Reviewerly/exHarmony huggingface dataset:
Language Attitude of State High School Students 3 Waeapo, Buru Regency Towards The Use of Indonesian Language in The Millenial Era

SMA Negeri 3 is a favorite school in Waeapo District, Buru Regency, Maluku. There are several ethnic groups in the school environment at SMA Negeri 3, including the indigenous tribes, namely the Buru tribe and immigrant tribes such as Ambon, Southeast Maluku (especially Kei), Ambalau, Islands Sula (especially Sanana), Butonese, Bugis, Makassarese, Javanese, Sundanese. He also uses various languages, including Ambonese Malay dialect, Butonese, Bugis, Makassarese, Javanese, and also Sundanese. The general objectives of this study are: 1) to identify the influence of language attitudes of students at SMA Negeri 3 Waeapo, Buru district on the use of Indonesian in the Millennial Era; 2) revealing the positive and negative effects of the Language Attitudes of Students of SMA Negeri 3 Waeapo, Buru Regency on the Use of Indonesian in the Millennial Era. The type of research used in this research is a descriptive qualitative field research which examines linguistic phenomena. The data and data sources in this study are primary data and secondary data. The location of this research is SMA Negeri 3 Waeapo. The method of data collection was done through non-participating observation. The data collection technique was carried out through the free-of-conversation listening technique, recording technique, and note-taking technique. The data that has been classified is then analyzed using qualitative descriptive analysis techniques. The results of the study are positive factors and there are negative factors from the use of the Ambonese Malay dialect. The positive factor is that students continue to use Ambonese Malay dialect which is a language loyalty and encourages people of a language to maintain their language, this is a language pride that encourages people to develop language and use it as a symbol of identity and community unity, as well as awareness of the existence of language norms that encourage people to use their language carefully and politely, so that students from the Javanese and Sundanese tribes are finally interested in using Ambonese Malay dialect. The negative factor is that the students who come from the Javanese tribe feel offended because they don't know the meaning of the Ambonese Malay dialect which is known to be rough according to the Javanese people who are known to be smooth in speech.
The Effect of Using Multi-Scroll Chaotic Systems on Chaos-Based Random Number Generators’ Performance

Chaotic and hyper-chaotic systems are used in various engineering applications such as encryption, communication, and artificial intelligence. Also, chaotic systems are widely used in chaos-based random number generator (RNG) designs as chaotic system signals are not periodic and produce different values continuously. Since multi-scroll chaotic systems (MSCSs) produce more than one scroll, the output values can take more different values than chaotic systems. In this study, the effects of different directional values and/or different numbers of scrolls of multi-scroll chaotic systems on chaos-based random number generators’ performance are investigated with NIST 800-22 (National Institute of Standards and Technology) and correlation coefficient tests. As a result of the research, it has been concluded that the use of multi-scroll chaotic systems with different directional values and/or different numbers of scrolls does not always have a direct positive effect on the performance of chaos-based random number generators. Thus, it is necessary to use a special pre-process method that will vary according to the multi-scroll chaotic system to be used for chaos-based RNG designs with good performance.
Classification of North and South Indian Handwritten Scripts using Gabor Wavelet Features

Objectives: Handwritten script identification plays a vital role in processing handwritten data electronically.Most of the methods fail to provide accuracy due to variation in handwriting, hence the classification of the Indic script before providing it to OCR is crucial.The anticipated work helps increase the accuracy by categorizing the handwritten documents as north or South Indic script before further classification.Methods: This study has proposed a method, using Gabor filters to extract features from the text image for recognizing the kind of script, and seven widely used Indian scripts were considered for this experiment.The handwritten documents were collected from distinct individuals on request, under supervision.The database was manually created by extracting portions of lines from the scanned document images.Findings: A recognition accuracy of 100% was obtained for classifying North and South scripts while an average accuracy of 92% was obtained for biscript classification using KNN classifier at a portion of the line level.Novelty: The proposed method improves the accuracy by acting as a pre-processor to the OCR system by classifying the script according to North Indian script or South Indian Script.Further, it can be processed to find out the script type within the North or South Indian Scripts.
Poetry at the first steps of Artificial Intelligence

