Reviewerly/exHarmony huggingface dataset:
A Cross-Modality Feature Transfer Method for Target Detection in SAR Images

Synthetic aperture radar (SAR) ship detection methods have achieved remarkable progress in recent years. However, unlike RGB images, the characteristics of SAR imaging will result in non-intuitive feature representations. Furthermore, due to the insufficient data of SAR images, existing methods relying on plenty of labeled SAR images may be hard to achieve promising performance. To address the aforementioned issues, a cross-modality feature transfer (CMFT) method is proposed in this article, which enhances feature representations in the SAR modality by transferring rich knowledge in the RGB modality. First, we propose a multilevel modality alignment network (MMAN), which encourages the model to effectively learn modality-invariant features and alleviate the large cross-modality discrepancies by aligning features from multilevels (scene level, local level, global level, and instance level). Second, to address the underperformance of samples with non-intuitive features in the modality alignment, we introduce a hard-sample supervision module (HSM) in the stage of feature extraction, which can thoroughly exploit the feature of hard-to-align samples by giving more optimization energy for them. Third, to enhance the discriminability of instance-level features, a feature complementary module (FCM) is customized to fully explore the potential complementary clues between instance-level features and context information for the instance-level feature alignment. Extensive experimental results demonstrate that the CMFT outperforms the state-of-the-art detectors. Compared to the baseline model, CMFT improves the accuracy by 3.1% mean average precision (mAP) on the SSDD dataset and 3.4% mAP on the HRSID dataset, demonstrating its superior SAR ship detection performance.
Turning backdoors for efficient privacy protection against image retrieval violations

Image retrieval, empowered by deep metric learning, is undoubtedly a building block in today’s media-sharing practices, but it also poses a severe risk of digging user privacy via retrieval. State-of-the-art countermeasures are built on adversarial learning, which would spoil the image-sharing mood with significant latency. To relieve the cumbersome experience of such data-centric approaches, we propose a plug-and-play privacy-preserving design (MIP) against image retrieval violations by exploring the rule-based triggering characteristics of model backdoors. The basic idea is to inject a privacy-preserving backdoor into the global retrieval model via backdoor learning, thus preventing shared images with such triggers from being searched. At its core, two types of triplet loss functions are invented, namely, imperceptible loss for normal retrieval performance and privacy-sensitive loss for disturbing retrieval with deliberate privacy backdoor injection. Extensive experiments on four widely used, realistic datasets showcase that MIP provides an outstanding privacy-preserving (backdoor) success rate, e.g., the poisoned retrieval mAP could be reduced to 0.33% (98.12%↓) in CUB-200, 0.04% (99.84%↓) in In-Shop, 0.64% (99.59%↓) in CARS196 and 0.01% (99.98%↓) in SOP, respectively, while maintaining similar normal retrieval performance (average 0.02%↓); provides a superior efficiency (7 orders of latency reduction) than the baselines. Besides, as a model-centric solution, MIP yields imperceptible visual changes and is demonstrated to resist potential black-box defenses (e.g., image filtering) and white-box defenses (e.g., fine-pruning). The code and data will be made available at https://github.com/lqsunshine/MIP.
A modified least squares-based tomography with density matrix perturbation and linear entropy consideration along with performance analysis

Abstract Quantum state tomography identifies target quantum states by performing repetitive measurements on identical copies. In this paper, we have two key contributions aimed at improving traditional post-processing computational complexity and sample complexity of quantum tomography protocols. In the first case, we propose a new low-cost positivity constraint method based on density matrix perturbation after the least squares (LS) estimation of the density matrix. In the second case, we propose a new cost function with the maximum linear entropy and LS method to improve the sample average trace distance with reasonably low sample complexity. We call it the LS with the maximum entropy (LSME) method. Our proposed algorithm does not follow the iterative optimization technique, which is true for existing maximum likelihood and entropy-based ones. Performance analysis is conducted for our proposed methods by studying how they compare to the existing techniques for different sample complexities and dimensionalities. Extensive numerical simulations have been conducted to demonstrate the advantages of the proposed tomography algorithms.
Partial-norm of entanglement: entanglement monotones that are not monogamous

