Reviewerly/exHarmony huggingface dataset:
Systematic Evaluation of GPT-3 for Zero-Shot Personality Estimation

Very large language models (LLMs) perform extremely well on a spectrum of NLP tasks in a zero-shot setting. However, little is known about their performance on human-level NLP problems which rely on understanding psychological concepts, such as assessing personality traits. In this work, we investigate the zero-shot ability of GPT-3 to estimate the Big 5 personality traits from users’ social media posts. Through a set of systematic experiments, we find that zero-shot GPT-3 performance is somewhat close to an existing pre-trained SotA for broad classification upon injecting knowledge about the trait in the prompts. However, when prompted to provide fine-grained classification, its performance drops to close to a simple most frequent class (MFC) baseline. We further analyze where GPT-3 performs better, as well as worse, than a pretrained lexical model, illustrating systematic errors that suggest ways to improve LLMs on human-level NLP tasks. The code for this project is available on Github.
Joint Learning Model for Low-Resource Agglutinative Language Morphological Tagging

Due to the lack of data resources, rule-based or transfer learning is mainly used in the morphological tagging of low-resource languages.However, these methods require expert knowledge, ignore contextual features, and have error propagation.Therefore, we propose a joint morphological tagger for low-resource agglutinative languages to alleviate the above challenges.First, we represent the contextual input with multi-dimensional features of agglutinative words.Second, joint training reduces the direct impact of part-of-speech errors on morphological features and increases the indirect influence between the two types of labels through a fusion mechanism.Finally, our model separately predicts part-of-speech and morphological features.Part-of-speech tagging is regarded as sequence tagging.When predicting morphological features, two-label adjacency graphs are dynamically reconstructed by integrating multilingual global features and monolingual local features.Then, a graph convolution network is used to learn the higherorder intersection of labels.A series of experiments show that the proposed model in this paper is superior to other comparative models.
Translating a low-resource language using GPT-3 and a human-readable dictionary

We investigate how well words in the polysynthetic language Inuktitut can be translated by combining dictionary definitions, without use of a neural machine translation model trained on parallel text.Such a translation system would allow natural language technology to benefit from resources designed for community use in a language revitalization or education program, rather than requiring a separate parallel corpus.We show that the text-to-text generation capabilities of GPT-3 allow it to perform this task with BLEU scores of up to 18.5.We investigate prompting GPT-3 to provide multiple translations, which can help slightly, and providing it with grammar information, which is mostly ineffective.Finally, we test GPT-3's ability to derive morpheme definitions from whole-word translations, but find this process is prone to errors including hallucinations.
The Paradox of Multilingual Emotion Detection

The dominance of English is a well-known issue in NLP research.In this position paper, I turn to state-of-the-art psychological insights to explain why this problem is especially persistent in research on automatic emotion detection, and why the seemingly promising approach of using multilingual models to include lower-resourced languages might not be the desired solution.Instead, I campaign for the use of models that acknowledge linguistic and cultural differences in emotion conceptualization and verbalization.Moreover, I see much potential in NLP to better understand emotions and emotional language use across different languages.
AdityaPatkar at WASSA 2023 Empathy, Emotion, and Personality Shared Task: RoBERTa-Based Emotion Classification of Essays, Improving Performance on Imbalanced Data

This paper presents a study on using the RoBERTa language model for emotion classification of essays as part of the 'Shared Task on Empathy Detection, Emotion Classification and Personality Detection in Interactions' (Barriere et al., 2023), organized as part of 'WASSA 2023' at 'ACL 2023'.Emotion classification is a challenging task in natural language processing, and imbalanced datasets further exacerbate this challenge.In this study, we explore the use of various data balancing techniques in combination with RoBERTa (Liu et al., 2019) to improve the classification performance.We evaluate the performance of our approach (denoted by adityapatkar on Codalab (Pavao et al., 2022)) on a multi-label dataset of essays annotated with eight emotion categories, provided by the Shared Task organizers.Our results show that the proposed approach achieves the best macro F1 score in the competition's training and evaluation phase.Our study provides insights into the potential of RoBERTa for handling imbalanced data in emotion classification.The results can have implications for the natural language processing tasks related to emotion classification.
YNU-HPCC at WASSA-2023 Shared Task 1: Large-scale Language Model with LoRA Fine-Tuning for Empathy Detection and Emotion Classification

This paper describes the system for the YNU-HPCC team in WASSA-2023 Shared Task 1: Empathy Detection and Emotion Classification.This task needs to predict the empathy, emotion, and personality of the empathic reactions.This system is mainly based on the Decodingenhanced BERT with disentangled attention (DeBERTa) model with parameter-efficient fine-tuning (PEFT) and the Robustly Optimized BERT Pretraining Approach (RoBERTa).Low-Rank Adaptation (LoRA) fine-tuning in PEFT is used to reduce the training parameters of large language models.Moreover, back translation is introduced to augment the training dataset.This system achieved relatively good results on the competition's official leaderboard.The code of this system is available here.
Coronavirus Pandemic and The Challenges of Oil Producing Economies in Africa: Nigeria And Angola In Comparative Perspective

