Reviewerly/exHarmony huggingface dataset:
Multi-class Token Transformer for Weakly Supervised Semantic Segmentation

This paper proposes a new transformer-based framework to learn class-specific object localization maps as pseudo labels for weakly supervised semantic segmentation (WSSS). Inspired by the fact that the attended regions of the one-class token in the standard vision transformer can be leveraged to form a class-agnostic localization map, we investigate if the transformer model can also effectively capture class-specific attention for more discriminative object localization by learning multiple class tokens within the transformer. To this end, we propose a Multi-class Token Transformer, termed as MCTformer, which uses multiple class tokens to learn interactions between the class tokens and the patch tokens. The proposed MCTformer can successfully produce class-discriminative object localization maps from the class-to-patch attentions corresponding to different class tokens. We also propose to use a patch-level pairwise affinity, which is extracted from the patch-to-patch transformer attention, to further refine the localization maps. Moreover, the proposed framework is shown to fully complement the Class Activation Mapping (CAM) method, leading to remarkably superior WSSS results on the PASCAL VOC and MS COCO datasets. These results underline the importance of the class token for WSSS. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> https://github.com/xulianuwa/MCTformer
SynthSeg: Segmentation of brain MRI scans of any contrast and resolution without retraining

Despite advances in data augmentation and transfer learning, convolutional neural networks (CNNs) difficultly generalise to unseen domains. When segmenting brain scans, CNNs are highly sensitive to changes in resolution and contrast: even within the same MRI modality, performance can decrease across datasets. Here we introduce SynthSeg, the first segmentation CNN robust against changes in contrast and resolution. SynthSeg is trained with synthetic data sampled from a generative model conditioned on segmentations. Crucially, we adopt a domain randomisation strategy where we fully randomise the contrast and resolution of the synthetic training data. Consequently, SynthSeg can segment real scans from a wide range of target domains without retraining or fine-tuning, which enables straightforward analysis of huge amounts of heterogeneous clinical data. Because SynthSeg only requires segmentations to be trained (no images), it can learn from labels obtained by automated methods on diverse populations (e.g., ageing and diseased), thus achieving robustness to a wide range of morphological variability. We demonstrate SynthSeg on 5,000 scans of six modalities (including CT) and ten resolutions, where it exhibits unparallelled generalisation compared with supervised CNNs, state-of-the-art domain adaptation, and Bayesian segmentation. Finally, we demonstrate the generalisability of SynthSeg by applying it to cardiac MRI and CT scans.
TRLG: Fragile blind quad watermarking for image tamper detection and recovery by providing compact digests with optimized quality using LWT and GA

In this paper, an efficient fragile blind quad watermarking scheme, named TRLG, is proposed for image tamper detection and recovery based on lifting wavelet transform and genetic algorithm. TRLG generates four compact digests with super quality based on lifting wavelet transform and halftoning technique by distinguishing the types of image blocks. In this way, for each 2 × 2 non-overlapping block, four chances for recovering the destroyed blocks are created. A special parameter estimation technique based on the genetic algorithm is performed to improve and optimize the quality of digests and the watermarked image. Furthermore, the Chebyshev System is used to determine the mapping block for embedding, encrypting, and shuffling the information. To improve the recovery rate, two techniques called Mirror-aside and Partner-block are proposed. Some experiments are conducted to prove the superiority of TRLG in terms of quality of the watermarked and recovered images, tamper localization, and security compared with the state-of-the-art methods. The results indicate that the average values of PSNR and SSIM of the watermarked image are about 46 dB and 1, respectively. Also, the average values of PSNR and SSIM for several recovered images that were destroyed about 90% reached to 24 dB and 0.86, respectively.
You Only Propagate Once: Accelerating Adversarial Training via Maximal Principle

Deep learning achieves state-of-the-art results in many tasks in computer vision and natural language processing. However, recent works have shown that deep networks can be vulnerable to adversarial perturbations, which raised a serious robustness issue of deep networks. Adversarial training, typically formulated as a robust optimization problem, is an effective way of improving the robustness of deep networks. A major drawback of existing adversarial training algorithms is the computational overhead of the generation of adversarial examples, typically far greater than that of the network training. This leads to the unbearable overall computational cost of adversarial training. In this paper, we show that adversarial training can be cast as a discrete time differential game. Through analyzing the Pontryagin's Maximal Principle (PMP) of the problem, we observe that the adversary update is only coupled with the parameters of the first layer of the network. This inspires us to restrict most of the forward and back propagation within the first layer of the network during adversary updates. This effectively reduces the total number of full forward and backward propagation to only one for each group of adversary updates. Therefore, we refer to this algorithm YOPO (You Only Propagate Once). Numerical experiments demonstrate that YOPO can achieve comparable defense accuracy with approximately 1/5 ~ 1/4 GPU time of the projected gradient descent (PGD) algorithm. Our codes are available at https://https://github.com/a1600012888/YOPO-You-Only-Propagate-Once.
Rates of Convergence for Sparse Variational Gaussian Process Regression

