Reviewerly/exHarmony huggingface dataset:
Pre-Service Technology Teachers’ Learning Experiences of Teaching Methods for Integrating the Use of Technologies for the Fourth Industrial Revolution

One of the challenges teacher educators face today is the need to integrate learning technologies into the learning experiences of pre-service teachers to equip them with innovative and responsive teaching methods to be able to teach in the Fourth Industrial Revolution. These responsive teaching methods will equip them to address and solve contextual problems faced by society and develop 21st-century skills. Case studies are a responsive teaching method that was embraced in the teaching of a technology education module. These case studies required pre-service teachers to use the Internet of Things, to equip them to be able to teach in the Fourth Industrial Revolution. The current chapter focuses on pre-service technology teachers’ learning experiences of using the Internet of Things when engaging in case studies to solve local contextual problems. There is a paucity of research on pre-service teachers’ learning experiences of teaching methods integrating the use of the Internet of Things in developing countries like South Africa. Hence the need for this study. Data was generated via reflective journals and focus group interviews from 18 pre-service teachers. Informed consent of pre-service teachers was sought, and they were assured of confidentiality and anonymity. Focus group interviews were audio-recorded and were transcribed verbatim. Thereafter transcripts were sent to participants for member checking, to ensure that the recordings were an accurate representation of what they meant to say. The findings revealed that pre-service technology teachers engaged in deep and surface approaches to learning when they used the Internet of Things, they encountered learning experiences regarding their teacher agency. They valued and enjoyed case studies that targeted to resolve contextual issues. These findings have implications for the kinds of tasks that are designed to prepare pre-service teachers to teach in the Fourth Industrial Revolution within an African context.
Operational nonclassicality in minimal autonomous thermal machines

Thermal machines exploit interactions with multiple heat baths to perform useful tasks, such as work production and refrigeration. In the quantum regime, tasks with no classical counterpart become possible. Here, we consider the minimal setting for quantum thermal machines, namely two-qubit autonomous thermal machines that use only incoherent interactions with their environment, and investigate the fundamental resources needed to generate entanglement. Our investigation is systematic, covering different types of interactions, bosonic and fermionic environments, and different resources that can be supplied to the machine. We adopt an operational perspective in which we assess the nonclassicality of the generated entanglement through its ability to perform useful tasks such as Einstein-Podolsky-Rosen steering, quantum teleportation and Bell nonlocality. We provide both constructive examples of nonclassical effects and general no-go results that demarcate the fundamental limits in autonomous entanglement generation. Our results open up a path toward understanding nonclassical phenomena in thermal processes.
Resource-Aware Min-Min (RAMM) Algorithm for Resource Allocation in Cloud Computing Environment

Resource allocation (RA) is a significant aspect of Cloud Computing. The Cloud resource manager is responsible to assign available resources to the tasks for execution in an effective way that improves system performance, reduce response time, lessen makespan and utilize resources efficiently. To fulfil these objectives, an effective Tasks Scheduling algorithm is required. The standard Max-Min and Min-Min Task Scheduling algorithms are not able to produce better makespan and effective resource utilization. In this paper, a Resource-Aware Min-Min (RAMM) Algorithm is proposed based on basic Min-Min algorithm. The proposed RAMM Algorithm selects shortest execution time task and assigns it to the resource which takes shortest completion time. If minimum completion time resource is busy, then the RAMM Algorithm selects next minimum completion time resource to reduce waiting time of the task and improve resource utilization. The experiment results show that the proposed RAMM Algorithm produces better makespan and load balance than Max-Min, Min-Min and improved Max-Min Algorithms
Motion Planning for a Humanoid Mobile Manipulator System

A high redundant non-holonomic humanoid mobile dual-arm manipulator system (MDAMS) is presented in this paper, where the motion planning to realize “human-like” autonomous navigation and manipulation tasks is studied. First, an improved MaxiMin NSGA-II algorithm, which optimizes five objective functions to solve the problems of singularity, redundancy and coupling between mobile base and manipulator simultaneously, is proposed to design the optimal pose to manipulate the target object. Then, in order to link the initial pose and that optimal pose, an off-line motion planning algorithm is designed. In detail, an efficient direct-connect bidirectional RRT and gradient descent algorithm is proposed to reduce the sampled nodes largely, and a geometric optimization method is proposed for path pruning. Besides, head forward behaviors are realized by calculating the reasonable orientations and assigning them to the mobile base to improve the quality of human-robot interaction. Third, the extension to online planning is done by introducing real-time sensing, collision-test and control cycles to update robotic motion in dynamic environments. Fourth, an EEs’ via-point-based multi-objective genetic algorithm (MOGA) is proposed to design the “human-like” via-poses by optimizing four objective functions. Finally, numerous simulations are presented to validate the effectiveness of proposed algorithms.
Secure Similar Patients Query on Encrypted Genomic Data

