Reviewerly/exHarmony huggingface dataset:
Sending-or-not-sending twin-field quantum key distribution in practice

We present results of practical sending-or-not quantum key distribution. In real-life implementations, we need consider the following three requirements, a few different intensities rather than infinite number of different intensities, a phase slice of appropriate size rather than infinitely small size and the statistical fluctuations. We first show the decoy-state method with only a few different intensities and a phase slice of appropriate size. We then give a statistical fluctuation analysis for the decoy-state method. Numerical simulation shows that, the performance of our method is comparable to the asymptotic case for which the key size is large enough. Our results show that practical implementations of the sending-or-not quantum key distribution can be both secure and efficient.
Privacy-Preserving Tensor Decomposition Over Encrypted Data in a Federated Cloud Environment

Tensors are popular and versatile tools which model multidimensional data. Tensor decomposition has emerged as a powerful technique dealing with multidimensional data. With the booming development of cloud computing, a large number of users are inclined to outsource big data storage and computations to the cloud. However, because of the rise of various privacy concerns, sensitive data usually need to be encrypted prior to being outsourced to a cloud. Computations over encrypted data in the cloud without compromising the privacy of data is still a challenge. This paper presents a novel privacy-preserving tensor decomposition approach over semantically secure encrypted big data. The proposed approach leverages properties of homomorphic encryption and employs a federated cloud to securely decompose an encrypted tensor for multiple users, without the clouds learning any knowledge about users' data. This is, to our knowledge, the first attempt to solve privacy-preserving tensor decomposition without requiring interaction between users and cloud service providers. In addition, in our approach, we present the first secure integer division and integer square root schemes over encrypted data (the dividend, divisor and radicand are in encrypted format). Finally, we prove the security of our approach under semi-trusted model and empirically analyze its effectiveness, which demonstrates the utility of our proposed approach in cloud deployments.
Partial Label Learning with Self-Guided Retraining

Partial label learning deals with the problem where each training instance is assigned a set of candidate labels, only one of which is correct. This paper provides the first attempt to leverage the idea of self-training for dealing with partially labeled examples. Specifically, we propose a unified formulation with proper constraints to train the desired model and perform pseudo-labeling jointly. For pseudo-labeling, unlike traditional self-training that manually differentiates the ground-truth label with enough high confidence, we introduce the maximum infinity norm regularization on the modeling outputs to automatically achieve this consideratum, which results in a convex-concave optimization problem. We show that optimizing this convex-concave problem is equivalent to solving a set of quadratic programming (QP) problems. By proposing an upper-bound surrogate objective function, we turn to solving only one QP problem for improving the optimization efficiency. Extensive experiments on synthesized and real-world datasets demonstrate that the proposed approach significantly outperforms the state-of-the-art partial label learning approaches.
Transferable Curriculum for Weakly-Supervised Domain Adaptation

Domain adaptation improves a target task by knowledge transfer from a source domain with rich annotations. It is not uncommon that “source-domain engineering” becomes a cumbersome process in domain adaptation: the high-quality source domains highly related to the target domain are hardly available. Thus, weakly-supervised domain adaptation has been introduced to address this difficulty, where we can tolerate the source domain with noises in labels, features, or both. As such, for a particular target task, we simply collect the source domain with coarse labeling or corrupted data. In this paper, we try to address two entangled challenges of weaklysupervised domain adaptation: sample noises of the source domain and distribution shift across domains. To disentangle these challenges, a Transferable Curriculum Learning (TCL) approach is proposed to train the deep networks, guided by a transferable curriculum informing which of the source examples are noiseless and transferable. The approach enhances positive transfer from clean source examples to the target and mitigates negative transfer of noisy source examples. A thorough evaluation shows that our approach significantly outperforms the state-of-the-art on weakly-supervised domain adaptation tasks.
Semi-Supervised Learning With Deep Embedded Clustering for Image Classification and Segmentation

