Open Access. Powered by Scholars. Published by Universities.®

Computer Sciences Commons™

Open Access. Powered by Scholars. Published by Universities.®

Discipline
Institution
Keyword
Publication Year
Publication
Publication Type
File Type

Articles 12991 - 13020 of 63010

Full-Text Articles in Computer Sciences

Factored Adaptation For Non-Stationary Reinforcement Learning, Fan Feng, Biwei Huang, Kun Zhang, Sara Magliacane Dec 2022

Factored Adaptation For Non-Stationary Reinforcement Learning, Fan Feng, Biwei Huang, Kun Zhang, Sara Magliacane

Machine Learning Faculty Publications

Dealing with non-stationarity in environments (e.g., in the transition dynamics) and objectives (e.g., in the reward functions) is a challenging problem that is crucial in real-world applications of reinforcement learning (RL). While most current approaches model the changes as a single shared embedding vector, we leverage insights from the recent causality literature to model non-stationarity in terms of individual latent change factors, and causal graphs across different environments. In particular, we propose Factored Adaptation for Non-Stationary RL (FANS-RL), a factored adaption approach that learns jointly both the causal structure in terms of a factored MDP, and a factored representation of …

Go to article

Data Processing Under Fuzzy Uncertainty: Towards More Accurate Algorithms, Marina Tuyako Mizukoshi, Weldon Lodwick, Martine Ceberio, Olga Kosheleva, Vladik Kreinovich Dec 2022

Data Processing Under Fuzzy Uncertainty: Towards More Accurate Algorithms, Marina Tuyako Mizukoshi, Weldon Lodwick, Martine Ceberio, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

Data that we process comes either from measurements or from experts -- or from the results of previous data processing that were also based on measurements and/or expert estimates. In both cases, the data is imprecise. To gauge the accuracy of the results of data processing, we need to take the corresponding data uncertainty into account. In this paper, we describe a new algorithm for taking fuzzy uncertainty into account, an algorithm that, for small number of inputs, leads to the same or even better accuracy than the previously proposed methods.

Go to article

Non-Localized Physical Processes Can Help Speed Up Computations, Be It Hidden Variables In Quantum Physics Or Non-Localized Energy In General Relativity, Michael Zakharevich, Olga Kosheleva, Vladik Kreinovich Dec 2022

Non-Localized Physical Processes Can Help Speed Up Computations, Be It Hidden Variables In Quantum Physics Or Non-Localized Energy In General Relativity, Michael Zakharevich, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

While most physical processes are localized -- in the sense that each event can only affect events in its close vicinity -- many physicists believe that some processes are non-local. These beliefs range from more heretic -- such as hidden variables in quantum physics -- to more widely accepted, such as the non-local character of energy in General Relativity. In this paper, we attract attention to the fact that non-local processes bring in the possibility of drastically speeding up computations.

Go to article

Graph Approach To Uncertainty Quantification, Hector A. Reyes, Cliff Joslyn, Vladik Kreinovich Dec 2022

Graph Approach To Uncertainty Quantification, Hector A. Reyes, Cliff Joslyn, Vladik Kreinovich

Departmental Technical Reports (CS)

Traditional analysis of uncertainty of the result of data processing assumes that all measurement errors are independent. In reality, there may be common factor affecting these errors, so these errors may be dependent. In such cases, the independence assumption may lead to underestimation of uncertainty. In such cases, a guaranteed way to be on the safe side is to make no assumption about independence at all. In practice, however, we may have information that a few pairs of measurement errors are indeed independent -- while we still have no information about all other pairs. Alternatively, we may suspect that for …

Go to article

Systems Approach Explains Why Low Heart Rate Variability Is Correlated With Depression (And Suicidal Thoughts), Francisco Zapata, Eric Smith, Vladik Kreinovich Dec 2022

Systems Approach Explains Why Low Heart Rate Variability Is Correlated With Depression (And Suicidal Thoughts), Francisco Zapata, Eric Smith, Vladik Kreinovich

Departmental Technical Reports (CS)

Depression is a serious medical problem. If diagnosed early, it can usually be cured, but if left undetected, it can lead to suicidal thoughts and behavior. The early stages of depression are difficult to diagnose. Recently, researchers found a promising approach to such diagnosis -- it turns out that depression is correlated with low heart rate variability. In this paper, we show that the general systems approach can explain this empirical relation.

