Reviewerly/exHarmony huggingface dataset:
Event Temporal Relation Extraction with Bayesian Translational Model

Existing models to extract temporal relations between events lack a principled method to incorporate external knowledge. In this study, we introduce Bayesian-Trans, a Bayesian learning-based method that models the temporal relation representations as latent variables and infers their values via Bayesian inference and translational functions. Compared to conventional neural approaches, instead of performing point estimation to find the best set parameters, the proposed model infers the parameters’ posterior distribution directly, enhancing the model’s capability to encode and express uncertainty about the predictions. Experimental results on the three widely used datasets show that Bayesian-Trans outperforms existing approaches for event temporal relation extraction. We additionally present detailed analyses on uncertainty quantification, comparison of priors, and ablation studies, illustrating the benefits of the proposed approach.
Nationality Bias in Text Generation

Pranav Narayanan Venkit, Sanjana Gautam, Ruchi Panchanadikar, Ting-Hao Huang, Shomir Wilson. Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics. 2023.
Corpus Annotation Graph Builder (CAG): An Architectural Framework to Create and Annotate a Multi-source Graph

Roxanne El Baff, Tobias Hecking, Andreas Hamm, Jasper W. Korte, Sabine Bartsch. Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations. 2023.
EVALIGN: Visual Evaluation of Translation Alignment Models

This paper presents EvAlign, a visual analytics framework for quantitative and qualitative evaluation of automatic translation alignment models. EvAlign offers various visualization views enabling developers to visualize their models’ predictions and compare the performance of their models with other baseline and state-of-the-art models. Through different search and filter functions, researchers and practitioners can also inspect the frequent alignment errors and their positions. EvAlign hosts nine gold standard datasets and the predictions of multiple alignment models. The tool is extendable, and adding additional datasets and models is straightforward. EvAlign can be deployed and used locally and is available on GitHub.
MAUPQA: Massive Automatically-created Polish Question Answering Dataset

Recently, open-domain question answering systems have begun to rely heavily on annotated datasets to train neural passage retrievers. However, manually annotating such datasets is both difficult and time-consuming, which limits their availability for less popular languages. In this work, we experiment with several methods for automatically collecting weakly labeled datasets and show how they affect the performance of the neural passage retrieval models. As a result of our work, we publish the MAUPQA dataset, consisting of nearly 400,000 question-passage pairs for Polish, as well as the HerBERT-QA neural retriever.
Global Constraints with Prompting for Zero-Shot Event Argument Classification

Determining the role of event arguments is a crucial subtask of event extraction. Most previous supervised models leverage costly annotations, which is not practical for open-domain applications. In this work, we propose to use global constraints with prompting to effectively tackles event argument classification without any annotation and task-specific training. Specifically, given an event and its associated passage, the model first creates several new passages by prefix prompts and cloze prompts, where prefix prompts indicate event type and trigger span, and cloze prompts connect each candidate role with the target argument span. Then, a pre-trained language model scores the new passages, making the initial prediction. Our novel prompt templates can easily adapt to all events and argument types without manual effort. Next, the model regularizes the prediction by global constraints exploiting cross-task, cross-argument, and cross-event relations. Extensive experiments demonstrate our model’s effectiveness: it outperforms the best zero-shot baselines by 12.5% and 10.9% F1 on ACE and ERE with given argument spans and by 4.3% and 3.3% F1, respectively, without given argument spans. We have made our code publicly available.
How academic majors in non-STEM affect digital literacy: the empirical study

Digital literacy is important capacity for students’ learning in the world of rapid changes in technology. This article aimed to study digital literacy among non-STEM undergraduate students. The study covered the component of 4 skills, i.e., operation skills, thinking skills, collaborative skills, and awareness skills. The hypothesis set in the study was academic major in non-STEM has different digital literacy. When each component was separately studied, digital literacy among non-STEM students should be different as well. The participants in the study were 378 non-STEM undergraduate students of Prince of Songkla University, Surat Thani Campus. The research instrument was digital literacy measurement. Inferential statistics were used in the data analysis to compare two independent groups. The results support hypotheses that the sample in non-STEM undergraduate students with different academic majors had different levels of digital literacy. Students major in a language had higher mean scores than students major in management. When each component was separately studied, students with different academic majors had different levels of digital literacy in each component. Students major in language had higher mean scores than students major in management in all components. This article mentioned an important point about digital literacy of non-STEM undergraduate students so as to be a guideline for stakeholders to determine strategies, set policies and make plans to develop digital skills among non-STEM students, which is highly important to students in the era of rapid changing technology.
Usability Analysis of Atma Jaya Catholic University E-Learning Based on Human Computer Interaction

