Reviewerly/exHarmony huggingface dataset:
Toward convergence of effective-field-theory simulations on digital quantum computers

We report results for simulating an effective field theory to compute the binding energy of the deuteron nucleus using a hybrid algorithm on a trapped-ion quantum computer. Two increasingly complex unitary coupled-cluster ansatze have been used to compute the binding energy to within a few percent for successively more complex Hamiltonians. By increasing the complexity of the Hamiltonian, allowing more terms in the effective field theory expansion, and calculating their expectation values, we present a benchmark for quantum computers based on their ability to scalably calculate the effective field theory with increasing accuracy. Our result of ${E}_{4}=\ensuremath{-}2.220\ifmmode\pm\else\textpm\fi{}0.179$ MeV may be compared with the exact deuteron ground-state energy $\ensuremath{-}2.224$ MeV. We also demonstrate an error mitigation technique using Richardson extrapolation on ion traps. The error mitigation circuit represents a record for deepest quantum circuit on a trapped-ion quantum computer.
Secure and Scalable mHealth Data Management Using Blockchain Combined With Client Hashchain: System Design and Validation

Blockchain is emerging as an innovative technology for secure data management in many areas, including medical practice. A distributed blockchain network is tolerant against network fault, and the registered data are resistant to tampering and revision. The technology has a high affinity with digital medicine like mobile health (mHealth) and provides reliability to the medical data without labor-intensive third-party contributions. On the other hand, the reliability of the medical data is not insured before registration to the blockchain network. Furthermore, there are issues with regard to how the clients' mobile devices should be dealt with and authenticated in the blockchain network in order to avoid impersonation.The aim of the study was to design and validate an mHealth system that enables the compatibility of the security and scalability of the medical data using blockchain technology.We designed an mHealth system that sends medical data to the blockchain network via relay servers. The architecture provides scalability and convenience of operation of the system. In order to ensure the reliability of the data from clients' mobile devices, hash values with chain structure (client hashchain) were calculated in the clients' devices and the results were registered on the blockchain network.The system was applied and deployed in mHealth for insomnia treatment. Clinical trials for mHealth were conducted with insomnia patients. Medical data of the recruited patients were successfully registered with the blockchain network via relay servers along with the hashchain calculated on the clients' mobile devices. The correctness of the data was validated by identifying illegal data, which were made by simulating fraudulent access.Our proposed mHealth system, blockchain combined with client hashchain, ensures compatibility of security and scalability in the data management of mHealth medical practice.UMIN Clinical Trials Registry UMIN000032951; https://upload.umin.ac.jp/cgi-open- bin/ctr_e/ctr_view.cgi?recptno=R000037564 (Archived by WebCite at http://www.webcitation.org/78HP5iFIw).
Acceleration of LSTM With Structured Pruning Method on FPGA

This paper focuses on accelerating long short-term memory (LSTM), which is one of the popular types of recurrent neural networks (RNNs). Because of the large number of weight memory accesses and high computation complexity with the cascade-dependent structure, it is a big challenge to efficiently implement the LSTM on field-programmable gate arrays (FPGAs). To speed up the inference on FPGA, considering its limited resource, a structured pruning method that can not only reduce the LSTM model's size without loss of prediction accuracy but also eliminate the imbalance computation and irregular memory accesses is proposed. Besides that, the hardware architecture of the compressed LSTM is designed to pursue high performance. As a result, the implementation of an LSTM language module on Stratix V GXA7 FPGA can achieve 85.2 GOPS directly on the sparse LSTM network by our method, corresponding to 681.6-GOPS effective throughput on the dense one, which shows that the proposed structured pruning algorithm makes 7.82 times speedup when only 1/8 parameters are reserved. We hope that our method can give an efficient way to accelerate the LSTM and similar recurrent neural networks when the resource-limited environment is emphasized.
Selection of Software Product Line Implementation Components Using Recommender Systems: An Application to Wordpress