Deep neural networks usually require large labeled datasets to construct accurate models; however, in many real-world scenarios, such as medical image segmentation, labelling data is a time-consuming and costly human (expert) intelligent task. Semi-supervised methods leverage this issue by making use of a small labeled dataset and a larger set of unlabeled data. In this article, we present a flexible framework for semi-supervised learning that combines the power of supervised methods that learn feature representations using state-of-the-art deep convolutional neural networks with the deep embedded clustering algorithm that assigns data points to clusters based on their probability distributions and feature representations learned by the networks. Our proposed semi-supervised learning algorithm based on deep embedded clustering (SSLDEC) learns feature representations via iterations by alternatively using labeled and unlabeled data points and computing target distributions from predictions. During this iterative procedure the algorithm uses labeled samples to keep the model consistent and tuned with labeling, as it simultaneously learns to improve feature representation and predictions. SSLDEC requires few hyper-parameters and thus does not need large labeled validation sets, which addresses one of the main limitations of many semi-supervised learning algorithms. It is also flexible and can be used with many state-of-the-art deep neural network configurations for image classification and segmentation tasks. To this end, we implemented and tested our approach on benchmark image classification tasks as well as in a challenging medical image segmentation scenario. In benchmark classification tasks, SSLDEC outperformed several state-of-the-art semi-supervised learning methods, achieving 0.46% error on MNIST with 1000 labeled points, and 4.43% error on SVHN with 500 labeled points. In the iso-intense infant brain MRI tissue segmentation task, we implemented SSLDEC on a 3D densely connected fully convolutional neural network where we achieved significant improvement over supervised-only training as well as a semi-supervised method based on pseudo-labelling. Our results show that SSLDEC can be effectively used to reduce the need for costly expert annotations, enhancing applications such as automatic medical image segmentation.
Cybercrime and Cybersecurity in Africa

Africa has been among the fastest growing regions in terms of cybercrime activities. The continent is also a source of significant cyberattacks targeting the rest of the world. However, a number of measures have been taken to address cyber-threats and improve cybersecurity in the continent. Many countries in the continent have developed legislation to fight cyber-threats. They have also strengthened enforcement measures. Private sector efforts have also been undertaken to strengthen cybersecurity.
Collision free 4D path planning for multiple UAVs based on spatial refined voting mechanism and PSO approach

In this paper, a four-dimensional coordinated path planning algorithm for multiple UAVs is proposed, in which time variable is taken into account for each UAV as well as collision free and obstacle avoidance. A Spatial Refined Voting Mechanism (SRVM) is designed for standard Particle Swarm Optimization (PSO) to overcome the defects of local optimal and slow convergence. For each generation candidate particle positions are recorded and an adaptive cube is formed with own adaptive side length to indicate occupied regions. Then space voting begins and is sorted based on voting results, whose centers with bigger voting counts are seen as sub-optimal positions. The average of all particles of corresponding dimensions are calculated as the refined solutions. A time coordination method is developed by generating specified candidate paths for every UAV, making them arrive the same destination with the same time consumption. A spatial-temporal collision avoidance technique is introduced to make collision free. Distance to destination is constructed to improve the searching accuracy and velocity of particles. In addition, the objective function is redesigned by considering the obstacle and threat avoidance, Estimated Time of Arrival (ETA), separation maintenance and UAV self-constraints. Experimental results prove the effectiveness and efficiency of the algorithm.
A Color Image Encryption Using Dynamic DNA and 4-D Memristive Hyper-Chaos

In this paper, a color image encryption is proposed based on the dynamic DNA and the 4-D memristive hyper-chaos. First, chaotic matrices are generated from the 4-D memristive hyper-chaos by using the plain image, the salt key, and control parameters. Second, the dynamic encoding process is performed on three components of the plain image to obtain three DNA matrices. Third, to enhance both the security and robustness of encrypted image, dynamic confusion and diffusion are applied to the encoded DNA matrices. Finally, the encrypted image is generated by DNA decoding and components combining. The main feature of our proposed algorithm is that dynamic DNA mechanism based on hyper-chaos is performed on the processes of encoding, confusion, and diffusion. Simulation results and security analysis further demonstrate that it has a strong resistance against various attacks and outperforms other methods in the literature.
On the Word Alignment from Neural Machine Translation

Prior researches suggest that neural machine translation (NMT) captures word alignment through its attention mechanism, however, this paper finds attention may almost fail to capture word alignment for some NMT models.This paper thereby proposes two methods to induce word alignment which are general and agnostic to specific NMT models.Experiments show that both methods induce much better word alignment than attention.This paper further visualizes the translation through the word alignment induced by NMT.In particular, it analyzes the effect of alignment errors on translation errors at word level and its quantitative analysis over many testing examples consistently demonstrate that alignment errors are likely to lead to translation errors measured by different metrics.
Robust Estimation of Similarity Transformation for Visual Object Tracking

Most of existing correlation filter-based tracking approaches only estimate simple axis-aligned bounding boxes, and very few of them is capable of recovering the underlying similarity transformation. To tackle this challenging problem, in this paper, we propose a new correlation filter-based tracker with a novel robust estimation of similarity transformation on the large displacements. In order to efficiently search in such a large 4-DoF space in real-time, we formulate the problem into two 2-DoF sub-problems and apply an efficient Block Coordinates Descent solver to optimize the estimation result. Specifically, we employ an efficient phase correlation scheme to deal with both scale and rotation changes simultaneously in log-polar coordinates. Moreover, a variant of correlation filter is used to predict the translational motion individually. Our experimental results demonstrate that the proposed tracker achieves very promising prediction performance compared with the state-of-the-art visual object tracking methods while still retaining the advantages of high efficiency and simplicity in conventional correlation filter-based tracking methods.
Merge and Label: A Novel Neural Network Architecture for Nested NER

