What counts as good evidence

Making better use of evidence is essential if public services are to deliver more for less. Central to this challenge is the need for a clearer understanding about standards of evidence that can be applied to the research informing social policy. This paper reviews the extent to which it is possible to reach a workable consensus on ways of identifying and labelling evidence. It does this by exploring the efforts made to date and the debates that have ensued. Throughout, the focus is on evidence that is underpinned by research, rather than other sources of evidence such as expert opinion or stakeholder views.Publisher PD

Morphing and Sampling Network for Dense Point Cloud Completion

3D point cloud completion, the task of inferring the complete geometric shape from a partial point cloud, has been attracting attention in the community. For acquiring high-fidelity dense point clouds and avoiding uneven distribution, blurred details, or structural loss of existing methods' results, we propose a novel approach to complete the partial point cloud in two stages. Specifically, in the first stage, the approach predicts a complete but coarse-grained point cloud with a collection of parametric surface elements. Then, in the second stage, it merges the coarse-grained prediction with the input point cloud by a novel sampling algorithm. Our method utilizes a joint loss function to guide the distribution of the points. Extensive experiments verify the effectiveness of our method and demonstrate that it outperforms the existing methods in both the Earth Mover's Distance (EMD) and the Chamfer Distance (CD).Comment: 8pages, 7 figures, AAAI202

Teacher Evaluator Training & Certification: Lessons Learned From the Measures of Effective Teaching Project

Makes recommendations for the design and implementation of programs to train and certify principals in conducting teacher evaluations, including content, format, and length of training, scoring practice, and criteria for certification tests

Reinforcement Learning for Sparse-Reward Object-Interaction Tasks in First-person Simulated 3D Environments

First-person object-interaction tasks in high-fidelity, 3D, simulated environments such as the AI2Thor virtual home-environment pose significant sample-efficiency challenges for reinforcement learning (RL) agents learning from sparse task rewards. To alleviate these challenges, prior work has provided extensive supervision via a combination of reward-shaping, ground-truth object-information, and expert demonstrations. In this work, we show that one can learn object-interaction tasks from scratch without supervision by learning an attentive object-model as an auxiliary task during task learning with an object-centric relational RL agent. Our key insight is that learning an object-model that incorporates object-attention into forward prediction provides a dense learning signal for unsupervised representation learning of both objects and their relationships. This, in turn, enables faster policy learning for an object-centric relational RL agent. We demonstrate our agent by introducing a set of challenging object-interaction tasks in the AI2Thor environment where learning with our attentive object-model is key to strong performance. Specifically, we compare our agent and relational RL agents with alternative auxiliary tasks to a relational RL agent equipped with ground-truth object-information, and show that learning with our object-model best closes the performance gap in terms of both learning speed and maximum success rate. Additionally, we find that incorporating object-attention into an object-model's forward predictions is key to learning representations which capture object-category and object-state

Vision-and-Language Navigation: A Survey of Tasks, Methods, and Future Directions

A long-term goal of AI research is to build intelligent agents that can communicate with humans in natural language, perceive the environment, and perform real-world tasks. Vision-and-Language Navigation (VLN) is a fundamental and interdisciplinary research topic towards this goal, and receives increasing attention from natural language processing, computer vision, robotics, and machine learning communities. In this paper, we review contemporary studies in the emerging field of VLN, covering tasks, evaluation metrics, methods, etc. Through structured analysis of current progress and challenges, we highlight the limitations of current VLN and opportunities for future work. This paper serves as a thorough reference for the VLN research community.Comment: 19 pages. Accepted to ACL 202

Explainability for Machine Learning Models: From Data Adaptability to User Perception

This thesis explores the generation of local explanations for already deployed machine learning models, aiming to identify optimal conditions for producing meaningful explanations considering both data and user requirements. The primary goal is to develop methods for generating explanations for any model while ensuring that these explanations remain faithful to the underlying model and comprehensible to the users. The thesis is divided into two parts. The first enhances a widely used rule-based explanation method. It then introduces a novel approach for evaluating the suitability of linear explanations to approximate a model. Additionally, it conducts a comparative experiment between two families of counterfactual explanation methods to analyze the advantages of one over the other. The second part focuses on user experiments to assess the impact of three explanation methods and two distinct representations. These experiments measure how users perceive their interaction with the model in terms of understanding and trust, depending on the explanations and representations. This research contributes to a better explanation generation, with potential implications for enhancing the transparency, trustworthiness, and usability of deployed AI systems.Comment: PhD Thesi

Learning Prior Feature and Attention Enhanced Image Inpainting

Many recent inpainting works have achieved impressive results by leveraging Deep Neural Networks (DNNs) to model various prior information for image restoration. Unfortunately, the performance of these methods is largely limited by the representation ability of vanilla Convolutional Neural Networks (CNNs) backbones.On the other hand, Vision Transformers (ViT) with self-supervised pre-training have shown great potential for many visual recognition and object detection tasks. A natural question is whether the inpainting task can be greatly benefited from the ViT backbone? However, it is nontrivial to directly replace the new backbones in inpainting networks, as the inpainting is an inverse problem fundamentally different from the recognition tasks. To this end, this paper incorporates the pre-training based Masked AutoEncoder (MAE) into the inpainting model, which enjoys richer informative priors to enhance the inpainting process. Moreover, we propose to use attention priors from MAE to make the inpainting model learn more long-distance dependencies between masked and unmasked regions. Sufficient ablations have been discussed about the inpainting and the self-supervised pre-training models in this paper. Besides, experiments on both Places2 and FFHQ demonstrate the effectiveness of our proposed model. Codes and pre-trained models are released in https://github.com/ewrfcas/MAE-FAR.Comment: ECCV 202

Stakeholder network as a determinant to the degree of synchronization between a firm’s values and its stakeholder management strategies. A comparison between public and private companies using mission statements and corporate charitable donations.

Stakeholder theory and stakeholder management theories have gained popularity among practitioners and scholars in recent decades for both its normative and positive power. Intuitively, it is easy to assume that firms who manage for stakeholders utilize various stakeholder management strategies to realize their corporate values. Thus, this study intends to examine the degree of synchronization, or the lack thereof, between a firm’s publicly endorsed values and the values embedded in its CSR stakeholder management activity, specifically, charitable donations. More importantly, due to the different sizes and nature of the stakeholder networks faced by private and public firms, we expect the levels of synchronization to differ between the two, with the distinction that such values stray further from each other for public firms. We found that public and private firms differ in the levels of synchronization between their endorsed values and their charitable recipient organizations’ values on many semantic and psychological domains (17 categories). Interesting, contrary to our initial hypothesis, the level of discrepancy is greater among private firms than that of public firms on most domains (16 categories), which entices further research into determinants of firms’ behavior affected by institutionalized rituals