Reviewerly/exHarmony huggingface dataset:
Phase unwrapping in ICF target interferometric measurement via deep learning

This paper proposes an unwrapping algorithm based on deep learning for inertial confinement fusion (ICF) target interferograms. With a deep convolutional neural network (CNN), the task of phase unwrapping is transferred into a problem of semantic segmentation. A method for producing the data set for the ICF target measurement system is demonstrated. The noisy wrapped phase is preprocessed using a guided filter. Postprocessing is introduced to refine the final result, ensuring the proposed method can still accurately unwrap the phase even when the segmentation result of the CNN is not perfect. Simulations and actual interferograms show that our method has better accuracy and antinoise ability than some classical unwrapping approaches. In addition, the generalization capability of our method is verified by successfully applying it to an aspheric nonnull test system. By adjusting the data set, the proposed method may be transferred to other systems.
Identification of Scams in Initial Coin Offerings With Machine Learning

Following the emergence of cryptocurrencies, the field of digital assets experienced a sudden explosion of interest among institutional investors. However, regarding ICOs, there were a lot of scams involving the disappearance of firms after they had collected significant amounts of funds. We study how well one can predict if an offering will turn out to be a scam, doing so based on the characteristics known ex-ante. We therefore examine which of these characteristics are the most important predictors of a scam, and how they influence the probability of a scam. We use detailed data with 160 features from about 300 ICOs that took place before March 2018 and succeeded in raising most of their required capital. Various machine learning algorithms are applied together with novel XAI tools in order to identify the most important predictors of an offering’s failure and understand the shape of relationships. It turns out that based on the features known ex-ante, one can predict a scam with an accuracy of about 65–70%, and that nonlinear machine learning models perform better than traditional logistic regression and its regularized extensions.
Data-Driven 3D Reconstruction of Dressed Humans From Sparse Views

Recently, data-driven single-view reconstruction methods have shown great progress in modeling 3D dressed humans. However, such methods suffer heavily from depth ambiguities and occlusions inherent to single view inputs. In this paper, we tackle this problem by considering a small set of input views and investigate the best strategy to suitably exploit information from these views. We propose a data-driven end-to-end approach that reconstructs an implicit 3D representation of dressed humans from sparse camera views. Specifically, we introduce three key components: first a spatially consistent reconstruction that allows for arbitrary placement of the person in the input views using a perspective camera model; second an attention-based fusion layer that learns to aggregate visual information from several viewpoints; and third a mechanism that encodes local 3D patterns under the multi-view context. In the experiments, we show the proposed approach outperforms the state of the art on standard data both quantitatively and qualitatively. To demonstrate the spatially consistent reconstruction, we apply our approach to dynamic scenes. Additionally, we apply our method on real data acquired with a multi-camera platform and demonstrate our approach can obtain results comparable to multi-view stereo with dramatically less views.
RDFsim: Similarity-Based Browsing over DBpedia Using Embeddings

Browsing has been the core access method for the Web from its beginning. Analogously, one good practice for publishing data on the Web is to support dereferenceable URIs, to also enable plain web browsing by users. The information about one URI is usually presented through HTML tables (such as DBpedia and Wikidata pages) and graph representations (by using tools such as LODLive and LODMilla). In most cases, for an entity, the user gets all triples that have that entity as subject or as object. However, sometimes the number of triples is numerous. To tackle this issue, and to reveal similarity (and thus facilitate browsing), in this article we introduce an interactive similarity-based browsing system, called RDFsim, that offers “Parallel Browsing”, that is, it enables the user to see and browse not only the original data of the entity in focus, but also the K most similar entities of the focal entity. The similarity of entities is founded on knowledge graph embeddings; however, the indexes that we introduce for enabling real-time interaction do not depend on the particular method for computing similarity. We detail an implementation of the approach over specific subsets of DBpedia (movies, philosophers and others) and we showcase the benefits of the approach. Finally, we report detailed performance results and we describe several use cases of RDFsim.
Verifying single-mode nonclassicality beyond negativity in phase space

While negativity in phase space is a well-known signature of nonclassicality, a wide variety of nonclassical states require their characterization beyond negativity. We establish a framework of nonclassicality in phase space that addresses nonclassical states comprehensively with a direct experimental evidence. This includes the negativity of phase-space distribution as a special case and further analyzes quantum states with positive distributions effectively. We prove that it detects all nonclassical Gaussian states and all non-Gaussian states of arbitrary dimension remarkably by examining three phase-space points only. Our formalism also provides an experimentally accessible lower bound for a nonclassicality measure based on trace distance. Importantly, this foundational approach can be further adapted to constitute practical tests in two directions looking into particle and wave nature of bosonic systems, via an array of nonideal on-off detectors and coarse-grained homodyne measurement, respectively. All these tests are practically powerful in characterizing nonclasssical states reliably against noise, making a versatile tool for a broad range of quantum systems in quantum technologies.
A quantum Hopfield associative memory implemented on an actual quantum processor

