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Relationship Between Strategic Dexterity, Absorptive Capacity, And Competitive Advantage, Ifechide Monyei Jan 2023

Relationship Between Strategic Dexterity, Absorptive Capacity, And Competitive Advantage, Ifechide Monyei

Walden Dissertations and Doctoral Studies

Small- and medium-sized enterprise (SME) manufacturing executives and managers are concerned with the rapid technological changes involving artificial intelligence (AI), machine learning, and big data. To compete in the global landscape, effectively managing digital and artificial intelligence changes among SME manufacturing executives and managers is critical for leaders to compete in 2023 and beyond. Grounded in the dynamic capabilities view theory, the purpose of this quantitative correlation study was to examine the relationship between strategic dexterity, absorptive capacity, and competitive advantage. The participants were 66 executives and managers of SME manufacturing organizations who use big data and analytics daily and …

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Perceptions And Knowledge Of Information Security Policy Compliance In Organizational Personnel, Jesus M. Mosqueda Jan 2023

Perceptions And Knowledge Of Information Security Policy Compliance In Organizational Personnel, Jesus M. Mosqueda

Walden Dissertations and Doctoral Studies

All internet connected organizations are becoming increasingly vulnerable to cyberattacks due to information security policy noncompliance of personnel. The problem is important to information technology (IT) firms, organizations with IT integration, and any consumer who has shared personal information online, because noncompliance is the single greatest threat to cybersecurity, which leads to expensive breaches that put private information in danger. Grounded in the protection motivation theory, the purpose of this quantitative study was to use multiple regression analysis to examine the relationship between perceived importance, organizational compliance, management involvement, seeking guidance, and rate of cybersecurity attack. The research question for …

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Knowledge-Sharing Practice As A Tool In Organizational Development In Nigerian Higher Educational Institutions, Emmanuel Ajiri Ojo Jan 2023

Knowledge-Sharing Practice As A Tool In Organizational Development In Nigerian Higher Educational Institutions, Emmanuel Ajiri Ojo

Walden Dissertations and Doctoral Studies

AbstractUnderstanding knowledge management is key to understanding organizational development and innovations. Inadequate knowledge-sharing practices in Nigerian educational institutions has impeded innovation and management development. The purpose of this qualitative modified Delphi study was to seek consensus among administrators from Nigerian educational institutions and scholars from Nigerian universities regarding knowledge-sharing practices that nourish innovation in Nigerian higher educational institutions. The organizational development framework was used to guide the study. Data collection included a nonprobability purposive sampling of 25 participants and three rounds of surveys administered online. A consensus was reached on eight factors after coding and thematic analysis: setting knowledge-sharing expectations, …

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Effective Strategies For Using Telecommuting By Owners Of Small Businesses, Thomas Law Jan 2023

Effective Strategies For Using Telecommuting By Owners Of Small Businesses, Thomas Law

Walden Dissertations and Doctoral Studies

Small business owners who lack effective strategies to incorporate telecommuting may be unable to retain teleworking employees, create a flexible working environment, or improve workforce morale, negatively impacting company productivity and profitability. Grounded in transformational leadership theory and sociotechnical systems theory, the purpose of this qualitative multiple case study was to explore strategies small business owners use to incorporate telecommuting to retain teleworking employees. Data were collected from five small business owners in Texas with at least 1 year of management experience and created and maintained remote working strategies. Data collection included semistructured interviews and company documents. Three themes emerged …

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Perceptions And Knowledge Of Information Security Policy Compliance In Organizational Personnel, Jesus M. Mosqueda Jan 2023

Perceptions And Knowledge Of Information Security Policy Compliance In Organizational Personnel, Jesus M. Mosqueda

Walden Dissertations and Doctoral Studies

All internet connected organizations are becoming increasingly vulnerable to cyberattacks due to information security policy noncompliance of personnel. The problem is important to information technology (IT) firms, organizations with IT integration, and any consumer who has shared personal information online, because noncompliance is the single greatest threat to cybersecurity, which leads to expensive breaches that put private information in danger. Grounded in the protection motivation theory, the purpose of this quantitative study was to use multiple regression analysis to examine the relationship between perceived importance, organizational compliance, management involvement, seeking guidance, and rate of cybersecurity attack. The research question for …

