Reviewerly/exHarmony huggingface dataset:
Generating Animations from Instructional Text

Generating animations from text finds application in numerous areas like screenplay writing, instructional videos, public safety, and video user manuals, etc.However, translating natural language text into animations is a challenging task.We develop a text-to-animation system which can handle any simple instructional text.We have created an NLP pipeline to extract action sequences from cooking recipes and map them to appropriate atomic actions present in the system's knowledge base.This paper explores a novel approach of linking Graphics with Natural Language Processing (NLP).Our goal is to obviate the necessity of having a large collection of stored graphics.
Challenges in target selection for automatic question generation

Today's educational systems need an efficient tool to perform competently assessment of students on their major concepts they learnt from study material. Preparing a set of questions for assessment can be time consuming for teachers. Automatic Question Generation (AQG) is the technique for generating a right set of questions from a content. A key problem with AQG systems is the selection of the appropriate target sentences and concepts. In this paper, we investigate the difficulties in automatic target selection using unannotated document sources. As the result of the analysis, we identified the following key issues: hard to measure the topic relevance; the large segmentation of the information content and the incomplete background knowledge bases.
Genesis-modeling Method in the Educational Process

Innovative development ideas in the modern educational process lead to the search for adequate methods that should provide, firstly, humanistic orientation, secondly, be interactive, thirdly, be based on the ideas of higher psychological mechanisms of consciousness and self-consciousness. The genesis-modeling method has to provide an educational strategy for the growing personality. The peculiarity of the genesis-modeling method is that it aims at both the model (ideal representation) of a particular personal formation (value) and the optimal way to achieve it. Due to a dual purpose, this method can be qualified as an innovative psychological-pedagogical project, which has its own value, because its content allows productive educational variations. Genesis-modeling method involves the means of organizing own behavior by a person at the stage of perception as well as at the stage of arbitrary acceptance of an ethical requirement (spiritual value). These characteristics of the genesis-modeling method are deduced from a number of methodological principles: 1) The principle of activity of the situation components, which states the relationship between the subject and social environment; 2) The principle of systematic personal development reveals in internal relations the difficult stage of their transformation in the process of cultural development of the subject; 3) The principle of the active nature of personal development explains the peculiarities of their origin: from natural-reactive to arbitrary-sustainable. The transformative possibilities of the genesis-modeling method are considered using the example of such a value as diligence, which is ranked from high to low level. It reveals the four stages of the personality's growth to the designated value. For each of them, in the task-oriented way, the research presents the specific purpose of the educator’s actions, tactics, direction, content, mode of presentation, conditions of action, as well as the nature of the student’s response. The task-oriented form of realization of these stages allows carrying out timely postoperative control. The fifth stage is the reflexive one, which includes the appropriate exercises aimed at providing the development of student’s value of diligence. The research also presents quantitative results, which testify to the high efficiency of the genesis-modeling method in the general educational process.
PENGEMBANGAN BUKU PENUNJANG TEMATIK TERPADU BERBASIS KETERAMPILAN PROSES TEMA INDAHNYA KEBERSAMAAN UNTUK KELAS 4 SD/MI

The integrated thematic supporting book based on process skills that will be developedÂ to reduce students' dependence on teachers, provide direct learning experiences, can be a tool for students to learn about themselves and the environment, and can be applied independently by students to solve problems they meet in daily life. This type of research is categorized as a research development with a 4-D model that are defining, designing, developing, and distributing. The subjects in this study are integrated thematic support books based on process skills that are tested on fourth grade students in elementary school. The limited test is carried out on 10 students in grade IV Hidayatul Mubtadiin Pasuruan City who are randomly selected. Meanwhile, the field trial is conducted in three classes IV A Hidayatul Mubtadiin Pasuruan City, which Consist of 25 students. The research data are validity data, practicality data, and effectiveness data from integrated thematic supporting books. Furthermore, data collection is done through observation, questionnaires, and tests. Data Analysis is done by analyzing the level of validity and reliability, analyzing the level of difficulty, analyzing obstacles, analyzing the results of process skills, and analyzing student responses. The results obtained by the validity level of 3.63 and reliability of 0.97. Meanwhile, the level of difficulty of the book based on the percentage of difficult words, difficult sentences and difficult to understand images are 1.03%, 2.18%, and 10.83, respectively. The result of students 'process skills is analyzed with the t-test formula on the result of the students' pretest and posttest shows that the value of sig. (2- tailed) is 0,000. Because the value of sig. sig. (2-tailed) &lt;0.05, it can be concluded that there are differences in the significance and response of students to the use of an overall book average of 97.73% which means the book is interesting.
Understanding and Detecting Majority Attacks

