Reviewerly/exHarmony huggingface dataset:
S The Security MicroVisor: A Formally-Verified Software-Based Security Architecture for the Internet of Things

The Internet of Things (IoT) is shaped by the increasing number of low-cost Internet-connected embedded devices that are becoming ubiquitous in every aspect of modern life. With their cost-sensitive design, integrating hardware-based security mechanisms into such devices is undesirable. Therefore, securing these devices is a particularly difficult challenge, especially, due to their growing popularity as attack targets, via remote malware infestations. The vast majority of such devices are bare-metal, where they execute programs in fully-accessible and unprotected memories without any operating system and even without including any form of security. This is beside the fact that IoToperating systems offer little or no protection. This paper addresses this problem through the concept of a Security MicroVisor (S <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">μ</sub> V), which provides embedded devices that lack hardware-based memory protection units with memory isolation using software virtualisation and assembly-level code verification. More specifically, our contribution is two-fold. First, we propose S <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">μ</sub> V as a software-based memory isolation technique. We then formally verify the software architecture, written in C, to prove that it is memory-safe and crash-free. Second, we propose a software-based remote attestation, as an example of a fundamental security service that can be implemented on top of S <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">μ</sub> V, to detect malware-infected devices. We first describe the design and implementation of S <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">μ</sub> V. Then, we highlight the formal verification of software architecture, and characterize the remote attestation protocol. We evaluate the S <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">μ</sub> V implementation using an 8-bit AVR microcontroller that is widely used in IoT devices. Evaluation results show that S <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">μ</sub> V provides strong security guarantees while maintaining extremely low overhead in terms of memory footprint, performance, and power consumption. Furthermore, we extend the performance evaluation also to the remote attestation scheme, illustrating its limited overhead.
Experimental evaluation of the strain intensity factor at the inclusion tip using digital photoelasticity

Digital photoelasticity is used for the experimental evaluation of the strain intensity factor for a rigid line inclusion embedded in an elastic matrix. The multi-parameter stress field equations are derived using the Airy’s stress function approach. The ten-step phase shifting technique (PST) is used to obtain the whole field isochromatic and isoclinic data, and the adaptive quality-guided phase unwrapping (AQGPU) algorithm is used for unwrapping the phase map. An over-deterministic linear least squares approach is used to solve the multi-parameter stress field equations in an optimization framework to obtain the inclusion tip parameters and the actual inclusion tip location. The relations for strain intensity factor and stress intensity factor (SIF) with the parameters is presented. Photoelastic experiments are performed on two inclusion configurations where inclusion is kept along and inclined to the direction of loading. The obtained strain intensity factor is compared with the analytical solution, and a good match is observed.
Feasibility of Machine Learning in Predicting Features Related to Congenital Nystagmus

Congenital nystagmus is an ocular-motor disease affecting people’s visual acuity since their first years of life. Electrooculography is used to perform eye tracking in these patients, giving the possibility to extract a wide variety of parameters. The relationships among all these variables were analysed in the past and the aim of this paper is to perform a new analysis employing more recent techniques, those of machine learning. The electrooculography of 20 patients was recorded, signals were pre-processed, and some parameters were extracted through a custom-made software. Knime analytics platform was chosen in order to build predictive models using Random Forests and Logistic Regression Tree algorithms and some evaluation metrics were computed. The visual acuity and the variability of eye positioning were predicted employing five and six variables, respectively. In terms of coefficient of determination, visual acuity had values over 0.72 and variability of eye positioning over 0.70. Compared to the results obtained without machine learning algorithms during the past years, these values become more valuable. In conclusion, this approach showed its feasibility in detecting relationships among variables related to congenital nystagmus; it could be tested in order to find new and stronger relationships among these variables and be of support for clinicians.
A Single-Image Super-Resolution Method Based on Progressive-Iterative Approximation