Go to article

An Argument In Favor Of Piecewise-Constant Membership Functions, Marina Tuyako Mizukoshi, Weldon Lodwick, Martine Ceberio, Olga Kosheleva, Vladik Kreinovich Dec 2022

An Argument In Favor Of Piecewise-Constant Membership Functions, Marina Tuyako Mizukoshi, Weldon Lodwick, Martine Ceberio, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

Theoretically, we can have membership functions of arbitrary shape. However, in practice, at any given moment of time, we can only represent finitely many parameters in a computer. As a result, we usually restrict ourselves to finite-parametric families of membership functions. The most widely used families are piecewise linear ones, e.g., triangular and trapezoid membership functions. The problem with these families is that if we know a nonlinear relation y = f(x) between quantities, the corresponding relation between membership functions is only approximate -- since for piecewise linear membership functions for x, the resulting membership function for y is not …

Go to article

Will Nanotechnology Bring In The Judgement Day?, Olga Kosheleva, Vladik Kreinovich Dec 2022

Will Nanotechnology Bring In The Judgement Day?, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

There are many current and prospective positive aspects of nanotechnology. However, while we look forward to its future successes, we need to keep our eyes open and be prepared for what will really be a future shock: that quantum computing – an inevitable part of nanotechnology – will enable the future folks to read all our encrypted messages and thus, learn everything that we wanted to keep secret. This will be really the Judgement Day, when all our sins will be open to everyone. How we will react to it? Will this destroy our civilization? Let us hope that the …

Go to article

Eureka: Euphemism Recognition Enhanced Through Knn-Based Methods And Augmentation, Sedrick Scott Keh, Rohit Bharadwaj, Emmy Liu, Simone Tedeschi, Varun Gangal, Roberto Navigli Dec 2022

Eureka: Euphemism Recognition Enhanced Through Knn-Based Methods And Augmentation, Sedrick Scott Keh, Rohit Bharadwaj, Emmy Liu, Simone Tedeschi, Varun Gangal, Roberto Navigli

Computer Vision Faculty Publications

We introduce EUREKA, an ensemble-based approach for performing automatic euphemism detection. We (1) identify and correct potentially mislabelled rows in the dataset, (2) curate an expanded corpus called EuphAug, (3) leverage model representations of Potentially Euphemistic Terms (PETs), and (4) explore using representations of semantically close sentences to aid in classification. Using our augmented dataset and kNN-based methods, EUREKA was able to achieve state-of-the-art results on the public leaderboard of the Euphemism Detection Shared Task, ranking first with a macro F1 score of 0.881.

Go to article

Rare Gems: Finding Lottery Tickets At Initialization, Kartik Sreenivasan, Jy Yong Sohn, Liu Yang, Matthew Grinde, Alliot Nagle, Hongyi Wang, Eric Xing, Kangwook Lee, Dimitris Papailiopoulos Dec 2022

Rare Gems: Finding Lottery Tickets At Initialization, Kartik Sreenivasan, Jy Yong Sohn, Liu Yang, Matthew Grinde, Alliot Nagle, Hongyi Wang, Eric Xing, Kangwook Lee, Dimitris Papailiopoulos

Machine Learning Faculty Publications

Large neural networks can be pruned to a small fraction of their original size, with little loss in accuracy, by following a time-consuming “train, prune, re-train” approach. Frankle & Carbin [9] conjecture that we can avoid this by training lottery tickets, i.e., special sparse subnetworks found at initialization, that can be trained to high accuracy. However, a subsequent line of work [11, 41] presents concrete evidence that current algorithms for finding trainable networks at initialization, fail simple baseline comparisons, e.g., against training random sparse subnetworks. Finding lottery tickets that train to better accuracy compared to simple baselines remains an open …

Go to article

Standard Interval Computation Algorithm Is Not Inclusion-Monotonic: Examples, Marina Tuyako Mizukoshi, Weldon Lodwick, Martine Ceberio, Olga Kosheleva, Vladik Kreinovich Dec 2022

Standard Interval Computation Algorithm Is Not Inclusion-Monotonic: Examples, Marina Tuyako Mizukoshi, Weldon Lodwick, Martine Ceberio, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

