Reviewerly/exHarmony huggingface dataset:
Publicly Verifiable Boolean Query Over Outsourced Encrypted Data

Outsourcing storage and computation to the cloud has become a common practice for businesses and individuals. As the cloud is semi-trusted or susceptible to attacks, many researches suggest that the outsourced data should be encrypted and then retrieved by using searchable symmetric encryption (SSE) schemes. Since the cloud is not fully trusted, we doubt whether it would always process queries correctly or not. Therefore, there is a need for users to verify their query results. Motivated by this, in this paper, we propose a publicly verifiable dynamic searchable symmetric encryption scheme based on the accumulation tree. We first construct an accumulation tree based on encrypted data and then outsource both of them to the cloud. Next, during the search operation, the cloud generates the corresponding proof according to the query result by mapping Boolean query operations to set operations, while keeping privacy preservation and achieving the verification requirements: freshness, authenticity, and completeness. Finally, we extend our scheme by dividing the accumulation tree into different small accumulation trees to make our scheme scalable. The security analysis and performance evaluation show that the proposed scheme is secure and practical.
Chebyshev’s bias for analytic<i>L</i>-functions

Abstract We discuss the generalizations of the concept of Chebyshev’s bias from two perspectives. First, we give a general framework for the study of prime number races and Chebyshev’s bias attached to general L -functions satisfying natural analytic hypotheses. This extends the cases previously considered by several authors and involving, among others, Dirichlet L -functions and Hasse–Weil L -functions of elliptic curves over Q . This also applies to new Chebyshev’s bias phenomena that were beyond the reach of the previously known cases. In addition, we weaken the required hypotheses such as GRH or linear independence properties of zeros of L -functions. In particular, we establish the existence of the logarithmic density of the set $ \{x \ge 2:\sum\nolimits_{p \le x} {\lambda _f}(p) \ge 0\}$ for coefficients ( λ f ( p )) of general L -functions conditionally on a much weaker hypothesis than was previously known.
Communication efficient quantum secret sharing

A quantum secret sharing scheme is a cryptographic protocol by which a dealer can share a secret among a group of $n$ players so that only certain subsets of players can recover the secret by collaboration. In this paper we propose communication efficient quantum threshold secret sharing schemes. They minimize the amount of quantum communication required to reconstruct the secret when more than the necessary number of players collaborate. They are based on a class of staircase codes proposed by Bitar and El Rouayheb. In a standard $((k,n))$ quantum threshold scheme, any subset of $k$ or more players can recover the secret. The quantum communication cost for reconstruction in such schemes is $k$ qudits for each secret qudit. Using the proposed construction, any subset of $d\ensuremath{\ge}k$ players can also collaborate to recover the secret with a communication cost of $d$ qudits for $d\ensuremath{-}k+1$ secret qudits. In other words, for the proposed schemes the quantum communication cost is only $\frac{d}{d\ensuremath{-}k+1}$ qudits for every secret qudit. For $d>k$, proposed schemes are communication efficient with respect to standard schemes; and when $d=2k\ensuremath{-}1$, the quantum communication cost is reduced by a factor $O(k)$. Further, when $n=2k\ensuremath{-}1$, the proposed schemes have optimal communication cost for secret reconstruction.
Practical and reliable error bars for quantum process tomography

Current techniques in quantum process tomography typically return a single point estimate of an unknown process based on a finite albeit large amount of measurement data. Due to statistical fluctuations, however, other processes close to the point estimate can also produce the observed data with near certainty. Unless appropriate error bars can be constructed, the point estimate does not carry any sound operational interpretation. Here, we provide a solution to this problem by constructing a confidence region estimator for quantum processes. Our method enables reliable estimation of essentially any figure of merit for quantum processes on few qubits, including the diamond distance to a specific noise model, the entanglement fidelity, and the worst-case entanglement fidelity, by identifying error regions which contain the true state with high probability. We also provide a software package, QPtomographer, implementing our estimator for the diamond norm and the worst-case entanglement fidelity. We illustrate its usage and performance with several simulated examples. Our tools can be used to reliably certify the performance of, e.g., error correction codes, implementations of unitary gates, or more generally any noise process affecting a quantum system.
Graphicle: Exploring Units, Networks, and Context in a Blended Visualization Approach

