Reviewerly/exHarmony huggingface dataset:
Towards the Tipping Point for FAIR Implementation

This article explores the global implementation of the FAIR Guiding Principles for scientific management and data stewardship, which provide that data should be findable, accessible, interoperable and reusable. The implementation of these principles is designed to lead to the stewardship of data as FAIR digital objects and the establishment of the Internet of FAIR Data and Services (IFDS). If implementation reaches a tipping point, IFDS has the potential to revolutionize how data is managed by making machine and human readable data discoverable for reuse. Accordingly, this article examines the expansion of the implementation of FAIR Guiding Principles, especially how and in which geographies (locations) and areas (topic domains) implementation is taking place. A literature review of academic articles published between 2016 and 2019 on the use of FAIR Guiding Principles is presented. The investigation also includes an analysis of the domains in the IFDS Implementation Networks (INs). Its uptake has been mainly in the Western hemisphere. The investigation found that implementation of FAIR Guiding Principles has taken firm hold in the domain of bio and natural sciences. To achieve a tipping point for FAIR implementation, it is now time to ensure the inclusion of non-European ascendants and of other scientific domains. Apart from equal opportunity and genuine global partnership issues, a permanent European bias poses challenges with regard to the representativeness and validity of data and could limit the potential of IFDS to reach across continental boundaries. The article concludes that, despite efforts to be inclusive, acceptance of the FAIR Guiding Principles and IFDS in different scientific communities is limited and there is a need to act now to prevent dampening of the momentum in the development and implementation of the IFDS. It is further concluded that policy entrepreneurs and the GO FAIR INs may contribute to making the FAIR Guiding Principles more flexible in including different research epistemologies, especially through its GO CHANGE pillar.
"Is my phone hacked?" Analyzing Clinical Computer Security Interventions with Survivors of Intimate Partner Violence

Intimate partner abusers use technology to track, monitor, harass, and otherwise harm their victims, and prior work reports that victims have few resources for obtaining help with such attacks. This paper presents a qualitative analysis of data from a field study of an approach to helping survivors of intimate partner violence (IPV) with technology abuse. In this approach, called clinical computer security, a trained technologist performs a face-to-face consultation with an IPV survivor to help them understand and navigate technology issues. Findings from consultations with 31 survivors, as well as IPV professionals working on their behalf, uncovered a range of digital security and privacy vulnerabilities exacerbated by the nuanced social context of such abuse. In this paper we explore survivor experiences with, and reactions to, the consultations, discussing (1) the ways in which survivors present their tech concerns, (2) the cooperative work required to guide survivors towards understanding probable causes of tech insecurity, (3) survivors' reactions to the consultations, particularly when security vulnerabilities or spyware are discovered, and (4) the role we play as consultants and interventionists in the complex socio-technical systems involved in mitigating IPV. We conclude by discussing some of the broad ethical and sustainability challenges raised by our work, and provide design opportunities for tech platforms to better support survivors of IPV.
Dynamic three-dimensional shape measurement with a complementary phase-coding method

The traditional phase-coding (TPC) method has been widely used for three-dimensional (3D) shape measurement. However, the fringe order determined from the coding patterns cannot be precisely aligned with the wrapped phase calculated from the sinusoidal patterns when measuring dynamic objects. Consequently, phase unwrapping errors will occur around the 2π phase jumps of the wrapped phase. To address this problem, the present work proposes a complementary phase-coding (CPC) method for dynamic 3D shape measurement. Specifically, the projection of the sinusoidal and coding patterns forms a cycle and the se patterns are shifted by a half-period between even and odd cycles. Combining two fringe orders of adjacent even and odd cycles, the absolute phase map can be correctly recovered. Moreover, to improve the measurement speed, all 8-bit gray patterns are converted into 1-bit binary patterns by a binary dithering technique. Both simulations and experiments demonstrate the robustness and efficiency of the proposed method.
Weakly Supervised Disentanglement with Guarantees

Learning disentangled representations that correspond to factors of variation in real-world data is critical to interpretable and human-controllable machine learning. Recently, concerns about the viability of learning disentangled representations in a purely unsupervised manner has spurred a shift toward the incorporation of weak supervision. However, there is currently no formalism that identifies when and how weak supervision will guarantee disentanglement. To address this issue, we provide a theoretical framework—including a calculus of disentanglement— to assist in analyzing the disentanglement guarantees (or lack thereof) conferred by weak supervision when coupled with learning algorithms based on distribution matching. We empirically verify the guarantees and limitations of several weak supervision methods (restricted labeling, match-pairing, and rank-pairing), demonstrating the predictive power and usefulness of our theoretical framework.
Preserving differential privacy in deep neural networks with relevance-based adaptive noise imposition

