Reviewerly/exHarmony huggingface dataset:
New Frontiers of Profit and Risk: The Fourth Industrial Revolution’s Impact on Business and Human Rights

The Fourth Industrial Revolution, characterised by the fusion of physical, digital, and biological technologies, will have profound social and economic consequences. But what impact will it have on human rights? This article critically interrogates key writings and speeches about the Fourth Industrial Revolution, which requires reading between the lines of technical, business, and policy-orientated materials that rarely address human rights explicitly. The findings are a mix of good and bad news. First, the traditional linkage between economic competition and violations of bodily integrity is weakening as new forms of profitability in tomorrow's digital ecosystems require empowered and creative individuals. However, these celebratory visions of the profitable interpenetration of our bodies, hardware and virtual worlds has a dark side. The next-generation of human rights violations arising from transnational business activities will be increasingly subtle, diffuse, and sophisticated.
A MIMO Detector With Deep Learning in the Presence of Correlated Interference

In this paper, we investigate the classical detection problem for vehicle networks with multiple antennas, by considering practical communication scenarios, where the interfering signals are correlated over time or frequency. In such cases, the conventional detector requires to estimate the joint distribution of the interfering signals, which imposes a huge computational complexity. To tackle this issue, we propose the joint use of a maximum likelihood detector (MLD) and a deep convolutional neural network (DCNN), where MLD is used to produce an initial detection result and DCNN improves the detection by exploiting the local correlation to suppress the interference. Furthermore, the improved DCNN is enhanced by devising the loss function through the cross-entropy of the detection, which can help to suppress the interfering signals and simultaneously force the residual interference to approach the Gaussian distribution. Simulation results are presented to verify the effectiveness of the proposed detector compared to the conventional one. The trained model and source code for this work are available at https://github.com/skypitcher/project_dcnnmld.
Quantifying Quantum Speedups: Improved Classical Simulation From Tighter Magic Monotones

Consumption of magic states promotes the stabilizer model of computation to universal quantum computation. Here, we propose three different classical algorithms for simulating such universal quantum circuits, and characterize them by establishing precise connections with a family of magic monotones. Our first simulator introduces a new class of quasiprobability distributions and connects its runtime to a generalized notion of negativity. We prove that this algorithm has significantly improved exponential scaling compared to all prior quasiprobability simulators for qubits. Our second simulator is a new variant of the stabilizer-rank simulation algorithm, extended to work with mixed states and with significantly improved runtime bounds. Our third simulator trades precision for speed by discarding negative quasiprobabilities. We connect each algorithm’s performance to a corresponding magic monotone and, by comprehensively characterizing the monotones, we obtain a precise understanding of the simulation runtime and error bounds. Our analysis reveals a deep connection between all three seemingly unrelated simulation techniques and their associated monotones. For tensor products of single-qubit states, we prove that our monotones are all equal to each other, multiplicative and efficiently computable, allowing us to make clear-cut comparisons of the simulators’ performance scaling. Furthermore, our monotones establish several asymptotic and nonasymptotic bounds on state interconversion and distillation rates. Beyond the theory of magic states, our classical simulators can be adapted to other resource theories under certain axioms, which we demonstrate through an explicit application to the theory of quantum coherence.5 MoreReceived 13 March 2020Revised 11 September 2020Accepted 22 February 2021DOI:https://doi.org/10.1103/PRXQuantum.2.010345Published 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 AreasQuantum algorithmsQuantum computationQuantum simulationResource theoriesQuantum Information
Shape sensing of variable cross-section beam using the inverse finite element method and isogeometric analysis

The inverse finite element method (IFEM), which is used to reconstruct the displacement field from the discrete surface strain measurements, is of great significance to the management, control and driving of smart structures. However, the iFEM method based on constant cross-section beam elements proposed in previous works were no longer suitable for variable cross-section beam elements. To solve this problem, this paper proposes a new iFEM method for reconstructing the displacement field of variable cross-section beam based on isogeometric analysis. Firstly, the mechanical parameters of beam section are linearized, including section area, axial rigidity, shear rigidity, torsional rigidity and bending rigidity, and a new constitutive relations are established. Then, adhering to the constitutive equations and the small-strain hypothesis, the displacement equations of the theoretical deformation field are deduced. Nevertheless, considering that the deduced displacement equations can not be applied to the iFEM, this paper proposes a method for using isogeometric analysis instead of the original function, and the least-square method is used to establish the strain-displacement relation. Finally, to verify the validity and accuracy of the methodology, a concentrated load and a distributed load were applied to one airfoil in the experiment tests. The predicted displacements with previous iFEM and presented iFEM are compared with those experimentally measured values, respectively. The results show that the presented iFEM exhibited higher accuracy than the previous iFEM in the variable cross-section beam problem.
A heuristic representation learning based on evidential memberships: Case study of UCI-SPECTF

