Reviewerly/exHarmony huggingface dataset:
Mathematical Methods and Models in Linguistics

In this article, relevant approaches to creating and using mathematical models in linguistics are examined. Analyzed are the types of mathematical methods used to solve linguistic tasks. Also investigated are the fundamental principles for creating mathematical models. Stressed in particular is that the approach usually applied in solving physical tasks can be effective. Its typical feature is that a universal theory explaining the interaction among a system’s functional elements on the most general level is the basis of the model. In this case, the necessary approximating dependencies are not conjectured, as is often the case, but obtained or calculated on the basis of the fundamental theory. The prospects for applying this approach in linguistics are defined. Examples of the modeling of linguistic systems and a comparative analysis of their various methods are provided. Much attention is paid to the universality of the mathematical models used to analyze linguistic material. It is noted that if the approximate dependencies used are based on a system’s general universal properties, this can significantly complicate the quantitative analysis of the data. The advantages and shortcomings of mathematical modeling in solving applied linguistic tasks are illustrated. Besides “classic” models (such as Zipf’s law for the rank distribution of words in a text), other modeling approaches are described. In particular, attention is paid to the methods of constructing mathematical models based on neural networks. In this case, the approximation of dependence is realized in the form of sequential essentially nonlinear transformations. The main disadvantage of this approach is connected with the technical complications of execution and with the absence of overt analytical dependence. The main advantage is hidden in the potential possibility of creating approximate dependencies of practically any type. As a comparison of various modeling approaches, the article provides examples of solving the same linguistic tasks using several methods.
Applying the UTAUT to Understand Factors Affecting the Use of Indonesia Public Administration

Decision-making will always be based on valid and reliable data. Information disclosure is a necessity for people in a country to know something. Through a system, a country will be greatly assisted in disseminating information to its people. The transparent public administration process is the hope of the citizen to the government. Innovation based on the use of the system plays an essential role in fulfilling government-owned social services. Claiming Old Age Security during the pandemic has forced the government to use a system that can run a fast and controlled administrative process. This study wants to evaluate a system used in the employees' social services of state-community administration using the UTAUT method. This study indicates that the performance expectancy, social influence, facilitating conditions, and behavior variables have a significant and positive effect. However, the effort expectancy variable does not influence behavior intention. The optimal system is a system that can be used easily by users; if the data processor cannot use the system, a system failure will occur because the data generated will be biased and invalid. Suppose the user has difficulty operating the system. In that case, it is best to immediately evaluate it in the form of input and training to improve further the skills of the user who uses it. Participants who process claims can be processed faster, increasing participant satisfaction.
Sistem Pakar Identifikasi Hama Dan Penyakit Pada Tumbuhan Kakao Menggunakan Metode BFS (Breadth First Search) Dan Certainty Factor Berbasis Android

Abstract - Cocoa (Theobroma cacao L.) is a plantation commodity other than coffee that is suitable for smallholder plantations, because this plant can produce throughout the year, so it can be a source of daily or weekly income for cocoa farmers. One of the factors that affect the decline in productivity is pests and diseases. Diseases and pests of cocoa plants can be distinguished based on signs and symptoms. Symptoms of pests and diseases can be observed with the naked eye which characterizes the type that causes these pests and diseases. To overcome these problems, an expert system was created to identify diseases and pests on cocoa plants using the BFS (Breadth First Search) method and certainty factor. The BFS (Breadth First Search) method is used in the manufacture of this system because it can perform periodic search for symptoms and search broadly for each symptom. In addition, the certainty factor method is added to the making of this system to calculate the certainty value of the identification results, so finding conclusions or results from identification using the BFS (Breadth First Search) method will be more accurate with the certainty value of the certainty factor method.
A Study on the Fast Fourier Transform Applications

A fundamental issue in many branches of mathematics, science, and engineering is polynomial evaluation. Numerous applications, such as cryptography, banking, and the fields of economic analysis, numerical analysis, and sign processing, can advantage considerably from the use of environment friendly polynomial contrast algorithms. We seemed at some of the most famous polynomial comparison methods in this topic, which includes Horner's approach, Newton-Raphson method, and Lagrange interpolation method. We additionally reviewed extra state-of-the-art strategies that can consider polynomials extra correctly in some situations, such as the Fast Fourier Transform (FFT) and Fast Multipoint Evaluation (FME). The preference of algorithm will matter on the specific hassle and software at hand as every algorithm has benefits and cons of its own. It is indispensable to take computational complexity into account.
Forms and Functions of Indonesian-English Code Switching: A Case Study of Elites in Makassar City

