arXiv Computer Science @arxiv_cs@qoto.org

AI-based systems, including Large Language Models (LLMs), impact millions by supporting diverse tasks but face issues like misinformation, bias, and misuse. Ethical AI development is crucial as new technologies and concerns emerge, but objective, practical ethical guidance remains debated. This study examines LLMs in developing ethical AI systems, assessing how trustworthiness-enhancing techniques affect ethical AI output generation. Using the Design Science Research (DSR) method, we identify techniques for LLM trustworthiness: multi-agents, distinct roles, structured communication, and multiple rounds of debate. We design the multi-agent prototype LLM-BMAS, where agents engage in structured discussions on real-world ethical AI issues from the AI Incident Database. The prototype's performance is evaluated through thematic analysis, hierarchical clustering, ablation studies, and source code execution. Our system generates around 2,000 lines per run, compared to only 80 lines in the ablation study. Discussions reveal terms like bias detection, transparency, accountability, user consent, GDPR compliance, fairness evaluation, and EU AI Act compliance, showing LLM-BMAS's ability to generate thorough source code and documentation addressing often-overlooked ethical AI issues. However, practical challenges in source code integration and dependency management may limit smooth system adoption by practitioners. This study aims to shed light on enhancing trustworthiness in LLMs to support practitioners in developing ethical AI-based systems.

Dementia is a neurodegenerative condition that combines several diseases and impacts millions around the world and those around them. Although cognitive impairment is profoundly disabling, it is the noncognitive features of dementia, referred to as Neuropsychiatric Symptoms (NPS), that are most closely associated with a diminished quality of life. Agitation and aggression (AA) in people living with dementia (PwD) contribute to distress and increased healthcare demands. Current assessment methods rely on caregiver intervention and reporting of incidents, introducing subjectivity and bias. Artificial Intelligence (AI) and predictive algorithms offer a potential solution for detecting AA episodes in PwD when utilized in real-time. We present a 5-year study system that integrates a multimodal approach, utilizing the EmbracePlus wristband and a video detection system to predict AA in severe dementia patients. We conducted a pilot study with three participants at the Ontario Shores Mental Health Institute to validate the functionality of the system. The system collects and processes raw and digital biomarkers from the EmbracePlus wristband to accurately predict AA. The system also detected pre-agitation patterns at least six minutes before the AA event, which was not previously discovered from the EmbracePlus wristband. Furthermore, the privacy-preserving video system uses a masking tool to hide the features of the people in frames and employs a deep learning model for AA detection. The video system also helps identify the actual start and end time of the agitation events for labeling. The promising results of the preliminary data analysis underscore the ability of the system to predict AA events. The ability of the proposed system to run autonomously in real-time and identify AA and pre-agitation symptoms without external assistance represents a significant milestone in this research field.

Delivering prompt information and guidance to farmers is critical in agricultural decision-making. Farmers helpline centres are heavily reliant on the expertise and availability of call centre agents, leading to inconsistent quality and delayed responses. To this end, this article presents Kisan Query Response System (KisanQRS), a Deep Learning-based robust query-response framework for the agriculture sector. KisanQRS integrates semantic and lexical similarities of farmers queries and employs a rapid threshold-based clustering method. The clustering algorithm is based on a linear search technique to iterate through all queries and organize them into clusters according to their similarity. For query mapping, LSTM is found to be the optimal method. Our proposed answer retrieval method clusters candidate answers for a crop, ranks these answer clusters based on the number of answers in a cluster, and selects the leader of each cluster. The dataset used in our analysis consists of a subset of 34 million call logs from the Kisan Call Centre (KCC), operated under the Government of India. We evaluated the performance of the query mapping module on the data of five major states of India with 3,00,000 samples and the quantifiable outcomes demonstrate that KisanQRS significantly outperforms traditional techniques by achieving 96.58% top F1-score for a state. The answer retrieval module is evaluated on 10,000 samples and it achieves a competitive NDCG score of 96.20%. KisanQRS is useful in enabling farmers to make informed decisions about their farming practices by providing quick and pertinent responses to their queries.

