Reviewerly/exHarmony huggingface dataset:
Levels of explainable artificial intelligence for human-aligned conversational explanations

Over the last few years there has been rapid research growth into eXplainable Artificial Intelligence (XAI) and the closely aligned Interpretable Machine Learning (IML). Drivers for this growth include recent legislative changes and increased investments by industry and governments, along with increased concern from the general public. People are affected by autonomous decisions every day and the public need to understand the decision-making process to accept the outcomes. However, the vast majority of the applications of XAI/IML are focused on providing low-level `narrow' explanations of how an individual decision was reached based on a particular datum. While important, these explanations rarely provide insights into an agent's: beliefs and motivations; hypotheses of other (human, animal or AI) agents' intentions; interpretation of external cultural expectations; or, processes used to generate its own explanation. Yet all of these factors, we propose, are essential to providing the explanatory depth that people require to accept and trust the AI's decision-making. This paper aims to define levels of explanation and describe how they can be integrated to create a human-aligned conversational explanation system. In so doing, this paper will survey current approaches and discuss the integration of different technologies to achieve these levels with Broad eXplainable Artificial Intelligence (Broad-XAI), and thereby move towards high-level `strong' explanations.
Node Selection Toward Faster Convergence for Federated Learning on Non-IID Data

Federated Learning (FL) is a distributed learning paradigm that enables a large number of resource-limited nodes to collaboratively train a model without data sharing. The non-independent-and-identically-distributed (non-i.i.d.) data samples invoke discrepancies between the global and local objectives, making the FL model slow to converge. In this paper, we proposed <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Optimal Aggregation</monospace> algorithm for better aggregation, which finds out the optimal subset of local updates of participating nodes in each global round, by identifying and excluding the adverse local updates via checking the relationship between the local gradient and the global gradient. Then, we proposed a <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</monospace> robabilistic <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</monospace> ode <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</monospace> election framework ( <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FedPNS</monospace> ) to dynamically change the probability for each node to be selected based on the output of <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Optimal Aggregation</monospace> . <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FedPNS</monospace> can preferentially select nodes that propel faster model convergence. The convergence rate improvement of <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FedPNS</monospace> over the commonly adopted Federated Averaging ( <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FedAvg</monospace> ) algorithm is analyzed theoretically. Experimental results demonstrate the effectiveness of <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FedPNS</monospace> in accelerating the FL convergence rate, as compared to <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FedAvg</monospace> with random node selection.
CDFI: Compression-Driven Network Design for Frame Interpolation

DNN-based frame interpolation—that generates the intermediate frames given two consecutive frames—typically relies on heavy model architectures with a huge number of features, preventing them from being deployed on systems with limited resources, e.g., mobile devices. We propose a compression-driven network design for frame interpolation (CDFI), that leverages model pruning through sparsity-inducing optimization to significantly reduce the model size while achieving superior performance. Concretely, we first compress the recently proposed AdaCoF model and show that a 10× compressed AdaCoF performs similarly as its original counterpart; then we further improve this compressed model by introducing a multi-resolution warping module, which boosts visual consistencies with multi-level details. As a consequence, we achieve a significant performance gain with only a quarter in size compared with the original AdaCoF. Moreover, our model performs favorably against other state-of-the-arts in a broad range of datasets. Finally, the proposed compression-driven framework is generic and can be easily transferred to other DNN-based frame interpolation algorithm. Our source code is available at https://github.com/tding1/CDFI.
Measuring User Experience, Usability and Interactivity of a Personalized Mobile Augmented Reality Training System

Innovative technology has been an important part of firefighting, as it advances firefighters’ safety and effectiveness. Prior research has examined the implementation of training systems using augmented reality (AR) in other domains, such as welding, aviation, army, and mathematics, offering significant pedagogical affordances. Nevertheless, firefighting training systems using AR are still an under-researched area. The increasing penetration of AR for training is the driving force behind this study, and the scope is to analyze the main aspects affecting the acceptance of AR by firefighters. The current research uses a technology acceptance model, extended by the external constructs of perceived interactivity and personalization, to consider both the system and individual level. The proposed model was evaluated by a sample of 200 users, and the results show that both the external variables of perceived interactivity and perceived personalization are prerequisite factors in extending the TAM model. The findings reveal that the usability is the strongest predictor of firefighters’ behavioral intentions to use the AR system, followed by the ease of use with smaller, yet meaningful, direct and indirect effects on firefighters’ intentions. The identified acceptance factors help AR developers enhance the firefighters’ experience in training operations.
RTA’s Employees’ Perceptions Toward the Efficiency of Artificial Intelligence and Big Data Utilization in Providing Smart Services to the Residents of Dubai