Both individuals and enterprises produce genomic data rapidly and continuously. There is a need to outsource such data to the cloud for better flexibility. Outsourcing also helps data owners by eliminating the local storage management problem. To protect data privacy and security, data owners must encrypt the sensitive data before outsourcing. Since genomic data are enormous in volume, executing researchers queries securely, and efficiently is a challenging task. In this paper, we introduce an indexing algorithm based on the prefix-tree to support similar patient queries. The proposed method guarantees the following: data privacy, query privacy, and output privacy. The privacy is guaranteed through encryption and garbled circuits considering the semi-honest adversary model. The overall computation is scalable and fast enough for real-life biomedical applications. Moreover, experimental results show that our method performs better than existing state-of-art techniques in this domain.
DecidArch: Playing Cards as Software Architects

Teaching software architecture is a challenge because of the difficulty to expose students to actual meaningful design situations. Games can provide a useful illustration of the design decision making process, and teach students the power of team interaction for making sound decisions. We introduce a game –DecidArch– developed to achieve three learning objectives: _x0001_1) create awareness about the rationale involved in design decision making, _x0002_2) enable appreciation of the reasoning behind candidate design decisions proposed by others, and _x0003_3) create awareness about interdependencies between design decisions. The game has been played by _x0002__x0002_ groups with a total of _x0008__x0003_ players, all of them students of the VU software architecture course. We present some of the lessons learned, both from our observation and through participant survey. We conclude that the game well supports our three learning objectives, and we identify several improvement points for future game editions.
Exploring Symmetry of Binary Classification Performance Metrics

Selecting the proper performance metric constitutes a key issue for most classification problems in the field of machine learning. Although the specialized literature has addressed several topics regarding these metrics, their symmetries have yet to be systematically studied. This research focuses on ten metrics based on a binary confusion matrix and their symmetric behaviour is formally defined under all types of transformations. Through simulated experiments, which cover the full range of datasets and classification results, the symmetric behaviour of these metrics is explored by exposing them to hundreds of simple or combined symmetric transformations. Cross-symmetries among the metrics and statistical symmetries are also explored. The results obtained show that, in all cases, three and only three types of symmetries arise: labelling inversion (between positive and negative classes); scoring inversion (concerning good and bad classifiers); and the combination of these two inversions. Additionally, certain metrics have been shown to be independent of the imbalance in the dataset and two cross-symmetries have been identified. The results regarding their symmetries reveal a deeper insight into the behaviour of various performance metrics and offer an indicator to properly interpret their values and a guide for their selection for certain specific applications.
Reproducibility evaluation of SLANT whole brain segmentation across clinical magnetic resonance imaging protocols

Whole brain segmentation on structural magnetic resonance imaging (MRI) is essential for understanding neuroanatomical-functional relationships. Traditionally, multi-atlas segmentation has been regarded as the standard method for whole brain segmentation. In past few years, deep convolutional neural network (DCNN) segmentation methods have demonstrated their advantages in both accuracy and computational efficiency. Recently, we proposed the spatially localized atlas network tiles (SLANT) method, which is able to segment a 3D MRI brain scan into 132 anatomical regions. Commonly, DCNN segmentation methods yield inferior performance under external validations, especially when the testing patterns were not presented in the training cohorts. Recently, we obtained a clinically acquired, multi-sequence MRI brain cohort with 1480 clinically acquired, de-identified brain MRI scans on 395 patients using seven different MRI protocols. Moreover, each subject has at least two scans from different MRI protocols. Herein, we assess the SLANT method’s intra- and inter-protocol reproducibility. SLANT achieved less than 0.05 coefficient of variation (CV) for intra-protocol experiments and less than 0.15 CV for inter-protocol experiments. The results show that the SLANT method achieved high intra- and inter- protocol reproducibility.
Fuzzy c-means clustering based segmentation and the filtering method for discontinuous ESPI fringe patterns

The filtering of ESPI fringe patterns with both noise and discontinuity is a challenging problem raised in recent years. Discontinuity-detectable and discontinuity-aware processing techniques are demanded. In this paper, a fuzzy c-means (FCM) clustering based fringe segmentation method is proposed. By applying the FCM clustering method to the estimated fringe orientation, we segment the discontinuous ESPI fringe patterns into continuous segments, thus the discontinuous region is identified and a discontinuous region mask is generated. Then, the discontinuous region mask is introduced into the controlling speed function, and an adaptive shape-preserving oriented partial differential equation (OPDE) model is proposed for discontinuous ESPI fringe patterns denoising. According to our method, the discontinuous regions are effectively found and with the proposed adaptive shape-preserving OPDE, the noise is well eliminated, the shape of fringes and the discontinuity are well kept. The performance of our method is illustrated with three computer-simulated and one experimentally obtained discontinuous ESPI fringe patterns and comparison with related segmentation methods and OPDEs.
Pattern-based NN control for uncertain pure-feedback nonlinear systems