This paper is about Artificial Intelligence (AI) attempts at writing poetry, usually referred to with the term “poetry generation”. Poetry generation started out from Digital Humanities, which developed out of humanities computing; nowadays, however, it is part of Computational Creativity, a field that tackles several areas of art and science. In the paper it is examined, first, why poetry was chosen among other literary genres as a field for experimentation. Mention is made to the characteristics of poetry (namely arbitrariness and absurdity) that make it fertile ground for such endeavors and also to various text- and reader-centered literary approaches that favored experimentation even by human poets. Then, a rough historic look at poetry generation is attempted, followed by a review of the methods employed, either for fun or as academic projects, along Lamb et al.’s (2017) taxonomy which distinguishes between mere poetry generation and result enhancement. Another taxonomy by Gonçalo Oliveira (2017), dividing between form and content issues in poetry generation, is also briefly presented. The results of poetry generators are evaluated as generally poor and the reasons for this failure are examined: inability of computers to understand any word as a sign with a signified, lack of general intelligence, process- (rather than output-) driven attempts, etc. Then, computer-like results from a number of human poetic movements are also presented as a juxtaposition: DADA, stream of consciousness, OuLiPo, LangPo, Flarf, blackout/erasure poetry. The equivalence between (i) human poets that are concerned more with experimentation more than with good results and (ii) computer scientists who are process-driven leads to a discussion of the characteristics of humanness, of the possibility of granting future AI personhood and of the need to see our world in terms of a new, more refined ontology.
The Effect of Using Artificial Intelligence on Learning Performance in Iraq: The Dual Factor Theory Perspective

The adoption of artificial intelligence applications in higher education plays an important role in the improvement of the quality of education and learning practices and overcomes many educational' issues. The purpose of this study is to examine the factors influencing learning performance by using artificial intelligence in the educational process. The research model has been developed based on dual factor concept through examining “enablers” and “inhibitors” factors of artificial intelligence adoption in higher education toward improving the Learning Performance. The research model has been built based on a combination of the following theories, constructivism, TAM3, UTAUT, BM, status quo bias theory. The hypothesized model is validated empirically via a questionnaire including 57-item based on 5-point Likert scales completed by 383 respondents (random sampled). Structural equation modeling was used to evaluate the proposed model by analyzing the confirmatory factor and path effects across the AMOS software. The results demonstrate that the indicators of model fitness showed good fit. As for the results of the hypothesis test, it is clear that the results of the analysis show that the interaction and the engagement of learning have a significant effect on the collaboration for learning and thus have a significant effect on the learning performance. Perceived enjoyment, perceived usefulness, and perceived ease of use have a significant effect on using artificial intelligence. Facilitating conditions have no significant impact on using artificial intelligence. Consciousness has a positive effect on use while perceived risks and resistance do not significantly affect use and learning performance. The use of artificial intelligence has a positive and significant effect on learning performance.
Towards Debiasing Translation Artifacts

Cross-lingual natural language processing relies on translation, either by humans or machines, at different levels, from translating training data to translating test sets. However, compared to original texts in the same language, translations possess distinct qualities referred to as translationese. Previous research has shown that these translation artifacts influence the performance of a variety of cross-lingual tasks. In this work, we propose a novel approach to reducing translationese by extending an established bias-removal technique. We use the Iterative Null-space Projection (INLP) algorithm, and show by measuring classification accuracy before and after debiasing, that translationese is reduced at both sentence and word level. We evaluate the utility of debiasing translationese on a natural language inference (NLI) task, and show that by reducing this bias, NLI accuracy improves. To the best of our knowledge, this is the first study to debias translationese as represented in latent embedding space.
APLIKASI NAÏVE BAYES CLASSIFIER (NBC) PADA KLASIFIKASI STATUS GIZI BALITA STUNTING DENGAN PENGUJIAN K-FOLD CROSS VALIDATION

The case of stunting in Indonesia is a problem that has been discussed for a long time. One of many efforts to overcome this problem is through an accelerated stunting reduction program to improve the nutritional status of the community and also to reduce the prevalence of stunting or stunted toddlers. Generally, the index used to determine the nutritional status of stunting toddlers height compared to age. This study aims to identify the classification results, evaluate the model, and predict the nutritional status of stunting toddlers using the Naïve Bayes Classifier algorithm with K-Fold Cross Validation testing. The data processing system used is the GUI-R (Graphical User Interface) in order to facilitate the analysis process by implementing the Shiny Package in the Rstudio program. The results of accuracy using Naïve Bayes Classifier with 10-Fold Cross Validation test obtained the highest accuracy on the 6th iteration with an accuracy 94.39%, while the lowest accuracy on the 8th iteration with an accuracy 82.08%. Overall, the average accuracy in each iteration is 88.46%, so it can be concluded that Naïve Bayes Classifier model considered good enough to classified data on the nutritional status of stunting toddlers.Keywords: Stunting, Data Mining, Naïve Bayes Classifier, K-Fold Cross Validation, Shiny Package
Towards Unified Prompt Tuning for Few-shot Text Classification

