Miracle-dz/UniFAR-FTUs huggingface dataset:
Meta-Learning for Low-Resource Speech Emotion Recognition

While emotion recognition is a well-studied task, it remains unexplored to a large extent in cross-lingual settings. Speech Emotion Recognition (SER) in low-resource languages poses difficulties as existing approaches for knowledge transfer do not generalize seamlessly. Probing the learning process of generalized representations across languages, we propose a meta-learning approach for low-resource speech emotion recognition. The proposed approach achieves fast adaptation on a number of unseen target languages simultaneously. We evaluate the Model Agnostic Meta-Learning (MAML) algorithm on three low-resource target languages -Persian, Italian, and Urdu. We empirically demonstrate that our proposed method - MetaSER1, considerably outperforms multitask and transfer learning-based methods for speech emotion recognition task, and discuss the benefits, efficiency, and challenges of MetaSER on limited data settings.
Towards Practical Near-Maximum-Likelihood Decoding of Error-Correcting Codes: An Overview

While in the past several decades the trend to go towards increasing error-correcting code lengths was predominant to get closer to the Shannon limit, applications that require short block length are developing. Therefore, decoding techniques that can achieve near-maximum-likelihood (near-ML) are gaining momentum. This overview paper surveys recent progress in this emerging field by reviewing the GRAND algorithm, linear programming decoding, machine-learning aided decoding and the recursive projection-aggregation decoding algorithm. For each of the decoding algorithms, both algorithmic and hardware implementations are considered, and future research directions are outlined.
An Efficient and Responsive Robot Motion Controller for Safe Human-Robot Collaboration

Safety and efficiency are two crucial factors for human-robot collaboration. It is challenging to ensure human safety while not sacrificing the task efficiency. In this letter, we present a reinforcement learning (RL) based method with a hazard estimator to balance these two factors. Our method has two phases. In the training phase, an RL control policy and a hazard estimator are trained; in the testing phase, we dynamically select a guiding goal along a given task path to balance between human avoidance and task execution. The proposed method is compared among three previous methods: another RL based method, a reactive method, and a motion planner both in simulated and real-world experiments. Results show that our method can 1) enable a robot to follow a demonstrated (reference) path if the human stays far from the robot; 2) apply responsive online motion adaption to balance human avoidance and task efficiency if the human moves closer toward the robot. In addition, the dynamic goal selection method is easy to use, and can effectively increase the success rate and provide a better trade-off between safety and efficiency.
Cue-Preserving MMSE Filter with Bayesian SNR Marginalization for Binaural Speech Enhancement

Binaural speech enhancement has often suffered from the trade-off between noise reduction and spatial cue preservation. The common-gain filtering of noisy speech under minimum mean-square error (MMSE) turned out as a viable approach, which resembles the format of Wiener-filtering spectral enhancement. Those techniques critically require the estimation of the local time-varying a-priori SNR. In single-channel approaches, it has been recently shown that local a-priori SNR can be marginalized in a Bayesian sense with an MMSE approach. In this paper, we translate the single-channel approach into a binaural Bayesian SNR marginalization, based on a binaural a-priori SNR definition and a related hyperprior. The overall MMSE solution then turns into a posterior expectation of an informed cue-preserving Wiener filter function, the computation of which is governed by binaural a-posteriori SNR and global SNR (i.e., the hyper-prior mean). The resulting MMSE solution is thus easy to implement and performance consistently stands at the top of our evaluation by segmental SNR, PESQ, and STOI computational metrics.
Latent Space Motion Analysis for Collaborative Intelligence

When the input to a deep neural network (DNN) is a video signal, a sequence of feature tensors is produced at the intermediate layers of the model. If neighboring frames of the input video are related through motion, a natural question is, "what is the relationship between the corresponding feature tensors? " By analyzing the effect of common DNN operations on optical flow, we show that the motion present in each channel of a feature tensor is approximately equal to the scaled version of the input motion. The analysis is validated through experiments utilizing common motion models.
Neural Noise Embedding for End-To-End Speech Enhancement with Conditional Layer Normalization

Most of the deep learning based speech enhancement methods focus on the modeling of complicated relationship between the noisy speech and the clean speech without the consideration of noise information. In order to cope with various complex noise scenes, we introduce a novel enhancement architecture that integrates a deep autoencoder with neural noise embedding. In this study, a new normalization method, termed conditional layer normalization (CLN), is introduced to improve the generalization of deep learning based speech enhancement approaches for unseen environments. The noise embedding is passed through the CLN layers to regularize the network for speech enhancement task. The proposed network can be adaptively adjusted according to different noise information extracted from the noisy speech input. The network in overall is trained in an end-to-end manner and the experimental results show that the proposed scheme produces satisfactory enhancement performance comparing the other methods. The visualization shows that our proposed network captures noise information, which is helpful to improve robustness to unseen environments for speech enhancement.
Encoder-Decoder Based Pitch Tracking and Joint Model Training for Mandarin Tone Classification

