Reviewerly/exHarmony huggingface dataset:
Multi-layer Aggregation as a Key to Feature-Based OOD Detection

Deep Learning models are easily disturbed by variations in the input images that were not observed during the training stage, resulting in unpredictable predictions. Detecting such Out-of-Distribution (OOD) images is particularly crucial in the context of medical image analysis, where the range of possible abnormalities is extremely wide. Recently, a new category of methods has emerged, based on the analysis of the intermediate features of a trained model. These methods can be divided into 2 groups: single-layer methods that consider the feature map obtained at a fixed, carefully chosen layer, and multi-layer methods that consider the ensemble of the feature maps generated by the model. While promising, a proper comparison of these algorithms is still lacking. In this work, we compared various feature-based OOD detection methods on a large spectra of OOD (20 types), representing approximately 7800 3D MRIs. Our experiments shed the light on two phenomenons. First, multi-layer methods consistently outperform single-layer approaches, which tend to have inconsistent behaviour depending on the type of anomaly. Second, the OOD detection performance is variable depending on the architecture of the underlying neural network. We provide our implementation of the feature-based OOD detectors at https://github.com/benolmbrt/MedicOOD .
Numerical Uncertainty of Convolutional Neural Networks Inference for Structural Brain MRI Analysis

This paper investigates the numerical uncertainty of Convolutional Neural Networks (CNNs) inference for structural brain MRI analysis. It applies Random Rounding—a stochastic arithmetic technique— to CNN models employed in non-linear registration (SynthMorph) and whole-brain segmentation (FastSurfer), and compares the resulting numerical uncertainty to the one measured in a reference image-processing pipeline (FreeSurfer recon-all). Results obtained on 32 representative subjects show that CNN predictions are substantially more accurate numerically than traditional image-processing results (non-linear registration: 19 vs 13 significant bits on average; whole-brain segmentation: 0.99 vs 0.92 Sørensen-Dice score on average), which suggests a better reproducibility of CNN results across execution environments.
TriadNet: Sampling-Free Predictive Intervals for Lesional Volume in 3D Brain MR Images

The volume of a brain lesion (e.g. infarct or tumor) is a powerful indicator of patient prognosis and can be used to guide the therapeutic strategy. Lesional volume estimation is usually performed by segmentation with deep convolutional neural networks (CNN), currently the state-of-the-art approach. However, to date, few work has been done to equip volume segmentation tools with adequate quantitative predictive intervals, which can hinder their usefulness and acceptation in clinical practice. In this work, we propose TriadNet, a segmentation approach relying on a multi-head CNN architecture, which provides both the lesion volumes and the associated predictive intervals simultaneously, in less than a second. We demonstrate its superiority over other solutions on BraTS 2021, a large-scale MRI glioblastoma image database. Our implementation of TriadNet is available at https://github.com/benolmbrt/TriadNet .
RR-CP: Reliable-Region-Based Conformal Prediction for Trustworthy Medical Image Classification

Conformal prediction (CP) generates a set of predictions for a given test sample such that the prediction set almost always contains the true label (e.g., 99.5% of the time). CP provides comprehensive predictions on possible labels of a given test sample, and the size of the set indicates how certain the predictions are (e.g., a set larger than one is 'uncertain'). Such distinct properties of CP enable effective collaborations between human experts and medical AI models, allowing efficient intervention and quality check in clinical decision-making. In this paper, we propose a new method called Reliable-Region-Based Conformal Prediction (RR-CP), which aims to impose a stronger statistical guarantee so that an extremely low error rate (e.g., 0.5%) can be achieved in the test time, and under this constraint, the size of the prediction set is optimized to be small. We consider a small prediction set size an important measure only when the low error rate is achieved. Experiments on five public datasets show that our RR-CP performs well: with a reasonably small-sized prediction set, it achieves the user-specified low error rate significantly more frequently than exiting CP methods.
Sentiment Analysis “Using SVM, KNN and SVM with PCA”

There is a lot of scope of sentimental analysis in the field of natural language processing. Sentiment analysis gives the ability to a programmable system to analyse the sentiment behind some natural language behaving like a human. It does not make the system a human but the ability to understand sentiments behind some natural language that humans use to communicate with each other can be analysed via a computer or system. Sentiment analysis is an excellent way to discover people's views about places, architecture, film, brand, book, story, news, novel and restaurant reviews, etc. In this paper, we compared various sentimental analysis techniques to classify the restaurant reviews as positive or negative sentiments to find the most suitable one to use to achieve maximum accuracy in terms of classification between positive or negative reviews and also automated the approach. This greatly helps the owners of the restaurants to have a clear view of what the customers like about the restaurant and its service and the negative part too. Necessary steps can then be taken by the owner and staff to improve through those reviews. Analyzing every individual review by customers is a hectic task and thus automating this process will reduce costing of labor as well as time. We have used different techniques and methodologies for analyzing the sentiments of restaurant reviews using Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Support Vector Machine with PCA (Principal Component Analysis). And we achieved an accuracy of about 96% through the SVM model beside other classification models.
Multi-Level Curriculum Learning for Multi-Turn Dialogue Generation

