Learning with softmax cross-entropy on one-hot labels often leads to overconfident predictions and poor robustness under noise or perturbations. Label smoothing mitigates this by redistributing some confidence uniformly, but treats all samples equally, ignoring intra-class variability. We propose a hybrid quantum–classical framework that leverages quantum non-determinism to refine data labels into probabilistic ones, offering more nuanced, human-like uncertainty representations than label smoothing or Bayesian approaches. A variational quantum circuit (VQC) encodes inputs into multi-qubit quantum states, using entanglement and superposition to capture subtle feature correlations. Measurement via the Born rule extracts probabilistic soft labels that reflect input-specific uncertainty. These labels are then used to train a classical convolutional neural network (CNN) with soft-target cross-entropy loss. On MNIST and Fashion-MNIST, our method improves robustness—achieving up to 50% higher accuracy under noise—while maintaining competitive clean-data accuracy. It also enhances model calibration and interpretability, as CNN outputs better reflect quantum-derived uncertainty. This work introduces Quantum Probabilistic Label Refining, which bridges quantum measurement and classical deep learning to enable robust training via refined, correlation-aware labels, without architectural changes or adversarial techniques.

Current deep learning-based shadow detection and removal algorithms exhibit critical limitations in adequately modeling the physical properties of shadows and exploring their inherent luminance and chrominance characteristics. To tackle these challenges, this paper proposes a shadow detection and removal model via illumination estimation and chromaticity guidance (CI-GAN). Specifically, a three-branch generative adversarial network is designed to explicitly model shadow physical characteristics through simultaneous shadow mask generation, shadow brightness simulation, and inverse illumination estimation, which establishes an interpretable framework for single-image shadow removal. To prevent the loss of shadow feature information, multi-dimensional features from the three-branch network is integrated into the shadow removal encoder by cross-channel stacking. Then, an encoder-decoder structure based on the Patch-based Generative Adversarial Network (PatchGAN) is used to generate high-quality shadow-free images and mitigate the model collapse problem during training. Additionally, we design a shadow detection loss that integrates the pixel loss, the feature loss and the cross loss to generate high-precision shadow masks. A perceptual loss and an edge loss are designed to preserve the details and edge structure of images. Finally, we adopt an adaptive weighted fusion strategy to integrate multiple loss functions, thereby ensuring the robustness and generalization ability of the model. Extensive experiments on public datasets demonstrate that our method outperforms the state-of-the-art methods in terms of preventing shadow brightness distortion and chromaticity inconsistency.

In the current noisy intermediate-scale quantum (NISQ) Era, Quantum Computing faces significant challenges due to noise, which severely restricts the application of computing complex algorithms. Superconducting quantum chips, one of the pioneer quantum computation technologies, introduce additional noise when moving qubits to adjacent locations for operation on designated two-qubit gates. The current compilers rely on decision models that either count the swap gates or multiply the gate errors when choosing swap paths at the routing stage. Our research has unveiled the overlooked situations for error propagations through the circuit, leading to accumulations that may affect the final output.

Quantum computers in the current noisy intermediate-scale quantum (NISQ) era face two major limitations - size and error vulnerability. Although quantum error correction (QEC) methods exist, they are not applicable at the current size of computers, requiring thousands of qubits, while NISQ systems have nearly one hundred at most. One common approach to improve reliability is to adjust the compilation process to create a more reliable final circuit, where the two most critical compilation decisions are the qubit allocation and qubit routing problems. We focus on solving the qubit allocation problem and identifying initial layouts that result in a reduction of error. To identify these layouts, we combine reinforcement learning with a graph neural network (GNN)-based Q-network to process the mesh topology of the quantum computer, known as the backend, and make mapping decisions, creating a Graph Neural Network Assisted Quantum Compilation (GNAQC) strategy. We train the architecture using a set of four backends and six circuits and find that GNAQC improves output fidelity by roughly 12.7% over pre-existing allocation methods.

Summary Nowadays, wireless radio signals are ubiquitous and are around us; some signals pass through us, and some reflect off us. Substantial advancements in recent years demonstrate that such signals are utilized for diverse emerging applications, including people activity, motion watches, healthcare, and so forth. A few questions would be that may raise severe concerns in future cybersecurity and private domains. For example, what if Wi‐Fi signals are utilized to watch a person doings and actions, which are mostly without the person's authorization and authentication. How far such signal utilization can attack privacy intrusively, silently, more particularly, what/where we do, say, command, see, write, draw, go, perform, everything can be known. In this article, we investigate watching human activities by leveraging Wi‐Fi signals and discuss a few application prototypes. We attempt to learn whether or not attackers have the ability to passively watch our Internet activity as well as physical activities and motions through Wi‐Fi. With all‐new advances, one must be aware that cyberattackers may apply unauthorized use of these advances to their benefit. We discuss some of the countermeasures and approaches to mitigate these risks with Wi‐Fi signal leveraging.

