Reviewerly/exHarmony huggingface dataset:
Top 10 Blockchain Predictions for the (Near) Future of Healthcare

To review blockchain lessons learned in 2018 and near-future predictions for blockchain in healthcare, Blockchain in Healthcare Today (BHTY) asked the world's blockchain in healthcare experts to share their insights. Here, our internationally-renowned BHTY peer-review board discusses their major predictions.Based on their responses, presented in detail below, ten major themes (Table ) for the future of blockchain in healthcare will emerge over the 12 months.&#x0D; &#x0D; CORRIGENDUM:&#x0D; This following paragraph has been corrected (page 3, first paragraph) from:&#x0D; "Fourth, with over 1000 insurance companies in the country, filling out paperwork to documentprovider training and licensure is a nightmare. The Synaptic Health Alliance aims to simplifythis process by putting all credentialing information on a distributed public ledger for all stakeholders to access.1"&#x0D; To:&#x0D; "Fourth, keeping health care provider directories maintained by health plans up-to-date is a critical, complex issue facing organizations across the health care system. The first project of the Synaptic Health Alliance aims to simplify this process by putting provider demographic information on a permissioned blockchain for Alliance members to access and maintain.1"
Learning Fast Matching Models from Weak Annotations

We propose a novel training scheme for fast matching models in Search Ads, motivated by practical challenges. The first challenge stems from the pursuit of high throughput, which prohibits the deployment of inseparable architectures, and hence greatly limits model accuracy. The second problem arises from the heavy dependency on human provided labels, which are expensive and time-consuming to collect, yet how to leverage unlabeled search log data is rarely studied. The proposed training framework targets on mitigating both issues, by treating the stronger but undeployable models as annotators, and learning a deployable model from both human provided relevance labels and weakly annotated search log data. Specifically, we first construct multiple auxiliary tasks from the enumerated relevance labels, and train the annotators by jointly learning from those related tasks. The annotation models are then used to assign scores to both labeled and unlabeled training samples. The deployable model is firstly learnt on the scored unlabeled data, and then fine-tuned on scored labeled data, by leveraging both labels and scores via minimizing the proposed label-aware weighted loss. According to our experiments, compared with the baseline that directly learns from relevance labels, training by the proposed framework outperforms it by a large margin, and improves data efficiency substantially by dispensing with 80% labeled samples. The proposed framework allows us to improve the fast matching model by learning from stronger annotators while keeping its architecture unchanged. Meanwhile, it offers a principled manner to leverage search log data in the training phase, which could effectively alleviate our dependency on human provided labels.
Anderson localization in synthetic photonic lattice with random coupling

A synthetic photonic lattice (SPL) is a re-configurable test-bed for studying the dynamics of one-dimensional mesh lattices including the photonic implementations of discrete time quantum walks. Unlike other realizations of photonic lattices, SPL possesses easy and fast control of lattice parameters. Here we consider disordered SPL where the coupling ratio between the two fiber loops realizing the lattice is random but does not change between the round trips. We obtain a new analytical result for the localization length (inverse Lyapunov exponent) for a practical case of weak coupling disorder. We also numerically study the dynamics of the pulse train circulating within the loops and observe that despite delocalization transition at the band center the pulse spreading is arrested even at small values of the disorder.
Multi-objective-optimization-based control parameters auto-tuning for aerial manipulators

The aerial manipulator has recently attracted much research attention due to its wide applications such as aerial cleaning, aerial transportation, and aerial manipulation. It is important to design a reliable controller for the aerial manipulator to robustly perform aerial tasks with different settings. However, current controllers still employ manual parameters tuning methods, which is mostly limited to a specific setting like a fixed aerial manipulator configuration or an unchanged environment. In fact, there could be diverse configurations of aerial manipulators and uncertain environments in practice, which requires the manual tuning process to be frequently repeated. This repetition is easy to be unavailable due to its significant cost of time and expensive involvements of control-tuning experts. To solve these problems, a novel multi-objective-optimization-based control parameters auto-tuning method is proposed for the aerial manipulator. Based on a conventional proportional–integral–derivative control structure, an evolutionary-algorithm-based optimization is used to automatically find optimal proportional–integral–derivative control parameters to satisfy conflicting objectives such as minimizing the integrated time square error and the control rate. Simulation results prove that the proposed method can achieve better control performances like smaller overshoots and faster stabilization time than manual tuning methods.
Efficient Ciphertext-Policy Attribute-Based Online/Offline Encryption with User Revocation

