Reviewerly/exHarmony huggingface dataset:
Modeling and evaluating a complex edge computing based systems: An emergency management support system case study

The edge computing paradigm enhances the exploitability of cloud computing by providing, in principle, the means to overcome the limitations in terms of responsiveness, bandwidth needs, privacy and availability in critical applications. By moving some of the components of the system towards the physical location where results are timely needed, it is possible to support mission critical applications and back them up with the flexibility and the resource availability and scalability offered by the cloud, while keeping mission costs lower (and response efficiency higher) than classical approaches. The main challenge in merging cloud and edge components lies in their correct balance to allow for the best results at the lowest costs. This means that performance-oriented evaluation models are crucial in the design, deployment and execution phases of the system lifetime. In this paper we present a modeling approach for complex, critical-edge computing-based systems relying on the use of queuing networks, applied to a novel architecture aiming at supporting operations in case of medium or large-scale accidents that involve interoperability among responders during the emergency phase. The proposed architecture allows the coordination of fire brigade teams, equipped with sensors and augmented reality devices, to minimize mission problems and timely exploit local and external information, as well as supporting interoperations with other first responders. The results show that, even with a standard modeling approach, these systems are extremely interesting and show non-trivial behaviors. Security and dependability issues of the proposed architecture are discussed.
Robust Image Hashing Scheme Based on Low-Rank Decomposition and Path Integral LBP

This paper presents a robust image hashing algorithm that exploits low-rank decomposition and path integral local binary pattern (pi-LBP), referred to LRPL hashing. The proposed algorithm generates a compact binary sequence from a low-rank component of the normalized image as the hash code. Considering the excellent texture structure description ability of pi-LBP features, the new hashing algorithm extracts a feature vector from a low-rank feature matrix with pi-LBP. The pi-LBP feature vector is then encrypted by using the logistic map to produce the final hash sequence. The Hamming distance between the hash sequences is employed to authenticate the tested image. The experimental results demonstrate that the proposed LRPL hashing method has better robustness and discrimination compared with existing hashing algorithms.
Emotion and Experience in Negotiating HIV-Related Digital Resources

While digital technologies are increasingly being used to provide support and diagnoses remotely, it is unclear whether they offer adequate emotional support and appropriate messages in navigating complex, stigmatised and sensitive conditions that can have a momentous impact on people's lives. In this paper, we investigate how and why people access existing HIV resources, and their experiences of using these resources through a survey with 197 respondents and an interview and think-aloud study with 28 participants. Our findings indicate that many HIV-related resources do not address the anxiety-provoking reasons for access, reinforce stigma and neglect to provide important information and emotional support. We finally discuss potential ways of addressing these issues in the current environment where more sexual health services are being delivered online.
A Decade of Mal-Activity Reporting

This paper focuses on reporting of Internet malicious activity (or mal-activity in short) by public blacklists with the objective of providing a systematic characterization of what has been reported over the years, and more importantly, the evolution of reported activities. Using an initial seed of 22 blacklists, covering the period from January 2007 to June 2017, we collect more than 51 million mal-activity reports involving 662K unique IP addresses worldwide. Leveraging the Wayback Machine, antivirus (AV) tool reports and several additional public datasets (e.g., BGP Route Views and Internet registries) we enrich the data with historical meta-information including geo-locations (countries), autonomous system (AS) numbers and types of mal-activity. Furthermore, we use the initially labelled dataset of ~1.57 million mal-activities (obtained from public blacklists) to train a machine learning classifier to classify the remaining unlabeled dataset of ~44 million mal-activities obtained through additional sources. We make our unique collected dataset (and scripts used) publicly available for further research. The main contributions of the paper are a novel means of report collection, with a machine learning approach to classify reported activities, characterization of the dataset and, most importantly, temporal analysis of mal-activity reporting behavior. Inspired by P2P behavior modeling, our analysis shows that some classes of mal-activities (e.g., phishing) and a small number of mal-activity sources are persistent, suggesting that either blacklist-based prevention systems are ineffective or have unreasonably long update periods. Our analysis also indicates that resources can be better utilized by focusing on heavy mal-activity contributors, which constitute the bulk of mal-activities.
"This Girl is on Fire"

