In laparoscopic surgical training and evaluation, real-time recognition of surgical actions with transparency outputs is crucial for automated, objective, and immediate instructional feedback to support skills improvement. However, we face challenges due to limited dataset sizes and variability in surgical environments. This study presents AutoMedTS , an end-to-end automated machine learning framework customized for medical time-series data, enabling rapid deployment using surgical suturing trajectories collected from both expert and novice surgeons. The proposed method features key improvements including: (i) a novel temperature-scaled Softmax resampling technique effectively addressing severe class imbalance, and (ii) an uncertainty-aware ensemble selection mechanism ensuring robust predictions across surgeons with varying skill levels. Additionally, the approach emphasizes model transparency to meet the high standards of reliability and transparency required in medical applications. Compared to deep learning methods, traditional machine learning models not only facilitate efficient rapid deployment but also offer significant transparency advantages. Experimental results demonstrate that our method provides fast, stable, and reliable real-time surgical action recognition in clinical training environments. Code and data are publicly available at https://github.com/baobingzhang/AutoMedTS .

Repeatable and accurate tests are important when designing hardware and algorithms for solar-powered wireless sensor networks (WSNs). Since no two days are exactly alike with regard to energy harvesting, tests must be carried out indoors. Solar simulators are traditionally used in replicating the effects of sunlight indoors; however, solar simulators are expensive, have lighting elements that have short lifetimes, and are usually not designed to carry out the types of tests that hardware and algorithm designers require. As a result, hardware and algorithm designers use tests that are inaccurate and not repeatable (both for others and also for the designers themselves). In this article, we propose an indoor test methodology that does not rely on solar simulators. The test methodology has its basis in astronomy and photovoltaic cell design. We present a generic design for a test apparatus that can be used in carrying out the test methodology. We also present a specific design that we use in implementing an actual test apparatus. We test the efficacy of our test apparatus and, to demonstrate the usefulness of the test methodology, perform experiments akin to those required in projects involving solar-powered WSNs. Results of the said tests and experiments demonstrate that the test methodology is an invaluable tool for hardware and algorithm designers working with solar-powered WSNs.

We present the CISpaces framework, a collaborative virtual space for intelligence analysts for the elaboration of information to explain a situation. CISpaces supports the analysis of conflicting information in collaboration exploiting argumentation schemes to structure and share analyses, crowd-sourcing to collect information and provenance to establish the credibility of hypotheses.

This paper examines the use of the Geometric Arithmetic Parallel Processor (GAPP) IC systolic array chip as the major processing element in the design and construction of a high-speed, parallel, relational database machine. This chip is of particular interest because it is one of the first commercially available systolic array chips. A brief review of the architectural features of the GAPP is presented. Parallel algorithms for handling basic relational database operations-simple queries, joins, and sorts-are presented. Building blocks of cascaded systolic arrays are developed to implement these algorithms. A database machine architecture to incorporate these building blocks is described and compared to existing designs.