Reviewerly/exHarmony huggingface dataset:
Hybrid digital-analog simulation of many-body dynamics with superconducting qubits

In recent years, there has been a significant progress in the development of digital quantum processors. The state-of-the-art quantum devices are imperfect, and fully-algorithmic fault-tolerant quantum computing is a matter of future. Until technology develops to the state with practical error correction, computational approaches other than the standard digital one can be used to avoid execution of the most noisy quantum operations. We demonstrate how a hybrid digital-analog approach allows simulating dynamics of a transverse-field Ising model without standard two-qubit gates, which are currently one of the most problematic building blocks of quantum circuits. We use qubit-qubit crosstalks (couplings) of IBM superconducting quantum processors to simulate Trotterized dynamics of spin clusters and then we compare the obtained results with the results of conventional digital computation based on two-qubit gates from the universal set. The comparison shows that digital-analog approach significantly outperforms standard digital approach for this simulation problem, despite of the fact that crosstalks in IBM quantum processors are small. We argue that the efficiency of digital-analog quantum computing can be improved with the help of more specialized processors, so that they can be used to efficiently implement other quantum algorithms. This indicates the prospect of a digital-to-analog strategy for near-term noisy intermediate-scale quantum computers.
BERT-Based Multi-head Selection for Joint Entity-Relation Extraction

In this paper, we report our method for the Information Extraction task in 2019 Language and Intelligence Challenge. We incorporate BERT into the multi-head selection framework for joint entity-relation extraction. This model extends existing approaches from three perspectives. First, BERT is adopted as a feature extraction layer at the bottom of the multi-head selection framework. We further optimize BERT by introducing a semantic-enhanced task during BERT pre-training. Second, we introduce a large-scale Baidu Baike corpus for entity recognition pre-training, which is of weekly supervised learning since there is no actual named entity label. Third, soft label embedding is proposed to effectively transmit information between entity recognition and relation extraction. Combining these three contributions, we enhance the information extracting ability of the multi-head selection model and achieve F1-score 0.876 on testset-1 with a single model. By ensembling four variants of our model, we finally achieve F1 score 0.892 (1st place) on testset-1 and F1 score 0.8924 (2nd place) on testset-2 .
Image encryption algorithm based on image hashing, improved chaotic mapping and DNA coding

In order to improve the problems of security and robustness for existing image encryption algorithms and to reduce the security risks of encryption algorithms against statistical analysis, differential attacks, exhaustive attacks, cropping and noise attacks etc., a novel image encryption algorithm based on image hashing, improved chaotic mapping and DNA coding is proposed. Firstly, extracting the image features and evenly block after pre-processing the original image and fingerprint image. Secondly, the features are generated to be a binary hash sequence through the image hash algorithm, and the generated hash sequence is iterated as the initial parameter of the improved chaotic map and Chen's chaotic system. Finally, Chen's chaotic system is used to generate a random sequence, and the matrix generated by the improved chaotic map and the original image is subjected to DNA calculation and encoding operations to obtain an encrypted image. Experimental results show that the proposed algorithm has better performance on security, larger key space and higher key sensitivity, the pixel correlation coefficient close to 0, the information entropy close to 8, unified average changing intensity and number of pixels change rate values are close to ideal values, and it has better robustness on noise and cropping attacks.
An Overview of Semiparametric Extensions of Finite Mixture Models

Finite mixture models have offered a very important tool for exploring complex data structures in many scientific areas, such as economics, epidemiology and finance.Semiparametric mixture models, which were introduced into traditional finite mixture models in the past decade, have brought forth exciting developments in their methodologies, theories, and applications.In this article, we not only provide a selective overview of the newly-developed semiparametric mixture models, but also discuss their estimation methodologies, theoretical properties if applicable, and some open questions.Recent developments are also discussed.
How Cloud Traffic Goes Hiding

The growing demand for an ever-increasing number of cloud services is profoundly transforming the Internet's interconnection or peering ecosystem, and one example is the emergence of "virtual private interconnections (VPIs)". However, due to the underlying technologies, these VPIs are not publicly visible and traffic traversing them remains largely hidden as it bypasses the public Internet. In particular, existing techniques for inferring Internet interconnections are unable to detect these VPIs and are also incapable of mapping them to the physical facility or geographic region where they are established.
Quantum Flags and New Bounds on the Quantum Capacity of the Depolarizing Channel

