Reviewerly/exHarmony huggingface dataset:
A framework for Model-Driven Engineering of resilient software-controlled systems

Abstract Emergent paradigms of Industry 4.0 and Industrial Internet of Things expect cyber-physical systems to reliably provide services overcoming disruptions in operative conditions and adapting to changes in architectural and functional requirements. In this paper, we describe a hardware/software framework supporting operation and maintenance of software-controlled systems enhancing resilience by promoting a Model-Driven Engineering (MDE) process to automatically derive structural configurations and failure models from reliability artifacts. Specifically, a reflective architecture developed around digital twins enables representation and control of system Configuration Items properly derived from SysML Block Definition Diagrams, providing support for variation. Besides, a plurality of distributed analytic agents for qualitative evaluation over executable failure models empowers the system with runtime self-assessment and dynamic adaptation capabilities. We describe the framework architecture outlining roles and responsibilities in a System of Systems perspective, providing salient design traits about digital twins and data analytic agents for failure propagation modeling and analysis. We discuss a prototype implementation following the MDE approach, highlighting self-recovery and self-adaptation properties on a real cyber-physical system for vehicle access control to Limited Traffic Zones.
Meta weight learning via model-agnostic meta-learning

While meta learning approaches have achieved remarkable success, obtaining a stable and unbiased meta-learner remains a significant challenge, since the initial model of a meta-learner could be too biased towards existing tasks to adapt to new tasks. In order to avoid a biased meta-learner and improve its generalizability, this paper proposes a generic meta learning method that aims to learn an unbiased meta-learner towards a variety of tasks before its initial model is adapted to unseen tasks. Specifically, this paper presents a meta weight learning method for minimizing the inequality of performance across different training tasks. An end-to-end training approach is introduced for the proposed algorithm that allows for effectively learning weight and initializing the network model. Alternatively, a variety of measurement methods of weight is also designed to test the effectiveness of different weight learning methods on the improvement of model-agnostic meta-learning algorithm. The simulation results show that the proposed meta weight learning method not only outperforms state-of-the-art meta learning algorithms, but also is superior to other manually designed measurement methods of weight on discrete and continuous control problems.
New MDS Euclidean Self-Orthogonal Codes

In this paper, two criterions of MDS Euclidean self-orthogonal codes are presented. New MDS Euclidean self-dual codes and self-orthogonal codes are constructed via the criterions. In particular, among our constructions, for large square q, about 1/8·q new MDS Euclidean (almost) self-dual codes over F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> can be produced. Moreover, we can construct about 1/4· q new MDS Euclidean self-orthogonal codes with different even lengths n and dimension n/2-1.
Digital transformation risk management in forensic science laboratories

Technological advances are changing how forensic laboratories operate in all forensic disciplines, not only digital. Computers support workflow management, enable evidence analysis (physical and digital), and new technology enables previously unavailable forensic capabilities. Used properly, the integration of digital systems supports greater efficiency and reproducibility, and drives digital transformation of forensic laboratories. However, without the necessary preparations, these digital transformations can undermine the core principles and processes of forensic laboratories. Pertinent examples of problems involving technology that have occurred in laboratories are provided, along with opportunities and risk mitigation strategies, based on the authors’ experiences. Forensic preparedness concentrating on digital data reduces the cost and operational disruption of responding to various kinds of problems, including misplaced exhibits, allegations of employee misconduct, disclosure requirements, and information security breaches. This work presents recommendations to help forensic laboratories prepare for and manage these risks, to use technology effectively, and ultimately strengthen forensic science. The importance of involving digital forensic expertise in risk management of digital transformations in laboratories is emphasized. Forensic laboratories that do not adopt forensic digital preparedness will produce results based on digital data and processes that cannot be verified independently, leaving them vulnerable to challenge. The recommendations in this work could enhance international standards such as ISO/IEC 17025 used to assess and accredit laboratories.
Recommendations for patient similarity classes: results of the AMIA 2019 workshop on defining patient similarity

Abstract Defining patient-to-patient similarity is essential for the development of precision medicine in clinical care and research. Conceptually, the identification of similar patient cohorts appears straightforward; however, universally accepted definitions remain elusive. Simultaneously, an explosion of vendors and published algorithms have emerged and all provide varied levels of functionality in identifying patient similarity categories. To provide clarity and a common framework for patient similarity, a workshop at the American Medical Informatics Association 2019 Annual Meeting was convened. This workshop included invited discussants from academics, the biotechnology industry, the FDA, and private practice oncology groups. Drawing from a broad range of backgrounds, workshop participants were able to coalesce around 4 major patient similarity classes: (1) feature, (2) outcome, (3) exposure, and (4) mixed-class. This perspective expands into these 4 subtypes more critically and offers the medical informatics community a means of communicating their work on this important topic.
Multi-view feature transfer for click-through rate prediction

