Reviewerly/exHarmony huggingface dataset:
Self-supervised driven consistency training for annotation efficient histopathology image analysis

Training a neural network with a large labeled dataset is still a dominant paradigm in computational histopathology. However, obtaining such exhaustive manual annotations is often expensive, laborious, and prone to inter and intra-observer variability. While recent self-supervised and semi-supervised methods can alleviate this need by learning unsupervised feature representations, they still struggle to generalize well to downstream tasks when the number of labeled instances is small. In this work, we overcome this challenge by leveraging both task-agnostic and task-specific unlabeled data based on two novel strategies: (i) a self-supervised pretext task that harnesses the underlying multi-resolution contextual cues in histology whole-slide images to learn a powerful supervisory signal for unsupervised representation learning; (ii) a new teacher-student semi-supervised consistency paradigm that learns to effectively transfer the pretrained representations to downstream tasks based on prediction consistency with the task-specific unlabeled data. We carry out extensive validation experiments on three histopathology benchmark datasets across two classification and one regression based tasks, i.e., tumor metastasis detection, tissue type classification, and tumor cellularity quantification. Under limited-label data, the proposed method yields tangible improvements, which is close to or even outperforming other state-of-the-art self-supervised and supervised baselines. Furthermore, we empirically show that the idea of bootstrapping the self-supervised pretrained features is an effective way to improve the task-specific semi-supervised learning on standard benchmarks. Code and pretrained models are made available at: https://github.com/srinidhiPY/SSL_CR_Histo.
PAQ: 65 Million Probably-Asked Questions and What You Can Do With Them

Abstract Open-domain Question Answering models that directly leverage question-answer (QA) pairs, such as closed-book QA (CBQA) models and QA-pair retrievers, show promise in terms of speed and memory compared with conventional models which retrieve and read from text corpora. QA-pair retrievers also offer interpretable answers, a high degree of control, and are trivial to update at test time with new knowledge. However, these models fall short of the accuracy of retrieve-and-read systems, as substantially less knowledge is covered by the available QA-pairs relative to text corpora like Wikipedia. To facilitate improved QA-pair models, we introduce Probably Asked Questions (PAQ), a very large resource of 65M automatically generated QA-pairs. We introduce a new QA-pair retriever, RePAQ, to complement PAQ. We find that PAQ preempts and caches test questions, enabling RePAQ to match the accuracy of recent retrieve-and-read models, whilst being significantly faster. Using PAQ, we train CBQA models which outperform comparable baselines by 5%, but trail RePAQ by over 15%, indicating the effectiveness of explicit retrieval. RePAQ can be configured for size (under 500MB) or speed (over 1K questions per second) while retaining high accuracy. Lastly, we demonstrate RePAQ’s strength at selective QA, abstaining from answering when it is likely to be incorrect. This enables RePAQ to “back-off” to a more expensive state-of-the-art model, leading to a combined system which is both more accurate and 2x faster than the state-of-the-art model alone.
Fast Camouflaged Object Detection via Edge-based Reversible Re-calibration Network

Camouflaged Object Detection (COD) aims to detect objects with similar patterns (e.g., texture, intensity, colour, etc) to their surroundings, and recently has attracted growing research interest. As camouflaged objects often present very ambiguous boundaries, how to determine object locations as well as their weak boundaries is challenging and also the key to this task. Inspired by the biological visual perception process when a human observer discovers camouflaged objects, this paper proposes a novel edge-based reversible re-calibration network called ERRNet. Our model is characterized by two innovative designs, namely Selective Edge Aggregation (SEA) and Reversible Re-calibration Unit (RRU), which aim to model the visual perception behaviour and achieve effective edge prior and cross-comparison between potential camouflaged regions and background. More importantly, RRU incorporates diverse priors with more comprehensive information comparing to existing COD models. Experimental results show that ERRNet outperforms existing cutting-edge baselines on three COD datasets and five medical image segmentation datasets. Especially, compared with the existing top-1 model SINet, ERRNet significantly improves the performance by ∼6% (mean E-measure) with notably high speed (79.3 FPS), showing that ERRNet could be a general and robust solution for the COD task.
Visual vs internal attention mechanisms in deep neural networks for image classification and object detection