When we usually process data, we, in effect, implicitly assume that we know the exact values of all the inputs. In practice, these values comes from measurements, and measurements are never absolutely accurate. In many cases, the only information about the actual (unknown) values of each input is that this value belongs to an appropriate interval. Under this interval uncertainty, we need to compute the range of all possible results of applying the data processing algorithm when the inputs are in these intervals. In general, the problem of exactly computing this range is NP-hard, which means that in feasible time, …

Go to article

Independence Testing-Based Approach To Causal Discovery Under Measurement Error And Linear Non-Gaussian Models, Haoyue Dai, Peter Spirtes, Kun Zhang Dec 2022

Independence Testing-Based Approach To Causal Discovery Under Measurement Error And Linear Non-Gaussian Models, Haoyue Dai, Peter Spirtes, Kun Zhang

Machine Learning Faculty Publications

Causal discovery aims to recover causal structures generating the observational data. Despite its success in certain problems, in many real-world scenarios the observed variables are not the target variables of interest, but the imperfect measures of the target variables. Causal discovery under measurement error aims to recover the causal graph among unobserved target variables from observations made with measurement error. We consider a specific formulation of the problem, where the unobserved target variables follow a linear non-Gaussian acyclic model, and the measurement process follows the random measurement error model. Existing methods on this formulation rely on non-scalable over-complete independent component …

Go to article

Post Pandemic Cyberbiosecurity Threats From Terrorist Groups, Haley D. Dodge Dec 2022

Post Pandemic Cyberbiosecurity Threats From Terrorist Groups, Haley D. Dodge

Master's Theses

The research in this thesis explored the research question: Are United States (US) health systems accessible to cyber-bio terrorist attacks post-pandemic, within the context of the emerging discipline of cyberbiosecurity? Key findings of the analysis demonstrated how US health systems are more accessible to cyber-bio terrorist attacks specifically from cyber hacking groups based on the increasing sophistication of their cyber capabilities and the lack of cyber protection for biological systems. The concept of cyberbiosecurity was first introduced in 2018 by researchers exploring the converging threat landscape of the cyber and biology domains. As biology is growing more dependent upon vulnerable …

Go to article

An Ultrasensitive Bacterial Detection Platform For Culture-Free Diagnosis Of Infections, Xuyang Shi Dec 2022

An Ultrasensitive Bacterial Detection Platform For Culture-Free Diagnosis Of Infections, Xuyang Shi

ETD Archive

The current methods of the diagnosis of bloodstream infections are based on bacterial culture growth, a process that requires considerable time, e.g., 12-16 hours, to obtain a result. This long wait time for the result creates many problems, including the generation of multi-drug resistant organisms (MDROs). At the same time, infected bloodstream usually contains a very low concentration of bacteria, i.e., lower than 5 CFU/mL. The long diagnosis time and the extremely low concentration of bacteria in the infected bloodstream make such infections difficult to diagnose. Here, we demonstrate a culture-free approach for the diagnosis of bloodstream infections using a …

Go to article

Text And Image Frames Affect Message's Sharing And Acceptance Of Social Media Users, Xin Sa Dec 2022

Text And Image Frames Affect Message's Sharing And Acceptance Of Social Media Users, Xin Sa

ETD Archive

No abstract provided.

Go to article

System And Methods For Wireless Power Transfer Scheduling In A Wireless Power Transfer Network, Muhammad Omer Farooq Dec 2022

System And Methods For Wireless Power Transfer Scheduling In A Wireless Power Transfer Network, Muhammad Omer Farooq

Department of Computer Science Publications

A transmitter in a wireless power transfer network (WPTN) needs to deliver wireless power to a number of wireless power receiver. There can be diverse set of wireless power receivers, for example, Internet of Things devices, smart phones, laptops, etc. The wireless power requirements for receivers vary depending upon their type and state. Commercially deployed WPTNs may also have different subscription plans for users, hence each user’s device should receive wireless power transfer (WPT) service based on the subscribed plan. Usually, a transmitter has to provide service to a number of receivers. Hence, the transmitter needs to have a WPT …

Go to article

How Viscosity Of An Asphalt Binder Depends On Temperature: Theoretical Explanation Of An Empirical Dependence, Edgar Daniel Rodriguez Velasquez, Vladik Kreinovich Dec 2022

