Reviewerly/exHarmony huggingface dataset:
Logistic regression-based side-channel analysis attacks

Advanced Encryption Standard (AES) is a modern concept in cryptography. Most of modern encryption schemes and devises are built based on this standard. Those encryption systems have really high resistance to most of modern attacking methods. However, this passage will introduce the most powerful way of attacking: Side-Channel Analysis (SCA). By performing such attacking by artificial intelligence model, it shows that they can break the encryption system in a very efficient and effective way. By using ASCAD database, this passage analysis some properties when using logistic regression to perform Side-Channel-Attack on the traces of the encryption system. Since in the original article, the authors only analysis the performance of multilayer perceptron (MLP) and convolution neural network (CNN), this passage aims to apply similar methodology to logistic regression and analyse its performance in different circumstances. Moreover, some interesting properties about logistic regression was found, and it can sometimes perform better than systems in the original passage in certain situation.
WEB APPLICATION AS MEDIA INFORMATION AND TOURISM PROMOTION IN THOUSAND ISLANDS PRAMUKA ISLAND IN THE INDUSTRIAL AGE 4.0

Tourism is one of the driving wheels of the economy for people living in Thousand Island. Tourists must get any information as easily as possible. Unfortunately, the Rumah Hijau Community still used traditional ways to handle ticketing, promoting, and handling other tourism activities in Pulau Pramuka and Pulau Panggang. Tourists would not get the newest information about tourism activities. Tourists will find it difficult to book homestays on Pramuka Island and Panggang Island. Likewise, if local MSMEs have souvenirs or other specialties that can be sold, so far the information they sell to tourists is only through word of mouth. Therefore, information media is needed, namely a website, which can distribute information quickly and precisely. The website will make it easier for Rumah Hijau to organize ordering and sales data. Tourists will also be able to view information easily. In this technology era, website applications can help to boost the promotion and sharing of information for tourism activities.
FreCDo: A Large Corpus for French Cross-Domain Dialect Identification

We present a novel corpus for French dialect identification comprising 413,522 French text samples collected from public news websites in Belgium, Canada, France and Switzerland. To ensure an accurate estimation of the dialect identification performance of models, we designed the corpus to eliminate potential biases related to topic, writing style, and publication source. More precisely, the training, validation and test splits are collected from different news websites, while searching for different keywords (topics). This leads to a French cross-domain (FreCDo) dialect identification task. We conduct experiments with four competitive baselines, a fine-tuned CamemBERT model, an XGBoost based on fine-tuned CamemBERT features, a Support Vector Machines (SVM) classifier based on fine-tuned CamemBERT features, and an SVM based on word n-grams. Aside from presenting quantitative results, we also make an analysis of the most discriminative features learned by CamemBERT. Our corpus is available in open-source format at https://github.com/MihaelaGaman/FreCDo.
Kapin (Smart Card) Media: Evaluating Learning Tools Efficacy to Improve Reading Skills of Second Grads

In the context of classroom learning, particularly at the elementary school level, various methods are employed. This study aims to achieve three key research objectives: (1) to establish the reliability of Chapin learning media, (2) to ensure its effective implementation, and (3) to assess its overall efficacy. The research methodology utilized the Research and Development (R&amp;D) approach, following the Addie development model, consisting of five stages: Analysis, Design, Development, Implementation, and Evaluation. The study's population consisted of 12 students from Class II at SDN Tiron III and 23 students from Class II at SDN Tiron IV. Data collection involved the use of validation questionnaires for media, content, and learning specialists, supplemented by both small- and large-scale experiments. Data analysis encompassed validity testing, practicality assessment through teacher response questionnaires (involving 2 teachers and 35 students), and effectiveness evaluation based on students' learning outcomes, analyzed using the N-Gain Score. The results of this study indicate that the smart card learning media achieved a high level of validity, with percentages of 90%, 84%, and 84% for content, media, and overall validity, respectively. Furthermore, the practicality assessment revealed that students' responses averaged 88%, while expert teacher responses averaged 84%. The N-Gain score, with values of 0.77 for the wider class scale and 0.79 for the limited class scale, both fall into the high category, demonstrating the efficacy of the learning media.
NutriColoring: designing a doodling toolkit to support daily self-reported dietary assessment among office workers