Excellent variational approximations to Gaussian process posteriors have been developed which avoid the $\mathcal{O}\left(N^3\right)$ scaling with dataset size $N$. They reduce the computational cost to $\mathcal{O}\left(NM^2\right)$, with $M\ll N$ being the number of inducing variables, which summarise the process. While the computational cost seems to be linear in $N$, the true complexity of the algorithm depends on how $M$ must increase to ensure a certain quality of approximation. We address this by characterising the behavior of an upper bound on the KL divergence to the posterior. We show that with high probability the KL divergence can be made arbitrarily small by growing $M$ more slowly than $N$. A particular case of interest is that for regression with normally distributed inputs in D-dimensions with the popular Squared Exponential kernel, $M=\mathcal{O}(\log^D N)$ is sufficient. Our results show that as datasets grow, Gaussian process posteriors can truly be approximated cheaply, and provide a concrete rule for how to increase $M$ in continual learning scenarios.
Planning with Goal-Conditioned Policies

Planning methods can solve temporally extended sequential decision making problems by composing simple behaviors. However, planning requires suitable abstractions for the states and transitions, which typically need to be designed by hand. In contrast, reinforcement learning (RL) can acquire behaviors from low-level inputs directly, but struggles with temporally extended tasks. Can we utilize reinforcement learning to automatically form the abstractions needed for planning, thus obtaining the best of both approaches? We show that goal-conditioned policies learned with RL can be incorporated into planning, such that a planner can focus on which states to reach, rather than how those states are reached. However, with complex state observations such as images, not all inputs represent valid states. We therefore also propose using a latent variable model to compactly represent the set of valid states for the planner, such that the policies provide an abstraction of actions, and the latent variable model provides an abstraction of states. We compare our method with planning-based and model-free methods and find that our method significantly outperforms prior work when evaluated on image-based tasks that require non-greedy, multi-staged behavior.
Blind Super-Resolution Kernel Estimation using an Internal-GAN

Super resolution (SR) methods typically assume that the low-resolution (LR) image was downscaled from the unknown high-resolution (HR) image by a fixed `ideal’ downscaling kernel (e.g. Bicubic downscaling). However, this is rarely the case in real LR images, in contrast to synthetically generated SR datasets. When the assumed downscaling kernel deviates from the true one, the performance of SR methods significantly deteriorates. This gave rise to Blind-SR - namely, SR when the downscaling kernel (``SR-kernel’’) is unknown. It was further shown that the true SR-kernel is the one that maximizes the recurrence of patches across scales of the LR image. In this paper we show how this powerful cross-scale recurrence property can be realized using Deep Internal Learning. We introduce ``KernelGAN’’, an image-specific Internal-GAN, which trains solely on the LR test image at test time, and learns its internal distribution of patches. Its Generator is trained to produce a downscaled version of the LR test image, such that its Discriminator cannot distinguish between the patch distribution of the downscaled image, and the patch distribution of the original LR image. The Generator, once trained, constitutes the downscaling operation with the correct image-specific SR-kernel. KernelGAN is fully unsupervised, requires no training data other than the input image itself, and leads to state-of-the-art results in Blind-SR when plugged into existing SR algorithms.
Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression

Bounding box regression is the crucial step in object detection. In existing methods, while $\ell_n$-norm loss is widely adopted for bounding box regression, it is not tailored to the evaluation metric, i.e., Intersection over Union (IoU). Recently, IoU loss and generalized IoU (GIoU) loss have been proposed to benefit the IoU metric, but still suffer from the problems of slow convergence and inaccurate regression. In this paper, we propose a Distance-IoU (DIoU) loss by incorporating the normalized distance between the predicted box and the target box, which converges much faster in training than IoU and GIoU losses. Furthermore, this paper summarizes three geometric factors in bounding box regression, \ie, overlap area, central point distance and aspect ratio, based on which a Complete IoU (CIoU) loss is proposed, thereby leading to faster convergence and better performance. By incorporating DIoU and CIoU losses into state-of-the-art object detection algorithms, e.g., YOLO v3, SSD and Faster RCNN, we achieve notable performance gains in terms of not only IoU metric but also GIoU metric. Moreover, DIoU can be easily adopted into non-maximum suppression (NMS) to act as the criterion, further boosting performance improvement. The source code and trained models are available at https://github.com/Zzh-tju/DIoU.
Never Give Up: Learning Directed Exploration Strategies

