Reviewerly/exHarmony huggingface dataset:
Study on the Strategy of Layered English Teaching in Applied Undergraduate Universities

In order to implement the spirit of the Ministry of education on College English teaching reform, implement the student-centered education concept, improve the teaching level of English and highlight the characteristics of English teaching, the quality and comprehensive quality of students' English learning are uneven with the increase of social demand for talents. How to improve the overall quality of English teaching has become a key issue in the English Teaching of applied undergraduate universities. Layered teaching mode can well solve this contradiction, stimulate students' learning initiative, optimize teaching resources and improve teaching level.
Pengembangan Sistem Informasi Media Promosi UMKM Menerapkan Metode User Centered Design

One of the requirements of globalization in the business world is to integrate restaurant management with information systems and computerized technology. Therefore, the web is a medium and publication that can be easily searched anytime and anywhere through the internet without being limited by geographical areas that can be used for food vendors. The purpose of this research is to build a web-based advertising media system. The research flow is through collecting data through interviews, observation, documentation, and literature studies. by applying the User Centered Design process to the system using the Unified Modeling Language (UML). Apply the PHP programming language, create a front-end design using the Bootstrap framework, and a MySQL database to create a web. As part of this research, developed a web-based advertising medium for the Ketupat Sayur Uni Epi.
A Control Theoretic Framework for Adaptive Gradient Optimizers in Machine Learning

Adaptive gradient methods have become popular in optimizing deep neural networks; recent examples include AdaGrad and Adam. Although Adam usually converges faster, variations of Adam, for instance, the AdaBelief algorithm, have been proposed to enhance Adam's poor generalization ability compared to the classical stochastic gradient method. This paper develops a generic framework for adaptive gradient methods that solve non-convex optimization problems. We first model the adaptive gradient methods in a state-space framework, which allows us to present simpler convergence proofs of adaptive optimizers such as AdaGrad, Adam, and AdaBelief. We then utilize the transfer function paradigm from classical control theory to propose a new variant of Adam, coined AdamSSM. We add an appropriate pole-zero pair in the transfer function from squared gradients to the second moment estimate. We prove the convergence of the proposed AdamSSM algorithm. Applications on benchmark machine learning tasks of image classification using CNN architectures and language modeling using LSTM architecture demonstrate that the AdamSSM algorithm improves the gap between generalization accuracy and faster convergence than the recent adaptive gradient methods.
RLx2: Training a Sparse Deep Reinforcement Learning Model from Scratch

Training deep reinforcement learning (DRL) models usually requires high computation costs. Therefore, compressing DRL models possesses immense potential for training acceleration and model deployment. However, existing methods that generate small models mainly adopt the knowledge distillation-based approach by iteratively training a dense network. As a result, the training process still demands massive computing resources. Indeed, sparse training from scratch in DRL has not been well explored and is particularly challenging due to non-stationarity in bootstrap training. In this work, we propose a novel sparse DRL training framework, "the Rigged Reinforcement Learning Lottery" (RLx2), which builds upon gradient-based topology evolution and is capable of training a sparse DRL model based entirely on a sparse network. Specifically, RLx2 introduces a novel multi-step TD target mechanism with a dynamic-capacity replay buffer to achieve robust value learning and efficient topology exploration in sparse models. It also reaches state-of-the-art sparse training performance in several tasks, showing 7.5\times-20\times model compression with less than 3% performance degradation and up to 20\times and 50\times FLOPs reduction for training and inference, respectively.
Remember the Past: Distilling Datasets into Addressable Memories for Neural Networks

We propose an algorithm that compresses the critical information of a large dataset into compact addressable memories. These memories can then be recalled to quickly re-train a neural network and recover the performance (instead of storing and re-training on the full original dataset). Building upon the dataset distillation framework, we make a key observation that a shared common representation allows for more efficient and effective distillation. Concretely, we learn a set of bases (aka ``memories'') which are shared between classes and combined through learned flexible addressing functions to generate a diverse set of training examples. This leads to several benefits: 1) the size of compressed data does not necessarily grow linearly with the number of classes; 2) an overall higher compression rate with more effective distillation is achieved; and 3) more generalized queries are allowed beyond recalling the original classes. We demonstrate state-of-the-art results on the dataset distillation task across six benchmarks, including up to 16.5% and 9.7% in retained accuracy improvement when distilling CIFAR10 and CIFAR100 respectively. We then leverage our framework to perform continual learning, achieving state-of-the-art results on four benchmarks, with 23.2% accuracy improvement on MANY. The code is released on our project webpage https://github.com/princetonvisualai/RememberThePast-DatasetDistillation.
Completeness of inpatient medical resume filling

