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Articles 781 - 810 of 27405
Full-Text Articles in Entire DC Network
Traffic Forecasting With Vset-Nets: A Vgae Spatial Embedding For Temporal Networks Approach, Mrunmayee Dhapre
Traffic Forecasting With Vset-Nets: A Vgae Spatial Embedding For Temporal Networks Approach, Mrunmayee Dhapre
Master's Projects
Traffic forecasting is important for improving transportation systems by enabling better traffic management, congestion reduction, and urban planning. However, predicting traffic accurately is challenging due to the strong spatial dependencies between different road segments and the temporal changes in traffic patterns over time. Traditional time-series and graph models often struggle to capture both of these aspects effectively. In response, recent research has focused on temporal graph representation learning methods that jointly consider spatial relationships and temporal features in networks. This project proposes a hybrid model called VSET-Nets (VGAE Spatial Embedding for Temporal Networks) that employs Variational Graph Autoencoders (VGAEs) for …
Link Failure Localization In Hierarchical, Multidomain Optical Networks, Martin Mihailov Bojinov
Link Failure Localization In Hierarchical, Multidomain Optical Networks, Martin Mihailov Bojinov
Master's Projects
Optical networks transport data encoded on light signals over optical fiber cables. Individual optical networks are managed by domain administrators, such as service providers, vendors, or regional bodies. Multidomain optical networking explores the possibility of enabling seamless data transmission across domain boundaries. In this project, we explore how we can perform network monitoring in a hierarchical multidomain optical network while preserving domain security and autonomy. This is done by allocating dedicated monitoring trails across various broker abstractions of our network topologies. We propose three heuristic functions that expedite trail selection. Of the three heuristics, our least monitored algorithm performs the …
Optimizing Analytics Storage Strategies For Search Engines And Wiki Platforms, Sujith Kakarlapudi
Optimizing Analytics Storage Strategies For Search Engines And Wiki Platforms, Sujith Kakarlapudi
Master's Projects
Modern search engines and wiki platforms generate vast quantities of user inter-
/="/">action data such as page views, edits, clicks, and session events. This data must be stored and aggregated efficiently to enable scalable analytics and responsive querying. Yioop, an open-source search engine framework, serves as our primary case study, processing millions of such events to power its indexing and recommendation features. This report explores a shift from the conventional database storage based model to a log based model, in order to improve scalability and write efficiency. A size-limited, append-only logging facility was provided to log analytics events: the …
Optimizing Integrated Access And Backhaul Topology Using Monte Carlo Tree Search, Srimanth Reddy Ummenthala
Optimizing Integrated Access And Backhaul Topology Using Monte Carlo Tree Search, Srimanth Reddy Ummenthala
Master's Projects
Integrated Access and Backhaul (IAB) plays a central role in enabling scalability and high-throughput in wireless networks, especially in areas where wired backhaul is not feasible. Optimizing the topology of IAB networks is a complex task, involving trade-offs between path loss, node capacity, and link quality. To address this, this project investigates a Monte Carlo Tree Search (MCTS)-based approach to improve overall network performance by maximizing the capacity of network, considering Signal-to-Noise Ratio (SNR) on wireless links. MCTS provides a guided search mechanism to construct topologies efficiently under the constraints and is evaluated against baseline topologies. Experimental results show that …
Comparative Analysis Of Embedding Techniques With Clustering Algorithms For Malware Opcodes, Ayush Koul
Comparative Analysis Of Embedding Techniques With Clustering Algorithms For Malware Opcodes, Ayush Koul
Master's Projects
Malware detection and classification remain critical challenges in cybersecurity, especially as malicious software becomes increasingly sophisticated and prevalent. While much of the work involving embeddings has traditionally relied on supervised learning approaches, there is significant potential in leveraging unsupervised learning techniques to discern hidden structures in malware data. By employing embedding techniques to convert malware samples into high-dimensional vector representations, we can capture the subtle and complex patterns inherent in malicious code without relying on pre-labeled data. This unsupervised approach helps categorize malware into predefined malware families, greatly aiding in developing cybersecurity solutions. In contrast to traditional supervised models that …
Application Of Advanced Convolutional Neural Network With Robust Hashing On Obfuscated Image Based Malware Dataset, Sanket Shekhar Kulkarni
Application Of Advanced Convolutional Neural Network With Robust Hashing On Obfuscated Image Based Malware Dataset, Sanket Shekhar Kulkarni
Master's Projects
