Reviewerly/exHarmony huggingface dataset:
Tensor feature hallucination for few-shot learning

Few-shot learning addresses the challenge of learning how to address novel tasks given not just limited supervision but limited data as well. An attractive solution is synthetic data generation. However, most such methods are overly sophisticated, focusing on high-quality, realistic data in the input space. It is unclear whether adapting them to the few-shot regime and using them for the downstream task of classification is the right approach. Previous works on synthetic data generation for few-shot classification focus on exploiting complex models, e.g. a Wasserstein GAN with multiple regularizers or a network that transfers latent diversities from known to novel classes.We follow a different approach and investigate how a simple and straightforward synthetic data generation method can be used effectively. We make two contributions, namely we show that: (1) using a simple loss function is more than enough for training a feature generator in the few-shot setting; and (2) learning to generate tensor features instead of vector features is superior. Extensive experiments on miniImagenet, CUB and CIFAR-FS datasets show that our method sets a new state of the art, outperforming more sophisticated few-shot data augmentation methods. The source code can be found at https://github.com/MichalisLazarou/TFH_fewshot.
TensorCrypto: High Throughput Acceleration of Lattice-Based Cryptography Using Tensor Core on GPU

Tensor core is a newly introduced hardware unit in NVIDIA GPU chips that allows matrix multiplication to be computed much faster than in the integer and floating-point units.In this paper, we show that for the first time, tensor core can be used to accelerate state-of-the-art lattice-based cryptosystems.We employed tensor core to speed up polynomial convolution, which is the most time consuming operation in lattice-based cryptosystems.Towards that aim, several parallel algorithms are proposed to allow the tensor core to handle flexible matrix sizes and ephemeral key pairs.Experimental results show that the polynomial convolution computed using the tensor core is at least 2× faster than the version implemented with conventional integer units of the NVIDIA GPU.The proposed tensor-core-based polynomial convolution technique was applied to NTRU, one of the finalists in NIST post-quantum cryptography (PQC) standardization.It achieved 2.02×/1.98×(encapsulation) and 1.56×/1.90×(decapsulation) higher throughput on two parameter sets (ntruhps2048509 and ntruhps2048677), compared to the conventional integer-based implementations on a GPU.In particular, the proposed implementation techniques achieved throughput up to 793651 key encapsulations per second and 505051 decapsulations per second on a RTX2060 GPU.To demonstrate the flexibility of the proposed technique, we extend the implementation to other lattice-based cryptosystems that have a small modulus: LAC and two variant parameter sets in FrodoKEM.Considering that the IoT gateway devices and cloud servers need to handle massive connections from the sensor nodes, the proposed high throughput implementation on GPU is very useful in securing the IoT communication.
Is it possible to establish the link between drug busts and the cryptocurrency market? Yes, we can

Following the rampant increase in Bitcoin prices, there has been a proliferation of cryptocurrencies, which have become a major way of doing business across national boundaries. This paper investigates the link between cryptocurrency markets and drug trafficking activities. More specifically, we explore the impact of the announcement of 24 major drug busts on the systematic risk and return of the world cryptocurrency market. We deploy an event study methodology to estimate the abnormal returns associated with drug trafficking activities in the cryptocurrency market. We find that the relationship between the two is quite strong in the case of some cryptocurrencies, albeit weaker in others. However, we show that drug bust news tends to create uncertainty, and accordingly impart risk into cryptocurrency markets. This study confirms the predictions of convenience theories of crime as to the relative attractiveness of cryptocurrencies to criminals, and the extent to which not only general, but also their own future interests, sacrificed readily on the altar of accessibility. We highlight how when social and regulatory foundations are weak, criminal behaviour may overwhelm virtual spaces, marginalizing more orthodox businesses, no matter how altruistic the intentions of their founders.
Practical Privacy-Preserving Federated Learning in Vehicular Fog Computing

