Fuzzing is widely used for software robustness testing. However, its application in commercial communication modules remains limited due to several key challenges, including labor-intensive template generation, lack of coverage collection support, limited testing performance, and inconsistencies between practical hardware and software CI/CD processes. In collaboration with China Mobile IoT, we present FuzzCM, a comprehensive protocol fuzzing framework tailored for commercial communication modules. FuzzCM employs a Retrieval-Augmented Generation (RAG)-enhanced large language model (LLM) to automate template generation and utilizes GPIO-based instrumentation for efficient runtime coverage data collection. Additionally, it leverages a knowledge base constructed from prior tests to guide hybrid mutation strategies and integrates CI/CD across both software and hardware layers, enabling continuous and environment-aware testing. We conducted an industrial practice with FuzzCM on five LTE Cat.1 bis modules, identifying 21 previously unknown bugs, 15 of which have been fixed. The results demonstrate that FuzzCM outperforms both manual methods and the Peach* approach, achieving average coverage improvements of 51% and 29%, respectively, with overall coverage reaching 85%.

Recognizing biota in microscopic activated sludge images is essential for effective wastewater treatment. General object detection methods face challenges in accurately recognizing the biota with various sizes and shapes, especially under constraints of computational efficiency in industry practice. This paper presents a framework called BiotaFormer to detect biota in microscopic activated sludge images for wastewater treatment. BiotaFormer recognizes and monitors biota in microscopic images to evaluate the condition of activated sludge, which optimizes the subsequent treatment process. To evaluate BiotaFormer, we introduce the Biota-12, the first dataset specifically for biota detection. Experimental results show that BiotaFormer has significant advantages in performance, demonstrating its overall effectiveness and efficiency in industry practice

Current 3D detection methods, whether single-modal or multimodal, face notable limitations. Single-modal detectors, using either camera or LiDAR, struggle with spatial accuracy and object differentiation due to insufficient depth information or difficulty distinguishing semantically similar objects. Existing multimodal fusion techniques, while improving performance, often suffer from high computational costs, false positives, and complex architectures, especially when utilizing anchor-based pipelines. To address these challenges, we propose an efficient pointwise fusion method that directly extracts point features from enhanced RGB images and fuses them with corresponding point cloud features, preserving essential spatial and semantic information. This fused data is then processed through a three-dimensional neural network, significantly improving inference speed and detection performance. Our framework is designed for multi-class 3D object detection, leveraging the complementary strengths of LiDAR and camera data without the need for multiple backbones or complex synchronization steps. Extensive experiments on the KITTI benchmark demonstrate that the proposed method outperforms state-of-the-art LiDAR-camera fusion techniques, achieving 92.5% AP for 3D detection and 95.41% AP for BEV detection, making it particularly suitable for autonomous driving systems. These results highlight the effectiveness of the proposed fusion strategy in balancing accuracy, computational efficiency, and robustness in complex 3D environments.

Network protocol reverse engineering is crucial for a wide range of security applications. Many existing techniques accomplish this task by analyzing network traces. However, these methods globally cluster messages and analyze each cluster separately, which causes the loss of valuable field information. To address this problem, we present MDIplier, a protocol reverse engineering tool that leverages the hierarchical structure of protocol messages and performs tailored analysis at each message layer. MDIplier performs an iterative inference process. During each iteration, it identifies the message delimiter for layer separation and infers the format for each layer separately, optimizing the use of available field information. Our evaluation of eight widely used protocols shows that MDIplier outperforms state-of-the-art methods. It identifies fields with a perfection score 4.6×, 1.4×, 5.8×, and 1.8× higher than that of Netzob, Netplier, FieldHunter, and BinaryInferno, respectively. Furthermore, the experiments on proprietary protocols used in three IoT devices demonstrate the effectiveness of MDIplier in real-world scenarios.

