Related papers: An Exploratory Study of Hardware Reverse Engineeri…
Spatial cognition is essential for human intelligence, enabling problem-solving through visual simulations rather than solely relying on verbal reasoning. However, existing AI benchmarks primarily assess verbal reasoning, neglecting the…
Cognitive and metacognitive strategy had demonstrated a significant role in self-regulated learning (SRL), and an appropriate use of strategies is beneficial to effective learning or question-solving tasks during a human-computer…
Systems that use Machine Learning (ML) have become commonplace for companies that want to improve their products and processes. Literature suggests that Requirements Engineering (RE) can help address many problems when engineering…
Behavioural testing -- verifying system capabilities by validating human-designed input-output pairs -- is an alternative evaluation method of natural language processing systems proposed to address the shortcomings of the standard…
Ideas about how to increase the unconscious participation in interaction between 'a human' and 'a computer' are developed in this paper. Evidence of impact of the unconscious functioning is presented. The unconscious is characterised as…
Humans can leverage hierarchical structures to split a task into sub-tasks and solve problems efficiently. Both imitation and reinforcement learning or a combination of them with hierarchical structures have been proven to be an efficient…
Virtual Reality (VR) is seeing increased adoption across many fields. The field of experimental cognitive science is also testing utilization of the technology combined with physiological measures such as electroencephalography (EEG) and…
Despite the availability of refactoring as a feature in popular IDEs, recent studies revealed that developers are reluctant to use them, and still prefer the manual refactoring of their code. At JetBrains, our goal is to fully support…
The integration of human factors (HF) knowledge is crucial when developing safety-critical systems, such as automated vehicles (AVs). Ensuring that HF knowledge is considered continuously throughout the AV development process is essential…
Recourse generators provide actionable insights, often through feature-based counterfactual explanations (CFEs), to help negatively classified individuals understand how to adjust their input features to achieve a positive classification.…
An important current challenge in Human-Robot Interaction (HRI) is to enable robots to learn on-the-fly from human feedback. However, humans show a great variability in the way they reward robots. We propose to address this issue by…
Modern industrial systems require updated approaches to safety management, as the tight interplay between cyber-physical, human, and organizational factors has driven their processes toward increasing complexity. In addition to dealing with…
The purpose of requirements engineering (RE) is to make sure that the expectations and needs of the stakeholders of a software system are met. Emotional needs can be captured as emotional requirements that represent how the end user should…
Neuromorphic hardware platforms can significantly lower the energy overhead of a machine learning inference task. We present a design-technology tradeoff analysis to implement such inference tasks on the processing elements (PEs) of a Non-…
Human beings construct perception of space by integrating sparse observations into massively interconnected synapses and neurons, offering a superior parallelism and efficiency. Replicating this capability in AI finds wide applications in…
Emulating firmware for microcontrollers is challenging due to the tight coupling between the hardware and firmware. This has greatly impeded the application of dynamic analysis tools to firmware analysis. The state-of-the-art work…
Efficiently detecting human intent to interact with ubiquitous robots is crucial for effective human-robot interaction (HRI) and collaboration. Over the past decade, deep learning has gained traction in this field, with most existing…
Feature discovery from complex unstructured data is fundamentally a reasoning problem: it requires identifying abstractions that are predictive of a target outcome while avoiding leakage, proxies, and post-outcome signals. With the…
Traditional machine learning depends on high-precision arithmetic and near-ideal hardware assumptions, which is increasingly challenged by variability in aggressively scaled semiconductor devices. Compute-in-memory (CIM) architectures…
Deep learning approaches have shown promising performance for compressed sensing-based Magnetic Resonance Imaging. While deep neural networks trained with mean squared error (MSE) loss functions can achieve high peak signal to noise ratio,…