This project report provides in-depth details on the creation of a malware classification system that makes use of Convolutional Neural Networks (CNNs) that have been strengthened by data set obfuscation and strong hashing. We test many CNN architectures, including MobileNet, ResNet, and DenseNet, using rigorous hashing and obfuscation techniques on datasets. The entire pipeline is described in this research, which ranges from the gathering and preprocessing of data sets to the application of novel hashing techniques that boost overall accuracy in classification and increase resilience against malicious attacks. Parallel to this, we show that dataset obfuscation adds an additional level …
Malware Generation And Classification Using Pixelcnn, Mounika Krishna Teja Karumudi
Malware Generation And Classification Using Pixelcnn, Mounika Krishna Teja Karumudi
Master's Projects
Malware poses a serious threat to both data privacy and system security. With the wide variety of malware families and the surge in cyber-attacks, the accurate classification of malware is crucial for building effective detection and prevention systems. In recent years, deep learning (DL) methods in computer vision have shown promise in classifying malware by converting malware files into visual representations and applying DL algorithms to classify the resulting images. Among the different approaches to malware family classification, image-based methods have gained significant interest. This research focuses on leveraging DL techniques for image-based classification of malware. The success of identifying …
Reinforcement Learning-Based End-To-End Monitoring Path Selection In Multi-Domain Optical Networks, Soham Choudhury
Reinforcement Learning-Based End-To-End Monitoring Path Selection In Multi-Domain Optical Networks, Soham Choudhury
Master's Projects
This paper presents a novel approach for optimizing network monitoring in optical communication systems using Reinforcement Learning (RL). Assuming a multi-domain architecture with limited domain visibility, we simulate multiple optical connections using an optical communications simulation software, GNPy, obtaining key network metrics to model the system. We developed two RL agents: the first agent selects near-optimal monitoring paths based on network states, and the second agent dynamically adapts its selected paths in response to state changes, such as fiber failures or issues with ROADMs. This adaptive approach allows for continuous improvement of network monitoring, ensuring resilience and efficient fault detection. …
Transforming Chatbot Text: A Sequence-To-Sequence Approach To Human-Like Text Generation, Natesh Reddy
Transforming Chatbot Text: A Sequence-To-Sequence Approach To Human-Like Text Generation, Natesh Reddy
Master's Projects
With the advancements in Large Language Models (LLMs) such as ChatGPT, the boundary between text written by a human and that created by AI has become blurred. This poses a threat to systems designed to detect AI-generated content. In this work, we adopt a novel strategy to adversarially transform GPT-generated text using sequence-to-sequence (Seq2Seq) models, to make the text more human-like. We concentrate on improving GPT-generated sentences by including significant linguistic, structural, and semantic components that are typical of human-authored text. The goal is to then use this transformed data to train a more robust detection model. Our experiments show …
Enhancing Robustness Of Cnn Model For Malware Detection Using Gan-Based Data Augmentation And Transfer Learning, Milind Anand Pathak
Enhancing Robustness Of Cnn Model For Malware Detection Using Gan-Based Data Augmentation And Transfer Learning, Milind Anand Pathak
Master's Projects
Malware classification is a critical component in the field of cybersecurity. Accurate identification of a malware family can enable timely threat detection and response. In this thesis, we propose a robust image-based malware classification pipeline using Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs), with a focus on improving performance for underrepresented malware families. We train a baseline CNN model on the Malimg dataset across 25 malware families, but observe misclassifications in classes with limited data and overlapping visual features. To address this, we apply targeted augmentations and generate class-specific synthetic data using StyleGAN2-ADA. A CNN trained on the …
Comparative Analysis Of Adversarial Permeability In Cpu-Native, Qat And Onnx-Based Quantized Transformer Models, Fahad Siddiqui
Comparative Analysis Of Adversarial Permeability In Cpu-Native, Qat And Onnx-Based Quantized Transformer Models, Fahad Siddiqui
Master's Projects
This work explores securing and optimization of Transformer-based time series forecasting models. We employ several quantization techniques, including quantization-aware training (QAT), and tested the robustness of quantized models by adversarially attacking them. The preliminary results of this work, in our controlled setup, indicate that quantized models outperform the full precision model in terms of robustness against adversarial attacks. They achieved this robustness while showing a very minimal decrease in their forecasting performance.
