Digital Commons Network^™

Towards A Not Obtrusive Low Cost Biosystem To Assess Risk Perception In Workplace Through Stress Detection, Emanuele Bellini, Serena Benevenuti, Chiara Batistini

H-Workload 2017: Models and Applications (Works in Progress)

The main aim of the article is to build a method to assess risk perception in real time in order to early detect and prevent risk behaviors and possible human errors. To this end, the relation between mental workload and stress as critical factors affecting risk perception has been investigated. In particular the mental-physical activation generated by an increment of the workload has the effect of reducing the resources needed to perceive risk increasing the worker vulnerability. The complexity of the stress phenomenon suggested the adoption of an integrated view. The Functional Model has been adopted to for its holistic …

Smart Homes Enhance Seniors’ Safety, Singapore Management University

Research@SMU: Connecting the Dots

Professor Tan Hwee Pink and researchers at iCity Lab are using sensors to increase the safety of seniors who live independently in their own homes.

See the papers:

• Online detection of behavioral change using unobtrusive eldercare monitoring system
• Improving the sensitivity of unobtrusive inactivity detection in sensor-enabled homes for the elderly
• SHINESeniors: Personalized services for active ageing-in-place

Establishing A-Priori Performance Guarantees For Robot Missions That Include Localization Software, Damian Lyons, Ron Arkin, Shu Jiang, Matt O'Brien, Feng Tang, Peng Tang

Faculty Publications

One approach to determining whether an automated system is performing correctly is to monitor its performance, signaling when the performance is not acceptable; another approach is to automatically analyze the possible behaviors of the system a-priori and determine performance guarantees. Thea authors have applied this second approach to automatically derive performance guarantees for behaviorbased, multi-robot critical mission software using an innovative approach to formal verification for robotic software. Localization and mapping algorithms can allow a robot to navigate well in an unknown environment. However, whether such algorithms enhance any specific robot mission is currently a matter for empirical validation. Several …

Regulating Religious Robots: Free Exercise And Rfra In The Time Of Superintelligent Artificial Intelligence, Ignatius Michael D. Ingles

Ateneo School of Law Publications

No abstract provided.

Ai Education: Open-Access Educational Resources On Ai, Todd W. Neller

Computer Science Faculty Publications

Open-access AI educational resources are vital to the quality of the AI education we offer. Avoiding the reinvention of wheels is especially important to us because of the special challenges of AI Education. AI could be said to be “the really interesting miscellaneous pile of Computer Science”. While “artificial” is well-understood to encompass engineered artifacts, “intelligence” could be said to encompass any sufficiently difficult problem as would require an intelligent approach and yet does not fall neatly into established Computer Science subdisciplines. Thus AI consists of so many diverse topics that we would be hard-pressed to individually create quality learning …

An Ensemble Learning Framework For Anomaly Detection In Building Energy Consumption, Daniel B. Araya, Katarina Grolinger, Hany F. Elyamany, Miriam Am Capretz, Girma T. Bitsuamlak

Electrical and Computer Engineering Publications

During building operation, a significant amount of energy is wasted due to equipment and human-related faults. To reduce waste, today's smart buildings monitor energy usage with the aim of identifying abnormal consumption behaviour and notifying the building manager to implement appropriate energy-saving procedures. To this end, this research proposes a new pattern-based anomaly classifier, the collective contextual anomaly detection using sliding window (CCAD-SW) framework. The CCAD-SW framework identifies anomalous consumption patterns using overlapping sliding windows. To enhance the anomaly detection capacity of the CCAD-SW, this research also proposes the ensemble anomaly detection (EAD) framework. The EAD is a generic framework …

Deep Neural Networks With Confidence Sampling For Electrical Anomaly Detection, Norman L. Tasfi, Wilson A. Higashino, Katarina Grolinger, Miriam A. M. Capretz

Electrical and Computer Engineering Publications

The increase in electrical metering has created tremendous quantities of data and, as a result, possibilities for deep insights into energy usage, better energy management, and new ways of energy conservation. As buildings are responsible for a significant portion of overall energy consumption, conservation efforts targeting buildings can provide tremendous effect on energy savings. Building energy monitoring enables identification of anomalous or unexpected behaviors which, when corrected, can lead to energy savings. Although the available data is large, the limited availability of labels makes anomaly detection difficult. This research proposes a deep semi-supervised convolutional neural network with confidence sampling for …

Machine Learning With Personal Data: Is Data Protection Law Smart Enough To Meet The Challenge?, Fred H. Cate, Christopher Kuner, Dan Jerker B. Svantesson, Orla Lynskey, Christopher Millard

Articles by Maurer Faculty

No abstract provided.