Attribute-Based Encryption (ABE) must provide an efficient revocation mechanism since a user’s private key can be compromised or expired over time. The existing revocable ABE schemes have the drawbacks of heavy computational costs on key updates and encryption operations, which make the entities for performing these operations a possible bottleneck in practice applications. In this paper, we propose an efficient Ciphertext-Policy Attribute-Based Online/Offline Encryption with user Revocation (R-CP-ABOOE). We integrate the subset difference method with ciphertext-policy ABE to significantly improve key-update efficiency on the side of the trusted party from <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mi mathvariant="normal">O</mml:mi><mml:mo stretchy="false">(</mml:mo><mml:mi>r</mml:mi><mml:mrow><mml:mrow><mml:mi mathvariant="normal">log</mml:mi></mml:mrow><mml:mo>⁡</mml:mo><mml:mrow><mml:mo stretchy="false">(</mml:mo><mml:mi>N</mml:mi><mml:mo>/</mml:mo><mml:mi>r</mml:mi><mml:mo stretchy="false">)</mml:mo></mml:mrow></mml:mrow><mml:mo stretchy="false">)</mml:mo></mml:math> to <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:mi mathvariant="normal">O</mml:mi><mml:mo stretchy="false">(</mml:mo><mml:mi>r</mml:mi><mml:mo stretchy="false">)</mml:mo></mml:math>, where <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M3"><mml:mrow><mml:mi>N</mml:mi></mml:mrow></mml:math> is the number of users and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M4"><mml:mrow><mml:mi>r</mml:mi></mml:mrow></mml:math> is the number of revoked users. To reduce the encryption burden for mobile devices, we use the online/offline technology to shift the majority of encryption work to the offline phase, and then mobile devices only need to execute a few simple computations to create a ciphertext. In addition, we exploit a novel trick to prove its selective security under the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M5"><mml:mrow><mml:mi>q</mml:mi></mml:mrow></mml:math>-type assumption. Performance analysis shows that our scheme greatly improves the key-update efficiency for the trusted party and the encryption efficiency for mobile devices.
Multi-scale aggregation network for temporal action proposals

Temporal action detection is a very challenging and valuable task for video analysis and applications. The detection results, to a great extent, rely on the quality of temporal action proposals. However, temporal actions in videos vary dramatically, e.g. from a fraction of a second to minutes, which causes much difficulties for accurate temporal action proposals. In this paper, we propose a multi-scale aggregation network to overcome those variations for temporal action proposals. Our proposed network generates an actionness score sequence for the input video to automatically perceive the duration of actions, and thus can dynamically generate corresponding lengths of action proposals for them. For more reliable actionness prediction, we propose to adaptively explore the intrinsic short and long dependencies in action by two multi-scale aggregation strategies: unit level multi-scale aggregation and proposal level multi-scale aggregation. We also propose to take the soft labelling to facilitate the actionness prediction for the units near the action boundaries. Extensive experiments on THUMOS14 dataset have demonstrated the effectiveness of our proposed method.
Energy/Area-Efficient Scalar Multiplication with Binary Edwards Curves for the IoT

Making Elliptic Curve Cryptography (ECC) available for the Internet of Things (IoT) and related technologies is a recent topic of interest. Modern IoT applications transfer sensitive information which needs to be protected. This is a difficult task due to the processing power and memory availability constraints of the physical devices. ECC mainly relies on scalar multiplication (kP)-which is an operation-intensive procedure. The broad majority of kP proposals in the literature focus on performance improvements and often overlook the energy footprint of the solution. Some IoT technologies-Wireless Sensor Networks (WSN) in particular-are critically sensitive in that regard. In this paper we explore energy-oriented improvements applied to a low-area scalar multiplication architecture for Binary Edwards Curves (BEC)-selected given their efficiency. The design and implementation costs for each of these energy-oriented techniques-in hardware-are reported. We propose an evaluation method for measuring the effectiveness of these optimizations. Under this novel approach, the energy-reducing techniques explored in this work contribute to achieving the scalar multiplication architecture with the most efficient area/energy trade-offs in the literature, to the best of our knowledge.
Bernstein polynomial model for nonparametric multivariate density

In this paper, we study the Bernstein polynomial model for estimating the multivariate distribution functions and densities with bounded support. As a mixture model of multivariate beta distributions, the maximum (approximate) likelihood estimate can be obtained using EM algorithm. A change-point method of choosing optimal degrees of the proposed Bernstein polynomial model is presented. Under some conditions, the optimal rate of convergence in the mean χ2-divergence of new density estimator is shown to be nearly parametric. The method is illustrated by an application to a real data set. Finite sample performance of the proposed method is also investigated by simulation study and is shown to be much better than the kernel density estimate but close to the parametric ones.
Trends and Patterns of Mobile Learning: A Study of Mobile Learning Management System Access

