This paper proposes a novel framework, HyperGAT-BERT-RAS, that integrates (1) a HyperGraph Attention Network (HyperGAT) with BERT for enhanced semantic representation; (2) a Reference Answer Set (RAS) constructed via clustering of full-score answers;...
We propose an approach to non-deterministic finite automaton (NFA) inductive synthesis that is based on answer set programming (ASP) solvers. To that end, we explain how an NFA and its response to input samples can be encoded as rules in a logic prog...
Food recommendation systems must satisfy dietary preferences and nutritional, health, and ingredient constraints. In constrained food knowledge-base question answering, unequal positive training-answer frequency may influence ranking without necessar...
This research explores the results that an examinee would obtain if taking a multiple-choice questions test in which they have doubts as to what the true answer is among different options. This problem is analyzed by making use of combinatorics and a...
Multimodal knowledge graph completion (MMKGC) aims to predict missing entities by exploiting structural, visual, and textual information and is important for semantic retrieval, recommendation, and intelligent question answering. Existing relation-aw...
The complexity of a mathematical expression is a measure that can be used to compare the expression with other mathematical expressions and judge which one is simpler. In the paper, we analyze three effect factors for the complexity of a mathematical...
Answer Set Programming (ASP) is a well established logical approach in artificial intelligence that is widely used for knowledge representation and problem solving. Equilibrium logic extends answer set semantics to more general classes of programs an...
Short-answer questions can encourage students to express their understanding. However, these answers can vary widely, leading to subjective assessments. Automatic short answer grading (ASAG) has become an important field of research. Recent studies h...
Recent advances in deep neural networks, language modeling and language generation have introduced new ideas to the field of conversational agents. As a result, deep neural models such as sequence-to-sequence, memory networks, and the Transformer hav...
This paper focuses mainly on the problem of Chinese medical question answer matching, which is arguably more challenging than open-domain question answer matching in English due to the combination of its domain-restricted nature and the language-spec...
Machine Reading Comprehension (MRC) research concerns how to endow machines with the ability to understand given passages and answer questions, which is a challenging problem in the field of natural language processing. To solve the Chinese MRC task...
To allow the intelligent detection of correct answers in the rice-related question-and-answer (Q&A) communities of the “China Agricultural Technology Extension Information Platform”, we propose an answer selection model with dynamic a...
We address the problem of evolutionary system design (ESD) by means of answer set programming modulo difference constraints (AMT). The goal of this design approach is to synthesize new product variants or generations from existing products. We start...
Automatic short answer scoring methods have been developed with various algorithms over the decades. In the Indonesian language, the string-based similarity is more commonly used. This method is difficult to accurately measure the similarity of two s...
Knowledge base question answering (KBQA) aims to analyze the semantics of natural language questions and return accurate answers from the knowledge base (KB). More and more studies have applied knowledge bases to question answering systems, and when...
Rice has a wide planting area as one of the essential food crops in China. The problem of diseases and pests in rice production has always been one of the main factors affecting its quality and yield. It is essential to provide treatment methods and...
Question-answering systems have become an important tool for learning and knowledge acquisition. However, current answer selection models often rely on representing features using whole sentences, which leads to neglecting individual words and losing...
Large language models are increasingly used as automated graders, yet their reliability under answer-side manipulation and their behavior in multi-model panels remain insufficiently understood. This paper introduces EvalHack, a matrix benchmark in wh...
With the improved reasoning capabilities of large language models (LLMs), their applications have rapidly expanded across a wide range of tasks. In recent question answering tasks, performance gains have been achieved through Self-Consistency, where...
An evaluation of early answers must choose which questions to release and how to read their answers. Using one scoring convention for both choices can make two procedures look similar while concealing their sensitivity to either choice. We measured t...
Traditional approaches to marking short-answer questions face limitations in timeliness, scalability, inter-rater reliability, and faculty time costs. Harnessing generative artificial intelligence (AI) to address some of these shortcomings is attract...
Multiple-choice tests are efficient and objectively scored, but they provide limited insights into the reasoning underlying students’ responses. Requiring students to justify their answers may encourage more generative processing while also pro...
Machine Reading Comprehension (MRC) has become a key benchmark for evaluating natural language processing systems, and multiple-choice question answering is widely adopted for assessing reasoning, comprehension, and commonsense inference capabilities...
Question Answer System (QAS) automatically answers the question asked in natural language. Due to the varying dimensions and approaches that are available, QAS has a very diverse solution space, and a proper bibliometric study is required to paint th...
This paper relates two interesting paradigms in fuzzy logic programming from a semantical approach: core fuzzy answer set programming and multi-adjoint normal logic programming. Specifically, it is shown how core fuzzy answer set programs can be tran...
