Reviewerly/exHarmony huggingface dataset:
The Unleashing Your Writing Potential: Enhancing Skill With Mind Mapping Technique

Abstract&#x0D; This research aimed to find a way to improve writing skills through mind mapping. This study was conducted in three cycles following the procedures for action research, namely planning, implementing, observing, and reflecting. This research used pre experimental research consisting of two cycles, and each cycle consisted of two sessions. Besides, the research was preceded by an initial reflection to know the subjects' pre-existing ability in the pre-cycle. The whole process of the present study consisted of three main phases: pre-cycle, cycle I, and cycle II, in which each cycle consisted of four interconnected activities: planning, action, observation, and reflection. The researcher used a pre-test, post-test, and questionnaire to analyse the significant improvement. The researcher used statistics using the main score formula to analyse the data. The finding shows that the mean post-test score is higher (87) than the pre-test score (70). Based on these results of the post-test, the questionnaire result showed positive responses from the subjects toward implementing the mind mapping technique. In other words, this study establishes that 8th-grade students' writing ability in SMPN 3 Abiansemal, Badung, Bali could be enhanced by her mind mapping technique.&#x0D; &#x0D; Keywords: Improving , Mind Mapping Technique , Writing Skill , English language &#x0D;
Hybrid Method Between Fireworks Algorithm and Competitive Neural Network

The main goal of this book is to develop a hybrid method of competitive learning using Fireworks Algorithm and a Competitive Neural NetworkCompetitive Neural Network (CNN).
Parallelizable Simple Recurrent Units with Hierarchical Memory

Recurrent neural networks and its many variants have been widely used in language modeling, text generation, machine translation, speech recognition and so forth, due to the excellent ability to process sequence data. However, the above-mentioned networks are constructed in a multi-layer stacking way, which makes the memory-dependent information in the distant past continuously decay. To this end, this paper proposes a parallelizable simple recurrent unit with hierarchical memory (PSRU-HM) to preserve more long-term historical information for inference. It is achieved by the nested SRU structure, and realizes the information interaction between inner and outer memory cell through the connection between inner and outer layers. The depth of network can be dynamically adjusted according to the task complexity. Meanwhile, a jump connection that combines high-level and low-level features is added to the outermost layer. It maximizes the utilization of effective input information. In order to accelerate the training and inference of the network, the weights of PSRU-HM are reorganized to enable the parallelization deployment in the CUDA framework. Extensive experiments are conducted to verify the proposed method using several public datasets, including text classification, language modeling and question answering. Experimental results show that PSRU-HM outperforms the traditional methods and achieves 2 $$\times $$ speed-up compared to cuDNN-optimized LSTM.
Conclusions to the Hybrid Method Between Fireworks Algorithm and Competitive Neural Network

In this book, the proposed methodProposed Method is sectioned in two parts to achieve the main goal of design a hybrid method of competitive learning. In the first section, FWA adaptationFireworks Algorithm for Clustering (F2FWAC) (FWAC, FFWAC and F2FWAC) as a methodFuzzy Fireworks Algorithm for Clustering (FFWAC) forFireworks Algorithm optimizing the optimal number of clusters based on the number of centroids for clustering problems is presented; and, Type 1 (T1FIS) and Interval Type 2 Fuzzy Inference Systems (IT2FIS) joined a Competitive Neural NetworkCompetitive Neural Network (CNN) for classification problems, in the second section.
Simulation Studies of the Hybrid of Fuzzy Fireworks and Competitive Neural Networks

In this chapter we present results obtained throughout of this book. The results are partitioned by methods, that is to say, firstly, the results of the optimization methods are presented, and secondly, the results of the classification methods with fuzzy classifiers are shown.
The Correlation Between Vocabulary Mastery and Students’ Descriptive Writing Skills

This research aims to find out whether there is a correlation between vocabulary mastery (X) and students' descriptive writing skills (Y) class X.2 at SMAN 4 Palangka Raya. The research method is a quantitative, correlational research design. Using random sampling so that the class chosen is X.2. Data collection tools use tests. Data were analyzed using simple linear regression. Based on research results. The average student test score was 82.07 for vocabulary mastery and an average score of 67.69 for descriptive writing skills. The research results show that there is no correlation between vocabulary mastery and students' descriptive writing skills, with a correlation significance of 0.744 which is greater than 0.05 (probability value) which means H0 is accepted while Ha is rejected. The correlation value (R) is 0.063 and the R square value is 0.004, which means the influence of variables X and Y is 0.4%. So it can be concluded that there is no correlation between vocabulary mastery and students' descriptive writing skills.
Modified Multiple Decrement Table and Its Credibility Based on Factor Characteristics

