Miracle-dz/UniFAR-FTUs huggingface dataset:
Literary Intertextual Semantic Change Detection: Application and Motivation for Evaluating Models on Small Corpora

Lexical semantic change detection is the study of how words change meaning between corpora. While Schlechtweg et al. (2020) standardized both datasets and evaluation metrics for this shared task, for those interested in applying semantic change detection models to small corpora—e.g., in the digital humanities—there is a need for evaluation involving much smaller datasets. We present a method and open-source code pipeline for downsampling the SemEval-2020 Task 1 corpora while preserving gold standard measures of semantic change. We then evaluate several state-of-the-art models trained on these downsampled corpora and find both dramatically decreased performance (average 67% decrease) and high variance. We also propose a novel application to the digital humanities and provide a case study demonstrating that semantic change detection can be used in an exploratory manner to produce insightful avenues of investigation for literary scholars.
USTHB at NADI 2023 shared task: Exploring Preprocessing and Feature Engineering Strategies for Arabic Dialect Identification

In this paper, we conduct an in-depth analysis of several key factors influencing the performance of Arabic Dialect Identification NADI’2023, with a specific focus on the first subtask involving country-level dialect identification. Our investigation encompasses the effects of surface preprocessing, morphological preprocessing, FastText vector model, and the weighted concatenation of TF-IDF features. For classification purposes, we employ the Linear Support Vector Classification (LSVC) model. During the evaluation phase, our system demonstrates noteworthy results, achieving an F_1 score of 62.51%. This achievement closely aligns with the average F_1 scores attained by other systems submitted for the first subtask, which stands at 72.91%.
Legal Judgment Prediction: If You Are Going to Do It, Do It Right

The field of Legal Judgment Prediction (LJP) has witnessed significant growth in the past decade, with over 100 papers published in the past three years alone. Our comprehensive survey of over 150 papers reveals a stark reality: only ~7% of published papers are doing what they set out to do - predict court decisions. We delve into the reasons behind the flawed and unreliable nature of the remaining experiments, emphasising their limited utility in the legal domain. We examine the distinctions between predicting court decisions and the practices of legal professionals in their daily work. We explore how a lack of attention to the identity and needs of end-users has fostered the misconception that LJP is a near-solved challenge suitable for practical application, and contributed to the surge in academic research in the field. To address these issues, we examine three different dimensions of ‘doing LJP right’: using data appropriate for the task; tackling explainability; and adopting an application-centric approach to model reporting and evaluation. We formulate a practical checklist of recommendations, delineating the characteristics that are required if a judgment prediction system is to be a valuable addition to the legal field.
Zelda Rose: a tool for hassle-free training of transformer models

Zelda Rose is a command line interface for pretraining transformer-based models. Its purpose is to enable an easy start for users interested in training these ubiquitous models, but unable or unwilling to engage with more comprehensive — but more complex — frameworks and the complex interactions between libraries for managing models, datasets and computations. Training a model requires no code on the user’s part and produce models directly compatible with the HuggingFace ecosystem, allowing quick and easy distribution and reuse. A particular care is given to lowering the cost of maintainability and future-proofing, by making the code as modular as possible and taking advantage of third-party libraries to limit ad-hoc code to the strict minimum.
HTE at ArAIEval Shared Task: Integrating Content Type Information in Binary Persuasive Technique Detection

Propaganda frequently employs sophisticated persuasive strategies in order to influence public opinion and manipulate perceptions. As a result, automating the detection of persuasive techniques is critical in identifying and mitigating propaganda on social media and in mainstream media. This paper proposes a set of transformer-based models for detecting persuasive techniques in tweets and news that incorporate content type information as extra features or as an extra learning objective in a multitask learning setting. In addition to learning to detect the presence of persuasive techniques in text, our best model learns specific syntactic and lexical cues used to express them based on text genre (type) as an auxiliary task. To optimize the model and deal with data imbalance, a focal loss is used. As part of ArabicNLP2023-ArAIEval shared task, this model achieves the highest score in the shared task 1A out of 13 participants, according to the official results, with a micro-F1 of 76.34% and a macro-F1 of 73.21% on the test dataset.
NOMOS: Navigating Obligation Mining in Official Statutes

The process of identifying obligations in a legal text is not a straightforward task, because not only are the documents long, but the sentences therein are long as well. As a result of long elements in the text, law is more difficult to interpret (Coupette et al., 2021). Moreover, the identification of obligations relies not only on the clarity and precision of the language used but also on the unique perspectives, experiences, and knowledge of the reader. In particular, this paper addresses the problem of identifyingobligations using machine and deep learning approaches showing a full comparison between both methodologies and proposing a new approach called NOMOS based on the combination of Positional Embeddings (PE) and Temporal Convolutional Networks (TCNs). Quantitative and qualitative experiments, conducted on legal regulations 1, demonstrate the effectiveness of the proposed approach.
UL & UM6P at ArAIEval Shared Task: Transformer-based model for Persuasion Techniques and Disinformation detection in Arabic

