Reviewerly/exHarmony huggingface dataset:
Analysis of the Communication Method of National Traditional Sports Culture Based on Deep Learning

Like the Chinese nation, China's national traditional sports culture has a long history and is a treasure of Chinese national culture, reflecting the life, customs, wisdom, and pursuit of the people of all ethnic groups in China. The dissemination of national traditional sports culture is not only a necessary method for our country's cultural export but also a necessary means to maintain the diversity of world sports culture. However, there are still serious deficiencies in the dissemination of national traditional sports culture in our country, and some traditional national sports cultures are not even known to the public. Starting from the two communication methods of sports events and network communication, this paper studies the three mature national traditional sports cultures of martial arts (Wushu), Go, and Chinese chess and proposes a quantitative method for communication methods and communication effects. We further analyze the role of national traditional sports culture in cultural communication.
Critical Classes and Samples Discovering for Partial Domain Adaptation

Partial domain adaptation (PDA) attempts to learn transferable models from a large-scale labeled source domain to a small unlabeled target domain with fewer classes, which has attracted a recent surge of interest in transfer learning. Most conventional PDA approaches endeavor to design delicate source weighting schemes by leveraging target predictions to align cross-domain distributions in the shared class space. Accordingly, two crucial issues are overlooked in these methods. First, target prediction is a double-edged sword, and inaccurate predictions will result in negative transfer inevitably. Second, not all target samples have equal transferability during the adaptation; thus, “ambiguous” target data predicted with high uncertainty should be paid more attentions. In this article, we propose a critical classes and samples discovering network (CSDN) to identify the most relevant source classes and critical target samples, such that more precise cross-domain alignment in the shared label space could be enforced by co-training two diverse classifiers. Specifically, during the training process, CSDN introduces an adaptive source class weighting scheme to select the most relevant classes dynamically. Meanwhile, based on the designed target ambiguous score, CSDN emphasizes more on ambiguous target samples with larger inconsistent predictions to enable fine-grained alignment. Taking a step further, the weighting schemes in CSDN can be easily coupled with other PDA and DA methods to further boost their performance, thereby demonstrating its flexibility. Extensive experiments verify that CSDN attains excellent results compared to state of the arts on four highly competitive benchmark datasets.
Exploiting Session Information in BERT-based Session-aware Sequential Recommendation

In recommendation systems, utilizing the user interaction history as sequential information has resulted in great performance improvement. However, in many online services, user interactions are commonly grouped by sessions that presumably share preferences, which requires a different approach from ordinary sequence representation techniques. To this end, sequence representation models with a hierarchical structure or various viewpoints have been developed but with a rather complex network structure. In this paper, we propose three methods to improve recommendation performance by exploiting session information while minimizing additional parameters in a BERT-based sequential recommendation model: using session tokens, adding session segment embeddings, and a time-aware self-attention. We demonstrate the feasibility of the proposed methods through experiments on widely used recommendation datasets.
Is BERT Robust to Label Noise? A Study on Learning with Noisy Labels in Text Classification

Incorrect labels in training data occur when human annotators make mistakes or when the data is generated via weak or distant supervision.It has been shown that complex noise-handling techniques -by modeling, cleaning or filtering the noisy instances -are required to prevent models from fitting this label noise.However, we show in this work that, for text classification tasks with modern NLP models like BERT, over a variety of noise types, existing noisehandling methods do not always improve its performance, and may even deteriorate it, suggesting the need for further investigation.We also back our observations with a comprehensive analysis.
Augmented Reality-Based Surgery on the Human Cadaver Using a New Generation of Optical Head-Mounted Displays: Development and Feasibility Study

Although nearly one-third of the world's disease burden requires surgical care, only a small proportion of digital health applications are directly used in the surgical field. In the coming decades, the application of augmented reality (AR) with a new generation of optical-see-through head-mounted displays (OST-HMDs) like the HoloLens (Microsoft Corp) has the potential to bring digital health into the surgical field. However, for the application to be performed on a living person, proof of performance must first be provided due to regulatory requirements. In this regard, cadaver studies could provide initial evidence.The goal of the research was to develop an open-source system for AR-based surgery on human cadavers using freely available technologies.We tested our system using an easy-to-understand scenario in which fractured zygomatic arches of the face had to be repositioned with visual and auditory feedback to the investigators using a HoloLens. Results were verified with postoperative imaging and assessed in a blinded fashion by 2 investigators. The developed system and scenario were qualitatively evaluated by consensus interview and individual questionnaires.The development and implementation of our system was feasible and could be realized in the course of a cadaver study. The AR system was found helpful by the investigators for spatial perception in addition to the combination of visual as well as auditory feedback. The surgical end point could be determined metrically as well as by assessment.The development and application of an AR-based surgical system using freely available technologies to perform OST-HMD-guided surgical procedures in cadavers is feasible. Cadaver studies are suitable for OST-HMD-guided interventions to measure a surgical end point and provide an initial data foundation for future clinical trials. The availability of free systems for researchers could be helpful for a possible translation process from digital health to AR-based surgery using OST-HMDs in the operating theater via cadaver studies.
Nearest Neighbor Knowledge Distillation for Neural Machine Translation

