Reviewerly/exHarmony huggingface dataset:
Interacting with Next-Generation Smart Patient-Centric Healthcare Systems

There is a recognised move towards more personalised health with citizens at the centre of healthcare provision. In particular, there is an emphasis on the right of citizens to decide who, why and when, should have access to their medical records. The EU project SERUMS is developing a tool-chain for the secure access of distributed medical information preserving the privacy levels imposed by GDPR, national and/or organisational regulations. We propose a user-centred approach to demonstrate how technologies can converge to enable doctors and patients to interact with integrated healthcare records. In addition, it will allow us to evaluate and evolve our tool-chain.
Cost-Sensitive Canonical Correlation Analysis for Semi-Supervised Multi-View Learning

Canonical correlation analysis (CCA) is a cost insensitive method. It assumes the same loss for different classification errors and aims to attain a low error rate by maximizing the cross-view correlation. However, in some real-world applications, different classification errors will lead to unequal misclassification losses. In addition, in practice, only limited cost label information is available in training set due to the expensive costs of labelling. This paper aims to perform label propagation with CCA in a unified cost-sensitive learning framework. By learning jointly, both the label propagation and CCA can feed back to each other. Thus, more discriminative and cost-sensitive projections will be learned for feature fusion. We test the proposed method on the cost-sensitive application of door-locker system based on multi-view face recognition. The results in comparison with eight label propagation methods, eleven CCA related methods and eight cost-sensitive single-view methods demonstrate its effectiveness.
Modeling the Semantics of States and State Machines

A system's behavior is typically specified through models such as state diagrams that describe how the system should behave.According to researchers, it is not clear what a state actually represents regarding the system to be modeled.Standards do not provide adequate definitions of or sufficient guidance on the use of states.Studies show these inconsistencies can lead to poor or incomplete specifications, which in turn could result in project delays or increase the cost of the system design.This paper aims to establish a precise definition of the notion of states and state machines, a goal motivated by system modelers' (e.g., requirement engineers') need to understand key concepts and vocabulary such as states and state machine, which are major behavioral modeling tools (e.g., in UML)."State" is the main notion of a state machine in which events drive state changes.This raises questions about the nature of these state-related notations.The semantics of these concepts is based on a new modeling methodology called the thinging machine applied to a number of examples of existing models.The thinging machine semantics is founded on five elementary actions that divide the static model into changes/states upon which events are defined.
Discovering Related Users in Location-based Social Networks

Users from Location-Based Social Networks can be characterised by how and where they move. However, most of the works that exploit this type of information neglect either its sequential or its geographical properties. In this article, we focus on a specific family of recommender systems, those based on nearest neighbours; we define related users based on common check-ins and similar trajectories and analyse their effects on the recommendations. For this purpose, we use a real-world dataset and compare the performance on different dimensions against several state-of-the-art algorithms. The results show that better neighbours could be discovered with these approaches if we want to promote novel and diverse recommendations.
Demographic classification through pupil analysis

An area of biometrics that has recently attracted much attention is gender and age classification. Its applications can be found not only in the fields of security and surveillance, but also in the context of marketing and demographic information gathering. In addition, extracting this information from a biometric sample can help to decrease the time to identify the exact individual. In this paper, we exploit pupil size as a discriminating feature for the estimation of gender and age. Data obtained from the free observation of face images have been used to train two classifiers (Adaboost and SVM), considering both the best results produced by each classifier and their fusion through weighted means. With experiments involving more than 100 participants, we have found that pupil size can provide significant results, better than those achievable using data on fixations and gaze paths. Pupil Diameter Mean (PDM) has proved to be the best discriminating feature for both gender and age. To the best of our knowledge, there are no other studies trying to perform such a classification using pupil size only.
Automated Detection of Multi-Rotor UAVs Using a Machine-Learning Approach

The objective of this article is to propose and verify a reliable detection mechanism of multi-rotor unmanned aerial vehicles (UAVs). Such a task needs to be solved in many areas such as in the protection of vulnerable buildings or in the protection of privacy. Our system was firstly realized by standard computer vision methods using the Oriented FAST and Rotated BRIEF (ORB) feature detector. Due to the low success rate achieved in real-world conditions, the machine-learning approach was used as an alternative detection mechanism. The “Common Objects in Context dataset” was used as a predefined dataset and it was extended by 1000 samples of UAVs from the SafeShore dataset. The effectiveness and the reliability of our system are proven by four basic experiments—drone in a static image and videos which are displaying a drone in the sky, multiple drones in one image, and a drone with another flying object in the sky. The successful detection rate achieved was 97.3% in optimal conditions.
Learning Soft Mask Based Feature Fusion with Channel and Spatial Attention for Robust Visual Object Tracking

