Gait disabilities are among the most frequent worldwide. Their treatment relies on rehabilitation therapies, in which smart walkers are being introduced to empower the user's recovery and autonomy, while reducing the clinicians effort. For that, these should be able to decode human motion and needs, as early as possible. Current walkers decode motion intention using information of wearable or embedded sensors, namely inertial units, force and hall sensors, and lasers, whose main limitations imply an expensive solution or hinder the perception of human movement. Smart walkers commonly lack a seamless human-robot interaction, which intuitively understands human motions. A contactless approach is proposed in this work, addressing human motion decoding as an early action recognition/detection problematic, using RGB-D cameras. We studied different deep learning-based algorithms, organised in three different approaches, to process lower body RGB-D video sequences, recorded from an embedded camera of a smart walker, and classify them into 4 classes (stop, walk, turn right/left). A custom dataset involving 15 healthy participants walking with the device was acquired and prepared, resulting in 28800 balanced RGB-D frames, to train and evaluate the deep networks. The best results were attained by a convolutional neural network with a channel attention mechanism, reaching accuracy values of 99.61% and above 93%, for offline early detection/recognition and trial simulations, respectively. Following the hypothesis that human lower body features encode prominent information, fostering a more robust prediction towards real-time applications, the algorithm focus was also evaluated using Dice metric, leading to values slightly higher than 30%. Promising results were attained for early action detection as a human motion decoding strategy, with enhancements in the focus of the proposed architectures.

Non-invasive brain imaging techniques allow understanding the behavior and macro changes in the brain to determine the progress of a disease. However, computational pathology provides a deeper understanding of brain disorders at cellular level, able to consolidate a diagnosis and make the bridge between the medical image and the omics analysis. In traditional histopathology, histology slides are visually inspected, under the microscope, by trained pathologists. This process is time-consuming and labor-intensive; therefore, the emergence of Computational Pathology has triggered great hope to ease this tedious task and make it more robust. This chapter focuses on understanding the state-of-the-art machine learning techniques used to analyze whole slide images within the context of brain disorders. We present a selective set of remarkable machine learning algorithms providing discriminative approaches and quality results on brain disorders. These methodologies are applied to different tasks, such as monitoring mechanisms contributing to disease progression and patient survival rates, analyzing morphological phenotypes for classification and quantitative assessment of disease, improving clinical care, diagnosing tumor specimens, and intraoperative interpretation. Thanks to the recent progress in machine learning algorithms for high-content image processing, computational pathology marks the rise of a new generation of medical discoveries and clinical protocols, including in brain disorders.

One particularly interesting aspect of these models is that their behavior can be configured by a prompt, the initial text provided to the model, which establishes a pattern that the model attempts to continue. General purpose Large Language models can be fine-tuned on specific corpora to provide expertise in a particular domain. One such model is the OpenAI Codex model [3], a 12 billion parameter version of GPT-3 [2, 11], fine-tuned on code samples from 54 million public software repositories on GitHub. This model powers Github Co-Pilot [5], which primarily provides code-completion services within an Integrated Development Environment. We wondered whether such a model could power a conversational programming assistant and perhaps approach the vision laid out by Rich and Waters for their Programmer's Apprentice [15]. We developed the Programmer's Assistant prototype to explore this possibility, and to test whether potential users would find this sort of system useful and desirable [16]. In this paper we will review the steps taken to engineer the prompt for the Programmer's Assistant that used the Codex model to power an interactive conversational assistant, and how we evolved the prompt to establish the desired persona and behavior.

Recent advances in deep neural networks (DNNs) have significantly enhanced the capabilities of optical character recognition (OCR) technology, enabling its adoption to a wide range of real-world applications. Despite this success, DNN-based OCR is shown to be vulnerable to adversarial attacks, in which the adversary can influence the DNN model's prediction by carefully manipulating input to the model. Prior work has demonstrated the security impacts of adversarial attacks on various OCR languages. However, to date, no studies have been conducted and evaluated on an OCR system tailored specifically for the Thai language. To bridge this gap, this work presents a feasibility study of performing adversarial attacks on a specific Thai OCR application -- Thai License Plate Recognition (LPR). Moreover, we propose a new type of adversarial attack based on the \emph{semi-targeted} scenario and show that this scenario is highly realistic in LPR applications. Our experimental results show the feasibility of our attacks as they can be performed on a commodity computer desktop with over 90% attack success rate.

