Reviewerly/exHarmony huggingface dataset:
Challenges of Adoption of Cloud Computing Solutions in Higher Education: Case Study Republic of Kosovo

Cloud Computing has begun to be widely used in higher education institutions in the last few years. The transition from on-premises systems to cloud solutions in education is a smart initiative with its own set of rewards and obstacles and affects various stakeholders willing to accept this change. The research tries to highlight the challenges that could affect the Cloud Computing (CC) solution in higher education institutions in the Republic of Kosovo (RKS) as a developing country, regarding security, infrastructure, and performance. Potential challenges affecting the implementation of CC in education will be assessed by different stakeholders, such as students, academic and IT staff of public and private universities, who are the target participants of this research. Also, this paper tries to highlight the level of user awareness of CC technology, the advantages of this platform, as well as the willingness of shareholders to apply it in educational institutions. Expected results foresee the identification of challenges and benefits that can be encountered in the case of CC implementation in HEIs, with the case study of the RKS. The findings of the research can help institutions to assess the challenges during the integration of a CC solution in their HEIs.
An Overview of Diffusion Models for Text Generation

Given the great success that diffusion models have achieved in generating various types of continuous data, including image, video and audio, there has been a growing interest in the application of these models to text generation. However, the discrete nature of text presents a challenge for diffusion models initially designed for application in a continuous feature space. The two main lines of work that aim to bring together diffusion models and natural language processing are focused on either defining the diffusion process in continuous space by converting discrete tokens to embeddings or defining the diffusion process in discrete space. These recent works attempt to combine diffusion models with leading sequence-to-sequence generation Transformer architecture as well as with existing pre-trained language models. In this work, we give a detailed overview of the approaches developed to date. We present and analyze the benefits and limitations that each model introduces, along with how they compare to the autoregressive models that dominate this field.
Application of Hybrid Project Management Methodology in Development of Software Systems

Selection of project management methodology in development of software systems is very challenging, especially taking into consideration that every project is unique. In this paper hybrid approach as a combination of traditional plan-driven and agile methodologies of project management, with emphasis on applying Scrum in waterfall project management, in development of web-applications is analyzed. This paper provides results of seven qualitative researches done through interviews with project managers and statistical data analysis of survey respondents (n=47). Results of qualitative research show that most projects follow a pattern in which waterfall methodology of project management is combined with agile practices -agile was used in almost all projects which were the subject of research in project phase of Development. Results of quantitative research conducted through a survey confirmed that Scrum and waterfall are the most frequently used methodologies in web application development projects and also indicated the frequency of overlapping of Development and Testing project phases. Paper provides guidelines for combining waterfall model with agile models and emphasizes the importance of planning, analyzing user requirements and defining software architecture at the beginning of the project, as well as delivering multiple minor development versions to users for testing.
Network Model of Multiagent Communication of Traffic Inspection for Supervision and Control of Passenger Transportation in Road and City Traffic

As a rule, the supervision and control of the transportation of persons in different types of traffic is the responsibility of the Traffic Inspection of the state inspectorates of individual countries. This paper investigates the intelligent supervision and control of the transportation of persons in road and city traffic managed by the Traffic Inspection in the geo-space of the Republic of Srpska (RS). The aim of the research is to create a multiagent communication (MAC) network model of an ensemble of intelligent interactive teams of human agents (HA), software agents (SA) and cyber-physical agents (CPA) located in six distributed nodes of a star network topology. All nodes of this topology represent Intelligent communication agents, and a special team of intelligent management agents for Inspection Affairs is located in the sixth node of the MAC network model. In the proposed model, the communication platform for MAC is supported by process-adaptable applications of machine learning methods, and the ability of agents to learn from experience and predict based on digital data processing. The main goal of MAC model is optimization of solutions for timely planning, reliable functioning, continuous monitoring, preventive supervision and effective management of Traffic Inspection tasks in uncertain situations contexts.
AI Chatbot For Job Interview