The researchers aim at examining the efficiency of AI and Big Data utilization in providing smart services to the residents of the Emirate of Dubai from the perceptions of RTA's employees. The research undertook a quantitative study of employees at Dubai, intending to understand their perspective on whether the governance structure in place to oversee AI and Big Data application helps enhance service delivery to the citizens and residents. For this purpose, a sample of 56 employees is requested to complete an online survey. The employees are drawn using a simple random sample from the mid-level management to have the opinion in representing both the management and subordinates. The online questionnaire of the survey is included two main sections; one section devoted to collect the basic demographic information about the respondents, and the other section will be comprised of Likert-scale-type questions aimed at collecting information concerning the efficiency of RTA's AI and big data in providing smart services to the residents of Dubai. Descriptive statistical techniques are used to analyze the data. The correlation analysis is conducted to describe and explore the relationship between all the study variables. Since RTA, Dubai has been at the forefront in acknowledging Dubai as a smart city, while technology improvement has been enhancing service delivery to the residents of the Emirate of Dubai. The research will show the role of RTA's IT governance and its use of AI and Big Data to ensure the efficiency of smart service delivery.
Sequential Graph Convolutional Network for Active Learning

We propose a novel pool-based Active Learning framework constructed on a sequential Graph Convolution Network (GCN). Each images feature from a pool of data represents a node in the graph and the edges encode their similarities. With a small number of randomly sampled images as seed labelled examples, we learn the parameters of the graph to distinguish labelled vs unlabelled nodes by minimising the binary cross-entropy loss. GCN performs message-passing operations between the nodes, and hence, induces similar representations of the strongly associated nodes. We exploit these characteristics of GCN to select the unlabelled examples which are sufficiently different from labelled ones. To this end, we utilise the graph node embeddings and their confidence scores and adapt sampling techniques such as CoreSet and uncertainty-based methods to query the nodes. We flip the label of newly queried nodes from unlabelled to labelled, re-train the learner to optimise the downstream task and the graph to minimise its modified objective. We continue this process within a fixed budget. We evaluate our method on 6 different benchmarks: 4 real image classification, 1 depth-based hand pose estimation and 1 synthetic RGB image classification datasets. Our method outperforms several competitive baselines such as VAAL, Learning Loss, CoreSet and attains the new state-of-the-art performance on multiple applications.
Primitive Representation Learning for Scene Text Recognition

Scene text recognition is a challenging task due to di-verse variations of text instances in natural scene images. Conventional methods based on CNN-RNN-CTC or encoder-decoder with attention mechanism may not fully investigate stable and efficient feature representations for multi-oriented scene texts. In this paper, we propose a primitive representation learning method that aims to exploit intrinsic representations of scene text images. We model elements in feature maps as the nodes of an undirected graph. A pooling aggregator and a weighted aggregator are proposed to learn primitive representations, which are transformed into high-level visual text representations by graph convolutional networks. A Primitive REpresentation learning Network (PREN) is constructed to use the visual text representations for parallel decoding. Furthermore, by integrating visual text representations into an encoder-decoder model with the 2D attention mechanism, we pro-pose a framework called PREN2D to alleviate the misalignment problem in attention-based methods. Experimental results on both English and Chinese scene text recognition tasks demonstrate that PREN keeps a balance between ac-curacy and efficiency, while PREN2D achieves state-of-the-art performance.
AdderSR: Towards Energy Efficient Image Super-Resolution

This paper studies the single image super-resolution problem using adder neural networks (AdderNets). Com-pared with convolutional neural networks, AdderNets utilize additions to calculate the output features thus avoid massive energy consumptions of conventional multiplications. However, it is very hard to directly inherit the existing success of AdderNets on large-scale image classification to the image super-resolution task due to the different calculation paradigm. Specifically, the adder operation cannot easily learn the identity mapping, which is essential for image processing tasks. In addition, the functionality of high-pass filters cannot be ensured by AdderNets. To this end, we thoroughly analyze the relationship between an adder operation and the identity mapping and insert shortcuts to enhance the performance of SR models using adder networks. Then, we develop a learnable power activation for adjusting the feature distribution and refining details. Experiments conducted on several benchmark models and datasets demonstrate that, our image super-resolution models using AdderNets can achieve comparable performance and visual quality to that of their CNN baselines with an about 2.5× reduction on the energy consumption. The codes are available at: https://github.com/huawei-noah/AdderNet.
mmMesh

