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2019

Operations Research, Systems Engineering and Industrial Engineering

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Articles 1 - 30 of 187

Full-Text Articles in Engineering

Predicting The Daily Return Direction Of The Stock Market Using Hybrid Machine Learning Algorithms, X. Zhong, David Lee Enke Dec 2019

Predicting The Daily Return Direction Of The Stock Market Using Hybrid Machine Learning Algorithms, X. Zhong, David Lee Enke

Engineering Management and Systems Engineering Faculty Research & Creative Works

Big data analytic techniques associated with machine learning algorithms are playing an increasingly important role in various application fields, including stock market investment. However, few studies have focused on forecasting daily stock market returns, especially when using powerful machine learning techniques, such as deep neural networks (DNNs), to perform the analyses. DNNs employ various deep learning algorithms based on the combination of network structure, activation function, and model parameters, with their performance depending on the format of the data representation. This paper presents a comprehensive big data analytics process to predict the daily return direction of the SPDR S&P ...


Hedge Fund Replication Using Strategy Specific Factors, Sujit Subhash, David Lee Enke Dec 2019

Hedge Fund Replication Using Strategy Specific Factors, Sujit Subhash, David Lee Enke

Engineering Management and Systems Engineering Faculty Research & Creative Works

Hedge funds have traditionally served wealthy individuals and institutional investors with the promise of delivering protection of capital and uncorrelated positive returns irrespective of market direction, allowing them to better manage portfolio risk. However, the financial crisis of 2008 has heightened investor sensitivity to the high fees, illiquidity, lack of transparency, and lockup periods typically associated with hedge funds. Hedge fund replication products, or clones, seek to answer these challenges by providing daily liquidity, transparency, and immediate exposure to a desired hedge fund strategy. Nonetheless, although lowering cost and adding simplicity by using a common set of factors, traditional replication ...


Adaptive Randomized Rounding In The Big Parsimony Problem, Sangho Shim, Sunil Chopra, Eunseok Kim Oct 2019

Adaptive Randomized Rounding In The Big Parsimony Problem, Sangho Shim, Sunil Chopra, Eunseok Kim

Annual Symposium on Biomathematics and Ecology: Education and Research

No abstract provided.


The Use Of Sacrificial Support Structures In A Rapid Machining Process, Wutthigrai Boonsuk, Matthew C. Frank Sep 2019

The Use Of Sacrificial Support Structures In A Rapid Machining Process, Wutthigrai Boonsuk, Matthew C. Frank

Wutthigrai Boonsuk

Rapid prototyping techniques for CNC machining have been developed in an effort to produce functional prototypes in appropriate materials. One of the major challenges is to develop an automatic fixturing system for the part during the milling process. The current proposed method, sacrificial support fixturing, is similar to the support structures used in existing rapid processes, such as Stereolithography. During the machining process, the sacrificial supports emerge incrementally and, at the end of the process, are the only entities connecting the part to the stock material. In this paper, we propose methodologies for the design of sacrificial support structures for ...


Effects Of Three Dry Powder Inhalers On Deposition Of Aerosolized Medicine In The Human Oral-Pharyngeal-Laryngeal Regions, Mohammed Ali Sep 2019

Effects Of Three Dry Powder Inhalers On Deposition Of Aerosolized Medicine In The Human Oral-Pharyngeal-Laryngeal Regions, Mohammed Ali

Mohammed Ali

The dry powder inhaler (DPI) is a popular, effective and convenient drug delivery device for inhalation therapy to treat asthma. However, a large quantity (approximately 54%) of inhaled aerosols deposit in the oropharyngeal region. Deposition in this region is undesirable because it provides minimum therapeutic benefits and has adverse localized or systemic side effects. This study reports a method of examining electrostatic charge effects on deposition of three DPI aerosols (Spiriva Handihaler, Advair Diskus, and Pulmicort Turbohaler) in a cadaver-based cast of the human oral-pharyngeal-laryngeal (OPL) regions. Experimental aerosols were generated from the three commercially available DPIs by means of ...


