Reviewerly/exHarmony huggingface dataset:
Semi-automated analysis of digital whole slides from humanized lung-cancer xenograft models for checkpoint inhibitor response prediction

We propose a deep learning workflow for the classification of hematoxylin and eosin stained histological whole-slide images of non-small-cell lung cancer. The workflow includes automatic extraction of meta-features for the characterization of the tumor. We show that the tissue-classification produces state-of-the-art results with an average F1-score of 83%. Manual supervision indicates that experts, in practice, accept a far higher percentage of predictions. Furthermore, the extracted meta-features are validated via visualization revealing relevant biomedical relations between the different tissue classes. In a hypothetical decision-support scenario, these meta-features can be used to discriminate the tumor response with regard to available treatment options with an estimated accuracy of 84%. This workflow supports large-scale analysis of tissue obtained in preclinical animal experiments, enables reproducible quantification of tissue classes and immune system markers, and paves the way towards discovery of novel features predicting response in translational immune-oncology research.
The impact of institutional quality and governance on financial inclusion in Africa: A two-step system generalised method of moments approach

Orientation: Literature emphasises that institutional quality and governance are important elements in enhancing financial inclusion. Studies on institutions, governance and financial inclusion in developing economies found that governance and institutions have positively influenced people wanting to make savings and open a formal bank account. Research purpose: This study investigated the impact of institutional quality and governance on financial inclusion in Africa. Motivation of the study: The significance of how governance and institutions affect access to finance has largely been overlooked in previous research. Thus the principal objective of this study is to address this research gap. Research approach/design and method: A system generalised method of moments technique for a panel data of 49 countries for the period 2004–2016 was employed. Main findings: The results obtained suggest a positive impact of institutional quality and governance on financial inclusion within the region. Our study also found a significant positive effect of the lagged value of financial inclusion and banking sector size on financial inclusion for African countries. However, rural to total population and natural resources negatively influenced financial inclusion in Africa. Practical/managerial implications: This study provides implications for policymakers which are fruitful if implemented. Policymakers should facilitate the existence of a transparent legal framework, removal of corruption and enhancing fair administration and judicial proceedings so as to enhance the prospects of financial inclusion. In addition, improving economic freedom and governance levels minifies the informality levels in the financial markets. Contribution/value-add: This study adds value and knowledge to the current body on financial inclusion and governance issues in Africa, which has not received much attention in developing economies.
Digital pathology for intraoperative frozen section diagnosis of thoracic specimens: an evaluation of a system using remote sampling and whole slide imaging diagnosis

Background Digital pathology is now used for primary diagnostic work as well as teaching, research and consultation. In our multisite institution service reorganisation led to histopathology being located in a separate hospital from some surgical specialities. We implemented remotely supervised specimen sampling and frozen section diagnosis using digital pathology. In this study we assessed the concordance of glass and digital slide diagnosis using this system. Methods We reviewed cases from the first 2 years of digital frozen section reporting at our institution. Cases with potential digital to glass slide discordance were reviewed by three experienced thoracic histopathologists. The reasons for discordance were determined and common themes identified. We also reviewed critical incidents relating to digital pathology during the study period. Results The study population comprised 211 cases. Frozen section to final diagnosis concordance between digital and glass slide diagnosis was found in 196 (92.6%) cases. The 15 potentially discordant cases were reviewed. Intraobserver concordance between glass and digital slide review ranged from 9/15 to 12/15 cases across the three pathologists. Glass slide review diagnosis showed better concordance with ground truth in two cases; digital slide review was more accurate in two cases. One relevant critical incident was identified during the study period. Discussion This is the largest study to examine digital pathology for thoracic frozen section diagnosis and shows that this is a safe and feasible alternative to glass slide diagnosis. Discordance between digital and glass slide diagnoses were unrelated to the processes of whole slide imaging and digital microscopy.
Three-domain fuzzy wavelet broad learning system for tremor estimation

This paper proposes a new three-domain fuzzy wavelet broad learning system (TDFW-BLS) for tremor estimation in tele-operation. In the feature layer of our novel method, feature nodes can be mapped by a three-domain fuzzy wavelet sub-systems (3DFWs) and the k-means method is applied to determine the parameters in the TDFWs. The architecture of the new proposed system maps the input of different dimensions to different groups feature nodes by 3DFWS. In the enhancement layer, feature nodes are mapped to enhancement nodes. Moreover, feature nodes and increment nodes can be concatenated into a matrix to map the total output of the novel system by the full connection layer. Finally, the semi-physical simulation experiment is designed to demonstrate the effectiveness of the novel TDFW-BLS. Meanwhile, it is compared with some existing methods and the results have shown superior performance.
Contour-Aware Long-Term Tracking With Reliable Re-Detection