We propose a reinforcement learning agent to solve hard exploration games by learning a range of directed exploratory policies. We construct an episodic memory-based intrinsic reward using k-nearest neighbors over the agent's recent experience to train the directed exploratory policies, thereby encouraging the agent to repeatedly revisit all states in its environment. A self-supervised inverse dynamics model is used to train the embeddings of the nearest neighbour lookup, biasing the novelty signal towards what the agent can control. We employ the framework of Universal Value Function Approximators to simultaneously learn many directed exploration policies with the same neural network, with different trade-offs between exploration and exploitation. By using the same neural network for different degrees of exploration/exploitation, transfer is demonstrated from predominantly exploratory policies yielding effective exploitative policies. The proposed method can be incorporated to run with modern distributed RL agents that collect large amounts of experience from many actors running in parallel on separate environment instances. Our method doubles the performance of the base agent in all hard exploration in the Atari-57 suite while maintaining a very high score across the remaining games, obtaining a median human normalised score of 1344.0%. Notably, the proposed method is the first algorithm to achieve non-zero rewards (with a mean score of 8,400) in the game of Pitfall! without using demonstrations or hand-crafted features.
Proving the Lottery Ticket Hypothesis: Pruning is All You Need

The lottery ticket hypothesis (Frankle and Carbin, 2018), states that a randomly-initialized network contains a small subnetwork such that, when trained in isolation, can compete with the performance of the original network. We prove an even stronger hypothesis (as was also conjectured in Ramanujan et al., 2019), showing that for every bounded distribution and every target network with bounded weights, a sufficiently over-parameterized neural network with random weights contains a subnetwork with roughly the same accuracy as the target network, without any further training.
Using the Semantic Web in digital humanities: Shift from data publishing to data-analysis and serendipitous knowledge discovery

This paper discusses a shift of focus in research on Cultural Heritage semantic portals, based on Linked Data, and envisions and proposes new directions of research. Three generations of portals are identified: Ten years ago the research focus in semantic portal development was on data harmonizatio n, aggregation, search, and browsing (“first generation systems”). At the moment, the rise of Digital Humanities research has started to shift the focus to providing the user with integrated tools for solving research problems in interactive ways (“second generation systems”). This paper envisions and argues that the next step ahead to “third generation systems” is based on Artificial Intelligence: future portals not only provide tools for the human to solve problems but are used for finding research problems in the first place, for addressing them, and even for solving them automatically under the constraints set by the human researcher. Such systems should preferably be able to explain their reasoning, which is an important aspect in the source critical humanities research tradition. The second and third generation systems set new challenges for both computer scientists and humanities researchers.
Tracing surveillance and auto-regulation in Singapore: ‘smart’ responses to COVID-19

The wealthy and ‘smart’ city-state of Singapore was one of the first to develop a mobile tracing app called TraceTogether during the coronavirus outbreak. It then pivoted towards developing a wearable tech device in order to reach all 5.7 million residents, brushing off concerns about privacy and surveillance. This article tracks the development of TraceTogether and engages in critical debates that have ensued around the use of the app, namely around the twin implications of privacy protection and the conduct of surveillance in a panoptic and auto-regulatory society that privileges socio-political discipline and control. With health crises and pandemics becoming more commonplace, more people around the world are being persuaded to wear some loss of privacy to trust ‘smart’ technologies to aid us in fighting enemies that are deadly and invisible. Singapore could already be offering a glimpse of how this can be done now, and in the future.
Towards a unified criteria model for usability evaluation in the context of open source software based on a fuzzy Delphi method