Composition Of Templates For Transitional Pedipulation Behaviors, Thomas T. Topping, Vasileios Vasilopoulos, Avik De, Daniel E. Koditschek Sep 2019

Composition Of Templates For Transitional Pedipulation Behaviors, Thomas T. Topping, Vasileios Vasilopoulos, Avik De, Daniel E. Koditschek

Departmental Papers (ESE)

Abstract. We document the reliably repeatable dynamical mounting and dismounting of wheeled stools and carts, and of fixed ledges, by the Minitaur robot. Because these tasks span a range of length scales that preclude quasi-static execution, we use a hybrid dynamical systems framework to variously compose and thereby systematically reuse a small lexicon of templates (low degree of freedom behavioral primitives). The resulting behaviors comprise the key competences beyond mere locomotion required for robust implementation on a legged mobile manipulator of a simple version of the warehouseman’s problem.


Assessing Values-Based Sourcing Strategies In Regional Food Supply Networks: An Agent-Based Approach, Caroline C. Krejci, Michael C. Dorneich, Richard T. Stone Sep 2019

Assessing Values-Based Sourcing Strategies In Regional Food Supply Networks: An Agent-Based Approach, Caroline C. Krejci, Michael C. Dorneich, Richard T. Stone

Michael C. Dorneich

The recent increase in demand for regionally produced food has resulted in a need for more efficient distribution methods. To connect regional food producers and consumers, intermediated regional food supply networks have developed. The intermediary, known as a regional food hub, serves as an aggregation point for products and information. It may also act as a filter to ensure that the requirements of both producers and consumers are consistently met. This paper describes an empirically based agent-based model of a regional food network in central Iowa that is intermediated by a food hub. The model was used to test a ...


Maize Yield And Nitrate Loss Prediction With Machine Learning Algorithms, Mohsen Shahhosseini, Rafael A. Martinez-Feria, Guiping Hu, Sotirios Archontoulis Aug 2019

Maize Yield And Nitrate Loss Prediction With Machine Learning Algorithms, Mohsen Shahhosseini, Rafael A. Martinez-Feria, Guiping Hu, Sotirios Archontoulis

Mohsen Shahhosseini

Pre-season prediction of crop production outcomes such as grain yields and N losses can provide insights to stakeholders when making decisions. Simulation models can assist in scenario planning, but their use is limited because of data requirements and long run times. Thus, there is a need for more computationally expedient approaches to scale up predictions. We evaluated the potential of five machine learning (ML) algorithms as meta-models for a cropping systems simulator (APSIM) to inform future decision-support tool development. We asked: 1) How well do ML meta-models predict maize yield and N losses using pre-season information? 2) How many data ...


Optimizing Ensemble Weights And Hyperparameters Of Machine Learning Models For Regression Problems, Mohsen Shahhosseini, Guiping Hu, Hieu Pham Aug 2019

Optimizing Ensemble Weights And Hyperparameters Of Machine Learning Models For Regression Problems, Mohsen Shahhosseini, Guiping Hu, Hieu Pham

Mohsen Shahhosseini

Aggregating multiple learners through an ensemble of models aims to make better predictions by capturing the underlying distribution more accurately. Different ensembling methods, such as bagging, boosting and stacking/blending, have been studied and adopted extensively in research and practice. While bagging and boosting intend to reduce variance and bias, respectively, blending approaches target both by finding the optimal way to combine base learners to find the best trade-off between bias and variance. In blending, ensembles are created from weighted averages of multiple base learners. In this study, a systematic approach is proposed to find the optimal weights to create ...


Backhaul Profit Maximization Problem Instances, Yuanyuan Dong, Yulan Bai, Eli V. Olinick, Andrew Junfang Yu Aug 2019

Backhaul Profit Maximization Problem Instances, Yuanyuan Dong, Yulan Bai, Eli V. Olinick, Andrew Junfang Yu

Engineering Management, Information, and Systems Research

This archive contains data for the problem instances described in the technical report "An Empirical Study of Mixed Integer Programming Formulations of the Backhaul Profit Maximization Problem" by Yulan Bai and Eli V. Olinick.