Recently discriminative correlation filter (DCF) based methods have gained much popularity for their excellent performance and high efficiency. However, most of them perform poorly in long-term tracking as they are not equipped with an effective mechanism to evaluate the quality of tracking results and correct tracking errors. To resolve such issue, this paper proposes a long-term tracking method, which consists of two components, including tracking-by-detection and re-detection. The tracking-by-detection part is built upon the DCF framework by incorporating a contour constraint map, which could identify non-target samples and refine the tracking results efficiently in presence of some challenging situations, such as deformation and occlusion. Benefited by our proposed re-detection strategy, the missing/occluded target could be captured immediately after it reappears. Moreover, a re-detected result is allowed to replace the original tracking result only when it owns a performance gap against the original one, which can reduce the risk of wrong substitution and well enhance the long-term tracking robustness. Extensive experiments on OTB2015, Temple-Color, UAV20L and VOT-LT2018 show that the proposed long-term tracking method outperforms state-of-the-art hand-crafted based methods, and even some deep learning based methods.
Quick Insight of Research Literature Using Topic Modeling

With the development in Information technology and advancement in education and research, a huge number of research publications and articles are generated every year. Crucial knowledge and information about innovative technology are embedded in these documents. It has become necessary to find a text analytics technique that gives quick insight into the research content from such enormous unstructured text data. Here we proposed information retrieval technique using topic modeling for taking quick outlook of the data. Latent Dirichlet is a generative probabilistic model which uses probability distribution of words in document to extract theme of documents. Topics generated show many hidden themes, correlated terminologies to main theme of documents which gives a quick overview of these documents. In this work Blockchain Technology, emergent technology publications are considered.
Same App, Different Countries: A Preliminary User Reviews Study on Most Downloaded iOS Apps

Prior work on mobile app reviews has demonstrated that user reviews contain a wealth of information and are seen as a potential source of requirements. However, most of the studies done in this area mainly focused on mining and analyzing user reviews from the US App Store, leaving reviews of users from other countries unexplored. In this paper, we seek to understand if the perception of the same apps between users from other countries and that from the US differs through analyzing user reviews. We retrieve 300,643 user reviews of the 15 most downloaded iOS apps of 2018, published directly by Apple, from nine English-speaking countries over the course of 5 months. We manually classify 3,358 reviews into several software quality and improvement factors. We leverage a random forest based algorithm to identify factors that can be used to differentiate reviews between the US and other countries. Our preliminary results show that all countries have some factors that are proportionally inconsistent with the US.
Effective Taxation System by Blockchain Technology

One of the basic functions of a government is to deliver public services to citizens. Service delivery of this kind requires public expenditure. Hence, governments require resources to finance their expenditure. Although there are a number of different methods available to fund public expenditure, the most important one is taxation. However, governments incur costs when collecting taxes. It is, therefore, important for a government to ensure the efficiency of its tax collection system and to collect taxes in such a way that only minimal costs are incurred. Providing transparent, controllable, secure, and real-time information is vital in terms of ensuring the effectiveness of a tax collection system. Changes and developments in information and communication technologies have prompted the public sector to identify new ways to collect taxes. In recent years, discussions regarding the applicability of blockchain technology (or, more commonly referred to as crypto coins), for the public sector have emerged. In this study, the applicability of blockchain technology for use in a tax system is discussed. The properties and benefits of different blockchain technologies are analyzed in terms of both data and transparency. It has been concluded that blockchain technology could be applied in a number of areas to reduce the administrative tax burden and the costs associated with tax collection. This study, therefore, attempts to explain the applicability of blockchain technology in relation to taxation, and it clarifies (1) how blockchain technology represents a new approach to taxation, (2) how blockchain technology reduces tax expenditure, (3) how blockchain technology increases both transparency and accountability, (4) how tax evasion can be reduced using blockchain technology, and (5) how blockchain technology can reduce the administrative tax burden.
Operator-in-the-Loop Deep Sequential Multi-Camera Feature Fusion for Person Re-Identification