We pursue an interpretable pitch tracking model and a jointly trained tone model for Mandarin tone classification. For pitch tracking, present deep learning based pitch model structure seldom considers the Viterbi decoding commonly implemented in prevalent manually designed pitch tracking algorithms. We propose RNN based Encoder-Decoder framework with gating mechanism which underlying models both the state cost estimation and Viterbi back-tracing pass implemented in the RAPT algorithm. Then we apply the pitch extractor to a down-stream Mandarin tone classification task. The basic motivation is to combine together the two conventional components in tone classification (i.e., the pitch extractor and tone classifier) and then the whole network are trained simultaneously in an end-to-end fashion. Various cascade methods are evaluated. We carry out pitch extraction and tone classification experiments on Mandarin continuous speech database to show the superiority of the proposed models. Experimental results on pitch extraction show proposed pitch tracking model outperforms the DNN-RNN and bi-directional variants. Tone classification experimental results show the composite model outperforms the traditional cascade tone classification framework which makes use of pitch related feature and a back-end classifier.
First-Order Fast Algorithm for Structurally Optimal Multi-Group Multicast Beamforming in Large-Scale Systems

We consider multi-group multicast beamforming in large-scale systems to minimize the transmit power subject to the signal-to-interference-plus-noise ratio (SINR) requirements. Based on the optimal multicast beamforming structure, we propose a fast first-order algorithm to obtain the beamforming solution. The algorithm utilizes the successive convex approximation (SCA) method and solves each SCA subproblem by dual reformulation along with the extra-gradient method for fast closed-form updates. Initialization methods are also explored, including an extragradient-based fast initialization approach that is proposed to generate initial feasible points for SCA. Simulations show that the proposed algorithm provides a near-optimal performance with substantially lower computational complexity for large-scale systems than the existing algorithm.
Adversarial Attacks Against Face Recognition: A Comprehensive Study

Face recognition (FR) systems have demonstrated reliable verification performance, suggesting suitability for real-world applications ranging from photo tagging in social media to automated border control (ABC). In an advanced FR system with deep learning-based architecture, however, promoting the recognition efficiency alone is not sufficient, and the system should also withstand potential kinds of attacks. Recent studies show that (deep) FR systems exhibit an intriguing vulnerability to imperceptible or perceptible but natural-looking adversarial input images that drive the model to incorrect output predictions. In this article, we present a comprehensive survey on adversarial attacks against FR systems and elaborate on the competence of new countermeasures against them. Further, we propose a taxonomy of existing attack and defense methods based on different criteria. We compare attack methods on the orientation, evaluation process, and attributes, and defense approaches on the category. Finally, we discuss the challenges and potential research direction.
Effective Automation of Distribution Systems With Joint Integration of DGs/ SVCs Considering Reconfiguration Capability by Jellyfish Search Algorithm

Power system operators and planners have progressively shown an interest in maximizing distribution automation technologies. The automated distribution systems (ADS) provide the capability of efficient and reliable control which require an optimal operation strategy to control the status of the line switches and also dispatch the controllable devices. Therefore, this paper introduces an efficient and robust technique based on Jellyfish Search Algorithm (JFSA) for optimal Volt/VAr coordination in ADSs based on joint distribution system reconfiguration (DSR), distributed generation units (DGs) integration and Distribution static VAr compensators (SVCs) operation. The suggested technique is used for the dynamic operation of ADS in order to minimize losses and reduce emissions when considering regular daily loading conditions. The 33-bus and 69-bus delivery DSs have been subjected to a variety of scenarios. These situations are mostly concerned with achieving optimum distribution system operation and control, as well as validating the proposed methodology. Despite the problem’s complexity, the proposed technique based on JFSA is shown to be the best solution in all of the cases considered. Furthermore, a comparison of the proposed JFSA with other similar approaches demonstrates its usefulness as a method to be used in modern ADS control centers.
Exploring the use of Common Label Set to Improve Speech Recognition of Low Resource Indian Languages

In many Indian languages, written characters are organized on sound phonetic principles, and the ordering of characters is the same across many of them. However, while training conventional end-to-end (E2E) Multilingual speech recognition systems, we treat characters or target subword units from different languages as separate entities. Since the visual rendering of these characters is different, in this paper, we explore the benefits of representing such similar target subword units (e.g., Byte Pair Encoded(BPE) units) through a Common Label Set (CLS). The CLS can be very easily created using automatic methods since the ordering of characters is the same in many Indian Languages. E2E models are trained using a transformer-based encoder-decoder architecture. During testing, given the Mel-filterbank features as input, the system outputs a sequence of BPE units in CLS representation. Depending on the language, we then map the recognized CLS units back to the language-specific grapheme representation. Results show that models trained using CLS improve over monolingual baseline and a multilingual framework with separate symbols for each language. Similar experiments on a subset of the Voxforge dataset also confirm the benefits of CLS. An extension of this idea is to decode an unseen language (Zero-resource) using CLS trained model.
Aquarius

