Reviewerly/exHarmony huggingface dataset:
An in-depth analysis of passage-level label transfer for contextual document ranking

Pre-trained contextual language models such as BERT, GPT, and XLnet work quite well for document retrieval tasks. Such models are fine-tuned based on the query-document/query-passage level relevance labels to capture the ranking signals. However, the documents are longer than the passages and such document ranking models suffer from the token limitation (512) of BERT. Researchers proposed ranking strategies that either truncate the documents beyond the token limit or chunk the documents into units that can fit into the BERT. In the later case, the relevance labels are either directly transferred from the original query-document pair or learned through some external model. In this paper, we conduct a detailed study of the design decisions about splitting and label transfer on retrieval effectiveness and efficiency. We find that direct transfer of relevance labels from documents to passages introduces label noise that strongly affects retrieval effectiveness for large training datasets. We also find that query processing times are adversely affected by fine-grained splitting schemes. As a remedy, we propose a careful passage level labelling scheme using weak supervision that delivers improved performance (3–14% in terms of nDCG score) over most of the recently proposed models for ad-hoc retrieval while maintaining manageable computational complexity on four diverse document retrieval datasets.
Research on the Reform of the Teaching Mode of Rural English Education Assistance Based on the Technical Support of Network Technology

Abstract Under the background of the development of network technology, this paper aims to promote rural English teaching and constructs an English teaching model that combines English recognition technology and rural teaching. The main process of speech recognition is examined by analyzing different speech recognition technologies. Using a deep learning network, an English speech recognition model has been established. Combined with the English acoustic features in the network data, fluency of English speech is evaluated. Data embedding is performed on the English sequences in the network, combined with the sequence probability in the English data, so as to determine whether the English speech is correct or not. The Eval value for the English recognition model based on deep learning is 5.49%, while the test value is 5.89%, as per the results. As the English dataset increases, so does the English recognition technique proposed in this paper, and the accuracy remains above 0.6, and when the dataset is 500, the speech recognition accuracy is 0.8. The teaching model that combines speech recognition techniques with English teaching improves students’ English to a certain extent.
Handwriting identification and verification using artificial intelligence-assisted textural features

Abstract Intelligent process control and automation systems require verification authentication through digital or handwritten signatures. Digital copies of handwritten signatures have different pixel intensities and spatial variations due to the factors of the surface, writing object, etc. On the verge of this fluctuating drawback for control systems, this manuscript introduces a Spatial Variation-dependent Verification (SVV) scheme using textural features (TF). The handwritten and digital signatures are first verified for their pixel intensities for identification point detection. This identification point varies with the signature’s pattern, region, and texture. The identified point is spatially mapped with the digital signature for verifying the textural feature matching. The textural features are extracted between two successive identification points to prevent cumulative false positives. A convolution neural network aids this process for layered analysis. The first layer is responsible for generating new identification points, and the second layer is responsible for selecting the maximum matching feature for varying intensity. This is non-recurrent for the different textures exhibited as the false factor cuts down the iterated verification. Therefore, the maximum matching features are used for verifying the signatures without high false positives. The proposed scheme’s performance is verified using accuracy, precision, texture detection, false positives, and verification time.
The Influence of Co-Curricular Learning on Cadets' Skills in the Construction and Stability of Ships Course at Malahayati Maritime Polytechnic

To achieve the goals of national education, efforts are needed, such as maximizing the learning experiences of students because learning experiences greatly influence learning outcomes, especially in vocational education. Students' learning experiences can be accommodated through three activities: curricular, extracurricular, and co-curricular. While curricular and extracurricular activities are frequently heard of and discussed, it seems that co-curricular activities have not been fully utilized for strengthening learning. Co-curricular activities are carried out to reinforce, deepen, or enrich curricular activities. They are conducted outside regular class hours (including during breaks) and can take place in school or off-campus to support curricular implementation. Co-curricular activities must directly support the curricular activities and the students' learning interests with an emphasis on a more realistic context. Therefore, it is not solely focused on the curricular content, as it emphasizes real-world problem contexts, co-curricular activities have numerous benefits for students. Some of the benefits of co-curricular activities include character development, the cultivation of good values, the development of social skills and leadership abilities, the promotion of awareness and appreciation of diversity, increased enthusiasm in addressing evolving global issues, and the development of literacy and numeracy competencies. This can also be observed at Malahayati School of Maritime Polytechnic where co-curricular learning is one of the mandatory activities applied to cadets to help reinforce the learning materials they acquire in the classroom.
QuComm: Optimizing Collective Communication for Distributed Quantum Computing

