Reviewerly/exHarmony huggingface dataset:
Decentralizing science: Towards an interoperable open peer review ecosystem using blockchain

Scientific publication and its Peer Review system strongly rely on a few major industry players controlling most journals (e.g. Elsevier), databases (e.g. Scopus) and metrics (e.g. JCR Impact Factor), while keeping most articles behind paywalls. Critics to such system include concerns about fairness, quality, performance, cost, unpaid labor, transparency, and accuracy of the evaluation process. The Open Access movement has tried to provide free access to the published research articles, but most of the aforementioned issues remain. In such context, decentralized technologies such as blockchain offer an opportunity to experiment with new models for scientific production and dissemination relying on a decentralized infrastructure, aiming to tackle multiple of the current system shortcomings. This paper makes a proposal for an interoperable decentralized system for an open peer review ecosystem, relying on emerging distributed technologies such as blockchain and IPFS. Such system, named "Decentralized Science" (DecSci), aims to enable a decentralized reviewer reputation system, which relies on an Open Access by-design infrastructure, together with transparent governance processes. Two prototypes have been implemented: a proof-of-concept prototype to validate DecSci's technological feasibility, and a Minimum Viable Product (MVP) prototype co-designed with journal editors. In addition, three evaluations have been carried out: an exploratory survey to assess interest on the issues tackled; two sets of interviews to confirm both the main problems for editors and to validate the MVP prototype; and a cost analysis of the main operations, both execution cost and actual price. Additionally, the paper discusses the multiple interoperability challenges such proposal faces, including an architecture to tackle them. This work finishes with a review of some of the open challenges that this ambitious proposal may face.
CaPBug-A Framework for Automatic Bug Categorization and Prioritization Using NLP and Machine Learning Algorithms

Bug reports facilitate software development teams in improving the quality of software.These reports include significant information related to problems encountered within a software, possible enhancement suggestions, and other potential issues.Bug reports are typically complex and are too detailed; hence a lot of resources are required to analyze and process them manually.Moreover, it leads to delays in the resolution of high priority bugs.Accurate and timely processing of bug reports based on their category and priority plays a significant role in improving the quality of software maintenance.Therefore, an automated process of categorization and prioritization of bug reports is needed to address the aforementioned issues.Automated categorization and prioritization of bug reports have been explored recently by many researchers; however, limited progress has been made in this regard.In this research, we present a novel framework, titled CaPBug, for automated categorization and prioritization of bug reports.The framework is implemented using Natural Language Processing (NLP) and supervised machine learning algorithms.A baseline corpus is built with six categories and five prioritization levels by analyzing more than 2000 bug reports of Mozilla and Eclipse repository.Four classification algorithms i.e., Naive Bayes, Random Forest, Decision Tree, and Logistic Regression have been used to categorize and prioritize bug reports.We demonstrate that the CaPBug framework achieved an accuracy of 88.78% by using a Random Forest classifier with a textual feature for predicting the category.Similarly, using the CaPBug framework, an accuracy of 90.43% was achieved in predicting the priority of bug reports.Synthetic Minority Over-Sampling Technique (SMOTE) has been applied to address the class imbalance issue in priority classes.
Multi-view subspace clustering networks with local and global graph information

This study investigates the problem of multi-view subspace clustering, the goal of which is to explore the underlying grouping structure of data collected from different fields or measurements. Since data do not always comply with the linear subspace models in many real-world applications, most existing multi-view subspace clustering methods based on the shallow linear subspace models may fail in practice. Furthermore, the underlying graph information of multi-view data is usually ignored in most existing multi-view subspace clustering methods. To address the aforementioned limitations, we proposed the novel multi-view subspace clustering networks with local and global graph information, termed MSCNLG, in this paper. Specifically, autoencoder networks are employed on multiple views to achieve latent smooth representations that are suitable for the linear assumption. Simultaneously, by integrating fused multi-view graph information into self-expressive layers, the proposed MSCNLG obtains the common shared multi-view subspace representation, which can be used to get clustering results by employing the standard spectral clustering algorithm. As an end-to-end trainable framework, the proposed method fully investigates the valuable information of multiple views. Comprehensive experiments on six benchmark datasets validate the effectiveness and superiority of the proposed MSCNLG.
Decentralizing Supply Chain Anti-Counterfeiting and Traceability Systems Using Blockchain Technology

