arXiv subjects

Jiajun He

Publications and source records attributed to Jiajun He.

58 records · Page 4Linked to original sources

arXiv2023

ed-cec: improving rare word recognition using asr postprocessing based on error detection and context-aware error correction

Automatic speech recognition (ASR) systems often encounter difficulties in accurately recognizing rare words, leading to errors that can have a negative impact on downstream tasks such as keyword spotting, intent detection, and text summarization. To address this challenge, we present a novel ASR postprocessing method that focuses on improving the recognition of rare words through error detection and context-aware error correction. Our method optimizes the decoding process by targeting only the predicted error positions, minimizing unnecessary computations. Moreover, we leverage a rare word list to provide additional contextual knowledge, enabling the model to better correct rare words. Experimental results across five datasets demonstrate that our proposed method achieves significantly lower word error rates (WERs) than previous approaches while maintaining a reasonable inference speed. Furthermore, our approach exhibits promising robustness across different ASR systems.

cs.AI↗

arXiv2022

An Input-Aware Mimic Defense Theory and its Practice

The current security problems in cyberspace are characterized by strong and complex threats. Defenders face numerous problems such as lack of prior knowledge, various threats, and unknown vulnerabilities, which urgently need new fundamental theories to support. To address these issues, this article proposes a generic theoretical model for cyberspace defense and a new mimic defense framework, that is, Spatiotemporally heterogeneous, Input aware, and Dynamically updated Mimic Defense (SIDMD). We make the following contributions: (1) We first redefine vulnerabilities from the input space perspective to normalize the diverse cyberspace security problem. (2) We propose a novel unknown vulnerability discovery method and a dynamic scheduling strategy considering temporal and spatial dimensions without prior knowledge. Theoretical analysis and experimental results show that SIDMD has the best security performance in complex attack scenarios, and the probability of successful attacks is greatly reduced compared to the state-of-the-art.

cs.CR↗

arXiv2021

A New Entity Extraction Method Based on Machine Reading Comprehension

Entity extraction is a key technology for obtaining information from massive texts in natural language processing. The further interaction between them does not meet the standards of human reading comprehension, thus limiting the understanding of the model, and also the omission or misjudgment of the answer (ie the target entity) due to the reasoning question. An effective MRC-based entity extraction model-MRC-I2DP, which uses the proposed gated attention-attracting mechanism to adjust the restoration of each part of the text pair, creating problems and thinking for multi-level interactive attention calculations to increase the target entity It also uses the proposed 2D probability coding module, TALU function and mask mechanism to strengthen the detection of all possible targets of the target, thereby improving the probability and accuracy of prediction. Experiments have proved that MRC-I2DP represents an overall state-of-the-art model in 7 from the scientific and public domains, achieving a performance improvement of up to compared to the model model in F1.

cs.CL↗

arXiv2020

A Statistical Characterization of Localization Performance in Millimeter-Wave Cellular Networks

Millimeter-wave (mmWave) communication is a promising solution for achieving high data rate and low latency in 5G wireless cellular networks. Since directional beamforming and antenna arrays are exploited in the mmWave networks, accurate angle-of-arrival (AOA) information can be obtained and utilized for localization purposes. The performance of a localization system is typically assessed by the Cramer-Rao lower bound (CRLB) evaluated based on fixed node locations. However, this strategy only produces a fixed value for the CRLB specific to the scenario of interest. To allow randomly distributed nodes, stochastic geometry has been proposed to study the CRLB for time-of-arrival-based localization. To the best of our knowledge, this methodology has not yet been investigated for AOA-based localization. In this work, we are motivated to consider the mmWave cellular network and derive the CRLB for AOA-based localization and its distribution using stochastic geometry. We analyze how the CRLB is affected by the node locations' spatial distribution, including the target and participating base stations. To apply the CRLB on a network setting with random node locations, we propose an accurate approximation of the CRLB using the L/4-th value of ordered distances where L is the number of participating base stations. Furthermore, we derive the localizability of the mmWave network, which is the probability that a target is localizable, and examine how the network parameters influence the localization performance. These findings provide us deep insight into optimum network design that meets specified localization requirements.

cs.IT↗