Multilingual Sentiment Analysis Using Ensemble Learning, Farhan Ansari
Multilingual Sentiment Analysis Using Ensemble Learning, Farhan Ansari
Master's Projects
The widespread use of multiple social media platforms has amplified the expression of public opinions over the Internet in languages such as English, Hindi and Spanish. With the aid of technological advancements in machine learning, we can analyze opinions posted on the Internet and gauge public sentiments. There are organizations and businesses that are interested in the evaluation of these sentiments as these type of data can generally be used to obtain the opinion of a product, restaurant, a candidate, etc. In this study, we perform a comparative analysis of three popular ensemble learning methodologies (Boosting, Bagging and Stacking) based …
Large Language Models For Bacterial Genomic Analysis, Manvendra Chavan
Large Language Models For Bacterial Genomic Analysis, Manvendra Chavan
Master's Projects
Identification of bacterial gene sequences with agricultural applications has the potential to transform agricultural biotechnology. These genes can be used in environmentally friendly pest control strategies. One such use case is identifying genes with potential insecticidal properties. With an increasing number of genomic information and decreasing numbers of available annotated sequences, finding new insecticidal genes has become more challenging.The traditional methods relying on sequence alignment and annotated databases are not effective in detecting functionally relevant genes lacking close homology to known cases. This project investigates the data-driven classification of genes by sequence modeling. This research is focused on learning DNA …
Genegate: Genetic Gating In A Mixture-Of-Experts For Real-Time Multi-Objective Traffic Signal Control, Rashmi Vishwanath Bhat
Genegate: Genetic Gating In A Mixture-Of-Experts For Real-Time Multi-Objective Traffic Signal Control, Rashmi Vishwanath Bhat
Master's Projects
Urban traffic signal control often needs to juggle between competing goals. It needs to minimize delays, reduce emissions, prevent crashes, and prioritize emergency vehicles all while the demand is constantly fluctuating. Traditional fixed-time or statically blended policies cannot reallocate priorities quickly when conditions change. We introduce GeneGate, a mixture-of-experts framework that uses a lightweight genetic gate to fuse four specialist controllers (throughput, emissions, safety, emergency) and adjusts their weights in real time. A short offline genetic search produces a robust initial blend, and an online micro-evolution step refines it every few cycles based on live traffic feedback. GeneGate’s adaptive gating …
Retrieval-Augmented Generation (Rag) Chatbots: A Comparative Study Of Claude, Gpt-4o, Deepseek, And Llama, Kalindi Vijesh Parekh
Retrieval-Augmented Generation (Rag) Chatbots: A Comparative Study Of Claude, Gpt-4o, Deepseek, And Llama, Kalindi Vijesh Parekh
Master's Projects
The use of Retrieval-augmented generation (RAG) in chatbot platforms has transformed academic spaces by significantly improving information accessibility. RAG has become a viable approach to upgrading Large Language Models (LLMs) with external knowledge access in real time. With the growing availability of advanced LLMs such as GPT, DeepSeek, Claude, Gemini, and Llama, there is a growing need to compare RAG systems based on different LLMs. This study compares the responses of four different RAG chatbots using popular LLMs against a uniquely designed evaluation dataset. Specifically, the study compares the responses and performance of closed-source (GPT-4o and Claude) and open-source models …
Ai-Enabled Anticipatory Handover Predictions In 5g Networks, Ojas Ankush Naik
Ai-Enabled Anticipatory Handover Predictions In 5g Networks, Ojas Ankush Naik
Master's Projects
As users move across network cells in 5G, it is critical to maintain seamless connectivity through efficient and fast handovers. However, as 5G networks have a very dense deployment of cells and higher carrier frequencies, handovers are often more frequent and challenging, leading to failures or the ping-pong effect. In this research, we are going to use Artificial Intelligence (AI) techniques to enable predicting handover(HO) events proactively as opposed to reactively, aiming to reduce HO failures and unnecessary handovers. We develop Long-Short-Term Memory (LSTM) and Bidirectional LSTM (Bi-LSTM) models to forecast future signal measurements, predict handover trigger points, and compare …
Smithian Merchant Towns And Good Government In The Wealth Of Nations, John B. Estill
Smithian Merchant Towns And Good Government In The Wealth Of Nations, John B. Estill
Faculty Research, Scholarly, and Creative Activity
