Reviewerly/exHarmony huggingface dataset:
Designing IoT Resources to Support Outdoor Play for Children

We describe a Research-through-Design (RtD) project that explores the Internet of Things (IoT) as a resource for children's free play outdoors. Based on initial insights from a design ethnography, we developed four RtD prototypes for social play in different scenarios of use outdoors, including congregating on a street or in a park to play physical games with IoT. We observed these prototypes in use by children in their free play in two community settings, and report on the qualitative analysis of our fieldwork. Our findings highlight the designs' material qualities that encouraged social and physical play under certain conditions, suggesting social affordances that are central to the success of IoT designs for free play outdoors. We provide directions for future research that addresses the challenges faced when deploying IoT with children, contributing new considerations for interaction design with children in outdoor settings and free play contexts.
Temporally Discounted Differential Privacy for Evolving Datasets on an Infinite Horizon

The following topics are dealt with: learning (artificial intelligence); temporal logic; formal verification; optimisation; mobile robots; security of data; cyber-physical systems; smart power grids; Markov processes; control system synthesis.
Blockchain Enabled IoT Edge Computing

The earlier work related to this paper described blockchain enabled IoT edge computing architecture, where any compute resource owner can join the ecosystem and lend out the compute resources required for edge computing/analytics. This paper lists possible set of challenges implementing the above solution. The primary challenges are ensuring data privacy for the input data files and ensuring computation integrity when executing the computation remotely. The paper talks about current state research and possible practical implementation to address the privacy and integrity challenges using hardware assisted trusted executing environment primarily Intel SGX. The paper proposes using unidirectional payment channels to address payment related challenges.
Four-Year Experience of Digital Slide Telepathology for Intraoperative Frozen Section Consultations in a Two-Site French Academic Department of Pathology

Abstract Objectives To share our experience with digital slide telepathology for intraoperative frozen section consultations (IOCs) and to describe its evolution over time by reporting performance metrics and addressing organizational and economic aspects. Methods Since 2013, a technician has been alone at the surgical site. At the other site, the pathologist opens the digital slide from a local server via the intranet. Three periods were compared: a 6-month period of conventional IOC (period 1), a 24-month period of telepathology at 6 months after implementation (period 2), and a 12-month period of telepathology at 3.5 years after implementation (period 3). Results In total, 87 conventional IOCs and 464 and 313 IOCs on digital slides were performed respectively during periods 1, 2, and 3; mean turnaround time was 27, 36, and 38 minutes, respectively, and there were a mean number of 1.1, 1.1, and 1.3 slides, respectively, per IOC. Diagnostic accuracy was achieved in 95.4%, 92.7%, and 93.9%, respectively, of IOCs (not significant). The additional cost is in the same range as the cost of urgent transport by courier. Conclusions Developing IOC with digital slides is a challenge but is necessary to optimize medical time in the current context of pathologist shortage and budget restrictions.
Generalization of intensity distribution of medical images using GANs

Abstract The performance of a CNN based medical-image classification network depends on the intensities of the trained images. Therefore, it is necessary to generalize medical images of various intensities against degradation of performance. For lesion classification, features of generalized images should be carefully maintained. To maintain the performance of the medical image classification network and minimize the loss of features, we propose a method using a generative adversarial network (GAN) as a generator to adapt the arbitrary intensity distribution to the specific intensity distribution of the training set. We also select CycleGAN and UNIT to train unpaired medical image data sets. The following was done to evaluate each method’s performance: the similarities between the generalized image and the original were measured via the structural similarity index (SSIM) and histogram, and the original domain data set was passed to a classifier that trained only the original domain images for accuracy comparisons. The results show that the performance evaluation of the generalized images is better than that of the originals, confirming that our proposed method is a simple but powerful solution to the performance degradation of a classification network.
A Personal Health Trajectory API: Addressing Problems in Health Institution-Oriented Systems

Each person interacts with multiple health institution’s systems along their life. These systems are usually developed to fulfill the specific needs of sanitary organizations or Web of Medical Things manufacturers. However, most of the times these information systems aren’t interconnected, making it very difficult to put in common the information of a patient scattered in various systems. Thus, it’s necessary to develop solutions that allow health information systems developers to consult all the information of an user across the multiple health’s information systems it is scattered and to offer this information organized as the Personal Health Trajectory of the user (a succession of Personal Health Records ordered by time). This paper proposes a solution for the integration of heterogeneous health information systems, the processing of their data and its provisioning in a health trajectory perspective through an API. Thus, software developers of healthcare solutions can leverage this API to develop a new generation of person-oriented solutions.
Complete Bottom-Up Predicate Invention in Meta-Interpretive Learning