Contact tracing is the approach to identifying physical contact between human beings using a variety of data such as personal details and locations to discover the potential infection of diseases. Since the outbreak of the COVID-19 pandemic, contact tracing has been used extensively to quarantine the people at risk to stop the spread. Moreover, the data collected during contact tracing are typical spatiotemporal data, which can be used to study the disease and discover the spread pattern. However, both traditional labor-intensive and modern digital-based approaches have limitations in terms of cost and privacy concerns. In this paper, we proposed GeauxTrace, a Blockchain-based privacy-protecting contact tracing platform, which separates private data from proof of contact. Sensitive data collected by the front-end app via Bluetooth-based methods are stored locally, and only the proofs of contacts are uploaded onto the immutable private blockchain, which forms a global contact graph at the backend. Our approach not only enables multi-hop risky users to be notified but also reveals the infection patterns via the global graph, which could help study diseases and assist the policymaker. Our implementation shows the feasibility of the proposed platform in real-world scenarios and achieves the performance of 20-30 user requests per second.

Summary In recent times, many location‐based service providers (LBSPs) choose to outsource data query services to third‐party cloud service providers (CSPs). This allows users to easily search for points of interests (POIs), such as restaurants and parking lots in their vicinity, using their mobile devices and in‐vehicle infotainment units. Skyline query is one potential technique to be deployed for road networks. However, the untrusted CSPs may forge or omit query results, intentionally or not. Therefore, in this article, we posit that by observing the unique properties of skyline query results in road networks, we can bind each POI with four nearby POIs with special properties using signature chain technology. Our proposed approach not only provides users with skyline query result authentication ability over the road network, but also have low communication overhead. Specifically, the overhead analysis and experimental results show that our proposed approach decreases the communication overhead.

With the emergence and development in 5G cellular systems and ultra dense networks, recent years have witnessed a surge of interest in Dense Wireless Sensor Network (DWSN) in academia and industry. Although many existing works have explored the Steiner tree problem (STP) like multicast and topology design in WSN scenarios, new challenges introduced by growing node number and redundant links receive much less attention yet. The complexity of the topology usually makes algorithms that find the optimal solution unacceptable in terms of running time. Meanwhile, other approximate algorithms with lower time complexity cause too much performance loss. In this paper, we propose a physarum-based pre-processing algorithm called PBA for boosting up a traditional algorithm that can find STP’s optimal solution. The proposed algorithm could select crucial edges and vertices quickly by imitating the physarum’s foraging process. Our algorithm could eliminate edges and vertices irrelevant to the optimal solution by 80% and 50% in the original graph, respectively. This simplified graph produced by PBA is used in the traditional algorithm for obtaining the final result. PBA could also be transformed into a distributed version with minor changes for large-scale topology. Simulation results demonstrate that the proposed algorithm can shorten the original algorithm’s running time by one to two orders of magnitude with less than 5% performance loss in general.

The frequent outbreaks of agriculture pests have caused heavy losses in crop production. And the small size and high similarity of agricultural pests bring challenges to the prompt and accurate pest detection using imaging technologies. The key impetus of this paper is to achieve a good balance between efficiency and accuracy for pest detection on the basis of agricultural image data mining. This paper proposes Pest-YOLO which is a real-time agriculture pest detection method based on the improved convolutional neural network (CNN) and YOLOv4. First, a squeeze-and-excitation attention mechanism module is introduced to CNN for mining image data, extracting key features, and suppressing unrelated features. Then, a cross-stage multi-feature fusion method is designed to improve the structure of feature pyramid network and path aggregation network, thus enhancing the feature expressiveness of small targets like pests. Finally, our Pest-YOLO realizes end-to-end real-time pest detection with high accuracy based on improved CNN and YOLOv4. We evaluate the performance of our method on a typical large-scale pest dataset including 28k images and 24 classes. Experimental results demonstrate that our method outperforms the state-of-the-art solutions including Faster R-CNN and YOLO-based detectors, and achieves good performance with 71.6% mAP and 83.5% Recall. The proposed method is effective and applicable for accurate and real-time intelligent pest detection without expertise feature engineering.