Commentators on Adam Smith’s Inquiry into the Nature and Causes of the Wealth of Nations often characterize Smith as a proponent of government in multiple areas, including security in person, property, contract, and some public goods. However, Smith understood government more expansively than people today. In volume I, book III, chapters I–IV, Smith describes the evolution of merchant towns in England that led to “good governance” that dismantled the feudal system. This limited government not only included the security provided by the formal laws and institutions. It also aligned the informal elements of individual civic ethics and self-reliance with the …
Physiotrack: A Gamified Physiotherapy System, Pranavi Chaturvedula
Physiotrack: A Gamified Physiotherapy System, Pranavi Chaturvedula
Master's Projects
Traditional physiotherapy methods tend to be non-interactive and provide little to no personalized instruction, even though physiotherapy is critical to stroke recovery. This thesis explores a fully adaptive, sensor-based, feedback architecture intended for stroke patients which remotely supervises movement and personalizes exercises enabled by multimodal sensors. The system uses filtering and windowed segmentation of accelerometer and skeletal data to compute features like jerk, speed, and joint movement angular range. A game engine applies accelerometer and skeletal features together with optimized, lightweight ML models to drive adaptive feedback, scoring, and difficulty adjustment. The architecture supports responsive continuous sensor streaming within the …
Edurag: Improving Ai Teaching Assistants With Retrieval-Augmented Generation, Geethika Vadlamudi
Edurag: Improving Ai Teaching Assistants With Retrieval-Augmented Generation, Geethika Vadlamudi
Master's Projects
This paper is based on the emerging need for AI-driven teaching assistants to deliver personalized, effective, and responsive educational assistance. Earlier proposals for educational technology such as rule-based systems and adaptive learning platforms have been limited by the lack of flexibility, domain accuracy, and slow, non-interactive support. With the launch of large language models (LLMs) like GPT-3, GPT-4, they have demonstrated that they can generate the kind of human-responsive text. When it comes to more substantive education matters, though, such models suffer from domain accuracy, in addition to whether accurate information can be imparted. And that is where the aspect …
Human Vs. Llm: Interpreting Emotion From Neutral Faces In Contextually Charged Backgrounds, Sai Mounika Peteti
Human Vs. Llm: Interpreting Emotion From Neutral Faces In Contextually Charged Backgrounds, Sai Mounika Peteti
Master's Projects
In this study, we investigate the intersection of cultural context, visual perception, and implicit bias in interpreting neutral human expressions. Specifically, we explore how emotionally charged backgrounds can shape viewers interpretations of neutral facial expressions. Using an experimental setup, participants are shown neutral human portraits paired with varying background types ranging from emotionally neutral or pleasant to evocative scenes. Participants are tasked with selecting which background best matches the emotional state of the person depicted in the portraits. Eye-tracking data is collected to analyze visual attention patterns and cognitive processing. In parallel, we also evaluate how large language models (LLMs) …
Why We Need To Fill In Our Mixed-Use Communities’ Business Vacancies And How We Can Do That, Victor Huynh
Why We Need To Fill In Our Mixed-Use Communities’ Business Vacancies And How We Can Do That, Victor Huynh
Master's Projects
This planning report provides an in-depth exploration as to why so many mixed use communities, including transit-oriented developments, in metropolitan regions across the country are facing challenges with filling in their retail business vacancies at the ground floor. It also provides a current snapshot of best practices to remedy this widespread barrier to cities functioning more effectively and sustainably. This report provides a combination of findings from a literature review which identifies the most common reasons and remedies for business vacancies, and a research design which identifies which urban planning or urban design characteristics correlate most frequently with case sites …
Leveraging Diverse Knowledge To Support Collection Development: Maximizing Student Engagement And Community Resource Value In Varied Community College Settings, Jose Aguiñaga, Edward Mckennon
Leveraging Diverse Knowledge To Support Collection Development: Maximizing Student Engagement And Community Resource Value In Varied Community College Settings, Jose Aguiñaga, Edward Mckennon
Faculty Research, Scholarly, and Creative Activity
No abstract provided.
