Reviewerly/exHarmony huggingface dataset:
A deep hypersphere approach to high-dimensional anomaly detection

The term of Curse of Dimensionality implicitly expresses the challenge for anomaly detection in a high-dimensional space. Because the distribution of anomalies in the high-dimensional spatial data is usually too sparse to provide sufficient information for detecting anomalies. In addition, irrelevant attributes may be seen as noise in the input data, which masks the true anomalies, so that it is difficult to choose a subspace of the input data that highlights the relevant attributes. In this case, the task becomes even harder if one aims at learning a compact boundary to distinguish anomalies from normal data. To address this issue, we proposed a detection method using the combination of an autoencoder and a hypersphere. In addition, an angle kernel and a radius kernel are also derived in order to learn a compact boundary of distinguishing anomalous and normal instances. Results show that our method outperforms the state-of-the-art detection methods in anomalous detection accuracy and the ability of learning a compact boundary. Moreover, our method also addresses the issue of blurred boundary in searching normal data in high dimensional dataset and when the information is insufficient due to a limited number of potential anomalies. We find that the measurement of angle similarity between data points during searching gains more advantages for learning a compact boundary than using the measurement of distance similarity. Since angle similarity is not only helpful for flexibly controlling search in normal data region, but also tightens the searched region of anomalies nearby the boundary. We also find that noise in data as a negative factor can deteriorate detection accuracy much more quickly than dimensionality does. Our findings indicate that the determination of hypersphere radius relies more on data dimensionality in a high-dimensional space than that in a low-dimensional space. However, in a low-dimensional space the radius is more likely correlated with data volume.
Serverless Architecture for Healthcare Management Systems

Serverless computing is an emerging cloud service architecture for executing distributed applications, where services are provided based on a pay-as-you-go basis. It allows the developers to deploy and run their applications without worrying about the underlying architecture. Serverless architecture has been popular due to its cost-effective policies, auto-scaling, independent, and simplified code deployment. The healthcare service can be made available as a serverless application that consists of distributed cloud services achieving the various requirements in the healthcare industry. The services offered may be made available on premise on a cloud infrastructure to users and health service providers. This chapter presents a novel model for serverless architecture for healthcare systems, where the services are provided as functional units to various stakeholders of the healthcare system. Two case studies related to healthcare systems that have adopted serverless frameworks are discussed.
Imagination and the Future University

Abstract This essay argues that thinking about university futures requires not only practices of critique and desire, but practices of rigorous and reflexive imagination. Building on Bill Sharpe's three horizons framework, it argues that debates about university futures are dominated by horizon 1 thinking (critique of the current situation) and horizon 3 thinking (normative aspirations toward desirable futures) but that there is limited exploration of horizon 2 (the emerging possibilities that may create radical disruption). The article draws on futures and anticipation studies, in particular Ziauddin Sardar and John Sweeney's “postnormal menagerie,” to model a set of imaginative inquiries into the blind spots, blank spots, and different forms of ignorance through which highly divergent university futures might be explored. It concludes by proposing two scenarios for university futures—the “Campus of the Sky” and the “Pirate University”—as sites of generative experimentation and further research, and with a call for a radical diversification of participation in dialogues about the future of the university.
Debugging with Stack Overflow: Web Search Behavior in Novice and Expert Programmers

Debugging can be challenging for novice and expert programmers alike. Programmers routinely turn to online resources such as Stack Overflow for help, but understanding of debugging search practices, as well as tool support to find debugging resources, remains limited. Existing tools that mine online help forums are generally not aimed at novices, and programmers face varying levels of success when looking for online resources. Furthermore, training online code search skills is pedagogically challenging, as we have little understanding of how expertise impacts programmers’ web search behavior while debugging code.We help fill these knowledge gaps with the results of a study of 40 programmers investigating differences in Stack Overflow search behavior at three levels of expertise: novices, experienced programmers who are novices in Python (the language we use in our study), and experienced Python programmers. We observe significant differences between all three levels in their ability to find posts helpful for debugging a given error, with both general and language-specific expertise facilitating Stack Overflow search efficacy and debugging success. We also conduct an exploratory investigation of factors that correlate with this difference, such as the display rank of the selected link and the number of links checked per search query. We conclude with an analysis of how online search behavior and results vary by Python error type. Our findings can inform online code search pedagogy, as well as inform the development of future automated tools.
Writer Recognition Using Off-line Handwritten Single Block Characters