Click-through rate prediction is an important method for online advertising and marketing evaluations. However, for environmental reasons, there is a scarcity and imbalance in the advertising data available. We found that a feature transfer can be applied in a transfer learning method to obtain potential connections from less relevant advertisement data. Considering the complexity and diverse features of advertisement data, a feature transfer cannot allow researchers to discover the relationships among such features within the advertisement data. Therefore, we propose a click-through rate method based on a multi-view feature transfer (MFT). MFT divides the data into common and selected features during the data pretreatment process. It then combines the important feature vectors obtained using a Laplacian matrix with the common features obtained during the pretreatment process to form groups of views. Therefore, combining feature transfer matrix with mutli-view clustering is an innovation of the CTR data prediction process. In our experiments, the MFT model achieved good results. Experiments on a large number of datasets of different sizes and the application of three evaluation indicators show that the MFT method delivers excellent prediction results using the transfer relationships among the characteristics of an advertising dataset, and its performance is better than that of many other advertising click-through rate prediction methods.
Affirmative and silent cyber coverage in traditional insurance policies: Qualitative content analysis of selected insurance products from the German insurance market

Abstract This paper examines the design of affirmative and silent coverage in view of the cyber risks in traditional insurance policies for select product lines on the German market. Given the novelty and complexity of the topic and the insufficient coverage in the literature, we use two different sources. We analysed the general insurance terms and conditions of different traditional insurance lines using Mayring’s qualitative content analysis. Also, we conducted interviews with experts from the German insurance industry to evaluate how insurers understand their silent cyber exposures, and what measures they take to deal with this new exposure. The study shows a considerable cyber liability risk potential for insurers in the considered insurance lines. This arises from the affirmative as well as silent cover inclusions and exclusions for cyber risks, which result from imprecise wordings of insurance clauses and insufficient descriptions of the contractually specified scope of the insurance coverage.
Blockchain-based architecture for the control of logistics activities: Pharmaceutical utilities case study

Abstract Logistics services involve a wide range of transport operations between distributors and clients. Currently, the large number of intermediaries are a challenge for this sector, as it makes all the processes more complicated. To face that problem, we propose a system that uses smart contracts to remove intermediaries and speed up logistics activities. Our new model combines smart contracts and a multi-agent system in a single platform to improve the current logistics system by increasing organization, security and getting rid of several human intermediaries to automate its processes, making distribution times significantly faster. Also, with this kind of approach, it is possible to apply penalties to parties that do not comply with the terms of using this platform.
Benchmarking deep network architectures for ethnicity recognition using a new large face dataset

Abstract Although in recent years we have witnessed an explosion of the scientific research in the recognition of facial soft biometrics such as gender, age and expression with deep neural networks, the recognition of ethnicity has not received the same attention from the scientific community. The growth of this field is hindered by two related factors: on the one hand, the absence of a dataset sufficiently large and representative does not allow an effective training of convolutional neural networks for the recognition of ethnicity; on the other hand, the collection of new ethnicity datasets is far from simple and must be carried out manually by humans trained to recognize the basic ethnicity groups using the somatic facial features. To fill this gap in the facial soft biometrics analysis, we propose the VGGFace2 Mivia Ethnicity Recognition (VMER) dataset, composed by more than 3,000,000 face images annotated with 4 ethnicity categories, namely African American, East Asian, Caucasian Latin and Asian Indian. The final annotations are obtained with a protocol which requires the opinion of three people belonging to different ethnicities, in order to avoid the bias introduced by the well-known other race effect. In addition, we carry out a comprehensive performance analysis of popular deep network architectures, namely VGG-16, VGG-Face, ResNet-50 and MobileNet v2. Finally, we perform a cross-dataset evaluation to demonstrate that the deep network architectures trained with VMER generalize on different test sets better than the same models trained on the largest ethnicity dataset available so far. The ethnicity labels of the VMER dataset and the code used for the experiments are available upon request at https://mivia.unisa.it .
A Novel Multiple-Model Adaptive Kalman Filter for an Unknown Measurement Loss Probability