As Large Language Models (LLMs) become more prevalent, concerns about their safety, ethics, and potential biases have risen. Systematically evaluating LLMs' risk decision-making tendencies and attitudes, particularly in the ethical domain, has become crucial. This study innovatively applies the Domain-Specific Risk-Taking (DOSPERT) scale from cognitive science to LLMs and proposes a novel Ethical Decision-Making Risk Attitude Scale (EDRAS) to assess LLMs' ethical risk attitudes in depth. We further propose a novel approach integrating risk scales and role-playing to quantitatively evaluate systematic biases in LLMs. Through systematic evaluation and analysis of multiple mainstream LLMs, we assessed the "risk personalities" of LLMs across multiple domains, with a particular focus on the ethical domain, and revealed and quantified LLMs' systematic biases towards different groups. This research helps understand LLMs' risk decision-making and ensure their safe and reliable application. Our approach provides a tool for identifying and mitigating biases, contributing to fairer and more trustworthy AI systems. The code and data are available.

Inaccuracies in polygraph tests often lead to wrongful convictions, false information, and bias, all of which have significant consequences for both legal and political systems. Recently, analyzing facial micro-expressions has emerged as a method for detecting deception; however, current models have not reached high accuracy and generalizability. The purpose of this study is to aid in remedying these problems. The unique multimodal transformer architecture used in this study improves upon previous approaches by using auditory inputs, visual facial micro-expressions, and manually transcribed gesture annotations, moving closer to a reliable non-invasive lie detection model. Visual and auditory features were extracted using the Vision Transformer and OpenSmile models respectively, which were then concatenated with the transcriptions of participants micro-expressions and gestures. Various models were trained for the classification of lies and truths using these processed and concatenated features. The CNN Conv1D multimodal model achieved an average accuracy of 95.4%. However, further research is still required to create higher-quality datasets and even more generalized models for more diverse applications.

The growing integration of digitized infrastructure with Internet of Things (IoT) networks has transformed the management and optimization of building energy consumption. By leveraging IoT-based monitoring systems, stakeholders such as building managers, energy suppliers, and policymakers can make data-driven decisions to improve energy efficiency. However, accurate energy forecasting and analytics face persistent challenges, primarily due to the inherent physical constraints of buildings and the diverse, heterogeneous nature of IoT-generated data. In this study, we conduct a comprehensive benchmarking of two publicly available IoT datasets, evaluating the performance of time series foundation models in the context of building energy analytics. Our analysis shows that single-modal models demonstrate significant promise in overcoming the complexities of data variability and physical limitations in buildings, with future work focusing on optimizing multi-modal models for sustainable energy management.

In disaster scenarios, effective communication is crucial, yet language barriers often hinder timely and accurate information dissemination, exacerbating vulnerabilities and complicating response efforts. This paper presents a novel, multilingual, voice-based social network specifically designed to address these challenges. The proposed system integrates advanced artificial intelligence (AI) with blockchain technology to enable secure, asynchronous voice communication across multiple languages. The application operates independently of external servers, ensuring reliability even in compromised environments by functioning offline through local networks. Key features include AI-driven real-time translation of voice messages, ensuring seamless cross-linguistic communication, and blockchain-enabled storage for secure, immutable records of all interactions, safeguarding message integrity. Designed for cross-platform use, the system offers consistent performance across devices, from mobile phones to desktops, making it highly adaptable in diverse disaster situations. Evaluation metrics demonstrate high accuracy in speech recognition and translation, low latency, and user satisfaction, validating the system's effectiveness in enhancing communication during crises. This solution represents a significant advancement in disaster communication, bridging language gaps to support more inclusive and efficient emergency response.