Block characters are often used when filling paper forms for a variety of purposes. We investigate if there is biometric information contained within individual digits of handwritten text. In particular, we use personal identity numbers consisting of the six digits of the date of birth, DoB. We evaluate two recognition approaches, one based on handcrafted features that compute contour directional measurements, and another based on deep features from a ResNet50 model. We use a self-captured database of 317 individuals and 4920 written DoBs in total. Results show the presence of identity-related information in a piece of handwritten information as small as six digits with the DoB. We also analyze the impact of the amount of enrolment samples, varying its number between one and ten. Results with such small amount of data are promising. With ten enrolment samples, the Top-1 accuracy with deep features is around 94%, and reaches nearly 100% by Top-10. The verification accuracy is more modest, with EER>20% with any given feature and enrolment set size, showing that there is still room for improvement.
An entity-weights-based convolutional neural network for large-sale complex knowledge embedding

Knowledge graph (KG) has increasingly been seen as a significant resource in financial applications (e.g., risk control, auditing and anti-fraud). However, there are few prior studies that focus on multi-relational circles, extracting additional information under the completed KG and selecting similarity measures for knowledge representation. In this paper, we introduce multi-relational circles and propose a novel embedding model, which considers entity weights calculated by PageRank algorithm to improve TransE method. In order to extract additional information, we use entity weights to convert embeddings into an on-map mining problem, and propose a model called CNNe based on entity weights and a convolutional neural network with three hidden layers, which converts vectors of entities, entity weights and relationships into matrices to perform link prediction in the same way as image processing. With the help of ten different similarity measures, it is demonstrated that the choice of distance measure greatly effect the results of the translation embedding models. Moreover, we propose two embedding methods, sMFE and tMFE, to enhance the results using matrix factorization. The complete incidence matrix is first applied to knowledge embedding, which contains the most comprehensive topological properties of the graph. Experimental results on standard benchmark datasets demonstrate that the proposed models are effective. In particular, CNNe achieves a mean rank of 166 less than the baseline method and an improvement of 2.1% on the proportion of correct entities ranked in the top ten on YAGO3-10 dataset.
Identification Strategy Design with the Solution of Wavelet Singular Spectral Entropy Algorithm for the Aerodynamic System Instability

In order to effectively identify the signs of instability in the aerodynamic system of an axial compressor, a wavelet singular spectral entropy algorithm incorporated within the wavelet transform, singular value decomposition and information entropy is proposed to describe the distribution complexity of the spatial modalities in the flow field. This kind of identification design can accurately distinguish the boundary between the stable and unstable states of the internal flow field from the view of a dynamic system. On the basis of the information entropy algorithm, the wavelet singular spectral entropy algorithm is designed to integrate with the advantages of wavelet transform analysis on the time-frequency localization and singular value decomposition for signal processing and data mining together. So that the quantitative analysis of the definition of rebuilding a system image can be achieved by the solution of wavelet singular spectral entropy. This method can automatically extract the transient information of the space mode in the time-frequency domain. It effectively avoids the shortcoming that the feature extraction on spatial information cannot be accomplished from multiple angles with the single information entropy algorithm. In the data processing of instability signals under different speeds, the wavelet singular spectral entropy algorithm shows a greater advantage in the early warning for compressor stall. The result shows that the value of the wavelet singular spectral shows an obvious mutation when the aerodynamic system approaches the instability boundary. According to the threshold set, the identification hybrid algorithm can detect the stall precursor about 23~96 r in advance. Compared to the single information entropy algorithm, the hybrid wavelet singular spectral entropy algorithm is able to shift to an earlier precursor identification by about 11~82 r. This established hybrid identification algorithm accounts for the nonlinearity of the aerodynamic system, providing a new perspective for the nonlinear system instability identification.
Integrating Data Directly into Publications with Augmented Reality and Web-Based Technologies – Schol-AR

Scientific research has become highly intertwined with digital information, however scientific publication remains based on the static text and figures of principal articles. This discrepancy constrains complex scientific data into 2D static figures, hindering our ability to effectively exchange the complex and extensive information that underlies modern research. Here, we demonstrate how the viewing of digital data can be directly integrated into the existing publication system through both web based and augmented reality (AR) technologies. We additionally provide a framework that makes these capabilities available to the scientific community. Ultimately, augmenting articles with data can modernize scientific communication by bridging the gap between the digital basis of present-day research and the natural limitations of printable articles.
An Interoperable Electronic Health Record System for Clinical Cardiology