How Viscosity Of An Asphalt Binder Depends On Temperature: Theoretical Explanation Of An Empirical Dependence, Edgar Daniel Rodriguez Velasquez, Vladik Kreinovich

Departmental Technical Reports (CS)

Pavement must be adequate for all the temperatures, ranging from the winter cold to the summer heat. In particular, this means that for all possible temperatures, the viscosity of the asphalt binder must stay within the desired bounds. To predict how the designed pavement will behave under different temperatures, it is desirable to have a general idea of how viscosity changes with temperature. Pavement engineers have come up with an empirical approximate formula describing this change. However, since this formula is purely empirical, with no theoretical justification, practitioners are often somewhat reluctant to depend on this formula. In this paper, …

Go to article

Why In Mond -- Alternative Gravitation Theory -- A Specific Formula Works The Best: Complexity-Based Explanation, Olga Kosheleva, Vladik Kreinovich Dec 2022

Why In Mond -- Alternative Gravitation Theory -- A Specific Formula Works The Best: Complexity-Based Explanation, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

Based on the rotation of the stars around a galaxy center, one can estimate the corresponding gravitational acceleration -- which turns out to be much larger than what Newton's theory predicts based on the masses of all visible objects. The majority of physicists believe that this discrepancy indicates the presence of "dark" matter, but this idea has some unsolved problems. An alternative idea -- known as Modified Newtonian Dynamics (MOND, for short) is that for galaxy-size distances, Newton's gravitation theory needs to be modified. One of the most effective versions of this idea uses so-called simple interpolating function. In this …

Go to article

Epistemic Vs. Aleatory: Case Of Interval Uncertainty, Marina Tuyako Mizukoshi, Weldon Lodwick, Martine Ceberio, Vladik Kreinovich Dec 2022

Epistemic Vs. Aleatory: Case Of Interval Uncertainty, Marina Tuyako Mizukoshi, Weldon Lodwick, Martine Ceberio, Vladik Kreinovich

Departmental Technical Reports (CS)

Interval computations usually deal with the case of epistemic uncertainty, when the only information that we have about a value of a quantity is that this value is contained in a given interval. However, intervals can also represent aleatory uncertainty -- when we know that each value from this interval is actually attained for some object at some moment of time. In this paper, we analyze how to take such aleatory uncertainty into account when processing data. We show that in case when different quantities are independent, we can use the same formulas for dealing with aleatory uncertainty as we …

Go to article

Intuitive Robot Integration Via Virtual Reality Workspaces, Minh Tram Dec 2022

Intuitive Robot Integration Via Virtual Reality Workspaces, Minh Tram

Computer Science and Engineering Theses - Archive

As robots become increasingly prominent in diverse industrial settings, the desire for an accessible and reliable system has correspondingly increased. Yet, the task of meaningfully assessing the feasibility of introducing a new robotic component, or adding more robots into an existing infrastructure, remains a challenge. This is due to both the logistics of acquiring a robot and the need for expert knowledge in setting it up. In this paper, we address these concerns by developing a purely virtual simulation of a robotic system. Our proposed framework enables natural human-robot interaction through a visually immersive representation of the workspace. The main …

Go to article

Data Discovery Analysis On Complex Time Series Data, Peter Lawrence Severynen Dec 2022

Data Discovery Analysis On Complex Time Series Data, Peter Lawrence Severynen

Computer Science and Engineering Theses - Archive

Complex time series are a ubiquitous form of data in the modern world. They have wide application across many different fields of scientific inquiry and business endeavor. Time series are used to understand and forecast weather patterns, voting patterns, computer network traffic, population health outcomes, demographic changes, the results of scientific experiments, and the performance of stocks and mutual funds. But time series can be difficult to analyze by conventional methods when the data is multivariate, incomplete, or in different formats. To address these issues, an investigation of several multivariate time series datasets was performed using the methods of automatic …

Go to article

An Ai-Based Framework For Studying Visual Diversity Of Urban Neighborhoods And Its Relationship With Socio-Demographic Variables, Md Amiruzzaman, Ye Zhao, Stefanie Amiruzzaman, Aryn C. Karpinski, Tsung Heng Wu Dec 2022

An Ai-Based Framework For Studying Visual Diversity Of Urban Neighborhoods And Its Relationship With Socio-Demographic Variables, Md Amiruzzaman, Ye Zhao, Stefanie Amiruzzaman, Aryn C. Karpinski, Tsung Heng Wu

Computer Science Faculty Publications

This study presents a framework to study quantitatively geographical visual diversities of urban neighborhood from a large collection of street-view images using an Artificial Intelligence (AI)-based image segmentation technique. A variety of diversity indices are computed from the extracted visual semantics. They are utilized to discover the relationships between urban visual appearance and socio-demographic variables. This study also validates the reliability of the method with human evaluators. The methodology and results obtained from this study can potentially be used to study urban features, locate houses, establish services, and better operate municipalities.

