Reviewerly/exHarmony huggingface dataset:
Multilevel Techniques for Compression and Reduction of Scientific Data-Quantitative Control of Accuracy in Derived Quantities

Although many compression algorithms are focused on preserving pointwise values of the data, application scientists are generally more concerned with derived quantities. Equally well, the user may even be willing to accept a high level of lossiness in the compression provided that the compressed data respect certain invariants, such as mass conservation. In the current work, we develop a mathematical framework and techniques that enable data to be adaptively compressed while maintaining a specified tolerance on a class of user-prescribed quantities. The algorithm is used to augment the functionality of the data reduction package MGARD developed in previous work and the functionality is illustrated by a range of application including data from computational simulation of autocatalytic reaction simulation, turbulent combustion simulation, experimental data obtained from magnetic confinement fusion experiment, and simulation of turbulent flow along a rectangular channel. In each case, we consider one or more relevant quantities of interest and reduce the data so as to preserve these quantities.
Towards Optimal Transport with Global Invariances

Many problems in machine learning involve calculating correspondences between sets of objects, such as point clouds or images. Discrete optimal transport provides a natural and successful approach to such tasks whenever the two sets of objects can be represented in the same space, or at least distances between them can be directly evaluated. Unfortunately neither requirement is likely to hold when object representations are learned from data. Indeed, automatically derived representations such as word embeddings are typically fixed only up to some global transformations, for example, reflection or rotation. As a result, pairwise distances across two such instances are ill-defined without specifying their relative transformation. In this work, we propose a general framework for optimal transport in the presence of latent global transformations. We cast the problem as a joint optimization over transport couplings and transformations chosen from a flexible class of invariances, propose algorithms to solve it, and show promising results in various tasks, including a popular unsupervised word translation benchmark.
A Multi-Layer Dual Attention Deep Learning Model With Refined Word Embeddings for Aspect-Based Sentiment Analysis

Although the sentiment analysis domain has been deeply studied in the last few years, the analysis of social media content is still a challenging task due to the exponential growth of multimedia content.Natural language ambiguities and indirect sentiments within the social media text have made it hard to classify.Aspect-based sentiment analysis creates a need to develop explicit extraction techniques using syntactic parsers to exploit the relationship between the aspect and sentiment terms.Along with the extraction approaches, word embeddings are generated through Word2Vec models for the continuous low-dimensional vector representation of text that fails to capture the significant sentiment information.This paper presents a co-extraction model with refined word embeddings to exploit the dependency structures without using syntactic parsers.For this purpose, a deep learning-based multilayer dual-attention model is proposed to exploit the indirect relation between the aspect and opinion terms.In addition, word embeddings are refined by providing distinct vector representations to dissimilar sentiments, unlike the Word2Vec model.For this, we have employed a sentiment refinement technique for pre-trained word embedding model to overcome the problem of similar vector representations of opposite sentiments.Performance of the proposed model is evaluated on three benchmark datasets of SemEval Challenge 2014 and 2015.The experimental results indicate the effectiveness of our model compared to the existing state-of-the-art models for the aspect-based sentiment analysis.
AttentionXML: Label Tree-based Attention-Aware Deep Model for High-Performance Extreme Multi-Label Text Classification

Extreme multi-label text classification (XMTC) is an important problem in the era of {\it big data}, for tagging a given text with the most relevant multiple labels from an extremely large-scale label set. XMTC can be found in many applications, such as item categorization, web page tagging, and news annotation. Traditionally most methods used bag-of-words (BOW) as inputs, ignoring word context as well as deep semantic information. Recent attempts to overcome the problems of BOW by deep learning still suffer from 1) failing to capture the important subtext for each label and 2) lack of scalability against the huge number of We propose a new label tree-based deep learning model for XMTC, called AttentionXML, with two unique features: 1) a multi-label attention mechanism with raw text as input, which allows to capture the most relevant part of text to each label; and 2) a shallow and wide probabilistic label tree (PLT), which allows to handle millions of labels, especially for labels. We empirically compared the performance of AttentionXML with those of eight state-of-the-art methods over six benchmark datasets, including Amazon-3M with around 3 million AttentionXML outperformed all competing methods under all experimental settings. Experimental results also show that AttentionXML achieved the best performance against tail labels among label tree-based methods. The code and datasets are available at \url{http://github.com/yourh/AttentionXML} .
On the fly synthesis of edit suggestions

