Reviewerly/exHarmony huggingface dataset:
Image retrieval via learning content-based deep quality model towards big data

Image retrieval aims to search specific image from large-scale datasets. Traditional text-based and content-based image retrieval approaches have shown competitive performance. However, both of which are limited by semantic gap, i.e., they cannot reflect human perception of images. To narrow semantic gap in image retrieval, this paper proposes a deep neural network (DNN) based image retrieval method, where saliency map is derived to form human gaze shifting paths by constraint metrics. More specifically, we first design a DNN-based image saliency prediction. Subsequently, we leverage image quality assessment (IQA) algorithm to select high-quality salient regions, which will be concatenated in sequence by using proposed constraint metrics to mimic human visual perception. Afterwards, we leverage the CNN-based architecture for deep representation acquisition of each images, where spatial structure among salient regions can be well preserved. Subsequently, based on the quality score of the query image, a series of candidate images whose quality scores are similar to that of the query image are derived. Finally, we engineer a ranking distance metric to refine the candidate images to achieve image retrieval. Extend experiments demonstrate that our method outperforms several state-of-the-art algorithms.
A novel particle swarm optimisation with mutation breeding

The diversity of the population is a key factor for particle swarm optimisation (PSO) when dealing with most optimisation problems. The best previously visited positions of each particle are the exemplar in PSO to guide particle swarm to search, and the diversity of the population can be controlled by these best previously visited positions. Base on this idea of to control the diversity of population to improve the performance of PSO, this paper proposes a novel PSO with mutation breeding (MBPSO), which performs a mutation breeding operation periodically, to control the diversity of the population to improve the global optimisation ability. The mutation breeding operation can be divided into two steps: breeding and mutation. The breeding step is to replace all of best previously visited positions of each particle with the global best previously visited position, and the mutation step is to perform a mutation operation for those new generated best previously visited positions. In addition, we adopt a new updating mechanism of the global best previously position to avoid falling into local optimum. The experimental results on a suit of benchmark functions verifies that the proposed PSO is a competitive algorithm when compare with other PSO variants.
Exploiting “Uncertain” Deep Networks for Data Cleaning in Digital Pathology

With the advent of digital pathology, there has been an increasing interest in providing pathologists with machine learning tools, often based on deep learning, to obtain faster and more robust image assessment. Nonetheless, the accuracy of these tools relies on the generation of large training sets of pre-labeled images. This is typically a challenging and cumbersome process, requiring extensive pre-processing to remove spurious samples that may lead the training to failure. Unlike their plain counterparts, which tend to provide overconfident decisions and cannot identify samples they have not been specifically trained for, Bayesian Convolutional Neural Networks provide a reliable measure of classification uncertainty. In this study, we exploit this inherent capability to automatize the data cleaning phase of histopathological image assessment. Our experiments on a case study of Colorectal Cancer image classification demonstrate that our approach can boost the accuracy of downstream classification by 15% at least.
Correlation of test results and influence of a mass balance constraint on risks in conformity assessment of a substance or material

When components of a substance or material are subject to a mass balance constraint, test results of the components’ contents are intrinsically correlated because of the constraint. This so-called ‘spurious’ correlation is observed in addition to possible metrologically-related correlation of test results, and natural and/or technological correlation of the components’ contents. Such correlations may influence understanding of test results and evaluation of risks of false decisions, due to measurement uncertainty, in conformity assessment of the substance or material. The objective of the present paper is the development of a technique for appropriate evaluation of the risks. A Bayesian multivariate approach to evaluate the conformance probability of materials or objects and relevant risks is discussed for different scenarios of the data modelling, taking into account all observed correlations. A Monte Carlo method, including the mass balance constraint, written in the R programming environment, is provided for the necessary calculations.
Is Digital Inequality a Part of Preservice Teachers’ Reasoning About Technology Integration Decisions?

