Reviewerly/exHarmony huggingface dataset:
Self-Path: Self-Supervision for Classification of Pathology Images With Limited Annotations

While high-resolution pathology images lend themselves well to 'data hungry' deep learning algorithms, obtaining exhaustive annotations on these images for learning is a major challenge. In this article, we propose a self-supervised convolutional neural network (CNN) framework to leverage unlabeled data for learning generalizable and domain invariant representations in pathology images. Our proposed framework, termed as Self-Path, employs multi-task learning where the main task is tissue classification and pretext tasks are a variety of self-supervised tasks with labels inherent to the input images. We introduce novel pathology-specific self-supervision tasks that leverage contextual, multi-resolution and semantic features in pathology images for semi-supervised learning and domain adaptation. We investigate the effectiveness of Self-Path on 3 different pathology datasets. Our results show that Self-Path with the pathology-specific pretext tasks achieves state-of-the-art performance for semi-supervised learning when small amounts of labeled data are available. Further, we show that Self-Path improves domain adaptation for histopathology image classification when there is no labeled data available for the target domain. This approach can potentially be employed for other applications in computational pathology, where annotation budget is often limited or large amount of unlabeled image data is available.
High-Fidelity Pluralistic Image Completion with Transformers

Image completion has made tremendous progress with convolutional neural networks (CNNs), because of their powerful texture modeling capacity. However, due to some inherent properties (e.g., local inductive prior, spatial-invariant kernels), CNNs do not perform well in understanding global structures or naturally support pluralistic completion. Recently, transformers demonstrate their power in modeling the long-term relationship and generating diverse results, but their computation complexity is quadratic to input length, thus hampering the application in processing high-resolution images. This paper brings the best of both worlds to pluralistic image completion: appearance prior reconstruction with transformer and texture replenishment with CNN. The former transformer recovers pluralistic coherent structures together with some coarse textures, while the latter CNN enhances the local texture details of coarse priors guided by the high-resolution masked images. The proposed method vastly outperforms state-of-the-art methods in terms of three aspects: 1) large performance boost on image fidelity even compared to deterministic completion methods; 2) better diversity and higher fidelity for pluralistic completion; 3) exceptional generalization ability on large masks and generic dataset, like ImageNet. Code and pre-trained models have been publicly released at https://github.com/raywzy/ICT.
SSUR: An Approach to Optimizing Virtual Machine Allocation Strategy Based on User Requirements for Cloud Data Center

Cloud data centers provide services for an increasing number of applications. The virtual machines (VMs) that perform the corresponding application tasks need to be allocated to physical machines (PMs). For VM allocation, cloud service centers consider both energy consumption and quality of service (QoS), while cloud users are primarily concerned with their own needs, such as throughput and reliability. This paper proposes an allocation scheme for optimization based on user requirements in a cloud data center. First, various application requests from mobile phones and devices, which are regarded as a group of VM lists in the data center, are submitted to the cloud platform. Our method first allocates these arriving VMs to appropriate PMs based on their usage of hardware resources and the current throughput of the PMs in the data center. Second, due to dynamic workloads, the loads of the PMs that host these VMs may become very high. CPU utilization thresholds are set to determine whether migration is required, and the energy consumption before and after allocation is used to choose which VMs are reallocated. A suitable strategy for VM migration and PM shutdown can improve reliability and reduce energy consumption. Finally, it is shown through experimental simulations that compared with two existing algorithms, on the premise that the user requirements are met, the proposed method offers good performance in terms of total energy consumption, CPU utilization, number of PMs used and number of service-level agreement (SLA) violation.
A survey of federated learning for edge computing: Research problems and solutions

Federated Learning is a machine learning scheme in which a shared prediction model can be collaboratively learned by a number of distributed nodes using their locally stored data. It can provide better data privacy because training data are not transmitted to a central server. Federated learning is well suited for edge computing applications and can leverage the the computation power of edge servers and the data collected on widely dispersed edge devices. To build such an edge federated learning system, we need to tackle a number of technical challenges. In this survey, we provide a new perspective on the applications, development tools, communication efficiency, security & privacy, migration and scheduling in edge federated learning.
A systematic review of AI technology-based service encounters: Implications for hospitality and tourism operations

