. It is shown that the desired memory patterns are stored as the stable equilibrium points of the attractor network. To retrieve a memory pattern, an initial stimulus input is presented …

Context-Driven Automatic Subgraph Creation For Literature-Based Discovery, Delroy H. Cameron, Ramakanth Kavuluru, Thomas Rindflesch, Amit P. Sheth, Krishnaprasad Thirunarayan, Olivier Bodenreider

Kno.e.sis Publications

Background: Literature-based discovery (LBD) is characterized by uncovering hidden associations in non-interacting scientific literature. Prior approaches to LBD include use of: 1) domain expertise and structured background knowledge to manually filter and explore the literature, 2) distributional statistics and graph-theoretic measures to rank interesting connections and 3) heuristics to help eliminate spurious connections. However, manual approaches to LBD are not scalable and purely distributional approaches may not be sufficient to obtain insights into the meaning of poorly understood associations. While several graph-based approaches have the potential to elucidate associations, their effectiveness has not been fully demonstrated. A considerable degree of …

Towards An Integrated Model Of The Mental Lexicon, Natawut Monaikul

All Student Theses and Dissertations

Several models have been proposed attempting to describe the mental lexicon-the abstract organization of words in the human mind. Numerous studies have shown that by representing the mental lexicon as a network, where nodes represent words and edges connect similar words using a metric based on some word feature, a small-world structure is formed. This property, pervasive in many real-world networks, implies processing efficiency and resiliency to node deletion within the system, explaining the need for such a robust network as the mental lexicon. However, each model considered a single word feature at a time, such as semantic or phonological …

Leading Undergraduate Students To Big Data Generation, Jianjun Yang, Ju Shen

Computer Science Faculty Publications

People are facing a flood of data today. Data are being collected at unprecedented scale in many areas, such as networking, image processing, virtualization, scientific computation, and algorithms. The huge data nowadays are called Big Data. Big data is an all encompassing term for any collection of data sets so large and complex that it becomes difficult to process them using traditional data processing applications. In this article, the authors present a unique way which uses network simulator and tools of image processing to train students abilities to learn, analyze, manipulate, and apply Big Data. Thus they develop students hands-on …

A Web-Based Temperature Monitoring System For The College Of Arts And Letters, Rigoberto Solorio

Electronic Theses, Projects, and Dissertations

In general, server rooms have restricted access requiring that staff possess access codes, keys, etc. Normally, only administrators are provided access to protect the physical hardware and the data stored in the servers. Servers also have firewalls to restrict outsiders from accessing them via the Internet. Servers also cost a lot of money. For this reason, server rooms also need to be protected against overheating. This will prolong the lifecycle of the units and can prevent data loss from hardware failure.

The California State University San Bernardino (CSUSB), Specifically the College of Arts and Letters server room has faced power …

Smart Data - How You And I Will Exploit Big Data For Personalized Digital Health And Many Other Activities, Amit P. Sheth

Kno.e.sis Publications

No abstract provided.

On Using Synthetic Social Media Stimuli In An Emergency Preparedness Functional Exercise, Andrew Hampton, Shreyansh Bhatt, Gary Alan Smith, Jeremy S. Brunn, Hemant Purohit, Valerie L. Shalin, John M. Flach, Amit P. Sheth

Kno.e.sis Publications

This paper details the creation and use of a massive (over 32,000 messages) artificially constructed 'Twitter' microblog stream for a regional emergency preparedness functional exercise. By combining microblog conversion, manual production, and a control set, we created a web based information stream providing valid, misleading, and irrelevant information to public information officers (PIOs) representing hospitals, fire departments, the local Red Cross, and city and county government officials. PIOs searched, monitored, and (through conventional channels) verified potentially actionable information that could then be redistributed through a personalized screen name. Our case study of a key PIO reveals several capabilities that social …

Hole Detection And Shape-Free Representation And Double Landmarks Based Geographic Routing In Wireless Sensor Networks, Jianjun Yang, Zongming Fei, Ju Shen

