Reviewerly/exHarmony huggingface dataset:
Scheduled sampling for one-shot learning via matching network

Considering human can learn new object successfully from just one sample, one-shot learning, where each visual class just has one labeled sample for training, has attracted more and more attention. In the past years, most researchers achieve one-shot learning by training a matching network to map a small labeled support set and an unlabeled image to its label. The support set is combined by one image with the same label as unlabeled image and few images with other labels generated by random sampling. This random sampling strategy easily generates massive over-easy support sets in which most labels are less relevant to the label of unlabeled image. It leads to the limitation of matching network for one-shot prediction over indistinguishable label sets. For this issue, we propose a novel metric to evaluate the learning difficulty of support set, where this metric jointly considers the semantic diversity and similarity of visual labels. Based on the metric, we introduce a scheduled sampling strategy to train the matching network from easy to difficult. Extensive experimental results on three datasets, including mini-Imagenet, Birds and Flowers, indicate that our method could achieve significant improvements over other previous methods.
A Hierarchical Sharding Protocol for Multi-Domain IoT Blockchains

Internet of Things (IoT), an significant support for strategic emerging industries, is re-building industrial systems, such as transportation, healthcare and energy, while a number of new features and requirements like data cross-industry sharding appear in recent years. The emerging Blockchain technology has provided a promising opportunity to break information island and single-domain management in multi-domain IoT systems by leveraging distributed storage. On the other hand, the transaction consensus throughput in the mainstream blockchain systems, such as Ethereum and Fabric, is far from meeting the demand for massive data storage in time in multi-domain IoT systems. In this paper, we design a sharding protocol called MDIoTSP. The protocol first partitions the overall blockchains into many small shards, each of which generally recognized as a micro-blockchain according to the multiple domains, and then make the final consensus by merging the hash digests of the sub-blocks which generated from the shards for the multi-domain IoT blockchains ecosystem specifically. We also develop MicrothingsChains to run MDIoTSP in multi-domain IoT blockchains to prove our assumption that MDIoTSP scales up the transaction consensus throughput near linearly with the number of shards.
Estimation of daily, weekly and monthly global solar radiation using ANNs and a long data set: a case study of Fortaleza, in Brazilian Northeast region

A 14-year-long data set containing daily values of meteorological variables was used to train three artificial neural networks (ANNs) for daily, weekly averaged and monthly averaged global solar radiation prediction for Fortaleza, in the Brazilian Northeast region. Local climate is semiarid coastal. Day of the year, maximum temperature, minimum temperature, irradiance, precipitation, cloudiness, extraterrestrial radiation, relative humidity, evaporation and wind speed were adopted as predictors. The ANNs were developed by an in-house code and trained with the Broyden–Fletcher–Goldfarb–Shanno (BFGS) algorithm. Besides the lack of explicit predictors able to model El Niño and La Niña phenomena, which have strong influence on local weather, the accuracy of the predictions was considered excellent according to its values of normalized root-mean-square error (nRMSE) and good relative to mean absolute percentage error (MAPE) values. Both error metrics presented the smallest values for the monthly case study.
History-driven build failure fixing: how far are we?

Build systems are essential for modern software development and maintenance since they are widely used to transform source code artifacts into executable software. Previous work shows that build systems break frequently during software evolution. Therefore, automated build-fixing techniques are in huge demand. In this paper we target a mainstream build system, Gradle, which has become the most widely used build system for Java projects in the open-source community (e.g., GitHub). HireBuild, state-of-the-art build-fixing tool for Gradle, has been recently proposed to fix Gradle build failures via mining the history of prior fixes. Although HireBuild has been shown to be effective for fixing real-world Gradle build failures, it was evaluated on only a limited set of build failures, and largely depends on the quality/availability of historical fix information. To investigate the efficacy and limitations of the history-driven build fix, we first construct a new and large build failure dataset from Top-1000 GitHub projects. Then, we evaluate HireBuild on the extended dataset both quantitatively and qualitatively. Inspired by the findings of the study, we propose a simplistic new technique that generates potential patches via searching from the present project under test and external resources rather than the historical fix information. According to our experimental results, the simplistic approach based on present information successfully fixes 2X more reproducible build failures than the state-of-art HireBuild based on historical fix information. Furthermore, our results also reveal various findings/guidelines for future advanced build failure fixing.
A novel secondary decomposition learning paradigm with kernel extreme learning machine for multi-step forecasting of container throughput

