OpenRaiser/Intern-Atlas huggingface dataset:
Robot Hand-Eye Calibration using Structure-from-Motion
Venue: arXiv cs.RO 2023
Year: 2023

In this paper we propose a new flexible method for hand-eye calibration. The vast majority of existing hand-eye calibration techniques requires a calibration rig which is used in conjunction with camera pose estimation methods. Instead, we combine structure-from-motion with known robot motions and we show that the solution can be obtained in linear form. The latter solves for both the hand-eye parameters and for the unknown scale factor inherent with structure-from-motion methods. The algebraic analysis that is made possible with such a linear formulation allows to investigate not only the well known case of general screw motions but also such singular motions as pure translations, pure rotations, and planar motions. In essence, the robot-mounted camera looks to an unknown rigid layout, tracks points over an image sequence and estimates the camera-to-robot relationship. Such a self calibration process is relevant for unmanned vehicles, robots working in remote places, and so forth. We conduct a large number of experiments which validate the quality of the method by comparing it with existing ones.
Deepparse : An Extendable, and Fine-Tunable State-Of-The-Art Library for Parsing Multinational Street Addresses
Venue: arXiv cs.CL 2023
Year: 2023

Segmenting an address into meaningful components, also known as address parsing, is an essential step in many applications from record linkage to geocoding and package delivery. Consequently, a lot of work has been dedicated to develop accurate address parsing techniques, with machine learning and neural network methods leading the state-of-the-art scoreboard. However, most of the work on address parsing has been confined to academic endeavours with little availability of free and easy-to-use open-source solutions. This paper presents Deepparse, a Python open-source, extendable, fine-tunable address parsing solution under LGPL-3.0 licence to parse multinational addresses using state-of-the-art deep learning algorithms and evaluated on over 60 countries. It can parse addresses written in any language and use any address standard. The pre-trained model achieves average $99~\%$ parsing accuracies on the countries used for training with no pre-processing nor post-processing needed. Moreover, the library supports fine-tuning with new data to generate a custom address parser.
FATURA: A Multi-Layout Invoice Image Dataset for Document Analysis and Understanding
Venue: arXiv cs.CV 2023
Year: 2023

Document analysis and understanding models often require extensive annotated data to be trained. However, various document-related tasks extend beyond mere text transcription, requiring both textual content and precise bounding-box annotations to identify different document elements. Collecting such data becomes particularly challenging, especially in the context of invoices, where privacy concerns add an additional layer of complexity. In this paper, we introduce FATURA, a pivotal resource for researchers in the field of document analysis and understanding. FATURA is a highly diverse dataset featuring multi-layout, annotated invoice document images. Comprising $10,000$ invoices with $50$ distinct layouts, it represents the largest openly accessible image dataset of invoice documents known to date. We also provide comprehensive benchmarks for various document analysis and understanding tasks and conduct experiments under diverse training and evaluation scenarios. The dataset is freely accessible at https://zenodo.org/record/8261508, empowering researchers to advance the field of document analysis and understanding.
A PTAS for Triangle-Free 2-Matching
Venue: arXiv cs.DS 2023
Year: 2023

In the Triangle-Free (Simple) 2-Matching problem we are given an undirected graph $G=(V,E)$. Our goal is to compute a maximum-cardinality $M\subseteq E$ satisfying the following properties: (1) at most two edges of $M$ are incident on each node (i.e., $M$ is a 2-matching) and (2) $M$ does not induce any triangle. In his Ph.D. thesis from 1984, Harvitgsen presents a complex polynomial-time algorithm for this problem, with a very complex analysis. This result was never published in a journal nor reproved in a different way, to the best of our knowledge. In this paper we have a fresh look at this problem and present a simple PTAS for it based on local search. Our PTAS exploits the fact that, as long as the current solution is far enough from the optimum, there exists a short augmenting trail (similar to the maximum matching case).
A New Angle: On Evolving Rotation Symmetric Boolean Functions
Venue: arXiv cs.NE 2023
Year: 2023

Rotation symmetric Boolean functions represent an interesting class of Boolean functions as they are relatively rare compared to general Boolean functions. At the same time, the functions in this class can have excellent properties, making them interesting for various practical applications. The usage of metaheuristics to construct rotation symmetric Boolean functions is a direction that has been explored for almost twenty years. Despite that, there are very few results considering evolutionary computation methods. This paper uses several evolutionary algorithms to evolve rotation symmetric Boolean functions with different properties. Despite using generic metaheuristics, we obtain results that are competitive with prior work relying on customized heuristics. Surprisingly, we find that bitstring and floating point encodings work better than the tree encoding. Moreover, evolving highly nonlinear general Boolean functions is easier than rotation symmetric ones.
AMES: A Differentiable Embedding Space Selection Framework for Latent Graph Inference
Venue: arXiv cs.LG 2023
Year: 2023

