Machine learning detects survey validity from user behavior.
problem Detecting valid responses in web surveys.
method Uses mouse activity and machine learning models (LSTM, HMM).
result Predicts survey validity without analyzing specific answers.
On-device research index
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48 results for “User surveys”
Survey visual analytics methods for detecting anomalous user behaviors.
problem Understanding and detecting anomalous user behaviors in various domains.
method Survey and classification of visual analytics methods in four categories.
result Discussion of findings and potential research directions.
Paper proposes using pairwise feature comparisons to infer modification costs for user recourse.
problem Learning and inferring user preferences for modifying features in black-box models.
method Bradley-Terry model for inferring feature-wise costs from non-exhaustive human comparison surveys.
result Non-exhaustive human surveys can efficiently learn feature costs, enabling recourse finding.
This paper surveys and classifies attribute-aware CF models.
problem Rating prediction with user and item attributes.
method Mathematical classification of attribute-aware CF models into four categories.
result Comprehensive comparison of effectiveness among different categories.
This paper optimizes survey questions to reduce bias and select key tokens for QoE analysis.
problem Reducing bias in user surveys and selecting informative tokens for Quality of Experience analysis.
method Randomized question order and greedy submodular maximization for selecting tokens.
result Randomizing token order can significantly reduce bias, and a subset of 30% tokens captures 94% of the information.
Smartphone data shows promise but accuracy issues remain.
problem Inaccurate travel surveys from smartphone data.
method Training algorithms on data quality and validating labels.
result Machine learning methods are limited by data quality.
Survey shows users value usability over functionality in process discovery tools.
problem Users prioritize usability over functional aspects in process discovery tools.
method A survey was conducted with 66 respondents to gather feedback on process discovery tools.
result Users prefer usability over functionality in process discovery tools.
This survey analyzes knowledge discovery in cryptocurrency transactions.
problem Understanding user behaviors and collective actions in cryptocurrency transactions.
method Data mining techniques and literature review.
result Classified existing research into three aspects and discussed major findings.
New algorithms reduce costly feature collection in bandits.
problem Costly feature collection in contextual bandits.
method Proposes algorithms avoiding unnecessary feature collection.
result Strong regret guarantees maintained with reduced feature collection.
Survey on using knowledge graphs for better recommender systems.
problem Data sparsity and cold start issues in recommender systems.
method Utilizes knowledge graphs to improve recommendation accuracy and provide explanations.
result Advantages of knowledge graph-based recommender systems.
Survey on security and privacy in decentralized federated learning.
problem New threats in decentralized federated learning due to the removal of the server.
method Thorough security analysis and overview of defense mechanisms.
result Challenges and threats in decentralized federated learning.
This article provides the first survey of computational models of emotion in reinforcement learning (RL) agents. The survey focuses on agent/robot emotions, and mostly ignores human user emotions. Emotions are recognized as functional in decision-making by influencing motivation and action selection. Therefore, computa…
This paper surveys differential privacy methods for transportation spatiotemporal data.
problem Protecting user privacy in public release of spatiotemporal data.
method Review of differential privacy mechanisms and their application in transportation.
result Challenges in deploying and adopting differential privacy in transportation.
Survey on fake news identification and mitigation techniques.
problem Proliferation of fake news on social media impacts public opinion.
method Identification and mitigation techniques, focusing on content and user engagement.
result Significant advances in identification and mitigation methods.
Online surveys have the potential to support adaptive questions, where later questions depend on earlier responses. Past work has taken a rule-based approach, uniformly across all respondents. We envision a richer interpretation of adaptive questions, which we call dynamic question ordering (DQO), where question order …
An efficient LDP protocol for QMLE with improved practicality and theoretical guarantees.
problem Difficult implementation of existing LDP QMLE for large-scale surveys.
method Developed an alternative LDP protocol without long waiting time, high communication cost, and derivative boundedness assumptions.
result Sufficient conditions for consistency and asymptotic normality of the protocol.
regvis.net offers a visual survey of regulatory visualization.
problem Lack of a comprehensive resource for regulatory visualization.
method Collection and manual tagging of 80+ publications, creation of a searchable webpage.
result First publication set tailored for regulatory visualization.