E-learning UAJ is a system provided by Unika Atma Jaya to facilitate the learning process for both students and lecturers. However, this system is still rarely used, especially among engineering faculties. This forms the basis for conducting the following analysis. The purpose of this research is to measure the level of usability of the UAJ e-learning system. Usability analysis is carried out using five components of usability outlined by Jakob Nielsen: Learnability, Efficiency, Memorability, Errors, and Satisfaction. The indicators within these five components are then compiled into a questionnaire, which will be distributed to active lecturers and students across all faculties who utilize the UAJ e-learning platform. This study employs a Likert scale with five categories, ranging from category 1 indicating "very bad" to category 5 indicating "very good." Among the five usability criteria assessed for both students and lecturers, the majority of responses fell within category 3, indicating sufficiency. The average scores ranged from 14.122 to 18.731 for lecturers and from 19.825 to 21.845 for students. Overall, the usability level of the UAJ e-learning platform is deemed satisfactory, enabling more effective utilization by both lecturers and students. Additionally, the study results propose several solutions for enhancing the development of the UAJ e-learning system.
Online data poisoning attack against edge AI paradigm for IoT-enabled smart city

&lt;abstract&gt; &lt;p&gt;The deep integration of edge computing and Artificial Intelligence (AI) in IoT (Internet of Things)-enabled smart cities has given rise to new edge AI paradigms that are more vulnerable to attacks such as data and model poisoning and evasion of attacks. This work proposes an online poisoning attack framework based on the edge AI environment of IoT-enabled smart cities, which takes into account the limited storage space and proposes a rehearsal-based buffer mechanism to manipulate the model by incrementally polluting the sample data stream that arrives at the appropriately sized cache. A maximum-gradient-based sample selection strategy is presented, which converts the operation of traversing historical sample gradients into an online iterative computation method to overcome the problem of periodic overwriting of the sample data cache after training. Additionally, a maximum-loss-based sample pollution strategy is proposed to solve the problem of each poisoning sample being updated only once in basic online attacks, transforming the bi-level optimization problem from offline mode to online mode. Finally, the proposed online gray-box poisoning attack algorithms are implemented and evaluated on edge devices of IoT-enabled smart cities using an online data stream simulated with offline open-grid datasets. The results show that the proposed method outperforms the existing baseline methods in both attack effectiveness and overhead.&lt;/p&gt; &lt;/abstract&gt;
Label-Dependent Graph Neural Network

Graph neural network (GNN) provides a powerful expressive way to embed graph-structured data, which has been widely applied and spans many fields. This article found an interesting but unreasonable phenomenon in some classical GNNs, namely label confusion. Theoretically, the dependence between prediction label and ground-truth should gradually increase and tend to converge with training epochs. On the contrary, label confusion shows that the dependence between the predicted label and ground-truth is strong first and then weak. This article explains it from the perspective of feature-noise dependence. Specifically, traditional GNN prediction usually assumes that the ground-truth consists of a mutually independent prediction label (dependent on node features) and noise (independent of node features). However, the propagation aggregation mechanism of GNN will integrate irrelevant information from neighboring nodes into the prediction label, resulting in the prediction label no longer being completely dependent on the node feature, or the noise no longer being completely independent of the node feature. Hence, this article proposes a Label-Dependent GNN to alleviate this problem, called LDGNN. LDGNN mainly consists of two limitations, namely feature-noise (the difference between predicted label and ground-truth) independence, and expectation-variance (EV) separation. Specifically, LDGNN introduces the Hilbert-Schmidt independence criterion (HSIC) as a regularization to minimize the dependence between input features and noise. Note that the main reason for adopting HSIC is that it can measure the nonlinear relationship between any two spatial variables. In this way, HSIC can guide GNN to retain more label-dependence information. Then, LDGNN designs an EV separation to centralize nodes within a class and disperse them between classes to further retain label-dependence information. Through these two strategies, the GNN’s expression ability can be enhanced. Next, we theoretically prove the essential reason why LDGNN alleviates label confusion and has been verified in experiments. To verify the performance of LDGNN, we apply it to four classical GNN models on three datasets, and experimental results demonstrate the effectiveness of LDGNN.
FEATURES OF INTERNET COMMUNICATION GENRES