The phenomenon of code switching is one of the many social contexts that can help any interaction with the transition from one language to another. The aims of this research are to find out the general patterns of linguistic configuration and socio-pragmatic functions of Indonesian-English code switching. This research used descriptive qualitative and quantitative and the data were collected from 40 elites of different professions in four different types of interactions; seminars, meetings, dialogues on TV, and chitchats employing a direct observation technique including recording and jotting down, and questionnaires. The result of this research is demonstrated that code switching is a crucial phenomenon for a multilingual society. It plays a significant role in governing the social interaction of the community because it has a very wide spectrum of socio-pragmatic implication which significantly contribute to the lives of man whether as an individual being or as a social one
Improving Beginning Reading Abilty for Dylexian Children through Letter Puzzle Media in Class IV SDN 08 Sarilamak

This research is motivated by the problems that exist in the field, namely a child with learning difficulties who have difficulty in reading (dyslexia) who is in fourth grade at SD N 08 Sarilamak. Children have problems reading words with KV-KVK patterns so that children cannot follow the lessons at school. This study aims to improve children's ability to read early, especially reading words with a KV-KVK pattern. This research is an experimental study in the form of single subject research (SSR) with an A-B-A research design, the subjects in this study are dyslexic children with problems in early reading. especially in reading words with KV-KVK pattern. Data collection techniques in this study used test instruments and documentation of the ability to read words with KV-KVK patterns in children. Data analysis techniques in this study used analysis techniques under conditions and analysis between conditions.&#x0D; Keywords: Letter Puzzle, Beginning Reading, Dyslexia
The Influence of Think Pair Share Cooperative Learning Models on Outcome in Integrated Thematic Learning for Grade V Elementery School

The goal of this research is to see how the Think Pair Share (TPS) cooperative model affects student learning outcomes in integrated theme learning in class V Cluster I, Jurai District IV. This study used a non-equivalent control group design, which is a type of quasi-experimental methodology. This research covered all pupils in class V Cluster I, Jurai District IV. The experimental and control classes were created through purposeful sampling, with class V A acting as the experimental class and class V B functioning as the control class. The hypothesis was evaluated with a t test at a 95% confidence level (α = 0.05), and the results indicated that tcount &gt; ttable (2,434 &gt; 2,045), showing that H1 is correct. As a consequence, using the Think Pair Share (TPS) cooperative learning technique in integrated theme learning in class V of Elementary School had a significant influence on student learning outcomes.
EVALUASI SISTEM DRAINASE DALAM UPAYA PENANGGULANGAN BANJIR (STUDI KASUS KELURAHAN HONIPOPU KOTA AMBON)

Urban drainage systems serve the disposal of excess water in a city by draining it through the soil surface or passing below the ground surface, to be discharged into rivers, lakes and the sea. The excess water can be in the form of rainwater, domestic wastewater and industrial wastewater. Therefore, in order to prevent flooding in urban areas, an urban drainage system must be integrated with sanitation, garbage, city flood control and also the state of the surrounding area. The objectives of this research are 1) to identify the drainage system that always causes flooding in Honipopu Village, Ambon City; 2) Determine the direction of the right drainage system in an effort to overcome flooding in Honipopu Village, Ambon City. Data collection methods used in this study include: observation, interview techniques, agency surveys, and documentation. Analysis of the data used is an analysis of the basic physical conditions of the research area, including slope, hydrology, nalysis of the data used is an analysis of the basic physical conditions of the research area, including slope, hydrology, rainfall conditions and land use. Drainage conditions in Honipopu Village are poor due to very high sedimentation and waste water discharge causing drainage to become shallow and also to inadequate drainage system. The direction of the drainage system in an effort to cope with flooding in the Honipopu village is the rehabilitation and normalization of drainage strategies.
Interactive Power Point Analysis as a Learning Media during Limited Face-to-Face Meeting in Class 1 SD Muhammadiyah 13 Surabaya

&#x0D; The purpose of this study is to analyze the interactive power point media used by teacher as learning media during limited face-to-face meetings in class I SD Muhammadiyah 13 Surabaya. This study used descriptive qualitative research method, research techniques of observation, interviews and documentation, the source of this data was shown to the principal, teacher and student. The results of this study indicated that the use of interactive power point media during learning in the era of limited face-to-face meetings is very helpful in increasing students' enthusiasm for learning by using interactive power point media, students' understanding of the material and students' ability to answer the questions given.&#x0D;
GIFT: Graph-Induced Fine-Tuning for Multi-Party Conversation Understanding

