Reviewerly/exHarmony huggingface dataset:
Laplacian Salience-Gated Feature Pyramid Network for Accurate Liver Vessel Segmentation

Liver vessels generated from computed tomography are usually pretty small, which poses major challenges for satisfactory vessel segmentation, including 1) the scarcity of high-quality and large-volume vessel masks, 2) the difficulty in capturing vessel-specific features, and 3) the heavily imbalanced distribution of vessels and liver tissues. To advance, a sophisticated model and an elaborated dataset have been built. The model has a newly conceived Laplacian salience filter that highlights vessel-like regions and suppresses other liver regions to shape the vessel-specific feature learning and to balance vessels against others. It is further coupled with a pyramid deep learning architecture to capture different levels of features, thus improving the feature formulation. Experiments show that this model markedly outperforms the state-of-the-art approaches, achieving a relative improvement of Dice score by at least 1.63% compared to the existing best model on available datasets. More promisingly, the averaged Dice score produced by the existing models on the newly constructed dataset is as high as 0.734±0.070 , which is at least 18.3% higher than that obtained from the existing best dataset under the same settings. These observations suggest that the proposed Laplacian salience, together with the elaborated dataset, can be helpful for liver vessel segmentation.
Expressive-VC: Highly Expressive Voice Conversion with Attention Fusion of Bottleneck and Perturbation Features

Voice conversion for highly expressive speech is challenging. Current approaches struggle with the balance between speaker similarity, intelligibility, and expressiveness. To address this problem, we propose Expressive-VC, a novel end-to-end voice conversion framework that leverages advantages from both the neural bottleneck feature (BNF) approach and the information perturbation approach. Specifically, we use a BNF encoder and a Perturbed-Wav encoder to form a content extractor to learn linguistic and para-linguistic features respectively, where BNFs come from a robust pre-trained ASR model and the perturbed wave becomes speaker-irrelevant after signal perturbation. We further fuse the linguistic and para-linguistic features through an attention mechanism, where speaker-dependent prosody features are used as the attention query, which results from a prosody encoder with target speaker embedding and normalized pitch and energy of source speech as input. Finally, the decoder consumes the integrated features and the speaker-dependent prosody feature to generate the converted speech. Experiments show that Expressive-VC is superior to several popular systems, achieving both high expressiveness captured from the source speech and high speaker similarity with the target speaker; meanwhile intelligibility is well maintained.
BCDAIoD: An Efficient Blockchain-Based Cross-Domain Authentication Scheme for Internet of Drones

During long-distance flight, unmanned aerial vehicles (UAVs) need to perform cross-domain authentication to prove their identity and receive information from the ground control station (GCS). However, the GCS needs to verify all drones arriving at the area it is responsible for, which leads to the GCS being unable to complete authentication in time when facing cross-domain requests from a large number of drones. Additionally, due to potential threats from attackers, drones and GCSs are likely to be deceived. To improve the efficiency and security of cross-domain authentication, we propose an efficient blockchain-based cross-domain authentication scheme for the Internet of Drones (BCDAIoD). By using a consortium chain with a multi-chain architecture, the proposed method can query and update different types of data efficiently. By mutual authentication before cross-domain authentication, drones can compose drone groups to lighten the authentication workload of domain management nodes. BCDAIoD uses the notification mechanism between domains to enable path planning for drones in advance, which can further improve the efficiency of cross-domain authentication. The performance of BCDAIoD was evaluated through experiments. The results show that the cross-domain authentication time cost and computational overhead of BCDAIoD are significantly lower those of than existing methods when the number of drones is large.
Improving the digital capacity of schools by using the <scp>SELFIE</scp> tool for collective reflection

Abstract Schools play a crucial role in preparing students for a technology‐driven world. Although schools incorporate technology for teaching and learning in promising ways, reviewing their organisational strategies would enhance their digital capacity—namely, the effective integration of technology in teaching and learning practices. This article presents insights for improving schools' digital capacity provided by school leaders, teachers, and students from fourteen European countries, through open‐ended questions on the SELFIE online tool for collective reflection. This article presents the thematic analysis conducted on 5247 excerpts collected through SELFIE questionnaires, referring to participants' perceptions of their schools' digital infrastructure, the usefulness of technology for teaching and learning, and the perceived need to make the most of technology. Findings align with international studies, which confirm that schools' digital infrastructure still needs improving, while teachers need more training on embedding technology in their teaching practices. Findings also reveal differences in school leaders, teachers, and students' viewpoints and experiences on technology use in their schools. Comparing viewpoints and experiences regarding a school's digital capacity by using a comprehensive and structured process of collective reflection, the SELFIE tool is unique within the field and can offer insights to school communities, education policymakers and researchers.
Frequency-Aware Attentional Feature Fusion for Deepfake Detection

