Algorithm of Abnormal Audio Recognition Based on Improved MFCC

AbstractCharacteristics extraction has a great effect on the audio training and recognition in the audio recognition system. MFCC algorithm is a typical characteristics extraction method with stable performance and high recognition rate. For the situation that MFCC has a large amount of computation, an improved algorithm MFCC_E is introduced. The computation of MFCC_E is reduced by 50% compared with the standard algorithm MFCC, and it make the hardware implementation is easy. The experimental result indicated that MFCC_E and MFCC have the same recognition rate roughly, yet the computational complexity of MFCC_E is much smaller

Does learning different script systems affect configural visual processing? ERP evidence from early readers of Chinese and German

Reading is a complex cultural skill requiring considerable training, apparently affecting also the processing of non-linguistic visual stimuli. We examined whether the different visual demands involved in reading different script systems—alphabetic German versus logographic Chinese script—would differentially influence configural visual processing. Our main dependent measure was the N170 component of the ERP, which is considered as a signature of configural processing. In the present study, German and Chinese children (N = 28 vs. 27) who had received about one year of formal instruction in their native script system, worked on a series of one-back tasks with naturalistic faces, two-tone Mooney faces and doodles, and on an adaptation task with pairs of faces were either identical or differed in their second-order relations. Chinese children showed larger N170 amplitudes than German children for naturalistic and Mooney faces, specifically indicating superior holistic processing in Chinese children. In contrast, there was no superiority in Chinese children on the second-order adaptation effect at the N170, providing no evidence for differences in second-order relations processing of facial configurations between the groups. Given the sensitivity of the visual system to reading acquisition, these findings suggest that these group differences in holistic processing might be due to the extensive training with the highly complex logographic script system learned by Chinese children, imposing high demands on higher-order visual perception.China Scholarship Council http://dx.doi.org/10.13039/501100004543National Natural Science Foundation of China http://dx.doi.org/10.13039/501100001809Peer Reviewe

Fighting Pandemics with Augmented Reality and Smart Sensing-based Social Distancing

In a postpandemic world, remaining vigilant and maintaining social distancing are still crucial so societies can contain the virus and the public can avoid disproportionate health impacts. Augmented reality (AR) can visually assist users in understanding the distances in social distancing. However, integrating external sensing and analysis is required for social distancing beyond the users’ local environment. We present DistAR, an android-based application for social distancing leveraging AR and smart sensing using on-device analysis of optical images and environment crowdedness from smart campus data. Our prototype is one of the first efforts to combine AR and smart sensing technologies to create a real-time social distancing application.Peer reviewe

Case Report: A novel mutation in TNFAIP3 in a patient with type 1 diabetes mellitus and haploinsufficiency of A20

BackgroundHaploinsufficiency of A20 (HA20) is a monogenic autosomal-dominant genetic autoinflammatory disease caused by loss of function mutations in the TNFAIP3 gene. The predominant autoimmune phenotype associated with HA20 varies significantly, presenting with fever, recurrent oral and genital ulcers, skin rash, gastrointestinal and musculoskeletal symptoms, and other clinical manifestations, all of which indicate an early-onset of autoinflammatory disorder. Genetic linkage between TNFAIP3 and T1DM was reported in GWAS studies. However, only a few cases of HA20 combined with T1DM have been reported.Case descriptionA 39-year-old man with a history of type 1 diabetes mellitus since 19 years was admitted to the Department of Endocrinology and Metabolism, First Affiliated Hospital of China Medical University. He also suffered from recurring and minor mouth ulcers since early childhood. His laboratory evaluation results revealed reduced islet function, normal lipid profile, HbA1c of 7%, elevated glutamate decarboxylase antibodies, elevated hepatic transaminases, and elevated thyroid-related antibodies with normal thyroid function. Notably, the patient was diagnosed in adolescence and never had ketoacidosis, the islets were functioning despite the long disease duration, his abnormal liver function could not be reasonably explained, and he had early onset Behcet’s-like disease symptom. Hence, although he was on routine follow-up for diabetes, we communicated with him and obtained consent for genetic testing. Whole-exome sequencing revealed a novel c.1467_1468delinsAT heterozygous mutation in the gene TNFAIP3, which is located in exon 7, resulting in a stop-gained type mutation p.Q490*. With good but mild fluctuating glycemic control, the patient received intensive insulin therapy with long-acting and short-acting insulin. The liver function was improved by using ursodeoxycholic acid 0.75 mg/d during the follow-up.ConclusionWe report a novel pathogenic mutation in TNFAIP3 that results in HA20 in a patient with T1DM. In addition, we analyzed the clinical feathers of such patients and summarized the cases of five patients with HA20 co-presented with T1DM. When T1DM co-occurs with autoimmune diseases or other clinical manifestations, such as oral and/or genital ulcers and chronic liver damage, the possibility of an HA20 must be considered. Early and definitive diagnosis of HA20 in such patients may inhibit the progression of late-onset autoimmune diseases, including T1DM

Clinical analysis of infectious mononucleosis complicated with acute acalculous cholecystitis

