Reviewerly/exHarmony huggingface dataset:
Novel binary differential evolution algorithm for knapsack problems

The capability of the conventional differential evolution algorithm to solve optimization problems in continuous spaces has been well demonstrated and documented in the literature. However, differential evolution has been commonly considered inapplicable for several binary/permutation-based real-world problems because of its arithmetic reproduction operator. Moreover, many limitations of the standard differential evolution algorithm, such as slow convergence and becoming trapped in local optima, have been defined. In this paper, a novel technique which makes a simple differential evolution algorithm suitable and very effective for solving binary-based problems, such as binary knapsack ones, is proposed. It incorporates new components, such as representations of solutions, a mapping method and a diversity technique. Also, a new efficient fitness evaluation approach for calculating and, at the same time, repairing knapsack candidate solutions, is introduced. To assess the performance of this new algorithm, four datasets with a total of 44 instances of binary knapsack problems are considered. Its performance and those of 22 state-of-the-art algorithms are compared, with the experimental results demonstrating its superiority in terms of both the quality of solutions and computational times. It is also capable of finding new solutions which are better than the current best ones for five large knapsack problems.
Function-Level Bottleneck Analysis of Private Proof-of-Authority Ethereum Blockchain

Private Ethereum blockchain-based systems are demanded in many industry sectors. However, the throughput performance of these systems does not meet their expectations. Many researchers have analyzed the performance of private blockchains, but their studies have failed to analyze root causes. In this paper, we perform a deep function-level bottleneck analysis for the private Ethereum blockchain. As the Ethereum client application is developed with golang, we leverage pprof, which is a resource-profiling tool for golang, and custom golang functions to measure the time taken by functions. To easily configure parameters and conduct our test, we code a shell script that automates the building process of a private Ethereum blockchain with docker containers. We conducted a series of experiments and identified the bottleneck function that is called every time a transaction arrives at an Ethereum node. In addition, we also found that the multi-threading is not well utilized, meaning that there is much room for improvement.
SEENS: Nuclei segmentation in Pap smear images with selective edge enhancement

Accurate nuclei segmentation, as an indispensable basis and core link for multi-cell cervical image analysis, plays an important role in automatic pre-cancer detection. However, poor image quality due to the uneven staining, complex backgrounds and overlapped cell clusters poses a great challenge in nuclei segmentation. In this paper, we propose a new Selective-Edge-Enhancement-based Nuclei Segmentation method (SEENS). In the proposed method, selective search is integrated with mathematical operators to segment whole slide cervical images into small regions of interest (ROI) while automatically avoiding repeated segmentation as well as eliminating non-nuclei regions. In addition, an edge enhancement method based on the canny operator and mathematical morphology is presented to extract edge information as a weight to enhance the nucleus edge selectively. As a result, the enhanced ROI is then segmented by the Chan–Vese model with a higher accuracy. We evaluate our method with 18 whole slide images for a total of 395 cell nuclei. Experimental results demonstrate that SEENS achieves higher accuracy in cervical nuclei segmentation. Notably our method performs particularly better in low-contrast scenarios than baselines.
Latent Complete Row Space Recovery for Multi-View Subspace Clustering

Multi-view subspace clustering has been applied to applications such as image processing and video surveillance, and has attracted increasing attention.Most existing methods learn view-specific self-representation matrices, and construct a combined affinity matrix from multiple views.The affinity construction process is time-consuming, and the combined affinity matrix is not guaranteed to reflect the whole true subspace structure.To overcome these issues, the Latent Complete Row Space Recovery (LCRSR) method is proposed.Concretely, LCRSR is based on the assumption that the multi-view observations are generated from an underlying latent representation, which is further assumed to collect the authentic samples drawn exactly from multiple subspaces.LCRSR is able to recover the row space of the latent representation, which not only carries complete information from multiple views but also determines the subspace membership under certain conditions.LCRSR does not involve the graph construction procedure and is solved with an efficient and convergent algorithm, thereby being more scalable to largescale datasets.The effectiveness and efficiency of LCRSR are validated by clustering various kinds of multi-view data and illustrated in the background subtraction task.
GAN Enhanced Membership Inference: A Passive Local Attack in Federated Learning

Federated learning has lately received great attention for its privacy protection feature. However, recent researches found that federated learning models are susceptible to various inference attacks. In this paper, we point out a membership inference attack method that can cause a serious privacy leakage in federated learning. An adversary who is a participant in federated learning can train a classification attack model to launch the membership inference attack, which determines if a data record is in the model's training dataset. The existing membership inference method is dissatisfied due to a lack of attack data since the training data of each participant are independent. To overcome the lack of attack data, an adversary can enrich attack data using the generative adversarial network (GAN), which is a practical method to increase data diversity. We substantiate that this GAN enhanced membership inference attack method has a 98% attack accuracy. We perform experiments to show that data diversity and the overfitting make federated learning models susceptible.
Investigating the Accessibility of Voice Assistants With Impaired Users: Mixed Methods Study