The outbreak of the covid-19 pandemic has forced many economics of the world to diversify their economy, because of the negative impact of the pandemic on global economy. The IMF report shows that the global GDP fall by 3% in 2020, and an estimate of 2% growth was experienced before the outbreak of the covid-19 pandemic. This study seeks to engage in a comparative analysis of the challenges of oil producing economies in Africa with emphasis on the Nigeria and Angola economy. Secondary data were used such as textbooks, journals and other internet sources. This study reason why these economies were negatively affected was that both countries dependent heavily on the export of crude oil as their major source of foreign exchange earnings. This study argues that if both countries had diversified their economy respectively, the impact of the decline in the price of crude oil in the international market would not have affected their economy to the extent that it has affect it. Therefore, this study further argues that poor macroeconomic policies as well as corruption that characterized the mismanagement of the oil revenue has contributed in a no small measure in affecting the economies of both countries. It is against this backdrop this study makes the following recommendations as measures that will help address the challenges facing both Nigeria and Angola especially in the area of diversification of the economy and curbing corruption.
NANO-AES Security Algorithm for Image Cryptography

Abstract: Data transmission security is now essential in any wireless communication. The fundamental problem in wireless communication is ensuring the security of data transfer from source to destination through data encryption and decryption. We give a literature review on the security of the encryption AES method and its contemporary applications in communication, data transfer, and wireless communication in this study. To provide security, we employ the Advanced Encryption Standard (AES), which works with 128-bit data and encrypts it with 128-bit keys. In this paper, we will conduct a literature review of AES algorithms and pick AES algorithms for wireless communication applications, as well as design a Verilog AES subblock add round key, mix column, and s-box for a Spartan3 FPGA device using Xilinx ISE 9.1i software
Modeling an Intelligent Agriculture System Simulation Based on Mobile Cloud Internet of Things (IoT)

Intelligent systems based on the Internet of Things (IoT) used for analysis and monitoring needs are present in all areas of human life and agriculture. Through the smart system was created, it can provide recommendations for management and also improving for agricultural and livestock production. Monitoring is done by collecting data (data collecting) and being modeled from several temperatures, humidity, gas, distance measurement, light indicators. The concept of E-Agriculture (Electronic Agriculture) is limited to monitoring environmental conditions of agricultural production and also provides an overview of E-Agriculture or E-Farm modeling on a larger scale. The E-AgriFarm intelligent system simulation using Arduino Controller and Blynk's Cloud IoT has a temperature measurement range of 26 - 31 0C, Relative humidity (RH) from 47% - 64%, and detection of a water level of at least 40-50 cm meters for drinking containers for each livestock with a capacity of 20 liters, and the systems can detect the level of pollution of gas leaks. Thus the E-AgriFarm smart system can provide management recommendations and increase the livestock production system to be better monitored and controlled.
TRANSFORMASI PUSTAKAWAN REFERENSI

Reference service is one type of service in the library. University library users, especially in reference services, really need information assistance to assist the research activities that they are currently doing. The needs of university library users have changed from was raw data information, with the development of information technology and computers, has turned into data that can be visualized and presented. The ability of librarians is required to change from being based on quick reference to becoming a data scientist. The method used is descriptive qualitative where the results of this study will describe an object in detail and in depth not only until data collection and then told but the data is further processed and then interpreted. The type of data used is qualitative obtained from speech, information, documents or files. In this paper, the type of data used is obtained from the librarian in charge of the reference room. To become a data scientist librarian, there are several steps that need to be taken apart from having a basic knowledge of mathematics and computer programming. These skills include the use of software, basic statistical knowledge, machine learning, calculus and linear equations, data munging, visualizing and communicating data, thinking like a data scientist.
Digital Competence and Job Performance in University Teachers in the Public Sector

