Reviewerly/exHarmony huggingface dataset:
QuPath: The global impact of an open source digital pathology system

QuPath, originally created at the Centre for Cancer Research & Cell Biology at Queen’s University Belfast as part of a research programme in digital pathology (DP) funded by Invest Northern Ireland and Cancer Research UK, is arguably the most wildly used image analysis software program in the world. On the back of the explosion of DP and a need to comprehensively visualise and analyse whole slides images (WSI), QuPath was developed to address the many needs associated with tissue based image analysis; these were several fold and, predominantly, translational in nature: from the requirement to visualise images containing billions of pixels from files several GBs in size, to the demand for high-throughput reproducible analysis, which the paradigm of routine visual pathological assessment continues to struggle to deliver. Resultantly, large-scale biomarker quantification must increasingly be augmented with DP. Here we highlight the impact of the open source Quantitative Pathology & Bioimage Analysis DP system since its inception, by discussing the scope of scientific research in which QuPath has been cited, as the system of choice for researchers.
A sustainable framework for spatial planning of photovoltaic solar farms using GIS and multi-criteria assessment approach in Central Anatolia, Turkey

Convenient photovoltaic (PV) solar farms location choosing is a substantial issue in terms of renewable energy policies and spatial planning. This study is carried out to contribute to the growth, expansion, advancement, and location selection of PV farms using Geographical Information System (GIS), Analytic Hierarchy Process, Weighted Linear Combination, and Inverse Distance Weighting approaches. All maps were prepared for 17 evaluation criteria and suitability classification in GIS environment in the province of Kayseri (Turkey). Global Horizontal Irradiation was identified to be the most essential sub-criteria, followed by aspect, land use/cover, slope, distance from power network, temperature, precipitation, total snowy days, relative humidity, distance from land transportation network and others. The results show that an extensive percentage (3.70 % of the study area is highly suitable, and 40.01 % is moderately suitable) of the study area has a unique capacity for the establishment of PV farms. The results indicate that the western region of the study area is the most convenient for the installation of PV farms. In this study, the location selection decisions of existing PV farms investments were evaluated. Most of the existing PV farm’s locations frequently match up with the convenient zones presented in this study. This study provides a considerable, utility, and sustainable framework for spatial planning of PV farms in order to develop regional renewable energy and environmental conservation policies and to raise the awareness of the public.
Designing a GDPR compliant blockchain-based IoV distributed information tracking system

Blockchain technologies and distributed ledgers enable the design and implementation of trustable data logging systems that can be used by multiple parties to produce a non-repudiable database. The case of Internet of Vehicles may greatly benefit of such a possibility to track the chain of responsibility in case of accidents or damages due to bad or omitted maintenance, improving the safety of circulation and helping granting a correct handling of related legal issues. However, there are privacy issues that have to be considered, as tracked information potentially include data about private persons (position, personal habits), commercially relevant information (state of the fleet of a company, freight movement and related planning, logistic strategies), or even more critical knowledge (e.g., considering vehicles belonging to police, public authorities, governments or officers in sensible positions). In the European Union, all this information is covered by the General Data Protection Regulation (GDPR). In this paper we propose a reference model for a system that manages relevant information to show how blockchain can support GDPR compliant solutions for Internet of Vehicles, taking as a reference an integrated scenario based on Italy, and analyze a subset of its use cases to show its viability with reference to privacy issues.
O<sup>3</sup>-R-CP-ABE: An Efficient and Revocable Attribute-Based Encryption Scheme in the Cloud-Assisted IoMT System

With the processes of collecting, analyzing, and transmitting the data in the Internet of Things (IoT), the Internet of Medical Things (IoMT) comprises the medical equipment and applications connected to the healthcare system and offers an entity with real time, remote measurement, and analysis of healthcare data. However, the IoMT ecosystem deals with some great challenges in terms of security, such as privacy leaking, eavesdropping, unauthorized access, delayed detection of life-threatening episodes, and so forth. All these negative effects seriously impede the implementation of the IoMT ecosystem. To overcome these obstacles, this article presents an efficient, outsourced online/offline revocable ciphertext policy attribute-based encryption scheme with the aid of cloud servers and blockchains in the IoMT ecosystem. Our proposal achieves the characteristics of fine-grained access control, fast encryption, outsourced decryption, user revocation, and ciphertext verification. It is noteworthy that based on the chameleon hash function, we construct the private key of the data user with collision resistance, semantically secure, and key-exposure free to achieve revocation. To the best of our knowledge, this is the first protocol for a revocation mechanism by means of the chameleon hash function. Through formal analysis, it is proven to be secure in a selectively replayable chosen-ciphertext attack (RCCA) game. Finally, this scheme is implemented with the Java pairing-based cryptography library, and the simulation results demonstrate that it enables high efficiency and practicality, as well as strong reliability for the IoMT ecosystem.
Latent Replay for Real-Time Continual Learning