The 2019 Indonesian Mortality Table IV (TMI IV) involved 52 life insurance companies in Indonesia during the study period from 2013 to 2017. From the data, there may be differences in the characteristics of company customers so that the use of TMI IV is not in accordance with these characteristics. In life insurance companies, there are types of coverage (causes), namely: NDPA, which means death due to illness or accident; PAD, which means reimbursement of medical expenses, and SRD, which is the cancellation of the policy so that the coverage ends. The Companies can construct a Life Table involving multiple causes called a Multiple Decrement (MD) Table. This table is modified into a Modified Multiple Decrement Table (MDT) by adding factors to the causes in the form of regions. The clustering of factors needs to be done to reduce the complexity of the calculation. Using the K-means method, the grouping of regions R1R9 is divided into the following: PAD causes (3 groups) and SRD (2 groups). MDT is obtained from the relationship between MD and the Associated Single Decrement (ASD). The Annual Exposure Method was used to calculate the probability of causes. Furthermore, extrapolation is performed on the probability of cause, for which there is no value, and graduation is performed on the less smooth probability of cause. Then, credibility theory is used to determine the credibility level of the industry. The industry-credible probability of cause has a value between the observed value and the industry value (TMI IV).
Exploration and Practice of the Reform of Programming Courses' Assessment Methods—Taking the Course of Website Front-end Design and Development as an Example

With the rapid development of information technology, programming skills become more and more important in contemporary society. In the field of website front-end design and development, its market demand also continues to rise. However, for a long time, traditional assessment methods such as paper-and-pencil exams and simple programming tasks no longer seem to be able to comprehensively assess the students' ability to apply themselves in a real working environment, which not only is out of touch with the actual needs of the industrial world, but also lacks the cultivation of students' teamwork and project management skills. Considering these problems, the article takes the course "Web Front-end Design and Development" as an example to explore and practice the assessment method of programming courses, expecting to provide a more close to the reality and more effective assessment strategy for modern programming education.
Classification

Generally, classification is about predicting a label, whereas regression is usually used to predict a quantity. Classification models approximate a mapping function from inputs to produce a class or category. Classification is an important task that allows us to group objects we deem as similar together by finding common underlying attributes. This allows us to overlook the small differences of the individual objects and make generalizations about the items within each category. Logistic regression is commonly used instead of linear regression for binary classification problems and produces a value ranging from 0 to 1 that can be interpreted as a probability that an event occurred. In this chapter, we will cover various algorithms that can be used to classify objects into distinct categories and demonstrate how performance of these algorithms can be improved using data processing techniques.
Chatbot, Speech, and NLP

Chatbots are programs that are capable of conversing with people trained for various tasks, such as providing parents with information about a school. These programs use natural language processing and machine learning algorithms to understand and respond to human language in a way that feels like a conversation. Chatbots allow organizations to gain valuable insights into customer behavior, preferences, and sentiment. By analyzing customer interactions and conversations, organizations can identify patterns, trends, and customer needs. This chapter will provide the skills required to create a basic chatbot that can converse though speech. Speech to text tools will allow us to convert speech data into text data, which then allow us to ingest the text data using natural language processing techniques. We will then train an encoder–decoder architecture model using long short-term memory units for a question and answer task to simulate a conversation.
Convolutional Neural Networks

Text mining is the process of extracting meaning from unstructured text documents using both machine learning and natural language processing techniques. Text data might be easily ingested by humans but can be challenging for machines as they do not understand the meaning of words like humans do. Instead, text data have to be represented in structured numerical representations that capture information about each word or token in order for machine learning algorithms to be able to ingest them. Only after this process, then text data can be ingested by artificial intelligence algorithms for various tasks such as sentence topic classification and keyword extraction. By analyzing text documents, organizations can gain a deeper understanding of customer feedback, market trends, social media sentiment, and other relevant information that can inform business decisions. In this chapter, we will cover various methods to convert text into structured numerical representations and utilize classification algorithms to perform topic prediction.
Clustering

Supervised algorithms use labeled data as an input for developing a prediction model. However, the amount of unlabeled data collected often far exceeds that of labeled data. Unsupervised algorithms, such as clustering algorithms, are algorithms that are able to make use of these unlabeled data to extract useful insights. These algorithms are able to help us identify underlying patterns and properties across different data points and group them into clusters for us to analyze. In this chapter, we will introduce the concept of clustering as a form of unsupervised learning and cover the K-means algorithm that utilizes a distance-based metric to cluster the data into K different clusters. Afterward, we will introduce methods to select a suitable number of clusters such as the elbow method. By the end of this chapter, readers should have a stable foundation in clustering techniques.
Introduction to Artificial Intelligence

