Reviewerly/exHarmony huggingface dataset:
Dampak Tumpang Tindih Bahasa Ibu Dalam Perkembangan Bahasa Anak

This study aims to collaborate theory with practice that occurs in the field, also knowing that the use of mother tongue in early childhood is very important.The research method used is descriptive qualitative with literature study in the form of photos, notes, and books.The results of this study are the occurrence of overlap in language development in children is very influential and has an impact on self and communication.The stimulation given cannot be instant but is carried out continuously.Because the child's grasping power will adjust how the language is given so that what kind of reinforcement will be generated.The use of mother tongue that is not optimal will cause language overlap.The occurrence of overlaps can result in difficulties in developing children's language development in everyday life; such as to communicate and interact with other people and the environment, impaired concentration, and unstable behavior.
Does the Geometry of the Data Control the Geometry of Neural Predictions? (Student Abstract)

This paper studies the over-parameterization of deep neural networks using the Fisher Information Matrix from information geometry. We identify several surprising trends in the structure of its eigenspectrum, and how this structure relates to the eigenspectrum of the data correlation matrix. We identify how the eigenspectrum relates to the topology of the predictions of the model and develop a "model reduction'' method for deep networks. This ongoing investigation hypothesizes certain universal trends in the FIM of deep networks that may shed light on their effectiveness.
Usage of user hate speech index for improving hate speech detection in Twitter posts

Social media is an important source of real-world data for sentiment analysis. Hate speech detection models can be trained on data from Twitter and then utilized for content filtering and removal of posts which contain hate speech. This work proposes a new algorithm for calculating user hate speech index based on user post history. Three available datasets were merged for the purpose of acquiring Twitter posts which contained hate speech. Text preprocessing and tokenization was performed, as well as outlier removal and class balancing. The proposed algorithm was used for determining hate speech index of users who posted tweets from the dataset. The preprocessed dataset was used for training and testing multiple machine learning models: k-means clustering without and with principal component analysis, naïve Bayes, decision tree and random forest. Four different feature subsets of the dataset were used for model training and testing. Anomaly detection, data transformation and parameter tuning were used in an attempt to improve classification accuracy. The highest F1 measure was achieved by training the model using a combination of user hate speech index and other user features. The results show that the usage of user hate speech index, with or without other user features, improves the accuracy of hate speech detection.
Understanding Stochastic Optimization Behavior at the Layer Update Level (Student Abstract)

Popular first-order stochastic optimization methods for deep neural networks (DNNs) are usually either accelerated schemes (e.g. stochastic gradient descent (SGD) with momentum) or adaptive step-size methods (e.g. Adam/AdaMax, AdaBelief). In many contexts, including image classification with DNNs, adaptive methods tend to generalize poorly compared to SGD, i.e. get stuck in non-robust local minima; however, SGD typically converges slower. We analyze possible reasons for this behavior by modeling gradient updates as vectors of random variables and comparing them to probabilistic bounds to identify "meaningful" updates. Through experiments, we observe that only layers close to the output have "definitely non-random" update behavior. In the future, the tools developed here may be useful in rigorously quantifying and analyzing intuitions about why some optimizers and particular DNN architectures perform better than others.
Knowledge-Enhanced Scene Graph Generation with Multimodal Relation Alignment (Student Abstract)

Existing scene graph generation methods suffer the limitations when the image lacks of sufficient visual contexts. To address this limitation, we propose a knowledge-enhanced scene graph generation model with multimodal relation alignment, which supplements the missing visual contexts by well-aligned textual knowledge. First, we represent the textual information into contextualized knowledge which is guided by the visual objects to enhance the contexts. Furthermore, we align the multimodal relation triplets by co-attention module for better semantics fusion. The experimental results show the effectiveness of our method.
Pengaruh Media Pembelajaran IPS Berbasis Powtoon Terhadap Minat Belajar Siswa Kelas IV SDN Jenggot Krembung Sidoarjo

This study aims to determine the effect of Powtoon-based social studies learning media on the learning interest of fourth-grade students at Jenggot Elementary School, Krembung, Sidoarjo. The type of this research is Pre-Experimental Design which uses One Group Pretest-Posttest Design research design. The sample in this study were 18 students in class fourth-graders at Jenggot Elementary School, Krembung, Sidoarjo. The data collection technique used in this research is a questionnaire/questionnaire. Data on student interest in learning shows the average value (mean) of the pretest is 42.61 while the average value (mean) of the posttest is 70.44. While the tcount value is 10.820 and the ttable value is 2.11991 with a significance value of 0.000 ttable 2.11991, this indicates that there is a significant or real difference between pretest learning interest and posttest learning interest, so Ha is accepted or Powtoon-based social studies learning media has a significant effect on fourth-grade students learning interest, Jenggot Elementary School, Krembung, Sidoarjo.
English Expressions Used in Handling Complaint Emails