A new upper bound for the quantum capacity of the d-dimensional depolarizing channels is presented. Our derivation makes use of a flagged extension of the map where the receiver obtains a copy of a state σ_{0} whenever the messages are transmitted without errors, and a copy of a state σ_{1}, when instead the original state gets fully depolarized. By varying the overlap between the flag states, the resulting transformation nicely interpolates between the depolarizing map (when σ_{0}=σ_{1}), and the d-dimensional erasure channel (when σ_{0} and σ_{1} have orthogonal support). We find sufficient conditions for degradability of the flagged channel, which let us calculate its quantum capacity in a suitable parameter region. From this last result we get the upper bound for the depolarizing channel, which by a direct comparison appears to be tighter than previous available results for d>2, and for d=2 it is tighter in an intermediate regime of noise. In particular, in the limit of large d values, our findings present a previously unnoticed O(1) correction.
Fault prognostics by an ensemble of Echo State Networks in presence of event based measurements

Fault prognostics aims at predicting the degradation of equipment for estimating the Remaining Useful Life (RUL). Traditional data-driven fault prognostic approaches face the challenge of dealing with incomplete and noisy data collected at irregular time steps, e.g. in correspondence of the occurrence of triggering events in the system. Since the values of all the signals are missing at the same time and the number of missing data largely exceeds the number of triggering events, missing data reconstruction approaches are difficult to apply. In this context, the objective of the present work is to develop a one-step method, which directly receives in input the event-based measurement and produces in output the system RUL with the associated uncertainty. Two strategies based on the use of ensembles of Echo State Networks (ESNs), properly adapted to deal with data collected at irregular time steps, have been proposed to this aim. A synthetic and a real-world case study are used to show their effectiveness and their superior performance with respect to state-of-the-art prognostic methods.
Novel Meaningful Image Encryption Based on Block Compressive Sensing

This paper proposes a new image compression-encryption algorithm based on a meaningful image encryption framework. In block compressed sensing, the plain image is divided into blocks, and subsequently, each block is rendered sparse. The zigzag scrambling method is used to scramble pixel positions in all the blocks, and subsequently, dimension reduction is undertaken via compressive sensing. To ensure the robustness and security of our algorithm and the convenience of subsequent embedding operations, each block is merged, quantized, and disturbed again to obtain the secret image. In particular, landscape paintings have a characteristic hazy beauty, and secret images can be camouflaged in them to some extent. For this reason, in this paper, a landscape painting is selected as the carrier image. After a 2-level discrete wavelet transform (DWT) of the carrier image, the low-frequency and high-frequency coefficients obtained are further subjected to a discrete cosine transform (DCT). The DCT is simultaneously applied to the secret image as well to split it. Next, it is embedded into the DCT coefficients of the low-frequency and high-frequency components, respectively. Finally, the encrypted image is obtained. The experimental results show that, under the same compression ratio, the proposed image compression-encryption algorithm has better reconstruction effect, stronger security and imperceptibility, lower computational complexity, shorter time consumption, and lesser storage space requirements than the existing ones.
Construction of New S-Box Using Action of Quotient of the Modular Group for Multimedia Security

Substitution box (S-box) is a vital nonlinear component for the security of cryptographic schemes. In this paper, a new technique which involves coset diagrams for the action of a quotient of the modular group on the projective line over the finite field is proposed for construction of an S-box. It is constructed by selecting vertices of the coset diagram in a special manner. A useful transformation involving Fibonacci sequence is also used in selecting the vertices of the coset diagram. Finally, all the analyses to examine the security strength are performed. The outcomes of the analyses are encouraging and show that the generated S-box is highly secure.
Decolonizing design through the perspectives of cosmological others: Arguing for an ontological turn in design research and practice

A closer attention to cultural and cosmological difference as the basis for thinking about how we redesign our own modern technological infrastructures may be the way to decolonize design research.
Heart Disease Prediction Using Frequent Item Set Mining and Classification Technique

The heart is the most important part of the human body.Any abnormality in heart results heart related illness in which it obstructs blood vessels which causes heart attack, chest pain or stroke.Care and improvement of the health by the help of identification, prevention, and care of any kind of diseases is the main goal.So for this various prediction analysis methods are used which job is to identify the illness at prelim phase so that prevention and care of heart disease is done.This paper emphasizes on the care of heart diseases at a primitive phase so that it will lead to a successful cure.In this paper, diverse data mining classification method like Decision tree classification, Naive Bayes classification, Support Vector Machine classification, and k-NN classification are used for determination and safeguard of the diseases.
Predicting IMDb Rating of Movies by Machine Learning Techniques

