Reviewerly/exHarmony huggingface dataset:
Hyperspectral Image Denoising via Group Sparsity Regularized Hybrid Spatio-Spectral Total Variation

In this paper, we propose a new hyperspectral image (HSI) denoising model with the group sparsity regularized hybrid spatio-spectral total variation (GHSSTV) and low-rank tensor decomposition, which is based on the analysis of structural sparsity of HSIs. First, the global correlations among all modes are explored by the Tucker decomposition, which applies low-rank constraints to the clean HSIs. To avoid over-smoothing, we propose GHSSTV regularization to ensure the group sparsity not only in the first-order gradient domain but also in the second-order ones along the spatio-spectral dimensions. Then, the sparse noise in HSI can be detected by the ℓ1 norm. Furthermore, strong Gaussian noise is simulated by the Frobenius norm. The alternating direction multiplier method (ADMM) algorithm is employed to effectively solve the GHSSTV model. Finally, experimental results from a series of simulations and real-world data suggest a superior performance of the GHSSTV method in HSIs denoising.
Super-Efficient Super Resolution for Fast Adversarial Defense at the Edge

Autonomous systems are highly vulnerable to a variety of adversarial attacks on Deep Neural Networks (DNNs). Training-free model-agnostic defenses have recently gained popularity due to their speed, ease of deployment, and ability to work across many DNNs. To this end, a new technique has emerged for mitigating attacks on image classification DNNs, namely, preprocessing adversarial images using super resolution - upscaling low-quality inputs into high-resolution images. This defense requires running both image classifiers and super resolution models on constrained autonomous systems. However, super resolution incurs a heavy computational cost. Therefore, in this paper, we investigate the following question: Does the robustness of image classifiers suffer if we use tiny super resolution models? To answer this, we first review a recent work called Super-Efficient Super Resolution (SESR) [1] that achieves similar or better image quality than prior art while requiring 2 × to 330 × fewer Multiply-Accumulate (MAC) operations. We demonstrate that despite being orders of magnitude smaller than existing models, SESR achieves the same level of robustness as significantly larger networks. Finally, we estimate end-to-end performance of super resolution-based defenses on a commercial Arm Ethos-U55 micro-NPU. Our findings show that SESR achieves nearly 3 × higher FPS than a baseline while achieving similar robustness.
PGADA: Perturbation-Guided Adversarial Alignment for Few-Shot Learning Under the Support-Query Shift

Few-shot learning methods aim to embed the data to a low-dimensional embedding space and then classify the unseen query data to the seen support set. While these works assume that the support set and the query set lie in the same embedding space, a distribution shift usually occurs between the support set and the query set, i.e., the Support-Query Shift, in the real world. Though optimal transportation has shown convincing results in aligning different distributions, we find that the small perturbations in the images would significantly misguide the optimal transportation and thus degrade the model performance. To relieve the misalignment, we first propose a novel adversarial data augmentation method, namely Perturbation-Guided Adversarial Alignment (PGADA), which generates the hard examples in a self-supervised manner. In addition, we introduce Regularized Optimal Transportation to derive a smooth optimal transportation plan. Extensive experiments on three benchmark datasets manifest that our framework significantly outperforms the eleven state-of-the-art methods on three datasets. Our code is available at https://github.com/772922440/PGADA .
The Implementation of Smart Locker in Edge Computing

This implementation completes a set of smart locker system with face as identification, which improves the traditional locker problem with keys or passwords, and improves security and operational efficiency. Face comparison is based on the core technology of FaceNet, which compares the face image after correction and feature conversion of the depth model. Since the depth model requires a lot of computation and reduces the dependence on cloud computing, this implementation is performed by edge computing equipment. The experimental results confirm that the system can operate correctly and stably to achieve practical purposes.
Corpus Design of Chinese Medicine English Vocabulary Translation Teaching System Based on Python

The current corpus has the problem of imperfect span retrieval function, which leads to a large classification noise. This paper designs a Python-based corpus of Chinese medicine English vocabulary translation teaching system. Here, we select the script material of web crawler, extract topic tags in the form of tag window, calculate the amount of information carried by words, use Python to extract the characteristics of Chinese medicine English vocabulary, and according to the observation value of exploration strategy, use instant time difference learning algorithm to construct the translation mode of teaching system, limit the scope of key words, and design the cross-range retrieval function of corpus. Experimental results: the average classification noise of the designed corpus and the other two corpora is 25.007[Formula: see text]dB, 33.877[Formula: see text]dB and 32.166[Formula: see text]dB, which proves that the integrated Python corpus has higher comprehensive value.
Continuous variable multipartite entanglement in cascaded nonlinearities

