Routine and repetitive infrastructure inspections present safety, efficiency, and consistency challenges as they are performed manually, often in challenging or hazardous environments. They can also introduce subjectivity and errors into the process, resulting in undesirable outcomes. Simultaneous localization and mapping (SLAM) presents an opportunity to generate high-quality 3D maps that can be used to extract accurate and objective inspection data. Yet, many SLAM algorithms are limited in their ability to align 3D maps from repeated inspections in GPS-denied settings automatically. This limitation hinders practical long-term asset health assessments by requiring tedious manual alignment for data association across scans from previous inspections. This paper introduces a versatile map alignment algorithm leveraging both visual and lidar data for improved place recognition robustness and presents an infrastructure-focused dataset tailored for consecutive inspections. By detaching map alignment from SLAM, our approach enhances infrastructure inspection pipelines, supports monitoring asset degradation over time, and invigorates SLAM research by permitting exploration beyond existing multi-session SLAM algorithms.

In this paper, we investigate the problem of decomposing 2D environments for robot coverage planning. Coverage path planning (CPP) involves computing a cost-minimizing path for a robot equipped with a coverage or sensing tool so that the tool visits all points in the environment. CPP is an NP-Hard problem, so existing approaches simplify the problem by decomposing the environment into the minimum number of sectors. Sectors are sub-regions of the environment that can each be covered using a lawnmower path (i.e., along parallel straight-line paths) oriented at an angle. However, traditional methods either limit the coverage orientations to be axis-parallel (horizontal/vertical) or provide no guarantees on the number of sectors in the decomposition. We introduce an approach to decompose the environment into possibly overlapping rectangular sectors. We provide an approximation guarantee on the number of sectors computed using our approach for a given environment. We do this by leveraging the submodular property of the sector coverage function, which enables us to formulate the decomposition problem as a submodular set cover (SSC) problem with well-known approximation guarantees for the greedy algorithm. Our approach improves upon existing coverage planning methods, as demonstrated through an evaluation using maps of complex real-world environments.

Artificial intelligence (AI) has made significant advances in recent years and opened up new possibilities in exploring applications in various fields such as biomedical, robotics, education, industry, etc. Among these fields, human hand gesture recognition is a subject of study that has recently emerged as a research interest in robotic hand control using electromyography (EMG). Surface electromyography (sEMG) is a primary technique used in EMG, which is popular due to its non-invasive nature and is used to capture gesture movements using signal acquisition devices placed on the surface of the forearm. Moreover, these signals are pre-processed to extract significant handcrafted features through time and frequency domain analysis. These are helpful and act as input to machine learning (ML) models to identify hand gestures. However, handling multiple classes and biases are major limitations that can affect the performance of an ML model. Therefore, to address this issue, a new mixture of experts extra tree (MEET) model is proposed to identify more accurate and effective hand gesture movements. This model combines individual ML models referred to as experts, each focusing on a minimal class of two. Moreover, a fully trained model known as the gate is employed to weigh the output of individual expert models. This amalgamation of the expert models with the gate model is known as a mixture of experts extra tree (MEET) model. In this study, four subjects with six hand gesture movements have been considered and their identification is evaluated among eleven models, including the MEET classifier. Results elucidate that the MEET classifier performed best among other algorithms and identified hand gesture movement accurately.

Recent works in field robotics highlighted the importance of resiliency against different types of terrains. Boreal forests, in particular, are home to many mobility-impeding terrains that should be considered for off-road autonomous navigation. Also, being one of the largest land biomes on Earth, boreal forests are an area where autonomous vehicles are expected to become increasingly common. In this paper, we address this issue by introducing BorealTC, a publicly available dataset for proprioceptive-based terrain classification (TC). Recorded with a Husky A200, our dataset contains 116 min of Inertial Measurement Unit (IMU), motor current, and wheel odometry data, focusing on typical boreal forest terrains, notably snow, ice, and silty loam. Combining our dataset with another dataset from the state-of-the-art, we evaluate both a Convolutional Neural Network (CNN) and the novel state space model (SSM)-based Mamba architecture on a TC task. Interestingly, we show that while CNN outperforms Mamba on each separate dataset, Mamba achieves greater accuracy when trained on a combination of both. In addition, we demonstrate that Mamba's learning capacity is greater than a CNN for increasing amounts of data. We show that the combination of two TC datasets yields a latent space that can be interpreted with the properties of the terrains. We also discuss the implications of merging datasets on classification. Our source code and dataset are publicly available online: https://github.com/norlab-ulaval/BorealTC.