Humans can accomplish tasks that scientists are still trying to fathom, such tasks are hard to write algorithms for. Artificial intelligence programs are thus written in a way that allows these algorithms to learn from data. This makes data quality crucial to the performance of the algorithm. Data exploration and investigation are a must for artificial intelligence developers to identify appropriate charts, present data to visualize its core characteristics, and tell stories observed.
Short-Term Solar Irradiance Forecasting from Future Sky Images Generation

Solar irradiance prediction is critical for the integration of the solar power to the existing power system. A recent trend in the literature is to adopt deep learning-based methods to predict future solar irradiance from sky images. While these models have achieved significant improvements, they are only capable of making predictions for a fixed forecasting horizon due to their non-recurrent nature. To this end, we propose a deep learning network that is capable of predicting solar irradiance in an autoregressive manner, which allows predictions across a long time horizon. Particularly, we reduce the problem to first generating future sky images which are then used to predict future solar irradiance. We evaluate our models on TSI880 and ASI16 datasets, and show that our model achieves superior performance compared to previous works for 4-h ahead-of-time predictions. Furthermore, we also demonstrate that the solar irradiance forecast of our model is not limited to only 4 h, but can be extended for even longer horizon.
S5TR: Simple Single Stage Sequencer for Scene Text Recognition

As an active research topic in computer vision, scene text recognition (STR) aims to recognize character sequences in natural scenes. Currently, mainstream STR approaches consist of two main modules: a visual model for feature extraction and a sequence model for text translation. The two modules function separately and sequentially, which increases the complexity of the STR model. In this study, we propose a novel Simple Single Stage Sequencer for Scene Text Recognition (S5TR), which allows to transform text instance images into string sequences directly. Specifically, our S5TR contains stacks of Sequencers made of horizontal and vertical Long Short Term Memory Networks (LSTMs). On the one hand, S5TR extracts visual representations of images by modeling long-range dependencies via LSTM, which is similar to self-attention in Vision Transformer (ViT). On the other hand, LSTM serving as a sequence modeling module is able to capture contextual information within the character sequence for predicting the character. Experimental results demonstrate that our S5TR achieves highly competitive performance compared to existing STR methods.
GAN-LM: Generative Adversarial Network using Language Models for Downstream Applications

In this work, we investigate Data Augmentation methods to improve the performance of state-of-the-art models for four different downstream tasks. Specifically, we propose Generative Adversarial Network using Language Models (GAN-LM) approach that combines a deep generative model with a pre-trained language model to produce diverse augmentations. We compare the GAN-LM to various conventional methods in non-contextual- and contextual-levels on four public datasets: ZESHEL for zero-shot entity linking, TREC for question classification, STS-B for sentence pairs semantic textual similarity (STS), and mSTS for multilingual sentence pairs STS. Additionally, we subsample these datasets to study the impact of such augmentations in low-resource settings where limited amounts of training data is available. Compared to the state-of-the-art methods in downstream tasks, we mostly achieve the best performance using GAN-LM approach. Finally, we investigate the way of combining the GAN-LM with other augmentation methods to complement our proposed approach. The developed code for reproducibility is included in the supplementary material.
System-Initiated Transitions from Chit-Chat to Task-Oriented Dialogues with Transition Info Extractor and Transition Sentence Generator

In this work, we study dialogue scenarios that start from chit-chat but eventually switch to task-related services, and investigate how a unified dialogue model, which can engage in both chit-chat and task-oriented dialogues, takes the initiative during the dialogue mode transition from chit-chat to task-oriented in a coherent and cooperative manner. We firstly build a transition info extractor (TIE) that keeps track of the preceding chit-chat interaction and detects the potential user intention to switch to a task-oriented service. Meanwhile, in the unified model, a transition sentence generator (TSG) is extended through efficient Adapter tuning and transition prompt learning. When the TIE successfully finds task-related information from the preceding chit-chat, such as a transition domain (“train” in Figure fig: system-initiated transition from chit-chat to task-oriented.), then the TSG is activated automatically in the unified model to initiate this transition by generating a transition sentence under the guidance of transition information extracted by TIE. The experimental results show promising performance regarding the proactive transitions. We achieve an additional large improvement on TIE model by utilizing Conditional Random Fields (CRF). The TSG can flexibly generate transition sentences while maintaining the unified capabilities of normal chit-chat and task-oriented response generation.
Building a dual dataset of text- and image-grounded conversations and summarisation in Gàidhlig (Scottish Gaelic)

