Reviewerly/exHarmony huggingface dataset:
Gradient Agreement Hinders the Memorization of Noisy Labels

The performance of deep neural networks (DNNs) critically relies on high-quality annotations, while training DNNs with noisy labels remains challenging owing to their incredible capacity to memorize the entire training set. In this work, we use two synchronously trained networks to reveal that noisy labels may result in more divergent gradients when updating the parameters. To overcome this, we propose a novel co-training framework named gradient agreement learning (GAL). By dynamically evaluating the gradient agreement coefficient of every pair of parameters from two identical DNNs to determine whether to update them in the training process. GAL can effectively hinder the memorization of noisy labels. Furthermore, we utilize the pseudo labels produced by the two DNNs as the supervision for the training of another network, thereby gaining further improvement by correcting some noisy labels while overcoming the confirmation bias. Extensive experiments on various benchmark datasets demonstrate the superiority of the proposed GAL.
Dynamic open set specific emitter identification via multi‐channel reconstructive discriminant network

Abstract Specific emitter identification (SEI) is an emerging device authentication technology, which depends on the inherent hardware characteristics of wireless devices. By analysing the received signal, the hardware characteristics of a specific emitter can be extracted at the receiver and used for device authentication and association. Most of the existing SEI schemes focus on the identification under closed sets. In view of the explosive growth of the number of IoT devices, based on generative adversarial networks, this study proposes a method that considers the identification of unknown emitters. The reconstruction network to reconstruct the signals of the known class and fully train the feature space of the signals of the known class is designed. In the discriminator, two channels are specifically designed to perform anomaly detection for unknown signals and end‐to‐end closed‐set classification for known signals. In order to better reject unknown signals and accept known signals, Receiver operating characteristic curve and Youden index are used to determine the optimal threshold for anomaly detection. Under the given optimal threshold conditions, the identified threshold points have larger True Positive Rate. In addition, the time‐frequency feature combination vector is designed to reveal the essential characteristics of emitters. The experimental results on the real‐world datasets collected from universal software radio peripherals in short‐range communication scenario show that compared with the existing open‐set recognition methods such as C2AE, Openmax and SoftMax, the average recognition accuracy of the proposed framework improved by 0.08, 0.15, and 0.2 respectively, and the Marco‐F1 score improved by 0.05, 0.1, and 0.16, respectively, which proves the superiority of the proposed framework. In addition to identifying unknown and known Universal software radio peripherals in this article, some potential and widespread applications of the proposed framework include access user security authentication in IoT, such as Hack RF and Blue tooth devices. In addition, as a relatively general signal processing framework, the model can be used for air safety management and maritime ship identity authentication in civil aspects. In the military aspect, it can be used in electronic support and various signal reconnaissance scenarios, such as automatic signal modulation recognition in open set scenarios, individual identity determination of radar emitter and unknown working state identification of transmitters, which proves reference for subsequent research on open‐set signal recognition.
Application and practice of information software technology in curriculum teaching reform

Education integrating information software technology is a new smart education model advocated in the field of higher education. The traditional curriculum education and teaching methods are not conducive to the cultivation of innovative talents. Many professional courses need the combination of theoretical knowledge and practical operation. Due to the limitation of practical operation space and cost, the courses tend to favor theoretical teaching, which makes it difficult for students to have an in-depth understanding of professional knowledge. In recent years, virtual simulation experiment platform teaching, professional software modeling and analysis, online network platform practical case teaching and so on have become new practical teaching modes. In some engineering courses, professional design and analysis software such as ABAQUS, MIDAS, Guanglianda, PKPM and BIM can well assist the course practice teaching, help stimulate students' learning interest, enhance students' professional practice ability, help students better understand professional theoretical knowledge and improve students' employment competitiveness. Smart classroom teaching information technology physical environment and online network learning platform provide a strong guarantee for curriculum smart teaching.
Decomposing Complementary and Substitutable Relations for Intercorporate Investment Recommendation