Many real-world datasets are large, multivariate, and relational in nature and relevant associated decisions frequently require a simultaneous consideration of both attributes and connections. Existing visualization systems and approaches, however, often make an explicit trade-off between either affording rich exploration of individual data units and their attributes or exploration of the underlying network structure. In doing so, important analysis opportunities and insights are potentially missed. In this study, we aim to address this gap by (1) considering visualizations and interaction techniques that blend the spectrum between unit and network visualizations, (2) discussing the nature of different forms of contexts and the challenges in implementing them, and (3) demonstrating the value of our approach for visual exploration of multivariate, relational data for a real-world use case. Specifically, we demonstrate through a system called Graphicle how network structure can be layered on top of unit visualization techniques to create new opportunities for visual exploration of physician characteristics and referral data. We report on the design, implementation, and evaluation of the system and effectiveness of our blended approach.
Many-particle interference to test Born's rule

Born's rule relates detection probabilities to the modulus square of the wave function. It is one of the central principles of quantum mechanics together with that of linear superposition. Single-particle interference is accordingly limited to pairs of quantum paths and higher-order interferences are prohibited. Deviations from Born's law have been quantified via the Sorkin parameter which is proportional to the third-order term. However, while the linearity of quantum theory has been experimentally tested to the level of 10−20 eV, the Sorkin parameter has only been measured to an accuracy of 2×10−3 in the quantum regime. We here investigate Born's law using many-particle interferences and demonstrate that all interference terms of order (2M+1) and greater vanish for M particles. We further introduce a family of many-particle Sorkin parameters and show that they are exponentially more sensitive to deviations from Born's rule than their single-particle counterpart.Received 19 October 2018Revised 12 November 2019Accepted 10 February 2020DOI:https://doi.org/10.1103/PhysRevResearch.2.012051Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasOptical tests of quantum theoryQuantum foundationsQuantum opticsAtomic, Molecular & OpticalGeneral Physics
Discontinuity Preserving Liver MR Registration With Three-Dimensional Active Contour Motion Segmentation

The sliding motion of the liver during respiration violates the homogeneous motion smoothness assumption in conventional non-rigid image registration and commonly results in compromised registration accuracy. This paper presents a novel approach, registration with 3D active contour motion segmentation (RAMS), to improve registration accuracy with discontinuity-aware motion regularization.A Markov random field-based discrete optimization with dense displacement sampling and self-similarity context metric is used for registration, while a graph cuts-based 3D active contour approach is applied to segment the sliding interface. In the first registration pass, a mask-free L1 regularization on an image-derived minimum spanning tree is performed to allow motion discontinuity. Based on the motion field estimates, a coarse segmentation finds the motion boundaries. Next, based on MR signal intensity, a fine segmentation aligns the motion boundaries with anatomical boundaries. In the second registration pass, smoothness constraints across the segmented sliding interface are removed by masked regularization on a minimum spanning forest and masked interpolation of the motion field.For in vivo breath-hold abdominal MRI data, the motion masks calculated by RAMS are highly consistent with manual segmentations in terms of Dice similarity and bidirectional local distance measure. These automatically obtained masks are shown to substantially improve registration accuracy for both the proposed discrete registration as well as conventional continuous non-rigid algorithms.The presented results demonstrated the feasibility of automated segmentation of the respiratory sliding motion interface in liver MR images and the effectiveness of using the derived motion masks to preserve motion discontinuity.
Data Management Practices in Academic Library Learning Analytics: A Critical Review

INTRODUCTION Data handling in library learning analytics plays a pivotal role in protecting patron privacy, yet the landscape of data management by librarians is poorly understood. METHODS This critical review examines data-handling practices from 54 learning analytics studies in academic libraries and compares them against the NISO Consensus Principles on User’s Digital Privacy in Library, Publisher, and Software-Provider Systems and data management best practices. RESULTS A number of the published research projects demonstrate inadequate data protection practices including incomplete anonymization, prolonged data retention, collection of a broad scope of sensitive information, lack of informed consent, and sharing of patron-identified information. DISCUSSION As with researchers more generally, libraries should improve their data management practices. No studies aligned with the NISO Principles in all evaluated areas, but several studies provide specific exemplars of good practice. CONCLUSION Libraries can better protect patron privacy by improving data management practices in learning analytics research.
Psycho-Pedagogical Support of Students Project Activities in Multi-functional Production Laboratories (Fab Lab) on the Basis of Technical University

The article deals with the organization of psychological and pedagogical support of project activities of students on the example of fab lab. The authors describe the model of psychological and pedagogical support of project activities of students, define the psychological and pedagogical support of this activity, and consider the principles of its organization. The article presents a study of communicative competence, social intelligence and motivational orientations in interpersonal communications.
Richness of Deep Echo State Network Dynamics

