Reviewerly/exHarmony huggingface dataset:
Language Embeddings Sometimes Contain Typological Generalizations

Abstract To what extent can neural network models learn generalizations about language structure, and how do we find out what they have learned? We explore these questions by training neural models for a range of natural language processing tasks on a massively multilingual dataset of Bible translations in 1,295 languages. The learned language representations are then compared to existing typological databases as well as to a novel set of quantitative syntactic and morphological features obtained through annotation projection. We conclude that some generalizations are surprisingly close to traditional features from linguistic typology, but that most of our models, as well as those of previous work, do not appear to have made linguistically meaningful generalizations. Careful attention to details in the evaluation turns out to be essential to avoid false positives. Furthermore, to encourage continued work in this field, we release several resources covering most or all of the languages in our data: (1) multiple sets of language representations, (2) multilingual word embeddings, (3) projected and predicted syntactic and morphological features, (4) software to provide linguistically sound evaluations of language representations.
Bentuk Penyingkatan Penulisan Istilah pada Forum Mahasiswa Universitas Malikussaleh di Media Sosial

This study aims to describe the form of writing terms in the Malikussaleh University student forum on social media. This type of research is qualitative using a descriptive analysis approach. The research data is in the form of the use of terms in student forums contained in WhatsApp groups. The data source for this research is the whatsapp group of students at the Unimal Student Association. Researchers took data sources from 5 groups, namely Himasa A2, Himasa 17 Family, Himasa PBSI, Non-Family KKN, Solidarity. Data collection techniques in this study are reading techniques and note-taking techniques. The stages of qualitative data analysis in this study are as follows (a) data reduction, (b) data presentation, and (c) drawing conclusions. The research results obtained are the form of terms used in the form of shortened words, proper names, and noun phrases. Abbreviation of words in the form of removing vowel sounds in basic words, cutting off syllables, removing letters other than the initial letter. Personal name in the form of a person's name, title name, institution or organization name. Noun phrases are terms by abbreviating noun phrases.
The Environmental Consequences of Foreign Finance Inflows and Economic Performance in Africa

The purpose of this study was to examine the impact of foreign finance inflows and economic performance on environmental degradation in Africa.The study was motivated by the quest to reexamine the validity of the environmental Kuznets curve (EKC) theory in 15 selected African economies by practically decomposing the total environmental effects of foreign finance into three strands, namely scale, technique, and composition effects.The panel dataset contained the 15 largest economies selected from the five regions of the African continent from 1990 to 2020 and ensured the used of 31 observations for each country.To simultaneously guarantee heterogeneity among the long-run and short-run coefficients, the study employed pooled mean group (PMG) estimator of the dynamic heterogeneous panel auto-regressive distributed lag (panel ARDL) model as its analytical technique.The study revealed that economic performance was negatively and positively related to environmental degradation in the short-run and long-run, respectively.This confirms the existence of an inverted U-shaped relationship between economic performance and environmental degradation and also validates the existence of the environmental Kuznets curve (EKC) hypothesis.Moreover, foreign finance was inversely related to environmental degradation, which implies that poor environmental quality cannot be directly linked to foreign capital inflows among the investigated countries.
Manufacturing Sector Development in Sub-Saharan Africa: Does Monetary Policy Matter?

This study investigated the effect of monetary policy on the manufacturing sector value added in 24 sub-Saharan African (SSA) countries.The study is motivated by the persistent state of underdevelopment of the manufacturing sector in the region and the palpable dearth of empirical evidence on the sector's response to monetary policy actions as suggested by theory.The study employed panel data from 1995 to 2020 and the framework of the panel ARDL model which is estimated using three dynamic panel estimators -Mean Group (MG), Pooled Mean Group (PMG), and Dynamic Fixed Effect (DFE).Findings from the study showed that the monetary variables are time sensitive and heterogeneous in their effects depending on the long or short-run.We document that while interest rate and the exchange rate had statistically significant negative impacts on manufacturing value added in the long-run only credit to the private sector had no real impact on manufacturing value added in the short and long-run.The implication is that policymakers have to conduct monetary policy in such a manner that mitigates the persistent rise in interest and the depreciation of the domestic currency exchange rates to boost manufacturing value added in the region.
Digitalization of Media Create Precocious Alpha Generation