Survey reviews code-switched speech and language processing.
problem Processing code-switched text and speech for multilingual communities.
method Reviews computational approaches and lists available resources.
result Essential for building intelligent agents that interact in multilingual settings.
A survey of existing methods for stopping active learning (AL) reveals the needs for methods that are: more widely applicable; more aggressive in saving annotations; and more stable across changing datasets. A new method for stopping AL based on stabilizing predictions is presented that addresses these needs. Furthermo…
As technology become more advanced, those who design, use and are otherwise affected by it want to know that it will perform correctly, and understand why it does what it does, and how to use it appropriately. In essence they want to be able to trust the systems that are being designed. In this survey we present assura…
Content based image retrieval, a technique which uses visual contents of image to search images from large scale image databases according to users' interests. This paper provides a comprehensive survey on recent technology used in the area of content based face image retrieval. Nowadays digital devices and photo shari…
Collaborative filtering (CF) and content-based filtering (CBF) have widely been used in information filtering applications. Both approaches have their strengths and weaknesses which is why researchers have developed hybrid systems. This paper proposes a novel approach to unify CF and CBF in a probabilistic framework, n…
ADGAN improves risk tolerance prediction by aligning cross-domain data.
problem Lack of professional knowledge and domain-specific models in risk tolerance studies.
method Asymmetric cross-Domain Generative Adversarial Network (ADGAN) for domain scale inequality.
result ADGAN better handles class imbalance and unqualified data than state-of-the-art methods.
New model shows most users on Q&A site Stack Overflow ignore badges.
problem Understanding user behavior in Q&A websites with badges.
method Probabilistic model applied to Stack Overflow data.
result Majority of users remain apathetic to badges but still contribute.
This survey reviews portfolio choice in settings where investment opportunities are stochastic due to, e.g., stochastic volatility or return predictability. It is explained how to heuristically compute candidate optimal portfolios using tools from stochastic control, and how to rigorously verify their optimality by mea…
Survey on deep learning for malware classification, including unknown threats.
problem Classifying and recognizing unknown malware variants.
method Review of deep learning techniques and OSR solutions.
result Deep learning can effectively classify known malware and recognize unknown threats.
Develops a measure for subjective explainability of ML predictions.
problem Ensuring transparency and trust in automated decision-making.
method Information-theoretic concepts applied to conditional entropy of predictions given user feedback.
result EERM principle balances subjective explainability and risk.
Most industrial recommender systems rely on the popular collaborative filtering (CF) technique for providing personalized recommendations to its users. However, the very nature of CF is adversarial to the idea of user privacy, because users need to share their preferences with others in order to be grouped with like-mi…
This work surveys attacks and defenses on edge neural networks.
problem Security challenges of edge neural networks due to their compute and memory intensity, data-independence, and privacy risks.
method Taxonomy of attacks and defenses on edge-deployed neural networks.
result New security considerations and approaches are needed for edge DNNs.
Survey of deep learning methods for recommender systems challenges.
problem Cold start and candidate generation challenges in recommender systems.
method Taxonomy of deep learning techniques addressing cold start and candidate generation problems.
result Taxonomy of deep learning techniques to address recommender systems challenges.
Machine Learning models become increasingly proficient in complex tasks. However, even for experts in the field, it can be difficult to understand what the model learned. This hampers trust and acceptance, and it obstructs the possibility to correct the model. There is therefore a need for transparency of machine learn…
Machine learning workflow development is anecdotally regarded to be an iterative process of trial-and-error with humans-in-the-loop. However, we are not aware of quantitative evidence corroborating this popular belief. A quantitative characterization of iteration can serve as a benchmark for machine learning workflow d…
When tracking user-specific online activities, each user's preference is revealed in the form of choices and comparisons. For example, a user's purchase history is a record of her choices, i.e. which item was chosen among a subset of offerings. A user's preferences can be observed either explicitly as in movie ratings …
Client-based machine learning uses mobile devices for computation, improving privacy and reducing data upload.
problem Exploiting mobile devices for machine learning tasks to protect privacy and reduce data upload.
method Leveraging local hardware and data on mobile devices for computation-intensive tasks, only uploading results.
result Client-based machine learning can relieve server burdens and protect user privacy.
