Miracle-dz/UniFAR-FTUs huggingface dataset:
Applications of Certainty Scoring for Machine Learning Classiﬁcation in Multi-modal Contexts

Quantitative characterizations and estimations of uncertainty are of fundamental importance for machine learning classiﬁcation, particularly in safety-critical settings such as the military battleﬁeld where continuous real-time monitoring requires explainable and reliable scoring. Reliance on the maximum a posteriori principle to determine label classiﬁcation can obscure a model’s certainty of label assignment. We develop quantitative scores of certainty and competence based on predicted probability estimates as an eﬀective tool for inferring the verity of positives across diﬀerent data modalities and architectures. Our theoretical results establish that competent models have distinct distributions of certainty for true and false positives. Our empirical results bear out that there are distinct distributions of certainty scores on training and holdout data, as well as data that is a priori out-of-distribution. Further, we ﬁnd that the most reliable test for out-of-distribution data is to compare the global True positive certainty score distribution against test data. At least 92.3% of out-of-distribution are successfully identiﬁed this way across our two experimental modalities at the tranche level. Further, 100% of the out-of-context images are identiﬁed as out-of-distribution using the stochastic form of our out-of-distribution detection test across all ﬁve stochastic variants of the ResNet models. Consequently, we ﬁnd that the use of our certainty framework provides a robust means of detecting out-of-distribution inputs, while also serving as a reliable mechanism for comparing model quality of accurately distinguishing between true and False positives, particularly in safety-critical contexts.
REHABILITATIVE AUDIOLOGY The Impact of Increasing Hearing Aid High-Frequency Bandwidth

Rationale The purpose of this study was to investigate the influence of high-frequency cut-off on speech perception in quiet and noise; specifically to determine if a significant benefit is observed on speech recognition testing in quiet and noise as high -frequency information is amplified with receiver-in-the canal (RIC) devices using a commonly used fitting rationale.
HIV/AIDS and the Millennium Development Goals: A Public Sentiment Analysis of World AIDS Day Twitter Chat

The Millennium Development Goals (MDGs) outlined a comprehensive vision and framework for the United Nations development activities [1]. The MDGs served as a guide for priority-setting, stakeholder mobilization and for the allocation of resources [1]. In the fight to combat HIV/AIDS, MDG 6 sought to ensure universal access to treatment by 2010 and to halt and begin the reversal of the spread by 2015 [2].
A more-than-human approach to researching AI at work: Alternative narratives for human and AI co-workers

Professional workers practice at the intersection of public narratives about artificial intelligence (AI), the AI industry, and regulatory frameworks. Yet, there is limited understanding of the interactions between workers, AI systems, and the publics they serve. To inform networked learning scholarship, there is a pressing need to study the knowledge that workers are developing as they learn to work with AI and the implications for networked learning within the workplace and higher education. We bring social and computing science perspectives alongside more-than-human sensitivities to explore how professional expertise, judgment, accountability, and control is being re-distributed between human workers and AI systems. By sketching the changes AI is provoking we highlight the fine-grained research and analysis necessary to ensure that AI design and deployment is critically informed by in-depth understandings of how people are actually engaging with algorithmic systems. We raise questions about what trust and confidence in new AI-infused work practices is needed (or possible). Attention is drawn to the complexities of AI-mediated work, which invites re-thinking ways to generate the evidence needed to inform networked work-learning practices. Highlighted throughout is the power of AI narratives and the importance of advancing alternative, more nuanced, narratives.
Density and biomass of planktonic rotifers in different habitats in upper Paraná River (PR, Brazil).

: Density and biomass of planktonic rotifers in different habitats in upper Paraná River (PR, Brazil). The present study intends to analyze the influence of the water level variation and other abiotic and biotic factors on the densities and biomass of dominating rotifers in different environments of the upper Paraná River. Samples were collected in lakes (connected or not with the main channel), inter-bar lakes, and in the Paraná River during the flood (February) and dry periods (August) of the year 2000. Rotifer samples were collected at the surface of the pelagic region of each environment. The highest number of individuals and biomass occurred in the flood season at the isolated lakes. The PCA and correlation analyses showed that chlorophyll-a, DOC, total phosphorus and water transparency influenced the density and biomass variation of dominating rotifers. The obtained results can be explained by the unusual hydrologic cycle in the period. The prevalence of low hydrological levels would promote a partial disconnection of the studied environments. This fact could have lead to a major stability, favouring a development of the dominating plankton groups during the flood period, increasing the interactions between communities. At optimal rotifer development, chlorophyll-a (phytoplankton biomass) played a major role as a food resource. Lower densities and biomass values of the dominating rotifers during the dry season were probably caused by a combination of a decrease of available food sources and temperature, and an increase of predation pressure.
Linking northeastern North Paciﬁc oxygen changes to upstream surface outcrop variations

