Reviewerly/exHarmony huggingface dataset:
THUNDER: Named Entity Recognition Using a Teacher-Student Model with Dual Classifiers for Strong and Weak Supervisions

Strong and weak supervisions have complementary characteristics. However, utilizing both supervisions for named entity recognition (NER) has not been extensively studied. Moreover, the existing works address only incomplete annotations and neglects inaccurate annotations during NER model training. To effectively utilize weak labels, we introduce an auxiliary classifier that learns from weak labels. Furthermore, we adopt the teacher-student framework to handle both incomplete and inaccurate weak labels. A teacher model is first trained using both strongly and weakly supervised data, and next generates pseudo labels to replace weak labels. Then, the student model is trained so that the main classifier learns from both strong labels and confident pseudo labels while the auxiliary classifier learns from less confident pseudo labels. We also incorporate data augmentation through ChatGPT to generate additional annotated sentences to improve model performance and generalization capabilities. The experimental results with different weak supervisions demonstrate that our proposed method surpasses existing techniques.
Dual-Scale Interest Extraction Framework with Self-Supervision for Sequential Recommendation

In the sequential recommendation task, the recommender generally learns multiple embeddings from a user’s historical behaviors, to catch the diverse interests of the user. Nevertheless, the existing approaches just extract each interest independently for the corresponding sub-sequence while ignoring the global correlation of the entire interaction sequence, which may fail to capture the user’s inherent preference for the potential interests generalization and unavoidably make the recommended items homogeneous with the historical behaviors. In this paper, we propose a novel Dual-Scale Interest Extraction framework (DSIE) to precisely estimate the user’s current interests. Specifically, DSIE explicitly models the user’s inherent preference with contrastive learning by attending over his/her entire interaction sequence at the global scale and catches the user’s diverse interests in a fine granularity at the local scale. Moreover, we develop a novel interest aggregation module to integrate the multi-interests according to the inherent preference to generate the user’s current interests for the next-item prediction. Experiments conducted on three real-world benchmark datasets demonstrate that DSIE outperforms the state-of-the-art models in terms of recommendation preciseness and novelty.
XFLT: Exploring Techniques for Generating Cross Lingual Factually Grounded Long Text

Multiple business scenarios require an automated generation of descriptive human-readable long text from structured input data, where the source is typically a high-resource language and the target is a low or medium resource language. We define the Cross-Lingual Fact to Long Text Generation (XFLT) as a novel natural language generation (NLG) task that involves generating descriptive and human-readable long text in a target language from structured input data (such as fact triples) in a source language. XFLT is challenging because of (a) hallucinatory nature of the state-of-the-art NLG models, (b) lack of good quality training data, and (c) lack of a suitable cross-lingual NLG metric. Unfortunately previous work focuses on different related problem settings (cross-lingual facts to short text or monolingual graph to text) and has made no efforts to handle hallucinations. In this paper, we contribute a novel dataset, XLALIGN with over 64,000 paragraphs across 12 different languages, and English facts. We propose a novel solution to the XFLT task which addresses these challenges by training multilingual Transformer-based encoder-decoder models with coverage prompts and grounded decoding. Further, it improves on the XFLT quality by defining task-specific reward functions and training on them using reinforcement learning. On XLALIGN, we compare this novel solution with several strong baselines using a new metric, cross-lingual PARENT. We also make our code and data publicly available https://drive.google.com/file/d/1sHgcwXKribjrm2grbs-LzXUUqXQitD2N/.
Zeta functions for tensor codes

In this work, we introduce a new class of optimal tensor codes related to the Ravagnani-type anticodes defined in 2023. We show that it extends the family of [Formula: see text]-maximum rank distance codes and contains the [Formula: see text]-binomial moment determined codes (with respect to the Ravagnani-type anticodes) as a proper subclass. We define and study the zeta function for tensor codes. We establish connections between this object and the weight enumerator of a tensor code with respect to the Ravagnani-type anticodes. We introduce a new refinement of the invariants of tensor codes exploiting the structure of product lattices of some classes of anticodes classified in 2023 and we derive the corresponding MacWilliams identities. In this framework, we also define a multivariate version of the tensor weight enumerator and we establish relations with the corresponding zeta function. As an application, we derive connections on the tensor weights.
Blockchain technology in education: The perspective, challenges, and concerns