Abstract Quantum entanglement is known to be monogamous, i.e. it obeys strong constraints on how the entanglement can be distributed among multipartite systems. Almost all the entanglement monotones so far are shown to be monogamous. We explore here a family of entanglement monotones with the reduced functions are concave but not strictly concave and show that they are not monogamous. They are defined by four kinds of the ‘partial-norm’ of the reduced state, which we call them partial-norm of entanglement , minimal partial-norm of entanglement, reinforced minimal partial-norm of entanglement, and partial negativity , respectively. This indicates that, the previous axiomatic definition of the entanglement monotone needs supplemental agreement that the reduced function should be strictly concave since such a strict concavity can make sure that the corresponding convex-roof extended entanglement monotone is monogamous. Here, the reduced function of an entanglement monotone refers to the corresponding function on the reduced state for the measure on bipartite pure states.
Performance Analysis of Transfer Learning Methods for Malaria Disease Identification

Malaria has become a widespread disease and one of the leading causes of many deaths worldwide. Malaria is a blood disease brought on by Plasmodium parasites, which are transmitted by the bite of a female Anopheles mosquito. To diagnose the condition, medical experts analyse thick and thin blood smears. However, their precision is dependent on the quality of the smear and experience in categorising and counting parasitized and uninfected cells. Such an investigation could be complicated and time-consuming for large-scale diagnosis, resulting in poor quality as well. Deep learning (DL) approaches such as Convolutional Neural Networks (CNN) offer highly scalable and improved performance with end-to-end feature extraction and classification in cutting-edge image analysis-based computer-aided-diagnosis (CAD) procedures. Automated malaria screening employing DL approaches could contribute in the development of an effective diagnostic aid. In this study, we assessed the efficacy of VGG16, EfficientNetB3, InceptionV3, and ResNet50 as feature extractors to categorise parasitized and uninfected cells and aid in enhanced malaria disease screening. Our results showed that optimum accuracy of 0.97 is achieved after 40 epochs. Our study demonstrated the successful application of deep learning techniques, specifically ResNet50 and EfficientNetB3, among the analysed models, for malaria disease screening and detection.
Link Value Estimation Based Graph Attention Network for Link Prediction in Complex Networks

Link prediction in complex networks aims to discover hidden or forthcoming links between network nodes, and it is widely used in areas such as knowledge graphs. Existing Graph Neural Networks (GNNs) often only consider whether nodes are connected or calculate the weight of the links through node features when they apply network structure and features to learn node representation in the manner of neighborhood aggregation, while neglecting the intrinsic value of links. This paper analyzes the value of links based on network structure and proposes corresponding evaluation metrics. It integrates the value of links into the construction and training of the link prediction graph attention network, not only improving the performance of the link prediction, but also providing theoretical support for the interpretability of the prediction results. Extensive link prediction experiments were carried out on representative open graph benchmark data, and the results show that the link prediction framework proposed in this paper has good performance and generalization capabilities.
SSRDet: Small Object Detection Based on Feature Pyramid Network

There are still many technological challenges in recognizing small objects in complex situations, and the performance of current detection algorithms for small objects is still unsatisfactory.Most existing methods mainly use feature pyramid networks to enrich shallow features using contextual features.However, due to the inconsistency of gradients between different layers of the feature pyramid network, the shallow features cannot be fully utilized resulting in the slow improvement of small object detection accuracy.To effectively improve the small object detection algorithm, we propose a new feature pyramid network-based small object detection algorithm, SSRDet.To effectively assign positive and negative sample labels and address the issue of sample scale imbalance, we first present RFLA.Then, to overcome the gradient inconsistency between various layers and enable the full utilization of the shallow features, we extend the feature pyramid network by including a scale enhancement module (SEM) and a scale selection module (SSM).Finally, we introduced the attention module (SPAM) to filter out the background noise in the shallow feature extraction to better extract small object features.We validated our method on VisDrone2019 and AI-TOD, and our method outperformed the state-of-the-art detectors.
Matrix Completion of Adaptive Jumping Graph Neural Networks for Recommendation Systems