Alpha generation is born after 2010. They grew up and live with sophisticated digital technology. They are used to operating modern technology in daily activities. They are used to using technology to communicate with others, in the real world and the virtual world. Many public spheres in cyberspaces have been explored by them such as YouTuber, TikTok, WhatsApp, Facebook, online game, online news, e-commerce, m-banking and so on. This technology is called a smartphone which provides various conveniences with information services and unlimited benefits. This causes the alpha generation to enjoy the virtual world and ignore their activities in the real world. The lack of censorship on content uploaded in cyberspace makes the mindset and behavior of the alpha generation, who are still unstable, forced to mature and mature before their age. So that the digitization of this media can trigger a premature alpha generation. Various anticipations have been created by technology, for example applications that can filter content that is not suitable for children such as adult content, violence, issues of intolerance, and others. However, this protection is not enough. The role of parents is very important to overcoming this problem. Children spend more of their free time at home. Full support and concern from the government, society, academia and the media will complement various efforts to make the alpha generation free from digital fantasy addiction.
PENGEMBANGAN MEDIA PEMBELAJARAN KATAR (KARTU PINTAR) PADA MATERI TEMBANG MACAPAT UNTUK SISWA KELAS V SD

Katar (Smart Card) research is a learning medium for fifth-grade students at SDN Panglungan 1 which has been validated and then tested. Katar (Smart Card) is a media or visual aid in the form of a card made with an attractive appearance. The purpose of this research is to develop Katar (Smart Card) development media. This type of research is research and development. Researchers use this type of research, namely qualitative data. The development model used in this research is Addie's development model. The stages used are 1) Analysis, 2) Design, 3) Development, 4) Implementation, 5) Evaluation. Data collection techniques used are questionnaires and questionnaires. The subjects of the study were fifth-grade students at SDN Panglungan 1. The results obtained were that the rotary wheel learning media obtained a validity percentage of 86% from material experts, 86% from media experts, and 85% practicality test from learning experts. Based on the product effectiveness test conducted, it was found that there was an increase above the KKM, namely 13 students out of 18 students scored above the KKM 86.65. So, it can be concluded that Katar media (Smart Cards) is feasible and effective to use.
Researches advanced in fine-grained image classification based on convolutional neural network

Due to practical needs, fine-grained image classification (FGIC) has been considered for many years as a direction of study in computer vision, which aims to subdivide images belonging to one coarse-grained category into multiple fine-grained classes. Traditional fine-grained image classification algorithms rely heavily on annotations. Recently, convolutional neural networks (CNN) have prefigured unprecedented opportunities for this research direction with the popularity and development in deep learning. To start, this study introduces the development history with various fine-grained image classification algorithms, as well as definition and research significance of the problem. After that, it compares and analyzes the different algorithms respectively in the aspects of strong supervision and weak supervision. This paper also compares the accuracy of these models on frequently used datasets. We conclude the paper by summarizing and evaluating the different aspects of these algorithms, and then discuss possible future research domains and challenges in this field.
Commercial video recognition system for short video (TikTok) based on machine learning

Short video has the features of short duration and high information carrying capacity, which is more in accordance with modern netizens' mobile phone using patterns. With the continual increase of the user scale of smart mobile terminals, many mobile phone users may make full use of the fragmented time to shoot and view short movies. Numerous Internet behemoths are fighting to invest in creating short video platforms since the amount of video user traffic generates enormous commercial prospects. For speeding up the audit teams effectiveness, video classification technology needs to be constantly developed and updated. The article proposed a commercial video detection model with a wide range of data analysis and processing. More specifically, Principal Component Analysis (PCA), feature selection by random forest and discretization using decision trees would be involved in order to transform the original data into features that better express the nature of the problem. The application of these features to Random Forest Model can improve the model prediction accuracy of data. Experimental results demonstrate that the recognition system fulfills outstanding performance. The model achieves 0.90 precision and 0.96 AUC score (area under ROC curve) of excellent evaluation in the corresponding test set.
Multi-modal sentiment analysis based on graph neural network

Thanks to popularity of social media, people are witnessing the rapid proliferation of posts with various modalities. It is worth noting that these multi-modal expressions share certain characteristics, including the interdependence of objects in the posted images, which is sometimes overlooked in previous researches as they focused on single image-text posts and pay little attention on obtaining the global features. In this paper, a neural network with multiple channels for image-text sentiment detection is proposed. The first step is to encode text and images to capture implicit tendencies. Then the introduction of this model obtains multi-modal expressions by collecting the shared characteristics of the dataset. Finally, the attention mechanism provides reliable predictions of the sentiment tendencies of the given pairs of image-text data. The results of experiments conducted on two publicly available datasets crawled from Twitter prove the reliability of the model on multi-modal sentiment detection, since the model precedes previously proposed models in the main evaluating criteria.
A new method for mandala image synthesis based on WGAN-GP

