Reviewerly/exHarmony huggingface dataset:
Automatic Searching and Pruning of Deep Neural Networks for Medical Imaging Diagnostic

The field of medical imaging diagnostic makes use of a modality of imaging tests, e.g., X-rays, ultrasounds, computed tomographies, and magnetic resonance imaging, to assist physicians with the diagnostic of patients' illnesses. Due to their state-of-the-art results in many challenging image classification tasks, deep neural networks (DNNs) are suitable tools for use by physicians to provide diagnostic support when dealing with medical images. To further advance the field, the present work proposes a two-phase algorithm capable of automatically generating compact DNN architectures given a database, called here DNNDeepeningPruning. In the first phase, also called the deepening phase, the algorithm grows a DNN by adding blocks of residual layers one after another until the model overfits the given data. In the second phase, called the pruning phase, the algorithm prunes the created DNN model from the first phase to produce a DNN with a small amount of floating-point operations guided by some preference given by the user. The proposed algorithm unifies the two separate fields of DNN architecture searching and pruning under a single framework, and it is tested in two medical imaging data sets with satisfactory results.
Generative Landmark Guided Face Inpainting

It is challenging to inpaint face images in the wild, due to the large variation of appearance, such as different poses, expressions and occlusions. A good inpainting algorithm should guarantee the realism of output, including the topological structure among eyes, nose and mouth, as well as the attribute consistency on pose, gender, ethnicity, expression, etc. This paper studies an effective deep learning based strategy to deal with these issues, which comprises of a facial landmark predicting subnet and an image inpainting subnet. Concretely, given partial observation, the landmark predictor aims to provide the structural information (e.g. topological relationship and expression) of incomplete faces, while the inpaintor is to generate plausible appearance (e.g. gender and ethnicity) conditioned on the predicted landmarks. Experiments on the CelebA-HQ and CelebA datasets are conducted to reveal the efficacy of our design and, to demonstrate its superiority over state-of-the-art alternatives both qualitatively and quantitatively. In addition, we assume that high-quality completed faces together with their landmarks can be utilized as augmented data to further improve the performance of (any) landmark predictor, which is corroborated by experimental results on the 300W and WFLW datasets. The code is available at https://github.com/YaN9-Y/lafin.
Action Completeness Modeling with Background Aware Networks for Weakly-Supervised Temporal Action Localization

The state-of-the-art of fully-supervised methods for temporal action localization from untrimmed videos has achieved impressive results. Yet, it remains unsatisfactory for the weakly-supervised temporal action localization, where only video-level action labels are given without the timestamp annotation on when the actions occur. The main reason comes from that, the weakly-supervised networks only focus on the highly discriminative frames, but there are some ambiguous frames in both background and action classes. The ambiguous frames in background class are very similar to the real actions, which may be treated as target actions and result in false positives. On the other hand, the ambiguous frames in action class which possibly contain action instances, are prone to be false negatives by the weakly-supervised networks and result in a coarse localization. To solve these problems, we introduce a novel weakly-supervised Action Completeness Modeling with Background Aware Networks (ACM-BANets). Our Background Aware Network (BANet) contains a weight-sharing two-branch architecture, with an action guided Background aware Temporal Attention Module (B-TAM) and an asymmetrical training strategy, to suppress both highly discriminative and ambiguous background frames to remove the false positives. Our action completeness modeling contains multiple BANets, and the BANets are forced to discover different but complementary action instances to completely localize the action instances in both highly discriminative and ambiguous action frames. In the i-th iteration, the i-th BANet discovers the discriminative features, which are then erased from the feature map. The partially-erased feature map is fed into the (i+1)-th BANet of the next iteration to force this BANet to discover discriminative features different from the i-th BANet. Evaluated on two challenging untrimmed video datasets, THUMOS14 and ActivityNet1.3, our approach outperforms all the current weakly-supervised methods for temporal action localization.
Fast Enhancement for Non-Uniform Illumination Images using Light-weight CNNs

This paper proposes a new light-weight convolutional neural network (~5k params) for non-uniform illumination image enhancement to handle color, exposure, contrast, noise and artifacts, etc., simultaneously and effectively. More concretely, the input image is first enhanced using Retinex model from dual different aspects (enhancing under-exposure and suppressing over-exposure), respectively. Then, these two enhanced results and the original image are fused to obtain an image with satisfactory brightness, contrast and details. Finally, the extra noise and compression artifacts are removed to get the final result. To train this network, we propose a semi-supervised retouching solution and construct a new dataset (~82k images) that contains various scenes and light conditions. Our model can enhance 0.5 mega-pixel (like 600×800) images in real-time (~50 fps), which is faster than existing enhancement methods. Extensive experiments show that our solution is fast and effective to deal with non-uniform illumination images.
Hybrid Global Positioning System-Adaptive Neuro-Fuzzy Inference System based autonomous mobile robot navigation

