Reviewerly/exHarmony huggingface dataset:
Fast Cross-Modal Hashing With Global and Local Similarity Embedding

Recently, supervised cross-modal hashing has attracted much attention and achieved promising performance. To learn hash functions and binary codes, most methods globally exploit the supervised information, for example, preserving an at-least-one pairwise similarity into hash codes or reconstructing the label matrix with binary codes. However, due to the hardness of the discrete optimization problem, they are usually time consuming on large-scale datasets. In addition, they neglect the class correlation in supervised information. From another point of view, they only explore the global similarity of data but overlook the local similarity hidden in the data distribution. To address these issues, we present an efficient supervised cross-modal hashing method, that is, fast cross-modal hashing (FCMH). It leverages not only global similarity information but also the local similarity in a group. Specifically, training samples are partitioned into groups; thereafter, the local similarity in each group is extracted. Moreover, the class correlation in labels is also exploited and embedded into the learning of binary codes. In addition, to solve the discrete optimization problem, we further propose an efficient discrete optimization algorithm with a well-designed group updating scheme, making its computational complexity linear to the size of the training set. In light of this, it is more efficient and scalable to large-scale datasets. Extensive experiments on three benchmark datasets demonstrate that FCMH outperforms some state-of-the-art cross-modal hashing approaches in terms of both retrieval accuracy and learning efficiency.
DWIE: An entity-centric dataset for multi-task document-level information extraction

This paper presents DWIE, the ‘Deutsche Welle corpus for Information Extraction’, a newly created multi-task dataset that combines four main Information Extraction (IE) annotation subtasks: (i) Named Entity Recognition (NER), (ii) Coreference Resolution, (iii) Relation Extraction (RE), and (iv) Entity Linking. DWIE is conceived as an entity-centric dataset that describes interactions and properties of conceptual entities on the level of the complete document. This contrasts with currently dominant mention-driven approaches that start from the detection and classification of named entity mentions in individual sentences. Further, DWIE presented two main challenges when building and evaluating IE models for it. First, the use of traditional mention-level evaluation metrics for NER and RE tasks on entity-centric DWIE dataset can result in measurements dominated by predictions on more frequently mentioned entities. We tackle this issue by proposing a new entity-driven metric that takes into account the number of mentions that compose each of the predicted and ground truth entities. Second, the document-level multi-task annotations require the models to transfer information between entity mentions located in different parts of the document, as well as between different tasks, in a joint learning setting. To realize this, we propose to use graph-based neural message passing techniques between document-level mention spans. Our experiments show an improvement of up to 5.5 F1 percentage points when incorporating neural graph propagation into our joint model. This demonstrates DWIE’s potential to stimulate further research in graph neural networks for representation learning in multi-task IE. We make DWIE publicly available at https://github.com/klimzaporojets/DWIE.
An Automated Deductive Verification Framework for Circuit-building Quantum Programs

Abstract While recent progress in quantum hardware open the door for significant speedup in certain key areas, quantum algorithms are still hard to implement right, and the validation of such quantum programs is a challenge. In this paper we propose Qbricks , a formal verification environment for circuit-building quantum programs, featuring both parametric specifications and a high degree of proof automation. We propose a logical framework based on first-order logic, and develop the main tool we rely upon for achieving the automation of proofs of quantum specification: PPS, a parametric extension of the recently developed path sum semantics. To back-up our claims, we implement and verify parametric versions of several famous and non-trivial quantum algorithms, including the quantum parts of Shor’s integer factoring , quantum phase estimation (QPE) and Grover’s search.
Echo-state networks for soft sensor design in an SRU process

The implementation of soft sensors for industrial processes is expanding in applications for recent machine learning techniques. In this work, strategies based on reservoir computing are applied to developing dynamical models of target variables in a sulfur recovery unit (SRU) of a refinery plant in Italy. In particular, a specific type of recurrent network, namely an echo-state network (ESN), is adopted to estimate key process variables on the SRU. Two process lines are considered to evaluate the proposed algorithm on different datasets in terms of estimation performance and computational effort of the learning process. The obtained results are evaluated in comparison with other recurrent networks, based on long short-term memory, and with other techniques reported in the literature, demonstrating the feasibility of the proposed approach. Furthermore, the introduction of intrinsic plasticity (IP) is also considered to adapt the reservoir parameters to the provided inputs, achieving a significant improvement in the statistical distribution of the results obtained for the pool of learned networks. The reported results show that ESN-IP represents a suitable solution for identifying dynamical models of the industrial processes, avoiding the time-consuming regressor selection procedure, which is needed when a static network is adopted to design a dynamical model.
SSentiA: A Self-supervised Sentiment Analyzer for classification from unlabeled data