An Empirical Study Of Mixed Integer Programming Formulations Of The Backhaul Profit Maximization Problem, Yulan Bai, Eli V. Olinick Aug 2019

An Empirical Study Of Mixed Integer Programming Formulations Of The Backhaul Profit Maximization Problem, Yulan Bai, Eli V. Olinick

Engineering Management, Information, and Systems Research

Solving an instance of the Backhaul Profit Maximization Problem (BPMP) requires simultaneously solving two problems: (1) determining how to route an empty delivery vehicle back from its current location to its depot by a scheduled arrival time, and (2) selecting a profit-maximizing subset of delivery requests between various locations on the route subject to the vehicle's capacity. We propose and test a series of enhancements to the node-arc and triples mixed integer programming formulations of BPMP found in the literature and develop a multi-criteria Composite Index Method (CIM) to evaluate the results. We find that CPLEX takes 5 to ...


Optimizing Ensemble Weights And Hyperparameters Of Machine Learning Models For Regression Problems, Mohsen Shahhosseini, Guiping Hu, Hieu Pham Aug 2019

Optimizing Ensemble Weights And Hyperparameters Of Machine Learning Models For Regression Problems, Mohsen Shahhosseini, Guiping Hu, Hieu Pham

Guiping Hu

Aggregating multiple learners through an ensemble of models aims to make better predictions by capturing the underlying distribution more accurately. Different ensembling methods, such as bagging, boosting and stacking/blending, have been studied and adopted extensively in research and practice. While bagging and boosting intend to reduce variance and bias, respectively, blending approaches target both by finding the optimal way to combine base learners to find the best trade-off between bias and variance. In blending, ensembles are created from weighted averages of multiple base learners. In this study, a systematic approach is proposed to find the optimal weights to create ...


Multi-Objective Evolutionary Neural Network To Predict Graduation Success At The United States Military Academy, Gene Lesinski, Steven Corns Aug 2019

Multi-Objective Evolutionary Neural Network To Predict Graduation Success At The United States Military Academy, Gene Lesinski, Steven Corns

Steven Corns

This paper presents an evolutionary neural network approach to classify student graduation status based upon selected academic, demographic, and other indicators. A pareto-based, multi-objective evolutionary algorithm utilizing the Strength Pareto Evolutionary Algorithm (SPEA2) fitness evaluation scheme simultaneously evolves connection weights and identifies the neural network topology using network complexity and classification accuracy as objective functions. A combined vector-matrix representation scheme and differential evolution recombination operators are employed. The model is trained, tested, and validated using 5100 student samples with data compiled from admissions records and institutional research databases. The inputs to the evolutionary neural network model are used to classify ...


Densenet For Anatomical Brain Segmentation, Ram Deepak Gottapu, Cihan H. Dagli Aug 2019

Densenet For Anatomical Brain Segmentation, Ram Deepak Gottapu, Cihan H. Dagli

Cihan H. Dagli

Automated segmentation in brain magnetic resonance image (MRI) plays an important role in the analysis of many diseases and conditions. In this paper, we present a new architecture to perform MR image brain segmentation (MRI) into a number of classes based on type of tissue. Recent work has shown that convolutional neural networks (DenseNet) can be substantially more accurate with less number of parameters if each layer in the network is connected with every other layer in a feed forward fashion. We embrace this idea and generate new architecture that can assign each pixel/voxel in an MR image of ...


Analysis Of Parkinson's Disease Data, Ram Deepak Gottapu, Cihan H. Dagli Aug 2019

Analysis Of Parkinson's Disease Data, Ram Deepak Gottapu, Cihan H. Dagli

Cihan H. Dagli

In this paper, we investigate the diagnostic data from patients suffering with Parkinson's disease (PD) and design classification/prediction model to simplify the diagnosis. The main aim of this research is to open possibilities to be able to apply deep learning algorithms to help better understand and diagnose the disease. To our knowledge, the capabilities of deep learning algorithms have not yet been completely utilized in the field of Parkinson's research and we believe that by having an in-depth understanding of data, we can create a platform to apply different algorithms to automate the Parkinson's Disease diagnosis ...