Since deep learning is the dominant paradigm in the multi-turn dialogue generation task, large-scale training data is the key factor affecting the model performance. To make full use of the training data, the existing work directly applied curriculum learning to the multi-turn dialogue generation task, training model in a “easy-to-hard” way. But the design of the current methodology does not consider dialogue-specific features. To close this gap, we propose a Multi-Level Curriculum Learning (MLCL) method for multi-turn dialogue generation by considering the word-level linguistic feature and utterance-level semantic relation in a dialogue. The motivation is that word-level knowledge is beneficial to understanding complex utterance-level dependency of dialogue. Thus, we design two difficulty measurements and a self-adaptive curriculum scheduler, making the model gradually shift the learning focus from word-level to utterance-level information during the training process. We also verify the independence and complementarity of the two measurements at different levels. We evaluate the performance on two widely used multi-turn dialogue datasets, and the results demonstrate that our proposed method outperforms the strong baselines and existing CL methods in terms of automated metrics and human evaluation. We will release the code files upon acceptance.
Rethinking Certification for Trustworthy Machine-Learning-Based Applications

Machine Learning (ML) is increasingly used to implement advanced applications with non-deterministic behavior, which operate on the cloud-edge continuum.The pervasive adoption of ML is urgently calling for assurance solutions assessing applications non-functional properties (e.g., fairness, robustness, privacy) with the aim to improve their trustworthiness.Certification has been clearly identified by policymakers, regulators, and industrial stakeholders as the preferred assurance technique to address this pressing need.Unfortunately, existing certification schemes are not immediately applicable to non-deterministic applications built on ML models.This article analyzes the challenges and deficiencies of current certification schemes, discusses open research issues, and proposes a first certification scheme for ML-based applications.1.
UTFNet: Uncertainty-Guided Trustworthy Fusion Network for RGB-Thermal Semantic Segmentation

In real-world scenarios, the information quality provided by RGB and thermal (RGB-T) sensors often varies across samples. This variation will negatively impact the performance of semantic segmentation models in utilizing complementary information from RGB-T modalities, resulting in a decrease in accuracy and fusion credibility. Dynamically estimating the uncertainty of each modality for different samples could help the model perceive such information quality variation and then provide guidance for a reliable fusion. With this in mind, we propose a novel uncertainty-guided trustworthy fusion network (UTFNet) for RGB-T semantic segmentation. Specifically, we design an uncertainty estimation and evidential fusion (UEEF) module to quantify the uncertainty of each modality and then utilize the uncertainty to guide the information fusion. In the UEEF module, we introduce the Dirichlet distribution to model the distribution of the predicted probabilities, parameterized with evidence from each modality and then integrate them with the Dempster-Shafer theory (DST). Moreover, illumination evidence gathering (IEG) and multi-scale evidence gathering (MEG) modules by considering illumination and target multi-scale information respectively are designed to gather more reliable evidence. In the IEG module, we calculate the illumination probability and model it as the illumination evidence. The MEG module can collect evidence for each modality across multiple scales. Both qualitative and quantitative results demonstrate the effectiveness of our proposed model in accuracy, robustness and trustworthiness. The code will be accessible at https://github.com/KustTeamWQW/UTFNet.
Teacher Leadership and Teacher Wellbeing: Any Relationship?

Teacher wellbeing and leadership are pivotal for effective education. This study examines the interplay between teacher leadership and teacher wellbeing. Utilizing a positivist approach, data was collected through surveys from 383 teachers across six countries. Statistical analysis including structural equation modeling, regression analysis, and correlation assessments were conducted. Findings imply a reciprocal relationship between teacher leadership and wellbeing in educational contexts, with leadership exerting a marginally stronger positive impact on wellbeing. Optimal wellbeing, encompassing positive physical, emotional, and cognitive states, equips teachers to engage in effective leadership practices, foster collaborations, and contribute actively to improved teaching and learning. Conversely, compromised wellbeing, stemming from heightened stress, burnout, or dissatisfaction, hinders effective leadership, dampening motivation, creativity, and problem-solving abilities. These findings underscore the pivotal role of cultivating teacher leadership in fostering an environment conducive to teacher wellbeing, and vice versa.
Exploring the impact of fairness of social bots on user experience