Interpretable machine learning tackles the important problem that humans cannot understand the behaviors of complex machine learning models and how these models arrive at a particular decision. Although many approaches have been proposed, a comprehensive understanding of the achievements and challenges is still lacking…
This review classifies electricity price models for risk management.
problem Choosing suitable models for risk management in electricity markets.
method Classification of models based on their ability to represent price behavior.
result Helps users select appropriate models for risk management.
Deriving the Traveler Behavior Information from Social Media: A Case Study in Manhattan with Twittercs.SI
This study uses Twitter to analyze traveler behavior in Manhattan.
problem Analyzing traveler behavior using social media data.
method Systematic method to extract displacement information from geo-tagged tweets.
result Twitter reveals unique demographics and travel behavior patterns.
This paper surveys gradient-based multi-objective deep learning methods.
problem Balancing multiple conflicting objectives in deep learning models.
method Gradient-based techniques adapted from Multi-Objective Optimization.
result Comprehensive survey of gradient-based multi-objective deep learning algorithms.
Deep learning has recently seen rapid development and received significant attention due to its state-of-the-art performance on previously-thought hard problems. However, because of the internal complexity and nonlinear structure of deep neural networks, the underlying decision making processes for why these models are…
Survey on random features for kernel approximation, focusing on algorithms, theory, and practical applications.
problem Efficiently approximating kernel methods for large-scale problems.
method Random features techniques to speed up kernel methods.
result Need for a high number of random features for good approximation quality.
Survey on making reinforcement learning models more understandable.
problem Complex reinforcement learning models are hard to interpret.
method Classification and assessment of current XRL methods.
result Most XRL methods simplify complex models rather than designing simple ones.
Measuring the morphological parameters of galaxies is a key requirement for studying their formation and evolution. Surveys such as the Sloan Digital Sky Survey (SDSS) have resulted in the availability of very large collections of images, which have permitted population-wide analyses of galaxy morphology. Morphological…
Bayesian ATM improves stability and efficiency in mobile health interventions.
problem Balancing intervention efficacy with user burden in mobile health interventions.
method Bayesian extension to ATM using Kalman filter-style updates.
result Bayesian ATM achieves comparable or improved scalarized returns with lower variance and more stable policy behavior.
Foot-mounted inertial positioning (FMIP) can face problems of inertial drifts and unknown initial states in real applications, which renders the estimated trajectories inaccurate and not obtained in a well defined coordinate system for matching trajectories of different users. In this paper, an approach adopting receiv…
A Study of Machine Learning Models in Predicting the Intention of Adolescents to Smoke Cigarettescs.LG
Study predicts adolescents' intention to smoke cigarettes using ML models.
problem Early identification of adolescents' smoking intentions.
method Five machine learning algorithms tested for predicting intention to smoke cigarettes.
result Gradient Boosting Classifier showed highest accuracy.
A method for reasoning on knowledge graphs using debate dynamics.
problem Automatic reasoning on knowledge graphs with interpretability.
method Reinforcement learning agents debate over facts, judge decides truth.
result Method outperforms baselines on triple classification and link prediction tasks.
Survey updates knowledge on homogeneous Einstein spaces.
problem Understanding homogeneous Einstein spaces.
method Building on previous surveys by Wang and Lauret.
result Current state of homogeneous Einstein spaces.
This paper explores neural models to improve modeling of Hawkes process intensity functions.
problem Traditional Hawkes process intensity function's parametrized kernel function biases future event predictions.
method Uses neural models to model the kernel function of Hawkes process intensity function.
result Neural models can better capture future event characteristics using past events data.