An interesting research problem in the supply chain industry is evaluating and determining the provenance of physical goods—demonstrating the authenticity of luxury goods such as bottled wine. However, many supply chain systems and networks have been built and implemented with centralized system architecture, relying on centralized authorities or any form of intermediary, and leading to issues such as single-point processing, storage and failure, which could be susceptible to malicious modifications to product records or various potential attacks to system components by dishonest participant nodes traversing along the supply chain. Blockchain technology has evolved from merely being a decentralized, distributed and immutable ledger of cryptocurrency transactions to a programmable interactive environment for building decentralized and reliable applications addressing different use-cases and existing problems in the world. In this research, with a chosen research method of proof-by-demonstration, the Decentralized NFC-Enabled Anti-Counterfeiting System (dNAS) is proposed and developed, decentralizing a legacy anti-counterfeiting system of the supply-chain industry using Blockchain technology to facilitate trustworthy data provenance retrieval, verification and management, as well as strengthening the capability of the product’s anti-counterfeiting and traceability qualities in the wine industry, with the capacity to further extend this to the supply chain industry as a whole. The proposed dNAS utilizes a decentralized blockchain network with a consensus protocol compatible with the concept of enterprise blockchain, programmable smart contracts and a distributed file storage system to develop a secure and immutable scientific-data provenance tracking and management platform on which provenance records, providing compelling properties of the data integrity of luxurious goods, are recorded, verified and validated automatically.
Blockchain Integration With Digital Technology and the Future of Health Care Ecosystems: Systematic Review

In the era of big data, artificial intelligence (AI), and the Internet of Things (IoT), digital data have become essential for our everyday functioning and in health care services. The sensitive nature of health care data presents several crucial issues such as privacy, security, interoperability, and reliability that must be addressed in any health care data management system. However, most of the current health care systems are still facing major obstacles and are lacking in some of these areas. This is where decentralized, secure, and scalable databases, most notably blockchains, play critical roles in addressing these requirements without compromising security, thereby attracting considerable interest within the health care community. A blockchain can be maintained and widely distributed using a large network of nodes, mostly computers, each of which stores a full replica of the data. A blockchain protocol is a set of predefined rules or procedures that govern how the nodes interact with the network, view, verify, and add data to the ledger.In this article, we aim to explore blockchain technology, its framework, current applications, and integration with other innovations, as well as opportunities in diverse areas of health care and clinical research, in addition to clarifying its future impact on the health care ecosystem. We also elucidate 2 case studies to instantiate the potential role of blockchains in health care.To identify related existing work, terms based on Medical Subject Headings were used. We included studies focusing mainly on health care and clinical research and developed a functional framework for implementation and testing with data. The literature sources for this systematic review were PubMed, Medline, and the Cochrane library, in addition to a preliminary search of IEEE Xplore.The included studies demonstrated multiple framework designs and various implementations in health care including chronic disease diagnosis, management, monitoring, and evaluation. We found that blockchains exhibit many promising applications in clinical trial management such as smart-contract application, participant-controlled data access, trustless protocols, and data validity. Electronic health records (EHRs), patient-centered interoperability, remote patient monitoring, and clinical trial data management were found to be major areas for blockchain usage, which can become a key catalyst for health care innovations.The potential benefits of blockchains are limitless; however, concrete data on long-term clinical outcomes based on blockchains powered and supplemented by AI and IoT are yet to be obtained. Nonetheless, implementing blockchains as a novel way to integrate EHRs nationwide and manage common clinical problems in an algorithmic fashion has the potential for improving patient outcomes, health care experiences, as well as the overall health and well-being of individuals.
Prospects and obstacles analysis of applying blockchain technology to power trading using a deeply improved model based on the DEMATEL approach

Blockchain technology is widely concerned, and its related applications can promote the process of smart cities and sustainable society. However, while mining the potential application scenarios in power trading, we must recognize the barriers, help it survive the hype stage, and promote its healthy development and technology landing. For the first time, hesitant fuzzy linguistic term set and K-mediods clustering algorithm are used to improve the decision-making trial and evaluation laboratory (DEMATEL) method, and the obstacle analysis model of the applied scene is constructed. Compared with the conventional DEMATEL method, the collection of evaluation information is more flexible and closer to reality. Besides, the classification of obstacle factors is more scientific and there can be more than two categories for effect degree. Firstly, thirteen barriers to its application in power trading are identified; at the same time, six specific application scenarios are summarized and analyzed. Then, a detailed discussion is conducted on each scenario: The quantification of the influence degree among obstacles, the classification and qualitative of the influence degree, and the causal mechanism analysis. The key obstacles identified can be used to guide practice. Finally, strategic solutions and policy recommendations are given to remove or alleviate these obstacles.
B-PROP