When working with a document, users often perform context-specific repetitive edits – changes to the document that are similar but specific to the contexts at their locations. Programming by demonstration/examples (PBD/PBE) systems automate these tasks by learning programs to perform the repetitive edits from demonstration or examples. However, PBD/PBE systems are not widely adopted, mainly because they require modal UIs – users must enter a special mode to give the demonstration/examples. This paper presents Blue-Pencil, a modeless system for synthesizing edit suggestions on the fly. Blue-Pencil observes users as they make changes to the document, silently identifies repetitive changes, and automatically suggests transformations that can apply at other locations. Blue-Pencil is parameterized – it allows the ”plug-and-play” of different PBE engines to support different document types and different kinds of transformations. We demonstrate this parameterization by instantiating Blue-Pencil to several domains – C# and SQL code, markdown documents, and spreadsheets – using various existing PBE engines. Our evaluation on 37 code editing sessions shows that Blue-Pencil synthesized edit suggestions with a precision of 0.89 and a recall of 1.0, and took 199 ms to return suggestions on average. Finally, we report on several improvements based on feedback gleaned from a field study with professional programmers to investigate the use of Blue-Pencil during long code editing sessions. Blue-Pencil has been integrated with Visual Studio IntelliCode to power the IntelliCode refactorings feature.
Nesterov Accelerated Gradient and Scale Invariance for Adversarial Attacks

Deep learning models are vulnerable to adversarial examples crafted by applying human-imperceptible perturbations on benign inputs. However, under the black-box setting, most existing adversaries often have a low transferability to attack other defense models. In this work, from the perspective of regarding the adversarial example generation as an optimization process, we propose two new methods to improve the transferability of adversarial examples, namely Nesterov Iterative Fast Gradient Sign Method (NI-FGSM) and Scale-Invariant attack Method (SIM). NI-FGSM aims to adapt Nesterov accelerated gradient into the iterative attacks so as to effectively look ahead and avoid the “missing” of the global maximum. While SIM is based on our discovery on the scale-invariant property of deep learning models, for which we leverage to optimize the adversarial perturbations over the scale copies of the input images so as to avoid “overfitting” on the white-box model being attacked and generate more transferable adversarial examples. NI-FGSM and SIM can be naturally integrated to build a robust gradient-based attack to generate more transferable adversarial examples against the defense models. Empirical results on ImageNet dataset and NIPS 2017 adversarial competition demonstrate that our attack methods exhibit higher transferability and achieve higher attack success rates than state-of-the-art gradient-based attacks.
Quantum and dual-tree complex wavelet transform-based image watermarking

Nowadays, multimedia applications are extensively utilized and communicated over Internet. Due to the use of public networks for communication, the multimedia data are prone to various security attacks. In the past few decades, image watermarking has been extensively utilized to handle this issue. Its main objective is to embed a watermark into a host multimedia data without affecting its presentation. However, the existing methods are not so effective against multiplicative attacks. Therefore, in this paper, a novel quantum-based image watermarking technique is proposed. It initially computes the dual-tree complex wavelet transform coefficients of an input cover image. The watermark image is then scrambled using Arnold transform. Thereafter, in the lower coefficient input the watermark image is embedded using quantum-based singular value decomposition (SVD). Finally, the covered image is obtained by applying the inverse dual-tree complex wavelet transform on the obtained coefficients. Comparative analyses are carried out by considering the proposed and the existing watermarking techniques. It has been found that the proposed technique outperforms existing watermarking techniques in terms of various performance metrics.
Literature Review on Transfer Learning for Human Activity Recognition Using Mobile and Wearable Devices with Environmental Technology

Activity recognition systems utilise data from sensors in mobile, environmental and wearable devices, ubiquitously available to individuals. It is a growing research area within intelligent systems that aims to model and identify human physical, cognitive and social actions, patterns and skills. They typically rely on supervised machine-learning approaches, in which the cost of gathering and labelling data is high due to the diverse, interleaved and dynamic nature of human behaviour. Transfer learning is an approach in which previously learned knowledge is utilised to model a new but related setting. For instance, it can reuse existing knowledge to recognise activities performed by different types of users, using different sensor technologies and in different environmental conditions. As the adoption of Internet of Thing devices increases, mobile and wearable sensing is becoming pervasive, and more challenging behaviour recognition activities are being tackled. Yet, the availability of more data does not necessarily translate to better recognition models, if these data are not properly labelled. Thus, the importance of taking advantage of transfer learning to advance the field of activity recognition. This literature review summarises the transfer learning techniques and explores the benefits of combining mobile and wearable devices with environmental sensors in support of transfer learning. We also discuss the maturity of transfer learning by analysing the validation method used in the papers reviewed. Overall, 170 selected articles published between 2014 and 2019 were reviewed following the Okali and Schabram methodology. Findings show an increase of 41% of publications when comparing the output of 2019 against the average number of papers published in the previous 5 years (2014–2018). Inertial sensors such as accelerometers and gyroscopes, are the most frequently used. Feature and instance representation are mature techniques for transfer knowledge. Unsupervised learning across users is a typical application, and shallow techniques and active learning are areas of opportunity in transfer learning methodologies.
Audio Encryption Scheme Using Self-Adaptive Bit Scrambling and Two Multi Chaotic-Based Dynamic DNA Computations