Training deep neural networks at the edge on light computational devices, embedded systems and robotic platforms is nowadays very challenging. Continual learning techniques, where complex models are incrementally trained on small batches of new data, can make the learning problem tractable even for CPU-only embedded devices enabling remarkable levels of adaptiveness and autonomy. However, a number of practical problems need to be solved: catastrophic forgetting before anything else. In this paper we introduce an original technique named "Latent Replay" where, instead of storing a portion of past data in the input space, we store activations volumes at some intermediate layer. This can significantly reduce the computation and storage required by native rehearsal. To keep the representation stable and the stored activations valid we propose to slow-down learning at all the layers below the latent replay one, leaving the layers above free to learn at full pace. In our experiments we show that Latent Replay, combined with existing continual learning techniques, achieves state-of-the-art performance on complex video benchmarks such as CORe50 NICv2 (with nearly 400 small and highly non-i.i.d. batches) and OpenLORIS. Finally, we demonstrate the feasibility of nearly real-time continual learning on the edge through the deployment of the proposed technique on a smartphone device.
Preprocessing of Breast Cancer Images to Create Datasets for Deep-CNN

Breast cancer is the most diagnosed cancer in Australia with crude incidence rates increasing drastically from 62.8 at ages 35-39 to 271.4 at ages 50-54 (cases per 100,000 women). Various researchers have proposed methods and tools based on Machine Learning and Convolutional Neural Networks for assessing mammographic images, but these methods have produced detection and interpretation errors resulting in false-positive and false-negative cases when used in the real world. We believe that this problem can potentially be resolved by implementing effective image pre-processing techniques to create training data for Deep-CNN. Therefore, the main aim of this research is to propose effective image pre-processing methods to create datasets that can save computational time for the neural network and improve accuracy and classification rates. To do so, this research proposes methods for background removal, pectoral muscle removal, adding noise to the images, and image enhancements. Adding noise without affecting the quality of details in the images makes the input images for the neural network more representative, which may improve the performance of the neural network model when used in the real world. The proposed method for background removal is the “Rolling Ball Algorithm” and “Huang's Fuzzy Thresholding”, which succeed in removing background from 100% of the images. For pectoral muscle removal “Canny Edge Detection” and “Hough's Line Transform” are used, which removed muscle from 99.06% of the images. “Invert”, “CTI_RAS” and “ISOCONTOUR” lookup tables (LUTs) were used for image enhancements to outline the ROIs and regions within the ROIs.
Social media as a language learning environment: a systematic review of the literature (2008-2019)

This review maps the scientific literature on social media as a language learning environment published between 2008 and 2019. Over 1,600 journals from the Scopus database were surveyed and 396 documents were analyzed using bibliometric analysis. The findings indicate that this field of interest has exponentially grown in the past 12 years and is likely to progress in the next several years. Although the scientific output is highly concentrated on the ten leading countries, the data also indicates its wide geographical distribution. As the first review to cover the 17 most popular social media platforms, the qualitative analysis of the findings of each study provided overwhelming evidence on the positive impact of social media as a language learning and teaching environment. The paper also reveals that high-profile platforms, such as Facebook, Skype, WhatsApp, and Twitter remain to attract the greatest attention from language learning scholars. This result was attributed to their multiple and flexible communication affordances, wide geographical distribution, and large number of active users. The paper concludes with implications for social media adoption and future studies.
Blind Image Quality Assessment With Active Inference

Blind image quality assessment (BIQA) is a useful but challenging task. It is a promising idea to design BIQA methods by mimicking the working mechanism of human visual system (HVS). The internal generative mechanism (IGM) indicates that the HVS actively infers the primary content (i.e., meaningful information) of an image for better understanding. Inspired by that, this paper presents a novel BIQA metric by mimicking the active inference process of IGM. Firstly, an active inference module based on the generative adversarial network (GAN) is established to predict the primary content, in which the semantic similarity and the structural dissimilarity (i.e., semantic consistency and structural completeness) are both considered during the optimization. Then, the image quality is measured on the basis of its primary content. Generally, the image quality is highly related to three aspects, i.e., the scene information (content-dependency), the distortion type (distortion-dependency), and the content degradation (degradation-dependency). According to the correlation between the distorted image and its primary content, the three aspects are analyzed and calculated respectively with a multi-stream convolutional neural network (CNN) based quality evaluator. As a result, with the help of the primary content obtained from the active inference and the comprehensive quality degradation measurement from the multi-stream CNN, our method achieves competitive performance on five popular IQA databases. Especially in cross-database evaluations, our method achieves significant improvements.
Deep Learning-Based Hookworm Detection in Wireless Capsule Endoscopic Image Using AdaBoost Classifier