We propose to improve the visual object tracking by introducing a soft mask based low-level feature fusion technique. The proposed technique is further strengthened by integrating channel and spatial attention mechanisms. The proposed approach is integrated within a Siamese framework to demonstrate its effectiveness for visual object tracking. The proposed soft mask is used to give more importance to the target regions as compared to the other regions to enable effective target feature representation and to increase discriminative power. The low-level feature fusion improves the tracker robustness against distractors. The channel attention is used to identify more discriminative channels for better target representation. The spatial attention complements the soft mask based approach to better localize the target objects in challenging tracking scenarios. We evaluated our proposed approach over five publicly available benchmark datasets and performed extensive comparisons with 39 state-of-the-art tracking algorithms. The proposed tracker demonstrates excellent performance compared to the existing state-of-the-art trackers.
Reserve Parking and Authentication of Guest Using QR Code

In the current world permeated by the use of technology, parking appears to be an issue that has not been given serious consideration. This paper developed and proposed an online digital parking application, with the help of Quick Response (QR) code for the reduction of workforce in parking areas. In Android technology, every compatible device can read QR codes and scan them to provide considerable information. The QR code is scanned by the system for the details of the user for authorized entrance. Following such scan, the user is allotted a parking space on the basis of availability, and upon leaving the allotted space, it will remain an empty slot to be allotted to the next user. This can mitigate human effort through the use of simple software and hardware, while ensuring authorization and safety to users.
BabelFish: Fusing Address Translations for Containers

Cloud computing has begun a transformation from using virtual machines to containers. Containers are attractive because multiple of them can share a single kernel, and add minimal performance overhead. Cloud providers leverage the lean nature of containers to run hundreds of them on a few cores. Furthermore, containers enable the serverless paradigm, which leads to the creation of short-lived processes.In this work, we identify that containerized environments create page translations that are extensively replicated across containers in the TLB and in page tables. The result is high TLB pressure and redundant kernel work during page table management. To remedy this situation, this paper proposes BabelFish, a novel architecture to share page translations across containers in the TLB and in page tables. We evaluate BabelFish with simulations of an 8-core processor running a set of Docker containers in an environment with conservative container co-location. On average, under BabelFish, 53% of the translations in containerized workloads and 93% of the translations in serverless workloads are shared. As a result, BabelFish reduces the mean and tail latency of containerized data-serving workloads by 11% and 18%, respectively. It also lowers the execution time of containerized compute workloads by 11%. Finally, it reduces serverless function bring-up time by 8% and execution time by 10%-55%.
A Study on the Influence of Scene Reality of VR Environment on English Learners' Learning Engagement and Learning Effectiveness

Technical support for language learning is considered a hot issue in education. However, most language learners are affected by the lack of social context and behavioral interaction. This study develops and designs an English learning system based on virtual reality technology, which aims to help learners understand the learning content by improving learners' immersion and presence. The research focuses on the reality of virtual scenes, and develops an analysis and a study of learning engagement and learning effectiveness. The research results found that the higher the reality of the virtual environment's scene, the higher the learner's engagement in learning. In addition, in high reality scenarios, there is a significant correlation between learning engagement and learning effectiveness.
Teleportation of single and bipartite states via a two qubits xxz Heizenberg spin chain in a non-Markovian environment

Quantum teleportation of single particle states and bipartite spin states via a channel composed of a two-qubits xxz Heisenberg spin chain under the effects of Dzyaloshinski-Moriya interaction, which interacts with a non-Markovian environment as a noisy quantum channel is investigated. The quantum channel and the initial state of the system are shown to be the fundamental elements that fully characterize entanglement and the teleportation average fidelity. For too low entanglement of the channel the output entanglement becomes zero (there is teleportation failure). For an initial entangled state, the Non-Markovian environment causes revivals in the average fidelity after it falls below the critical value of 23. The Dzyaloshinski-Moriya interactions are observed to enhance the revivals in the average fidelity; this is an interesting result because alongside the Non-Markovian bath, the effect of the Dzyaloshinski-Moriya interaction, can be used to improve the efficiency of the quantum channel for teleporting quantum states.
Prediction of Solar Activity Using Hybrid Artificial Bee Colony With Neighborhood Rough Sets

