Reviewerly/exHarmony huggingface dataset:
Adversarial Amendment is the Only Force Capable of Transforming an Enemy into a Friend

Adversarial attack is commonly regarded as a huge threat to neural networks because of misleading behavior. This paper presents an opposite perspective: adversarial attacks can be harnessed to improve neural models if amended correctly. Unlike traditional adversarial defense or adversarial training schemes that aim to improve the adversarial robustness, the proposed adversarial amendment (AdvAmd) method aims to improve the original accuracy level of neural models on benign samples. We thoroughly analyze the distribution mismatch between the benign and adversarial samples. This distribution mismatch and the mutual learning mechanism with the same learning ratio applied in prior art defense strategies is the main cause leading the accuracy degradation for benign samples. The proposed AdvAmd is demonstrated to steadily heal the accuracy degradation and even leads to a certain accuracy boost of common neural models on benign classification, object detection, and segmentation tasks. The efficacy of the AdvAmd is contributed by three key components: mediate samples (to reduce the influence of distribution mismatch with a fine-grained amendment), auxiliary batch norm (to solve the mutual learning mechanism and the smoother judgment surface), and AdvAmd loss (to adjust the learning ratios according to different attack vulnerabilities) through quantitative and ablation experiments.
Dual-view Correlation Hybrid Attention Network for Robust Holistic Mammogram Classification

Mammogram image is important for breast cancer screening, and typically obtained in a dual-view form, i.e., cranio-caudal (CC) and mediolateral oblique (MLO), to provide complementary information for clinical decisions. However, previous methods mostly learn features from the two views independently, which violates the clinical knowledge and ignores the importance of dual-view correlation in the feature learning. In this paper, we propose a dual-view correlation hybrid attention network (DCHA-Net) for robust holistic mammogram classification. Specifically, DCHA-Net is carefully designed to extract and reinvent deep feature maps for the two views, and meanwhile to maximize the underlying correlations between them. A hybrid attention module, consisting of local relation and non-local attention blocks, is proposed to alleviate the spatial misalignment of the paired views in the correlation maximization. A dual-view correlation loss is introduced to maximize the feature similarity between corresponding strip-like regions with equal distance to the chest wall, motivated by the fact that their features represent the same breast tissues, and thus should be highly-correlated with each other. Experimental results on the two public datasets, i.e., INbreast and CBIS-DDSM, demonstrate that the DCHA-Net can well preserve and maximize feature correlations across views, and thus outperforms previous state-of-the-art methods for classifying a whole mammogram as malignant or not.
Enhancing Sentiment Analysis with Word2Vec and LSTM: A Comparative Study

Sentiment analysis is an important natural language processing task that helps people understand the emotional information conveyed in texts. This paper aims to propose a sentiment classification model based on the combination of Word2Vec and LSTM (Long Short Term Memory). This paper will introduce two key technologies, Word2Vec and LSTM, combining them to build an effective sentiment analysis model. We conducted a comparative analysis between our model and other state-of-the-art methods including CNN, BiLSTM+CNN, Word2vec+SVM, among others. Through rigorous experimental evaluation, this paper showcases the effectiveness and superior performance of the proposed model in sentiment classification tasks. Our method attains an F1 score of 78.2% on benchmark dataset, indicating its strong performance in the task.
Exploring Effective Inter-Encoder Semantic Interaction for Document-Level Relation Extraction

In document-level relation extraction (RE), the models are required to correctly predict implicit relations in documents via relational reasoning. To this end, many graph-based methods have been proposed for this task. Despite their success, these methods still suffer from several drawbacks: 1) their interaction between document encoder and graph encoder is usually unidirectional and insufficient; 2) their graph encoders often fail to capture the global context of nodes in document graph. In this paper, we propose a document-level RE model with a Graph-Transformer Network (GTN). The GTN includes two core sublayers: 1) the graph-attention sublayer that simultaneously models global and local contexts of nodes in the document graph; 2) the cross-attention sublayer, enabling GTN to capture the non-entity clue information from the document encoder. Furthermore, we introduce two auxiliary training tasks to enhance the bidirectional semantic interaction between the document encoder and GTN: 1) the graph node reconstruction that can effectively train our cross-attention sublayer to enhance the semantic transition from the document encoder to GTN; 2) the structure-aware adversarial knowledge distillation, by which we can effectively transfer the structural information of GTN to the document encoder. Experimental results on four benchmark datasets prove the effectiveness of our model. Our source code is available at https://github.com/DeepLearnXMU/DocRE-BSI.
Efficient Sign Language Translation with a Curriculum-based Non-autoregressive Decoder

