Reviewerly/exHarmony huggingface dataset:
CIKQA: Learning Commonsense Inference with a Unified Knowledge-in-the-loop QA Paradigm

We propose a new commonsense reasoning benchmark to motivate commonsense reasoning progress from two perspectives: (1) Evaluating whether models can distinguish knowledge quality by predicting if the knowledge is enough to answer the question; (2) Evaluating whether models can develop commonsense inference capabilities that generalize across tasks. We first extract supporting knowledge for each question and ask humans to annotate whether the auto-extracted knowledge is enough to answer the question or not. After that, we convert different tasks into a unified question-answering format to evaluate the models’ generalization capabilities. We name the benchmark Commonsense Inference with Knowledge-in-the-loop Question Answering (\name). Experiments show that with our learning paradigm, models demonstrate encouraging generalization capabilities. At the same time, we also notice that distinguishing knowledge quality remains challenging for current commonsense reasoning models.
Linking the Neulateinische Wortliste to the LiLa Knowledge Base of Interoperable Resources for Latin

Federica Iurescia, Eleonora Litta, Marco Passarotti, Matteo Pellegrini, Giovanni Moretti, Paolo Ruffolo. Proceedings of the 7th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature. 2023.
ImaginE: An Imagination-Based Automatic Evaluation Metric for Natural Language Generation

Automatic evaluations for natural language generation (NLG) conventionally rely on token-level or embedding-level comparisons with text references. This differs from human language processing, for which visual imagination often improves comprehension. In this work, we propose ImaginE, an imagination-based automatic evaluation metric for natural language generation. With the help of StableDiffusion, a state-of-the-art text-to-image generator, we automatically generate an image as the embodied imagination for the text snippet and compute the imagination similarity using contextual embeddings. Experiments spanning several text generation tasks demonstrate that adding machine-generated images with our ImaginE displays great potential in introducing multi-modal information into NLG evaluation, and improves existing automatic metrics’ correlations with human similarity judgments in both reference-based and reference-free evaluation scenarios.
Quote Detection: A New Task and Dataset for NLP

Selma Tekir, Aybüke Güzel, Samet Tenekeci, Bekir Haman. Proceedings of the 7th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature. 2023.
Using Ensemble Learning in Language Variety Identification

The present work describes the solutions pro- posed by the UnibucNLP team to address the closed format of the DSL-TL task featured in the tenth VarDial Evaluation Campaign. The DSL-TL organizers provided approximately 11 thousand sentences written in three different languages and manually tagged with one of 9 classes. Out of these, 3 tags are considered common label and the remaining 6 tags are variety-specific. The DSL-TL task features 2 subtasks: Track 1 - a three-way and Track 2 - a two-way classification per language. In Track 2 only the variety-specific labels are used for scoring, whereas in Track 1 the common label is considered as well. Our team participated in both tracks, with three ensemble-based sub- missions for each. The meta-learner used for Track 1 is XGBoost and for Track 2, Logis- tic Regression. With each submission, we are gradually increasing the complexity of the en- semble, starting with two shallow, string-kernel based methods. To the first ensemble, we add a convolutional neural network for our second submission. The third ensemble submitted adds a fine-tuned BERT model to the second one. In Track 1, ensemble three is our highest ranked, with an F1 − score of 53.18%; 5.36% less than the leader. Surprisingly, in Track 2 the en- semble of shallow methods surpasses the other two, more complex ensembles, achieving an F 1 − score of 69.35%.
The Use of Quizizz to Develop Instructional PJBL Practices in Teaching Writing

This research aims to explain the use of Quizizz to develop instructional PJBL practices in teaching writing to the eighth-graders of MTs Ihsaniyah Balapulang, Tegal. This research applied mixed-methods research design and the concurrent embedded design. There were 30% quantitative data which were collected through students’ formative assessment and 70% qualitative data which were collected through observation, documents, and interview. This study involved an English teacher and 19 students of MTs.Ihsaniyah Balapulang. The instruments used were observation checklist, interview, documentation, and formative test. The researcherss did the validity, reliability, practicality, authenticity and T-test. The T-test result shows that the sig (2-tailed) value is 0.00 &lt;0.05 and the t-count (5.759) &gt; t-count (2.1009). Thus, it can be concluded that the hypothesis used in this study was accepted. It means that the use of Quizizz to develop instructional PJBL practices can be said to be effective. The teaching and learning observation in the classroom also show that the English teacher was able to effectively conduct the teaching of writing recount text, and the students were enthusiastic about participating in writing lessons using PJBL and Quizizz. Therefore, Quizizz and PJBL can be considered as an effective approach to improve students’ writing.
An Optimized Quantum Circuit Representation of Bayesian Networks

