Reviewerly/exHarmony huggingface dataset:
Flow-ER: A Flow-Based Embedding Regularization Strategy for Robust Speech Representation Learning

Over the recent years, various deep learning-based embedding methods were proposed. Although the deep learning-based embedding extraction methods have shown good performance in numerous tasks including speaker verification, language identification and anti-spoofing, their performance is limited when it comes to mismatched conditions due to the variability within them unrelated to the main task. In order to alleviate this problem, we propose a novel training strategy that regularizes the embedding network to have minimum information about the nuisance attributes. To achieve this, our proposed method directly incorporates the information bottleneck scheme into the training process, where the mutual information is estimated using an auxiliary normalizing flow network. The performance of the proposed method is evaluated on different speech processing tasks and found to provide improvement over the standard training strategy in all experimentations.
Dual Learning for Large Vocabulary On-Device ASR

Dual learning is a paradigm for semi-supervised machine learning that seeks to leverage unsupervised data by solving two opposite tasks at once. In this scheme, each model is used to generate pseudo-labels for unlabeled examples that are used to train the other model. Dual learning has seen some use in speech processing by pairing ASR and TTS as dual tasks. However, these results mostly address only the case of using unpaired examples to compensate for very small supervised datasets, and mostly on large, non-streaming models. Dual learning has not yet been proven effective for using unsupervised data to improve realistic on-device streaming models that are already trained on large supervised corpora. We provide this missing piece though an analysis of an on-device-sized streaming conformer trained on the entirety of Librispeech, showing relative WER improvements of 10.7%/5.2% without an LM and 11.7%/16.4% with an LM.
Graph neural network comparison for 2D nesting efficiency estimation

Minimizing the level of material consumption in textile production is a major concern. The cornerstone of this optimization task is the nesting problem, whose goal is to lay a set of irregular 2D parts out onto a rectangular surface, called the nesting zone, while respecting a set of constraints. Knowing the efficiency—ratio of usable to used up material enables the optimization of several textile production problems. Unfortunately, knowing the efficiency requires the nesting problem to be solved, which is computationally intensive and has been proven to be NP-hard. This paper introduces a regression approach to estimate efficiency without solving the nesting problem. Our approach models the 2D nesting problem as a graph where the nodes are images derived from parts and the edges hold the constraints. The method then consists of combining convolutional neural networks for addressing the image-based aspects and graph neural networks (GNNs) for the constraint aspects. We evaluate several neural message passing approaches on our dataset and obtain results that are sufficiently accurate for enabling several business use cases, where our model best solves this task with a mean absolute error of 1.65. We provide open access to our dataset, whose properties differ from those of other graph datasets found in the literature. This dataset is constructed on 100,000 real customers’ nesting data. Along the way, we compare the performance and generalization capabilities of four GNN architectures obtained from the literature on this dataset.
Metamorphic Testing of Relation Extraction Models

Relation extraction (RE) is a fundamental NLP task that aims to identify relations between some entities regarding a given text. RE forms the basis for many advanced NLP tasks, such as question answering and text summarization, and thus its quality is critical to the relevant downstream applications. However, evaluating the quality of RE models is non-trivial. On the one hand, obtaining ground truth labels for individual test inputs is tedious and even difficult. On the other hand, there is an increasing need to understand the characteristics of RE models in terms of various aspects. To mitigate these issues, this study proposes evaluating RE models by applying metamorphic testing (MT). A total of eight metamorphic relations (MRs) are identified based on three categories of transformation operations, namely replacement, swap, and combination. These MRs encode some expected properties of different aspects of RE. We further apply MT to three popular RE models. Our experiments reveal a large number of prediction failures in the subject RE models, confirming that MT is effective for evaluating RE models. Further analysis of the experimental results reveals the advantages and disadvantages of our subject models and also uncovers some typical issues of RE models.
Dynamic prediction model and visual spatial analysis technology of big data

With the continuous development of modern industry, the industrial control process is becoming more and more large-scale and automated, people have more and more requirements for control, and the control objectives are no longer single. According to the actual production process requirements, some controlled variables only need to meet certain The desired interval can be set. In addition, with the increasing complexity of industrial processes, the characteristics of controlled objects are also becoming more and more complex. Not only continuous dynamic characteristics such as physical and chemical reactions, but also discrete characteristics such as discrete events and logical rules, the hybrid characteristics are becoming more and more obvious. The main purpose of this paper is to analyze and study the dynamic prediction and visualization space of big data. This paper mainly adopts the methods of soft constraints and interval control, and studies the hybrid interval predictive control algorithm based on the mixed logic dynamic (MLD) model. Experiments show that the weighted interval predictive control algorithm based on MLD can reduce the maximum overshoot of the controlled variable, and at the same time, the controlled variable in the interval can return to the desired set interval faster after it exceeds the interval.
ISSUES OF THE THEORY AND PRACTICE OF PROFESSIONAL AND PEDAGOGICAL TRAINING OF UNIVERSITY TEACHERS

