The reborn Commodore 64 is an astonishing remake--but daunting if you weren't there the first time around. "Digital detox" approach is compelling. It's hard to overstate just how seismic an impact the Commodore 64 had on home computing. Launched in 1982, the 8-bit machine--iconic in its beige plastic shell with integrated keyboard--went on to become the best-selling personal computer of all time . Despite the success, manufacturer Commodore International folded in 1994, with rights to the name floating around for years.

The number and sophistication of cyberthreats have increased along with the internet's exponential expansion, especially those that are spread by bad URLs. A variety of assaults, such as phishing, drive-by downloads, command-and-control communications, and data exfiltration, are launched using malicious websites. Because attackers are constantly changing URLs to avoid detection, traditional blacklisting techniques are unable to keep up with the dynamic and hostile character of contemporary threats. As a result, intelligent algorithms that can recognize intricate patterns in URLs and instantly identify malicious ones have become crucial components of contemporary cybersecurity protection designs [1, 13]. Because machine learning (ML) and deep learning (DL) approaches can identify non-linear relationships in input data and generalize from observed patterns, they have shown considerable promise in the field of malicious URL detection [2, 3]. But there are still a number of obstacles to overcome: class imbalance (lack of labeled malicious data compared to benign URLs); attackers' adversarial techniques that produce highly obfuscated or anomalous URLs that undermine the effectiveness of traditional classifiers; and the majority of detection systems are restricted to monolingual user interfaces and lack real-time usability features.

Origami-inspired mechanisms can transform flat sheets into functional three-dimensional dynamic structures that are lightweight, compact, and capable of complex motion. These properties make origami increasingly valuable in robotic and deployable systems. However, accurately simulating their folding behavior and interactions with the environment remains challenging. To address this, we present a design framework for origami mechanism simulation that utilizes MuJoCo's deformable-body capabilities. In our approach, origami sheets are represented as graphs of interconnected deformable elements with user-specified constraints such as creases and actuation, defined through an intuitive graphical user interface (GUI). This framework allows users to generate physically consistent simulations that capture both the geometric structure of origami mechanisms and their interactions with external objects and surfaces. We demonstrate our method's utility through a case study on an origami catapult, where design parameters are optimized in simulation using the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) and validated experimentally on physical prototypes. The optimized structure achieves improved throwing performance, illustrating how our system enables rapid, simulation-driven origami design, optimization, and analysis.

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Convolutional Neural Networks (CNNs) have demonstrated exceptional performance in audio tagging tasks. However, deploying these models on resource-constrained devices like the Raspberry Pi poses challenges related to computational efficiency and thermal management. In this paper, a comprehensive evaluation of multiple convolutional neural network (CNN) architectures for audio tagging on the Raspberry Pi is conducted, encompassing all 1D and 2D models from the Pretrained Audio Neural Networks (PANNs) framework, a ConvNeXt-based model adapted for audio classification, as well as MobileNetV3 architectures. In addition, two PANNs-derived networks, CNN9 and CNN13, recently proposed, are also evaluated. To enhance deployment efficiency and portability across diverse hardware platforms, all models are converted to the Open Neural Network Exchange (ONNX) format. Unlike previous works that focus on a single model, our analysis encompasses a broader range of architectures and involves continuous 24-hour inference sessions to assess performance stability. Our experiments reveal that, with appropriate model selection and optimization, it is possible to maintain consistent inference latency and manage thermal behavior effectively over extended periods. These findings provide valuable insights for deploying audio tagging models in real-world edge computing scenarios.

User-friendly modeling and virtual simulation of urban traffic scenarios with different types of interacting agents such as pedestrians, cyclists and autonomous vehicles remains a challenge. We present CAR-JAN, a novel tool for semi-automated generation and simulation of such scenarios based on the multi-agent engineering framework AJAN and the driving simulator CARLA. CARJAN provides a visual user interface for the modeling, storage and maintenance of traffic scenario layouts, and leverages SPARQL Behavior Tree-based decision-making and interactions for agents in dynamic scenario simulations in CARLA. CARJAN provides a first integrated approach for interactive, intelligent agent-based generation and simulation of virtual traffic scenarios in CARLA.