The Aquarius project [1] has, as the fundamental goal of its research, to establish the principles by which very large improvements in performance can be achieved in machines specialized for calculating difficult problems in design automation, expert systems, and signal processing. These problems are characterized by having substantial numeric and symbolic components. We are commit ted to the eventual design of a very high performance heterogeneous MIMD multiprocessor tailored to the execution of both numeric and logic calculations. Aquarius began in 1983. By 1985 we had completed and demonstrated the Aquarius I system [15] which was a small heterogeneous multiprocessor. Aquarius I achieved about an order of magnitude higher performance than had been achieved up to tha t time. For example, the Japanese Fifth Generation Computer 'PSI ' had achieved 30 KLIPS in 1985. We are currently focusing on an experimental multiprocessor architecture (Aquarius II) for the high performance execution of Prolog that will contain 12 processors specialized for Prolog and others for a total of 16 processors.
Representation Learning with Spectro-Temporal-Channel Attention for Speech Emotion Recognition

Convolutional neural network (CNN) is found to be effective in learning representation for speech emotion recognition. CNNs do not explicitly model the associations or relative importance of features in the spectral/temporal/channel-wise axes. In this paper, we propose an attention module, named spectro-temporal-channel (STC) attention module that is integrated with CNN to improve representation learning ability. Our module infers an attention map along the three dimensions, namely time, frequency, and CNN channel. Experiments are conducted on the IEMOCAP database to evaluate the effectiveness of the proposed representation learning method. The results demonstrate that the proposed method outperforms the traditional CNN method by an absolute increase of 3.13% in terms of F1 score.
Fden: Mining Effective Information of Features in Detecting Network Anomalies

Network anomaly detection is important for detecting and reacting to the presence of network attacks. In this paper, we propose a novel method to effectively leverage the features in detecting network anomalies, named FDEn, consisting of flow-based Feature Derivation (FD) and prior knowledge incorporated Ensemble models (Enpk). To mine the effective information in features, 149 features are derived to enrich the feature set of the original data with covering more characteristics of network traffic. To leverage these features effectively, an ensemble model Enpk, including CatBoost and XGBoost, based on the bagging strategy is proposed to first detect anomalies by combining numerical features and categorical features. And then, Enpk adjusts the predicted label of specific data by incorporating the prior knowledge of network security. We conduct empirically experiments on the data set provided by the Network Anomaly Detection Challenge (NADC), in which we obtain average improvement up to 61.6%, 31.7%, 50.2%, and 45.0%, in terms of the cost score, precision, recall and F1-score, respectively.
Global Optimisation with Constructive Reals

We draw new connections between deterministic, complete, and general global optimisation of continuous functions and a generalised notion of regression, using constructive type theory and computable real numbers. Using this foundation we formulate novel convergence criteria for regression, derived from the convergence properties of global optimisations. We see this as possibly having an impact on optimisation-based computational sciences, which include much of machine learning. Using computable reals, as opposed to floating-point representations, we can give strong theoretical guarantees in terms of both precision and termination. The theory is fully formalised using the safe mode of the proof assistant AGDA. Some examples implemented using an off-the-shelf constructive reals library in JAVA indicate that the approach is algorithmically promising.
Modeling and Assessing the Temporal Behavior of Emotional and Depressive User Interactions on Social Networks

The way users interact on social media can indicate their well-being. When depressed, people’s feelings tend to be more evident, affecting how users interact and demonstrating their feelings on social media. This paper presents a new approach for the temporal assessment of emotional behavior and interaction among depressed users on social networks. We start by modeling user interactions using complex networks, grouping users through time using the Clauset-Newman-Moore greedy modularity maximization. We evaluate the built networks using metrics such as assortativity, density, clustering, diameter, and shortest path length, closeness, and coverage. Then, we propose EMUS, a method for establishing an emotional user score based on the extraction of emotional features in texts of posts and comments. To extract emotional features, we combine the use of the Empath framework and VADER lexicon. Finally, based on the standard deviation among users, we establish a metric for assessing mood levels. We evaluated users for 33 days, and the results show a sequence of mixed emotional behaviors with high correlations between the number of active users in the network communities, and the form and quality of interactions. The developed approach can be further applied to other database graphs, for different sequential pattern analysis and text-mining contexts.
HCAG: A Hierarchical Context-Aware Graph Attention Model for Depression Detection

