Reviewerly/exHarmony huggingface dataset:
A Comparative Study on Non-Autoregressive Modelings for Speech-to-Text Generation

Non-autoregressive (NAR) models simultaneously generate multiple outputs in a sequence, which significantly reduces the inference speed at the cost of accuracy drop compared to autoregressive baselines. Showing great potential for real-time applications, an increasing number of NAR models have been explored in different fields to mitigate the performance gap against AR models. In this work, we conduct a comparative study of various NAR modeling methods for end-to-end automatic speech recognition (ASR). Experiments are performed in the state-of-the-art setting using ESPnet. The results on various tasks provide interesting findings for developing an understanding of NAR ASR, such as the accuracy-speed trade-off and robustness against long-form utterances. We also show that the techniques can be combined for further improvement and applied to NAR end-to-end speech translation. All the implementations are publicly available to encourage further research in NAR speech processing.
Linkage of blockchain to enterprise resource planning systems for improving sustainable performance

Abstract The present article analyzes the use of enterprise resources planning (ERP) packages to be part of the solution for the main issues related to the blockchain. ERP modules have been listed and tested against seven propositions, to check the ability to better use ERP for solving blockchain concerns. The research is based on the previous study of Saberi et al. (2019; 10.1080/00207543.2018.1533261) about the relationship between sustainable supply chain management and blockchain. We use structural equation modeling (SEM) to check the validity of the propositions. Based on the results, we propose recommendations for companies to sustainably improve their supply chain strategy with the use of blockchain, along with ERP modules. The results show that the selected companies would benefit from ERP and blockchain technology integration, as they both improve their sustainable corporate performance and integrate them more successfully in the supply chain. ERP and blockchain are to be used together to approach today's challenges and better prepare companies for the uncertain future.
Mechanical arm obstacle avoidance path planning based on improved artificial potential field method

Purpose This paper aims to resolve issues of the traditional artificial potential field method, such as falling into local minima, low success rate and lack of ability to sense the obstacle shapes in the planning process. Design/methodology/approach In this paper, an improved artificial potential field method is proposed, where the object can leave the local minima point, where the algorithm falls into, while it avoids the obstacle, following a shorter feasible path along the repulsive equipotential surface, which is locally optimized. The whole obstacle avoidance process is based on the improved artificial potential field method, applied during the mechanical arm path planning action, along the motion from the starting point to the target point. Findings Simulation results show that the algorithm in this paper can effectively perceive the obstacle shape in all the selected cases and can effectively shorten the distance of the planned path by 13%–41% with significantly higher planning efficiency compared with the improved artificial potential field method based on rapidly-exploring random tree. The experimental results show that the improved artificial potential field method can effectively plan a smooth collision-free path for the object, based on an algorithm with good environmental adaptability. Originality/value An improved artificial potential field method is proposed for optimized obstacle avoidance path planning of a mechanical arm in three-dimensional space. This new approach aims to resolve issues of the traditional artificial potential field method, such as falling into local minima, low success rate and lack of ability to sense the obstacle shapes in the planning process.
Going Beyond One-Size-Fits-All Image Descriptions to Satisfy the Information Wants of People Who are Blind or Have Low Vision

Image descriptions are how people who are blind or have low vision (BLV) access information depicted within images. To our knowledge, no prior work has examined how a description for an image should be designed for different scenarios in which users encounter images. Scenarios consist of the information goal the person has when seeking information from or about an image, paired with the source where the image is found. To address this gap, we interviewed 28 people who are BLV to learn how the scenario impacts what image content (information) should go into an image description. We offer our findings as a foundation for considering how to design next-generation image description technologies that can both (A) support a departure from one-size-fits-all image descriptions to context-aware descriptions, and (B) reveal what content to include in minimum viable descriptions for a large range of scenarios.
CABiNet: Efficient Context Aggregation Network for Low-Latency Semantic Segmentation

With the increasing demand of autonomous machines, pixel-wise semantic segmentation for visual scene understanding needs to be not only accurate but also efficient for any potential real-time applications. In this paper, we propose CABiNet (Context Aggregated Bi-lateral Network), a dual branch convolutional neural network (CNN), with significantly lower computational costs as compared to the state-of-the-art, while maintaining a competitive prediction accuracy. Building upon the existing multi-branch architectures for high-speed semantic segmentation, we design a cheap high resolution branch for effective spatial detailing and a context branch with light-weight versions of global aggregation and local distribution blocks, potent to capture both long-range and local contextual dependencies required for accurate semantic segmentation, with low computational overheads. Specifically, we achieve 76.6% and 75.9% mIOU on Cityscapes validation and test sets respectively, at 76 FPS on an NVIDIA RTX 2080Ti and 8 FPS on a Jetson Xavier NX.
M2Met: The Icassp 2022 Multi-Channel Multi-Party Meeting Transcription Challenge