Various face manipulation techniques develop rapidly and can easily generate high-quality fake images or videos, posing significant ethical concerns when used for malicious purposes. Although recent works achieve significant performance in deepfake detection, they still suffer from overfitting issues. To deal with this problem, we propose a novel framework to aggregate diverse information for deepfake detection from both RGB and frequency. Specially, we first introduce a channel attention module to assemble local and global contexts to overcome the potential semantic inconsistency on local artifacts and global features. Then we design a spatial-frequency feature fusion module to fuse the RGB-frequency information comprehensively. Moreover, a variant attention module is further proposed to improve feature discrimination. Extensive experiments demonstrate that our method maintains comparable performance in intra-dataset and cross-dataset evaluation.
Unrolled Fourier Disparity Layer Optimization for Scene Reconstruction from Few-Shots Focal Stacks

This paper presents a novel unrolled optimization method to reconstruct a dense light field from a focal stack containing only very few images captured with different focus. The proposed unrolled method first reconstructs Fourier Disparity Layers (FDL) from which all the light field viewpoints can then be computed. By recovering details in regions that are out-of-focus in all the captured images, the produced FDL model is also suitable for post-capture scene refocusing from a sparse focal stack. Solving the optimization problem in the FDL domain allows us to derive a closed-form expression of the data-fit term of the inverse problem. We show that the proposed framework outperforms state-of-the-art methods from focal stack measurements for both light field reconstruction and image refocusing.
Learning Hybrid Representations of Semantics and Distortion for Blind Image Quality Assessment

Recently, some studies have shown that semantic and distortion representations both benefit the evaluation of image quality. However, the images of existing synthetic distortion databases are annotated with subjective quality scores and distortion types, lacking labels with semantic objects. Therefore, it is virtually infeasible to learn the representations of image semantics and distortion by co-guiding with semantic and distortion labels. To address this issue, we propose a dual-perception network (DPNet) via an end-to-end multi-task learning method, where knowledge distillation is lever-aged as a semantic label-free strategy. Specifically, semantic representation derived from pre-trained ResNet152 is applied to supervise the output of DPNet, while the output is utilized to construct a distortion recognition task. In this way, image semantics and distortion can be hybridly represented in an identical feature map. Finally, image quality is regressed based on the hybrid representations. Experimental results conducted on five benchmark databases validate that the proposed method can achieve state-of-the-art performance.
Improving Speech Prosody of Audiobook Text-To-Speech Synthesis with Acoustic and Textual Contexts

We present a multi-speaker Japanese audiobook text-to-speech (TTS) system that leverages multimodal context information of preceding acoustic context and bilateral textual context to improve the prosody of synthetic speech. Previous work either uses unilateral or single-modality context, which does not fully represent the context information. The proposed method uses an acoustic context encoder and a textual context encoder to aggregate context information and feeds it to the TTS model, which enables the model to predict context-dependent prosody. We conducted comprehensive objective and subjective evaluations on a multi-speaker Japanese audiobook dataset. Experimental results demonstrate that the proposed method significantly outperforms two previous works. Additionally, we present insights about the different choices of context - modalities, lateral information and length - for audiobook TTS that have never been discussed in the literature before.
Mingling or Misalignment? Temporal Shift for Speech Emotion Recognition with Pre-Trained Representations

Fueled by recent advances of self-supervised models, pre-trained speech representations proved effective for the downstream speech emotion recognition (SER) task. Most prior works mainly focus on exploiting pre-trained representations and just adopt a linear head on top of the pre-trained model, neglecting the design of the downstream network. In this paper, we propose a temporal shift module to mingle channel-wise information without introducing any parameter or FLOP. With the temporal shift module, three designed baseline building blocks evolve into corresponding shift variants, i.e. ShiftCNN, ShiftLSTM, and Shiftformer. Moreover, to balance the trade-off between mingling and misalignment, we propose two technical strategies, placement of shift and proportion of shift. The family of temporal shift models all outperforms the state-of-the-art methods on the benchmark IEMOCAP dataset under both finetuning and feature extraction settings. Our code is available at https://github.com/ECNU-Cross-Innovation-Lab/ShiftSER.
Networked Policy Gradient Play in Markov Potential Games

