Reviewerly/exHarmony huggingface dataset:
SegQNAS: Quantum-inspired Neural Architecture Search applied to Medical Image Semantic Segmentation

Semantic segmentation can be applied to a wide range of applications. One of the most interesting is medical image analysis. Applying semantic segmentation techniques in that field has great potential to assist physicians in diagnosing and analysing medical scans for the patient's benefit. Traditionally, several techniques have been applied in order to perform semantic segmentation. However, with the development of Deep Learning methods, there was a paradigm shift. Deep learning techniques are able to achieve great results, comparable to human-level performance. In order to achieve state-of-the-art results, researchers have to lean on the task of designing novel deep neural network architectures. That process is very time-consuming and heavily relies on experience and expert knowledge. Neural architecture search is the process of automatising the search for new deep neural network architectures. Quantum-Inspired Neural Architecture Search is a neural architecture search algorithm that leverages the benefits of quantum-inspired computing to search for neural network architectures efficiently. In this work, we adapt this to search for semantic segmentation neural networks and apply it to medical image analysis. The Spleen and Prostate datasets from the Medical Segmentation Decathlon challenge were used. Results show that our work was able to find better-performing semantic segmentation architectures for both datasets: <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$0.9583\pm 0.006$</tex> in comparison to ResU-Net <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$0.9525\pm 0.008$</tex> for the spleen dataset, and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$0.6887\pm 0.067$</tex> in comparison to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$0.6529\pm 0.070$</tex> for the prostate dataset.
Policy Optimization with Augmented Value Targets for Generalization in Reinforcement Learning

Our work aims to improve the generalization performance of a reinforcement learning (RL) agent in unseen environment variations. The value function used in RL agents is frequently overfitted, leading to poor generalization performance. In this work, we argue that the task completion time is highly impacted by the varying environmental conditions, thus resulting in variation in episode lengths, and consequently, the value estimation. Therefore, learning from a limited variation of the environments, the agent gets biased to the value estimates that correspond to the observed episode lengths. To this end, we introduce Augmented Value Targets (AVaTar), which generates multiple value function targets considering the possibility of episode length variation and optimizes the value function with the average of these targets. We demonstrate that optimizing the average of the augmented targets is computationally more feasible than independently leveraging those pseudotargets. Evaluations on the Procgen and Crafter benchmark show that our proposed approach is effective in generalizing the value estimates over unseen contexts and significantly outperforms the standard policy gradient algorithm Proximal Policy Optimization (PPO). Furthermore, comparison and integration with the recent generalizationspecific approach UCB-DrAC indicate that AVaTar outperforms UCB-DrAC in most of the environments from Procgen.
Natural Language Explanations for Machine Learning Classification Decisions

This paper addresses the challenge of providing understandable explanations for machine learning classification decisions. To do this, we introduce a dataset of expert-written textual explanations paired with numerical explanations, forming a data-to-text generation task. We fine-tune BART and T5 language models on this dataset to generate natural language explanations by linearizing the information represented by explainable output graphs. We find that the models can produce fluent and largely accurate textual explanations. We experiment with various configurations and see that an augmented dataset leads to a reduced error rate. Additionally, we probe the numerical explanations more directly by fine-tuning BART and T5 on a question-answer task and achieved an accuracy of 91% with T5.
GLQA: A Generation-based Method for Legal Question Answering

Legal Question Answering (LQA) aims to automatically answer questions in the legal domain, which is a challenging and meaningful task for Legal Intelligence. Most previous work modeled LQA as a retrieval task, which could obtain irrelevant answers due to the limitations of pre-constructed databases. In addition, it is costly to maintain an up-to-date database as legal knowledge is constantly being updated. To address these issues, we propose the Generation-based Method for Legal Question Answering (GLQA). GLQA instead models LQA as a generation task to flexibly produce new relevant answers specific to each question. To further make the answers more controllable and informative, GLQA incorporates laws as external knowledge into the answer generation process. Specifically, our method contains a retriever and a generator. The retriever is used to retrieve applicable law articles as external knowledge and the generator aims to generate answers with the help of external knowledge. The retriever and the generator are integrated into a single T5 model using multi-task learning to make them mutually reinforcing. Our design enables the model not only to produce highly relevant new answers but also to keep the knowledge in the answers up-to-date by modifying the law database at a low cost.
Causal Visual Feature Extraction for Image Classification Interpretation