Multi-hop knowledge graph question answer (KGQA) is a challenging task because it requires reasoning over multiple edges of the knowledge graph (KG) to arrive at the right answer. However, KGs are often incomplete with many missing links, posing addi...
Evaluating the response to open questions is a complex process since it requires prior knowledge of a specific topic and language. The computational challenge is to analyze the text by learning from a set of correct examples to train a model and then...
Internet of Things (IoT) application systems are increasingly adopted in factories, shops, offices, and governments. However, building such systems using various devices and modules remains difficult for non-experts, because they must confirm specifi...
Semantic answer caching reduces inference cost by reusing previous responses but may repeatedly serve incorrect answers. Prior research primarily addresses cache matching, verification, and freshness, whereas this study investigates an error-attribut...
Large language models can solve mathematics problems accurately yet compromise tutoring by revealing answers before learners complete the reasoning. Prompt- and reward-based alignment changes the response distribution but does not screen the particul...
Automated scoring systems have been revolutionized by natural language processing, enabling the evaluation of students’ diverse answers across various academic disciplines. However, this presents a challenge as students’ responses may var...
Medical Visual Question Answering (Med-VQA) faces significant limitations in application development due to sparse and challenging data acquisition. Existing approaches focus on multi-modal learning to equip models with medical image inference and na...
Programming has gained significant importance worldwide as societies increasingly rely on computer application systems. To support novices in learning various programming languages, we have developed the Programming Learning Assistant System (PLAS)....
A warehouse delivery problem consists of a set of robots that undertake delivery jobs within a warehouse. Items are moved around the warehouse in response to events. A solution to a warehouse delivery problem is a collision-free schedule of robot mov...
Answer selection, as a crucial method for intelligent medical service robots, has become more and more important in natural language processing (NLP). However, there are still some critical issues in the answer selection model. On the one hand, the m...
Grading short answers on a large scale is a significant challenge for academic institutions and online learning platforms. This study proposes a Square Root Exponential Inverse Document Frequency (SRE-IDF) weighting scheme to address this challenge....
When it comes to the writing of a new logic program or theory, it is of great importance to obtain a concise and minimal representation, for simplicity and ease of interpretation reasons. There are already a few methods and many tools, such as Karnau...
A rising number of people use online reviews to choose if they want to use or buy a service or product. Therefore, approaches for identifying fake reviews are in high request. This paper proposes a hybrid rule-based fact-checking framework based on A...
This paper introduces Answer-Augmented Prompting (AAP), an innovative approach that leverages the Response Consistency of History of Dialogue (HoD) phenomenon in Large Language Models (LLMs). AAP not only achieves significantly superior performance e...
Scheduling and optimization have a central place in the research area of computing because it is increasingly important to achieve fully automated production processes to adjust manufacturing systems to the requirements of Industry 4.0. In this paper...
Analyzing events in dynamic environments poses a fundamental challenge in the development of intelligent agents and robots capable of interacting with humans. Current approaches predominantly rely on visual–text models; however, these methods o...
The knowledge provided by user communities in question-answering (QA) forums is a highly valuable source of information for satisfying user information needs. However, finding the best answer for a posted question can be challenging. User-generated c...
Online social spaces, where users can exchange information, opinions and resources, have achieved wide popularity and are gaining attention in many research fields, including education. Their actual potential support to learning, however, still requi...
31 December 2023
Multimodal medical question answering (MMQA) is a vital area bridging healthcare and Artificial Intelligence (AI). This survey methodically examines the MMQA research published in recent years. We collect academic literature through Google Scholar, a...
This paper proposes an automated microfluidic system for molecular diagnostics that integrates the functions of a traditional polymerase chain reaction (PCR) laboratory into a palm-sized microfluidic cartridge (CARD) made of polystyrene. The CARD int...
We explore the use case of question answering (QA) by a contact centre for 130,000 Dutch government employees in the domain of questions about human resources (HR). HR questions can be answered using personnel files or general documentation, with the...
Elementary Flux Modes (EFMs) provide a rigorous basis to systematically characterize the steady state, cellular phenotypes, as well as metabolic network robustness and fragility. However, the number of EFMs typically grows exponentially with the size...
In cybersecurity, identifying and addressing vulnerabilities in source code is essential for maintaining secure IT environments. Traditional static and dynamic analysis techniques, although widely used, often exhibit high false-positive rates, elevat...
Background: Generative artificial intelligence (AI) tools including ChatGPT have the potential to augment the process of designing examinations and assessments for medical learners, leading to time and resource savings, and the ability to produce lar...
Efficient language analysis techniques and models are crucial in the artificial intelligence age for enhancing cross-lingual question answering. Transfer learning with state-of-the-art models has been beneficial in this regard, but the performance of...
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