Distributed quantum computing (DQC) is a scalable way to build a large-scale quantum computing system. Previous compilers for DQC focus on either qubit-to-qubit inter-node gates or qubit-to-node nonlocal circuit blocks, missing opportunities of optimizing collective communication which consists of nonlocal gates over multiple nodes. In this paper, we observe that by utilizing patterns of collective communication, we can greatly reduce the amount of inter-node communication required to implement a group of nonlocal gates. We propose QuComm, the first compiler framework which unveils and analyzes collective communication patterns hidden in distributed quantum programs and efficiently routes inter-node gates on any DQC architecture based on discovered patterns, cutting down the overall communication cost of the target program. We also provide the first formalization of the communication buffer concept in DQC compiling. The communication buffer utilizes data qubits to store remote entanglement so that we can ensure enough communication resources on any DQC architecture to support the proposed optimizations for collective communication. Experimental results show that, compared to the state-of-the-art baseline, QuComm reduces the amount of inter-node communication by 54.9% on average, over various distributed quantum programs and DQC hardware configurations.
Deep Learning Based Misogynistic Bangla Text Identification from Social Media

Misogyny is characterized by hostility, hatred, aversion, intimidation, and violence against women. With the rise of social media, it has become one of the most convenient platforms for expressing woman-hating speech. As a result, misogyny is gaining appeal and societal standards are being violated. With millions of Bangladeshi Facebook users, misogyny is growing increasingly prevalent in Bangla as well. In this paper, we have proposed automatically identifying misogynistic content in Bangla on social media platforms in order to evaluate the problem's challenges. As there is no existing Bangla dataset for analyzing misogynistic text, we generated our own. We have applied various deep-learning algorithms to improve the classification of misogynistic text categories. LSTM and RNN models are used for designing the model architecture in deep learning. Models are evaluated using the confusion matrix, accuracy, and f1-scores. The results indicate that LSTM outperforms RNN in terms of accuracy by 67 %.
Methods of Dimensions Reduction in Text Processing Algorithms

Paper describes methods of dimensionality reduction widely used in artificial intelligence in general, and in computer linguistics in particular, such as Non-negative matrix factorization and Singular value decomposition from the point of use in methods of Latent Semantic Analysis and Method of Principal Components. Advantages and disadvantages of each method are given. The computational complexity was investigated and a comparison of performance on dense and sparse matrices of different sizes was made. It is proposed to use them to reduce the dimensionality also of multidimensional linguistic data arrays.
Multiclass Classifier Based on Binary Logistic Regressions Obtained According to the Principles of GMDH

Introduction. The issue of accuracy improvement in classification tasks is always topical, and various approaches have been developed, applied in accordance with the peculiarities of the problem formulation and properties of the feature space. Among the most effective models, classifiers based on multiple logistic regressions have proved themselves. Purpose. The aim of the paper is to develop an algorithm for solving multiclassification problems on the basis of binary logistic models built by the stepwise multiple logistic regression algorithm of the Stepwise type, improved according to the principles of the method of group accounting of arguments. Methods. The paper proposes a modification of the stepwise algorithm for creating binary multivariate logistic regressions Stepwise, where it is proposed to optimize the algorithm parameters in accordance with the principles of the method of group consideration of arguments: significance levels by the logarithmic likelihood ratio test for inclusion and exclusion of model arguments. The choice of optimal parameters is realized in accordance with an external criterion that takes into account the balance of classification accuracy of training and test samples and the balance of class classification accuracy. Subsequently, the binary class models obtained by the one-versus-all principle are combined into a multiclass classifier that returns the answer according to the maximum likelihood of the class. The comparison of classification models obtained by the classical Stepwise algorithm and the one proposed in the robot is carried out on the medical data of the publicly available Internet resource Kaggle. Conclusion. The paper substantiates and demonstrates the advantages of classifiers based on logistic multivariate regressions optimized according to the principles of the method of group consideration of arguments relative to the classical version of the Stepwise algorithm. The effective application of the algorithm in solving multiclass classification problem is shown.
Knowledge Graphs and Large Language Models

Large Language Models(LLM) based on the Transformer architecture is nowadays one of the most widely used tool in Natural Language Processing(NLP) field. Nonetheless this approach has some limitations and flaws. In particular, these problems become crucial for the NLP-based expert systems. The LLMs may sometimes hallucinate and provide non-trustworthy responses. We will advocate the using of Knowledge Graphs for solving this problem.
Development and Implementation of Cloud Open Source Cryptocurrency BI Dashboards System

Cryptocurrencies have received a lot of attention from investors, researchers, regulators, and the media. It is a known fact that the cryptocurrency price is very volatile and has anomaly effects. However, not enough scientific research has been done in the Business Intelligence cryptocurrency calendar anomaly detection area. This study presented the development of Business Intelligence dashboards with real data. As a result of this application, anomaly detection was carried out in the values from the Finance Yahoo API collected data set. With the corresponding BI application, a time-dependent representation of cryptocurrency price can be captured and analyzed, and anomalies can be selected and evaluated. The factors that play a role in forming the model to be applied in detecting anomalies with the experimental results were formed and investigated.
Efektivitas Sistem Informasi Akuntansi Atas Pendapatan Jasa Penitipan Barang Pada PT. Pegadaian (Persero) UPS Sibuhuan