Predicate Invention in Meta-Interpretive Learning (MIL) is generally based on a top-down approach, and the search for a consistent hypothesis is carried out starting from the positive examples as goals. We consider augmenting top-down MIL systems with a bottom-up step during which the background knowledge is generalised with an extension of the immediate consequence operator for second-order logic programs. This new method provides a way to perform extensive predicate invention useful for feature discovery. We demonstrate this method is complete with respect to a fragment of dyadic datalog. We theoretically prove this method reduces the number of clauses to be learned for the top-down learner, which in turn can reduce the sample complexity. We formalise an equivalence relation for predicates which is used to eliminate redundant predicates. Our experimental results suggest pairing the state-of-the-art MIL system Metagol with an initial bottom-up step can significantly improve learning performance.
Determination of pupillary distance using YOLO algorithm

This study highlights the importance of one of the most important components when prescription glasses are developed for a patient, which is the Pupillary Distance (PD). PD pertains to the distance between the two pupils. This is an essential information when having prescription glasses created to ensure that the pupils remain in the center of the glasses. This paper explores the application of the You Only Look Once (YOLO) algorithm in order to determine the distance between the two pupils measured. It has been found that the YOLO algorithm can be an efficient tool in measuring distances between the objects, which was proven by linear regression where the computed value and measured value are similar. The research has concluded that it can replicate the results from the conventional method of measuring Pupillary Distance through the use of a ruler.
Multichannel Color Image Denoising via Weighted Schatten p-norm Minimization

The R, G and B channels of a color image generally have different noise statistical properties or noise strengths. It is thus problematic to apply grayscale image denoising algorithms to color image denoising. In this paper, based on the non-local self-similarity of an image and the different noise strength across each channel, we propose a MultiChannel Weighted Schatten p-Norm Minimization (MCWSNM) model for RGB color image denoising. More specifically, considering a small local RGB patch in a noisy image, we first find its nonlocal similar cubic patches in a search window with an appropriate size. These similar cubic patches are then vectorized and grouped to construct a noisy low-rank matrix, which can be recovered using the Schatten p-norm minimization framework. Moreover, a weight matrix is introduced to balance each channel’s contribution to the final denoising results. The proposed MCWSNM can be solved via the alternating direction method of multipliers. Convergence property of the proposed method are also theoretically analyzed . Experiments conducted on both synthetic and real noisy color image datasets demonstrate highly competitive denoising performance, outperforming comparison algorithms, including several methods based on neural networks.
Document Translation vs. Query Translation for Cross-Lingual Information Retrieval in the Medical Domain

We present a thorough comparison of two principal approaches to Cross-Lingual Information Retrieval: document translation (DT) and query translation (QT).Our experiments are conducted using the cross-lingual test collection produced within the CLEF eHealth information retrieval tasks in 2013-2015 containing English documents and queries in several European languages.We exploit the Statistical Machine Translation (SMT) and Neural Machine Translation (NMT) paradigms and train several domain-specific and task-specific machine translation systems to translate the non-English queries into English (for the QT approach) and the English documents to all the query languages (for the DT approach).The results show that the quality of QT by SMT is sufficient enough to outperform the retrieval results of the DT approach for all the languages.NMT then further boosts translation quality and retrieval quality for both QT and DT for most languages, but still, QT provides generally better retrieval results than DT.
Tchebycheff Procedure for Multi-task Text Classification

Multi-task Learning methods have achieved significant progress in text classification.However, existing methods assume that multi-task text classification problems are convex multiobjective optimization problems, which is unrealistic in real-world applications.To address this issue, this paper presents a novel Tchebycheff procedure to optimize the multitask classification problems without any convex assumption.The extensive experiments back up our theoretical analysis and validate the superiority of our proposals.
Parallel feature based calibration method for a trinocular vision sensor

In this paper, a new method to calibrate a trinocular vision sensor is presented. A planar target with several parallel lines is utilized. The trifocal tensor of three image planes can be calculated out according to line correspondences. Compatible essential matrix between each two cameras can be obtained. Then, rotation matrix and translation matrix can be deduced base on singular value decomposition of their corresponding essential matrix. In our proposed calibration method, image rectification is carried out to remove perspective distortion. As the feature utilized is straight line, precise point to point correspondence is not necessary. Experimental results show that our proposed calibration method can obtain precise results. Moreover, the trifocal tensor can also give a strict constraint for feature matching as descripted in our previous work. Root mean square error of measured distances is 0.029 mm with regards to the view field of about 250×250 mm. As parallel feature exists widely in natural scene, our calibration method also provides a new approach for self-calibration of a trinocular vision sensor.
Processing of the Residuals of Numbers in Real and Complex Numerical Domains