Reservoir Computing (RC) is a popular methodology for the efficient design of Recurrent Neural Networks (RNNs). Recently, the advantages of the RC approach have been extended to the context of multi-layered RNNs, with the introduction of the Deep Echo State Network (DeepESN) model. In this paper, we study the quality of state dynamics in progressively higher layers of DeepESNs, using tools from the areas of information theory and numerical analysis. Our experimental results on RC benchmark datasets reveal the fundamental role played by the strength of inter-reservoir connections to increasingly enrich the representations developed in higher layers. Our analysis also gives interesting insights into the possibility of effective exploitation of training algorithms based on stochastic gradient descent in the RC field.
Learning Low-Rank Regularized Generic Representation With Block-Sparse Structure for Single Sample Face Recognition

In this paper, we propose a novel low-rank regularized generic representation method to address the single sample per person problem in face recognition, which simultaneously employs the structure information from the probe dataset and the generic variation information.Each face image is divided into overlapped patches, whose classification results are aggregated to produce the final result.We generate a subspace for each patch by extracting its eight nearest neighbor patches and explore the relationships between subspaces by imposing a low-rank constraint on the reconstruction coefficients.Moreover, a block-sparsity constraint on the coefficient matrix is imposed to further promote the discrimination of representations.We also propose a dictionary learning method to learn the intra-class facial variations from the generic face datasets, which separates the face contour noise from the variation dictionary by an incoherence regularization item.The experimental results on four public face databases not only show the robustness of our approach to expression, illumination, occlusion, and time variation but also demonstrate the effectiveness of face sketch recognition.
Geochemical background and threshold for 47 chemical elements in Slovenian topsoil

Geochemical background and threshold values need to be established to identify areas with unusually high concentrations of elements. High concentrations are caused by natural or anthropogenic processes. The
Quantifying the Incompatibility of Quantum Measurements Relative to a Basis

Motivated by quantum resource theories, we introduce a notion of incompatibility for quantum measurements relative to a reference basis. The notion arises by considering states diagonal in that basis and investigating whether probability distributions associated with different quantum measurements can be converted into one another by probabilistic postprocessing. The induced preorder over quantum measurements is directly related to multivariate majorization and gives rise to families of monotones, i.e., scalar quantifiers that preserve the ordering. For the case of orthogonal measurement we establish a quantitative connection between incompatibility, quantum coherence and entropic uncertainty relations. We generalize the construction to include arbitrary positive-operator-valued measurements and report complete families of monotones.
Automatically Extracting Bug Reproducing Steps from Android Bug Reports

Many modern software projects use bug-tracking systems (e.g., Bugzilla, Google Code Issue Tracker) to track software issues and help developers reproduce these issues. There has been recent work on automatically translating the natural language text (i.e., steps to reproduce) of bug reports to reproducing scripts, targeted at Android apps, to facilitate app debugging process. The scripts describe the event sequences leading to the app issues and thus can be reused for testing newer versions of the apps. However, existing techniques require manually providing the text description of steps to reproduce for generating reproducing scripts, which is a non-trivial task because natural language text in bug reports can be complex and contain much information irrelevant for bug reproduction. In this paper, we propose an approach that can automatically extract the text description of steps to reproduce (S2R) from bug reports to advance automated software issue diagnosis and test script reuse. The approach is implemented as a tool, called S2RMiner, which combines HTML parsing, natural language processing, and machine learning techniques. We have evaluated S2RMiner on 1000 original Android bug reports. The results show that S2RMiner can extract S2R with high accuracy.
The advent of <scp>AI</scp> and deep learning in diagnostics and imaging

Machine learning systems are increasingly applied in health care and the life sciences with great potential for cancer diagnostics and optical microscopy.
A Novel Neural Network for Solving Nonsmooth Nonconvex Optimization Problems

In this paper, a novel recurrent neural network (RNN) is presented to deal with a kind of nonsmooth nonconvex optimization problem in which the objective function may be nonsmooth and nonconvex, and the constraints include linear equations and convex inequations. Under certain suitable assumptions, from an arbitrary initial state, each solution to the proposed RNN exists globally and is bounded, and it enters the feasible region within a limited time. Moreover, the solution to the RNN with an arbitrary initial state can converge to the critical point set of the optimization problem. In particular, the RNN does not need the following: 1) abounded feasible region; 2) the computation of an exact penalty parameter; or 3) the initial state being chosen from a given bounded set. Numerical experiments are provided to show the effectiveness and advantages of the RNN.
Feature Evolution Based Multi-Task Learning for Collaborative Filtering with Social Trust