In recent years, there has been a significant upsurge in the interest surrounding Quantum machine learning, with researchers actively developing methods to leverage the power of quantum technology for solving highly complex problems across various domains. However, implementing gate-based quantum algorithms on noisy intermediate quantum devices (NISQ) presents notable challenges due to limited quantum resources and inherent noise. In this paper, we propose an innovative approach for representing Bayesian networks on quantum circuits, specifically designed to address these challenges. Our aim is to minimize the required quantum resource needed to implement a Quantum Bayesian network (QBN) on a quantum computer. By carefully designing the sequence of quantum gates within the dynamic circuit, we can optimize the utilization of limited quantum resources while mitigating the impact of noise. Furthermore, we present an experimental study that demonstrates the effectiveness and efficiency of our proposed approach. Through simulations and experiments on NISQ devices, we show that our dynamic circuit representation significantly reduces the resource requirements and enhances the robustness of QBN implementation. These findings highlight the potential of our approach to pave the way for practical applications of Quantum Bayesian networks on currently available quantum hardware.
Innovation and Digital Transformation of Media Economy Based on Information Security Technology

This study aims to analyze the application of information security (IS) technology and blockchain in the media economy, and the impact of cross-border cooperation and value chain reconstruction combined with blockchain technology (BCT) on promoting innovation and digital transformation in the media economy. This study delves into the interplay between cross-border collaboration, value chain reconstruction, and BCT. It constructs a media economic digital platform by amalgamating blockchain and cross-border value chain principles, subsequently evaluating and analyzing its performance. Findings indicate that the proposed model exhibits a reduced block propagation time and enhanced security performance, boasting a 5% improvement. The platform’s latency remains stable at approximately 245 ms. Survey analysis reveals an agreement rate exceeding 57.94% across dimensions such as data mining, customer relationship management, and classification effectiveness, contributing to heightened user satisfaction. Consequently, this study offers valuable insights into user relationship security management within cross-border collaborations, shedding light on novel avenues for sustainable development and digital transformation in the realm of the media economy.
Enhancing Students' Writing Ability Using Clustering Methodology

This research aims to improve students’ writing skills at SMP Negeri 15 Bandar Lampung by using the Clustering technique. The participants of this research were the students in eighth grade. The total numbers of students were 33 students. Employing classroom action research, this study was carried out for two cycles at VIIIA. The main emphasis of this study was the improvement of students' learning activities and writing skills. In cycle 1, students were less engaged, less self-assured, and the classroom environment was still out of control. The cycle one learning activities' mean score for students was 6.53, which had a subpar predicate. Cycle 2 revealed an improvement in the students. They were more engaged and self-assured, and the environment in the classroom was entertaining and under control. The students' cycle two learning activities had an average score of 7.50 and a favourable predicate. Additionally, in cycle one writing test had a mean score of 64.81, with 16 students or 48.48% passing the goal. It indicates that students' writing abilities have improved. The writing test for students in cycle 2 had a mean score of 77.39; 27 students, or 81.82%, passed the target. From the data shown, it could be concluded that classroom action research using the Clustering technique can improve students' learning activities and writing skills.
P-norm Smooth Control Barrier Function for UAV Navigation in Clutter Environment

In this paper, a P-norm Smooth Control Barrier Function (PSCBF) based method is proposed to achieve target tracking for unmanned aerial vehicle (UAV) in clutter environment. Different from relative results in which the CBF is designed as the minimum of several functions, the proposed PSCBF is smooth and differentiable to avoid the existence of singularity and ensures the safety of UAV. The control performance of the proposed method and the Artificial Potential Fields (APFs) based method are compared in the context of obstacle avoidance on simple illustrative examples. Both simulation and experiment results are given to verify the effectiveness of the proposed method.
Keypoints Dictionary Learning for Fast and Robust Alignment

Sparse keypoints based methods allow to match two images in an efficient manner. However, even though they are sparse, not all generated keypoints are necessary. This uselessly increases the computational cost during the matching step and can even add uncertainty when these keypoints are not discriminatory enough, thus leading to imprecise, or even wrong, alignment. In this paper, we address the important case where the alignment deals with the same scene or the same type of object. This enables a preliminary learning of optimal keypoints, in terms of efficiency and robustness. Our fully unsupervised selection method is based on a statistical a contrario test on a small set of training images to build without any supervision a dictionary of the most relevant points for the alignment. We show the usefulness of the proposed method on two applications, the stabilization of video surveillance sequences and the fast alignment of industrial objects containing repeated patterns. Our experiments demonstrate an acceleration of the method by 20 factor and significant accuracy gain.
RepCo: Replenish sample views with better consistency for contrastive learning