In this paper, a novel single image super-resolution (SR) method based on progressive-iterative approximation is proposed. To preserve textures and clear edges, the image SR reconstruction is treated as an image progressive-iterative fitting procedure and achieved by iterative interpolation. Due to different features in different regions, we first employ the nonsubsampled contourlet transform (NSCT) to divide the image into smooth regions, texture regions, and edges. Then, a hybrid interpolation scheme based on curves and surfaces is proposed, which differs from the traditional surface interpolation methods. Specifically, smooth regions are interpolated by the non-uniform rational basis spline (NURBS) surface geometric iteration. To retain textures, control points are increased, and the progressive-iterative approximation of the NURBS surface is employed to interpolate the texture regions. By considering edges in an image as curve segments that are connected by pixels with dramatic changes, we use NURBS curve progressive-iterative approximation to interpolate the edges, which sharpens the edges and can maintain the image edge structure without jaggy and block artifacts. The experimental results demonstrate that the proposed method significantly outperforms the state-of-the-art methods in terms of both subjective and objective measures.
Supporting children’s outdoor science learning with mobile computers: integrating learning on-the-move strategies with context-sensitive computing

(2019). Supporting children's outdoor science learning with mobile computers: integrating learning on-the-move strategies with context-sensitive computing. Learning, Media and Technology: Vol. 44, No. 4, pp. 457-472.
Adversarial Examples Versus Cloud-based Detectors: A Black-box Empirical Study

Deep learning has been broadly leveraged by major cloud providers, such as Google, AWS and Baidu, to offer various computer vision related services including image classification, object identification, illegal image detection, etc. While recent works extensively demonstrated that deep learning classification models are vulnerable to adversarial examples, cloud-based image detection models, which are more complicated than classifiers, may also have similar security concern but not get enough attention yet. In this paper, we mainly focus on the security issues of real-world cloud-based image detectors. Specifically, (1) based on effective semantic segmentation, we propose four attacks to generate semantics-aware adversarial examples via only interacting with black-box APIs; and (2) we make the first attempt to conduct an extensive empirical study of black-box attacks against real-world cloud-based image detectors. Through the comprehensive evaluations on five major cloud platforms: AWS, Azure, Google Cloud, Baidu Cloud, and Alibaba Cloud, we demonstrate that our image processing based attacks can reach a success rate of approximately 100%, and the semantic segmentation based attacks have a success rate over 90% among different detection services, such as violence, politician, and pornography detection. We also proposed several possible defense strategies for these security challenges in the real-life situation.
Co-creating and Assessing Future Wellbeing Technology Using Design Fiction

In this article, we present our research using design fiction to explore future wellbeing technologies for older adults. “For Good Times and Bad Times” is a design fiction representing a range of emotional, qualitative, and functional goals associated with wellbeing health technology. We invited a small cohort of experts to participate in a wearable monitoring device co-creation session, and then invited older-aged participants to review and reflect on a design fiction crafted from the experts’ findings and a set of psychological needs and values that have been associated with wearable health devices. The results offer insight into the future of wellbeing monitoring technology, and demonstrate the power design fiction has to communicate and generate deep discussions on the psychological, cultural, and social implications of using technologies that mediate complex personal experiences. We discuss the challenges associated with creating and using design fiction in research with older adults, and the potential of fiction to shift perspectives and elicit prospective outcomes of using future technologies.
Extensions to rank-based prototype selection in k-Nearest Neighbour classification

The k-nearest neighbour rule is commonly considered for classification tasks given its straightforward implementation and good performance in many applications. However, its efficiency represents an obstacle in real-case scenarios because the classification requires computing a distance to every single prototype of the training set. Prototype Selection (PS) is a typical approach to alleviate this problem, which focuses on reducing the size of the training set by selecting the most interesting prototypes. In this context, rank methods have been postulated as a good solution: following some heuristics, these methods perform an ordering of the prototypes according to their relevance in the classification task, which is then used to select the most relevant ones. This work presents a significant improvement of existing rank methods by proposing two extensions: (i) a greater robustness against noise at label level by considering the parameter 'k' of the classification in the selection process; and (ii) a new parameter-free rule to select the prototypes once they have been ordered. The experiments performed in different scenarios and datasets demonstrate the goodness of these extensions. Also, it is empirically proved that the new full approach is competitive with respect to existing PS algorithms.
Improved predictive capability of coagulation process by extreme learning machine with radial basis function

