With the deep integration of artificial intelligence and interactive technology, Graphical User Interface (GUI) Agent, as the carrier connecting goal-oriented natural language and real-world devices, has received widespread attention from the community. Contemporary benchmarks aim to evaluate the comprehensive capabilities of GUI agents in GUI operation tasks, generally determining task completion solely by inspecting the final screen state. However, GUI operation tasks consist of multiple chained steps while not all critical information is presented in the final few pages. Although a few research has begun to incorporate intermediate steps into evaluation, accurately and automatically capturing this process information still remains an open challenge. To address this weakness, we introduce ProBench, a comprehensive mobile benchmark with over 200 challenging GUI tasks covering widely-used scenarios. Remaining the traditional State-related Task evaluation, we extend our dataset to include Process-related Task and design a specialized evaluation method. A newly introduced Process Provider automatically supplies accurate process information, enabling presice assessment of agent's performance. Our evaluation of advanced GUI agents reveals significant limitations for real-world GUI scenarios. These shortcomings are prevalent across diverse models, including both large-scale generalist models and smaller, GUI-specific models. A detailed error analysis further exposes several universal problems, outlining concrete directions for future improvements.

Humans naturally follow distinct patterns when conducting their daily activities, which are driven by established practices and processes, such as production workflows, social norms and daily routines. Human activity recognition (HAR) algorithms usually use neural networks or machine learning techniques to analyse inherent relationships within the data. However, these approaches often overlook the contextual information in which the data are generated, potentially limiting their effectiveness. We propose a novel approach that incorporates process information from context to enhance the HAR performance. Specifically, we align probabilistic events generated by machine learning models with process models derived from contextual information. This alignment adaptively weighs these two sources of information to optimise HAR accuracy. Our experiments demonstrate that our approach achieves better accuracy and Macro F1-score compared to baseline models.

Accurate prediction of Global Warming Potential (GWP) is essential for assessing the environmental impact of chemical processes and materials. Traditional GWP prediction models rely predominantly on molecular structure, overlooking critical process-related information. In this study, we present an integrative GWP prediction model that combines molecular descriptors (MACCS keys and Mordred descriptors) with process information (process title, description, and location) to improve predictive accuracy and interpretability. Using a deep neural network (DNN) model, we achieved an R-squared of 86% on test data with Mordred descriptors, process location, and description information, representing a 25% improvement over the previous benchmark of 61%; XAI analysis further highlighted the significant role of process title embeddings in enhancing model predictions. To enhance interpretability, we employed a Kolmogorov-Arnold Network (KAN) to derive a symbolic formula for GWP prediction, capturing key molecular and process features and providing a transparent, interpretable alternative to black-box models, enabling users to gain insights into the molecular and process factors influencing GWP. Error analysis showed that the model performs reliably in densely populated data ranges, with increased uncertainty for higher GWP values. This analysis allows users to manage prediction uncertainty effectively, supporting data-driven decision-making in chemical and process design. Our results suggest that integrating both molecular and process-level information in GWP prediction models yields substantial gains in accuracy and interpretability, offering a valuable tool for sustainability assessments. Future work may extend this approach to additional environmental impact categories and refine the model to further enhance its predictive reliability.

Machine-learning based generation of process models from natural language text process descriptions provides a solution for the time-intensive and expensive process discovery phase. Many organizations have to carry out this phase, before they can utilize business process management and its benefits. Yet, research towards this is severely restrained by an apparent lack of large and high-quality datasets. This lack of data can be attributed to, among other things, an absence of proper tool assistance for dataset creation, resulting in high workloads and inferior data quality. We explore two assistance features to support dataset creation, a recommendation system for identifying process information in the text and visualization of the current state of already identified process information as a graphical business process model. A controlled user study with 31 participants shows that assisting dataset creators with recommendations lowers all aspects of workload, up to $-51.0\%$, and significantly improves annotation quality, up to $+38.9\%$. We make all data and code available to encourage further research on additional novel assistance strategies.