Online health communities (OHCs) allow people living with a shared diagnosis or medical condition to connect with peers for social support and advice. OHCs have been well studied in conditions like diabetes and cancer, but less is known about their role in enigmatic diseases with unknown or complex causal mechanisms. In this paper, we study one such condition: Vulvodynia, a chronic pain syndrome of the vulvar region. Through observations of and interviews with members of a vulvodynia Facebook group, we found that while the interaction types are broadly similar to those found in other OHCs, the women spent more time seeking basic information and building individualized management plans. They also encounter significant emotional and interpersonal challenges, which they discuss with each other. We use this study to extend the field's understanding of OHCs, and to propose implications for the design of self-tracking tools to support sensemaking in enigmatic conditions.
Inalienability

This paper takes on one of the rarely articulated yet important questions pertaining to digital media objects: how do HCI and design researchers understand 'gifting' when the object can just as easily be 'shared'? This question has often been implied and occasionally answered, though only partially. We propose the concept of 'inalienability', taken from the gifting literature, as a useful theory for clarifying what design researchers mean by gifting in a digital context. We apply 'inalienability' to three papers from the ACM Digital Library and one ongoing project, spanning nearly two decades of HCI and design research, that combine 'gifting and 'sharing' in their frameworks. In this way we show how applying the concept of 'inalienability' can clarify behaviours that mark gifting as a unique activity, frame research questions around gifting and sharing, outline specific next steps for gifting research, and suggest design strategies in this area.
Optical homogeneity measurement of parallel plates by wavelength-tuning interferometry using nonuniform fast Fourier transform

Wavelength-tuning interferometry is commonly employed to measure the optical homogeneity of parallel plates. However, the nonlinearity of phase shifts caused by wavelength tuning errors and environmental vibration leads to a spatially uniform error in the calculated phase distribution. Herein, a wavelength-tuning interferometry method based on nonuniform fast Fourier transform (WTI-NUFFT) was developed, which solves the spectral aliasing resulting from the spatially uniform error. The characteristics of the WTI-NUFFT method were estimated through comparison with the FFT method. Both the simulated and experimental results showed that the WTI-NUFFT method can improve the accuracy of the optical homogeneity measurement of parallel plates.
Constructing distributed time-critical applications using cognitive enabled services

Time-critical analytics applications are increasingly making use of distributed service interfaces (e.g., micro-services) that support the rapid construction of new applications by dynamically linking the services into different workflow configurations. Traditional service-based applications, in fixed networks, are typically constructed and managed centrally and assume stable service endpoints and adequate network connectivity. Constructing and maintaining such applications in dynamic heterogeneous wireless networked environments, where limited bandwidth and transient connectivity are commonplace, presents significant challenges and makes centralized application construction and management impossible. In this paper we present an architecture which is capable of providing an adaptable and resilient method for on-demand decentralized construction and management of complex time-critical applications in such environments. The approach uses a Vector Symbolic Architecture (VSA) to compactly represent an application as a single semantic vector that encodes the service interfaces, workflow, and the time-critical constraints required. By extending existing services interfaces, with a simple cognitive layer that can interpret and exchange the vectors, we show how the required services can be dynamically discovered and interconnected in a completely decentralized manner. We demonstrate the viability of this approach by using a VSA to encode various time-critical data analytics workflows. We show that these vectors can be used to dynamically construct and run applications using services that are distributed across an emulated Mobile Ad-Hoc Wireless Network (MANET). Scalability is demonstrated via an empirical evaluation.
Kalman filtering based gradient estimation algorithms for observer canonical state-space systems with moving average noises

This paper focuses on the joint parameter and state estimation issue for observer canonical state-space systems with white noises in state equations and moving average noises in output equations. By means of the Kalman filtering and the gradient search, we derive a Kalman filtering based extended stochastic gradient algorithm. For purpose of achieving the higher parameter estimation accuracy, a Kalman filtering based multi-innovation extended stochastic gradient algorithm is proposed on the basis of the multi-innovation identification theory. Finally, the effectiveness of the proposed algorithms is validated through a numerical example.
Implementation of Deep Learning Algorithm with Perceptron using TenzorFlow Library

In recent years, Deep Learning, Machine Learning, and Artificial Intelligence are highly focused concepts of data science. Deep learning has achieved success in the field of Computer Vision, Speech and Audio Processing, and Natural Language Processing. It has the strong learning ability that can improve utilization of datasets for the feature extraction compared to traditional Machine Learning Algorithm. Perceptron is the essential building block for creating a deep Neural Network. The perceptron model is the more general computational model. It analyzes the unsupervised data, making it a valuable tool for data analytics. A key task of this paper is to develop and analyze learning algorithm. It begins with deep learning with perceptron and how to apply it using TensorFlow to solve various issues. The main part of this paper is to make perceptron learning algorithm well behaved with non-separable training datasets. This type of algorithm is suitable for Machine Learning, Deep Learning, Pattern Recognition, and Connectionist Expert System.
Identifying and Ranking Knowledge Management Tools and Techniques Affecting Organizational Information Security Improvement