Abstract Multipartite entanglement is the crucial requirement for quantum information processing, quantum computation, quantum communication and quantum sensing. Therefore, the generation and characterization of such entanglement are primary goals in quantum technologies. Here we theoretically study the multipartite continuous-variable quadrature entanglement generated by three symmetric cascaded nonlinear optical systems. Such cascaded systems allow us to study the bipartite, tripartite and quadripartite entanglement. We use different continuous-variable entanglement criteria to characterize the entanglement. We observe that only a few bipartite combinations show the entanglement out of all possible combinations, while most tripartite combinations are entangled. Interestingly such a system depicts a full quadripartite entanglement. Moreover, we show that, unlike the quadrature sum-variance entanglement criterion, the negative partial transpose criterion can show the entanglement in most cases. The present comprehensive analysis of the generation and characterization of the continuous-variable entanglement with cascaded systems would pave the way to realizing a complex quantum network.
The Determinants of the Growth of the European Bioplastics Sector—A Fuzzy Cognitive Maps Approach

The extensive use of plastics and the environmental burden associated with their disposal have attracted the attention of scientists, politicians and citizens in Europe. In this frame, the EU has adopted a European Strategy for Plastics aiming, on the one hand, at reducing the use of plastic and, on the other hand, promoting their reuse in the context of a circular economy directly linked with the recently adopted Bioeconomy Strategy. Bioplastics could be an alternative to the conventional plastics, but they still have a limited share in the market. In this paper, Fuzzy Cognitive Maps (FCMs), a soft computing technique for analysing complex decision-making problems, is applied to identify the factors acting as drivers or barriers towards a bio-based plastics industry, their relative importance and the interactions between them. Experts with diverse backgrounds (technical experts, policy makers, industry executives) were interviewed in order to capture their perceptions and create a collective FCM capturing the strong and the weak points of the system. The collective FCM has a total number of 38 factors, which reflect the different approaches and knowledge of the experts. Overall, the “bio-based plastics” system is influenced mainly by the following factors: “EU Legislation”, “Monomers purity”, “Properties of the product”, “Recycling potential”, “Research &amp; Development”, “National Legislation” and “Production cost”. The effect of the most significant political, social and techno-economic factors on the potential growth of the bioplastics sector has also been examined via simulations. The analysis demonstrated that the model is affected more (is more sensitive) to shifts in technoeconomic factors.
Cardiac Segmentation Method Based on Domain Knowledge

Echocardiography plays an important role in the clinical diagnosis of cardiovascular diseases. Cardiac function assessment by echocardiography is a crucial process in daily cardiology. However, cardiac segmentation in echocardiography is a challenging task due to shadows and speckle noise. The traditional manual segmentation method is a time-consuming process and limited by inter-observer variability. In this paper, we present a fast and accurate echocardiographic automatic segmentation framework based on Convolutional neural networks (CNN). We propose FAUet, a segmentation method serially integrated U-Net with coordinate attention mechanism and domain feature loss from VGG19 pre-trained on the ImageNet dataset. The coordinate attention mechanism can capture long-range dependencies along one spatial direction and meanwhile preserve precise positional information along the other spatial direction. And the domain feature loss is more concerned with the topology of cardiac structures by exploiting their higher-level features. In this research, we use a two-dimensional echocardiogram (2DE) of 88 patients from two devices, Philips Epiq 7C and Mindray Resona 7T, to segment the left ventricle (LV), interventricular septal (IVS), and posterior left ventricular wall (PLVW). We also draw the gradient weighted class activation mapping (Grad-CAM) to improve the interpretability of the segmentation results. Compared with the traditional U-Net, the proposed segmentation method shows better performance. The mean Dice Score Coefficient (Dice) of LV, IVS, and PLVW of FAUet can achieve 0.932, 0.848, and 0.868, and the average Dice of the three objects can achieve 0.883. Statistical analysis showed that there is no significant difference between the segmentation results of the two devices. The proposed method can realize fast and accurate segmentation of 2DE with a low time cost. Combining coordinate attention module and feature loss with the original U-Net framework can significantly increase the performance of the algorithm.
OTP System Based on ECC Key Exchange

The user id and corresponding passwords are generally used for identifying a user in cyberspace. However, this information is not enough to prove that the right person has provided these details. This is where authentication has a role to play. Authentication is the process of proving the identity of a user on a computer system. Identification is used to recognize a person or thing's identity, while authentication is the procedure for verifying that identity. The system can ensure that the right person accesses its resources through authentication. The user has to give some credentials that no one else possesses. It is sometimes called multifactor authentication. One form of multifactor authentication is the one time password (OTP). While using critical services like e-commerce, the user is authenticated using OTP before making the actual payment. Upon receiving the OTP, the user enters it on the client site, which is sent to the server for validation. Submitting OTP to the server through the open network makes it prone to all sorts of attacks that can happen on the open network. An OTP system based on Elliptic Curve Cryptography (ECC) is proposed to avoid sending OTP through an open network. Through the ECC key exchange mechanism, OTP can be generated simultaneously at the server-side and client-side. Hence it is not required to send OTP back to the server for verification. The client itself can verify the OTP without sending it to the server. Not only that, OTP can be used as a session key for all the transactions in the session.
SecNDP: Secure Near-Data Processing with Untrusted Memory