In this paper, we present mmMesh, the first real-time 3D human mesh estimation system using commercial portable millimeter-wave devices. mmMesh is built upon a novel deep learning framework that can dynamically locate the moving subject and capture his/her body shape and pose by analyzing the 3D point cloud generated from the mmWave signals that bounce off the human body. The proposed deep learning framework addresses a series of challenges. First, it encodes a 3D human body model, which enables mmMesh to estimate complex and realistic-looking 3D human meshes from sparse point clouds. Second, it can accurately align the 3D points with their corresponding body segments despite the influence of ambient points as well as the error-prone nature and the multi-path effect of the RF signals. Third, the proposed model can infer missing body parts from the information of the previous frames. Our evaluation results on a commercial mmWave sensing testbed show that our mmMesh system can accurately localize the vertices on the human mesh with an average error of 2.47 cm. The superior experimental results demonstrate the effectiveness of our proposed human mesh construction system.
Integrating Elman recurrent neural network with particle swarm optimization algorithms for an improved hybrid training of multidisciplinary datasets

There are several types of neural networks (NNs) that are widely used for data classification tasks. The supervised learning NN is an advanced network with a training algorithm for setting the weights and biases of the network in its training phase. However, traditional training algorithms such as backpropagation have some drawbacks, such as slow convergence speed and falling into local minima, which reduces the performance of the classifier. Therefore, different nature-inspired metaheuristic algorithms are integrated with the NN training algorithms to provide derivative-free solutions for complex classification problems. Consequently, this paper proposes the integration of a particle swarm optimization (PSO) algorithm with an improved Elman recurrent neural network (ERNN) to form a PSO-ERNN metaheuristic model. The key contribution of this study is the development of a new dimensional equation for ERNN architecture and the integration of PSO in ERNN learning to produce the PSO-ERNN model. The PSO is constructed to train the NN and ERNN models to achieve a fast convergence rate and avoid local minima problems. The PSO-ERNN model is validated by comparing it against the standard PSO-NN metaheuristic model and similar models from the literature. The PSO-NN and PSO-ERNN models are tested and evaluated using ten benchmark classification problems of breast cancer, heart, hepatitis, liver, wine, iris, lung cancer, yeast, Pima Indians diabetes, and ionosphere datasets. In the training phase, the results show that the PSO-ERNN model performs better than the PSO-NN model when the training set has a bigger size of samples. In the testing phase, the PSO-ERNN model outperforms the PSO-NN model for all the tested datasets except the lung cancer and yeast datasets, in which the accuracy percentage slightly decreases. In the validation phase, the PSO-ERNN model shows better performance quality in terms of accuracy percentage in six of the tested datasets. The average percentage of the training, testing, and validation accumulation show that the PSO-NN performs better than the PSO-ERNN in the lung cancer (87.27, 83.32), and heart (73.56, 70.64) datasets. On the other hand, the PSO-ERNN performs better than the PSO-NN in the iris (88.18, 86.74), hepatitis (88.60, 87.93), wine (89.16, 86.08), liver (73.56, 70.64), ionosphere (83.98, 78.94), and breast cancer (94.84, 91.17). PSO-NN and PSO-ERNN produce the same average results in the Pima Indians diabetes (84.00, 84.00) and yeast (91.31, 91.30) dataset. These results show clearly that the PSO-ERNN generally outperforms the PSO-NN when considering the overall results of the ten datasets. Nevertheless, the combinations of the PSO-NN and PSO-ERNN are proven to represent consistent and robust classification methods. The computational efficiencies of the training processes in both the PSO-NN and PSO-ERNN models are highly improved when coupled with the PSO.
Dynamic evolution of multi-graph based collaborative filtering for recommendation systems