In recent years, supervised machine learning (ML) methods have realized remarkable performance gains for sentiment classification utilizing labeled data. However, labeled data are usually expensive to obtain, thus, not always achievable. When annotated data are unavailable, the unsupervised tools are exercised, which still lag behind the performance of supervised ML methods by a large margin. Therefore, in this work, we focus on improving the performance of sentiment classification from unlabeled data. We present a self-supervised hybrid methodology SSentiA (Self-supervised Sentiment Analyzer) that couples an ML classifier with a lexicon-based method for sentiment classification from unlabeled data. We first introduce LRSentiA (Lexical Rule-based Sentiment Analyzer), a lexicon-based method to predict the semantic orientation of a review along with the confidence score of prediction. Utilizing the confidence scores of LRSentiA, we generate highly accurate pseudo-labels for SSentiA that incorporates a supervised ML algorithm to improve the performance of sentiment classification for less polarized and complex reviews. We compare the performances of LRSentiA and SSSentA with the existing unsupervised, lexicon-based and self-supervised methods in multiple datasets. The LRSentiA performs similarly to the existing lexicon-based methods in both binary and 3-class sentiment analysis. By combining LRSentiA with an ML classifier, the hybrid approach SSentiA attains 10%–30% improvements in macro F1 score for both binary and 3-class sentiment analysis. The results suggest that in domains where annotated data are unavailable, SSentiA can significantly improve the performance of sentiment classification. Moreover, we demonstrate that using 30%–60% annotated training data, SSentiA delivers similar performances of the fully labeled training dataset.
Variational quantum compiling with double Q-learning

Abstract Quantum compiling aims to construct a quantum circuit V by quantum gates drawn from a native gate alphabet, which is functionally equivalent to the target unitary U . It is a crucial stage for the running of quantum algorithms on noisy intermediate-scale quantum (NISQ) devices. However, the space for structure exploration of quantum circuit is enormous, resulting in the requirement of human expertise, hundreds of experimentations or modifications from existing quantum circuits. In this paper, we propose a variational quantum compiling (VQC) algorithm based on reinforcement learning, in order to automatically design the structure of quantum circuit for VQC with no human intervention. An agent is trained to sequentially select quantum gates from the native gate alphabet and the qubits they act on by double Q -learning with ϵ -greedy exploration strategy and experience replay. At first, the agent randomly explores a number of quantum circuits with different structures, and then iteratively discovers structures with higher performance on the learning task. Simulation results show that the proposed method can make exact compilations with less quantum gates compared to previous VQC algorithms. It can reduce the errors of quantum algorithms due to decoherence process and gate noise in NISQ devices, and enable quantum algorithms especially for complex algorithms to be executed within coherence time.
Visible-infrared person re-identification with data augmentation via cycle-consistent adversarial network

Visible-infrared person re-identification (VI-ReID) aims to search the same pedestrian images across different modalities, which is a challenging task for video surveillance. Compared to RGB-based re-identification (Re-ID) with sufficient single-modality training samples, VI-ReID suffers from imbalanced dual-modality data which affects the accuracy of deep learning classifiers. To this end, we present a image modality translation (IMT) network that learns to generate translated modality images from given modalities. It performs image modality translation by means of cycle-consistent adversarial network (CycleGAN) and serves as a data augmentation tool to restore balance to imbalanced training images. Concretely, our method mainly includes two steps: first, we train the IMT network on real images and generate target modality samples to enlarge the training dataset size and increase its diversity. Then the source images and modality transferred images are combined to train a Re-ID CNN model for improving cross-modality retrieval performance. To validate the effectiveness of our proposed approach, we perform our work over SYSU-MM01 and RegDB datasets. The experimental results indicate that our proposed method is significantly more accurate than the state-of-the-art methods.
Transform Domain-Based Invertible and Lossless Secret Image Sharing With Authentication