The use of mobile technology such as smartphones and tablets is deeply embedded in college life by Generation Z (students born between 1995 and 2010). Using reports generated by the Learning Management System (LMS), this research studied the mobile device access patterns of both undergraduate and graduate students enrolled in courses utilizing the Blackboard Learning Management System at a small university in South Texas over the 2015-2016 school year to determine patterns of usage. Frequency counts were employed to determine numbers of logins over 24 hours, logins over days of the weeks, and preferred operating systems. Reports determined that there were 14, 234 unique visitors, that Monday had the most logins of the days of the week, and that the most frequent time of day for logins was 10 a.m. Surprisingly, however, there were a robust number of logins between midnight and 6 a.m. The reports also show that iOS devices dominate the user base with 121,990 logins within the one year date range.
Video-Based Person Re-Identification Using Unsupervised Tracklet Matching

Despite the significant improvement in accuracy supervised learning has brought into person re-identification (re-id), the availability of sufficient fully annotated data from concerned camera-views poses a problem for real-life applications. To alleviate the burden of intensive data annotation, one way is to resort to unsupervised methods. This has motivated us to propose a novel algorithm for unsupervised video-based person re-id applications. To achieve this, the frames of a person video tracklet are divided into a set of clusters that are subsequently matched using a distance measure based on the Naive Bayes nearest neighbor algorithm and Spearman distance. Knowing that person’s sequences may suffer from substantial changes in viewpoint, pose, and illumination distortions, our technique allows the rejection of poor and noisy clusters while retaining the most discriminative ones for matching. Experiments on three widely used datasets for video person re-id PRID2011, iLIDS-VID and MARS have been carried out, and the results demonstrate the superiority of the proposed approach.
Decentralized autonomous imaging data processing using blockchain

Imaging studies are one of the leading drivers of modern medical decision making, and thus, their accessibility to healthcare providers and patients is of critical importance. However, current techniques for storage and transferring medical imaging data are inconvenient and sometimes wholly inadequate. In this paper, we propose a decentralized autonomous medical image processing approach using blockchain technology. Blockchain will enable the sharing of key relevant data using a distributed, decentralized, shared ledger that is available to participants. We outline a framework that utilizes blockchain to enable users to access imaging data in a secure and autonomous manner. A user case is experimentally investigated to validate our proposed approach.
Resource optimized quantum architectures for surface code implementations of magic-state distillation

Quantum computers capable of solving classically intractable problems are under construction, and intermediate-scale devices are approaching completion. Current efforts to design large-scale devices require allocating immense resources to error correction, with the majority dedicated to the production of high-fidelity ancillary states known as magic-states. Leading techniques focus on dedicating a large, contiguous region of the processor as a single "magic-state distillation factory" responsible for meeting the magic-state demands of applications. In this work we design and analyze a set of optimized factory architectural layouts that divide a single factory into spatially distributed factories located throughout the processor. We find that distributed factory architectures minimize the space-time volume overhead imposed by distillation. Additionally, we find that the number of distributed components in each optimal configuration is sensitive to application characteristics and underlying physical device error rates. More specifically, we find that the rate at which T-gates are demanded by an application has a significant impact on the optimal distillation architecture. We develop an optimization procedure that discovers the optimal number of factory distillation rounds and number of output magic states per factory, as well as an overall system architecture that interacts with the factories. This yields between a 10x and 20x resource reduction compared to commonly accepted single factory designs. Performance is analyzed across representative application classes such as quantum simulation and quantum chemistry.
Citation patterns in translation studies: a format-dependent bibliometric analysis

Citation distributions vary across the board among academic disciplines, which is the reason why field-oriented normalisation is necessary to compensate for this. In this vein, the aim of this study is three-fold. Firstly, to find out which document type is cited the most in translation studies (TS). Secondly, to determine the time distribution for citations and aging patterns in TS scientific literature. Thirdly, to define a discipline-weighted citation window or cited half-life, in order to establish the optimal citation window to be used in TS. Data enabling the present research will be retrieved from BITRA, which includes over 70,000 items covering the diversity of document types and languages used in TS research for all times and the citing information of over 10% of its entries. This database will thus allow us to carry out a study on citation and aging patterns in TS academic literature covering the 1960-2015 period. Both, global results, as well as a more detailed analysis focusing on different document types, will be provided. This bibliometric study aims to offer a discipline-focused approach in order to develop specific and realistic impact criteria for our discipline, while taking into account its actual research and communication practices.
Boosting Quantum Annealing Performance Using Evolution Strategies for Annealing Offsets Tuning

In this paper we introduce a novel algorithm to iteratively tune annealing offsets for qubits in a D-Wave 2000Q quantum processing unit (QPU). Using a (1+1)-CMA-ES algorithm, we are able to improve the performance of the QPU by up to a factor of 12.4 in probability of obtaining ground states for small problems, and obtain previously inaccessible (i.e., better) solutions for larger problems. We also make efficient use of QPU samples as a resource, using 100 times less resources than existing tuning methods. The success of this approach demonstrates how quantum computing can benefit from classical algorithms, and opens the door to new hybrid methods of computing.
Orthogonal least squares based center selection for fault-tolerant RBF networks