Assessing Total Mortality Following Seabird Wrecks Given Variable Data Quantity And Quality: The Cassin’S Auklet Die-Off, Timothy Jones, Julia K. Parrish, Parker Maccready, Lisa T. Ballance, David W. Bradley, Hillary K. Burgess, Jane E. Dolliver, James T. Harvey, Trevor Joyce, Kirsten Lindquist, Jacqueline Lindsey, Hannahrose M. Nevins, Jan Roletto, Laurie Wilson, Charlie Wright
Assessing Total Mortality Following Seabird Wrecks Given Variable Data Quantity And Quality: The Cassin’S Auklet Die-Off, Timothy Jones, Julia K. Parrish, Parker Maccready, Lisa T. Ballance, David W. Bradley, Hillary K. Burgess, Jane E. Dolliver, James T. Harvey, Trevor Joyce, Kirsten Lindquist, Jacqueline Lindsey, Hannahrose M. Nevins, Jan Roletto, Laurie Wilson, Charlie Wright
Faculty Research, Scholarly, and Creative Activity
Mass mortality events (MMEs) of seabirds are becoming more frequent as the global climate warms. Often documented via beached bird surveys, methods for estimating event-wide mortality are needed that can accommodate regional differences in carcass deposition and data quality/quantity. We develop a framework for estimating mortality from beached bird counts, extending existing approaches through the novel application of ocean circulation modeling to assess beaching likelihood. We applied our framework to the 2014/15 Cassin’s auklet (Ptychoramphus aleuticus) MME, which spread across three regions (central California, northern California-through-Washington, British Columbia) with varying data quality/quantity. Our best mortality estimate of ∼400 000 (estimates …
Conservation Benefits Of A Large Marine Protected Area Network That Spans Multiple Ecosystems, Joshua G. Smith, Cori Lopazanski, Christopher M. Free, Julien Brun, Clarissa Anderson, Mark H. Carr, Joachim Claudet, Jenifer E. Dugan, Jacob G. Eurich, Tessa B. Francis, David A. Gill, Scott L. Hamilton, Kristin Kaschner, David Mouillot, Peter T. Raimondi, Richard M. Starr, Shelby L. Ziegler, Daniel Malone, For Full Author List, See Comments Below
Conservation Benefits Of A Large Marine Protected Area Network That Spans Multiple Ecosystems, Joshua G. Smith, Cori Lopazanski, Christopher M. Free, Julien Brun, Clarissa Anderson, Mark H. Carr, Joachim Claudet, Jenifer E. Dugan, Jacob G. Eurich, Tessa B. Francis, David A. Gill, Scott L. Hamilton, Kristin Kaschner, David Mouillot, Peter T. Raimondi, Richard M. Starr, Shelby L. Ziegler, Daniel Malone, For Full Author List, See Comments Below
Faculty Research, Scholarly, and Creative Activity
Marine protected areas (MPAs) are widely implemented tools for long-term ocean conservation and resource management. Assessments of MPA performance have largely focused on specific ecosystems individually and have rarely evaluated performance across multiple ecosystems either in an individual MPA or across an MPA network. We evaluated the conservation performance of 59 MPAs in California's large MPA network, which encompasses 4 primary ecosystems (surf zone, kelp forest, shallow reef, deep reef) and 4 bioregions, and identified MPA attributes that best explain performance. Using a meta-analytic framework, we evaluated the ability of MPAs to conserve fish biomass, richness, and diversity. At the …
Contraceptive Use Among Transgender Men And Gender Diverse Individuals In The United States: Reasons For Use, Non-Use, And Methods Used For Pregnancy Prevention, Juno Obedin-Maliver, Ava Snow, Sachiko Ragosta, Laura Fix, Jen Hastings, Micah E. Lubensky, Matthew R. Capriotti, Zubin Dastur, Annesa Flentje, Mitchell R. Lunn, Heidi Moseson
Contraceptive Use Among Transgender Men And Gender Diverse Individuals In The United States: Reasons For Use, Non-Use, And Methods Used For Pregnancy Prevention, Juno Obedin-Maliver, Ava Snow, Sachiko Ragosta, Laura Fix, Jen Hastings, Micah E. Lubensky, Matthew R. Capriotti, Zubin Dastur, Annesa Flentje, Mitchell R. Lunn, Heidi Moseson
Faculty Research, Scholarly, and Creative Activity
Objectives: To describe contraceptive use for pregnancy prevention among transgender men and gender diverse (TGD) individuals assigned female or intersex at birth (AFIAB) and explore whether contraceptive use differs by testosterone use. Study design: We analyzed data from a cross-sectional, online survey of N = 1694 TGD individuals AFIAB recruited in 2019 through a community-facing website and a national community-engaged cohort study of sexual and/or gender minority (SGM) adults in the US. Descriptive and regression analyses characterized the current and ever use of contraceptive methods for pregnancy prevention, stratified by testosterone use, and described reasons for contraceptive use/non-use. Results: Most …
Examining Equity And Graduation Rates At Two Institutions Using A Course Deficit Model And The Collaborative Learning Through Active Sense-Making In Physics Curriculum, Cassandra A. Paul, David J. Webb
Examining Equity And Graduation Rates At Two Institutions Using A Course Deficit Model And The Collaborative Learning Through Active Sense-Making In Physics Curriculum, Cassandra A. Paul, David J. Webb
