UAS Path Planning for Dynamical Wildfire Monitoring with Uneven Importance

Unmanned Aircraft Systems (UASs) offer many benefits in wildfire monitoring when compared to traditional wildfire monitoring technologies. When planning the path of an UAS for wildfire monitoring, it is important to consider the uneven propagation nature of the wildfire because different parts of the fire boundary demand different levels of monitoring attention (importance) based on the propagation speed. In addition, many of the existing works adopt a centralized approach for the path planning of the UASs. However, the use of centralized approaches is often limited in terms of applicability and adaptability. This work focuses on developing decentralized UAS path planning algorithms to autonomously monitor a spreading wildfire considering uneven importance. The algorithms allow the UASs to focus on the most active regions of a wildfire while still covering the entire fire perimeter. When monitoring a relatively smaller and spatially static fire, a single UAS might be adequate for the task. However, when monitoring a larger wildfire that is evolving dynamically in space and time, efficient and optimized use of multiple UASs is required. Based on this need, we also focus on decentralized and importance-based multi-UAS path planning for wildfire monitoring. The design, implementation, analysis, and simulation results have been discussed in details for both single-UAS and multi-UAS path planning algorithms. Experiment results show the effectiveness and robustness of the proposed algorithms for dynamic wildfire monitoring

Exploring the Relationship between LLM Hallucinations and Prompt Linguistic Nuances: Readability, Formality, and Concreteness

As Large Language Models (LLMs) have advanced, they have brought forth new challenges, with one of the prominent issues being LLM hallucination. While various mitigation techniques are emerging to address hallucination, it is equally crucial to delve into its underlying causes. Consequently, in this preliminary exploratory investigation, we examine how linguistic factors in prompts, specifically readability, formality, and concreteness, influence the occurrence of hallucinations. Our experimental results suggest that prompts characterized by greater formality and concreteness tend to result in reduced hallucination. However, the outcomes pertaining to readability are somewhat inconclusive, showing a mixed pattern

FACTIFY-5WQA: 5W Aspect-based Fact Verification through Question Answering

Automatic fact verification has received significant attention recently. Contemporary automatic fact-checking systems focus on estimating truthfulness using numerical scores which are not human-interpretable. A human fact-checker generally follows several logical steps to verify a verisimilitude claim and conclude whether its truthful or a mere masquerade. Popular fact-checking websites follow a common structure for fact categorization such as half true, half false, false, pants on fire, etc. Therefore, it is necessary to have an aspect-based (delineating which part(s) are true and which are false) explainable system that can assist human fact-checkers in asking relevant questions related to a fact, which can then be validated separately to reach a final verdict. In this paper, we propose a 5W framework (who, what, when, where, and why) for question-answer-based fact explainability. To that end, we present a semi-automatically generated dataset called FACTIFY-5WQA, which consists of 391, 041 facts along with relevant 5W QAs - underscoring our major contribution to this paper. A semantic role labeling system has been utilized to locate 5Ws, which generates QA pairs for claims using a masked language model. Finally, we report a baseline QA system to automatically locate those answers from evidence documents, which can serve as a baseline for future research in the field. Lastly, we propose a robust fact verification system that takes paraphrased claims and automatically validates them. The dataset and the baseline model are available at https: //github.com/ankuranii/acl-5W-QAComment: Accepted at ACL main conference 202

The Troubling Emergence of Hallucination in Large Language Models -- An Extensive Definition, Quantification, and Prescriptive Remediations