Teachers’ ability to design meaningful uses of technology for all learners in any classrooms has a potential to narrow digital gaps among K-12 students. However, we know little about whether teachers are prepared to consider these issues when making technology integration decisions. This study explored preservice teachers’ knowledge use and their considerations about teaching practices related to digital inequality while reasoning about technology integration decisions. We analyzed interviews with and documents of a group of preservice teachers ( N = 14) who completed a technology integration task in a technology integration course. Findings showed that although they used multiple domains of teacher knowledge throughout their reasoning processes, they paid limited attention to sociocultural aspects of teaching that demonstrated the ability to care about digital inequality issues. Implications are discussed in terms of ways to better prepare preservice teachers to deal with digital inequalities.
Understanding Software Security from Design to Deployment

Analyzing, implementing and maintaining security requirements of software-intensive systems and achieving truly secure software requires planning for security from ground up, and continuously assuring that security is maintained across the software's lifecycle and even after deployment when software evolves. Given the increasing complexity of software systems, new application domains, dynamic and often critical operating conditions, the distributed nature of many software systems, and fast moving markets which put pressure on software vendors, building secure systems from ground up becomes even more challenging. Security-related issues have previously been targeted in software engineering sub-communities and venues. In the second edition of the International Workshop on Security from Design to Deployment (SEAD) at the International Conference on Automated Software Engineering (ASE) 2020, we aimed to bring the research and practitioner communities of requirements engineers, security experts, architects, developers, and testers together to identify foundations, and challenges, and to formulate solutions related to automating the analysis, design, implementation, testing, and maintenance of secure software systems.
Real-Time and Accurate Face Detection Networks Based on Deep Learning

In this paper, we propose a real-time and accurate face detection network based on MobileNets, SSD (Single Shot MultiBox Detector) and FPN (Feature Pyramid Networks). Our network adopts MobileNets and SSD as the basic networks and introduces FPN to fuse the multi-layer information, which makes the features extracted from the network more abundant and plays a positive role in face detection. In addition, the design of the network loss function draws on the loss function form of MultiBox, divides the whole loss function into two parts of confidence loss and localization loss, and expands the binary cross entropy loss of MultiBox into the loss function in the form of multi-category focal loss, which solves the unbalanced phenomenon of category during the training process.
Blockchain adoption drivers: The rationality of irrational choices

Summary There has been a huge increase in interest in blockchain technology. However, little is known about the drivers behind the adoption of this technology. In this article we identify and analyze these drivers, using six real‐world and representative scenarios. We confirm in our analysis that blockchain is not an appropriate technology for some scenarios, from a purely technical point of view. The choice for blockchain technology in such scenarios may therefore seem as an irrational choice. However, our analysis reveals that there are nontechnical drivers at play that drive the adoption of blockchain, such as philosophical beliefs, network effects, and economic incentives. These nontechnical drivers may explain the rationality behind the choice for blockchain adoption.
On compression rate of quantum autoencoders: Control design, numerical and experimental realization

Quantum autoencoders which aim at compressing quantum information in a low-dimensional latent space lie in the heart of automatic data compression in the field of quantum information. In this paper, we establish an upper bound of the compression rate for a given quantum autoencoder and present a learning control approach for training the autoencoder to achieve the maximal compression rate. The upper bound of the compression rate is theoretically proven using eigen-decomposition and matrix differentiation, which is determined by the eigenvalues of the density matrix representation of the input states. Numerical results on 2-qubit and 3-qubit systems are presented to demonstrate how to train the quantum autoencoder to achieve the theoretically maximal compression, and the training performance using different machine learning algorithms is compared. Experimental results of a quantum autoencoder using quantum optical systems are illustrated for compressing two 2-qubit states into two 1-qubit states.
Approach for shadow detection and removal using machine learning techniques

In this work, the authors have proposed a method for shadow detection and removal from videos by utilising methods of machine learning. From literature, various algorithms on shadow detection and removal have been accounted with advantages and disadvantages. Here some algorithms have a need for manual alignment and predefined explicit parameters, but fail to give precise outcome in different lighting and ecological surroundings. In this work, the authors propose a three-phase framework. In first stage, key frames are chosen by utilising features based K-means clustering which selects the key frames using features like colour, shape and surface. In second stage, they utilised two-stage segmentation techniques to segment the shadows by marking the region of interest. In the final step, they use threshold based segmentation to remove the shadow in videos. The performance of the proposed method is compared by performance evaluation of all state-of-the-art methods. The proposed strategies are established to achieve superior results in comparison to other state-of-the-art methods.
Early prediction of quality of service using interface-level metrics, code-level metrics, and antipatterns