Benefitting from the outstanding capabilities of intelligent controlling and prediction, federated learning (FL) has been widely applied in Internet of Vehicle (IoV). However, applying FL into fog-computing-based IoV still suffers from two crucial problems: (i) how to achieve the privacy-preserving FL under the flexible architecture of fog computing with no assistance of cloud server, and (ii) how to guarantee the privacy-preserving FL to perform with high efficiency and low overhead in fog-computing settings. For addressing the above issues, we propose a practical framework, named <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Galaxy</small> , the first of its kind in the regime of privacy-preserving FL under the setting of non-cloud-assisted fog computing. Based on the secure multi-party computation (MPC) technology, our framework satisfies the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\boldsymbol{(T,N)}$</tex-math></inline-formula> -threshold property, permitting <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\boldsymbol{N}$</tex-math></inline-formula> (a scalable number) fog nodes to cooperate with multiple users for implementing privacy-preserving FL, while resisting the collusion up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\boldsymbol{T}-\boldsymbol{1}$</tex-math></inline-formula> fog nodes, and being robust to at most <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\boldsymbol{N}-\boldsymbol{T}$</tex-math></inline-formula> fog nodes simultaneously dropping out. Besides, considering the practical scenario that low-quality data may negatively impair the FL model convergence, our scheme can handle users’ low-quality data while protecting all user-related information under our secure framework. Based on the above superior properties, our scheme can perform with high scalability, high processing efficiency, and low resource overhead, being practical for fog-computing-based IoV. Extensive experiment results demonstrate our scheme with high-level performance.
IAFNet: Few-Shot Learning for Modulation Recognition in Underwater Impulsive Noise

Deep learning (DL)-based modulation recognition methods are challenging in the case of few labeled samples and underwater impulsive noise. In this letter, we propose a novel network structure named IAFNet to achieve higher recognition accuracy of modulation signals with fewer samples in underwater impulsive noise environment. The IAFNet integrates impulsive noise preprocessing (INP), attention network (AN) and few-shot learning (FSL) to extract features more effectively through denoising and task-driven. Experimental results on simulation and practical data show that the IAFNet attains stronger anti-noise performance and better recognition performance on fewer labeled samples. Compared with other methods, the classification accuracy is improved by about 7&#x0025;.
Semantic Segmentation Guided Real-World Super-Resolution

Real-world single image Super-Resolution (SR) aims to enhance the resolution and reconstruct High-Resolution (HR) details of real Low-Resolution (LR) images.This is different from the traditional SR setting, where the LR images are synthetically created, typically with bicubic downsampling.As the degradation process for real-world LR images are highly complex, SR of such images is much more challenging.Recent promising approaches to solve the Real-World Super-Resolution (RWSR) problem include the use of domain adaptation to create realistic trainingpairs, and self-learning based methods which learn an image specific SR model at test time.However, as domain adaptation is an inherently challenging problem in itself, SR models based solely on this approach are limited by the domain gap.In contrast, while self-learning based methods remove the need for paired-training data by utilizing internal information in the LR image, these methods come with the cost of slow prediction times.This paper proposes a novel framework, Semantic Segmentation Guided Real-World Super-Resolution (SSG-RWSR), which uses an auxiliary semantic segmentation network to guide the SR learning.This results in noise-free reconstructions with accurate object boundaries, and enables training on real LR images.The latter allows our SR network to adapt to the image specific degradations, without Ground-Truth (GT) reference images.We support the guidance with domain adaptation to faithfully reconstruct realistic textures, and ensure color consistency.We evaluate our proposed method on two public available datasets, and present State-of-the-Art results in terms of perceptual image quality on both real and synthesized LR images.
Sub-nanosecond operations on superconducting quantum register based on Ramsey patterns

Abstract An ultrafast qubit control concept is proposed and analyzed theoretically to reduce the duration of operations with single and multiple superconducting qubits. It is based on the generation of Ramsey fringes due to unipolar sub-nanosecond control pulses. The interference of waves of qubit states population propagating forward and backward in time play a key role in the concept. The influence of the shape and duration of control pulses on the contrast of the interference pattern is revealed in the frame of Ramsey’s paradigm. Protocols for the observation of Ramsey oscillations and an implementation of various gate operations are developed for flux qubits. We also suggest a notional engineering solution for creating the required sub-nanosecond control pulses with the desired shape and amplitude. It is demonstrated that this makes it possible to control the quantum states of the system with a fidelity of more than 99%.
Self-Supervised Correlation Learning for Cross-Modal Retrieval

Cross-modal retrieval aims to retrieve relevant data from another modality when given a query of one modality. Although most existing methods that rely on the label information of multimedia data have achieved promising results, the performance benefiting from labeled data comes at a high cost since labeling data often requires enormous labor resources, especially on large-scale multimedia datasets. Therefore, unsupervised cross-modal learning is of crucial importance in real-world applications. In this paper, we propose a novel unsupervised cross-modal retrieval method, named Self-supervised Correlation Learning (SCL), which takes full advantage of large amounts of unlabeled data to learn discriminative and modality-invariant representations. Since unsupervised learning lacks the supervision of category labels, we incorporate the knowledge from the input as a supervisory signal by maximizing the mutual information between the input and the output of different modality-specific projectors. Besides, for the purpose of learning discriminative representations, we exploit unsupervised contrastive learning to model the relationship among intra- and inter-modality instances, which makes similar samples closer and pushes dissimilar samples apart. Moreover, to further eliminate the modality gap, we use a weight-sharing scheme and minimize the modality-invariant loss in the joint representation space. Beyond that, we also extend the proposed method to the semi-supervised setting. Extensive experiments conducted on three widely-used benchmark datasets demonstrate that our method achieves competitive results compared with current state-of-the-art cross-modal retrieval approaches.
A Stepwise Domain Adaptive Segmentation Network With Covariate Shift Alleviation for Remote Sensing Imagery