Ai Education: Machine Learning Resources, Todd W. Neller

Computer Science Faculty Publications

In this column, we focus on resources for learning and teaching three broad categories of machine learning (ML): supervised, unsupervised, and reinforcement learning. In ournext column, we will focus specifically on deep neural network learning resources, so if you have any resource recommendations, please email them to the address above. [excerpt]

Ai Education: Deep Neural Network Learning Resources, Todd W. Neller

Computer Science Faculty Publications

In this column, we focus on resources for learning and teaching deep neural network learning. Many exciting advances have been made in this area of late, and so many resources have become available online that the flood of relevant concepts and techniques can be overwhelming. Here, we hope to provide a sampling of high-quality resources to guide the newcomer into this booming field. [excerpt]

A Systems Approach To Predicting And Measuring Workload In Rail Traffic Management Systems, Joanna Evans

H-Workload 2017: Models and Applications (Works in Progress)

The introduction of systems such as Traffic Management (TM) will result in a number of changes in how the railway is managed for operations and maintenance staff such as, an increase in collaborative working styles and shared responsibilities. In order to react to these changing operational demands and user needs, TM workstation designs need to have greater flexibility and be configurable to support the information requirements for each specific role as well as support each role during different scenarios. Although this flexibility in system design has the potential to enhance performance, it increases the complexity of measuring operator workload. The …

Intelligent Profiling Of Blood Donors In Ireland, Joanna Kossakowska

Theses

The demand for blood products in Ireland is constantly rising due to population growth and population ageing. It is believed that within the next decade these two factors will present challenges to blood donor recruitment and the availability of blood supplies. Improving the retention of blood donors will have a positive impact on the availability of blood products. Identification of suitable donors with the potential for long-term donating can potentially enhance the predictability of blood supply levels.

This research proposes that the patterns of blood donation behaviours of donors can be isolated from blood donor databases held by blood collecting …

K-Mer Analysis Pipeline For Classification Of Dna Sequences From Metagenomic Samples, Russell Kaehler

Graduate Student Theses, Dissertations, & Professional Papers

Biological sequence datasets are increasing at a prodigious rate. The volume of data in these datasets surpasses what is observed in many other fields of science. New developments wherein metagenomic DNA from complex bacterial communities is recovered and sequenced are producing a new kind of data known as metagenomic data, which is comprised of DNA fragments from many genomes. Developing a utility to analyze such metagenomic data and predict the sample class from which it originated has many possible implications for ecological and medical applications. Within this document is a description of a series of analytical techniques used to process …

Xic Clustering By Baseyian Network, Kyle J. Handy

Graduate Student Theses, Dissertations, & Professional Papers

No abstract provided.

An Alternative Approach To Training Sequence-To-Sequence Model For Machine Translation, Vivek Sah

Honors Theses

Machine translation is a widely researched topic in the field of Natural Language Processing and most recently, neural network models have been shown to be very effective at this task. The model, called sequence-to-sequence model, learns to map an input sequence in one language to a vector of fixed dimensionality and then map that vector to an output sequence in another language without any human intervention provided that there is enough training data. Focusing on English-French translation, in this paper, I present a way to simplify the learning process by replacing English input sentences by word-by-word translation of those sentences. …

Presenting A Labelled Dataset For Real-Time Detection Of Abusive User Posts, Hao Chen, Susan Mckeever, Sarah Jane Delany

Conference papers

Social media sites facilitate users in posting their own personal comments online. Most support free format user posting, with close to real-time publishing speeds. However, online posts generated by a public user audience carry the risk of containing inappropriate, potentially abusive content. To detect such content, the straightforward approach is to filter against blacklists of profane terms. However, this lexicon filtering approach is prone to problems around word variations and lack of context. Although recent methods inspired by machine learning have boosted detection accuracies, the lack of gold standard labelled datasets limits the development of this approach. In this work, …

An Introduction To The Theory And Applications Of Bayesian Networks, Anant Jaitha

CMC Senior Theses

Bayesian networks are a means to study data. A Bayesian network gives structure to data by creating a graphical system to model the data. It then develops probability distributions over these variables. It explores variables in the problem space and examines the probability distributions related to those variables. It conducts statistical inference over those probability distributions to draw meaning from them. They are good means to explore a large set of data efficiently to make inferences. There are a number of real world applications that already exist and are being actively researched. This paper discusses the theory and applications of …