Most existing studies on Sign Language Translation (SLT) employ AutoRegressive Decoding Mechanism (AR-DM) to generate target sentences. However, the main disadvantage of the AR-DM is high inference latency. To address this problem, we introduce Non-AutoRegressive Decoding Mechanism (NAR-DM) into SLT, which generates the whole sentence at once. Meanwhile, to improve its decoding ability, we integrate the advantages of curriculum learning and NAR-DM and propose a Curriculum-based NAR Decoder (CND). Specifically, the lower layers of the CND are expected to predict simple tokens that could be predicted correctly using source-side information solely. Meanwhile, the upper layers could predict complex tokens based on the lower layers' predictions. Therefore, our CND significantly reduces the model's inference latency while maintaining its competitive performance. Moreover, to further boost the performance of our CND, we propose a mutual learning framework, containing two decoders, i.e., an AR decoder and our CND. We jointly train the two decoders and minimize the KL divergence between their outputs, which enables our CND to learn the forward sequential knowledge from the strengthened AR decoder. Experimental results on PHOENIX2014T and CSL-Daily demonstrate that our model consistently outperforms all competitive baselines and achieves 7.92/8.02× speed-up compared to the AR SLT model respectively. Our source code is available at https://github.com/yp20000921/CND.
Multi-level Graph Contrastive Prototypical Clustering

Recently, graph neural networks (GNNs) have drawn a surge of investigations in deep graph clustering. Nevertheless, existing approaches predominantly are inclined to semantic-agnostic since GNNs exhibit inherent limitations in capturing global underlying semantic structures. Meanwhile, multiple objectives are imposed within one latent space, whereas representations from different granularities may presumably conflict with each other, yielding severe performance degradation for clustering. To this end, we propose a novel Multi-Level Graph Contrastive Prototypical Clustering (MLG-CPC) framework for end-to-end clustering. Specifically, a Prototype Discrimination (ProDisc) objective function is proposed to explicitly capture semantic information via cluster assignments. Moreover, to alleviate the issue of objectives conflict, we introduce to perceive representations of different granularities within individual feature-, prototypical-, and cluster-level spaces by the feature decorrelation, prototype contrast, and cluster space consistency respectively. Extensive experiments on four benchmarks demonstrate the superiority of the proposed MLG-CPC against the state-of-the-art graph clustering approaches.
Learning to Act for Perceiving in Partially Unknown Environments

Autonomous agents embedded in a physical environment need the ability to correctly perceive the state of the environment from sensory data. In partially observable environments, certain properties can be perceived only in specific situations and from certain viewpoints that can be reached by the agent by planning and executing actions. For instance, to understand whether a cup is full of coffee, an agent, equipped with a camera, needs to turn on the light and look at the cup from the top. When the proper situations to perceive the desired properties are unknown, an agent needs to learn them and plan to get in such situations. In this paper, we devise a general method to solve this problem by evaluating the confidence of a neural network online and by using symbolic planning. We experimentally evaluate the proposed approach on several synthetic datasets, and show the feasibility of our approach in a real-world scenario that involves noisy perceptions and noisy actions on a real robot.
Beyond Pure Text: Summarizing Financial Reports Based on Both Textual and Tabular Data

Abstractive text summarization is to generate concise summaries that well preserve both salient information and the overall semantic meanings of the given documents. However, real-world documents, e.g., financial reports, generally contain rich data such as charts and tabular data which invalidates most existing text summarization approaches. This paper is thus motivated to propose this novel approach to simultaneously summarize both textual and tabular data. Particularly, we first manually construct a “table+text → summary” dataset. Then, the tabular data is respectively embedded in a row-wise and column-wise manner, and the textual data is encoded at the sentence-level via an employed pre-trained model. We propose a salient detector gate respectively performed between each pair of row/column and sentence embeddings. The highly correlated content is considered as salient information that must be summarized. Extensive experiments have been performed on our constructed dataset and the promising results demonstrate the effectiveness of the proposed approach w.r.t. a number of both automatic and human evaluation criteria.
BARA: Efficient Incentive Mechanism with Online Reward Budget Allocation in Cross-Silo Federated Learning