Named entity recognition (NER) is one of the best studied tasks in natural language processing.However, most approaches are not capable of handling nested structures which are common in many applications.In this paper we introduce a novel neural network architecture that first merges tokens and/or entities into entities forming nested structures, and then labels each of them independently.Unlike previous work, our merge and label approach predicts real-valued instead of discrete segmentation structures, which allow it to combine word and nested entity embeddings while maintaining differentiability.We evaluate our approach using the ACE 2005 Corpus, where it achieves state-of-the-art F1 of 74.6, further improved with contextual embeddings (BERT) to 82.4, an overall improvement of close to 8 F1 points over previous approaches trained on the same data.Additionally we compare it against BiLSTM-CRFs, the dominant approach for flat NER structures, demonstrating that its ability to predict nested structures does not impact performance in simpler cases. 1
Modeling Graph Structure in Transformer for Better AMR-to-Text Generation

Recent studies on AMR-to-text generation often formalize the task as a sequence-tosequence (seq2seq) learning problem by converting an Abstract Meaning Representation (AMR) graph into a word sequence.Graph structures are further modeled into the seq2seq framework in order to utilize the structural information in the AMR graphs.However, previous approaches only consider the relations between directly connected concepts while ignoring the rich structure in AMR graphs.In this paper we eliminate such a strong limitation and propose a novel structure-aware selfattention approach to better modeling the relations between indirectly connected concepts in the state-of-the-art seq2seq model, i.e., the Transformer.In particular, a few different methods are explored to learn structural representations between two concepts.Experimental results on English AMR benchmark datasets show that our approach significantly outperforms the state of the art with 29.66 and 31.82BLEU scores on LDC2015E86 and LDC2017T10, respectively.To the best of our knowledge, these are the best results achieved so far by supervised models on the benchmarks.
μVulDeePecker: A Deep Learning-Based System for Multiclass Vulnerability Detection

Fine-grained software vulnerability detection is an important and challenging problem. Ideally, a detection system (or detector) not only should be able to detect whether or not a program contains vulnerabilities, but also should be able to pinpoint the type of a vulnerability in question. Existing vulnerability detection methods based on deep learning can detect the presence of vulnerabilities (i.e., addressing the binary classification or detection problem), but cannot pinpoint types of vulnerabilities (i.e., incapable of addressing multiclass classification). In this paper, we propose the first deep learning-based system for multiclass vulnerability detection, dubbed $\mu$VulDeePecker. The key insight underlying $\mu$VulDeePecker is the concept of code attention, which can capture information that can help pinpoint types of vulnerabilities, even when the samples are small. For this purpose, we create a dataset from scratch and use it to evaluate the effectiveness of $\mu$VulDeePecker. Experimental results show that $\mu$VulDeePecker is effective for multiclass vulnerability detection and that accommodating control-dependence (other than data-dependence) can lead to higher detection capabilities.
Large-Scale Mobile App Identification Using Deep Learning

Many network services and tools (e.g.network monitors, malware-detection systems, routing and billing policy enforcement modules in ISPs) depend on identifying the type of traffic that passes through the network.With the widespread use of mobile devices, the vast diversity of mobile apps, and the massive adoption of encryption protocols (such as TLS), large-scale encrypted traffic classification becomes increasingly difficult.In this paper, we propose a deep learning model for mobile app identification that works even with encrypted traffic.The proposed model only needs the payload of the first few packets for classification, and, hence, it is suitable even for applications that rely on early prediction, such as routing and QoS provisioning.The deep model achieves between 84% to 98% accuracy for the identification of 80 popular apps.We also perform occlusion analysis to bring insight into what data is leaked from SSL/TLS protocol that allows accurate app identification.Moreover, our traffic analysis shows that many apps generate not only app-specific traffic, but also numerous ambiguous flows.Ambiguous flows are flows generated by common functionality modules, such as advertisement and traffic analytics.Because such flows are common among many different apps, identifying the source app that generates ambiguous flows is challenging.To address this challenge, we propose a CNN+LSTM model that uses adjacent flows to learn the order and pattern of multiple flows, to better identify the app that generates them.We show that such flow association considerably improves the accuracy, particularly for ambiguous flows.Furthermore, we show that our approach is robust to mixed traffic scenarios where some unrelated flows may appear in adjacent flows.To the best of our knowledge, this is the first work that identifies the source app for ambiguous flows.
How to Quantify a Dynamical Quantum Resource