Information system security is one of the most important challenges of today’s organisations. Although most organisations use security technologies, they have come to the conclusion that technology alone is not enough. In this study, we analysed and ranked the opinions of IT experts, managers, and Ph.D. students concerning the role of knowledge management in creating, recording, sharing, and applying information security knowledge. First, we identified 62 knowledge management techniques and tools. Then, 20 techniques and tools were investigated in knowledge management cycle using fuzzy screening through interviews and questionnaires. Finally, the network analysis methods, DEMATEL, and VIKOR were employed for weighting, internal relationships, and ranking each of the tools in the evaluation cycle. According to the results, “ Knowledge Base” was the most important factor in creating and recording knowledge. In sharing and diffusing knowledge, “ Categorisation of Knowledge ”scored the top, while “Knowledge Maps” were the most important tool in knowledge application.
Long-Term Value of Social Robots through the Eyes of Expert Users

Socially-enabled digital technologies have attracted academic interest for decades, with recent commercial examples of Siri and Alexa, capturing public attention. However, despite ubiquitous visions of a robotic future, very few fully-fledged social robots are currently available to consumers. To improve their designs, studies of their long-term use are particularly valuable, but are currently unavailable. To address this gap, we report on interviews with four long-term users of Pepper - a social robot introduced in 2014. Our thematic analysis elicited insights across three kinds of value Pepper brought to its users: utilitarian functionality; the community that formed around Pepper; and a personal value of affection. We focus on two contributions those values bring to social robot design: social robots as social proxies, alleviating disabilities or acting akin to social media profiles; and robot nurturing as a design construct, going beyond purely utilitarian or hedonistic perspectives on robots.
Image Water Marking using DWT to Encapsulate Data in Medical Image

At present various watermarking techniques are in use in the field of digital images. The medical image watermarking creates a greater degree of the distortion as a result of the embedded image, which is a big challenge in the medical applications as it affects the decision making process. Certain watermarking technique results vary with different images and also the performance seems to be inconsistent when various images are considered for watermarking. The key point of this work is to enhance the quality and security in order to accomplish distortion free watermarking by utilizing a watermarking technique which subject to pixel weight. For this 2 level DWT is used to obtain the best region for embedding for which the dragonfly optimization algorithm is applied.
Hyper2vec: Biased Random Walk for Hyper-network Embedding

Network embedding aims to obtain a low-dimensional representation of vertices in a network, meanwhile preserving structural and inherent properties of the network. Recently, there has been growing interest in this topic while most of the existing network embedding models mainly focus on normal networks in which there are only pairwise relationships between the vertices. However, in many realistic situations, the relationships between the objects are not pairwise and can be better modeled by a hyper-network in which each edge can join an uncertain number of vertices. In this paper, we propose a deep model called Hyper2vec to learn the embeddings of hyper-networks. Our model applies a biased $$2^{nd}$$ order random walk strategy to hyper-networks in the framework of Skip-gram, which can be flexibly applied to various types of hyper-networks.
Multiple pedestrian tracking by combining particle filter and network flow model

A novel network flow model is proposed for multiple pedestrian tracking in this paper. Based on tracklets, only a short and reliable detection sequence is needed for effective tracking. Our model fuses the local and global data association strategies to compensate for their respective shortcomings, and can be divided into two stages: a local stage and a global stage. In the local stage, we follow the tracking-by-detection framework to generate confident tracklets by employing a boosted particle filter. In the global stage, the data association is formulated as a Maximum-a-Posteriori (MAP) problem and solved by a typical min-cost flow algorithm. A double-step optimization is designed to solve the long term occlusion. The experimental results show that our method outperforms several state-of-the-art methods for multiple pedestrian tracking.
T-Box: A Forensics-Enabled Trusted Automotive Data Recording Method