One of the information systems that is really needed in companies, without good information, company management will experience difficulties in making decisions. The information system needed in a company is an accounting information system, because basically an Accounting Information System (AIS) is a system that processes data and transactions to produce information that is useful for planning, controlling and operating a business. This research aims to analyze the effectiveness of information systems. Accounting for Revenue from Goods Safekeeping Services at PT. Pegadaian (Persero) UPS Sibuhuan. The research method used in this research is descriptive qualitative with a field approach through interviews and documentation, then analysis is carried out. The results of this research indicate that the accounting information system for goods deposit services revenue has been implemented effectively at PT Pegadaian (Persero) UPS Sihubuan. PT Pegadaian (Persero) UPS Sihubuan uses source documents to collect initial data about business activities, and then transfers the data to a computer. With well-designed source documents, data entry can be used properly for speed in recording business activities. When data about business activities has been collected, the next step is the process of updating previously stored information about the resources affected by these activities. The updating process is carried out directly, transactions occur and then produce output, namely financial reports. There is an accounting information system reporting process for deposit service revenues in the form of recognition, measurement, disclosure, reporting and accounting report notes.
Exploring Use of Explanative Illustrations to Communicate Differential Privacy Models

Proper communication is key to the adoption and implementation of differential privacy (DP). In this work, we designed explanative illustrations of three DP models (Central DP, Local DP, Shuffler DP) to help laypeople conceptualize how random noise is added to protect individuals’ privacy and preserve group utility. Following a pilot survey and an interview, we conducted an online experiment ( N = 300) exploring participants’ comprehension, privacy and utility perception, and data-sharing decisions across the three DP models. We obtained empirical evidence showing participants’ acceptance of the Shuffler DP model for data privacy protection. We discuss the implications of our findings.
Optimalisasi Pemutakhiran Data Sarana dan Prasarana Dinas Pemadam Kebakaran dan Penyelamatan Kota Samarinda Berbasis Sistem Informasi Geografis sebagai Upaya Pengurangan Risiko Bencana

Samarinda is the capital city of East Kalimantan. As the capital city on the second largest island in Indonesia, Samarinda is home to 834,824 residents. A problem that often arises in densely populated cities is fire. In Samarinda, from 2015 to 2022, there have been at least 2,349 fire incidents with a burned area of 8,090,755 m2 so this city has become one of the cities that most often experience fires on Kalimantan. Several supporting facilities and infrastructure owned by the Samarinda Fire and Rescue Agency, such as hydrants and fire posts have been expanded in number. However, most of these supporting facilities have not been integrated with a good coordinate system. So, when a fire occurs, it takes time to identify the location of these vital facilities. As an effort to solve this problem, the Geological Engineering Study Program, Faculty of Engineering, Mulawarman University and Samarinda State Polytechnic of Agriculture held community engagement activities in the form of geographic information system (GIS)-based application training for Samarinda Fire and Rescue Agency officers. This program aims to provide additional competencies needed by field officers in integrating data on facilities and infrastructure such as hydrants and fire posts into the Samarinda geoportal system. Based on the results of this activity, Samarinda Fire and Rescue Agency officers are skilled in operating applications that can integrate coordinate-based facilities and infrastructure data into a geoportal system, including measuring the burned areas accurately and precisely so that fire incidents in Samarinda can be minimized.
Peran dari Layanan Makerspaces dalam Kegiatan Komunitas Gerakan SUMUT Mengajar

According to the Ministry of Trade of the Republic of Indonesia, the creative economy is an industry that originates from the use of individual creativity, skills and talents to create prosperity and employment opportunities through the creation and utilization of the individual's creativity and inventiveness. The creative economic activity carried out by a group of volunteers in Nagur Village, Tanjung Beringin District, Serdang Bedagai is making pencil boxes from used bottles. Makerspace activities in the North Sumatra Teaching Movement community activities have a role as an educational space, creation or creativity space, social space and academic introduction to early childhood. As an educational space and a space for creation or creativity, these are activities that almost all volunteer groups do, such as teaching the Koran, teaching elementary school and middle school children what they don't know. And in the creation or creativity space, volunteer groups also carry out activities such as making crafts from used materials. With the existence of a makerspace, those of us who have creative ideas or knowledge that we want to share with the wider community can channel it directly and use a building or place that can be repurposed as a makerspace.
the_linguists at BLP-2023 Task 1: A Novel Informal Bangla Fasttext Embedding for Violence Inciting Text Detection