We show that the generalization of the relative entropy of a resource from states to channels is not unique, and there are at least six such generalizations. Then, we show that two of these generalizations are asymptotically continuous, satisfy a version of the asymptotic equipartition property, and their regularizations appear in the power exponent of channel versions of the quantum Stein's lemma. To obtain our results, we use a new type of "smoothing" that can be applied to functions of channels (with no state analog). We call it "liberal smoothing" as it allows for more spread in the optimization. Along the way, we show that the diamond norm can be expressed as a max relative entropy distance to the set of quantum channels, and prove a variety of properties of all six generalizations of the relative entropy of a resource.
Pose-Guided Multi-Granularity Attention Network for Text-Based Person Search

Text-based person search aims to retrieve the corresponding person images in an image database by virtue of a describing sentence about the person, which poses great potential for various applications such as video surveillance. Extracting visual contents corresponding to the human description is the key to this cross-modal matching problem. Moreover, correlated images and descriptions involve different granularities of semantic relevance, which is usually ignored in previous methods. To exploit the multilevel corresponding visual contents, we propose a pose-guided multi-granularity attention network (PMA). Firstly, we propose a coarse alignment network (CA) to select the related image regions to the global description by a similarity-based attention. To further capture the phrase-related visual body part, a fine-grained alignment network (FA) is proposed, which employs pose information to learn latent semantic alignment between visual body part and textual noun phrase. To verify the effectiveness of our model, we perform extensive experiments on the CUHK Person Description Dataset (CUHK-PEDES) which is currently the only available dataset for text-based person search. Experimental results show that our approach outperforms the state-of-the-art methods by 15 % in terms of the top-1 metric.
Using Hindsight to Anchor Past Knowledge in Continual Learning

In continual learning, the learner faces a stream of data whose distribution changes over time. Modern neural networks are known to suffer under this setting, as they quickly forget previously acquired knowledge. To address such catastrophic forgetting, many continual learning methods implement different types of experience replay, re-learning on past data stored in a small buffer known as episodic memory. In this work, we complement experience replay with a new objective that we call ``anchoring'', where the learner uses bilevel optimization to update its knowledge on the current task, while keeping intact the predictions on some anchor points of past tasks. These anchor points are learned using gradient-based optimization to maximize forgetting, which is approximated by fine-tuning the currently trained model on the episodic memory of past tasks. Experiments on several supervised learning benchmarks for continual learning demonstrate that our approach improves the standard experience replay in terms of both accuracy and forgetting metrics and for various sizes of episodic memory.
Reconfigurable Hexapartite Entanglement by Spatially Multiplexed Four-Wave Mixing Processes

Multipartite entanglement serves as a vital resource for quantum information processing. Generally, its generation requires complex beam splitting processes which limit scalability. A promising trend is to integrate multiple nonlinear processes into a single device via frequency or time multiplexing. The generated states in these schemes are useful for quantum computation. However, they are confined in one or two beams and hard to be spatially separated for applications in quantum communication. Here, we experimentally demonstrate a scheme to generate spatially separated hexapartite entangled states by means of spatially multiplexing seven concurrent four-wave mixing processes. In addition, we show that the entanglement structure characterized by subsystem entanglement distribution can be modified by appropriately shaping the pump characteristics. Such reconfigurability of the entanglement structure gives the possibility to target a desired multipartite entangled state for a specific quantum communication protocol. Our results here provide a new platform for generating large scale spatially separated reconfigurable multipartite entangled beams.
A Full Quantum Eigensolver for Quantum Chemistry Simulations

Quantum simulation of quantum chemistry is one of the most compelling applications of quantum computing. It is of particular importance in areas ranging from materials science, biochemistry, and condensed matter physics. Here, we propose a full quantum eigensolver (FQE) algorithm to calculate the molecular ground energies and electronic structures using quantum gradient descent. Compared to existing classical-quantum hybrid methods such as variational quantum eigensolver (VQE), our method removes the classical optimizer and performs all the calculations on a quantum computer with faster convergence. The gradient descent iteration depth has a favorable complexity that is logarithmically dependent on the system size and inverse of the precision. Moreover, the FQE can be further simplified by exploiting a perturbation theory for the calculations of intermediate matrix elements and obtaining results with a precision that satisfies the requirement of chemistry application. The full quantum eigensolver can be implemented on a near-term quantum computer. With the rapid development of quantum computing hardware, the FQE provides an efficient and powerful tool to solve quantum chemistry problems.