Reviewerly/exHarmony huggingface dataset:
Hyperbolic Visual Embedding Learning for Zero-Shot Recognition

This paper proposes a Hyperbolic Visual Embedding Learning Network for zero-shot recognition. The network learns image embeddings in hyperbolic space, which is capable of preserving the hierarchical structure of semantic classes in low dimensions. Comparing with existing zeroshot learning approaches, the network is more robust because the embedding feature in hyperbolic space better represents class hierarchy and thereby avoid misleading resulted from unrelated siblings. Our network outperforms exiting baselines under hierarchical evaluation with an extremely challenging setting, i.e., learning only from 1,000 categories to recognize 20,841 unseen categories. While under flat evaluation, it has competitive performance as state-of-the-art methods but with five times lower embedding dimensions. Our code is publicly available.
Photonic-Crystal Josephson Traveling-Wave Parametric Amplifier

An amplifier combining noise performances as close as possible to the quantum limit with large bandwidth and high saturation power is highly desirable for many solid state quantum technologies such as high fidelity qubit readout or high sensitivity electron spin resonance for example. Here we introduce a new Traveling Wave Parametric Amplifier based on Superconducting QUantum Interference Devices. It displays a 3 GHz bandwidth, a -102 dBm 1-dB compression point and added noise near the quantum limit. Compared to previous state-of-the-art, it is an order of magnitude more compact, its characteristic impedance is in-situ tunable and its fabrication process requires only two lithography steps. The key is the engineering of a gap in the dispersion relation of the transmission line. This is obtained using a periodic modulation of the SQUID size, similarly to what is done with photonic crystals. Moreover, we provide a new theoretical treatment to describe the non-trivial interplay between non-linearity and such periodicity. Our approach provides a path to co-integration with other quantum devices such as qubits given the low footprint and easy fabrication of our amplifier.
Triangular color codes on trivalent graphs with flag qubits

Abstract The color code is a topological quantum error-correcting code supporting a variety of valuable fault-tolerant logical gates. Its two-dimensional version, the triangular color code, may soon be realized with currently available superconducting hardware despite constrained qubit connectivity. To guide this experimental effort, we study the storage threshold of the triangular color code against circuit-level depolarizing noise. First, we adapt the Restriction Decoder to the setting of the triangular color code and to phenomenological noise. Then, we propose a fault-tolerant implementation of the stabilizer measurement circuits, which incorporates flag qubits. We show how information from flag qubits can be used in an efficient and scalable way with the Restriction Decoder to maintain the effective distance of the code. We numerically estimate the threshold of the triangular color code to be 0.2%, which is competitive with the thresholds of other topological quantum codes. We also prove that 1-flag stabilizer measurement circuits are sufficient to preserve the full code distance, which may be used to find simpler syndrome extraction circuits of the color code.
SFace: Sigmoid-Constrained Hypersphere Loss for Robust Face Recognition

Deep face recognition has achieved great success due to large-scale training databases and rapidly developing loss functions. The existing algorithms devote to realizing an ideal idea: minimizing the intra-class distance and maximizing the inter-class distance. However, they may neglect that there are also low quality training images which should not be optimized in this strict way. Considering the imperfection of training databases, we propose that intra-class and inter-class objectives can be optimized in a moderate way to mitigate overfitting problem, and further propose a novel loss function, named sigmoid-constrained hypersphere loss (SFace). Specifically, SFace imposes intra-class and inter-class constraints on a hypersphere manifold, which are controlled by two sigmoid gradient re-scale functions respectively. The sigmoid curves precisely re-scale the intra-class and inter-class gradients so that training samples can be optimized to some degree. Therefore, SFace can make a better balance between decreasing the intra-class distances for clean examples and preventing overfitting to the label noise, and contributes more robust deep face recognition models. Extensive experiments of models trained on CASIA-WebFace, VGGFace2, and MS-Celeb-1M databases, and evaluated on several face recognition benchmarks, such as LFW, MegaFace and IJB-C databases, have demonstrated the superiority of SFace.
Quantum machine learning in high energy physics

Abstract Machine learning has been used in high energy physics (HEP) for a long time, primarily at the analysis level with supervised classification. Quantum computing was postulated in the early 1980s as way to perform computations that would not be tractable with a classical computer. With the advent of noisy intermediate-scale quantum computing devices, more quantum algorithms are being developed with the aim at exploiting the capacity of the hardware for machine learning applications. An interesting question is whether there are ways to apply quantum machine learning to HEP. This paper reviews the first generation of ideas that use quantum machine learning on problems in HEP and provide an outlook on future applications.
Improved YOLOv3 Based on Attention Mechanism for Fast and Accurate Ship Detection in Optical Remote Sensing Images