In recent years, deep learning achieves remarkable results in the field of artificial intelligence. However, the training process of deep neural networks may cause the leakage of individual privacy. Given the model and some background information of the target individual, the adversary can maliciously infer the sensitive feature of the target individual. Therefore, it is imperative to preserve the sensitive information in the training data. Differential privacy is a state-of-the-art paradigm for providing the privacy guarantee of datasets, which protects the private and sensitive information from the attack of adversaries significantly. However, the existing privacy-preserving models based on differential privacy are less than satisfactory since traditional approaches always inject the same amount of noise into parameters to preserve the sensitive information, which may impact the trade-off between the model utility and the privacy guarantee of training data. In this paper, we present a general differentially private deep neural networks learning framework based on relevance analysis, which aims to bridge the gap between private and non-private models while providing an effective privacy guarantee of sensitive information. The proposed model perturbs gradients according to the relevance between neurons in different layers and the model output. Specifically, during the process of backward propagation, more noise is added to gradients of neurons that have less relevance to the model output, and vice-versa. Experiments on five real datasets demonstrate that our mechanism not only bridges the gap between private and non-private models, but also prevents the disclosure of sensitive information effectively.
Prediction of an Acute Hypotensive Episode During an ICU Hospitalization With a Super Learner Machine-Learning Algorithm

Acute hypotensive episodes (AHE), defined as a drop in the mean arterial pressure (MAP) <65 mm Hg lasting at least 5 consecutive minutes, are among the most critical events in the intensive care unit (ICU). They are known to be associated with adverse outcome in critically ill patients. AHE prediction is of prime interest because it could allow for treatment adjustment to predict or shorten AHE.The Super Learner (SL) algorithm is an ensemble machine-learning algorithm that we specifically trained to predict an AHE 10 minutes in advance. Potential predictors included age, sex, type of care unit, severity scores, and time-evolving characteristics such as mechanical ventilation, vasopressors, or sedation medication as well as features extracted from physiological signals: heart rate, pulse oximetry, and arterial blood pressure. The algorithm was trained on the Medical Information Mart for Intensive Care dataset (MIMIC II) database. Internal validation was based on the area under the receiver operating characteristic curve (AUROC) and the Brier score (BS). External validation was performed using an external dataset from Lariboisière hospital, Paris, France.Among 1151 patients included, 826 (72%) patients had at least 1 AHE during their ICU stay. Using 1 single random period per patient, the SL algorithm with Haar wavelets transform preprocessing was associated with an AUROC of 0.929 (95% confidence interval [CI], 0.899-0.958) and a BS of 0.08. Using all available periods for each patient, SL with Haar wavelets transform preprocessing was associated with an AUROC of 0.890 (95% CI, 0.886-0.895) and a BS of 0.11. In the external validation cohort, the AUROC reached 0.884 (95% CI, 0.775-0.993) with 1 random period per patient and 0.889 (0.768-1) with all available periods and BSs <0.1.The SL algorithm exhibits good performance for the prediction of an AHE 10 minutes ahead of time. It allows an efficient, robust, and rapid evaluation of the risk of hypotension that opens the way to routine use.
Generic Authenticated Key Exchange in the Quantum Random Oracle Model

We propose $$\mathsf {FO_\mathsf {AKE}}$$ , a generic construction of two-message authenticated key exchange (AKE) from any passively secure public key encryption (PKE) in the quantum random oracle model (QROM). Whereas previous AKE constructions relied on a Diffie-Hellman key exchange or required the underlying PKE scheme to be perfectly correct, our transformation allows arbitrary PKE schemes with non-perfect correctness. Dealing with imperfect schemes is one of the major difficulties in a setting involving active attacks. Our direct construction, when applied to schemes such as the submissions to the recent NIST post-quantum competition, is more natural than previous AKE transformations. Furthermore, we avoid the use of (quantum-secure) digital signature schemes which are considerably less efficient than their PKE counterparts. As a consequence, we can instantiate our AKE transformation with any of the submissions to the recent NIST competition, e.g., ones based on codes and lattices. $$\mathsf {FO_\mathsf {AKE}}$$ can be seen as a generalisation of the well known Fujisaki-Okamoto transformation (for building actively secure PKE from passively secure PKE) to the AKE setting. As a helper result, we also provide a security proof for the Fujisaki-Okamoto transformation in the QROM for PKE with non-perfect correctness which is tighter and tolerates a larger correctness error than previous proofs.
Exposing Fake Images With Forensic Similarity Graphs