Intercorporate investment has a large impact in financial performance and long-term development of a corporate. Among all the concerns for a company ’s investment strategy, complementary and substitutable investments are two fundamental factors. However, these two relations are implicit and entangled in the complex corporate network, requiring extra caution before investment. To this end, in this paper, we proposed a novel graph convolutional network called Series-Parallel decomposed Graph Convolutional Network (SPGCN). We first decompose the complementary and substitutable relations as two information propagating directions in company dependency graph, producing multifaceted node features. Then, with an Attentive Aggregation Module, we are able to further measure the impact of both features to the final investment decision making, producing an interpretable analysis for investment strategy. Finally, we conduct experiments on a real-world dataset, to show the effectiveness of decomposing two concerns on investment recommendation task. With visualization and case studies, our method also shows great potential to help understand and conduct complementary and substitutable investment decisions. We open source our code to support future research: https://github.com/lem0n1e/SPGCN.
△-Closure Structure for Studying Data Distribution

In this paper, we revisit pattern mining and study the distribution underlying a binary dataset thanks to the closure structure which is based on passkeys, i.e., minimum generators in equivalence classes robust to noise.We introduce ∆-closedness, a generalization of the closure operator, where ∆ measures how a closed set differs from its upper neighbors in the partial order induced by closure.A ∆-class of equivalence includes minimum and maximum elements and allows us to characterize the distribution underlying the data.Moreover, the set of ∆classes of equivalence can be partitioned into the so-called ∆closure structure.In particular, a ∆-class of equivalence with a high ∆ is supported by more observations and thus is more stable.In the experiments, we study the ∆-closure structure of several real-world datasets and show that this structure is very stable for large ∆ and does not substantially depend on the data sampling used for the analysis.
An Empirical Study on Chinese Student Translators’ Ability in Identifying In-text Terminology : With a Specific Focus on Terms of Computer Science

This empirical study is designed to investigate how well Chinese student translators identify terms in specialized Chinese texts. Through contrastive comparisons on the findings based on the data processed in SPSS 19.00, it attempts to uncover how translation education, student translators’ terminology ability and texts of different degree of technicality relate, and accordingly offer some insights to current translation education in China’s mainland, automatic term extraction and terminology access strategy for online e-dictionaries.
Sparsified Subgraph Memory for Continual Graph Representation Learning

Memory replay, which stores a subset of representative historical data from previous tasks to replay while learning new tasks, exhibits state-of-the-art performance for various continual learning applications on Euclidean data. While topological information plays a critical role in characterizing graph data, existing memory replay based graph learning techniques only store individual nodes for replay and do not consider their associated edge information. To this end, we propose a sparsified subgraph memory (SSM), which sparsifies the selected computation graphs into fixed size before storing them into the memory. In this way, we can reduce the memory consumption of a computation subgraph from $\mathcal{O}(d^{L})$ to $\mathcal{O}(1)$, and for the first time enable GNNs to utilize the explicit topological information for memory replay. Finally, our empirical studies show that SSM outperforms state-of-the-art approaches by up to 27.8% on four different public datasets. Unlike existing methods which focus on task incremental learning (task-IL) setting, SSM succeeds in the challenging class incremental learning (class-IL) setting in which a model is required to distinguish all learned classes without task indicators, and even achieves comparable performance to joint training which is the performance upper bound for continual learning. Our code is available at https://github.com/QueuQ/SSM.
From zero to hero

Graph databases employ graph structures such as nodes, attributes and edges to model and store relationships among data. To access this data, graph query languages (GQL) such as Cypher are typically used, which might be difficult to master for end-users. In the context of relational databases, sequence to SQL models, which translate natural language questions to SQL queries, have been proposed. While these Neural Machine Translation (NMT) models increase the accessibility of relational databases, NMT models for graph databases are not yet available mainly due to the lack of suitable parallel training data. In this short paper we sketch an architecture which enables the generation of synthetic training data for the graph query language Cypher.
Construction of Online Teaching Platform for College English Course under the Mixed Teaching Mode

Based on Web technology, we have built an online teaching platform for college English by using B/S architecture, SSM framework, MySQL database and other technologies. By setting up the functions of course learning, key breakthrough, homework assessment, communication and feedback, it can improve the problems of poor autonomous learning ability of students, insufficient communication and interaction between teachers and students, and lack of after-class evaluation in the current college English teaching process. The emergence of online teaching platform can combine network information technology with college English teaching, and form a network learning model with students as the main body of study and teachers as the main body of supervision. With the cooperation of traditional classroom teaching, we can create an efficient and flexible online and offline mixed teaching mode, realize the optimization of college English teaching effect, and make a new attempt for the informatization reform of college education.
Enhancing Robust Text Classification via Category Description

