Software bots have attracted increasing interest and popularity in both research and society. Their contributions span automation, digital twins, game characters with conscious-like behavior, and social media. However, there is still a lack of intelligent bots that can adapt to the variability and dynamic nature of digital web environments. Unlike human users, they have difficulty understanding and exploiting the affordances across multiple virtual environments. Despite the hype, bots with human user-like cognition do not currently exist. Chatbots, for instance, lack situational awareness on the digital platforms where they operate, preventing them from enacting meaningful and autonomous intelligent behavior similar to human users. In this survey, we aim to explore the role of cognitive architectures in supporting efforts towards engineering software bots with advanced general intelligence. We discuss how cognitive architectures can contribute to creating intelligent software bots. Furthermore, we highlight key architectural recommendations for the future development of autonomous, user-like cognitive bots.

Automated disease, weed and crop classification with computer vision will be invaluable in the future of agriculture. However, existing model architectures like ResNet, EfficientNet and ConvNeXt often underperform on smaller, specialised datasets typical of such projects. We address this gap with informed data collection and the development of a new CNN architecture, PhytNet. Utilising a novel dataset of infrared cocoa tree images, we demonstrate PhytNet's development and compare its performance with existing architectures. Data collection was informed by analysis of spectroscopy data, which provided useful insights into the spectral characteristics of cocoa trees. Such information could inform future data collection and model development. Cocoa was chosen as a focal species due to the diverse pathology of its diseases, which pose significant challenges for detection. ResNet18 showed some signs of overfitting, while EfficientNet variants showed distinct signs of overfitting. By contrast, PhytNet displayed excellent attention to relevant features, no overfitting, and an exceptionally low computation cost (1.19 GFLOPS). As such PhytNet is a promising candidate for rapid disease or plant classification, or precise localisation of disease symptoms for autonomous systems.

Lip segmentation is crucial in computer vision, especially for lip reading. Despite extensive face segmentation research, lip segmentation has received limited attention. The aim of this study is to compare state-of-the-art lip segmentation models using a standardized setting and a publicly available dataset. Five techniques, namely EHANet, Mask2Former, BiSeNet V2, PIDNet, and STDC1, are qualitatively selected based on their reported performance, inference time, code availability, recency, and popularity. The CelebAMask-HQ dataset, comprising manually annotated face images, is used to fairly assess the lip segmentation performance of the selected models. Inference experiments are conducted on a Raspberry Pi4 to emulate limited computational resources. The results show that Mask2Former and EHANet have the best performances in terms of mIoU score. BiSeNet V2 demonstrate competitive performance, while PIDNet excels in recall but has lower precision. Most models present inference time ranging from 1000 to around 3000 milliseconds on a Raspberry Pi4, with PIDNet having the lowest mean inference time. This study provides a comprehensive evaluation of lip segmentation models, highlighting their performance and inference times. The findings contribute to the development of lightweight techniques and establish benchmarks for future advances in lip segmentation, especially in IoT and edge computing scenarios.

People naturally understand emotions, thus permitting a machine to do the same could open new paths for human-computer interaction. Facial expressions can be very useful for emotion recognition techniques, as these are the biggest transmitters of non-verbal cues capable of being correlated with emotions. Several techniques are based on Convolutional Neural Networks (CNNs) to extract information in a machine learning process. However, simple CNNs are not always sufficient to locate points of interest on the face that can be correlated with emotions. In this work, we intend to expand the capacity of emotion recognition techniques by proposing the usage of Facial Action Units (AUs) recognition techniques to recognize emotions. This recognition will be based on the Facial Action Coding System (FACS) and computed by a machine learning system. In particular, our method expands over EmotiRAM, an approach for multi-cue emotion recognition, in which we improve over their facial encoding module.

Large neural networks pretrained on web-scale corpora are central to modern machine learning. In this paradigm, the distribution of the large, heterogeneous pretraining data rarely matches that of the application domain. This work considers modifying the pretraining distribution in the case where one has a small sample of data reflecting the targeted test conditions. We propose an algorithm motivated by a recent formulation of this setting as an online, bilevel optimization problem. With scalability in mind, our algorithm prioritizes computing gradients at training points which are likely to most improve the loss on the targeted distribution. Empirically, we show that in some cases this approach is beneficial over existing strategies from the domain adaptation literature but may not succeed in other cases. We propose a simple test to evaluate when our approach can be expected to work well and point towards further research to address current limitations.

The task of multispectral image segmentation (segmentation of images with numerous channels/bands, each capturing a specific range of wavelengths of electromagnetic radiation) has been previously explored in contexts with large amounts of labeled data. However, these models tend not to generalize well to datasets of smaller size. In this paper, we propose a novel approach for improving few-shot segmentation performance on multispectral images using reinforcement learning to generate representations. These representations are generated in the form of mathematical expressions between channels and are tailored to the specific class being segmented. Our methodology involves training an agent to identify the most informative expressions, updating the dataset using these expressions, and then using the updated dataset to perform segmentation. Due to the limited length of the expressions, the model receives useful representations without any added risk of overfitting. We evaluate the effectiveness of our approach on several multispectral datasets and demonstrate its effectiveness in boosting the performance of segmentation algorithms.

Despite advancements in real estate appraisal methods, this study primarily focuses on two pivotal challenges. Firstly, we explore the often-underestimated impact of Points of Interest (POI) on property values, emphasizing the necessity for a comprehensive, data-driven approach to feature selection. Secondly, we integrate road-network-based Areal Embedding to enhance spatial understanding for real estate appraisal. We first propose a revised method for POI feature extraction, and discuss the impact of each POI for house price appraisal. Then we present the Areal embedding-enabled Masked Multihead Attention-based Spatial Interpolation for House Price Prediction (AMMASI) model, an improvement upon the existing ASI model, which leverages masked multi-head attention on geographic neighbor houses and similar-featured houses. Our model outperforms current baselines and also offers promising avenues for future optimization in real estate appraisal methodologies.

All types of research, development, and policy work can have unintended, adverse consequences - work in responsible artificial intelligence (RAI), ethical AI, or ethics in AI is no exception.

Business automation processes have gained popularity in recent times. Robot Process Automation (RPA) reached its peak in September 2018, according to Google Trends data [1]. In this article, we provide an in-depth analysis of selected papers that describe the current state-of-the-art on RPA and Intelligent Process Automation (IPA). The main objective of this article is to present the latest research and understanding of intelligent methods for processing business rules, especially related to service order handling. The methods discussed involve the use of machine processing techniques and natural language processing. The article is structured as follows: Section 2 describe the research methodology. Section 3 focuses on Robot Process Automation (RPA).

Large Language Models (LLMs) demonstrate impressive reasoning ability and the maintenance of world knowledge not only in natural language tasks, but also in some vision-language tasks such as open-domain knowledge-based visual question answering (OK-VQA). As images are invisible to LLMs, researchers convert images to text to engage LLMs into the visual question reasoning procedure. This leads to discrepancies between images and their textual representations presented to LLMs, which consequently impedes final reasoning performance. To fill the information gap and better leverage the reasoning capability, we design a framework that enables LLMs to proactively ask relevant questions to unveil more details in the image, along with filters for refining the generated information. We validate our idea on OK-VQA and A-OKVQA. Our method continuously boosts the performance of baselines methods by an average gain of 2.15% on OK-VQA, and achieves consistent improvements across different LLMs.