Computer Science Faculty Publications

In wireless sensor networks, an important issue of geographic routing is “local minimum” problem, which is caused by a “hole” that blocks the greedy forwarding process. Existing geographic routing algorithms use perimeter routing strategies to find a long detour path when such a situation occurs. To avoid the long detour path, recent research focuses on detecting the hole in advance, then the nodes located on the boundary of the hole advertise the hole information to the nodes near the hole. Hence the long detour path can be avoided in future routing. We propose a heuristic hole detecting algorithm which identifies …

Gender-Based Violence In 140 Characters Or Fewer: A #Bigdata Case Study Of Twitter, Hemant Purohit, Tanvi Banerjee, Andrew Hampton, Valerie L. Shalin, Nayanesh Bhandutia, Amit P. Sheth

Kno.e.sis Publications

Public institutions are increasingly reliant on data from social media sites to measure public attitude and provide timely public engagement. Such reliance includes the exploration of public views on important social issues such as gender-based violence (GBV). In this study, we examine big (social) data consisting of nearly fourteen million tweets collected from Twitter over a period of ten months to analyze public opinion regarding GBV, highlighting the nature of tweeting practices by geographical location and gender. We demonstrate the utility of Computational Social Science to mine insight from the corpus while accounting for the influence of both transient events …

Knowledge Enabled Approach To Predict The Location Of Twitter Users, Revathy Krishnamurthy, Pavan Kapanipathi, Amit P. Sheth, Krishnaprasad Thirunarayan

Kno.e.sis Publications

Knowledge bases have been used to improve performance in applications ranging from web search and event detection to entity recognition and disambiguation. More recently, knowledge bases have been used to analyze social data. A key challenge in social data analysis has been the identification of the geographic location of online users in a social network such as Twitter. Existing approaches to predict the location of users, based on their tweets, rely solely on social media features or probabilistic language models. These approaches are supervised and require large training dataset of geo-tagged tweets to build their models. As most Twitter users …

Mining Behavior Of Citizen Sensor Communities To Improve Cooperation With Organizational Actors, Hemant Purohit

Kno.e.sis Publications

Web 2.0 (social media) provides a natural platform for dynamic emergence of citizen (as) sensor communities, where the citizens generate content for sharing information and engaging in discussions. Such a citizen sensor community (CSC) has stated or implied goals that are helpful in the work of formal organizations, such as an emergency management unit, for prioritizing their response needs. This research addresses questions related to design of a cooperative system of organizations and citizens in CSC. Prior research by social scientists in a limited offline and online environment has provided a foundation for research on cooperative behavior challenges, including ‘ …

Value Oriented Big Data Processing With Applications, Krishnaprasad Thirunarayan

Kno.e.sis Publications

We discuss the nature of Big Data and address the role of semantics in analyzing and processing Big Data that arises in the context of Physical-Cyber-Social Systems. To handle Volume, we advocate semantic perception that can convert low-level observational data to higher-level abstractions more suitable for decision- making. To handle Variety, we resort to semantic models and annotations of data so that intelligent processing can be done independent of heterogeneity of data formats and media. To handle Velocity, we seek to use continuous semantics capability to dynamically create event or situation specific models and recognize relevant new concepts, entities and …

Extracting City Traffic Events From Social Streams, Pramod Anantharam, Payam Barnaghi, Krishnaprasad Thirunarayan, Amit P. Sheth

Kno.e.sis Publications

Cities are composed of complex systems with physical, cyber, and social components. Current works on extracting and understanding city events mainly rely on technology enabled infrastructure to observe and record events. In this work, we propose an approach to leverage citizen observations of various city systems and services such as traffic, public transport, water supply, weather, sewage, and public safety as a source of city events. We investigate the feasibility of using such textual streams for extracting city events from annotated text. We formalize the problem of annotating social streams such as microblogs as a sequence labeling problem. We present …

Understanding Social Effects In Online Networks, Huda Alhazmi, Swapna S. Gokhale, Derek Doran

Kno.e.sis Publications

Understanding the motives behind people’s interactions online can offer sound bases to predict how a social network may evolve and also support a host of applications. We hypothesize that three offline social factors, namely, stature, relationship strength, and egocentricity may also play an important role in driving users’ interactions online. Therefore, we study the influence of these three social factors in online interactions by analyzing the transitivity in triads or three-way relationships among users. Analyzing transitivity through the lens of triad census for four popular social networks, namely, Facebook, Twitter, YouTube and Slashdot, we find that: (i) users’ interactions are …