Using graph neural networks to model recommendation scenarios can effectively capture high-order relationship features between objects, thereby helping the model better handle recommendation problems. However, the over-smoothing phenomenon poses a performance constraint for recommendation algorithms based on graph convolutional node aggregation. In realistic recommendation scenarios that involve social relationships, the imbalance of node degrees can deepen the impact of over-smoothing on recommendation accuracy. To address these issues, we propose an adaptive matrix completion algorithm for collaborative filtering recommendation, which is based on the aggregation rules of relational graph convolutional networks, and introduces jumping knowledge connection for adaptive selection of user-item feature aggregation results of deep graph convolutional networks. And in order to overcome the limitations of existing interlayer aggregation mechanisms, we design a self-attention-based aggregation mechanism to integrate the output of each layer and enhance the generalization ability of the model. In addition, we introduce normalization in the process of data transmission between layers to ensure the distinguishability between nodes. Finally, we conduct experiments on three real recommendation datasets to compare the algorithm’s performance and perform ablation analysis. Our model achieves RMSEs of 0.9058, 0.8346 and 0.7176 on the three datasets respectively. The results show that the recommendation performance of our model achieves a leading level when compared with current state-of-the-art algorithms and verifies the influence of node degree distribution on the recommendation process.
Medicare Fraud Detection Using Graph Analysis: A Comparative Study of Machine Learning and Graph Neural Networks

Insurance companies have focused on medicare fraud detection to reduce financial losses and reputational harm because medicare fraud causes tens of billions of dollars in damage annually.This study demonstrates that medicare fraud detection can be significantly enhanced by introducing graph analysis with considering the relationships among medical providers, beneficiaries, and physicians.We use open-source tabular datasets containing beneficiary information, inpatient claims, outpatient claims, and indications about potential fraudulent providers.We then aggregated them into a single dataset by converting them into a graph structure.Furthermore, we developed medicare fraud detection models using two approaches to reflect graph information, i.e., graph neural network (GNN) models and traditional machine learning models using graph centrality measures.Therefore, the machine learning model with graph centrality features showed improved precision of 4 percent point (%p), recall of 24 %p, and F1-score of 14 %p compared to the best GNN model.The improvement in recall to this extent could result in substantial cost savings of 3.1 billion euros and 5 billion dollars in the United States and Europe, respectively, benefiting governmental institutions and insurance companies involved in healthcare insurance operations.Furthermore, the required learning time of the best GNN model was approximately 250-300 times more than that of the best machine-learning model.This outcome suggests that successful and efficient detection of medicare fraud can be achieved if graph centrality measures are used to capture the relationships among medical providers, physicians, and beneficiaries.
FecNet: A Feature Enhancement and Cascade Network for Object Detection Using Roadside LiDAR

Roadside light detection and ranging (LiDAR) is commonly used to record the traffic data of the whole intersection scene or road segment in intelligent transportation systems (ITSs). However, general deep-learning object detection methods do not adequately consider the static background captured by roadside LiDAR. Moreover, critical issues remain to be overcome in object detection using roadside LiDAR: false alarms caused by complex background interference and multiscale objects with limited characteristics. To this end, a feature enhancement and cascade network (FecNet) is proposed to alleviate the problems. From the perspective of feature enhancement, FecNet improves foreground feature discrimination by extracting foreground information and fusing it with feature maps of multiple stages. Also, from the perspective of feature cascade, a feature cascade backbone is proposed to enhance the localization and contextual information of multiscale objects with limited characteristics. Comprehensive experiments are conducted using a roadside LiDAR dataset. The experimental results suggest that FecNet is superior to the benchmark detectors and achieves better computational efficiency and detection accuracy.
Long-Frame-Shift Neural Speech Phase Prediction With Spectral Continuity Enhancement and Interpolation Error Compensation

Speech phase prediction, which is a significant research focus in the field of signal processing, aims to recover speech phase spectra from amplitude-related features. However, existing speech phase prediction methods are constrained to recovering phase spectra with short frame shifts, which are considerably smaller than the theoretical upper bound required for exact waveform reconstruction of short-time Fourier transform (STFT). To tackle this issue, we present a novel long-frame-shift neural speech phase prediction (LFS-NSPP) method which enables precise prediction of long-frame-shift phase spectra from long-frame-shift log amplitude spectra. The proposed method consists of three stages: interpolation, prediction and decimation. The short-frame-shift log amplitude spectra are first constructed from long-frame-shift ones through frequency-by-frequency interpolation to enhance the spectral continuity, and then employed to predict short-frame-shift phase spectra using an NSPP model, thereby compensating for interpolation errors. Ultimately, the long-frame-shift phase spectra are obtained from short-frame-shift ones through frame-by-frame decimation. Experimental results show that the proposed LFS-NSPP method can yield superior quality in predicting long-frame-shift phase spectra than the original NSPP model and other signal-processing-based phase estimation algorithms.
Self-Supervised Imitation for Offline Reinforcement Learning With Hindsight Relabeling

