Reviewerly/exHarmony huggingface dataset:
CORK: A privacy-preserving and lossless federated learning scheme for deep neural network

With the advance of machine learning technology and especially the explosive growth of big data, federated learning, which allows multiple participants to jointly train a high-quality global machine learning model, has gained extensive attention. However, in federated learning, it has been proved that inference attacks could reveal sensitive information from both local updates and global model parameters, which threatens user privacy greatly. Aiming at the challenge, in this paper, a privacy-preserving and lossless federated learning scheme, named CORK, is proposed for deep neural network. With CORK, multiple participants can train a global model securely and accurately with the assistance of an aggregation server. Specifically, we first design a drop-tolerant secure aggregation algorithm FTSA, which ensures the confidentiality of local updates. Then, a lossless model perturbation mechanism PTSP is proposed to protect sensitive data in global model parameters. Furthermore, the neuron pruning operation in PTSP can reduce the scale of models, which thus improves the computation and communication efficiency significantly. Detailed security analysis shows that CORK can resist inference attacks on both local updates and global model parameters. In addition, CORK is implemented with real MNIST and CIFAR-10 datasets, and the experimental results demonstrate that CORK is indeed effective and efficient.
Massively Multilingual ASR: A Lifelong Learning Solution

The development of end-to-end models has largely sped up the research in massively multilingual automatic speech recognition (MMASR). Previous research has demonstrated the feasibility to build high quality MMASR models. In this work, we study the impact of adding more languages and propose a lifelong learning approach to build high quality MMASR systems. Experiments on a 66-language Voice Search task show that we can take a model built on 15 languages and continue training to obtain a 32-language model and similarly to further build a 67-language model. More importantly, models developed in this way achieve better quality compared to those trained from scratch. It maintains similar performance on old languages and achieves competitive results on new ones. This would potentially speed up the development of universal ASR models that recognize speech from any language, any domain and any environment by reusing knowledge learned beforehand.
A Wavelet-Based Dual-Stream Network for Underwater Image Enhancement

We present a wavelet-based dual-stream network that addresses color cast and blurry details in underwater images. We handle these artifacts separately by decomposing an input image into multiple frequency bands using discrete wavelet transform, which generates the downsampled structure image and detail images. These sub-band images are used as input to our dual-stream network that incorporates two sub-networks: the multi-color space fusion network and the detail enhancement network. The multi-color space fusion network takes the decomposed structure image as input and estimates the color corrected output by employing the feature representations from diverse color spaces of the input. The detail enhancement network addresses the blurriness of the original underwater image by improving the image details from high-frequency sub-bands. We validate the proposed method on both real-world and synthetic underwater datasets and show the effectiveness of our model in color correction and blur removal with low computational complexity.
A Novel 3D Unsupervised Domain Adaptation Framework for Cross-Modality Medical Image Segmentation

We consider the problem of volumetric (3D) unsupervised domain adaptation (UDA) in cross-modality medical image segmentation, aiming to perform segmentation on the unannotated target domain (e.g. MRI) with the help of labeled source domain (e.g. CT). Previous UDA methods in medical image analysis usually suffer from two challenges: 1) they focus on processing and analyzing data at 2D level only, thus missing semantic information from the depth level; 2) one-to-one mapping is adopted during the style-transfer process, leading to insufficient alignment in the target domain. Different from the existing methods, in our work, we conduct a first of its kind investigation on multi-style image translation for complete image alignment to alleviate the domain shift problem, and also introduce 3D segmentation in domain adaptation tasks to maintain semantic consistency at the depth level. In particular, we develop an unsupervised domain adaptation framework incorporating a novel quartet self-attention module to efficiently enhance relationships between widely separated features in spatial regions on a higher dimension, leading to a substantial improvement in segmentation accuracy in the unlabeled target domain. In two challenging cross-modality tasks, specifically brain structures and multi-organ abdominal segmentation, our model is shown to outperform current state-of-the-art methods by a significant margin, demonstrating its potential as a benchmark resource for the biomedical and health informatics research community.
CT image quality enhancement via a dual-channel neural network with jointing denoising and super-resolution