The problems which were analyzed in this paper are about the kinds and uses of the functions of the English expressions that are communicated by the reservation staff to their client. The number of reservation staff which was used as the data source were five people, they were three males and two females. The collected data were analyzed by taking multiple samples of complaints on email correspondence by the staff of Baliku Tours and Travel, especially with their client and colleague. The purpose of this paper was to find out the kind of expressions are used and to investigate the formality of English expressions used by the staff in handling complaints by email. The theories applied in this study were based on the theory of John Blundell in his book entitled Function in English (1987) as the main theory. The supporting theory was taken from the book of Leo Jones entitled Function of English (1981). According to the analysis, the staff of Baliku Tours and Travel who were chosen as respondents used several functions of English Expressions when communicating with their clients or colleagues. English expressions for greeting, English expression for giving information, English expression for getting information, English expression for offering things, English expression for apologizing, English expression for thanking, English expression for inviting, English expression for saying goodbye, and English expression for giving suggestions were the English expressions they communicated to their client. Most of the staff used formal language expressions and polite way when they handled complaints. The neutral, less formal, and impolite expressions which they communicated to the client or colleagues were not in large number.&#x0D; Keywords: sociolinguistic, language function, emails by Baliku Tours and Travel
Using Word Wall Technique and Vocabulary Mastery on Students’ Writing Skill

The study's purpose was to determine the effect of the word wall approach and vocabulary acquisition on students' writing skills. For this quasi-experimental study, students in MA Negeri 1 Serang's class XI IPA 1 are randomly assigned to one of four different factorial designs, each with a 2x2 nonequivalent control group. Data on learning outcomes were gathered via pre- and post-test activities. The samples consisted of 60 students picked at random and matched to the study's circumstances and objectives. The findings indicate a substantial difference between the experimental and control groups on the posttest. The Fo was 5.225, and the Sig. was 0.025&lt;0.05. Thus, although the null hypothesis was rejected, the research hypothesis was supported. Additionally, it suggests that the impact of combining the word wall approach with vocabulary knowledge on pupils' writing ability varies.
SAQENet: A Quality Enhancement Network for Compressed Video with Self-attention

Existing block-based encoding frameworks often use inaccurate quantification and motion compensation techniques, which result in many compression artifacts due to the loss of high-frequency information. In particular, the blurring of content edges and significant compression distortion can negatively impact the subjective video quality given limited coding resources. Hence, there is an urgent need to build a quality enhancement method for improving the quality of the compressed video at the receiving end given the same coding resources.
Nested-PCR for diagnosis of Hymenolepis nana in the patients of Wasit Province

The current study was carried out at Wasit Province / Iraq in cooperation with AL-Karama Teaching Hospital. Samples were collected from many places at Wasit Province. It included 100 stool samples from Iraqi patients conducted during the period from October to December, 2021 to investigate the prevalence of Hymenolepis nana. Only 23 stool sample were positive for H.nana. These samples were collected from the villages and rural areas of Wasit Province, the villages of Sayed-Shati and Al-Battar, the area of al-Anwar, the peasants, the expatriates, and the Dujaili area. The results showed that the percentage of H.nana infection among patients were 23 (23%). With regard to parasitic infection, the highest infection rate was recorded for the group (4-15) years old, as for sex, the percentage of males reached about 78.26% and females 21.74% in this study with no significant differences. According to residence, The highest infection was appeared in the rural areas 75% and the lowest was in the urban areas 25% with a significant differences.
PENTINGNYA MENGUASAI KOMUNIKASI BAHASA INGGRIS PADA MAHASISWA MANAJEMEN PEMASARAN

Abstract--For students majoring in English, it is clearly a major part of being able to communicate proficiently using English, but for non-English majors such as majoring in management, this is something that is not uncommon, even tends to be difficult for them. The problem that then arises is what learning techniques are appropriate and interesting for them to apply in order to be proficient, and how they apply the learning. Therefore, the research questions in this study are: (i) Why are English communication skills needed by students? (ii) what learning techniques are applied by management students? This study aims to: (i) find the mastery of English communication by law students (ii) learn learning techniques for management students to apply.Kata kunci: English Learning, Management Student, Communication Strategy
Blur Face Recognition using Blur Metric and Some Variants of Supervised Distance Preserving Projection