Autonomous weapons systems (AWS) change the way humans make decisions, the effect of those decisions and who is accountable for decisions made. We must remain vigilant, informed and human-centred as we tackle our deliberations on developing norms regarding their development, use and justification. Ways to enhance compliance in international humanitarian law (IHL) include: Training weapons decision makers in IHL; developing best practice in weapons reviews including requirements for industry to ensure that any new weapon, means or method of warfare is capable of being used lawfully; develop human-centred test and evaluation methods; invest in digital infrastructure to increase knowledge of the civilian environment in a conflict and its dynamics; invest in research on the real effects and consequences of civilian harms to the achievement of military and political objectives; improve secure communications between stakeholders in a conflict; and finally to upskill governments and NGOs in what is technically achievable with emerging technologies so that they can contribute to system requirements, test and evaluation protocols and operational rules of use and engagement. Governments are responsible for setting requirements for weapons systems. They are responsible for driving ethicality as well as lethality. Governments can require systems to be made and used to better protect civilians and protected objects. The UN can advocate for compliance with IHL, human rights, human-centred use of weapons systems and improved mechanisms to monitor and trace military decision making including those decisions affected by autonomous functionality.

Recently, large language models (LLMs) have been increasingly used to support various decision-making tasks, assisting humans in making informed decisions. However, when LLMs confidently provide incorrect information, it can lead humans to make suboptimal decisions. To prevent LLMs from generating incorrect information on topics they are unsure of and to improve the accuracy of generated content, prior works have proposed Retrieval Augmented Generation (RAG), where external documents are referenced to generate responses. However, traditional RAG methods focus only on retrieving documents most relevant to the input query, without specifically aiming to ensure that the human user's decisions are well-calibrated. To address this limitation, we propose a novel retrieval method called Calibrated Retrieval-Augmented Generation (CalibRAG), which ensures that decisions informed by the retrieved documents are well-calibrated. Then we empirically validate that CalibRAG improves calibration performance as well as accuracy, compared to other baselines across various datasets.

Education is being transformed by rapid advances in Artificial Intelligence (AI), including emerging Generative Artificial Intelligence (GAI). Such technology can significantly support academics and students by automating monotonous tasks and making personalised suggestions. However, despite the potential of the technology, there are significant concerns regarding AI misuse, particularly by students in assessments. There are two schools of thought: one advocates for a complete ban on it, while the other views it as a valuable educational tool, provided it is governed by a robust usage policy. This contradiction clearly indicates a major policy gap in academic practices, and new policies are required to uphold academic standards while enabling staff and students to benefit from technological advancements. We surveyed 117 academics from three countries (UK, UAE, and Iraq), and identified that most academics retain positive opinions regarding AI in education. For example, the majority of experienced academics do not favour complete bans, and they see the potential benefits of AI for students, teaching staff, and academic institutions. Importantly, academics specifically identified the particular benefits of AI for autonomous assessment (71.79% of respondents agreed). Therefore, for the first time, we propose a novel AI framework for autonomously evaluating students' work (e.g., reports, coursework, etc.) and automatically assigning grades based on their knowledge and in-depth understanding of the submitted content. The survey results further highlight a significant lack of awareness of modern AI-based tools (e.g., ChatGPT) among experienced academics, a gap that must be addressed to uphold educational standards.

In AI planning, it is common to distinguish between planning domains and problem instances, where a "domain" is generally understood as a set of related problem instances. This distinction is important, for example, in generalised planning, which aims to find a single, general plan or policy that solves all instances of a given domain. In PDDL, domains and problem instances are clearly separated: the domain defines the types, predicate symbols, and action schemata, while the problem instance specifies the concrete set of (typed) objects, the initial state, and the goal condition. In this paper, we show that it is quite easy to define a PDDL domain such that any propositional planning problem instance, from any domain, becomes an instance of this (lifted) "universal" domain. We construct different formulations of the universal domain, and discuss their implications for the complexity of lifted domain-dependent or generalised planning.