In this work, we present a Quantum Hopfield Associative Memory (QHAM) and demonstrate its capabilities in simulation and hardware using IBM Quantum Experience. The QHAM is based on a quantum neuron design which can be utilized for many different machine learning applications and can be implemented on real quantum hardware without requiring mid-circuit measurement or reset operations. We analyze the accuracy of the neuron and the full QHAM considering hardware errors via simulation with hardware noise models as well as with implementation on the 15-qubit ibmq_16_melbourne device. The quantum neuron and the QHAM are shown to be resilient to noise and require low qubit overhead and gate complexity. We benchmark the QHAM by testing its effective memory capacity and demonstrate its capabilities in the NISQ-era of quantum hardware. This demonstration of the first functional QHAM to be implemented in NISQ-era quantum hardware is a significant step in machine learning at the leading edge of quantum computing.
<b>BNPmix</b>: An <i>R</i> Package for Bayesian Nonparametric Modeling via Pitman-Yor Mixtures

BNPmix is an R package for Bayesian nonparametric multivariate density estimation, clustering, and regression, using Pitman-Yor mixture models, a flexible and robust generalization of the popular class of Dirichlet process mixture models. A variety of model specifications and state-of-the-art posterior samplers are implemented. In order to achieve computational efficiency, all sampling methods are written in C++ and seamless integrated into R by means of the Rcpp and RcppArmadillo packages. BNPmix exploits the ggplot2 capabilities and implements a series of generic functions to plot and print summaries of posterior densities and induced clustering of the data.
Assessing design buildability through virtual reality from the perspective of construction students

Purpose The purpose of this paper is to evaluate the impact of visual models on the ability of construction students to assess design buildability. Design/methodology/approach The study engaged 45 construction students from one selected tertiary education institute in New Zealand. The data collection process involved meeting the students face-to-face and demonstrating the VR model to them, after which the students completed an online questionnaire and assessed design buildability using both 2D drawing and virtual reality (VR) models. To make this assessment, the participants considered a residential earth building modelled to promote sustainable building features. The assessment process required the participants to evaluate the design buildability of the same building design using a 2D drawing and a 3D VR model. Findings The study found that VR models have significant advantages for assessing design buildability. Students measured 16.80% higher average buildability with the 3D VR model compared to the 2D drawing. The participants in the evaluation felt that the visual model significantly improved the comprehensibility of complex designs, which helped identify and manage design buildability (overall, 83% of participants strongly supported this). Originality/value The paper showed construction digitisation such as VR, augmented reality and building information modelling is highly cooperative as it can easily be made available for online learning. Thus, the findings support construction educators use online-based VR learning to promote efficient teaching of design buildability to students.
How to Put It Plainly? Findings From Two Randomized Controlled Studies on Writing Plain Language Summaries for Psychological Meta-Analyses

Plain language summaries (PLS) aim to communicate research findings to laypersons in an easily understandable manner. Despite the societal relevance of making psychological research findings available to the public, our empirical knowledge on how to write PLS of psychology studies is still scarce. In this article, we present two experimental studies investigating six characteristics of PLS for psychological meta-analyses. We specifically focused on approaches for (1) handling technical terms, (2) communicating the quality of evidence by explaining the methodological approach of meta-analyses, (3) explaining how synthesized studies operationalized their research questions, (4) handling statistical terms, (5) structuring PLS, and (6) explaining complex meta-analytic designs. To develop empirically validated guidelines on writing PLS, two randomized controlled studies including large samples stratified for education status, age, and gender (NStudy1=2,288 and NStudy2=2,211) were conducted. Eight PLS of meta-analyses from different areas of psychology were investigated as study materials. Main outcome variables were user experience (i.e., perceived accessibility, perceived understanding, and perceived empowerment) and knowledge acquisition, as well as understanding and knowledge of the quality of evidence. Overall, our hypotheses were partially confirmed, with our results underlining, among other things, the importance of explaining or replacing content-related technical terms (i.e., theoretical concepts) and indicating the detrimental effects of providing too many details on statistical concepts on user experience. Drawing on these and further findings, we derive five empirically well-founded rules on the lay-friendly communication of meta-analytic research findings in psychology. Implications for PLS authors and future research on PLS are discussed.
Maternal health and Indigenous traditional midwives in southern Mexico: contextualisation of a scoping review

