Reviewerly/exHarmony huggingface dataset:
A GRU and chaos-based novel image encryption approach for transport images

An Intelligent Transport System (ITS) uses smart devices to capture the traffic data in the form of images. However, the adversary can steal and misuse this traffic information. Hence, it becomes essential to have an efficient encryption strategy to save data from various types of attacks. This paper proposes a novel encryption algorithm that uses the Gated Recurrent Unit (GRU) and Sine-Cosine chaotic map to encrypt transport images. The encryption scheme is divided into three phases. In the first phase, two intermediate keys and the seed value required for creating chaotic sequence are generated using unique combinations of 128-bit share key and 128-bit initial vector. In the second phase, permutation is performed using one of the intermediate keys and the chaotic sequence generated by the novel Sine-Cosine chaotic map. The final phase performs the diffusion process using the other intermediate key and GRU approach that uses the chaotic sequence generated by the Sine-Cosine map. The performance of the proposed encryption approach is analyzed using various standard encryption metrics, attacks and decryption parameters. The obtained results and comparative results with existing approaches reveal that the proposed method is suitable for implementing secure and efficient transport image cryptosystems.
Cooperative multi-target hunting by unmanned surface vehicles based on multi-agent reinforcement learning

To solve the problem of multi-target hunting by an unmanned surface vehicle (USV) fleet, a hunting algorithm based on multi-agent reinforcement learning is proposed. Firstly, the hunting environment and kinematic model without boundary constraints are built, and the criteria for successful target capture are given. Then, the cooperative hunting problem of a USV fleet is modeled as a decentralized partially observable Markov decision process (Dec-POMDP), and a distributed partially observable multi-target hunting Proximal Policy Optimization (DPOMH-PPO) algorithm applicable to USVs is proposed. In addition, an observation model, a reward function and the action space applicable to multi-target hunting tasks are designed. To deal with the dynamic change of observational feature dimension input by partially observable systems, a feature embedding block is proposed. By combining the two feature compression methods of column-wise max pooling (CMP) and column-wise average-pooling (CAP), observational feature encoding is established. Finally, the centralized training and decentralized execution framework is adopted to complete the training of hunting strategy. Each USV in the fleet shares the same policy and perform actions independently. Simulation experiments have verified the effectiveness of the DPOMH-PPO algorithm in the test scenarios with different numbers of USVs. Moreover, the advantages of the proposed model are comprehensively analyzed from the aspects of algorithm performance, migration effect in task scenarios and self-organization capability after being damaged, the potential deployment and application of DPOMH-PPO in the real environment is verified.
Volume-awareness and outlier-suppression co-training for weakly-supervised MRI breast mass segmentation with partial annotations

Segmenting breast mass from magnetic resonance imaging (MRI) scans is an important step in the breast cancer diagnostic procedure for physicians and computer-aided diagnosis systems. Sufficient high-quality annotation is essential for establishing an automatic segmentation model, particularly for MRI breast masses with complex backgrounds and various sizes. In this study, we have proposed a novel approach for training an MRI breast mass segmentation network with partial annotations and reinforcing it with two weakly supervised constraint losses. Specifically, following three user-friendly partial annotation methods were designed to alleviate annotation costs: single-slice, orthogonal slice, and interval slice annotations. With the guidance of partial annotations, we first introduced a volume awareness loss that supports the additional constraint for masses with various scales. Moreover, to reduce false-positive predictions, we proposed an end-to-end differentiable outlier-suppression loss to suppress noise activation outside the target during training. We validated our method on 140 patients. The Dice similarity coefficient (DSC) of the proposed three partial annotation methods are 0.674, 0.835, and 0.837 respectively. Quantitative and qualitative evaluations demonstrate that our method can achieve competitive performance compared to state-of-the-art methods with complete annotations.
Performance of the Swiss Digital Contact-Tracing App Over Various SARS-CoV-2 Pandemic Waves: Repeated Cross-sectional Analyses