This article introduces a new hybrid technique for predicting solar activity. The proposed hybrid technique consists of four phases. The first phase is feature selection, where the most relevant features (variables) are selected based on an artificial bee colony using a neighborhood rough set as the fitness function. The second phase employs the training support vector regression (SVR) using a part of the solar activity data set based on the selected features. Sequential minimal optimization is carried out to determine the optimal parameters of SVR. The third phase tests the regression model based on the second part of the data set. The fourth phase is the process of predicting solar activity. According to the prediction system, the maximum amplitude of cycle 25 is 80 ± 12, and it will occur in 2026. The solution quality is assessed by using three indices called average absolute percent relative error (AAPRE), root-mean-square error (RMSE), and coefficient of determination. These indices prove the high quality of the forecast sunspot number value and the actual ones. In addition, it can be concluded that the proposed system is significantly improved the predicting solar activity performance at acceptable assessment indices.
PSU: Particle Stacking Undersampling Method for Highly Imbalanced Big Data

Imbalanced classes are a common problem in machine learning, and the computational costs required for proper resampling increases with the data size.In this study, a simple and effective undersampling method, named particle stacking undersampling (PSU) was proposed.Compared with other competing undersampling methods, PSU can significantly reduce the computational costs, while minimizing information loss to prevent a prediction bias.The performance benchmark applied on 55 binary classification problems indicated that the proposed method not only achieved an enhanced classification performance over other well-known undersampling methods (random undersampling, NearMiss-1, NearMiss-2, cluster centroid, edited nearest neighbor, condensed nearest neighbor, and Tomek Links) but also provided a computational simplicity that can be scalable to large data.Moreover, an experiment verified that two propositions forming the basis of the PSU algorithm can also be applied to other undersampling methods to achieve methodological improvements.
PyramidTags: Context-, Time- and Word Order-Aware Tag Maps to Explore Large Document Collections

It is difficult to explore large text collections if no or little information is available on the contained documents. Hence, starting analytic tasks on such corpora is challenging for many stakeholders from various domains. As a remedy, recent visualization research suggests to use visual spatializations of representative text documents or tags to explore text collections. With PyramidTags, we introduce a novel approach for summarizing large text collections visually. In contrast to previous work, PyramidTags in particular aims at creating an improved representation that incorporates both temporal evolution and semantic relationship of visualized tags within the summarized document collection. As a result, it equips analysts with a visual starting point for interactive exploration to not only get an overview of the main terms and phrases of the corpus, but also to grasp important ideas and stories. Analysts can hover and select multiple tags to explore relationships and retrieve the most relevant documents. In this work, we apply PyramidTags to hundreds of thousands of web-crawled news reports. Our benchmarks suggest that PyramidTags creates time- and context-aware layouts, while preserving the inherent word order of important pairs.
Manipulation Planning Using Object-Centered Predicates and Hierarchical Decomposition of Contextual Actions

Current approaches combining task and motion planning require intensive geometric and symbolic reasoning to find feasible motions for task execution. The poor expressiveness of task planning domains for characterizing geometric changes with actions and the difficulties faced by current approaches to efficiently identify motion dependencies for plan execution produce expensive callings to motion planning on unfeasible actions and intensive reasoning to find realizable plans. In this work we combine two recent approaches to address these problems. Task planning is carried out using an object-centered description of geometric relations that consistently characterizes changes in the object configuration space. Plan execution is implemented using a symbol to motion hierarchical decomposition that depends on consecutive actions in the plan, rather than on single actions, which permits considering motion dependencies across plan actions for a successful execution.
FastDerainNet: A Deep Learning Algorithm for Single Image Deraining

Existing neural network-based methods for de-raining single images exhibit dissatisfactory results owing to the inefficient propagation of features when objects with sizes and shapes similar to those of rain streaks are present in images. Furthermore, existing methods do not consider that the abundant information included in rain streaked images could interfere with the training process. To overcome these limitations, in this paper, we propose a deep residual learning algorithm called FastDerainNet for removing rain streaks from single images. We design a deep convolutional neural network architecture, based on a deep residual network called the share-source residual module (SSRM), by substituting the origins of all shortcut connections for one point. To further improve the de-raining performance, we adopt the SSRM as the parameter layers in FastDerainNet and use image decomposition to modify the loss function. Finally, we train FastDerainNet on a synthetic dataset. By learning the residual mapping between rainy and clean image detail layers, it is able to reduce the mapping range and simplify the training process. Experiments on both synthetic and real-world images demonstrate that the proposed method achieves increased performance with regard to de-raining, in addition to preserving original details, in comparison with other state-of-the-art methods.
Certificateless Signcryption Scheme From Lattice

