Recognizing and navigating client resistance is critical for effective mental health counseling, yet detecting such behaviors is particularly challenging in text-based interactions.Existing NLP approaches oversimplify resistance categories, ignore the sequential dynamics of therapeutic interventions, and offer limited interpretability.To address these limitations, we propose Psy-FIRE, a theoretically grounded framework capturing 13 fine-grained resistance behaviors alongside collaborative interactions.Based on PsyFIRE, we construct the ClientResistance corpus with 23,930 annotated utterances from real-world Chinese text-based counseling, each supported by context-specific rationales.Leveraging this dataset, we develop RECAP, a twostage framework that detects resistance and fine-grained resistance types with explanations.RECAP achieves 91.25% F1 for distinguishing collaboration and resistance and 66.58% macro-F1 for fine-grained resistance categories classification, outperforming leading promptbased LLM baselines by over 20 points.Applied to a separate counseling dataset and a pilot study with 62 counselors, RECAP reveals the prevalence of resistance, its negative impact on therapeutic relationships and demonstrates its potential to improve counselors' understanding and intervention strategies.

Bug localization refers to the process of identifying the locations of bugs in software through a series of testing and analysis methods during software development or maintenance. An important research direction focuses on localizing buggy components given a bug report. Existing bug localization methods can be broadly categorized into two approaches. The first is traditional information retrieval-based bug localization, which treats bug reports and source files as plain text and relies on keyword matching for localization. The second is deep learning-based bug localization, which leverages deep learning models to capture semantic information in bug reports and source files. Although deep learning models have advantages in semantic understanding, a significant semantic gap still exists between source files (written in programming languages) and bug reports (written in natural language), which impacts model performance. In this work, we propose a novel bug localization framework named WisdomLoc. This framework matches bug reports with the source files by extracting both information retrieval features and semantic features from the source files. Furthermore, WisdomLoc enhances the semantic representation of source files by incorporating natural language descriptions translated from the corresponding program instructions, using context-aware code translation techniques. This helps further bridge the semantic gap between source files and bug reports. Specifically, WisdomLoc consists of four modules. The first module extracts shallow semantic features from the source files based on their textual content and structural information. The second module captures deep semantic features by translating program instructions into natural language descriptions. The third module incorporates traditional IR-based software features. Finally, the fourth module integrates the outputs of the previous three modules to compute the final matching score between the bug reports and the source files. We evaluated the performance of WisdomLoc on four benchmark datasets. Experimental results demonstrate that WisdomLoc outperforms eight state-of-the-art models.

Routing is essential in communication as it determines the most efficient path for data packets to travel from the source to the destination, ensuring reliable network connectivity. However, with the emergence of cross-domain communication scenarios involving air, surface, and underwater environments, the traditional routing protocol—which considers only limited information including bandwidth or delay as static link cost during route selection—falls short of meeting the rapid and efficient demands of cross-domain heterogeneous communication. To address this issue, this paper proposes a dynamic cost intelligent routing algorithm. The algorithm incorporates an effective bandwidth estimation module and a link priority prediction module, which dynamically process the extracted link information from air, surface, and underwater communication networks. The processed data is then fed into the deep learning model, where it takes multi-dimensional link attribute information as input and outputs a one-dimensional cost corresponding to each link. The effective cost are then integrated back into the network, dynamically adjusted the routing decisions using the shortest path algorithm to optimize overall network performance. The algorithm ensures more reliable data transmission and efficient utilization of network resources. To simulate real-world scenarios, this study utilized the NS3 platform to construct a cross-domain simulation environment that encompasses radio communication, underwater acoustic communication, and optical communication across air, surface, and underwater domains. We conducted experiments by constructing a heterogeneous network consisting of 36 nodes. The results show that this algorithm outperforms the routing algorithms based on the traditional OSPF routing algorithm, and several other algorithms, in relation to throughput, packet loss rate and average delay.