Reviewerly/exHarmony huggingface dataset:
TransLine: Transfer Learning for Accurate Power Line Anomaly Detection with Insufficient Data

Accurate and automatic power line anomaly detection is critical to smart grid. However, effective solutions are yet available due to the insufficiency of anomaly data. In this paper, we first collect a dataset from various sources consisting both normal and abnormal power line images. With this dataset, anomaly detection becomes feasible though with limited accuracy due to the limited size of the dataset. As such, we propose TransLine, an approach based on transfer learning to apply the existing knowledge extracted from large-scale datasets to complement the data insufficiency of power line anomaly detection. TransLine customizes and optimizes the knowledge to automate the power line anomaly detection with high accuracy. The experiment results show that TransLine can achieve superb accuracy of 96.1% on average and up to 98.1% given only a hundred abnormal images for model training. TransLine can be a key enabler of smart grid for great stability and efficiency and can inspire the other industrial applications facing data insufficiency issues.
Time Parallel Denoising Algorithm Based on P-M Equation for Real Image

This paper combines the regularized Perona and Malik model with the time parareal algorithm to denoise the real image which has the characteristics of typical Gaussian noise. This method can remove the noise stably and effectively and has high calculation efficiency. For the real color image of <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mn>6000</mn> <mo>×</mo> <mn>4000</mn> </math> pixels, the time cost can be shortened from one minute in serial computing to a few seconds based on our combination method if we do not care about the requirement of computing hardware. This provides some reference for practical engineering application.
Implementing Quasi-Experimental in Educational Intervention Towards a Mobile Application Approach to Mute and Deaf Students

Mobile applications have become an important tool in various fields especially education.However, the implementation of technology in education to students with mute and deaf disabilities is still less than normal students.This is due to the lack of digital materials that suit the needs of students.Innovation in education is needed to translate research results into life as practice.This study aims to implement a quasi-experimental research method on the technology intervention of teaching and learning materials in Education.This study reviewed articles and books that discuss the design of quasi-experimental studies.Findings showed that, the test question items for section A that were tested showed students who answered correctly at the level above average.There is one item that is below average.While for sections B and C all items tested students can answer well at a level above average.Based on these findings, the implementation of quasi-experimental methods to determine the effectiveness of an innovation technology in Education is appropriate to determine the maximum impact on the use of technology.
An Online English Writing Evaluation System Using Deep Learning Algorithm

In English writing, automatic evaluation systems are becoming more and more common and are receiving more and more attention. As a teaching application, an automatic evaluation system uses information technologies such as corpus and artificial intelligence to quickly score and modify students’ writing. To expedite the realization of teaching information, this system records students’ writing processes, which are closely related to the instruction requirements of the college’s English program and meets the personalized needs by combining the current situation of English writing and teaching. This study investigates the impact of field cognitive style and automatic evaluation system on college writing training. From the perspective of cognitive style differences, this paper summarizes the application strategies of an automatic evaluation system in College English writing and teaching, to better realize the integration of information technology and subject teaching and improve students’ English writing ability and level. The deep learning-based system for grading students in the classroom is completed by this paper. The network data transmission module allows several ends to all experience stable data interaction.
Simulation Path of Network Microvideo Personalized Recommendation Based on Improved Ant Colony Algorithm

With the rapid development of Internet technology, network information not only brings many conveniences to the majority of users but also brings about the problem of information overload. It is increasingly difficult for users to accurately obtain the information they need from the vast ocean of information. This problem has led to the research of recommendation technology, especially the simulation path of online microvideo personalized recommendations. The video recommendation model in this study adopts the idea of an ant colony algorithm. The evaluation data and browsing record data generated by users are the basis of video recommendation, and the microvideo feature model and user preference model are abstracted from the original data by improving the ant colony algorithm. The recommendation algorithm is then used to recommend online microvideos that meet users’ preferences. There are two recommendation methods, one is to find similar students through the user preference model of each student, and find the microvideo content that the students have not learned; the other is to use the user preference model and the content feature model to match the content that conforms to the user’s preference. Compared with the traditional BP neural network algorithm, GA-BP algorithm, and PSO-BP algorithm, the mean square error MSE value of the ACO algorithm for microvideo scoring classification is reduced by 11.41%, 5.93%, and 2.41%, respectively. It can be seen that the classification of video ratings by the improved ACO algorithm has lower average errors than other algorithms. The network microvideo personalized recommendation scheme designed in this study has very practical significance.
AdaAX

