Reviewerly/exHarmony huggingface dataset:
The Problems Encountered by Non-English Department Students in Speaking English

Speaking is one of the difficult English skills for non-English department students. This is because students must understand how to produce good and correct sentences to pronounce. To produce a sentence, they must master a lot of vocabulary and master how to pronounce it. In addition, to be able to master speaking skills, students must also have self-confidence. This research aimed to know the problems encountered by Non-English Department students in speaking English and their causes and to find out the problem-solving encountered by non-English majoring students in speaking English. The researcher used descriptive qualitative research with a questionnaire and an interview to collect the data. The results of this study indicate that almost all students find problems (linguistic and nonlinguistic) in speaking English. Linguistic problems that are often found are a lack of grammar, poor pronunciation, and the last is lack of vocabulary. The non-linguistics are anxiety, shyness &amp; lack of confidence. The factors causing the problem of speaking English in this study were low motivation, habits &amp; mother tongue, lack of practice &amp; opportunity, and fear of making mistakes. To solve the problem of speaking English is to improve vocabulary and pronunciation skills. Some ways to improve it are by listening and singing English songs, watching English movies to understand English sentences, and using Google Translate to look up the meaning of difficult words and how to pronounce them. In addition, to improve their speaking skills, sometimes students also practice speaking in English with their friends or independently.
PecidRL: Petition expectation correction and identification based on deep reinforcement learning

Identifying petition expectation for government response plays an important role in government administrative service. Although some petition platforms allow citizens to label the petition expectation when they submit e-petitions, the misunderstanding and misselection of petition labels still has necessitated manual classification involved. Automatic petition expectation identification has faced challenges in poor context information, heavy noise and casual syntactic structure of the petition text. In this paper we propose a novel deep reinforcement learning based method for petition expectation (citizens’ demands for the level of government response) correction and identification named PecidRL. We collect a dataset from Message Board for Leaders, the largest official petition platform in China, containing 237,042 petitions. Firstly, we introduce a deep reinforcement learning framework to automatically correct the mislabeled and ambiguous labels of the petitions. Then, multi-view textual features, including word-level and document-level semantic features, sentiment features and different textual graph representations are extracted and integrated to enrich more auxiliary information. Furthermore, based on the corrected petitions, 19 novel petition expectation identification models are constructed by extending 11 popular machine learning models for petition expectation detection. Finally, comprehensive comparison and evaluation are conducted to select the final petition expectation identification model with the best performance. After performing correction by PecidRL, each metric on all extended petition expectation identification models improves by an average of 8.3% with the highest increase ratio reaching 14.2%. The optimal model is determined as Peti-SVM-bert with the highest accuracy 93.66%. We also analyze the petition expectation label variation of the dataset by using PecidRL. We derive that 16.9% of e-petitioners tend to exaggerate the urgency of their petitions to make the government pay high attention to their appeals and 4.4% of the petitions urgency are underestimated. This study has substantial academic and practical value in improving government efficiency. Additionally, a web-server is developed to facilitate government administrators and other researchers, which can be accessed at http://www.csbg-jlu.info/PecidRL/.
Similar classes latent distribution modelling-based oversampling method for imbalanced image classification

Learning an unbiased classifier from imbalanced image datasets is challenging since the classifier may be strongly biased toward the majority class. To address this issue, some generative model-based oversampling methods have been proposed. However, most of these methods pay little attention to boundary samples, which may contribute tiny to learning an unbiased classifier. In this paper, we focus on boundary samples and propose a similar classes latent distribution modelling-based oversampling method. Specifically, first, we model each class as different von Mises–Fisher distributions, thereby aligning feature learning with the class distributions. Furthermore, we develop a distance minimization loss function, which makes latent representations from similar classes close to each other. In this way, the generator can capture more shared features during training. In addition, we propose a boundary sampling strategy, which uses latent variables near the decision boundary to generate boundary samples. These samples expand the minority decision region and reshape the decision boundary. Experiments on four imbalanced image datasets show that the proposed method achieves promising performance in terms of Recall, Precision, F1-score, and G-mean.
An Improvised Approach of Deep Learning Neural Networks in NLP Applications