k-nearest-neighbor machine translation (kNN-MT), proposed by Khandelwal et al. (2021), has achieved many state-of-the-art results in machine translation tasks.Although effective, kNN-MT requires conducting kNN searches through the large datastore for each decoding step during inference, prohibitively increasing the decoding cost and thus leading to the difficulty for the deployment in real-world applications.In this paper, we propose to move the time-consuming kNN search forward to the preprocessing phase, and then introduce k Nearest Neighbor Knowledge Distillation (kNN-KD) that trains the base NMT model to directly learn the knowledge of kNN.Distilling knowledge retrieved by kNN can encourage the NMT model to take more reasonable target tokens into consideration, thus addressing the overcorrection problem.Extensive experimental results show that, the proposed method achieves consistent improvement over the stateof-the-art baselines including kNN-MT, while maintaining the same training and decoding speed as the standard NMT model. 1
Recent Advances in Concept Drift Adaptation Methods for Deep Learning

In the ``Big Data'' age, the amount and distribution of data have increased wildly and changed over time in various time-series-based tasks, e.g weather prediction, network intrusion detection. However, deep learning models may become outdated facing variable input data distribution, which is called concept drift. To address this problem, large number of samples are usually required to update deep learning models, which is impractical in many realistic applications. This challenge drives researchers to explore the effective ways to adapt deep learning models to concept drift. In this paper, we first mathematically describe the categories of concept drift including abrupt drift, gradual drift, recurrent drift, incremental drift. We then divide existing studies into two categories (i.e., model parameter updating and model structure updating), and analyze the pros and cons of representative methods in each category. Finally, we evaluate the performance of these methods, and point out the future directions of concept drift adaptation for deep learning.
Recurrent Neural Networks Are Universal Approximators With Stochastic Inputs

In this article, we investigate the approximation ability of recurrent neural networks (RNNs) with stochastic inputs in state space model form. More explicitly, we prove that open dynamical systems with stochastic inputs can be well-approximated by a special class of RNNs under some natural assumptions, and the asymptotic approximation error has also been delicately analyzed as time goes to infinity. In addition, as an important application of this result, we construct an RNN-based filter and prove that it can well-approximate finite dimensional filters which include Kalman filter (KF) and Beneš filter as special cases. The efficiency of RNN-based filter has also been verified by two numerical experiments compared with optimal KF.
Evaluating English Language Teaching Quality in Classrooms Using OLAP and SVM Algorithms

Currently, the existing techniques of evaluating quality of teaching quality colleges and universities within China are based on classical and statistical methods. However, these methods are unable to accurately reflect the real and actual evaluation of teaching quality. Furthermore, in this era of computerization, education has also revamped itself and is not limited to the conventional lecturing approach. Nowadays, English has become one of the most important skills for foreigners as countries move toward internationalization resulting in a huge amount of data being collected in educational databases, which remains unused. Powerful tools are required to improve the quality of English teaching and reap the benefits of big data generated in classrooms. In this paper, online analytical processing (OLAP) in combination with a support vector machine (SVM) classification algorithm is adopted, and then the algorithm constructs the linear optimal decision function in the feature space. Through the training of sample data by the SVM algorithm, relatively high-quality classification results can be obtained on the target object, especially for high-dimensional cases. The proposed approach has an extremely efficient application value. Compared with the existing methods, the error of evaluation results can be greatly improved, which leads to further improvement of accuracy in terms of evaluation results.
Chatbots as Cognitive, Educational, Advisory &amp; Coaching Systems

Chatbots are software applications assimilating human communication with the aim to raise adherence and engagement between human-systems interaction. Text messaging-based conversational agents (CAs) make an interesting use of natural language processing and improve by learning, allowing coherent two-way communication with humans, either oral or written as well as real-time decision-making. Chatbots serve as means of learning and teaching, as virtual assistants and social companions. Machine learning algorithms embedded into Chatbots simulate human cognition, including cognitive learning, decision making and adaptation to the environment. Thus, the future of artificial intelligence in Chatbots lies in the development of a global, reliable and sustainable ecosystem of knowledge, skills and values by bringing together all interested stakeholders, such as scientists, consumers, businesses and the state. Such effective customized/personalized learning frameworks depend on sophisticated conversational flows based on user models, which cluster user preferences and attributes in combination with learning analytics to induce end users’ personal skills, knowledge mastery, learning ability as well as professional development.
On Lattice-Free Boosted MMI Training of HMM and CTC-Based Full-Context ASR Models