Semantic Gateway As A Service Architecture For Iot Interoperability, Pratikkumar Desai, Amit P. Sheth, Pramod Anantharam

Kno.e.sis Publications

The Internet of Things (IoT) is set to occupy a substantial component of future Internet. The IoT connects sensors and devices that record physical observations to applications and services of the Internet. As a successor to technologies such as RFID and Wireless Sensor Networks (WSN), the IoT has stumbled into vertical silos of proprietary systems, providing little or no interoperability with similar systems. As the IoT represents future state of the Internet, an intelligent and scalable architecture is required to provide connectivity between these silos, enabling discovery of physical sensors and interpretation of messages between things. This paper proposes a …

Use Of Entropy For Feature Selection With Intrusion Detection System Parameters, Frank Acker

CCAC Theses and Dissertations

The metric of entropy provides a measure about the randomness of data and a measure of information gained by comparing different attributes. Intrusion detection systems can collect very large amounts of data, which are not necessarily manageable by manual means. Collected intrusion detection data often contains redundant, duplicate, and irrelevant entries, which makes analysis computationally intensive likely leading to unreliable results. Reducing the data to what is relevant and pertinent to the analysis requires the use of data mining techniques and statistics. Identifying patterns in the data is part of analysis for intrusion detections in which the patterns are categorized …

Motivation And Learning Of Non-Traditional Computing Education Students In A Web-Based Combined Laboratory, Michael Jesse Green

CCAC Theses and Dissertations

Hands-on experiential learning activities are an important component of computing education disciplines. Laboratory environments provide learner access to real world equipment for completing experiments. Local campus facilities are commonly used to host laboratory classes. While campus facilities afford hands-on experience with real equipment high maintenance costs, restricted access, and limited flexibility diminish laboratory effectiveness. Web-based simulation and remote laboratory formats have emerged as low cost options, which allow open access and learner control. Simulation lacks fidelity and remote laboratories are considered too complex for novice learners.

A web-based combined laboratory format incorporates the benefits of each format while mitigating the …

Well-Being Technologies: Meditation Using Virtual Worlds, Laura Downey

CCAC Theses and Dissertations

In a technologically overloaded world, is it possible to use technology to support well-being activities and enhance human flourishing? Proponents of positive technology and positive computing are striving to answer yes to that question. However, the impact of technology on well-being remains unresolved. Positive technology combines technology and positive psychology. Positive psychology focuses on well-being and the science of human flourishing. Positive computing includes an emphasis on designing with well-being in mind as a way to support human potential. User experience (UX) is critical to positive technology and positive computing. UX researchers and practitioners are advocating for experience-driven design and …

Knowledge-Driven Personalized Contextual Mhealth Service For Asthma Management In Children, Pramod Anantharam, Tanvi Banerjee, Amit P. Sheth, Krishnaprasad Thirunarayan, Surendra Marupudi, Vaikunth Sridharan

Kno.e.sis Publications

Wide adoption of smartphones and availability of low-cost sensors has resulted in seamless and continuous monitoring of physiology, environment, and public health notifications. However, personalized digital health and patient empowerment can become a reality only if the complex multisensory and multimodal data is processed within the patient context. Contextual processing of patient data along with personalized medical knowledge can lead to actionable information for better and timely decisions. We present a system called kHealth capable of aggregating multisensory and multimodal data from sensors (passive sensing) and answers to questionnaire (active sensing) from patients with asthma. We present our preliminary data …

Using Ehrs For Heart Failure Therapy Recommendation Using Multidimensional Patient Similarity Analytics, Maryam Panahiazar, Vahid Taslimitehrani, Naveen L. Pereira, Jyotishman Pathak

Kno.e.sis Publications

Electronic Health Records (EHRs) contain a wealth of information about an individual patient’s diagnosis, treatment and health outcomes. This information can be leveraged effectively to identify patients who are similar to each for disease diagnosis and prognosis. In recent years, several machine learning methods 1 have been proposed to assessing patient similarity, although the techniques have primarily focused on the use of patient diagnoses data from EHRs for the learning task. In this study, we develop a multidimensional patient similarity assessment technique that leverages multiple types of information from the EHR and predicts a medication plan for each new patient …