Reviewerly/exHarmony huggingface dataset:
THE USE OF PODCAST TO PROMOTE STUDENTS’ SPEAKING ABILITY IN ONLINE LEARNING AT THE COVID PANDEMIC 19

The objective of this research was to promote students' speaking ability by using podcasts in teaching and learning. the subject of the research was taken in one of the senior high schools in Bogor 11th grades and the participant was 6 students consists 3 of females and 3 of males. The method used in this study was Classroom Action Research (CAR), conducted in two cycles. The phases are planning, acting, observing, and reflecting as the process of teaching and learning. For collecting the data the researcher used the quantitative data that were obtained from the documents, observations, and interviews to investigate the students' promotion in speaking ability such a promoting their grammar, pronunciation, vocabulary, and self-confidence. It can be concluded that the students' speaking ability could be promoted by using podcasts in English teaching and learning.Keywords:Podcasts, Promote,Speaking,Online Learning
Graded Readers: An Empirical Study Measuring the Impact on Low-Proficiency EFL Students’ Writing Fluency in Morocco

The writing ability of Moroccan students is generally characterized by low language fluency. This article examines the impact of the implementation of graded readers as a syllabus intervention alternative rather than the textbook-based approach regarding teaching writing skill. The research design for this study is a mixed method. The qualitative method was twofold, in-class and online. The first tool was conducted via a series of observations and structured discussions with students. Likewise, the second tool was conducted through online forum interactions between the students one another and students with their instructor so as to measure their perceptions on the use of graded readers as a new method of dealing with the writing skill in preparation for the formative assessment quiz. Microsoft Teams platform, commissioned by the Ministry of Education, was used as a complementary medium to enhance in-class discussions where students are required to answer some open-ended questions. On the other hand, the quantitative method was used to measure the students’ outcomes by using the Rubric Calculator Software issued by Smekenseducation.com. The findings of this study demonstrate that the use of graded readers to improve the writing fluency was encouraging according to the positive results obtained from the analyzed data regardless of the time restrictions. The paper is concluded with several recommendations for more research in the field of study and the requirements needed for the best implementation of this creative method to boost the learners’ writing fluency.
Implementasi Deep Learning Menggunakan Metode CNN dan LSTM untuk Menentukan Berita Palsu dalam Bahasa Indonesia

This research aims to implement deep learning techniques to determine fact and fake news in Indonesian language. The methods used are Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM). The stages of the research consisted of collecting data, labeling data, preprocessing data, word embedding, splitting data, forming CNN and LSTM models, evaluating, testing new input data and comparing evaluations of the established CNN and LSTM models. The Data are collected from a fact and fake news provider site that is valid, namely TurnbackHoax.id. There are 1786 news used in this study, with 802 fact and 984 fake news. The results indicate that the CNN and LSTM methods were successfully applied to determine fact and fake news in Indonesian language properly. CNN has an accuracy test, precision and recall value of 0.88, while the LSTM model has an accuracy test and precision value of 0.84 and a recall of 0.83. In testing the new data input, all of the predictions obtained by CNN are correct, while the prediction results obtained by LSTM have 1 wrong prediction. Based on the evaluation results and the results of testing the new data input, the model produced by the CNN method is better than the model produced by the LSTM method.
How to Save your Annotation Cost for Panoptic Segmentation?

How to properly reduce the annotation cost for panoptic segmentation? How to leverage and optimize the cost-quality trade-off for training data and model? These questions are key challenges towards a label-efficient and scalable panoptic segmentation system due to its expensive instance/semantic pixel-level annotation requirements. By closely examining different kinds of cheaper labels, we introduce a novel multi-objective framework to automatically determine the allocation of different annotations, so as to reach a better segmentation quality with a lower annotation cost. Specifically, we design a Cost-Quality Balanced Network (CQB-Net) to generate the panoptic segmentation map, which distills the crucial relations between various supervisions including panoptic labels, image-level classification labels, bounding boxes, and the semantic coherence information between the foreground and background. Instead of ad-hoc allocation during training, we formulate the optimization of cost-quality trade-off as a Multi-Objective Optimization Problem (MOOP). We model the marginal quality improvement of each annotation and approximate the Pareto-front to enable a label-efficient allocation ratio. Extensive experiments on COCO benchmark show the superiority of our method, e.g. achieving a segmentation quality of 43.4% compared to 43.0% of OCFusion while saving 2.4x annotation cost.
Visualizing NLP in Undergraduate Students' Learning about Natural Language