The so-called “attention mechanisms” in Deep Neural Networks (DNNs) denote an automatic adaptation of DNNs to capture representative features given a specific classification task and related data. Such attention mechanisms perform both globally by reinforcing feature channels and locally by stressing features in each feature map. Channel and feature importance are learnt in the global end-to-end DNNs training process. In this paper, we present a study and propose a method with a different approach, adding supplementary visual data next to training images. We use human visual attention maps obtained independently with psycho-visual experiments, both in task-driven or in free viewing conditions, or powerful models for prediction of visual attention maps. We add visual attention maps as new data alongside images, thus introducing human visual attention into the DNNs training and compare it with both global and local automatic attention mechanisms. Experimental results show that known attention mechanisms in DNNs work pretty much as human visual attention, but still the proposed approach allows a faster convergence and better performance in image classification tasks.
Segmentation information with attention integration for classification of breast tumor in ultrasound image

Breast cancer is one of the most common forms of cancer among women worldwide. The development of computer-aided diagnosis (CAD) technology based on ultrasound imaging to promote the diagnosis of breast lesions has attracted the attention of researchers and deep learning is a popular and effective method. However, most of the deep learning based CAD methods neglect the relationship between two vision tasks tumor region segmentation and classification. In this paper, taking into account some prior knowledges of medicine, we propose a novel segmentation-to-classification scheme by adding the segmentation-based attention (SBA) information to the deep convolution network (DCNN) for breast tumors classification. A segmentation network is trained to generate tumor segmentation enhancement images. Then two parallel networks extract features for the original images and segmentation enhanced images and one channel attention based feature aggregation network is to automatically integrate the features extracted from two feature networks to improve the performance of recognizing malignant tumors in the breast ultrasound images. To validate our method, experiments have been conducted on breast ultrasound datasets. The classification results of our method have been compared with those obtained by eleven existing approaches. The experimental results show that the proposed method achieves the highest Accuracy (90.78%), Sensitivity (91.18%), Specificity (90.44%), F1-score (91.46%), and AUC (0.9549).
Blockchain adoption in the maritime supply chain: Examining barriers and salient stakeholders in containerized international trade

This study aimed to investigate the relationships between blockchain adoption barriers and identified the salient stakeholders for blockchain adoption in containerized international trade. The interpretative structural modelling and Cross-Impact Matrix Multiplication Applied to Classification analyses indicated that the most impactful among the eight barriers are lack of support from influential stakeholders, lack of understanding regarding blockchain, and lack of government regulations. The stakeholder mapping analysis demonstrated that the high salient stakeholders among 11 legitimate stakeholders are container lines, ports, beneficial cargo owners, freight forwarders/third party logistics, and customs authorities. The study is original and contributes to theory and practice as it uncovers both impactful barriers and critical stakeholders by adopting a stakeholder theory perspective and offers significant implications to practice, policy, and theory by combining these two analyses.
Min-Max Cost Optimization for Efficient Hierarchical Federated Learning in Wireless Edge Networks

Federated learning is a distributed machine learning technology that can protect users&#x2019; data privacy, so it has attracted more and more attention in the industry and academia. Nonetheless, most of the existing works focused on the cost optimization of the entire process, while the cost of individual participants cannot be considered. In this article, we explore a min-max cost-optimal problem to guarantee the convergence rate of federated learning in terms of cost in wireless edge networks. In particular, we minimize the cost of the worst-case participant subject to the delay, local CPU-cycle frequency, power allocation, local accuracy, and subcarrier assignment constraints. Considering that the formulated problem is a mixed-integer nonlinear programming problem, we decompose it into several sub-problems to derive its solutions, in which the subcarrier assignment and power allocation are obtained by utilizing the Lagrangian dual decomposition method, the CPU-cycle frequency is obtained by a heuristic algorithm, and the local accuracy is obtained by an iteration algorithm. Simulation results show the convergence of the proposed algorithm and reveal that the proposed scheme can accomplish a tradeoff between the cost and fairness by comparing the proposed scheme with the existing schemes.
Consensus Mechanism for Blockchain-Enabled Vehicle-to-Vehicle Energy Trading in the Internet of Electric Vehicles