In this paper, we propose an interactive AI chatbot in French language which able to ask questions to a potential candidate, to detect incomplete answers and to ask additional questions in order to obtain a complete answer to a given question. Additionally, the proposed chatbot allows an interactive environment where candidates can also ask questions during the interview. We also propose a system of interview based on an exhaustive behavioral diagram. All the chatbot’s functionalities have been validated by experimentation. The results show that our chatbot is complete in terms of the questions asked and information to be collected during the interview. Moreover, experimentation has shown that the diagram covers all cases of scenarios between an interviewer and a candidate during a job interview.
Learning Semantic Representations and Discriminative Features in Unsupervised Domain Adaptation

In domain adaptation, the goal is to train a neural network on the source domain and obtain a good accuracy on the target domain. In such a scenario, it is important to transfer the knowledge from the labelled source domain to the unlabelled target domain due to the expensive cost of manual labelling. Following the trail of works in the recent time, feature level alignment seems to be the most promising direction in unsupervised domain adaptation. In most of the recent works using this feature alignment, the semantic information present in the labelled source domain has not been exploited. Among the works that have tried to learn this semantic representations, the discriminative features have not been taken into consideration which results in lower accuracy on target domain. In this paper, we present a novel approach, joint discriminative and semantic transfer network (JDSTN) that not only aligns the semantic representations of source and target domain, but also enhances the discriminative features and thereby improving the accuracy significantly. This is achieved by using pseudo-labels to align the feature centroids of source and target domains while introducing losses that promote the learning of discriminative features.
Underwater Object Detection Using Synthetic Data

The ability to detect life in challenging underwater environments holds the potential to preserve many aquatic species and coral reefs. Recent object detection research has witnessed a remarkable upsurge in natural images but not in Underwater, due to the imbalanced lighting, inadequate contrast, frequent occlusions, and the mimicry displayed by aquatic life forms. The assessment of object recognition models utilized in various contexts has augmented the need for annotated datasets. Due to the labor-intensive nature of generating these datasets, we have opted to undertake training using synthetic images as an alternative. In this study, we train the cutting-edge YOLO object detection system on a synthetic underwater dataset, with the aim of achieving category-agnostic object detection and then evaluated through practical assessments conducted on real underwater images. In addition, we provide benchmarking results for different YOLO versions in this work, assessing their performance on both real-world and synthetic datasets. Our investigation reveal that YOLOv5 shines in its ability to perform on synthetic data, whereas the latest YOLOv8, excels in real data domains, outpacing other two models tested. These findings have far reaching implications for the design and development of object detection in underwater environments.
An approach to align socio-technical dependencies in large-scale software development

Seeking the advantages delivered by agile methods in small-scale software development, large organisations are also adopting agile methods.However, scaling results in a huge growth of socio-technical dependencies that can lead to waiting time, delays, and defects and hinder the teams' ability to recognize their own responsibilities.This research proposes an approach to enable teams' autonomy and clarifes teams' responsibility assignments by aligning socio-technical dependencies.By utilising compile-time, run-time, and task dependencies, our approach identifes the wasteful dependencies between the social structures (teams) and the corresponding technical structures (architecture) and also suggests improvements.The initial results suggest that the approach correctly identifes the wasteful dependencies that are hindering teams' responsibility assignments.The suggested solution proposals are also considered useful.Awareness of such wasteful dependencies is the frst step toward being able to handle them successfully.
A Survey Based on Privacy-Preserving Over Health Care Data Analysis

Modern communications, sensors, and cloud services have recently been revolutionizing the traditional public health system. However, privacy concerns have been growing due to the convergence of advancements. Therefore, it is essential to thoroughly investigate the privacy concerns related to e-health information. To implement quality and effective health services seamlessly, hospitals in India and worldwide have shifted from paper-based human data processing to Healthcare Information Systems (HIS) and Digital Health Records (DHR). Because patient information is now nearly instantly accessible from anywhere globally, the increasing use of digital data in the healthcare sector has the potential to yield tremendous benefits. However, making DHR accessible online and linking HIS to the internet expose data to security risks and compromise patient privacy. This paper provides a survey on the privacy of healthcare data and information on healthcare systems (HIS), their automation, threats to the privacy protection of healthcare information as a result of automation, and how to identify these threats using new technologies to promote the quality of healthcare.
Rewriting Math Word Problems to Improve Learning Outcomes for Emerging Readers: A Randomized Field Trial in Carnegie Learning’s MATHia