We report on the use of open-source natural language processing capabilities in a web-based interface to allow undergraduate students to apply what they have learned about formal natural language structures. The learning activities encourage students to interpret data in new ways, think originally about natural language, and critique the back-end NLP models and algorithms visualized on the user front end. This work is of relevance to AI resources developed for education by focusing on inclusivity of students from many disciplinary backgrounds. Specifically, we comprehensively extended a web-based system with new resources. To test the students' reactions to NLP analyses that offer insights into both the strengths and limitations of AI systems, we incorporated a range of automated analyses focused on language-independent processing or meaning representations which still represent challenges for NLP. We conducted a survey-based evaluation with students in open-ended case-based assignments in undergraduate coursework. Responses indicated that the students reinforced their knowledge, applied critical thinking about language and NLP applications, and used the application not to solve the assignment for them, but as a tool in their own effort to address the task. We further discuss how using interpretable visualizations of system decisions is an opportunity to learn about ethical issues in NLP, and how making AI systems interpretable may broaden multidisciplinary interest in AI in early educational experiences.
A Block-Based Triangle Counting Algorithm on Heterogeneous Environments

Triangle counting is a fundamental building block in graph algorithms. In this article, we propose a block-based triangle counting algorithm to reduce data movement during both sequential and parallel execution. Our block-based formulation makes the algorithm naturally suitable for heterogeneous architectures. The problem of partitioning the adjacency matrix of a graph is well-studied. Our task decomposition goes one step further: it partitions the set of triangles in the graph. By streaming these small tasks to compute resources, we can solve problems that do not fit on a device. We demonstrate the effectiveness of our approach by providing an implementation on a compute node with multiple sockets, cores and GPUs. The current state-of-the-art in triangle enumeration processes the Friendster graph in 2.1 seconds, not including data copy time between CPU and GPU. Using that metric, our approach is 20 percent faster. When copy times are included, our algorithm takes 3.2 seconds. This is 5.6 times faster than the fastest published CPU-only time.
Block Compression and Invariant Pruning for SAT-based Totally-Ordered HTN Planning

Translations into propositional logic are currently one of the most efficient techniques for solving Totally-Ordered HTN planning problems. The two current encodings both iterate over the maximum allowed depth of decomposition. Given this depth, they compute a tree that represents all possible decompositions up to this depth. Based on this tree, a formula in propositional logic is created. We show that much of the computed tree is actually useless as it cannot possibly belong to a solution. We provide a technique for removing (parts of) these useless structures using state invariants. We further show that is often not necessary to encode all leafs of this tree as separate timesteps, as the prior encodings did. Instead, we can compress the leafs into blocks and encode all leafs of a block at one timestep. We show that these changes provide an improvement over the state-of-the-art in HTN planning.
Cross-Lingual Transfer in Zero-Shot Cross-Language Entity Linking

Cross-language entity linking grounds mentions written in several languages to a monolingual knowledge base.We use a simple neural ranking architecture for this task that uses multilingual BERT representations of both the mention and the context as input, so as to explore the ability of a transformer model to perform well on this task.We find that the multilingual ability of BERT leads to good performance in monolingual and multilingual settings.Furthermore, we explore zero-shot language transfer and find surprisingly robust performance.We conduct several analyses to identify the sources of performance degradation in the zero-shot setting.Results indicate that while multilingual transformer models transfer well between languages, issues remain in disambiguating similar entities unseen in training.
Summary of Research Methods on Pre-Training Models of Natural Language Processing

In recent years, deep learning technology has been widely used and developed.In natural language processing tasks, pre-training models have been more widely used.Whether it is sentence extraction or sentiment analysis of text, the pre-training model plays a very important role.The use of a large-scale corpus for unsupervised pre-training of models has proven to be an excellent and effective way to provide models.This article summarizes the existing pre-training models and sorts out the improved models and processing methods of the relatively new pre-training models, and finally summarizes the challenges and prospects of the current pre-training models.
The Sample Complexity of Teaching by Reinforcement on Q-Learning