Accurate 3-D object detection is vital in autonomous driving. Traditional LiDAR models struggle with sparse point clouds. We propose a novel approach integrating LiDAR and camera data to maximize sensor strengths while overcoming individual limitations for enhanced 3-D object detection. Our research introduces the channelwise and spatially guided multimodal feature fusion network (CSMNET) for 3-D object detection. First, our method enhances LiDAR data by projecting it onto a 2-D plane, enabling the extraction of class-specific features from a probability map. Second, we design class-based farthest point sampling (C-FPS), which boosts the selection of foreground points by utilizing point weights based on geometric or probability features while ensuring diversity among the selected points. Third, we developed a parallel attention (PAT)-based multimodal fusion mechanism achieving higher resolution compared to raw LiDAR points. This fusion mechanism integrates two attention mechanisms: channel attention for LiDAR data and spatial attention for camera data. These mechanisms enhance the utilization of semantic features in a region of interest (ROI) to obtain more representative point features, leading to a more effective fusion of information from both LiDAR and camera sources. Specifically, CSMNET achieves an average precision (AP) in bird’s eye view (BEV) detection of 90.16% (easy), 85.18% (moderate), and 80.51% (hard), with a mean AP (mAP) of 85.12%. In 3-D detection, CSMNET attains 82.05% (easy), 72.64% (moderate), and 67.10% (hard) with an mAP of 73.75%. For 2-D detection, the scores are 95.47% (easy), 93.25% (moderate), and 86.68% (hard), yielding an mAP of 91.72% for the KITTI dataset.

In linguistics, the uncertainty of context due to polysemy is widespread, which attracts much attention. Quantum-inspired complex word embedding based on Hilbert space plays an important role in natural language processing (NLP), which fully leverages the similarity between quantum states and word tokens. A word containing multiple meanings could correspond to a single quantum particle which may exist in several possible states, and a sentence could be analogous to the quantum system where particles interfere with each other. Motivated by quantum-inspired complex word embedding, interpretable complex-valued word embedding (ICWE) is proposed to design two end-to-end quantum-inspired deep neural networks (ICWE-QNN and CICWE-QNN representing convolutional complex-valued neural network based on ICWE) for binary text classification. They have the proven feasibility and effectiveness in the application of NLP and can solve the problem of text information loss in CE-Mix [1] model caused by neglecting the important linguistic features of text, since linguistic feature extraction is presented in our model with deep learning algorithms, in which gated recurrent unit (GRU) extracts the sequence information of sentences, attention mechanism makes the model focus on important words in sentences and convolutional layer captures the local features of projected matrix. The model ICWE-QNN can avoid random combination of word tokens and CICWE-QNN fully considers textual features of the projected matrix. Experiments conducted on five benchmarking classification datasets demonstrate our proposed models have higher accuracy than the compared traditional models including CaptionRep BOW, DictRep BOW and Paragram-Phrase, and they also have great performance on F1-score. Eespecially, CICWE-QNN model has higher accuracy than the quantum-inspired model CE-Mix as well for four datasets including SST, SUBJ, CR and MPQA. It is a meaningful and effictive exploration to design quantum-inspired deep neural networks to promote the performance of text classification.

A quantum circuit learning approach is studied to carry out the fast-fitting of Gaussian functions. First, a parameterized structure is designed for quantum circuits based on the boson sampling model. And then, the training procedure of exploiting gradient-based optimizations is presented to iteratively update the gradient of the loss function concerning circuit parameters. For efficiency, two kinds of circuit loss, the kernel maximum mean discrepancy and the mean absolute error, are used in the training procedure, which are both competent to achieve quantum circuit learning well. It is significant that the two circuit losses assist in reducing the variance to$2.54 \times 10^{-6}$and$6.91 \times 10^{-6}$, respectively. Finally, a kind of quantum circuit fixed structure is developed with the boson sampling model that can decrease the model complexity as the circuit depth d grows. Sets of experiments have been conducted to evaluate the proposed quantum circuit learning scheme, and demonstrate that our parameterized approach is efficient and promising, and it is worth looking forward to solving practical application problems with quantum computers since valid quantum circuits for Gaussian function fast-fitting can be designed indeed.