Abstract: In recent years, natural language processing (NLP) has drawn a lot of interest for its ability to computationally represent and analyze human language. Its uses have expanded to include machine translation, email spam detection, information extraction, summarization, medical diagnosis, and question answering, among other areas. The purpose of this research is to investigate how deep learning and neural networks are used to analyze the syntax of natural language. This research first investigates a feed-forward neural network-based classifier for a transfer-based dependent syntax analyzer. This study presents a long-term memory neural network-based dependent syntactic analysis paradigm. This model, which will serve as a feature extractor, is based on the feed-forward neural network model mentioned before. After the feature extractor is learned, we train a recursive neural network classifier that is optimized by sentences using a long short-term memory neural network as a classifier of the transfer action and the characteristics retrieved by the syntactic analyzer as its input. Syntactic analysis replaces the method of modeling independent analysis with one that models the analysis of the entire sentence as a whole. The experimental findings demonstrate that the model has improved its performance more than the benchmark techniques.
Robot path planning using fusion algorithm of ant colony optimization and genetic algorithm

Aiming at the shortcomings of single ant colony optimization such as many redundant nodes, slow convergence and low efficiency, based on the idea of “selection-crossover” of genetic algorithm, an improved fusion algorithm of ant colony optimization and genetic algorithm is proposed. In this paper, the fusion algorithm includes “optimal strategy” and “genetic region strategy”. The optimal strategy is that high-quality parents are selected by roulette in the first [Formula: see text] paths of each generation; genetic region strategy is that according to the path information of the parents, the grid map is divided into genetic area and nongenetic area. Genetic area refers to the area where the offspring ants can pass, and nongenetic area refers to the area where the offspring ants can’t pass; finally, the offspring ant searches the path in the genetic region to reduce the search range of the offspring ant and improve the convergence speed. Simulation results show that the fusion algorithm has faster searching speed and more stable convergence than the basic ant colony optimization and other improved ant colony optimization.
Digital Image Watermarking with Hybrid Structure of DWT, DCT, SVD Techniques and The Optimization with BFO Algorithm

The copyright violations in digital images and the violations of the privacy of personal data are happened with the development of technology and the widespread use of the internet. The usage of watermarks in digital images provides high protection to image owners in copyright protection and in protection of personal data. In this paper, watermarks have been added to digital images by using discrete wavelet transform (DWT), discrete cosine transform (DCT) and singular value decomposition (SVD) methods, respectively. “Gaussian”, “shot”, “salt &amp;amp; pepper” and “speckle” noises are added to watermarked images, respectively. The original image and the watermark have been extracted from the watermarked image after adding noise. The original image and watermark have been re-extracted from the optimized watermarked image by using the bacterial foraging optimization (BFO) technique. In this step, the particle swarm optimization (PSO) algorithm has been used to set the best position of the chemotactic parameters of the BFO algorithm. The peak signal to noise ratio (PSNR), normalized cross correlation (NCC) and image fidelity (IF) values have been calculated and compared to see the success performance of watermarking techniques and optimization techniques.
Students’ Perception of the Use of Youtube in English Language Learning

Digital learning has become a new learning mode in higher education. Learners have become heavily dependent on YouTube channels and other technology-enhanced language learning platforms. As the YouTube platform has become an inexorable part of students’ lives, language teachers can explore their perceptions and assist them to learn English language with the help of this platform. This study aimed to find out the learners’ perception of using YouTube as a learning medium to enhance their language learning. In addition, a few pedagogical implications will also be provided. The research respondents comprised sixty-six undergraduate students of the Department of English, Jagannath University, Dhaka, Bangladesh. The data were collected using a questionnaire and semi-structured interviews. The findings revealed that the majority of the students reported that they benefitted by watching YouTube educational videos; however, in preparing assignments, being attentive while studying, and acquiring grammatical competence, learners reported that this platform had little impact. Most of the students reported that YouTube helped them to learn English vocabulary, to make learning English very interesting, to comprehend the contents of YouTube videos in a very easy manner, get relevant materials to learn English language, acquire English language better by writing down the major points in a diary while watching videos, to develop speaking skills watching YouTube videos, and to enhance listening comprehension. Therefore, based on the findings of this study, it can be concluded that the use of YouTube has numerous positive impacts on EFL learners’ English language proficiency.
PENERAPAN METODE SIMPLE ADDITIVE WEIGHTING (SAW) DALAM PERANCANGAN APLIKASI PEMILIHAN PASANGAN BERBASIS WEB

As social beings, humans have a desire to socialize, to build deeper relationships with individuals they like, and also to get a spouse according to the criteria they like. However, there are often obstacles in finding a spouse, one of which is due to being too busy and an increasing workload. Luckily we live in an era of technology that makes it easier for us to interact with other people. The spouse selection application itself is quite common for several circles in Indonesia, but some of these applications are considered not fully providing services to recommend a spouse based on the criteria. Therefore, it is necessary to design an application that helps users find a spouse according to the criteria they like. In this application, the Simple Additive Weighting (SAW) method will be used to process data. The SAW method itself has the ability to find optimal alternatives from a number of alternatives with certain criteria.
Larger Foraminifera of Middle and Late Cenomanian (Biostrome Association), Southern Iraq