This article proposes a novel adaptive Kalman filter (AKF) to estimate the unknown probability of measurement loss using the interacting multiple-model (IMM) filtering framework, yielding the IMM-AKF algorithm. In the proposed IMM-AKF algorithm, the state, Bernoulli random variable, and measurement loss probability are jointly inferred based on the variational Bayesian (VB) approach. In particular, a new likelihood definition is derived for the mode probability update process of the IMM-AKF algorithm. Experiments demonstrate the superiority of the proposed IMM-AKF algorithm over existing filtering algorithms by adaptively estimating the unknown time-varying measurement loss probability.
Implicit authentication method for smartphone users based on rank aggregation and random forest

Currently, the smartphone devices have become an essential part of our daily activities. Smartphone' users run various essential applications (such as banking and e-health Apps), which contains very confidential information (e.g., credit card number and its PIN). Typically, the smartphone's user authentication is achieved using mechanisms (password or security pattern) to verify the user identity. Although these mechanisms are cheap, simple, and quick enough for frequent logins, they are vulnerable to attacks such as shoulder surfing or smudge attack. This problem could be addressed by authenticating the users using their behaviour (i.e., touch behaviour) while using their smartphones. Such behaviours include finger's pressure, size, and pressure time while tapping keys. Selecting features (from these behaviours) could play an important role in the authentication process's performance. This paper aims to propose an efficient authentication method providing an implicit authentication for smartphone users while not imposing an additional cost of special hardware and addressing the limited smartphone capabilities. We first investigated feature selection techniques from the filter and wrapper approaches and then used the best one to propose our implicit authentication method. The random forest classifier is used to evaluate these techniques. It is also used to achieve the classification task in our authentication method. Using a public dataset, the experimental results showed that the filter-based technique (i.e., rank aggregation) is the best feature selection to build an implicit authentication method for the smartphone environment. It showed accuracy results around 97.80% using only 25 features out of 53 features (i.e., require less mobile resources (memory and processing power) to authenticate users. At the same time, the results showed that our method has less error rate: 2.03 FAR, 0.04 FRR, and 1.04 ERR, comparing to the related work. These promising results would be used to develop a mobile application that allows implicit authentication of legitimate owners while avoiding the traditional authentication problems and using fewer smartphone resources.
Exploration and Mapping with Groups of Robots: Recent Trends

Multi-robot exploration—i.e., the problem of mapping unknown features of an environment—is fundamental in many tasks, including search and rescue, planetary exploration, and environmental monitoring. This article surveys recent literature with the aim at identifying current research trends, open challenges, and future directions. Since the first formalization of the exploration problem, current research has extended systems and algorithms to map 3D environments and more complex phenomena and considered real-world constraints. Deep learning-based approaches have seen some preliminary applications in decision-making. While current research has been progressing towards systems that can work in the real world, long-term exploration in large unstructured environments with complex phenomena to map is still an open problem. This introduces opportunities for exciting research, including but not limited to generalized frameworks that bridge the gap between theory and practice, learning-based approaches, and robustness to failure and attacks, so that robots can be deployed safely in real-world environments.
A novel method for reducing motion sickness susceptibility through training visuospatial ability – A two-part study

Everyone can be susceptible to motion sickness (except those with complete loss of labyrinth function) and around one in three are known to be servery susceptible. Motion sickness can be experienced in many domains, including car travel, on a boat, using virtual reality headsets and simulator use amongst others. It is expected that due to potential designs and use cases, self-driving cars will increase motion sickness onset likelihood and severity for many car travellers. Besides medication, there are limited methods through which one can actively reduce their motion sickness susceptibility. This research develops a novel visuospatial training tool and explores the effect of visuospatial training on motion sickness. With a combined sample of 42 participants split between driving simulator trials (n = 20), and on-road trials (n = 22) baseline visuospatial skills and motion sickness were first measured. After a 14-day training period where participates completed 15-min of pen and paper tasks per day, it was found that visuospatial skills improved by 40%. This increase in visuospatial ability was shown to be directly responsible for a reduction in motion sickness by 51% in the simulator (with a 60% reduction in participant dropouts) and a 58% reduction in the on-road trial. This research has successfully identified a new method to reduce motion sickness susceptibility and the impact of these findings have wide reaching implications for motion sickness research, especially in the field of self-driving vehicles.
Rare earth mineral diversity controlled by REE pattern shapes