Deep neural networks (DNNs) have achieved great success in image classification, but can be very vulnerable to adversarial attacks with small perturbations to images. To improve adversarial image generation for DNNs, we develop a novel method, called mFI-PSO, which utilizes a Manifold-based First-order Influence measure for vulnerable image and pixel selection and the Particle Swarm Optimization for various objective functions. Our mFI-PSO can thus effectively design adversarial images with flexible, customized options on the number of perturbed pixels, the misclassification probability, and the targeted incorrect class. Experiments demonstrate the flexibility and effectiveness of our mFI-PSO in adversarial attacks and its appealing advantages over some popular methods.

Recently, unsupervised representation learning has been extremely successful in the field of natural language processing. More and more pre-trained language models are proposed and achieved the most advanced results especially in machine reading comprehension. However, these proposed pre-trained language models are huge with hundreds of millions of parameters that have to be trained. It is quite time consuming to use them in actual industry. Thus we propose a method that employ a distillation traditional reading comprehension model to simplify the pre-trained language model so that the distillation model has faster reasoning speed and higher inference accuracy in the field of machine reading comprehension. We evaluate our proposed method on the Chinese machine reading comprehension dataset CMRC2018 and greatly improve the accuracy of the original model. To the best of our knowledge, we are the first to propose a method that employ the distillation pre-trained language model in Chinese machine reading comprehension.

The authors propose an efficient spectrum sharing scheme in cooperative cognitive radio networks, where an energy‐constrained secondary transmitter (ST) first scavenges radio frequency (RF) energy from the received primary signals, and then the ST assists the primary transmission to obtain the opportunity of spectrum access. Specifically, the ST can forward the primary signal with its own signal by adopting both the Alamouti coding technique and superposition scheme only if the harvested energy is sufficient while the primary data is decoded correctly by the ST. Otherwise, the ST will continue to harvest RF energy. The authors use the discrete Markov chain to model the processes of charging and discharging of the battery. Moreover, two different joint decoding and interference cancellation schemes are employed at the receivers to restore the desired data. Closed‐form expressions of outage probabilities for both the primary and secondary systems are derived. Aiming to minimise the outage probability of the secondary system with guaranteeing the primary transmission, an optimal power allocation factor for the ST is determined by Monte‐Carlo simulation. Numerical results demonstrate that the proposed scheme can effectively improve the transfer performance of the secondary system while realising the transfer requirement of the primary system.

In this article, we present a novel radial visualization of IDS alerts, named IDSPlanet, which helps administrators identify false positives, analyze attack patterns, and understand evolving network conditions. Inspired by celestial bodies, IDSPlanet is composed of Chrono Rings, Alert Continents, and Interactive Core. These components correspond with temporal features of alert types, patterns of behavior in affected hosts, and correlations amongst alert types, attackers and targets. The visualization provides an informative picture for the status of the network. In addition, IDSPlanet offers different interactions and monitoring modes, which allow users to interact with high-interest individuals in detail as well as to explore overall pattern.

A quantum TITO (Two-Input-Two-Output) diversity transmission scheme for the entangled-state message is proposed by generalizing the wireless transmission technique to the quantum field. The TITO quantum teleportation can be implemented with non-maximally entangled Bell states in order to enhance the security and fidelity of the quantum channel, in which a quantum signal sequence with n entangled quantum states can be transmitted through the TITO quantum channel by applying the diversity technology. The analysis shows that the quantum TITO transmission can be achieved securely and with an expected fidelity.

A novel quantum anonymous communication scheme is proposed on the basis of Dining Cryptographers (DC) Problem and nonmaximally entangled qubit pair. The scheme takes advantage of quantum-mechanical impossibility of local unitary transformation between certain nonmaximally entangled states to provide truly random number which can be brightly used in anonymous communication protocols based on DC-Nets. The analysis and discussions demonstrate that the proposed quantum anonymous communication scheme can be performed securely with high capacity and untraceability. The scheme can also be extended to a (2, 2) quantum secret sharing (QSS) scheme.

A novel (n, n) scheme of multiparty quantum group signature of classical or quantum message is proposed based on the discrete quantum Fourier transform. The generation and verification of the signature can be processed only if all the n participants work in concert. Moreover, a new verification manner, in which the message owner and the signing group separately verify the signature on both side by using the entangled state of EPR sequence, is involved in this paper. Security analysis shows that it is feasible to achieve a secure quantum group signature with the secure quantum computation.