Sensitivity Analysis Method To Address User Disparities In The Analytic Hierarchy Process, Marie Ivanco, Gene Hou, Jennifer Michaeli

Mechanical & Aerospace Engineering Faculty Publications

Decision makers often face complex problems, which can seldom be addressed well without the use of structured analytical models. Mathematical models have been developed to streamline and facilitate decision making activities, and among these, the Analytic Hierarchy Process (AHP) constitutes one of the most utilized multi-criteria decision analysis methods. While AHP has been thoroughly researched and applied, the method still shows limitations in terms of addressing user profile disparities. A novel sensitivity analysis method based on local partial derivatives is presented here to address these limitations. This new methodology informs AHP users of which pairwise comparisons most impact the derived …

Decision Process In Mcdm With Large Number Of Criteria And Heterogeneous Risk Preferences, Jian Liu, Hong Kuan Zhao, Zhao Bin Li, Si Feng Liu

Electrical and Computer Engineering Faculty Research & Creative Works

A new decision process is proposed to address the challenge that a large number of criteria in the multi-criteria decision making (MCDM) problem and the decision makers with heterogeneous risk preferences. First, from the perspective of objective data, the effective criteria are extracted based on the similarity relations between criterion values and the criteria are weighted, respectively. Second, the corresponding types of theoretic model of risk preferences expectations will be built, based on the possibility and similarity between criterion values to solve the problem for different interval numbers with the same expectation. Then, the risk preferences (Risk-seeking, risk-neutral and risk-aversion) …

Security Analytics: Using Deep Learning To Detect Cyber Attacks, Glenn M. Lambert Ii

UNF Graduate Theses and Dissertations

Security attacks are becoming more prevalent as cyber attackers exploit system vulnerabilities for financial gain. The resulting loss of revenue and reputation can have deleterious effects on governments and businesses alike. Signature recognition and anomaly detection are the most common security detection techniques in use today. These techniques provide a strong defense. However, they fall short of detecting complicated or sophisticated attacks. Recent literature suggests using security analytics to differentiate between normal and malicious user activities.

The goal of this research is to develop a repeatable process to detect cyber attacks that is fast, accurate, comprehensive, and scalable. A model …

Novel Neuroevolution Techniques For The Life Science Domain, Timothy Manning

Theses

The life science domain is a high value research area, both in terms of the benefits in increased knowledge and in societal impact. Much of the research funding has focused on wet lab based approaches to increase visibility into biological processes and producing maximal relevant information on which to make decisions. Given the complexity of biological functions, in many cases this has led to an information overload. Researchers are now able to routinely generate and access petabytes of data as a result of high throughput experiments, and this capability is growing. This data can be difficult to interpret and intractable …

Complex Affect Recognition In The Wild, Behnaz Nojavanasghari

Electronic Theses and Dissertations

Artificial social intelligence is a step towards human-like human-computer interaction. One important milestone towards building socially intelligent systems is enabling computers with the ability to process and interpret the social signals of humans in the real world. Social signals include a wide range of emotional responses from a simple smile to expressions of complex affects. This dissertation revolves around computational models for social signal processing in the wild, using multimodal signals with an emphasis on the visual modality. We primarily focus on complex affect recognition with a strong interest in curiosity. In this dissertation,we ?rst present our collected dataset, EmoReact. …

Learning Conditional Preference Networks From Optimal Choices, Cory Siler

Theses and Dissertations--Computer Science

Conditional preference networks (CP-nets) model user preferences over objects described in terms of values assigned to discrete features, where the preference for one feature may depend on the values of other features. Most existing algorithms for learning CP-nets from the user's choices assume that the user chooses between pairs of objects. However, many real-world applications involve the the user choosing from all combinatorial possibilities or a very large subset. We introduce a CP-net learning algorithm for the latter type of choice, and study its properties formally and empirically.