Context: With the current high trends of deploying and using web services in practice, effective techniques for maintaining high quality of Service are becoming critical for both service providers and subscribers/users. Service providers want to predict the quality of service during early stages of development before releasing them to customers. Service clients consider the quality of service when selecting the best one satisfying their preferences in terms of price/budget and quality between the services offering the same features. The majority of existing studies for the prediction of quality of service are based on clustering algorithms to classify a set of services based on their collected quality attributes. Then, the user can select the best service based on his expectations both in terms of quality and features. However, this assumption requires the deployment of the services before being able to make the prediction and it can be time-consuming to collect the required data of running web services during a period of time. Furthermore, the clustering is only based on well-known quality attributes related to the services performance after deployment. Objective: In this paper, we start from the hypothesis that the quality of the source code and interface design can be used as indicators to predict the quality of service attributes without the need to deploy or run the services by the subscribers. Method: We collected training data of 707 web services and we used machine learning to generate association rules that predict the quality of service based on the interface and code quality metrics, and antipatterns. Results: The empirical validation of our prediction techniques shows that the generated association rules have strong support and high confidence which confirms our hypothesis that source code and interface quality metrics/antipatterns are correlated with web service quality attributes which are response time, availability, throughput, successability, reliability, compliance, best practices, latency, and documentation. Conclusion: To the best of our knowledge, this paper represents the first study to validate the correlation between interface metrics, source code metrics, antipatterns and quality of service. Another contribution of our work consists of generating association rules between the code/interface metrics and quality of service that can be used for prediction purposes before deploying new releases.
Super-Resolution and Self-Attention with Generative Adversarial Network for Improving Malignancy Characterization of Hepatocellular Carcinoma

The slice thickness of MR imaging may remarkably degrade the clarity of 3D lesion images within through-plane slices (coronal or sagittal views) so as to influence the performance of lesion characterization. To alleviate the problem, we propose an end-to-end super-resolution and self-attention framework based on Generative adversarial networks (GAN) for improving the malignancy characterization of hepatocellular carcinoma (HCC). Specifically, a super-resolution subnetwork is designed to enhance the low-resolution patches of coronal or sagittal views based on the high resolution patches of the axial view, and then the enhanced patches are fed into the classification subnetwork for malignancy characterization. Furthermore, a self-attention mechanism is utilized to extract multi-level features for better super-resolution and lesion characterization. Experimental results of clinical HCCs demonstrate the superior performance of the proposed method compared with conventional CNN-based methods and show the potential in clinical practice.
Estimating the number of components in finite mixture models via the Group-Sort-Fuse procedure

Estimation of the number of components (or order) of a finite mixture model is a long standing and challenging problem in statistics. We propose the Group-Sort-Fuse (GSF) procedure—a new penalized likelihood approach for simultaneous estimation of the order and mixing measure in multidimensional finite mixture models. Unlike methods which fit and compare mixtures with varying orders using criteria involving model complexity, our approach directly penalizes a continuous function of the model parameters. More specifically, given a conservative upper bound on the order, the GSF groups and sorts mixture component parameters to fuse those which are redundant. For a wide range of finite mixture models, we show that the GSF is consistent in estimating the true mixture order and achieves the n−1/2 convergence rate for parameter estimation up to polylogarithmic factors. The GSF is implemented for several univariate and multivariate mixture models in the R package GroupSortFuse. Its finite sample performance is supported by a thorough simulation study, and its application is illustrated on two real data examples.
Underrated or overstated? The need for technological competencies in scholarly communication librarianship

A number of attempts have been made to determine the key competencies for librarians supporting scholarly communications (SC), but it remains unclear what technical competencies are required. To explore this issue, the study undertook thematic analysis of in-depth semi-structured interviews with UK based librarians working in SC. Typical technologies in use were identified and six typical ways of talking about technologies uncovered. Partly because of the out-sourcing of key technologies, SC librarians need soft skills and certain positive orientations to technology rather than practical technical skills. They acquire these skills through learning on the job and through communities of learning. A concept of socio-technological competence is proposed to encapsulate this skillset.
generateData—A 2D data generator

generateData is a MATLAB/Octave function for generating 2D data clusters. Data is created along straight lines, which can be more or less parallel depending on the selected input parameters. The function also allows to fine-tune the generated data with respect to number of clusters, total data points, average cluster separation and several other distributional properties.
Detecting Hoaxes in Indonesian News Using TF/TDM and K Nearest Neighbor