The hospitality and tourism industry faces serious challenges during public health emergencies such as COVID-19. Managers are concerned not only about how to maintain business and provide humanized services but also about social distancing. This study presented artificial intelligence (AI) technology-based service encounters as a possible solution and examined the antecedents and consequences of the encounter triad including customers, employees, and AI. Based on a systematic literature review, the study identified 4 modes of AI technology-based service encounters: AI-supplemented, AI-generated, AI-mediated, and AI-facilitated encounters. In addition, the study developed an integrated model to specify the factors that influence AI technology-infused service encounters in general and the customer service outcomes that result from the encounters. The findings contribute to service management and AI application theoretically and practically.
RAPTOR: Robust and Perception-Aware Trajectory Replanning for Quadrotor Fast Flight

Recent advances in trajectory replanning have enabled quadrotor to navigate autonomously in unknown environments. However, high-speed navigation still remains a significant challenge. Given very limited time, existing methods have no strong guarantee on the feasibility or quality of the solutions. Moreover, most methods do not consider environment perception, which is the key bottleneck to fast flight. In this article, we present RAPTOR, a robust and perception-aware replanning framework to support fast and safe flight, which addresses these issues systematically. A path-guided optimization approach that incorporates multiple topological paths is devised, to ensure finding feasible and high-quality trajectories in very limited time. We also introduce two perception-aware planning approaches to actively observe and avoid unknown obstacles. A risk-aware trajectory refinement ensures that unknown obstacles which may endanger the quadrotor can be observed earlier and avoid in time. The motion of yaw angle is planned to actively explore the surrounding space that is relevant for safe navigation. The proposed methods are tested extensively through benchmark comparisons and challenging indoor and outdoor aggressive flights. We release our implementation as an open-source package <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> for the community.
CReST: A Class-Rebalancing Self-Training Framework for Imbalanced Semi-Supervised Learning

Semi-supervised learning on class-imbalanced data, although a realistic problem, has been under studied. While existing semi-supervised learning (SSL) methods are known to perform poorly on minority classes, we find that they still generate high precision pseudo-labels on minority classes. By exploiting this property, in this work, we propose Class-Rebalancing Self-Training (CReST), a simple yet effective framework to improve existing SSL methods on class-imbalanced data. CReST iteratively retrains a baseline SSL model with a labeled set expanded by adding pseudolabeled samples from an unlabeled set, where pseudolabeled samples from minority classes are selected more frequently according to an estimated class distribution. We also propose a progressive distribution alignment to adaptively adjust the rebalancing strength dubbed CReST+. We show that CReST and CReST+ improve state-of-the-art SSL algorithms on various class-imbalanced datasets and consistently outperform other popular rebalancing methods. Code has been made available at https://github.com/google-research/crest.
Learning Salient Boundary Feature for Anchor-free Temporal Action Localization

Temporal action localization is an important yet challenging task in video understanding. Typically, such a task aims at inferring both the action category and localization of the start and end frame for each action instance in a long, untrimmed video. While most current models achieve good results by using pre-defined anchors and numerous actionness, such methods could be bothered with both large number of outputs and heavy tuning of locations and sizes corresponding to different anchors. Instead, anchor-free methods is lighter, getting rid of redundant hyper-parameters, but gains few attention. In this paper, we propose the first purely anchor-free temporal localization method, which is both efficient and effective. Our model includes (i) an end-to-end trainable basic predictor, (ii) a saliency-based refinement module to gather more valuable boundary features for each proposal with a novel boundary pooling, and (iii) several consistency constraints to make sure our model can find the accurate boundary given arbitrary proposals. Extensive experiments show that our method beats all anchor-based and actionness-guided methods with a remarkable margin on THUMOS14, achieving state-of-the-art results, and comparable ones on ActivityNet v1.3. Code is available at https://github.com/TencentYoutuResearch/ActionDetection-AFSD.
Deep learning-based transformation of H&amp;E stained tissues into special stains

Pathology is practiced by visual inspection of histochemically stained slides. Most commonly, the hematoxylin and eosin (H&E) stain is used in the diagnostic workflow and it is the gold standard for cancer diagnosis. However, in many cases, especially for non-neoplastic diseases, additional "special stains" are used to provide different levels of contrast and color to tissue components and allow pathologists to get a clearer diagnostic picture. In this study, we demonstrate the utility of supervised learning-based computational stain transformation from H&E to different special stains (Masson's Trichrome, periodic acid-Schiff and Jones silver stain) using tissue sections from kidney needle core biopsies. Based on evaluation by three renal pathologists, followed by adjudication by a fourth renal pathologist, we show that the generation of virtual special stains from existing H&E images improves the diagnosis in several non-neoplastic kidney diseases sampled from 58 unique subjects. A second study performed by three pathologists found that the quality of the special stains generated by the stain transformation network was statistically equivalent to those generated through standard histochemical staining. As the transformation of H&E images into special stains can be achieved within 1 min or less per patient core specimen slide, this stain-to-stain transformation framework can improve the quality of the preliminary diagnosis when additional special stains are needed, along with significant savings in time and cost, reducing the burden on healthcare system and patients.
Deep Learning-Based Real-Time AI Virtual Mouse System Using Computer Vision to Avoid COVID-19 Spread