Return On Investment Of The Cftp Framework With And Without Risk Assessment, Anne Lim Lee

Walden Dissertations and Doctoral Studies

In recent years, numerous high tech companies have developed and used technology roadmaps when making their investment decisions. Jay Paap has proposed the Customer Focused Technology Planning (CFTP) framework to draw future technology roadmaps. However, the CFTP framework does not include risk assessment as a critical factor in decision making. The problem addressed in this quantitative study was that high tech companies are either losing money or getting a much smaller than expected return on investment when making technology investment decisions. The purpose of this research was to determine the relationship between returns on investment before and after adding risk …

Plithogeny, Plithogenic Set, Logic, Probability, And Statistics, Florentin Smarandache

Branch Mathematics and Statistics Faculty and Staff Publications

In this book we introduce for the first time, as generalization of dialectics and neutrosophy, the philosophical concept called plithogeny. And as its derivatives: the plithogenic set (as generalization of crisp, fuzzy, intuitionistic fuzzy, and neutrosophic sets), plithogenic logic (as generalization of classical, fuzzy, intuitionistic fuzzy, and neutrosophic logics), plithogenic probability (as generalization of classical, imprecise, and neutrosophic probabilities), and plithogenic statistics (as generalization of classical, and neutrosophic statistics).

Plithogeny is the genesis or origination, creation, formation, development, and evolution of new entities from dynamics and organic fusions of contradictory and/or neutrals and/or non-contradictory multiple old entities.

Plithogenic …

Artificial Intelligence And Its Potential Adverse Impacts On The Philippine Economy, Krista Danielle Yu, Caesar Cororaton, Joel P. Ilao, Charibeth K. Cheng, Kathleen Aviso, Christina D. Cayamanda, Michael Angelo B. Promentilla, Ringgold P. Atienza, Roman Julio B. Infante, Raymond R. Tan

Angelo King Institute for Economic and Business Studies (AKI)

Recent developments in artificial intelligence (AI) and deep learning techniques are expected to reshape the nature of the working environment in many economic sectors through the automation of many white collar jobs. This technological breakthrough poses threats of job obsolescence in several industries, particularly for a labor abundant country such as the Philippines. With human capital as one of its largest resources, the services sector is a major contributor to the country’s economy, contributing around 60% of the total gross domestic product and employing about 22.8 million workers (Philippine Statistics Authority, 2017).

Robot Lives Matter?, Christopher Boolukos (Class Of 2017)

Writing Across the Curriculum

It’s 2016 and slavery is still a brutal reality around the world and a crime against humanity. The human race has never been shy when it comes to enslaving fellow human beings, so with progress in robotics and AI, we will soon be able to enslave robots to do our bidding. This poses a serious moral dilemma as to what rights such entities would possess and what responsibility we have, if any, on how we use them in society. Should it make any difference whether an entity is made of silicon or carbon, or whether its brain uses semi-conductors or …

Human-Intelligence/Machine-Intelligence Decision Governance: An Analysis From Ontological Point Of View, Faisal Mahmud, Teddy Steven Cotter

Engineering Management & Systems Engineering Faculty Publications

The increasing CPU power and memory capacity of computers, and now computing appliances, in the 21st century has allowed accelerated integration of artificial intelligence (AI) into organizational processes and everyday life. Artificial intelligence can now be found in a wide range of organizational processes including medical diagnosis, automated stock trading, integrated robotic production systems, telecommunications routing systems, and automobile fuzzy logic controllers. Self-driving automobiles are just the latest extension of AI. This thrust of AI into organizations and everyday life rests on the AI community’s unstated assumption that “…every aspect of human learning and intelligence could be so precisely described …

2d Vector Map And Database Design For Indoor Assisted Navigation, Luciano Caraciolo Albuquerque

Dissertations and Theses

In this paper we implemented a 2D Vector Map, map editor and Database design intended to provide an efficient way to convert cad files from indoor environments to a set of vectors representing hallways, doors, exits, elevators, and other entities embedded in a floor plan, and save them in a database for use by other applications, such as assisted navigation for blind people.

A graphical application as developed in C++ to allow the user to input a CAD DXF file, process the file to automatically obtain nodes and edges, and save the nodes and edges to a database for posterior …

Designing 2d Interfaces For 3d Gesture Retrieval Utilizing Deep Learning, Spencer Southard

UNF Graduate Theses and Dissertations

Gesture retrieval can be defined as the process of retrieving the correct meaning of the hand movement from a pre-assembled gesture dataset. The purpose of the research discussed here is to design and implement a gesture interface system that facilitates retrieval for an American Sign Language gesture set using a mobile device. The principal challenge discussed here will be the normalization of 2D gestures generated from the mobile device interface and the 3D gestures captured from video samples into a common data structure that can be utilized by deep learning networks. This thesis covers convolutional neural networks and auto encoders …