Pre-training and fine-tuning have achieved remarkable success in many downstream natural language processing (NLP) tasks. Recently, pre-training methods tailored for information retrieval (IR) have also been explored, and the latest success is the PROP method which has reached new SOTA on a variety of ad-hoc retrieval benchmarks. The basic idea of PROP is to construct therepresentative words prediction (ROP) task for pre-training inspired by the query likelihood model. Despite its exciting performance, the effectiveness of PROP might be bounded by the classical unigram language model adopted in the ROP task construction process. To tackle this problem, we propose a bootstrapped pre-training method (namely B-PROP) based on BERT for ad-hoc retrieval. The key idea is to use the powerful contextual language model BERT to replace the classical unigram language model for the ROP task construction, and re-train BERT itself towards the tailored objective for IR. Specifically, we introduce a novel contrastive method, inspired by the divergence-from-randomness idea, to leverage BERT's self-attention mechanism to sample representative words from the document. By further fine-tuning on downstream ad-hoc retrieval tasks, our method achieves significant improvements over PROP and other baselines, and further pushes forward the SOTA on a variety of ad-hoc retrieval tasks.
Proactive safety monitoring: A functional approach to detect safety-related anomalies using unmanned aerial vehicle video data

The advent of smart cities has motivated the field of transportation safety to transition towards a proactive approach that anticipates and mitigates risks before crashes occur. One of the stepping-stones to complete this transition is to monitor the risk levels of the transportation roadway system continuously. With the advancement in emerging technologies, computer-vision-based safety analysis approaches that focus on risk assessment using traffic conflicts quantified by surrogate safety measures extracted from traffic videos have been proposed. As the detailed movements of all the vehicles are logged in the video data, the quantified traffic conflicts can depict the whole picture of risk levels for a specific location. However, the capability of using traffic videos and the quantified traffic conflicts for proactive safety monitoring has yet to be investigated widely. In addition, one of the limitations of past safety research is that safety-related data are mostly aggregated into summary statistics (such as the total number of crashes/conflicts), which constitutes a major simplification of the real risk levels that change over time. Therefore, this study contributes to the literature by proposing a novel functional approach that models time series of a variant of the time exposed time-to-collision safety indicator directly to detect safety-related anomalies from traffic video data. Functional data smoothing methods that can mitigate overfitting and account for the nonnegative characteristics of safety indicator are applied. Nine commonly used functional anomaly detection methods are summarized and compared using approximately one-hour of traffic video data recorded by an unmanned aerial vehicle at a signalized intersection. Ground truth safety-related anomalies are identified by manually reviewing the video data, which is used to validate the performances of anomaly detection methods. Good separation between safety-related anomalies and non-anomalies (0.85 area under the receiver operating characteristics and 0.8 area under the precision-recall curve) has been found. By applying the proposed safety-related anomaly detection method, significant amount of human resources can be liberated.
Text mining using nonnegative matrix factorization and latent semantic analysis

Text clustering is considered one of the most important topics in modern data mining. Nevertheless, text data require tokenization which usually yields a very large and highly sparse term-document matrix, which is usually difficult to process using conventional machine learning algorithms. Methods such as latent semantic analysis have helped mitigate this issue, but are nevertheless not completely stable in practice. As a result, we propose a new feature agglomeration method based on nonnegative matrix factorization, which is employed to separate the terms into groups, and then each group’s term vectors are agglomerated into a new feature vector. Together, these feature vectors create a new feature space much more suitable for clustering. In addition, we propose a new deterministic initialization for spherical K-means, which proves very useful for this specific type of data. In order to evaluate the proposed method, we compare it to some of the latest research done in this field, as well as some of the most practiced methods. In our experiments, we conclude that the proposed method either significantly improves clustering performance or maintains the performance of other methods, while improving stability in results.
Mathematics Education Students’ Experiences during Lockdown: Managing Collaboration in eLearning

