A machine learning-based monitoring system for attention and stress detection for children with Autism Spectrum Disorders

© 2021 ACM, Inc. This is the accepted manuscript version of an article which has been published in final form at https://doi.org/10.1145/3484377.3484381The majority of children with Autism Spectrum Disorders (ASD) have faced difficulties in sensory processing, which affect their ability of effective attention and stress management. Children with ASD also have unique patterns of sensory processing when responding to the stimuli in the environment. In this study, a real-time monitoring system has been designed and developed for attention and stress detection. Comprehensive sensory information, including environmental, physiological, and sensory profile data can be collected by the system using sensors, smart devices, and a standard sensory profiling questionnaire. Data acquisition with 35 ASD children using the system prototype was successfully conducted. With the acquired data set, different machine learning models were trained to predict attentional and stress level. Among all the investigated models, Gradient Boosting Decision Tree and Random Forest obtained the best prediction accuracies of 86.67% and 99.05% on attention and stress detection respectively. The two models were then implemented into the system for automatic detection. Future work could be focusing on exploring more supportive features to improve the prediction accuracy for attention detection. Such an easily-accessed monitoring system tailored for children with ASD could be widely-used in daily life to assist ASD users with their attention and stress management

Tensor Decomposition Based Attention Module for Spiking Neural Networks

The attention mechanism has been proven to be an effective way to improve spiking neural network (SNN). However, based on the fact that the current SNN input data flow is split into tensors to process on GPUs, none of the previous works consider the properties of tensors to implement an attention module. This inspires us to rethink current SNN from the perspective of tensor-relevant theories. Using tensor decomposition, we design the \textit{projected full attention} (PFA) module, which demonstrates excellent results with linearly growing parameters. Specifically, PFA is composed by the \textit{linear projection of spike tensor} (LPST) module and \textit{attention map composing} (AMC) module. In LPST, we start by compressing the original spike tensor into three projected tensors using a single property-preserving strategy with learnable parameters for each dimension. Then, in AMC, we exploit the inverse procedure of the tensor decomposition process to combine the three tensors into the attention map using a so-called connecting factor. To validate the effectiveness of the proposed PFA module, we integrate it into the widely used VGG and ResNet architectures for classification tasks. Our method achieves state-of-the-art performance on both static and dynamic benchmark datasets, surpassing the existing SNN models with Transformer-based and CNN-based backbones.Comment: 11 page

Prompt-driven Target Speech Diarization

We introduce a novel task named `target speech diarization', which seeks to determine `when target event occurred' within an audio signal. We devise a neural architecture called Prompt-driven Target Speech Diarization (PTSD), that works with diverse prompts that specify the target speech events of interest. We train and evaluate PTSD using sim2spk, sim3spk and sim4spk datasets, which are derived from the Librispeech. We show that the proposed framework accurately localizes target speech events. Furthermore, our framework exhibits versatility through its impressive performance in three diarization-related tasks: target speaker voice activity detection, overlapped speech detection and gender diarization. In particular, PTSD achieves comparable performance to specialized models across these tasks on both real and simulated data. This work serves as a reference benchmark and provides valuable insights into prompt-driven target speech processing.Comment: Accepted by ICASSP 202

USED: Universal Speaker Extraction and Diarization

Speaker extraction and diarization are two crucial enabling techniques for speech applications. Speaker extraction aims to extract a target speaker's voice from a multi-talk mixture, while speaker diarization demarcates speech segments by speaker, identifying `who spoke when'. The previous studies have typically treated the two tasks independently. However, the two tasks share a similar objective, that is to disentangle the speakers in the spectral domain for the former but in the temporal domain for the latter. It is logical to believe that the speaker turns obtained from speaker diarization can benefit speaker extraction, while the extracted speech offers more accurate speaker turns than the mixture speech. In this paper, we propose a unified framework called Universal Speaker Extraction and Diarization (USED). We extend the existing speaker extraction model to simultaneously extract the waveforms of all speakers. We also employ a scenario-aware differentiated loss function to address the problem of sparsely overlapped speech in real-world conversations. We show that the USED model significantly outperforms the baselines for both speaker extraction and diarization tasks, in both highly overlapped and sparsely overlapped scenarios. Audio samples are available at https://ajyy.github.io/demo/USED/.Comment: Submitted to ICASSP 202

Amplification Refractory Mutation System (ARMS)-PCR for waxy sorghum authentication with single-nucleotide resolution

Waxy sorghum has greater economic value than wild sorghum in relation to their use in food processing and the brewing industry. Thus, the authentication of the waxy sorghum species is an important issue. Herein, a rapid and sensitive Authentication Amplification Refractory Mutation System-PCR (aARMS-PCR) method was employed to identify sorghum species via its ability to resolve single-nucleotide in genes. As a proof of concept, we chose a species of waxy sorghum containing the wxc mutation which is abundantly used in liquor brewing. The aARMS-PCR can distinguish non-wxc sorghum from wxc sorghum to guarantee identification of specific waxy sorghum species. It allowed to detect as low as 1% non-wxc sorghum in sorghum mixtures, which ar one of the most sensitive tools for food authentication. Due to its ability for resolving genes with single-nucleotide resolution and high sensitivity, aARMS-PCR may have wider applicability in monitoring food adulteration, offering a rapid food authenticity verification in the control of adulteration