Medical resume is an important and basic sheet in inpatient medical records so it needs to be filled out completely because it is important in the process of patient treatment, insurance claims, compliance with accreditation standards, and legal evidence. However, in reality there are still many medical resumes that have not been filled out completely. The purpose of this study was to determine and the causes of incomplete filling of inpatient medical resumes. This study uses a literature review method that analyzes the results of six previous studies with relevant themes. Based on the results of this literature review study, it can be said that filling in inpatient medical resumes based on quantitative analysis of the six journals reviewed has not reached 100% (51.60% - 87.42%) and the dominant cause of incomplete filling is due to resource factors. human beings, besides that there are no SOPs and socialization of filling out medical resumes and limited facilities.&#x0D; &#x0D; Keywords: completeness, causes of completeness, medical resume.
SigMaNet: One Laplacian to Rule Them All

This paper introduces SigMaNet, a generalized Graph Convolutional Network (GCN) capable of handling both undirected and directed graphs with weights not restricted in sign nor magnitude. The cornerstone of SigMaNet is the Sign-Magnetic Laplacian ($L^{\sigma}$), a new Laplacian matrix that we introduce ex novo in this work. $L^{\sigma}$ allows us to bridge a gap in the current literature by extending the theory of spectral GCNs to (directed) graphs with both positive and negative weights. $L^{\sigma}$ exhibits several desirable properties not enjoyed by other Laplacian matrices on which several state-of-the-art architectures are based, among which encoding the edge direction and weight in a clear and natural way that is not negatively affected by the weight magnitude. $L^{\sigma}$ is also completely parameter-free, which is not the case of other Laplacian operators such as, e.g., the Magnetic Laplacian. The versatility and the performance of our proposed approach is amply demonstrated via computational experiments. Indeed, our results show that, for at least a metric, SigMaNet achieves the best performance in 15 out of 21 cases and either the first- or second-best performance in 21 cases out of 21, even when compared to architectures that are either more complex or that, due to being designed for a narrower class of graphs, should -- but do not -- achieve a better performance.
Identifikasi Potensi Bahaya dan Analisis Risiko Penyebaran Virus Covid-19 pada Proses Pembelajaran Tatap Muka (PTM) Terbatas di SMK XYZ

&lt;p&gt;&lt;strong&gt;The COVID-19 pandemic caused by the coronavirus has caused most human activities to be hampered, ranging from trade bans and office closures to students who have to do online learning activities. However, over time, the rate of positive coronavirus cases began to decline so that governments in various countries began to open schools and allow limited face-to-face learning processes. This research was carried out as a step to prevent the spread of the COVID-19 virus in the school environment and was carried out at one of the vocational high schools in Jakarta using Job Safety Analysis (JSA) to identify activities and risks that could potentially cause the spread of the coronavirus in the school environment. Then, the activity with the highest average fuzzy risk level is determined using the fuzzy approach. Activities with very high risk are analyzed for their causes using the domino theory and given recommendations for their control. From the results of this study, it is known that the level of compliance of XYZ Vocational School residents in implementing health protocols, especially when outside the home, is good. It is also known that there are 6 types of activities that are categorized as very high, with 10 direct causes that can cause incidents. Therefore, the most dominant risk control suggestion is administrative steps to minimize the spread of the virus due to weak control, which is the root cause of the basic cause and immediate cause, according to the domino theory sequence.&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;&lt;strong&gt;&lt;em&gt;Keywords &lt;/em&gt;&lt;/strong&gt;- &lt;/strong&gt;&lt;em&gt;Covid-19, Domino theory, Fis mamdani, FTA, JSA, The Limited face-to-face learning process&lt;/em&gt;&lt;/p&gt;
THE ANALYSIS OF INTEREST IN WRITING POETRY TEXT OF X GRADER STUDENTS OF VOCATIONAL HIGH SCHOOL STUDENT

This research was made to find out how interested students are in writing poetry texts and some factors that cause them. The research method which used in this research is descriptive qualitative with the type of research in the form of field research (field research). The research was conducted at SMK 2 Pacet from April 8th to 16th 2021 with numbers of samples as much as 38 people. From this study, the writer found that only about 10.8% of students expressed interest in literature, especially poetry while 83.8% of other students stated that they were not as that interested to poetry because poetry and about 6% of other students stated that they were not interested. From the research data, it can be concluded that students' interest in poetry writing activities is still under expectation.
Penerapan Algoritma K-Medoids Untuk Pengelompokan Data Penerima Bantuan Uang Kuliah Tunggal Bagi Mahasiswa Terdampak Covid-19

