Bi-directional Ontology Versioning BOV

his paper defines a new type of ontology versioning: Bi-directional Ontology Versioning: BOV. BOV provides bi-directional mappings and transformations between concepts in two ontology versions. BOV is identified by two levels mapping processes: linguistic mapping and structural mapping. BOV can satisfy the requirement of mapping in distributed environment

A robust modulation classification method using convolutional neural networks

Automatic modulation classification (AMC) is a core technique in noncooperative communication systems. In particular, feature-based (FB) AMC algorithms have been widely studied. Current FB AMC methods are commonly designed for a limited set of modulation and lack of generalization ability; to tackle this challenge, a robust AMC method using convolutional neural networks (CNN) is proposed in this paper. In total, 15 different modulation types are considered. The proposed method can classify the received signal directly without feature extracion, and it can automatically learn features from the received signals. The features learned by the CNN are presented and analyzed. The robust features of the received signals in a specific SNR range are studied. The accuracy of classification using CNN is shown to be remarkable, particularly for low SNRs. The generalization ability of robust features is also proven to be excellent using the support vector machine (SVM). Finally, to help us better understand the process of feature learning, some outputs of intermediate layers of the CNN are visualized

Automatic Truss Design with Reinforcement Learning

Truss layout design, namely finding a lightweight truss layout satisfying all the physical constraints, is a fundamental problem in the building industry. Generating the optimal layout is a challenging combinatorial optimization problem, which can be extremely expensive to solve by exhaustive search. Directly applying end-to-end reinforcement learning (RL) methods to truss layout design is infeasible either, since only a tiny portion of the entire layout space is valid under the physical constraints, leading to particularly sparse rewards for RL training. In this paper, we develop AutoTruss, a two-stage framework to efficiently generate both lightweight and valid truss layouts. AutoTruss first adopts Monte Carlo tree search to discover a diverse collection of valid layouts. Then RL is applied to iteratively refine the valid solutions. We conduct experiments and ablation studies in popular truss layout design test cases in both 2D and 3D settings. AutoTruss outperforms the best-reported layouts by 25.1% in the most challenging 3D test cases, resulting in the first effective deep-RL-based approach in the truss layout design literature.Comment: IJCAI2023. The codes are available at https://github.com/StigLidu/AutoTrus

Current Situation and Development Trend of Mobile Communication Systems

This paper introduces the development background of mobile communication and the development of mobilecommunication. It introduces the application principle, network structure, main technology, the advantages anddisadvantages of the three generations of mobile communication system respectively, and introduces the currentthird generation mobile communication system, including its technical support and research direction, analysis andcomparison of the European WCDMA system, the United States CDMA2000 system and China's TD-SCDMA systemtechnical characteristics. Finally, the development trend and prospect of future mobile communication system arediscussed

Coding the negative emotions of family members and patients among the high-risk preoperative conversations with the Chinese version of VR-CoDES

Abstract Background Little is known about family members' and patients' expression of negative emotions among high‐risk preoperative conversations. Objectives This study aimed to identify the occurrence and patterns of the negative emotions of family members and patients in preoperative conversations, to investigate the conversation themes and to explore the correlation between the negative emotions and the conversation themes. Methods A retrospective study was conducted using the Chinese version of Verona Coding Definitions of Emotional Sequences (VR‐CoDES‐C) to code 297 conversations on high‐risk procedures. Inductive content analysis was used to analyse the topics in which negative emotions nested. The χ2 Test was used to test the association between the cues and the conversation themes. Results The occurrence rate of family members' and patients' negative emotions was very high (85.9%), much higher when compared to most conversations under other medical settings. The negative emotions were mainly expressed by cues (96.4%), and cue‐b (67.4%) was the most frequent category. Cues and concerns were mostly elicited by family members and patients (71.6%). Negative emotions were observed among seven themes, in which ‘Psychological stress relating to illness severity, family's care and financial burden’ (30.3%) ranked the top. Cue‐b, cue‐c and cue‐d had a significant correlation (p < .001) with certain themes. Conclusions Family members and patients conveyed significantly more negative emotions in the high‐risk preoperative conversations than in other medical communications. Certain categories of cues were induced by specific emotional conversation contents. Patient Contribution Family members and patients contributed to data

Boosting Few-Shot Text Classification via Distribution Estimation

Distribution estimation has been demonstrated as one of the most effective approaches in dealing with few-shot image classification, as the low-level patterns and underlying representations can be easily transferred across different tasks in computer vision domain. However, directly applying this approach to few-shot text classification is challenging, since leveraging the statistics of known classes with sufficient samples to calibrate the distributions of novel classes may cause negative effects due to serious category difference in text domain. To alleviate this issue, we propose two simple yet effective strategies to estimate the distributions of the novel classes by utilizing unlabeled query samples, thus avoiding the potential negative transfer issue. Specifically, we first assume a class or sample follows the Gaussian distribution, and use the original support set and the nearest few query samples to estimate the corresponding mean and covariance. Then, we augment the labeled samples by sampling from the estimated distribution, which can provide sufficient supervision for training the classification model. Extensive experiments on eight few-shot text classification datasets show that the proposed method outperforms state-of-the-art baselines significantly.Comment: Accepted to AAAI 202

Tunable topological phase transition in soft Rayleigh beam system with imperfect interfaces

Acoustic metamaterials, particularly the topological insulators, exhibit exceptional wave characteristics that have sparked considerable research interest. The study of imperfect interfaces affect is of significant importance for the modeling of wave propagation behavior in topological insulators. This paper models a soft Rayleigh beam system with imperfect interfaces, and investigates its topological phase transition process tuned by mechanical loadings. The model reveals that the topological phase transition process can be observed by modifying the distance between imperfect interfaces in the system. When a uniaxial stretch is applied, the topological phase transition points for longitudinal waves decrease within a limited frequency range, while they increase within a larger frequency scope for transverse waves. Enhancing the rigidity of the imperfect interfaces also enables shifting of the topological phase transition point within a broader frequency range for longitudinal waves and a confined range for transverse waves. The transition of topologically protected interface modes in the transmission performance of a twenty-cell system is verified, which include altering frequencies, switching from interface mode to edge mode. Overall, this study provides a new approach and guideline for controlling topological phase transition in composite and soft phononic crystal systems.Comment: 39 pages,8 figure

XNET: A Real-Time Unified Secure Inference Framework Using Homomorphic Encryption

Homomorphic Encryption (HE) presents a promising solution to securing neural networks for Machine Learning as a Service (MLaaS). Despite its potential, the real-time applicability of current HE-based solutions remains a challenge, and the diversity in network structures often results in inefficient implementations and maintenance. To address these issues, we introduce a unified and compact network structure for real-time inference in convolutional neural networks based on HE. We further propose several optimization strategies, including an innovative compression and encoding technique and rearrangement in the pixel encoding sequence, enabling a highly efficient batched computation and reducing the demand for time-consuming HE operations. To further expedite computation, we propose a GPU acceleration engine to leverage the massive thread-level parallelism to speed up computations. We test our framework with the MNIST, Fashion-MNIST, and CIFAR-10 datasets, demonstrating accuracies of 99.14%, 90.8%, and 61.09%, respectively. Furthermore, our framework maintains a steady processing speed of 0.46 seconds on a single-thread CPU, and a brisk 31.862 milliseconds on an A100 GPU for all datasets. This represents an enhancement in speed more than 3000 times compared to pervious work, paving the way for future explorations in the realm of secure and real-time machine learning applications