Federated learning (FL) is a prospective distributed machine learning framework that can preserve data privacy. In particular, cross-silo FL can complete model training by making isolated data islands of different organizations collaborate with a parameter server (PS) via exchanging model parameters for multiple communication rounds. In cross-silo FL, an incentive mechanism is indispensable for motivating data owners to contribute their models to FL training. However, how to allocate the reward budget among different rounds is an essential but complicated problem largely overlooked by existing works. The challenge of this problem lies in the opaque feedback between reward budget allocation and model utility improvement of FL, making the optimal reward budget allocation complicated. To address this problem, we design an online reward budget allocation algorithm using Bayesian optimization named BARA (Budget Allocation for Reverse Auction). Specifically, BARA can model the complicated relationship between reward budget allocation and final model accuracy in FL based on historical training records so that the reward budget allocated to each communication round is dynamically optimized so as to maximize the final model utility. We further incorporate the BARA algorithm into reverse auction-based incentive mechanisms to illustrate its effectiveness. Extensive experiments are conducted on real datasets to demonstrate that BARA significantly outperforms competitive baselines by improving model utility with the same amount of reward budget.
Generalized Discriminative Deep Non-Negative Matrix Factorization Based on Latent Feature and Basis Learning

As a powerful tool for data representation, deep NMF has attracted much attention in recent years. Current deep NMF builds the multi-layer structure by decomposing either basis matrix or feature matrix into multiple factors, and probably complicates the learning process when data is insufficient or exhibits simple structure. To overcome the limitations, a novel method called Generalized Deep Non-negative Matrix Factorization (GDNMF) is proposed, which generalizes several NMF and deep NMF methods in a unified framework. GDNMF simultaneously performs decomposition on both features and bases, which learns a hierarchical data representation based on multi-level basis. To further improve the latent representation and enhance its flexibility, GDNMF mutually reinforces shallow linear model and deep non-linear model. Moreover, semi-supervised GDNMF is proposed by treating partial label information as soft constraints in the multi-layer structure. An efficient two-phase optimization algorithm is developed, and experiments on five real-world datesets verify its superior performance compared with state-of-the-art methods.
Report on the Workshop on Factorized Databases

The workshop took place in Zurich and online from August 2 to 4, 2022. It was attended by researchers from 17 academic institutions and industry labs, including Microsoft Gray Systems Lab, Omics Data Automation, Oracle Labs Zurich, RelationalAI, and TigerGraph. It featured 18 talks and plenty of opportunities for discussions. The vast majority of participants attended in person.
Penerapan Model Pembelajaran Word Square dalam Meningkatkan Aktivitas dan Hasil Belajar IPS pada Siswa Kelas V SDN 1 Beringin Raya Tahun pelajaran 2022/2023

The problems in this study are 1) student learning outcomes are still low, 2) some students are classified as passive in learning 3) when learning students are easily distracted by surrounding conditions and 4) the use of learning models has not varied. One way that can be done to overcome these problems is to apply the word square learning model. The purpose of this study is to increase social studies learning activities and outcomes using the word square learning model in grade 5 students at SDN 1 Beringin Raya Kemiling for the 2022/2023 academic year. The method in this study uses Classroom Action Research (CAR). The subjects in this study were 30 class 5A students at SDN 1 Beringin Raya Kemiling. Data collection techniques in this study were in the form of observation sheets, learning achievement tests, and documentation. The indicator of learning outcomes in this study is to achieve a percentage of 80% of the total number of students who score above the KKM. The research results obtained were (1) student activity in cycle I using the word square learning model obtained a percentage of 64.29% and increased in cycle II to 88.10% and (2) social studies learning outcomes after implementing word square learning every the cycle has increased, where in the pre-cycle the mastery of 50% was obtained, then it increased in cycle I to obtain a percentage of 70% and increased again in cycle II to obtain a percentage of 90%. Based on the results of this study, it can be concluded that by applying the word square learning model it can increase the activity and learning outcomes of Social Sciences students in grade 5 SDN 1 Beringin Raya Kemiling in the 2022/2023 academic year.
PRINCIPLES OF CONSTRUCTION OF THE INFORMATION AND CONTROL SYSTEM OF THE DEPARTMENT OF THE UNIVERSITY

The concept of the information and control system of the university department, which trains specialists in the electrical engineering profile, is systematized. The principles of construction and the block diagram of such a system are proposed. The variants of application in the educational process are described. A feature of the proposed system is the integration of the computer and microcontroller educational infrastructure of the department with the "smart home" system of the premises of the university building.
CROSS-PLATFORM UNITY APPLICATION FOR DISPLAYING 3D MODELS OF AUGMENTED REALITY USING ARCORE

Recently, augmented reality technology has taken a qualitative step in development, which has enabled it to be useful in many areas of life. Augmented reality applications are unique in that they annotate or augment the user's reality. Practice has shown that augmented reali-ty technology has unlimited potential and requires further research in the direction of creat-ing innovative immersive applications. This work proposes a cross-platform Unity applica-tion for displaying 3D models of augmented reality using ARCore
Which markets need Central Bank Digital Currency?