We study the sample complexity of teaching, termed as ``teaching dimension" (TDim) in the literature, for the teaching-by-reinforcement paradigm, where the teacher guides the student through rewards. This is distinct from the teaching-by-demonstration paradigm motivated by robotics applications, where the teacher teaches by providing demonstrations of state/action trajectories. The teaching-by-reinforcement paradigm applies to a wider range of real-world settings where a demonstration is inconvenient, but has not been studied systematically. In this paper, we focus on a specific family of reinforcement learning algorithms, Q-learning, and characterize the TDim under different teachers with varying control power over the environment, and present matching optimal teaching algorithms. Our TDim results provide the minimum number of samples needed for reinforcement learning, and we discuss their connections to standard PAC-style RL sample complexity and teaching-by-demonstration sample complexity results. Our teaching algorithms have the potential to speed up RL agent learning in applications where a helpful teacher is available.
Programmatic Strategies for Real-Time Strategy Games

Search-based systems have shown to be effective for planning in zero-sum games. However, search-based approaches have important disadvantages. First, the decisions of search algorithms are mostly non-interpretable, which is problematic in domains where predictability and trust are desired such as commercial games. Second, the computational complexity of search-based algorithms might limit their applicability, especially in contexts where resources are shared among other tasks such as graphic rendering. In this work we introduce a system for synthesizing programmatic strategies for a real-time strategy (RTS) game. In contrast with search algorithms, programmatic strategies are more amenable to explanations and tend to be efficient, once the program is synthesized. Our system uses a novel algorithm for simplifying domain-specific languages (DSLs) and a local search algorithm that synthesizes programs with self play. We performed a user study where we enlisted four professional programmers to develop programmatic strategies for mRTS, a minimalist RTS game. Our results show that the programs synthesized by our approach can outperform search algorithms and be competitive with programs written by the programmers.
Selecting Informative Contexts Improves Language Model Fine-tuning

Language model fine-tuning is essential for modern natural language processing, but is computationally expensive and timeconsuming.Further, the effectiveness of finetuning is limited by the inclusion of training examples that negatively affect performance.Here we present a general fine-tuning method that we call information gain filtration for improving the overall training efficiency and final performance of language model fine-tuning.We define the information gain of an example as the improvement on a validation metric after training on that example.A secondary learner is then trained to approximate this quantity.During fine-tuning, this learner selects informative examples and skips uninformative ones.We show that our method has consistent improvement across datasets, finetuning tasks, and language model architectures.For example, we achieve a median perplexity of 54.0 on a books dataset compared to 57.3 for standard fine-tuning.We present statistical evidence that offers insight into the improvements of our method over standard finetuning.The generality of our method leads us to propose a new paradigm for language model fine-tuning -we encourage researchers to release pretrained secondary learners on common corpora to promote efficient and effective fine-tuning, thereby improving the performance and reducing the overall energy footprint of language model fine-tuning.
Influence of GUJarati STEmmeR in Supervised Learning of Web Page Categorization

With the large quantity of information offered on-line, it's equally essential to retrieve correct information for a user query. A large amount of data is available in digital form in multiple languages. The various approaches want to increase the effectiveness of on-line information retrieval but the standard approach tries to retrieve information for a user query is to go looking at the documents within the corpus as a word by word for the given query. This approach is incredibly time intensive and it's going to miss several connected documents that are equally important. So, to avoid these issues, stemming has been extensively utilized in numerous Information Retrieval Systems (IRS) to extend the retrieval accuracy of all languages. These papers go through the problem of stemming with Web Page Categorization on Gujarati language which basically derived the stem words using GUJSTER algorithms [1]. The GUJSTER algorithm is based on morphological rules which is used to derived root or stem word from inflected words of the same class. In particular, we consider the influence of extracted a stem or root word, to check the integrity of the web page classification using supervised machine learning algorithms. This research work is intended to focus on the analysis of Web Page Categorization (WPC) of Gujarati language and concentrate on a research problem to do verify the influence of a stemming algorithm in a WPC application for the Gujarati language with improved accuracy between from 63% to 98% through Machine Learning supervised models with standard ratio 80% as training and 20% as testing.
A Face Occlusion Removal and Privacy Protection Method for IoT Devices Based on Generative Adversarial Networks

The device group based on the Internet of Things (IoT) has been used in face recognition in real life, so it is more necessary to discuss the current data security issues and social hot issues. The Internet of Things device combines edge conditions and many recognizers to generative adversarial networks. On the premise of meeting the needs of partial occlusion of users, face recovery is completed through information reorganization. CelebA training set is used to simulate face occlusion, and the model is trained and tested. The results show that the method can recover the complete image of the protection for the facial privacy of specific people. At the same time, the IoT device using this method ensures that the face information is not easy to have tampered with when attacked.
AuthNet: A Deep Learning Based Authentication Mechanism Using Temporal Facial Feature Movements (Student Abstract)