There exists literary evidence that there is an established relationship between humanizing bots and customer perceptions of fairness and consequential user experience. This study seeks to evaluate factors that impact the perception surrounding the fairness of social bots and the impact of these factors on user experience. A qualitative approach was used to examine the fairness factors related to social bots and their impact on user experience with the help of data collected from 32 professionals with diverse backgrounds. The results obtained from manual coding and NVivo were assessed to evaluate the convergence of themes for the confluence of results and establish thematic validity. The findings revealed two major themes namely, artificial intelligence (AI) technology governance and AI technology outcome. This research furthers user experience literature and provides a theoretical framework delineating antecedents (AI technology governance and AI technology outcomes) of positive user experience while deploying AI-based social bots.
On the use of matrix profiles and optimal transport theory for multivariate time series anomaly detection within structural health monitoring

In order for a practical application of structural health monitoring to be considered successful, not only is the detection of anomalies important but so is the tracking of various other behaviors, which may be induced by changing operational conditions or environmental variations. Rather than being problems of anomaly detection, these are known as problems of changing contexts or changing semantics. In this paper, we propose a novel, and efficient method which is able to perform both tasks: anomaly detection, and semantic analysis, simultaneously, and which is also able to scale to a multi-sensor network array. The proposed methodology works to combine a recent development from the field of time series data mining known as matrix profiling, as well as ideas from a popular area of mathematics known as optimal transport theory. In particular, matrix profiles will be used to generate curves of semantic segmentation, over multivariate time series data. From here, it will be demonstrated that by finding the unbalanced optimal transport barycenter one can successfully pool together information from a sensor array network in such a way that the presence of false positive transition points is reduced significantly when compared to the baseline mean. Moreover, this will be demonstrated to occur in a hierarchical fashion by employing a variational mode decomposition across each sensor in the network array. Overall, the proposed methodology: (i) Can work in the small data context without requiring a large amount of data for model training and validation, (ii) Applies readily to unsupervised and semi-supervised learning settings, (iii) Can extend to the online learning setting, (iv) Is adaptable and robust for sensor array network problems, (v) Does not require specifying any threshold, and (vi) Is highly interpretable. Finally, the proposed methodology will be validated on three different levels of investigation: (i) A numerical dataset of an aerospace model based on a two-dimensional plate model, (ii) An experimental dataset of a building from Los Alamos National Laboratories, and finally, (iii) A field investigation on a cable-stayed bridge located in Sydney, Australia.
Ensemble Learning using Motion Sensors and Location for Human Activity Recognition

This study describes the human activity recognition method for the Sussex-Huawei Locomotion-Transportation (SHL) recognition challenge by team TDU_BSA. The user and phone location of the test data were estimated; the phone location of the test data was expected to be in the hand of the user. For each segment of the test data, users and phone location were then estimated. By combining motion sensors (accelerometer, gyroscope, and magnetometer) with location, we achieved a higher accuracy than in the case of previous challenges. The classification accuracy was improved by ensemble learning of LSTM(Long Short-Term Memory)-based deep learning and XGBoost model. Finally, by applying a mode filter to the estimation results, the F-measure of the SHL validation set was 98.5% at the submission stage.
Enhancing Transportation Mode Detection using Multi-scale Sensor Fusion and Spatial-topological Attention

Mobile sensors have improved traffic prediction through transportation mode recognition. Researchers are interested in exploring mobile sensor-based recognition methods for transportation modes. The SHL Recognition Challenge is a prominent competition in this field. SHL Challenge 2023 introduced a diverse dataset with GNSS-based and motion sensor data for transportation mode recognition. Our team, "we-can-fly," presents a fine-grained method using a multi-scale fusion approach that incorporates motion sensor and GNSS information. With temporal and topological attention mechanisms, we capture scene characteristics and enhance contextual understanding for transportation mode recognition. On the validation set, our method achieves an impressive 72.39% accuracy and 71.25% F1 score, confirming the effectiveness of our multimodal fusion transportation mode recognition algorithm.
Towards Adaptive User-centered Neuro-symbolic Learning for Multimodal Interaction with Autonomous Systems

Recent advances in deep learning and data-driven approaches have facilitated the perception of objects and their environments in a perceptual subsymbolic manner. Thus, these autonomous systems can now perform object detection, sensor data fusion, and language understanding tasks. However, there is an increasing demand to further enhance these systems to attain a more conceptual and symbolic understanding of objects to acquire the underlying reasoning behind the learned tasks. Achieving this level of powerful artificial intelligence necessitates considering both explicit teachings provided by humans (e.g., explaining how to act) and implicit teaching obtained through observing human behavior (e.g., through system sensors). Hence, it is imperative to incorporate symbolic and subsymbolic learning approaches to support implicit and explicit interaction models. This integration enables the system to achieve multimodal input and output capabilities. In this Blue Sky paper, we argue for considering these input types, along with human-in-the-loop and incremental learning techniques, to advance the field of artificial intelligence and enable autonomous systems to emulate human learning. We propose several hypotheses and design guidelines aimed at achieving this objective.
SmoothLander: A Quadrotor Landing Control System with Smooth Trajectory Guarantee Based on Reinforcement Learning

