The Cascade Correlation learning algorithm is a special supervised learning algorithm for artificial neural network architecture. The optimization algorithm in the traditional neural network has the disadvantages of a single optimization goal, slow convergence speed, and can easily fall into local area, which cannot fully meet the key elements in the cascade correlation learning algorithm. In comparison, the group intelligence optimization algorithm can take into account these key elements in the optimization process at the same time, and obtain better optimization results. In this paper, we propose the single-objective optimization algorithm jDE-B and the multi-objective optimization algorithm MOEA-T, and improve the network expansion mode in the learning process of Cascade Correlation neural networks. We investigate the effect of applying the group intelligent optimization algorithm in the Cascade Correlation learning algorithm. Experimental results show that our improved algorithm is able to enhance the ability of the Cascade Correlation neural network to fit problems, reduce the number of hidden units and the depth of the network, and optimize the network structure.

Zero-shot Speech Emotion Recognition (SER) enables machines to perceive unseen-emotional speech without knowing any samples from these emotional states, which is helpful in audio-based autonomous affective computing. However, existing works on zero-shot SER directly employ original prototypes and only consider inter-domain knowledge transfer through learning unseen-emotional classifiers. In this regard, we propose a zero-shot SER approach using generative learning with reconstructed prototypes in this paper. Within the proposed approach, we first reconstruct prototypes using the alignment from paralinguistic features to semantic prototypes. Then, generative learning is performed to build the connection from the reconstructed prototypes to the features. Afterwards, zero-shot experiments on emotional-speech data demonstrate that the proposed approach achieves better performance compared with the state-of-the-art approaches.

Speech Emotion Recognition (SER) makes it possible for machines to perceive affective information. Our previous research differed from conventional SER endeavours in that it focused on recognising unseen emotions in speech autonomously through machine learning. Such a step would enable the automatic leaning of unknown emerging emotional states. This type of learning framework, however, still relied on manual annotations to obtain multiple samples of each emotion. In order to reduce this additional workload, herein, we propose a zero-shot SER framework employing a per-emotion semantic-embedding paradigm to describe emotions in zero-shot SER, instead of using the sample-wise descriptors. Aiming to optimise the relationship between emotions, prototypes, and speech samples, this framework includes two types of learning strategies: Sample-wise learning and emotion-wise learning. These strategies apply a novel learning process to speech samples and emotions, respectively, via specifically designed semantic-embedding prototypes. We verify the utility of these approaches by performing an extensive experimental evaluation on two corpora on three aspects, namely the influence of different types of learning strategies, emotional-pair comparison, and the selections of semantic-embedding prototypes and paralinguistic features. The experimental results indicate that it is applicable to use semantic-embedding prototypes for zero-shot emotion recognition in speech, despite the influence of choosing optimal strategies and prototypes.

Zero-shot speech emotion recognition (SER) endows machines with the ability of sensing unseen-emotional states in speech, compared with conventional SER endeavors on supervised cases. On addressing the zero-shot SER task, auditory affective descriptors (AADs) are typically employed to transfer affective knowledge from seen- to unseen-emotional states. However, it remains unknown which types of AADs can well describe emotional states in speech during the transfer. In this regard, we define and research on three types of AADs, namely, per-emotion semantic-embedding, per-emotion manually annotated, and per-sample manually annotated AADs, through zero-shot emotion recognition in speech. This leads to a systematic design including prototype- and annotation-based zero-shot SER modules, relying on the input from per-emotion and per-sample AADs, respectively. We then perform extensive experimental comparisons between human and machines’ AADs on the French emotional speech corpus CINEMO for positive-negative (PN) and within-negative (WN) tasks. The experimental results indicate that semantic-embedding prototypes from pretrained models can outperform manually annotated emotional dimensions in zero-shot SER. The results further demonstrate that it is possible for machines to understand and describe affective information in speech better than human beings, with the help of sufficient pretrained models.

Grasping unknown objects with multi-fingered hands at high success rates and in real-time is an unsolved problem. Existing methods are limited in the speed of grasp synthesis or the ability to synthesize a variety of grasps from the same observation. We introduce Five-finger Hand Net (FFHNet), an ML model which can generate a wide variety of high-quality multi-fingered grasps for unseen objects from a single view. Generating and evaluating grasps with FFHNet takes only 30ms on a commodity GPU. To the best of our knowledge, FFHNet is the first ML-based real-time system for multi-fingered grasping with the ability to perform grasp inference at 30 frames per second (FPS). For training, we synthetically generate 180k grasp samples for 129 objects. We are able to achieve 91% grasping success for unknown objects in simulation and we demonstrate the model's capabilities of synthesizing high-quality grasps also for real unseen objects.