Today&#x2019;s data-intensive applications increasingly suffer from significant performance bottlenecks due to the limited memory bandwidth of the classical von Neumann architecture. Near-Data Processing (NDP) has been proposed to perform computation near memory or data storage to reduce data movement for improving performance and energy consumption. However, the untrusted NDP processing units (PUs) bring in new threats to workloads that are private and sensitive, such as private database queries and private machine learning inferences. Meanwhile, most existing secure hardware designs do not consider off-chip components trustworthy. Once data leaving the processor, they must be protected, e.g., via block cipher encryption. Unfortunately, current encryption schemes do not support computation over encrypted data stored in memory or storage, hindering the adoption of NDP techniques for sensitive workloads.In this paper, we propose SecNDP, a lightweight encryption and verification scheme for untrusted NDP devices to perform computation over ciphertext and verify the correctness of linear operations. Our encryption scheme leverages arithmetic secret sharing in secure Multi-Party Computation (MPC) to support operations over ciphertext, and uses counter-mode encryption to reduce the decryption latency. The security of the encryption and verification algorithm is formally proven. Compared with a non-NDP baseline, secure computation with SecNDP significantly reduces the memory bandwidth usage while providing security guarantees. We evaluate SecNDP for two workloads of distinct memory access patterns. In the setting of eight NDP units, we show a speedup up to 7.46&#x00D7; and energy savings of 18% over an unprotected non-NDP baseline, approaching the performance gain attained by native NDP without protection. Furthermore, SecNDP does not require any security assumption on NDP to hold, thus, using the same threat model as existing secure processors. SecNDP can be implemented without changing the NDP protocols and their inherent hardware design.
DEDGCN: Dual Evolving Dynamic Graph Convolutional Network

With the wide application of graph data in many fields, the research of graph representation learning technology has become the focus of scholars’ attention. Especially, dynamic graph representation learning is an important part of solving the problem of change graph in reality. On the one hand, most dynamic graph representation methods focus either on graph structure changes or node embedding changes, ignoring the internal relationship. On the other hand, most dynamic graph neural networks require learn node embeddings from specific tasks, resulting in poor universality of node embeddings and cannot be used in unsupervised tasks. Hence, Dual Evolving Dynamic Graph Convolutional Network (DEDGCN) was proposed to solve the above problems. DEDGCN uses the recurrent neural network to push the evolvement of GCN and nodes, from which it can extract the structural features of dynamic graph and learns the stability features of nodes, respectively, forming an adaptive dynamic graph convolution network. DEDGCN can be classified as unsupervised graph convolutional network. Thus, it is capable of training the unlabeled dynamic graph, it has more extensive application scenarios, and the calculated node embedding has strong generality. We evaluate our proposed method on experimental data in three tasks which are node classification, edge classification, and link prediction. In the classification task, facing the graph with large scale, complex connection relationship, and uncertain change rule, the F1 value of node classification task obtained by DEDGCN reaches 77%, and the F1 value of edge classification task reaches more than 90%. The results show that DEDGCN is effective in capturing graph features, and the effect of DEDGCN is much higher than other baseline methods, which proves the importance of capturing node stability features in dynamic graph representation learning. At the same time, the ability of DEDGCN in unsupervised tasks is further verified by using clustering and anomaly detection tasks, which proves that DEDGCN learning network embedding is widely used.
Towards a privacy protection-capable noise fingerprinting for numerically aggregated data