The article characterizes the target, content, activity and resultant components of the professional and pedagogi- cal training of university teachers; an analysis of the main tasks for the formation and development of teachers’ pedagogical consciousness and pedagogical convictions, the pedagogical qualities of the individual, their mastery of the skills and abilities of pedagogical activity in all its types is presented; personal and activity components are singled out, which determine the level of professional and pedagogical readiness of teachers to solve educational problems at the university; the features of professional and pedagogical training of the teaching staff of universities are characterized.
A Novel Teaching Case Design for Complex Engineering Problems in Embedded Training Courses

Training students' ability to solve complex engineering problems is the core of graduation requirements in engineering certification. In view of the outdated teaching cases and serious lack of innovation in embedded training courses, this paper takes the design of livestock temperature monitoring system as an example to solve the pain point problem in the real society. Through project background investigation, definition of design objectives, hardware design, software design and system performance evaluation, etc, the support of the courses for graduation requirements can be enhanced, and students can be cultivated to comprehensive and applied talents with embedded engineering awareness, practical ability and teamwork ability.
Improving Robustness of Medical Image Diagnosis System by Using Multi-loss Hybrid Adversarial Function with Heuristic Projection

Medical image diagnosis system by using deep neural networks (DNN) can improve the sensitivity and speed of interpretation of chest CT for COVID-19 screening. However, DNN based medical image diagnosis is known to be influenced by the adversarial perturbations. In order to improve the robustness of medical image diagnosis system, this paper proposes an adversarial attack training method by using multi-loss hybrid adversarial function with heuristic projection. Firstly, the effective adversarial attacks which contain the noise style that can puzzle the network are created with a multi-loss hybrid adversarial function (MLAdv). Then, instead of adding these adversarial attacks to the training data directly, we consider the similarity between the original samples and adversarial attacks by using an adjacent loss during the training process, which can improve the robustness and the generalization of the network for unanticipated noise perturbations. Experiments are finished on COVID-19 dataset. The average attack success rate of this method for three DNN based medical image diagnosis systems is 63.9%, indicating that the created adversarial attack has strong attack transferability and can puzzle the network effectively. In addition, with the adversarial attack training, the augmented networks by using adversarial attacks can improve the diagnosis accuracy by 4.75%. Therefore, the augmented network based on MLAdv adversarial attacks can improve the robustness of medical image diagnosis system.
Collapse and revival structure of information backflow for a central spin coupled to a finite spin bath

The Markovianity of quantum dynamics is an important property of open quantum systems determined by various ingredients of the system and bath. Apart from the system-bath interaction, the initial state of the bath, etc., the dimension of the bath plays a critical role in determining the Markovianity of quantum dynamics, as a strict decay of the bath correlations requires an infinite dimension for the bath. In this work, we investigate the role of finite bath dimension in the Markovianity of quantum dynamics by considering a simple but nontrivial model in which a central spin is isotropically coupled to a finite number of bath spins, and show how the dynamics of the central spin transits from non-Markovian to Markovian as the number of the bath spins increases. The non-Markovianity is characterized by the information backflow from the bath to the system in terms of the trace distance of the system states. We derive the time evolution of the trace distance analytically, and find periodic collapse-revival patterns in the information flow. The mechanism underlying this phenomenon is investigated in detail, and it shows that the period of the collapse-revival pattern is determined by the competition between the number of the bath spins, the system-bath coupling strength, and the frequency detuning. When the number of bath spins is sufficiently large, the period of the collapse-revival structure as well as the respective collapse and revival times increase in proportion to the number of the bath spins, which characterizes how the information backflow decays with a large dimension of the bath. We also analyze the effect of the system-bath interaction strength and frequency detuning on the collapse-revival patterns of the information flow, and obtain the condition for the existence of the collapse-revival structure. The results are illustrated by numerical computation.
Can One Hear the Position of Nodes?