Digital proximity-tracing apps have been deployed in multiple countries to assist with SARS-CoV-2 pandemic mitigation efforts. However, it is unclear how their performance and effectiveness were affected by changing pandemic contexts and new viral variants of concern.The aim of this study is to bridge these knowledge gaps through a countrywide digital proximity-tracing app effectiveness assessment, as guided by the World Health Organization/European Center for Prevention and Disease Control (WHO/ECDC) indicator framework to evaluate the public health effectiveness of digital proximity-tracing solutions.We performed a descriptive analysis of the digital proximity-tracing app SwissCovid in Switzerland for 3 different periods where different SARS-CoV-2 variants of concern (ie, Alpha, Delta, and Omicron, respectively) were most prevalent. In our study, we refer to the indicator framework for the evaluation of public health effectiveness of digital proximity-tracing apps of the WHO/ECDC. We applied this framework to compare the performance and effectiveness indicators of the SwissCovid app.Average daily registered SARS-CoV-2 case rates during our assessment period from January 25, 2021, to March 19, 2022, were 20 (Alpha), 54 (Delta), and 350 (Omicron) per 100,000 inhabitants. The percentages of overall entered authentication codes from positive tests into the SwissCovid app were 9.9% (20,273/204,741), 3.9% (14,372/365,846), and 4.6% (72,324/1,581,506) during the Alpha, Delta, and Omicron variant phases, respectively. Following receipt of an exposure notification from the SwissCovid app, 58% (37/64, Alpha), 44% (7/16, Delta), and 73% (27/37, Omicron) of app users sought testing or performed self-tests. Test positivity among these exposure-notified individuals was 19% (7/37) in the Alpha variant phase, 29% (2/7) in the Delta variant phase, and 41% (11/27) in the Omicron variant phase compared to 6.1% (228,103/3,755,205), 12% (413,685/3,443,364), and 41.7% (1,784,951/4,285,549) in the general population, respectively. In addition, 31% (20/64, Alpha), 19% (3/16, Delta), and 30% (11/37, Omicron) of exposure-notified app users reported receiving mandatory quarantine orders by manual contact tracing or through a recommendation by a health care professional.In constantly evolving pandemic contexts, the effectiveness of digital proximity-tracing apps in contributing to mitigating pandemic spread should be reviewed regularly and adapted based on changing requirements. The WHO/ECDC framework allowed us to assess relevant domains of digital proximity tracing in a holistic and systematic approach. Although the Swisscovid app mostly worked, as reasonably expected, our analysis revealed room for optimizations and further performance improvements. Future implementation of digital proximity-tracing apps should place more emphasis on social, psychological, and organizational aspects to reduce bottlenecks and facilitate their use in pandemic contexts.
A metalens-based analog computing system for ultrasonic Fourier transform calculations

Wave-based analog computing is a new computing paradigm heralded as a potentially superior alternative to existing digital computers. Currently, there are optical and low-frequency acoustic analog Fourier transformers. However, the former suffers from phase retrieval issues, and the latter is too physically bulky for integration into CMOS-compatible chips. This paper presents a solution to these problems: the Ultrasonic Fourier Transform Analog Computing System (UFT-ACS), a metalens-based analog computer that utilizes ultrasonic waves to perform Fourier transform calculations. Through wave propagation simulations on MATLAB, the UFT-ACS has been shown to calculate the Fourier transform of various input functions with a high degree of accuracy. Moreover, the optimal selection of parameters through sufficient zero padding and appropriate truncation and bandlimiting to minimize errors is also discussed.
Analysis and Design of Power System Transformer Standard Based on Knowledge Graph

The transformer can convert one kind of electric energy such as AC current and AC voltage into another kind of electric energy with the same frequency. Knowledge graph (KG) can describe various entities and concepts in the real world and their relationships, and it can be considered as a semantic network for power system transformer. Hence, it is of vital importance to analyze and design the power system transformer standard based on the knowledge graph. To this end, we firstly examine the power system transformer with one KG node and one eavesdropper E, where the eavesdropper E can overhear the network from the source, which may cause physical-layer secure issue and an outage probability event. To deal with the issue, we analyze and design the system secure performance under the eavesdropper and define the outage probability for system security, by providing analytical expression of outage probability. We further investigate the power system transformer with multiple KG nodes which can help strengthen the system security and reliability. For such a system, we analyze and design the system secure performance under the eavesdropper and define the outage probability for system security, by providing analytical expression of outage probability. Finally, we give some simulations to analyze the impact of secure transformer standard on the power system, and verify the accuracy of our proposed analytical expression for the the power system transformer standard based on the knowledge graph.
Information Support of Social Audit

The process of decision making based on analytical information in social audit is described in this paper. Possibility of data-driven models using during decision making on different levels of management is considered. Analysis of the mechanisms of formation and use of information support provides an opportunity to optimize the processes of collection, processing, storage and use of information in various areas, in particular in social audit. To improve the order of formation of information support of social audit, information requests at different levels of government are analyzed: global; macro levels; meso levels; micro level, which includes strategic, tactical and operational sublevels. The conceptual model of information support of social audit developed on this basis, with the description of its tasks, functions, components and the order of formation of information base, provides reception of the full and reliable information necessary for acceptance of the corresponding administrative decisions of a social direction.
Optical tomography dynamics induced by qubit-resonator interaction under intrinsic decoherence