A plethora of models are available for open-source software (OSS) usability evaluation. However, these models lack consensus between scholars as well as standard bodies on a specific set of usability evaluation criteria. Retaining irrelevant criteria and omitting essential ones will mislead the direction of the usability evaluation. This study introduces a three-step method to develop a usability evaluation model in the context of OSS. The fuzzy Delphi method has been employed to unify the usability evaluation criteria in the context of OSS. The first step in the method is the usability criteria analysis, which involves redefining and restructuring all collected usability criteria reported in the literature. The second step is fuzzy Delphi analysis, which includes the design and validates the fuzzy Delphi instrument and the utilisation of the fuzzy Delphi method to analyse the fuzziness consensus of experts' opinions on the usability evaluation criteria. The third step is the proposal of the OSS usability evaluation model. A total of 124 usability criteria were identified, redefined, and restructured by creating groups of related meaning criteria. The result of the groupings generated 11 main criteria; the findings of the fuzzy Delphi narrowed down the criteria to only seven. The final set of criteria was sent back to the panellists for reconsideration of their responses. The panellists verified that these criteria are suitable in the evaluation of the usability of OSS. The empirical analysis confirmed that the proposed evaluation model is acceptable in assessing the usability of OSS. Therefore, this model can be used as a reference metric for OSS usability evaluation which will have a practical benefit for the community in public and private organisations in helping the decision-maker to select the best OSS software package amongst the alternatives.
Unsupervised 3D Human Pose Representation with Viewpoint and Pose Disentanglement

Learning a good 3D human pose representation is important for human pose related tasks, e.g. human 3D pose estimation and action recognition. Within all these problems, preserving the intrinsic pose information and adapting to view variations are two critical issues. In this work, we propose a novel Siamese denoising autoencoder to learn a 3D pose representation by disentangling the pose-dependent and view-dependent feature from the human skeleton data, in a fully unsupervised manner. These two disentangled features are utilized together as the representation of the 3D pose. To consider both the kinematic and geometric dependencies, a sequential bidirectional recursive network (SeBiReNet) is further proposed to model the human skeleton data. Extensive experiments demonstrate that the learned representation 1) preserves the intrinsic information of human pose, 2) shows good transferability across datasets and tasks. Notably, our approach achieves state-of-the-art performance on two inherently different tasks: pose denoising and unsupervised action recognition. Code and models are available at: https://github.com/NIEQiang001/unsupervised-human-pose.git .
End-to-End Emotion-Cause Pair Extraction based on Sliding Window Multi-Label Learning

Emotion-cause pair extraction (ECPE) is a new task that aims to extract the potential pairs of emotions and their corresponding causes in a document. The existing methods first perform emotion extraction and cause extraction independently, and then perform emotion-cause pairing and filtering. However, the above methods ignore the fact that the cause and the emotion it triggers are inseparable, and the extraction of the cause without specifying the emotion is pathological, which greatly limits the performance of the above methods in the first step. To tackle these shortcomings, we propose two joint frameworks for ECPE: 1) multi-label learning for the extraction of the cause clauses corresponding to the specified emotion clause (CMLL) and 2) multi-label learning for the extraction of the emotion clauses corresponding to the specified cause clause (EMLL). The window of multi-label learning is centered on the specified emotion clause or cause clause and slides as their positions move. Finally, CMLL and EMLL are integrated to obtain the final result. We evaluate our model on a benchmark emotion cause corpus, the results show that our approach achieves the best performance among all compared systems on the ECPE task.
Backpropagation through nonlinear units for the all-optical training of neural networks

We propose a practical scheme for end-to-end optical backpropagation in neural networks. Using saturable absorption for the nonlinear units, we find that the backward-propagating gradients required to train the network can be approximated in a surprisingly simple pump-probe scheme that requires only simple passive optical elements. Simulations show that, with readily obtainable optical depths, our approach can achieve equivalent performance to state-of-the-art computational networks on image classification benchmarks, even in deep networks with multiple sequential gradient approximation. With backpropagation through nonlinear units being an outstanding challenge to the field, this work provides a feasible path toward truly all-optical neural networks.
UAV Path Planning Based on Multi-Layer Reinforcement Learning Technique

Unmanned aerial vehicles (UAVs) have been widely used in many applications due to its small size, swift mobility and low cost. Therefore, the study of guidance, navigation and control (GNC) system of UAV has becoming a popular research direction. Path planning plays an important role in the GNC system. In this paper, a multi-layer path planning algorithm based on reinforcement learning (RL) technique is proposed. Compared to the classic Q-learning, the proposed multi-layer algorithm has a distinct advantage that it collects both global and local information which greatly improves overall performance. The proposed RL algorithm has two layers, the higher layer deals with the local information, which could be considered as a short-term strategy. The lower layer deals with the global information, which could be considered as a long-term strategy. Both the higher layer and lower layer are working in harmony to plan a collision-free path. B-spline curve approach is applied for on-line path smoothing. Simulation results in different scenarios prove the effectiveness of multi-layer Q-learning algorithm.