Abstract The line connecting rare earth elements (REE) in chondrite-normalised plots can be represented by a smooth polynomial function using λ shape coefficients as described by O'Neill (2016). In this study, computationally generated λ combinations are used to construct artificial chondrite-normalised REE patterns that encompass most REE patterns likely to occur in natural materials. The dominant REE per pattern is identified, which would lead to its inclusion in a hypothetical mineral suffix, had this mineral contained essential REE. Furthermore, negative Ce and Y anomalies, common in natural minerals, are considered in the modelled REE patterns to investigate the effect of their exclusion on the relative abundance of the remainder REE. The dominant REE in a mineral results from distinct pattern shapes requiring specific fractionation processes, thus providing information on its genesis. Minerals dominated by heavy lanthanides are rare or non-existent, even though the present analysis shows that REE patterns dominated by Gd, Dy, Er and Yb are geologically plausible. This discrepancy is caused by the inclusion of Y, which dominates heavy REE budgets, in mineral name suffixes. The focus on Y obscures heavy lanthanide mineral diversity and can lead to various fractionation processes to be overlooked. Samarium dominant minerals are known, even though deemed unlikely by the computational model, suggesting additional fractionation processes that are not well described by λ shape coefficients. Positive Eu anomalies only need to be moderate in minerals depleted in the light REE for Eu to be the dominant REE, thus identifying candidate rocks in which the first Eu dominant mineral might be found. Here, I present an online tool, called ALambdaR that allows interactive control of λ shape coefficients and visualisation of resulting REE patterns.
PGC: Decentralized Confidential Payment System with Auditability

Many existing cryptocurrencies fail to provide transaction anonymity and confidentiality. As the privacy concerns grow, a number of works have sought to enhance privacy by leveraging cryptographic tools. Though strong privacy is appealing, it might be abused in some cases. In decentralized payment systems, anonymity poses great challenges to system's auditability, which is a crucial property for scenarios that require regulatory compliance and dispute arbitration guarantee. Aiming for a middle ground between privacy and auditability, we introduce the notion of decentralized confidential payment (DCP) system with auditability. In addition to offering confidentiality, DCP supports privacy-preserving audit in which an external party can specify a set of transactions and then request the participant to prove their compliance with a large class of policies. We present a generic construction of auditable DCP system from integrated signature and encryption scheme and non-interactive zero-knowledge proof systems. We then instantiate our generic construction by carefully designing the underlying building blocks, yielding a standalone cryptocurrency called PGC. In PGC, the setup is transparent, transactions are less than 1.3 KB and take under 38ms to generate and 15 ms to verify. At the core of PGC is an additively homomorphic public-key encryption scheme that we newly introduce, twisted ElGamal, which is not only as secure as standard exponential ElGamal, but also friendly to Sigma protocols and Bulletproofs. This enables us to easily devise zero-knowledge proofs for basic correctness of transactions as well as various application-dependent policies in a modular fashion.
MGRL: Graph neural network based inference in a Markov network with reinforcement learning for visual navigation

Visual navigation is an essential task for indoor robots and usually uses the map as assistance to providing global information for the agent. Because the traditional maps match the environments, the map-based and map-building-based navigation methods are limited in the new environments for obtaining maps. Although the deep reinforcement learning navigation method, utilizing the non-map-based navigation technique, achieves satisfactory performance, it lacks the interpretability and the global view of the environment. Therefore, we propose a novel abstract map for the deep reinforcement learning navigation method with better global relative position information and more reasonable interpretability. The abstract map is modeled as a Markov network which is used for explicitly representing the regularity of objects arrangement, influenced by people activities in different environments. Besides, a knowledge graph is utilized to initialize the structure of the Markov network, as providing the prior structure for the model and reducing the difficulty of model learning. Then, a graph neural network is adopted for probability inference in the Markov network. Furthermore, the update of the abstract map, including the knowledge graph structure and the parameters of the graph neural network, are combined into an end-to-end learning process trained by a reinforcement learning method. Finally, experiments in the AI2THOR framework and the physical environment indicate that our algorithm greatly improves the success rate of navigation in case of new environments, thus confirming the good generalization.
Low Visual Distortion and Robust Morphing Attacks Based on Partial Face Image Manipulation

Face verification is a popular way for verifying identities in access control systems. In this work, a partial face manipulation-based morphing attack (MA) is proposed to compromise the uniqueness of face templates. Different from existing research, this work changes MA from a holistic face level to component level, and only the most effective facial components (eyes and nose) are used. Therefore, a manipulated face is more similar to a bona fide one in terms of visual quality, texture, and noise characteristics. To validate the effectiveness of the proposed attack, a novel metric called actual mated morph presentation match rate (AMPMR) is proposed to evaluate MA performance under real-world conditions. With a collected dataset containing different attack types, image qualities, and manipulation parameters, the results indicate the proposed attack has better anti-detectability compared with the existing complete, splicing, and combined MAs. Moreover, it has low visual distortion and can reach a better tradeoff among facial biometrics verification, anti-detectability, and visual differences.