Recurrent neural networks (RNN) are widely used for handling sequence data. However, their black-box nature makes it difficult for users to interpret the decision-making process. We propose a new method to construct deterministic finite automata to explain RNN. In an automaton, states are abstracted from hidden states produced by the RNN, and the transitions represent input symbols. Thus, users can follow the paths of transitions, called patterns, to understand how a prediction is produced. Existing methods for extracting automata partition the hidden state space at the beginning of the extraction, which often leads to solutions that are either inaccurate or too large in size to comprehend. Unlike previous methods, our approach allows the automata states to be formed adaptively during the extraction. Instead of defining patterns on pre-determined clusters, our proposed model, AdaAX, identifies small sets of hidden states determined by patterns with finer granularity in data. Then these small sets are gradually merged to form states, allowing users to trade fidelity for lower complexity. Experiments show that our automata can achieve higher fidelity while being significantly smaller in size than baseline methods on synthetic and complex real datasets.
Teachers' and Parents' Viewpoints of Game-Based Learning: An Exploratory Study

During the recent global pandemic, children’s screen time has been raised due to learning at home programs. Unexpectedly, teachers and parents have been forced to adapt to digital learning technology for children’s education. This study aimed to explore the anxiety and dilemmas faced by teachers and parents in using game-based digital learning. The researcher argues that their role as educators and facilitators in digital learning and playing activity is critical. It is essential to understand how they view, deal with, and experience game-based learning before developing an educational game. A gap was found in the existing studies about teachers’ and parents’ perspectives of game-based learning. Two questionnaires were developed separately for teachers and parents of primary school children in East Java, Indonesia. The results provided information about the teachers’ and parents’ concerns about digital learning, including their frustrations and motivations.&#x0D; Keywords: game design, game-based learning, educational game, digital learning, human-centered design
IMPROVING STUDENTS’ VOCABULARY COMPETENCE BY CONSTRUCTING GRAMMATICAL AND LEXICAL COLLOCATION AT GR STUDENTS’ VOCABULARY COMPETENCE BY CONSTRUCTING GRAMMATICAL AND LEXICAL COLLOCATION AT GRADE XI IN WELLINGTON INTELLIGENCE SCHOOL

This study aims to: (1) find out whether collocation improves students' vocabulary competence (2) find the type of collocation that is most developed in students' vocabulary competence. This research combined qualitative data and quantitative data, where quantitative data was used to support qualitative data. The subject in this research were high school students of grade XI Wellington Intelligence School. The number of students was 39 students consisting of two classes (22 students in science class and 17 students in social class). The instruments used consist of interviews, observations, documents, and tests. This study adopted descriptive statistics to describe the main features of the data set. The average score of each test, including pre-test and post-test, were used to analyze the data. The results showed that collocation can improve students' vocabulary competence. The students' initial average was 70 for science students and 68.23 for social students. While the final average value showed an increase in the value of 82.72 for science students and 80.58 for social studies students. There were more student errors in the interpretation of lexical collocations than in the interpretation of grammatical collocations.&#x0D; Keywords: Vocabulary Competence, Building Grammar, Lexical Collocation
A Case-Based Approach for Content Planning in Data-to-Text Generation

The problem of Data-to-Text Generation (D2T) is usually solved using a modular approach by breaking the generation process into some variant of planning and realisation phases. Traditional methods have been very good at producing high quality texts but are difficult to build for complex domains and also lack diversity. On the other hand, current neural systems offer scalability and diversity but at the expense of being inaccurate. Case-Based approaches try to mitigate the accuracy and diversity trade-off by providing better accuracy than neural systems and better diversity than traditional systems. However, they still fare poorly against neural systems when measured on the dimensions of content selection and diversity. In this work, a Case-Based approach for content-planning in D2T, called CBR-Plan, is proposed which selects and organises the key components required for producing a summary, based on similar previous examples. Extensive experiments are performed to demonstrate the effectiveness of the proposed method against a variety of benchmark and baseline systems, ranging from template-based, to case-based and neural systems. The experimental results indicate that CBR-Plan is able to select more relevant and diverse content than other systems.
A Factorial Study of Neural Network Learning from Differences for Regression