Abstract Container throughput has been perceived as one of the most important factors of port economic development. Accurate forecasting of container throughput can not only improve the efficiency of container operation but also meet the requirements of financial trading. This paper proposes a secondary decomposition (SD) learning approach, which is integrated with a complementary ensemble empirical mode decomposition (CEEMD), sample entropy (SE), wavelet packet decomposition (WPD), extreme learning machine (ELM) and kernel extreme learning machine (KELM), to implement monthly container throughput forecasting. The CEEMD is conducted to decompose the original container data into several intrinsic mode function components and a residual term. The complexity of the subseries is measured by SE. Subsequently, the WPD is used to further smooth the most complex component. Then, ELM is executed to forecast the primary decomposition results, and KELM is employed to predict the secondary decomposition results. The final ensemble results can be obtained by integrating all of the forecasting results. The empirical results indicate that the SD learning approach is an excellent tool for forecasting nonlinear and nonstationary container throughput.
Can cryptocurrency, mobile phones, and internet herald sustainable financial sector development in emerging markets?

This study investigates the relationship between cryptocurrency, internet, mobile phones, financial inclusion, and financial sector development in China, India, Nigeria, and South Africa for the period 2009–2017 using fully modified ordinary least square (FM-OLS) and causality analysis. The empirical results show that cryptocurrency, internet usage, and mobile subscriptions have a significant positive relationship with financial inclusion and financial sector development, suggesting that countries with higher levels of cryptocurrency, internet usage, and mobile subscriptions have higher levels of financial inclusion and financial sector development. This finding is further buttressed by the causality analysis which shows that cryptocurrency, internet usage, and mobile subscriptions cause financial inclusion and financial sector development in the four countries.
Implementation of Machine Learning Algorithms in Arabic Sentiment Analysis Using N-Gram Features

Sentiment analysis (SA) is a scholarly process of extricating and classifying individuals’ emotions and feedbacks expressed in source text content. It is one of the pursued subfields of Computational Linguistics (CL) and Natural Language Processing (NLP). The evolution of social media based applications has generated a big amount of personalized reviews of different related information on the Web in the form of tweets, status updates, and many others. Several approaches have come into the spotlight in recent years to accomplish SA, the most part of SA researches have been applied utilizing the English language. SA in Arabic online social media may be slacking behind commonly because of the difficulties with handling the morphologically complex Arabic natural language and the lack and absence of accessible tools and assets for extracting Arabic opinions from the text. This research is aimed to analyze the collected twitter posts in different Arabic Dialects and a comparison between the various algorithms used for SA with various n-gram as a feature extraction method. The measurement of the performance of different algorithms is evaluated in terms of recall, precision, f-measure, and accuracy. The experiment results show that unigram with Passive Aggressive (PA) or Ridge Regression (RR) gives the highest accuracy 99.96 %.
Quantum multi-level wavelet transforms

The classical wavelet transform has been widely applied in the information processing field. It implies that quantum wavelet transform (QWT) may play an important role in quantum information processing. In this paper, we present the iteration equations of the general QWT using generalized tensor product. Then, Haar QWT (HQWT), Daubechies D4 QWT (DQWT), and their inverse transforms are proposed respectively. Meanwhile, the circuits of the two kinds of multi-level HQWT are designed. What’s more, the multi-level DQWT based on the periodization extension is implemented for the first time. The complexity analysis shows that the proposed multi-level QWTs on 2n elements can be implemented by O(n3) basic operations. Simulation experiments demonstrate that the proposed QWTs are correct and effective.
Direct Characteristic-Function Tomography of Quantum States of the Trapped-Ion Motional Oscillator

We implement direct readout of the symmetric characteristic function of quantum states of the motional oscillation of a trapped calcium ion. Using suitably chosen internal electronic state-dependent displacements based on bi-chromatic laser fields we map the expectation value of the real or imaginary part of the displacement operator to the internal states, which are subsequently read out using fluorescence detection. Combining the two readout results provides full information about the symmetric characteristic function. We characterize the performance of the technique by applying it to a range of archetypal quantum oscillator states, including displaced and squeezed Gaussian states as well as two and three component superpositions of displaced squeezed states which have applications in continuous variable quantum computing. For each, we discuss relevant features of the characteristic function and Wigner phase-space quasi-probability distribution. The direct reconstruction of these highly non-classical oscillator states using a reduced number of measurements is an essential tool for understanding and optimizing the control of these systems for quantum sensing and quantum information applications.
Formal Verification of Blockchain Smart Contract Based on Colored Petri Net Models