Hookworm is an illness caused by an internal sponger called a roundworm.Inferable from deprived cleanliness in the developing nations, hookworm infection is a primary source of concern for both motherly and baby grimness.The current framework for hookworm detection is composed of hybrid convolutional neural networks; explicitly an edge extraction framework alongside a hookworm classification framework is developed.To consolidate the cylindrical zones obtained from the edge extraction framework and the trait map acquired into the hookworm scientific categorization framework, pooling layers are proposed.The hookworms display different profiles, widths, and bend directions.These challenges make it difficult for customized hookworm detection.In the proposed method, a contourlet change was used with the development of the Hookworm detection.In this study, standard deviation, skewness, entropy, mean, and vitality were used for separating the highlights of the each form.These estimations were found to be accurate.AdaBoost classifier was utilized to characterize the hookworm pictures.In this paper, the exactness and the territory under bend examination in identifying the hookworm demonstrate its scientific relevance.
Multi-objective PSO based online feature selection for multi-label classification

Feature selection approaches aim to select a set of prominent features that best describe the data to improve the efficiency without degrading the performance of the model. In many real-world applications such as social networks, it is not easy to get a static feature set; rather, new features arrive continuously in the system. Therefore, online feature selection (OFS) strategies have become popular in dealing with such problems. Recent years have also witnessed the prominence of multi-label classification frameworks where multiple class labels can be associated with a single instance. The proposed method considers the multi-label learning and the arrival of features in an online fashion. The method automatically determines the best subset of features that is suitable for multi-label classification. A three-phase filtering process is applied to select the appropriate features. The first phase is an evolutionary-based particle swarm optimization (PSO) technique that applies to the group of incoming features in a multi-objective framework. The second phase checks the redundancy of features selected in the current group to the already selected features and finally, the third phase finds the features in the already selected feature list that becomes non-significant on the selection of newly arrived features and discards them. The proposed algorithm is tested on fourteen multi-label data sets collected from various domains such as biology, music, and text. From the results, it is observed that the first and second phases are sufficient to select the appropriate feature set. The efficacy of the proposed algorithm can be verified from the obtained results. It outperforms the results obtained by state-of-the-art approaches in most cases.
Detection of Prostate Cancer in Whole-Slide Images Through End-to-End Training With Image-Level Labels

Prostate cancer is the most prevalent cancer among men in Western countries, with 1.1 million new diagnoses every year. The gold standard for the diagnosis of prostate cancer is a pathologists' evaluation of prostate tissue. To potentially assist pathologists deep /learning /based cancer detection systems have been developed. Many of the state-of-the-art models are patch/based convolutional neural networks, as the use of entire scanned slides is hampered by memory limitations on accelerator cards. Patch-based systems typically require detailed, pixel-level annotations for effective training. However, such annotations are seldom readily available, in contrast to the clinical reports of pathologists, which contain slide-level labels. As such, developing algorithms which do not require manual pixel-wise annotations, but can learn using only the clinical report would be a significant advancement for the field. In this paper, we propose to use a streaming implementation of convolutional layers, to train a modern CNN (ResNet /34) with 21 million parameters end-to-end on 4712 prostate biopsies. The method enables the use of entire biopsy images at high-resolution directly by reducing the GPU memory requirements by 2.4 TB. We show that modern CNNs, trained using our streaming approach, can extract meaningful features from high-resolution images without additional heuristics, reaching similar performance as state-of-the-art patch-based and multiple-instance learning methods. By circumventing the need for manual annotations, this approach can function as a blueprint for other tasks in histopathological diagnosis. The source code to reproduce the streaming models is available at https://github.com/DIAGNijmegen/ pathology-streaming-pipeline.
AI applications of data sharing in agriculture 4.0: A framework for role-based data access control

Industry 4.0 and the associated IoT and data applications are evolving rapidly and expand in various fields. Industry 4.0 also manifests in the farming sector, where the wave of Agriculture 4.0 provides multiple opportunities for farmers, consumers and the associated stakeholders. Our study presents the concept of Data Sharing Agreements (DSAs) as an essential path and a template for AI applications of data management among various actors. The approach we introduce adopts design science principles and develops role-based access control based on AI techniques. The application is presented through a smart farm scenario while we incrementally explore the data sharing challenges in Agriculture 4.0. Data management and sharing practices should enforce defined contextual policies for access control. The approach could inform policymaking decisions for role-based data management, specifically the data-sharing agreements in the context of Industry 4.0 in broad terms and Agriculture 4.0 in specific.
Feature selection and classification in breast cancer prediction using IoT and machine learning