In recent years, computed tomography (CT) has been widely used in various clinical diagnosis. Given potential health risks bring by the X-ray radiation, the major objective of the current research is to achieve high-quality CT imaging while reducing X-ray radiation. However, most existing studies on low-dose CT image super-resolution reconstruction do not focus on the interaction between the denoising task and the super-resolution task. In this paper, we propose a dual-channel joint learning framework to accurately reconstruct high-resolution CT images from low-resolution CT images. Unlike the previous cascaded models which directly combine the denoising network and the super-resolution network, our method can process the denoising reconstruction and the super-resolution reconstruction in parallel. Additionally, we design a filter gate module that can filter features from the denoising branch and highlight important features which can benefit the super-resolution task. We evaluate the performance of our method in medical image enhancement by testing on the 2016 Low-Dose CT Grand Challenge dataset and the piglet dataset. The experimental results show that the proposed network is superior to other state-of-the-art methods in terms of both peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). We also demonstrate that our method can better remove noise and recover details. Furthermore, the method achieves competitive results not only for super-resolution reconstruction of low-dose CT, but also for super-resolution reconstruction of sparse-view CT.
Big-Hypergraph Factorization Neural Network for Survival Prediction From Whole Slide Image

Survival prediction for patients based on histopa- thological whole-slide images (WSIs) has attracted increasing attention in recent years. Due to the massive pixel data in a single WSI, fully exploiting cell-level structural information (e.g., stromal/tumor microenvironment) from the gigapixel WSI is challenging. Most of the current studies resolve the problem by sampling limited image patches to construct a graph-based model (e.g., hypergraph). However, the sampling scale is a critical bottleneck since it is a fundamental obstacle of broadening samples for transductive learning. To overcome the limitation of the sampling scale for constructing a big hypergraph model, we propose a factorization neural network that embeds the correlation among large-scale vertices and hyperedges into two low-dimensional latent semantic spaces separately, empowering the dense sampling. Thanks to the compressed low-dimensional correlation embedding, the hypergraph convolutional layers generate the high-order global representation for each WSI. To minimize the effect of the uncertainty data as well as to achieve the metric-driven learning, we also propose a multi-level ranking supervision to enable the network learning by a queue of patients on the global horizon. Extensive experiments are conducted on three public carcinoma datasets (i.e., LUSC, GBM, and NLST), and the quantitative results demonstrate the proposed method outperforms state-of-the-art methods across-the-board.
Resource Allocation With Workload-Time Windows for Cloud-Based Software Services: A Deep Reinforcement Learning Approach

As the workloads and service requests in cloud computing environments change constantly, cloud-based software services need to adaptively allocate resources for ensuring the Quality-of-Service (QoS) while reducing resource costs. However, it is very challenging to achieve adaptive resource allocation for cloud-based software services with complex and variable system states. Most of the existing methods only consider the current condition of workloads, and thus cannot well adapt to real-world cloud environments subject to fluctuating workloads. To address this challenge, we propose a novel Deep Reinforcement learning based resource Allocation method with workload-time Windows (DRAW) for cloud-based software services that considers both the current and future workloads in the resource allocation process. Specifically, an original Deep Q-Network (DQN) based prediction model of management operations is trained based on workload-time windows, which can be used to predict appropriate management operations under different system states. Next, a new feedback-control mechanism is designed to construct the objective resource allocation plan under the current system state through iterative execution of management operations. Extensive simulation results demonstrate that the prediction accuracy of management operations generated by the proposed DRAW method can reach 90.69%. Moreover, the DRAW can achieve the optimal/near-optimal performance and outperform other classic methods by 3 <inline-formula><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 13% under different scenarios.
Transaction Fees on a Honeymoon: Ethereum's EIP-1559 One Month Later

Ethereum Improvement Proposal (EIP) 1559 was recently implemented to transform Ethereum's transaction fee market. EIP–1559 utilizes an algorithmic update rule with a constant learning rate to estimate a base fee. The base fee reflects prevailing network conditions and hence provides a more reliable oracle for current gas prices. Using on-chain data from the period after its launch, we evaluate the impact of EIP–1559 on the user experience and market performance. Our empirical findings suggest that although EIP–1559 achieves its goals on average, short-term behavior is marked by intense, chaotic oscillations in block sizes (as predicted by our recent theoretical dynamical system analysis [1]) and slow adjustments during periods of demand bursts (e.g., NFT drops). Both phenomena lead to unwanted inter-block variability in mining rewards. To address this issue, we propose an alternative base fee adjustment rule in which the learning rate varies according to an additive increase, multiplicative decrease (AIMD) update scheme. Our simulations show that the latter robustly outperforms the EIP–1559 protocol under various demand scenarios. These results provide evidence that variable learning rate mechanisms may constitute a promising alternative to the default EIP–1559-based format and contribute to the ongoing discussion on the design of more efficient transaction fee markets.
Enabling Edge-Cloud Video Analytics for Robotics Applications