Journal Article Which markets need Central Bank Digital Currency? Get access Christian Hofmann Christian Hofmann Christian Hofmann is an Associate Professor at the National University of Singapore, Faculty of Law, Singapore lawch@nus.edu.sg Search for other works by this author on: Oxford Academic Google Scholar Capital Markets Law Journal, kmad015, https://doi.org/10.1093/cmlj/kmad015 Published: 12 August 2023 Article history Accepted: 05 May 2023 Published: 12 August 2023
Practice and Explore of the Application of Visualization in College English Teaching

This study aims to explore the research and practice of visual teaching in college English classrooms.Visual teaching is a kind of teaching method that helps students understand and remember knowledge by using visual media tools such as charts, pictures and videos.In today's information age, students are more and more receptive to visual information, so the application of visual teaching in college English classroom can improve students' learning effect.This study will explore the advantages of visual teaching, common visual teaching tools, and how to choose and design suitable visual teaching materials.This paper analyses the effects of visual teaching in college English classes and aims to provide teachers with guidance and suggestions on how to effectively use visual teaching to improve students' learning outcomes.
A study on the value and implementation strategies of autonomous management in elementary school classrooms

Primary school class management as an important part of elementary school education.Currently, classroom self-management, as a new model of classroom management, has somehow broken through the traditional model of classroom construction, taking students as the main subjects and giving full play to their subjective initiative and creativity.This paper discusses the value of classroom selfmanagement in elementary school and proposes effective strategies for implementing student selfmanagement in elementary school classroom management, so as to lay the foundation for the generation of students' core literacy and their own growth and development.
The Exploration of the Rhetorical Motives in Sons and Lovers through the Perspective of Dramatic Rhetorical Criticism

The dramatism paradigm proposed by the famous American rhetorician, Kenneth Burke, is widely favoured in the field of contemporary rhetorical criticism.Based on Burke's theoretical framework of the "dramatic five-position", this paper analyzes the five elements of the three stages of self-formation, self-exploration and self-reconstruction of the protagonist, Paul, in Lawrence's Sons and Lovers and their relationship ratio, revealing the repressed nature and complex psychology of the low and middle class in late nineteenth century in England.Paul's reverence for primitive human instincts and the pursuit of freedom criticized the aberrant emphasis on material civilization in industrial Britain at that time, invisibly exposing the writer's rhetorical motivations for his work.
Investigation on the Path and Performance Evaluation of Internet Finance Supporting Rural Revitalization in Yunnan under the Background of “Digital Economy”

With the development of the digital economy, research on RR (Rural Revitalization) and financial support in Yunnan Province is constantly improving.The path analysis of financial support for rural areas and the design of performance evaluation models based on DEA (Data Envelopment Analysis) are becoming increasingly important.In the entire research on the path and performance evaluation of RR, how to enhance the integrated development of rural industries in Yunnan Province is currently a key issue that needs to be urgently addressed.Through the research on the development process of rural industrial integration and RR and financial support in Yunnan Province, with the help of the Linear programming model of DEA evaluation decision-making unit, and combined with the questionnaire survey experiment, the following conclusions were drawn based on the data results: Based on the development of digital economy, the statistical yearbooks of six counties and districts in Yunnan Province were selected to calculate their pure TE (Technical Efficiency), scale efficiency and comprehensive TE.It was concluded that only the pure TE and scale efficiency of Fugong County and Weixin County reached 1 at the same time.That is to say, their performance evaluation was more effective.At the same time, in the questionnaire survey experiment, ten returning college students had a high satisfaction evaluation on strategies such as improving the ability of financial institutions to serve RR and continuously improving rural basic financial services for RR.This indicated that the financial support for RR path policy was effective and the performance evaluation model based on DEA had good practical application results.
Research on the Path of Constructive News of Disaster Report in the Era of Convergence-Media—Taking the Coal Mine Collapse Accident in Alxa Left Banner, Inner Mongolia as an Example

In the age of convergence media, how can the news media report social emergencies in a more constructive way, how can they intervene and participate in the governance of social opinion, and how can they contribute to the formation, analysis and solutions of the causes of disasters and accidents?In this paper, ten video news reports with high overall popularity are selected from the "TikTok" platform, and the short video news reports on the coal mine collapse in Alxa Left Banner, Inner Mongolia, are analysed in terms of the topic, content, form, angle and target audience of the reports, in order to explore the role of constructive journalism in the news reporting of disaster events.It is found that there is a contradiction between authenticity and timeliness, between "acceleration" and "alienation" in news production and between "rationality" and "emotion" in reporting.The balance between "rational" and "emotional" reporting.On this basis, we reflect on and explore the possibilities of constructive news reporting and the progress of news production.