Given a target image as query, person re-identification systems retrieve a ranked list of candidate matches on a per-camera basis. In deployed systems, a human operator scans these lists and labels sighted targets by touch or mouse-based selection. However, classical re-id approaches generate per-camera lists independently. Therefore, target identifications by operator in a subset of cameras cannot be utilized to improve ranking of the target in remaining set of network cameras. To address this shortcoming, we propose a novel sequential multi-camera re-id approach. The proposed approach can accommodate human operator inputs and provides early gains via a monotonic improvement in target ranking. At the heart of our approach is a fusion function which operates on deep feature representations of query and candidate matches. We formulate an optimization procedure custom-designed to incrementally improve query representation. Since existing evaluation methods cannot be directly adopted to our setting, we also propose two novel evaluation protocols. The results on two large-scale re-id datasets (Market-1501, DukeMTMC-reID) demonstrate that our multi-camera method significantly outperforms baselines and other popular feature fusion schemes. Additionally, we conduct a comparative subject-based study of human operator performance. The superior operator performance enabled by our approach makes a compelling case for its integration into deployable video-surveillance systems.
Arithmetic Semantics of Feature and Goal Models for Adaptive Cyber-Physical Systems

Many Cyber-Physical Systems (CPSs) today are self-adaptive, in order to handle frequent changes in environmental conditions and requirements. In CPSs, goal-based reasoning is often used to include stakeholder and social concerns in decision making during design and runtime adaptation activities. To better support some of these activities, arithmetic semantics for goal models were proposed to enable the generation of mathematical functions usable by systems. However, goal models often allow invalid combinations of alternatives, which can be prevented by companion feature models. In this paper, to enable the generation of valid and optimal configurations for adaptive CPSs, we propose new arithmetic semantics for feature models that enable their transformations to mathematical functions (in several programming languages) further restricting the ones generated from goal models. The composition of feature and goal functions results in a smaller design space, leading to fewer but valid solutions that can be generated (e.g., through optimization) and used in simulations and running adaptive CPSs with social concerns. Finally, a simulation model in SysML is proposed in this paper to demonstrate the feasibility and usefulness of this composition.
Using Graph Convolutional Networks for Approximate Reasoning with Abstract Argumentation Frameworks: A Feasibility Study

We employ graph convolutional networks for the purpose of determining the set of acceptable arguments under preferred semantics in abstract argumentation problems. While the latter problem is complexity-wise one of the hardest problems in reasoning with abstract argumentation problems, approximate methods are needed here in order to obtain a practically relevant runtime performance. This first study shows that deep neural network models such as graph convolutional networks significantly improve the runtime while keeping the accuracy of reasoning at about $$80\%$$ or even more.
A B-Spline Method With AIS Optimization for 2-D IoT-Based Overpressure Reconstruction

In blast wave monitoring, a traditional travel time tomography method is encountered with local minimum travel time and low coverage density of rays. In this article, a novel B-spline fitting method with the knot-optimization artificial immune system (AIS) is proposed for 2-D overpressure reconstruction. It possesses the advantages of handling point sets of large sizes and adjusts the knot vector flexibly. Based on the overpressure value in the explosion from the travel time tomography method, the proposed method combining the advantages of B-splines and knot point optimization AIS is able to achieve the optimal sensor distribution and raise the reconstruction precision. The detailed experimental results about the comparison of linear fitting interpolation, cubic fitting interpolation, natural neighbor fitting interpolation, v4 fitting interpolation, Delaunay triangulation fitting, and B-spline method are also given. Furthermore, for the knot optimization issue in B-spline, the proposed adaptive fitting method with knot-optimization AIS has a smaller root-mean-square (RMS) error with eight knot nodes in comparison with the classic B-spline fitting method. This article is conducted to provide new insights to reconstructing 2-D Internet-of-Things-based (IoT-based) overpressure in blast wave monitoring more precisely under limited sensor deployment and further give a new approach to overpressure reconstruction scenarios.
Performance evaluation of a cost-sensitive differential evolution classifier using spark – Imbalanced binary classification

Nowadays, the amount of data that has been collected or generated in many sectors has been growing exponentially because of the rapid development of technologies such as the Internet of Things (IoT). Additionally, the nature of this data is imbalanced. The need for extracting valuable information for decision support from this data poses a challenge to the scientific community to find a solution to cope with large imbalanced data. In previous work, our cost-sensitive differential evolution classification algorithm showed efficient performance for handling highly imbalanced data sets. However, our algorithm shows inefficient performance when applied to big data sets, thus lacking to scale with data size increases. In this paper, we design and implement a parallel version of our cost-sensitive differential evolution classifier using the Apache Spark framework (SCDE). The aim is to handle large and binary imbalanced data. The main idea of the algorithm is to find the optimal centroid for each target label using differential evolution by minimizing the total misclassification cost and then assign unlabeled data points to the closest centroid. Our experiments include a real data set that is based on intrusion detection in order to evaluate our algorithm's scalability and performance. The experimental results show that SCDE efficiently handles imbalanced binary data and scales very well with data size increases. Moreover, the speedup and scaleup results that are obtained by SCDE are close to linear.
Personalising Safe Sleep Messaging for Infant Caregivers in the United States