A superconducting circuit with a qubit and a resonator coupled via a two-photon interaction is considered. When the resonator is initially in a superposition of coherent states, optical tomography and quantum coherence dynamics are examined in the context of intrinsic decoherence. The results reveal that optical tomography is a good quantifier of the quantum coherence produced by the qubit-resonator interaction. The effects of qubit-resonator detuning and intrinsic decoherence on the dynamics of optical tomography distributions for coherent and even coherent states are investigated. The dynamics of optical tomography distributions are highly dependent on detuning and intrinsic decoherence. Our numerical simulations reveal that there is a relation between the optical tomography and the generated quantum coherence. When the qubit-resonator detuning and intrinsic decoherence are augmented, the amplitude and intensity, as well as the structure of the optical tomography, change substantially.
Edge intelligent collaborative privacy protection solution for smart medical

In the era of big data, competent medical care has entered people’s lives. However, the existing intelligent diagnosis models have low accuracy and poor universality. At the same time, there is a risk of privacy leakage in the process of health monitoring and auxiliary diagnosis. This paper combines edge computing and federated learning ensure model accuracy and protect patient privacy by proposing an Edge intelligent collaborative privacy protection solution for smart medical (EICPP). First, we offer a lightweight edge intellectual collaborative federated learning framework named KubeFL to support health monitoring and auxiliary diagnosis; secondly, we design a federated learning training model based on device-edge-cloud layering, with complete accuracy of up to 95.8%; Finally, a differential privacy algorithm for edge-cloud model transmission is proposed, which can exchange a lower accuracy loss for solid privacy protection.
A defense method against backdoor attacks on neural networks

Due to computational complexities of artificial neural networks (ANNs), there is an increasing demand for third parties and MLaaS (machine learning as a service) to take charge of the training procedure. Therefore, making ANNs robust against adversarial attacks has received a lot of attention. Backdoor attacks, which causes targeted mis-classification while the accuracy on clean data is not affected, are among the most efficient attacks. In this paper, we propose a method called link-pruning with scale-freeness (LPSF), in which the dormant threatening links from the neurons in the input layer to other neurons of feed-forward neural network are eliminated according to the information gained from a portion of clean input data and the essential links are strengthened by changing the fully-connected networks to scale-free structures. To the best of our knowledge, it is the first defense method that makes the network significantly robust against backdoor attack (BD) before the network is attacked. LPSF is evaluated on feed-forward neural networks and with malicious MNIST, FMNIST, handwritten Chinese characters and HODA datasets. Through LPSF strategy, we achieve a sufficiently high and stable accuracy on clean data and an exceeding reduction range of 50%−94% for attack success rate.
Patch-NetVLAD+: Learned patch descriptor and weighted matching strategy for place recognition

Visual Place Recognition (VPR) in areas with similar scenes such as urban or indoor scenarios is a major challenge. Existing VPR methods using global descriptors have difficulty capturing local specific region (LSR) in the scene and are therefore prone to localization confusion in such scenarios. As a result, finding the LSRs that are critical for location recognition becomes key. To address this challenge, we introduced Patch-NetVLAD+, which was inspired by patch-based VPR researches. Our method proposed a fine-tuning strategy with triplet loss to make NetVLAD suitable for extracting patch-level descriptors. Moreover, unlike existing methods that treat all patches in an image equally, our method extracts patches of LSR, which present less frequently throughout the dataset, and makes them play an important role in VPR by assigning proper weights to them. Experiments on Pittsburgh30k and Tokyo247 datasets show that our approach achieved up to 9.3% performance improvement than existing patch-based methods.
An image layered scrambling encryption algorithm based on a novel discrete chaotic map

At present, the image encryption algorithm based on the bit-plane has the problem that the encryption speed and the encryption effect cannot be well balanced. To address the problem, an image layered scrambling encryption algorithm is proposed in this paper. First, a new one-dimensional Cosine Logistic compound chaotic map (1DCLC) is proposed. Several characteristics of the new chaotic map are analyzed by using various measures, such as bifurcation diagram, Lyapunov exponent, sample entropy etc. And the results show that the novel chaotic map has a better chaotic performance. Second, based on the chaotic map, an image encryption algorithm is proposed. The algorithm first performs an XOR encryption operation on the image. Second, the layered scrambling is applied to the XOR encrypted image. Then the layered and scrambled image is recombined for the second XOR encryption operation. In particular, in the layered scrambling, not only can the position of the pixel be changed but also the value of the pixel can be modified, which improves the security of the algorithm. In addition, in order to defend against various plaintext attacks and increase the security of the overall algorithm, the Hash function is introduced to update the key of the proposed algorithm. Finally, the original algorithm is improved and the improved algorithm's ability to resist differential attacks is analyzed. The experimental simulation and security test results show that the proposed encryption algorithm has a comparable encryption effect and a faster encryption speed.
A Machine Learning Algorithm for Supplier Credit Risk Assessment Based on Supply Chain Management