The mouse is one of the wonderful inventions of Human-Computer Interaction (HCI) technology. Currently, wireless mouse or a Bluetooth mouse still uses devices and is not free of devices completely since it uses a battery for power and a dongle to connect it to the PC. In the proposed AI virtual mouse system, this limitation can be overcome by employing webcam or a built-in camera for capturing of hand gestures and hand tip detection using computer vision. The algorithm used in the system makes use of the machine learning algorithm. Based on the hand gestures, the computer can be controlled virtually and can perform left click, right click, scrolling functions, and computer cursor function without the use of the physical mouse. The algorithm is based on deep learning for detecting the hands. Hence, the proposed system will avoid COVID-19 spread by eliminating the human intervention and dependency of devices to control the computer.
Sensors and healthcare 5.0: transformative shift in virtual care through emerging digital health technologies

Emerging digital technologies continues to evolve posing unprecedented opportunities in health systems globally to improve healthcare services delivery. There has been significant progress in healthcare. However, the lack of emotive recognition coupled with a dearth of personalized and pervasive health applications and emotive smart devices calls for the integration of intelligent sensors health systems through emerging technologies. Although there has been significant progress in smart and connected health care, more research innovation, dissemination and technologies are needed to unbundle new opportunities and move towards healthcare 5.0. Healthcare is at the dawn of a paradigm change to reach the new era of smart disease control and detection, virtual care, smart health management, smart monitoring, and decision-making. Therefore, this study discusses the roles and capacities of sensors, their capabilities and other emerging technologies such as nanotechnology, 5 G technologies, drone technology, blockchain, robotics, big data, internet of things, artificial intelligence, and cloud computing. Healthcare 5.0 provides healthcare services including patient remote monitoring, tracking and virtual clinics, emotive telemedicine, ambient assisted living, smart self-management, wellness monitoring and control, smart treatment reminders, compliance and adherence, and personalized and connected health care. However, building resilience and robust healthcare 5.0 is not immune to challenges. Organizational challenges, technological and infrastructural barriers, lack of legal and regulatory frameworks and e-health policies, individual perceptions, misalignment with hospitals’ strategy, lack of funding, religious and cultural barriers are identified as potential barriers to the successful implementation of healthcare 5.0. Therefore, there is a need for building resilient technology-driven healthcare systems. To achieve this, there is a need for expanding technological infrastructure, provision of budgetary support based on sustainable business models, develop appropriate legal and e-health policies, standardization and synchronization of protocols, improving stakeholders’ engagement and involvement and establishment of private and public partnerships and investments.
Asymmetric Loss For Multi-Label Classification

In a typical multi-label setting, a picture contains on average few positive labels, and many negative ones. This positive-negative imbalance dominates the optimization process, and can lead to under-emphasizing gradients from positive labels during training, resulting in poor accuracy. In this paper, we introduce a novel asymmetric loss ("ASL"), which operates differently on positive and negative samples. The loss enables to dynamically down-weights and hard-thresholds easy negative samples, while also discarding possibly mislabeled samples. We demonstrate how ASL can balance the probabilities of different samples, and how this balancing is translated to better mAP scores. With ASL, we reach state-of-the-art results on multiple popular multi-label datasets: MS-COCO, Pascal-VOC, NUS-WIDE and Open Images. We also demonstrate ASL applicability for other tasks, such as single-label classification and object detection. ASL is effective, easy to implement, and does not increase the training time or complexity. Implementation is available at: https://github.com/Alibaba-MIIL/ASL.
Monitoring the public opinion about the vaccination topic from tweets analysis

The paper presents an intelligent system to automatically infer trends in the public opinion regarding the stance towards the vaccination topic: it enables the detection of significant opinion shifts, which can be possibly explained with the occurrence of specific social context-related events. The Italian setting has been taken as the reference use case. The source of information exploited by the system is represented by the collection of vaccine-related tweets, fetched from Twitter according to specific criteria; subsequently, tweets undergo a textual elaboration and a final classification to detect the expressed stance towards vaccination (i.e. in favor, not in favor, and neutral). In tuning the system, we tested multiple combinations of different text representations and classification approaches: the best accuracy was achieved by the scheme that adopts the bag-of-words, with stemmed n-grams as tokens, for text representation and the support vector machine model for the classification. By presenting the results of a monitoring campaign lasting 10 months, we show that the system may be used to track and monitor the public opinion about vaccination decision making, in a low-cost, real-time, and quick fashion. Finally, we also verified that the proposed scheme for continuous tweet classification does not seem to suffer particularly from concept drift, considering the time span of the monitoring campaign.
Distributed, decentralized, and democratized artificial intelligence