Abstract The presence of the internet and the rapid growth of social media had given rise to the blossoming of hoax creation and distribution through it. A hoax can cause anxiety and reactivity to its readers and could harm a certain party. Thereby, it is important to detect and report hoaxes to stop its spreading as soon as possible. This research aims to utilize the K Nearest Neighbor (KNN) classification algorithm to detect whether a piece of news is a hoax or not. Experiments were done by using 74 hoaxes compiled from Indonesian hoax-debunking community websites and were being compared against 74 real news from various reputable news websites in Indonesia. The result showed that the model could give detection/classification accuracy up to 83.6% and that the model is prone to false positives detections. The characteristics of the resulted model and further research directions are then discussed.
Research on analysis method of event importance and fault model in space fault network

To study the event importance and their role in the system fault evolution process (SFEP), and the possibility of fault mode, the analysis methods of event importance and possibility of fault mode are proposed. This paper is the series of research in related fields including four methods. The event importance in fault process to fault evolution is studied from fault mode. Change to Field theory is used to study the relationships between events in the fault process to judge the event importance. For the structure of space fault network (SFN), the possibility of fault mode is studied. Based on some events have occurred, the potential possibility of fault mode is studied. At the same time, some examples are given to illustrate the use and processes of these methods. These methods can study the event importance and the characteristics of fault occurrence in SFEP within the framework of SFN. The methods are suitable for computer information storage and data processing, and are suitable for intelligent analysis and data processing of SFEP, and enrich the theory of SFN
Smoke and Scan: A Content Analysis of QR Code-Directed Websites Found on Cigarette Packs in China

Abstract Introduction Quick Response codes, or “QR” codes, are used widely in China—for mobile payment, marketing, public transportation, and various other applications. In this study, we examined the content of websites linked from QR codes on cigarette packs collected in China. Aims and Methods In February 2017, 738 unique cigarette packs were collected from five Chinese cities (Beijing, Guangzhou, Shanghai, Kunming, and Chengdu) using a systematic protocol. Cigarette packs were coded for presence of QR codes on packaging. Packs containing QR codes were then scanned using the WeChat app. Websites sourced from QR codes were coded for required verification, website type, age- and health-related statements, engagement strategies, and marketing appeals. Results From the sample of 738 unique packs, 109 packs (14.8%) had a QR code on the packaging. The QR codes were linked to 24 unique websites of which 23 could be analyzed. All 23 unique websites were either brand-specific or social media websites; none focused on health or quit information. Of the 23 websites, only three (13.0%) websites had age-restricted site access and just six (26.1%) had any mention of health-related risks associated with product usage. Engagement strategies and/or marketing appeals were found on 20 (87.0%) websites. Conclusions The Chinese tobacco industry uses QR codes on cigarette packaging to link to web-based marketing content including social media recruitment, contests and giveaways, and product advertisement. It is important to understand where packs send consumers online and what messages they receive, and to consider QR codes on packaging when drafting policy. Implications Scanning QR codes in China is a commonplace activity. The authors are aware of no published studies on the role QR codes play on the marketing of cigarette packs, in China or elsewhere. This study demonstrates QR codes on cigarette packs can expose users and nonusers to cigarette marketing on interactive websites and protobacco social media pages, mostly without restrictive access or health warnings. This is an area that health authorities can consider regulating, given that this is a channel through which the tobacco industry can communicate with current and potential consumers.
Guidelines for the Interface Design of AR Systems for Manual Assembly

Augmented Reality (AR) allows for the augmentation of relevant in-situ information in real-time, making it an effective assistive tool to support and enhance task performance. Manual assembly is a promising area in which AR can be used to assist users. Findings from various user studies evaluating the use of AR-based support systems for manual assembly have shown that it is able to reduce mental workload and improve task performance, and that its usability is greatly influenced by user interface (UI) design. Many prototypes have been developed to identify the optimal information presentation, with each study implementing its own interface design approach. The challenge lies in determining a generalized and standardized approach to interface design: what information should be present and how should they be presented? The paper reviews the types of visual features that have been implemented in existing systems and how it had assisted users in performing tasks. The questions of what information should be available and how to present them, guides the discussion from which a set of guidelines is formulated, aided by theories from cognitive science. This paper proposes the following guidelines for the interface design of AR systems for manual assembly: to include information which are present in traditional paper-based instructions; to use exogenous cues for the tasks of identifying, locating and picking parts; to use endogenous cues to guide in assembling parts. The paper contributes to the design AR systems for manual assembly by proposing a standardized approach to interface design.