Faculty Research, Scholarly, and Creative Activity
We have previously described the reformed introductory physics course, collaborative learning through active sense-making in physics (CLASP), for bioscience students at a large public research one university (Original University) and presented evidence that the course was more successful and more equitable than the course it replaced by several measures. Now we compare the original success of CLASP with an implementation at a second institution. We find that the original results hold at another institution despite some changes to the original curriculum and a somewhat different student population. We find that students who take CLASP are (1) less likely to drop, …
From Shocks To Solidarity And Superstition: Exploring The Foundations Of Faith, Aidin Hajikhameneh, Laurence R. Iannaccone
From Shocks To Solidarity And Superstition: Exploring The Foundations Of Faith, Aidin Hajikhameneh, Laurence R. Iannaccone
Faculty Research, Scholarly, and Creative Activity
Additive shocks can substantially increase cooperation in otherwise standard public goods game experiments. We study shocks that randomly adjust players’ earnings by a fixed positive or negative amount reported at the end of each round. These adjustments change neither the return to players’ contributions nor the information about other group members. We compare results across four treatments that employ the same group-level adjustment algorithm but frame it differently, with pre-play descriptions that range from omitting all useful information to accurately revealing its 50/50 random nature. In each treatment, overall contributions run about 50% higher than those obtained in the standard …
How We Could Have Libertarian Free Will Even If God Were A Total Know-It-All About The Future, Mark Balaguer, Rebecca Chan
How We Could Have Libertarian Free Will Even If God Were A Total Know-It-All About The Future, Mark Balaguer, Rebecca Chan
Faculty Research, Scholarly, and Creative Activity
We argue that libertarianism (roughly, the thesis that we have indeterministic, libertarian free will) is compatible with God's infallible foreknowledge. We use eternalism (roughly, the thesis that reality is a 4-dimensional block and that past, present, and future objects exist) as an explanatory stepping stone between libertarianism and God's foreknowledge: eternalism entails that (and comes close to explaining how) an omniscient God would know what we decide in the future even if we have libertarian free will. This account also explains what is wrong with standard fatalist arguments for the incompatibility of free will and God's foreknowledge.
Working With Similarities And Differences: Relational Processes In Transdisciplinary Qualitative Research With Diverse Teams, Michael S. Dao, Soma De Bourbon, Melissa Mcclure Fuller, Miranda Worthen
Working With Similarities And Differences: Relational Processes In Transdisciplinary Qualitative Research With Diverse Teams, Michael S. Dao, Soma De Bourbon, Melissa Mcclure Fuller, Miranda Worthen
Faculty Research, Scholarly, and Creative Activity
Transdisciplinary research and research teams are becoming increasingly valued in academic spaces. The potential of transdisciplinary research is that the diversity of thought and experience can create robust research processes from project inception, methodological protocol, data collection, data analysis and reporting output. Yet, transdisciplinary research teams can also bring about complications pertaining to conflicting epistemological perspectives, areas of expertise, and objectives for applied impact. In noting the benefits and downsides of transdisciplinary research, this article aims to detail how a transdisciplinary research team navigated a qualitative and participatory longitudinal research project. Drawing from a larger community-based participatory research project, the …
Could Password Sharing Entitle You To Monitor Your Partner's Social Media Accounts?, Irum Saeed Abbasi, David Khabaz, Maryam Abbasi, Greg Feist
Could Password Sharing Entitle You To Monitor Your Partner's Social Media Accounts?, Irum Saeed Abbasi, David Khabaz, Maryam Abbasi, Greg Feist
Faculty Research, Scholarly, and Creative Activity
Social media offers a plethora of strangers cum friends whose photo shopped images seem idealistic and more appealing than one's primary partner. Due to the physical absence and lack of non-verbal cues, online communications can quickly become aggressive and emotionally intimate. Emotional intimacy between online friends is considered a betrayal in a dyadic relationship. To protect mates from external relationship threats, romantic partners may request and/or coerce their significant other for social media account passwords. In a sample of 299 adults (women = 246) between18 and 72 years (M = 21.12, SD = 5.39), we explored the participants’ attitudes toward …