Collate published evidence of factors that affect maternal health in Indigenous communities and contextualise the findings with stakeholder perspectives in the Mexican State of Guerrero.Scoping review and stakeholder fuzzy cognitive mapping.The scoping review included empirical studies (quantitative, qualitative or mixed methods) that addressed maternal health issues among Indigenous communities in the Americas and reported on the role or influence of traditional midwives before June 2020. The contextualisation drew on two previous studies of traditional midwife and researcher perspectives in southern Mexico.The initial search identified 4461 references. Of 87 selected studies, 63 came from Guatemala and Mexico. Three small randomised trials involved traditional midwives. One addressed the practice of traditional midwifery. With diverse approaches to cultural differences, the studies used contrasting definitions of traditional midwives. A fuzzy cognitive map graphically summarised the influences identified in the scoping review. When we compared the literature's map with those from 29 traditional midwives in Guerrero and eight international researchers, the three sources coincided in the importance of self-care practices, rituals and traditional midwifery. The primary concern reflected in the scoping review was access to Western healthcare, followed by maternal health outcomes. For traditional midwives, the availability of hospital or health centre in the community was less relevant and had negative effects on other protective influences, while researchers conditioned its importance to its levels of cultural safety. Traditional midwives highlighted the role of violence against women, male involvement and traditional diseases.The literature and stakeholder maps showed maternal health resulting from complex interacting factors in which promotion of cultural practices was compatible with a protective effect on Indigenous maternal health. Future research challenges include traditional concepts of diseases and the impact on maternal health of gender norms, self-care practices and authentic traditional midwifery.
Graph Neural Networks: Graph Matching

The problem of graph matching that tries to establish some kind of structural correspondence between a pair of graph-structured objects is one of the key challenges in a variety of real-world applications. In general, the graph matching problem can be classified into two categories: i) the classic graph matching problem which finds an optimal node-to-node correspondence between nodes of a pair of input graphs and ii) the graph similarity problem which computes a similarity metric between two graphs. While recent years have witnessed the great success of GNNs in learning node representations of graphs, there is an increasing interest in exploring GNNs for the graph matching problem in an end-to-end manner. This chapter focuses on the state of the art of graph matching models based on GNNs. We start by introducing some backgrounds of the graph matching problem. Then, for each category of graph matching problem, we provide a formal definition and discuss state-of-the-art GNN-based models for both the classic graph matching problem and the graph similarity problem, respectively. Finally, this chapter is concluded by pointing out some possible future research directions.
Limits of Preprocessing for Single-Server PIR

Previous chapter Next chapter Full AccessProceedings Proceedings of the 2022 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA)Limits of Preprocessing for Single-Server PIRGiuseppe Persiano and Kevin YeoGiuseppe Persiano and Kevin Yeopp.2522 - 2548Chapter DOI:https://doi.org/10.1137/1.9781611977073.99PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract We present lower bounds for the static cryptographic data structure problem of single-server private information retrieval (PIR). PIR considers the setting where a server holds a database of n entries and a client wishes to privately retrieve the i-th entry without revealing the index i to the server. In our work, we focus on PIR with preprocessing where an r-bit hint may be computed in a preprocessing stage and stored by the server to be used to perform private queries in expected time t. As our main result, we prove that for any single-server, computationally secure PIR with preprocessing, it must be that tr = Ω(n log n) when r = Ω(log n). If r = O(log n), then we show that t = Ω(n). Our lower bound holds even when the scheme errs with probability 1/n2 and the adversary's distinguishing advantage is 1/n. Our work improves upon the tr = Ω(n) lower bound of Beimel, Ishai and Malkin [JoC'04]. For information-theoretic security, we present a stronger lower bound of t + r = Ω(n) and show a matching construction. Both our lower bounds apply for public-key doubly-efficient PIRs of Boyle, Ishai, Pass and Wootters [TCC'17]. Additionally, our lower bound for information-theoretic security also applies for offline-online PIRs as defined by Corrigan-Gibbs and Kogan [Eurocrypt'20], where the hint is private and only viewed by the client. We prove our lower bounds in a variant of the cell probe model where only accesses to the database are charged cost and computation and accesses to the hint are free. Our main technical contribution is a novel use of the cell sampling technique (also known as the incompressibility technique) used to obtain lower bounds on data structures. In previous works, this technique only leveraged the correctness guarantees to prove lower bounds even when used for cryptographic primitives. Our work combines the cell sampling technique with the privacy guarantees of PIR to construct a powerful, polynomial-time adversary that is critical to proving our higher lower bounds. Previous chapter Next chapter RelatedDetails Published:2022eISBN:978-1-61197-707-3 https://doi.org/10.1137/1.9781611977073Book Series Name:ProceedingsBook Code:PRDA22Book Pages:xvii + 3771
Liver segmentation in CT imaging with enhanced mask region-based convolutional neural networks