Supporting Shrinkage: Better Planning And Decision-Making For Legacy Cities, Michael P. Johnson Jr., Justin B. Hollander, Eliza W. Kinsey, George Chichirau, Charla Burnett Aug 2019

Supporting Shrinkage: Better Planning And Decision-Making For Legacy Cities, Michael P. Johnson Jr., Justin B. Hollander, Eliza W. Kinsey, George Chichirau, Charla Burnett

Michael P. Johnson

Planning and policy design for shrinking and distressed regions is challenging. Traditionally, planners use
a variety of tools and incentives to encourage land uses that accommodate changes in populations,
infrastructure and activities to maximize quality of life and social and environmental sustainability.
These are generally designed for growing cities and politically and socially active communities. Since
many regions face significant disparities in social supports, financial resources and quality of life, use of
these tools is thus problematic.

Information technology and the Internet have transformed the production of goods and services. The
‘big data’, ‘smart cities’ and ‘e-government’ movements make it ...


Design Thinking: An Approach With Various Perceptions, Sanne Bouwman, Jesper Voorendt, Boris Eisenbart, Seda Mckilligan Aug 2019

Design Thinking: An Approach With Various Perceptions, Sanne Bouwman, Jesper Voorendt, Boris Eisenbart, Seda Mckilligan

Seda McKilligan

Design Thinking has become increasingly popular across different disciplines. However, what it exactly entails is becoming more and more vague, leading to the term being used for many different approaches and applications. This paper presents an interview study with experts on the application and training of Design Thinking in academia and industry. We find a divide with some seeing Design Thinking as a mere toolbox of methods, while others see it as an umbrella term for the mindset that determines how designers think and act. Subjects unanimously attest the approach large potential to support certain types of businesses, when applied ...


Extreme-Point Tabu Search Heuristics For Fixed-Charge Generalized Network Problems, Angelika Leskovskaya Aug 2019

Extreme-Point Tabu Search Heuristics For Fixed-Charge Generalized Network Problems, Angelika Leskovskaya

Engineering Management, Information, and Systems Research Theses and Dissertations

While researchers have studied generalized network flow problems extensively, the powerful addition of fixed charges on arcs has received scant attention. This work describes network-simplex-based algorithms that efficiently exploit the quasi-tree basis structure of the problem relaxations, proposes heuristics that utilize a candidate list, a tabu search with short and intermediate term memories to do the local search, a diversification approach to solve fixed-charge transportation problems, as well as a dynamic linearization of objective function extension for the transshipment fixed-charge generalized problems. Computational testings for both heuristics demonstrate their effectiveness in terms of speed and quality of solutions to these ...


Design Thinking: An Approach With Various Perceptions, Sanne Bouwman, Jesper Voorendt, Boris Eisenbart, Seda Mckilligan Aug 2019

Design Thinking: An Approach With Various Perceptions, Sanne Bouwman, Jesper Voorendt, Boris Eisenbart, Seda Mckilligan

Industrial Design Conference Presentations, Posters and Proceedings

Design Thinking has become increasingly popular across different disciplines. However, what it exactly entails is becoming more and more vague, leading to the term being used for many different approaches and applications. This paper presents an interview study with experts on the application and training of Design Thinking in academia and industry. We find a divide with some seeing Design Thinking as a mere toolbox of methods, while others see it as an umbrella term for the mindset that determines how designers think and act. Subjects unanimously attest the approach large potential to support certain types of businesses, when applied ...