Film Industry is not only a industry or a centre of entertainment, rather it is now a centre of global business. All over the world is now excited about a movie's box office success, popularity etc. A huge data is available online about these movies success or popularity. We have used hollywood movie list from Wikipedia and their rating from IMDb movie rating website to create our data set. Then machine learning classification algorithms are applied of the data set. Lastly an efficient model is developed to predict a movie's IMDb rating. The model gives good classification measures with the data set.
Differentially Private and Fair Classification via Calibrated Functional Mechanism

Machine learning is increasingly becoming a powerful tool to make decisions in a wide variety of applications, such as medical diagnosis and autonomous driving. Privacy concerns related to the training data and unfair behaviors of some decisions with regard to certain attributes (e.g., sex, race) are becoming more critical. Thus, constructing a fair machine learning model while simultaneously providing privacy protection becomes a challenging problem. In this paper, we focus on the design of classification model with fairness and differential privacy guarantees by jointly combining functional mechanism and decision boundary fairness. In order to enforce ϵ-differential privacy and fairness, we leverage the functional mechanism to add different amounts of Laplace noise regarding different attributes to the polynomial coefficients of the objective function in consideration of fairness constraint. We further propose an utility-enhancement scheme, called relaxed functional mechanism by adding Gaussian noise instead of Laplace noise, hence achieving (ϵ, δ)-differential privacy. Based on the relaxed functional mechanism, we can design (ϵ, δ)-differentially private and fair classification model. Moreover, our theoretical analysis and empirical results demonstrate that our two approaches achieve both fairness and differential privacy while preserving good utility and outperform the state-of-the-art algorithms.
GeoBroker: Leveraging geo-contexts for IoT data distribution

In the Internet of Things, the relevance of data often depends on the geographic context of data producers and consumers. Today's data distribution services, however, mostly focus on data content and not on geo-context, which could help to reduce the dissemination of excess data in many IoT scenarios. In this paper, we propose to use the geo-context information associated with devices to control data distribution. We define what geo-context dimensions exist and compare our definition with concepts from related work. Furthermore, we designed GeoBroker, a data distribution service that uses the location of things, as well as geofences for messages and subscriptions, to control data distribution. This way, we enable new IoT application scenarios while also increasing overall system efficiency for scenarios where geo-contexts matter by delivering only relevant messages. We evaluate our approach based on a proof-of-concept prototype and several experiments.
The Effectiveness of ECOLA Technique to Improve Reading Comprehension in Relation to Motivation and Self-Efficacy

This study was aimed at finding out whether; (1) the use of the Extending Concept through Language Activities (ECOLA) technique is more effective than brainstorming technique in teaching reading comprehension; (2) there is any difference of reading comprehension achievement of students taught by using ECOLA technique; and (3) there is an interaction between ECOLA technique, motivation, and self-efficacy towards students' reading comprehension. This study is a quasi-experimental research with a nonequivalent group design. Two classes were taken as the sample by using cluster random sampling. One class, as an experimental class, was taught by using the ECOLA technique. Another class, as a control class, was taught by employing the brainstorming technique. To attain the data, instruments in the form of a reading test and the questionnaires were employed. Afterwards, the data were analyzed by using Multifactor Analysis of Variance (Two Way ANOVA). The finding of this study showed that; (1) the ECOLA technique was more effective than the brainstorming technique in teaching reading comprehension; (2) there was the difference of reading achievement of students taught using the ECOLA technique viewed from the motivation and self-efficacy and it was indicated by the value of sig. (0.001) and (0.004); and (3) there was the interaction between ECOLA technique and self-efficacy towards students’ reading comprehension but there was no interaction between the technique and motivation. In addition, there was no significant interaction between the technique, motivation, and self-efficacy because it was indicated by the value of sig (0.302) was higher than significant level 0.05. To improve students’ reading comprehension, the ECOLA technique is recommended and the attention of moderate variables is encouraged.
Privacy Profiles and Amplification by Subsampling