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Strategies Information Technology Managers Use To Retain Qualified Information Technology Employees, Wayne Arnold Reu Jan 2023

Strategies Information Technology Managers Use To Retain Qualified Information Technology Employees, Wayne Arnold Reu

Walden Dissertations and Doctoral Studies

Retaining qualified information technology (IT) personnel can take time and effort, given the high demand for skilled positions. Business leaders are concerned with the high turnover of IT employees because of the cost of recruiting and training personnel and the disruption to organizational processes and performance. Grounded in job characteristics theory, the purpose of this qualitative pragmatic inquiry was to explore IT managers' strategies to retain qualified IT employees in organizations across the southwestern United States. Eight IT leaders participated because of their years of experience implementing strategies to retain qualified IT professionals. Data were collected using semistructured interviews and …

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Understanding U.S. Customers' Intention To Adopt Robo-Advisor Technology, Deborah Wall Jan 2023

Understanding U.S. Customers' Intention To Adopt Robo-Advisor Technology, Deborah Wall

Walden Dissertations and Doctoral Studies

Finance and information technology scholars wrote that there is a literature gap on what factors drive investors in Western financial markets to use a Robo-advisor to manage their investments. The purpose of this qualitative, single case study with embedded units is to understand the adoption intentions of retail investors in U.S. markets to use a Robo-advisor instead of a human advisor. A single case study design addressed the literature gap, and qualitative data from seven semi=structured interviews, reflective field notes, and archival data were triangulated to answer the research question. This study was grounded in a theoretical framework that includes …

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Cooperative Deep Q -Learning Framework For Environments Providing Image Feedback, Krishnan Raghavan, Vignesh Narayanan, Sarangapani Jagannathan Jan 2023

Cooperative Deep Q -Learning Framework For Environments Providing Image Feedback, Krishnan Raghavan, Vignesh Narayanan, Sarangapani Jagannathan

Publications

In this article, we address two key challenges in deep reinforcement learning (DRL) setting, sample inefficiency, and slow learning, with a dual-neural network (NN)-driven learning approach. In the proposed approach, we use two deep NNs with independent initialization to robustly approximate the action-value function in the presence of image inputs. In particular, we develop a temporal difference (TD) error-driven learning (EDL) approach, where we introduce a set of linear transformations of the TD error to directly update the parameters of each layer in the deep NN. We demonstrate theoretically that the cost minimized by the EDL regime is an approximation …

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Moment-Based Reinforcement Learning For Ensemble Control, Yao-Chi Yu, Vignesh Narayanan, Jr-Shin Li Jan 2023

Moment-Based Reinforcement Learning For Ensemble Control, Yao-Chi Yu, Vignesh Narayanan, Jr-Shin Li

Publications

Problems involving controlling the collective behavior of a population of structurally similar dynamical systems, the so-called ensemble control, arise in diverse emerging applications and pose a grand challenge in systems science and control engineering. Owing to the severely under-actuated nature and the difficulty of placing large-scale sensor networks, ensemble systems are limited to being actuated and monitored at the population level. Moreover, mathematical models describing the dynamics of ensemble systems are often elusive. Therefore, it is essential to design broadcast controls that excite the entire population in such a way that the heterogeneity in system dynamics are robustly compensated. In …

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Twitter Bot Detection Using Nlp And Graph Classification, Warada Jayant Kulkarni Jan 2023

Twitter Bot Detection Using Nlp And Graph Classification, Warada Jayant Kulkarni

Master's Projects

Social media platforms are one of the primary resources for information as it is easily accessible, low in cost, and provides a high rate of information spread. Online social media (OSM) have become the main source of news information around the world, but because of the distributed nature of the web, it has increased the risk of fake news spread. Fake news is misleading information that is published as real news. Therefore, identifying fake news and flagging them as such, as well as detecting sources that generate them is an ongoing task for researchers and OSM companies. Bots are artificial …

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An Interactive System For Generating Music From Moving Images, Hanlin Wang Jan 2023

An Interactive System For Generating Music From Moving Images, Hanlin Wang

Dartmouth College Master’s Theses

Moving images contain a wealth of information pertaining to motion. Motivated by the interconnectedness of music and movement, we present a framework for transforming the kinetic qualities of moving images into music. We developed an interactive software system that takes video as input and maps its motion attributes into the musical dimension based on perceptually grounded principles. The system combines existing sonification frameworks with theories and techniques of generative music. To evaluate the system, we conducted a two-part experiment. First, we asked participants to make judgements on video-audio correspondence from clips generated by the system. Second, we asked participants to …