Hybrid automatic speech recognition (ASR) models are typically sequentially trained with CTC or LF-MMI criteria. However, they have vastly different legacies and are usually implemented in different frameworks. In this paper, by decoupling the concepts of modeling units and label topologies and building proper numerator/denominator graphs accordingly, we establish a generalized framework for hybrid acoustic modeling (AM). In this framework, we show that LF-MMI is a powerful training criterion applicable to both limited-context and full-context models, for wordpiece/mono-char/bi-char/chenone units, with both HMM/CTC topologies. From this framework, we propose three novel training schemes: chenone(ch)/wordpiece(wp)-CTC-bMMI, and wordpiece(wp)-HMM-bMMI with different advantages in training performance, decoding efficiency and decoding time-stamp accuracy. The advantages of different training schemes are evaluated comprehensively on Librispeech, and wp-CTC-bMMI and ch-CTC-bMMI are evaluated on two real world ASR tasks to show their effectiveness. Besides, we also show bi-char(bc) HMM-MMI models can serve as better alignment models than traditional non-neural GMM-HMMs.
Learning Smooth Representation for Unsupervised Domain Adaptation

Typical adversarial-training-based unsupervised domain adaptation (UDA) methods are vulnerable when the source and target datasets are highly complex or exhibit a large discrepancy between their data distributions. Recently, several Lipschitz-constraint-based methods have been explored. The satisfaction of Lipschitz continuity guarantees a remarkable performance on a target domain. However, they lack a mathematical analysis of why a Lipschitz constraint is beneficial to UDA and usually perform poorly on large-scale datasets. In this article, we take the principle of utilizing a Lipschitz constraint further by discussing how it affects the error bound of UDA. A connection between them is built, and an illustration of how Lipschitzness reduces the error bound is presented. A local smooth discrepancy is defined to measure the Lipschitzness of a target distribution in a pointwise way. When constructing a deep end-to-end model, to ensure the effectiveness and stability of UDA, three critical factors are considered in our proposed optimization strategy, i.e., the sample amount of a target domain, dimension, and batchsize of samples. Experimental results demonstrate that our model performs well on several standard benchmarks. Our ablation study shows that the sample amount of a target domain, the dimension, and batchsize of samples, indeed, greatly impact Lipschitz-constraint-based methods' ability to handle large-scale datasets. Code is available at https://github.com/CuthbertCai/SRDA.
Hybrid FAST-SIFT-CNN (HFSC) approach for Vision-Based Indian Sign Language Recognition

Indian Sign Language (ISL) is the conventional means of communication for the deaf-mute community in the Indian subcontinent.Accurate feature extraction is one of the prime challenges in automatic gesture recognition of ISL gestures.In this paper, a hybrid approach, namely HFSC, integrating FAST and SIFT with CNN has been proposed for automatic and accurate recognition of ISL's static and single-hand gestures.Features from accelerated segment test (FAST) and scale-invariant feature transform (SIFT) provides the basic framework for feature extraction while CNN is used for classification.The performance of HFSC is compared with existing sign language recognition approaches by testing on standard benchmark (MNIST, Jochen-Trisech, and NUS hand postureII datasets.The HFSC algorithm's efficiency has been shown by comparing it with CNN and SIFT CNN for a uniform dataset with an accuracy of 97.89%.Furthermore, the Computational results of the HFSC on complex background dataset achieve comparable accuracy of 95%.
Toward a Secure and Usable User Authentication Mechanism for Mobile Passenger ID Devices for Land/Sea Border Control

Nowadays the critical sector of transport becomes progressively more dependent on digital technologies to perform essential activities and develop novel efficient transport services and infrastructure to empower economic and social cohesion exploiting the economic strengths of the European Union (EU). However, although the continuously increasing number of visitors, entering the EU through land-border crossing points or seaports, brings immense economic value, novel border control solutions, such as mobile devices for passenger identification for land/sea border control, are essential to precisely identify passengers “on the fly” ensuring their comfort. Nevertheless, these devices are expected to handle highly confidential personal data and thus, it is very likely to become an attractive target to malicious actors. Therefore, to ensure high level of device security without interrupting border control activities, strong secure and usable user authentication mechanisms are required. Towards this direction, we, firstly, discuss risk-based and adaptive authentication for mobile devices as a suitable approach to deal with the security vs. usability challenge and a novel risk-based adaptive user authentication mechanism is proposed to address this challenge. Afterwards, a set of popular Machine Learning (ML) classification algorithms for risk-based authentication was tested and evaluated on the HuMIdb (Human Mobile Interaction database) dataset to identify the most appropriate ones for the proposed mechanism. The evaluation results demonstrated impact of overfitting (i.e., accuracy: 1,0000) and therefore, we considered novelty detection algorithms to overcome this challenge and demonstrate high performance. To the best of our knowledge, this is the first time that novelty detection algorithms have been considered for risk-based adaptive user authentication showing promising results (OneClassSVM 0,9536, LOF 0,9740, KNN_average 0,9998).
Unsupervised domain adaptation based on adaptive local manifold learning

