A very popular model-agnostic technique for explaining predictive models is the SHapley Additive exPlanation (SHAP). The two most popular versions of SHAP are a conditional expectation version and an unconditional expectation version (the latter is also known as interventional SHAP). Except for tree-based methods, usually the unconditional version is used (for computational reasons). We provide a (surrogate) neural network approach which allows us to efficiently calculate the conditional version for both neural networks and other regression models, and which properly considers the dependence structure in the feature components. This proposal is also useful to provide drop1 and anova analyses in complex regression models which are similar to their generalized linear model (GLM) counterparts, and we provide a partial dependence plot (PDP) counterpart that considers the right dependence structure in the feature components.

Yet, these considerations are often overlooked, leading to issues of bias, discrimination, and perceived untrustworthiness. In response, we conducted a Systematic Review to unearth challenges and solutions relating to D&I in AI. Our rigorous search yielded 48 research articles published between 2017 and 2022. Open coding of these papers revealed 55 unique challenges and 33 solutions for D&I in AI, as well as 24 unique challenges and 23 solutions for enhancing such practices using AI. This study, by offering a deeper understanding of these issues, will enlighten researchers and practitioners seeking to integrate these principles into future AI systems.

Accurate and timely prediction of sea fog is very important for effectively managing maritime and coastal economic activities. Given the intricate nature and inherent variability of sea fog, traditional numerical and statistical forecasting methods are often proven inadequate. This study aims to develop an advanced sea fog forecasting method embedded in a numerical weather prediction model using the Yangtze River Estuary (YRE) coastal area as a case study. Prior to training our machine learning model, we employ a time-lagged correlation analysis technique to identify key predictors and decipher the underlying mechanisms driving sea fog occurrence. In addition, we implement ensemble learning and a focal loss function to address the issue of imbalanced data, thereby enhancing the predictive ability of our model. To verify the accuracy of our method, we evaluate its performance using a comprehensive dataset spanning one year, which encompasses both weather station observations and historical forecasts. Remarkably, our machine learning-based approach surpasses the predictive performance of two conventional methods, the weather research and forecasting nonhydrostatic mesoscale model (WRF-NMM) and the algorithm developed by the National Oceanic and Atmospheric Administration (NOAA) Forecast Systems Laboratory (FSL). Specifically, in regard to predicting sea fog with a visibility of less than or equal to 1 km with a lead time of 60 hours, our methodology achieves superior results by increasing the probability of detection (POD) while simultaneously reducing the false alarm ratio (FAR).

These notions vary across conceptualization Intersectionality is a critical framework that, through inquiry and (e.g., group, individual fairness [8]) and operationalization (e.g., praxis, allows us to examine how social inequalities persist through pre/in/post-processing [2]) [54]; nevertheless, the literature generally domains of structure and discipline. Given AI fairness' raison d'être agrees on the goal of minimizing negative outcomes across of "fairness," we argue that adopting intersectionality as an analytical demographic groups, including groups associated with multiple, framework is pivotal to effectively operationalizing fairness. "intersectional" demographic attributes (e.g., Black women) [92]. Through a critical review of how intersectionality is discussed in However, Kong [66] observes that AI fairness papers often narrowly 30 papers from the AI fairness literature, we deductively and inductively: interpret intersectional subgroup fairness as intersectionality, the 1) map how intersectionality tenets operate within the critical framework from which the term originates [29, 67]. This AI fairness paradigm and 2) uncover gaps between the conceptualization myopic conceptualization of intersectionality has non-trivial consequences and operationalization of intersectionality. We find that for just AI design and epistemology (i.e., ways of knowing).

In this paper, we introduce a new and simple method for comparing speech utterances without relying on text transcripts. Our speech-to-speech comparison metric utilizes state-of-the-art speech2unit encoders like HuBERT to convert speech utterances into discrete acoustic units. We then propose a simple and easily replicable neural architecture that learns a speech-based metric that closely corresponds to its text-based counterpart. This textless metric has numerous potential applications, including evaluating speech-to-speech translation for oral languages, languages without dependable ASR systems, or to avoid the need for ASR transcription altogether. This paper also shows that for speech-to-speech translation evaluation, ASR-BLEU (which consists in automatically transcribing both speech hypothesis and reference and compute sentence-level BLEU between transcripts) is a poor proxy to real text-BLEU even when ASR system is strong.

