Current Issue : October-December Volume : 2026 Issue Number : 4 Articles : 5 Articles
In this paper, we present an agent-based model of a controlled detonation system for dynamic sandbox analysis of suspicious software. Instead of treating the sandbox as a passive observer, the model places an AI operator inside the analysis loop and allows it to perform adaptive GUI interactions in a plausible, isolated execution environment. The controlled detonation process is formulated as a partially observable Markov decision process (POMDP), while the proposed proof-of-concept architecture combines initial profiling, VM preparation, multi-layer telemetry, and an RL policy with visual perception and temporal memory. Evaluation in a controlled emulation setting on 180 malware samples from three threat classes shows higher Activity Rates and Coverage, and shorter Time-to- Reveal than passive and fixed scripted baselines. These results support the feasibility of adaptive interactions as a promising direction for sandbox analysis, while broader external validation, matched comparisons with prior systems, and component-wise ablation remain future work....
Additive manufacturing (AM) enables customized, efficient restorative workflows, though the accuracy of 3D-printed restorations may be compromised by polymerization, sintering shrinkage, and post-processing. This study evaluated the geometric accuracy of 3D-printed partial restorations compared with the computer-aided design (CAD) reference. The null hypothesis stated that no significant differences would be found between Varseo Smile Crownplus (by BEGO, Italy) and IRIXMax (by DWS System, Italy) materials, which are printed and cured with different technologies. A model was prepared for an overlay and designed with a 1.5 mm uniform thickness. Restorations were produced in two groups with two different printing processes: DLP (digital light processing)-printed Varseo Smile Crownplus and SLA (stereolithography)-printed IRIXMax. Six samples per group were printed at 90◦ orientation and scanned. Meshes were aligned to the master geometry via pre-alignment and ICP (Iterative Closest Point) registration. Deviations were quantified in CloudCompare using mean, standard deviation (SD), and 90th percentile values. IRIXMax showed the lowest deviations from the ideal geometry, while Varseo Smile Crownplus exhibited greater variability. Pairwise comparisons found IRIXMax significantly more accurate than Varseo Smile Crownplus. Color maps confirmed material-specific deviation patterns. IRIXMax provided the highest geometric accuracy. Material-specific calibration is essential for reliable 3D-printed definitive restorations....
In this work, we present HiAER-Spike, a modular, reconfigurable, event-driven neuromorphic computing platformdesigned to execute large spiking neural networks with up to 160 million neurons and 40 billion synapses - roughly twice the neurons of a mouse brain at faster than real time. This system, assembled at the UC San Diego Supercomputer Center, comprises a co-designed hard- and software stack that is optimized for run-time massively parallel processing and hierarchical addressevent routing (HiAER) of spikes while promotingmemory-efficient network storage and execution. The architecture efficiently handles both sparse connectivity and sparse activity for robust and low-latency event-driven inference for both edge and cloud computing. A Python programming interface to HiAER-Spike, agnostic to hardware-level detail, shields the user from complexity in the configuration and execution of general spiking neural networks with minimal constraints in topology. The system is made easily available over a web portal for use by the widercommunity. In the following, we provide an overview of the hard- and software stack, explain the underlying design principles, demonstrate some of the system’s capabilities, and solicit feedback from the broader neuromorphic community. Examples are shown demonstrating HiAER-Spike’s capabilities for event-driven vision on benchmark CIFAR-10, DVS event-based gesture, MNIST, and Pong tasks....
This paper advances a theoretical argument that generative AI is accelerating the democratization of computational thinking and, in turn, reshaping education, professional practice, and the nature of computing itself. Traditionally, computational thinking has been closely tied to learning to program, thereby limiting who could effectively employ it. The emergence of large language models (LLMs) challenges this linkage by decoupling many forms of computational problem solving from direct programming. In response to this shift, the paper explores the implications for curriculum design and workforce roles through a theoretical and interpretive lens. Drawing on prior literature, historical context, and illustrative examples from recent scholarship and practice, we develop a conceptual account of AI-augmented computing. We argue that LLMs lower barriers to entry by abstracting away much of manual coding and reallocating effort toward problem framing, prompt engineering, oversight, and validation. We further argue that this transition is redistributing computational skills across disciplines, positioning prompt engineering as an emerging engineering practice, and increasing pressure on universities to redesign curricula around AI literacy, fluency, and mastery....
The development of reliable and scalable control software is a key requirement for the Cherenkov Telescope Array Observatory, where distributed subsystems must operate coherently and support increasingly automated observing strategies. This paper presents the architecture and design of the Telescope Control System of the Small-Sized Telescopes of the observatory, addressing the need for modularity, deterministic behavior, and long-term maintainability. The proposed solution adopts a set of software managers implementing well-defined interfaces and state machines, enabling predictable control flows and consistent interaction with heterogeneous hardware. Modern software engineering practices were applied, including containerized services, automated deployment workflows, and a comprehensive simulation environment. These elements were evaluated through prototypes and pathfinder activities that allowed us to explore design alternatives, validate the behavior of individual components, and assess the scalability of the overall architecture. Results from these exploratory tests indicate that the interface-driven and modular design supports robust operation, facilitates integration, and reduces the effort required for system evolution. While full implementation is currently in progress, the findings confirm that the proposed architecture provides a solid foundation for the test readiness review phase (the phase preceding formal integration testing) and can be effectively extended to future facilities requiring flexible, maintainable, and resilient control software....
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