In the construction of a radial basis function (RBF) network, one of the most important issues is to determine the number of RBF nodes in the hidden layer and the corresponding centers. However, most existing center selection methods for RBF networks are designed for the faultless implementation situation only. In practice, the implementation of the network may be perturbed by faults and thus fault tolerance should be considered in selecting centers. This paper addresses this problem by focusing on the center selection problem of fault-tolerant RBF networks. In particular, we consider RBF networks under the concurrent fault situation and reformulate the fault-tolerant training objective function as a linear regression problem. A center selection and learning algorithm is then proposed based on the orthogonal least squares (OLS) algorithm. The proposed algorithm takes fault-tolerance into consideration both in center selection and network training, and it is able to determine the number of RBF nodes, the RBF centers and the trained connecting weights simultaneously. Simulation results show that the proposed algorithm is superior than state-of-the-art center selection algorithms.
Nonlinear vibrations of thin plates with variable thickness: Application to sound synthesis of cymbals

Geometrically nonlinear vibrations of thin plates and shells with variable thickness are investigated numerically with the purpose of synthesizing the sound of cymbals. In cymbal making, taper refers to the gradual change in thickness from the centre to the rim and is known to be a key feature that determines the tone of the instrument. It is generally used in conjunction with shape variations in order to enable the cymbal to play a bell-like sound when hit near its centre, or a crash sound when struck close to the edge. The von Kármán equations for thin plates with thickness and shape variations are derived, and a numerical method combining a Rayleigh-Ritz approach together with a Störmer-Verlet scheme for advancing the problem in time is detailed. One main advantage of the method is its ability to implement easily any frequency-dependent loss mechanism which is a key property for sound synthesis. Also, the accuracy of the computation of the nonlinear restoring force is especially preserved. The method is employed to synthesize the sounds of cymbal-like instruments. The impact of taper is addressed and the relative effects of both thickness and shape variations, are contrasted.
Multi Similarity Metric Fusion in Graph-Based Semi-Supervised Learning

In semi-supervised label propagation (LP), the data manifold is approximated by a graph, which is considered as a similarity metric. Graph estimation is a crucial task, as it affects the further processes applied on the graph (e.g., LP, classification). As our knowledge of data is limited, a single approximation cannot easily find the appropriate graph, so in line with this, multiple graphs are constructed. Recently, multi-metric fusion techniques have been used to construct more accurate graphs which better represent the data manifold and, hence, improve the performance of LP. However, most of these algorithms disregard use of the information of label space in the LP process. In this article, we propose a new multi-metric graph-fusion method, based on the Flexible Manifold Embedding algorithm. Our proposed method represents a unified framework that merges two phases: graph fusion and LP. Based on one available view, different simple graphs were efficiently generated and used as input to our proposed fusion approach. Moreover, our method incorporated the label space information as a new form of graph, namely the Correlation Graph, with other similarity graphs. Furthermore, it updated the correlation graph to find a better representation of the data manifold. Our experimental results on four face datasets in face recognition demonstrated the superiority of the proposed method compared to other state-of-the-art algorithms.
A Solution for Secure Certified Electronic Mail Using Blockchain as a Secure Message Board

The certified mail is a value-added service that is widely used in the paper world. However, the scientific community has not yet provided a solution for certified e-mail that has achieved widespread acceptance. This lack of a certified e-mail solution is not due to a lack of proposed approaches; because over the past 40 years, more than 100 protocols have been reported in journals and at conferences. The vast majority of these proposed protocols use a trusted third party (TTP) to achieve fairness. The few solutions without a TTP have not been successful due to their high computational and/or communication cost. Blockchain provides a new approach to develop the protocols without a TTP but without the prior drawbacks of the previous solutions without a TTP. Here, we present a new protocol for certified e-mail based on a blockchain without a conventional TTP that is integrated with the conventional e-mail infrastructure. The protocol is secure, efficient, and viable from a practical perspective.
One-Lead Electrocardiogram for Biometric Authentication using Time Series Analysis and Support Vector Machine

In this research, a person identification system has been simulated using electrocardiogram (ECG) signals as biometrics.Ten adult people were participated as the subjects in this research taken from their signal ECG using the one-lead ECG machine.A total of 65 raw ECG waves from the 10 subjects were analyzed.This raw signal is then processed using the Hjorth Descriptor and Sample Entropy (SampEn) to get the signal features.Support Vector Machine (SVM) algorithm was used as the classifier for the subject authentication based upon the record of ECG signal.The results of the research showed that the highest accuracy value of 93.8% was found in Hjorth Descriptor.Compared to SampEn, this method is quite promising to be implemented for having a good performance and fewer features.