The article discusses general ideas about Internet communication, a new type of communication that has appeared in connection with the rapid development of the Internet and information technologies, describes the variety of terms, considers differences in definitions given by scientists, highlights the formation of different views on genre classification. On the basis of these conclusions, the author grouped the genre of Internet communication according to structural-compositional, communicative parameters. These include e-mail, the genre of instant messages, guest book, electronic bulletin boards, forum, chat, blog. The paper analyzes each of the listed genres, reveals their similarities and distinctive features. It has been described that a common feature, characteristic of all of them, is: communication is carried out using some device; features of colloquial speech are transmitted using graphic symbols; communication is established in a certain virtual space, on an asynchronous and synchronous basis, without the physical presence of people; no limit on the number of participants and requirements for their records. And the differences include the nature of participation (monologue, dialogue, polylogue) and the degree of interactivity, phatic function and compliance with the norms of the literary language, etc. They were named as the main features that distinguish it from other genres of the language.
An Improved Harris Hawks Optimization Algorithm and Its Application in Grid Map Path Planning

Aimed at the problems of the Harris Hawks Optimization (HHO) algorithm, including the non-origin symmetric interval update position out-of-bounds rate, low search efficiency, slow convergence speed, and low precision, an Improved Harris Hawks Optimization (IHHO) algorithm is proposed. In this algorithm, a circle map was added to replace the pseudo-random initial population, and the population boundary number was reduced to improve the efficiency of the location update. By introducing a random-oriented strategy, the information exchange between populations was increased and the out-of-bounds position update was reduced. At the same time, the improved sine-trend search strategy was introduced to improve the search performance and reduce the out-of-bound rate. Then, a nonlinear jump strength combining escape energy and jump strength was proposed to improve the convergence accuracy of the algorithm. Finally, the simulation experiment was carried out on the test function and the path planning application of a 2D grid map. The results show that the Improved Harris Hawks Optimization algorithm is more competitive in solving accuracy, convergence speed, and non-origin symmetric interval search efficiency, and verifies the feasibility and effectiveness of the Improved Harris Hawks Optimization in the path planning of a grid map.
Model for Estimating Waste Generation in Pekanbaru Using Backpropagation Algorithm

Waste generation in Pekanbaru City cannot be managed optimally. Based on 2020 data, less than 50% of the waste that reaches the Final Disposal Site (TPA) reaches. To overcome this problem, this study aims to create an estimation model that can estimate the amount of waste generated each year. So that it can help the authorities to implement various policies to control waste generation. The estimation model is created using the backpropagation algorithm. The attributes used are those related to population and waste generation. Based on the results of experiments conducted using RapidMiner, the best network architecture model is the 6-6-1 model, namely six nodes in the input layer, six nodes in the hidden layer, and one node in the output layer. The six nodes in the input layer refer to the number of attributes used. The activation function used is binary sigmoid. The RMSE value generated from the best model is very low, namely 0.0181. So it can be concluded that this model can be used to estimate the generation of solid waste in Pekanbaru City
Comparison of Machine Learning and Deep Learning Algorithms for Classification of Breast Cancer

Statistical data from the American Cancer Society which shows that breast cancer ranks first with the highest number of cases of all types of cases of malignant tumors (cancer) worldwide. through a data mining process that is used to extract information and data analysis, a classification process can be carried out to carry out further analysis of the pattern of a data. The dataset used in this study is the Breast Cancer Wisconsin (Diagnostic) Dataset obtained from UCI Machine Learning. The purpose of this study is to compare five algorithms, namely Logistic Regression, K Neighbors Classifier (KNN), Decision Tree Classifier, Deep Neural Network, Genetic Algorithm. The results showed that deep neural network algorithms and multilayer perceptron-genetic algorithms get 96% accuracy, logistic regression algorithms have 96% accuracy, then KNN with 94%, and decision tree classifier with 92%.
Using adaptive learning rate to generate adversarial images