Understanding the response of the ocean to global warming, including the renewal of ocean waters from the surface (ventilation), is important for future climate predictions. Oxygen distributions in the ocean thermocline have proven an effective way to infer changes in ventilation because physical processes (ventilation and circulation) that supply oxygen are thought to be primarily responsible for changes in interior oxygen concentrations. Here, the focus is on the North Paciﬁc thermocline, where some of the world’s oceans’ largest oxygen variations have been observed. These variations, described as bi-decadal cycles on top of a small declining trend, are strongest on subsurface isopycnals that outcrop into the mixed layer of the northwestern North Pa-ciﬁc in late winter. In this study, surface density time series are reconstructed in this area using observational data only and focusing on the time period from 1982, the ﬁrst full year of the satellite sea surface temperature record, to 2020. It is found that changes in the annual maximum outcrop area of the densest isopycnals outcropping in the northwestern North Paciﬁc are correlated with interannual oxygen variability observed at Ocean Station P (OSP) downstream at about a 10-year lag. The hypothesis is that ocean ventilation and uptake of oxygen is greatly reduced when the outcrop areas are small and that this signal travels within the North Paciﬁc Current to OSP, with 10 years being at the higher end of transit times re-ported in other studies. It is also found that sea surface salinity (SSS) dominates over sea surface temperature (SST) in driving interannual ﬂuctuations in annual maximum surface density in the northwestern North Paciﬁc, highlighting the role that salinity may play in altering ocean ventilation. In contrast, SSS and SST contribute about equally to the long-term declining surface density trends that are superimposed on the interannual cycles.
A Scenario for the Creation of H2S Heterogeneities in Acid Gas Reservoirs in Contact with an Active Aquifer: a Simulation Study

Compositional heterogeneities of H2S have been noticed in many sour gas reservoirs. Its occurrence is an important factor of economic depreciation. Thus, the knowledge of its content and distribution is a critical parameter when planning field development. The paper aims at exploring the role of an active aquifer in the creation of H2S heterogeneities in high H2S-bearing gas reservoir. Indeed, under conditions of pressure and temperature of typical reservoirs, H2S is far more soluble than hydrocarbons and other gases. A preferential leaching of H2S (e.g. versus CH4) over time is thus possible. This mechanism is controlled by: (1) Differential solubility of gases, which change the relative amounts of each gas near the gas-water contact (GWC); (2) Contact with an active aquifer, which can export the dissolved gases thus enhancing dissolution on the long-term; (3) Diffusional transport in the gas phase, which transfers the compositional anomalies farther from the gas-water contact; (4) Geological parameters (type of aquifer, permeability heterogeneities) which can modify the transport scenario. To illustrate and quantify this process, we show the results of numerical simulations, performed with the two-phase transport and geochemical software Hytec. First, a very schematic reservoir with a composition considered uniformly distributed within the reservoir, has been simulated to quantify the leaching of H2S. The results highlight the potential role of the active aquifer, which can leach the gases and export them outside the reservoir. In a second phase, the effect of geological parameters on the H2S heterogeneity development was studied: additional simulations were performed on geometries closer to natural cases. The amount of leached H2S depends strongly on the geometry: the larger the GWC area, the larger the amounts leached.
Temporal variation of attenuation of the visible radiation in four lakes of Parque Estadual do Rio Doce (PERD), Minas Gerais, Brasil.

: Temporal variation of attenuation of the visible radiation in four lakes of Parque Estadual do Rio Doce (PERD), Minas Gerais. The present study aimed at characterizing seasonal variation of photosynthetic solar radiation (PAR) within the water column of four natural lakes of the middle Rio Doce basin (Carioca, Dom Helvécio, Gambazinho, and Jacaré lakes) during the period July 2004 to June 2005. Depths of PAR penetration within the water columns were measured in order to estimate the scalar light attenuation coefficient ( K o ) for each lake. Chemical and biological water variables were also determined (total suspended solids - TSS, dissolved organic carbon -DOC, colored dissolved organic matter -CDOM, and chlorophyll a -Chl a) in order to determine their relative contribution to light attenuation. Measurements of K o in the analyzed lakes showed marked variation with higher (0.92-1.74 m -1 ) and lower K o values (0.58-0.77 m -1 ) recorded for lakes Carioca and Gambazinho, respectively. Among the lakes the variation of K o can be explained mainly due to changes of Chl a and CDOM concentrations. When all lakes are considered together CDOM is the major component of K o and explains 76% (p < 0.001) of its variation. Patterns of visible radiation penetration were analysed with potential contributions of allochthonous material and argued the possible causes in optical water quality differences among the studied lakes
Smith ScholarWorks Smith ScholarWorks