Monitoring Activity And Climate Impact In Market Hogs With Activity Ball, Brad Aronson, Eli Sents, Kyle Wenck, Derek Yegge, Joseph R. Vanstrom, Jacek A. Koziel, Steven Hoff, Brett Ramirez Aug 2019

Monitoring Activity And Climate Impact In Market Hogs With Activity Ball, Brad Aronson, Eli Sents, Kyle Wenck, Derek Yegge, Joseph R. Vanstrom, Jacek A. Koziel, Steven Hoff, Brett Ramirez

Brett Ramirez

Prairie Systems is a web-based data management provider, primarily servicing the swine industry. Their products include the Feed Allocation System, which helps producers manage feed inputs, Smart Order, which helps feed manufacturers and distributors manage feed order fulfillment, and LeeO, which tracks individual animals through RFID technologies. Prairie Systems’ line of products is implementing precision agriculture practices into the livestock industry.

To help producers better manage their operations, Prairie Systems is seeking to gain a better understanding of the daily activity of market hogs. To do this, we were tasked with designing and fabricating a container to collect activity data ...


Automated Finishing Pig Feeder Adjustment, Ryan Godfredsen, Kelley Mabeus, Michael Meyer, Brett C. Ramirez, Shweta Chopra, Jacek A. Koziel Aug 2019

Automated Finishing Pig Feeder Adjustment, Ryan Godfredsen, Kelley Mabeus, Michael Meyer, Brett C. Ramirez, Shweta Chopra, Jacek A. Koziel

Brett Ramirez

Feed costs in the swine industry are typically 60-70% of the total swine production costs. Feeder adjustment is used to decrease feed costs. When feeders are properly adjusted, the growth rate can be improved while the wasted feed is minimized. Currently, feeders are manually monitored and adjusted by daily caretaker inspection, which is very laborious. Automated adjustment would reduce labor need and allow for the integration of precision management in the future.


Evaluating Machine Learning Performance In Predicting Injury Severity In Agribusiness Industries, Fatemeh Davoudi Kakhki, Steven A. Freeman, Gretchen A. Mosher Aug 2019

Evaluating Machine Learning Performance In Predicting Injury Severity In Agribusiness Industries, Fatemeh Davoudi Kakhki, Steven A. Freeman, Gretchen A. Mosher

Agricultural and Biosystems Engineering Publications

Although machine learning methods have been used as an outcome prediction tool in many fields, their utilization in predicting incident outcome in occupational safety is relatively new. This study tests the performance of machine learning techniques in modeling and predicting occupational incidents severity with respect to accessible information of injured workers in agribusiness industries using workers’ compensation claims. More than 33,000 incidents within agribusiness industries in the Midwest of the United States for 2008–2016 were analyzed. The total cost of incidents was extracted and classified from workers’ compensation claims. Supervised machine learning algorithms for classification (support vector machines ...


Optimizing Ensemble Weights For Machine Learning Models: A Case Study For Housing Price Prediction, Mohsen Shahhosseini, Guiping Hu, Hieu Pham Jul 2019

Optimizing Ensemble Weights For Machine Learning Models: A Case Study For Housing Price Prediction, Mohsen Shahhosseini, Guiping Hu, Hieu Pham

Guiping Hu

Designing ensemble learners has been recognized as one of the significant trends in the field of data knowledge especially in data science competitions. Building models that are able to outperform all individual models in terms of bias, which is the error due to the difference in the average model predictions and actual values, and variance, which is the variability of model predictions, has been the main goal of the studies in this area. An optimization model has been proposed in this paper to design ensembles that try to minimize bias and variance of predictions. Focusing on service sciences, two well-known ...


Optimizing Ensemble Weights For Machine Learning Models: A Case Study For Housing Price Prediction, Mohsen Shahhosseini, Guiping Hu, Hieu Pham Jul 2019

Optimizing Ensemble Weights For Machine Learning Models: A Case Study For Housing Price Prediction, Mohsen Shahhosseini, Guiping Hu, Hieu Pham

Mohsen Shahhosseini

Designing ensemble learners has been recognized as one of the significant trends in the field of data knowledge especially in data science competitions. Building models that are able to outperform all individual models in terms of bias, which is the error due to the difference in the average model predictions and actual values, and variance, which is the variability of model predictions, has been the main goal of the studies in this area. An optimization model has been proposed in this paper to design ensembles that try to minimize bias and variance of predictions. Focusing on service sciences, two well-known ...