Wave propagation through nodes and links of a network forms the basis of spectral graph theory. Nevertheless, the sound emitted by nodes within the resonating chamber formed by a network are not well studied. The sound emitted by vibrations of individual nodes reflects the structure of the overall network topology but also the location of the node within the network. In this article a sound recognition neural network is trained to infer centrality measures from the nodes’ wave-forms. In addition to advancing network representation learning, sounds emitted by nodes are plausible in most cases. Auralization of the network topology may open new directions in arts, competing with network visualization.
Self-Regulation Feature Network for Person Reidentification

The performance of person reidentification (ReID) has been significantly improved with the development of convolutional neural networks in the past few years. Most existing ReID methods focus on modifying the backbone network architecture to learn feature representations of persons; however, there are only a few methods that explore the potential performance of the network architecture to relearn features. In this article, we propose a self-regulation feature (SRF) scheme based on the network model, called SRFnet, to address the problem of the limited ability of the model for learning person features. The proposed SRFnet utilizes global branches to supervise local branches and deeply mine potential features of the network from the perspective of optimization to obtain more distinguished feature representations of persons. Different from methods that add an attention mechanism, our SRFnet can self-adjust feature learning by only adding loss functions between the local and global features of the network, and it does not require any additional convolution parameters. Extensive experiments on three public datasets show that the proposed SRFnet method achieves the results of mean average precision (mAP)/Rank-1 as 93.65%/95.28% on Market1501, 89.65%/91.79% on DuckMTMC-reID, 83.79%/82.00% on CUHK03 (labeled), and 81.92%/80.21% on CUHK03 (detected) datasets, respectively. Furthermore, experimental results on cross-domain datasets also demonstrate the effectiveness of the proposed SRFnet.
Design and Implementation of Global Program with Compulsory Course-taking Study Abroad for Nurturing Global Engineers and Scientists

Shibaura Institute of Technology, since the adoption for the Top Global University Project in 2014, has been conducting the globalization of education and research with the goal of nurturing global engineers and scientists. The College of Systems Engineering and Science launched Global Program in three departments in 2017 and then extended to all the departments in 2019. Compulsory long-term course-taking study abroad, which is considered as one of the effective measures for globalization, is installed as the core component of the Global Program. This paper discusses the design and implementation of the Global Program and describes the current situation and issues based on the statistical information of the number of students in the Global Program. Moreover, we will also present the results of an outcome survey of graduates of the Global Program and discuss the evaluations of our global education.
Global Engineering Education and Practical English for the Career Development: Introduction of TOEIC Classes from the 3rd Grade in KOSEN and the Longitudinal Assessment

There is a growing consensus of using the TOEIC as an English proficiency measure in many university admission exams and company promotions in Japan. Yet, many KOSEN students and graduates struggle to use English both at school and work. To fulfill the needs, starting from 2023, Hachinohe KOSEN will introduce the new curriculum for 4th grade students that will require them to have a TOEIC score of least 400 points in order to move on to the 5th grade. To prepare for this requirement, TOEIC-related English classes were introduced in 2019 from the 3rd year. In this study, TOEIC scores of the same students who took two years in a row (the 3rd and 4th grades) were compared and the students’ responses to the English classes were reported.
Scaling and Cutout Data Augmentation for Cardiac Segmentation

Convolutional neural network (CNN) has a compelling learning capability, especially on spatial data representation, crucial in dealing with complex learning tasks. However, it requires extensive training data to optimally fit the model, making it susceptible to overfitting problems when the data is scarce, thus limiting its generalization ability. Therefore, it is essential to collect enough data or supplement the dataset with artificial data to improve the performance of the CNN model. In this paper, simple data augmentation through the geometry transformations of scaling and cutting is explored to augment the training dataset for cardiac segmentation. The generated images and labels are combined with the original dataset to double the training data size. Three state-of-the-art semantic segmentation models, which are U-Net, TernausNet, and DabNet, were used to validate the performance improvement of the proposed data augmentation method. The best performance improvement is returned by DabNet with an increment of 0.24% and 5.14% for mean accuracy and mean intersection over union, respectively. Hence, a better segmentation performance will enable medical practitioners to localize the organs effectively and efficiently.
Vector calculus

Fluid mechanics makes heavy use of vectors. This is due to the fact that the position, velocity and acceleration are vectors. The velocity thus constitutes three of the six state variables required to fully describe a flow. Students tend to have difficulties with vector calculus mainly due to the fact that it is somewhat more difficult to visualize the mathematical operations carried out. However, in many ways, vector calculus is not different from regular calculus. Vectors can be added and subtracted just like numbers. However, there are a couple of concepts inherent to vector calculus which are worth noting and studying. Mainly, the different operators and the respective mathematical operations for which they are applied. Also, the study of coordinate systems and their transformations is important when solving the fundamental equations of fluid mechanics which can often be simplified conveniently if choosing the correct coordinate system. Even though the Cartesian is the most widely used coordinate system the fluid mechanics of a system may be significantly simpler to study when using a different coordinate system. A typical example are microfluidic channels with round cross-sections as commonly found, e.g., in capillaries. In this chapter, we will introduce the four most important coordinate systems, i.e., the Cartesian, cylindrical, polar and spherical coordinate systems. We will derive the respective position, velocity acceleration vectors as well as the transformations that allow us to convert one coordinate system into the other. Finally, we will derive the most important operators in each of the coordinate systems which we require for the solving the fundamental equations of fluid mechanics. We will also introduce four of the most commonly encountered theorems of vector calculus, i.e., the theorems of Gauß, Stokes, Green and Reynold's transport theorem.
Kbpt: Knowledge-Based Prompt Tuning for Zero-Shot Relation Triplet Extraction

