Polynomial multiplication is a significant bottleneck in mainstream postquantum cryptography (PQC) schemes. To speed it up, number theoretic transform (NTT) is widely used, which decreases the time complexity from${O}(n^{2})$to$O[n\log _{2}(n)]$. However, it is challenging to ensure optimal hardware efficiency in conjunction with scalability. This brief proposes a novel pipelined NTT/inverse-NTT (INTT) architecture on field-programmable gate array (FPGA). A group-based pairwise memory access (GPMA) scheme is proposed, and a scratchpad and reordering unit (SRU) is designed to form an efficient dataflow that simplifies control units and achieves almost$n/2$processing cycles on average for n-point NTT. Moreover, our architecture can support varying parameters. Compared to the state-of-the-art works, our architecture achieves up to$4.8\times $latency improvements and up to$4.3\times $improvements on area time product (ATP).

Slot filling and intent detection are two major tasks for spoken language understanding.In most existing work, these two tasks are built as joint models with multi-task learning with no consideration of prior linguistic knowledge.In this paper, we propose a novel joint model that applies a graph convolutional network over dependency trees to integrate the syntactic structure for learning slot filling and intent detection jointly.Experimental results show that our proposed model achieves state-of-the-art performance on two public benchmark datasets and outperforms existing work.At last, we apply the BERT model to further improve the performance on both slot filling and intent detection.

A goal-oriented visual dialogue involves multi-turn interactions between two agents, Questioner and Oracle. During which, the answer given by Oracle is of great significance, as it provides golden response to what Questioner concerns. Based on the answer, Questioner updates its belief on target visual content and further raises another question. Notably, different answers drive into different visual beliefs and future questions. However, existing methods always indiscriminately encode answers after much longer questions, resulting in a weak utilization of answers. In this paper, we propose an Answer-Driven Visual State Estimator (ADVSE) to impose the effects of different answers on visual states. First, we propose an Answer-Driven Focusing Attention (ADFA) to capture the answer-driven effect on visual attention by sharpening question-related attention and adjusting it by answer-based logical operation at each turn. Then based on the focusing attention, we get the visual state estimation by Conditional Visual Information Fusion (CVIF), where overall information and difference information are fused conditioning on the question-answer state. We evaluate the proposed ADVSE to both question generator and guesser tasks on the large-scale GuessWhat?! dataset and achieve the state-of-the-art performances on both tasks. The qualitative results indicate that the ADVSE boosts the agent to generate highly efficient questions and obtains reliable visual attentions during the reasonable question generation and guess processes.