Secret Image Sharing (SIS) as a secure data sharing scheme in multiple cover images, has become an increasing researchers' concern. In traditional SIS, the cover image can't be recovered losslessly. The distorted cover images would reduce the practicability of the scheme, especially in medical and military images. The lossless recovery of cover images is required since all details of these images are very critical. In current Invertible SIS (ISIS), the secret image and the cover image may not be reconstructed losslessly. In addition, the authentication capability, visual quality of the stego cover image and embedding rate are limited in spatial domain-based ISIS. As an important carrier, the binary cover image is desired in real applications. Therefore, this paper proposes Transform domain-based Invertible and Lossless Secret Image Sharing schemes with Authentication (T-ILSISA), namely Integer Wavelet Transform-based ILSISA (IWT-ILSISA) and Binarization Transform-based ILSISA (BT-ILSISA) respectively. In (k,n) threshold IWT-ILSISA, the pixels of secret image and the data of cover image are regarded as the coefficients of the (k-1) degree polynomial. The values of generated share are embedded into IWT domain of the cover image. In BT-ILSISA, many different cover images are applied. The generated shares are transformed to the meaningful images since noise-like shares are easy to attract the attacker's attention, are suspected to censors and are difficult for identification and management. In the two schemes, the original secret image and the cover image can be recovered losslessly. The experimental results and theoretical analysis demonstrate that the performances of IWT-ILSISA are better than other similar schemes in the terms of embedding capacity, authentication capability and visual quality of the stego cover image. The BT-ILSISA has a lower computational complexity of the recovery.
Learning deep part-aware embedding for person retrieval

Person retrieval is an important vision task, aiming at matching the images of the same person under various camera views. The key challenge of person retrieval lies in the large intra-class variations among the person images. Therefore, how to learn discriminative feature representations becomes the core problem. In this paper, we propose a deep part-aware representation learning method for person retrieval. First, an improved triplet loss is introduced such that the global feature representations from the same identity are closely clustered. Meanwhile, a localization branch is proposed to automatically localize those discriminative person-wise parts or regions, only using identity labels in a weakly supervised manner. Via the learning simultaneously guided by the global branch and the localization branch, the proposed method can further improve the performance for person retrieval. Through an extensive set of ablation studies, we verify that the localization branch and the improved triplet loss each contributes to the performance boosts of the proposed method. Our model obtains superior (or comparable) performance compared to state-of-the-art methods for person retrieval on the four public person retrieval datasets. On the CUHK03-labeled dataset, for instance, the performance increases from 73.0% mAP and 77.9% rank-1 accuracy to 80.8% (+7.8%) mAP and 83.9% (+6.0%) rank-1 accuracy.
Wearable Sensor Clothing for Body Movement Measurement during Physical Activities in Healthcare

This paper presents a wearable wireless system for measuring human body activities, consisting of small inertial sensor nodes and the main hub for data transmission via Bluetooth for further analysis. Unlike optical and ultrasonic technologies, the proposed solution has no movement restrictions, such as the requirement to stay in the line of sight, and it provides information on the dynamics of the human body’s poses regardless of its location. The problem of the correct placement of sensors on the body is considered, a simplified architecture of the wearable clothing is described, an experimental set-up is developed and tests are performed. The system has been tested by performing several physical exercises and comparing the performance with the commercially available BTS Bioengineering SMART DX motion capture system. The results show that our solution is more suitable for complex exercises as the system based on digital cameras tends to lose some markers. The proposed wearable sensor clothing can be used as a multi-purpose data acquisition device for application-specific data analysis, thus providing an automated tool for scientists and doctors to measure patient’s body movements.
SSRCNN: A Semi-Supervised Learning Framework for Signal Recognition

Due to the emergence of deep learning, signal recognition has made great strides in performance improvement. The success of most deep learning methods relies on the accessibility of abundant labeled training data. However, the annotation of signals is quite expensive, making it challenging to train deep learning models substantially. This calls for the development of semi-supervised learning (SSL) method to fully utilize the unlabeled data to assist the training of deep learning models. To achieve this goal, three types of loss functions, tailored to the task of SLL-based signal recognition, are carefully designed in this paper. Together with a carefully selected neural network structure, the proposed SSL method can effectively extract the information from unlabeled training data and thus overcome the difficulty of insufficient training. Extensive numerical results using open source datasets are presented to show the superior performance of the proposed SSL method.
A<sup>3</sup>GAN: An Attribute-Aware Attentive Generative Adversarial Network for Face Aging