Blockchain technology has altered the way transactions are conducted since its introduction. It offers a risk-free and secure way to exchange digital currency that is impervious to manipulation by those with bad intentions. In blocks that store the data, it stores all transactional data. The best example of decentralized, systematic data collection is blockchain technology. By storing the block in many locations across a distributed network arrangement, it offers a public ledger system that combines public key encryption of all transactions to solve the double spending problem, making change impossible and therefore more secure. This article explores the usefulness and benefits of Blockchain technology compared to traditional data management by outlining its fundamental concepts and how it has been adopted in different industries. The writers also discuss the factors that led to the adoption of Bitcoin and other cryptocurrencies as well as the shift to a decentralized crypto ledger. In-depth information on this technology's potential hazards, drawbacks, security features, threats, and vulnerabilities is also included in the article.
Los aportes de la lingüística forense contra el cibercrimen

This article presents different tasks of linguistic analysis that can contribute to the prosecution of cybercriminals during an investigation or to aid legal experts to reach decisions through an expert report. This article shows the application of different areas of forensic linguistics, such as linguistic profiling, authorship analysis, the analysis of meaning or criminal language, to different cases related to cybercrime. The cybercrimes analyzed include cyberbullying, cyberattacks through malware, hate speech or cyberfraud. Thus, this contribution highlights the potential of forensic linguistics in the fight against cybercrime.
Immersive Modeling Framework for Training Applications

This article describes a framework for modeling and executing training in augmented and virtual reality environments. The framework was designed based on characteristics observed in existing training applications for complex tasks, such as the use of tools, control panels and the need for step-by-step instructions. Unlike other frameworks, in the proposed system, it is possible to create the training entirely within the virtual/augmented environment, avoiding constant switching between 2D and 3D environments. The framework allows for the definition of steps in a training program, each of which includes textual instructions, videos, and 3D objects, static or animated, anchored in the real world. To demonstrate the capabilities of the framework, a training program for operating a Universal Testing Machine was created as a case study. Overall, the proposed framework allows for the creation of effective and efficient AR training programs for a variety of tasks and industries.
Nilai Pendidikan Karakter Dalam Novel Si Anak Savana Karya Tere Liye Dan Implikasinya Pada Pembelajaran Sastra Di SMA

This study aims to describe (1) the value of character education in Tere Liye's Novel Si Anak Savana (2) the implications for learning literature in high school.This study uses qualitative methods and types of library research. Qualitative research was used to describe the value of character education in Tere Liye's Novel Si Anak Savana and to describe its implications for teaching literature in high school. The results of the study were 14 values of character education and 28 quotes of character education values in Tere Liye's novel Si Anak Savana by Tere Liye. The 14 values of character education are: (1) Religious (form: obedient to worship, always pray, be grateful, believe, put your trust in and pray). (2) Honest (form: not lying) (3) Discipline (form: not delaying work). (4) Hard Work (its form: always trying and never giving up), (5) Independent (its form: not depending on other people) (6) Creative (its form: always having ideas and ideas) (7) Democratic (its form: expressing opinions ) (8) Curiosity (its form: curiosity), (9) Friendly (its form: always doing work together), (10) Peace-loving (its form: not wanting to fight), (11) Caring for the environment (its form : environmental cleanliness and natural preservation), (12) Social care (its form: helping others and social service). (13) Likes to read (form: likes to read) (14) Responsibility (form: obligation that must be carried out. The most dominant character education value in Tere Liye's novel Si Anak Savana by Tere Liye is the value of social care character education, and curiosity From the results of the analysis of the 28 values of character education in Tere Liye's novel Si Anak Savana, it can be implicated in teaching literature in class XI high school, according to KD 3.20 analyzing the messages from the two fiction books (novels and poetry) that have been read. , students can learn the value of character education.Students can also find the value of character education in novel.
Penggunaan Model PJBL Pada Pembelajaran Menulis Puisi Dengan Pemanfaatan Media Youtube Pada Peserta Didik Kelas X IPS-1 Semester II SMA Negeri 5 Palangka Raya Tahun Pembelajaran 2022/2023

This study aims to determine: (1) the use of the PjBL model in learning to write poetry by&#x0D; utilizing YouTube media for students, (2) the effect of using the PjBL model in learning to write poetry by using YouTube media for students.&#x0D; The approach used in this study is a quantitative approach in the form of writing poetry and qualitative with descriptive methods. The variables in this study are the use of YouTube media and the learning outcomes of students in learning to write poetry. The population in this study were all students of class X IPS SMA Negeri 5 Palangka Raya. The sample in this study were students of class X IPS-1 SMA Negeri 5 Palangka Raya who were obtained by purposive sampling technique. Data collection techniques used observation sheets of teacher and student activities as well as tests on writing poetry.
Construction of Music Popular Trend Prediction Model Based on Big Data Algorithm