Despite the success of deep neural networks on text classification, their large capacity also leads to capturing task-irrelevant patterns such as label noise. Label noise is usually introduced into the data during label collection and causes nontrivial declines in performance due to the memorization effect. Though effort has been devoted to combating the label noise in other systems such as image classification, high-quality input features are necessary for discovering task-relevant patterns before memorizing the label noise. However, such a high-quality input feature requirement is hard to be satisfied for text classification due to the nature of natural language. To combat the label noise with low-quality input features in the text classification, we propose a novel framework that exploits external category descriptions to construct prototypes that can be used to denoise the input representation and alleviate the over-fitting. However, there still remains a challenge that the external category descriptions from other corpora could be semantically discrepant with the underlying task-specific classes in the training corpus. To align their semantics, we propose two regularizers that penalize sample-wise semantic-based deviations at the local level and class-wise structure-based deviations at the global level, respectively. Our extensive experiments across two open datasets and one real-world case study demonstrate that our method is superior to state-of-the-art baselines under various settings of label noise.
Evaluation modeling of grass-roots teaching organizations in Colleges and Universities Based on FCE

Through the discussion on the organizational form, operation status and evaluation indicators of grass-roots teaching organizations, this paper constructs the evaluation model of grass-roots teaching organizations in Colleges and Universities through the fuzzy comprehensive evaluation(FCE) method, and takes the teaching and Research Office of a department of a university as an application example to carry out qualitative and quantitative research, in order to expect to obtain more objective and fair evaluation results, so as to enhance the vitality of grassroots teaching organizations in Colleges and universities, It provides a guarantee for the sustainable development of grassroots teaching organizations in Colleges and universities.
Formative Evaluation and Performance Management System of Curriculum under the Background of Engineering Certification

This paper analyzes the problems of digitization, timeliness, interaction and diversification in the formative evaluation of curriculum under the background of engineering certification, and develops a performance management system for formative evaluation under the background of engineering certification using Java as the development technology. Teacher and student users can realize the two-way management of curriculum, evaluation, teaching and learning. The system can provide real-time feedback for ongoing educational activities and improve the level of teaching activities.
Marker Correspondence Initialization in an IR Motion Capturing System

Marker-based motion capturing (MoCap) systems are of great importance among the different visual referencing systems, because of their robustness, speed, and precision. Due to the lack of highly descriptive visual features in captured images in such systems, establishing the correspondences between the multi-view observations of the markers is a major challenge. This problem is even more challenging when there is no preceding or low reliable data on the markers’ positions. Under these conditions, the correspondences have to be found from a large space of possible matches. This paper aims to provide a solution for this correspondence initialization problem in multi-view tracking systems based on graph theory and Girvan-Newman community detection. The validity and robustness of the algorithm are verified via experimental analysis.
Detach and Enhance: Learning Disentangled Cross-modal Latent Representation for Efficient Face-Voice Association and Matching

Many researches in cognitive science have shown that humans often perform face-voice association for various perception tasks, and some recent data mining works have been designed in emulating such ability intelligently. Nevertheless, most methods often suffer from the degraded performance when there exist semantically irrelevant interference factors across different modalities. To alleviate this concern, this paper presents an efficient Disentangled Cross-modal Latent Representation (DCLR) method to adaptively detach the discriminative feature attributes and enhance the face-voice association. To be specific, the proposed DCLR framework consists of two-stage cross-modal disentangling process. First, the former stage employs the supervised contrastive learning to push the representations of face-voice data from the same person closer while pulling those representations of different person away. Then, the latter stage freezes all the parameters of the former stage, and further innovates a multi-layer orthogonal decoupling scheme to learn the disentangled latent representations, while filtering out the modality-dependent irrelevant factors. Besides, the cross-modal reconstruction loss is further utilized to narrow down the semantic gap between heterogeneous feature expressions. Through the joint exploitation of the above, the proposed framework can well associate the face-voice data to benefit various kinds of cross-modal perception tasks. Extensive experiments verify the superiorities of the proposed face-voice association framework and show its competitive performances.
A comprehensive evaluation of classroom teaching quality based on a comprehensive-weighted cloud model - From the perspective of expert comments