Mid To Late Season Weed Detection In Soybean Production Fields Using Unmanned Aerial Vehicle And Machine Learning, Arun Narenthiran Veeranampalayam Sivakumar Jul 2019

Mid To Late Season Weed Detection In Soybean Production Fields Using Unmanned Aerial Vehicle And Machine Learning, Arun Narenthiran Veeranampalayam Sivakumar

Biological Systems Engineering--Dissertations, Theses, and Student Research

Mid-late season weeds are those that escape the early season herbicide applications and those that emerge late in the season. They might not affect the crop yield, but if uncontrolled, will produce a large number of seeds causing problems in the subsequent years. In this study, high-resolution aerial imagery of mid-season weeds in soybean fields was captured using an unmanned aerial vehicle (UAV) and the performance of two different automated weed detection approaches – patch-based classification and object detection was studied for site-specific weed management. For the patch-based classification approach, several conventional machine learning models on Haralick texture features were compared ...


Minimodal: Dimensional Domain Of Miniature Shipping Containers For Intermodal Freight Transportation, Lee Stapley Jul 2019

Minimodal: Dimensional Domain Of Miniature Shipping Containers For Intermodal Freight Transportation, Lee Stapley

Ursidae: The Undergraduate Research Journal at the University of Northern Colorado

This study explores the feasibility of miniature shipping container usage within existing intermodal transportation (IT) supply chains. Smaller intermodal container shipments may help realign freight shipments with the most efficient transportation mode, rail. These containers embolden the dimensional domain (DD) of shipping. The shipping container dimensional domain (container size variation and modal fluidity) is widespread and results in shipments that are often larger or more infrequent than needed. The DD impacts transport mode, shipping frequency, shipment velocity, intermodal supply chain accessibility, and regional shipping networks. This study suggests that container size impacts the DD and, therefore, mode choice. As miniature ...


Transportation Safety Performance Of Us Bus Transit Agencies And Population Density: A Cross-Sectional Analysis (2008-2014), Ilker Karaca, Peter T. Savolainen Jul 2019

Transportation Safety Performance Of Us Bus Transit Agencies And Population Density: A Cross-Sectional Analysis (2008-2014), Ilker Karaca, Peter T. Savolainen

Ilker Karaca

The paper examines the transportation safety performance of transit agencies providing public bus service in the US by using data from the National Transit Database (NTD)

Uses NTD data for a seven-year period from 2008 to 2014 • 3,853 observations for 651 public transportation agencies in 50 states

Seven types of bus transit fatalities and injuries (including passengers, operators, pedestrians, bicyclists)

Main explanatory variable: urban density obtained from the US Census figures

Other explanatory variables: total agency revenue miles, unlinked passenger trips, agency fleet size, and urban population


Seven Hci Grand Challenges, Constantine Stephanidis, Gavriel Salvendy, Margherita Antona, Jessie Chen, Jianming Dong, Vincent Duffy, Xiaowen Fang, Cali Fidopiastis, Gino Fragomeni, Limin Fu, Yinni Guo, Don Harris, Andri Ioannou, Kyeong-Ah (Kate) Jeong, Shin'ichi Konomi, Heidi Kromker, Masaaki Kurosu, James Lewis, Aaron Marcus, Gabriele Meiselwitz, Abbas Moallem, Hirohiko Mori, Fiona Fui-Hoon Nah, Stavroula Ntoa, Pei-Luen Rau, Dylan Schmorrow, Keng Siau, Norbert Streitz, Wentao Wang, Sakae Yamamoto, Panayiotis Zaphiris, Jia Zhou Jul 2019

Seven Hci Grand Challenges, Constantine Stephanidis, Gavriel Salvendy, Margherita Antona, Jessie Chen, Jianming Dong, Vincent Duffy, Xiaowen Fang, Cali Fidopiastis, Gino Fragomeni, Limin Fu, Yinni Guo, Don Harris, Andri Ioannou, Kyeong-Ah (Kate) Jeong, Shin'ichi Konomi, Heidi Kromker, Masaaki Kurosu, James Lewis, Aaron Marcus, Gabriele Meiselwitz, Abbas Moallem, Hirohiko Mori, Fiona Fui-Hoon Nah, Stavroula Ntoa, Pei-Luen Rau, Dylan Schmorrow, Keng Siau, Norbert Streitz, Wentao Wang, Sakae Yamamoto, Panayiotis Zaphiris, Jia Zhou