The chapter discusses the procedures for the formation and use of real residuals of real numbers on a real module, as well as complex and real residues of an integer complex number on a complex module. The chapter focuses on the processing of complex and real residuals of an integer complex number by a complex module. This procedure is based on using the results of the first fundamental Gauss theorem. The chapter of the proposed procedure provides examples of determining deductions in a complex numerical domain. On the basis of the considered procedure, an algorithm was developed for determining the real deduction of an integral complex number using a complex module in accordance with which the device was synthesized for its technical implementation. The device received a patent of Ukraine for the invention, which confirms the novelty and practical value of research results. The results obtained in the chapter are advisable to be used when implementing tasks and algorithms in real and complex numerical domains. In particular, the use of real numbers for cryptographic applications was considered.
ReXNet: Diminishing Representational Bottleneck on Convolutional Neural Network.

This paper addresses representational bottleneck in a network and propose a set of design principles that improves model performance significantly. We argue that a representational bottleneck may happen in a network designed by a conventional design and results in degrading the model performance. To investigate the representational bottleneck, we study the matrix rank of the features generated by ten thousand random networks. We further study the entire layer's channel configuration towards designing more accurate network architectures. Based on the investigation, we propose simple yet effective design principles to mitigate the representational bottleneck. Slight changes on baseline networks by following the principle leads to achieving remarkable performance improvements on ImageNet classification. Additionally, COCO object detection results and transfer learning results on several datasets provide other backups of the link between diminishing representational bottleneck of a network and improving performance. Code and pretrained models are available at this https URL.
A deep learning-based automated diagnostic system for classifying mammographic lesions

Background: Screening mammography has led to reduced breast cancer-specific mortality and is recommended worldwide. However, the resultant doctors’ workload of reading mammographic scans needs to be addressed. Although computer-aided detection (CAD) systems have been developed to support readers, the findings are conflicting regarding whether traditional CAD systems improve reading performance. Rapid progress in the artificial intelligence (AI) field has led to the advent of newer CAD systems using deep learning-based algorithms which have the potential to reach human performance levels. Those systems, however, have been developed using mammography images mainly from women in western countries. Because Asian women characteristically have higher-density breasts, it is uncertain whether those AI systems can apply to Japanese women. In this study, we will construct a deep learning-based CAD system trained using mammography images from a large number of Japanese women with high quality reading. Methods: We will collect digital mammography images taken for screening or diagnostic purposes at multiple institutions in Japan. A total of 15,000 images, consisting of 5000 images with breast cancer and 10,000 images with benign lesions, will be collected. At least 1000 images of normal breasts will also be collected for use as reference data. With these data, we will construct a deep learning-based AI system to detect breast cancer on mammograms. The primary endpoint will be the sensitivity and specificity of the AI system with the test image set. Discussion: When the ability of AI reading is shown to be on a par with that of human reading, images of normal breasts or benign lesions that do not have to be read by a human can be selected by AI beforehand. Our AI might work well in Asian women who have similar breast density, size, and shape to those of Japanese women. Trial registration: UMIN, trial number UMIN000039009. Registered 26 December 2019, https://www.umin.ac.jp/ctr/
Multilingual Scene Text Detection Using Gradient Morphology

Text detection in natural scene images is an interesting problem in the field of information retrieval. Several methods have been proposed over the past few decades for scene text detection. However, the robustness and efficiency of these methods are downgraded due to high sensitivity towards various complexities of an image. Also, in multi-lingual environment where texts may occur in multiple languages, a method may not be suitable for detecting scene texts in certain languages. To counter these challenges, a gradient morphology-based method is proposed in this paper that proves to be robust against image complexities and efficiently detects scene texts irrespective of their languages. The method is validated using low quality images from standard multi-lingual datasets like MSRA-TD500 and MLe2e. The performance of the method is compared with that of some state-of-the-art methods, and comparably better results are observed.
HE-ELM Technique Based Transformer Protection