We present a randomized field trial delivered in Carnegie Learning’s MATHia’s intelligent tutoring system to 12,374 learners intended to test whether rewriting content in “word problems” improves student mathematics performance within this content, especially among students who are emerging as English language readers. In addition to describing facets of word problems targeted for rewriting and the design of the experiment, we present an artificial intelligence-driven approach to evaluating the effectiveness of the rewrite intervention for emerging readers. Data about students’ reading ability is generally neither collected nor available to MATHia’s developers. Instead, we rely on a recently developed neural network predictive model that infers whether students will likely be in this target sub-population. We present the results of the intervention on a variety of performance metrics in MATHia and compare performance of the intervention group to the entire user base of MATHia, as well as by comparing likely emerging readers to those who are not inferred to be emerging readers. We conclude with areas for future work using more comprehensive models of learners.
Intelligent Textbooks: The Fifth International Workshop

Textbooks have evolved over the last several decades in many aspects. Most textbooks can be accessed online, many of them freely. They often come with libraries of supplementary educational resources or online educational services built on top of them. As a result of these enrichments, new research challenges and opportunities emerge that call for the application of AIED methods to enhance digital textbooks and learners' interaction with them. Therefore, we ask: How can we use intelligent and adaptive technologies to facilitate the access to digital textbooks and improve the learning process? What new insights about knowledge and learning can be extracted from textbook content and data-mined from the logs of students interacting with it? How can these insights be leveraged to develop improved intelligent texts? How can we leverage new language technology to manage and augment textbooks? The Fifth International Workshop on Intelligent Textbooks features research contributions addressing these and other research questions related to intelligent textbooks. It brings together researchers working on different aspects of learning technologies to establish intelligent textbooks as a new, interdisciplinary research field.
Automated Assessment of Comprehension Strategies from Self-explanations Using Transformers and Multi-task Learning

Self-explanation practice is an effective method to support students in better understanding complex texts. This study focuses on automatically assessing the comprehension strategies employed by readers while understanding STEM texts. Data from 3 datasets (N = 11,833) with self-explanations annotated on different comprehension strategies (i.e., bridging, elaboration, and paraphrasing) and an overall quality score was used to train various machine learning models in both single-task and multi-task setups. Our end-to-end neural architecture considers RoBERTa as an encoder applied to the target and self-explanation texts, combined with handcrafted features for assessing text cohesion and filtering out low-quality examples. The best configuration obtained a .699 weighted F1-score for the overall self-explanation quality.
A Case Study on AIED Unplugged Applied to Public Policy for Learning Recovery Post-pandemic in Brazil

This article discusses the opportunities and challenges of applying Artificial Intelligence in Education (AIED) unplugged in Brazil's post-pandemic context from a Public Policy that considers the national reality. In this context, an intelligent platform is being adopted to support learning recovery, focusing on developing writing skills at elementary school. To meet this objective, AI algorithms were developed with computer vision for transcribing hand-written texts and natural language processing techniques for correcting texts in Portuguese and giving feedback to students to improve their writing skills.
Automated Essay Scoring Incorporating Multi-level Semantic Features

Essay writing might reveal the language proficiency of a student. Utilizing intelligent technology to automatically grade essays is an effective method of saving significant manpower and time resources, and improving the accuracy of score. The present models typically rely on shallow semantic features, deep semantic features and multi-level semantic features. Existing models, however, struggle to be superior in both scoring accuracy and generalization performance. As a result, we propose a model that incorporates multi-level semantic features. Specifically, we manually define and automatically extracted the shallow semantic features; we use the BERT pre-training model, convolutional neural networks and recurrent neural networks to extract the deep semantic features; and last, feature fusion is used to score essay automatically. The proposed model outperforms three state-of-the-art baseline methods, according to experimental results on two datasets. Additionally, the generalization of the model has been greatly enhanced. The study has a significant impact on automated essay scoring theoretical investigations and practical applications.
Empowering Education with LLMs - The Next-Gen Interface and Content Generation