Purpose: The objective of the study is to describe and evaluate the association of digital competence and job performance in public sector university teachers located in Lima, Peru. Theoretical framework: Being the digital competence necessary to improve the performance of teachers and increase their labor remuneration (Martín-Cuadrado et al., 2021). Design/Methodology/Approach: The same that has been possible to verify; thanks to the cross-sectional design and correlational descriptive level. In a sample of 184 university professors; who decided to participate freely and voluntarily to answer the questionnaires. Findings: The results show that the digital competence of teachers is at the advanced level at 45.7%, followed by the intermediate level with 38.9% and low at 15.5%. And in terms of work performance, the average level comes out with 36.2%, high 32.9% and low 13.3%. And by associating digital competence and job performance; it is found that the correlation value is (Rho = .290, p&lt;.001); which expresses being of low magnitude and positive direction. Existing a nexus of positive and significant direction between both. Research, practical &amp; social implications: Evidencing that the development of digital skills is associated with the job performance of university teachers in the public sector according to their praxis and academic work. That is, the optimization of the work performance of university teachers improves to the extent that they develop their digital skills, favoring teaching, the use of active strategies; problem solving and timely information management using digital media. Originality/Value: Because digital competence acts as a conditioning element for job performance in the exercise of university teaching.
Industrial Safety Management System using Wireless Communication Technology

The industrial safety management scheme is essential now-a-days due to the drastic growth of industries in the globe. The industrial accidents are majorly caused due to the power problems and power leakages. For ensuring the safety of human lives there is a necessity of new system to provide proper safety to guard the workers from electrical leakages or problems. When an electric current flows through a living organism, it causes a condition known as an electric shock. One milliampere is the smallest current a person can likely detect (mA). Tingling is the mildest symptom; more severe effects include muscular spasms, tissue damage, cardiac fibrillation, consciousness loss and sometimes even death. Electrocution is the term used to describe the result of a fatal electric shock. The complete power unit of a plant or department may be controlled by an embedded assisted shock prevention method, protecting workers from electrical shocks. This system provides the proper safety mechanism to industries and avoids accidental scenarios in proper way as well as the resulting section provides the outcome efficiency of the proposed approach in clear manner.
Applying Inverse Propensity Score Weighting and Doubly Robust for Treatment Effect Estimation in Sepsis

Sepsis is a severe disease with a relatively high fatality rate. The treatment assignment of antibiotics is one of the most crucial factors in reducing the mortality rate. Existing machine learning models are designed to estimate the individual time-to-treatment effect of the antibiotic. However, most models need more efficiency and stability in overcoming the selection bias problem. This paper applies two methods of debiasing samples and estimating individual treatment effects. By re-weighting, we create pseudo mini-batches that mimic the corresponding randomized controlled trial (RCT) process. We also adopt Inverse Propensity Score Weighting (IPW) method to assign appropriate weight to each sample in the observation dataset by estimating the propensity scores. We assume Doubly Robust (DR) to combine IPW and outcome regression model. On a real-world dataset, the experiments exhibit the ability of the model to successfully identify effective timing of treatment.
A novel multi-modal feature extraction system for news video

Advancements in artificial intelligence technology are leading to significant transformations in the generation of news. This article presents the News Video Style Feature Automatic Extraction System (NVFAE), which leverages state-of-the- art open-source technologies such as Automatic Speech Recognition, Optical Character Recognition, and Natural Language Processing. The NVFAE extracts news video style features from multiple modalities: textual features, visual features, and delivery features. The system uses automated annotation to extract five distinct style features from news videos: high-frequency words, overall tense, video color tone, main character, and video length. 38 BBC videos about the topic of "Turkey earthquake" from the YouTube platform are chosen for testing NVFAE, whose five features can be all successfully extracted. Experimental results show that the system proposed in this paper can provide a digital and structured way to explore the narrative characteristics of news videos, only with a mouse click and style features of news videos can be obtained in batches. NVFAE is aimed to overcome the difficulties of manual annotation for video news, as well as supply news workers with valuable references to study the news styles and content planning.
Image dehazing algorithm based on improved dark channel prior and heterogeneous optimization

This paper presents an image dehazing algorithm based on the improved dark channel prior and heterogeneous optimization. The improved dark channel prior simplifies the window-based dark channel prior principle to pixel based dark channel prior, and employs heterogeneous acceleration techniques to improve the processing speed. To further optimize the algorithm's performance, we implement the improved dark channel prior dehazing algorithm on CPU, GPU, and FPGA. We compared the processing effects, runtime, and pixel output under unit power consumption of the dark channel prior dehazing methods based on slice bilateral filtering and guided filtering on these different types of processors. The experimental results show that the algorithm proposed in this paper runs faster on GPU and FPGA than on CPU, and also exhibits higher energy efficiency. Moreover, compared to the dark channel prior dehazing methods based on slice bilateral filtering and guided filtering, this algorithm has higher efficiency and lower power consumption while improving the image quality.
PENGEMBANGAN ELECTRONIC BOOK BERBASIS STEM UNTUK MENINGKATKAN LITERASI SAINS MATERI ENERGI DAN PERUBAHANNYA PADA SISWA KELAS IV SEKOLAH DASAR