A smart contract is a computer protocol intended to digitally facilitate and enforce the negotiation of a contract in undependable environment. However, the number of attacks using the vulnerabilities of the smart contracts is also growing in recent years. Many solutions have been proposed in order to deal with them, such as documenting vulnerabilities or setting the security strategies. Among them, the most influential progress is made by the formal verification method. In this paper, we propose a formal verification method based on Colored Petri Nets (CPN) to verify smart contracts in blockchain system. First, we develop the smart contract models with possible attacker models based on hierarchical CPN modeling, then the smart contract models are executed by step-by-step simulation to validate their functional correctness, and finally we utilize the branch timing logic ASK-CTL based model checking technology in the CPN tools to detect latent vulnerabilities in smart contracts. We demonstrate that our CPN modeling based verification method can not only detect the logical vulnerabilities of the smart contract, but also consider the impacts of users behavior to find out potential non-logical vulnerabilities in the contracts, such as the vulnerabilities caused by the limitations of the Solidity language.
Facial Recognition Technology: A Primer for Plastic Surgeons

Summary: The face is arguably the most unique and defining feature of the human body. From birth, humans are conditioned to perceive, interpret, and react to information conveyed by faces both familiar and unfamiliar. Although face recognition is routine for humans, only recently has it become possible for a computer to accurately recognize a human face in an image or video. With advances in artificial intelligence, image capture technology, and Internet connectivity, facial recognition technology has entered the forefront of personal and commercial technology. Plastic surgeons should be prepared to answer questions from patients about the fundamentals of facial recognition technology, and the potential effects of plastic surgery on facial recognition technology performance. This article provides an overview of facial recognition technology, describes its present applications, discusses its relevance within the field of plastic surgery, and provides recommendations for plastic surgeons to consider during preoperative discussions with patients.
Automated Hypersafety Verification

We propose an automated verification technique for hypersafety properties, which express sets of valid interrelations between multiple finite runs of a program. The key observation is that constructing a proof for a small representative set of the runs of the product program (i.e. the product of the several copies of the program by itself), called a reduction, is sufficient to formally prove the hypersafety property about the program. We propose an algorithm based on a counterexample-guided refinement loop that simultaneously searches for a reduction and a proof of the correctness for the reduction. We demonstrate that our tool Weaver is very effective in verifying a diverse array of hypersafety properties for a diverse class of input programs.
Auto-weighted multi-view constrained spectral clustering

Constrained clustering is a new fashion of semi-supervised learning which focused on enhancing the quality of the partition by utilizing pairwise constraints. Though many constrained clustering methods have an excellent performance in single-view clustering, they can’t be directly applied to multi-view scenario. In this paper, we propose a novel constrained spectral clustering approach for multi-view data, which explicitly imposes pairwise constraints as a series of linear constraints on the unified indicator matrix. To our best knowledge, this is the first work on multi-view constrained spectral clustering. Our approach can differ the importance of different views via the auto-weight learning strategy. Simultaneously, the views which contain much noisy or irrelevant information are also automatically eliminated, thereby improving the prediction performance. Extensive experiments conducted on various multi-view datasets demonstrate that the proposed approach can efficiently utilize pairwise constraints and outperforms the state-of-the-art approaches.
Data-Efficient Graph Embedding Learning for PCB Component Detection

This paper presents a challenging computer vision task, namely the detection of generic components on a PCB, and a novel set of deep-learning methods that are able to jointly leverage the appearance of individual components and the propagation of information across the structure of the board to accurately detect and identify various types of components on a PCB. Due to the expense of manual data labeling, a highly unbalanced distribution of component types, and significant domain shift across boards, most earlier attempts based on traditional image processing techniques fail to generalize well to PCB images with various quality, lighting conditions, etc. Newer object detection pipelines such as Faster R-CNN, on the other hand, require a large amount of labeled data, do not deal with domain shift, and do not leverage structure. To address these issues, we propose a three stage pipeline in which a class-agnostic region proposal network is followed by a low-shot similarity prediction classifier. In order to exploit the data dependency within a PCB, we design a novel Graph Network block to refine the component features conditioned on each PCB. To the best of our knowledge, this is one of the earliest attempts to train a deep learning based model for such tasks, and we demonstrate improvements over recent graph networks for this task. We also provide in-depth analysis and discussion for this challenging task, pointing to future research.
On the Transferability of Adversarial Examples against CNN-based Image Forensics