In this paper, we propose a method to replan coverage paths for a robot operating in an environment with initially unknown static obstacles. Existing coverage approaches reduce coverage time by covering along the minimum number of coverage lines (straight-line paths). However, recomputing such paths online can be computationally expensive resulting in robot stoppages that increase coverage time. A naive alternative is greedy detour replanning, i.e., replanning with minimum deviation from the initial path, which is efficient to compute but may result in unnecessary detours. In this work, we propose an anytime coverage replanning approach named OARP-Replan that performs near-optimal replans to an interrupted coverage path within a given time budget. We do this by solving linear relaxations of mixed-integer linear programs (MILPs) to identify sections of the interrupted path that can be optimally replanned within the time budget. We validate our approach in simulation using maps of real-world environments and compare our approach against a greedy detour replanner and other state-of-the-art approaches.

Sentiment analysis of social media data is an emerging field with vast applications in various domains. In this study, we developed a sentiment analysis model to analyze social media sentiment, especially tweets, during global conflicting scenarios. To establish our research experiment, we identified a recent global dispute incident on Twitter and collected around 31,000 filtered Tweets for several months to analyze human sentiment worldwide.

Ensuring diagnostic performance of AI models before clinical use is key to the safe and successful adoption of these technologies. Studies reporting AI applied to digital pathology images for diagnostic purposes have rapidly increased in number in recent years. The aim of this work is to provide an overview of the diagnostic accuracy of AI in digital pathology images from all areas of pathology. This systematic review and meta-analysis included diagnostic accuracy studies using any type of artificial intelligence applied to whole slide images (WSIs) in any disease type. The reference standard was diagnosis through histopathological assessment and / or immunohistochemistry. Searches were conducted in PubMed, EMBASE and CENTRAL in June 2022. We identified 2976 studies, of which 100 were included in the review and 48 in the full meta-analysis. Risk of bias and concerns of applicability were assessed using the QUADAS-2 tool. Data extraction was conducted by two investigators and meta-analysis was performed using a bivariate random effects model. 100 studies were identified for inclusion, equating to over 152,000 whole slide images (WSIs) and representing many disease types. Of these, 48 studies were included in the meta-analysis. These studies reported a mean sensitivity of 96.3% (CI 94.1-97.7) and mean specificity of 93.3% (CI 90.5-95.4) for AI. There was substantial heterogeneity in study design and all 100 studies identified for inclusion had at least one area at high or unclear risk of bias. This review provides a broad overview of AI performance across applications in whole slide imaging. However, there is huge variability in study design and available performance data, with details around the conduct of the study and make up of the datasets frequently missing. Overall, AI offers good accuracy when applied to WSIs but requires more rigorous evaluation of its performance.

Avidbots Corp., which makes commercial floor-scrubbing robots, said it has raised US$70-million in new equity, marking one of the biggest financing rounds for a Canadian startup in recent months as the tech sector grapples with a prolonged downturn. Jeneration Capital, which has offices in Hong Kong and Beijing, led the round, which Avidbots said Tuesday brought its total venture financing to US$107-million to date. Numerous existing investors including BDC Capital, Golden Ventures and Kensington Capital Partners returned for the financing round, as did new investors such as BMO Capital Partners. Founded in 2014, Avidbots has focused its robotics efforts on a suite of floor-cleaning robots called Neo. The Kitchener, Ont.-based company says more than 1,000 have been sold worldwide in more than a dozen countries for clients in sectors such as warehousing, health care and manufacturing.

Two things can be said about human beings: we like building machines, and we tend to freak out about the machines we build. The Luddites of 19th-century England, an oath-based secret society, looked to the industrial era and saw not liberation but destitution. The most radical among them formed paramilitary groups to raid textile factories and destroy knitting machines and mechanical looms -- devices that would replace workers. Their political descendants include the lamplighters of early-20th-century New York who went on strike to protest the advent of electric streetlights, and the switchboard operators of Bloomington-Normal, Illinois, who in the 1930s took action against the rotary dial system. Did predictions of automation and mass joblessness come true?

Many key technology areas that were already growing prior to the pandemic have gotten accelerated, producing an even greater need for solution providers to focus in on areas such as cybersecurity, cloud and AI, a panel of channel chiefs said during the Best of Breed Virtual event Wednesday. Without a doubt, the IT industry has become even more essential amid the impacts of COVID-19, said Ron Dupler, CEO of Kittery, Maine-based solution provider GreenPages, who served as moderator for the panel. "Essentially, we kept the world running to a large degree during this," said Dupler (pictured top left) during the session at the Best of Breed Virtual Spring 2021 event, which was hosted by CRN parent The Channel Company. Now, the opportunity is to meet the increased demand for digital transformation among customers going forward--using expertise in segments such as AI and automation, advanced security and a variety of cloud technologies, panelists said. An emphasis on speed to market and customer experience have gotten "amplified" in the environment shaped by the pandemic, said Ryan Walsh (pictured bottom right), chief product officer and channel chief at cloud distributor Pax8.