We assume that the values of propositional variables are not defined Discounting future costs and rewards is a common in the terminal state t. The agent a has multiple actions in practice in accounting, game theory, and machine each game state. These actions are depicted in Figure 1 using learning. In spite of this, existing logics for reasoning directed edges. The cost of each action to agent a is shown as about strategies with cost and resource constraints a label on the directed edge. For instance, the directed edge do not account for discounting. The paper from state w to state u with label 2 means that the agent a proposes a sound and complete logical system for has an action with cost 2 to transition the game from state w reasoning about budget-constrained strategic abilities to state u. Transitioning to the terminal state t represents the that incorporates discounting into its semantics.

Post-hoc explanation methods are an important class of approaches that help understand the rationale underlying a trained model's decision. But how useful are they for an end-user towards accomplishing a given task? In this vision paper, we argue the need for a benchmark to facilitate evaluations of the utility of post-hoc explanation methods. As a first step to this end, we enumerate desirable properties that such a benchmark should possess for the task of debugging text classifiers. Additionally, we highlight that such a benchmark facilitates not only assessing the effectiveness of explanations but also their efficiency.

Achieving human-level performance on some of Machine Reading Comprehension (MRC) datasets is no longer challenging with the help of powerful Pre-trained Language Models (PLMs). However, it is necessary to provide both answer prediction and its explanation to further improve the MRC system's reliability, especially for real-life applications. In this paper, we propose a new benchmark called ExpMRC for evaluating the explainability of the MRC systems. ExpMRC contains four subsets, including SQuAD, CMRC 2018, RACE$^+$, and C$^3$ with additional annotations of the answer's evidence. The MRC systems are required to give not only the correct answer but also its explanation. We use state-of-the-art pre-trained language models to build baseline systems and adopt various unsupervised approaches to extract evidence without a human-annotated training set. The experimental results show that these models are still far from human performance, suggesting that the ExpMRC is challenging. Resources will be available through https://github.com/ymcui/expmrc

Model selection, via penalized likelihood type criteria, is a standard task in many statistical inference and machine learning problems. Progress has led to deriving criteria with asymptotic consistency results and an increasing emphasis on introducing non-asymptotic criteria. We focus on the problem of modeling non-linear relationships in regression data with potential hidden graph-structured interactions between the high-dimensional predictors, within the mixture of experts modeling framework. In order to deal with such a complex situation, we investigate a block-diagonal localized mixture of polynomial experts (BLoMPE) regression model, which is constructed upon an inverse regression and block-diagonal structures of the Gaussian expert covariance matrices. We introduce a penalized maximum likelihood selection criterion to estimate the unknown conditional density of the regression model. This model selection criterion allows us to handle the challenging problem of inferring the number of mixture components, the degree of polynomial mean functions, and the hidden block-diagonal structures of the covariance matrices, which reduces the number of parameters to be estimated and leads to a trade-off between complexity and sparsity in the model. In particular, we provide a strong theoretical guarantee: a finite-sample oracle inequality satisfied by the penalized maximum likelihood estimator with a Jensen-Kullback-Leibler type loss, to support the introduced non-asymptotic model selection criterion. The penalty shape of this criterion depends on the complexity of the considered random subcollection of BLoMPE models, including the relevant graph structures, the degree of polynomial mean functions, and the number of mixture components.

Vector representations have become a central element in semantic language modelling, leading to mathematical overlaps with many fields including quantum theory. Compositionality is a core goal for such representations: given representations for 'wet' and 'fish', how should the concept 'wet fish' be represented? This position paper surveys this question from two points of view. The first considers the question of whether an explicit mathematical representation can be successful using only tools from within linear algebra, or whether other mathematical tools are needed. The second considers whether semantic vector composition should be explicitly described mathematically, or whether it can be a model-internal side-effect of training a neural network. A third and newer question is whether a compositional model can be implemented on a quantum computer. Given the fundamentally linear nature of quantum mechanics, we propose that these questions are related, and that this survey may help to highlight candidate operations for future quantum implementation.

On May 4th, Infosys announced that it is planning to hire 1,000 workers in the next three years to support the UK economy post the pandemic. These fresh hires would be working in the innovative digital space with disruptive technologies like artificial intelligence, cloud computing, and data analytics. The employees will also be provided with critical training and mentoring. Infosys said that it will mostly hire fresh graduates from different universities in the UK and the new recruits will be working in Infosys' design studio in Shoreditch, an innovation center in Canary Wharf, proximity centers in Nottingham, and other client locations across the country. Infosys is globally recognized as a top employer and this initiative will enable to bridge the gap that occurred in recent digital transformations across different industries.

Up-to-date poverty maps are an important tool for policy makers, but until now, have been prohibitively expensive to produce. We propose a generalizable prediction methodology to produce poverty maps at the village level using geospatial data and machine learning algorithms. We tested the proposed method for 25 Sub-Saharan African countries and validated them against survey data. The proposed method can increase the validity of both single country and cross-country estimations leading to higher precision in poverty maps of 44 Sub-Saharan African countries than previously available. More importantly, our cross-country estimation enables the creation of poverty maps when it is not practical or cost-effective to field new national household surveys, as is the case with many low- and middle-income countries.

Many research papers that propose models to predict the course of the COVID-19 pandemic either use handcrafted statistical models or large neural networks. Even though large neural networks are more powerful than simpler statistical models, they are especially hard to train on small datasets. This paper not only presents a model with grater flexibility than the other proposed neural networks, but also presents a model that is effective on smaller datasets. To improve performance on small data, six regularisation methods were tested. The results show that the GRU combined with 20% Dropout achieved the lowest RMSE scores. The main finding was that models with less access to data relied more on the regulariser. Applying Dropout to a GRU model trained on only 28 days of data reduced the RMSE by 23%.

User and item attributes are essential side-information; their interactions (i.e., their co-occurrence in the sample data) can significantly enhance prediction accuracy in various recommender systems. We identify two different types of attribute interactions, inner interactions and cross interactions: inner interactions are those between only user attributes or those between only item attributes; cross interactions are those between user attributes and item attributes. Existing models do not distinguish these two types of attribute interactions, which may not be the most effective way to exploit the information carried by the interactions. To address this drawback, we propose a neural Graph Matching based Collaborative Filtering model (GMCF), which effectively captures the two types of attribute interactions through modeling and aggregating attribute interactions in a graph matching structure for recommendation. In our model, the two essential recommendation procedures, characteristic learning and preference matching, are explicitly conducted through graph learning (based on inner interactions) and node matching (based on cross interactions), respectively. Experimental results show that our model outperforms state-of-the-art models. Further studies verify the effectiveness of GMCF in improving the accuracy of recommendation.

Privacy protection has recently attracted the attention of both academics and industries. Society protects individual data privacy through complex legal frameworks. This has become a topic of interest with the increasing applications of data science and artificial intelligence that have created a higher demand to the ubiquitous application of the data. The privacy protection of the broad Data-InformationKnowledge-Wisdom (DIKW) landscape, the next generation of information organization, has not been in the limelight. Next, we will explore DIKW architecture through the applications of popular swarm intelligence and differential privacy. As differential privacy proved to be an effective data privacy approach, we will look at it from a DIKW domain perspective. Swarm Intelligence could effectively optimize and reduce the number of items in DIKW used in differential privacy, this way accelerating both the effectiveness and the efficiency of differential privacy for crossing multiple modals of conceptual DIKW. The proposed approach is proved through the application of personalized data that is based on the open-sourse IRIS dataset. This experiment demonstrates the efficiency of Swarm Intelligence in reducing computing complexity.