Considering the high cost of manually annotated large-scale datasets for superior sound event classifier performance, the data collection process has shifted to using the Internet, which facilitates easier user-contributed audio and metadata collection. However, label noise is inevitable. To address the problems caused by label noise, several types of noise-robust loss functions have been proposed recently as alternatives to the commonly categorical cross-entropy (CCE) loss, one of which is the generalized cross-entropy (GCE) loss, which demonstrates state-of-the-art performance. However, GCE cannot realize sufficient noise robustness and satisfactory accuracy simultaneously. Thus, we propose adaptive GCE loss, which automatically adapts to noisy labels in every batch to achieve adequate noise robustness and sufficient accuracy. We conducted experiments and found that the classification accuracy of the proposed loss demonstrated 4.7% and 1.2% absolute improvement over the CCE and GCE baselines, respectively. We also demonstrate that clean data consumption in the proposed loss is dramatically reduced by more than 75% compared with CCE.

The task of Mask-Speech Identification (MSI) aims at judging whether a chunk of speech is pronounced when the speaker is wearing a facial mask or not. Most of the existing related research focuses on investigating the influence of wearing a mask, which only adapts in some certain cases to speech analysis. Thus in order to generalise the research on MSI, we propose an MSI approach using deep networks on Low-Level Aggregation (LLA) for speech chunks. The proposed approach benefits from data augmentation on Low-Level Descriptors (LLDs), resulting in more adaptation to deep models through inputting much more samples in training without employing pre-trained knowledge. Experiments are performed on the dataset of Mask Augsburg Speech Corpus (MSC) used in the INTERSPEECH 2020 ComParE challenge, considering the influence from employing different strategies. The experimental results show effectiveness of the proposed approach compared with the ComParE challenge baselines.

Contact-rich manipulation tasks are commonly found in modern manufacturing settings. However, manually designing a robot controller is considered hard for traditional control methods as the controller requires an effective combination of modalities and vastly different characteristics. In this paper, we first consider incorporating operational space visual and haptic information into a reinforcement learning (RL) method to solve the target uncertainty problems in unstructured environments. Moreover, we propose a novel idea of introducing a proactive action to solve a partially observable Markov decision process (POMDP) problem. With these two ideas, our method can either adapt to reasonable variations in unstructured environments or improve the sample efficiency of policy learning. We evaluated our method on a task that involved inserting a random-access memory (RAM) using a torque-controlled robot and tested the success rates of different baselines used in the traditional methods. We proved that our method is robust and can tolerate environmental variations.

Perceptual audio coding is heavily and successfully applied for audio compression. However, perceptual audio coders may inject audible coding artifacts when encoding audio at low bitrates. Low-bitrate audio restoration is a challenging problem, which tries to recover a high-quality audio sample close to the uncompressed original from a low-quality encoded version. In this paper, we propose a novel data-driven method for audio restoration, where temporal and spectral dynamics are explicitly captured by a deep time-frequency-LSTM recurrent neural networks. Leveraging the captured temporal and spectral information can facilitate the task of learning a nonlinear mapping from the magnitude spectrogram of low-quality audio to that of high-quality audio. The proposed method substantially attenuates audible artifacts caused by codecs and is conceptually straightforward. Extensive experiments were carried out and the experimental results show that for low-bitrate audio at 96 kbps (mono), 64 kbps (mono), and 96 kbps (stereo), the proposed method can efficiently generate improved-quality audio that is competitive or even superior in perceptual quality to the audio produced by other state-of-the-art deep neural network methods and the LAME-MP3 codec.

An unstable grasp pose can lead to slip, thus an unstable grasp pose can be predicted by slip detection. A regrasp is required afterwards to correct the grasp pose in order to finish the task. In this work, we propose a novel regrasp planner with multi-sensor modules to plan grasp adjustments with the feedback from a slip detector. Then a regrasp planner is trained to estimate the location of center of mass, which helps robots find an optimal grasp pose. The dataset in this work consists of 1 025 slip experiments and 1 347 regrasps collected by one pair of tactile sensors, an RGB-D camera and one Franka Emika robot arm equipped with joint force/torque sensors. We show that our algorithm can successfully detect and classify the slip for 5 unknown test objects with an accuracy of 76.88% and a regrasp planner increases the grasp success rate by 31.0% compared to the state-of-the-art vision-based grasping algorithm.