This paper introduces a novel informal Bangla word embedding for designing a cost-efficient solution for the task “Violence Inciting Text Detection” which focuses on developing classification systems to categorize violence that can potentially incite further violent actions. We propose a semi-supervised learning approach by training an informal Bangla FastText embedding, which is further fine-tuned on lightweight models on task specific dataset and yielded competitive results to our initial method using BanglaBERT, which secured the 7th position with an f1-score of 73.98%. We conduct extensive experiments to assess the efficiency of the proposed embedding and how well it generalizes in terms of violence classification, along with it’s coverage on the task’s dataset. Our proposed Bangla IFT embedding achieved a competitive macro average F1 score of 70.45%. Additionally, we provide a detailed analysis of our findings, delving into potential causes of misclassification in the detection of violence-inciting text.
nlpBDpatriots at BLP-2023 Task 2: A Transfer Learning Approach towards Bangla Sentiment Analysis

In this paper, we discuss the entry of nlpBDpatriots to some sophisticated approaches for classifying Bangla Sentiment Analysis. This is a shared task of the first workshop on Bangla Language Processing (BLP) organized under EMNLP. The main objective of this task is to identify the sentiment polarity of social media content. There are 30 groups of NLP enthusiasts who participate in this shared task and our best-performing approach for the task is transfer learning with data augmentation. Our group ranked 12th position in this competition with this methodology securing a micro F1 score of 0.71.
Offensive Language Identification in Transliterated and Code-Mixed Bangla

Md Nishat Raihan, Umma Tanmoy, Anika Binte Islam, Kai North, Tharindu Ranasinghe, Antonios Anastasopoulos, Marcos Zampieri. Proceedings of the First Workshop on Bangla Language Processing (BLP-2023). 2023.
nlpBDpatriots at BLP-2023 Task 1: Two-Step Classification for Violence Inciting Text Detection in Bangla - Leveraging Back-Translation and Multilinguality

In this paper, we discuss the nlpBDpatriots entry to the shared task on Violence Inciting Text Detection (VITD) organized as part of the first workshop on Bangla Language Processing (BLP) co-located with EMNLP. The aim of this task is to identify and classify the violent threats, that provoke further unlawful violent acts. Our best-performing approach for the task is two-step classification using back translation and multilinguality which ranked 6th out of 27 teams with a macro F1 score of 0.74.
NUS-IDS at PragTag-2023: Improving Pragmatic Tagging of Peer Reviews through Unlabeled Data

We describe our models for the Pragmatic Tagging of Peer Reviews Shared Task at the 10th Workshop on Argument Mining at EMNLP-2023. We trained multiple sentence classification models for the above competition task by employing various state-of-the-art transformer models that can be fine-tuned either in the traditional way or through instruction-based fine-tuning. Multiple model predictions on unlabeled data are combined to tentatively label unlabeled instances and augment the dataset to further improve performance on the prediction task. In particular, on the F1000RD corpus, we perform on-par with models trained on 100% of the training data while using only 10% of the data. Overall, on the competition datasets, we rank among the top-2 performers for the different data conditions.
LANS: Large-scale Arabic News Summarization Corpus

Text summarization has been intensively studied in many languages, and some languages have reached advanced stages. Yet, Arabic Text Summarization (ATS) is still in its developing stages. Existing ATS datasets are either small or lack diversity. We build, LANS, a large-scale and diverse dataset for Arabic Text Summarization task. LANS offers 8.4 million articles and their summaries extracted from newspapers websites’ metadata between 1999 and 2019. The high-quality and diverse summaries are written by journalists from 22 major Arab newspapers and include an eclectic mix of at least more than 7 topics from each source. We conduct an intrinsic evaluation on LANS by both automatic and human evaluations. Human evaluation of 1,000 random samples reports 95.4% accuracy for our collected summaries, and automatic evaluation quantifies the diversity and abstractness of the summaries.
Argumentative Stance Prediction: An Exploratory Study on Multimodality and Few-Shot Learning

To advance argumentative stance prediction as a multimodal problem, the First Shared Task in Multimodal Argument Mining hosted stance prediction in crucial social topics of gun control and abortion. Our exploratory study attempts to evaluate the necessity of images for stance prediction in tweets and compare out-of-the-box text-based large-language models (LLM) in few-shot settings against fine-tuned unimodal and multimodal models. Our work suggests an ensemble of fine-tuned text-based language models (0.817 F1-score) outperforms both the multimodal (0.677 F1-score) and text-based few-shot prediction using a recent state-of-the-art LLM (0.550 F1-score). In addition to the differences in performance, our findings suggest that the multimodal models tend to perform better when image content is summarized as natural language over their native pixel structure and, using in-context examples improves few-shot learning of LLMs performance.