For regression tasks, using neural networks in a supervised way typically requires to repeatedly (over several iterations called epochs) present a set of items described by a number of features and the expected value to the network, so that it can learn to predict those values from those features. Inspired by case-based reasoning, several previous studies have made the hypothesis that there could be some advantages in training such neural networks on differences between sets of features, to predict differences between values. To test such a hypothesis, we applied a systematic factorial study on seven datasets and variants of datasets. The goal is to understand the impact on the performance of a neural network trained on differences, as compared to one trained in the usual way, of parameters such as the size of the training set, the number of epochs of training or the number of similar cases retrieved. We find that learning from differences achieves similar or better results than the ones of a neural network trained in the usual way. Our most significant finding however is that, in all cases, difference-based networks start obtaining good results from a low number of epochs, compared to the one required by a neural network trained in the usual manner. In other words, they achieve similar results while requiring less training.
Sentiment Analysis About COVID-19 Booster Vaccine on Twitter Using Deep Learning

The rapid spread of COVID-19 cases to various countries has made the COVID-19 outbreak a global pandemic by the World Health Organization (WHO). The effect of the designation of COVID-19 as a pandemic has prompted the government to take preventive action against vaccination, as well as the WHO which has asked the public to immediately get a third or booster dose of vaccine. Various responses regarding the COVID-19 booster vaccine continue to emerge on social media such as Twitter. Twitter is often used by its users to express emotions about something either positive or negative. People tend to believe what they find on social networks, which makes them vulnerable to rumors and fake news. Sentiment analysis or opinion mining is one solution to overcome the problem of automatically classifying opinions or reviews into positive or negative opinions. In this study, the Deep Learning algorithm was used to analyze public opinion sentiment regarding the COVID-19 booster vaccine on Twitter. The data collection method used is crawling data using an access token obtained from the Twitter API. Meanwhile, to evaluate the model, the K-fold Cross-Validation method is used. The results of testing the model obtained the highest accuracy value at iterations = 10, which is 82.78% with AUC value = 0.836, precision = 83.33% and recall = 95.89%.
Performance Comparison of K-Means and DBScan Algorithms for Text Clustering Product Reviews

The purpose of this study was to compare the accuracy performance of the K-Means and DBScan algorithms in clustering product reviews. This comparison evaluated to determine which algorithm is better in terms of accuracy. The two algorithms were chosen because they have different methods of clustering, K-Means uses centroid-based while DBScan uses density-based. Text clustering results can be implemented on e-commerce platforms, marketplaces or product review platforms. This can help customers in deciding what product they will buy. One of the factors that customers have difficulty in determining what product they will buy is the number of reviews that each product has, and the difficulty in concluding the advantages of each product that will be matched their needs or desires. With text clustering, it can be easier and faster for customer to determine whether the product is worth buying or not based on the product reviews they read. The data set used in this study is a review of the Cetaphil Facial Wash product from the Female Daily website. Firstly, data set goes through the Text Pre-Processing stage; then it will be clustered using two algorithms, K-Means and DBScan. After that, the results of the clustering of the two algorithms calculated for their accuracy performance and the performance results obtained. From the results of this study, it concluded that, in the review clustering of Cetaphil Facial Wash products, DBScan has 99.80% accuracy, which higher to compare with K-Means with only has 99.50% accuracy.
Api Service Infrastructure Using Kubernetes And Terraform Based On Microservices Ngoorder.Id

The growing number of ngorder.id service users causes traffic to Api Ngorder to be higher, so a new infrastructure is needed in order to maintain Api Ngorder uptime during high traffic and also maintain service stability. In the implementation process, Api scripts that are currently running on a monolith cluster will be divided into several categories and will be split into several kubernetes clusters. To support autoscale, a Horizontal Pod Autoscaler was added, and to route traffic it would use the Api Gateway from Amazon Web Service. In this infrastructure test, it is done by testing the logic script function using Katalon Studio and testing at the infrastructure level by doing a crash test in the form of failing to deploy and terminating the pod, as well as performing a stress test to test autoscaling in the cluster, the entire test can be run by performing a stress test on the php service pods. by setting the autoscaler parameter Memory Utilization Percentage 125%, 150% and 250%, proving that the HorizontalPodAutoscaler (HPA) as an autoscaler handler can function according to the targets and parameters that have been determined.
Application of Huffman Algorithm and Unary Codes for Text File Compression