In software products line (SPL), there may be features which can be implemented by different components, which means there are several implementations for the same feature.In this context, the selection of the best components set to implement a given configuration is a challenging task due to the high number of combinations and options which could be selected.In certain scenarios, it is possible to find information associated with the components which could help in this selection task, such as user ratings.In this paper, we introduce a component-based recommender system, called (REcommender System that suggests implementation Components from selecteD fEatures), which uses information associated with the implementation components to make recommendations in the domain of the SPL configuration.We also provide a RESDEC reference implementation that supports collaborative-based and content-based filtering algorithms to recommend (i.e., implementation components) regarding WordPress-based websites configuration.The empirical results, on a knowledge base with 680 plugins and 187 000 ratings by 116 000 users, show promising results.Concretely, this indicates that it is possible to guide the user throughout the implementation components selection with a margin of error smaller than 13% according to our evaluation.
Distributed quantum metrology with a single squeezed-vacuum source

The authors present a technique to overcome the technological limitations of interferometry in quantum metrology by using squeezed light as the quantum resource, on-off detectors, and thus avoiding the need of any auxiliary interferometric channels. Their set-up achieves Heisenberg-limited sensitivity in the estimation of a linear combination of multiple phases
Recommending Differentiated Code to Support Smart Contract Update

Blockchain has attracted wide attention. A smart contract is a program that runs on the blockchain, and there is evidence that most of the smart contracts on the Ethereum are highly similar, as they share lots of repetitive code. In this study, we empirically study the repetitiveness of the smart contracts via cluster analysis and try to extract the differentiated code from the similar contracts. Differentiated code is defined as the source code except the repeated ones in two similar smart contracts, which usually illustrates how a software feature is implemented or a programming issue is solved. Then, differentiated code might be used to guide the update of a smart contract in its next version. In this paper, to support the update of a target smart contract, we apply syntax and semantic similarities to discover its similar smart contracts from more than 120,000 smart contracts, and recommend the differentiated code to the target smart contract. The promising experimental results demonstrated the differentiated code can effectively support smart contract update.
Online sequential class-specific extreme learning machine for binary imbalanced learning

Many real-world applications suffer from the class imbalance problem, in which some classes have significantly fewer examples compared to the other classes. In this paper, we focus on online sequential learning methods, which are considerably more preferable to tackle the large size imbalanced classification problems effectively. For example, weighted online sequential extreme learning machine (WOS-ELM), voting based weighted online sequential extreme learning machine (VWOS-ELM) and weighted online sequential extreme learning machine with kernels (WOS-ELMK), etc. handle the imbalanced learning effectively. One of our recent works class-specific extreme learning machine (CS-ELM) uses class-specific regularization and has been shown to perform better for imbalanced learning. This work proposes a novel online sequential class-specific extreme learning machine (OSCSELM), which is a variant of CS-ELM. OSCSELM supports online learning technique in both chunk-by-chunk and one-by-one learning mode. It targets to handle the class imbalance problem for both small and larger datasets. The proposed work has less computational complexity in contrast with WOS-ELM for imbalanced learning. The proposed method is assessed by utilizing benchmark real-world imbalanced datasets. Experimental results illustrate the effectiveness of the proposed approach as it outperforms the other methods for imbalanced learning.
Policy poisoning in batch reinforcement learning and control

We study a security threat to batch reinforcement learning and control where the attacker aims to poison the learned policy. The victim is a reinforcement learner / controller which first estimates the dynamics and the rewards from a batch data set, and then solves for the optimal policy with respect to the estimates. The attacker can modify the data set slightly before learning happens, and wants to force the learner into learning a target policy chosen by the attacker. We present a unified framework for solving batch policy poisoning attacks, and instantiate the attack on two standard victims: tabular certainty equivalence learner in reinforcement learning and linear quadratic regulator in control. We show that both instantiation result in a convex optimization problem on which global optimality is guaranteed, and provide analysis on attack feasibility and attack cost. Experiments show the effectiveness of policy poisoning attacks.
Reverse KL-Divergence Training of Prior Networks: Improved Uncertainty and Adversarial Robustness