Depression is one of the most common mental health disorders, it’s crucial to design an effective and robust model for automatic depression detection (ADD). Although current approaches rely on extra topic models or manually topic-selection procedures which is time-consuming, they still haven’t thoroughly explored the sufficient context information among clinical interviews. In this paper, we propose HCAG, a novel Hierarchical Context-Aware Graph attention model for ADD. Our model mirrors the hierarchical structure of depression assessment and leverages the Graph Attention Network (GAT) to grasp relational contextual information of text/audio modality. Experiments on the DAIC-WOZ dataset show a great performance improvement, with the Fl-score of 0.92, a Mean Absolute Error (MAE) of 2.94, and a Root Mean Square Error (RMSE) of 3.80. To the best of our knowledge, our model outperforms the existing state-of-the-art methods.
F-Net: Fusion Neural Network for Vehicle Trajectory Prediction in Autonomous Driving

Recent research has been remarkable in recurrent neural networks (RNNs) on sequence-to-sequence problems for image caption, and promising in convolutional neural networks (CNNs) on spatial analysis problems for image detection and sematic segmentation problems. In this paper, based on recurrent neural networks and convolutional neural networks, we propose a fusion neural network architecture named F-Net to deal with vehicle trajectory prediction on highway and urban scenarios in autonomous driving applications. The novelty of the proposed method is the attention mechanism that affects effectively in the progress of both RNN and CNN feature extraction. Besides, our sufficient usage of raw sensor data protects scene texture information of environment and interaction among surrounding vehicles. Experimental results on the nuScene dataset show that our proposed method outperforms the state-of-the-art methods.
Low-Latency Polar Decoder Using Overlapped SCL Processing

In this paper, we present a novel scheduling method that reduces the latency of polar decoders significantly. Unlike the prior pruning-based successive cancellation list (SCL) decoding that suffers from a number of idle cycles, the proposed overlapped SCL scheme immediately begins node operations without waiting for the list to be sorted, being exempt from such unfavorable cycles. All possible candidates for the next node operations are precomputed in parallel with the pruning operations, and are readily selected to minimize the latency. For the 5G New Radio systems, the proposed method shortens the decoding latency of the state-of-the-art approaches by up to 22% without degrading the error-correcting performance.
Neural Layered Min-Sum Decoding for Protograph LDPC Codes

In this paper, layered min-sum (MS) iterative decoding is formulated as a customized neural network following the sequential scheduling of check node (CN) updates. By virtue of the lifting structure of protograph low-density parity-check (LDPC) codes, identical network parameters are shared among all derived edges originating from the same edge in the protograph, which makes the number of learn- able parameters manageable. The proposed neural layered MS decoder can support arbitrary codelengths consequently. Moreover, an iteration-wise greedy training method is proposed to tune the parameters such that it avoids the vanishing gradient problem and accelerates the decoding convergence.
Admm-Based Fast Algorithm for Robust Multi-Group Multicast Beamforming

We consider robust multi-group multicast beamforming design in massive multiple-input multiple-output (MIMO) large-scale systems. The goal is to minimize the transmit power subject to the minimum signal-to-interference-plus-noise-ratio (SINR) targets under channel uncertainty. Using the exact worst-case SINR constraints, we transform the problem into a non-convex optimization problem. We develop an alternating direction method of multipliers (ADMM)based fast algorithm to solve this problem directly with convergence guarantee. Our two-layer ADMM-based algorithm decomposes the non-convex problem into a sequence of convex subproblems, for which we obtain the semi-closed-form or closed-form solutions. Simulation studies show that our algorithm provides a considerable computational advantage over the conventional interior-point method non-convex solver with nearly identical performance.
Litesing: Towards Fast, Lightweight and Expressive Singing Voice Synthesis

LiteSing proposed in this paper is a high-quality singing voice synthesis (SVS) system, which is fast, lightweight and expressive. This model mainly stacks several non-autoregressive WaveNet blocks in the encoder and decoder under a generative adversarial architecture, predicts full conditions from the musical score, and generates acoustic features from these conditions. The full conditions in this paper consist of dynamic spectrogram energy, voiced/unvoiced (V/UV) decision and dynamic pitch curve, which are proven related to the expressiveness. We predict the pitch and the timbre features separately, avoiding the interdependence between these two features. Instead of neural network vocoders, a parametric WORLD vocoder is employed for the pitch curve consistency. Experiment results show that LiteSing outperforms the baseline model using feed-forward Transformer by 1.386 times faster on inference speed, 15 times smaller on training parameters number, and achieves a similar MOS on sound quality. Through an A/B test, LiteSing achieves 67.3% preference rate over baseline in pitch curve and dynamic spectrogram energy prediction. which demonstrates the advantage of LiteSing over the other compared models.