Emerging deep learning-based video analytics tasks demand computation-intensive neural networks and powerful computing resources on the cloud to achieve high accuracy. Due to the latency requirement and limited network bandwidth, edge-cloud systems adaptively compress the data to strike a balance between overall analytics accuracy and bandwidth consumption. However, the degraded data leads to another issue of poor <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">tail accuracy</i> , which means the extremely low accuracy of a few semantic classes and video frames. Autonomous robotics applications especially value the tail accuracy performance but suffer using the prior edge-cloud systems. We present Runespoor, an edge-cloud video analytics system to manage the tail accuracy and enable emerging robotics applications. We train and deploy a super-resolution model tailored for the tail accuracy of analytics tasks on the server to significantly improves the performance on hard-to-detect classes and sophisticated frames. During online operation, we use an adaptive data rate controller to further improve the tail performance by instantly adjusting the data rate policy according to the video content. Our evaluation shows that Runespoor improves class-wise tail accuracy by up to 300%, frame-wise 90%/99% tail accuracy by up to 22%/54%, and greatly improves the overall accuracy and bandwidth trade-off.
Genetically-modified Multi-objective Particle Swarm Optimization approach for high-performance computing workflow scheduling

Nowadays, scientific research, industry, and many other fields are greedy regarding computing resources. Therefore, Cloud Computing infrastructures are now attracting pervasive interest thanks to their excellent hallmarks such as scalability, high performance, reliability, and the pay-per-use strategy. The execution of these high-performant applications on such kind of computing environments in respect of optimizing many conflicting objectives brings us to a challenging issue commonly known as the multi-objective workflows scheduling on large scale distributed systems. Having this in mind, we outline in the present paper our proposed approach called Genetically-modified Multi-objective Particle Swarm Optimization (GMPSO) for scheduling application workflows on hybrid Clouds in the context of high-performance computing in an attempt to optimize Makespan and Cost. The GMPSO consists of incorporating genetic operations into the Multi-objective Particle Swarm Optimization to enhance the resulting solutions. To achieve this, we have designed a novel solution encoding that represents the task ordering, the task mapping and the resource provisioning processes of the workflow scheduling problem in hybrid Clouds. In addition, a set of particular adaptive evolutionary operators have been designed. Conducted simulations lead to significant results compared with a set of well-performed algorithms such NSGA-II, OMOPSO and SMPSO, especially, for the most-demanding workload of workflows.
Arabic chatbot technologies: A scoping review

Chatbots have been widely used in many spheres of life from customer services to mental health companions. Despite the breakthroughs in achieving human-like conversations, Arabic language chatbots driven by AI and NLP are relatively scarce due to the complex nature of the Arabic language. We aim to review published literature on Arabic chatbots to gain insight into the technologies used highlighting the gap in this emerging field. To identify relevant studies, we searched eight bibliographic databases and conducted backward and forward reference checking. Two reviewers independently performed study selection and data extraction. The extracted data was synthesized using a narrative approach. We included 18 of 1755 retrieved publications. Thirteen unique chatbots were identified from the 18 studies. ArabChat was the most common chatbot in the included studies (n=5). The type of Arabic language in most chatbots (n=13) was Modern Standard Arabic. The input and output modalities used in 17 chatbots were only text. Most chatbots (n=14) were able to have long conversations. The majority of the chatbots (n=14) were developed to serve a specific purpose (Closed domain). A retrieval-based model was used for developing most chatbots (n=17). Despite a large number of chatbots worldwide, there is relatively a small number of Arabic language chatbots. Furthermore, the available Arabic language chatbots are less advanced than other language chatbots. Researchers should develop more Arabic language chatbots that are based on more advanced input and output modalities, generative-based models, and natural language processing (NLP).
Progressive Dual-Attention Residual Network for Salient Object Detection

Due to the rapid development of deep learning, the performance of salient object detection has been constantly refreshed. Nevertheless, it is still challenging for existing methods to distinguish the location of salient objects and retain fine structural details. In this paper, a novel progressive dual-attention residual network (PDRNet) is proposed to exploit two complementary attention maps to guide residual learning, thus progressively refining prediction in a coarse-to-fine manner. We design a dual-attention residual module (DRM) to achieve residual refinement with the help of the dual attention (DA) scheme. Specifically, an attention map and its corresponding reverse attention map are used to make the network be aware of learning residual details from the perspective of the salient and non-salient regions, thus utilizing their complementarity to correct the mistakes of object parts and boundary details. Besides, a hierarchical feature screening module (HFSM) is designed to capture more powerful global contextual knowledge for locating salient objects. It establishes cross-scale skip connections among multi-scale features and utilizes the intra-channel dependency of these scales to enhance information interaction and feature representation. Extensive experiments have proved that our proposed PDRNet performs favorably against 18 state-of-the-art competitors on five benchmark datasets, demonstrating the effectiveness and superiority of our method.
Toward a No-Reference Quality Metric for Camera-Captured Images