Semantic segmentation for remote sensing images (RSI) is critical for the Earth monitoring system. However, the covariate shift between RSI datasets under different capture conditions cannot be alleviated by directly using the unsupervised domain adaptation (UDA) method, which negatively affects the segmentation accuracy in RSI. We propose a stepwise domain adaptive segmentation network with covariate shift alleviation (Cov-DA) for RSI parsing to solve this issue. Specifically, to alleviate domain shift generated by different sensors, both the source and target domains are projected into a colorspace with normalized distribution through an elaborate colorspace mapping unified module (CMUM). The color distributions of these two domains tend to be more uniform. Furthermore, in the target domain, the multistatistics joint evaluation module (MJEM) is proposed to capture different statistical characteristics of subscenarios for selecting plain scenarios regarded as high-confidence segmentation results to assist the further improvement of segmentation performance. In addition, a pyramid perceptual attention module (PPAM) containing omnidirectional features without computational burdens is added to our network for effectively enhancing the multiscale feature capture ability. In the cross-city DA experiments based on the International Society for Photogrammetry and Remote Sensing (ISPRS) and aerial benchmarks, the superiority of our algorithm is significantly demonstrated. Furthermore, we release a large-scale Martian terrain dataset noted as “Mars-Seg” containing 5 K images with pixel-level accurate annotations regarding issues, such as the lack of semantic segmentation datasets for unknown scenes.
Extended particle swarm optimization for feature selection of high‐dimensional biomedical data

Abstract This paper proposes a novel Extended Particle Swarm Optimization model (EPSO) that potentially enhances the search process of PSO for optimization problem. Evidently, gene expression profiles are significantly important measurement factor in molecular biology that is used in medical diagnosis of cancer types. The challenge to certain classification methodologies for gene expression profiles lies in the thousands of features recorded for each sample. A modified Wrapper feature selection model is applied with the aim of addressing the gene classification challenge by replacing its randomness approach with EPSO and PSO, respectively. EPSO is initializing the random size of the population and dividing them into two groups in order to promote the exploration and reduce the probability of falling in stagnation. Experimentally, EPSO has required less processing time to select the optimal features (average of 62.14 s) than PSO (average of 95.72 s). Furthermore, EPSO accuracy has provided better classification results (start from 54% to 100%) than PSO (start from 52% to 96%).
Grey-box modelling and fuzzy logic control of a Leader–Follower robot manipulator system: A hybrid Grey Wolf–Whale Optimisation approach

This study presents the development of a grey-box modelling approach and fuzzy logic control for real time trajectory control of an experimental four degree-of-freedom Leader-Follower​ Robot (LFR) manipulator system using a hybrid optimisation algorithm, known as Grey Wolf Optimiser (GWO) - Whale Optimisation Algorithm (WOA). The approach has advantages in achieving an accurate model of the LFR manipulator system, and together with a better trajectory tracking performance. In the first instance, the white box model is formed by modelling the dynamics of the follower manipulator using the Euler-Lagrange formulation. This white-box model is then improved upon by re-tuning the model's parameters using GWO-WOA and experimental data from the real LFR manipulator system, thus forming the grey-box model. A minimum improvement of 73.9% is achieved by the grey-box model in comparison to the white-box model. In the latter part of this investigation, the developed grey-box model is used for the design, tuning and real-time implementation of a fuzzy PD＋I controller on the experimental LFR manipulator system. A 78% improvement in the total mean squared error is realised after tuning the membership functions of the fuzzy logic controller using GWO-WOA. Experimental results show that the approach significantly improves the trajectory tracking performance of the LFR manipulator system in terms of mean squared error, steady state error and time delay.
Aspect-Based Sentiment Analysis With Heterogeneous Graph Neural Network