The ongoing Covid-19 pandemic period greatly affects various aspects of life, one of which is the issue of the economy. This problem has an impact on the field of education, one of which is at the university level. where many students whose parents/insurers of tuition fees are experiencing financial constraints due to the impact of the Covid-19 pandemic. So we need an effective way as a recommendation in analyzing student data based on the socioeconomic status of each student's parents in determining the group of UKT recipients. There are many ways that can be used, one of which is by utilizing data mining to group data for students who are entitled to get UKT using the K-Medoids method. The application of the K-Medoids method is used to group data on students who are eligible to receive UKT assistance funds with the aim of being a recommendation in analyzing student data based on the socioeconomic status of each student's parents in determining the UKT recipient group for the university. Whatever the results of the application of the K-medoids method, a group that deserves to be recommended is based on the results of Cluster / Grouping 0 with a total student data of 8 people based on the results of consideration of the criteria used, namely Parents' Occupation, Home Ownership Status and Parents' Income
STUDY ON THE SPATIAL-TEMPORAL EVOLUTION OF SUBSIDENCE IN THE BEIBU GULF REGION BY TIME-SERIES INSAR

Abstract. In this paper, based on the 24 scenes Sentinel-1A images covering the Beibu Gulf region from 2018 to 2020, we used the PSInSAR technique to obtain the surface subsidence information in the study area and analyzed the surface subsidence spatial-temporal characteristics. The results showed that (1) The subsidence rate of most subsidence areas in Beibu Gulf is from −10.0 mm/year to −1.1 mm/year. And most of the subsidence areas are located in the coastal areas in the south of the study area. The distribution was obvious, and the maximum subsidence rate can reach −17.2 mm/a. However, the subsidence areas of villages and towns located in Qinzhou in the north and inland of the study area are relatively stable. (2) Subsidence was mainly concentrated in Yinhai District, Haicheng District, Tieshan Port District and Fangcheng District of Fangchenggang in Beihai City. The annual average Subsidence range from −10.0 mm/year to −1.1 mm/year. Among them, the Tieshan Port area and Fangcheng District had obvious subsidence trends, with the maximum cumulative subsidence reaching −35.9 mm. (3) The spatial-temporal characteristics of wide-area surface subsidence and the relationship between surface subsidence and influencing factors in the Beibu Gulf region were determined.
Teachers’ Perception in Implementing Communicative Language Teaching (CLT) at Maritime English Class

The objectives of this research were to find out the Teachers‟ perception in implementing communicative language teaching (CLT) at Maritime English class.&#x0D; This research applied qualitative description. The population were the teachers at Polytechnic Maritime Barombong. The respondent consisted of 14 teachers. The research data were collected by questionnaire and analyzed by using goggle form to see the teachers‟ perception about the implementation of Communicative Language Teaching at Maritime English class.&#x0D; The results of the research was: the teachers‟ perception in implementing communicative language teaching (CLT) at Maritime English class result which disclosed familiar and understand communicative language teaching (CLT). They also believed that Communicative language teaching (CLT) can improve the students communicative competence. The Teachers‟ perception in implementing communicative language teaching (CLT) at Maritime English class the researcher conclude that overall the teachers satisfy with communicative language teaching (CLT).
PHILOSOPHY IN METHODOLOGICAL GROWTH FUTURE TEACHER: ROLE AND STRUCTURING THE CONTENT

Philosophi in metodological drowning of future teacher: role and structure of contecst. In the state we discribe duestions of philosophi in professional preparing of teacher as a metodological sight of this component. This part of philosophy can be strongering by efectsonal structure of philosophical study.
Sequential Nature of Recommender Systems Disrupts the Evaluation Process

Datasets are often generated in a sequential manner, where the previous samples and intermediate decisions or interventions affect subsequent samples. This is especially prominent in cases where there are significant human-AI interactions, such as in recommender systems. To characterize the importance of this relationship across samples, we propose to use adversarial attacks on popular evaluation processes. We present sequence-aware boosting attacks and provide a lower bound on the amount of extra information that can be exploited from a confidential test set solely based on the order of the observed data. We use real and synthetic data to test our methods and show that the evaluation process on the MovieLense-100k dataset can be affected by $\sim1\%$ which is important when considering the close competition. Codes are publicly available.
Characteristics of Telugu in Middle Dravidian Languages

Telugu language can be mentioned next to tamil in terms of antiquity and pronuncication. Telugu is spoken in andhra pradesh and place like south africa in our indian country telugu is the second most widely spoken language after hindi. It is a literary language like tamil. Those who study the reson for the name o this language will think in various ways that the word thrillingam was changed to telugu and it is also believed that the word ‘Tenuge’ which meens sweetness was given knowing that it was telugu. Many grammars have been written in the language from the western country. Northern Scholars, such as nannayapattar have written a number of literatures in this language. The language, which is spoken by more people than tamil is thriving with a variety of creativity. In terms of gender discrimination, telugu can be seen to be different from other dravidian languages the sound and writing script of the telugu language are considered to be the most excellent. The people of chennai who migrated to places like sumatra and java are considered as telugus. That is why the telugu language is still existing as very prominent today.
STOCHASTIC BIOLOGICAL MODEL OF NEURON

