Reviewerly/exHarmony huggingface dataset:
Exploring Machine Speech Chain For Domain Adaptation

Machine Speech Chain integrates both end-to-end (E2E) automatic speech recognition (ASR) and neural text-to-speech (TTS) into one circle for joint training. It has been proven that it can effectively leverage a large amount of unpaired data in the spirit of data augmentation. In this paper, we explore the TTS→ASR pipeline in machine speech chain to perform domain adaptation for both E2E ASR and neural TTS models with only text data from the target domain. We conduct experiments by adapting from audiobook domain (i.e., LibriSpeech) to presentation domain (i.e., TED-LIUM). There is a relative word error rate (WER) reduction of 19.7% for the E2E ASR model on the TED-LIUM test set, and a relative WER reduction of 29.4% in synthetic speech generated by neural TTS in the presentation domain. Moreover, we observe that the gains from the proposed method and conventional adaptation methods of language models are additive.
Fuzzy hierarchical network embedding fusing structural and neighbor information

Many network-based tasks need powerful feature expression to capture the diversity of networks. It can be provided by network embedding learning of nodes. The related researches led to a significant progress so far. Nevertheless, suffering from paying unique attention on the structure or neighborhoods of nodes, most of methods cannot describe the vector representations of nodes well enough. In addition, random walk plays a key role during the learning procedure in many advanced methods. The successor node is selected according t the proximity of its direct prior node in each walk step. It is easy to lead a similarity drifting in a long walk sequence and influence the sequence quality. To address these issues, a fuzzy hierarchical network embedding approach (FHNE) is put forward to learn the vector expression fusing structure and neighbor information. Firstly, a multi-granular graph is constructed by a fuzzy k-core decomposition to encode the structural and neighborhood information of nodes. Then, inspired by the epidemic method, a biased random walk is designed to solve the similarity drifting. Many numerical experiments demonstrate that our method exhibits superior performance on various tasks in some real datasets. It verifies that FHNE can learn the efficient network representations.
Dataset of international students’ acceptance of online distance learning during COVID-19 pandemic: A preliminary investigation

The dataset describes factors affecting international students' acceptance of Online Distance Learning (ODL) mode while pursuing oversea education during COVID-19 pandemic. The recruited respondents comprised of international students who were pursuing undergraduate degree programmes in the institutions of higher learning (IHLs) in Malaysia. Respondents were invited to participate in an online survey via Google Forms. A purposive sampling technique was adopted in this research whereby a total of 207 valid questionnaires were obtained and used for data analysis. Data outputs such as respondents' profile, Partial Least Squares Structural Equation Modelling, and importance-performance matrix analysis were presented. The data can be used as a reference source to identify areas of improvement by educators, academic management, and policy makers of IHLs.
A survey on multimodal-guided visual content synthesis

With the increasing interest in various creative scenes such as social media, film production, and intelligence courses, people expect to be able to compile rich visual content according to their subjective ideas and actual needs. In this context, visual content synthesis technique based on multimodal data has attracted much attention in recent years. Compared to traditional generative methods, multimodal data offer more flexible and concrete clues that provide an interactive and controllable way to generate the desired visual content. In this survey, we comprehensively summarize the improvements in multimodal-guided visual content synthesis. We first formulate the taxonomy of visual content synthesis and divide it into four different subfields depending on the input modality, including visual-guided visual content synthesis, text-guided visual content synthesis, audio-guided visual content synthesis, and visual content synthesis guided by other modalities. In each subfield, we describe the paradigm of different modality-guided visual content synthesis, and also discuss the signature methods mainly based on Generative Adversarial Networks (GANs). Next, we present commonly used benchmark datasets and metrics for evaluating models, as well as detailed comparisons between different methods. Finally, we provide insight into current research challenges and possible future research directions.
Accelerated Frequent Closed Sequential Pattern Mining for uncertain data

Data uncertainty has been taken into a consideration for mining and discovery of its hidden knowledge in a variety of applications. Due to the fact that the nature of closed sequences is closely related to possible world, more recent studies on uncertain closed sequential data mining has usually been challenged by the explosive possible worlds, which is exponential to the number of uncertain sequences in the database. Although basic Probabilistic Frequent Closed Sequences Mining (PFCSM-FF) strategy can solve this problem preliminarily, the inclusion–exclusion rules and closure checking methods used in PFCSM-FF makes mining algorithm very inefficient. And on this basis, another two improvements, PFCSM-CF and PFCSM-CC algorithms, are designed to reduce the search space and simplify the candidate sequence database, which significantly compress the computational scale. Substantial experiments on the real and synthetic datasets have demonstrated the efficiency improvement on the proposed PFCSM-CC and PFCSM-CF methods. Besides, the high usability of the proposed PFCSM-CC algorithm is further demonstrated according to the similarity of the time spent on existing probabilistic frequent sequence mining algorithm.
JDAN: Joint Detection and Association Network for Real-Time Online Multi-Object Tracking