In today’s complex and ever-changing world, a distribution network in lending impact analysis is an evaluation of a client’s procedures, rules, and financial well-being to evaluate as considerable risk and it provides to the contracting company. A creditor’s capability to pay the current lender’s obligations is considered while doing a lender’s threat assessment. Traditionally, it refers to the concern that the borrower may not be able to collect the sequence and interest. The challenges in lenders’ threat assessment are a lack of adequate data storage and retrieval, problematic delays caused by a lack of access to the relevant data at the right time, extended lead times that lead their shipments at risk, and demand for speedier deliveries. This paper introduces a machine learning-based linear regression algorithm (ML-LRA) for supplier credit risk (SCR) assessment based on supply chain management (SCM) in credit risk frameworks that depend significantly on modeling ML. Regression models are logistical constraints that can be used to simulate the impacts of multiple variables on a customer’s creditworthiness. The chain of distribution forecasting tool assesses specific decisions based on assumptions in variability. As a result of the findings in this study, it can be assumed that ML-LR approaches have a significant role in a variety of business processes such as supplier selection, risk prediction along with the supply chain, and demand and sales estimation. Finally, the study’s consequences for the most critical constraints and obstacles are examined to enhance the supply chain management system and ensure overall system sustainability.
Quantum information transfer between a two-level and a four-level quantum systems

Quantum mechanics provides a disembodied way to transfer quantum information from one quantum object to another. In theory, this quantum information transfer can occur between quantum objects of any dimension, yet the reported experiments of quantum information transfer to date have mainly focused on the cases where the quantum objects have the same dimension. Here, we theoretically propose and experimentally demonstrate a scheme for quantum information transfer between quantum objects of different dimensions. By using an optical qubit-ququart entangling gate, we observe the transfer of quantum information between two photons with different dimensions, including the flow of quantum information from a four-dimensional photon to a two-dimensional photon and vice versa. The fidelities of the quantum information transfer range from 0.700 to 0.917, all above the classical limit of 2/3. Our work sheds light on a new direction for quantum information transfer and demonstrates our ability to implement entangling operations beyond two-level quantum systems.
Capacity of entanglement for a nonlocal Hamiltonian

The notion of capacity of entanglement is the quantum information theoretic counterpart of the heat capacity which is defined as the second cumulant of the entanglement spectrum. Given any bipartite pure state, we can define the capacity of entanglement as the variance of the modular Hamiltonian in the reduced state of any of the subsystems. Here, we study the dynamics of this quantity under non-local Hamiltonian. Specifically, we address the question: Given an arbitrary non-local Hamiltonian what is the capacity of entanglement that the system can possess? As an useful application, we show that the quantum speed limit for creating the entanglement is not only governed by the fluctuation in the non-local Hamiltonian, but also depends inversely on the time average of square root of the capacity of entanglement. Furthermore, we discuss this quantity for a general self-inverse Hamiltonian and provide a bound on the rate of the capacity of entanglement. Towards the end, we generalise the capacity of entanglement for bipartite mixed states based on the relative entropy of entanglement and show that the above definition reduces to the capacity of entanglement for pure bipartite states. Our results can have several applications in diverse areas of physics.
SalientVR

Mobile 360° video streaming has grown significantly in popularity but the quality of experience (QoE) suffers from insufficient wireless network bandwidth. The state-of-the-art solutions are limited by the temporal correlation assumption. Recent studies are aware of the potential of saliency to further QoE improvement, but several fundamental challenges about saliency judgment, saliency acquirement, and quality adaptation are still not fully addressed. To solve these challenges, we present SalientVR, a saliency-driven mobile 360° video streaming system integrated with gaze information. We design (i) a precise gaze-driven saliency judging criterion for mobile VR viewers, (ii) two pragmatic gaze-driven, tile-level saliency acquiring methods based on cross-user similarity and a specific content-aware deep neural network respectively, and (iii) a lightweight saliency-aware quality adaptation algorithm with a motion-assisted online correction, which is robust to wireless bandwidth vagaries and saliency bias. Moreover, we contribute a gaze-annotated dataset and a gaze-driven quality assessment metric for 360° videos. By extensive prototype evaluations (based on dataset tests and user studies), compared to alternatives, SalientVR significantly enhances the video quality and reduces the rebuffering ratio over 4G/LTE network emulations and in the wild, which achieves a 43.68% QoE improvement.