Reinforcement learning (RL) requires a lot of interactions with the environment, which is usually expensive or dangerous in real-world tasks. To address this problem, offline RL considers learning policies from fixed datasets, which is promising in utilizing large-scale datasets, but still suffers from the unstable estimation for out-of-distribution data. Recent developments in RL via supervised learning methods offer an alternative to learning effective policies from suboptimal datasets while relying on oracle information from the environment. In this article, we present an offline RL algorithm that combines hindsight relabeling and supervised regression to predict actions without oracle information. We use hindsight relabeling on the original dataset and learn a command generator and command-conditional policies in a supervised manner, where the command represents the desired return or goal location according to the corresponding task. Theoretically, we illustrate that our method optimizes the lower bound of the goal-conditional RL objective. Empirically, our method achieves competitive performance in comparison with existing approaches in the sparse reward setting and favorable performance in continuous control tasks.
Efficient Construction of Verifiable Timed Signatures and Its Application in Scalable Payments

Despite the myriad benefits offered by blockchain technology, most of them still face several interrelated issues, such as limited transaction throughput, exorbitant transaction fees, and protracted confirmation times. Payment channel networks have emerged as a promising scalability solution, allowing two mutually distrustful users to engage in multiple off-chain transactions. However, existing schemes based on Hash Time Lock Contract or Anonymous Multi-hop Lock generally cannot ensure strong unlinkability of payments, due to the fact that the time-lock information still remains on the blockchain. To enhance on-chain privacy, a versatile tool was recently proposed by Thyagarajan et al. ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CCS’20</i> ), named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Verifiable Timed Signatures</i> , but it suffers from the dual insufficiencies of linear-increasing performance and time unverifiability (i.e., performance is linear to the number of signature shares, and signatures cannot be ensured recoverable after the specified time). In this paper, we first propose an approach to reduce computational overhead of VTS, which can be applied to enhance other established schemes, such as VTD ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S&amp;P’22</i> ) and VTLRS ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ESORICS’22</i> ). To further reduce the computational complexity from <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O(n)</i> to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</i> (1), we introduce a new cryptographic primitive called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Verifiable Timed Adaptor Signatures</i> . Moreover, we extend the VTAS to VTAS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> which provides the security property of verifiable recovery. We demonstrate the practicality of our proposal via presenting a concrete instantiation and constructing a privacy-enhanced payment channel network. Finally, the comprehensive evaluation reveals that our solutions exhibit superior performance than the state-of-the-art schemes.
A UAV-Assisted Edge Framework for Real-Time Disaster Management

Unmanned Aerial Vehicles (UAV) equipped with onboard embedded platforms and camera sensors provide access to difficult-to-reach areas and facilitate in remote sensing and autonomous decision-making capabilities in disaster recovery and management applications. Onboard computations are preferred due to connectivity, privacy, and latency problems. However, edge implementation becomes challenging because of limited onboard hardware resources (in terms of area, power, and storage). In this paper, we propose a UAV assisted edge computation framework that compresses the Convolutional Neural Networks (CNN) models to be run on an onboard embedded Graphics Processing Unit (GPU) for real-time disaster scenario classification. We use an imbalanced dataset named, Aerial Image Database for Emergency Response (AIDER), to replicate real-world disaster scenarios. Our experimental results show that optimized compressed model’s throughput is increased by about 99% which is up to 92x faster than the native model. Furthermore, the model size reduction enabled through proposed framework is about 84% without compromising accuracy and thus makes it suitable for edge GPUs.
Grafting Laplace and Gaussian Distributions: A New Noise Mechanism for Differential Privacy

The framework of differential privacy protects an individual’s privacy while publishing query responses on congregated data. In this work, a new noise addition mechanism for differential privacy is introduced where the noise added is sampled from a hybrid density that resembles Laplace in the centre and Gaussian in the tail. With a sharper centre and light, sub-Gaussian tail, this density has the best characteristics of both distributions. We theoretically analyze the proposed mechanism, and we derive the necessary and sufficient condition in one dimension and a sufficient condition in high dimensions for the mechanism to guarantee (ϵ, δ)-differential privacy. Numerical simulations corroborate the efficacy of the proposed mechanism compared to other existing mechanisms in achieving a better trade-off between privacy and accuracy.