Blockchain technologies are becoming increasingly popular for financial and business solutions thanks to their many features and adaptability. However, their security is not assured. Of particular concern are majority attacks, which exploit the consensus algorithm used to resolve competing chains to modify past and present transactions on the blockchain. Despite numerous in-the-wild majority attacks, detecting in-progress attacks remains difficult, with most attacks remaining undetected for hours or days. to understand better these attacks and why they are so difficult to detect, in this paper we present a series of increasingly sophisticated attacks performed in an experimental environment and analyze the resulting network traffic. We then propose a possible detection mechanism based on the shared characteristics of these attacks. The implementation of such an algorithm is finally tested in a variety of experimental environments.
Blended Agile Approach: A Mobile Application Approach

Consumers' demand for mobile applications has significantly contributed most software-solution companies and individuals to continuously develop applications that will understand the domain needed by the market.These applications provide mobile users with rich applications and services to support our activities for entertainment, productivity, lifestyle, communication, games and many more.In order to meet these demands, high-quality software must be produced and delivered with the help of various application frameworks that have been constructed to test which among the selections fit best.Consequently, due to the emerging technologies, developers need to strictly follow a development cycle that will be beneficial for them in terms of time delivery, features complexity, platform compatibility and target market.Hence, the software engineering approach is undeniably an important phase in mobile application development.With this study, the researchers proposed an Agile Approach fused with Scrum Methodology, Extreme Programming and Feature-driven Development.The framework was evaluated and classified according to characteristic categories and has defined as a tool for fast-paced mobile application development.
Investigating Perceptions of Social Intelligence in Simulated Human-Chatbot Interactions

With the ongoing penetration of conversational user interfaces, a better understanding of social and emotional characteristic inherent to dialogue is required. Chatbots in particular face the challenge of conveying human-like behaviour while being restricted to one channel of interaction, i.e., text. The goal of the presented work is thus to investigate whether characteristics of social intelligence embedded in human-chatbot interactions are perceivable by human interlocutors and if yes, whether such influences the experienced interaction quality. Focusing on the social intelligence dimensions Authenticity, Clarity and Empathy, we first used a questionnaire survey evaluating the level of perception in text utterances, and then conducted a Wizard of Oz study to investigate the effects of these utterances in a more interactive setting. Results show that people have great difficulties perceiving elements of social intelligence in text. While on the one hand they find anthropomorphic behaviour pleasant and positive for the naturalness of a dialogue, they may also perceive it as frightening and unsuitable when expressed by an artificial agent in the wrong way or at the wrong time.
Resilience and Plasticity of Deep Network Interpretations for Aerial Imagery

This paper aims at visualizing deep convolutional neural network interpretations for aerial imagery and understanding how these interpretations change across datasets or when network weights are damaged. Our visualization results offer insights on the generalization power and resilience of commonly used networks, such as VGG16, ResNet50, and DenseNet121. Our experiments on the AID and the UCM aerial datasets demonstrate the emergence of object and texture detectors in convolutional networks commonly used for classification. We further analyze these interpretations when the network is trained on one dataset and tested on another to demonstrate the robustness of feature learning across aerial datasets. We also explore the shift in interpretations when performing transfer learning from an aerial dataset (AID) to a generic object dataset (MS-COCO). These results illustrate how transfer learning benefits the network's internal representations. For analyzing the effects of damages on activation maps, our work proposes to simulate damages by randomly zeroing network weights at different levels of the network. The paper carries out experiments with retraining the network to check if it can recover the lost interpretations. Visualizing changes in the neural network's interpretation when the undamaged weights are updated, allows us to assess the resilience of a network visually. Finally, we propose a new metric for the quantitative assessment of network resilience.
Sistem Pakar untuk Mendiagnosa Penyakit Persendian Menggunakan Metode Certainty Factor

If someone feels unwell, they will usually make a diagnosis and find the solutions before deciding to consult a doctor. As with joint disease with symptoms of pain that are still mild, there is no time to go to the doctor, fees, or other reasons. Especially now through information via the internet can be easily obtained. To assist in identifying and improving the accuracy of diagnosis, it is necessary to have a web-based expert system application to diagnose joint disease using certainty factor methods. The research method used is using SDLC (Software Development Life Cycle). An expert system that has been made can be used as early detection and get solutions for joint diseases and preventive measures to treatment
A New Design of Permutation Function Using Spiral Fibonacci in Block Cipher