Recent studies have shown that Convolutional Neural Networks (CNN) are relatively easy to attack through the generation of so called adversarial examples. Such vulnerability also affects CNN-based image forensic tools. Research in deep learning has shown that adversarial examples exhibit a certain degree of transferability, i.e., they maintain part of their effectiveness even against CNN models other than the one targeted by the attack. This is a very strong property undermining the usability of CNN's in security-oriented applications. In this paper, we investigate if attack transferability also holds in image forensics applications. With specific reference to the case of manipulation detection, we analyse the results of several experiments considering different sources of mismatch between the CNN used to build the adversarial examples and the one adopted by the forensic analyst. The analysis ranges from cases in which the mismatch involves only the training dataset, to cases in which the attacker and the forensic analyst adopt different architectures. The results of our experiments show that, in the majority of the cases, the attacks are not transferable, thus easing the design of proper countermeasures at least when the attacker does not have a perfect knowledge of the target detector.
Asynchronous parallel algorithms for nonconvex optimization

We propose a new asynchronous parallel block-descent algorithmic framework for the minimization of the sum of a smooth nonconvex function and a nonsmooth convex one, subject to both convex and nonconvex constraints. The proposed framework hinges on successive convex approximation techniques and a novel probabilistic model that captures key elements of modern computational architectures and asynchronous implementations in a more faithful way than current state-of-the-art models. Other key features of the framework are: (1) it covers in a unified way several specific solution methods; (2) it accommodates a variety of possible parallel computing architectures; and (3) it can deal with nonconvex constraints. Almost sure convergence to stationary solutions is proved, and theoretical complexity results are provided, showing nearly ideal linear speedup when the number of workers is not too large.
Supporting and challenging learners through pedagogical agents: Addressing ethical issues through designing for values

Abstract Pedagogical Agents (PAs) that would guide interactions in intelligent learning environments were envisioned two decades ago. These early animated characters had been shown to deliver learning benefits. However, little was understood regarding what aspects were beneficial for learning and what sort of learning PAs were suitable for. This article considers the current and future use of PAs to support and challenge learners from three perspectives. Firstly, we look at PAs from a practical perspective to consider what Intelligent Virtual Agents are, the roles they play in education and beyond and the underlying technologies and theories driving them. Next we take a pedagogical perspective to consider the vision, pedagogical approaches supported and new possible uses of PAs. This leads us to the political perspective to consider the values, ethics and societal impacts of PAs. Drawing all three perspectives together we present a design for values approach to designing ethical and socially responsible PAs.
All orders structure and efficient computation of linearly reducible elliptic Feynman integrals

A bstract We define linearly reducible elliptic Feynman integrals, and we show that they can be algorithmically solved up to arbitrary order of the dimensional regulator in terms of a 1-dimensional integral over a polylogarithmic integrand, which we call the inner polylogarithmic part (IPP). The solution is obtained by direct integration of the Feynman parametric representation. When the IPP depends on one elliptic curve (and no other algebraic functions), this class of Feynman integrals can be algorithmically solved in terms of elliptic multiple polylogarithms (eMPLs) by using integration by parts identities. We then elaborate on the differential equations method. Specifically, we show that the IPP can be mapped to a generalized integral topology satisfying a set of differential equations in ϵ -form. In the examples we consider the canonical differential equations can be directly solved in terms of eMPLs up to arbitrary order of the dimensional regulator. The remaining 1-dimensional integral may be performed to express such integrals completely in terms of eMPLs. We apply these methods to solve two- and three-points integrals in terms of eMPLs. We analytically continue these integrals to the physical region by using their 1-dimensional integral representation.
Transfer Learning of Language-independent End-to-end ASR with Language Model Fusion

This work explores better adaptation methods to low-resource languages using an external language model (LM) under the framework of transfer learning. We first build a language-independent ASR system in a unified sequence-to-sequence (S2S) architecture with a shared vocabulary among all languages. During adaptation, we perform LM fusion transfer, where an external LM is integrated into the decoder network of the attention-based S2S model in the whole adaptation stage, to effectively incorporate linguistic context of the target language. We also investigate various seed models for transfer learning. Experimental evaluations using the IARPA BABEL data set show that LM fusion transfer improves performances on all target five languages compared with simple transfer learning when the external text data is available. Our final system drastically reduces the performance gap from the hybrid systems.