Recent development of speech signal processing, such as speech recognition, speaker diarization, etc., has inspired numerous applications of speech technologies. The meeting scenario is one of the most valuable and, at the same time, most challenging scenarios for the deployment of speech technologies. Speaker diarization and multi-speaker automatic speech recognition in meeting scenarios have attracted much attention recently. However, the lack of large public meeting data has been a major obstacle for advancement of the field. Therefore, we make available the AliMeeting corpus, which consists of 120 hours of recorded Mandarin meeting data, including far-field data collected by 8-channel microphone array as well as near-field data collected by headset microphone. Each meeting session is composed of 2-4 speakers with different speaker overlap ratio, recorded in meeting rooms with different size. Along with the dataset, we launch the ICASSP 2022 Multi-channel Multi-party Meeting Transcription Challenge (M2MeT) with two tracks, namely speaker diarization and multi-speaker ASR, aiming to provide a common testbed for meeting rich transcription and promote reproducible research in this field. In this paper we provide a detailed introduction of the AliMeeting dateset, challenge rules, evaluation methods and baseline systems.
A dual opposition-based learning for differential evolution with protective mechanism for engineering optimization problems

Opposition-based learning (OBL), which plays an important role in soft computing, has recently drawn attention. The paramount challenge of OBL is to design and find an OBL strategy that is suitable for the problem structure. Besides, for the OBL variants proposed so far, there is no clear taxonomy guideline. To solve these issues, this paper proposes a novel opposition, called dual opposition-based learning (DOBL), which contains two opposition strategies and a protective mechanism. Firstly, a diversity-based taxonomy is proposed, which categorizes existing state-of-the-art OBL variants according to dimension-wise diversity. The subpopulation strategy is used and embedded in the classified OBL variants to generate explorative opposition and exploitative opposition. Secondly, for a successful algorithm, a good ratio between exploration and exploitation is required. Therefore, a protective mechanism is designed to obtain a good equilibrium between exploration and exploitation. Finally, the performance of DOBL is compared with eight state-of-the-art OBL variants on DE and advanced DE named jSO to find the CEC 2017 test suite’s best solution. Besides, DOBL is applied to CEC 2011 as well as CEC 2020 real-world optimization problems, and compared with nine novel metaheuristic algorithms as well as the top three algorithms in CEC 2020, respectively. Two statistical tests, the Wilcoxon rank-sum test and the Friedman test, are used to analyze the experiment results. The experiment results of 29 functions and 60 real-world problems demonstrate that the proposed DOBL is better than its competitors on CEC2011, CEC 2017, and CEC 2020 test suites.
Towards Self-Explainable Graph Neural Network

Graph Neural Networks (GNNs), which generalize the deep neural networks to graph-structured data, have achieved great success in modeling graphs. However, as an extension of deep learning for graphs, GNNs lack explainability, which largely limits their adoption in scenarios that demand the transparency of models. Though many efforts are taken to improve the explainability of deep learning, they mainly focus on i.i.d data, which cannot be directly applied to explain the predictions of GNNs because GNNs utilize both node features and graph topology to make predictions. There are only very few work on the explainability of GNNs and they focus on post-hoc explanations. Since post-hoc explanations are not directly obtained from the GNNs, they can be biased and misrepresent the true explanations. Therefore, in this paper, we study a novel problem of self-explainable GNNs which can simultaneously give predictions and explanations. We propose a new framework which can find K-nearest labeled nodes for each unlabeled node to give explainable node classification, where nearest labeled nodes are found by interpretable similarity module in terms of both node similarity and local structure similarity. Extensive experiments on real-world and synthetic datasets demonstrate the effectiveness of the proposed framework for explainable node classification.
Blockchain-Enabled Resource Trading and Deep Reinforcement Learning-Based Autonomous RAN Slicing in 5G