Faculty Publications

This article aims to investigate the Grand Challenges which arise in the current and emerging landscape of rapid technological evolution towards more intelligent interactive technologies, coupled with increased and widened societal needs, as well as individual and collective expectations that HCI, as a discipline, is called upon to address. A perspective oriented to humane and social values is adopted, formulating the challenges in terms of the impact of emerging intelligent interactive technologies on human life both at the individual and societal levels. Seven Grand Challenges are identified and presented in this article: Human-Technology Symbiosis; Human-Environment Interactions; Ethics, Privacy and Security ...


Maquiladoras In Central America: An Analysis Of Workforce Schedule, Productivity And Fatigue., Jose L. Barahona Jul 2019

Maquiladoras In Central America: An Analysis Of Workforce Schedule, Productivity And Fatigue., Jose L. Barahona

Masters Theses & Specialist Projects

Textile factories or Maquiladoras are very abundant and predominant in Central American economies. However, they all do not have the same standardized work schedule or routines. Most of the Maquiladoras only follow schedules and regulations established by the current labor laws without taking into consideration many variables within their organization that could affect their overall performance. As a result, the purpose of the study is to analyze the current working structure of a textile Maquiladora and determine the most suitable schedule that will abide with the current working structure but also increase production levels, employee morale and decrease employee fatigue ...


Seven Hci Grand Challenges, Constantine Stephanidis, Gavriel Salvendy, Margherita Antona, Jessie Y. C. Chen, Jianming Dong, Vincent G. Duffy, Xiaowen Fang, Cali Fidopiastis, Gino Fragomeni, Limin Paul Fu, Yinni Guo, Don Harris, Andri Ioannou, Kyeong-Ah (Kate) Jeong, Shin'ichi Konomi, Heidi Kromker, Masaaki Kurosu, James R. Lewis, Aaron Marcus, Gabriele Meiselwitz, Abbas Moallem, Hirohiko Mori, Fiona Fui-Hoon Nah, Stavroula Ntoa, Pei-Luen Patrick Rau, Dylan Schmorrow, Keng Siau, Norbert Streitz, Wentao Wang, Sakae Yamamoto, Panayiotis Zaphiris, Jia Zhou Jun 2019

Seven Hci Grand Challenges, Constantine Stephanidis, Gavriel Salvendy, Margherita Antona, Jessie Y. C. Chen, Jianming Dong, Vincent G. Duffy, Xiaowen Fang, Cali Fidopiastis, Gino Fragomeni, Limin Paul Fu, Yinni Guo, Don Harris, Andri Ioannou, Kyeong-Ah (Kate) Jeong, Shin'ichi Konomi, Heidi Kromker, Masaaki Kurosu, James R. Lewis, Aaron Marcus, Gabriele Meiselwitz, Abbas Moallem, Hirohiko Mori, Fiona Fui-Hoon Nah, Stavroula Ntoa, Pei-Luen Patrick Rau, Dylan Schmorrow, Keng Siau, Norbert Streitz, Wentao Wang, Sakae Yamamoto, Panayiotis Zaphiris, Jia Zhou

Abbas Moallem

This article aims to investigate the Grand Challenges which arise in the current and emerging landscape of rapid technological evolution towards more intelligent interactive technologies, coupled with increased and widened societal needs, as well as individual and collective expectations that HCI, as a discipline, is called upon to address. A perspective oriented to humane and social values is adopted, formulating the challenges in terms of the impact of emerging intelligent interactive technologies on human life both at the individual and societal levels. Seven Grand Challenges are identified and presented in this article: Human-Technology Symbiosis; Human-Environment Interactions; Ethics, Privacy and Security ...