The recent advancements in Large Language Models (LLMs) have garnered widespread acclaim for their remarkable emerging capabilities. However, the issue of hallucination has parallelly emerged as a by-product, posing significant concerns. While some recent endeavors have been made to identify and mitigate different types of hallucination, there has been a limited emphasis on the nuanced categorization of hallucination and associated mitigation methods. To address this gap, we offer a fine-grained discourse on profiling hallucination based on its degree, orientation, and category, along with offering strategies for alleviation. As such, we define two overarching orientations of hallucination: (i) factual mirage (FM) and (ii) silver lining (SL). To provide a more comprehensive understanding, both orientations are further sub-categorized into intrinsic and extrinsic, with three degrees of severity - (i) mild, (ii) moderate, and (iii) alarming. We also meticulously categorize hallucination into six types: (i) acronym ambiguity, (ii) numeric nuisance, (iii) generated golem, (iv) virtual voice, (v) geographic erratum, and (vi) time wrap. Furthermore, we curate HallucInation eLiciTation (HILT), a publicly available dataset comprising of 75,000 samples generated using 15 contemporary LLMs along with human annotations for the aforementioned categories. Finally, to establish a method for quantifying and to offer a comparative spectrum that allows us to evaluate and rank LLMs based on their vulnerability to producing hallucinations, we propose Hallucination Vulnerability Index (HVI). We firmly believe that HVI holds significant value as a tool for the wider NLP community, with the potential to serve as a rubric in AI-related policy-making. In conclusion, we propose two solution strategies for mitigating hallucinations

Effect of varying the Mg with Ca content in highly porous phosphate-based glass microspheres

Natural ventilation is a low energy strategy used in many building types. Design approaches are mature but are dependent on variables with high uncertainty, such as the aerodynamic behaviour of purpose provided openings (PPOs), which need improved characterisation. An analytical framework is used to define different types of flow through openings based on the balance of environmental forces that drive flow, and the different flow structures they create. This allows a comprehensive literature review to be made, where different studies and descriptive equations can be compared on a like-for-like basis, and from which clear gaps in knowledge, technical standards, and design data are identified. Phenomena whose understanding could be improved by analysis of existing data are identified and explored. A Statistical Effective Area Model (SEAM) is developed from academic data to estimate the performance of butt hinged openings during the design stage, that accounts for the impact of aspect ratio and opening angle. Its predictions are compared against available empirical data and are found to have a standard error of 1.2%, which is substantially lower than the 15–25% prediction errors of free area models commonly used in practice. An analytical model is made based on entrainment theory to explain the increase in flow rate that occurs through two aligned openings. This model defines characteristic design parameters and predicts a detrimental impact on the ventilation of the wider space. Finally, an analytical model is created to explain the reduction in discharge coefficient that occurs when a large temperature difference exists across an opening. This model defines novel dimensionless parameters that characterise the flow, and predicts empirical data well, suggesting that it should be integrated into design equations

Development and Characterisation of Phosphate-Based Glass Coatings via Suspension High Velocity Oxy-Fuel (SHVOF) Thermal Spray Process

Phosphate based glasses (PBGs) are promising materials for biomedical applications due to their biocompatible and fully resorbable characteristics in aqueous environments. These glasses can be coated on to metal substrate via the technique of suspension high velocity oxy-fuel (SHVOF) thermal spraying to produce nano structured coatings with improved physical and mechanical properties. PBGs coatings were produced using SHVOF thermal spray process at 50 and 75 kW flame power. The 75 kW coating was rougher (R a = 3.6 ± 0.1 µm) than the 50 kW coating (R a = 2.7 ± 0.1 µm), whereas the 50 kW coating was much thicker (24.6 ± 2.3 µm) than the 75 kW coating (16.0 ± 3.4 µm). Due to the rougher surface, the 75 kW coating showed high degradation and ion release rates. Moreover, structural changes were observed by Raman analysis, the initial glass formulation contained Q 1 (phosphate tetrahedra with one bridging oxygen) and Q 2 (phosphate tetrahedra with two bridging oxygen) species. However, the coatings showed a reduction of Q 2 species and higher concentrations of Q 1 and Q 0 (phosphate tetrahedra with no-bridging-oxygen) species, which led to lower degradation rates and reduced ion release profiles in the glass coating compared to the initial glass

Counter Turing Test CT^2: AI-Generated Text Detection is Not as Easy as You May Think -- Introducing AI Detectability Index