This study was motivated by a desire to help working-age individuals gain a better understanding of their daily nutritional intakes with a new self-reported dietary assessment method because an unhealthy eating behavior increases the risks of developing chronic diseases. In this study, we present the design and evaluation of NutriColoring, a food diary that leverages doodling on sketches to report and reflect on everyday diet in the working context. Through a 2-week field study involving 18 participants, the usefulness of NutriColoring in facilitating dietary assessment was tested by making comparisons with the typical bullet diary method. Our quantitative results showed that NutriColoring provided users with improved dietary assessment experience and intrinsic motivations, with significantly low task frustration and high enjoyment. Because of the freedom and playfulness in reporting intakes at work, the interview findings showed a high acceptance of employing NutriColoring at work. This article is concluded with a set of implications for the design and development of a Doodling toolkit to support healthy eating behaviors among office workers.
Deep learning enabled single-shot absolute phase recovery in high-speed composite fringe pattern profilometry of separated objects

A recent article in the <italic>Opto-Electronic Advances</italic> (OEA) journal from Prof. Qian Chen and Prof. Chao Zuo’s group introduced a new and efficient 3D imaging system that captures high-speed images using deep learning-enabled fringe projection profilometry (FPP). In this News &amp; Views article, we explore potential avenues for future advancements, including expanding the measurement range through an extended number-theoretical approach, enhancing quality through the incorporation of horizontal fringes, and integrating data from other modalities to broaden the system's applications.
SPEECH CREATIVITY OF FUTURE TEACHERS: CULTIVATING THROUGH WORDS(IN THE CONTEXT OF V.A. SUKHOMLINSKY’S IDEAS)

The author in his article explores the role and significance of speech creativity in the development of a future teacher and is based on the ideas of the outstanding teacher V.A. Sukhomlinsky devoted to the verbal component of the teaching profession. The study presents a brief analysis of theoretical concepts and practical approaches to the development of speech creativity of a future teacher in the context of modern requirements and the challenge of the educational environment. The analysis was carried out taking into account reliable historical and factually accurate data on the work and experience of V.A. Sukhomlinsky, which included the study of his approaches to upbringing and education, the concept of personality development through the word, as well as real examples of the practical implementation of his ideas in schools and educational institutions. Particular attention is paid to the importance of word education, which allows the teacher to stimulate creative thinking, communication skills and develop the emotional expressiveness of students, which has a positive impact on the results of educational activities. The results of the study confirm that the active development and use of speech creativity in pedagogical practice contributes to the formation of the personal readiness of the future teacher to implement an individual and collective educational process, prevents professional burnout, and allows to quickly adapt to a new audience and any category of the team (parental, work, educational). This article represents an important contribution to the development of speech creativity of future teachers and substantiates the importance of using the ideas of V.A. Sukhomlinsky in pedagogical practice, since the results and recommendations obtained in the course of this study can be used by educational institutions to develop and implement effective programs for the development of speech creativity and improve the quality of domestic education.
The Inevitable Menace of Cyber-warfare : A War by the Computer &amp; for the Computer

The future wars shall not be fought on Land, probably no bullets shall be fired, it will be fought in the Cyber Space, and it will be fought through the Internet or the Dark Web. Espionage will not be done by spies on enemy soils, but now it will done virtually on enemy data through cyber space. Radar stations will not be bombed anymore, but they will be jammed/hacked. Bridges and ammunition depots will not be targeted anymore, but the Data Centers will be targeted. The aforementioned cataclysm is the prediction of new age warfare- “The Cyber Warfare”. The aim of this paper is to bring forth the devastating capabilities of the Cyber Warfare. Also this paper aims to point the techniques which may be used to inflict wounds on enemy data/communication along with the loop holes present and finally finding ways to avert these clear and future dangers.
The Influence of the LIPIRTUP Strategy on the Short Story Writing Skills of Class VII Students at SMPN 24 Medan

This research aims to determine the effect of the LIPIRTUP strategy on the short story writing skills of class VII students at SMPN 24 Medan. The method used in this research is the one-group pre-test post-test design experimental method. Hypothesis testing was carried out using the "t" test. The data obtained showed that students' skills in writing short stories before using the LIPIRTUP strategy were in the sufficient category with an average score of 65.90, while students' skills in writing short stories after using the LIPIRTUP strategy were in the moderate category. good with an average value of 73.45. Hypothesis testing shows tcount &gt; ttable, namely 3.99470 &gt; 1.69913, so the null hypothesis (H0) is rejected and the alternative hypothesis (Ha) is accepted. This shows that there is an influence of the LIPIRTUP strategy on the short story writing skills of class VII students at SMPN 24 Medan.
Systematic Generalization by Finetuning? Analyzing Pretrained Language Models Using Constituency Tests