The diagnosed features (samples) with multiple attributes of medical images always demand experts to reveal insight. Up to today, machine learning often cannot be a helpful expert. The reason lies in lacking evidential granules carrying knowledge and evidence for inferential learning. The shortage slows down representation learning which aims at discovering expressions for featuring concepts. Therefore, this paper proposes evidential memberships carrying preferential relevance to build a heuristic representation learning. Empirically, it solves local features and global representations with maximum coverage under challenges of shallow bury. For illustration, it is implemented on the testing data set of UCI-SPECTF.
Enhancement of backtracking search algorithm for identifying soil parameters

Summary In this paper, an enhanced backtracking search algorithm (so‐called MBSA‐LS) for parameter identification is proposed with two modifications: (a) modifying the mutation of original backtracking search algorithm (BSA) considering the contribution of current best individual for accelerating convergence speed and (b) novelly incorporating an efficient differential evolution (DE) as local search for improving the quality of population. The proposed MBSA‐LS is first validated with better performance than the original BSA and some other typical state‐of‐the‐art optimization algorithms on a benchmark of soil parameter identification in terms of effectiveness, efficiency, and robustness. Then, the efficiency of the MBSA‐LS is further illustrated by two representative cases: identifying soil parameters from both laboratory tests and field measurements. All comparisons demonstrate that the proposed MBSA‐LS algorithm can give accurate results in a short time. Finally, to conveniently solve the problems of parameter identification, a practical tool ErosOpt for parameter identification is developed by integrating the proposed MBSA‐LS and some other efficient algorithms for readers to conduct the parameter identification using optimisation algorithms.
An optimized blind watermarking scheme based on principal component analysis in redundant discrete wavelet domain

The frequency of digital data transmission or sharing online has heavily increased due to the number of people regularly accessing the internet. This guide explores how easily copyright can be breached on a large scale. Digital multimedia in the form of images, text, audio and video can easily be forged, faked and then shared for profit. To address this issue, digital watermarking has been introduced as a possible solution. In this guide, an optimized blind image watermarking approach based on principal component analysis (PCA) in redundant discrete wavelet (R-DW) domain is proposed. In addition, several attacks are applied to a watermarked image by extracting the message from the attacked image to show the robustness of the proposed PCA-R-DW scheme. Furthermore, an improved grey-wolf optimizer (IGWO) algorithm which has emerged as an efficient meta-heuristic optimization technique is utilized to optimize the performance of the proposed PCA-R-DW blind watermarking (BW) approach. Extensive comparison of test results with conventional BW approaches show the robustness of the proposed framework in terms of quality metrics such as normalized correlation coefficient (NCC), peak signal-to-noise ratio (PSNR), mean square error (MSE) and structural similarity (SSIM) index.
Uncertainty-Aware Audiovisual Activity Recognition Using Deep Bayesian Variational Inference

Deep neural networks (DNNs) provide state-of-the-art results for a multitude of applications, but the approaches using DNNs for multimodal audiovisual applications do not consider predictive uncertainty associated with individual modalities. Bayesian deep learning methods provide principled confidence and quantify predictive uncertainty. Our contribution in this work is to propose an uncertainty aware multimodal Bayesian fusion framework for activity recognition. We demonstrate a novel approach that combines deterministic and variational layers to scale Bayesian DNNs to deeper architectures. Our experiments using in- and out-of-distribution samples selected from a subset of Moments-in-Time (MiT) dataset show a more reliable confidence measure as compared to the non-Bayesian baseline and the Monte Carlo dropout (MC dropout) approximate Bayesian inference. We also demonstrate the uncertainty estimates obtained from the proposed framework can identify out-of-distribution data on the UCF101 and MiT datasets. In the multimodal setting, the proposed framework improved precision-recall AUC by 10.2% on the subset of MiT dataset as compared to non-Bayesian baseline.
Scalable Data Storage Design for Nonstationary IoT Environment With Adaptive Security and Reliability