Existing no-reference (NR) image quality assessment (IQA) metrics are still not convincing for evaluating the quality of the camera-captured images. Toward tackling this issue, we, in this article, establish a novel NR quality metric for quantifying the quality of the camera-captured images reliably. Since the image quality is hierarchically perceived from the low-level preliminary visual perception to the high-level semantic comprehension in the human brain, in our proposed metric, we characterize the image quality by exploiting both the low-level image properties and the high-level semantics of the image. Specifically, we extract a series of low-level features to characterize the fundamental image properties, including the brightness, saturation, contrast, noiseness, sharpness, and naturalness, which are highly indicative of the camera-captured image quality. Correspondingly, the high-level features are designed to characterize the semantics of the image. The low-level and high-level perceptual features play complementary roles in measuring the image quality. To infer the image quality, we employ the support vector regression (SVR) to map all the informative features to a single quality score. Thorough tests conducted on two standard camera-captured image databases demonstrate the effectiveness of the proposed quality metric in assessing the image quality and its superiority over the state-of-the-art NR quality metrics. The source code of the proposed metric for camera-captured images is released at https://github.com/YT2015?tab=repositories.
ACE: Anchor-Free Corner Evolution for Real-Time Arbitrarily-Oriented Object Detection

Objects with different orientations are ubiquitous in the real world (e.g., texts/hands in the scene image, objects in the aerial image, etc.), and the widely-used axis-aligned bounding box does not compactly enclose the oriented objects. Thus arbitrarily-oriented object detection has attracted rising attention in recent years. In this paper, we propose a novel and effective model to detect arbitrarily-oriented objects. Instead of directly predicting the angles of oriented bounding boxes like most existing methods, we evolve the axis-aligned bounding box to the oriented quadrilateral box with the assistance of dynamically gathering contour information. More specifically, we first obtain the axis-aligned bounding box in an anchor-free manner. After that, we set the key points based on the sampled contour points of the axis-aligned bounding box. To improve the localization performance, we enrich the feature representations of these key points by exploiting a dynamic information gathering mechanism. This technique propagates the geometrical and semantic information along the sampled contour points, and fuses the information from the semantic neighbors of each sampled point, which varies for different locations. Finally, we estimate the offsets between the axis-aligned bounding box key points and the oriented quadrilateral box corner points. Extensive experiments on two frequently-used aerial image benchmarks HRSC2016 and DOTA, as well as scene text/hand datasets ICDAR2015, TD500, and Oxford-Hand, demonstrate the effectiveness and advantage of our proposed model.
MonoJSG: Joint Semantic and Geometric Cost Volume for Monocular 3D Object Detection

Due to the inherent ill-posed nature of 2D-3D projection, monocular 3D object detection lacks accurate depth recovery ability. Although the deep neural network (DNN) enables monocular depth-sensing from high-level learned features, the pixel-level cues are usually omitted due to the deep convolution mechanism. To benefit from both the pow-erful feature representation in DNN and pixel-level geomet-ric constraints, we reformulate the monocular object depth estimation as a progressive refinement problem and propose a joint semantic and geometric cost volume to model the depth error. Specifically, we first leverage neural networks to learn the object position, dimension, and dense normal-ized 3D object coordinates. Based on the object depth, the dense coordinates patch together with the corresponding object features is reprojected to the image space to build a cost volume in a joint semantic and geometric error man-ner. The final depth is obtained by feeding the cost volume to a refinement network, where the distribution of semantic and geometric error is regularized by direct depth supervision. Through effectively mitigating depth error by the re-finement framework, we achieve state-of-the-art results on both the KITTI and Waymo datasets. <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> Code available at https://github.com/lianqingll/MonoJSG
Efficient and Interpretable Robot Manipulation With Graph Neural Networks

Manipulation tasks, like loading a dishwasher, can be seen as a sequence of spatial constraints and relationships between different objects. We aim to discover these rules from demonstrations by posing manipulation as a classification problem over a graph, whose nodes represent task-relevant entities like objects and goals, and present a graph neural network (GNN) policy architecture for solving this problem from demonstrations. In our experiments, a single GNN policy trained using imitation learning (IL) on 20 expert demos can solve blockstacking, rearrangement, and dishwasher loading tasks; once the policy has learned the spatial structure, it can generalize to a larger number of objects, goal configurations, and from simulation to the real world. These experiments show that graphical IL can solve complex long-horizon manipulation problems without requiring detailed task descriptions. Videos can be found at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://youtu.be/POxaTDAj7aY</uri> .