Gàidhlig (Scottish Gaelic; gd) is spoken by about 57k people in Scotland, but remains an under-resourced language with respect to natural language processing in general and natural language generation (NLG) in particular. To address this gap, we developed the first datasets for Scottish Gaelic NLG, collecting both conversational and summarisation data in a single setting. Our task setup involves dialogues between a pair of speakers discussing museum exhibits, grounding the conversation in images and texts. Then, both interlocutors summarise the dialogue resulting in a secondary dialogue summarisation dataset. This paper presents the dialogue and summarisation corpora, as well as the software used for data collection. The corpus consists of 43 conversations (13.7k words) and 61 summaries (2.0k words), and will be released along with the data collection interface.
Using SimAM and SSD to Detect Prostate Capsule

Aiming at the problem of accurate detection of the prostate capsule, this paper designed an accurate detection network of the prostate capsule by integrating 3d non-parametric residual attention mechanism SimAM and SSD, whcih called Attention based on Single Shot Multibox Detector (ASSD). Compared with other mentioned methods, ASSD got the best detetion precision.
E-Learning Application Training for AL-SAFITRI Vocational School Teachers in South Buton Regency

Combining face-to-face meetings, in this case in class, with electronic learning can increase the contribution and interactivity between students. Through face-to-face students can get to know fellow students and their accompanying teachers. This familiarity greatly supports virtual collaborative work. Improving the quality of education at SMK 1 Al-Safitri is one of them by conducting online learning (in the network). E-Learning can be interpreted as a science of learning without having to use printed paper/hand out the material presented. On average, people working in the field of e-learning, especially from the fields of education, psychology, and computer science, know and use Moodle. teacher accounts that provide teaching topics or provide other subject matters besides being able to provide teaching topics there are several advantages of this Moodle LMS, namely there is a quiz feature, or videos.
The timing bottleneck: Why timing and overlap are mission-critical for conversational user interfaces, speech recognition and dialogue systems

Speech recognition systems are a key intermediary in voice-driven human-computer interaction. Although speech recognition works well for pristine monologic audio, real-life use cases in open-ended interactive settings still present many challenges. We argue that timing is mission-critical for dialogue systems, and evaluate 5 major commercial ASR systems for their conversational and multilingual support. We find that word error rates for natural conversational data in 6 languages remain abysmal, and that overlap remains a key challenge (study 1). This impacts especially the recognition of conversational words (study 2), and in turn has dire consequences for downstream intent recognition (study 3). Our findings help to evaluate the current state of conversational ASR, contribute towards multidimensional error analysis and evaluation, and identify phenomena that need most attention on the way to build robust interactive speech technologies.
A New Dataset for Causality Identification in Argumentative Texts

Existing datasets for causality identification in argumentative texts have several limitations, such as the type of input text (e.g., only claims), causality type (e.g., only positive), and the linguistic patterns investigated (e.g., only verb connectives). To resolve these limitations, we build the Webis-Causality-23 dataset, with sophisticated inputs (all units from arguments), a balanced distribution of causality types, and a larger number of linguistic patterns denoting causality. The dataset contains 1485 examples derived by combining the two paradigms of distant supervision and uncertainty sampling to identify diverse, high-quality samples of causality relations, and annotate them in a cost-effective manner.
Frame-oriented Summarization of Argumentative Discussions

Online discussions on controversial topics with many participants frequently include hundreds of arguments that cover different framings of the topic. But these arguments and frames are often spread across the various branches of the discussion tree structure. This makes it difficult for interested participants to follow the discussion in its entirety as well as to introduce new arguments. In this paper, we present a new rank-based approach to extractive summarization of online discussions focusing on argumentation frames that capture the different aspects of a discussion. Our approach includes three retrieval tasks to find arguments in a discussion that are (1) relevant to a frame of interest, (2) relevant to the topic under discussion, and (3) informative to the reader. Based on a joint ranking by these three criteria for a set of user-selected frames, our approach allows readers to quickly access an ongoing discussion. We evaluate our approach using a test set of 100 controversial Reddit ChangeMyView discussions, for which the relevance of a total of 1871 arguments was manually annotated.
An End-to-End Deep Learning Approach for Video Captioning Through Mobile Devices

Video captioning is a computer vision task that aims at generating a description for video content. This can be achieved using deep learning approaches that leverage image and audio data. In this work, we have developed two strategies to tackle this task in the context of resource-constrained devices: (i) generating one caption per frame combined with audio classification, and (ii) generating one caption for a set of frames combined with audio classification. In these strategies, we have utilized one architecture for the image data and another for the audio data. We have developed an application tailored for resource-constrained devices, where the image sensor captures images at a specific frame rate. The audio data is captured from a microphone for a predefined duration at time. Our application combines the results from both modalities to create a comprehensive description. The main contribution of this work is the introduction of a new end-to-end application that can utilize the developed strategies and be beneficial for environment monitoring. Our method has been implemented on a low-resource computer, which poses a significant challenge.