Quantum computation has taken over as one of the largest current research areas in computer architecture and information theory. With the potential to make a large number of factorization-based encryption methods obsolete, companies and governments around the globe are racing to build the first large-scale quantum computer. Currently, most quantum computers are noisy intermediate-scale quantum (NISQ), using a relatively small collection of unreliable qubits. While error correction methods exist, they require a large number of ancilla qubits to protect the data qubits which is not practical for use on current NISQ machines. However, following the Dowling-Neven Law, available qubits on a superconducting chip are growing at an exponential rate similar to Moore's Law. Looking toward larger scale quantum machines, we examine a method to increase usable qubit density of quantum machines implementing error correction by using quantum caches that utilize simpler error correction codes. Alternatively, this also allows for the design of reliable systems while meeting the performance and qubit requirements for quantum algorithms. We modify the Qiskit quantum simulation library to work with caches and investigate the effects of region size and topology on the swap characteristics of algorithm execution. We also present our results and discuss recommended topologies for each algorithm. Lastly, we present mix scale-out simulations to examine the impact of cache on future large-scale machines. The default central cache topology gains a maximum performance increase of 2.15 times compared to the worst topology, which creates a robust cache-aware quantum processor layout.

Different risk evaluation approaches exist to protect users from potentially malicious apps. However, while assessing risks, the existing approaches ignore users' functional needs, and that app funGuojun Wangctions help their developers compete in the marketplace. In this paper, we propose a function-centric risk assessment approach for Android apps. The proposed approach combines operation research and machine learning methods to calculate the risks of an app and offers five competitive apps in the same app-category. We evaluate the proposed approach using 1,377 apps in sixteen app-categories, obtained from the most popular app store of 2019 in China, “Ying Yong Bao.” The experimental evaluation demonstrates the feasibility of the proposed approach. This approach can help users select safe apps in the marketplace that offer competitive functions.

Recent studies identify that Deep learning Neural Networks (DNNs) are vulnerable to subtle perturbations, which are not perceptible to the human visual system but can fool the DNN models and lead to wrong outputs. These algorithms are the first efforts to move forward to secure deep learning by providing an avenue to train future defense networks. We propose the first hardware accelerator for adversarial attacks based on memristor crossbar arrays. Our design significantly improves the throughput of a visual adversarial perturbation system, which can further improve the robustness and security of future deep learning systems. Based on the algorithm uniqueness, we propose four implementations for the adversarial attack accelerator (A^3) to improve the throughput, energy efficiency, and computational efficiency.

Current reputation systems for peer-to-peer (P2P) file sharing systems either fail to utilize existing trust within social networks or suffer from certain attacks (e.g., free-riding and collusion). To handle these problems, we introduce a trust management system, called SocialLink, that utilizes social network and historical transaction links. SocialLink manages file transactions through both the social network and a novel weighted transaction network, which is built based on previous file transaction history. First, SocialLink exploits the trust among friends in social networks by enabling two friends to share files directly. Second, the weighted transaction network is utilized to 1) deduce the trust of the client on a server in reliably providing the requested file and 2) check the fairness of the transaction. In this way, SocialLink prevents potential misbehaving transactions (i.e., providing faulty files), encourages nodes to contribute file resources to non-friends, and avoids free-riding. Furthermore, the weighted transaction network helps SocialLink resist whitewashing, collusion and Sybil attacks. Extensive simulation demonstrates that SocialLink can efficiently ensure trustable and fair P2P file sharing and resist the aforementioned attacks.

Abstract The anti‐spoofing community has been intensively proposing new methods for defending against new web‐spoofing techniques. In this paper, we analyze the problems within current anti‐spoofing mechanisms, and propose a new SSL protected trust model. Then, we describe the attacks on SSL protected trusted communication. In this paper, we also propose the new Automatic Detecting Security Indicator scheme (ADSI) to defend against spoofing attacks on SSL protected web servers. In a secure transaction, ADSI will randomly choose a picture and embed it into the current web browser at a random place. This can be triggered by any security relevant event that has occurred on the browser, and then automatic checking will be performed on the current active security status. When a mismatch of embedded pictures is detected, an alarm goes off to alert the users. Since an adversary is hard to replace or mimic the randomly embedded picture, the web‐spoofing attack cannot be mounted easily. In comparison with existing schemes, (1) the proposed scheme has the weakest security assumption, and places a very low burden on the user by automating the process of detection and recognition of web‐spoofing for SSL‐enabled trusted communication; (2) it has little intrusiveness on the browser; and (3) it can be implemented in a trusted PC at an Internet Cafe. Copyright © 2009 John Wiley & Sons, Ltd.

The primary goal of the secure socket layer protocol (SSL) is to provide confidentiality and data integrity between two communicating entities. Since the most computationally expensive step in the SSL handshake protocol is the server's RSA decryption, it is introduced that the proposed secret exchange algorithm can be used to speedup SSL session initialization. The optimization strategy, which is based on the constrained model considering the user's requirements for Quality of Service (QoS), such as security ranking, focuses on the optimal result in different public key size. It is also introduced that the parameter is optimized when integrating user's requirements for Internet QoS such as the stability of the system and the tolerable response time. Finally, the proposed algorithm is evaluated to be practical and efficient through both analysis and simulation studies.