This paper is focused on face recognition techniques in uncontrolled scenarios, specifically on the recognition of face images with blur effects. At first, the blur level of the testing image is determined using recently proposed blur metric. This blur metric value is used to blur the training set of gallery images using Gaussian filter. The blur level of training images is the same as that of the testing image. Two variants of Supervised Distance Preserving Projection (SDPP), SDPP as Semidefinite Least Square (SLS-SDPP) and Regularized Supervised Distance Preserving Projection (RSDPP), are used for extracting effective features of training and testing images. K-Nearest Neighbor classifier is used for matching. Numerical experiments were carried out on two benchmarking face data ORL and Yale. The performances of SLS-SDPP and RSDPP are compared with one of the leading methods Eigenface method. Experimental results show that the combination of blur metric and the feature extraction methods achieved outstanding performance in recognizing blur images of different levels and also outperforms the base methods and Eigenface method. Dhaka Univ. J. Sci. 69(3): 154-160, 2022 (June)
PESTO: Switching Point Based Dynamic and Relative Positional Encoding for Code-Mixed Languages (Student Abstract)

NLP applications for code-mixed (CM) or mix-lingual text have gained a significant momentum recently, the main reason being the prevalence of language mixing in social media communications in multi-lingual societies like India, Mexico, Europe, parts of USA etc. Word embeddings are basic building blocks of any NLP system today, yet, word embedding for CM languages is an unexplored territory. The major bottleneck for CM word embeddings is switching points, where the language switches. These locations lack in contextually and statistical systems fail to model this phenomena due to high variance in the seen examples. In this paper we present our initial observations on applying switching point based positional encoding techniques for CM language, specifically Hinglish (Hindi - English). Results are only marginally better than SOTA, but it is evident that positional encoding could be an effective way to train position sensitive language models for CM text.
Model AI Assignments 2022

The Model AI Assignments session seeks to gather and disseminate the best assignment designs of the Artificial Intelligence (AI) Education community. Recognizing that assignments form the core of student learning experience, we here present abstracts of six AI assignments from the 2022 session that are easily adoptable, playfully engaging, and flexible for a variety of instructor needs. Assignment specifications and supporting resources may be found at http://modelai.gettysburg.edu.
An Overview of Platform Economy and Its Algorithms: Strengths, Weaknesses and Regulatory Initiatives

Algorithm-based Platform Economy is growing rapidly, with digital capitalism as its core. Its advantage of accurately matching supply with demand has attracted an enormous amount of capital. However, the monopolistic nature of platform and the intelligent algorithms are constantly violating the bottom line of consumer privacy, making digital economy a weapon for capital to exploit the laboring people. Therefore, they should be strictly regulated, which is the only way for platform economy to deliver real benefits to working people.
Survey On Feature Extraction Approach for Human Action Recognition in Still Images and Videos

Human Action Recognition (HAR) has been a challenging problem yet it needs to be solved. Recently the detection and recognition of human action has broad range of applications and is popularized in the field of computer vision. It mainly focuses on to understand human behaviour and name a label to each action. There are many approaches for action recognizing from both image and video based actions. Now it is time to review these existing approaches in order to help for future research. The main aim of this work is to study the various action recognition techniques in videos and images. The paper presents a brief overview of features of human actions by categorizing as still image-based and video-based. All related datasets are also introduced in this paper, which will be helpful for future research.
The Regulatory Regime for Cryptocurrency in the Present Global Order

Cryptocurrency has been one of the tools that originated from the blockchain technology used in the financial world and have been a topic of debate among several diasporas concerned with the financial and money markets.It is known that cryptocurrencies such as Bitcoin, Ethereum etc., are in demand and used for transactions securing the financial purposes that illustrate the need and utility of such cryptocurrencies.Cryptocurrencies are a product of insecurity and instability in the financial markets; however, the measurement of the pricing of cryptocurrency itself is based on the perception of people, which makes it highly volatile and disadvantageous in many ways.It's also evidence that there are many threats and challenges that these cryptocurrencies present that come from using them for crime, terrorism, and money laundering, among others.This calls for a significant change in the cryptoasset market, which has the potential to claim a greater stake in the financial market in the coming years.Relevance and utility of the cryptocurrency cannot be neglected because of its misappropriation; instead, regulations must be made to minimize such negative actions with the positive utility of such cryptocurrency.The present paper focuses on the lack of regulation for the cryptocurrency and their allied sectors, a major concern among the international community.Many nations such as Singapore and China have already started to venture out the prospects in the research fields related to regulating the cryptocurrency, and hence more such steps are needed by nations across the world.Cryptocurrency is a property or currency which could be decided as per the following policy of the respective state; however, when it comes to regularizing the cryptocurrency as a medium of transactions, there needs to be a global consensus among the states for holistic development of such filed of exploration.
Models of Role-Structure in Project-Based Games for Children in Kindergarten About 5-6 Years Old