Searching one of the most important reservoirs of the Cretaceous in Iraq is the aim of many researchers. The studied sequences are in the Mishrif Formation in southern Iraq. The goal is to define fauna and design biostratigraphic zoning. Forty genus and species were identified as associated with the most capable reservoir facies (the biostrome). Identification of biostrome facies mainly was in the Late Cenomanian part of the studying sequences. Suggested biozones are based on eight biomarkers, two of which appear and disappear together, leading to four interval biozones. They are; (1) Nezzazata simplex and Nezzazata conica concurrent biozone delineating the Middle Cenomanian age. (2) Praealveoilina cretacea and Pseudorhapydionian concurrent biozone. (3) Praealveolina tenuis and Cisalveolina lehneri concurrent biozone. (4) Chrysalidina gradate and Pseudolituonella reicheli concurrent biozone delineating the Late Cenomanian.
Three on Three Optimizer: A New Metaheuristic with Three Guided Searches and Three Random Searches

This paper presents a new swarm intelligencebased metaheuristic called a three-on-three optimizer (TOTO).This name is chosen based on its novel mechanism in adopting multiple searches into a single metaheuristic.These multiple searches consist of three guided searches and three random searches.These three guided searches are searching toward the global best solution, searching for the global best solution to avoid the corresponding agent, and searching based on the interaction between the corresponding agent and a randomly selected agent.The three random searches are the local search of the corresponding agent, the local search of the global best solution, and the global search within the entire search space.TOTO is challenged to solve the classic 23 functions as a theoretical optimization problem and the portfolio optimization problem as a real-world optimization problem.There are 13 bank stocks from Kompas 100 index that should be optimized.The result indicates that TOTO performs well in solving the classic 23 functions.TOTO can find the global optimal solution of eleven functions.TOTO is superior to five new metaheuristics in solving 17 functions.These metaheuristics are grey wolf optimizer (GWO), marine predator algorithm (MPA), mixed leader-based optimizer (MLBO), golden search optimizer (GSO), and guided pelican algorithm (GPA).TOTO is better than GWO, MPA, MLBO, GSO, and GPA in solving 22, 21, 21, 19, and 15 functions, respectively.It means TOTO is powerful to solve highdimension unimodal, multimodal, and fixed-dimension multimodal problems.TOTO performs as the second-best metaheuristic in solving a portfolio optimization problem.
Development of Chatbot-Psychologist: Dataset, Architecture, Design and Chatbot in Use

Virtual assistants are software applications that allow users to have conversations with the software in the same way as they would in real life. Creating functional conversational agents have been the most challenging aspect of working with AI ever since the field was first introduced. Virtual conversational agents' primary responsibility is to properly interpret natural communication and reply in an acceptable manner, in spite of the fact that they may do a range of activities. In the past, AI agents were created either via the use of handwritten rules and commands or by straightforward statistical approaches. End-to-end Automation has mostly supplanted these models because of its superior potential for learning new skills. The encoder-decoder deep learning approach is currently the method that has the greatest amount of interest for simulating conversations. The domain of linguistic understanding served as a source of motivation for the development of this concept. In this article, we present an overview o the findings from our study into the development of an immersive digital conversational agent that might potentially provide sufferers psychological aid. We used Rasa NLU approach, which is based on NLP practices, in order to construct and train the chatbot. The findings of the study indicated that providing appropriate replies to patients' questions and concerns during conversations had a prediction accuracy about 75 percent.
Improving MapReduce Speculative Executions with Global Snapshots

Hadoop's MapReduce implementation has been employed for distributed storage and computation.Although efficient for parallelizing large-scale data processing, the challenge of handling poor-performing jobs persists.Hadoop does not fix straggler tasks but instead launches equivalent tasks (also called a backup task).This process is called Speculative Execution in Hadoop.Current speculative execution approaches face challenges like incorrect estimation of tasks run times, high consumption of system resources and inappropriate selection of backup tasks.In this paper, we propose a new speculative execution approach, which determines task run times with consistent global snapshots and K-Means clustering.Task run times are captured during data processing.Two categories of tasks (i.e.fast and stragglers) are detected with K-Means clustering.A silhouette score is applied as decision tool to determine when to process backup tasks, and to prevent extra iterations of K-Means.This helped to reduce the overhead incurred in applying our approached.We evaluated our approach on different data centre configurations with two objectives: i) the overheads caused by implementing our approach and ii) job performance improvements.Our results showed that i) the overheads caused by applying our approach is becoming more negligible as data centre sizes increase.The overheads reduced by 1.9%, 1.5% and 1.3% (comparatively) as the size of the data centre and the task run times increased, ii) longer mapper tasks runs have better chances for improvements, regardless of the amount of straggler tasks.The graphs of the longer mappers were below 10% relative to the disruptions introduced.This showed that the effects of the disruptions were reduced and became more negligible, while there was more improvement in job performance.
Implementasi Algoritma Exclusive OR (XOR) Dalam Pengembangan Aplikasi Chat Berbasis Android