We propose a networked policy gradient play algorithm for solving Markov potential games. In a Markov game, each agent has a reward function that depends on the actions of all the agents and a common dynamic state. A differentiable Markov potential game admits a potential value function that has local gradients equal to the gradients of agents’ local value functions. In the proposed algorithm, agents use parameterized policies that depend on the state and other agents’ policies. Agents use stochastic gradients and local parameter values received from their neighbors to update their policies. We show that the joint policy parameters converge to a first-order stationary point of a Markov potential game in expectation for general action and state spaces. Numerical results on the lake game exemplify the convergence of the proposed method.
Autotts: End-to-End Text-to-Speech Synthesis Through Differentiable Duration Modeling

Parallel text-to-speech (TTS) models have recently enabled fast and highly-natural speech synthesis. However, they typically require external alignment models, which are not necessarily optimized for the decoder as they are not jointly trained. In this paper, we propose a differentiable duration method for learning monotonic alignments between input and output sequences. Our method is based on a soft-duration mechanism that optimizes a stochastic process in expectation. Using this differentiable duration method, we introduce AutoTTS, a direct text-to-waveform speech synthesis model. AutoTTS enables high-fidelity speech synthesis through a combination of adversarial training and matching the total ground-truth duration. Experimental results show that our model obtains competitive results while enjoying a much simpler training pipeline. Audio samples are available online <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Generalized Invariant Matching Property Via Lasso

Learning under distribution shifts is a challenging task. One principled approach is to exploit the invariance principle via the structural causal models. However, the invariance principle is violated when the response is intervened, making it a difficult setting. In a recent work, the invariant matching property has been developed to shed light on this scenario and shows promising performance. In this work, by formulating a high-dimensional problem with intrinsic sparsity, we generalize the invariant matching property for an important setting when only the target is intervened. We propose a more robust and computation-efficient algorithm by leveraging a variant of Lasso, improving upon the existing algorithms.
Graph-Graph Context Dependency Attention for Graph Edit Distance

As a popular similarity measurement, Graph Edit Distance (GED) can be applied to various downstream tasks. Traditional GED solvers have difficulty in generalization and time efficiency, so deep graph similarity learning models have emerged as a promising trend. However, existing methods lack distinguishing embeddings for cross-dependencies between graphs and intra-graph representations. In this paper, we propose a deep network architecture GED-CDA by introducing a graph-graph context dependency attention module to enhance embeddings. Our graph-graph context dependency attention module consists of a cross-attention layer and a self-attention layer, which can both learn the interactions between graph pairs and capture the connections within graphs. In addition, we also introduce a spectral encoding strategy to bring topological information of graphs into node representations. Extensive experimental results on three real-world graph datasets demonstrate the effectiveness of our proposed GED-CDA in the GED task.
Understanding Shared Speech-Text Representations

Recently, a number of approaches to train speech models by incorporating text into end-to-end models have been developed, with Maestro advancing state-of-the-art automatic speech recognition (ASR) and Speech Translation (ST) performance. In this paper, we expand our understanding of the resulting shared speech-text representations with two types of analyses. First we examine the limits of speech-free domain adaptation, finding that a corpus-specific duration model for speech-text alignment is the most important component for learning a shared speech-text representation. Second, we inspect the similarities between activations of unimodal (speech or text) encoders as compared to the activations of a shared encoder. We find that the shared encoder learns a more compact and overlapping speech-text representation than the uni-modal encoders. We hypothesize that this partially explains the effectiveness of the Maestro shared speech-text representations.
SCSGNet: Spatial-Correlated and Shape-Guided Network for Breast Mass Segmentation

Automatic and accurate breast mass segmentation plays a crucial role in the early diagnosis of breast cancer. However, it has been a challenging task for two main reasons: (1) Breast masses are diverse; and (2) The boundaries of masses are ambiguous. To address these problems, we propose a Spatial-Correlated and Shape-Guided Network (SCSGNet), which combines global context extraction with local boundary refinement. Specifically, the high-level features are aggregated to produce a global map as the initial guidance area, and a Series-Parallel Feature Fusion (SPFF) module is added to capture masses of different shapes and sizes. Besides, we design a Dynamic Long-range Correlation Capture (DLCC) module to capture the spatial correlation of masses at different positions. Finally, we devise a Triplet Attention Guide (TAG) module to iteratively update the feature map and refine the boundary. Experiments on two public datasets demonstrate that our method achieves superior performance over other state-of-the-art methods.
MHLAT: Multi-Hop Label-Wise Attention Model for Automatic ICD Coding