Ship detection is an important but challenging task in the field of computer vision, partially due to the minuscule ship objects in optical remote sensing images and the interference of clouds occlusion and strong waves. Most of the current ship detection methods focus on boosting detection accuracy while they may ignore the detection speed. However, it is also indispensable to increase ship detection speed because it can provide timely ocean rescue and maritime surveillance. To solve the above problems, we propose an improved YOLOv3 (ImYOLOv3) based on attention mechanism, aiming to achieve the best trade-off between detection accuracy and speed. First, to realize high-efficiency ship detection, we adopt the off-the-shelf YOLOv3 as our basic detection framework due to its fast speed. Second, to boost the performance of original YOLOv3 for small ships, we design a novel and lightweight dilated attention module (DAM) to extract discriminative features for ship targets, which can be easily embedded into the basic YOLOv3. The integrated attention mechanism can help our model learn to suppress irrelevant regions while highlighting salient features useful for ship detection task. Furthermore, we introduce a multi-class ship dataset (MSD) and explicitly set supervised subclass according to the scales and moving states of ships. Extensive experiments verify the effectiveness and robustness of ImYOLOv3, and show that our method can accurately detect ships with different scales in different backgrounds, while at a real-time speed.
DialogueCRN: Contextual Reasoning Networks for Emotion Recognition in Conversations

Emotion Recognition in Conversations (ERC) has gained increasing attention for developing empathetic machines.Recently, many approaches have been devoted to perceiving conversational context by deep learning models.However, these approaches are insufficient in understanding the context due to lacking the ability to extract and integrate emotional clues.In this work, we propose novel Contextual Reasoning Networks (DialogueCRN) to fully understand the conversational context from a cognitive perspective.Inspired by the Cognitive Theory of Emotion, we design multiturn reasoning modules to extract and integrate emotional clues.The reasoning module iteratively performs an intuitive retrieving process and a conscious reasoning process, which imitates human unique cognitive thinking.Extensive experiments on three public benchmark datasets demonstrate the effectiveness and superiority of the proposed model.
SMART Frame Selection for Action Recognition

Video classification is computationally expensive. In this paper, we address theproblem of frame selection to reduce the computational cost of video classification.Recent work has successfully leveraged frame selection for long, untrimmed videos,where much of the content is not relevant, and easy to discard. In this work, however,we focus on the more standard short, trimmed video classification problem. Weargue that good frame selection can not only reduce the computational cost of videoclassification but also increase the accuracy by getting rid of frames that are hard toclassify. In contrast to previous work, we propose a method that instead of selectingframes by considering one at a time, considers them jointly. This results in a moreefficient selection, where “good" frames are more effectively distributed over thevideo, like snapshots that tell a story. We call the proposed frame selection SMARTand we test it in combination with different backbone architectures and on multiplebenchmarks (Kinetics [5], Something-something [14], UCF101 [31]). We showthat the SMART frame selection consistently improves the accuracy compared toother frame selection strategies while reducing the computational cost by a factorof 4 to 10 times. Additionally, we show that when the primary goal is recognitionperformance, our selection strategy can improve over recent state-of-the-art modelsand frame selection strategies on various benchmarks (UCF101, HMDB51 [21],FCVID [17], and ActivityNet [4]).
Embodied intelligence via learning and evolution

The intertwined processes of learning and evolution in complex environmental niches have resulted in a remarkable diversity of morphological forms. Moreover, many aspects of animal intelligence are deeply embodied in these evolved morphologies. However, the principles governing relations between environmental complexity, evolved morphology, and the learnability of intelligent control, remain elusive, because performing large-scale in silico experiments on evolution and learning is challenging. Here, we introduce Deep Evolutionary Reinforcement Learning (DERL): a computational framework which can evolve diverse agent morphologies to learn challenging locomotion and manipulation tasks in complex environments. Leveraging DERL we demonstrate several relations between environmental complexity, morphological intelligence and the learnability of control. First, environmental complexity fosters the evolution of morphological intelligence as quantified by the ability of a morphology to facilitate the learning of novel tasks. Second, we demonstrate a morphological Baldwin effect i.e., in our simulations evolution rapidly selects morphologies that learn faster, thereby enabling behaviors learned late in the lifetime of early ancestors to be expressed early in the descendants lifetime. Third, we suggest a mechanistic basis for the above relationships through the evolution of morphologies that are more physically stable and energy efficient, and can therefore facilitate learning and control.
FL-PMI: Federated Learning-Based Person Movement Identification through Wearable Devices in Smart Healthcare Systems