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Murder On The Vr Express: Studying The Impact Of Thought Experiments At A Distance In Virtual Reality, Andrew Kissel, Krzysztof J. Rechowicz, John B. Shull Jan 2023

Murder On The Vr Express: Studying The Impact Of Thought Experiments At A Distance In Virtual Reality, Andrew Kissel, Krzysztof J. Rechowicz, John B. Shull

Philosophy Faculty Publications

Hypothetical thought experiments allow researchers to gain insights into widespread moral intuitions and provide opportunities for individuals to explore their moral commitments. Previous thought experiment studies in virtual reality (VR) required participants to come to an on-site laboratory, which possibly restricted the study population, introduced an observer effect, and made internal reflection on the participants’ part more difficult. These shortcomings are particularly crucial today, as results from such studies are increasingly impacting the development of artificial intelligence systems, self-driving cars, and other technologies. This paper explores the viability of deploying thought experiments in commercially available in-home VR headsets. We conducted …

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Energy-Efficient Multi-Rate Opportunistic Routing In Wireless Mesh Networks, Mohammad Ali Mansouri Khah, Neda Moghim, Nasrin Gholami, Sachin Shetty Jan 2023

Energy-Efficient Multi-Rate Opportunistic Routing In Wireless Mesh Networks, Mohammad Ali Mansouri Khah, Neda Moghim, Nasrin Gholami, Sachin Shetty

VMASC Publications

Opportunistic or anypath routing protocols are focused on improving the performance of traditional routing in wireless mesh networks. They do so by leveraging the broadcast nature of the wireless medium and the spatial diversity of the network. Using a set of neighboring nodes, instead of a single specific node, as the next hop forwarder is a crucial aspect of opportunistic routing protocols, and the selection of the forwarder set plays a vital role in their performance. However, most opportunistic routing protocols consider a single transmission rate and power for the nodes, which limits their potential. To address this limitation, this …

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Optimizing Stochastic Task Migration In Vehicular Edge Computing, Ankur Nahar, Debasis Das, Sajal K. Das Jan 2023

Optimizing Stochastic Task Migration In Vehicular Edge Computing, Ankur Nahar, Debasis Das, Sajal K. Das

Computer Science Faculty Research & Creative Works

The performance of vehicular edge computing (VEC) depends on the effective optimization of task offloading. However, uneven distribution of vehicular traffic, rapidly changing network conditions, and stochastic nature of vehicular networks motivate us to innovate approaches to efficient resource management while maintaining system's stability. to address these challenges, we propose a novel queue length-Based stochastic task migration strategy that leverages model predictive control (MPC) and Lyapunov optimization techniques. Our approach employs the queue length at the edge node as the criterion for offloading decisions. the MPC controller dynamically allocates the processing power and bandwidth resources to vehicles based on their …

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Lasa: Location-Aware Scheduling Algorithm In Industrial Iot Networks With Mobile Nodes, Marco Pettorali, Francesca Righetti, Carlo Vallati, Sajal K. Das, Giuseppe Anastasi Jan 2023

Lasa: Location-Aware Scheduling Algorithm In Industrial Iot Networks With Mobile Nodes, Marco Pettorali, Francesca Righetti, Carlo Vallati, Sajal K. Das, Giuseppe Anastasi

Computer Science Faculty Research & Creative Works

The Synchronized Single-hop Multiple Gateway (SHMG) is a framework recently proposed to support mobility into 6TiSCH, the standard network architecture defined for Industrial Internet of Things (IIoT) deployments. SHMG supports industrial applications with stringent requirements by adopting the Shared-Downstream Dedicated-Upstream (SD-DU) scheduling policy, which allocates to Mobile Nodes (MNs) a set of dedicated transmission opportunities for uplink data. Such allocation is performed on all the Border Routers (BRs) of the network without considering the location of MNs. Transmission opportunities are reserved also in BRs far from the current location of the MN, resulting in a waste of resources that limits …

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Combating Fake News: A Gravity Well Simulation To Model Echo Chamber Formation In Social Media, Jeremy E. Thompson Jan 2023