Curating knowledge from multiple siloed sources that contain both structured and unstructured data is a major challenge in many real-world applications. Pattern matching and querying represent fundamental tasks in modern data analytics that leverage this curated knowledge. The development of such applications necessitates overcoming several research challenges, including data extraction, named entity recognition, data modeling, and designing query interfaces. Moreover, the explainability of these functionalities is critical for their broader adoption. The emergence of Large Language Models (LLMs) has accelerated the development lifecycle of new capabilities. Nonetheless, there is an ongoing need for domain-specific tools tailored to user activities. The creation of digital assistants has gained considerable traction in recent years, with LLMs offering a promising avenue to develop such assistants utilizing domain-specific knowledge and assumptions. In this context, we introduce an advanced query and reasoning system, GraphAide, which constructs a knowledge graph (KG) from diverse sources and allows to query and reason over the resulting KG. GraphAide harnesses both the KG and LLMs to rapidly develop domain-specific digital assistants. It integrates design patterns from retrieval augmented generation (RAG) and the semantic web to create an agentic LLM application. GraphAide underscores the potential for streamlined and efficient development of specialized digital assistants, thereby enhancing their applicability across various domains.

People with learning disabilities have a higher mortality rate and premature deaths compared to the general public, as reported in published research in the UK and other countries. This study analyses hospitalisations of 9,618 patients identified with learning disabilities and long-term conditions for the population of Wales using electronic health record (EHR) data sources from the SAIL Databank. We describe the demographic characteristics, prevalence of long-term conditions, medication history, hospital visits, and lifestyle history for our study cohort, and apply machine learning models to predict the length of hospital stays for this cohort. The random forest (RF) model achieved an Area Under the Curve (AUC) of 0.759 (males) and 0.756 (females), a false negative rate of 0.224 (males) and 0.229 (females), and a balanced accuracy of 0.690 (males) and 0.689 (females). After examining model performance across ethnic groups, two bias mitigation algorithms (threshold optimization and the reductions algorithm using an exponentiated gradient) were applied to minimise performance discrepancies. The threshold optimizer algorithm outperformed the reductions algorithm, achieving lower ranges in false positive rate and balanced accuracy for the male cohort across the ethnic groups. This study demonstrates the potential of applying machine learning models with effective bias mitigation approaches on EHR data sources to enable equitable prediction of hospital stays by addressing data imbalances across groups.

Hunt for predatory journals is based on binary division of the publishing world to legitime and predatory journals. This leads to difficulties in labeling publishers like MDPI with questionable practices. However, the root cause is not the publisher itself but the problems in the peer review system. These problems have created markets for the for-profit publishers shaking the understanding of conservative researchers and their belief about what is good science. In this paper, we analyze the problems via examples based on our own experience as author, reviewer, guest editor, associate editor, and editor-in-chief. We discuss how MDPI has addressed these problems.

The Generalist Repository Ecosystem Initiative (GREI), funded by the NIH, developed an AI taxonomy tailored to data repository roles to guide AI integration across repository management. It categorizes the roles into stages, including acquisition, validation, organization, enhancement, analysis, sharing, and user support, providing a structured framework for implementing AI in repository workflows.

This paper presents a monitoring framework that infers the level of autonomous vehicle (AV) collision risk based on its object detector's performance using only monocular camera images. Essentially, the framework takes two sets of predictions produced by different algorithms and associates their inconsistencies with the collision risk via fuzzy inference. The first set of predictions is obtained through retrieving safety-critical 2.5D objects from a depth map, and the second set comes from the AV's 3D object detector. We experimentally validate that, based on Intersection-over-Union (IoU) and a depth discrepancy measure, the inconsistencies between the two sets of predictions strongly correlate to the safety-related error of the 3D object detector against ground truths. This correlation allows us to construct a fuzzy inference system and map the inconsistency measures to an existing collision risk indicator. In particular, we apply various knowledge- and data-driven techniques and find using particle swarm optimization that learns general fuzzy rules gives the best mapping result. Lastly, we validate our monitor's capability to produce relevant risk estimates with the large-scale nuScenes dataset and show it can safeguard an AV in closed-loop simulations.