Social recommendation could address the data sparsity and cold-start problems for collaborative filtering by leveraging user trust relationships as auxiliary information for recommendation. However, most existing methods tend to consider the trust relationship as preference similarity in a static way and model the representations for user preference and social trust via a common feature space. In this paper, we propose TrustEV and take the view of multi-task learning to unite collaborative filtering for recommendation and network embedding for user trust. We design a special feature evolution unit that enables the embedding vectors for two tasks to exchange their features in a probabilistic manner, and further harness a meta-controller to globally explore proper settings for the feature evolution units. The training process contains two nested loops, where in the outer loop, we optimize the meta-controller by Bayesian optimization, and in the inner loop, we train the feedforward model with given feature evolution units. Experiment results show that TrustEV could make better use of social information and greatly improve recommendation MAE over state-of-the-art approaches.
Multilayer ion trap with three-dimensional microwave circuitry for scalable quantum logic applications

We present a multilayer surface-electrode ion trap with embedded 3D microwave circuitry for implementing entangling quantum logic gates. We discuss the electromagnetic full-wave simulation procedure that has led to the trap design and the characterization of the resulting microwave field pattern using a single ion as a local field probe. The results agree with simulations within the uncertainty; compared to previous traps, this design reduces detrimental AC Zeeman shifts by three orders of magnitude. The design presented here can be viewed as an entangling gate component in a library for surface-electrode ion traps intended for quantum logic operations.
Remote explainability faces the bouncer problem

The concept of explainability is envisioned to satisfy society’s demands for transparency about machine learning decisions. The concept is simple: like humans, algorithms should explain the rationale behind their decisions so that their fairness can be assessed. Although this approach is promising in a local context (for example, the model creator explains it during debugging at the time of training), we argue that this reasoning cannot simply be transposed to a remote context, where a model trained by a service provider is only accessible to a user through a network and its application programming interface. This is problematic, as it constitutes precisely the target use case requiring transparency from a societal perspective. Through an analogy with a club bouncer (who may provide untruthful explanations upon customer rejection), we show that providing explanations cannot prevent a remote service from lying about the true reasons leading to its decisions. More precisely, we observe the impossibility of remote explainability for single explanations by constructing an attack on explanations that hides discriminatory features from the querying user. We provide an example implementation of this attack. We then show that the probability that an observer spots the attack, using several explanations for attempting to find incoherences, is low in practical settings. This undermines the very concept of remote explainability in general. When automated decisions are provided by a company without providing the full model, users and law makers might demand a ‘right to an explanation’. Le Merrer and Trédan show that malicious manipulations of these explanations are hard to detect, even for simple strategies to obscure the model’s decisions.
Copyright as an obstacle or an enabler? A European perspective on text and data mining and its role in the development of AI creativity

Text and data mining (TDM) may be performed in a variety of fields and for different purposes. Among other things, TDM techniques may be used to ‘train’ Artificial Intelligence (AI), also for the purpose of AI-driven creativity. In this context, copyright restrictions might be in place, even if copies made of pre-existing content are only used internally and are instrumental to the creation of something ‘new’. Recently, in the context of the Directive 2019/790 on Copyright in the Digital Single Market (DSM Directive), the European Union (EU) legislature introduced two new mandatory exceptions for TDM. This article discusses the interplay between TDM and AI creativity by focusing, first, on the potential and technicalities of TDM and the interplay with legal restrictions. Second, it reviews the copyright-related issues facing TDM and the debate underlying the adoption of this new piece of EU legislation. Third, it reflects on the future of AI creativity in Europe. This article concludes that, even despite the adoption of two new mandatory TDM exceptions in the DSM Directive which are now to be transposed by individual EU Member States, copyright restrictions might continue affecting and restricting significantly the possibility of undertaking TDM activities in Europe.
Computational Strategies for Scalable Genomics Analysis

The revolution in next-generation DNA sequencing technologies is leading to explosive data growth in genomics, posing a significant challenge to the computing infrastructure and software algorithms for genomics analysis. Various big data technologies have been explored to scale up/out current bioinformatics solutions to mine the big genomics data. In this review, we survey some of these exciting developments in the applications of parallel distributed computing and special hardware to genomics. We comment on the pros and cons of each strategy in the context of ease of development, robustness, scalability, and efficiency. Although this review is written for an audience from the genomics and bioinformatics fields, it may also be informative for the audience of computer science with interests in genomics applications.