The landing process of the quadrotors can be affected by the disturbance from the ground effect when approaching the landing surface. Such a disturbance significantly increases the chances of collision and jittering of the quadrotors, thereby posing threats to the safety of both the quadrotors and the mounted equipment. In light of this, we propose SmoothLander, an aerodynamics and reinforcement learning-based control system to stabilize the quadrotors under the influence of the ground effect and control noise. First, we design a landing trajectory for the quadrotor in accordance with aerodynamics. Then we design a reinforcement learning-based command generator to effectively optimize the quadrotor’s landing behavior. We evaluate our control system through physical feature-based simulation and in-field experiments. The results show that our method can enable the quadrotor to land more smoothly and stably against control noise than the baseline.
Designing Dissolving Wearables

Bio-based materials facilitate the development of more sustainable devices and wearables, expanding the range of design possibilities beyond conventional materials. Our work with biofoam explores one such quality, dissolving, as a unique affordance for designing and interacting with wearables. We developed techniques to make biofoam yarns, and used them to craft three wearables: “Seasonal Footwear", a “Reveal Bralette", and an “Unfolding Lace Top". These wearables incorporate sections that dissolve in water, allowing customization to suit the user’s needs. These wearables illustrate short-term use cases, such as a one-time reveal or shape change. We explore this novel design space as sustainable ephemeral fashion, where bio-based dissolving materials enable revealing, transformative, and interactive functionalities.
The UEA Digital Humans entry to the GENEA Challenge 2023

This paper describes our entry to the GENEA (Generation and Evaluation of Non-verbal Behaviour for Embodied Agents) Challenge 2023. This year’s challenge focuses on generating gestures in a dyadic setting – predicting a main-agent’s motion from the speech of both the main-agent and an interlocutor. We adapt a Transformer-XL architecture for this task by adding a cross-attention module that integrates the interlocutor’s speech with that of the main-agent. Our model is conditioned on speech audio (encoded using PASE+), text (encoded using FastText) and a speaker identity label, and is able to generate smooth and speech appropriate gestures for a given identity. We consider the GENEA Challenge user study results and present a discussion of our model strengths and where improvements can be made.
Comprehensive Analysis of the Trade of NFTs at Major Auction Houses: From Hype to Reality

On 11 March 2021, amidst the lingering grip of the COVID-19 pandemic, the art world witnessed an extraordinary event. Christie’s, the renowned auction house, hosted a groundbreaking auction counting just one lot: a Non-Fungible Token (NFT)—a digital asset that had been generating buzz in recent times. The astounding price fetched by the NFT sent shockwaves through the art world. While the 255-year-old auction house was known for selling unique assets, its auctioning of an NFT was surprising as Christie’s online marketplace was not on the blockchain, contrarily to NFT platforms such as Opensea, Nifty Gateway, etc. The resounding success, however, of its historic auction was followed by a surge of NFT off-chain sales at Christie’s, Sotheby’s, and Phillips. While extensive research has been done on the trade of NFTs on the blockchain, little research exists on the trade of NFTs at public auction houses. Based on more than two years’ tracking of NFTs auctioned at major auction houses, our research identifies three phases in the development of the trade and provides valuable insights into the unique factors that contributed to the growth of NFTs at public auctions between the springs of 2021 and 2023.
A Lightweight Robust Distance Estimation Method for Navigation Aiding in Unsupervised Environment Using Monocular Camera

This research addresses the challenges of visually impaired individuals’ independent travel by avoiding obstacles. The study proposes a distance estimation method for uncontrolled three-dimensional environments to aid navigation towards labeled target objects. Utilizing a monocular camera, the method captures cuboid objects (e.g., fences, pillars) for near-front distance estimation. A Field of View (FOV) model calculates the camera’s angle and arbitrary pitch relative to the target Point of Interest (POI) within the image. Experimental results demonstrate the method’s proficiency in detecting distances between objects and the source camera, employing the FOV and Point of View (POV) principles. The approach achieves a mean absolute percentage error (MAPE) of 6.18% and 6.24% on YOLOv4-tiny and YOLOv4, respectively, within 10 m. The distance model only contributes a maximum error of 4% due to POV simplification, affected by target object characteristics, height, and selected POV. The proposed distance estimation method shows promise in drone racing navigation, EV autopilot, and aiding visually impaired individuals. It offers valuable insights into dynamic 3D environment distance estimation, advancing computer vision and autonomous systems.