Internet-of-Things (IoT) environment has a dynamic nature with high risks of confidentiality, integrity, and availability violations. The loss of information, denial of access, information leakage, collusion, technical failures, and data security breaches are difficult to predict and anticipate in advance. These types of nonstationarity are one of the main issues in the design of the reliable IoT infrastructure capable of mitigating their consequences. It is not sufficient to propose solutions for a given scenario, but mechanisms to adapt the current solution to changes in the environment. In this article, we present a multicloud storage architecture called WA-MRC-RRNS that combines the weighted access scheme, threshold secret sharing, and redundant residue number system with multiple failure detection/recovery mechanisms and homomorphic ciphers. We provide a theoretical analysis of the probability of information loss, data redundancy, speed of encoding/decoding, and show how to dynamically configure parameters to cope with different objective preferences, workloads, and cloud properties. We propose a multiobjective optimization mechanism to adjust redundancy, encryption-decryption speed, and data loss probability. Comprehensive experimental analysis with real data shows that our approach provides a secure way to mitigate the uncertainty of the use of untrusted and not reliable IoT infrastructure.
Blockchain-based Interoperable Healthcare using Zero-Knowledge Proofs and Proxy Re-Encryption

The development of a robust, transparent and interoperable E-healthcare infrastructure has been a difficult task due to many regulations and legislatures like HIPAA (Health Insurance Portability and Accountability Act) and GDPR (General Data Protection Regulation). Healthcare service providers prefer to store data about their patients in locked up silos, behind often inadequate layers of security and firewalls. Such an approach results in data breaches and limits the ability to get a holistic view of the medical history of a patient. The obscure cost of treatment is another issue brought to attention recently in media. In this paper, we have proposed a national blockchain framework for managing patients' Electronic Health Records (EHRs) access control and funds in the context of India's National healthcare scheme. We introduce a transparent insurance claim process for healthcare providers and an auditable trail of EHR access using smart contracts. We use a smart card approach allowing beneficiaries to authenticate their identity using zero-knowledge proofs and delegate access to service the providers via proxy re-encryption.
A Secure Anonymous Identity-Based Scheme in New Authentication Architecture for Mobile Edge Computing

Mobile edge computing (MEC) provides computing and data storage capabilities within the range of the wireless access network close to the users, which is driving the need for new type of security technologies. Faced with rapid traffic growth and increasing user experience requirements, the existing security schemes can no longer meet the new requirements of real time and lightweight. New solutions that do not require complex calculations, such as identity-based cryptography, attract great attention from researchers. To meet these demands, we design a new authentication architecture for MEC environment, which provides secure and efficient communication. Based on this authentication architecture, an anonymous identity-based scheme is proposed for lightweight devices. In the meantime, user anonymity is also fully protected. The proposed scheme is proved to be secure under a specific security model. In addition, we describe the security properties our scheme meets. The comparisons of time consumption and communicational cost are given at the end of the article, to demonstrate that our scheme performs prior to several previous schemes.
Benchmarking on improvement and site-adaptation techniques for modeled solar radiation datasets

High-accuracy solar radiation data are needed in almost every solar energy project for bankability. Time series of solar irradiance components that spans decades can be supplied by satellite-derived irradiance or by reanalysis models, with very various types of uncertainty associated to the specific approaches taken and quality of boundary conditions information. In order to improve the reliability of these modeled datasets, comparison with ground measurements over a short period of time can be used for correcting some aspects, bias mainly, of the modeled data by using different methodologies; this procedure is known as site adaptation. Therefore, a benchmarking exercise that uses different site adaptation techniques was proposed within the Task 16 IEA-PVPS activities. In this work, over ten different site-adaptation techniques have been used for assessing the accuracy improvement, using ten different datasets covering both satellite-derived and reanalysis solar radiation data. The effectiveness of these methods is found not universal or spatially homogeneous, but in general, it can be stated that significant improvements can be achieved eventually in most sites and datasets.
Efficient synthetical clustering validity indexes for hierarchical clustering

Clustering validation and identifying the optimal number of clusters are of great importance in expert and intelligent systems. However, the commonly used similarity measures for validating are not versatile to measure the complex data structure, in reality, some of which are not as effective as that of the used clustering algorithm which gives the clustering results. This paper studies the validity indexes for the hierarchical clustering algorithm and proposes a unified validity index framework. For the single-linkage agglomerative hierarchical clustering we propose two efficient synthetical clustering validity (SCV) indexes using the minimum spanning tree to calculate the intra-cluster compactness to overcome the deficiencies of the measurements in the existing validity indexes. For the general hierarchical clustering, a self-adaptive similarity measure strategy and two generalized synthetical clustering validity (GSCV) indexes, which are the extension of the proposed SCV indexes, are developed. The proposed SCV and GSCV indexes constitute a unified validity index framework, where SCV index is a special case of GSCV index, can avoid the incompatibility of the similarity measure between the clustering and validation. The experimental comparisons with the state-of-the-art validity indexes on artificial and real-world data sets demonstrate the efficiency of the proposed validity indexes in discovering the true number of clusters and dealing with various sorts of data sets, including imbalanced data sets.
A Survey on Privacy Properties for Data Publishing of Relational Data