Modern vehicles are equipped with numerous electric control units which exchange vehicular status data, providing drivers with convenience, efficiency, and safety. In addition, the autonomous vehicles adopt various sensors that produce high volumes of high-speed data to process and assess internal and external situations. This data is particularly useful to automotive service providers such as car insurers, rental companies, and manufacturers. One way to understand how this data is used is to imagine the scenario in which an automobile insurer would provide a discount to a customer with an accident-free or near accident-free driving record. However, it is still possible that a less than the honest customer could manipulate their driving data in order to receive premium insurance services at preferential rates. To prevent this and similar scenarios, it is then critical to ensure that all data generated in a vehicle upholds integrity, continuity, and non-repudiation. Unfortunately, no such trustworthy data recording system of this caliber exists in any manufactured vehicle to date. This paper attempts to respond to this need, and we present a reliable automotive data recording system that satisfies these requirements and detects malicious manipulations from data deletion, replacement, replaying, and truncation. The proposed method additionally satisfies forward integrity of message authentication keys and is designed to utilize recorded data as automotive forensic evidence. Finally, the evaluation results demonstrate that our system can manage bandwidths of up to 64 MB/s.
Multiclass Brain Tissue Segmentation in 4D CT Using Convolutional Neural Networks

4D CT imaging has a great potential for use in stroke workup. A fully convolutional neural network (CNN) for 3D multiclass segmentation in 4D CT is presented, which can be trained end-to-end from sparse 2D annotations. The CNN was trained and validated on 42 4D CT acquisitions of the brain of patients with suspicion of acute ischemic stroke. White matter, gray matter, cerebrospinal fluid, and vessels were annotated by two trained observers. The mean Dice coefficients, contour mean distances, and absolute volume differences were, respectively, 0.87 ± 0.04, 0.52 ± 0.47 mm, and 11.78 ± 9.55 % on a separate test set of five patients, which were similar to the average interobserver variability scores of 0.88 ± 0.03, 0.72 ± 0.93 mm, and 8.86 ± 7.65 % outperforming the current state of the art. The proposed method is, therefore, a promising deep neural network for multiclass segmentation in 4D spatiotemporal imaging data.
Partially safe evolution of software product lines

Software Product Lines allow the automatic generation of related products built with reusable artefacts. In this context, developers may need to perform changes and check whether products are affected. A strategy to perform such analysis is verifying behaviour preservation through the use of formal theories. The product line refinement notion requires behaviour preservation for all existing products. Nevertheless, in evolution scenarios like bug fixes, some products intentionally have their behaviour changed. To support developers in these and other unsafe scenarios, we define a theory of partial product line refinement that helps to precisely understand which products are affected by a change. This provides a kind of impact analysis that could, for example, reduce test effort, since only affected products need to be tested. We provide properties such as compositionality, which deals with changes to a specific product line element, and general properties to support developers when safe and partially safe scenarios are combined. We also define a set of transformation templates, which are classified according to their compatibility to specific types of product lines. To evaluate our work, we analyse two product lines: Linux and Soletta, to discover if our templates could be helpful in evolving these systems.
Reading Face, Reading Health

With the recent advancement in computer vision, Artificial Intelligence (AI), and mobile technologies, it has become technically feasible for computerized Face Reading Technologies (FRTs) to learn about one's health in everyday settings. However, how to design FRT-based applications for everyday health practices remains unexplored. This paper presents a design study with a technology probe called Faced, a mobile health checkup application based on the facial diagnosis method from Traditional Chinese Medicine (TCM). A field trial of Faced with 10 participants suggests potential usage modes and highlights a number of critical design issues in the use of FRTs for everyday health, including adaptability, practicality, sensitivity, and trustworthiness. We end by discussing design implications to address the unique challenges of fully integrating FRTs into everyday health practices.
Preaching Voxels: An Alternative Approach to Mixed Reality

For mixed reality applications, where reality and virtual reality are spatially merged and aligned in interactive real-time, we propose a pure voxel representation as a rendering and interaction method of choice. We show that voxels\textemdash gap-less volumetric pixels in a regular grid in space\textemdash allow for an actual user experience of a mixed reality environment, for a seamless blending of virtual and real, and for a sense of presence and co-presence in such an environment. If everything is based on voxels, even if coarse, visual coherence is achieved inherently. We argue the case for voxels by (1) conceptually defining and illustrating voxel-based mixed reality, (2) describing the computational feasibility, (3) presenting a fully functioning, low resolution prototype, (4) empirically exploring the user experience, and finally (5) discussing current work and future directions for voxel-based mixed reality. This work is not the first that utilises voxels for mixed reality, but is the first that has an holistic view on voxel-based mixed realities as an effective way of experiencing and interacting with mixed reality environments.