Combating Fake News: A Gravity Well Simulation To Model Echo Chamber Formation In Social Media, Jeremy E. Thompson

Dartmouth College Ph.D Dissertations

Fake news has become a serious concern as distributing misinformation has become easier and more impactful. A solution is critically required. One solution is to ban fake news, but that approach could create more problems than it solves, and would also be problematic from the beginning, as it must first be identified to be banned. We initially propose a method to automatically recognize suspected fake news, and to provide news consumers with more information as to its veracity. We suggest that fake news is comprised of two components: premises and misleading content. Fake news can be condensed down to a …

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Claimdistiller: Scientific Claim Extraction With Supervised Contrastive Learning, Xin Wei, Md Reshad Ul Hoque, Jian Wu, Jiang Li Jan 2023

Claimdistiller: Scientific Claim Extraction With Supervised Contrastive Learning, Xin Wei, Md Reshad Ul Hoque, Jian Wu, Jiang Li

Computer Science Faculty Publications

The growth of scientific papers in the past decades calls for effective claim extraction tools to automatically and accurately locate key claims from unstructured text. Such claims will benefit content-wise aggregated exploration of scientific knowledge beyond the metadata level. One challenge of building such a model is how to effectively use limited labeled training data. In this paper, we compared transfer learning and contrastive learning frameworks in terms of performance, time and training data size. We found contrastive learning has better performance at a lower cost of data across all models. Our contrastive-learning-based model ClaimDistiller has the highest performance, boosting …

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Identifying The Serious Clinical Outcomes Of Adverse Reactions To Drugs By A Multi-Task Deep Learning Framework, Haochen Zhao, Peng Ni, Qichang Zhao, Xiao Liang, Di Ai, Shannon Erhardt, Jun Wang, Yaohang Li, Jiianxin Wang Jan 2023

Identifying The Serious Clinical Outcomes Of Adverse Reactions To Drugs By A Multi-Task Deep Learning Framework, Haochen Zhao, Peng Ni, Qichang Zhao, Xiao Liang, Di Ai, Shannon Erhardt, Jun Wang, Yaohang Li, Jiianxin Wang

Computer Science Faculty Publications

Adverse Drug Reactions (ADRs) have a direct impact on human health. As continuous pharmacovigilance and drug monitoring prove to be costly and time-consuming, computational methods have emerged as promising alternatives. However, most existing computational methods primarily focus on predicting whether or not the drug is associated with an adverse reaction and do not consider the core issue of drug benefit-risk assessment-whether the treatment outcome is serious when adverse drug reactions occur. To this end, we categorize serious clinical outcomes caused by adverse reactions to drugs into seven distinct classes and present a deep learning framework, so-called GCAP, for predicting the …

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Power, Stress, And Uncertainty: Experiences With And Attitudes Toward Workplace Surveillance During A Pandemic, Jessica Vitak, Michael Zimmer Jan 2023

Power, Stress, And Uncertainty: Experiences With And Attitudes Toward Workplace Surveillance During A Pandemic, Jessica Vitak, Michael Zimmer

Computer Science Faculty Research and Publications

There is a rich literature on technology’s role in facilitating employee monitoring in the workplace. The COVID-19 pandemic created many challenges for employers, and many companies turned to new forms of monitoring to ensure remote workers remained productive; however, these technologies raise important privacy concerns as the boundaries between work and home are further blurred. In this paper, we present findings from a study of 645 US workers who spent at least part of 2020 working remotely due to the pandemic. We explore how their work experiences (job satisfaction, stress, and security) changed between January and November 2020, as well …

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Comparing Phishing Training And Campaign Methods For Mitigating Malicious Emails In Organizations, Jackie Christopher Scott Jan 2023

Comparing Phishing Training And Campaign Methods For Mitigating Malicious Emails In Organizations, Jackie Christopher Scott

CCAC Theses and Dissertations

Although there have been numerous technological advancements in the last several years, there continues to be a real threat as it pertains to social engineering, especially phishing, spear-phishing, and Business Email Compromise (BEC). While the technologies to protect corporate employees and network borders have gotten better, there are still human elements to consider. No technology can protect an organization completely, so it is imperative that end users are provided with the most up-to-date and relevant Security Education, Training, and Awareness (SETA). Phishing, spear-phishing, and BEC are three primary vehicles used by attackers to infiltrate corporate networks and manipulate end users …