Contrastive learning methods aim to learn shared representations by minimizing distances between positive pairs, and maximizing distances between negative pairs in the embedding space. To achieve better performance of contrastive learning, one of the key problems is to design appropriate sample pairs. In most previous works, random cropping on the input image is utilized to obtain two views as positive pairs. However, such strategies lead to suboptimal performance since the sampled crops may have inconsistent semantic information, which consequently degrades the quality of contrastive views. To address this limitation, we explore to replenish sample views with better consistency of the image and propose a novel self-supervised learning (SSL) framework RepCo. Instead of searching for semantically consistent patches between two different views, we select patches on the same image as the replenishment of positive/negative pairs, encourage patches that are similar but come from different positions as positive pairs, and force patches that are dissimilar but come from adjacent positions to have different representations, i.e. construct negative pairs to enrich the learned representations. Our method effectively generates high-quality contrastive views, explores the untapped semantic consistency on images, and provides more informative representations for downstream tasks. Experiments on adequate downstream tasks have shown that, our approach achieves ＋2.1 AP50 (COCO pre-trained) and ＋1.6 AP50 (ImageNet pre-trained) gains on Pascal VOC object detection, ＋2.3 mIoU gains on Cityscapes semantic segmentation, respectively.
Online Shopping Website

In the era of digital technology, consumers all over the world are increasingly embracing online shopping. This online store is an example of a platform that provides users with a convenient and easy way to browse and buy a selection of things. Users can easily search for products, add them to their basket, and check out securely with a variety of payment choices thanks to the website's easy-to-use layout. Customers can simply identify what they're looking for, place an order, and have it delivered directly to their door thanks to the website's hassle-free shopping experienceThe website offers a wide range of goods, including electronics, apparel, home appliances, toys, cosmetics, and more. Customers can use keywords or particular categories to find products fast utilising the website's search engine. Customers can easily find the things they need on the website because to its clear, visually appealing, and user-friendly style. The quick and simple checkout process on the website enables users to finish their purchases in just a few clicks. Clients have access to a variety of safe payment methods, including bank transfers, e-wallets, credit cards, and debit cards. The website uses encryption technology to safeguard sensitive data, further ensuring the security of consumer information. Customers can create an account on the website, keep their shipping and payment information, examine their order history, and monitor their shipments using the customizable account system. Overall, this online store offers clients a safe, dependable, and easy-to-use platform for online shopping. The website has been a popular option for customers searching for a hassle-free shopping experience because to its wide selection of products, aggressive pricing, and dependable shipping.
Online Shopping Website

In the era of digital technology, consumers all over the world are increasingly embracing online shopping. This online store is an example of a platform that provides users with a convenient and easy way to browse and buy a selection of things. Users can easily search for products, add them to their basket, and check out securely with a variety of payment choices thanks to the website's easy-to-use layout. Customers can simply identify what they're looking for, place an order, and have it delivered directly to their door thanks to the website's hassle-free shopping experienceThe website offers a wide range of goods, including electronics, apparel, home appliances, toys, cosmetics, and more. Customers can use keywords or particular categories to find products fast utilising the website's search engine. Customers can easily find the things they need on the website because to its clear, visually appealing, and user-friendly style. The quick and simple checkout process on the website enables users to finish their purchases in just a few clicks. Clients have access to a variety of safe payment methods, including bank transfers, e-wallets, credit cards, and debit cards. The website uses encryption technology to safeguard sensitive data, further ensuring the security of consumer information. Customers can create an account on the website, keep their shipping and payment information, examine their order history, and monitor their shipments using the customizable account system.
Revolutionizing Fan Engagement in the Music Industry with Blockchain Technology

The music industry is facing challenges in engaging fans and providing transparency, feedback, and rewards. Blockchain technology presents a potential solution by enabling new forms of fan engagement and participation. This paper proposes a blockchain-based music platform that leverages Ethereum’s decentralized platform and smart contract functionality. Ethereum’s Proof of Stake (PoS) consensus algorithm makes it more energy-efficient than the Proof of Work (PoW) algorithm used by Bitcoin. The platform could facilitate investment in up-and-coming artists, feedback mechanisms, and rewards for fans. The scalability and decentralization of Ethereum make it an attractive choice for building a platform that can accommodate a large number of users and transactions without compromising performance. The proposed platform offers a secure, scalable, and decentralized solution that provides novel ways for fans to engage and participate in the music industry while being energy-efficient, sustainable, and accessible to everyone with a stake in the system.
AN EXPLORATORY STUDY ON STUDENTS’ ANXIETY IN THE PROCESS OF COMPOSING AN ENGLISH ESSAY