In the last few years, enormous strides have been made for object detection and data association, which are vital subtasks for one-stage online multi-object tracking (MOT). However, the two separated submodules involved in the whole MOT pipeline are processed or optimized separately, resulting in a complex method design and requiring manual settings. In addition, few works integrate the two subtasks into a single end-to-end network to optimize the overall task. In this study, we propose an end-to-end MOT network called joint detection and association network (JDAN) that is trained and inferred in a single network. All layers in JDAN are differentiable, and can be optimized jointly to detect targets and output an association matrix for robust multi-object tracking. What’s more, we generate suitable pseudo-labels to address the data inconsistency between object detection and association. The detection and association submodules could be optimized by the composite loss function that is derived from the detection results and the generated pseudo association labels, respectively. The proposed approach is evaluated on two MOT challenge datasets, and achieves promising performance compared with classic and latest methods.
A DEM Super-Resolution Reconstruction Network Combining Internal and External Learning

The study of digital elevation model (DEM) super-resolution reconstruction algorithms has solved the problem of the need for high-resolution DEMs. However, the DEM super-resolution reconstruction algorithm itself is an inverse problem, and making full use of the DEM a priori information is an effective way to solve this problem. In our work, a new DEM super-resolution reconstruction method is proposed based on the complementary relationship between internally learned super-resolution reconstruction methods and externally learned super-resolution reconstruction methods. The method is based on the presence of a large amount of repetitive information within the DEM. Using an internal learning approach to learn the internal prior of the DEM, a low-resolution dataset of the DEM rich in detailed features is generated, and based on this, the training of a constrained external learning network is constructed for the discrepancy data pair. Finally, it introduces residual learning based on the network model to accelerate the operation rate of the network and to solve the model degradation problem brought about by the deepening of the network. This enables the better transfer of learned detailed features in deeper network mappings, which in turn ensures accurate learning of the DEM prior information. The network utilizes the internal prior of the specific DEM as well as the external prior of the DEM dataset and achieves better super-resolution reconstruction results in the experimental results. The results of super-resolution reconstruction by the Bicubic method, Super-Resolution Convolutional Neural Networks (SRCNN), very deep convolutional networks (VDSR), ”Zero-Shot” Super-Resolution networks (ZSSR) and the new method in this paper were compared, and the average RMSE of the super-resolution reconstruction results of the five methods were 8.48 m, 8.30 m, 8.09 m, 7.02 m and 6.65 m, respectively. The mean elevation error at the same resolution is 21.6% better than that of the Bicubic method, 19.9% better than that of the SRCNN, 17.8% better than that of the VDSR method, and 5.3% better than that of the ZSSR method.
Generation for Unsupervised Domain Adaptation: A Gan-Based Approach for Object Classification with 3D Point Cloud Data

Recent deep networks have achieved good performance on a variety of 3d points classification tasks. However, these models often face challenges in "wild tasks" where there are considerable differences between the labeled training/source data collected by one Lidar and unseen test/target data collected by a different Lidar. Unsupervised domain adaptation (UDA) seeks to overcome such a problem without target domain labels. Instead of aligning features between source data and target data, we propose a method that uses a Generative Adversarial Network (GAN) to generate synthetic data from the source domain so that the output is close to the target domain. Experiments show that our approach performs better than state-of-the-art UDA methods in three popular 3D object/scene datasets (i.e., ModelNet, ShapeNet and ScanNet) for cross-domain 3D object classification.
SAR-BagNet: An Ante-hoc Interpretable Recognition Model Based on Deep Network for SAR Image

Convolutional neural networks (CNNs) have been widely used in SAR image recognition and have achieved high recognition accuracy on some public datasets. However, due to the opacity of the decision-making mechanism, the reliability and credibility of CNNs are insufficient at present, which hinders their application in some important fields such as SAR image recognition. In recent years, various interpretable network structures have been proposed to discern the relationship between a CNN’s decision and image regions. Unfortunately, most interpretable networks are based on optical images, which have poor recognition performance for SAR images, and most of them cannot accurately explain the relationship between image parts and classification decisions. Based on the above problems, in this study, we present SAR-BagNet, which is a novel interpretable recognition framework for SAR images. SAR-BagNet can provide a clear heatmap that can accurately reflect the impact of each part of a SAR image on the final network decision. Except for the good interpretability, SAR-BagNet also has high recognition accuracy and can achieve 98.25% test accuracy.
Improving CTC-Based Speech Recognition Via Knowledge Transferring from Pre-Trained Language Models