In this paper, we introduce our participating system to the ArAIEval Shared Task, addressing both the detection of persuasion techniques and disinformation tasks. Our proposed system employs a pre-trained transformer-based language model for Arabic, alongside a classifier. We have assessed the performance of three Arabic Pre-trained Language Models (PLMs) for sentence encoding. Additionally, to enhance our model’s performance, we have explored various training objectives, including Cross-Entropy loss, regularized Mixup loss, asymmetric multi-label loss, and Focal Tversky loss. On the official test set, our system has achieved micro-F1 scores of 0.7515, 0.5666, 0.904, and 0.8333 for Sub-Task 1A, Sub-Task 1B, Sub-Task 2A, and Sub-Task 2B, respectively. Furthermore, our system has secured the 4th, 1st, 3rd, and 2nd positions, respectively, among all participating systems in sub-tasks 1A, 1B, 2A, and 2B of the ArAIEval shared task.
Rosetta Stone at KSAA-RD Shared Task: A Hop From Language Modeling To Word–Definition Alignment

A Reverse Dictionary is a tool enabling users to discover a word based on its provided definition, meaning, or description. Such a technique proves valuable in various scenarios, aiding language learners who possess a description of a word without its identity, and benefiting writers seeking precise terminology. These scenarios often encapsulate what is referred to as the “Tip-of-the-Tongue” (TOT) phenomena. In this work, we present our winning solution for the Arabic Reverse Dictionary shared task. This task focuses on deriving a vector representation of an Arabic word from its accompanying description. The shared task encompasses two distinct subtasks: the first involves an Arabic definition as input, while the second employs an English definition. For the first subtask, our approach relies on an ensemble of finetuned Arabic BERT-based models, predicting the word embedding for a given definition. The final representation is obtained through averaging the output embeddings from each model within the ensemble. In contrast, the most effective solution for the second subtask involves translating the English test definitions into Arabic and applying them to the finetuned models originally trained for the first subtask. This straightforward method achieves the highest score across both subtasks.
HW-TSC’s Participation in the WMT 2023 Automatic Post Editing Shared Task

The paper presents the submission by HW-TSC in the WMT 2023 Automatic Post Editing (APE) shared task for the English-Marathi (En-Mr) language pair. Our method encompasses several key steps. First, we pre-train an APE model by utilizing synthetic APE data provided by the official task organizers. Then, we fine-tune the model by employing real APE data. For data augmentation, we incorporate candidate translations obtained from an external Machine Translation (MT) system. Furthermore, we integrate the En-Mr parallel corpus from the Flores-200 dataset into our training data. To address the overfitting issue, we employ R-Drop during the training phase. Given that APE systems tend to exhibit a tendency of ‘over-correction’, we employ a sentence-level Quality Estimation (QE) system to select the final output, deciding between the original translation and the corresponding output generated by the APE model. Our experiments demonstrate that pre-trained APE models are effective when being fine-tuned with the APE corpus of a limited size, and the performance can be further improved with external MT augmentation. Our approach improves the TER and BLEU scores on the development set by -2.42 and +3.76 points, respectively.
NLP_CUET at BLP-2023 Task 1: Fine-grained Categorization of Violence Inciting Text using Transformer-based Approach

The amount of online textual content has increased significantly in recent years through social media posts, online chatting, web portals, and other digital platforms due to the significant increase in internet users and their unprompted access via digital devices. Unfortunately, the misappropriation of textual communication via the Internet has led to violence-inciting texts. Despite the availability of various forms of violence-inciting materials, text-based content is often used to carry out violent acts. Thus, developing a system to detect violence-inciting text has become vital. However, creating such a system in a low-resourced language like Bangla becomes challenging. Therefore, a shared task has been arranged to detect violence-inciting text in Bangla. This paper presents a hybrid approach (GAN+Bangla-ELECTRA) to classify violence-inciting text in Bangla into three classes: direct, passive, and non-violence. We investigated a variety of deep learning (CNN, BiLSTM, BiLSTM+Attention), machine learning (LR, DT, MNB, SVM, RF, SGD), transformers (BERT, ELECTRA), and GAN-based models to detect violence inciting text in Bangla. Evaluation results demonstrate that the GAN+Bangla-ELECTRA model gained the highest macro f_1-score (74.59), which obtained us a rank of 3rd position at the BLP-2023 Task 1.
Extending scene-to-patch models: Multi-resolution multiple instance learning for Earth observation