Convolutional neural networks have achieved great success in the field of image recognition, however, their inherent black-box properties hinder their application in highly sensitive fields such as healthcare and autonomous driving. Therefore, interpretable artificial intelligence (XAI) has become a hot topic of academic research, which aims at exploring the working mechanism inside deep convolutional neural networks. Causality is an important means to explore the working mechanism of neural networks. For image classification, the image classification results depend on the causal features or background features of the input image. Grad-CAM is a popular interpretation method to visualize these features, and its visualization results in a deep integration of causal and context features. In this paper, we propose a background category-based feature integration method to estimate the context features, and then apply an ensemble theory approach to extract causal features from Grad-CAM. The experimental results show that our Grad-CAM-based extracted causal features not only have better causality in ResNet50, VGG16, and GoogleNet compared with Grad-CAM and Grad-CAM++, but also are similar to the visualization results of Grad-CAM ++ in terms of target object localization of visualization results. In addition, we verify that our method is applicable not only to high category fine-grained datasets but also to low fine-grained datasets through category fine-grained experiments.
Sequential Recommendation with Latent Relation Information

Sequential recommendation has attracted a lot of research due to the great value it has shown in online applications. Recently, relation information in sequences has become the focus of sequential recommendation, as it can reflect the re-lationships between items and the long-term interest tendencies of users. However, existing models are largely based directly on user-item interaction and fail to make effective use of the latent relation information. To address this problem, we propose a method to integrate global relation information into the model through the creation of item and user graphs. Furthermore, we propose a graph convolutional network-based relation information extraction module that alleviates the sparsity problem in sequence data. Combining these two designs, we propose a model named SRLRI for the sequential recommendation. Experiments on five real datasets demonstrate its superiority over existing state-of-the-art baselines.
Bandit-NAS: Bandit Sampling Method for Neural Architecture Search

Existing NAS (Neural Architecture Search) algorithms achieve a low error rate on vision tasks such as image classification by training each child network with equal resources during the search. However, it is not necessary to train with the equal resource or use the fully converge score to obtain the relative performance of each child network, and there is computational redundancy in training all child networks with the equal resource. In this paper, we propose Bandit-NAS to automatically compute the required data slicing and training time for each child network. i): We first model the search of the best child network training time for a given resource into an M-armed bandit problem. ii): Then we propose a reward-flexible bandit algorithm in conjunction with existing reinforcement learning-based NAS algorithms to determine an update strategy. The proposed Bandit-NAS can train M child networks simultaneously under a given resource constraint (training time for one epoch), and the amount of training data is allocated according to the current accuracy of the child networks, thus minimizing the error rate of the child networks. Experiments on CIFAR-10 show that proposed Bandit-NAS performs better the baseline NAS algorithm, e.g., ENAS, with lower error rate and faster searching time.
Occlusion Robust 3D Human Pose Estimation with StridedPoseGraphFormer and Data Augmentation

Occlusion is an omnipresent challenge in 3D human pose estimation (HPE). In spite of the large amount of research dedicated to 3D HPE, only a limited number of studies address the problem of occlusion explicitly. To fill this gap, we propose to combine exploitation of spatio-temporal features with synthetic occlusion augmentation during training to deal with occlusion. To this end, we build a spatio-temporal 3D HPE model, StridedPoseGraphFormer based on graph convolution and transformers, and train it using occlusion augmentation. Unlike the existing occlusion-aware methods, that are only tested for limited occlusion, we extensively evaluate our method for varying degrees of occlusion. We show that our proposed method compares favorably with the state-of-the-art (SoA). Our experimental results also reveal that in the absence of any occlusion handling mechanism, the performance of SoA 3D HPE methods degrades significantly when they encounter occlusion.
Recommendation with Dynamic Natural Language Explanations

Explaining recommendation with natural languages has shown to be an effective strategy to improve the recommendation pervasiveness and user satisfaction. While recent years have witnessed many promising models, they mostly consider the user-item interactions as independent samples. However, in real-world scenarios, the user preference is always dynamic and evolving, and the current user behaviors may have strong correlations with the previous ones. To bridge this gap, in this paper, we propose to build an explainable recommender model by considering the user dynamic preference. Our general idea is to build a sequential model to capture the user history behaviors, and then the explanations are generated by summarizing all the past interactions. In specific, we firstly deploy two independent components to model the user sequential interactions and reviews separately. Then, we design a duration-aware attention mechanism to discriminate the importance of different items and reviews. For more effectively modeling the history information, we introduce a denoising module to remove the user behaviors which are less important for the current prediction. We conduct extensive experiments to demonstrate the effectiveness of our model based on three real-world datasets, in which the best performance can be improved by about 13.3%, 6.5%, 5.0% and 1.9% on the metrics of BLEU-1, ROUGE-1, ROUGE-2 and MAE, respectively. In addition, we also evaluate the generated explanations from both qualitative and qualitative perspectives.
Towards Carbon Footprint Management in Hybrid Multicloud