Ensemble approaches for uncertainty estimation have recently been applied to the tasks of misclassification detection, out-of-distribution input detection and adversarial attack detection. Prior Networks have been proposed as an approach to efficiently emulate an ensemble of models for classification by parameterising a Dirichlet prior distribution over output distributions. These models have been shown to outperform alternative ensemble approaches, such as Monte-Carlo Dropout, on the task of out-of-distribution input detection. However, scaling Prior Networks to complex datasets with many classes is difficult using the training criteria originally proposed. This paper makes two contributions. First, we show that the appropriate training criterion for Prior Networks is the reverse KL-divergence between Dirichlet distributions. This addresses issues in the nature of the training data target distributions, enabling prior networks to be successfully trained on classification tasks with arbitrarily many classes, as well as improving out-of-distribution detection performance. Second, taking advantage of this new training criterion, this paper investigates using Prior Networks to detect adversarial attacks and proposes a generalized form of adversarial training. It is shown that the construction of successful adaptive whitebox attacks, which affect the prediction and evade detection, against Prior Networks trained on CIFAR-10 and CIFAR-100 using the proposed approach requires a greater amount of computational effort than against networks defended using standard adversarial training or MC-dropout.
Exploring blockchain technology in international trade

Purpose The purpose of this paper is to explore the applicability of blockchain technology in international trade process from a perspective of letter of credit payment. Design/methodology/approach A blockchain-based re-engineering process is designed by employing the blockchain and its affiliated smart contract technology to harvest the benefits of distributed ledger and distributed business workflow automation. Findings Comparative analysis and feasibility study were conducted to identify and validate the prospects, in terms of facilitating process flow and enhancing overall trade performance, of the proposed blockchain-based international trade process model. Practical implications Traditional trade processes suffer from a great number of issues about intermediaries, information latency and trust, which, in turn, hinder overall process efficiency. The emerging blockchain technology may have potentials to mitigate those issues by revolutionizing business processes across enterprise borders in various industries. Originality/value This study contributes to the conceptual design of a blockchain- and smart-contract-based process along with a provision of practical case in business process re-engineering. Further endeavors devoted to blockchain research and application across different sectors are suggested to reach better performance of business process operations.
Autoencoder and Evolutionary Algorithm for Level Generation in Lode Runner

Procedural content generation can be used to create arbitrarily large amounts of game levels automatically, but traditionally the PCG algorithms needed to be developed or adapted for each game manually. Procedural Content Generation via Machine Learning (PCGML) harnesses the power of machine learning to semi-automate the development of PCG solutions, training on existing game content so as to create new content from the trained models. One of the machine learning techniques that have been suggested for this purpose is the autoencoder. However, very limited work has been done to explore the potential of autoencoders for PCGML. In this paper, we train autoencoders on levels for the platform game Lode Runner, and use them to generate levels. Compared to previous work, we use a multi-channel approach to represent content in full fidelity, and we compare standard and variational autoencoders. We also evolve the values of the hidden layer of trained autoencoders in order to find levels with desired properties.
Evaluating Machine Learning Articles

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Ensemble Methods for Instance-Based Arabic Language Authorship Attribution

The Authorship Attribution (AA) is considered as a subfield of authorship analysis and it is an important problem as the range of anonymous information increased with fast-growing of internet usage worldwide. In other languages such as English, Spanish and Chinese, such issue is quite well studied. However, in the Arabic language, the AA problem has received less attention from the research community due to the complexity and nature of Arabic sentences. The paper presented an intensive review of previous studies for Arabic language. Based on that, this study has employed the Technique for Order Preferences by Similarity to Ideal Solution (TOPSIS) method to choose the base classifier of the ensemble methods. In terms of attribution features, hundreds of stylometric features and distinct words using several tools have been extracted. Then, AdaBoost and Bagging ensemble methods have been applied to Arabic enquires (Fatwa) dataset. The findings showed an improvement of the effectiveness of the authorship attribution task in the Arabic language.
Integrated photonic neural network based on silicon metalines

An integrated photonic neural network is proposed based on on-chip cascaded one-dimensional (1D) metasurfaces. High-contrast transmitarray metasurfaces, termed as metalines in this paper, are defined sequentially in the silicon-on-insulator substrate with a distance much larger than the operation wavelength. Matrix-vector multiplications can be accomplished in parallel and with low energy consumption due to intrinsic parallelism and low-loss of silicon metalines. The proposed on-chip whole-passive fully-optical meta-neural-network is very compact and works at the speed of light, with very low energy consumption. Various complex functions that are performed by digital neural networks can be implemented by our proposal at the wavelength of 1.55 µm . As an example, the performance of our optical neural network is benchmarked on the prototypical machine learning task of classification of handwritten digits images from the Modified National Institute of Standards and Technology (MNIST) dataset, and an accuracy comparable to the state of the art is achieved.
Emotion-Cause Pair Extraction as Sequence Labeling Based on A Novel Tagging Scheme