The recommendation system is an important and widely used technology in the era of Big Data. Current methods have fused side information into it to alleviate the sparsity problem, one of the key problems of recommendation systems. However, not all the side information can be obtained with high quality, and the specific methods based on side information are not universal. In addition, side information has not been mined by the existing graph-based methods. To address these problems, we propose a Dynamic evolution of Multi-Graph Collaborative Filtering (DMGCF) model to mine and reuse side information. Specifically, we first construct user graph and item graph based on user-item bipartite graph and embeddings to exploit inter-user and inter-item relationships. The two new graphs simulate side information in latent space. Next, we perform a dual-path graph convolution network (GCN) on these three graphs for collaborative filtering. Then, a novel dynamic evolution mechanism is proposed to update and promote the embeddings and graphs collaboratively during the learning process, which produces better embeddings, user and item relationships, as well as the rating scores. We conduct a series of experiments on real-world datasets, and experimental results show the effectiveness of our approach.
HyperMorph: Amortized Hyperparameter Learning for Image Registration

We present HyperMorph, a learning-based strategy for deformable image registration that removes the need to tune important registration hyperparameters during training. Classical registration methods solve an optimization problem to find a set of spatial correspondences between two images, while learning-based methods leverage a training dataset to learn a function that generates these correspondences. The quality of the results for both types of techniques depends greatly on the choice of hyperparameters. Unfortunately, hyperparameter tuning is time-consuming and typically involves training many separate models with various hyperparameter values, potentially leading to suboptimal results. To address this inefficiency, we introduce amortized hyperparameter learning for image registration, a novel strategy to learn the effects of hyperparameters on deformation fields. The proposed framework learns a hypernetwork that takes in an input hyperparameter and modulates a registration network to produce the optimal deformation field for that hyperparameter value. In effect, this strategy trains a single, rich model that enables rapid, fine-grained discovery of hyperparameter values from a continuous interval at test-time. We demonstrate that this approach can be used to optimize multiple hyperparameters considerably faster than existing search strategies, leading to a reduced computational and human burden as well as increased flexibility. We also show several important benefits, including increased robustness to initialization and the ability to rapidly identify optimal hyperparameter values specific to a registration task, dataset, or even a single anatomical region, all without retraining the HyperMorph model. Our code is publicly available at http://voxelmorph.mit.edu .
The Nexus between Digital Finance and Economic Development: Evidence from China

In the context of the digital economy and based on the characteristics of digital financial development in China, this paper investigates the effect of digital finance on economic growth and explores its influencing mechanism. A panel econometric model, mediating effect model, and instrumental variable method were employed to evaluate yearly data from 30 provinces of China from 2011 to 2018. The results show that the development of digital finance has significantly driven economic growth, which is quantitatively robust after the selection of historical data as instrumental variables and other robustness tests. A heterogeneity analysis proved that provinces in the central and western regions, which have a lower urbanization rate and lower physical capital, more clearly embody the facilitating impacts of digital finance on economic growth compared to their counterparts in other regions. Further analysis found that the development of digital finance has spurred the liberation of regional entrepreneurship, which in turn promoted economic growth—that is, there is an entrepreneurial channel by which digital finance could boost economic growth.
Bandwidth-Agile Image Transmission With Deep Joint Source-Channel Coding

We propose deep learning based communication methods for adaptive-bandwidth transmission of images over wireless channels. We consider the scenario in which images are transmitted progressively in layers over time or frequency, and such layers can be aggregated by receivers in order to increase the quality of their reconstructions. We investigate two scenarios, one in which the layers are sent sequentially, and incrementally contribute to the refinement of a reconstruction, and another in which the layers are independent and can be retrieved in any order. Those scenarios correspond to the well known problems of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">successive refinement</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">multiple descriptions</i> , respectively, in the context of joint source-channel coding (JSCC). We propose DeepJSCC- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$l$ </tex-math></inline-formula> , an innovative solution that uses convolutional autoencoders, and present three architectures with different complexity trade-offs. To the best of our knowledge, this is the first practical multiple-description JSCC scheme developed and tested for practical information sources and channels. Numerical results show that DeepJSCC- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$l$ </tex-math></inline-formula> can learn to transmit the source progressively with negligible losses in the end-to-end performance compared with a single transmission. Moreover, DeepJSCC- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$l$ </tex-math></inline-formula> has comparable performance with state of the art digital progressive transmission schemes in the challenging low signal-to-noise ratio (SNR) and small bandwidth regimes, with the additional advantage of graceful degradation with channel SNR.