Deep learning algorithms are widely used to extend modern biometric authentication mechanisms in resource-constrained environments like smartphones, providing ease-of-use and user comfort, while maintaining a non-invasive nature. In this paper, an alternative is proposed, that uses both facial recognition and the unique movements of that particular face while uttering a password. The proposed model is language independent, the password doesn't necessarily need to be a set of meaningful words or numbers, and also, is a contact-less system. When evaluated on the standard MIRACL-VC1 dataset, the proposed model achieved a testing accuracy of 98.1%, underscoring its effectiveness.
Assessing Emoji Use in Modern Text Processing Tools

Emojis have become ubiquitous in digital communication, due to their visual appeal as well as their ability to vividly convey human emotion, among other factors.This also leads to an increased need for systems and tools to operate on text containing emojis.In this study, we assess this support by considering test sets of tweets with emojis, based on which we perform a series of experiments investigating the ability of prominent NLP and text processing tools to adequately process them.In particular, we consider tokenization, part-of-speech tagging, dependency parsing, as well as sentiment analysis.Our findings show that many systems still have notable shortcomings when operating on text containing emojis.
Deep Graph Spectral Evolution Networks for Graph Topological Evolution

Characterizing the underlying mechanism of graph topological evolution from a source graph to a target graph has attracted fast increasing attention in the deep graph learning domain. However, it is very challenging to build expressive and efficient models that can handle global and local evolution patterns between source and target graphs. On the other hand, graph topological evolution has been investigated in the graph signal processing domain historically, but it involves intensive labors to manually determine suitable prescribed spectral models and prohibitive difficulty to fit their potential combinations and compositions. To address these challenges, this paper proposes the deep Graph Spectral Evolution Network (GSEN) for modeling the graph topology evolution problem by the composition of newly-developed generalized graph kernels. GSEN can effectively fit a wide range of existing graph kernels and their combinations and compositions with the theoretical guarantee and experimental verification. GSEN has outstanding efficiency in terms of time complexity (O(n)) and parameter complexity (O(1)), where n is the number of nodes of the graph. Extensive experiments on multiple synthetic and real-world datasets demonstrate outstanding performance.
Kebijakan Pemerintah dalam Penanggulangan Bencana di Indonesia

Abstract Purpose: This study aimed to seek consideration for disaster mitigation planning through observations of disasters that have occurred before. and can be used in decision-making regarding natural disaster management. Research methodology: This research used a descriptive method with a secondary data approach and implemented a conceptual approach accompanied by a comparative approach. Results: The results of this study, namely the orientation of disaster mitigation objectives, show that there is a process related to the preparation of development plans for both regions and cities and disaster management, as well as providing a general picture to the community regarding the stages of government making policy decisions in disaster management and disaster anticipation for communities affected by disasters. Limitations: The limitations in this study are regarding disaster mitigation, the process of making plans for the future, both at the regional and central levels, through the analysis and research process on previous policies as consideration and future evaluation for disaster prevention measures and structured planning for the worst possible disaster. Contribution: This research can be used as a reference to understand the context of disaster mitigation by the government and material for consideration in making decisions about what to take to plan future disaster management from existing disaster experiences.
Audit Logs Management and Security - A Survey

Audit logs are key resources that show the current state of the systems and user activities and are used for cyber forensics and maintenance. These logs are the only source that can help in finding traces of some malicious activities or troubleshooting a system failure. Insight view for troublefree availability of computing resources and performance monitoring and meaningful forensic audit depends on the management and archival system of audit logs. These logs are prone to multidimensional threats and superusers or system administrators have unprecedented access to these logs and can alter these logs as and when required. Similarly, repudiation is another serious issue in computer forensics and non-repudiation can be provided by a secure recording of event logs. Periodic backups, encrypted data transfer, off-site storage and certificate based storage of these logs are commonly being used. In this survey, we searched for the requirements of securing audit logs and available approaches to secure these logs. Based on the available literature, a taxonomy of audit log management is developed. We have drawn a comparison between these approaches and also highlighted the current challenges to these logs security and their available options.