Developing algorithms for real-life problems that perform well in practice highly depends on the availability of realistic data for testing. Obtaining real-life data for optimization problems in health care, however, is often difficult. This is especially true for any patient related optimization problems, e.g., for patient-to-room assignment, due to data privacy policies. Furthermore, obtained real-life data usually cannot be published which prohibits reproducibility of results by other researchers. Therefore, often artificially generated instances are used. We use these insights to develop a configurable instance generator for PRA with an easy-to-use graphical user interface. Configurability is in this case especially important as we observed in an extensive analysis of real-life data that, e.g., the probability distribution for patients' age and length of stay depends on the respective ward. Introduction The development of algorithms for real-world optimization problems that perform well in practice heavily relies on the availability of realistic data for testing.

In the coming years, agents are widely expected to take over more and more chores on behalf of humans, including using computers and smartphones. For now, though, they're too error prone to be much use. A new agent called S2, created by the startup Simular AI, combines frontier models with models specialized for using computers. The agent achieves state-of-the-art performance on tasks like using apps and manipulating files--and suggests that turning to different models in different situations may help agents advance. "Computer-using agents are different from large language models and different from coding," says Ang Li, cofounder and CEO of Simular. In Simular's approach, a powerful general-purpose AI model, like OpenAI's GPT-4o or Anthropic's Claude 3.7, is used to reason about how best to complete the task at hand--while smaller open source models step in for tasks like interpreting web pages.

Machine learning (ML) has become crucial in modern life, with growing interest from researchers and the public. Despite its potential, a significant entry barrier prevents widespread adoption, making it challenging for non-experts to understand and implement ML techniques. The increasing desire to leverage ML is counterbalanced by its technical complexity, creating a gap between potential and practical application. This work introduces asanAI, an offline-first, open-source, no-code machine learning toolkit designed for users of all skill levels. It allows individuals to design, debug, train, and test ML models directly in a web browser, eliminating the need for software installations and coding. The toolkit runs on any device with a modern web browser, including smartphones, and ensures user privacy through local computations while utilizing WebGL for enhanced GPU performance. Users can quickly experiment with neural networks and train custom models using various data sources, supported by intuitive visualizations of network structures and data flows. asanAI simplifies the teaching of ML concepts in educational settings and is released under an open-source MIT license, encouraging modifications. It also supports exporting models in industry-ready formats, empowering a diverse range of users to effectively learn and apply machine learning in their projects. The proposed toolkit is successfully utilized by researchers of ScaDS.AI to swiftly draft and test machine learning ideas, by trainers to effectively educate enthusiasts, and by teachers to introduce contemporary ML topics in classrooms with minimal effort and high clarity.

Large language models (LLMs) are transforming the landscape of chemistry and materials science. Recent examples of LLM-accelerated experimental research include virtual assistants for parsing synthesis recipes from the literature, or using the extracted knowledge to guide synthesis and characterization. Despite these advancements, their application is constrained to labs with automated instruments and control software, leaving much of materials science reliant on manual processes. Here, we demonstrate the rapid deployment of a Python-based control module for a Keithley 2400 electrical source measure unit using ChatGPT-4. Through iterative refinement, we achieved effective instrument management with minimal human intervention. Additionally, a user-friendly graphical user interface (GUI) was created, effectively linking all instrument controls to interactive screen elements. Finally, we integrated this AI-crafted instrument control software with a high-performance stochastic optimization algorithm to facilitate rapid and automated extraction of electronic device parameters related to semiconductor charge transport mechanisms from current-voltage (IV) measurement data. This integration resulted in a comprehensive open-source toolkit for semiconductor device characterization and analysis using IV curve measurements. We demonstrate the application of these tools by acquiring, analyzing, and parameterizing IV data from a Pt/Cr$_2$O$_3$:Mg/$\beta$-Ga$_2$O$_3$ heterojunction diode, a novel stack for high-power and high-temperature electronic devices. This approach underscores the powerful synergy between LLMs and the development of instruments for scientific inquiry, showcasing a path for further acceleration in materials science.