In this paper, a prediction model of music pop trend based on big data algorithm is proposed, and the approximate representation method of time series symbol aggregation based on starting and ending distance is used to predict music pop trend. This method puts forward a new distance measure by combining the symbolic distance and the starting and ending distance, which solves the problem that it is impossible to measure the consistency of symbols in each sequence segment, fully reflects the distance between time series, and effectively improves the effect of distance measurement. The research results show that the average song playing amount is better than the original song playing amount. The F value of the model predicted in this paper is 5064. It is superior to other time series music trend prediction models. Experiments show that this paper uses the approximate representation method of time series symbol aggregation based on the distance from beginning to end to predict the music fashion trend, which solves the problem of predicting the amount of songs played in the research of music fashion trend prediction and the problem of inaccurate prediction caused by the sudden increase of the amount of music data.
A Quality Control Model for Lung Cancer Based on A Standardized Treatment Mode

In the medical field, quality of care is one of the most important goals, and only by achieving good quality of care can we provide better health services to the society. Therefore, it is particularly important to implement quality control of the diagnosis and treatment process. In this paper, we propose a lung cancer quality control model based on the standardized judgment of the treatment model to address the requirements of lung cancer treatment quality control, the complex content of medical text data, and the irregular recording time. First, the word vector is trained by BERT pre-training model; then, the K-medoids algorithm is used to mine the standardized treatment patterns, and the time-aware LSTM unit is used to deal with the uneven input interval on this basis; finally, the attention mechanism is introduced to highlight the different contributions of the visit days to complete the prediction of the next cycle of lung cancer treatment items. The experimental results showed that the recall and average accuracy of the BERT-TLSTM-ATT fusion model reached 0.895 and 0.782, respectively, which performed well compared with other models and effectively controlled the quality of next-cycle treatment, achieving the goal of quality control of all aspects of lung cancer treatment.
Üsküdar University became one of the first universities entered WhatsApp ‘Channels’

Üsküdar University became one of the first universities entered WhatsApp ‘Channels’
Deep Learning for Malaria Diagnosis: Leveraging Convolutional Neural Networks for Accurate Parasite Detection

malaria is one of the most severe diseases worldwide. However, the current diagnostic method that involves examining blood smears under a microscope is unreliable and heavily relies on the examiner's expertise. Recent attempts to use deep-learning algorithms for malaria diagnosis have not produced satisfactory results. But, a new CNN-based machine learning model has been proposed in a research paper that can automatically detect and predict infected cells in thin blood smears with 94.63% accuracy. This model accurately accentuates the region of interest for the stained parasite in the images, which increases its reliability, transparency, and comprehensibility, making it suitable for deployment in healthcare settings.
Square Attacks on Reduced-Round FEA-1 and FEA-2

FEA-1 and FEA-2 are the South Korean Format-Preserving Encryption (FPE) standards. In this paper, we discuss the security of FEA-1 and FEA-2 against the square attacks. More specifically, we present a three-round distinguishing attack against FEA-1 and FEA-2. The data complexity of this three-round distinguisher is $$2^8$$ plaintexts. We use this three-round distinguisher for key recovery against four rounds of FEA-1. The time complexity of this key recovery attacks is $$2^{137.6}$$ , for both 192-bit and 256-bit key sizes. In addition, we extend the three-round distinguisher to a five-round distinguisher for FEA-2 using the tweak schedule. We use this distinguisher to mount six round key recovery attack with complexity $$2^{137.6}$$ , for 192-bit and 256-bit key sizes.
Modeling and Reconstruction of Data with Applications as Matrix of Pixels

Probabilistic Features Combination method (PFC), which is proposed by the author, is the approach of multidimensional data modeling, extrapolation and interpolation using the set of high-dimensional feature vectors. This method is a hybridization of numerical methods and probabilistic methods. Identification of faces or fingerprints need modeling and each model of the pattern is built by a choice of multi-dimensional probability distribution function and feature combination. PFC modeling via nodes combination and parameter γ as N-dimensional probability distribution function enables data interpolation for feature vectors. Multi-dimensional data is modeled and interpolated via nodes combination and different functions as probability distribution functions for each feature treated as random variable.
IASAT Conference 2019: an impression

IASAT is the International Association for the Study of Affective Touch, founded in 2015. September 11 –13, 2019, the third international conference was held in Linköping, Sweden. Participants came from, among others, Sweden, Germany, Russia, Great Britain, France, Canada, USA, Australia, and – last but not least – The Netherlands
Feasibility of Using Zero-Shot Learning in Transformer-Based Natural Language Processing Algorithm for Key Information Extraction from Head and Neck Tumor Board Notes