Face aging has received significant research attention in recent years. Although great progress has been achieved with the success of Generative Adversarial Networks (GANs) in synthesizing realistic images, most existing GAN-based face aging methods have two main problems: 1) unnatural changes of high-level semantic information due to the insufficient consideration of prior knowledge of input faces, and 2) distortions of low-level image content (e.g. modifications in age-irrelevant regions). In this article, we introduce A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> GAN, an Attribute-Aware Attentive face aging model to address the above issues. Facial attribute vectors are regarded as the conditional information and embedded into both the generator and discriminator, encouraging synthesized faces to be faithful to attributes of corresponding inputs. To improve the visual fidelity of generation results, we leverage the attention mechanism to restrict modifications to age-related areas and preserve image details. Unlike previous works with attention modules, we introduce face parsing maps to help the generator distinguish image regions of interest and suppress attention activation elsewhere. Moreover, the wavelet packet transform is employed to capture textural features at multiple scales in the frequency space. Extensive experimental results demonstrate the effectiveness of our model in synthesizing photo-realistic aged face images and achieving state-of-the-art performance on popular datasets.
Bridging Offline Reinforcement Learning and Imitation Learning: A Tale of Pessimism

Offline reinforcement learning (RL) algorithms seek to learn an optimal policy from a fixed dataset without active data collection. Based on the composition of the offline dataset, two main methods are used: imitation learning which is suitable for expert datasets, and vanilla offline RL which often requires uniform coverage datasets. From a practical standpoint, datasets often deviate from these two extremes and the exact data composition is usually unknown. To bridge this gap, we present a new offline RL framework, called single-policy concentrability, that smoothly interpolates between the two extremes of data composition, hence unifying imitation learning and vanilla offline RL. Under this new framework, we ask: can one develop an algorithm that achieves a minimax optimal rate adaptive to unknown data composition? To address this question, we consider a lower confidence bound (LCB) algorithm developed based on pessimism in the face of uncertainty in offline RL. We study finite-sample properties of LCB as well as information-theoretic limits in multi-armed bandits, contextual bandits, and Markov decision processes (MDPs). Our analysis reveals surprising facts about optimality rates. In particular, in both contextual bandits and RL, LCB achieves a fast convergence rate for nearly-expert datasets, analogous to the one achieved by imitation learning, contrary to the slow rate achieved in offline RL. In contextual bandits, we prove that LCB is adaptively optimal for the entire data composition range, achieving a smooth transition from imitation learning to offline RL. We further show that LCB is almost adaptively optimal in MDPs.
VR-ORCA: Variable Responsibility Optimal Reciprocal Collision Avoidance

As one of the most popular multi-agent path planning approaches, the optimal reciprocal collision avoidance (ORCA) algorithm assumes that each agent takes half the responsibility for collision avoidance. However, due to the asymmetric situation faced by adjacent agents, they are expected to take different responsibilities for collision avoidance to improve the entire crowd's navigation performance. Thus, in this letter, we propose the variable responsibility optimal reciprocal collision avoidance (VR-ORCA) algorithm, which relaxes the original assumption in ORCA and only requires the responsibilities of a pair of agents sum to one. In particular, the responsibility division between a pair of nearby agents is determined independently for each agent by minimizing a cost function involving their common neighbors. We validate our approach on a variety of simulated benchmarks and the results demonstrate that our novel method is beneficial in reducing the collision probability, travel time and distance of ORCA.
Simulation for cybersecurity: state of the art and future directions

Abstract In this article, we provide an introduction to simulation for cybersecurity and focus on three themes: (1) an overview of the cybersecurity domain; (2) a summary of notable simulation research efforts for cybersecurity; and (3) a proposed way forward on how simulations could broaden cybersecurity efforts. The overview of cybersecurity provides readers with a foundational perspective of cybersecurity in the light of targets, threats, and preventive measures. The simulation research section details the current role that simulation plays in cybersecurity, which mainly falls on representative environment building; test, evaluate, and explore; training and exercises; risk analysis and assessment; and humans in cybersecurity research. The proposed way forward section posits that the advancement of collecting and accessing sociotechnological data to inform models, the creation of new theoretical constructs, and the integration and improvement of behavioral models are needed to advance cybersecurity efforts.
Quantum Mechanics-Based Signal and Image Representation: Application to Denoising

Decomposition of digital signals and images into other basis or dictionaries than time or space domains is a very common approach in signal and image processing and analysis. Such a decomposition is commonly obtained using fixed transforms (e.g., Fourier or wavelet) or dictionaries learned from example databases or from the signal or image itself. In this work, we investigate in detail a new approach of constructing such a signal or image-dependent bases inspired by quantum mechanics tools, i.e., by considering the signal or image as a potential in the discretized Schroedinger equation. To illustrate the potential of the proposed decomposition, denoising results are reported in the case of Gaussian, Poisson, and speckle noise and compared to the state of the art algorithms based on wavelet shrinkage, total variation regularization or patch-wise sparse coding in learned dictionaries, non-local means image denoising, and graph signal processing.