In this paper, we propose new image forgery detection and localization algorithms by recasting these problems as graph-based community detection problems. To do this, we introduce a novel graph-based representation of an image, which we call the Forensic Similarity Graph, that captures key forensic relationships among regions in the image. In this representation, small image patches are represented by graph vertices with edges assigned according to the forensic similarity between patches. Localized tampering introduces unique structure into this graph, which aligns with a concept called “community structure” in graph-theory literature. In the Forensic Similarity Graph, communities correspond to the tampered and unaltered regions in the image. As a result, forgery detection is performed by identifying whether multiple communities exist, and forgery localization is performed by partitioning these communities. We present two community detection techniques, adapted from literature, to detect and localize image forgeries. We experimentally show that our proposed community detection methods outperform existing state-of-the-art forgery detection and localization methods, which do not capture such community structure.
2D Compass Codes

The compass model on a square lattice provides a natural template for building subsystem stabilizer codes. The surface code and the Bacon-Shor code represent two extremes of possible codes depending on how many gauge qubits are fixed. We explore threshold behavior in this broad class of local codes by trading locality for asymmetry and gauge degrees of freedom for stabilizer syndrome information. We analyze these codes with asymmetric and spatially inhomogeneous Pauli noise in the code capacity and phenomenological models. In these idealized settings, we observe considerably higher thresholds against asymmetric noise. At the circuit level, these codes inherit the bare-ancilla fault-tolerance of the Bacon-Shor code.
Learning semantic program embeddings with graph interval neural network

Learning distributed representations of source code has been a challenging task for machine learning models. Earlier works treated programs as text so that natural language methods can be readily applied. Unfortunately, such approaches do not capitalize on the rich structural information possessed by source code. Of late, Graph Neural Network (GNN) was proposed to learn embeddings of programs from their graph representations. Due to the homogeneous (i.e. do not take advantage of the program-specific graph characteristics) and expensive (i.e. require heavy information exchange among nodes in the graph) message-passing procedure, GNN can suffer from precision issues, especially when dealing with programs rendered into large graphs. In this paper, we present a new graph neural architecture, called Graph Interval Neural Network (GINN), to tackle the weaknesses of the existing GNN. Unlike the standard GNN, GINN generalizes from a curated graph representation obtained through an abstraction method designed to aid models to learn. In particular, GINN focuses exclusively on intervals (generally manifested in looping construct) for mining the feature representation of a program, furthermore, GINN operates on a hierarchy of intervals for scaling the learning to large graphs. We evaluate GINN for two popular downstream applications: variable misuse prediction and method name prediction. Results show in both cases GINN outperforms the state-of-the-art models by a comfortable margin. We have also created a neural bug detector based on GINN to catch null pointer deference bugs in Java code. While learning from the same 9,000 methods extracted from 64 projects, GINN-based bug detector significantly outperforms GNN-based bug detector on 13 unseen test projects. Next, we deploy our trained GINN-based bug detector and Facebook Infer, arguably the state-of-the-art static analysis tool, to scan the codebase of 20 highly starred projects on GitHub. Through our manual inspection, we confirm 38 bugs out of 102 warnings raised by GINN-based bug detector compared to 34 bugs out of 129 warnings for Facebook Infer. We have reported 38 bugs GINN caught to developers, among which 11 have been fixed and 12 have been confirmed (fix pending). GINN has shown to be a general, powerful deep neural network for learning precise, semantic program embeddings.
Augmented bricklaying

Abstract Augmented bricklaying explores the manual construction of intricate brickwork through visual augmentation, and applies and validates the concept in a real-scale building project—a fair-faced brickwork facade for a winery in Greece. As shown in previous research, robotic systems have proven to be very suitable to achieve various differentiated brickwork designs with high efficiency but show certain limitations, for example, in regard to spatial freedom or the usage of mortar on site. Hence, this research aims to show that through the use of a craft-specific augmented reality system, the same geometric complexity and precision seen in robotic fabrication can be achieved with an augmented manual process. Towards this aim, a custom-built augmented reality system for in situ construction was established. This process allows bricklayers to not depend on physical templates, and it enables enhanced spatial freedom, preserving and capitalizing on the bricklayer’s craft of mortar handling. In extension to conventional holographic representations seen in current augmented reality fabrication processes that have limited context-awareness and insufficient geometric feedback capabilities, this system is based on an object-based visual–inertial tracking method to achieve dynamic optical guidance for bricklayers with real-time tracking and highly precise 3D registration features in on-site conditions. By integrating findings from the field of human–computer interfaces and human–machine communication, this research establishes, explores, and validates a human–computer interactive fabrication system, in which explicit machine operations and implicit craftsmanship knowledge are combined. In addition to the overall concept, the method of implementation, and the description of the project application, this paper also quantifies process parameters of the applied augmented reality assembly method concerning building accuracy and assembly speed. In the outlook, this paper aims to outline future directions and potential application areas of object-aware augmented reality systems and their implications for architecture and digital fabrication.
Digital Twins for Intelligent Authorization in the B5G-Enabled Smart Grid