The current world crisis of COVID-19 has enforced international lockdowns in educational institutions, necessitating that these institutions quickly transition to online learning. In mathematics education studies, where collaborative problem-solving is considered a necessary pedagogical approach, lecturers have had challenges incorporating collaborative problem-solving in an authentic manner. There are now also complex living and learning contexts in which the students have to undertake their learning processes. This paper examines students’ experiences of the rapid transition to online learning and their perceptions of the advantages and barriers that ensued. A small comparative case study was used to explore the complexity of the situation, within rich, authentic settings. Questionnaires and semi-structured interviews were used. The participants found that personal circumstances related to having children and partners at home while they studied, and aspects such as not interacting directly with other students to clarify and mediate their thinking, led to major initial frustrations with collaborating. While some of these continued throughout lockdown they did adjust and recognize that the advantages, such as a mix of formal and informal digital pedagogical media, gave them greater flexibility and led to deeper reflective thinking. We contend that some elements related to the adaptions made should become permanent features of face-to-face learning.
Blockchain based solution to improve the Supply Chain Management in Indian agriculture

India is one of the agriculture-based country in the world. The history of agriculture in India starts from Indus Valley Civilization. But in such a historical country, many farmers are starving for their income while foreign countries are earning lakhs from the same. One of the reasons for the above cause is the very poor supply chain management. It is a well-known fact that most of the farming items decay very fast if not maintained properly. In the existing supply chain management, though the government provides storage houses for the farmers, the farmers could not track the status of their goods. Also, there is no proper system for tracking the status of their goods in the various stages of transportation. Hence, an improved supply chain management is needed with the desired properties such as checkpoints at every stage in the supply chain management, verification of goods status by both the farmers and the government officials. In this paper, a system is proposed that uses Blockchain technology to attain the transparency about the goods' status that leads to a healthier relationship between the producer and consumer. By storing the details in the blockchain, all the process is visible both to the farmers and officials involved in the transportation. Also, the records that are immutable can remain forever.
RETRACTED ARTICLE: COVID 19 response: An analysis of teachers’ perception on pedagogical successes and challenges of digital teaching practice during new normal

The purpose of this exploratory study undertaken between June and August 2020 was to capture teachers' perspectives to explore (a) what kind of pedagogies they have successfully implemented in the face of a pandemic; (b) what hurdles and successes did they encounter while implementing virtual teaching-learning; and (c) how virtual pedagogies can be improved. Data was collected using purposive sampling via 47 social media groups and pages, using internet survey as an instrument from 141 teachers, teaching kindergarten and elementary students, from different regions (continents) of the world. Findings revealed, six success themes and eight major challenges from the voice of teachers experiencing a rapid and unprepared shift to virtual education. Suggestions for improving digital education revolve around four areas which suggest that pupils from marginal socioeconomic households were significantly disadvantaged during the COVID-driven virtual education scheme. This research is not preoccupied with identifying universal outcomes but, instead, is focused on how the real virtual teaching experience can help in informing areas of focus for reimagining the approach to education for an uncertain future.
Soft-sensor design via task transferred just-in-time-learning coupled transductive moving window learner

Data based approaches have recently gained extensive attention in modern process industries. Accordingly, data based soft sensing technology used for making online predictions of quality variables plays currently an important role in process control and monitoring. Drifts in operating conditions and process characteristics, however, demand novel online learning methods to be developed for maintaining predictive accuracy of soft sensors. While moving window (MW) and just-in-time-learning (JITL) are the most frequently used online learning methods in soft sensor design, they are, indeed, effective against different types of drifts. In the current study, in order to exploit the merits of both methods within a perspective offered by the recent transfer learning paradigm, we propose combining a task transferred JITL model with a MW learner in a transductive learning setting (JITLTT-MWtr). The proposed method, tuned via a historical training set, is easy to implement and robust due to cooperative/complementary actions of JITL and MW methods. High prediction accuracy of JITLTT-MWtr is demonstrated on one semi-synthetic and five publicly available real datasets, indicating its efficiency and potential for industrial implementation.
From Pandemic Control to Data-Driven Governance: The Case of China’s Health Code

Current debates over digital contact tracing mainly focus on the tools and experiences in the West. China’s health code, while often seen as one of the earliest and most widely adopted apps since the outbreak of COVID-19, has not been studied specifically. This article provides a detailed analysis of the health code, draws comparison with the contact tracing apps developed by Google and Apple, and seeks to understand the specifications and contradictions internal to the health code’s development and deployment in China. Looking at both technical features and the mode and process of its adoption, the article argues that the health code is strictly speaking not a contact tracing tool, but a technology of population control which is integrated in traditional forms of control and facilitates the enhancement of such control. As a technology of ruling the population, rather than the virus as such, the health code also reveals crucial problems in the modernization and informatization of the state governance and public administration. A critique on the health code solely informed by privacy and personal data protection runs the risk of being co-opted by the government and technology companies deploying such tools to expand their surveillance and regulatory power.