Early interventions in mental health conditions such as Major Depressive Disorder (MDD) are critical to improved health outcomes, as they can help reduce the burden of the disease. As the efficient diagnosis of depression severity is therefore highly desirable, the use of behavioural cues such as speech characteristics in diagnosis is attracting increasing interest in the field of quantitative mental health research. However, despite the widespread use of machine learning methods in the depression analysis community, the lack of adequate labelled data has become a bottleneck preventing the broader application of techniques such as deep learning. Accordingly, we herein describe a deep learning approach that combines unsupervised learning, knowledge transfer and hierarchical attention for the task of speech-based depression severity measurement. Our novel approach, a Hierarchical Attention Transfer Network (HATN), uses hierarchical attention autoencoders to learn attention from a source task, followed by speech recognition, and then transfers this knowledge into a depression analysis system. Experiments based on the depression sub-challenge dataset of the Audio/Visual Emotion Challenge (AVEC) 2017 demonstrate the effectiveness of our proposed model. On the test set, our technique outperformed other speech-based systems presented in the literature, achieving a Root Mean Square Error (RMSE) of 5.51 and a Mean Absolute Error (MAE) of 4.20 on a Patient Health Questionnaire (PHQ)-8 scale [0, 24]. To the best of our knowledge, these scores represent the best-known speech results on the AVEC 2017 depression corpus to date.

Conventionally, speech emotion recognition is achieved using passive learning approaches. Differing from such approaches, we herein propose and develop a dynamic method of autonomous emotion learning based on zero-shot learning. The proposed methodology employs emotional dimensions as the attributes in the zero-shot learning paradigm, resulting in two phases of learning, namely attribute learning and label learning. Attribute learning connects the paralinguistic features and attributes utilising speech with known emotional labels, while label learning aims at defining unseen emotions through the attributes. The experimental results achieved on the CINEMO corpus indicate that zero-shot learning is a useful technique for autonomous speech-based emotion learning, achieving accuracies considerably better than chance level and an attribute-based gold-standard setup. Furthermore, different emotion recognition tasks, emotional attributes, and employed approaches strongly influence system performance.

Speech emotion recognition (SER) is a powerful tool for endowing computers with the capacity to process information about the affective states of users in human-machine interactions. Recent research has shown the effectiveness of graph embedding-based subspace learning and extreme learning machine applied to SER, but there are still various drawbacks in these two techniques that limit their application. Regarding subspace learning, the change from linearity to nonlinearity is usually achieved through kernelization, whereas extreme learning machines only take label information into consideration at the output layer. In order to overcome these drawbacks, this paper leverages extreme learning machines for dimensionality reduction and proposes a novel framework to combine spectral regression-based subspace learning and extreme learning machines. The proposed framework contains three stages-data mapping, graph decomposition, and regression. At the data mapping stage, various mapping strategies provide different views of the samples. At the graph decomposition stage, specifically designed embedding graphs provide a possibility to better represent the structure of data through generating virtual coordinates. Finally, at the regression stage, dimension-reduced mappings are achieved by connecting the virtual coordinates and data mapping. Using this framework, we propose several novel dimensionality reduction algorithms, apply them to SER tasks, and compare their performance to relevant state-of-the-art methods. Our results on several paralinguistic corpora show that our proposed techniques lead to significant improvements.

This paper proposes a comprehensive study on machine listening for localisation of snore sound excitation. Here we investigate the effects of varied frame sizes, and overlap of the analysed audio chunk for extracting low-level descriptors. In addition, we explore the performance of each kind of feature when it is fed into varied classifier models, including support vector machines, k-nearest neighbours, linear discriminant analysis, random forests, extreme learning machines, kernel-based extreme learning machines, multilayer perceptrons, and deep neural networks. Experimental results demonstrate that, wavelet packet transform energy can outperform most other features. A deep neural network trained with subband energy ratios reaches the highest performance achieving an unweighted average recall of 72.8% from four types for snoring.