The collision-free navigation of a mobile robot in clutter environments is challenging. Global Positioning System (GPS) and adaptive neuro-fuzzy inference system (ANFIS) are well-known techniques widely used for navigation and control, respectively. This paper proposes a hybrid GPS-ANFIS based method for collision-free navigation of autonomous mobile robots. The GPS-based controller keeps the navigation direction of the robot toward the static or dynamic target. It uses the coordinates received from the two GPS modules on the edges of the longitudinal axis of the robot all together with the coordinates of the target to divert it from the current path making a certain angle towards the target. The performance of the proposed method in navigating a mobile robot in clutter environments and its effectiveness in comparison with the other collision-free navigation methods has been evaluated through simulations. The evaluation criteria are on the basis of the obstacle avoidance behavior and the length of the discovered collision-free path by the robot. The results have shown that our hybrid GPS-ANFIS method navigates the robot toward the goal via a shorter path while avoiding the obstacles.
Incomplete Cross-modal Retrieval with Dual-Aligned Variational Autoencoders

Learning the relationship between the multi-modal data, e.g., texts, images and videos, is a classic task in the multimedia community. Cross-modal retrieval (CMR) is a typical example where the query and the corresponding results are in different modalities. Yet, a majority of existing works investigate CMR with an ideal assumption that the training samples in every modality are sufficient and complete. In real-world applications, however, this assumption does not always hold. Mismatch is common in multi-modal datasets. There is a high chance that samples in some modalities are either missing or corrupted. As a result, incomplete CMR has become a challenging issue. In this paper, we propose a Dual-Aligned Variational Autoencoders (DAVAE) to address the incomplete CMR problem. Specifically, we propose to learn modality-invariant representations for different modalities and use the learned representations for retrieval. We train multiple autoencoders, one for each modality, to learn the latent factors among different modalities. These latent representations are further dual-aligned at the distribution level and the semantic level to alleviate the modality gaps and enhance the discriminability of representations. For missing instances, we leverage generative models to synthesize latent representations for them. Notably, we test our method with different ratios of random incompleteness.Extensive experiments on three datasets verify that our method can consistently outperform the state-of-the-arts.
Subspace Sparse Discriminative Feature Selection

In this article, we propose a novel feature selection approach via explicitly addressing the long-standing subspace sparsity issue. Leveraging l2,1 -norm regularization for feature selection is the major strategy in existing methods, which, however, confronts sparsity limitation and parameter-tuning trouble. To circumvent this problem, employing the l2,0 -norm constraint to improve the sparsity of the model has gained more attention recently whereas, optimizing the subspace sparsity constraint is still an unsolved problem, which only can acquire an approximate solution and without convergence proof. To address the above challenges, we innovatively propose a novel subspace sparsity discriminative feature selection (S2DFS) method which leverages a subspace sparsity constraint to avoid tuning parameters. In addition, the trace ratio formulated objective function extremely ensures the discriminability of selected features. Most important, an efficient iterative optimization algorithm is presented to explicitly solve the proposed problem with a closed-form solution and strict convergence proof. To the best of our knowledge, such an optimization algorithm of solving the subspace sparsity issue is first proposed in this article, and a general formulation of the optimization algorithm is provided for improving the extensibility and portability of our method. Extensive experiments conducted on several high-dimensional text and image datasets demonstrate that the proposed method outperforms related state-of-the-art methods in pattern classification and image retrieval tasks.
Divide-and-Rule: Self-Supervised Learning for Survival Analysis in Colorectal Cancer

With the long-term rapid increase in incidences of colorectal cancer (CRC), there is an urgent clinical need to improve risk stratification. The conventional pathology report is usually limited to only a few histopathological features. However, most of the tumor microenvironments used to describe patterns of aggressive tumor behavior are ignored. In this work, we aim to learn histopathological patterns within cancerous tissue regions that can be used to improve prognostic stratification for colorectal cancer. To do so, we propose a self-supervised learning method that jointly learns a representation of tissue regions as well as a metric of the clustering to obtain their underlying patterns. These histopathological patterns are then used to represent the interaction between complex tissues and predict clinical outcomes directly. We furthermore show that the proposed approach can benefit from linear predictors to avoid overfitting in patient outcomes predictions. To this end, we introduce a new well-characterized clinicopathological dataset, including a retrospective collective of 374 patients, with their survival time and treatment information. Histomorphological clusters obtained by our method are evaluated by training survival models. The experimental results demonstrate statistically significant patient stratification, and our approach outperformed the state-of-the-art deep clustering methods.
On deep learning techniques to boost monocular depth estimation for autonomous navigation