Prompt-based fine-tuning has boosted the performance of Pre-trained Language Models (PLMs) on few-shot text classification by employing task-specific prompts. Yet, PLMs are unfamiliar with prompt-style expressions during pre-training, which limits the few-shot learning performance on downstream tasks. It would be desirable if the models can acquire some prompting knowledge before adaptation to specific NLP tasks. We present the Unified Prompt Tuning (UPT) framework, leading to better few-shot text classification for BERT-style models by explicitly capturing prompting semantics from non-target NLP datasets. In UPT, a novel paradigm Prompt-Options-Verbalizer is proposed for joint prompt learning across different NLP tasks, forcing PLMs to capture task-invariant prompting knowledge. We further design a self-supervised task named Knowledge-enhanced Selective Masked Language Modeling to improve the PLM's generalization abilities for accurate adaptation to previously unseen tasks. After multi-task learning across multiple tasks, the PLM can be better prompt-tuned towards any dissimilar target tasks in low-resourced settings. Experiments over a variety of NLP tasks show that UPT consistently outperforms state-of-the-arts for prompt-based fine-tuning.
Analysis on the Development Trend and Influencing Factors of Intelligent Agriculture in Anhui Province

With the implementation of the "Internet +" strategy, the modern integration of information technology and agriculture has also entered the fast lane of development. A large number of high-tech equipment and technologies have been gradually integrated into agricultural production, greatly improving the efficiency of agriculture. Realizing agricultural intellectualization and modernization has become an important goal of China's agricultural and rural work at this stage. This paper establishes a model to analyze the current development trend and main influencing factors of smart agriculture in Anhui Province, so as to provide empirical reference for the development of smart agriculture. Firstly, the key factors affecting agricultural productivity in Anhui Province are the change of agricultural scale and agricultural productivity by using tobit-u model, and then the research results show that the change of agricultural productivity in Anhui Province is the key factor to improve agricultural productivity The level of industrialization has a significant role in promoting the production efficiency of smart agriculture in Anhui Province, and the level of financial agricultural expenditure and urbanization rate have a significant negative effect.
An automatic reading method of pointer meter based on YOLOv5s

Pointer meters are widely used in transformer substations and other places because of their good robustness. Generally, the reading of pointer instrument cannot be read automatically, and can only rely on manual reading. With the development of robot and computer vision technology, it is possible to use inspection robot to obtain the reading of pointer instrument. The automatic reading of the analog meter should first detect the meter from the image, and then identify its reading. For the instability of traditional instrument detection, an analog meter automatic reading method based on YOLOv5s detector is proposed in this paper. Hough transform algorithm is used to detect the pointer and range, and then the meter reading is realized based on the angle method. Experiments show that the average processing time of a single image is 0.3s, and the reading error is less than 5%, which can meet the requirements of automatic reading in patrol inspection.
Proposal Framework to Light Weight Cryptography Primitives

Light weighting encryption algorithm is the best solution for IoT networks • All cryptography primitives (block, stream, hashing, asymmetric) must be light weighted.• Lightweight importance in time will decrease and complexity and randomness still same.• Lightweights encryption save energy and computation consuming of smart devices.Due to manufacturing cost and portability limitations, the computing power, storage capacity, and energy of the Internet of Things (IoT) hardware are still slowly developing.From above, the proposed security system based on encryption must consider the resources, time, memory used, and the lifespan of related sensors.In addition, some applications need simple encryption, especially after the emergence of IoT and the Web of Things (WoT).Providing solutions suitable for resource-constrained devices can be achieved by using lightweight cryptography.In this paper, building a framework that includes proposals for producing lightweight security algorithms for cryptography primitives was highly recommended.For the block cipher, some suggestions have been applied to an example of block encryption, Advance Encryption Standard 128 (AES-128), to produce lightweight AES-128.For lightweight stream cipher, the system applied the proposals on Ronald Rivest Cryptography algorithms (RC4).Rivest-Shamir-Adleman (RSA) algorithm is used to produce a lightweight asymmetric cipher by key partition and using the Chinese Remainder Theorem (CRT) in the decryption process to produce a lightweight RSA algorithm.Several proposals have been used for hash functions, the most important of which is reducing the number of rounds and simplifying the functions in SHA-256.Depending on the proposed framework, all the produced lightweight algorithms passed the National Institute of Standards and Technology (NIST) statistical tests for test randomness.The produced algorithms showed better processing time than standard algorithms, less memory usage for a lightweight version of each standard algorithm, and higher throughput than standard algorithms.
Enhanced Conditional Random Field-Long Short-Term Memory for Name Entity Recognition in English Texts