The establishment of reasonable evaluation standards and the application of proper evaluation methods are the prerequisites and bases for effectively evaluating and promoting teaching quality. In this study, we coded and analyzed the evaluation indexes of classroom teaching quality by extracting key information and carrying out comprehensive weighting, and optimized the weights of current teaching quality evaluation indexes; according to the improved standards, teachers were then scored by various experts. We selected the results of a teacher and re-evaluated the overall classroom teaching quality performance as well as the performance of each sub-index based on a cloud model. Finally, we analyzed their strengths and weaknesses, so as to provide guidance to improve teaching quality in a more targeted manner.
Research on Group Interaction in College English Teaching Based on Educational Informatization

With the continuous development of science and technology, the application of information technology in college English education is increasing day by day. Educational informatization has become the main theme of teaching reform and development in higher education. In the process of English education, teachers must use multimedia network and other information education means to improve teaching efficiency, and use rich network textbooks to make the teaching content more vivid, so that students can interact with their peers in groups to learn independently, so as to achieve the teaching purpose. This paper will explore the application strategies and methods of group interaction mode in college English classroom teaching under the background of educational informatization, hopefully to provide some experience and support for modern college English teaching.
Task-level Relations Modelling for Graph Meta-learning

Graph meta-learning which is used to deal with graph few-shot learning attracts more and more research interests. Existing graph meta-learning methods mainly focus on capturing node-level relations, but they ignore task-level relations which are beneficial for improving the performance of few-shot node classification. Furthermore, contrastive learning which can learn knowledge without labeled data is suitable for few-shot scenario, but existing graph few-shot learning methods have never exploited it. To tackle above problems, in this paper, we combine conventional graph meta-learning framework with graph contrastive learning and propose a novel joint model named -${\underline T}$asklevel -${\underline R}$elations Modelling for -${\underline G}$raph ${\underline M}$eta-learning (TRGM). By constructing auxiliary contrastive pretext tasks, TRGM can fully capture the inter-task relations (task correlation and task discrepancy) and promote the primary few-shot learning. Finally, we conduct extensive experiments on six benchmark datasets to validate the effectiveness and efficiency of TRGM. Experimental results show that our model outperforms several strong baselines and achieves the new state-of-the-art.
Research on the Construction of English Intelligent Learning Mode Based on Big Data

In today's era of big data, the traditional English learning model can no longer meet the personalized needs of users, and it is the general trend to reconstruct, optimize and innovate the English learning model. Therefore, based on Internet big data technology and related intelligent algorithms, this paper constructs an English learning system based on big data, so as to optimize and innovate the learning mode and create an intelligent English learning mode, with a view to further improving the English learning quality of users.
Robustness May be at Odds with Stability in Adversarial Training based Feature Selection?

As an important stage in machine learning pipeline, feature selection techniques are mainly used to improve the generalization performance and training efficiency of machine learning model, but few works have focused on the robustness of machine learning models from the perspective of feature selection when dealing with adversarial attacks. In this paper, we propose the adversarial training (AT) based feature selection framework, i.e. AT based feature selection, to improve the robustness of machine learning model built on the feature selection result, which is inspired by using adversarial training to improve the robustness of deep learning model. AT based feature selection framework is the combination of adversarial training with some traditional feature selection algorithm, which can be divided into AT in-processing and AT post-processing feature selection. On the other hand, stability is also a very important property for feature selection. Then we experimentally analyze the relationship between robustness and stability of AT based feature selection, especially theoretically analyze the stability of $\ell_{2}$ regularized AT in-processing feature selection algorithm in two different adversarial training forms. Our experimental results on benchmark data sets show that AT based feature selection algorithm is effective to improve the robustness of machine learning model, however, obtain lower stability than corresponding feature selection model without AT.
Hypernode: Entity Fusion for Data Traceability and Link Prediction

In the era of big data, fragmented knowledge, multisource heterogeneity, and different representation forms of the same entities in various data sources have posed considerable challenges to entity fusion. How to effectively integrate multisource knowledge for the same entities has provoked vast amounts of attention and research from multiple disciplines. Most existing methods for entity fusion can be categorized into two classes: one is to establish an association between the same entities, and the other is to delete duplicate entities after knowledge fusion and create a new fusion entity. However, in these two classes of methods, the former does not achieve true knowledge fusion and semantic interoperability, while the latter may cause irreversible loss of original information. In this paper, we propose a novel entity fusion scheme: Hypernode. Hypernode fuses the same entity in different data sources into a new entity while retaining the original data. We verify the effectiveness of Hypernode on multiple models of link prediction experiments. Several practical application cases illustrate the applicability of Hypernode in data traceability, open domain knowledge fusion, and multi-modal knowledge graph fusion.