In real-world scenarios, although data entities may possess inherent relationships, the specific graph illustrating their connections might not be directly accessible. Latent graph inference addresses this issue by enabling Graph Neural Networks (GNNs) to operate on point cloud data, dynamically learning the necessary graph structure. These graphs are often derived from a latent embedding space, which can be modeled using Euclidean, hyperbolic, spherical, or product spaces. However, currently, there is no principled differentiable method for determining the optimal embedding space. In this work, we introduce the Attentional Multi-Embedding Selection (AMES) framework, a differentiable method for selecting the best embedding space for latent graph inference through backpropagation, considering a downstream task. Our framework consistently achieves comparable or superior results compared to previous methods for latent graph inference across five benchmark datasets. Importantly, our approach eliminates the need for conducting multiple experiments to identify the optimal embedding space. Furthermore, we explore interpretability techniques that track the gradient contributions of different latent graphs, shedding light on how our attention-based, fully differentiable approach learns to choose the appropriate latent space. In line with previous works, our experiments emphasize the advantages of hyperbolic spaces in enhancing performance. More importantly, our interpretability framework provides a general approach for quantitatively comparing embedding spaces across different tasks based on their contributions, a dimension that has been overlooked in previous literature on latent graph inference.
Ovarian Cancer Data Analysis using Deep Learning: A Systematic Review from the Perspectives of Key Features of Data Analysis and AI Assurance
Venue: arXiv cs.LG 2023
Year: 2023

Background and objectives: By extracting this information, Machine or Deep Learning (ML/DL)-based autonomous data analysis tools can assist clinicians and cancer researchers in discovering patterns and relationships from complex data sets. Many DL-based analyses on ovarian cancer (OC) data have recently been published. These analyses are highly diverse in various aspects of cancer (e.g., subdomain(s) and cancer type they address) and data analysis features. However, a comprehensive understanding of these analyses in terms of these features and AI assurance (AIA) is currently lacking. This systematic review aims to fill this gap by examining the existing literature and identifying important aspects of OC data analysis using DL, explicitly focusing on the key features and AI assurance perspectives. Methods: The PRISMA framework was used to conduct comprehensive searches in three journal databases. Only studies published between 2015 and 2023 in peer-reviewed journals were included in the analysis. Results: In the review, a total of 96 DL-driven analyses were examined. The findings reveal several important insights regarding DL-driven ovarian cancer data analysis: - Most studies 71% (68 out of 96) focused on detection and diagnosis, while no study addressed the prediction and prevention of OC. - The analyses were predominantly based on samples from a non-diverse population (75% (72/96 studies)), limited to a geographic location or country. - Only a small proportion of studies (only 33% (32/96)) performed integrated analyses, most of which used homogeneous data (clinical or omics). - Notably, a mere 8.3% (8/96) of the studies validated their models using external and diverse data sets, highlighting the need for enhanced model validation, and - The inclusion of AIA in cancer data analysis is in a very early stage; only 2.1% (2/96) explicitly addressed AIA through explainability.
Leveraging Previous Facial Action Units Knowledge for Emotion Recognition on Faces
Venue: arXiv cs.CV 2023
Year: 2023

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.
H-COAL: Human Correction of AI-Generated Labels for Biomedical Named Entity Recognition
Venue: arXiv cs.CL 2023
Year: 2023

With the rapid advancement of machine learning models for NLP tasks, collecting high-fidelity labels from AI models is a realistic possibility. Firms now make AI available to customers via predictions as a service (PaaS). This includes PaaS products for healthcare. It is unclear whether these labels can be used for training a local model without expensive annotation checking by in-house experts. In this work, we propose a new framework for Human Correction of AI-Generated Labels (H-COAL). By ranking AI-generated outputs, one can selectively correct labels and approach gold standard performance (100% human labeling) with significantly less human effort. We show that correcting 5% of labels can close the AI-human performance gap by up to 64% relative improvement, and correcting 20% of labels can close the performance gap by up to 86% relative improvement.
Exploring Lip Segmentation Techniques in Computer Vision: A Comparative Analysis
Venue: arXiv cs.CV 2023
Year: 2023

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.
Risk-averse Batch Active Inverse Reward Design
Venue: arXiv cs.LG 2023
Year: 2023

Designing a perfect reward function that depicts all the aspects of the intended behavior is almost impossible, especially generalizing it outside of the training environments. Active Inverse Reward Design (AIRD) proposed the use of a series of queries, comparing possible reward functions in a single training environment. This allows the human to give information to the agent about suboptimal behaviors, in order to compute a probability distribution over the intended reward function. However, it ignores the possibility of unknown features appearing in real-world environments, and the safety measures needed until the agent completely learns the reward function. I improved this method and created Risk-averse Batch Active Inverse Reward Design (RBAIRD), which constructs batches, sets of environments the agent encounters when being used in the real world, processes them sequentially, and, for a predetermined number of iterations, asks queries that the human needs to answer for each environment of the batch. After this process is completed in one batch, the probabilities have been improved and are transferred to the next batch. This makes it capable of adapting to real-world scenarios and learning how to treat unknown features it encounters for the first time. I also integrated a risk-averse planner, similar to that of Inverse Reward Design (IRD), which samples a set of reward functions from the probability distribution and computes a trajectory that takes the most certain rewards possible. This ensures safety while the agent is still learning the reward function, and enables the use of this approach in situations where cautiousness is vital. RBAIRD outperformed the previous approaches in terms of efficiency, accuracy, and action certainty, demonstrated quick adaptability to new, unknown features, and can be more widely used for the alignment of crucial, powerful AI models.
Automated Detection of hidden Damages and Impurities in Aluminum Die Casting Materials and Fibre-Metal Laminates using Low-quality X-ray Radiography, Synthetic X-ray Data Augmentation by Simulation, and Machine Learning
Venue: arXiv cs.CV 2023
Year: 2023