Various unwanted phenomena that are taken place in the transformer may occasionally mal-operate selected fault classification based protective schemes. Hence, it is necessary to discriminate internal fault from external abnormal conditions for unit protection of power transformer. This paper presents a new Hierarchical Ensemble Extreme Learning Machine (HE-ELM) based classifier technique to identify faults in & out of the transformer. The component ELMs is structured hierarchically to improve its fault data classification accuracy. The developed algorithm is evaluated by simulating multiple disorders on 100 MVA, 132/220 kV transformer with the help of PSCAD software. DWT is used to extract features from acquired current signals from the transformer. The feature vector formed after the extraction process is fed to the HE-ELM algorithm for data classification. The fault discrimination accuracy of the HE-ELM technique is 99.91%. This shows its effectiveness concerning other classifier techniques. Moreover, the developed algorithm is successfully tested on hardware prototype in a laboratory environment under various inrush and fault conditions using a Cortex M4 microcontroller (STM32F407) with maximum identification time of 27 ms. The proposed HE-ELM technique is compared with existing SVM, PNN, and ELM techniques for identical fault data. Results demonstrate that HE-ELM outperforms than existing schemes in the cross-domain recognition task.
Component-based face recognition using statistical pattern matching analysis

The aim of this research is to develop a fusion concept to component-based face recognition algorithms for features analysis of binary facial components (BFCs), which are invariant to illumination, expression, pose variations and partial occlusion. To analyze the features, using statistical pattern matching concepts, which are the combination of Chi-square (CSQ), Hu moment invariants (HuMIs), absolute difference probability of white pixels (AbsDifPWPs) and geometric distance values (GDVs) have been proposed for face recognition. The individual grayscale face image is cropped by applying the Viola–Jones face detection algorithm from a face database having variations in illumination, appearance, pose and partial occlusion with complex backgrounds. Doing illumination correction through histogram linearization technique, the grayscale face components such as eyes, nose and mouth regions are extracted using the 2D geometric positions. The binary face image is created by applying cumulative probability distribution function with Otsu adaptive thresholding method and then extracted BFCs such as eyes, nose and mouth regions. Five statistical pattern matching tools such as the standard deviation of CSQ values with probability of white pixels (PWPs), standard deviation of HuMIs with Hu’s seven moment invariants, AbsDifPWPs and GDVs are developed for recognition purpose. GDVs are determined between two similar facial corner points (FCPs) and nine FCPs are extracted from binary whole face and BFCs. Pixel Intensity Values (PIVs) which are determined using L2 norms from grayscale values of the whole face and grayscale values of the face components. Experiment is performed using BioID Face Database on the basis of these pattern matching tools and appropriate threshold values with logical and conditional operators and gives the best expected results from true positive rate perspective.
IAS: An IoT Architectural Self-adaptation Framework

This paper develops a generic approach to model control loops and their interaction within the Internet of Things (IoT) environments. We take advantage of MAPE-K loops to enable architectural self-adaptation. The system’s architectural setting is aligned with the adaptation goals and the components run-time situation and constraints. We introduce an integrated framework for IoT Architectural Self-adaptation (IAS) where functional control elements are in charge of environmental adaptation and autonomic control elements handle the functional system’s architectural adaptation. A Queuing Networks (QN) approach was used for modeling the IAS. The IAS-QN can model control levels and their interaction to perform both architectural and environmental adaptations. The IAS-QN was modeled on a smart grid system for the Melle-Longchamp area (France). Our architectural adaptation approach successfully set the propositions to enhance the performance of the electricity transmission system. This industrial use-case is a part of CPS4EU European industrial innovation project (CPS4EU is a three years project funded by the H2020-ECSEL-2018-IA. The project develops four vital IoT technologies, namely computing, connectivity, sensing, and cooperative systems. It incorporates those IoT technologies through pre-integrated architectures and design tools. It instantiates the architectures in dedicated use-cases from a strategic application viewpoint for automotive, smart grid, and industrial automation https://cps4eu.eu ).
Failover Cluster Nodes and ISCSI Storage Area Network on Virtualization Windows Server 2016

The use of data in this current digital era, the traditional model of connecting the storage media with servers, cannot meet the need for fast access to a very large amount of data. Storage Area Network can be the solution because this technology can handle a large amount of storage media (TeraByte), enable to be a share of storage resources, as well as giving data access in real-time, quick, and easy. Internet Small Computer System Interface (iSCSI) is a concept of storage media that use Internet Protocol as a medium for connecting storage media and data transfer through network service. Testing of availability server in this research use failover cluster technology, after testing done, then the result is obtained, when a failure or error occurs on the primary server, the primary server role will be automatically replaced by backup server with the same resource as the main server. As for the time automatic displacement server, when an active server makes failure, then it will only take less than 5 seconds. So, it can be concluded that this technology can minimize the value of the downtime in the system.