Forensic footwear impressions play a crucial role in criminal investigations, assisting in possible suspect identification. The quality of an impression collected from a crime scene directly impacts the forensic information that can be garnered from any ...

The complex topology of actual road networks and the interlinked nature of traffic flow with spatiotemporal factors pose challenges to traditional node-static correlation models. Therefore, the DUTNG model—a short-term traffic flow prediction model ...

Recent studies [48, 7] have demonstrated that Large Language Models (LLMs), like ChatGPT [3, 46] and LLAMA [59], can assist with routine teaching tasks and have the potential to revolutionize traditional education. However, other studies [35] highlight ...

Visual object navigation, a classical problem in embodied intelligence tasks, requires agents to find a specified target using only the first-person view of visual information. A number of methods that search only for an object in the same category are ...

This editorial summarizes the content of the Special Issue on Software Engineering and AI for Data Quality of the Journal of Data and Information Quality (JDIQ).

Given a large-scale mobile network with a variety of equipment and radio access network technologies for an approximate 20 million subscribers, there are many types of data that can be used for big data analytics and machine learning (ML) tasks for ...

Transformer-based models have demonstrated much success in various natural language processing tasks. However, they are often vulnerable to adversarial attacks, such as data poisoning, which can intentionally fool the model into generating incorrect ...

Deep learning algorithms have become increasingly popular over the years, having proved their efficiency in input-output functions for distinct types of data. This technology is particularly useful in medical imaging, where complex image structures often ...

Machine Learning (ML) models have the potential to support decision-making in healthcare by grasping complex patterns within data. However, decisions in this domain are sensitive and require active involvement of domain specialists with deep knowledge of ...

Machine learning (ML) and artificial intelligence (AI) systems rely heavily on human-annotated data for training and evaluation. A major challenge in this context is the occurrence of annotation errors, as their effects can degrade model performance. This ...

Data quality is crucial in modern software systems, like data-driven decision support systems. However, data quality is affected by data anomalies, which represent instances that deviate from most of the data. These anomalies affect the reliability and ...

Human activity recognition (HAR) is an active research field that has seen great success in recent years due to advances in sensory data collection methods and activity recognition systems. Deep artificial intelligence (AI) models have contributed to the ...

The importance of context in data quality (DQ) was shown many years ago and nowadays is widely accepted. Early approaches and surveys defined DQ as fitness for use and showed the influence of context on DQ. This article presents a Systematic Literature ...

There is no doubt that the popularity of smart devices and the development of deep learning models bring individuals too much convenience. However, some rancorous attackers can also implement unexpected privacy inferences on sensed data from smart devices ...

Predicting students’ academic success is crucial for educational institutions to provide targeted support and interventions to those at risk of underperforming. With the increasing adoption of digital learning management systems (LMS), there has been a ...

Data validation is a primary concern in any data-driven application, as undetected data errors may negatively affect machine learning models and lead to suboptimal decisions. Data quality issues are usually detected manually by experts, which becomes ...

Time series data are widely used and provide a wealth of information for countless applications. However, some applications are faced with a limited amount of data, or the data cannot be used due to confidentiality concerns. To overcome these obstacles, ...

In this work, we address the challenging and open problem of involving non-expert users in the data repairing problem as first-class citizens. Despite a large number of proposals that have been devoted to cleaning data from the point of view of expert ...

Data quality plays a vital role in scientific research and decision-making across industries. Thus, it is crucial to incorporate the data quality control (DQC) process, which comprises various actions and operations to detect and correct data errors. The ...

In entity resolution, blocking pre-partitions data for further processing by more expensive methods. Two entity mentions are in the same block if they share identical or related blocking-keys. Previous work has sometimes related blocking keys by grouping ...