Beyond fifth generation (B5G) communication networks and computation paradigms in the edge are expected to be integrated into power grid infrastructures over the coming years. In this sense, AI technologies will play a fundamental role to efficiently manage dynamic information flows of future applications, which impacts the authorization policies applied in such a complex scenario. This article studies how digital twins can evolve their context awareness capabilities and simulation technologies to anticipate faults or to detect cyber-security issues in real time, and update access control policies accordingly. Our study analyzes the evolution of monitoring platforms and architecture decentralization, including the application of machine learning and blockchain technologies in the smart grid, toward the goal of implementing autonomous and self-learning agents in the medium and long term. We conclude this study with future challenges on applying digital twins to B5G-based smart grid deployments.
Real masks and spoof faces: On the masked face presentation attack detection

Face masks have become one of the main methods for reducing the transmission of COVID-19. This makes face recognition (FR) a challenging task because masks hide several discriminative features of faces. Moreover, face presentation attack detection (PAD) is crucial to ensure the security of FR systems. In contrast to the growing number of masked FR studies, the impact of face masked attacks on PAD has not been explored. Therefore, we present novel attacks with real face masks placed on presentations and attacks with subjects wearing masks to reflect the current real-world situation. Furthermore, this study investigates the effect of masked attacks on PAD performance by using seven state-of-the-art PAD algorithms under different experimental settings. We also evaluate the vulnerability of FR systems to masked attacks. The experiments show that real masked attacks pose a serious threat to the operation and security of FR systems.
Motion Planning for Mobile Manipulators—A Systematic Review

One of the fundamental fields of research is motion planning. Mobile manipulators present a unique set of challenges for the planning algorithms, as they are usually kinematically redundant and dynamically complex owing to the different dynamic behavior of the mobile base and the manipulator. The purpose of this article is to systematically review the different planning algorithms specifically used for mobile manipulator motion planning. Depending on how the two subsystems are treated during planning, sampling-based, optimization-based, search-based, and other planning algorithms are grouped into two broad categories. Then, planning algorithms are dissected and discussed based on common components. The problem of dealing with the kinematic redundancy in calculating the goal configuration is also analyzed. While planning separately for the mobile base and the manipulator provides convenience, the results are sub-optimal. Coordinating between the mobile base and manipulator while utilizing their unique capabilities provides better solution paths. Based on the analysis, challenges faced by the current planning algorithms and future research directions are presented.
The portrayal of online shaming in contemporary online news media: A media framing analysis

Online shaming, where individuals participate in social policing by shaming supposed wrongdoings on the internet, is a rapidly increasing and global phenomenon. The potential impacts of online shaming are said to be extensive and wide-reaching, however minimal empirical research on this topic has been conducted to date, with existing coverage being largely anecdotal and media-based. The current study aims to demonstrate how online shaming is constructed in contemporary online news media. Qualitative analysis using Giles and Shaw’s (2009) media framing analysis was completed on 69 online news articles published within the last two years concerning online shaming. Two overarching representations of online shaming were uncovered: a dominant narrative framing online shaming as a dangerous threat with serious consequences, and a smaller frame representing online shaming as more constructive and capable of resulting in positive outcomes. Variations in conditions presented, as well as the many rationalisations, consequences, and recommendations posed for mitigating online shaming embedded within the articles collectively represent online shaming as a multifaceted and morally ambiguous phenomenon. Understanding media depictions of online shaming is important, as it may have broader implications for public perceptions, debate, and support of policies and other related processes.
Living optical random neural network with three dimensional tumor spheroids for cancer morphodynamics

Abstract Optical neural networks process information at the speed of light and are energetically efficient. Photonic artificial intelligence allows speech recognition, image classification, and Ising machines. Modern machine learning paradigms, as extreme learning machines, reveal that disordered and biological materials may realize optical neural networks with thousands of nodes trained only at the input and at the readout. May we use living matter for machine learning? Here, we employ living three-dimensional tumor brain models to demonstrate a random optical learning machine (ROM) for the investigation of glioblastoma. The tumor spheroid act as a computational reservoir. The ROM detects cancer morphodynamics by laser-induced hyperthermia, quantifies chemotherapy, and cell metabolism. The ROM is a sensitive noninvasive smart probe for cytotoxicity assay and enables real-time investigation of tumor dynamics. We hence design and demonstrate a novel bio-hardware for optical computing and the study of light/complex matter interaction.