Android is an operating system that is widely used in this modern era. This is inseparable from Android features that are increasingly being developed to facilitate the user experience. Along with these developments, security problems also arise when exchanging information or data. Security is an important aspect of the development of modern information technology era, so the development of the branch of science that studies data security or known as cryptography. This study aims to design and build a secure chat application based on Android and implement the XOR algorithm. This algorithm is used to secure information so that it cannot be read before it reaches the recipient. The calculation of this algorithm generates a random message (ciphertext) that can only be read by the recipient. The result of this search is a chat application in which there is a security feature where the recipient must enter the same key as the sender in order for the message to be read. Testing of this application is done by sending a message then entering the same key between sender and receiver.
Non-blind Image Watermarking Algorithm based on Non-Separable Haar Wavelet Transform against Image Processing and Geometric Attacks

The protection of digital media become crucial and needs to be developed as technology continues to become more advanced. This is to cater to the problems in copyright protection and data security. This paper presents a new non-blind image watermarking algorithm applying modified non-separable Haar wavelet transform (NSHWT), singular value decomposition (SVD), Arnold’s cat map and Rabin-p cryptosystem. The traditional transform domain watermarking (DWT) is resource-consuming, especially when performed on large image data. In order to improve on this issue, the proposed algorithm applied the modified NSHWT, a much more efficient alternative to the DWT. Another concern of digital watermarking algorithms is it is often publicly known, so encryptions are important to keep the image secure. The embedded watermark image is protected by scrambling the image using Arnold’s cat map, and the scrambling parameters are encrypted with the Rabin-p cryptosystem to keep the algorithm secure. In general, the application of modified NSHWT and SVD makes the watermark highly robust against image processing and geometric processing attacks, and its security is ensured with the protection by Arnold’s cat map and Rabin-p cryptosystem. There are already many algorithms with high imperceptibility and robustness, but in finding the perfect balance, authors often forsake other metrics such as efficiency, security, and flexibility. These are also essential when the algorithm is considered in real applications. Thus, the proposed algorithm is evaluated in terms of imperceptibility, robustness, and efficiency. The algorithm achieved imperceptibility results of 51.5157 average PSNR and 0.9991 average SSIM, and robustness results from 0.9710 to 0.9966 NC values. The algorithm is also around 40% faster to execute with modified NSHWT compared to the traditional DWT. Finally, it has a high embedding capacity of 6 bits per pixel. Overall, results show that the algorithm is highly imperceptible and robust against image processing and geometric attacks, while additionally being secure and flexible.
A Survey Paper On CROSS-SITE SCRIPTING(XSS)

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A Closed-Loop Network for Single Infrared Remote Sensing Image Super-Resolution in Real World

Single image super-resolution (SISR) is to reconstruct a high-resolution (HR) image from a corresponding low-resolution (LR) input. It is an effective way to solve the problem that infrared remote sensing images are usually suffering low resolution due to hardware limitations. Most previous learning-based SISR methods just use synthetic HR-LR image pairs (obtained by bicubic kernels) to learn the mapping from LR images to HR images. However, the underlying degradation in the real world is often different from the synthetic method, i.e., the real LR images are obtained through a more complex degradation kernel, which leads to the adaptation problem and poor SR performance. To handle this problem, we propose a novel closed-loop framework that can not only make full use of the learning ability of the channel attention module but also introduce the information of real images as much as possible through a closed-loop structure. Our network includes two independent generative networks for down-sampling and super-resolution, respectively, and they are connected to each other to get more information from real images. We make a comprehensive analysis of the training data, resolution level and imaging spectrum to validate the performance of our network for infrared remote sensing image super-resolution. Experiments on real infrared remote sensing images show that our method achieves superior performance in various training strategies of supervised learning, weakly supervised learning and unsupervised learning. Especially, our peak signal-to-noise ratio (PSNR) is 0.9 dB better than the second-best unsupervised super-resolution model on PROBA-V dataset.