Electric Vehicles (EVs) have emerged as one of the most promising solutions for reducing carbon emissions in smart cities. However, due to the limited battery life of EVs and the scarcity of charging stations, EV drivers are not willing to travel long distances. Thus, blockchain-enabled energy trading (BET) has lately been used to securely share energy among EVs via wireless power transfer (WPT) technology. Blockchain is used to ensure the security and privacy of transactions between untrustworthy EVs in the WPT process. Nevertheless, previous works on BET have relied on existing consensus mechanisms built on the requirements of the cryptocurrency systems. These consensus mechanisms have faced significant challenges in maintaining high reliability, throughput, low latency, and network scalability in V2V energy trading that requires real-time services. To address these issues, we propose a new consensus mechanism that leverages the benefits of Practical Byzantine Fault Tolerance (PBFT) and Proof of Reputation (PoR) called PBFT-based PoR (PPoR). The energy trading process runs in a clustered vehicular network, where validator selection, block generation, and consensus processes are performed in each cluster. We adopt an incentive mechanism based on a Stackelberg game model to optimize the utility of sellers, buyers, and validator nodes, which motivates honest and cooperative nodes. The simulation results show that the proposed scheme reduces buyers&#x2019; costs by 21.1&#x0025; while increasing the utility of sellers by 18&#x0025;. Moreover, compared to benchmarks, the proposed scheme reduces the transaction processing delay and increases the throughput by more than 47.1&#x0025; and 15.7&#x0025;, respectively.
The use of 360-degree virtual tours to promote mountain walking tourism: stimulus–organism–response model

Based on the stimulus–organism–response (S–O–R) theory, this study attempts to investigate how the use of 360-degree virtual mountain walking tours can motivate audiences’ intention to take a real walking in the mountains. The survey results from 320 samples after watching a 360-degree virtual video reveal the positive influence of vividness (stimulus) on presence, emotional involvement, flow state, and enjoyment (organism), leading to the intention to take mountain walking tourism (response). This study also examines the interrelationship between four organism variables to explain the underlying mechanism of 360-degree virtual travel experience in stimulating audiences’ visit intention. It shows how technology helps the development of nature-based tourism. It also offers implications for developers to develop influential 360-degree virtual tourism and destination marketers to promote mountain walking tourism.
Chaotic binary Group Search Optimizer for feature selection

Feature selection (FS) is recognized as one of the majority public and challenging problems in the Machine Learning domain. FS can be examined as an optimization problem that needs an effective optimizer to determine its optimal subset of more informative features. This paper proposes a wrapper FS method that combines chaotic maps (CMs) and binary Group Search Optimizer (GSO) called CGSO, which is used to solve the FS problem. In this method, five chaotic maps are incorporated with the GSO algorithm’s main procedures, namely, Logistic, Piecewise, Singer, Sinusoidal, and Tent. The GSO algorithm is used as a search strategy, while k-NN is employed as an induction algorithm. The objective function is to integrate three main objectives: maximizing the classification accuracy value, minimizing the number of selected features, and minimizing the complexity of generated k-NN models. To evaluate the proposed methods’ performance, twenty well-known UCI datasets are used and compared with other well-known published methods in the literature. The obtained results reveal the superiority of the proposed methods in outperforming other well-known methods, especially when using binary GSO with Tent CM. Finally, it is a beneficial method to be utilized in systems that require FS pre-processing.
Towards Safe Weakly Supervised Learning

In this paper, we study weakly supervised learning where a large amount of data supervision is not accessible. This includes i) incomplete supervision, where only a small subset of labels is given, such as semi-supervised learning and domain adaptation; ii) inexact supervision, where only coarse-grained labels are given, such as multi-instance learning and iii) inaccurate supervision, where the given labels are not always ground-truth, such as label noise learning. Unlike supervised learning which typically achieves performance improvement with more labeled examples, weakly supervised learning may sometimes even degenerate performance with more weakly supervised data. Such deficiency seriously hinders the deployment of weakly supervised learning to real tasks. It is thus highly desired to study safe weakly supervised learning, which never seriously hurts performance. To this end, we present a generic ensemble learning scheme to derive a safe prediction by integrating multiple weakly supervised learners. We optimize the worst-case performance gain and lead to a maximin optimization. This brings multiple advantages to safe weakly supervised learning. First, for many commonly used convex loss functions in classification and regression, it is guaranteed to derive a safe prediction under a mild condition. Second, prior knowledge related to the weight of the base weakly supervised learners can be flexibly embedded. Third, it can be globally and efficiently addressed by simple convex quadratic or linear program. Finally, it is in an intuitive geometric interpretation with the least square loss. Extensive experiments on various weakly supervised learning tasks, including semi-supervised learning, domain adaptation, multi-instance learning and label noise learning demonstrate our effectiveness.
The potentials of combining Blockchain technology and Internet of Things for digital reverse supply chain: A case study

