Search Results

Medical Visual Question Answering (MedVQA) is crucial for enhancing the efficiency of clinical diagnosis by providing accurate and timely responses to clinicians' inquiries regarding medical images. Existing MedVQA models suffered from modality ...

Cognitive Diagnosis (CD), which leverages students and exercise data to predict students' proficiency levels on different knowledge concepts, is one of fundamental components in Intelligent Education. Due to the scarcity of student-exercise interaction ...

Learning to respond to a computer program that is not working as intended is often characterized as finding a singular bug causing a singular problem. This framing underemphasizes the wide range of ways that students and teachers could notice ...

Evaluating the performance of Microservices Architectures (MSA) is essential to ensure their proper functioning and meet end-user satisfaction. For MSA performance analysts, one of the most challenging tasks is to determine the cause of any deviation of ...

Testing Autonomous Driving Systems (ADS) is essential for safe development of self-driving cars. For thorough and realistic testing, ADS are usually embedded in a simulator and tested in interaction with the simulated environment. However, their high ...

In recent years, the field of recommendation systems has witnessed significant advancements, with explainable recommendation systems gaining prominence as a crucial area of research. These systems aim to enhance user experience by providing transparent ...

Due to increasing cyber crimes, cyber security analyst and problem solvers are in huge demand. These designations entail certain skills which an individual needs to acquire such as analyzing logs, system debugging etc. Through teacher-student interviews ...

Existing methods for video question answering (VideoQA) often suffer from spurious correlations between different modalities, leading to a failure in identifying the dominant visual evidence and the intended question. Moreover, these methods function as ...

This paper proposes a new evolutionary strategy - called Evolutionary Abduction (EVA) - designed to target a class of problems called Combinatorial Causal Optimization Problems (CCOP). In a CCOP, the goal is to find combinations of causes that best ...

As autonomous decision making becomes ubiquitous, researchers agree that developing trust is required for adoption and proficient use of AI systems [20, 35, 39], and it is widely accepted that autonomous agents that can explain their decisions help ...

With software systems becoming increasingly pervasive and autonomous, our ability to test for their quality is severely challenged. Many systems are called to operate in uncertain and highly-changing environment, not rarely required to make ...

Membership inference (MI) attacks highlight a privacy weakness in present stochastic training methods for neural networks. It is not well understood, however, why they arise. Are they a natural consequence of imperfect generalization only? Which ...

Artificial Intelligence (AI), and especially machine learning can be used to find statistical patterns in datasets with thousands of variables with ease. But an understanding of causality is difficult to learn for a machine. For humans however, ...

Compositional action recognition is a novel challenge in the computer vision community and focuses on revealing the different combinations of verbs and nouns instead of treating subject-object interactions in videos as individual instances only. ...

The general aim of the recommender system is to provide personalized suggestions to users, which is opposed to suggesting popular items. However, the normal training paradigm, i.e., fitting a recommender model to recover the user behavior data with ...

Mediation analysis is commonly performed using regressions or Bayesian network analysis in statistics, psychology, and health science; however, it is not effectively supported in existing visualization tools. The lack of assistance poses great risks ...

As computing systems are more frequently and more actively intervening to improve people's work and daily lives, it is critical to correctly predict and understand the causal effects of these interventions. Conventional machine learning methods, built ...

Stragglers are exceptionally slow tasks within a job that delay its completion. Stragglers, which are uncommon within a single job, are pervasive in datacenters with many jobs. We present Hound, a statistical machine learning framework that infers the ...

Stragglers are exceptionally slow tasks within a job that delay its completion. Stragglers, which are uncommon within a single job, are pervasive in datacenters with many jobs. A large body of research has focused on mitigating datacenter stragglers, but ...

To ensure complex systems reliability and to extent their life cycle, it is crucial to properly and timely detect and localize the causes of eventual faults. In this context, this paper describes a new intelligent approach to diagnose (single and ...