Enterprises today aspire to optimize the operating costs and carbon footprint (CFP) of their IT operations jointly without compromising their business imperatives. This has given rise to a hybrid approach in which enterprises retain the dynamic choice to leverage private data centers and one or more public clouds in conjunction. While cloud service providers (CSPs) have long provided APIs for estimating, reconciling, and optimizing operating costs, they have only recently started exposing APIs related to CFP.
Scene Understanding for Autonomous Driving Using Visual Question Answering

This paper investigates the feasibility of dot-products present in self-attention mechanisms as an explainability technique for autonomous driving. A Visual Question Answering (VQA) framework is implemented with three types of questions pertaining to the presence or absence of road signs and traffic lights. The models are evaluated for the encoding of uni- and multimodal encodings: a standard version and a modified version of the Learning Cross-Modality Encoder Representations from Transformers (LXMERT) framework. We present numerical results for the two model architectures on the question answering task, with overall accuracies of 79.7% and 78.5% respectively, and overall F1-scores of 0.749 and 0.693 respectively. Moreover, we show that these questions, despite containing and asking no information on the objects' positions, indirectly tune the model such that the self-attention dot-products provide scenic understanding to the questions in the form of a visual map. The choice of pooling for the model's output and the plotting parameters for the visual maps influence the reliability and accuracy of visualization. Finally, an argument is made for the benefit of this approach to autonomous driving.
CCFD-GAN: Credit Card Fraud Detection Based on Generative Adversarial Networks Enhanced by Penalty Mechanism

With the prevalence of credit cards worldwide, credit card fraud detection becomes increasingly meaningful and necessary for ensuring financial security. However, approaches that directly mine fraud patterns are not practical due to the lack of fraud transaction samples for training and the unpredictability of fraud behavior. Meanwhile, most existing works, lacking the ability to model the complex spatio-temporal dependencies in transaction sequences, fail to show satisfactory detection performance. To solve the problems above, this work proposes a Credit Card Fraud Detection model based on Generative Adversarial Network (CCFD-GAN). Instead of modeling the fraud transaction directly, CCFD-GAN learns the normal transaction distribution and detects samples outside the normal transaction distribution as fraudulent, therefore, no fraud transaction training samples are needed. Meanwhile, for effectively modeling the complex dependencies, not only Convolutional GRU (Conv-GRU) is utilized in CCFD-GAN to capture the spatio-temporal dependencies together, but temporal aggregated features and multi-scope outlier degrees are also employed to obtain a larger feature space that provides richer information. Moreover, in order to solve inaccurate detections caused by generating data outside the normal manifold, the penalty mechanism based on complementary distribution is introduced in the training phase to lead the GAN model towards the normal manifold. Experimental results show that our model outperforms classical credit card fraud detection models.
Anomalous Activity Detection from Ego View Camera of Surveillance Robots

Can a surveillance robot autonomously detect anomalous activity from its ego view camera perception? This is a challenging task as it requires identifying what is normal and what is an abnormal pattern - given the variations of possible anomalies and abnormalities. This paper presents an architecture and method based on a spatio-temporal convolution neural network to detect and classify anomalies. This work is inspired by the ‘Konio-Magno-Parvocellular’ cells of the human brain, which is claimed to aid humans in organizing changes in perceived scenes. The model is trained and tested on a benchmark video dataset [1] of human activity. We have obtained 91% testing accuracy on this dataset. Experiments in simulation as well as deployment on a real robot shows that the proposed methodology can identify anomalous activities effectively. We have also listed down the observations from practical deployment of the model.
Deep Reinforcement Learning Based Multi-Task Automated Channel Pruning for DNNs

Model compression is a key technique that enables deploying Deep Neural Networks (DNNs) on Internet-of-Things (IoT) devices with constrained computing resources and limited power budgets. Channel pruning has become one of the representative compression approaches, but how to determine the compression ratio for different layers of a model still remains as a challenging task. Current automated pruning solutions address this issue by searching for an optimal strategy according to the target compression ratio. Nevertheless, when given a series of tasks with multiple compression ratios and different training datasets, these approaches have to carry out the pruning process repeatedly, which is inefficient and time-consuming. In this paper, we propose a Multi-Task Automated Channel Pruning (MTACP) framework, which can simultaneously generate a number of feasible compressed models satisfying different task demands for a target DNN model. To learn MTACP, the layer-by-layer multi-task channel pruning process is transformed into a Markov Decision Process (MDP), which seeks to solve a series of decision-making problems. Based on this MDP, we propose an actor-critic-based multi-task Reinforcement Learning (RL) algorithm to learn the optimal policy, working based on the IMPortance weighted Actor-Learner Architectures (IMPALA). IMPALA is known as a distributed RL architecture, in which the learner can learn from a set of actors that continuously generate trajectories of experience in their own environments. Extensive experiments on CIFAR10/100 and FLOWER102 datasets for MTACP demonstrate its unique capability for multi-task settings, as well as its superior performance over state-of-the-art solutions.
MHRNet: A Multi-stage Image Deblurring Approach with High-Resolution Representation Learning