Mandala, as an ancient image art, has been found to have many unexpected applications in recent years, including use in art therapies, meditation induction and human body state assessment. Despite the omnipresent applications of convolutional neural networks in imaging Synthesis, it can be found that there is no work on Mandala Image Synthesis yet. To fill this research gap, deep learning algorithms were considered in this study. With existing research on Generative Adversarial Network (GAN), a typical GAN network called WGAN-GP was used to produce Mandala images. The generator and discriminators in the WGAN-GP network are fully trained in this study. To prove the robustness of the employed method, this study also investigates and compares the performances of WGAN-GP and other GANs. The experimental results demonstrated the effectiveness and satisfactory performance of the employed method. In brief, our completed work can effectively help provide Mandala images for further research on it.
Comparison of processing results of median filter and mean filter on Gaussian noise

In people's daily lives, if the image is polluted with noise, it will become very blurred, so it is pretty necessary to use filtering to denoise the image and get a clearer image to assist people in work and study. The purpose of this article is by comparing the denoising result of the Median filter and Mean filter on Gaussian noise, and the filtering method which is more suitable for reducing Gaussian noise is found. Matlab is a very accurate and reliable scientific calculation standard software tool, which is very common in people's lives. The Median filter is a kind of nonlinear filter. It is so virtual at decreasing impulse noise. The common basic theory of the Median filter is to supersede the gray value of pixels with the median of the gray value in a neighborhood of the pixels, and not use the average proportion. The Mean filter is a sort of plain sliding-window space filter that takes the place of the central value with the mean of all the pixel values in the plain window. This essay takes advantage of Matlab to complete the process of adding Gaussian noise in photos, reducing Gaussian noise, and calculating the PSNR. The processing result of two types of noise reducers on Gaussian noise is compared. And by observing the image clarity, PSNR and MSE evaluation techniques to find which is a better filter to decrease Gaussian noise.
Impact of different transaction features on credit card fraud detection by neural networks

A perfect credit card fraud detection model is necessary because of the economic loss caused by credit card fraud. Since many models have already gained great performance, this research focuses on the influences of different credit card transaction features on the accuracy of the fraud detection model constructed by a neural network, which is favorable for economists to study credit card fraud better. The data used in this research from Kaggle contains a million credit card transaction records with seven features for each. To analyze the importance of different details, different parameters are used in the input layer of the neural network model to compare the performance. Furthermore, the result is that the feature ratio to the median purchase price is the most significant one. The second important feature is the distance from home, and then online order is followed. Compared with the accuracy when inputting all the seven features(99.81%), the model performs well with only the above three features in the input layer(95.43%).
Comparing one-stage and two-stage learning strategy in object detection

Object detection plays a vital role in computer social perception and computer vision. It could be applied to computer navigation, video monitoring, industrial detection, and so on. It greatly reduces the human labours by automatically locate and identify objects. Nowadays, the mainstream methods of object detection could be separated into the one- and two-stage method. The one-stage method leverages Convolutional Neural Network (CNN) for obtaining features and directly locate the target objects and their corresponding category probabilities. Different from the two-stage solutions, its accuracy is lower and the recognition speed is higher. The two-stage method is a straight forward solution, which process is mainly completed through a complete CNN, so CNN features will be leveraged to extract the feature description of the target among candidate regions through a CNN. The accuracy of the two-step method has been greatly improved, but the running speed is much slower than the one-step method. While the one-step method is less accurate, it is much faster. In this work, representative works for object detection are conducted and compared. The results could further demonstrate their respective advantages.
Image recognition system based on deep learning technology

In recent years, people have been able to easily obtain a large amount of visual information through various devices, but not all of these visual information are useful, and there is a lot of useless information mixed with them, which makes it difficult for people to identify and track the target parent in the picture. Due to its various characteristics, deep learning technology can quickly obtain information from massive information and compare, analyze and utilize it, so it has become the key to solving the problem of image recognition and tracking. This paper will summarize the existing literature, focus on the structure of the image detection system based on deep learning technology, explain its basic principles, and briefly introduce the basis of deep learning technology - neural network. This paper finds that there are many technical routes for image recognition systems based on deep learning technology, each of which has its own advantages and disadvantages.
English handwriting recognition based on the convolutional neural network