Writing proficiency is a crucial language skill for students to acquire, necessitating the production of compositions such as essays. This research aims to identify the manifestations of anxiety experienced by students during the composition of English essays and to ascertain the underlying factors precipitating such apprehension. Participants consisted of 34 fifth-semester students from the Department of English Education at Universitas Syiah Kuala. Employing qualitative methodology, the study utilized questionnaires and interviews as research instruments to collect data. Questionnaires were used to capture data concerning diverse forms of student anxiety, while interviews were conducted to elucidate the determinants of anxiety specifically related to composing English essays. Outcomes revealed that cognitive anxiety was predominant, afflicting 43.5% of fifth-semester students, showcasing its prevalence in this cohort. Additionally, language complexities, self-confidence deficits, fear of critical evaluation, and the pursuit of perfection emerged as pivotal sources of essay-writing apprehension. These findings underline the multifaceted nature of student anxiety during English essay composition, thereby serving as a valuable resource for educational stakeholders seeking insights to enhance students' writing competencies.
AMSC: Adaptive Masking and Structure-Constraint Learning for Domain Adaptive Semantic Segmentation Under Adverse Conditions

Unsupervised domain adaptation (UDA) aims to enable autonomous vehicles to understand complex road information without semantic labels. However, traditional UDA methods face challenges under adverse conditions, leading to suboptimal segmentation. These methods grapple with two core problems: distinguishing visually similar classes and misclassifying sudden dynamic classes in the target domain. To tackle these issues, we introduce Adaptive Masking and Structure-Constraint Learning (AMSC), comprising: (1) Adaptive Masking module for context leverage. (2) Structure-Constraint Learning to address structural loss from masking. Through these modules, we effectively tackle the aforementioned challenges. Simulation results showcase AMSC's state-of-the-art UDA performance, achieving a mIoU of 73.2 on Cityscapes to ACDC, a standard benchmark for normal-to-adverse semantic segmentation in UDA.
Domain-Adaptive Graph Attention-Supervised Network for Cross-Network Edge Classification

Graph neural networks (GNNs) have shown great ability in modeling graphs; however, their performance would significantly degrade when there are noisy edges connecting nodes from different classes. To alleviate negative effect of noisy edges on neighborhood aggregation, some recent GNNs propose to predict the label agreement between node pairs within a single network. However, predicting the label agreement of edges across different networks has not been investigated yet. Our work makes the pioneering attempt to study a novel problem of cross-network homophilous and heterophilous edge classification (CNHHEC) and proposes a novel domain-adaptive graph attention-supervised network (DGASN) to effectively tackle the CNHHEC problem. First, DGASN adopts multihead graph attention network (GAT) as the GNN encoder, which jointly trains node embeddings and edge embeddings via the node classification and edge classification losses. As a result, label-discriminative embeddings can be obtained to distinguish homophilous edges from heterophilous edges. In addition, DGASN applies direct supervision on graph attention learning based on the observed edge labels from the source network, thus lowering the negative effects of heterophilous edges while enlarging the positive effects of homophilous edges during neighborhood aggregation. To facilitate knowledge transfer across networks, DGASN employs adversarial domain adaptation to mitigate domain divergence. Extensive experiments on real-world benchmark datasets demonstrate that the proposed DGASN achieves the state-of-the-art performance in CNHHEC.
RANCANG BANGUN SISTEM MONITORING KUALITAS UDARA BERBASIS ANDROID

In this era of rapid technological development, it is a crucial period where technology has become an inseparable part of everyday life. The significant increase in human activity has triggered various air pollution problems, including burning, waste, industrial waste, vehicle fumes, industrial fumes, and the use of electronic equipment, among others. Additionally, besides the aforementioned problem of air pollution, its impact can also lead to a variety of serious problems and the occurrence of health issues such as asthma, ISPA, lung cancer, and so on. Therefore, an Android-based application was created that can provide information about the current air quality in the user's area. This application can be used to monitor air quality in specific areas based on measurements and data collection. The method used for this research is the RAD (Rapid Application Development) method, which is suitable for developing software in a short time. We have also added a real-time air quality monitoring feature that can monitor air levels to determine if they are within normal levels or not. This allows users to combat air pollution and carry out continuous monitoring without being limited by space and time.