Addressing the issues of who saying what to whom in multi-party conversations (MPCs) has recently attracted a lot of research attention. However, existing methods on MPC understanding typically embed interlocutors and utterances into sequential information flows, or utilize only the superficial of inherent graph structures in MPCs. To this end, we present a plug-and-play and lightweight method named graph-induced fine-tuning (GIFT) which can adapt various Transformer-based pre-trained language models (PLMs) for universal MPC understanding. In detail, the full and equivalent connections among utterances in regular Transformer ignore the sparse but distinctive dependency of an utterance on another in MPCs. To distinguish different relationships between utterances, four types of edges are designed to integrate graph-induced signals into attention mechanisms to refine PLMs originally designed for processing sequential texts. We evaluate GIFT by implementing it into three PLMs, and test the performance on three downstream tasks including addressee recognition, speaker identification and response selection. Experimental results show that GIFT can significantly improve the performance of three PLMs on three downstream tasks and two benchmarks with only 4 additional parameters per encoding layer, achieving new state-of-the-art performance on MPC understanding.
Continual Learning for End-to-End ASR by Averaging Domain Experts

Continual learning for end-to-end automatic speech recognition has to contend with a number of difficulties. Fine-tuning strategies tend to lose performance on data already seen, a process known as catastrophic forgetting. On the other hand, strategies that freeze parameters and append tunable parameters must maintain multiple models. We suggest a strategy that maintains only a single model for inference and avoids catastrophic forgetting. Our experiments show that a simple linear interpolation of several models' parameters, each fine-tuned from the same generalist model, results in a single model that performs well on all tested data. For our experiments we selected two open-source end-to-end speech recognition models pre-trained on large datasets and fine-tuned them on 3 separate datasets: SGPISpeech, CORAAL, and DiPCo. The proposed average of domain experts model performs well on all tested data, and has almost no loss in performance on data from the domain of original training.
Decision-based iterative fragile watermarking for model integrity verification

Typically, foundation models are hosted on cloud servers to meet the high demand for their services. However, this exposes them to security risks, as attackers can modify them after uploading to the cloud or transferring from a local system. To address this issue, we propose an iterative decision-based fragile watermarking algorithm that transforms normal training samples into fragile samples that are sensitive to model changes. We then compare the output of sensitive samples from the original model to that of the compromised model during validation to assess the model's completeness.The proposed fragile watermarking algorithm is an optimization problem that aims to minimize the variance of the predicted probability distribution outputed by the target model when fed with the converted sample.We convert normal samples to fragile samples through multiple iterations. Our method has some advantages: (1) the iterative update of samples is done in a decision-based black-box manner, relying solely on the predicted probability distribution of the target model, which reduces the risk of exposure to adversarial attacks, (2) the small-amplitude multiple iterations approach allows the fragile samples to perform well visually, with a PSNR of 55 dB in TinyImageNet compared to the original samples, (3) even with changes in the overall parameters of the model of magnitude 1e-4, the fragile samples can detect such changes, and (4) the method is independent of the specific model structure and dataset. We demonstrate the effectiveness of our method on multiple models and datasets, and show that it outperforms the current state-of-the-art.
What In-Context Learning "Learns" In-Context: Disentangling Task Recognition and Task Learning

Large language models (LLMs) exploit in-context learning (ICL) to solve tasks with only a few demonstrations, but its mechanisms are not yet well-understood. Some works suggest that LLMs only recall already learned concepts from pre-training, while others hint that ICL performs implicit learning over demonstrations. We characterize two ways through which ICL leverages demonstrations. Task recognition (TR) captures the extent to which LLMs can recognize a task through demonstrations -- even without ground-truth labels -- and apply their pre-trained priors, whereas task learning (TL) is the ability to capture new input-label mappings unseen in pre-training. Using a wide range of classification datasets and three LLM families (GPT-3, LLaMA and OPT), we design controlled experiments to disentangle the roles of TR and TL in ICL. We show that (1) models can achieve non-trivial performance with only TR, and TR does not further improve with larger models or more demonstrations; (2) LLMs acquire TL as the model scales, and TL's performance consistently improves with more demonstrations in context. Our findings unravel two different forces behind ICL and we advocate for discriminating them in future ICL research due to their distinct nature.
ADDSL: Hand Gesture Detection and Sign Language Recognition on Annotated Danish Sign Language