Handwriting recognition is widely used in the retrieval, recognition, and management of various information to improve efficiency in various industries. Convolutional neural networks help people get rid of the feature of extracting information feature sets manually and significantly improve recognition efficiency. In this paper, a generalization-enhanced network recognition model is proposed using an improved lightweight convolutional neural network model. An improved method is used to adapt word recognition by the idea of pre-recognition segmentation. In addition, the generalization is enhanced by diversifying and pre-processing the dataset so that the algorithm can obtain noise-resistant performance and detail retention and allow the recognition system to recognize various types of scenes. The results show that the model achieves an accuracy of 93% on the test set. Compared with other classical network models, the model has higher recognition accuracy, faster convergence, and better generalization ability. The system elaborated in this paper can be used for devices with weak computer processing power.
The advance of recommendation system with graph neural network

The field of e-commerce provides customers with many kinds of goods to choose from. It is also a challenge for consumers to choose among tens of thousands of product categories. The emergence of recommender systems solves such problems for consumers to a certain extent because it would recommend products that may fulfil their needs for consumers. Finding user preferences or rankings for a specific item can be aided by recommender systems. It uses specialized software and techniques to offer suggestions for items utilized by various users. To increase customer satisfaction or sell different things for e-commerce platforms, recommender systems advise consumers about specific items they may be interested in or desire to utilize. Graph neural networks generalize effectively to new datasets because they can learn the graph structure of various datasets. To put it another way, this makes Graph Neural Networks (GNNs) the perfect recommender systems for various real-world issues. As a result, the growth and usage of neural graph networks in recommender systems and the limitations of graph neural networks will be covered in detail in this paper.
The performance analysis of Adam and SGD in image classification and generation tasks

Optimization problems have a very important leading position in machine learning. A great deal of machine learning algorithms ends up solving optimization problems. Among all the optimization algorithms, gradient methods are the simplest and most commonly used compared to algorithms like Particle Swarm Optimization and Ant Colony Optimization. In the gradient methods, Adaptive Moment Estimation (Adam) and stochastic parallel gradient descent (SGD) are both outstanding algorithms that have helped solve all kinds of deep learning tasks. But which one is better in some certain conditions is still unknown, which means programmers need to try many of the optimizers to have the best choice. Based on some previous researches, this paper study the impact of L2 regularization and weight decay in Adam and SGD with momentum, which turns out in adaptive methods, L2 regularization is not as effective as it is in SGD. It gives the intuition that SGD should outperform Adam in image classification tasks. However, this paper finds things go the other way around by running an experiment using Lenet-5 on MINST. Besides, this paper describes an experiment on Fashion-MINST using DCGAN with both Adam and SGD as optimizers in Generator and Discriminator. The result shows that the generator with SGD produces fake images with higher quality.
Text classification algorithms exploration on sentiment analysis

Internet provides us with an abundance of useful tools and data. However, it also generates a vast quantity of data that may bewilder us. There must be a technique for automatically processing these data. Here, text classification becomes useful. Text classification is the algorithm-based process of categorizing data inputs into distinct labels. For instance, email software utilizes it to assess if an email should be filtered into the spam folder, social media forums use it to classify postings into labels that are relevant to the topic, etc. Text categorization is utilized in a variety of businesses, including search engines, sentiment analysis, emergency response systems, chatbots, etc. Review websites have emerged in recent years where customers may share their opinions on a business or a product. The review is extremely emotive but crucial to the company. It is possible to accurately assess the reviews for the sentiment they present through text classification. This paper compares the efficacy of various text classification algorithms for sentiment analysis.
Federated transfer learning: Concept and application

As the development of Machine Learning, more and more data are used to train models in order to guide peoples daily life. Faced with privacy and efficiency challenges these days, Federated Transfer Learning (FTL) has grown in popularity, as it has a great ability to protect data privacy while also dealing with the problem of data scarcity. In this passage, the author studies the FTL and its different applications, including sales industry, Industrial Internet of Things Devices, Finance application, Medical Application and Autonomous Driving, analyses the usage in the different industries. Further, the author discusses the privacy and robustness of FTL, which are the core features of FTL. In the end, the author concluded the features of FTL and also gave a prospection of FTL.