Ratiometric G-quadruplex assay for robust lead detection in food samples

Lead (Pb2+) pollution is a serious food safety issue, rapid detection of Pb2+ residual in food is vital to guarantee food quality and safety. Here we proposed ratiometric aptamer probes, allowing robust Pb2+ supervision in food samples. Pb2+ specific aptamer can bolster a transition of G-quadruplex structural response to Pb2+; this process can be monitored by N-methyl mesoporphyrin IX (NMM), which is highly specific to G-quadruplex. Particularly, the utilization of G-quadruplex specific dye and terminal-labeled fluorophore allowed to endue ratiometric signal outputs towards Pb2+, dramatically increase the robustness for lead detection. The ratiometric G-quadruplex assay allowed a facile and one-pot Pb2+ detection at room temperature using a single-stranded DNA aptamer. We demonstrated its feasibility for detecting lead pollution in fresh eggs and tap water samples. The ratiometric G-quadruplex design is expected to be used for on-site Pb2+ testing associated with food safet

Real-time qPCR for the detection of puffer fish components from Lagocephalus in food: L. inermis, L. lagocephalus, L. gloveri, L. lunaris, and L. spadiceus

Puffer fish is a type of precious high-end aquatic product, is widely popular in Asia, especially in China and Japan, even though it naturally harbors a neurotoxin known as tetrodotoxin (TTX) that is poisonous to humans and causes food poisoning. With the increasing trade demand, which frequently exceeds existing supply capacities, fostering fraudulent practices, such as adulteration of processed products with non-certified farmed wild puffer fish species. To determine the authenticity of puffer fish processed food, we developed a real-time qPCR method to detect five common puffer fish species in aquatic products: Lagocephalus inermis, Lagocephalus lagocephalus, Lagocephalus gloveri, Lagocephalus lunaris, and Lagocephalus spadiceus. The specificity, cross-reactivity, detection limit, efficiency, and robustness of the primers and probes created for five species of puffer fish using TaqMan technology have been determined. No cross-reactivity was detected in the DNA of non-target sample materials, and no false-positive signal was detected; the aquatic products containing 0.1% of a small amount of wild puffer fish materials without certification can be reliably tracked; the statistical p-value for each method’s Ct value was greater than 0.05. The developed qPCR method was sensitive, highly specific, robust, and reproducibility, which could be used to validate the authenticity of wild puffer fish in aquatic products sold for commercial purposes

Why models underestimate West African tropical forest primary productivity

Tropical forests dominate terrestrial photosynthesis, yet there are major contradictions in our understanding due to a lack of field studies, especially outside the tropical Americas. A recent field study indicated that West African forests have among the highest forests gross primary productivity (GPP) yet observed, contradicting models that rank them lower than Amazonian forests. Here, we show possible reasons for this data-model mismatch. We found that biometric GPP measurements are on average 56.3% higher than multiple global GPP products at the study sites. The underestimation of GPP largely disappears when a standard photosynthesis model is informed by local field-measured values of (a) fractional absorbed photosynthetic radiation (fAPAR), and (b) photosynthetic traits. Remote sensing products systematically underestimate fAPAR (33.9% on average at study sites) due to cloud contamination issues. The study highlights the potential widespread underestimation of tropical forests GPP and carbon cycling and hints at the ways forward for model and input data improvement

Financial Futures Prediction Using Fuzzy Rough Set and Synthetic Minority Oversampling Technique

In this research, a novel approach called SMOTE-FRS is proposed for movement prediction and trading simulation of the Chinese Stock Index 300 (CSI300) futures, which is the most crucial financial futures in the Chinese A-share market. First, the SMOTE- (Synthetic Minority Oversampling Technique-) based method is employed to address the sample unbalance problem by oversampling the minority class and undersampling the majority class of the futures price change. Then, the FRS- (fuzzy rough set-) based method, as an efficient tool for analyzing complex and nonlinear information with high noise and uncertainty of financial time series, is adopted for the price change multiclassification of the CSI300 futures. Next, based on the multiclassification results of the futures price movement, a trading strategy is developed to execute a one-year simulated trading for an out-of-sample test of the trained model. From the experimental results, it is found that the proposed method averagely yielded an accumulated return of 6.36%, a F1-measure of 65.94%, and a hit ratio of 62.39% in the four testing periods, indicating that the proposed method is more accurate and more profitable than the benchmarks. Therefore, the proposed method could be applied by the market participants as an alternative prediction and trading system to forecast and trade in the Chinese financial futures market