Abstract Land cover classification (LCC) and natural disaster response (NDR) are important issues in climate change mitigation and adaptation. Existing approaches that use machine learning with Earth observation (EO) imaging data for LCC and NDR often rely on fully annotated and segmented datasets. Creating these datasets requires a large amount of effort, and a lack of suitable datasets has become an obstacle in scaling the use of machine learning for EO. In this study, we extend our prior work on Scene-to-Patch models: an alternative machine learning approach for EO that utilizes Multiple Instance Learning (MIL). As our approach only requires high-level scene labels, it enables much faster development of new datasets while still providing segmentation through patch-level predictions, ultimately increasing the accessibility of using machine learning for EO. We propose new multi-resolution MIL architectures that outperform single-resolution MIL models and non-MIL baselines on the DeepGlobe LCC and FloodNet NDR datasets. In addition, we conduct a thorough analysis of model performance and interpretability.
Introduction and Analysis of M-TSLand: A Multi-Temporal and Multi-Scale Remote Sensing Farmland Dataset

Farmland is an important resource for human survival and development. Intelligent segmentation of farmland using remote sensing is an effective measure to protect farmland. (1) However, farmland present different states during different seasons, such as appearing green in winter or spring and appearing yellow in autumn, which pose challenges for segmentation models. (2) Segmentation of remote sensing imagery at different resolutions also poses challenges. To address this issue, this paper proposes a multi-temporal and multi-scale remote sensing farmland dataset(M-TSLand). This dataset were constructed based on 1m and 2m resolutions, respectively, and images were extracted from different seasons. To validate the effectiveness of the dataset, we used some mainstream segmentation models such as SegNext, DeepLabV3+, etc., and achieved mIoU of 67.65%, 66.09%, which provide a research basis for the generalization of the model as well as a high accuracy rate.
Exploring the Predictors of Satisfaction in a Blended Learning Course: Investigating the Role of Interaction and Self-Efficacy

The purpose of this study was to explore the joint impact of self-efficacy and interaction on student satisfaction in English as a second language learning (ESL). A total of 91 Chinese freshmen students participated, with self-efficacy, teacher-student interaction, and student-student interaction being measured as the independent variables, and satisfaction as the dependent variable. Multiple regression analysis was used to investigate the relationship between the variables. The results indicated that self-efficacy and teacher-student interaction significantly predicted satisfaction, while student-student interaction had no significant effect. This study suggests that educators should create a learning environment that promotes self-efficacy and teacher-student interaction to enhance ESL student satisfaction. The limitations of the study were acknowledged, and future research directions were discussed. These findings have implications for educational practices, particularly for second language learning.
Towards a Compact Routed Internet

The BGP routing table continues to grow. Reducing the table size means that memory can be made available for other in-router processing. Compact routing provides a method for reducing table size whilst sacrificing the shortest path property. We propose a deployable compact routing scheme and consider the challenges of such an implementation. We evaluate using large-scale emulations and compare against current routing protocols.
Adversarial Machine Learning in Recommender Systems

Recommender systems are ubiquitous. Our digital lives are influenced by their use when, for instance, we select the news to read, the product to buy, the friend to connect with, and the movie to watch. While enormous academic research efforts have been mainly focused on getting high-quality recommendations to reach maximum user satisfaction, little effort has been devoted to studying the integrity and security of these systems. Is there an underlying relationship between the characteristics of the historical user-item interactions and the efficacy of injection of false users/feedback strategies against collaborative models? Can public semantic data be used to perform attacks more potent in raising the recommendability of victim items? Can a malicious user poison or evade the image data of visual recommenders with adversarial perturbed product images? Is the family of model-based recommenders more vulnerable to multi-step gradient-based adversarial perturbations? Furthermore, is the adversarial training robustification still effective in the last scenario? Is this training defense influencing the beyond-accuracy and bias performance? This dissertation intends to pave the way towards more robust recommender systems, beginning with understanding how to robustify a model, what is the cost of robustness in terms of reduction of recommendation accuracy, and which are the novel adversarial risks of modern recommenders. This thesis, getting inspiration from the literature on the security of collaborative models against the insertion of hand-engineered fake profiles and the recent advances of adversarial machine learning methods in other research areas like computer vision, contributes to several directions: (i) the proposal of a practical framework to interpret the impact of data characteristics on the robustness of collaborative recommenders [Deldjoo et al., 2020], (ii) the design of powerful attack strategies using publicly available semantic data [Anelli et al., 2020], (iii) the identification of severe adversarial vulnerabilities of visual-based recommender models where adversaries can break the recommendation integrity by pushing products to the highest recommendation positions with a simple and human-imperceptible perturbation of products' images [Anelli et al., 2021b], (iv) the proposal of robust adversarial perturbation methods capable of completely breaking the accuracy of matrix factorization recommenders [Anelli et al., 2021a], and (v) a formal study that examines the effects of adversarial training in reducing the recommendation quality of state-of-the-art model-based recommenders Anelli et al. [2021c]. Awarded by: Politecnico di Bari, Bari, Italy on 24 January 2022. Supervised by: Tommaso DI Noia. Available at: https://iris.poliba. it/retrieve/dd89f8a6-faa4-ccdd-e053-6605fe0a1b87/Adversarial_Machine_Learning_in_Recommender_Systems. pdf.
Disaggregate Applications Along End-Host Data-Path