We address the problem of robot guided assembly tasks, by using a learning-based approach to identify contact model parameters for known and novel parts. First, a Variational Autoencoder (VAE) is used to extract geometric features of assembly parts. Then, we combine the extracted features with physical knowledge to derive the parameters of a contact model using our newly proposed neural network structure. The measured force from real experiments is used to supervise the predicted forces, thus avoiding the need for ground truth model parameters. Although trained only on a small set of assembly parts, good contact model estimation for unknown objects were achieved. Our main contribution is the network structure that allows us to estimate contact models of assembly tasks depending on the geometry of the part to be joined. Where current system identification processes have to record new data for a new assembly process, our method only requires the 3D model of the assembly part. We evaluate our method by estimating contact models for robot-guided assembly tasks of pin connectors as well as electronic plugs and compare the results with real experiments.

Computational neuroscience is a field of study that seeks to understand how the brain works by using mathematical models, simulations, and computer simulations. It is an interdisciplinary field that involves expertise in biology, physics, mathematics, computer science, and engineering. In recent years, computational neuroscience has become increasingly important in the development of artificial intelligence systems. Biological systems learn, adapt, and perform complex tasks by using networks of neurons that communicate with each other through synapses. These networks of neurons are highly interconnected and are able to process information in parallel, allowing for fast and efficient computation.

Although two different entities, humans and machines have coexisted for many centuries now. Binary, the language that enables machines to compute and process information, is a system of two elements. It is the basis of all the coding and forms the foundation of how machines interact with the world. How does, then, the wide spectrum of human thinking and imagination accommodate the co-existence of binary machines? When a machine is given a task, it is translated into a series of binary bits, ones and zeros, a language that it comprehends and uses to complete the assigned job.

Artificial intelligence is going to reshape the world we know in many different ways. AI is being used to make calculations in autonomous vehicles, to do mundane tasks like create reports and in security like facial/gait recognition. But, can a computer algorithm actually be creative, too? The simple answer is, "Yes." We've seen AI take prompts and turn them into small, simple art pieces. But that technology is improving at an insane pace, as we see with Lensa.ai.

We are initiating coverage of BrainChip Holdings (OTC:BRCHF) with a valuation of $0.75 per share. BrainChip is the first company to offer a commercial neuromorphic processor and the associated IP to the market. The company's Akida IP brings artificial intelligence (AI) tools to the "edge" with on-device computing and "one-shot" learning capabilities. The company licenses its intellectual property to OEMs, semiconductor designers and semiconductor manufacturers. On-device Artificial Intelligence or "Edge AI" holds significant promise as a low-power alternative to Cloud AI tools currently in the marketplace.

Researchers from the University of Glasgow's School of Psychology and Neuroscience have developed a novel approach to understanding whether the human brain and its DNN models recognize things in the same way Deep Neural Networks have become very significant in everyday real-world applications such as automated face recognition systems and self-driving cars. Deep Neural Network is used by researchers to model the processing of information and examine how this processing is equivalent to that of humans. While how DNNs perform computations can be very different from the human brain. Hence, researchers have invented a unique approach to understanding whether the human brain and its DNN models recognize things in the same way, using similar steps of computations. Prof Philippe Schyns, Dean of Research Technology at the University of Glasgow, said: "Having a better understanding of whether the human brain and its DNN models recognize things the same way would allow for more accurate real-world applications using DNNs. This article defines a new approach to better this understanding of how the process works: first, researchers must show that both the brain and the DNNs recognize the same things – such as a face – using the same face features; and, secondly, that the brain and the DNN must process these features in the same way, with the same steps of computations. This research would overcome the main hurdle in AI development i.e. understanding the process of machine learning, which matches how humans process information. "Creating human-like AI is about more than mimicking human behavior – technology must also be able to process information, or'think', like or better than humans if it is to be fully relied upon.