Inferring the depth of images is a fundamental inverse problem within the field of Computer Vision since depth information is obtained through 2D images, which can be generated from infinite possibilities of observed real scenes. Benefiting from the progress of Convolutional Neural Networks (CNNs) to explore structural features and spatial image information, Single Image Depth Estimation (SIDE) is often highlighted in scopes of scientific and technological innovation, as this concept provides advantages related to its low implementation cost and robustness to environmental conditions. In the context of autonomous vehicles, state-of-the-art CNNs optimize the SIDE task by producing high-quality depth maps, which are essential during the autonomous navigation process in different locations. However, such networks are usually supervised by sparse and noisy depth data, from Light Detection and Ranging (LiDAR) laser scans, and are carried out at high computational cost, requiring high-performance Graphic Processing Units (GPUs). Therefore, we propose a new lightweight and fast supervised CNN architecture combined with novel feature extraction models which are designed for real-world autonomous navigation. We also introduce an efficient surface normals module, jointly with a simple geometric 2.5D loss function, to solve SIDE problems. We also innovate by incorporating multiple Deep Learning techniques, such as the use of densification algorithms and additional semantic, surface normals and depth information to train our framework. The method introduced in this work focuses on robotic applications in indoor and outdoor environments and its results are evaluated on the competitive and publicly available NYU Depth V2 and KITTI Depth datasets.
Neural Logic Reasoning

Recent years have witnessed the success of deep neural networks in many research areas. The fundamental idea behind the design of most neural networks is to learn similarity patterns from data for prediction and inference, which lacks the ability of cognitive reasoning. However, the concrete ability of reasoning is critical to many theoretical and practical problems. On the other hand, traditional symbolic reasoning methods do well in making logical inference, but they are mostly hard rule-based reasoning, which limits their generalization ability to different tasks since difference tasks may require different rules. Both reasoning and generalization ability are important for prediction tasks such as recommender systems, where reasoning provides strong connection between user history and target items for accurate prediction, and generalization helps the model to draw a robust user portrait over noisy inputs.
C-Face

C-Face (Contour-Face) is an ear-mounted wearable sensing technology that uses two miniature cameras to continuously reconstruct facial expressions by deep learning contours of the face. When facial muscles move, the contours of the face change from the point of view of the ear-mounted cameras. These subtle changes are fed into a deep learning model which continuously outputs 42 facial feature points representing the shapes and positions of the mouth, eyes and eyebrows. To evaluate C-Face, we embedded our technology into headphones and earphones. We conducted a user study with nine participants. In this study, we compared the output of our system to the feature points outputted by a state of the art computer vision library (Dlib) from a font facing camera. We found that the mean error of all 42 feature points was 0.77 mm for earphones and 0.74 mm for headphones. The mean error for 20 major feature points capturing the most active areas of the face was 1.43 mm for earphones and 1.39 mm for headphones. The ability to continuously reconstruct facial expressions introduces new opportunities in a variety of applications. As a demonstration, we implemented and evaluated C-Face for two applications: facial expression detection (outputting emojis) and silent speech recognition. We further discuss the opportunities and challenges of deploying C-Face in real-world applications.
XI Commandments of Kubernetes Security: A Systematization of Knowledge Related to Kubernetes Security Practices

Kubernetes is an open-source software for automat- ing management of computerized services. Organizations, such as IBM, Capital One and Adidas use Kubernetes to deploy and manage their containers, and have reported benefits related to deployment frequency. Despite reported benefits, Kubernetes deployments are susceptible to security vulnerabilities, such as those that occurred at Tesla in 2018. A systematization of Kubernetes security practices can help practitioners mitigate vulnerabilities in their Kubernetes deployments. The goal of this paper is to help practitioners in securing their Kubernetes installations through a systematization of knowledge related to Kubernetes security practices. We systematize knowledge by applying qualitative analysis on 104 Internet artifacts. We identify 11 security practices that include (i) implementation of role-based access control (RBAC) authorization to provide least privilege, (ii) applying security patches to keep Kubernetes updated, and (iii) implementing pod and network specific security policies.
Interactive Program Synthesis by Augmented Examples

