Collaborative Writing Support Tools on the Cloud

Automatic question generation from text or paragraph is a great challenging task which attracts broad attention in natural language processing. Because of the verbose texts and fragile ranking methods, the quality of top generated questions is poor. In this paper, we present a novel framework Automatic Chinese Question Generation (ACQG) to generate questions from text or paragraph. In ACQG, we use an adopted TextRank to extract key sentences and a template-based method to construct questions from key sentences. Then a multi-feature neural network model is built for ranking to obtain the top questions. The automatic evaluation result reveals that the proposed framework outperforms the state-of-the-art systems in terms of perplexity. In human evaluation, questions generated by ACQG rate a higher score.

<span>The importance of automatic question generation (AQG) systems in education is recognized for automating tasks and providing adaptive assessments. Recent research focuses on improving quality with advanced neural networks and machine learning techniques. However, selecting the appropriate target sentences and concepts remains challenging in AQG systems. To address this problem, the authors created a novel system that combined sentence structure analysis, dependency parsing approach, and named entity recognition techniques to select the relevant target words from the given sentence. The main goal of this paper is to develop an AQG system using syntactic and semantic sentence structure analysis. Evaluation using manual and automatic metrics shows good performance on simple and short sentences, with an overall score of 3.67 out of 5.0. As the field of AQG continues to evolve rapidly, future research should focus on developing more advanced models that can generate a wider range of questions, especially for complex sentence structures.</span>

Automatic question generator (AQG) technology is a system developed to create questions automatically from input in the form of text, images, and videos. AQG has been developed using various approaches such as natural language processing (NLP), statistical approaches, and other machine approaches. AQG has a very important role in the world of education, especially in independent education, because it can be used as a good evaluation medium for students. Utilizing AQG in independent education gives students full control to determine their learning. AQG turns learning into a more interactive experience by generating questions that can trigger critical thinking and problem-solving skills. AQG technology developed in independent learning will encourage students to respond actively to the material and understand concepts more deeply. Approximately 60% of research related to AQG has been conducted for assessment, 18% for knowledge acquisition, and the remainder for validation and other purposes. This research was conducted by conducting a comprehensive review of 63 articles related to AQG in education.

In this world of ever growing technology, humans have tried to develop systems to make their work as easy as possible. Education sector is no exception to this as we have got introduced to a lot of Ed-tech systems and can access any information or course just with the help of our smartphones. Carrying out educational activities is not an easy task. There are a lot of things to manage or taken care by the professors. To reduce the work load of the professors we have developed a Web Application to generate automatic questions based on the data being provided to the system. This will not only make the jobs of the professors easy but will also allow them to process huge educational syllabus and generate any type of questions using that data.

The advent of the Internet resulted in availability of e-content in huge quantities; this e-content includes information in different domains. This information is being exploited for various reasons and applications. As the formal evaluation and assessment techniques are undergoing transformations, there is also a need for a voluminous Question Bank (QB). The manual creation of questions is a time-consuming and expensive task as it requires domain experts and there is a growing trend towards question bank based question paper generation for assessment. Hence the problem of automatic question generation has attracted attention of the Natural Language Processing (NLP) research community; this problem can be handled by NLP. Automatic generation of high-quality questions is the long-term goal of the Question Generation (QG) research community. The automatic question generation aims to generate all possible questions from the input text or document. This survey article presents the issues, challenges and approaches as well as reviews the reported research work in the context of automatic question generation for different languages at national and international level.

Abstract
 In this global age, it is of critical importance for future workforces to possess both higher intellectual and social competencies crucially sustaining their careers. In accord with this fact, second language educators engaging in worldwide second language learning circumstances have taken a critical stance to promote collaborative learning activities earlier in senior high school contexts to maximize these future generations’ potentials in terms of problem-solving, critical thinking, social, and conflict resolution skills. This was a qualitative study conducted in a narrative way to decipher the observed recurring phenomenon accurately based on people’s stories and experiences. Open-ended interviews were also utilized in this study to obtain more natural and authentic portrayals regarding the stories told by participants. 2 EFL teachers having different expertise and experiences in running collaborative learning activities in distinctive Senior High School Contexts were also involved in this study to yield richer and reliable results out of the data gathered. The study revealed that collaborative learning activities will run more efficiently when supportive learning assistances are extensively addressed and a higher degree of reverence occurred among diverse group members. 
 Keywords: EFL Teachers’ Perspectives, Collaborative Learning Activities, Senior High School Contexts