Implementing human-like learning and control for nonlinear dynamical systems operating in different control situations is an important and challenging issue. This paper presents a pattern-based neural network (NN) control strategy for nonlinear pure-feedback systems via deterministic learning (DL). Firstly, an appropriately designed adaptive neural dynamic surface controller is proposed to achieve the finite time tracking control. By analyzing the recurrent property of NN input signals, a partial persistent excitation (PE) condition for radial basis function (RBF) network is established, the implicit desired control dynamics under different control situations are accurately identified via DL in the case that the dimension of NN input is reduced. And a set of pattern-based experienced actual and virtual controllers is constructed using the learned knowledge. Secondly, to classify different control situations, when the system is operating in different control situations but controlled by current normal experienced controller, the dynamics of each subsystem are accurately identified via DL, n sets of dynamical estimators are constructed using the learned knowledge. Thirdly, in the recognition phase, n sets of residuals are achieved by comparing each set of estimators with the monitored system, sudden change in the control situation is rapidly recognized based on the principle of the earliest occurrence of the minimum residual. Finally, in the control phase, according to the recognition result, the correct experienced actual and virtual controllers will be selected to control the plant, guaranteed stability and superior control performance are achieved without any further re-adaptation online. Simulation studies are given to verify the proposed scheme can not only acquire and memorize knowledge like humans, but also reuse the learned knowledge to achieve rapid recognition and control of current control situation.
From Zadeh’s Computing with Words Towards eXplainable Artificial Intelligence

The European Commission has identified Artificial Intelligence (AI) as the “most strategic technology of the 21st century” [7].
EMOTE – Multilayered encryption system for protecting medical images based on binary curve

Digital images used in business, government and medical sectors insist the need for image security against various threats. An exclusive system that can make use of color spaces has been proposed to process sensitive images based on the binary curve. The proposed Encryption systeM fOr proTecting imagEs has been coined as EMOTE. This multilayered system works on binary curves as binary operations have no carries. Since binary squaring is lightweight, it is claimed to be smaller and faster in hardware than prime-field ones. Logistic mapping has been done to perform chaotic masking, which is then followed by DNA encoding in the next layer. ECC over GF(2m) that works on the spatial domain has been chosen as it is one of the proven technique for its mathematical strength. This proposed system can facilitate both symmetric and asymmetric combination and hence hybrid. Experimental results and the histogram analysis obtained comprehend this proposed system for its extensive deployment to process sensitive images, especially in the medical domain. Ideal measures attained for MSE, PSNR values for the standard benchmark images taken from “The whole brain atlas” database from Harvard University, substantiate the proposed EMOTE system as an expedient choice for processing sensitive images.
Multi-Stage Self-Supervised Learning for Graph Convolutional Networks.

Graph Convolutional Networks(GCNs) play a crucial role in graph learningtasks, however, learning graph embedding with few supervised signals is still adifficult problem. In this paper, we propose a novel training algorithm forGraph Convolutional Network, called Multi-Stage Self-Supervised(M3S) TrainingAlgorithm, combined with self-supervised learning approach, focusing onimproving the generalization performance of GCNs on graphs with few labelednodes. Firstly, a Multi-Stage Training Framework is provided as the basis ofM3S training method. Then we leverage DeepCluster technique, a popular form ofself-supervised learning, and design corresponding aligning mechanism on theembedding space to refine the Multi-Stage Training Framework, resulting in M3STraining Algorithm. Finally, extensive experimental results verify the superiorperformance of our algorithm on graphs with few labeled nodes under differentlabel rates compared with other state-of-the-art approaches.
Long-standing issues in adjective order and corpus evidence for a multifactorial approach

Abstract In this paper, we introduce the issue of adjective order and show that different approaches vary in their answers to the question of how fine-grained the semantic categories determining adjective order are. We report on a corpus study that we conducted and that illustrates that a clear answer to the question of what general factors exactly determine adjective order is elusive, given the multifactorial nature of the problem. We then present the individual contributions to this special issue, and how they attempt to add new observations from Germanic languages to the general issues revolving around the topic of adjective order.
IFFLC: An Integrated Framework of Feature Learning and Classification for Multiple Diagnosis Codes Assignment