Image deblurring is a classic task in computer vision that aims to recover clean images from blurred images and is widely used in surveillance, medical imaging and so on. Existing methods typically apply a multi-stage encoder-decoder architecture to learn features at different scales, and they have achieved remarkable results. However, these methods usually map the input to a low-resolution (LR) image to expand its receptive field and then gradually reverse this image to the original resolution. Although these approaches obtain rich semantic information via spatial resolution reduction, they lose a large amount of spatial information, which is essential for image deblurring and extremely difficult to recover. To solve this problem, we propose a novel multi-stage model with high-resolution (HR) representation learning (MHRNet). In this model, HR representations are always preserved to reduce the loss of spatial information, and the features across all the scales at each resolution are fused to obtain spatially accurate and semantically rich features. Extensive experiments conducted on the GoPro, HIDE and RealBlur datasets demonstrate that MHRNet outperforms the state-of-the-art (SOTA) methods and reaches a 33.99-dB peak signal-to-noise ratio (PSNR) on the GoPro dataset.
A New Spatial Pooler Algorithm Based on Heterogeneous Hash Group

Hierarchical Temporal Memory (HTM) is an emerging neural network model that simulates the working principles of human brain neocortex. HTM forms sparse distributed representation (SDR) and learns temporal relations of sequential data through two core learning algorithms, spatial pooler (SP) and temporal memory (TM). However, the conventional SP algorithm is time consuming as all columns need to be iteratively trained and a large amount of computations is required to generate the SDR. Moreover, the stability of SDR is hard to guarantee. To improve efficiency and construct stable representation, we propose a new SP algorithm based on heterogeneous hash group (SP_HHG), where three different hash functions are designed for fast data representation. The former two functions originate from the locality sensitive hash and use different sensitive fields, and the third function is essentially a random hash. The representations generated by the combined hash values satisfy the principles of SDR in HTM. Extensive experiments are carried out on three sequential datasets and the results show that SP_HHG can effectively improve the efficiency of SP and produce more stable SDR which lead to the enhanced performance of HTM.
Triple Consistency-based Self-ensembling Model for Unsupervised Domain Adaption in Medical Image Segmentation

Deep learning models typically suffer significant performance degradation when they are applied directly to an unseen new dataset with a distribution inconsistent with the training data. Unsupervised domain adaptation allows models to generalize better on new data by learning domain shared feature representations to narrow the domain distribution differences. In this paper, we propose a new triple consistency-based self-ensembling model for unsupervised domain adaptation in medical image segmentation. We first propose a Fourier transform-based data augmentation method for generating semantic-preserving cross-domain augmented images, which can be easily incorporated into model training with only a small computational cost. Then we input the source domain image, target domain image and cross-domain augmented image into the self-ensembling model, and then propose three consistency strategies to guarantee the consistency of the original semantic information in the target domain from the output level, structure level and class level, respectively, penalizing the inconsistent prediction of the segmentation network, thus providing additional supervised signals to the segmentation model and improving the generalization performance of the model on the target domain data. We have conducted extensive experiments on a cross-modality cardiac structure segmentation dataset, and the experimental results show that our approach is significantly better than the state-of-the-art comparison algorithms. Our code will be released soon.
LCCN: A Lightweight Capture Context Network for Image Super-Resolution

In recent years, with the development of deep learning, many lightweight convolution neural networks (CNN) have achieved remarkable results in the field of single image super-resolution (SISR). However, it is difficult to capture long-range dependencies due to the limited perceptual field of lightweight CNN. To solve this problem, we propose the lightweight capture context network (LCCN), which includes the hierarchical residual attention distillation fusion (HRADF) module and lightweight capture context dependency (LCCD) module. In HRADF, we propose a hierarchical feature fusion structure consisting of multiple residual attention distillation blocks to improve the reconstruction effect by fusing multiple layers of feature maps. Moreover, the tpaconv lrelu block (TLB) and mixed spatial channel attention (MSCA) are applied to make the network lightweight and take full advantage of feature information. In LCCD, we introduce asymmetrical double multi-head attention to achieve low resource consumption and improve the long-range context dependency capture capability of the network. Extensive experiments show that LCCN achieves a good balance between performance and model complexity, and obtains satisfactory results on several benchmark datasets.