Certificateless signcryption can simultaneously provide certificateless signature and encryption. In recent years, many certificateless signcryption schemes have been proposed. However, these schemes are based on traditional mathematical theory and not have the ability of resisting the quantum computing attacks. Up to now, lattice-based certificateless signature or encryption schemes have been proposed; however, these schemes only have one function and cannot fulfill two functions of certificateless signature and encryption at the same time. In consideration of this reason, in this article, a certificateless signcryption scheme from lattice (L-CLSS) is constructed. L-CLSS has three advantages. First, in the random oracle model, it is provably indistinguishable against adaptive ciphertext-chosen attacks under the intractability of the learning with errors (LWE) problem and unforgeable against adaptive message-chosen attacks under the hardness of small integer solution (SIS) problem. Second, comparison with the cryptographic algorithms under the difficulty of large integer decomposition, L-CLSS can resist the quantum computing attacks. Third, L-CLSS has higher computation efficiency and a lower communication cost than the existing schemes.
Determination of frequency response of MEMS microphone from sound field measurements using optical phase-shifting interferometry method

Accurate determination of microphone sensitivity is important to build reliable acoustical instruments. The sensitivity is usually determined by calibration. However, because available microphone calibration methods determine the sensitivity from a mathematical model derived from the geometry of a conventional condenser microphone, they cannot be applied to the calibration of microelectromechanical systems (MEMS) microphone straightforwardly. To compromise this geometry difference with the available calibration methods, some authors have proposed the development of adapters that fits the conventional calibration apparatus and modified the calibration procedure. In this paper, we propose a different approach to calibrate the MEMS microphone. The sensitivity is calculated directly from the measurement of the sound field applied to the MEMS microphone and its output voltage. The projection of the sound field is measured by parallel phase-shifting interferometry (PPSI), and sound pressure on the MEMS microphone is obtained by tomographic reconstruction. Experimental calibration of a MEMS microphone was performed and validated using a microphone substitution method to evaluate the discrepancies of the sensitivity result. It is shown that the proposed method can be used to determine the frequency response of the MEMS microphone in the frequency range of 1000 Hz to 12000 Hz.
A modified Canny edge detector based on weighted least squares

Edge detection is the front-end processing stage in most computer vision and image understanding systems. Among various edge detection techniques, Canny edge detector is the one of most commonly used. In this paper a modified Canny edge detection technique focusing on change of the Sobel operator is proposed. Instead of convolution kernels, the weighted least squares method is utilized to calculate the horizontal and vertical gradient. Experimental results show that the new detector can detect some edges which are not observed in the results using the Canny edge detector.
Using Hypotheses as a Debugging Aid

As developers debug, developers formulate hypotheses about the cause of the defect and gather evidence to test these hypotheses. To better understand the role of hypotheses in debugging, we conducted two studies. In a preliminary study, we found that, even with the benefit of modern internet resources, incorrect hypotheses can cause developers to investigate irrelevant information and block progress. We then conducted a controlled experiment where 20 developers debugged and recorded their hypotheses. We found that developers have few hypotheses, two per defect. Having a correct hypothesis early strongly predicted later success. We also studied the impact of two debugging aids: fault locations and potential hypotheses. Offering fault locations did not help developers formulate more correct hypotheses or debug more successfully. In contrast, offering potential hypotheses made developers six times more likely to succeed. These results demonstrate the potential of future debugging tools that enable finding and sharing relevant hypotheses.
Robust Image Classification Using a Low-Pass Activation Function and DCT Augmentation

Convolutional Neural Network's (CNN's) performance disparity on clean and corrupted datasets has recently come under scrutiny. In this work, we analyse common corruptions in the frequency domain, i.e., High Frequency corruptions (HFc, e.g., noise) and Low Frequency corruptions (LFc, e.g., blur). Although a simple solution to HFc is low-pass filtering, ReLU -- a widely used Activation Function (AF), does not have any filtering mechanism. In this work, we instill low-pass filtering into the AF (LP-ReLU) to improve robustness against HFc. To deal with LFc, we complement LP-ReLU with Discrete Cosine Transform based augmentation. LP-ReLU, coupled with DCT augmentation, enables a deep network to tackle the entire spectrum of corruption. We use CIFAR-10-C and Tiny ImageNet-C for evaluation and demonstrate improvements of 5% and 7.3% in accuracy respectively, compared to the State-Of-The-Art (SOTA). We further evaluate our method's stability on a variety of perturbations in CIFAR-10-P and Tiny ImageNet-P, achieving new SOTA in these experiments as well. To further strengthen our understanding regarding CNN's lack of robustness, a decision space visualisation process is proposed and presented in this work.