In this paper, a novel scheme for secure audio transmission is introduced.The novelty of this scheme is that it combines four different techniques for audio encryption in the same scheme which makes it more secure: self-adaptive scrambling, multi chaotic maps, dynamic DNA encoding and cipher feedback encryption.Also, it introduces two new designed multi chaotic maps as pseudo random generators that combine five different chaotic maps with eight control parameters.The scheme consists of three phases with three secret keys.The first phase is a self-adaptive bit scrambling where SHA512 of the input audio is computed to be used as a first secret key for cyclic shifting the input audio binary stream which efficiently reduces the strong correlation between neighboring audio samples.The second phase is a dynamic DNA encoding of the scrambled audio by using a second secret key obtained from a pseudo random generator (LCS) using a novel design of multi chaotic maps including Sine, Chebyshev and Logistic map with three control parameters.As DNA encoding provides fast computations and large capacity for data transmission, the third phase consists of two DNA algebraic operations ''AND'' and ''XOR'' using a pseudorandom DNA sequence as a third secret key generated by another new designed multi chaotic HLG map which combines Henon, Logistic and Gaussian chaotic maps and has five control parameters.The third phase is structured in a cipher feedback mode and so it achieves strong diffusion and confusion in the encrypted audio.Also combining five different chaotic maps in the proposed scheme increases the number of secret control parameters which achieves a huge key space and so robust strength against brute force attacks.The scheme is evaluated by using different measurements including signal to noise ratio (SNR), peak signal to noise ratio (PSNR), number of samples change rate (NSCR), unified average changing intensity (UACI), root mean square (RMS), crest factor (CF), correlation coefficient, histogram, key sensitivity and key space.From results, it is clear that the scheme is highly secure and stronger than many recent similar audio encryption schemes against different types of attacks.
Do Islamic indices provide diversification to bitcoin? A time-varying copulas and value at risk application

The emergence of new asset classes offers avenues to international investment community however understanding relationship between any two assets in a single portfolio is important. We investigate the risk dependence between daily Bitcoin and major Islamic equity markets spanning over from July 2010 to March 2018. We start by examining long memory properties of Bitcoin and sampled Islamic indices and report significant results. The residuals from fractionally integrated models are then used in bivariate time invariant and time varying copulas to investigate dependence structure. Among all Islamic indices, DJIUK, DJIJP and DJICA exhibit time varying dependence with Bitcoin. In addition, we apply VaR, CoVaR and ΔCoVaR as risk measure to examine spillover between Bitcoin and Islamic equity markets. VaR of Bitcoin exceeds from VaR of Islamic indices and CoVaR of both Islamic and Bitcoin exceeds their respective VaR, suggesting presence of risk spillover between each other. Our results also report asymmetry between downside and upside ΔCoVaR suggesting implications for investors with different risk preferences. Finally, the diversification benefits indicate that Islamic equity market serves as an effective hedge in a portfolio along with Bitcoin.
A Privacy-Preserving Secure Framework for Electric Vehicles in IoT Using Matching Market and Signcryption

The present world of vehicle technology is inclined to develop Electric Vehicles (EVs) with various optimized features. These vehicles need frequent charging which takes a longer time to charge up. Therefore, scheduling of vehicles in charging stations is required. Besides, the information of the EVs and its location is also stored by the charging stations and therefore creates a concern of EV privacy. Various researches are going on to solve these problems; however, an efficient privacy-preserving solution is less practiced till date. In this paper, a framework for Electric Vehicle (EV) charging is discussed. The framework uses the concept of Matching Market to identify a charging station and uses the lattice-based cryptography for secure communications. The matching market considers multiple factors to provide the best allocation of charging station and cryptography ensures security and privacy preservation. The use of lattice-based cryptographic hash SWIFFT avoids heavy computation. This usage of matching market and lattice cryptography, more specifically signcryption for EV charging framework are the highlights of the solution and add-ons to the novel features. Overall, the presented framework is efficient in terms of computation and communication cost, satisfaction ratio, slot ratio, charging latency and load balancing index. The performance metrics are compared with recent developments in this field.
Spacechain: A Three-Dimensional Blockchain Architecture for IoT Security