Recent advances in telecommunications and database systems have allowed the scientific community to efficiently mine vast amounts of information worldwide and to extract new knowledge by discovering hidden patterns and correlations. Nevertheless, all this shared information can be used to invade the privacy of individuals through the use of fusion and mining techniques. Simply removing direct identifiers such as name, SSN, or phone number is not anymore sufficient to prevent against these practices. In numerous cases, other fields, like gender, date of birth and/or zipcode, can be used to re-identify individuals and to expose their sensitive details, e.g. their medical conditions, financial statuses and transactions, or even their private connections. The scope of this work is to provide an in-depth overview of the current state of the art in Privacy-Preserving Data Publishing (PPDP) for relational data. To counter information leakage, a number of data anonymisation methods have been proposed during the past few years, including <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -anonymity, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell $ </tex-math></inline-formula> -diversity, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$t$ </tex-math></inline-formula> -closeness, to name a few. In this study we analyse these methods providing concrete examples not only to explain how each of them works, but also to facilitate the reader to understand the different usage scenarios in which each of them can be applied. Furthermore, we detail several attacks along with their possible countermeasures, and we discuss open questions and future research directions.
Déjà vu: A Contextualized Temporal Attention Mechanism for Sequential Recommendation

Predicting users’ preferences based on their sequential behaviors in history is challenging and crucial for modern recommender systems. Most existing sequential recommendation algorithms focus on transitional structure among the sequential actions, but largely ignore the temporal and context information, when modeling the influence of a historical event to current prediction.
Off-policy Learning in Two-stage Recommender Systems

Many real-world recommender systems need to be highly scalable: matching millions of items with billions of users, with milliseconds latency. The scalability requirement has led to widely used two-stage recommender systems, consisting of efficient candidate generation model(s) in the first stage and a more powerful ranking model in the second stage.
Candidate iO from Homomorphic Encryption Schemes

We propose a new approach to construct general-purpose indistinguishability obfuscation (iO). Our construction is obtained via a new intermediate primitive that we call split fully-homomorphic encryption (split FHE), which we show to be sufficient for constructing iO. Specifically, split FHE is FHE where decryption takes the following two-step syntactic form: (i) A secret decryption step uses the secret key and produces a hint which is (asymptotically) shorter than the length of the encrypted message, and (ii) a public decryption step that only requires the ciphertext and the previously generated hint (and not the entire secret key), and recovers the encrypted message. In terms of security, the hints for a set of ciphertexts should not allow one to violate semantic security for any other ciphertexts. Next, we show a generic candidate construction of split FHE based on three building blocks: (i) A standard FHE scheme with linear decrypt-and-multiply (which can be instantiated with essentially all LWE-based constructions), (ii) a linearly homomorphic encryption scheme with short decryption hints (such as the Damgård-Jurik encryption scheme, based on the DCR problem), and (iii) a cryptographic hash function (which can be based on a variety of standard assumptions). Our approach is heuristic in the sense that our construction is not provably secure and makes implicit assumptions about the interplay between these underlying primitives. We show evidence that this construction is secure by providing an argument in an appropriately defined oracle model. We view our construction as a big departure from the state-of-the-art constructions, and it is in fact quite simple.
Privacy-Preserving Approach PBCN in Social Network With Differential Privacy

Currently, lots of real social relations in social networks force users to face the potential risk of privacy leakage. Consequently, data holders would like to disturbor anonymize their individual data before publishing them, for the purpose of privacy protection. Due to the characteristics of high sensitivity and large volume data of social network graph structure, it is difficult for privacy protection schemes to enable a reasonable allocation of noises while keeping desirable data availability and execution efficiency. On the basis of differential privacy model, combining with clustering and randomization algorithms, a privacy protection approach PBCN (Privacy Preserving Approach Based on Clustering and Noise) is proposed. This proposal is composed of five algorithms including random disturbance based on clustering, graph reconstruction after disturbing degree sequence and noise nodes generation, etc. Furthermore, a privacy measure algorithm based on adjacency degree is put forward in order to objectively evaluate the privacy-preserving strength of various schemes against graph structure and degree attacks. Simulation experiments are conducted to achieve performance comparisons between PBCN, Spctr Add/Del, Spctr Switch, DER and HPDP. The experimental results show that PBCN realizes more satisfactory data availability and execution efficiency. Finally, parameters utility analysis demonstrates PBCN can achieve a “trade-off” between data availability and privacy protection level.