Differential privacy provides a robust quantifiable methodology to measure and control the privacy leakage of data analysis algorithms.A fundamental insight is that by forcing algorithms to be randomized, their privacy leakage can be characterized by measuring the dissimilarity between output distributions produced by applying the algorithm to pairs datasets differing in one individual.After the introduction of differential privacy, several variants of the original definition have been proposed by changing the measure of dissimilarity between distributions, including concentrated, zero-concentrated and R{\'e}nyi differential privacy.&#x0D; The first contribution of this paper is to introduce the notion of privacy profile of a mechanism.This profile captures all valid $(\varepsilon,\delta)$ differential privacy parameters satisfied by a given mechanism, and contrasts with the usual approach of providing guarantees in terms of a single point in this curve.We show that knowledge of this curve is equivalent to knowledge of the privacy guarantees with respect to the alternative definitions listed above.This sheds further light into the connections between different privacy definitions, and suggests that these should be considered alternative but otherwise equivalent points of view.&#x0D; The second contribution of this paper is to apply the privacy profiles machinery to study the so-called ``privacy amplification by subsampling'' principle, which ensures that a differentially private mechanism run on a random subsample of a population provides higher privacy guarantees than when run on the entire population.Several instances of this principle have been studied for different random subsampling methods, each with an ad-hoc analysis. In this paper we set out to study this phenomenon in detail with the aim to provide a general method capable of recovering prior analyses in a streamlined fashion.Our method makes extensive use of coupling argument, and introduces a new tool to analyse differential privacy for mixture distributions.
The Security-Utility Trade-off for Iris Authentication and Eye Animation for Social Virtual Avatars

The gaze behavior of virtual avatars is critical to social presence and perceived eye contact during social interactions in Virtual Reality. Virtual Reality headsets are being designed with integrated eye tracking to enable compelling virtual social interactions. This paper shows that the near infra-red cameras used in eye tracking capture eye images that contain iris patterns of the user. Because iris patterns are a gold standard biometric, the current technology places the user's biometric identity at risk. Our first contribution is an optical defocus based hardware solution to remove the iris biometric from the stream of eye tracking images. We characterize the performance of this solution with different internal parameters. Our second contribution is a psychophysical experiment with a same-different task that investigates the sensitivity of users to a virtual avatar's eye movements when this solution is applied. By deriving detection threshold values, our findings provide a range of defocus parameters where the change in eye movements would go unnoticed in a conversational setting. Our third contribution is a perceptual study to determine the impact of defocus parameters on the perceived eye contact, attentiveness, naturalness, and truthfulness of the avatar. Thus, if a user wishes to protect their iris biometric, our approach provides a solution that balances biometric protection while preventing their conversation partner from perceiving a difference in the user's virtual avatar. This work is the first to develop secure eye tracking configurations for VR/AR/XR applications and motivates future work in the area.
Bobtail: Improved Blockchain Security with Low-Variance Mining

Blockchain systems are designed to produce blocks at a constant average rate.The most popular systems currently employ a Proof of Work (PoW) algorithm as a means of creating these blocks.An unfortunate limitation of all deployed PoW blockchain systems is that the time between blocks has high variance.For example, Bitcoin produces, on average, one block every 10 minutes.However, 5% of the time, Bitcoin's inter-block time is at least 30 minutes.In this paper, we show that high variance is at the root of fundamental attacks on PoW blockchains.We propose an alternative process for PoW-based block discovery that results in an inter-block time with significantly lower variance.Our algorithm, called Bobtail, generalizes the current algorithm, which uses a single PoW sample, to one that incorporates k samples.We show that the variance of inter-block times decreases as k increases.Bobtail significantly thwarts doublespend and selfish mining attacks.For example, for Bitcoin and Ethereum, a doublespending attacker with 35% of the mining power will succeed with 44% probability when the merchant sets up an embargo of 1 block; however, when k ≥ 40, the probability of success for the same attacker falls to less than 1%.Similarly, for Bitcoin and Ethereum currently, a selfish miner with 45% of the mining power will claim about 71% of blocks; however, when k ≥ 20, the same miner will find that selfish mining is less successful than honest mining.We also investigate attacks newly made possible by Bobtail and show how they can be defeated.The primary costs of our approach are larger blocks and increased network traffic.
A Sharp Estimate on the Transient Time of Distributed Stochastic Gradient Descent

This paper is concerned with minimizing the average of $n$ cost functions over a network in which agents may communicate and exchange information with each other. We consider the setting where only noisy gradient information is available. To solve the problem, we study the distributed stochastic gradient descent (DSGD) method and perform a non-asymptotic convergence analysis. For strongly convex and smooth objective functions, DSGD asymptotically achieves the optimal network independent convergence rate compared to centralized stochastic gradient descent (SGD). Our main contribution is to characterize the transient time needed for DSGD to approach the asymptotic convergence rate, which we show behaves as $K_T=\mathcal{O}\left(\frac{n}{(1-\rho_w)^2}\right)$, where $1-\rho_w$ denotes the spectral gap of the mixing matrix. Moreover, we construct a "hard" optimization problem for which we show the transient time needed for DSGD to approach the asymptotic convergence rate is lower bounded by $\Omega \left(\frac{n}{(1-\rho_w)^2} \right)$, implying the sharpness of the obtained result. Numerical experiments demonstrate the tightness of the theoretical results.