Cryptography is an important part of information security that covers the investigation of algorithms and protocols for secure information.With the advance of technology, a new design of cryptographic algorithm is required to fulfil the new system requirements.This paper emphasizes on the new design of permutation function for improving security in modern block ciphers.The Spiral Fibonacci method is designed to rearrange or permute inputs of bits through the substitution boxes.Then, the experiment was conducted between the Spiral Fibonacci in the proposed block cipher and the ShiftRow in the Advanced Encryption Standard cipher.The correlation coefficient technique is used to examine the diffusion property of the new design of permutation function.The results showed that the values of Spiral Fibonacci indicate low correlation between plaintext and ciphertext.Thus, the proposed block cipher improved diffusion property between the plaintext and ciphertext.
A Speech-to-Knowledge-Graph Construction System

This paper presents a HAO-Graph system that generates and visualizes knowledge graphs from a speech in real-time. When a user speaks to the system, HAO-Graph transforms the voice into knowledge graphs with key phrases from the original speech as nodes and edges. Different from language-to-language systems, such as Chinese-to-English and English-to-English, HAO-Graph converts a speech into graphs, and is the first of its kind. The effectiveness of our HAO-Graph system is verified by a two-hour chairman's talk in front of two thousand participants at an annual meeting in the form of a satisfaction survey.
Semantic Oriented Data Modeling for Enterprise Application Engineering Using Semantic Web Languages

This article proposes the use of the RDF, RDFS and OWL as a basis for data modeling for semantic oriented development of enterprise applications.RDF and OWL ware introduced to guaranty a semantic web to permit a web of interconnected data instead of a static web so that computer can explore web contents.Several efforts have been made to establish rules to convert data of existing enterprise applications into equivalent RDF/OWL data, to make them available on the semantic web.However, the most applications were developed using classical relational database modeling methodologies.Here beyond publishing data for the semantic web, we give motivations for the necessity of a RDF/OWL semantic data modeling, as well as an approach in form of best practices for this modeling.We also show the effectiveness of our approach by comparing it to the classical relational data modeling.
Software Requirement Specification Sistem Informasi Manajemen Rumah Makan Berdasarkan ISO/IEC/IEEE 29148-2018

The rapid development of technology makes this technology have an impact on many fields, one of which is the business world. The number of businesses that have emerged both small and large businesses that have an impact on competition between these businesses. Today, business in the culinary field is getting tougher. The culinary business sector of restaurants is increasingly popping up in this age. We need to overcome the competition in the emerging restaurant business. By using the stages of making software requirements specifications based on ISO / IEC / IEEE 29148-2018, this restaurant business will have an international standard information system. The result of this method is a software requirements specification document (SKPL) as a reference document for all activities carried out during the development of this information system.
Overview of Network Dataset and Data Mining Technique

Network dataset has been widely used in network research.Network dataset has big challenges lead to certain matters, as an example huge time consumtion in data processing and transmission and false alarm rate increased in attack detection gaining the harm and unsafe condition to online user.Hence the nature of network dataset need to be analyzed.Many researchers try to come out with better algorithm to handle the network dataset, consequently to come out with better finding result.This paper is about to analyze the nature of network dataset and review the data mining techniques in supervised and unsupervised approach to handle and process the network dataset.
Goal Recognition Design - Survey

Goal recognition is the task of recognizing the objective of agents based on online observations of their behavior. Goal recognition design (GRD), the focus of this survey, facilitates goal recognition by the analysis and redesign of goal recognition models. In a nutshell, given a model of a domain and a set of possible goals, a solution to a GRD problem determines: (1) to what extent do actions performed by an agent reveal the agent’s objective? and (2) what is the best way to modify the model so that the objective of an agent can be detected as early as possible? GRD answers these questions by offering a solution for assessing and minimizing the maximal progress of any agent before recognition is guaranteed. This approach is relevant to any domain in which efficient goal recognition is essential and in which the model can be redesigned. Applications include intrusion detection, assisted cognition, computer games, and human-robot collaboration. This survey presents the solutions developed for evaluation and optimization in the GRD context, a discussion on the use of GRD in a variety of real-world applications, and suggestions of possible future avenues of GRD research.
Towards High-Level Intrinsic Exploration in Reinforcement Learning

Deep reinforcement learning (DRL) methods traditionally struggle with tasks where environment rewards are sparse or delayed, which entails that exploration remains one of the key challenges of DRL. Instead of solely relying on extrinsic rewards, many state-of-the-art methods use intrinsic curiosity as exploration signal. While they hold promise of better local exploration, discovering global exploration strategies is beyond the reach of current methods. We propose a novel end-to-end intrinsic reward formulation that introduces high-level exploration in reinforcement learning. Our curiosity signal is driven by a fast reward that deals with local exploration and a slow reward that incentivizes long-time horizon exploration strategies. We formulate curiosity as the error in an agent’s ability to reconstruct the observations given their contexts. Experimental results show that this high-level exploration enables our agents to outperform prior work in several Atari games.
Shortest Path of a Graph using Centrality Measures

