In this paper, we propose a task-oriented semantic communication system based on the cloud-network-end (C-N-E) architecture to improve the energy efficiency of image transmission. Within the system, a cloud server provides storage and computation resources for image data collected by multiple cameras. The semantic information of an image is modeled as a scene graph, enabling the analysis of end-user interests. To reduce communication overhead, only useful semantic information relevant to user interests is transmitted. Considering the delay constraint, we formulate an optimization problem to minimize the total energy consumption by jointly selecting semantic information and allocating computation and communication resources. To solve this problem efficiently, an iterative algorithm based on optimal matching and sequential convex approximation is developed. Comparative simulations validate the efficacy of our algorithm.

Accurately detecting out-of-scope queries is a challenging task in task-oriented dialog systems. Most existing research focus on adding an outlier detector after classification or designing an open world classification to identify unknown intents. There is still a major performance gap on achieving high efficiency and accuracy based on above methods. In our research, we tend to solve this problem by constructing an out-of-scope class in the classification. We propose an explainable centroid-based fine-tuning method including a modified decision metric (MDM) and a centroid-based cosine loss (CCL) on Pre-trained Transformer models for optimization. This loss function builds on Copernican structure and assigns the same margin to each in-scope class to resolve an ambiguous configuration on out-of-scope detection. Moreover, cosine similarity is utilized to remove radial variations of centroids. Experimental results show that our proposed method achieves improvement compared to other baseline methods.

Notary cross-chain transaction technologies have obtained broad affirmation from industry and academia as they can avoid data islands and enhance chain interoperability. However, the increased privacy concern in data sharing makes the participants hesitate to upload sensitive information without the trust foundation of the external network. To address this issue, this paper proposes a differential private notary mechanism (DPNM) to preserve privacy in blockchain interoperations. It establishes a fully trusted notary organization to conduct data perturbation before replying query to the external blockchain network. In addition, the DPNM contains two built-in privacy budget allocation schemes: Efficiency priority scheme (EPS) and Privacy priority scheme (PPS). These schemes unify the privacy preferences among different nodes based on multi-node consensus in the decentralized environment. The EPS can generate noise linearly and work efficiently, and the PPS reflects better on nodes’ preferences. This paper utilizes several metrics including mechanism errors, elapsed time, latency, and gas consumption to evaluate the performance of DPNM compared to the traditional mechanisms. The experiment results indicate that the proposed mechanism can meet privacy preferences among different nodes and provide better utility with little extra cost.

Text corpora annotated with language-related properties are an important resource for the development of Language Technology. The current work contributes a new resource for Chinese Language Technology and for Chinese-English translation, in the form of a set of TED talks (some originally given in English, some in Chinese) that have been annotated with discourse relations in the style of the Penn Discourse TreeBank, adapted to properties of Chinese text that are not present in English. The resource is currently unique in annotating discourse-level properties of planned spoken monologues rather than of written text. An inter-annotator agreement study demonstrates that the annotation scheme is able to achieve highly reliable results.