Asbestos has been considered as a hazardous waste in France since 1997 and it is neither recyclable nor recoverable. Hence, its storage and processing have a major impact on both human health and environment. In addition, its treatment is subject to very strict environmental regulations. Hence, to minimize the environmental regulations impact, the treatment of asbestos requires tools and new information technology (IT) enabling real-time information sharing with all reverse supply chain (RSC) actors. A successful integration of new IT such as Blockchain technology and Internet of Things (IoT) can lead to several advantages in order to help asbestos waste management. This study aims at integrating and combining Blockchain and IoT to digitize RSC for asbestos waste treatment. First, we investigate the roles and impacts of Blockchain and IoT on forward and reverse supply chain performances. Second, we propose two new conceptual models for both open and close loop digital RSC. Finally, to demonstrate the feasibility and effectiveness of integration and combination of these digital technologies, we propose a case study for asbestos waste management. The results show that the combination of Blockchain and IoT can be implemented to provide robust digital RSC.
Light Field Image Super-Resolution With Transformers

Light field (LF) image super-resolution (SR) aims at reconstructing high-resolution LF images from their low-resolution counterparts. Although CNN-based methods have achieved remarkable performance in LF image SR, these methods cannot fully model the non-local properties of the 4D LF data. In this paper, we propose a simple but effective Transformer-based method for LF image SR. In our method, an angular Transformer is designed to incorporate complementary information among different views, and a spatial Transformer is developed to capture both local and long-range dependencies within each sub-aperture image. With the proposed angular and spatial Transformers, the beneficial information in an LF can be fully exploited and the SR performance is boosted. We validate the effectiveness of our angular and spatial Transformers through extensive ablation studies, and compare our method to recent state-of-the-art methods on five public LF datasets. Our method achieves superior SR performance with a small model size and low computational cost. Code is available at https://github.com/ZhengyuLiang24/LFT.
A comprehensive survey on blockchain technology

In recent years of development, the interest in Blockchain technology and the essentials of its application have made it popular. The efficiency and availability of this technology have made it a widely accepted technology. Blockchain technology has completely changed the present-day concept of centralization. Different methodologies are used for the purpose of interfacing and monitoring the transactions in blockchain technology. Decentralization, immutability, transparency, and peer to-peer communication help to address the current trends effectively. This paper presents a comprehensive survey of blockchain technology, describing its features, classification, blockchain wallets, and applications.
Blockchain’s coming to hospital to digitalize healthcare services: Designing a distributed electronic health record ecosystem

The technological revolution in blockchain achieved the healthcare sector and offered a significant opportunity to lead this digital transformation. A significant problem is that various healthcare organizations keep numerous and fragmented patient medical records. The proposed platform solves this problem by integrating electronic medical reports in a private and permissioned blockchain to create a distributed electronic health record (EHR) ecosystem. In this context, the information processing theory (IPT) allowed us to design and validate a blockchain-based EHR system enhancing the storage of medical records and data exchange among healthcare providers and reducing environmental uncertainty. The potential benefits of the implementation of our distributed network regard clinical outcomes (e.g., improved quality, reduced medical errors), organizational outcomes (e.g., financial, operational benefits), and managerial outcomes (e.g., improved ability to conduct research, improved population health, reduced costs). Future research directions concern the application of the proposed blockchain platform to a vast extent of healthcare services and organizations.
RGB Image Encryption through Cellular Automata, S-Box and the Lorenz System

The exponential growth in transmission of multimedia over the Internet and unsecured channels of communications is putting pressure on scientists and engineers to develop effective and efficient security schemes. In this paper, an image encryption scheme is proposed to help solve such a problem. The proposed scheme is implemented over three stages. The first stage makes use of Rule 30 cellular automata to generate the first encryption key. The second stage utilizes a well-tested S-box, whose design involves a transformation, modular inverses, and permutation. Finally, the third stage employs a solution of the Lorenz system to generate the second encryption key. The aggregate effect of this 3-stage process insures the application of Shannon’s confusion and diffusion properties of a cryptographic system and enhances the security and robustness of the resulting encrypted images. Specifically, the use of the PRNG bitstreams from both of the cellular automata and the Lorenz system, as keys, combined with the S-box, results in the needed non-linearity and complexity inherent in well-encrypted images, which is sufficient to frustrate attackers. Performance evaluation is carried out with statistical and sensitivity analyses, to check for and demonstrate the security and robustness of the proposed scheme. On testing the resulting encrypted Lena image, the proposed scheme results in an MSE value of 8923.03, a PSNR value of 8.625 dB, an information entropy of 7.999, NPCR value of 99.627, and UACI value of 33.46. The proposed scheme is shown to encrypt images at an average rate of 0.61 Mbps. A comparative study with counterpart image encryption schemes from the literature is also presented to showcase the superior performance of the proposed scheme.