The task of emotion-cause pair extraction deals with finding all emotions and the corresponding causes in unannotated emotion texts.Most recent studies are based on the likelihood of Cartesian product among all clause candidates, resulting in a high computational cost.Targeting this issue, we regard the task as a sequence labeling problem and propose a novel tagging scheme with coding the distance between linked components into the tags, so that emotions and the corresponding causes can be extracted simultaneously.Accordingly, an end-to-end model is presented to process the input texts from left to right, always with linear time complexity, leading to a speed up.Experimental results show that our proposed model achieves the best performance, outperforming the state-of-the-art method by 2.26% (p < 0.001) in F 1 measure.
Weighted QMIX: Expanding Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning

QMIX is a popular $Q$-learning algorithm for cooperative MARL in the centralised training and decentralised execution paradigm. In order to enable easy decentralisation, QMIX restricts the joint action $Q$-values it can represent to be a monotonic mixing of each agent's utilities. However, this restriction prevents it from representing value functions in which an agent's ordering over its actions can depend on other agents' actions. To analyse this representational limitation, we first formalise the objective QMIX optimises, which allows us to view QMIX as an operator that first computes the $Q$-learning targets and then projects them into the space representable by QMIX. This projection returns a representable $Q$-value that minimises the unweighted squared error across all joint actions. We show in particular that this projection can fail to recover the optimal policy even with access to $Q^*$, which primarily stems from the equal weighting placed on each joint action. We rectify this by introducing a weighting into the projection, in order to place more importance on the better joint actions. We propose two weighting schemes and prove that they recover the correct maximal action for any joint action $Q$-values, and therefore for $Q^*$ as well. Based on our analysis and results in the tabular setting, we introduce two scalable versions of our algorithm, Centrally-Weighted (CW) QMIX and Optimistically-Weighted (OW) QMIX and demonstrate improved performance on both predator-prey and challenging multi-agent StarCraft benchmark tasks.
Task-Conditioned Domain Adaptation for Pedestrian Detection in Thermal Imagery

Pedestrian detection is a core problem in computer vision that sees broad application in video surveillance and, more recently, in advanced driving assistance systems. Despite its broad application and interest, it remains a challenging problem in part due to the vast range of conditions under which it must be robust. Pedestrian detection at nighttime and during adverse weather conditions is particularly challenging, which is one of the reasons why thermal and multispectral approaches have been become popular in recent years. In this paper, we propose a novel approach to domain adaptation that significantly improves pedestrian detection performance in the thermal domain. The key idea behind our technique is to adapt an RGB-trained detection network to simultaneously solve two related tasks. An auxiliary classification task that distinguishes between daytime and nighttime thermal images is added to the main detection task during domain adaptation. The internal representation learned to perform this classification task is used to condition a YOLOv3 detector at multiple points in order to improve its adaptation to the thermal domain. We validate the effectiveness of task-conditioned domain adaptation by comparing with the state-of-the-art on the KAIST Multispectral Pedestrian Detection Benchmark. To the best of our knowledge, our proposed task-conditioned approach achieves the best single-modality detection results.
LambdaNetworks: Modeling long-range Interactions without Attention

We present lambda layers -- an alternative framework to self-attention -- for capturing long-range interactions between an input and structured contextual information (e.g. a pixel surrounded by other pixels). Lambda layers capture such interactions by transforming available contexts into linear functions, termed lambdas, and applying these linear functions to each input separately. Similar to linear attention, lambda layers bypass expensive attention maps, but in contrast, they model both content and position-based interactions which enables their application to large structured inputs such as images. The resulting neural network architectures, LambdaNetworks, significantly outperform their convolutional and attentional counterparts on ImageNet classification, COCO object detection and COCO instance segmentation, while being more computationally efficient. Additionally, we design LambdaResNets, a family of hybrid architectures across different scales, that considerably improves the speed-accuracy tradeoff of image classification models. LambdaResNets reach excellent accuracies on ImageNet while being 3.2 - 4.4x faster than the popular EfficientNets on modern machine learning accelerators. When training with an additional 130M pseudo-labeled images, LambdaResNets achieve up to a 9.5x speed-up over the corresponding EfficientNet checkpoints.