In an effort to improve the predictive capability of artificial neural networks for the coagulation process in a water treatment plant, extreme learning machine (ELM) coupled with radial basis function (RBF) neural networks were employed. The ELM-RBF was selected to exploit the computational robustness of ELM and accuracy of RBF for sufficiently large number of data that were available from the plant. The coagulation data were divided into two categories based on low and high turbidity. The optimum number of input parameters for modeling the coagulation of low turbidity water was found to be 3, whereas the optimum number of input parameters for modeling the coagulation of high turbidity water was found to be 4. Re-selection of the number of input parameters was necessary considering that raw water alkalinity was a significant factor in improving the high turbidity model performance. The low turbidity model was capable of predicting the coagulant dosage with correlation coefficient exceeding 0.97. The high turbidity model was capable of predicting the coagulation dosage with reasonably acceptable correlation coefficient of at least 0.80.
Improved well-log classification using semisupervised label propagation and self-training, with comparisons to popular supervised algorithms

Machine-learning techniques allow geoscientists to extract meaningful information from data in an automated fashion, and they are also an efficient alternative to traditional manual interpretation methods. Many geophysical problems have an abundance of unlabeled data and a paucity of labeled data, and the lithology classification of wireline data reflects this situation. Training supervised algorithms on small labeled data sets can lead to overtraining, and subsequent predictions for the numerous unlabeled data may be unstable. However, semisupervised algorithms are designed for classification problems with limited amounts of labeled data, and they are theoretically able to achieve better accuracies than supervised algorithms in these situations. We explore this hypothesis by applying two semisupervised techniques, label propagation (LP) and self-training, to a well-log data set and compare their performance to three popular supervised algorithms. LP is an established method, but our self-training method is a unique adaptation of existing implementations. The well-log data were made public through an SEG competition held in 2016. We simulate a semisupervised scenario with these data by assuming that only one of the 10 wells has labels (i.e., core samples), and our objective is to predict the labels for the remaining nine wells. We generate results from these data in two stages. The first stage is applying all the algorithms in question to the data as is (i.e., the global data), and the results from this motivate the second stage, which is applying all algorithms to the data when they are decomposed into two separate data sets. Overall, our findings suggest that LP does not outperform the supervised methods, but our self-training method coupled with LP can outperform the supervised methods by a notable margin if the assumptions of LP are met.
Recommendation of Scheduling Tourism Routes using Tabu Search Method (Case Study Bandung)

In 2019, the Indonesian Ministry of Tourism is in the process of improving the Go Digital program for the industrial era 4.0, where the internet has become one of the ways to determine travel destinations. However, currently, tourists still have difficulties obtaining detailed and complete information about tourist destinations, when visiting several destinations in one trip. Tourists are still having trouble estimating the distance and time needed for tourism independently, without having to depend on travel agents. These problems are often referred to as Traveling Salesman Problems (TSP). Therefore, we provide a solution to solve this TSP problem in the form of a system scheduling and searching route tourist using the tabu search method which enables tourists to find the optimal solution based on travel time, operational hours of tourist attraction, and the time limit of visits per day. Calculations in the tabu search method are combined with the concept of MAUT (Multi-Attribute Utility Theory) to determine the optimal tour based on several criteria: popularity, cost, and the number of attractions to be visited. Then, the test results of the tabu search method are compared with the firefly method. The result shows that the tabu search method is better than the firefly method, where there is an increase in accuracy of 48% in the calculation of fitness values, 47% in running time average, and 27% in the number of tours to be visited during 3 days of tour visits.
Understanding the Vulnerabilities in Digital Components of An Integrated Bridge System (IBS)

The adoption of the global positioning system (GPS) within the marine industry has revolutionized the marine operations by condensing the navigation of a vessel into an integrated bridge system (IBS). An IBS acts as the main command and control of a vessel as it interconnects various digital devices used for navigation in open seas and is also connected to other on-board systems of a vessel e.g., navigation and control, propulsion and machinery management system, cargo management system and safety management system, core infra structure systems, administrative and crew welfare systems, etc. Additionally, it also provides a gateway to the Internet, thus, leaving not only an IBS vulnerable but also all the on-board systems vulnerable to cyber-attacks. We, in this study, have collected historical evidences about various vulnerable digital components in an IBS to better understand the security and privacy challenges associated with the vulnerable IBS components. Our study is the first of its kind that involves collection and review of 59 historical accidents reported in literature and has highlighted various vulnerability patterns, their causes and consequences, with geographical as well as temporal relationships for different vulnerable IBS components. The vulnerabilities of IBS components were reportedly exploited using various cyber-attack techniques e.g., jamming, spoofing, hijacking, etc. This review paper also forms a baseline for future work on vulnerabilities of IBS and maritime cyber security.
Graph Classification with 2D Convolutional Neural Networks

