In the open-set settings, classi ers are required to not only accurately classify new instances of known In many real-world classi cation or recognition tasks, it is often classes (whose samples are observed during training) but also e ectively di cult to collect training examples that exhaust all possible classes recognize the samples from unknown classes. In a nutshell, due to, for example, incomplete knowledge during training or ever open-set classi ers are capable of making the "none of the above" changing regimes. Therefore, samples from unknown/novel classes decision with respect to known classes. This is known as open-set may be encountered in testing/deployment. In such scenarios, the recognition (OSR) [38] and has received signi cant attention in classi ers should be able to i) perform classi cation on known recent years [11, 47]. Since many learning tasks in nance are naturally classes, and at the same time, ii) identify samples from unknown classi cation tasks, for instance, company classi cations using classes. This is known as open-set recognition. Although random Global Industry Classi cation Standard (GICS), fund categorization, forest has been an extremely successful framework as a generalpurpose risk pro ling, economic scenario classi cations, etc., where often a classi cation (and regression) method, in practice, it usually new company, fund or economic scenario may not belong to any operates under the closed-set assumption and is not able to identify of the existing categories, casting these recognition tasks as OSR samples from new classes when run out of the box. In this work, we instead of traditional closed-set classi cation tasks is more appropriate.

My research investigates the use of cutting-edge hybrid In the era of information technology, the distinction between deep learning models to accurately di erentiate between text generated by arti cial intelligence (AI) and that authored AI-generated text and human writing. I applied a robust by humans has become increasingly blurred. This convergence methodology, utilising a carefully selected dataset comprising has profound implications, not only for the eld of AI and human texts from various sources, each tagged natural language processing (NLP) but also for broader societal with instructions.

Graph data structures are widely used to store relational information between several entities. With data being generated worldwide on a large scale, we see a significant growth in the generation of knowledge graphs. Thing in the future is Orange's take on a knowledge graph in the domain of the Web Of Things (WoT), where the main objective of the platform is to provide a digital representation of the physical world and enable cross-domain applications to be built upon this massive and highly connected graph of things. In this context, as the knowledge graph grows in size, it is prone to have noisy and messy data. In this paper, we explore state-of-the-art knowledge graph embedding (KGE) methods to learn numerical representations of the graph entities and, subsequently, explore downstream tasks like link prediction, node classification, and triple classification. We also investigate Graph neural networks (GNN) alongside KGEs and compare their performance on the same downstream tasks. Our evaluation highlights the encouraging performance of both KGE and GNN-based methods on node classification, and the superiority of GNN approaches in the link prediction task. Overall, we show that state-of-the-art approaches are relevant in a WoT context, and this preliminary work provides insights to implement and evaluate them in this context.

A plethora of recent research has proposed several automated methods based on machine learning (ML) and deep learning (DL) to detect cybersickness in Virtual reality (VR). However, these detection methods are perceived as computationally intensive and black-box methods. Thus, those techniques are neither trustworthy nor practical for deploying on standalone VR head-mounted displays (HMDs). This work presents an explainable artificial intelligence (XAI)-based framework VR-LENS for developing cybersickness detection ML models, explaining them, reducing their size, and deploying them in a Qualcomm Snapdragon 750G processor-based Samsung A52 device. Specifically, we first develop a novel super learning-based ensemble ML model for cybersickness detection. Next, we employ a post-hoc explanation method, such as SHapley Additive exPlanations (SHAP), Morris Sensitivity Analysis (MSA), Local Interpretable Model-Agnostic Explanations (LIME), and Partial Dependence Plot (PDP) to explain the expected results and identify the most dominant features. The super learner cybersickness model is then retrained using the identified dominant features. Our proposed method identified eye tracking, player position, and galvanic skin/heart rate response as the most dominant features for the integrated sensor, gameplay, and bio-physiological datasets. We also show that the proposed XAI-guided feature reduction significantly reduces the model training and inference time by 1.91X and 2.15X while maintaining baseline accuracy. For instance, using the integrated sensor dataset, our reduced super learner model outperforms the state-of-the-art works by classifying cybersickness into 4 classes (none, low, medium, and high) with an accuracy of 96% and regressing (FMS 1-10) with a Root Mean Square Error (RMSE) of 0.03.

In the field of materials science, microscopy is the first and often only accessible method for structural characterization. There is a growing interest in the development of machine learning methods that can automate the analysis and interpretation of microscopy images. Typically training of machine learning models requires large numbers of images with associated structural labels, however, manual labeling of images requires domain knowledge and is prone to human error and subjectivity. To overcome these limitations, we present a semi-supervised transfer learning approach that uses a small number of labeled microscopy images for training and performs as effectively as methods trained on significantly larger image datasets. Specifically, we train an image encoder with unlabeled images using self-supervised learning methods and use that encoder for transfer learning of different downstream image tasks (classification and segmentation) with a minimal number of labeled images for training. We test the transfer learning ability of two self-supervised learning methods: SimCLR and Barlow-Twins on transmission electron microscopy (TEM) images. We demonstrate in detail how this machine learning workflow applied to TEM images of protein nanowires enables automated classification of nanowire morphologies (e.g., single nanowires, nanowire bundles, phase separated) as well as segmentation tasks that can serve as groundwork for quantification of nanowire domain sizes and shape analysis. We also extend the application of the machine learning workflow to classification of nanoparticle morphologies and identification of different type of viruses from TEM images.

Limb deficiency severely affects the daily lives of amputees and drives efforts to provide functional robotic prosthetic hands to compensate this deprivation. Convolutional neural network-based computer vision control of the prosthetic hand has received increased attention as a method to replace or complement physiological signals due to its reliability by training visual information to predict the hand gesture. Mounting a camera into the palm of a prosthetic hand is proved to be a promising approach to collect visual data. However, the grasp type labelled from the eye and hand perspective may differ as object shapes are not always symmetric. Thus, to represent this difference in a realistic way, we employed a dataset containing synchronous images from eye- and hand- view, where the hand-perspective images are used for training while the eye-view images are only for manual labelling. Electromyogram (EMG) activity and movement kinematics data from the upper arm are also collected for multi-modal information fusion in future work. Moreover, in order to include human-in-the-loop control and combine the computer vision with physiological signal inputs, instead of making absolute positive or negative predictions, we build a novel probabilistic classifier according to the Plackett-Luce model. To predict the probability distribution over grasps, we exploit the statistical model over label rankings to solve the permutation domain problems via a maximum likelihood estimation, utilizing the manually ranked lists of grasps as a new form of label. We indicate that the proposed model is applicable to the most popular and productive convolutional neural network frameworks.

A drawback of these methods is that they consider only global image context, which may contain information Accurate and e cient product classi cation is signi cant for E-irrelevant to the question. To overcome this, some methods commerce applications, as it enables various downstream tasks have proposed visual attention models that attend to local spatial such as recommendation, retrieval, and pricing. Items often contain regions pertaining to a given question, and then perform multimodal textual and visual information, and utilizing both modalities usually fusion to classify answers accurately [4, 19, 21, 22]. More outperforms classi cation utilizing either mode alone. In this recently, dual attention models have been proposed.