Privacy protection and traitor tracing have always been two separate research subjects. Meanwhile, digital fingerprinting, which tracks illegally distributed digital media by creating a unique code for each user, has long been regarded as one of the effective means of tracking down illegal users. However, most of the existing fingerprinting schemes focus on how to design a more robust or general fingerprint code to target illegal users more effectively, without paying attention to privacy protection itself. Our paper proposes a novel idea to simultaneously perform privacy protection and traitor tracing on massive aggregated data. And further, a detailed Secure Noise FingerPrinting based on Differential Privacy (SNFP-DP) scheme is proposed to demonstrate the feasibility of the idea by integrating differential privacy and digital fingerprinting. The SNFP-DP scheme builds the well-designed noise for each specific computing node into the numerically aggregated data set, realizing fingerprint inserting and noise disturbance in one step. The privacy of Noised Aggregated FingerPrint Data (NAFPD) generated by our SNFP-DP scheme can be protected by adding appropriate noise, while the source of illegal dissemination can be determined by identifying the fingerprint in the NAFPD. In addition, we instantiate the SNFP-DP scheme on real-world data by specifying the values of all key parameters. The experimental results show that the NAFPD meets the requirements of data privacy protection and statistical analysis accuracy. And it also has the robustness and security characteristics as the digital fingerprinting. Finally, we conduct a thorough performance analysis through mathematical formula derivation and security attack simulation.
Improved <scp>meta‐heuristic</scp> algorithm for selecting optimal features: A big data classification model

Abstract Many fields function with large databases constitute a high number of features. Feature selection strategies seek to exclude the features that are distracting, repetitive, or unnecessary, as they can degrade the classification results. Existing approaches lack the scalability needed to handle the datasets with millions of instances and they do not obtain favorable results in a timely manner. This study uses a unique feature selection approach based on an upgraded optimization model and deep machine learning‐based data classification. “(a) Feature extraction, (b) optimal feature selection, and (c) classification” are the three stages of the proposed model. Initially, the extracted big‐datasets are efficiently handled by the parallel pool map‐reduce architecture. Several features from the input big‐data are extracted using feature extraction (FE) approaches such as the suggested Tri‐Kernel principal component analysis (TK‐PCA), linear discriminant analysis, and linear square regression. Furthermore, the data obtained characteristics may contain data that is irrelevant, out‐of‐date, or noisy. The computing cost rises due to the larger feature space. As a result, the best features are selected using a new optimization technique known as Levy Adapted SLnO (LA‐SLnO), which is a superior variant of the original SLnO algorithm. This selection of appropriate features improves the classification accuracy. For classification, Convolutional Neural Network is used in this work. Finally, a comparative evaluation is undergone to validate the efficiency of the proposed model.
Securing Cloud Application using SHAKE-256 Hash Algorithm &amp; Antiforgery token

In present days, all important data are transferred to the cloud environment to reduce infrastructure in physical devices. But Cloud attackers can access and steal information from the cloud environment. As a result, it may affect authenticated users and they can't access the application. To overcome the scenario, SHAKE-256 Hash Algorithm & Antiforgery token was proposed to protect the application and safeguard it from hackers. In this paper, SHAKE-256 Hash algorithm and Antiforgery Token were proposed to protect web application and safeguard from hackers.
Multi-strategy Jaya algorithm for industrial optimization tasks

Jaya, a simple heuristic algorithm, has shown attractive features, especially parameter-free. However, the simple structure of Jaya algorithm may result in poor performances, to boost the performance, a multi-strategy Jaya (MJaya) algorithm based on multi-population has been proposed in this paper. Three strategies correspond to three groups of solutions. The first strategy based on the first population is to introduce an adaptive weight parameter to the position-updating equation to improve the local search. The second strategy is based on rank-based mutation to enhance the global search. The third strategy is to exploit around the best solution to reinforce the local search. Three strategies cooperate well during the evolution process. The experimental results based on CEC 2014 have proven that the proposed MJaya is superior compared with Jaya and its latest variants. Then, the proposed MJaya algorithm is used to solve three industrial problems and the results have shown that the proposed MJaya algorithm can also solve complex industrial applications effectively.
Dual Aligned Siamese Dense Regression Tracker

Anchor or anchor-free based Siamese trackers have achieved the astonishing advancement. However, their parallel regression and classification branches lack the tracked target information link and interaction, and the corresponding independent optimization maybe lead to task-misalignment, such as the reliable classification prediction with imprecisely localization and vice versa. To address this problem, we develop a general Siamese dense regression tracker (SDRT) with both task and feature alignments. It consists of two cooperative and mutual-guidance core branches: dense local regression with RepPoint representation, the global and local multi-classifier fusion with aligned features. They complement and boost each other to constrain the results with well-localized followed to also be well-classified. Specifically, a dense local regression with RepPoint representation, directly estimates and averages multiple dense local bounding box offsets for accurate localization. And then, the refined bounding boxes can be used to learn the global and local affine alignment features for reliable multi-classifier fusion. The classified scores in turn guide the assigned positive bounding boxes for the regression task. The mutual guidance operations can bridge the connection between classification and regression substantially, since the assigned labels of one task depend on the prediction quality of the other task. The proposed tracking module is general, and it can boost both the anchor or anchor-free based Siamese trackers to some extent. The extensive tracking comparisons on six tracking benchmarks verify its favorable and competitive performance over states-of-the-arts tracking modules.