Detection and characterization of hidden defects, impurities, and damages in layered composites like Fibre laminates, e.g., Fibre Metal Laminates (FML), as well as in monolithic materials, e.g., aluminum die casting materials, is still a challenge. This work discusses methods and challenges in data-driven modeling of automated damage and defect detectors using X-ray single- and multi-projection (CT) images. Three main issues are identified: Data and feature variance, data feature labeling (for supervised machine learning), and the missing ground truth. It will be shown that only simulation of data can deliver a ground truth data set and accurate labeling. Noise has significant impact on the feature detection and will be discussed. Data-driven feature detectors are implemented with semantic pixel- or z-profile Convolutional Neural Networks and LSTM Auto-encoders. Data is measured with three different devices: A low-quality and low-cost (Low-Q), a mid- and a high-quality (micro-CT, Mid-/High-Q) device. The goals of this work are the training of robust and generalized feature detectors with synthetic data and the transition from High- and Mid-Q laboratory measuring technologies towards in-field usable technologies and methods.
SecureBERT and LLAMA 2 Empowered Control Area Network Intrusion Detection and Classification
Venue: arXiv cs.CR 2023
Year: 2023

Numerous studies have proved their effective strength in detecting Control Area Network (CAN) attacks. In the realm of understanding the human semantic space, transformer-based models have demonstrated remarkable effectiveness. Leveraging pre-trained transformers has become a common strategy in various language-related tasks, enabling these models to grasp human semantics more comprehensively. To delve into the adaptability evaluation on pre-trained models for CAN intrusion detection, we have developed two distinct models: CAN-SecureBERT and CAN-LLAMA2. Notably, our CAN-LLAMA2 model surpasses the state-of-the-art models by achieving an exceptional performance 0.999993 in terms of balanced accuracy, precision detection rate, F1 score, and a remarkably low false alarm rate of 3.10e-6. Impressively, the false alarm rate is 52 times smaller than that of the leading model, MTH-IDS (Multitiered Hybrid Intrusion Detection System). Our study underscores the promise of employing a Large Language Model as the foundational model, while incorporating adapters for other cybersecurity-related tasks and maintaining the model's inherent language-related capabilities.
PhytNet -- Tailored Convolutional Neural Networks for Custom Botanical Data
Venue: arXiv cs.CV 2023
Year: 2023

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.
DAS: A Deformable Attention to Capture Salient Information in CNNs
Venue: arXiv cs.CV 2023
Year: 2023

Convolutional Neural Networks (CNNs) excel in local spatial pattern recognition. For many vision tasks, such as object recognition and segmentation, salient information is also present outside CNN's kernel boundaries. However, CNNs struggle in capturing such relevant information due to their confined receptive fields. Self-attention can improve a model's access to global information but increases computational overhead. We present a fast and simple fully convolutional method called DAS that helps focus attention on relevant information. It uses deformable convolutions for the location of pertinent image regions and separable convolutions for efficiency. DAS plugs into existing CNNs and propagates relevant information using a gating mechanism. Compared to the O(n^2) computational complexity of transformer-style attention, DAS is O(n). Our claim is that DAS's ability to pay increased attention to relevant features results in performance improvements when added to popular CNNs for Image Classification and Object Detection. For example, DAS yields an improvement on Stanford Dogs (4.47%), ImageNet (1.91%), and COCO AP (3.3%) with base ResNet50 backbone. This outperforms other CNN attention mechanisms while using similar or less FLOPs. Our code will be publicly available.
Formal Verification of Long Short-Term Memory based Audio Classifiers: A Star based Approach
Venue: arXiv cs.SD 2023
Year: 2023

Formally verifying audio classification systems is essential to ensure accurate signal classification across real-world applications like surveillance, automotive voice commands, and multimedia content management, preventing potential errors with serious consequences. Drawing from recent research, this study advances the utilization of star-set-based formal verification, extended through reachability analysis, tailored explicitly for Long Short-Term Memory architectures and their Convolutional variations within the audio classification domain. By conceptualizing the classification process as a sequence of set operations, the star set-based reachability approach streamlines the exploration of potential operational states attainable by the system. The paper serves as an encompassing case study, validating and verifying sequence audio classification analytics within real-world contexts. It accentuates the necessity for robustness verification to ensure precise and dependable predictions, particularly in light of the impact of noise on the accuracy of output classifications.
User-Like Bots for Cognitive Automation: A Survey
Venue: arXiv cs.HC 2023
Year: 2023

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 web environments' variability and dynamic nature. 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.