Programming-by-example (PBE) has become an increasingly popular component in software development tools, human-robot interaction, and end-user programming. A long-standing challenge in PBE is the inherent ambiguity in user-provided examples. This paper presents an interaction model to disambiguate user intent and reduce the cognitive load of understanding and validating synthesized programs. Our model provides two types of augmentations to user-given examples: 1) semantic augmentation where a user can specify how different aspects of an example should be treated by a synthesizer via light-weight annotations, and 2) data augmentation where the synthesizer generates additional examples to help the user understand and validate synthesized programs. We implement and demonstrate this interaction model in the domain of regular expressions, which is a popular mechanism for text processing and data wrangling and is often considered hard to master even for experienced programmers. A within-subjects user study with twelve participants shows that, compared with only inspecting and annotating synthesized programs, interacting with augmented examples significantly increases the success rate of finishing a programming task with less time and increases users? confidence of synthesized programs.
Enhanced Flower Pollination Algorithm for Task Scheduling in Cloud Computing Environment

Cloud computing technology refers to on-demand access to services, applications, and infrastructure that runs on a distributed network utilizing virtualized resources. In the cloud model, an efficient task scheduling algorithm plays an important role in order to achieve better functioning in general and resource utilization of the cloud. The end-users submit tasks, and the scheduling algorithm needs to allocate them to the available resources on time. Task scheduling issue is considered as NP-hard problems, and metaheuristics algorithms demonstrate high efficiency in solving such problems, thus, in this work, we propose enhanced flower pollination algorithm for the task scheduling. The major focus of this study is to reduce the makespan. We compared the results of the proposed method to other similar approaches, such as PBACO, ACO, Min-Min, and FCFS allocation strategies. The obtained results from the experiment show that the proposed EEFPA scheduler has the potential to allocate submitted tasks by the user to the available resources on the cloud.
Bidirectional Guided Attention Network for 3-D Semantic Detection of Remote Sensing Images

Semantic segmentation and disparity estimation are in the research frontier of the computer vision and remote sensing (RS) fields. However, existing methods mostly deal with these two problems separately or use a combination of multiple models to solve these two tasks. Due to a lack of sufficient information sharing and fusion, they still have difficulties in coping with seasonal appearance differences in 3-D RS problems. In this article, we propose a novel multitask learning architecture that considers the bottom–up and up–bottom visual attention mechanism for 3-D semantic detection, named bidirectional guided attention network (BGA-Net). BGA-Net consists of five modules: unified backbone module (UBM), bidirectional guided attention module (BGAM), semantic segmentation module (SSM), feature matching module (FMM), and bidirectional fusion module (BFM). First, in UBM, we use a shared backbone to extract unified features and share them with three branches/modules (BGAM, SSM, and FMM). Then, SSM and FMM branches are applied to estimate segmentation and disparity maps, whereas the third branch/module (BGAM) shares the global features to guide the task-specific learning via attention mechanism. Finally, we fuse the results of the two tasks by BFM to improve the final performance. Extensive experiments demonstrate that: 1) our BGA-Net can handle the two tasks simultaneously and can be trained in an end-to-end way; 2) these modules fully take advantage of the two tasks’ information to share features and enhance the scene understanding ability, effectively against seasons change of RS images; and 3) BGA-Net has notable superiority and greater flexibility and also sets a new state of the art on the urban semantic 3-D (US3D) benchmark. Moreover, BGA-Net also provides insights into the intelligent interpretation of RS data images.
A Fast Sand-Dust Image Enhancement Algorithm by Blue Channel Compensation and Guided Image Filtering

The images captured in sand-dust weather have the characteristics of color deviation and low visibility, which seriously affect computer vision systems. To solve the above problems, we propose a fast and effective algorithm to enhance the images captured in sand-dust weather conditions. First, we compensate for the loss value in the blue channel. Then, white balancing technology is used to correct the color of the sand-dust-degraded image. Finally, guided image filtering is used to enhance the image contrast and edge accuracy, and an adaptive method is used to calculate the magnification factor of the detail layer to enhance the image detail information. The experimental results on a large number of sand-dust-degraded images show that the method can effectively recover the fading characteristics of sand-dust-degraded images in a short time and improve the clarity of the images. Experimental results via qualitative and quantitative evaluations demonstrate that the proposed method can significantly improve the images captured during sand-dust weather conditions, and the results are better than those of other methods.
COVID-19 Contact Tracing Apps: A Technologic Tower of Babel and the Gap for International Pandemic Control

As the world struggles with the new COVID-19 pandemic, contact tracing apps of various types have been adopted in many jurisdictions for combating the spread of the SARS-CoV-2 virus. However, even if they are successful in containing the virus within national borders, these apps are becoming ineffective as international travel is gradually resumed. The problem rests in the plurality of apps and their inability to operate in a synchronized manner, as well as the absence of an international entity with the power to coordinate and analyze the information collected by the disparate apps. The risk of creating a useless Tower of Babel of COVID-19 contact tracing apps is very real, endangering global health. This paper analyzes legal barriers for realizing the interoperability of contact tracing apps and emphasizes the need for developing coordinated solutions to promote safe international travel and global pandemic control.