This study explores the situation of basic level community school boy and girl students’ science and health learning through collaborative culture. Fifth, sixth and seventh-grade students (both boys and girls) took part in this study. As per the aim of this study, a set of questionnaire was developed and administered to the sampled students. Two hundred and fifty-eight students were selected from the sampled school using the census sampling technique. All the students were involved in collaborative learning activities such as building trust and open communication, establishing group interaction, respect diversity and sharing creative ideas based on the basic level science and health curriculum aiming at promoting inquiry learning through collaboration. A quantitative analysis involving the use of the Chi-square test at 0.05 level of significance, Likelihood ratio and Somer’s symmetric was conducted to see the association between the variables. The results showed that science and health collaborative learning activities by gender were not associated significantly. There was no statistically significant difference (α=0.05) between the variables studied. The findings showed a weak association with gender and collaborative science and health learning activities in the classrooms.

Studies found that complex knowledge can be learned more effectively when students are engaged in a collaborative classroom environment. Therefore, conventional classroom learning should be redesigned into collaborative learning activities and study how students interact. In this study, Laurillard’s conversational framework was used to redesign collaborative university classroom environment which centered at developing a multimedia project. Student comments and interaction were collected from open-ended questions and web 2.0 tools which were used in the interaction process, before analyzed by using Cecez-Kecmanovic and Webb’s (2000) Communicative Model of Collaborative Learning (CMCL). Result showed that Laurillard’s conversational framework was effective in redesigning the classroom to capture and reflect collaborative learning activities, and that the CMCL framework was a viable instrument in analyzing students’ interaction in the collaborative learning activities. A flow diagram was developed to illustrate the pattern of conversation from Laurillard’s conversation framework and the forms of teacher-students’ interaction.

Computer programming is a complex field that requires rigorous practice in programming code writing and learning skills, which can be one of the critical challenges in learning and teaching programming. The complicated nature of computer programming requires an instructor to manage its learning resources and diligently generate programming-related questions for students that need conceptual programming and procedural programming rules. In this regard, automatic question generation techniques help teachers carefully align their learning objectives with the question designs in terms of relevancy and complexity. This also helps in reducing the costs linked with the manual generation of questions and fulfills the need of supplying new questions through automatic question techniques. This paper presents a theoretical review of automatic question generation (AQG) techniques, particularly related to computer programming languages from the year 2017 till 2022. A total of 18 papers are included in this study. one of the goals is to analyze and compare the state of the field in question generation before COVID-19 and after the COVID-19 period, and to summarize the challenges and future directions in the field. In congruence to previous studies, there is little focus given in the existing literature on generating questions related to learning programming languages through different techniques. Our findings show that there is a need to further enhance experimental studies in implementing automatic question generation especially in the field of programming. Also, there is a need to implement an authoring tool that can demonstrate designing more practical evaluation metrics for students.

Threat Actor Attribution is the task of identifying an attacker responsible for an attack. This often requires expert analysis and involves a lot of time. There had been attempts to detect a threat actor using machine learning techniques that use information obtained from the analysis of malware samples. These techniques will only be able to identify the attack, and it is trivial to guess the attacker because various attackers may adopt an attack method. A state-of-the-art method performs attribution of threat actors from text reports using Machine Learning and NLP techniques using Threat Intelligence reports. We use the same set of Threat Reports of Advanced Persistent Threats (APT). In this paper, we propose a Deep Learning architecture to attribute Threat actors based on threat reports obtained from various Threat Intelligence sources. Our work uses Neural Networks to perform the task of attribution and show that our method makes the attribution more accurate than other techniques and state-of-the-art methods.