More companies in Brazil are adopting intelligent automation solutions and services as part of a wave of digital transformation that began during the COVID-19 pandemic and continues to accelerate, according to a new research report published today by Information Services Group (ISG) (Nasdaq: III), a leading global technology research and advisory firm. "Leading service providers are helping organizations integrate these tools, along with governance frameworks and change management processes." The 2022 ISG Provider Lens Intelligent Automation -- Services and Solutions report for Brazil finds several types of automation are gaining traction in the country, which is one of the largest automation markets in South America. Companies are acting to increase efficiency, better understand their processes and solve IT problems before they disrupt operations. "The challenges that companies in Brazil have faced since the pandemic call for comprehensive, modern automation solutions," said Chip Wagner, CEO of ISG Automation.

Accurate estimation of output quantiles is crucial in many use cases, where it is desired to model the range of possibility. Modeling target distribution at arbitrary quantile levels and at arbitrary input attribute levels are important to offer a comprehensive picture of the data, and requires the quantile function to be expressive enough. The quantile function describing the target distribution using quantile levels is critical for quantile regression. Although various parametric forms for the distributions (that the quantile function specifies) can be adopted, an everlasting problem is selecting the most appropriate one that can properly approximate the data distributions. In this paper, we propose a non-parametric and data-driven approach, Neural Spline Search (NSS), to represent the observed data distribution without parametric assumptions. NSS is flexible and expressive for modeling data distributions by transforming the inputs with a series of monotonic spline regressions guided by symbolic operators. We demonstrate that NSS outperforms previous methods on synthetic, real-world regression and time-series forecasting tasks.

Visual question answering on document images that contain textual, visual, and layout information, called document VQA, has received much attention recently. Although many datasets have been proposed for developing document VQA systems, most of the existing datasets focus on understanding the content relationships within a single image and not across multiple images. In this study, we propose a new multi-image document VQA dataset, SlideVQA, containing 2.6k+ slide decks composed of 52k+ slide images and 14.5k questions about a slide deck. SlideVQA requires complex reasoning, including single-hop, multi-hop, and numerical reasoning, and also provides annotated arithmetic expressions of numerical answers for enhancing the ability of numerical reasoning. Moreover, we developed a new end-to-end document VQA model that treats evidence selection and question answering in a unified sequence-to-sequence format. Experiments on SlideVQA show that our model outperformed existing state-of-the-art QA models, but that it still has a large gap behind human performance. We believe that our dataset will facilitate research on document VQA.

Sample weighting is widely used in deep learning. A large number of weighting methods essentially utilize the learning difficulty of training samples to calculate their weights. In this study, this scheme is called difficulty-based weighting. Two important issues arise when explaining this scheme. First, a unified difficulty measure that can be theoretically guaranteed for training samples does not exist. The learning difficulties of the samples are determined by multiple factors including noise level, imbalance degree, margin, and uncertainty. Nevertheless, existing measures only consider a single factor or in part, but not in their entirety. Second, a comprehensive theoretical explanation is lacking with respect to demonstrating why difficulty-based weighting schemes are effective in deep learning. In this study, we theoretically prove that the generalization error of a sample can be used as a universal difficulty measure. Furthermore, we provide formal theoretical justifications on the role of difficulty-based weighting for deep learning, consequently revealing its positive influences on both the optimization dynamics and generalization performance of deep models, which is instructive to existing weighting schemes.

Wildfires are becoming more frequent and their effects more devastating every day. Climate change has directly and indirectly affected the occurrence of these, as well as social phenomena have increased the vulnerability of people. Consequently, and given the inevitable occurrence of these, it is important to have early warning systems that allow a timely and effective response. Artificial intelligence, machine learning and Computer Vision offer an effective and achievable alternative for opportune detection of wildfires and thus reduce the risk of disasters. YOLOv7 offers a simple, fast, and efficient algorithm for training object detection models which can be used in early detection of smoke columns in the initial stage wildfires. The developed model showed promising results, achieving a score of 0.74 in the F1 curve when the confidence level is 0.298, that is, a higher score at lower confidence levels was obtained. This means when the conditions are favorable for false positives. The metrics demonstrates the resilience and effectiveness of the model in detecting smoke columns.

The massive machine-type communications (mMTC) service will be part of new services planned to integrate the fifth generation of wireless communication (B5G). In this scenario, the massive random access (RA) problem arises when two or more devices collide when selecting the same resource block. There are several techniques to deal with this problem. One of them deploys Q-learning (QL), in which devices store in their Q-table the rewards sent by the central node that indicate the quality of the transmission performed. The device learns the best resource blocks to select and transmit to avoid collisions. The numerical results reveal that the best performance-complexity trade-off is obtained by using a higher number of power levels, typically eight levels. The proposed MPL-QL can deliver better throughput and lower latency compared to other recent QL-based algorithms found in the literature.