Currently in hospitals, there are several separate information systems that manage, very often autonomously, the patient’s personal, clinical and diagnostic data. An electronic health record system has been specifically developed for a cardiology ward and it has been designed “ab initio” to be fully integrated into the hospital information system and to exchange data with the regional health information infrastructure. All documents have been given as Health Level 7 (HL7) clinical document architecture and messages are sent as HL7-Version 2 (V2) and/or HL7 Fast Healthcare Interoperability Resources (FHIR). Specific decision support sections for specific aspects have also been included. The system has been used for more than three years with a good level of satisfaction by the users. In the future, the system can be the basis for secondary use for clinical studies, further decision support systems and clinical trials.
What Do You Want From Me? Adapting Systems to the Uncertainty of Human Preferences

Autonomous systems, like drones and self-driving cars, are becoming part of our daily lives.Multiple people interact with them, each with their own expectations regarding system behaviour.To adapt system behaviour to human preferences, we propose and explore a game-theoretic approach.In our architecture, autonomous systems use sensor data to build game-theoretic models of their interaction with humans.In these models, we represent human preferences with types and a probability distribution over them.Game-theoretic analysis then outputs a strategy, that determines how the system should act to maximise utility, given its beliefs over human types.We showcase our approach in a search-and-rescue (SAR) scenario, with a robot in charge of locating victims.According to social psychology, depending on their identity some people are keen to help others, while some prioritise their personal safety.These social identities define what a person favours, so we can map them directly to game-theoretic types.We show that our approach enables a SAR robot to take advantage of human collaboration, outperforming non-adaptive configurations in average number of successful evacuations.
Vector textures derived from higher order derivative domains for classification of colorectal polyps

Textures have become widely adopted as an essential tool for lesion detection and classification through analysis of the lesion heterogeneities. In this study, higher order derivative images are being employed to combat the challenge of the poor contrast across similar tissue types among certain imaging modalities. To make good use of the derivative information, a novel concept of vector texture is firstly introduced to construct and extract several types of polyp descriptors. Two widely used differential operators, i.e., the gradient operator and Hessian operator, are utilized to generate the first and second order derivative images. These derivative volumetric images are used to produce two angle-based and two vector-based (including both angle and magnitude) textures. Next, a vector-based co-occurrence matrix is proposed to extract texture features which are fed to a random forest classifier to perform polyp classifications. To evaluate the performance of our method, experiments are implemented over a private colorectal polyp dataset obtained from computed tomographic colonography. We compare our method with four existing state-of-the-art methods and find that our method can outperform those competing methods over 4%-13% evaluated by the area under the receiver operating characteristics curves.
Face Recognition in Children: A Longitudinal Study

The lack of high fidelity and publicly available longitudinal children face datasets is one of the main limiting factors in the development of face recognition systems for children. In this work, we introduce the Young Face Aging (YFA) dataset for analyzing the performance of face recognition systems over short age-gaps in children. We expand previous work by comparing YFA with several publicly available cross-age adult datasets to quantify the effects of short age-gap in adults and children. Our analysis confirms a statistically significant and matcher independent decaying relationship between the match scores of ArcFace-Focal, MagFace, and Facenet matchers and the age-gap between the gallery and probe images in children, even at the short age-gap of 6 months. However, our result indicates that the low verification performance reported in previous work might be due to the intra-class structure of the matcher and the lower quality of the samples. Our experiment using YFA and a state-of-the-art, quality-aware face matcher (MagFace) indicates 98.3% and 94.9% TAR at 0.1% FAR over 6 and 36 Months age-gaps, respectively, suggesting that face recognition may be feasible for children for age-gaps of up to three years.
Automatic evaluation of summary on fidelity, conciseness and coherence for text summarization based on semantic link network