Recently, end-to-end automatic speech recognition models based on connectionist temporal classification (CTC) have achieved impressive results, especially when fine-tuned from wav2vec2.0 models. Due to the conditional independence assumption, CTC-based models are always weaker than attention-based encoder-decoder models and require the assistance of external language models (LMs). To solve this issue, we propose two knowledge transferring methods that leverage pre-trained LMs, such as BERT and GPT2, to improve CTC-based models. The first method is based on representation learning, in which the CTC-based models use the representation produced by BERT as an auxiliary learning target. The second method is based on joint classification learning, which combines GPT2 for text modeling with a hybrid CTC/attention architecture. Experiment on AISHELL-1 corpus yields a character error rate (CER) of 4.2% on the test set. When compared to the vanilla CTC-based models fine-tuned from the wav2vec2.0 models, our knowledge transferring method reduces CER by 16.1% relatively without external LMs.
Multi-Task Learning Improves Synthetic Speech Detection

With the development of deep learning, synthetic speech has become more and more realistic and easier to spoof Automatic Speaker Verification (ASV) devices. Based on mining more effective hand-crafted features and proposing more powerful networks, many algorithms have been proposed to detect this malicious attack. In this paper, by observing that deepening the network impairs the performance of the network in detecting unknown attacks, we propose that the synthetic speech detection problem is an out-of-distribution (OOD) generalization problem and we enhance the robustness of networks by using multi-task learning. In our system, three auxiliary tasks are used to assist synthetic speech detection: bonafide speech reconstruction, spoofing voice conversion and speaker classification. Experimental results show that our approach can be applied to multiple architectures and can significantly improve the performance on both known attacks (development set) and unknown attacks (evaluation set). In addition, our best-performing network is quite competitive to recent state-of-the-art (SOTA) systems. It demonstrates the potential application of multi-task learning in synthetic speech detection.
A Novel Genetic Algorithm for Efficient Task Scheduling in Cloud Environment

Cloud Computing environment has enabled many useful and applications in many domains. Cloud environment can be used for various task scheduling algorithms for efficient resource utilization. The goals of task scheduling are to maximize the utilization time of resources, reduce the task execution time, achieve high system throughput, and improve load balancing. Many optimization algorithms are available in the literature to efficiently schedule the tasks independently to improve the time of completion, reduce the cost of execution, and certainly maximize the use of resources. The objective of this paper is to provide a genetic algorithm which can be used to design and implement an evolutionary system for efficient task scheduling. The proposed algorithm can evolve an optimal resource utilization and management strategy in the Cloud environment. Such a system would be very useful for reducing task and job level runtime.
Prior-Bert and Multi-Task Learning for Target-Aspect-Sentiment Joint Detection

Aspect-Based Sentiment Analysis (ABSA) is a fine-grained sentiment analysis task and has become a significant task with real-world scenario value. The challenge of this task is how to generate an effective text representation and construct an end-to-end model that can simultaneously detect (target, aspect, sentiment) triples from a sentence. Besides, the existing models do not take the heavily unbalanced distribution of labels into account and also do not give enough consideration to long-distance dependence of targets and aspect-sentiment pairs. To overcome these challenges, we propose a novel end-to-end model named Prior-BERT and Multi-Task Learning (PBERT-MTL), which can detect all triples more efficiently. We evaluate our model on SemEval-2015 and SemEval-2016 datasets. Extensive results show the validity of our work in this paper. In addition, our model also achieves higher performance on a series of subtasks of target-aspect-sentiment detection. Code is available at https://github.com/CQUPT-CaiKe/PBERT-MTL.
Multi-Relation Message Passing for Multi-Label Text Classification