Background Voice assistants allow users to control appliances and functions of a smart home by simply uttering a few words. Such systems hold the potential to significantly help users with motor and cognitive disabilities who currently depend on their caregiver even for basic needs (eg, opening a door). The research on voice assistants is mainly dedicated to able-bodied users, and studies evaluating the accessibility of such systems are still sparse and fail to account for the participants’ actual motor, linguistic, and cognitive abilities. Objective The aim of this work is to investigate whether cognitive and/or linguistic functions could predict user performance in operating an off-the-shelf voice assistant (Google Home). Methods A group of users with disabilities (n=16) was invited to a living laboratory and asked to interact with the system. Besides collecting data on their performance and experience with the system, their cognitive and linguistic skills were assessed using standardized inventories. The identification of predictors (cognitive and/or linguistic) capable of accounting for an efficient interaction with the voice assistant was investigated by performing multiple linear regression models. The best model was identified by adopting a selection strategy based on the Akaike information criterion (AIC). Results For users with disabilities, the effectiveness of interacting with a voice assistant is predicted by the Mini-Mental State Examination (MMSE) and the Robertson Dysarthria Profile (specifically, the ability to repeat sentences), as the best model shows (AIC=130.11). Conclusions Users with motor, linguistic, and cognitive impairments can effectively interact with voice assistants, given specific levels of residual cognitive and linguistic skills. More specifically, our paper advances practical indicators to predict the level of accessibility of speech-based interactive systems. Finally, accessibility design guidelines are introduced based on the performance results observed in users with disabilities.
Multimodal MR Image Synthesis Using Gradient Prior and Adversarial Learning

In magnetic resonance imaging (MRI), several images can be obtained using different imaging settings (e.g. T1, T2, DWI, and Flair). These images have similar anatomical structures but are with different contrasts, which provide a wealth of information for diagnosis. However, the images under specific imaging settings may not be available due to the limitation of scanning time or corruption caused by noises. It is attractive to derive missing images with some settings from the available MR images. In this paper, we propose a novel end-to-end multisetting MR image synthesis method. The proposed method is based on generative adversarial networks (GANs) - a deep learning model. In the proposed method, different MR images obtained by different settings are used as the inputs of a GANs and each image is encoded by an encoder. Each encoder includes a refinement structure which is used to extract a multiscale feature map from an input image. The multiscale feature maps from different input images are then fused to generate several desired target images under specific settings. Because the resultant images obtained with GANs have blurred edges, we fuse gradient prior information in the model to protect high frequency information such as important tissue textures of medical images. In the proposed model, the multiscale information is also adopted in the adversarial learning (not just in the generator or discriminator) so that we can produce high quality synthesized images. We evaluated the proposed method on two public datasets: BRATS and ISLES. Experimental results demonstrate that the proposed approach is superior to current state-of-the-art methods.
Exploring the digital competence of pre-service teachers on entry onto an initial teacher education programme in Ireland

This study aimed to explore the digital competence of recent entrants into a pre-service teacher education programme in an Irish University. The participants were drawn from a cohort of 208 undergraduate teacher education students. The study employed an online survey that captured both self-reported levels of digital competence and knowledge of key areas of cyber ethics and digital technology. The respondents were active users of technology and very frequent users of social media but reported levels of skills in the use of other digital technologies were lower. In addition, their knowledge of cyber-ethics and associated practices varied. The study also found that they were positively disposed to technology in teaching. The paper argues that, while there are limitations to surveys that aim to capture one's level of digital competence, they can help guide teacher educators in responding to pre-service teachers. However, digital competence is an evolving concept and care must be taken to ensure that frameworks and tools used to assess it do not stifle teachers' autonomy in relation to their utilisation of technology.
Toward Remote Sensing Image Retrieval Under a Deep Image Captioning Perspective