Go to article

Approximate Query Processing Using Deep Learning And Database Techniques, Shohedul Hasan Dec 2022

Approximate Query Processing Using Deep Learning And Database Techniques, Shohedul Hasan

Computer Science and Engineering Dissertations - Archive

Data is generated at an unprecedented rate surpassing our ability to analyze them. In real applications, it is often impractical to find an exact answer by traversing the entire data. As a result, Approximate Query Processing (AQP) is getting extremely popular, which finds an approximate answer in a quick time by sacrificing a fraction of accuracy. This dissertation focuses on developing different AQP techniques to solve fundamental database problems using deep learning. Moreover, we build a fast and scalable algorithm for Quantile Regression, a well-known regression technique that can help minimize the uncertainty in the recent deep learning-based AQP solutions, …

Go to article

Learning To Reason About Code With Assertions: An Exploration With Two Student Populations, Sarah Blankenship Dec 2022

Learning To Reason About Code With Assertions: An Exploration With Two Student Populations, Sarah Blankenship

All Theses

Code tracing is fundamental to students’ understanding of a program, and symbolic reasoning that entails learning to use assertions with abstract input and output values, as opposed to concrete values, enhances that understanding. Symbolic reasoning teaches students valuable abstraction and logic skills that will serve them well in all aspects of programming and their software
development careers.
We use lessons integrated into an online educational tool to supplement classroom instruction to help students learn symbolic reasoning. We explore two ways for students to learn about assertions: Writing assertions to capture the behavior of given code and solving Parsons-style problems in …

Go to article

Obstacles In Learning Algorithm Run-Time Complexity Analysis, Bailey Licht Dec 2022

Obstacles In Learning Algorithm Run-Time Complexity Analysis, Bailey Licht

Theses/Capstones/Creative Projects

Algorithm run-time complexity analysis is an important topic in data structures and algorithms courses, but it is also a topic that many students struggle with. Commonly cited difficulties include the necessary mathematical background knowledge, the abstract nature of the topic, and the presentation style of the material. Analyzing the subject of algorithm analysis using multiple learning theories shows that course materials often leave out key steps in the learning process and neglect certain learning styles. Students can be more successful at learning algorithm run-time complexity analysis if these missing stages and learning styles are addressed.

Go to article

Graph Representation Learning For Heterogeneous Multimodal Biomedical Data, Nhat Chau Tran Dec 2022

Graph Representation Learning For Heterogeneous Multimodal Biomedical Data, Nhat Chau Tran

Computer Science and Engineering Dissertations - Archive

The emergence of high-throughput sequencing technology has generated a wealth of “multi-omics” data, capturing information about different types of biomolecules at multiple levels. Since large-scale genomics, transcriptomics, and proteomics data are becoming publicly available, integrated systems analysis utilizing these data sources has taken the front seat in deriving valuable insights for identifying cancer biomarkers or predicting interactions and functions for novel molecules such as LncRNAs. The graph representation learning paradigm can address these challenging tasks as among the most promising approaches to improve predictions over sparsely annotated molecular entities and to provide representation capacity and interpretability over heterogeneous and hierarchically …

Go to article

Understanding Human Actions: Cognitive Assessment And Action Segmentation Using Human Object Interaction, Saif Sayed Dec 2022

Understanding Human Actions: Cognitive Assessment And Action Segmentation Using Human Object Interaction, Saif Sayed