Technique in carrying out data compression is an important point in technological developments. With compression in data in the form of text can include many uses, including for data transfer, copying and for backing up data. From its uses, this aspect is important for data security. There are many compression techniques on the data, including using huffman algorithms and unary code. One of its applications will be implemented on a text data that is widely used by digital actors in storing important data. The data must not be known by unauthorized parties in accessing the data. Therefore, huffman algorithms and unary code can solve this problem. By compressing the selected data also encrypts it as an extra security. The Huffman algorithm is a lossless compression algorithm or a technique that does not change the original data, by converting the unit of data content into bits. So this algorithm is widely used in the compression process. The Unary Codes algorithm is also a lossless compression technique that is generally used by combining several modification techniques. In this unary codes algorithm, each symbol in the string will be searched for its frequency. Then sorted from the last order (descending). The use of these two text data compression techniques results in a file size that is smaller than the original but can be returned to the original data
Analysis of Data classification accuracy using ANFIS algorithm modification with K-Medoids clustering

The ANFIS algorithm is a technique in data mining that can be used for the data classification process. The ANFIS algorithm still has weaknesses, especially in determining the initial parameters for the network training process. Thus, an additional algorithm or modification is needed for the determination of these parameters. In this study, a clustering method will be proposed, namely K-Medoids Clustering as an additional method to the ANFIS algorithm. Basically, the ANFIS algorithm uses the FCM (Fuzzy C-Means Clustering) algorithm for the initial initialization of network parameters. The use of this method can cause local minima problems, where the clustering results obtained are not optimal because the pseudo-partition matrix generation process is carried out randomly. The matrix value will determine the initial parameter value in the ANFIS algorithm used in the first layer. Based on the research that has been done, it can be concluded that the accuracy of data classification using the ANFIS algorithm which has been modified with the proposed method provides a fairly good influence in conducting training and classification testing. The increase that occurs in the proposed method is 0.73% for the average training accuracy and an increase of 0.66% for the average testing accuracy.
Supervised Model for Sentiment Analysis Based on Hotel Review Clusters using RapidMiner

Customer feedback in the modern era like today is mostly presented in the form of digital reviews, including customer feedback at an inn or hotel, customer feedback is very valuable data where from this data the management can find out, identify and analyze the customer experience and what they need. With customer feedback in the form of digital reviews, it will allow a lot of data that can be obtained by hotel management and will provide many benefits if the data is processed correctly. To take advantage of large text review data, a combination of data mining and natural language processing techniques was chosen to process text in depth and efficiently. Text mining in the form of creating an opinion mining model using the Naïve Bayes classification algorithm is applied to find information and measure the main sentiments expressed in the reviewed text dataset, then the application of K-Means text grouping aims to group texts and get information about the main topics discussed from the content of the review dataset text in each group . By applying the constructed sentiment analysis model, approximately 90.90% accuracy results were obtained in reading texts and measuring sentiments related to hotel customer feedback data.
Decoding quantum Tanner codes

We introduce sequential and parallel decoders for quantum Tanner codes. When the Tanner code construction is applied to a sufficiently expanding square complex with robust local codes, we obtain a family of asymptotically good quantum low-density parity-check codes. In this case, our decoders provably correct arbitrary errors of weight linear in the code length, respectively in linear or logarithmic time. The same decoders are easily adapted to the expander lifted product codes of Panteleev and Kalachev. Along the way, we exploit recently established bounds on the robustness of random tensor codes to give a tighter bound on the minimum distance of quantum Tanner codes.
HPO $$\times $$ ELA: Investigating Hyperparameter Optimization Landscapes by Means of Exploratory Landscape Analysis

Abstract Hyperparameter optimization (HPO) is a key component of machine learning models for achieving peak predictive performance. While numerous methods and algorithms for HPO have been proposed over the last years, little progress has been made in illuminating and examining the actual structure of these black-box optimization problems. Exploratory landscape analysis (ELA) subsumes a set of techniques that can be used to gain knowledge about properties of unknown optimization problems. In this paper, we evaluate the performance of five different black-box optimizers on 30 HPO problems, which consist of two-, three- and five-dimensional continuous search spaces of the XGBoost learner trained on 10 different data sets. This is contrasted with the performance of the same optimizers evaluated on 360 problem instances from the black-box optimization benchmark (BBOB). We then compute ELA features on the HPO and BBOB problems and examine similarities and differences. A cluster analysis of the HPO and BBOB problems in ELA feature space allows us to identify how the HPO problems compare to the BBOB problems on a structural meta-level. We identify a subset of BBOB problems that are close to the HPO problems in ELA feature space and show that optimizer performance is comparably similar on these two sets of benchmark problems. We highlight open challenges of ELA for HPO and discuss potential directions of future research and applications.