Constituents are groups of words that behave as a syntactic unit. Many linguistic phenomena (e.g., question formation, diathesis alternations) require the manipulation and rearrangement of constituents in a sentence. In this paper, we investigate how different finetuning setups affect the ability of pretrained sequence-to-sequence language models such as BART and T5 to replicate constituency tests — transformations that involve manipulating constituents in a sentence. We design multiple evaluation settings by varying the combinations of constituency tests and sentence types that a model is exposed to during finetuning. We show that models can replicate a linguistic transformation on a specific type of sentence that they saw during finetuning, but performance degrades substantially in other settings, showing a lack of systematic generalization. These results suggest that models often learn to manipulate sentences at a surface level unrelated to the constituent-level syntactic structure, for example by copying the first word of a sentence. These results may partially explain the brittleness of pretrained language models in downstream tasks.
Using Masked Language Model Probabilities of Connectives for Stance Detection in English Discourse

This paper introduces an approach which operationalizes the role of discourse connectives for detecting argument stance. Specifically, the study investigates the utility of masked language model probabilities of discourse connectives inserted between a claim and a premise that supports or attacks it. The research focuses on a range of connectives known to signal support or attack, such as because, but, so, or although. By employing a LightGBM classifier, the study reveals promising results in stance detection in English discourse. While the proposed system does not aim to outperform state-of-the-art architectures, the classification accuracy is surprisingly high, highlighting the potential of these features to enhance argument mining tasks, including stance detection.
ArSarcasMoji Dataset: The Emoji Sentiment Roles in Arabic Ironic Contexts

In digital communication, emoji are essential in decoding nuances such as irony, sarcasm, and humour. However, their incorporation in Arabic natural language processing (NLP) has been cautious because of the perceived complexities of the Arabic language. This paper introduces ArSarcasMoji, a dataset of 24,630 emoji-augmented texts, with 17. 5% that shows irony. Through our analysis, we highlight specific emoji patterns paired with sentiment roles that denote irony in Arabic texts. The research counters prevailing notions, emphasising the importance of emoji’s role in understanding Arabic textual irony, and addresses their potential for accurate irony detection in Arabic digital content.
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 f1-score (74.59), which obtained us a rank of 3rd position at the BLP-2023 Task 1.
Towards Fine-Grained Argumentation Strategy Analysis in Persuasive Essays

We define an argumentation strategy as the set of rhetorical and stylistic means that authors employ to produce an effective, and often persuasive, text. First computational accounts of such strategies have been relatively coarse-grained, while in our work we aim to move to a more detailed analysis. We extend the annotations of the Argument Annotated Essays corpus (Stab and Gurevych, 2017) with specific types of claims and premises, propose a model for their automatic identification and show first results, and then we discuss usage patterns that emerge with respect to the essay structure, the “flows” of argument component types, the claim-premise constellations, the role of the essay prompt type, and that of the individual author.
Qamosy at Arabic Reverse Dictionary shared task: Semi Decoder Architecture for Reverse Dictionary with SBERT Encoder

A reverse dictionary takes a descriptive phrase of a particular concept and returns words with definitions that align with that phrase. While many reverse dictionaries cater to languages such as English and are readily available online or have been developed by researchers, there is a notable lack of similar resources for the Arabic language. This paper describes our participation in the Arabic Reverse Dictionary shared task. Our proposed method consists of two main steps: First, we convert word definitions into multidimensional vectors. Then, we train these encoded vectors using the Semi-Decoder model for our target task. Our system secured 2nd place based on the Rank metric for both embeddings (Electra and Sgns).
AHJL at Qur’an QA 2023 Shared Task: Enhancing Passage Retrieval using Sentence Transformer and Translation

The Holy Qur’an is central to Islam, influencing around two billion Muslims globally, and is known for its linguistic richness and complexity. This article discusses our involvement in the PR task (Task A) of the Qur’an QA 2023 Shared Task. We used two models: one employing the Sentence Transformer and the other using OpenAI’s embeddings for document retrieval. Both models, equipped with a translation feature, help interpret and understand Arabic language queries by translating them, executing the search, and then reverting the results to Arabic. Our results show that incorporating translation functionalities improves the performance in Arabic Question-Answering systems. The model with translation enhancement performed notably better in all metrics compared to the non-translation model.
Large Language Models as SocioTechnical Systems