Transfer learning is known to an effective method dealing with domain shift. When the same task is shared in different domains, it is usually called domain adaptation. The problem of distribution difference is inevitable in domain adaption works. The subspace method can transform the data into a new feature representation, which is helpful to reduce the distribution differences. At present, many researchers have made extensive exploration on subspace learning in domain adaptation works. The weakness of many existed domain adaptation methods based on subspace learning either ignores the local manifold information or has the problem of parameter selection in local manifold regularization term which may limit the effectiveness of cross - domain image classification. Therefore, a novel transfer learning method termed unsupervised domain adaptation based on adaptive local manifold learning (UDA-ALML) is proposed in this paper, which is mainly utilized to cross-domain image classification. For the sake of preserving the structure information of original data, the proposed method combines sparse representation, manifold learning and low rank representation to learn the transformation matrix. To be specific, the weight matrix in traditional local manifold regularization term is replaced by the reconstruction coefficient matrix. Large quantities of experiments show that it has a remarkable performance in cross-domain image recognition.
Validations of an alpha version of the E3 Forensic Speech Science System (E3FS3) core software tools

This paper reports on validations of an alpha version of the E3 Forensic Speech Science System (E3FS3) core software tools. This is an open-code human-supervised-automatic forensic-voice-comparison system based on x-vectors extracted using a type of Deep Neural Network (DNN) known as a Residual Network (ResNet). A benchmark validation was conducted using training and test data (forensic_eval_01) that have previously been used to assess the performance of multiple other forensic-voice-comparison systems. Performance equalled that of the best-performing system with previously published results for the forensic_eval_01 test set. The system was then validated using two different populations (male speakers of Australian English and female speakers of Australian English) under conditions reflecting those of a particular case to which it was to be applied. The conditions included three different sets of codecs applied to the questioned-speaker recordings (two mismatched with the set of codecs applied to the known-speaker recordings), and multiple different durations of questioned-speaker recordings. Validations were conducted and reported in accordance with the "Consensus on validation of forensic voice comparison".
A Label Dependence-Aware Sequence Generation Model for Multi-Level Implicit Discourse Relation Recognition

Implicit discourse relation recognition (IDRR) is a challenging but crucial task in discourse analysis. Most existing methods train multiple models to predict multi-level labels independently, while ignoring the dependence between hierarchically structured labels. In this paper, we consider multi-level IDRR as a conditional label sequence generation task and propose a Label Dependence-aware Sequence Generation Model (LDSGM) for it. Specifically, we first design a label attentive encoder to learn the global representation of an input instance and its level-specific contexts, where the label dependence is integrated to obtain better label embeddings. Then, we employ a label sequence decoder to output the predicted labels in a top-down manner, where the predicted higher-level labels are directly used to guide the label prediction at the current level. We further develop a mutual learning enhanced training method to exploit the label dependence in a bottom-up direction, which is captured by an auxiliary decoder introduced during training. Experimental results on the PDTB dataset show that our model achieves the state-of-the-art performance on multi-level IDRR. We release our code at https://github.com/nlpersECJTU/LDSGM.
DREAM: Improving Situational QA by First Elaborating the Situation

When people answer questions about a specific situation, e.g., "I cheated on my mid-term exam last week.Was that wrong?", cognitive science suggests that they form a mental picture of that situation before answering.While we do not know how language models (LMs) answer such questions, we conjecture that they may answer more accurately if they are also provided with additional details about the question situation, elaborating the "scene".To test this conjecture, we train a new model, DREAM, to answer questions that elaborate the scenes that situated questions are about, and then provide those elaborations as additional context to a question-answering (QA) model.We find that DREAM is able to create better scene elaborations (more accurate, useful, and consistent) than a representative state-of-the-art, zero-shot model (Macaw).We also find that using the scene elaborations as additional context improves the answer accuracy of a downstream QA system, including beyond that obtainable by simply further fine-tuning the QA system on DREAM's training data.These results suggest that adding focused elaborations about a situation can improve a system's reasoning about it, and may serve as an effective way of injecting new scenario-based knowledge into QA models.Finally, our approach is dataset-neutral; we observe improved QA performance across different models, with even bigger gains on models with fewer parameters.