Abstract Convolutional neural networks (CNNs) have proved their efficiency in performing image classification tasks, as they can automatically extract the image features and make the corresponding prediction. Meanwhile, the CNNs application is highly challenged by their vulnerability to adversarial samples. These samples are slightly different from the legitimate samples, but the CNN gives wrong classification. There are various ways to find the adversarial samples. The most common method is using backpropagation to generate gradients as the directed perturbation. Contrarily to set a constrained limitation, in this paper, we use iterative fast gradient sign method to generate adversarial images with the minimum perturbation. The CNNs were trained to perform surgical tool recognition as a configuration for the modern operation room. The coefficient or the learning rate which influenced the modification per iteration, was set to be adaptive instead of a fixed number. A few functions were utilized to perform the learning rate decay to compare the performance. Especially, we propose a new adaptive learning rate algorithm that consider the loss as a part of influence factor constitute the learning rate for the rest iterations. According to the experiments, our loss adaptive learning rate method was proved to be efficient to get the minimal perturbations for adversarial attack.
Person development by immersion in a variety of art forms

Background. The article is devoted to such a topical problem of modern education as the formation of personality in the process of mastering various types of artistic activity. Currently, the issues of introducing the younger generation and youth to creative activity require careful study. The article highlights the features of research in the field of integrated poly-artistic education in the previous years of the Institute of Art Education and Cultural Studies of the RAE, which the author of this article relies on. The research allows to assert that art requires a special attitude of educational schools to it. Confirming the above, the article presents the dynamics of the child's (student's) development directions through various types of artistic activity from 1st to 11th grade, formulated as a result of comprehensive research. Objective. The article aims to highlight and substantiate the importance and significance of introducing children and youth to art in the modern situation, when it acquires a special meaning. Results. The article reveals the factors of integrated poly-artistic education on the example of three types of art: literary, theatrical, and visual. Conclusions. The problem of person development by immersion in art has always interested researchers. Currently, misunderstanding of the need for the humanitarian and artistic component of general education leads students to narrow thinking and underestimation of their professional activities.
Nature Inspired Data Placement Strategy in Distributed Cloud Environment using Improved Firefly Algorithm

The execution of scientific applications needs high-processing computers and requires massive storage. This resulted in deploying applications in a distributed environment with high performance and extensive storage. Applications processed in cloud platforms face intolerable delays due to data movement across the centres. Optimized distribution of datasets among the global data centres has become an essential issue in the distributed cloud environment. This work proposes an improved data placement called IFA Data Placement (IFA-DP) method for a heterogeneous cloud environment. An effective and efficient optimal data placement strategy is proposed using a metaheuristic global optimisation firefly algorithm. The metaheuristic behavior of fireflies finds a better optimal solution. The primary aim of this work is to reduce the response time and execution cost, which is then proved by the simulation results. The access time of the Proposed IFA-DP is less by at least 2s compared to the existing methods.
Emotional Competences of Primary Education Teachers: A Need in School Post COVID-19

The COVID-19 pandemic has increased the number of students with mental health problems: depression, anxiety, stress. Faced with this reality, teachers and schools must be prepared to respond quickly and effectively. Therefore, the objective of this article is to analyze the emotional competences of primary school teachers in the city of Valencia based on the following sociodemographic variables: sex, age, professional experience, type of center and whether they have children. For this purpose, a quantitative methodological approach has been followed, through which the emotional competencies of primary education teachers are analyzed. These results allow us to establish teacher profiles according to sociodemographic variables and help to detect possible training deficiencies. A sample of 371 teachers of primary education in the city of Valencia has been analyzed. The Questionnaire on Teaching Competences of Primary Education Teachers, carried out under the Planned Action Model, has been used, and descriptive, univariate, bivariate and cluster analyses have been carried out. The mean, the standard deviation and the interquartile range (IQR) have been analyzed, as well as non-parametric tests such as the Wilcoxon, Kruskal–Wallis or Z test. The most significant results are that teachers have a greater ability to interpret emotions and to listen to students. On the contrary, it is teachers who most reject prejudice, discrimination and racism. Younger teachers are the ones who implement more inclusive learning environments. Finally, in general, all teachers are very respectful of students and claim to know how to manage classroom conflicts. The results obtained, in general terms, coincide with most of the research on teachers’ emotional competencies. Some aspects simply do not coincide with the literature. The teachers who participated in our research perceive themselves as having a greater capacity to observe and interpret students’ emotions, to generate learning situations that cater to diversity and to listen to their students. Other studies place these competencies at lower levels.