The performance of remote sensing image retrieval (RSIR) systems depends on the capability of the extracted features in characterizing the semantic content of images.Existing RSIR systems describe images by visual descriptors that model the primitives (such as different land-cover classes) present in the images.However, the visual descriptors may not be sufficient to describe the high-level complex content of RS images (e.g., attributes and relationships among different land-cover classes).To address this issue, in this article, we present an RSIR system that aims at generating and exploiting textual descriptions to accurately describe the relationships between the objects and their attributes present in RS images with captions (i.e., sentences).To this end, the proposed retrieval system consists of three main steps.The first step aims to encode the image visual features and then translate the encoded features into a textual description that summarizes the content of the image with captions.This is achieved based on the combination of a convolutional neural network with a recurrent neural network.The second step aims to convert the generated textual descriptions into semantically meaningful feature vectors.This is achieved by using the recent word embedding techniques.Finally, the last step estimates the similarity between the vectors of the textual descriptions of the query image and those of the archive images, and then retrieve the most similar images to the query image.Experimental results obtained on two different datasets show that the description of the image content with captions in the framework of RSIR leads to an accurate retrieval performance.
Explainable classifier for improving the accountability in decision-making for colorectal cancer diagnosis from histopathological images

Pathologists are responsible for cancer type diagnoses from histopathological cancer tissues. However, it is known that microscopic examination is tedious and time-consuming. In recent years, a long list of machine learning approaches to image classification and whole-slide segmentation has been developed to support pathologists. Although many showed exceptional performances, the majority of them are not able to rationalize their decisions. In this study, we developed an explainable classifier to support decision making for medical diagnoses. The proposed model does not provide an explanation about the causality between the input and the decisions, but offers a human-friendly explanation about the plausibility of the decision. Cumulative Fuzzy Class Membership Criterion (CFCMC) explains its decisions in three ways: through a semantical explanation about the possibilities of misclassification, showing the training sample responsible for a certain prediction and showing training samples from conflicting classes. In this paper, we explain about the mathematical structure of the classifier, which is not designed to be used as a fully automated diagnosis tool but as a support system for medical experts. We also report on the accuracy of the classifier against real world histopathological data for colorectal cancer. We also tested the acceptability of the system through clinical trials by 14 pathologists. We show that the proposed classifier is comparable to state of the art neural networks in accuracy, but more importantly it is more acceptable to be used by human experts as a diagnosis tool in the medical domain.
In defence of digital contact-tracing: human rights, South Korea and Covid-19

Purpose The media has even been very critical of some East Asian countries’ use of digital contact-tracing to control Covid-19. For example, South Korea has been criticised for its use of privacy-infringing digital contact-tracing. However, whether their type of digital contact-tracing was unnecessarily harmful to the human rights of Korean citizens is open for debate. The purpose of this paper is to examine this criticism to see if Korea’s digital contact-tracing is ethically justifiable. Design/methodology/approach This paper will evaluate Korea’s digital contact-tracing through the lens of the four human rights principles to determine if their response is ethically justifiable. These four principles were originally outlined in the European Court of Human Rights, namely, necessary, proportional, scientifically valid and time-bounded (European Court of Human Rights 1950). Findings The paper will propose that while the use of Korea’s digital contact-tracing was scientifically valid and proportionate (albeit, in need for improvements), it meets the necessity requirement, but is too vague to meet the time-boundedness requirement. Originality/value The Covid-19 pandemic has proven to be one of the worst threats to human health and the global economy in the past century. There have been many different strategies to tackle the pandemic, from somewhat laissez-faire approaches, herd immunity, to strict draconian measures. Analysis of the approaches taken in the response to the pandemic is of high scientific value and this paper is one of the first to critically engage with one of these methods – digital contact-tracing in South Korea.
Content analysis of fake consumer reviews by survey-based text categorization

As the influence of online consumer reviews grows, deceptive reviews are a worsening problem, betraying consumers' trust in reviews by pretending to be authentic and informative. This research identifies factors that can separate deceptive reviews from genuine ones. First, we create a novel means of detection by contrasting authentic versus fake word patterns specific to a given domain (e.g., hotel services). We use a survey on a crowdsourcing platform to obtain both genuine and deceptive reviews of hotels. We learned the word patterns from each category to discriminate genuine reviews from fake ones for positively and negatively evaluated reviews, respectively. We show that our All Terms procedure outperforms current benchmark methods in computational linguistics and marketing. Our extended analysis reveals the factors that determine fake reviews (e.g., a lack of details, present- and future-time orientation, and emotional exaggeration) and the factors influencing people's willingness to write fake reviews (including social media trust, product quality consciousness, deal proneness, hedonic and utilitarian consumption, prosocial behavior, and individualism). We also use our procedure to analyze more than 250,000 real-world hotel reviews to detect fake reviews and identify the hotel and review characteristics influencing review fakery in the industry (e.g., star rating, franchise hotel, hotel size, room price, review timing, and review rating).
The non-linear relationship between ICT diffusion and financial development