The expectation of Large Language Models (LLMs) to solve various societal problems has ignored the larger socio-technical frame of reference under which they operate. From a socio-technical perspective, LLMs are necessary to look at separately from other ML models as they have radically different implications in society never witnessed before. In this article, we ground Selbst et al.(2019)’s five abstraction traps – The Framing Trap, The Portability Trap, The Formalism Trap, The Ripple Effect Trap and the Solutionism Trap in the context of LLMs discussing the problems associated with the abstraction and fairness of LLMs. Through learnings from previous studies and examples, we discuss each trap that LLMs fall into, and propose ways to address the points of LLM failure by gauging them from a socio-technical lens. We believe the discussions would provide a broader perspective of looking at LLMs through a sociotechnical lens and our recommendations could serve as baselines to effectively demarcate responsibilities among the various technical and social stakeholders and inspire future LLM research.
TADI: Topic-aware Attention and Powerful Dual-encoder Interaction for Recall in News Recommendation

News recommendation is one of the widest commercialization in natural language processing research area, which aims to recommend news according to user interests. New recall plays an important role in news recommendation. It is to recall candidates from a very large news database. Recent researches of news recall mostly adopt dual-encoder architecture as it provides a much faster recall scheme, and they encode each word equally. However, these works remain two challenges: irrelevant word distraction and weak dual-encoder interaction. Therefore, we propose a model Topic-aware Attention and powerful Dual-encoder Interaction for Recall in news recommendation (TADI). To avoid irrelevant word distraction, TADI designs a Topic-aware Attention (TA) which weights words according to news topics. To enhance dual-encoder interaction, TADI provides a cheap yet powerful interaction module, namely Dual-encoder Interaction (DI). DI helps dual encoders interact powerfully based on two aux targets. After performance comparisons between TADI and state-of-the-arts in a series of experiments, we verify the effectiveness of TADI.
Annotation Sensitivity: Training Data Collection Methods Affect Model Performance

When training data are collected from human annotators, the design of the annotation instrument, the instructions given to annotators, the characteristics of the annotators, and their interactions can impact training data. This study demonstrates that design choices made when creating an annotation instrument also impact the models trained on the resulting annotations. We introduce the term annotation sensitivity to refer to the impact of annotation data collection methods on the annotations themselves and on downstream model performance and predictions. We collect annotations of hate speech and offensive language in five experimental conditions of an annotation instrument, randomly assigning annotators to conditions. We then fine-tune BERT models on each of the five resulting datasets and evaluate model performance on a holdout portion of each condition. We find considerable differences between the conditions for 1) the share of hate speech/offensive language annotations, 2) model performance, 3) model predictions, and 4) model learning curves. Our results emphasize the crucial role played by the annotation instrument which has received little attention in the machine learning literature. We call for additional research into how and why the instrument impacts the annotations to inform the development of best practices in instrument design.
Rather a Nurse than a Physician - Contrastive Explanations under Investigation

Contrastive explanations, where one decision is explained *in contrast to another*, are supposed to be closer to how humans explain a decision than non-contrastive explanations, where the decision is not necessarily referenced to an alternative. This claim has never been empirically validated. We analyze four English text-classification datasets (SST2, DynaSent, BIOS and DBpedia-Animals). We fine-tune and extract explanations from three different models (RoBERTa, GTP-2, and T5), each in three different sizes and apply three post-hoc explainability methods (LRP, GradientxInput, GradNorm). We furthermore collect and release human rationale annotations for a subset of 100 samples from the BIOS dataset for contrastive and non-contrastive settings. A cross-comparison between model-based rationales and human annotations, both in contrastive and non-contrastive settings, yields a high agreement between the two settings for models as well as for humans. Moreover, model-based explanations computed in both settings align equally well with human rationales. Thus, we empirically find that humans do not necessarily explain in a contrastive manner.
GEM: Gestalt Enhanced Markup Language Model for Web Understanding via Render Tree

Inexhaustible web content carries abundant perceptible information beyond text. Unfortunately, most prior efforts in pre-trained Language Models (LMs) ignore such cyber-richness, while few of them only employ plain HTMLs, and crucial information in the rendered web, such as visual, layout, and style, are excluded. Intuitively, those perceptible web information can provide essential intelligence to facilitate content understanding tasks. This study presents an innovative Gestalt Enhanced Markup (GEM) Language Model inspired by Gestalt psychological theory for hosting heterogeneous visual information from the render tree into the language model without requiring additional visual input. Comprehensive experiments on multiple downstream tasks, i.e., web question answering and web information extraction, validate GEM superiority.