International Classification of Diseases (ICD) coding is the task of assigning ICD diagnosis codes to clinical notes. This can be challenging given the large quantity of labels (nearly 9,000) and lengthy texts (up to 8,000 tokens). However, unlike the single-pass reading process in previous works, humans tend to read the text and label definitions again to get more confident answers. Moreover, although pretrained language models have been used to address these problems, they suffer from huge memory usage. To address the above problems, we propose a simple but effective model called the Multi-Hop Label-wise ATtention (MHLAT), in which multi-hop label-wise attention is deployed to get more precise and informative representations. Extensive experiments on three benchmark MIMIC datasets indicate that our method achieves significantly better or competitive performance on all seven metrics, with much fewer parameters to optimize.
Diffusion Motion: Generate Text-Guided 3D Human Motion by Diffusion Model

We propose a simple and novel method for generating 3D human motion from complex natural language sentences, which describe different velocity, direction and composition of all kinds of actions. Different from existing methods that use classical generative architecture, we apply the Denoising Diffusion Probabilistic Model to this task, synthesizing diverse motion results under the guidance of texts. The diffusion model converts white noise into structured 3D motion by a Markov process with a series of denoising steps and is efficiently trained by optimizing a variational lower bound. To achieve the goal of text-conditioned image synthesis, we use the classifier-free guidance strategy to add text embedding into the model during training. Our experiments demonstrate that our model achieves competitive results on HumanML3D test set quantitatively and can generate more visually natural and diverse examples. We also show with experiments that our model is capable of zero-shot generation of motions for unseen text guidance.
Hybridformer: Improving Squeezeformer with Hybrid Attention and NSR Mechanism

SqueezeFormer has recently shown impressive performance in automatic speech recognition (ASR). However, its inference speed suffers from the quadratic complexity of softmax-attention (SA). In addition, limited by the large convolution kernel size, the local modeling ability of SqueezeFormer is insufficient. In this paper, we propose a novel method HybridFormer to improve SqueezeFormer in a fast and efficient way. Specifically, we first incorporate linear attention (LA) and propose a hybrid LASA paradigm to increase the model’s inference speed. Second, a hybrid neural architecture search (NAS) guided structural re-parameterization (SRep) mechanism, termed NSR, is proposed to enhance the ability of the model to extract local interactions. Extensive experiments conducted on the LibriSpeech dataset demonstrate that our proposed HybridFormer can achieve a 9.1% relative word error rate (WER) reduction over SqueezeFormer on the test-other dataset. Furthermore, when input speech is 30s, the HybridFormer can improve the model’s inference speed up to 18%. Our source code is available online <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
BREAST CANCER DETECTION WITH MACHINE LEARNING APPROACH

One of the most widespread diseases among women today is breast cancer. Early and accurate diagnosis is key in rehabilitation and treatment. The usage of mammograms has some uncertainties in the detection rate. To develop tools for physicians for effective and early detection and diagnosis, machine learning techniques can be adopted. The introduction of Machine Learning (ML) in developing the tool will increase the survival rate of patients with breast cancer. This research work proposed different six ML techniques; Logistic Regression, Linear Discriminant Analysis, Decision Tree (DT), KNN, Naïve Bayes (NB), and Support Vector Machine (SVM), and then recommended the model with the highest accuracy for breast cancer detection. The experiment was carried out in a python environment and all the aforementioned techniques were validated with Wisconsin Breast Cancer dataset and evaluated with accuracy, precision, and recall.
Adaptive Eccm for Mitigating Smart Jammers

This paper considers adaptive radar electronic counter-counter measures (ECCM) to mitigate ECM by an adversarial jammer. Our ECCM approach models the jammer-radar interaction as a Principal Agent Problem (PAP), a popular economics framework for interaction between two entities with an information imbalance. In our setup, the radar does not know the jammer’s utility. Instead, the radar learns the jammer’s utility adaptively over time using inverse reinforcement learning. The radar’s adaptive ECCM objective is two-fold (1) maximize its utility by solving the PAP, and (2) estimate the jammer’s utility by observing its response. Our adaptive ECCM scheme uses deep ideas from revealed preference in micro-economics and principal agent problem in contract theory. Our numerical results show that, over time, our adaptive ECCM both identifies and mitigates the jammer’s utility.