This research investigates the linear and non-linear effects of information and communication technology (ICT) diffusion on financial development for 81 countries over the period 1990–2015 by employing the generalized-momentum method (GMM) and panel smooth transition regression (PSTR). Some main conclusions are presented as follows. First, comparing the different effects of ICT on financial development between the high-income group and the middle- & low-income group, telephone and Internet positively influences both groups’ financial development, whereas mobile cellular causes a negative effect in high-income countries, but a positive effect in middle- & low-income countries. Second, the growth of the Internet and telephones raises the financial development in all regions, while mobile cellular growth positively affects financial development only in Africa. Finally, strong evidence appears that the PSTR models capture the smooth non-linear effects of ICT diffusion on financial development, in which the effect of ICT diffusion on financial development is positive in the lower level of ICT diffusion, but turns negative in the higher level of ICT diffusion.
Evolution and impact of bias in human and machine learning algorithm interaction

Traditionally, machine learning algorithms relied on reliable labels from experts to build predictions. More recently however, algorithms have been receiving data from the general population in the form of labeling, annotations, etc. The result is that algorithms are subject to bias that is born from ingesting unchecked information, such as biased samples and biased labels. Furthermore, people and algorithms are increasingly engaged in interactive processes wherein neither the human nor the algorithms receive unbiased data. Algorithms can also make biased predictions, leading to what is now known as algorithmic bias. On the other hand, human's reaction to the output of machine learning methods with algorithmic bias worsen the situations by making decision based on biased information, which will probably be consumed by algorithms later. Some recent research has focused on the ethical and moral implication of machine learning algorithmic bias on society. However, most research has so far treated algorithmic bias as a static factor, which fails to capture the dynamic and iterative properties of bias. We argue that algorithmic bias interacts with humans in an iterative manner, which has a long-term effect on algorithms' performance. For this purpose, we present an iterated-learning framework that is inspired from human language evolution to study the interaction between machine learning algorithms and humans. Our goal is to study two sources of bias that interact: the process by which people select information to label (human action); and the process by which an algorithm selects the subset of information to present to people (iterated algorithmic bias mode). We investigate three forms of iterated algorithmic bias (personalization filter, active learning, and random) and how they affect the performance of machine learning algorithms by formulating research questions about the impact of each type of bias. Based on statistical analyses of the results of several controlled experiments, we found that the three different iterated bias modes, as well as initial training data class imbalance and human action, do affect the models learned by machine learning algorithms. We also found that iterated filter bias, which is prominent in personalized user interfaces, can lead to more inequality in estimated relevance and to a limited human ability to discover relevant data. Our findings indicate that the relevance blind spot (items from the testing set whose predicted relevance probability is less than 0.5 and who thus risk being hidden from humans) amounted to 4% of all relevant items when using a content-based filter that predicts relevant items. A similar simulation using a real-life rating data set found that the same filter resulted in a blind spot size of 75% of the relevant testing set.
Polyp-Net: A Multimodel Fusion Network for Polyp Segmentation

Computer-aided diagnosis of disease primarily depends on proper vision-based measurement (VBM). The traditional approach followed for diagnosis of colorectal cancer includes a manual screening of colorectum via a colonoscope and resection of polyps for histopathological analysis to decide the grade of malignancy. This procedure is time-consuming and expensive, and removal of benign polyp for analysis signifies the inefficiency of the diagnosis system. These drawbacks inspired us to develop an automatic vision-based analysis method for preliminary in vivo malignancy analysis of the polyp region. In this work, we have proposed a fusion-based polyp segmentation network, namely, Polyp-Net. Recently, convolutional neural networks (CNNs) have shown immense success in the domain of medical image analysis as it can exploit in-depth significant features with high discrimination capabilities. Therefore, motivated by these insights, we have proposed an enriched version of CNN with a nascent pooling mechanism, namely dual-tree wavelet pooled CNN (DT-WpCNN). The resultant segmented mask contains some surplus high-intensity regions apart from the polyp region. These shortcomings are avoided using a new variation of the region-based level-set method, namely, the local gradient weighting-embedded level-set method (LGWe-LSM), which shows a significant reduction of false-positive rate. The pixel-level fusion of the two enhanced methods shows more potentiality in the segmentation of the polyp regions. Our proposed network is trained on CVC-colon DB and tested on CVC-clinic DB. It achieves a dice score of 0.839, volume-similarity of 0.863, precision of 0.836, recall of 0.811, F1-score of 0.823, F2-score of 0.815, and Hausdorff distance of 21.796 which outperforms the existing baseline CNN's and recent state-of-the-art methods.