Aspect-based sentiment analysis aims to predict sentiment polarities of given aspects in text. Most current approaches employ attention-based neural methods to capture semantic relationships between aspects and words in one sentence. However, these methods ignore the fact that sentences with the same aspect and sentiment polarity often share the structure and semantic information in a domain, which leads to lower model performance. To mitigate this problem, we propose a heterogeneous aspect graph neural network (HAGNN) to learn the structure and semantic knowledge from intersentence relationships. Our model is a heterogeneous graph neural network since it contains three different kinds of nodes: word nodes, aspect nodes, and sentence nodes. These nodes can pass structure and semantic information between each other and update their embeddings to improve the performance of our model. To the best of our knowledge, we are the first to use a heterogeneous graph to capture relationships between sentences and aspects. The experimental results on five public datasets show the effectiveness of our model outperforming some state-of-the-art models.
Security Enhancement in Coherent OFDM Optical Transmission With Chaotic Three-Dimensional Constellation Scrambling

In this paper, we propose and experimentally demonstrate a novel hybrid chaos-based three-dimensional (3-D) constellation scrambling scheme to simultaneously improve the physical layer security and transmission performance of the coherent optical orthogonal frequency division multiplexing (CO-OFDM) system. A 3-D regular hexahedron signal constellation is constructed by the constellation figure of merit principle, which not only expands the encryption dimension but improves the error performance. The dynamic parameters for constellation scrambling are generated by the 5-D hybrid chaotic scheme based on the combination of a 3-D hyperchaotic Hénon mapping and two independent 1-D Logistic mappings, as such a key space of ∼10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">133</sup> is introduced to enhance the security level of OFDM data encryption during transmission. Furthermore, a transmission experiment for encryption of 144 Gbps 16-quadrature-amplitude-modulation OFDM data over a 100 km standard single-mode fiber in a CO-OFDM system is demonstrated. Compared with the case of using the 3-D rectangular constellation, a 2 dB bit error rate performance improvement is achieved. The results show that the proposed scheme could effectively enhance the system security and transmission performance, which suggests a scalable strategy for future physically secured CO-OFDM systems.
Consensus-Based Labeled Multi-Bernoulli Filter With Event-Triggered Communication

This paper introduces a novel consensus-based labeled multi-Bernoulli (LMB) filter to tackle multi-target tracking (MTT) in a communication resource-sensitive distributed sensor network (DSN). Although consensus-based approaches provide effective tools for distributed fusion and MTT, the requirement of iterative communication makes it impractical in resource limited situations. To deal with this issue, two event-triggered strategies are proposed and incorporated into the consensus-based LMB. Focusing on the information discrepancy between the local multi-target probability density function (PDF) and the time prediction of the latest broadcast one, the integral-triggering strategy (ITS) is introduced. Furthermore, by proving that the information discrepancy (Kullback-Leibler divergence) between two LMB densities with the same label space can be decomposed into the sum of the information discrepancy of each LMB component pair (LMB components with the same label), the separated-triggering strategy (STS) is proposed. The performance of the proposed algorithms is demonstrated in a distributed multi-target tracking scenario via numerical simulations.
EventHPE: Event-based 3D Human Pose and Shape Estimation

Event camera is an emerging imaging sensor for capturing dynamics of moving objects as events, which motivates our work in estimating 3D human pose and shape from the event signals. Events, on the other hand, have their unique challenges: rather than capturing static body postures, the event signals are best at capturing local motions. This leads us to propose a two-stage deep learning approach, called EventHPE. The first-stage, FlowNet, is trained by unsupervised learning to infer optical flow from events. Both events and optical flow are closely related to human body dynamics, which are fed as input to the ShapeNet in the second stage, to estimate 3D human shapes. To mitigate the discrepancy between image-based flow (optical flow) and shape-based flow (vertices movement of human body shape), a novel flow coherence loss is introduced by exploiting the fact that both flows are originated from the identical human motion. An in-house event-based 3D human dataset is curated that comes with 3D pose and shape annotations, which is by far the largest one to our knowledge. Empirical evaluations on DHP19 dataset and our in-house dataset demonstrate the effectiveness of our approach.
Artificial Intelligence Computing at the Quantum Level

The extraordinary advance in quantum computation leads us to believe that, in the not-too-distant future, quantum systems will surpass classical systems. Moreover, the field’s rapid growth has resulted in the development of many critical tools, including programmable machines (quantum computers) that execute quantum algorithms and the burgeoning field of quantum machine learning, which investigates the possibility of faster computation than traditional machine learning. In this paper, we provide a thorough examination of quantum computing from the perspective of a physicist. The purpose is to give laypeople and scientists a broad but in-depth understanding of the area. We also recommend charts that summarize the field’s diversions to put the whole field into context.