The advent of radio access network (RAN) slicing is envisioned as a new paradigm for accommodating different virtualized networks on a single infrastructure in 5G and beyond. Consequently, infrastructure providers (InPs) desire virtualized networks to share their subleased resources for effective resource management. Nonetheless, security and privacy challenges in the wireless network deter operators from collaborating with one another for resource trading. Lately, blockchain technology has received overwhelming attention for secure resource trading thanks to its security features. This paper proposes a novel hierarchical framework for blockchain-based resource trading among peer-to-peer (P2P) mobile virtual network operators (MVNOs), for autonomous resource slicing in 5G RAN. Specifically, a consortium blockchain network that supports hyperledger smart contract (SC) is deployed to set up secure resource trading among seller and buyer MVNOs. With the aim of designing a fair incentive mechanism, we model the pricing and demand problem of the seller and buyers as a two-stage Stackelberg game, where the seller MVNO is the leader and buyer MVNOs are followers. To achieve a Stackelberg equilibrium (SE) for the formulated game, a dueling deep Q-network (Dueling DQN) scheme is designed to achieve optimal pricing and demand policies for autonomous resource allocation at negotiation interval. Comprehensive simulation results analysis prove that the proposed scheme reduces double spending attacks by 12% in resource trading settings, and maximizes the utilities of players. The proposed scheme also outperforms deep Q-Network (DQN), Q-learning (QL) and greedy algorithm (GA), in terms of slice and system level satisfaction and resource utilization.
Hyperbolic Hypergraphs for Sequential Recommendation

Hypergraphs have been becoming a popular choice to model complex, non-pairwise, and higher-order interactions for recommender systems. However, compared with traditional graph-based methods, the constructed hypergraphs are usually much sparser, which leads to a dilemma when balancing the benefits of hypergraphs and the modelling difficulty. Moreover, existing sequential hypergraph recommendation overlooks the temporal modelling among user relationships, which neglects rich social signals from the recommendation data. To tackle the above shortcomings of the existing hypergraph-based sequential recommendations, we propose a novel architecture named Hyperbolic Hypergraph representation learning method for Sequential Recommendation (H2SeqRec) with the pre-training phase. Specifically, we design three self-supervised tasks to obtain the pre-training item embeddings to feed or fuse into the following recommendation architecture (with two ways to use the pre-trained embeddings). In the recommendation phase, we learn multi-scale item embeddings via a hierarchical structure to capture multiple time-span information. To alleviate the negative impact of sparse hypergraphs, we utilize a hyperbolic space-based hypergraph convolutional neural network to learn the dynamic item embeddings. Also, we design an item enhancement module to capture dynamic social information at each timestamp to improve effectiveness. Extensive experiments are conducted on two real-world datasets to prove the effectiveness and high performance of the model.
Credit Card Fraud Detection with Autoencoder and Probabilistic Random Forest

This paper proposes a method, called autoencoder with probabilistic random forest (AE-PRF), for detecting credit card frauds. The proposed AE-PRF method first utilizes the autoencoder to extract features of low-dimensionality from credit card transaction data features of high-dimensionality. It then relies on the random forest, an ensemble learning mechanism using the bootstrap aggregating (bagging) concept, with probabilistic classification to classify data as fraudulent or normal. The credit card fraud detection (CCFD) dataset is applied to AE-PRF for performance evaluation and comparison. The CCFD dataset contains large numbers of credit card transactions of European cardholders; it is highly imbalanced since its normal transactions far outnumber fraudulent transactions. Data resampling schemes like the synthetic minority oversampling technique (SMOTE), adaptive synthetic (ADASYN), and Tomek link (T-Link) are applied to the CCFD dataset to balance the numbers of normal and fraudulent transactions for improving AE-PRF performance. Experimental results show that the performance of AE-PRF does not vary much whether resampling schemes are applied to the dataset or not. This indicates that AE-PRF is naturally suitable for dealing with imbalanced datasets. When compared with related methods, AE-PRF has relatively excellent performance in terms of accuracy, the true positive rate, the true negative rate, the Matthews correlation coefficient, and the area under the receiver operating characteristic curve.
Digital Teaching Competence in Higher Education: A Systematic Review

Higher education is one of the educational stages most affected by digital technologies, whose constant development has produced a favourable atmosphere for new approaches regarding the teaching–learning processes. The aim of the study was to identify the most relevant findings that specialists have analysed in terms of the digital competences of university teachers, through the analysis of the scientific production of the specialised literature in the last decade (2009–2019). A search was carried out on the Web of Science (WoS) and Scopus databases following the guidelines of the PRISMA statement (Preferred Reporting Items for Systematic reviews and Meta-Analyses). After applying the inclusion and exclusion criteria, the sample was made up of 20 articles. The main findings show the low level of digital competence among teachers and the need for training in both technological and pedagogical areas.
ConvNets vs. Transformers: Whose Visual Representations are More Transferable?