Recent technological developments, such as the Internet of Things (IoT), artificial intelligence, edge, and cloud computing, have paved the way in transforming traditional healthcare systems into smart healthcare (SHC) systems. SHC escalates healthcare management with increased efficiency, convenience, and personalization, via use of wearable devices and connectivity, to access information with rapid responses. Wearable devices are equipped with multiple sensors to identify a person's movements. The unlabeled data acquired from these sensors are directly trained in the cloud servers, which require vast memory and high computational costs. To overcome this limitation in SHC, we propose a federated learning-based person movement identification (FL-PMI). The deep reinforcement learning (DRL) framework is leveraged in FL-PMI for auto-labeling the unlabeled data. The data are then trained using federated learning (FL), in which the edge servers allow the parameters alone to pass on the cloud, rather than passing vast amounts of sensor data. Finally, the bidirectional long short-term memory (BiLSTM) in FL-PMI classifies the data for various processes associated with the SHC. The simulation results proved the efficiency of FL-PMI, with 99.67% accuracy scores, minimized memory usage and computational costs, and reduced transmission data by 36.73%.
Spam Detection Using Bidirectional Transformers and Machine Learning Classifier Algorithms

Spam email has accounted for a high percentage of email traffic and has created problems worldwide. The deep learning transformer model is an efficient tool in natural language processing. This study proposed an efficient spam detection approach using a pretrained bidirectional encoder representation from transformer (BERT) and machine learning algorithms to classify ham or spam emails. Email texts were fed into the BERT, and features obtained from the BERT outputs were usedto represent the texts. Four classifier algorithms in machine learning were employed to classify the features of the text into ham or spam categories. The proposed model was tested using two public datasets in the experiments. The results of the evaluation metrics demonstrate that the logistic regression algorithm achieved the best classification performance in both datasets. They also justified the efficient ability of the proposed model in detecting spam emails.
Implicit Dual-Domain Convolutional Network for Robust Color Image Compression Artifact Reduction

Several dual-domain convolutional neural network-based methods show outstanding performance in reducing image compression artifacts. However, they suffer from handling color images because the compression processes for gray-scale and color images are completely different. Moreover, these methods train a specific model for each compression quality and require multiple models to achieve different compression qualities. To address these problems, we proposed an implicit dual-domain convolutional network (IDCN) with the pixel position labeling map and the quantization tables as inputs. Specifically, we proposed an extractor-corrector framework-based dual-domain correction unit (DCU) as the basic component to formulate the IDCN. A dense block was introduced to improve the performance of extractor in DRU. The implicit dual-domain translation allows the IDCN to handle color images with the discrete cosine transform (DCT)-domain priors. A flexible version of IDCN (IDCN-f) was developed to handle a wide range of compression qualities. Experiments for both objective and subjective evaluations on benchmark datasets show that IDCN is superior to the state-of-the-art methods and IDCN-f exhibits excellent abilities to handle a wide range of compression qualities with little performance sacrifice and demonstrates great potential for practical applications.
A New Lattice-Based Signature Scheme in Post-Quantum Blockchain Network

Blockchain technology has gained significant prominence in recent years due to its public, distributed, and decentration characteristics, which was widely applied in all walks of life requiring distributed trustless consensus. However, the most cryptographic protocols used in the current blockchain networks are susceptible to the quantum attack with rapid development of a sufficiently large quantum computer. In this paper, we first give an overview of the vulnerabilities of the modern blockchain networks to a quantum adversary and some potential post-quantum mitigation methods. Then, a new lattice-based signature scheme has been proposed, which can be used to secure the blockchain network over existing classical channels. Meanwhile, the public and private keys are generated by the Bonsai Trees technology with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RandBasis</i> algorithm from the root keys, which not only ensure the randomness, but also construct the lightweight nondeterministic wallets. Then, the proposed scheme can be proved secure in random oracle model, and it is also more efficient than similar literatures. In addition, we also give the detailed description of the post-quantum blockchain transaction. Furthermore, this work can help to enrich the research on the future post-quantum blockchain (PQB).
Partial Multi-Label Learning by Low-Rank and Sparse Decomposition