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Commentary On Healthcare And Disruptive Innovation, Hilary Finch, Affia Abasi-Amefon, Woosub Jung, Lucas Potter, Xavier-Lewis Palmer Jan 2023

Commentary On Healthcare And Disruptive Innovation, Hilary Finch, Affia Abasi-Amefon, Woosub Jung, Lucas Potter, Xavier-Lewis Palmer

Electrical & Computer Engineering Faculty Publications

Exploits of technology have been an issue in healthcare for many years. Many hospital systems have a problem with “disruptive innovation” when introducing new technology. Disruptive innovation is “an innovation that creates a new market by applying a different set of values, which ultimately overtakes an existing market” (Sensmeier, 2012). Modern healthcare systems are historically slow to accept new technological advancements. This may be because patient-based, provider-based, or industry-wide decisions are tough to implement, giving way to dire consequences. One potential consequence is that healthcare providers may not be able to provide the best possible care to patients. For example, …

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Natural Language Processing In The Legal Domain, Daniel Martin Katz, Dirk Hartung, Lauritz Gerlach, Abhik Jana, Michael J. Ii Bommarito Jan 2023

Natural Language Processing In The Legal Domain, Daniel Martin Katz, Dirk Hartung, Lauritz Gerlach, Abhik Jana, Michael J. Ii Bommarito

Research Collection Yong Pung How School Of Law

In this paper, we summarize the current state of the field of NLP and Law with a specific focus on recent technical and substantive developments. To support our analysis, we construct and analyze a corpus of more than six hundred NLP and Law related papers published over the past decade. Our analysis highlights several major trends. Namely, we document an increasing number of papers written, tasks undertaken, and languages covered over the course of the past decade. We observe an increase in the sophistication of the methods which researchers deployed in this applied context. Slowly but surely, Legal NLP is …

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Adaptive Resolution Loss: An Efficient And Effective Loss For Time Series Self-Supervised Learning Framework, Kevin Garcia, Juan Manuel Perez, Yifeng Gao Jan 2023

Adaptive Resolution Loss: An Efficient And Effective Loss For Time Series Self-Supervised Learning Framework, Kevin Garcia, Juan Manuel Perez, Yifeng Gao

Computer Science Faculty Publications

Time series data is a crucial form of information that has vast opportunities. With the widespread use of sensor networks, largescale time series data has become ubiquitous. One of the most prominent problems in time series data mining is representation learning. Recently, with the introduction of self-supervised learning frameworks (SSL), numerous amounts of research have focused on designing an effective SSL for time series data. One of the current state-of-the-art SSL frameworks in time series is called TS2Vec. TS2Vec specially designs a hierarchical contrastive learning framework that uses loss-based training, which performs outstandingly against benchmark testing. However, the computational cost …

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A Cross-Domain Trust Model Of Smart City Iot Based On Self-Certification, Yao Wang, Yubo Wang, Zhenhu Ning, Sadaqat Ur Rehman, Muhammad Waqas Jan 2023

A Cross-Domain Trust Model Of Smart City Iot Based On Self-Certification, Yao Wang, Yubo Wang, Zhenhu Ning, Sadaqat Ur Rehman, Muhammad Waqas

Research outputs 2022 to 2026

Smart city refers to the information system with Internet of things and cloud computing as the core technology and government management and industrial development as the core content, forming a large-scale, heterogeneous and dynamic distributed Internet of things environment between different Internet of things. There is a wide demand for cooperation between equipment and management institutions in the smart city. Therefore, it is necessary to establish a trust mechanism to promote cooperation, and based on this, prevent data disorder caused by the interaction between honest terminals and malicious terminals. However, most of the existing research on trust mechanism is divorced …

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Leveraging Machine Learning To Analyze Sentiment From Covid-19 Tweets: A Global Perspective, Md Mahbubar Rahman, Nafiz Imtiaz Khan, Iqbal H. Sarker, Mohiuddin Ahmed, Muhammad Nazrul Islam Jan 2023

Leveraging Machine Learning To Analyze Sentiment From Covid-19 Tweets: A Global Perspective, Md Mahbubar Rahman, Nafiz Imtiaz Khan, Iqbal H. Sarker, Mohiuddin Ahmed, Muhammad Nazrul Islam