The International Classification of Diseases, Version 9 (ICD-9) is often used to identify patients with specific diagnoses. However, certain conditions may not be accurate reflected by the ICD-9 codes, and diagnoses code assignments are complex time-consuming processes. Although there are existing methods for automotive disease diagnostic assignment techniques, they have limitations on the descriptiveness and interpretability of diseases based on features. More importantly, they ignored the importance of different features with respect to different diseases. To address the above-mentioned challenges, we propose a novel framework, namely IFFLC, which can select the most relevant features, learn disease-specific features for each disease, and perform multiple diagnosis codes' assignment. Specifically, we first develop feature selection based on disease information entropy to remove redundant and irrelevant features in both medical chart data and medical laboratory data. Then, we build a novel multiple diagnosis codes' classifier by learning the disease-specific features and exploring the intra-correlations between diseases. We employ an alternating direction method of multipliers to iteratively solve the related optimization problem. The extensive experiments on a real-world ICU database verify the superiority of the proposed method over state-of-the-art approaches.
Pixel-by-pixel absolute phase retrieval assisted by an additional three-dimensional scanner

This paper presents a novel absolute phase unwrapping method assisted by a low-cost three-dimensional (3D) scanner. The proposed absolute phase unwrapping method leverages a low-cost 3D scanner to capture rough 3D data of the scene and transforms the rough 3D data to the world coordinate system to generate an artificial reference phase map ϕref. By referring to ϕref, we can do absolute phase unwrapping directly without projecting any additional patterns, such that the digital fringe projection (DFP) system can achieve higher measurement speed. We develop a multiresolution system consisting of a DFP system and a Kinect V2 to validate our method. Experiments demonstrate that our method works for a large depth range, and the speed of the low-cost 3D scanner is not necessarily the maximum speed of our proposed method. Assisted by the Kinect V2, whose maximum speed is only 30 Hz, our DFP system achieves 53 Hz with a resolution 1600×1000 pixels when we measure dynamic objects that are moving in a large depth range of 400 mm.
A Novel Hybrid Clustering Algorithm Based on Minimum Spanning Tree of Natural Core Points

Clustering analysis has been widely used in pattern recognition, image processing, machine learning, and so on.It is a great challenge for most existing clustering algorithms to discover clusters with complex manifolds or great density variation.Most of the existing clustering needs manually set neighborhood parameter K to search the neighbor of each object.In this paper, we use natural neighbor to adaptively get the value of K and natural density of each object.Then, we define two novel concepts, natural core point and the distance between clusters to solve the complex manifold problem.On the basis of above-proposed concept, we propose a novel hybrid clustering algorithm that only needs one parameter M (the number of final clusters) based on minimum spanning tree of natural core points, called NCP.The experimental results on the synthetic dataset and real dataset show that the proposed algorithm is competitive with the state-of-the-art methods when discovering with the complex manifold or great density variation.
LightTwinSVM: A Simple and Fast Implementation of Standard Twin Support Vector Machine Classifier

This paper presents the LightTwinSVM program and its features.It is a simple and fast implementation of twin support vector machine algorithm (TwinSVM).Numerical experiments on benchmark datasets show the effectiveness of the LightTwinSVM program in terms of accuracy.This program can be used by researchers, students, and practitioners to solve classifications tasks.
SSNE: Status Signed Network Embedding

This work studies the problem of signed network embedding, which aims to obtain low-dimensional vectors for nodes in signed networks. Existing works mostly focus on learning representations via characterizing the social structural balance theory in signed networks. However, structural balance theory could not well satisfy some of the fundamental phenomena in real-world signed networks such as the direction of links. As a result, in this paper we integrate another theory Status Theory into signed network embedding since status theory can better explain the social mechanisms of signed networks. To be specific, we characterize the status of nodes in the semantic vector space and well design different ranking objectives for positive and negative links respectively. Besides, we utilize graph attention to assemble the information of neighborhoods. We conduct extensive experiments on three real-world datasets and the results show that our model can achieve a significant improvement compared with baselines.
Non-interactive Cryptographic Timestamping Based on Verifiable Delay Functions

We present the first treatment of non-interactive publicly-verifiable timestamping schemes in the Universal Composability framework. Inspired by the timestamping properties of Bitcoin, we use non-parallelizable computational work that relates to elapsed time to avoid previous impossibility results on non-interactive timestamping. We introduce models of verifiable delay functions (VDF) related to a clock and non-interactive timestamping in the UC-framework. These are used to present a secure construction that provides improvements over previous concrete constructions. Namely, timestamps forged by the adversary are now limited to a certain time-window that depends only on the adversary’s ability to compute VDFs more quickly and on the length of corruption. Finally, we discuss how our construction can be added to non-PoW blockchain protocols to prevent costless simulation attacks.