The accelerating investment in artificial intelligence has vast implications for economic and cognitive development globally. However, AI is currently dominated by an oligopoly of centralized mega-corporations, who focus on the interests of their stakeholders. There is a now universal need for AI services by businesses who lack access to capital to develop their own AI services, and independent AI developers lack visibility and a source of revenue. This uneven playing field has a high potential to lead to inequitable circumstances with negative implications for humanity. Furthermore, the potential of AI is hindered by the lack of interoperability standards. The authors herein propose an alternative path for the development of AI: a distributed, decentralized, and democratized market for AIs run on distributed ledger technology. We describe the features and ethical advantages of such a system using SingularityNET, a watershed project being developed by Ben Goertzel and colleagues, as a case study. We argue that decentralizing AI opens the doors for a more equitable development of AI and AGI. It will also create the infrastructure for coordinated action between AIs that will significantly facilitate the evolution of AI into true AGI that is both highly capable and beneficial for humanity and beyond.
A comprehensive survey on impulse and Gaussian denoising filters for digital images

This review article provides a comprehensive survey on state-of-the-art impulse and Gaussian denoising filters applied to images and summarizes the progress that has been made over the years in all applications involving image processing. The random noise model in this survey is assumed to be comprised of impulse (salt and pepper) and Gaussian noise. Different noise models are addressed, and different types of denoising filters are studied in terms of their performance on digital images and in their various practical implications and domains of application. A comprehensive comparison is performed to cover all the denoising methods in details and the results they yield. With this extensive review, researchers in image processing will be able to ascertain which of these denoising methods will be best applicable to their research needs and the application domain where such methods are contemplated for implementation.
Proof-of-Stake Sidechains

Sidechains have long been heralded as the key enabler of blockchain scalability and interoperability. However, no modeling of the concept or a provably secure construction has so far been attempted. We provide the first formal definition of what a sidechain system is and how assets can be moved between sidechains securely. We put forth a security definition that augments the known transaction ledger properties of liveness and safety to hold across multiple ledgers and enhance them with a new “firewall” security property which safeguards each blockchain from its sidechains, limiting the impact of an otherwise catastrophic sidechain failure. We then provide a sidechain construction that is suitable for proof-of-stake (PoS) sidechain systems. As an exemplary concrete instantiation we present our construction for an epoch- based PoS system consistent with Ouroboros (Crypto 2017), the PoS blockchain protocol used in Cardano which is one of the largest pure PoS systems by market capitalisation, and we also comment how the construction can be adapted for other protocols such as Ouroboros Praos (Eurocrypt 2018), Ouroboros Genesis (CCS 2018), Snow White and Algorand. An important feature of our construction is merged-staking that prevents “goldfinger” attacks against a sidechain that is only carrying a small amount of stake. An important technique for pegging chains that we use in our construction is cross-chain certification which is facilitated by a novel cryptographic primitive we introduce called ad-hoc threshold multisignatures (ATMS) which may be of independent interest. We show how ATMS can be securely instantiated by regular and aggregate digital signatures as well as succinct arguments of knowledge such as STARKs and bulletproofs with varying degrees of storage efficiency.
Selectively Emotional: How Smartphone Use Changes User-Generated Content

User-generated content has become ubiquitous and very influential in the marketplace. Increasingly, this content is generated on smartphones rather than personal computers (PCs). This article argues that because of its physically constrained nature, smartphone (vs. PC) use leads consumers to generate briefer content, which encourages them to focus on the overall gist of their experiences. This focus on gist, in turn, tends to manifest as reviews that emphasize the emotional aspects of an experience in lieu of more specific details. Across five studies—two field studies and three controlled experiments—the authors use natural language processing tools and human assessments to analyze the linguistic characteristics of user-generated content. The findings support the thesis that smartphone use results in the creation of content that is less specific and privileges affect—especially positive affect—relative to PC-generated content. The findings also show that differences in emotional content are driven by the tendency to generate briefer content on smartphones rather than user self-selection, differences in topical content, or timing of writing. Implications for research and practice are discussed.