For a long time, detecting hand gestures and recognizing them as letters or numbers has been a challenging task. This creates communication barriers for individuals with disabilities. This paper introduces a new dataset, the Annotated Dataset for Danish Sign Language (ADDSL). Annota-tions for the dataset were made using the open-source tool LabelImg in the YOLO format. Using this dataset, a one-stage ob-ject detector model (YOLOv5) was trained with the CSP-DarkNet53 backbone and YOLOv3 head to recognize letters (A-Z) and numbers (0-9) using only seven unique images per class (without augmen-tation). Five models were trained with 350 epochs, resulting in an average inference time of 9.02ms per image and a best accu-racy of 92% when compared to previous research. Our results show that modified model is efficient and more accurate than existing work in the same field. The code repository for our model is available at the GitHub repository https://github.com/s4nyam/pvt-addsl.
Physical Layer Authentication and Security Design in the Machine Learning Era

Security at the physical layer (PHY) is a salient research topic in wireless systems, and machine learning (ML) is emerging as a powerful tool for providing new data-driven security solutions. Therefore, the application of ML techniques to the PHY security is of crucial importance in the landscape of more and more data-driven wireless services. In this context, we first summarize the family of bespoke ML algorithms that are eminently suitable for wireless security. Then, we review the recent progress in ML-aided PHY security, where the term "PHY security" is classified into two different types: i) PHY authentication and ii) secure PHY transmission. Moreover, we treat neural networks as special types of ML and present how to deal with PHY security optimization problems using neural networks. Finally, we identify some major challenges and opportunities in tackling PHY security challenges by applying carefully tailored ML tools.
CQural: A Novel CNN based Hybrid Architecture for Quantum Continual Machine Learning

Training machine learning models in an incremental fashion is not only important but also an efficient way to achieve artificial general intelligence. The ability that humans possess of continuous or lifelong learning helps them to not forget previously learned tasks. However, current neural network models are prone to catastrophic forgetting when it comes to continual learning. Many researchers have come up with several techniques in order to reduce the effect of forgetting from neural networks, however, all techniques are studied classically with a very less focus on changing the machine learning model architecture. In this research paper, we show that it is not only possible to circumvent catastrophic forgetting in continual learning with novel hybrid classical-quantum neural networks, but also explains what features are most important to learn for classification. In addition, we also claim that if the model is trained with these explanations, it tends to give better performance and learn specific features that are far from the decision boundary. Finally, we present the experimental results to show comparisons between classical and classical-quantum hybrid architectures on benchmark MNIST and CIFAR-10 datasets. After successful runs of learning procedure, we found hybrid neural network outperforms classical one in terms of remembering the right evidences of the class-specific features.
Clinical Note Owns its Hierarchy: Multi-Level Hypergraph Neural Networks for Patient-Level Representation Learning

Leveraging knowledge from electronic health records (EHRs) to predict a patient's condition is essential to the effective delivery of appropriate care. Clinical notes of patient EHRs contain valuable information from healthcare professionals, but have been underused due to their difficult contents and complex hierarchies. Recently, hypergraph-based methods have been proposed for document classifications. Directly adopting existing hypergraph methods on clinical notes cannot sufficiently utilize the hierarchy information of the patient, which can degrade clinical semantic information by (1) frequent neutral words and (2) hierarchies with imbalanced distribution. Thus, we propose a taxonomy-aware multi-level hypergraph neural network (TM-HGNN), where multi-level hypergraphs assemble useful neutral words with rare keywords via note and taxonomy level hyperedges to retain the clinical semantic information. The constructed patient hypergraphs are fed into hierarchical message passing layers for learning more balanced multi-level knowledge at the note and taxonomy levels. We validate the effectiveness of TM-HGNN by conducting extensive experiments with MIMIC-III dataset on benchmark in-hospital-mortality prediction.
DEVELOPMENT OF MOBILE AUGMENTED REALITY-BASED LEARNING MEDIA FOR HEAVY EQUIPMENT SAFETY IN INDONESIAN VOCATIONAL SCHOOL

Mobile augmented reality (AR) technology has become more prominent among researchers and academia in addition to complementing real life with leading technology over a graphical interface. This study developed and evaluated the implementation of AR-based mobile learning media for heavy equipment safety. It was developed as an alternative application with a module that can be scanned to show illustrations of heavy equipment in 3D visual to the actual physical tools and its safety procedures. It was tested among 37 students in the 12th grade majoring in Exploration and Mining Geology at an Indonesia Vocational School. Student experiences were gathered with a structured questionnaire with an average rating of 86,28% with the category “Very Worthy” and validation of the media with an average rating of 88,72% with the category “Very Worthy”. Feasibility test regarding aspects of content, media, and learning with an average rating of 83,94% with the category “Very Worthy”. Thus, it is proven that developed AR-based mobile learning media could help students to learn and understand heavy equipment safety before practicing in the real world.