Artificial Intelligence (AI) has emerged in popularity recently, recording great progress in various industries. However, the environmental impact of AI is a growing concern, in terms of the energy consumption and carbon footprint of Machine Learning (ML) and Deep Learning (DL) models, making essential investigate Green AI, an attempt to reduce the climate impact of AI systems. This paper presents an assessment of different programming languages and Feature Selection (FS) methods to improve computation performance of AI focusing on Network Intrusion Detection (NID) and cyber-attack classification tasks. Experiments were conducted using five ML models - Random Forest, XGBoost, LightGBM, Multi-Layer Perceptron, and Long Short-Term Memory - implemented in four programming languages - Python, Java, R, and Rust - along with three FS methods - Information Gain, Recursive Feature Elimination, and Chi-Square. The obtained results demonstrated that FS plays an important role enhancing the computational efficiency of AI models without compromising detection accuracy, highlighting languages like Python and R, that benefit from a rich AI libraries environment. These conclusions can be useful to design efficient and sustainable AI systems that still provide a good generalization and a reliable detection.

Quadrupedal locomotion is a complex, open-ended problem vital to expanding autonomous vehicle reach. Traditional reinforcement learning approaches often fall short due to training instability and sample inefficiency. We propose a novel method leveraging multi-objective evolutionary algorithms as an automatic curriculum learning mechanism, which we named Multi-Objective Learning (MOL). Our approach significantly enhances the learning process by projecting velocity commands into an objective space and optimizing for both performance and diversity. Tested within the MuJoCo physics simulator, our method demonstrates superior stability and adaptability compared to baseline approaches. As such, it achieved 19\% and 44\% fewer errors against our best baseline algorithm in difficult scenarios based on a uniform and tailored evaluation respectively. This work introduces a robust framework for training quadrupedal robots, promising significant advancements in robotic locomotion and open-ended robotic problems.

Recently, general fractional calculus was introduced by Kochubei (2011) and Luchko (2021) as a further generalisation of fractional calculus, where the derivative and integral operator admits arbitrary kernel. Such a formalism will have many applications in physics and engineering, since the kernel is no longer restricted. We first extend the work of Al-Refai and Luchko (2023) on finite interval to arbitrary orders. Followed by, developing an efficient Petrov-Galerkin scheme by introducing Jacobi convolution polynomials as basis functions. A notable property of this basis function, the general fractional derivative of Jacobi convolution polynomial is a shifted Jacobi polynomial. Thus, with a suitable test function it results in diagonal stiffness matrix, hence, the efficiency in implementation. Furthermore, our method is constructed for any arbitrary kernel including that of fractional operator, since, its a special case of general fractional operator.

We introduce a yat-product-powered neural network, the Neural Matter Network (NMN), a breakthrough in deep learning that achieves non-linear pattern recognition without activation functions. Our key innovation relies on the yat-product and yat-product, which naturally induces non-linearity by projecting inputs into a pseudo-metric space, eliminating the need for traditional activation functions while maintaining only a softmax layer for final class probability distribution. This approach simplifies network architecture and provides unprecedented transparency into the network's decision-making process. Our comprehensive empirical evaluation across different datasets demonstrates that NMN consistently outperforms traditional MLPs. The results challenge the assumption that separate activation functions are necessary for effective deep-learning models. The implications of this work extend beyond immediate architectural benefits, by eliminating intermediate activation functions while preserving non-linear capabilities, yat-MLP establishes a new paradigm for neural network design that combines simplicity with effectiveness. Most importantly, our approach provides unprecedented insights into the traditionally opaque "black-box" nature of neural networks, offering a clearer understanding of how these models process and classify information.