Encoding a driving scene into vector representations has been an essential task for autonomous driving that can benefit downstream tasks e.g. trajectory prediction. The driving scene often involves heterogeneous elements such as the different types of objects (agents, lanes, traffic signs) and the semantic relations between objects are rich and diverse. Meanwhile, there also exist relativity across elements, which means that the spatial relation is a relative concept and need be encoded in a ego-centric manner instead of in a global coordinate system. Based on these observations, we propose Heterogeneous Driving Graph Transformer (HDGT), a backbone modelling the driving scene as a heterogeneous graph with different types of nodes and edges. For heterogeneous graph construction, we connect different types of nodes according to diverse semantic relations. For spatial relation encoding, the coordinates of the node as well as its in-edges are in the local node-centric coordinate system. For the aggregation module in the graph neural network (GNN), we adopt the transformer structure in a hierarchical way to fit the heterogeneous nature of inputs. Experimental results show that HDGT achieves state-of-the-art performance for the task of trajectory prediction, on INTERACTION Prediction Challenge and Waymo Open Motion Challenge.

If aliens were to contact Earth, what would it sound like? Such a scenario has been imagined countless times in science fiction but in reality we have no proof extraterrestrials even exist. That hasn't dampened the excitement that an advanced civilisation might be out there, however, and the discovery of a mysterious stellar object which emits a five-minute pulse every 22 minutes will only serve to intensify that. What's more, the scientists who detected it aren't 100 per cent sure what it is. An international team of astronomers led by Australia's Curtin University think it could be an ultra-long period magnetar -- a rare type of star with the most powerful known magnetic fields in the universe.

Ubiquitous technologies, such as smartphones and wearables, are an integral part of our lives today [47, 90]. Their proliferation has given rise to Personal Informatics (PI), namely a class of systems that "help people collect personally relevant information for the purpose of self-reflection and gaining self-knowledge" [66]. Such systems enable people to keep track of their productivity [62], finances [60], and learning [45]. Yet, tracking various aspects of physical and mental health is particularly prevalent [33]. PI systems can continuously and unobtrusively measure and collect physiological and behavioral data, namely, "digital biomarkers", from users through integrated sensors. Digital biomarkers contain an uncanny amount of personal information. Even the coarser behavioral biomarkers acquired from consumer wearables (e.g., steps, calories) strongly correlate to a person's gender, height, and weight [61], while signals of finer granularity (e.g., accelerometer and heart rate), can predict variables associated with an individual's physical health, fitness, and demographics [89]. At the same time, consumer smartphones and wearables are now packed with an increasing number of advanced health tracking features, innovating in personal health, research, and care [7]. Flagship consumer wearable algorithms --some approved by the US Food and Drug Administration-- can now identify signs of atrial fibrillation (AFib) through electrocardiogram (ECG) or photoplethysmography (PPG) signals [37].

We propose a Statistically Enhanced Learning (aka. SEL) model to predict handball games. Our Machine Learning model augmented with SEL features outperforms state-of-the-art models with an accuracy beyond 80%. In this work, we show how we construct the data set to train Machine Learning models on past female club matches. We then compare different models and evaluate them to assess their performance capabilities. Finally, explainability methods allow us to change the scope of our tool from a purely predictive solution to a highly insightful analytical tool. This can become a valuable asset for handball teams' coaches providing valuable statistical and predictive insights to prepare future competitions.

Key Information Extraction (KIE) is a challenging multimodal task that aims to extract structured value semantic entities from visually rich documents. Although significant progress has been made, there are still two major challenges that need to be addressed. Firstly, the layout of existing datasets is relatively fixed and limited in the number of semantic entity categories, creating a significant gap between these datasets and the complex real-world scenarios. Secondly, existing methods follow a two-stage pipeline strategy, which may lead to the error propagation problem. Additionally, they are difficult to apply in situations where unseen semantic entity categories emerge. To address the first challenge, we propose a new large-scale human-annotated dataset named Complex Layout form for key information EXtraction (CLEX), which consists of 5,860 images with 1,162 semantic entity categories. To solve the second challenge, we introduce Parallel Pointer-based Network (PPN), an end-to-end model that can be applied in zero-shot and few-shot scenarios. PPN leverages the implicit clues between semantic entities to assist extracting, and its parallel extraction mechanism allows it to extract multiple results simultaneously and efficiently. Experiments on the CLEX dataset demonstrate that PPN outperforms existing state-of-the-art methods while also offering a much faster inference speed.