Graph learning is currently dominated by graph kernels, which, while powerful, suffer some significant limitations. Convolutional Neural Networks (CNNs) offer a very appealing alternative, but processing graphs with CNNs is not trivial. To address this challenge, many sophisticated extensions of CNNs have recently been introduced. In this paper, we reverse the problem: rather than proposing yet another graph CNN model, we introduce a novel way to represent graphs as multi-channel image-like structures that allows them to be handled by vanilla 2D CNNs. Experiments reveal that our method is more accurate than state-of-the-art graph kernels and graph CNNs on 4 out of 6 real-world datasets (with and without continuous node attributes), and close elsewhere. Our approach is also preferable to graph kernels in terms of time complexity. Code and data are publicly available ( https://github.com/Tixierae/graph_2D_CNN ).
Integer Self-Organizing Maps for Digital Hardware

The Self-Organizing Map algorithm has been proven and demonstrated to be a useful paradigm for unsupervised machine learning of two-dimensional projections of multidimensional data. The tri-state Self-Organizing Maps have been proposed as an accelerated resource-efficient alternative to the Self-Organizing Maps for implementation on field-programmable gate array (FPGA) hardware. This paper presents a generalization of the tri-state Self-Organizing Maps. The proposed generalization, which we call integer Self-Organizing Maps, requires only integer operations for weight updates. The presented experiments demonstrated that the integer Self-Organizing Maps achieve better accuracy in a classification task when compared to the original tri-state Self-Organizing Maps.
Lessons Learned: Recommendations For Implementing a Longitudinal Study Using Wearable and Environmental Sensors in a Health Care Organization

Although traditional methods of data collection in naturalistic settings can shed light on constructs of interest to researchers, advances in sensor-based technology allow researchers to capture continuous physiological and behavioral data to provide a more comprehensive understanding of the constructs that are examined in a dynamic health care setting. This study gives examples for implementing technology-facilitated approaches and provides the following recommendations for conducting such longitudinal, sensor-based research, with both environmental and wearable sensors in a health care setting: pilot test sensors and software early and often; build trust with key stakeholders and with potential participants who may be wary of sensor-based data collection and concerned about privacy; generate excitement for novel, new technology during recruitment; monitor incoming sensor data to troubleshoot sensor issues; and consider the logistical constraints of sensor-based research. The study describes how these recommendations were successfully implemented by providing examples from a large-scale, longitudinal, sensor-based study of hospital employees at a large hospital in California. The knowledge gained from this study may be helpful to researchers interested in obtaining dynamic, longitudinal sensor data from both wearable and environmental sensors in a health care setting (eg, a hospital) to obtain a more comprehensive understanding of constructs of interest in an ecologically valid, secure, and efficient way.
Probabilistic Multilayer Regularization Network for Unsupervised 3D Brain Image Registration

Brain image registration transforms a pair of images into one system with the matched imaging contents, which is of essential importance for brain image analysis. This paper presents a novel framework for unsupervised 3D brain image registration by capturing the feature-level transformation relationships between the unaligned image and reference image. To achieve this, we develop a feature-level probabilistic model to provide the direct regularization to the hidden layers of two deep convolutional neural networks, which are constructed from two input images. This model design is developed into multiple layers of these two networks to capture the transformation relationships at different levels. We employ two common benchmark datasets for 3D brain image registration and perform various experiments to evaluate our method. Experimental results show that our method clearly outperforms state-of-the-art methods on both benchmark datasets by a large margin.
Experiences

In this paper we introduce CoLTE, a solution for LTE-based community networks. CoLTE is a lightweight, Internet-only LTE core network (EPC) designed to facilitate the deployment and operation of small-scale, community owned and operated LTE networks in rural areas with limited and unreliable backhaul. The key differentiator of CoLTE, when compared to existing LTE solutions, is that in CoLTE the EPC is designed to be located in the field and deployed alongside a small number of cellular radios (eNodeBs), as opposed to the centralized model seen in large-scale telecom networks. We also provide performance results and lessons learned from a real-world CoLTE network deployed in rural Indonesia. This network has been sustainably operating for over six months, currently serves over 40 active users, and provides measured backhaul reductions of up to 45% when compared to cloud-core solutions.