The Royalflush System for VoxCeleb Speaker Recognition Challenge 2022

In this technical report, we describe the Royalflush submissions for the VoxCeleb Speaker Recognition Challenge 2022 (VoxSRC-22). Our submissions contain track 1, which is for supervised speaker verification and track 3, which is for semi-supervised speaker verification. For track 1, we develop a powerful U-Net-based speaker embedding extractor with a symmetric architecture. The proposed system achieves 2.06% in EER and 0.1293 in MinDCF on the validation set. Compared with the state-of-the-art ECAPA-TDNN, it obtains a relative improvement of 20.7% in EER and 22.70% in MinDCF. For track 3, we employ the joint training of source domain supervision and target domain self-supervision to get a speaker embedding extractor. The subsequent clustering process can obtain target domain pseudo-speaker labels. We adapt the speaker embedding extractor using all source and target domain data in a supervised manner, where it can fully leverage both domain information. Moreover, clustering and supervised domain adaptation can be repeated until the performance converges on the validation set. Our final submission is a fusion of 10 models and achieves 7.75% EER and 0.3517 MinDCF on the validation set

Re-ranking Method Based on Topic Word Pairs

PACLIC 20 / Wuhan, China / 1-3 November, 200

Measuring information integration model for CAD/CMM

A CAD/CMM workpiece modeling system based on IGES file is proposed. The modeling system is implemented by using a method of labelling the tolerance items of 3D workpiece. The concept-feature face is used in the method. Firstly the CAD data of workpiece are extracted and recognized automatically. Then a workpiece model is generated, which is the integration of pure 3D geometry form with its corresponding inspection items. The principle of workpiece modeling is also presented. At last, the experiment results are shown and correctness of the model is certified

Effects of magnesium supplementation on improving hyperglycemia, hypercholesterolemia, and hypertension in type 2 diabetes: A pooled analysis of 24 randomized controlled trials

BackgroundPrevious studies have demonstrated that diabetes is often accompanied with lower magnesium status. However, practical details regarding the influences of magnesium intervention on hyperglycemia, hypercholesterolemia, and hypertension in type 2 diabetes (T2D) need to be further investigated.MethodsWeb of Science, ScienceDirect, and PubMed were searched for relevant literatures published through April 30, 2022, and high-quality data were pooled to evaluate the effects of magnesium supplementation on glycemic, circulating lipids, and blood pressure control in T2D, and to explore the associated practical details.ResultsPooled analyses of 24 randomized controlled trials with 1,325 T2D individuals revealed that subjects who received magnesium supplementation had statistically significant reductions in fasting plasma glucose, glycated hemoglobin, systolic blood pressure and diastolic blood pressure, with WMD values of –0.20 mM (95% CI: –0.30, –0.09), –0.22% (95% CI: –0.41, –0.03), –7.69 mmHg (95% CI: –11.71, –3.66) and –2.71 mmHg (95% CI: –4.02, –1.40), respectively. Detailed subgroup analyses demonstrated that health status of participants including age, body mass index, country, duration of disease, baseline magnesium level and baseline glycemic control condition as well as magnesium formulation, dosage and duration of intervention influenced the effects of magnesium addition. Dose-effect analysis showed that 279 mg/d for 116 d, 429 mg/d for 88 d and 300 mg/d for 120 d are the average optimal dosages and durations for improving glycemic, circulating lipids, and blood pressure controls, respectively.ConclusionOur findings provide clinically relevant information on the adjuvant therapy of magnesium for improving hyperglycemia, hypercholesterolemia, and hypertension in T2D

LE-SSL-MOS: Self-Supervised Learning MOS Prediction with Listener Enhancement

Recently, researchers have shown an increasing interest in automatically predicting the subjective evaluation for speech synthesis systems. This prediction is a challenging task, especially on the out-of-domain test set. In this paper, we proposed a novel fusion model for MOS prediction that combines supervised and unsupervised approaches. In the supervised aspect, we developed an SSL-based predictor called LE-SSL-MOS. The LE-SSL-MOS utilizes pre-trained self-supervised learning models and further improves prediction accuracy by utilizing the opinion scores of each utterance in the listener enhancement branch. In the unsupervised aspect, two steps are contained: we fine-tuned the unit language model (ULM) using highly intelligible domain data to improve the correlation of an unsupervised metric - SpeechLMScore. Another is that we utilized ASR confidence as a new metric with the help of ensemble learning. To our knowledge, this is the first architecture that fuses supervised and unsupervised methods for MOS prediction. With these approaches, our experimental results on the VoiceMOS Challenge 2023 show that LE-SSL-MOS performs better than the baseline. Our fusion system achieved an absolute improvement of 13% over LE-SSL-MOS on the noisy and enhanced speech track. Our system ranked 1st and 2nd, respectively, in the French speech synthesis track and the challenge's noisy and enhanced speech track.Comment: accepted in IEEE-ASRU202

A Comparison of academic staff management practices in Chinese and Australian Universities

This study investigated five Chinese higher education institutions in relation to management of academic staff. The study compared these practices with those used in three Australian universities. The results demonstrated that the Chinese universities provide more freedom to academic staff in terms of how staff spend their time at the university. However, there are more strict measures to control teaching staff’s punctuality in attending their classes and to have detailed planning and teaching documentation. There are also additional teaching evaluations at both school and university levels, together with student evaluation. Chinese higher education staff management places greater emphasis on extrinsic financial rewards to improve staff performance than do their Australian counterparts. The income of Chinese academic staff is performance based and closely connected to their teaching, supervision, research and management workload. This approach initially came from the West and is now adopted by Chinese higher education management, reflecting Chinese socialist principles regarding income distribution. This measure of distribution is a very important motivational factor designed to enhance staff performance. This study provides an understanding as to the reasons why differences exist in management practices in China and Australia and offers some explanations from historical, political and social culture perspectives. This research identifies both positive and negative aspects of the two systems and suggests that learning good management practices from each other may bring positive changes to the productivity of higher education in both countries.C

ρ-uncertainty Anonymization by Partial Suppression

Abstract. We present a novel framework for set-valued data anonymiza-tion by partial suppression regardless of the amount of background knowl-edge the attacker possesses, and can be adapted to both space-time and quality-time trade-offs in a “pay-as-you-go ” approach. While minimizing the number of item deletions, the framework attempts to either preserve the original data distribution or retain mineable useful association rules, which targets statistical analysis and association mining, two major data mining applications on set-valued data.