Liver segmentation in computed tomography (CT) imaging has been widely investigated as a crucial step for analyzing liver characteristics and diagnosing liver diseases. However, obtaining satisfactory liver segmentation performance is highly challenging because of the poor contrast between the liver and its surrounding organs and tissues, the high levels of CT image noise, and the wide variability in liver shapes among patients.To overcome these challenges, we propose a novel method for liver segmentation in CT image sequences. This method uses an enhanced mask region-based convolutional neural network (Mask R-CNN) with graph-cut segmentation. Specifically, the k-nearest neighbor (k-NN) algorithm is employed to cluster the target liver pixels in order to get an appropriate aspect ratio. Then, anchors are adapted to the liver size using the ratio information. Thus, high-accuracy liver localization can be achieved using the anchors and rotation-invariant object recognition. Next, a fully convolutional network (FCN) is used to segment the foreground objects, and local fine-grained liver detection is realized by pixel prediction. Finally, a whole liver mask is obtained by Mask R-CNN proposed in this paper.We proposed a Mask R-CNN algorithm which achieved superior performance in comparison with the conventional Mask R-CNN algorithms in term of the dice similarity coefficient (DSC), and the Medical Image Computing and Computer-Assisted Intervention (MICCAI) metrics.Our experimental results demonstrate that the improved Mask R-CNN architecture has good performance, accuracy, and robustness for liver segmentation in CT image sequences.
Configuration Space Decomposition for Scalable Proxy Collision Checking in Robot Planning and Control

Real-time robot motion planning in complex high-dimensional environments remains an open problem. Motion planning algorithms, and their underlying collision checkers, are crucial to any robot control stack. Collision checking takes up a large portion of the computational time in robot motion planning. Existing collision checkers make trade-offs between speed and accuracy and scale poorly to high-dimensional, complex environments. We present a novel space decomposition method using K-Means clustering in the Forward Kinematics space to accelerate proxy collision checking. We train individual configuration space models using Fastron, a kernel perceptron algorithm, on these decomposed subspaces, yielding compact yet highly accurate models that can be queried rapidly and scale better to more complex environments. We demonstrate this new method, called Decomposed Fast Perceptron (D-Fastron), on the 7-DOF Baxter robot producing on average 29x faster collision checks and up to 9.8x faster motion planning compared to state-of-the-art geometric collision checkers.
Where horizontal and vertical surveillances meet: Sense-making of US COVID-19 contact-tracing apps during a health crisis

Analyzing user reviews of seven US digital contact-tracing apps for COVID-19, this article unpacks how the new form of surveillance technology is understood and experienced by individuals during a global health crisis. The findings suggest that the app users felt empowered via self-tracking capacity and expressed community-level care and concerns, including those regarding the marginalized. At the same time, the users were raising doubts over technical effectiveness, navigating varying levels of voluntary choice available, and negotiating privacy concerns depending on the (dis)trust they held of institutional entities behind the governance of the apps. We argue that it is critical to investigate how surveillance technologies are situated across horizontal and vertical relationships in people's everyday lives to fully understand the individual and societal acceptance and/or refusal of the very systems during crises.
How Tangible is TEI? Exploring Swatches as a New Academic Publication Format

After the successful organization of the first TEI Swatch Exchange as part of the SDC (Student Design Challenge) in 2021, we would like to propose Swatches as a new and physical publication format to TEI, the Conference on Tangible, Embedded, and Embodied Interaction. We believe TEI should take initiative in exploring possibilities of broadening how academic publishing can look like, and what novel publication formats could be inclusive towards new knowledges and new participants in order to advance academic practice. The focus and nature of the work presented at TEI is uniquely fitted to explore physical publication formats as a novel way to advance, communicate, and cite work that embeds and embodies computing in the physical world. However, we are aware that a physical publication format also poses very specific challenges, including questions of dissemination, accessibility, and citation, to name a few. We propose a Studio to deeply engage with the possibilities and potentials, as well as potential problems of physical publications. We see such discussion as a crucial first step in defining plausible submission processes, publication forms, and means of archiving of new academic formats.