Advances in science and technology encourage education for students to care about and be responsive to issues that develop in society, to have in-depth knowledge and understanding to be applied in solving problems through scientific literacy. Meanwhile, students' scientific literacy skills are still low. So that the use of technology in teaching materials in the form of science learning e-books with the integration of the STEM approach can be a solution to train students' scientific literacy abilities. The aims of this study were 1) to determine the validity of the results of STEM-based e-book development, 2) to determine the practicality of using STEM-based e-books, 3) to determine the effectiveness of STEM-based e-books in increasing students' reading literacy. The type of research used is development research using the ADDIE design. The subjects in this study were 30 students of grade IV SDN Lebo 01. Data collection methods used in this study are interviews, questionnaires, and tests. The data analysis used was quantitative descriptive analysis through requirements testing and hypothesis testing using the Paired Sample T-Test. The results showed that the STEM-based science learning e-book was declared valid by media experts with very good criteria (94%) and subject matter experts with very good criteria (86%). The practicality of the STEM-based science learning e-book was obtained from the average teacher response score of 42 in the very high category and the average score of student responses was 42.8 in the very high category. The results of the effectiveness test showed an average pretest score of 67.83 &lt; an average posttest result of 86.27 and it can be said that STEM-based e-books in science learning are effective in increasing students' scientific literacy.
PENGARUH MEDIA RITATOON TERHADAP HASIL BELAJAR MATEMATIKA SISWA KELAS III SD NEGERI 12 TANJUNG BATU

The background of the problem in this study is the lack of learning outcomes in mathematics learning caused by the application of mathematical material using only student books without the help of other media. This study aims to determine the effect of using ritatoon media on the learning outcomes of third grade students at SD Negeri 12 Tanjung Batu. The research method used is the experimental method with a pretest-posttest control group design. The subjects of this study were students in class III A and III B of SD Negeri 12 Tanjung Batu, totaling 43 students. Methods of data collection using tests and documentation. The test instrument is in the form of multiple choice questions totaling 10 questions to measure student learning outcomes. Data analysis technique using Independent Sample T-test. The results showed that the influence of ritatoon media can improve elementary school students' learning outcomes in mathematics material which can be seen from the learning outcomes of hypothesis testing (Independent Sample T-Test) in the form of a significance value in this study, namely 0.000 &gt; 0.05, which means Ho is rejected Ha is accepted.&#x0D; Keywords: ritatoon media, student learning outcomes
RANCANG BANGUN SISTEM INFORMASI PENGELOLAAN MATERIAL SPRAY PAINTING DI PT WOHLRAB INDONESIA

PT Wohlrab Indonesia has indeed managed materials well. But in its implementation it takes time, energy, and money. This is because doing inventory calculations takes a long time, because it has to be written down on paper and then inputted in Ms. Excel and stored in the file folder. It also requires writing utensils, paper and other equipment. When viewed from a cost perspective, this process requires quite a lot of money because you have to pay for various writing equipment and others, including employee salaries. The research method used is observation, interviews, analysis, conclusion drawing and then the design method used is the SDLC (System Development Life Cycle) method with the Waterfall model. The purpose of this research is to design a Spray Painting Material Management Information System at PT Wohlrab Indonesia and also to build a spray painting material management information system at PT Wohlrab Indonesia. The results of this study are the need for a spray painting material management system at PT Wohlrab Indonesia, it can be concluded that PT Wohlrab Indonesia's spray painting material management information system is designed to provide convenience to admins in managing spray painting material because it is designed with an easy-to-understand or user friendly interface. can also process data quickly and accurately. PT Wohlrab Indonesia's spray painting material management information system uses web-based programming, developed using the PHP programming language and MySQL database. This system can improve admin performance in managing material data and can reduce errors in making reports, loss of material data because data stored in the database cannot be manipulated by parties who do not have access, even conducting data searches can be done quickly and accurately.&#x0D; Keywords : Information; Management; Materials; Systems.
ANALISIS CLUSTERING DENGAN K-MEANS UNTUK PENGELOMPOKKAN PENJUALAN ELEKTRONIK PADA BATAM IT MART

This study aims to help Batam IT Mart in the process of grouping sales, and to find out data on the types of goods that are in demand and not in demand, with that sales data obtained by the store can be processed into useful information in the future. The method used in this research is the K-means Clustering which is done by grouping data into several groups and then carrying out a calculation process to get results in the form of groups of data on the types of goods that are very hot-selling, best-selling, and not-selling. The data used in this research is electronic sales data at Batam IT Mart from March 2019 to November 2022. The research has resulting 3 clusters such as cluster 0 that contained 19 items, cluster 1 that contained 2 items and cluster 2 that contained 4 items. So the conclusion drawn is the analysis and grouping using k-means clustering method can help in doing the segmentation and grouping of the sales data. From this research, it is obtained renewal in the form of a cluster that can assist in analyzing and providing segments for goods sold at Batam IT Mart.