To address the issues of high computational overhead, vulnerability to physical capture attacks, and insufficient in offering quantum resistance in traditional blockchain-based cross-domain authentication protocols for collaborative unmanned aerial vehicles (UAVs) missions, we propose APQE-CDA, a PUF-based post-quantum end-to-end cross-domain authentication protocol. The proposed protocol employs a lightweight bucket shifter PUF (BS-PUF) to reduce the computational overhead of UAVs while providing resilience against physical capture attacks. Static Random-Access Memory (SRAM) PUF-enhanced Kyber post-quantum cryptography is utilized to ensure secure quantum resistance. In APQE-CDA, we exploit the reversibility of BS-PUF to facilitate secure Challenge Response Pair (CRP) storage on the blockchain while achieving lightweight identity authentication through commutativity. We integrate SRAM PUF randomness into the Kyber key generation mechanism to eliminate key storage on UAVs while ensuring quantum-resistant security in the authentication interactions. Finally, Burrows Abadi Needham (BAN) logic, Real-or-Random (ROR), and informal security analysis are adopted to demonstrate the security of the proposed scheme. Experiments conducted on the Raspberry Pi 5 and FPGA platforms show superior computational efficiency, lower power consumption, and enhanced quantum-resistant security compared to existing solutions. Furthermore, extensive validation through the NS3 network simulator and Hyperledger Fabric framework substantiates the protocol’s authentication efficiency and practical viability in real environment conditions.

Safe and efficient path planning remains a key challenge for mobile robots, especially in cluttered and complex environments. Many existing methods struggle to balance global exploration and local optimization, often leading to suboptimal paths or high computational costs. This paper presents a bio-inspired Moss Growth Optimization (MGO) algorithm integrated with a graph-based approach for robotic navigation. The environment is efficiently represented using MAKLINK Graph Theory (MGT), enabling rapid initial path generation. MGO enhances global exploration by thoroughly searching the solution space, while an implicit memory mechanism supports local refinement of promising paths. To further improve search efficiency, a Line-of-Sight (LoS) Reduction technique minimizes unnecessary exploration. Simulation results and comparative evaluations show that the proposed approach achieves faster convergence and shorter paths compared to state-of-the-art methods in high-dimensional and cluttered spaces. With its nature-inspired design and memory-based updates, the MGO framework offers a robust, efficient, and scalable solution for optimal robot path planning in real-world autonomous navigation scenarios.

In order to enhance radar target recognition performance of high resolution range profile (HRRP) under low signal-to-noise ratio (SNR), a novel HRRP sequential recognition method utilizing the correlation among scattering centers is proposed in this letter. In this method, the correlative information among the prominent scattering centers is considered in covariance matrix and the sequential recognition is carried out through the long short-term memory (LSTM) network to fully excavate the information of HRRPs. Moreover, we introduce a noise-robust recognition algorithm to renew the Gaussian trained model by the estimated variance of the noise. Experimental results indicate that the proposed method can acquire higher recognition rates and better robustness by introducing correlative information and sequential processing.

We present a novel strategy for analyzing E-Com search logs called Differential Query Semantic Analysis (DQSA) to discover explicit interpretable knowledge from search logs in the form of a semantic lexicon that makes context-specific mapping from a query segment (word or phrase) to the preferred attribute values of a product. Evaluation on a set of size-related query segments and attribute values shows that DQSA can effectively discover meaningful mappings of size-related query segments to their preferred specific attributes and attributes values in the context of a product type. DQSA has many uses including improvement of E-Com search accuracy by bridging the vocabulary gap, comparative analysis of search intent, and alleviation of the problem of tail queries and products.

User representation learning is vital to capture diverse user preferences, while it is also challenging as user intents are latent and scattered among complex and different modalities of user-generated data, thus, not directly measurable. Inspired by the concept of user schema in social psychology, we take a new perspective to perform user representation learning by constructing a shared latent space to capture the dependency among different modalities of user-generated data. Both users and topics are embedded to the same space to encode users' social connections and text content, to facilitate joint modeling of different modalities, via a probabilistic generative framework. We evaluated the proposed solution on large collections of Yelp reviews and StackOverflow discussion posts, with their associated network structures. The proposed model outperformed several state-of-the-art topic modeling based user models with better predictive power in unseen documents, and state-of-the-art network embedding based user models with improved link prediction quality in unseen nodes. The learnt user representations are also proved to be useful in content recommendation, e.g., expert finding in StackOverflow.

A user-generated review document is a product between the item's intrinsic properties and the user's perceived composition of those properties. Without properly modeling and decoupling these two factors, one can hardly obtain any accurate user understanding nor item profiling from such user-generated data. In this paper, we study a new text mining problem that aims at differentiating a user's subjective composition of topical content in his/her review document from the entity's intrinsic properties. Motivated by the Item Response Theory (IRT), we model each review document as a user's detailed response to an item, and assume the response is jointly determined by the individuality of the user and the property of the item. We model the text-based response with a generative topic model, in which we characterize the items' properties and users' manifestations of them in a low-dimensional topic space. Via posterior inference, we separate and study these two components over a collection of review documents. Extensive experiments on two large collections of Amazon and Yelp review data verified the effectiveness of the proposed solution: it outperforms the state-of-art topic models with better predictive power in unseen documents, which is directly translated into improved performance in item recommendation and item summarization tasks.

User modeling is critical for understanding user intents, while it is also challenging as user intents are so diverse and not directly observable. Most existing works exploit specific types of behavior signals for user modeling, e.g., opinionated data or network structure; but the dependency among different types of user-generated data is neglected.

We propose to capture humans' variable and idiosyncratic sentiment via building personalized sentiment classification models at a group level. Our solution roots in the social comparison theory that humans tend to form groups with others of similar minds and ability, and the cognitive consistency theory that mutual influence inside groups will eventually shape group norms and attitudes, with which group members will all shift to align. We formalize personalized sentiment classification as a multi-task learning problem. In particular, to exploit the clustering property of users' opinions, we impose a non-parametric Dirichlet Process prior over the personalized models, in which group members share the same customized sentiment model adapted from a global classifier. Extensive experimental evaluations on large collections of Amazon and Yelp reviews confirm the effectiveness of the proposed solution: it outperformed user-independent classification solutions, and several state-of-the-art model adaptation and multi-task learning algorithms.

Motivated by the findings in social science that people's opinions are diverse and variable while together they are shaped by evolving social norms, we perform personalized sentiment classification via shared model adaptation over time.In our proposed solution, a global sentiment model is constantly updated to capture the homogeneity in which users express opinions, while personalized models are simultaneously adapted from the global model to recognize the heterogeneity of opinions from individuals.Global model sharing alleviates data sparsity issue, and individualized model adaptation enables efficient online model learning.Extensive experimentations are performed on two large review collections from Amazon and Yelp, and encouraging performance gain is achieved against several state-of-the-art transfer learning and multi-task learning based sentiment classification solutions.

Automatic detection and recognition of ship in satellite images is very important and has a wide array of applications. This paper concentrates on optical satellite sensor, which provides an important approach for ship monitoring. Graph-based fore/background segmentation scheme is used to extract ship candidant from optical satellite image chip after the detection step, from course to fine. Shadows on the ship are extracted in a CFAR scheme. Because all the parameters in the graph-based algorithms and CFAR are adaptively determined by the algorithms, no parameter tuning problem exists in our method. Experiments based on measured optical satellite images shows our method achieved good balance between computation speed and ship extraction accuracy.