There are multiple programmable components available on the data-path of end-host systems (i.e., NIC, kernel hooks). Offloading part of an application to these programmable components can increase its performance. Many applications can benefit from offloading functionality, in this paper we describe an approach to automate this process, thus reducing the effort for adopting offloads.
Realization for Implementing Federated Learning Based Intrusion Detection with Non-IID IoT Datasets

With the advance of technology, there is an increasing variety of Internet of Things (IoT) devices, which could be vulnerable to hacker attacks. Therefore, intrusion detection for these IoT devices is crucial. Federated learning, a novel distributed learning approach, which trains multiple clients together and aggregates their weights through a server to achieve collaborative learning. These weights do not disclose the clients' individual dataset information, thereby preserve client privacy while benefiting from the knowledge of other clients. As the intrusion detection datasets for IoT devices come from different domains, the attack categories in these datasets are often highly dissimilar. Therefore, it is necessary to address the Non-Independent and Identically Distributed (non-IID) nature of the data in federated learning. In this paper, we propose three methods to handle the issues for different numbers of class outputs: maximum value, union, and federated transfer learning.
Pixel-wise Wildfire Burn Severity Classification with Bi-temporal Sentinel-2 Data and Deep Learning

Wildfires result in extensive damage and pose significant threats to the natural environment and human society. Accurate and timely assessment of wildfire damage is critical for effective post-fire management and restoration efforts. Much progress has been made for monitoring and mapping burned areas based on manual feature extraction and machine learning. However, current approaches are often hindered by several limitations, including poor generalizability, complex and time-consuming procedures, and a primary focus on binary classification, only distinguishing between burned and non-burned areas. As such, this paper intends to develop an advanced framework that not only can reliably detect burned areas, but also has the capability to assess the burn severity (low, medium and high) for the detected areas. Within this framework, pixel-wise multiclass models for detecting wildfire burn severity level are developed based on the standard U-Net architecture using pre- and post-fire Sentinel-2 remote sensing images. The experimental results demonstrated the power of the Bi-temporal U-Net model for both multi-class and binary mapping of wildfire detection, achieving an overall accuracy (OA) of over 95%, and outperformed baseline algorithms, including fully convolutional network, random forest, eXtreme gradient boosting, and support vector machine, with an average improvement of 18% in F1-score and 15% in mean intersection over union.
Betweenness centrality distribution and its application in rumor propagation

This paper presents a betweenness centrality distribution based on the traditional degree distribution. The traditional degree distribution only considers the degree of its nodes, while the betweenness centrality distribution also considers the shortest distance of adjacent nodes and so on. The similarities and differences between nodes can be studied more intuitively. In rumor propagation, the shortest distance can directly make the propagation time shorter and faster. Then we conduct experimental investigation on the traditional BA (Barabasi-Albert) network and the emerging hypernetwork with the rumor as the experimental background, and get the rule. Similar to the traditional degree distribution, after taking the double logarithm, we can conclude that our proposed intermediate centrality distribution is also power-law distribution. Then the practical significance in the application of rumor is discussed, and the proposed new concept is of practical significance. Finally, the paper gives the future development direction of this direction.
An improvement of SAX representation for time series by using wavelet packet decomposition and FastDTW

Aiming at the shortcomings of conventional SAX that using the mean value as the eigenvalue may lead to misclassification, this paper comprehensively considers the numerical distribution difference and morphological fluctuation characteristics of time series data. Firstly, wavelet packet decomposition (WPD) can decompose various types of non-smooth random signals into wavelet components at different scales. Firstly, the WPD is used to extract the features of the time series, and then the Fast Dynamic Time Warping (FastDTW) is used to take advantage of the metrics, and the FastDTW metrics are applied to the features to define the global trend indicators. Then, the SAX distance is combined with the trend distance, and an improved distance measure is proposed. Finally, the experimental results on different time series datasets show that the proposed method has the highest classification accuracy in 11 of the 20 datasets, which proves that the representation method proposed in the paper is significantly better than the original SAX representation and the improved SAX representation.