Abstract Named Entity recognition (NER) is the essential topic in the real world during the advanced development of technologies. Hence, in this paper, to develop Enhanced Conditional Random Field-Long Short-Term Memory (ECRF-LSTM) for NER in English language. The proposed ECRF-LSTM is combination of Conditional Random Field-Long Short-Term Memory (ECRF-LSTM) and Arithmetic Optimization Algorithm (AOA). This proposed method is utilizing to NER from the English texts. The proposed method is working with three phases such as pre-processing phase, feature extraction phase, and NER phase. Initially, the datasets are collected from the online system. In the pre-processing phase, removal of URL, removal of special symbol, username removal, tokenization and stop word removal are done. After that, the essential features such as domain weight, event weight, textual similarity, spatial similarity, temporal similarity, and Relative Document-Term Frequency Difference (RDTFD) are extracted and then applied for training the proposed model. To empower the training phase of CRF-LSTM method, AOA is utilized to select optimal weight parameter coefficients of CRF-LSTM for training the model parameters. The proposed method is validated by statistical measurements and compared with the conventional methods such as Convolutional Neural Network- Particle Swarm Optimization (CNN-PSO) and Convolutional Neural Network (CNN) respectively.
Mining Association Rules for Classification Using Frequent Generator Itemsets in arules Package

Abstract Mining frequent itemsets is an attractive research activity in data mining whose main aim is to provide useful relationships among data. Consequently, several open-source development platforms are continuously developed to facilitate the users' exploitation of new data mining tasks. Among these platforms, the R language is one of the most popular tools. In this paper, we defend the usefulness of providing a new version of arules package that can mine frequent generator itemsets, we describe the possibility of mining frequent generator itemsets in arules package, and we discuss what generators can be used for a classification task through an application example.
The Effectiveness of Using Electronic Modules in Mathematics Subjects in the Material of Constructing Flat Sided Space

Current technological developments encourage the development of many electronic modules that affect the learning process. The problem of learning mathematics obtained lies in learning outcomes that are not optimal, one of the causes is the lack of learning resources and limited study time. This study aimed to analyze the effectiveness of the electronic module on cognitive learning outcomes in the side-plane material. The research approach used is quantitative. The type of research used is pre-experimental research. The design used in this research is One Group Pretest-Posttest. The subjects of this study were class VIII students, who collected 32 students consisting of 14 boys and 18 girls. Methods of data collection by observation, interviews, and tests. The test instrument used was an objective test with 10 items. The analysis of improving learning outcomes was analyzed using the N-Gain formula. N-Gain is used to improve learning outcomes. Based on the N-gain analysis, student learning outcomes show a value of 0.61 or, in percentage, 61%, with a reasonably practical category. It was concluded that learning using the electronic mathematics module was quite effective in improving students' mathematics learning outcomes. E-modules can help students in learning.
SAINTENS: Self-Attention and Intersample Attention Transformer for Digital Biomarker Development Using Tabular Healthcare Real World Data

Deep learning currently struggles with tabular data, but it can benefit from multimodal learning. SAINT is a deep learning model for tabular data on which we base our presented developments.In this study, we introduce SAINTENS as a new deep learning method, specifically for the in healthcare predominant tabular real world data.For this purpose, we compare SAINTENS with SAINT and the State of the Art Machine Learning methods for tabular data. We use tabular data from geriatrics to predict four different targets (dysphagia, pressure ulcers, decompensated heart failure and delirium). We determine the relevant feature sets and train the models on these sets.Both SAINTENS and SAINT models are at least on the same performance level as the current State of the Art (Gradient Boosting Decision Trees).In combination with multimodal learning SAINTENS and SAINT may be used on real world data comprising tabular, text and image data, for discovery and development of new digital biomarkers.
Critical behavior of quantum Fisher information in finite-size open Dicke model

We explore the steady-state critical behavior of the finite-size open Dicke model—a model that incorporates spontaneous emission decay of the collective atomic spin states and decay of the cavity field. From the perspective of quantum information theory, we can often better characterize the quantum phase transition. In this paper, we characterize the super-radiant phase transition of the steady state of the open Dicke model by numerically calculating the quantum Fisher information (QFI). We calculate the QFI for the atomic state and the cavity field state, as well as their derivatives. We find that the QFI of the cavity field state is more sensitive to atomic decay, and is suppressed more severely in the presence of atomic decay. In contrast, the QFI of the atomic state is less sensitive to the photon loss of the cavity field.
Detection and Localization of Abnormal Events for Smart Surveillance

In this study, the methods of anomaly detection are proposed. Background substitution (BG) is used for extracting the motion and indicating the attention region's locations, which are employed. Then the regions are fed into the “Deep Convolutional Neural Network (DCNN)”. With the advantages of DCNN, for properly exploiting the spatiotemporal relationships, a network is developed for distinguishing anomalous and normal events. Besides this, the anomaly detection techniques are also described. The related databases are provided in this study. Many techniques for anomaly detection are discussed in this study with the help of the neural network. The different types of anomaly events are discussed here. All the data related to these anomaly events are discussed in the dataset. Different types of models related to the CNN model are also discussed in this study. And the anomaly techniques are also considered for discussion in this study.