Research outputs 2022 to 2026

Since the advent of the worldwide COVID-19 pandemic, analyzing public sentiment has become one of the major concerns for policy and decision-makers. While the priority is to curb the spread of the virus, mass population (user) sentiment analysis is equally important. Though sentiment analysis using different state-of-the-art technologies has been focused on during the COVID-19 pandemic, the reasons behind the variations in public sentiment are yet to be explored. Moreover, how user sentiment varies due to the COVID-19 pandemic from a cross-country perspective has been less focused on. Therefore, the objectives of this study are: to identify the most effective …

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Machine Learning Application On Employee ‎Promotion, Muhannad Ilwani, Ghalia Nassreddine, Joumana Younis Jan 2023

Machine Learning Application On Employee ‎Promotion, Muhannad Ilwani, Ghalia Nassreddine, Joumana Younis

Mesopotamian Journal of Computer Science

Any company's most valuable asset, its workforce, is its employees. As a result, the company's primary goal should be to develop an excellent strategy for supporting and investing in its employees and staff by providing the best training and development. Employee Promotion denotes the advancement of an employee's rank. It raises the salary, position, duties, and benefits. It is a part of the job that propels employees to the highest commitment and loyalty to their organizations. Employee morale and loyalty are two critical components of any successful business. Employee promotion is the key to improving employee performance and engagement. It …

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Using Machine Learning For Web Accessibility, Tlamelo Makati Jan 2023

Using Machine Learning For Web Accessibility, Tlamelo Makati

Academic Posters Collection

This research will explore the potential of machine learning to enhance web accessibility. Web accessibility is typically defined in terms of Web Accessibility Guidelines (WCAG), which states that everyone should be able to perceive, operate, understand and interpret the web regardless of disability or use of assistive technology. We would like to consult digital accessibility experts through interviews and focus groups to understand the web accessibility auditing and remediation processes in detail, with a focus on web navigation. An important goal of this work is to establish development processes where all stakeholders can leverage machine-learning tools to produce more accessible …

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From Overlay To Interplay - Subverting The Message And Creating The Surreal With Augmented Reality, Nina Lyons Jan 2023

From Overlay To Interplay - Subverting The Message And Creating The Surreal With Augmented Reality, Nina Lyons

Academic Posters Collection

This proof of concept utilises content creation tools that create diegetic presentation which is not commonly utilised in AR systems. In this study, the virtual overlay that becomes visible through AR displays diegetic content that disrupts the meaning of the poster highlighting the potential for AR as a visual communication medium and the opportunities that AR has, as a medium, for creating narrative.

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Determining Child Sexual Abuse Posts Based On Artificial Intelligence, Susan Mckeever, Christina Thorpe, Vuong Ngo Jan 2023

Determining Child Sexual Abuse Posts Based On Artificial Intelligence, Susan Mckeever, Christina Thorpe, Vuong Ngo

Conference papers

The volume of child sexual abuse materials (CSAM) created and shared daily both surface web platforms such as Twitter and dark web forums is very high. Based on volume, it is not viable for human experts to intercept or identify CSAM manually. However, automatically detecting and analysing child sexual abusive language in online text is challenging and time-intensive, mostly due to the variety of data formats and privacy constraints of hosting platforms. We propose a CSAM detection intelligence algorithm based on natural language processing and machine learning techniques. Our CSAM detection model is not only used to remove CSAM on …

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A Lak Of Direction Misalignment Between The Goals Of Learning Analytics And Its Research Scholarship, Benjamin A. Motz, Yoav Bergner, Christopher A. Brooks, Anna Gladden, Geraldine Gray, Charles Lang, Warren Li, Fernando Marmolejo-Ramos, Joshua D. Quick Jan 2023

A Lak Of Direction Misalignment Between The Goals Of Learning Analytics And Its Research Scholarship, Benjamin A. Motz, Yoav Bergner, Christopher A. Brooks, Anna Gladden, Geraldine Gray, Charles Lang, Warren Li, Fernando Marmolejo-Ramos, Joshua D. Quick

Articles

Learning analytics defines itself with a focus on data from learners and learning environments, with corresponding goals of understanding and optimizing student learning. In this regard, learning analytics research, ideally, should be characterized by studies that make use of data from learners engaged in education systems, should measure student learning, and should make efforts to intervene and improve these learning environments.

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