Background Filarial nematodes cause serious and debilitating infections in human populations of tropical countries, contributing to an entrenched cycle of poverty. Only one human filarial parasite, Brugia malayi , can be maintained in rodents in the laboratory setting. It has been a widely used model organism in experiments that employ culture systems, the impact of which on the worms is unknown.
Population Estimates of Hyalella curvispina Shoemaker (Amphipoda) in Aquatic Vegetation of Northeastern Argentinian Ponds

: Population Estimates of Hyalella curvispina Shoemaker (Amphipoda) in Aquatic Vegetation of Northeastern Argentinian Ponds. Quantitative comparisons were made of the accuracy of different sampling nets (962 cm 2 , 707 cm 2 and 380 cm 2 ) and two techniques for extracting amphipods from plants. To test the spatial distribution of Hyalella curvispina, eight sites with different environmental conditions were sampled between October and December 1999. Temporal variations in abundance and biomass were quantified from January to December 1999 in one of these sites covered by Pistia stratiotes. Because smallest number of replicate samples for a given level of precision was required using a net of 962 cm 2 and Berlese funnels, three replicate samples were taken each month with this sampling size and extraction techniques. High densities populations were found in permanent ponds with electrical conductivity between 65 and 260 m S.cm -1 and dominance of Pistia stratiotes. Multiple regression analysis revealed that population density in different pond of the Northeastern Argentinean was significantly related to conductivity and this variable explained 36 % of the variability in abundance. Mean monthly abundance of H. curvispina varied between 338 and 3200 per m 2 and its biomass between 0.20 and 1.95 g.m -2 . The population peak of H. curvispina occurred during October, in which there was no increase in plant biomass.
Downstream impact of Mogi-Guaçu River damming on the benthic invertebrates (São Paulo State, Brazil).

: Downstream impact of Mogi-Guaçu River damming on the benthic invertebrates (São Paulo State, Brazil). Effects of damming the Mogi-Guaçu River (São Paulo State, Brazil) on the downstream invertebrate community were examined. Samplings were conducted at a site downstream of the dam prior to, during and after reservoir filling. In the pre and post-filling phases, taxa richness tended to increase during periods of low surface current velocity. This fact could be associated with higher organic matter availability and/or a lower remotion of organisms. Density and richness values remained in the range observed before damming whereas taxa composition changed after filling. However, the impact over taxa composition was not severe because extreme changes of similarity were not observed between pre and post-filling periods.
AI-Assisted Causal Pathway Diagram for Human-Centered Design

This paper explores the integration of causal pathway diagrams (CPD) into human-centered design (HCD), investigating how these diagrams can enhance the early stages of the design process. A dedicated CPD plugin for the online collaborative whiteboard platform Miro was developed to streamline diagram creation and offer real-time AI-driven guidance. Through a user study with designers (N=20), we found that CPD's branching and its emphasis on causal connections supported both divergent and convergent processes during design. CPD can also facilitate communication among stakeholders. Additionally, we found our plugin significantly reduces designers' cognitive workload and increases their creativity during brainstorming, highlighting the implications of AI-assisted tools in supporting creative work and evidence-based designs.
Research on Economic Dispatch Issues in Microgrids Using Trajectory-Unified Technology

The Trajectory-Unified Method is a widely used approach for solving global optimum solutions to nonlinear optimization problems, but it typically has a relatively large convergence region. To address this, a unified method for calculating multiple optimal solutions in constrained nonlinear optimization based on trust technology is proposed and applied to the economic dispatch problem of microgrids. The proposed method consists of two parts: the Trajectory-Unified Method and the Trust-Tech Method. In the first part, the Trajectory-Unified Method is used to converge to a local optimum, and in the second part, the Trust-Tech Method is used to jump out of a known local optimum and converge to nearby local optima. This paper tested the proposed optimization technique on four standard cases and practical economic dispatch problems, achieving favorable results, which validate the effectiveness and rationality of the proposed optimization algorithm.
AI-Powered Smart Energy Solutions: Combating Global Warming with Innovation

One of the most critical issues of our day is global warming, which is advancing relentlessly and has serious repercussions for the environment and economy. Individual nations are dealing with different effects of global warming at the national level, such as rising temperatures, changing weather patterns, and extreme weather occurrences. There has never been a time when it has been more important to fight climate change, and at this critical juncture, the fusion of cutting-edge technology and visionary innovation is positioned to play a crucial role in our joint efforts. The powerful potential of artificial intelligence (AI) lies at the center of this confluence. AI provides smart energy solutions that have the ability to fundamentally alter how we create, distribute, and use energy while also reducing the effects of global warming. This article provides a glimpse into the comprehensive plan of “AI-Powered Smart Energy Solutions” in combating climate change through innovative and astute energy sector applications.
Enhanced Image Quantization Through Hybrid Sine Cosine Optimization and K-Means Clustering