When it comes to finding the shortest path in a graph, most people think of Dijkstra's algorithm.While Dijkstra's algorithm is indeed very useful, there are some other parameters that can be used to find the shortest path while communicating in a weighted network.In a graph network, there are different types of centrality measures used to find the importance of a node.In that Degree Centrality, Closeness Centrality and Betweenness Centrality are useful for identifying the amount of importanceof a node in a graph.Centrality measures are used to find nodes that act as a bridge from one part of a network to another part of a network.In this paper, results shows that there is better possibility to find shortest path using Degree Centrality or Closeness Centrality or Betweenness centrality compared with Dijkstra's algorithm.
Web Scrapping: Data Extraction from Websites

Data is very important nowadays for almost all organizations for their existence as well as for their growth. The Internet has become the major source of data for individuals and almost all organizations. Authentic Websites are a major source of reliable data for many individuals and organizations. Extracting Data from websites is commonly referred as Web Scrapping, which refers to both manual and automated process. Extracting large amount of meaning full data from the websites manually is very difficult, tedious and redundant task. Automated Scrapping is done by writing specific programs to extract the required data from the websites. These programs are usually called as web scrappers. Web scrappers are written using many programming languages like Python, Node.js, Ruby, C++, PHP etc. Each language has its own unique features and built in libraries for performing data extraction. There are many web scrapping tools like Beautiful Soup, Octoparse, Parsehub etc. In this article we are going to analyses few recent Web Scrappers tools used in scrapping the Web.&#x0D;
An Improved Latent Low Rank Representation for Automatic Subspace Clustering

There is growing interest in low rank representation (LRR) for subspace clustering. Existing latent LRR methods can exploit the global structure of data when the observations are insufficient and/or grossly corrupted, but it cannot capture the intrinsic structure due to the neglect of the local information of data. In this paper, we proposed an improved latent LRR model with a distance regularization and a non-negative regularization jointly, which can effectively discover the global and local structure of data for graph learning and improve the expression of the model. Then, an efficiently iterative algorithm is developed to optimize the improved latent LRR model. In addition, traditional subspace clustering characterizes a fixed numbers of cluster, which cannot efficiently make model selection. An efficiently automatic subspace clustering is developed via the bias and variance trade-off, where the numbers of cluster can be automatically added and discarded on the fly.
Analisis Kebutuhan Pengembangan Media Pembelajaran Interaktif Berbasis Adobe Flash CS6

This study aims to describe the results of the needs analysis for the development of learning media based on Adobe Flash CS6. This research is a qualitative descriptive study using survey methods to identify the needs of students and teachers in learning assessment. In this study, needs analysis refers to the achievement of student learning outcomes, problems faced by students, and the media needed in the learning assessment process. This research uses observation and interview methods in collecting data. Observations and interviews were conducted for grade X students of basic graphic design subjects at SMKN 8Padang. The results showed that: 1) completeness of student learning 2) Interactive learning media needed on computer subjects and basic networks. The results of this study concluded that students need interactive learning media in learning activities. Thus, this research was used in designing learning based on Adobe Flash CS6
Recommender Systems: Algorithms, Evaluation and Limitations

Aims/ objectives: This paper presents the different types of recommender ﬁltering techniques. The main objective of the study is to provide a review of classical methods used in recommender systems such as collaborative ﬁltering, content-based ﬁltering and hybrid ﬁltering, highlighting the main advantages and limitations. This paper also discusses the state-of-art machine learning based recommendation models including Clustering models and Bayesian Classiﬁers. Further, we discuss the widespread application of recommender systems to a variety of areas such as e-learning and e-news. Finally, the paper evaluates the performance of matrix factorization-based models, nearest neighbours algorithms and co-clustering algorithms in terms of different metrics.
Teachers’ reading promotion activities: variation, structure and correlates

Peeking into the inner workings of BERT has shown that its layers resemble the classical NLP pipeline, with progressively more complex tasks being concentrated in later layers.To investigate to what extent these results also hold for a language other than English, we probe a Dutch BERT-based model and the multilingual BERT model for Dutch NLP tasks.In addition, through a deeper analysis of partof-speech tagging, we show that also within a given task, information is spread over different parts of the network and the pipeline might not be as neat as it seems.Each layer has different specialisations, so that it may be more useful to combine information from different layers, instead of selecting a single one based on the best overall performance.