The purpose of our study was to better understand why parents/caregivers might not practice safe sleep behaviours. In autumn 2016, we conducted ‘pulse’ interviews with 124 parents/caregivers of children under the age of one year at a variety of local community events, festivals and meetings in cities with high infant mortality rates around the Midwestern US state of Ohio. Through an inductive approach, pulse interviews were analysed using thematic coding and an iterative process which followed for further clarification of themes (Qualitative Research in Psychology, 2006, 3, 77; BMC Medical Research Methodology, 2013, 13, 117). The six major themes of underlying reasons why parents/caregivers might not practice safe sleep behaviours that were identified in our coding process included the following: (a) culture and family tradition, (b) knowledge about safe sleep practices, (c) resource access, (d) stressed out parents, (f) lack of support and (g) fear for safety of baby. Using the descriptive findings from the pulse interviews, qualitative themes and key informant validation feedback, we developed four diverse fictional characters or personas of parents/caregivers who are most likely to practice unsafe sleep behaviours. These personas are characteristic scenarios which imitate parent and caregiver experiences with unsafe sleep behaviours. The personas are currently being used to influence development of health promotion and education programs personalised for parents/caregivers of infants less than one year to encourage safe sleep practices.
Deep Learning for Facial Recognition on Single Sample per Person Scenarios with Varied Capturing Conditions

Single sample per person verification has received considerable attention because of its relevance in security, surveillance and border crossing applications. Nowadays, e-voting and bank of the future solutions also join this scenario, opening this field of research to mobile and low resources devices. These scenarios are characterised by the availability of a single image during the enrolment of the users into the system, so they require a solution able to extract knowledge from previous experiences and similar environments. In this study, two deep learning models for face recognition, which were specially designed for applications on mobile devices and resources saving environments, were described and evaluated together with two publicly available models. This evaluation aimed not only to provide a fair comparison between the models but also to measure to what extent a progressive reduction of the model size influences the obtained results.The models were assessed in terms of accuracy and size with the aim of providing a detailed evaluation which covers as many environmental conditions and application requirements as possible. To this end, a well-defined evaluation protocol and a great number of varied databases, public and private, were used.
Modulation recognition with pre‐denoising convolutional neural network

Modulation recognition (MR) is an important technology in modern communication systems. Traditional MR methods can be categorised as the maximum likelihood hypothesis, pattern recognition, and deep learning-based methods. The authors can achieve high-recognition accuracy when the signal-to-noise ratio (SNR) is high. However, their recognition accuracy is greatly reduced when the SNR is low, especially when the SNR is below 0 dB. In order to improve the MR accuracy at low SNRs, they propose a pre-denoising algorithm, which is used before MR methods. The model of the pre-denoising algorithm is a fully convolutional neural network, which is similar to an auto-encoder. They also use residual learning to speed up the training process. Experimental results show that the proposed pre-denoising algorithm can significantly enhance the SNRs of modulated signals and improve the accuracy of MR methods.
The Effects of Task Complexity on the Use of Different Types of Information in a Search Assistance Tool

In interactive information retrieval, an important research question is: How do task characteristics influence users’ needs and behaviors? We report on a laboratory study ( N =32) that investigated the effects of task complexity on the types of information used by participants while searching. Participants completed tasks of four complexity levels and had access to four different types of information provided through a search-assistance tool referred to as the InfoBoxes (IB). The IB tool presented the following types of task-related information ( info-types ) on different tabs: (1) facts, (2) concepts, (3) opinions, and (4) insights. Facts (and opinions) were defined as objective (and subjective) statements relevant to the task. Concepts were defined as important ideas, principles, or entities related to the task. Insights were defined as tips or advice about the task. The study investigated six research questions that considered the effects of task complexity on: (RQ1) participants’ pre-/post-task perceptions about useful info-types; (RQ2) use of different info-types during the task; (RQ3) motivations for engaging with the IB; (RQ4) gains from using it; (RQ5) the search stage participants were in while engaging with the IB; and (RQ6) motivations for sometimes avoiding the IB. Our results suggest that task complexity influenced all six types of outcomes. We discuss implications of our results for designing search assistance tools and systems that favor certain types of content based on task characteristics.