Natural language processing (NLP) technology has the potential to automate information aggregation and summarization in oncology. One example is the automation of patient registry creation. In this work, we aim to show (1) the feasibility of using modern NLP algorithms to extract key information from tumor board notes, and (2) the impact of prompt engineering on the quality of the results.In this IRB-approved study, we obtained the texts of head and neck tumor board notes for 306 unique patients. Five key pieces of information used to create a patient registry were predefined: age, gender, tumor histology, tumor stage, and primary location. The NLP algorithm used was a modified Text-To-Text Transfer Transformer (T5) model that was initially trained on the Colossal Clean Crawled Corpus (C4) dataset and subsequently fine-tuned on the Stanford Question Answering Dataset (SQuAD) to perform the downstream task of extractive question answering. The NLP model and trained weights were obtained from the Hugging Face platform. During inference, the entire body of the tumor board note and a related question were fed as inputs, and the model predicted a sequence of texts in response to the question. Two sets of questions of similar semantic meanings were used. Questions in prompt set #1 included "What is the gender?", "What is the age?", "What is the type of carcinoma in pathological diagnosis?", "What is the stage?", and "Where is the carcinoma located at?". Questions in prompt set #2 include "Is the patient male or female?", "How old is the patient?", "What kind of cancer?", "What is the cancer stage?", and "What is the tumor location?". Each model-predicted response was compared to the ground truth extracted from the tumor board notes. A response was classified as true if it is consistent with the ground truth, otherwise, it was deemed false. The response accuracy for each question was subsequently calculated.The median number of words in each tumor board note was 448 (range, 219 - 1505). The accuracy of the NLP algorithm for each question from either set is reported in Table 1. Algorithm performance is higher for extracting objective information such as age, gender, and histology. In addition, it was found that questions of similar semantic meanings but with different wording can lead to significantly different results.We demonstrated that a transformer-based extractive question-answering NLP algorithm can be successfully used for extracting information from head and neck tumor board notes with zero-shot learning. Furthermore, our results highlight the significance of prompt engineering for applying NLP for this task. Future work on finetuning these algorithms to oncology-specific texts can potentially enhance algorithm performance for more difficult tasks.
Lexical predictors of text complexity: The case of Russian as a foreign language

The article presents results of a comparative analysis of lexical complexity of educational texts in teaching Russian as a foreign language. The corpus size of the study is about 0.5 million words evenly distributed among six levels of language proficiency (A1-C2, Russian National System of Certification Levels of General Proficiency in Russian as a Foreign Language, further – RNSCL). The analysis algorithm is demonstrated based on B2 level texts, for which we estimated the values of the eight complexity predictors using the automatic analyzers RuLex (rulex.kpfu.ru) and RuLingva (rulingva.kpfu.ru): the number of tokens and types, sentence length, word length, lexical diversity (LD), terminological density, readability (MSIS) and frequency. B2 texts demonstrate significant differences in all the parameters, except for the word length. The validated B2 average word length is 2.26 syllables. The increase of lexical diversity from A1 to C2 is insignificant being within the range of 0.3 - 0.5. The complexity growth in RFL texts is accompanied by an increase of terminological density and the readability index. Since the RFL text is an important source of linguocultural information, the research findings may be useful to researchers, developers of educational resources and test materials, and teachers for text selection processes.
“Techno-R” technology in improving the quality of foreign language learning

The article describes the “Techno-R” pedagogical technology, which is being developed at the Department of Theory and Practice of Teaching Foreign Languages at the Institute of Philology and Intercultural Communication of Kazan Federal University. We characterize this technology in terms of goal-setting – improving students’ communicative competence; in terms of philosophical principles – the humanistic approach, recognizing the value of man as a person who has the right to develop and demonstrate their abilities; in terms of the organizational form of training – group and individual classes; in terms of diagnostic procedures for identifying the level of proficiency in a foreign language communicative competence and ways of achieving the goal. The article analyzes the theoretical foundation of the research, based on the theory of knowledge transfer by P. Galperin, the theory of transfer and the theory of cognitive-practical activity methods, created by the researchers of the Kazan didactic school. The article discloses the content and procedural aspects of the “Techno-R” technology and its place among other modern technologies. Of particular interest is the stage of the indicative basis of speech actions adjusting the skills, included in the structure of language competence, and speech skills in productive and receptive types of speech. This stage is the key to success in adjusting the level of students’ communicative competence. The article presents an overview of the experimental material on improving the quality of education based on teaching French at school and university using the method of mathematical statistics Student’s T-test. The “Techno-R” technology is included in the field of the humanistic paradigm of the society development, it helps people in acquiring the ability to speak a foreign language.