With the rise of prolific ChatGPT, the risk and consequences of AI-generated text has increased alarmingly. To address the inevitable question of ownership attribution for AI-generated artifacts, the US Copyright Office released a statement stating that 'If a work's traditional elements of authorship were produced by a machine, the work lacks human authorship and the Office will not register it'. Furthermore, both the US and the EU governments have recently drafted their initial proposals regarding the regulatory framework for AI. Given this cynosural spotlight on generative AI, AI-generated text detection (AGTD) has emerged as a topic that has already received immediate attention in research, with some initial methods having been proposed, soon followed by emergence of techniques to bypass detection. This paper introduces the Counter Turing Test (CT^2), a benchmark consisting of techniques aiming to offer a comprehensive evaluation of the robustness of existing AGTD techniques. Our empirical findings unequivocally highlight the fragility of the proposed AGTD methods under scrutiny. Amidst the extensive deliberations on policy-making for regulating AI development, it is of utmost importance to assess the detectability of content generated by LLMs. Thus, to establish a quantifiable spectrum facilitating the evaluation and ranking of LLMs according to their detectability levels, we propose the AI Detectability Index (ADI). We conduct a thorough examination of 15 contemporary LLMs, empirically demonstrating that larger LLMs tend to have a higher ADI, indicating they are less detectable compared to smaller LLMs. We firmly believe that ADI holds significant value as a tool for the wider NLP community, with the potential to serve as a rubric in AI-related policy-making.Comment: EMNLP 2023 Mai

Microstructural, thermal, crystallization, and water absorption properties of films prepared from never-dried and freeze-dried cellulose nanocrystals

In this paper, the microstructural, optical, thermal, crystallization, and water absorption properties of films prepared from never-dried (ND) and freeze-dried (FD) cellulose nanocrystals (CNCs) are reported. Morphology of the ND CNCs reveals a needle-like structure, while after freeze-drying, they show a flake-like morphology. Microstructural analysis of ND and FD CNCs are further studied via small angle X-ray scattering to probe interactions. ND CNCs yield a transparent film with a low surface roughness (14 ± 4 nm), while the FD CNC film evidence a significant reduction of their transparency due to their higher surface roughness (134 ± 20 nm). Although Fourier transform infrared spectroscopy and energy-dispersive X-ray spectroscopy analyses reveal no chemical change occurs during the freeze-drying process, yet a more intense thermal degradation profile is observed for FD CNC film, probably due to the higher oxygen ingress within the gaps created between the stacked flakes. This, in turn, results in a greater loss of crystallinity at a higher temperature (300 °C) compared to the ND CNC film. A rapid decrease in water contact angle of the FD CNC film proves that the morphology of flakes and their orientation within the film has a strong influence in increasing water absorption capacity

Characterization of potential nanoporous sodium titanate film formation on Ti6Al4V and TiO2 microspherical substrates via wet-chemical alkaline conversion

The authors present novel insights into the formation of nanoporous, wet-chemically produced sodium titanate films onto microspherical substrates of varying composition. Microspheres of Ti6Al4V (atomised; ca. 20–50 μm), which were utilised due to their ubiquitous industrial usage relative to metallic titanium, were compared with TiO2 microspheres (flame spheroidised anatase and rutile powders; average ca. 30–40 μm). These were then suspended in 5 M NaOH solutions (60 °C, 24 h), and then characterized (SEM, EDS, XRD, XPS) to determine the extent of sodium titanate generation, and the potential inhibition of formation due to oxygen content. It was found that excessive oxygen content (flame-spheroidised rutile and anatase powders) resulted in inhibition of nanoporous titanate formation, apart from the top few nanometres of the surface, since a diffusion barrier of TiO2 prevents further conversion. The characteristic nanoporous titanate structures were formed on the Ti6Al4V microspheres, ca. 1 μm (999 ± 25 nm) in thickness, whereas no visible alteration to the TiO2 microspheres were seen. High surface concentration (ca. 9.5–17.6 at.%) of Na was seen in all samples via XPS, including the TiO2 microspheres (despite no morphological change), however, only the Ti6Al4V microspheres exhibited moderate Na content (ca. 4.7 at.%) via EDS, illustrating a diffusion gradient during formation. The confirmation of these structures onto microspherical substrates opens the possibility for application in biomaterials, water treatment, and energy fields