ObjectiveThis study aimed to investigate specific clinical diagnostic methods for children with infectious mononucleosis (IM) complicated by acute acalculous cholecystitis (AAC).MethodsWe conducted a retrospective analysis of 171 cases of IM diagnosed in the infectious disease ward of Children's Hospital of Nanjing Medical University between January 2020 and December 2020. All IM patients underwent abdominal ultrasound examinations to assess the liver, gallbladder, and spleen. Fourteen patients with symptoms of AAC underwent a follow-up assessment one week later.ResultsThe estimated incidence of AAC in hospitalized IM children was 8.2%. Both groups of patients presented with fever, abdominal pain, and eyelid edema upon admission. Characteristic radiological findings of AAC were observed, including gallbladder (GB) distention, increased GB wall thickness and increased common bile duct diameter. Analysis of laboratory results revealed no statistically significant differences in leukocyte, absolute lymphocyte count, CD3+, CD3 + CD4+, CD3+ CD8+, Aspartate Aminotransferase (AST), Alanine Aminotransferase (ALT), or Gamma-Glutamyl Transferase (GGT) levels between the AAC(+) and AAC(−) groups on admission. However, these parameters were not significant risk factors for AAC. After discharge, relevant indicators in non-AAC patients gradually decreased to normal levels, while those in AAC(+) patients did not show a significant decrease.ConclusionWhile cases of IM complicated by AAC are relatively uncommon, the utilization of abdominal ultrasound offers a reliable tool for confirming this diagnosis. Routine abdominal ultrasound examinations are recommended for IM patients to improve early detection and treatment of associated conditions

Co-training with High-Confidence Pseudo Labels for Semi-supervised Medical Image Segmentation

Consistency regularization and pseudo labeling-based semi-supervised methods perform co-training using the pseudo labels from multi-view inputs. However, such co-training models tend to converge early to a consensus, degenerating to the self-training ones, and produce low-confidence pseudo labels from the perturbed inputs during training. To address these issues, we propose an Uncertainty-guided Collaborative Mean-Teacher (UCMT) for semi-supervised semantic segmentation with the high-confidence pseudo labels. Concretely, UCMT consists of two main components: 1) collaborative mean-teacher (CMT) for encouraging model disagreement and performing co-training between the sub-networks, and 2) uncertainty-guided region mix (UMIX) for manipulating the input images according to the uncertainty maps of CMT and facilitating CMT to produce high-confidence pseudo labels. Combining the strengths of UMIX with CMT, UCMT can retain model disagreement and enhance the quality of pseudo labels for the co-training segmentation. Extensive experiments on four public medical image datasets including 2D and 3D modalities demonstrate the superiority of UCMT over the state-of-the-art. Code is available at: https://github.com/Senyh/UCMT

Mobile Augmented Reality: User Interfaces, Frameworks, and Intelligence

Mobile Augmented Reality (MAR) integrates computer-generated virtual objects with physical environments for mobile devices. MAR systems enable users to interact with MAR devices, such as smartphones and head-worn wearables, and perform seamless transitions from the physical world to a mixed world with digital entities. These MAR systems support user experiences using MAR devices to provide universal access to digital content. Over the past 20 years, several MAR systems have been developed, however, the studies and design of MAR frameworks have not yet been systematically reviewed from the perspective of user-centric design. This article presents the first effort of surveying existing MAR frameworks (count: 37) and further discuss the latest studies on MAR through a top-down approach: (1) MAR applications; (2) MAR visualisation techniques adaptive to user mobility and contexts; (3) systematic evaluation of MAR frameworks, including supported platforms and corresponding features such as tracking, feature extraction, and sensing capabilities; and (4) underlying machine learning approaches supporting intelligent operations within MAR systems. Finally, we summarise the development of emerging research fields and the current state-of-the-art, and discuss the important open challenges and possible theoretical and technical directions. This survey aims to benefit both researchers and MAR system developers alike.Peer reviewe

Leveraging Endo- and Exo-Temporal Regularization for Black-box Video Domain Adaptation

To enable video models to be applied seamlessly across video tasks in different environments, various Video Unsupervised Domain Adaptation (VUDA) methods have been proposed to improve the robustness and transferability of video models. Despite improvements made in model robustness, these VUDA methods require access to both source data and source model parameters for adaptation, raising serious data privacy and model portability issues. To cope with the above concerns, this paper firstly formulates Black-box Video Domain Adaptation (BVDA) as a more realistic yet challenging scenario where the source video model is provided only as a black-box predictor. While a few methods for Black-box Domain Adaptation (BDA) are proposed in image domain, these methods cannot apply to video domain since video modality has more complicated temporal features that are harder to align. To address BVDA, we propose a novel Endo and eXo-TEmporal Regularized Network (EXTERN) by applying mask-to-mix strategies and video-tailored regularizations: endo-temporal regularization and exo-temporal regularization, performed across both clip and temporal features, while distilling knowledge from the predictions obtained from the black-box predictor. Empirical results demonstrate the state-of-the-art performance of EXTERN across various cross-domain closed-set and partial-set action recognition benchmarks, which even surpassed most existing video domain adaptation methods with source data accessibility.Comment: 9 pages, 4 figures, and 4 table