Breast cancer (BC) is the most commonly found disease among women all over the world. The early diagnosis of breast cancer can potentially reduce the mortality rate and increase the chances of a successful treatment. Paper focuses on proposing a methodology to conduct early diagnosis of breast cancer using the Internet of Things and Machine Learning. The main objective of the paper is to explore the machine learning techniques in predicting breast cancer with IoT devices.Proposed classifier resulted in 98%, 97%, 96% and 98% of precision, recall, F_Measure and accuracy, respectively. The minimum error rate for the classifier have also been determined and found to be 34.21%, 45.82%8, 64.47% of Mean Absolute Error (MAR), Root Mean Square Error (RMSE) and Relative Absolute Error (RAE), respectively. It was evident through the obtained results that the MLP classifier yields a higher accuracy with a minimum error rate when compared to LR and RF.
Red Belly: A Secure, Fair and Scalable Open Blockchain

Blockchain has found applications to track ownership of digital assets. Yet, several blockchains were shown vulnerable to network attacks. It is thus crucial for companies to adopt secure blockchains before moving them to production. In this paper, we present Red Belly Blockchain (RBBC), the first secure blockchain whose throughput scales to hundreds of geodistributed consensus participants. To this end, we drastically revisited Byzantine Fault Tolerant (BFT) blockchains through three contributions: (i) defining the Set Byzantine Con-sensus problem of agreeing on a superblock of all proposed blocks instead of a single block; (ii) adopting a fair leaderless design to offer censorship-resistance guaranteeing the commit of correctly requested transactions; (iii) introducing sharded verification to limit the number of signature verifications without hampering security. We evaluate RBBC on up to 1000 VMs of 3 different types, spread across 4 continents, and under attacks. Although its performance is affected by attacks, RBBC scales in that its throughput increases to hundreds of consensus nodes and achieves 30k TPS throughput and 3 second latency on 1000 VMs, hence improving by 3× both the latency and the throughput of its closest competitor.
Blockchain Applications in Agribusiness: A Systematic Review

Blockchain is a technology that can be applied in different sectors to solve various problems. As a complex system, agribusiness presents many possibilities to take advantage of blockchain technology. The main goal of this paper is to identify the purposes for which blockchain has been applied in the agribusiness sector, for which a PRISMA-based systematic review was carried out. The scientific literature corpus was accessed and selected from Elsevier’s Scopus and ISI of Knowledge’s Web of Science (WoS) platforms, using the PRISMA protocol procedures. Seventy-one articles were selected for analysis. Blockchain application in agribusiness is a novel topic, with the first publication dating from 2016. The technological development prevails more than blockchain applications since it has been addressed mainly in the Computer Sciences and Engineering. Blockchain applications for agribusiness management of financial, energy, logistical, environmental, agricultural, livestock, and industrial purposes have been reported in the literature. The findings suggest that blockchain brings many benefits when used in agribusiness supply chains. We concluded that the research on blockchain applications in agribusiness is only at an early stage, as many prototypes are being developed and tested in the laboratory. In the near future, blockchain will be increasingly applied across all economic sectors, including agribusiness, promoting greater reliability and agility in information with a reduced cost. Several gaps for future studies were observed, with significant value for science, industry, and society.
Analyzing blockchain adoption barriers in manufacturing supply chains by the neutrosophic analytic hierarchy process

Tools established for managing information flow in supply chain management and logistics should match digital transformations. This issue is particularly salient for developing nations that hope to achieve sustainable development goals in a globalized era. Modern technologies are required to ensure a secure, transparent, and traceable path of information flow in global supply chains; however, it is not always straightforward for businesses in developing economies to adopt new digital technologies while sustaining productivity. One of the foundational technologies that can be used to create a basis for economic and social systems and to affect manufacturing supply chains in developing economies is blockchain. In this study, we analyze the barriers to blockchain technology adoption in manufacturing supply chains using the neutrosophic analytic hierarchy process (N-AHP). We propose an action plan framework for the validation of blockchain technology in a developing economy. The findings demonstrate that “transaction-level uncertainties” comprise the most critical barrier and have the highest weight in the final ranking followed by “usage in the underground economy”, “managerial commitment”, “challenges in scalability”, and “privacy risks”. This paper can assist industrial managers and experts in emerging economies to more clearly identify barriers to the implementation of blockchain technology and show them how to successfully employ blockchain technology in their supply chains.