Automatic Question Generation (AQG) is essential in educational technology, enabling the creation of assessments, practice exercises, and intelligent tutoring systems. Although recent neural approaches have achieved promising results, they often lack interpretability and control, thereby limiting their use in structured educational contexts. To overcome these challenges, this study introduces a structurally modular, rule-based AQG framework that generates factoid questions from educational texts through explicit linguistic rules and deterministic transformations. The proposed framework draws on classical rule-based question generation methods and employs a two-stage algorithmic design that distinctly separates linguistic analysis from question generation. The first stage involves linguistic preprocessing, clause extraction, and auxiliary-verb determination to identify syntactically valid, question-worthy clauses. In the second stage, rule-based transformations generate various factoid question types, such as who, what, where, when, how many, and how much, followed by disambiguation, template-based surface real- ization, and validation. This separation enhances interpretability, extensibility, and control over rule execution. Unlike data-driven models, the proposed framework does not require annotated training data and avoids hallucination by relying on explicit syntactic and semantic patterns. The resulting system produces grammatically correct, semantically meaningful, and reproducible questions, making it well suited for educational and academic applications. The study demonstrates that carefully designed rule-based systems remain a viable and effective alternative for automatic question generation in controlled domains.

With the popularization of machine learning (ML) techniques and the increased chipset’s performance, the application of ML to pedestrian localization systems has received significant attention in the last years. Several survey papers have attempted to provide a state-of-the-art overview, but they usually limit their scope to a particular type of positioning system or technology. In addition, they are written from the point of view of ML techniques and their practice, not from the point of view of the localization system and the specific problems that ML techniques can help to solve. This article is intended to offer a comprehensive state-of-the-art survey of the ML techniques that have been adopted over the last ten years to improve the performance of pedestrian localization systems, addressing the applicability of ML techniques in this domain, along with the main localization strategies. It concludes by indicating the underlying open issues and challenges associated with the existing systems, and possible future directions in which ML techniques could improve the performance of pedestrian localization systems. Among other open issues, most previous authors have focused their attention on position estimation accuracy, which wastes the potential of ML techniques to improve other performance parameters (e.g., response time, computational complexity, robustness, scalability or energy efficiency). This study shows that there is a strong trend towards the application of supervised learning. Consequently, there are many potential research opportunities in the use of other learning types, such as unsupervised and reinforcement learning, to improve the performance of pedestrian localization systems.

A comprehensive review on detection of plant disease using machine learning and deep learning approaches

PerspectivesX is a multi-perspective elaboration tool designed to encourage learner submission and curation across a range of collaborative learning activities. In this paper, it is shown that the learning design objectives of collaborative learning activities can be evaluated using NLP techniques, but that careful analysis of learner impact and pedagogical intent are required in order to select appropriate techniques. In particular, this paper focuses on the NLP techniques required to deliver an instructor dashboard, personalized learner feedback and content recommendation within multi-perspective elaboration activities. Key NLP techniques considered for inclusion include summarization, topic modeling, paraphrase detection and diversified content recommendation.

Machine Learning (ML) techniques are pervasive tools in various emerging commercial applications, but have to be accommodated by powerful computer systems to process very large data. Although general-purpose CPUs and GPUs have provided straightforward solutions, their energy-efficiencies are limited due to their excessive supports for flexibility. Hardware accelerators may achieve better energy-efficiencies, but each accelerator often accommodates only a single ML technique (family). According to the famous No-Free-Lunch theorem in the ML domain, however, an ML technique performs well on a dataset may perform poorly on another dataset, which implies that such accelerator may sometimes lead to poor learning accuracy. Even if regardless of the learning accuracy, such accelerator can still become inapplicable simply because the concrete ML task is altered, or the user chooses another ML technique.In this study, we present an ML accelerator called PuDianNao, which accommodates seven representative ML techniques, including k-means, k-nearest neighbors, naive bayes, support vector machine, linear regression, classification tree, and deep neural network. Benefited from our thorough analysis on computational primitives and locality properties of different ML techniques, PuDianNao can perform up to 1056 GOP/s (e.g., additions and multiplications) in an area of 3.51 mm^2, and consumes 596 mW only. Compared with the NVIDIA K20M GPU (28nm process), PuDianNao (65nm process) is 1.20x faster, and can reduce the energy by 128.41x.