One of the major obstacles that has to be faced when applying automatic emotion recognition to realistic human–machine interaction systems is the scarcity of labeled data for training a robust model. Motivated by this concern, this paper seeks to utmost exploit unlabeled data that are pervasively available in the real-world and easy to be collected, by means of novel semi-supervised learning (SSL) approaches. Conventional SSL methods such as self-training, suffer from their inherent drawback of error accumulation, i.e., the samples that are misclassified by the system are continuously employed to train the model in the following learning iterations. To address this major issue, we first propose an enhanced learning strategy, by which we re-evaluate the previously automatically labeled samples in each learning iteration, in order to update the training set by correcting the mislabeled samples. We further exploit multiple modalities and models in the SSL system, by using collaborative SSL, where all modalities and models are considered simultaneously; samples are selected by means of minimizing the joint entropy. This strategy is supposed to not only improve the performance of the model for data annotation and consequently enhance the trustability of the automatically labeled data, but also to elevate the diversity of selected data. To evaluate the effectiveness of the proposed approaches, we performed extensive experiments on the remote collaborative and affective database, which includes multimodal recordings of spontaneous affective interactions of dyads. The empirical results show that the proposed approaches significantly outperform recently well-established SSL methods.

目的探讨丹红注射液(Danhong injection, DHI)对大鼠高脂血症的作用及机制。 方法采用高脂饲料制备大鼠高脂血症模型，检测各组大鼠肝脏腺苷酸激活蛋白激酶(AMPK)、p-AMPK、胆固醇调节元件结合蛋白(SREBP-1)、乙酰辅酶A羧化酶(ACC)、p-ACC的蛋白表达水平。 结果给予丹红注射液后，肝脏AMPK、SREBP-1和ACC的蛋白表达水平明显降低(P＜0.05)，p-ACC和p-AMPK的蛋白表达水平明显升高(P＜0.05)。 结论丹红注射液通过增强AMPK的活化，抑制SREBP-1和ACC的活性，促进脂肪酸氧化，减少脂质沉积，有效降低了高脂血症大鼠的血脂水平。

Throughout many present studies dealing with multi-modal fusion, decisions are synchronously forced for fixed time segments across all modalities.Varying success is reported, sometimes performance is worse than unimodal classification.Our goal is the synergistic exploitation of multimodality whilst implementing a real-time system for affect recognition in a naturalistic setting.Therefore we present a categorization of possible fusion strategies for affect recognition on continuous time frames of complete recording sessions and we evaluate multiple implementations from resulting categories.These involve conventional fusion strategies as well as novel approaches that incorporate the asynchronous nature of observed modalities.Some of the latter algorithms consider temporal alignments between modalities and observed frames by applying asynchronous neural networks that use memory blocks to model temporal dependencies.Others use an indirect approach that introduces events as an intermediate layer to accumulate evidence for the target class through all modalities.Recognition results gained on a naturalistic conversational corpus show a drop in recognition accuracy when moving from unimodal classification to synchronous multimodal fusion.However, with our proposed asynchronous and event-based fusion techniques we are able to raise the recognition system's accuracy by 7.83 percent compared to video analysis and 13.71 percent in comparison to common fusion strategies.

We describe a data collection for vocal expression of ironic utterances and anger based on an Android app that was specifically developed for this study. The main aim of the investigation is to find evidence for a non-verbal expression of irony. A data set of 937 utterances was collected and labeled by six listeners for irony and anger. The automatically recognized textual content was labeled for sentiment. We report on experiments to classify ironic utterances based on sentiment and tone-of-voice. Baseline results show that an ironic voice can be detected automatically solely based on acoustic features in 69.3 UAR (unweighted average recall) and anger with 64.1 UAR. The performance drops by about 4% when it is calculated with a leave-one-speaker-out cross validation.

Due to the increasing total value of the digital currency, the security of encryption wallet is becoming more and more important. The hardware-based wallet is safe, but it is inconvenient because users need to carry an additional physical device, the software-based wallet is convenient, but the safety cannot be guaranteed. All these wallets need to synchronize the blockchain, while most current mobile devices do not have the capability to store all blocks. To solve these problems, mobile devices can use simplified payment verification (SPV). Nevertheless, in existing methods, there is no good way to protect the verification process of the transaction. In this paper, we design a secure blockchain lightweight wallet based on Trustzone to protect SPV. It is more portable compared with the hardware wallet, and safer than the software wallet. Through the isolation, it can also protect the private key and the wallet's address from being stolen by the attackers no matter whether the Rich OS is malicious or not. Meanwhile, it can protect the verification process by verifying transactions in the secure execution environment (SEE), and keep the local block headers unreadable directly from the Rich OS through encryption. We deploy it on the RASPBERRY PI 3 MODEL B development board. The result of the experiment shows that it has little impact on the system.