Multi-Label Learning (MLL) aims to learn from the training data where each example is represented by a single instance while associated with a set of candidate labels. Most existing MLL methods are typically designed to handle the problem of missing labels. However, in many real-world scenarios, the labeling information for multi-label data is always redundant , which can not be solved by classical MLL methods, thus a novel Partial Multi-label Learning (PML) framework is proposed to cope with such problem, i.e. removing the the noisy labels from the multi-label sets. In this paper, in order to further improve the denoising capability of PML framework, we utilize the low-rank and sparse decomposition scheme and propose a novel Partial Multi-label Learning by Low-Rank and Sparse decomposition (PML-LRS) approach. Specifically, we first reformulate the observed label set into a label matrix, and then decompose it into a groundtruth label matrix and an irrelevant label matrix, where the former is constrained to be low rank and the latter is assumed to be sparse. Next, we utilize the feature mapping matrix to explore the label correlations and meanwhile constrain the feature mapping matrix to be low rank to prevent the proposed method from being overfitting. Finally, we obtain the ground-truth labels via minimizing the label loss, where the Augmented Lagrange Multiplier (ALM) algorithm is incorporated to solve the optimization problem. Enormous experimental results demonstrate that PML-LRS can achieve superior or competitive performance against other state-of-the-art methods.
Transfer Meets Hybrid: A Synthetic Approach for Cross-Domain Collaborative Filtering with Text

Collaborative Filtering (CF) is the key technique for recommender systems. CF exploits user-item behavior interactions (e.g., clicks) only and hence suffers from the data sparsity issue. One research thread is to integrate auxiliary information such as product reviews and news titles, leading to hybrid filtering methods. Another thread is to transfer knowledge from source domains such as improving the movie recommendation with the knowledge from the book domain, leading to transfer learning methods. In real-world applications, a user registers for multiple services across websites. Thus it motivates us to exploit both auxiliary and source information for recommendation in this paper. To achieve this, we propose a Transfer Meeting Hybrid (TMH) model for cross-domain recommendation with unstructured text. The proposed TMH model attentively extracts useful content from unstructured text via a memory network and selectively transfers knowledge from a source domain via a transfer network. On two real-world datasets, TMH shows better performance in terms of three ranking metrics by comparing with various baselines. We conduct thorough analyses to understand how the text content and transferred knowledge help the proposed model.
Context-Aware Three-Dimensional Mean-Shift With Occlusion Handling for Robust Object Tracking in RGB-D Videos

Depth cameras have recently become popular and many vision problems can be better solved with depth information. But, how to integrate depth information into a visual tracker to overcome the challenges such as occlusion and background distraction is still underinvestigated in current literature on visual tracking. In this paper, we investigate a 3-D extension of a classical mean-shift tracker whose greedy gradient ascend strategy is generally considered as unreliable in conventional 2-D tracking. However, through careful study of the physical property of 3-D point clouds, we reveal that objects which may appear to be adjacent on a 2-D image will form distinctive modes in the 3-D probability distribution approximated by kernel density estimation, and finding the nearest mode using 3-D mean-shift can always work in tracking. Based on the understanding of 3-D mean-shift, we propose two important mechanisms to further boost the tracker's robustness: one is to enable the tracker to be aware of potential distractions and make corresponding adjustments to the appearance model; and the other is to enable the tracker to detect and recover from tracking failures caused by total occlusion. The proposed method is both effective and computationally efficient. On a conventional personal computer, it runs at more than 60 FPS without graphical processing unit acceleration.
Multi-objective differential evolution based random forest for e-health applications

Many machine learning techniques have been used in past few decades for various medical applications. However, these techniques suffer from parameter tuning issue. Therefore, an efficient tuning of these parameters has an ability to improve the performance of existing machine learning techniques. Therefore, in this work, a novel multi-objective differential evolution based random forest technique is proposed. The proposed technique is able to tune the parameters of random forest in an efficient manner. Extensive experiments are carried out by considering the proposed and the existing competitive machine learning techniques on various medical applications. It is observed that the proposed technique outperforms existing techniques in terms of accuracy, f-measure, sensitivity and specificity.
Research on the Application of Cryptography on the Blockchain

Blockchain is an innovative application model that integrates distributed data storage, peer-to-peer transmission, consensus mechanisms, digital encryption technology and other computer technologies. It is decentralized, secure, and Information disclosure. In the blockchain, digital encryption technology has a core position. The security of user information and transaction data is a necessary condition for the promotion of blockchain. The development of cryptography technology promotes and restricts the further development of blockchain. This paper outlines the infrastructure of blockchain, including the data layer, network layer, consensus layer, contract layer and application layer. The principles of encryption technology is introduced briefly, such as hash function, asymmetric cryptosystem, digital signature. The application of cryptography in all levels of blockchain is analyzed, including data layer, network layer, consensus layer, etc. It shows that cryptography runs through the whole blockchain system. The existing security problems of blockchain is analyzed, and the future research direction is expected.