In this paper we report on the use of project-based learning in teaching the children about 5-6 years old. We call it “Project-based game”. It’s a way of proceeding through the organization of mutual collaboration of children to solve the real problems of games, from which can excise children on transferring knowledge and problem-solving ability. Children work as a team. Each team member has the different role and task. So how to integrate resources to play a team advantage, and how to tap the maximum potential of each team member are particularly important. This study was trying to build a role of structural models for children to explain the relationship between the different roles in the project learning and reference for researchers. This study accomplished the following works:we interviewed normal teachers and children which from different classes, aware of the problems of grouping when teachers carry out the project game. Then we tracked and observed a number of project cases in kindergarten, designed the questions and tasks and collected a wealth of experimental data. Through the collation and analysis, we finally obtained significant achievement in this study “The role of structure model in project-based games for the children in kindergarten about 5-6 years old”.
Risk and Return: An Empirical Analysis of the Optimal Trading Strategies of Gold and Bitcoin from the Perspective of Market Traders with Three Decision Types

This paper uses the daily prices of gold and bitcoin from September 2016 to September 2021 to discuss the optimal decision of market traders. LSTM algorithm is used to predict the future price trend of bitcoin and gold. Then, calculate the risk coefficient of Bitcoin and gold trading. Next, the expected returns of gold and bitcoin are obtained according to the determined price forecast value, and combined with the risk coefficient to construct the transaction function. Finally, because the objective function and constraint conditions are linear functions, the results are calculated by using the idea of linear programming. In the whole paper, considering the attitude of market traders in decision-making, three types of decision makers are discussed. Firstly, the time series model is used to predict the data of the same gold and bitcoin. Then, the data predicted by the LSTM model and the time series model are used to calculate the MAE and MSE. By comparing the numerical values, the LSTM model is significantly superior to the time series model, so that the optimal prediction method is proved. Next, for three different decision types of traders, respectively, using the decision model established in 6.1 to find whether the optimal decision point exists and meaningful. The results show that all three types of decision makers can make optimal decisions under the influence of the risk of the predicted data, that is, the risk of the decision model is controllable. Secondly, to demonstrate the sensitivity of transaction costs. Firstly, the fixed commission ratio in the requirements is adjusted, and the commission ratios of bitcoin and gold are adjusted respectively, floating up and down within 20 %. Then, it is brought into 6.1 to calculate the optimal decision-making profit value of traders of three decision-making types. Finally, it is found that the profit value of the optimal decision-making point is very stable. Compared with the profit value calculated by the given commission ratio, the deviation is less than 0.4 %, and the sensitivity analysis of transaction cost is well completed. Finally, but not the most important, it is hoped that the model studied in this paper can help market traders make optimal strategies when making decisions.
Less is More: Adaptive Curriculum Learning for Thyroid Nodule Diagnosis

Thyroid nodule classification aims at determining whether the nodule is benign or malignant based on a given ultrasound image. However, the label obtained by the cytological biopsy which is the golden standard in clinical medicine is not always consistent with the ultrasound imaging TI-RADS criteria. The information difference between the two causes the existing deep learning-based classification methods to be indecisive. To solve the Inconsistent Label problem, we propose an Adaptive Curriculum Learning (ACL) framework, which adaptively discovers and discards the samples with inconsistent labels. Specifically, ACL takes both hard sample and model certainty into account, and could accurately determine the threshold to distinguish the samples with Inconsistent Label. Moreover, we contribute TNCD: a Thyroid Nodule Classification Dataset to facilitate future related research on the thyroid nodules. Extensive experimental results on TNCD based on three different backbone networks not only demonstrate the superiority of our method but also prove that the less-is-more principle which strategically discards the samples with Inconsistent Label could yield performance gains. Source code and data are available at https://github.com/chenghui-666/ACL/.
The effect of scheduling distributed practice according to the kinetic behavior system in learning artistic performance and retain some basic football skills

The effect of scheduling distributed practice according to the kinetic behavior system in learning artistic performance and retain some basic behavior system in learning artistic performance and retain some basic football skills football skills