We propose the first annual workshop on Empowering Education with LLMs - the Next-Gen Interface and Content Generation. This full-day workshop explores ample opportunities in leveraging humans, AI, and learning analytics to generate content, particularly appealing to instructors, researchers, learning engineers, and many other roles. The process of humans and AI cocreating educational content involves many stakeholders (students, instructors, researchers, instructional designers, etc.), thus multiple viewpoints can help to inform what future generated content might be useful, new and better ways to assess the quality of such content and to spark potential collaborative efforts between attendees. Ultimately, we want to illustrate how everyone can leverage recent advancements in AI, making use of the latest machine learning methods and large language models (LLMs), and engage all participants in shaping the landscape of challenges and opportunities in this space. We wish to attract attendees interested in scaling the generation of instructional and assessment content and those interested in the use of online learning platforms.
Comparative Analysis of Learnersourced Human-Graded and AI-Generated Responses for Autograding Online Tutor Lessons

Machine learning and artificial intelligence (AI) are ubiquitous, although accessibility and application are often misunderstood and obscure. Automatic short answer grading (ASAG), leveraging natural language processing (NLP) and machine learning, has received notable attention as a method of providing instantaneous, corrective feedback to learners without the time and energy demands of human graders. However, ASAG systems are only as valid as the reference answers, or training sets, they are compared against. We introduce an AI-based, machine learning method of autograding online tutor lessons that is easily accessible and user friendly. We present two methods of training set creation using: a subset of learnersourced, human-graded tutor responses from the lessons; and a surrogate model using the recently released AI-chatbot, ChatGPT. Findings indicate human-created training sets perform considerably better than AI-generated training sets (F1 = 0.84 and 0.67, respectively). Our straightforward approach, although not accurate enough for wide use, demonstrates application of directly available machine learning based NLP methods and highlights a constructive use of ChatGPT for pedagogical purposes that is not without limitations.
Amortised Design Optimization for Item Response Theory

Item Response Theory (IRT) is a well known method for assessing responses from humans in education and psychology. In education, IRT is used to infer student abilities and characteristics of test items from student responses. Interactions with students are expensive, calling for methods that efficiently gather information for inferring student abilities. Methods based on Optimal Experimental Design (OED) are computationally costly, making them inapplicable for interactive applications. In response, we propose incorporating amortised experimental design into IRT. Here, the computational cost is shifted to a precomputing phase by training a Deep Reinforcement Learning (DRL) agent with synthetic data. The agent is trained to select optimally informative test items for the distribution of students, and to conduct amortised inference conditioned on the experiment outcomes. During deployment the agent estimates parameters from data, and suggests the next test item for the student, in close to real-time, by taking into account the history of experiments and outcomes.
User Adaptive Language Learning Chatbots with a Curriculum

Along with the development of systems for natural language understanding and generation, dialog systems have been widely adopted for language learning and practicing. Many current educational dialog systems perform chitchat, where the generated content and vocabulary are not constrained. However, for learners in a school setting, practice through dialog is more effective if it aligns with students’ curriculum and focuses on textbook vocabulary. Therefore, we adapt lexically constrained decoding to a dialog system, which urges the dialog system to include curriculum-aligned words and phrases in its generated utterances. We adopt a generative dialog system, BlenderBot3, as our backbone model and evaluate our curriculum-based dialog system with middle school students learning English as their second language. The constrained words and phrases are derived from their textbooks, suggested by their English teachers. The evaluation result demonstrates that the dialog system with curriculum infusion improves students’ understanding of target words and increases their interest in practicing English.
Towards Enabling Binary Decomposition for Partial Multi-Label Learning

Partial multi-label learning (PML) is an emerging weakly supervised learning framework, where each training example is associated with multiple candidate labels which are only partially valid. To learn the multi-label predictive model from PML training examples, most existing approaches work by identifying valid labels within candidate label set via label confidence estimation. In this paper, a novel strategy towards partial multi-label learning is proposed by enabling binary decomposition for handling PML training examples. Specifically, the widely used error-correcting output codes (ECOC) techniques are adapted to transform the PML learning problem into a number of binary learning problems, which refrains from using the error-prone procedure of estimating labeling confidence of individual candidate label. In the encoding phase, a ternary encoding scheme is utilized to balance the definiteness and adequacy of the derived binary training set. In the decoding phase, a loss weighted scheme is applied to consider the empirical performance and predictive margin of derived binary classifiers. Extensive comparative studies against state-of-the-art PML learning approaches clearly show the performance advantage of the proposed binary decomposition strategy for partial multi-label learning.