Knowledge representation is becoming an effective way of information extraction. However, considerable studies ignored its application scenarios in the zero-shot setting. In this paper, we propose a novel framework for prompt language models based on external ontology knowledge called Knowledge-Based Prompt Tuning for the Zero-shot Relation Triplet Extraction (KBPT), which encourages further investigation in low-resource regimes to address the data scarcity problem in Relation Triplet Extraction (RTE). The major task of relation triplet extraction in zero-shot learning is to extract multiple triplets that are consisted of head entities, tail entities, and relation labels from an input sentence where the extracted relation labels do not exist in the training set. The fundamental idea of prompt tuning is to construct a prompt template, then append it behind the input text as the pre-trained language models (PLMs) input, thus, transforming the classification task into a masked language model prediction. However, our proposed model does not involve the mask language model prediction but a well-designed prompt template in a structured text format to generate synthetic training data containing the unseen relation category. Concretely, we utilize the relation labels and incorporate virtual tokens sources from the relation semantics to construct a structured prompt template for generating synthetic training instances. Moreover, to further enrich and supplement prior knowledge, we draw on ontology schema based on external knowledge bases to enhance the capability of semantic representation in the prompt template. To address the problem of knowledge heterogeneity, we synergistically optimize these embedding representations by way of collective training. In addition, we carefully design a Multiple Triplets Decoding (MTD) algorithm to break through the limitation of extracting multiple relation triplets in a sentence, and our proposed model is model-agnostic and can be orthogonal to any kind of PLMs. Extensive experimental results on four publicly available datasets with low-resource and zero-shot settings demonstrate the effectiveness of our proposed method. Moreover, our model achieves better performance compared with the current State-Of-The-Art (SOTA) model in terms of ${\rm F_{1}}$ score, precision-recall (Pâ€“R) curves and AUC.
A Study on the Diplomatic Documents of Korea-China for Korean Language Major at Chinese University

외교문서는 대외 교류에 중요한 공문서인데 현재 중국 내 한국어학과 교육내용에서 다루지 않고 있다. 따라서 본 연구는 중국 대학 한국어 전공자에게 필요한 외교 문서 교육 내용을 정리하고자 한다. 이를 위해 본 연구는 다음과 같이 두 부분으로 나눠 살펴보았다. 첫째는 한중 외교 문서의 성격을 정리하고 외교 문서가 무엇인지 어떤 분야를 다루는지, 어떤 상황에서 쓰이는지를 중국 대학 한국어 전공자에게 소개하는 것이다. 둘째는 한중 외교 문서의 언어적 특징을 크게 한자어 사용의 특징, 외래어 표기상의 특징 및 양해각서·교환각서의 경어법 특징으로 분류하여 분석하는 것이다. 이 논문은 그동안 언어 학계에서 많이 다루어지지 않았던 ‘한중 외교 문서’를 대상으로 그 성격과 언어학적 특징을 밝혔다는 점에서 외국인 한국어 교육에 기여하는 바가 있을 것이라고 생각한다. It is found that specialized knowledge of external exchanges is necessary to meet the aims of cultivating people of foreign language talent. Although the diplomatic documents are important official documents for external exchanges, but they are not included into the contents of Korean language studies and education. Therefore, the objective of this research is to identify the essential contents of diplomatic document education to those undergraduates who major in Korean language in China. In order, this study examined from the following two parts. First, to summarize the characteristics of diplomatic documents between China and Korea, and to introduce them to the students who major in Korean language at Chinese universities what the diplomatic documents are, which fields are they involved, and when they can be used. Second, to classified the linguistic characteristics of diplomatic documents between Korea and China were largely into three categories: the characteristics of Chinese characters applying, the characteristics of foreign word notation, and the characteristics of the honorific method of memorandum of understanding and exchange. The characteristics and linguistic characteristics of "Korean-Chinese diplomatic documents" which were not covered much in the linguistics research field were revealed, and it is supposed to be contribute to the Korean language education of foreigners.