Automatic evaluation of summary is one of the basic problems of automatic text summarization research. The commonly used approaches evaluate the informativeness, conciseness and coherence of summary separately based on different representations with weak semantics such as n-gram and vector of words. By transforming text into semantic link network, this paper proposes a general framework for automatically evaluating summary from the following aspects: (1) fidelity, the extent of a summary that conveys the core of its source text; (2) conciseness, the extent of non-redundancy within summary and, (3) coherence, the extent of relatedness between representations within summary, which are based on a uniform semantic representation of text. Experiments on the evaluation of the summarization of scientific papers and news show that the proposed framework achieves comparable or higher correlations with human judgments than the popular evaluation models. This research also verifies the role of the semantic link network in representing and analysing texts.
Skinny as a Bird: Design fiction as a vehicle for reflecting on food futures

This article explores the use of design fiction as a vehicle for critically reflecting on the complex issue of sustainable food consumption and production. The paper presents the design fiction Bird, a food delivery service that provides food rations to its customers based on their exact nutritional needs and self-improvement goals. The service makes food consumption sustainable by design, leveraging individual lifestyle ambitions to circumvent the need to translate sustainability awareness into action. We discuss what it means to embed provocation, critique, and reflection in a design fiction that highlights potential preferable and non-preferable trajectories of change related to imaginaries of technocentric food futures. Through a design fiction artefact that reflects a complex set of ethical, social, cultural, political, and environmental issues related to food consumption, the aim is to examine how design fiction can serve as an entry point for imagining and critiquing possible futures.
GoogLeNet Application towards Gesture Recognition for ASL Character Identification

Gesture recognition plays a vital role in multiple applications; the applications may be related to simple aspects such as playing video games to complex tasks such as related to communication using sign symbols. In this paper, we propose a robust hand gesture recognition system using skin detection technique. Gesture recognition plays a key role for different applications. In this regard, we attempt to take out the type features of the fingers, for instance their orientation, their status in terms of raised or folded fingers and their respective image position, which require the processing of images as a matter of principle. In order to recognize the terms used for contact the program uses a complex approach to distinguish different hand movements. The dynamic sequence of the images that function as a contribution to the hand gesture recognition system is captures using the camera. GoogLeNet is used for hand gesture recognition training and detection. In order to train GoogLeNet, we use transfer learning. We also used American Sign Language (ASL) to map the sign to a particular alphabet.
Understanding the Security and Privacy Concerns About the Use of Identifiable Health Data in the Context of the COVID-19 Pandemic: Survey Study of Public Attitudes Toward COVID-19 and Data-Sharing

The COVID-19 pandemic increased the availability and use of population and individual health data to optimize tracking and analysis of the spread of the virus. Many health care services have had to rapidly digitalize in order to maintain the continuity of care provision. Data collection and dissemination have provided critical support for defending against the spread of the virus since the beginning of the pandemic; however, little is known about public perceptions of and attitudes toward the use, privacy, and security of data.The goal of this study is to better understand people's willingness to share data in the context of the COVID-19 pandemic.A web-based survey was conducted on individuals' use of and attitudes toward health data for individuals aged 18 years and older, and in particular, with a reported diagnosis of a chronic health condition placing them at the highest risk of severe COVID-19.In total, 4764 individuals responded to this web-based survey, of whom 4674 (98.1%) reported a medical diagnosis of at least 1 health condition (3 per person on average), with type 2 diabetes (n=2974, 62.7%), hypertension (n=2147, 45.2%), and type 1 diabetes (n=1299, 27.4%) being most prominent in our sample. In general, more people are comfortable with sharing anonymized data than personally identifiable data. People reported feeling comfortable sharing data that were able to benefit others; 66% (3121 respondents) would share personal identifiable data if its primary purpose was deemed beneficial for the health of others. Almost two-thirds (n=3026; 63.9%) would consent to sharing personal, sensitive health data with government or health authority organizations. Conversely, over a quarter of respondents (n=1297, 27.8%) stated that they did not trust any organization to protect their data, and 54% (n=2528) of them reported concerns about the implications of sharing personal information. Almost two-thirds (n=3054, 65%) of respondents were concerned about the provisions of appropriate legislation that seeks to prevent data misuse and hold organizations accountable in the case of data misuse.Although our survey focused mainly on the views of those living with chronic health conditions, the results indicate that data sensitivity is highly contextual. More people are more comfortable with sharing anonymized data rather than personally identifiable data. Willingness to share data also depended on the receiving body, highlighting trust as a key theme, in particular who may have access to shared personal health data and how they may be used in the future. The nascency of legal guidance in this area suggests a need for humanitarian guidelines for data responsibility during disaster relief operations such as pandemics and for involving the public in their development.