A well-known challenge associated with the multi-label classification problem is modelling dependencies between labels. Most attempts at modelling label dependencies focus on co-occurrences, ignoring the valuable information that can be extracted by detecting label subsets that rarely occur together. For example, consider customer product reviews; a product probably would not simultaneously be tagged by both "recommended" (i.e., reviewer is happy and recommends the product) and "urgent" (i.e., the review suggests immediate action to remedy an unsatisfactory experience). Aside from the consideration of positive and negative dependencies, the direction of a relationship should also be considered. For a multi-label image classification problem, the "ship" and "sea" labels have an obvious dependency, but the presence of the former implies the latter much more strongly than the other way around. These examples motivate the modelling of multiple types of bi-directional relationships between labels. In this paper, we propose a novel method, entitled Multi-relation Message Passing (MrMP), for the multi-label classification problem. Experiments on benchmark multi-label text classification datasets show that the MrMP module yields similar or superior performance compared to state-of-the-art methods. The approach imposes only minor additional computational and memory overheads. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
An Efficient Lightweight SAR Ship Target Detection Network with Improved Regression Loss Function and Enhanced Feature Information Expression

It is difficult to identify the ship images obtained by a synthetic aperture radar (SAR) due to the influence of dense ships, complex background and small target size, so a deep learning-based target detection algorithm was introduced to obtain better detection performance. However, in order to achieve excellent performance, most of the current target detection algorithms focus on building deep and high-width neural networks, resulting in bloated network structure and reduced detection speed, which is not conducive to the practical application of target detection algorithms. Thereby, an efficient lightweight network Efficient-YOLO for ship detection in complex situations is proposed in the present work. Firstly, a new regression loss function ECIOU is proposed to enhance the detection boxes localization accuracy and model convergence speed. Secondly, We propose the SCUPA module to enhance the multiplexing of picture feature information and the model generalization performance. Thirdly, The GCHE module is proposed to strengthen the network’s ability to extract feature information. At last, the effectiveness of our method is tested on the specialized ship dataset: SSDD and HRSID datasets. The results show that Efficient-YOLO outperforms other state-of-the-art algorithms in accuracy, recall and detection speed, with smaller model complexity and model size.
Automatic Assessment of the Degree of Clinical Depression from Speech Using X-Vectors

Depression is a frequent and curable psychiatric disorder, detrimentally affecting daily activities, harming both work-place productivity and personal relationships. Among many other symptoms, depression is associated with disordered speech production, which might permit its automatic screening by means of the speech of the subject. However, the choice of actual features extracted from the recordings is not trivial. In this study, we employ x-vectors, a DNN-based feature extractor technique, to detect depression from a Hungarian corpus. We experiment with training custom x-vector extractors, and we also explore the performance of an out-of-domain pre-trained one. Our findings confirm that x-vectors are able to capture meaningful speaker traits that contain information for depression discrimination. We also show that the language of the extractor is of secondary importance compared to the frame-level feature set: our best model, which achieved an AUC score of 0.940 and an RMSE score of 9.54, was trained on log-energies instead of MFCCs.
Occluded Person Re-Identification Via Relational Adaptive Feature Correction Learning

Occluded person re-identification (Re-ID) in images captured by multiple cameras is challenging because the target person is occluded by pedestrians or objects, especially in crowded scenes. In addition to the processes performed during holistic person Re-ID, occluded person Re-ID involves the removal of obstacles and the detection of partially visible body parts. Most existing methods utilize the off-the-shelf pose or parsing networks as pseudo labels, which are prone to error. To address these issues, we propose a novel Occlusion Correction Network (OCNet) that corrects features through relational-weight learning and obtains diverse and representative features without using external networks. In addition, we present a simple concept of a center feature in order to provide an intuitive solution to pedestrian occlusion scenarios. Furthermore, we suggest the idea of Separation Loss (SL) for focusing on different parts between global features and part features. We conduct extensive experiments on five challenging benchmark datasets for occluded and holistic Re-ID tasks to demonstrate that our method achieves superior performance to state-of-the-art methods especially on occluded scene.
Syntactic and typological properties of translational language: A comparative description of dependency treebank of academic abstracts

Translational language, the language of translated texts, is distinct from both the source and the target language. Although there are many studies on translational language, they present contradictory results. This paper aims to investigate the syntactic and typological properties of translational language by adopting the two main indices of dependency grammar: mean dependency distance (MDD) and dependency direction. A comparable dependency treebank, consisting of translated and non-translated English abstracts, was built and quantitatively described. The results show that (1) the MDD of translational English is significantly longer than that of non-translational English, which can be explained by the difference in MDD of the main grammatical relations (subject, object, attribute and adverbial); (2) the MDD of translational English is within the threshold of four; (3) translational English has more head-final and fewer head-initial dependencies, which can be explained by the distributional differences of the four grammatical relations; (4) translational English has the same word order preference for SV-VO-AdjN as non-translational English, but there are more AdjN and fewer SV pairs in translational English. These findings show the effect of source language interference and suggest that a quantitative method is valid for a holistic and systemic description of translational language.