Vision transformers have attracted much attention from computer vision researchers as they are not restricted to the spatial inductive bias of ConvNets. However, although Transformer-based backbones have achieved much progress on ImageNet classification, it is still unclear whether the learned representations are as transferable as or even more transferable than ConvNets’ features. To address this point, we systematically investigate the transfer learning ability of ConvNets and vision transformers in 15 single-task and multi-task performance evaluations. We observe consistent advantages of Transformer-based backbones on 13 downstream tasks (out of 15), including but not limited to fine-grained classification, scene recognition (classification, segmentation and depth estimation), open-domain classification, face recognition, etc. More specifically, we find that two ViT models heavily rely on whole network fine-tuning to achieve performance gains while Swin Transformer does not have such a requirement. Moreover, vision transformers behave more robustly in multi-task learning, i.e., bringing more improvements when managing mutually beneficial tasks and reducing performance losses when tackling irrelevant tasks. We hope our discoveries can facilitate the exploration and exploitation of vision transformers in the future.
CDAda: A Curriculum Domain Adaptation for Nighttime Semantic Segmentation

Autonomous driving needs to ensure all-weather safety, especially in unfavorable environments such as night and rain. However, the current daytime-trained semantic segmentation networks face significant performance degradation at night because of the huge domain divergence. In this paper, we propose a novel Curriculum Domain Adaptation method (CDAda) to realize the smooth semantic knowledge transfer from daytime to nighttime. Specifically, it consists of two steps: 1) inter-domain style adaptation: fine-tune the daytime-trained model on the labeled synthetic nighttime images through the proposed frequency-based style transformation method (replace the low-frequency components of daytime images with those of nighttime images); 2) intra-domain gradual self-training: separate the nighttime domain into the easy split nighttime domain and hard split nighttime domain based on the "entropy + illumination" ranking principle, then gradually adapt the model to the two sub-domains through pseudo supervision on easy split data and entropy minimization on hard split data. To the best of our knowledge, we first extend the idea of intra-domain adaptation to self-training and prove different treatments on two parts can reduce the distribution divergence in the nighttime domain itself. In particular, aimed at the adopted unlabeled day-night image pairs, the prediction of the daytime images can guide the segmentation on the nighttime images by ensuring patch-level consistency. Extensive experiments on Nighttime Driving, Dark Zurich, and BDD100K-night dataset highlight the effectiveness of our approach with the more favorable performance 50.9%, 45.0%, and 33.8% Mean IoU against existing state-of-the-art approaches.
Conditional Graph Attention Networks for Distilling and Refining Knowledge Graphs in Recommendation

Knowledge graph is generally incorporated into recommender systems to improve overall performance. Due to the generalization and scale of the knowledge graph, most knowledge relationships are not helpful for a target user-item prediction. To exploit the knowledge graph to capture target-specific knowledge relationships in recommender systems, we need to distill the knowledge graph to reserve the useful information and refine the knowledge to capture the users' preferences. To address the issues, we propose Knowledge-aware Conditional Attention Networks (KCAN), which is an end-to-end model to incorporate knowledge graph into a recommender system. Specifically, we use a knowledge-aware attention propagation manner to obtain the node representation first, which captures the global semantic similarity on the user-item network and the knowledge graph. Then given a target, i.e., a user-item pair, we automatically distill the knowledge graph into the target-specific subgraph based on the knowledge-aware attention. Afterward, by applying a conditional attention aggregation on the subgraph, we refine the knowledge graph to obtain target-specific node representations. Therefore, we can gain both representability and personalization to achieve overall performance. Experimental results on real-world datasets demonstrate the effectiveness of our framework over the state-of-the-art algorithms.
A Hybrid-Attention Nested UNet for Nuclear Segmentation in Histopathological Images

Nuclear segmentation of histopathological images is a crucial step in computer-aided image analysis. There are complex, diverse, dense, and even overlapping nuclei in these histopathological images, leading to a challenging task of nuclear segmentation. To overcome this challenge, this paper proposes a hybrid-attention nested UNet (Han-Net), which consists of two modules: a hybrid nested U-shaped network (H-part) and a hybrid attention block (A-part). H-part combines a nested multi-depth U-shaped network and a dense network with full resolution to capture more effective features. A-part is used to explore attention information and build correlations between different pixels. With these two modules, Han-Net extracts discriminative features, which effectively segment the boundaries of not only complex and diverse nuclei but also small and dense nuclei. The comparison in a publicly available multi-organ dataset shows that the proposed model achieves the state-of-the-art performance compared to other models.