Brain is the vital organ for action, learning, memory, thought formation and other cognitive activities. The neuron is the fundamental functional unit of the brain. It is a specialized cell created to transmit information and receive information from other nerve cells, muscle cells or gland cells. This paper describes the stochastic behavior of biological model of Neuron and its implementation in MATLAB Simulink to study the behavior of a neuron in a real-time environment. The Hodgkin–Huxley mathematical model describes how initiated and propagated action potentials in neurons takes place are for external stimulus. The stochastic behavior of a Hodgkin-Huxley neuron is studied for various external stimulus and helped to find various action potentials.
Peningkatan Keaktifan dan Hasil Belajar IPS melalui Model Pembelajaran Kooperatif Tipe Teams Game Tournament (Tgt) pada Siswa Kelas IV SD N Sumberwungu 1 Kepanewon Tepus Kabupaten Gunungkidul

The selection of learning models used by teachers in learning activities it one of the determining factors for the success of student learning. This study aims to improve student activity and learning result through a cooperative learning model of the type of team game tournament (TGT) for fourth graders at SDN Sumberwungu I in the 2020/2022 academic year. This research is a classroom action research. This research was conducted at SDN Sumberwungu I. The subjects in this study were class IV students, totaling 10 students. Data collection techniques in this study used observation sheets and learning outcomes tests. The data analysis technique used quantitative and qualitative descriptive with percentages. The results showed that 1) The application of the cooperative learning model of the type of tournament team game (TGT) can increase student learning activities in fourth grade students of SD Sumberwungu I, it is proven that in the pre-cycles students have an interest in learning with a good category of at least 30% increasing in cycles I. 80% and in the second cycles 90%. 2) The application of the cooperative learning model of the type of team game tournament (TGT) can improve student learning outcomes, especially grade IV students at SDN Sumberwungu I, this can be seen in student learning completeness, in the pre-cycles students who excel the KKM 4 score 40% increase in the first cycles 8 80% students and in the second cycles increased to 9 students 90%. The increase was because the teacher put more emphasis on tournament rules, and the division of tasks in groups.
An NGram-Based Copyright Protection for Digital Images

This paper introduces an NGram-based approach for digital images copyright protection. The advantage of the proposed approach compared to the existing works, is that it does not always require the whole elements (e.g., bits) of the watermark pattern to be embedded into the original digital image. This, in turn, allows us to protect the digital images while at the same time minimizing the chances of having low quality marked digital images. The best case occurs when no element of the pattern is embedded into the original digital image. In contrast, the worst case, which rarely happens, occurs when all elements are embedded into the digital image. Moreover, the use of an NGram approach allows us to efficiently and easily reach any part of the image using the corresponding level numbers and addresses. This makes it more efficient especially for complex and high-dimensional data (e.g., images and videos). Experimental results show the effectiveness of the proposed approach in terms of the ability to recover the watermark pattern from the marked digital image even if major changes are applied to the original digital image.
Optical Character Recognition for Image &amp; Handwriting to Text Conversion

Abstract: This paper combines the functionality of Optical Character Recognition and speech synthesizer. The idea is to develop stoner friendly operation which performs image to text conversion. Objective The advantage of proposed system that overcomes the disadvantage of the prevailing system is that it supports multiple functionalities like editing and searching. It also adds benefit by providing heterogeneous characters recognition
Spatial Domain-based Face Recognition using MSB, and MLTP with ANN Classifier

The human face images are an essential biometric trait in the authentication of individuals as the face images can be taken without the knowledge and physical contact of the person leading to several recent applications. We propose Spatial Domain-based Face Recognition using MSB and MLTP with ANN Classifier in this paper. The benchmarked face datasets are considered for testing the proposed method by converting color images into greyscale images and uniform image sizes. The 8-bit binary of each pixel is segmented into two groups viz., Most Significant Bits (MSB) and Least Significant Bits (LSB). The important information is available only in MSBs than LSBs hence MSBs are considered for feature extraction leading to compression of binary bits. The MSB four bits are converted to decimal values, and Histogram Equalization (HE) is used to enhance the contrast level of images and resize them. The Modified Local Ternary Pattern (MLTP) is applied to an image to extract compelling features. The Artificial Neural Network (ANN) classifies face images to recognize human beings. It is noticed that the proposed method's recognition results are better than the existing techniques. Keywords: Artificial Neural Network (ANN), Biometrics, Face Recognition, Histogram Equalization, MSB, MLTP.