Deploying blockchain in IoT is an effective way to address traditional security issues. However, existing approaches have two major limitations: since the blockchain itself is subject to attacks, including selfish mining, double spending, and distributed denial of service attacks, IoT smart devices are also vulnerable once hackers successfully invade blockchain systems; due to the heterogeneity and resource limitations of IoT devices, the deployment of the existing blockchain systems in the IoT scenario cannot reflect strong adaptability and meet IoT service requirements. In this article, we introduce Spacechain, a secure and high-performance blockchain system with three-dimensional ledger architecture, to enable blockchain open in IoT. Specifically, we first design a three-dimensional architecture with novel data structures to deal with the heterogeneity and scalability of IoT networks. Then, we propose the Three-Dimensional Greedy Heaviest-Observed Sub-Tree (3D-GHOST) consensus mechanism for Spacechain to improve security and network performance. Additionally, we conduct detailed security analysis and extensive experimental verification to demonstrate the performance of Spacechain.
Large-Scale Quantum Network over 66 Orbital Angular Momentum Optical Modes

Multipartite entanglement (ME) is the fundamental ingredient for building quantum networks. The scale of ME determines its quantum information carrying and processing capability. Most of the current efforts for boosting the scale of ME focus on increasing the number of entangled nodes. However, the number of channels for broadcasting ME is also an important index for characterizing its scale. In this Letter, we experimentally exploit orbital angular momentum multiplexing and the spatial pump shaping technique to simultaneously and deterministically generate 11 channels of individually accessible and mutually orthogonal continuous variable (CV) spatially separated hexapartite entangled states over 66 optical modes in a single quantum system. These results suggest that our method can greatly expand the scale of ME and provide a new perspective and platform to construct a CV quantum network.
Photon superbunching from a generic tunnel junction

Photon pairs are generated in an electrically driven tunnel junction.
Light stage super-resolution

The light stage has been widely used in computer graphics for the past two decades, primarily to enable the relighting of human faces. By capturing the appearance of the human subject under different light sources, one obtains the light transport matrix of that subject, which enables image-based relighting in novel environments. However, due to the finite number of lights in the stage, the light transport matrix only represents a sparse sampling on the entire sphere. As a consequence, relighting the subject with a point light or a directional source that does not coincide exactly with one of the lights in the stage requires interpolation and resampling the images corresponding to nearby lights, and this leads to ghosting shadows, aliased specularities, and other artifacts. To ameliorate these artifacts and produce better results under arbitrary high-frequency lighting, this paper proposes a learning-based solution for the "super-resolution" of scans of human faces taken from a light stage. Given an arbitrary "query" light direction, our method aggregates the captured images corresponding to neighboring lights in the stage, and uses a neural network to synthesize a rendering of the face that appears to be illuminated by a "virtual" light source at the query location. This neural network must circumvent the inherent aliasing and regularity of the light stage data that was used for training, which we accomplish through the use of regularized traditional interpolation methods within our network. Our learned model is able to produce renderings for arbitrary light directions that exhibit realistic shadows and specular highlights, and is able to generalize across a wide variety of subjects. Our super-resolution approach enables more accurate renderings of human subjects under detailed environment maps, or the construction of simpler light stages that contain fewer light sources while still yielding comparable quality renderings as light stages with more densely sampled lights.
Rahasak—Scalable blockchain architecture for enterprise applications

Blockchain-based decentralized infrastructure has been adapted in various industries to handle the sensitive data in a privacy-preserving manner without trusting third parties. However, integrating state-of-the-art blockchain platforms with the scalable, enterprise-level applications result in several challenges. Current blockchain platforms do not support high transaction throughput, lack high scalability, and cannot provide real-time transaction processing and back-pressure operation handling in high transaction throughput applications (e.g Big data, IoT). In this paper, we propose a novel permissioned blockchain platform “Rahasak” for highly scalable, enterprise applications. Rahasak blockchain adopts the Apache Kafka-based consensus on top of a “Validate-Execute-Group” blockchain architecture to handle real-time transaction execution on the blockchain. The architecture is equipped with a functional programming and actor-based smart contract platform that enables concurrent execution of transactions in the blockchain. Rahasak supports high transaction throughput, high scalability, concurrent transaction execution, data analytics features. With Rahasak, we make blockchain more scalable, secure, structured and meaningful for further data analytics.