Computer Science and Engineering Dissertations - Archive

Automatic understanding of human behavior has several applications in medicine and surveillance. Analysing human actions can enable cognitive assessment of children by measuring their hyperactivity and response inhibition which can give physicians better understanding of their cognitive state. Automatic and non-invasive assessment for cognitive disorders will increase the affordability and reach for these detection methods and can prove life-changing in child’s development. Human activity can also be analysed in common settings such as cooking in kitchen and understanding the information of human object interaction can give priors on the underlying activity they are performing. In the first section, we focus …

Go to article

Expressiveness Of Real-Time Motion Captured Avatars Influences Perceived Animation Realism And Perceived Quality Of Social Interaction In Virtual Reality, Alan D. Fraser, Isabella Branson, Ross C. Hollett, Craig P. Speelman, Shane L. Rogers Dec 2022

Expressiveness Of Real-Time Motion Captured Avatars Influences Perceived Animation Realism And Perceived Quality Of Social Interaction In Virtual Reality, Alan D. Fraser, Isabella Branson, Ross C. Hollett, Craig P. Speelman, Shane L. Rogers

Research outputs 2022 to 2026

Using motion capture to enhance the realism of social interaction in virtual reality (VR) is growing in popularity. However, the impact of different levels of avatar expressiveness on the user experience is not well understood. In the present study we manipulated levels of face and body expressiveness of avatars while investigating participant perceptions of animation realism and interaction quality when disclosing positive and negative experiences in VR. Moderate positive associations were observed between perceptions of animation realism and interaction quality. Post-experiment questions revealed that many of our participants (approximately 40 %) indicated the avatar with the highest face and body …

Go to article

Scalable Distributional Robustness In A Class Of Non Convex Optimization With Guarantees, Avinandan Bose, Arunesh Sinha, Tien Mai Dec 2022

Scalable Distributional Robustness In A Class Of Non Convex Optimization With Guarantees, Avinandan Bose, Arunesh Sinha, Tien Mai

Research Collection School Of Computing and Information Systems

Distributionally robust optimization (DRO) has shown lot of promise in providing robustness in learning as well as sample based optimization problems. We endeavor to provide DRO solutions for a class of sum of fractionals, non-convex optimization which is used for decision making in prominent areas such as facility location and security games. In contrast to previous work, we find it more tractable to optimize the equivalent variance regularized form of DRO rather than the minimax form. We transform the variance regularized form to a mixed-integer second order cone program (MISOCP), which, while guaranteeing near global optimality, does not scale enough …

Go to article

S-Prompts Learning With Pre-Trained Transformers: An Occam's Razor For Domain Incremental Learning, Yabin Wang, Zhiwu Huang, Xiaopeng. Hong Dec 2022

S-Prompts Learning With Pre-Trained Transformers: An Occam's Razor For Domain Incremental Learning, Yabin Wang, Zhiwu Huang, Xiaopeng. Hong

Research Collection School Of Computing and Information Systems

State-of-the-art deep neural networks are still struggling to address the catastrophic forgetting problem in continual learning. In this paper, we propose one simple paradigm (named as S-Prompting) and two concrete approaches to highly reduce the forgetting degree in one of the most typical continual learning scenarios, i.e., domain increment learning (DIL). The key idea of the paradigm is to learn prompts independently across domains with pre-trained transformers, avoiding the use of exemplars that commonly appear in conventional methods. This results in a win-win game where the prompting can achieve the best for each domain. The independent prompting across domains only …

Go to article

What Should Streamers Communicate In Livestream E-Commerce? The Effects Of Social Interactions On Live Streaming Performance, Danyang Song, Xi Chen, Zhiling Guo, Xiao Liu Liu, Ruijin. Jin Dec 2022

What Should Streamers Communicate In Livestream E-Commerce? The Effects Of Social Interactions On Live Streaming Performance, Danyang Song, Xi Chen, Zhiling Guo, Xiao Liu Liu, Ruijin. Jin

Research Collection School Of Computing and Information Systems

Compared with traditional e-commerce, livestreaming e-commerce is characterized by direct and intimate communication between streamers and consumers that stimulates instant social interactions. This study focuses on streamers’ three types of information exchange (i.e., product information, social conversation, and social solicitation) and examines their roles in driving both short-term and long-term livestreaming performance (i.e., sales and customer base growth). We find that the informational role of product information (nonpromotional and promotional) is beneficial not only to sales performance, but also to the growth of the customer base. We also find that social conversation has a relationship-building effect that positively impacts both …

Go to article

First Prev Next Last