The need for quantization of color images arises because of limitations of image display and hardcopy, data storage, and data transmission devices. Many of the current methods for color simplification in images don't provide the best results. This can lead to noticeable color changes and the appearance of false outlines, especially when trying to represent images with a small set of colors. This paper describes a new approach to finding the optimal solutions of the color image quantization problem using a Sine Cosine Optimization (SCO) algorithm. The nature and the difficulty of the problem and its formulation are discussed. Then, the SCO algorithm is presented, and the representation of the problem with this method is explained. The effect of theparameters such as the Sine and Cosine phase updating rulesand population size on the quality of solutions is studied. Solutions obtained with the SCO algorithm are compared with those of heuristics and the K-means clustering algorithm, demonstrating the superior quality of the results achieved with the SCO algorithm.
A Fast Approach for Generating Unidentifiable Adversarial Samples

In recent years, Deep Neural Networks (DNN) have been widely used to solve many complicated tasks. However, several literatures have reported small perturbations in the target can obviously injury the performance of DNN system. This hinders the wide applications of DNN system in some safety-critical scenarios. Previous studies on adversarial samples generation usually arbitrarily and blindly to change all pixels to some extent. Unfortunately, the results can be identified by naked eyes although they can fool the DNN system. But in some cases, DNN system is just an auxiliary tool, people will also be employed to validate the identified results. This paper proposed a fast approach to generating adversarial samples with slightly changed pixels, with the purpose of deceiving people’s eyes. Firstly, the important pixels which makes more impact on output are identified by layer-by-layer weight analysis; secondly, these pixels are changed slightly by a fast gradient ascent method to check the output of DNN system; finally, the indicator of imperceptibility is used to valuation the change into an eye unrecognizable extent. Two well-known datasets are employed to validate the effectiveness of our approach.
Classification of stellar spectral data based on improved ConvNeXt-V2

The evolving landscape of astronomy necessitates the continuous development of novel methodologies to dissect astronomical data, with a particular emphasis on star spectral classification. In the initial phase of this study, we procured representative spectral data belonging to F5, G5, and K5 spectral types from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope's Data Release 9(LAMOST DR9). We then broadened our datasets to encompass five distinct stellar spectra classifications, specifically B, A, F, G, and K. This extension aligns more closely with the requirements of practical tasks within the field. The approach undertaken here commences with the application of one-dimensional convolution processing to the spectral data obtained from LAMOST, further enhancing its salience through the utilization of the short-time Fourier transform. This study primarily uses the improved ConvNeXt-V2 and conducts a comparative analysis with multiple excellent deep learning models. The findings indicate that ConvNeXt-V2 exhibits notable advantages in both experiments.
Research on Monthly Runoff Prediction based on VMD-BP model

Accurately predicting monthly runoff is crucial for studying hydrology and adjusting ecological resources. However, the complexity of monthly runoff data is nonlinear and unstable, which poses significant challenges to improving the accuracy of runoff prediction. This study attempts to use multiple interpretation algorithms and combination models for data preprocessing for comparative experimental analysis. The data preprocessing of this study used VMD algorithm and VMD CEEMDAN algorithm; the prediction model in this article uses BP model and RMF model. Firstly, this article uses the VMD algorithm to decompose monthly runoff data into multiple intrinsic mode functions (IMF), and uses sample entropy (SE) to determine the optimal number of decomposition times. Then, a fully integrated empirical mode decomposition (CEEMDAN) with adaptive noise is used to further decompose the residual components after decomposition. Finally, the BP model and RMF model are predicted by using all decomposed subsequences as inputs. The results show that the VMD algorithm is superior to the VMD-CEEMDAN algorithm, the VMD-BP model is superior to the VMD-RMF model, and the VMD-CEEMDAN-RMF model is superior to the VMD-CEEMDAN-BP model. This article will continue to conduct in-depth research on data algorithm models and prediction models in the future, as well as explore the application of AI intelligence in hydrological prediction models.
Fault Diagnosis of On-board Signaling Equipment Based on Bayesian Network

Aiming at the problems of excessive dependence on expert experience and low diagnosis accuracy rate of high-speed railway train-controlled on-board equipment, a fault diagnosis method based on expert knowledge and Bayesian network as the core is proposed. Firstly, taking the on-board equipment of the CASCO U888 CBTC signaling system as the research object, a Bayesian network based on expert knowledge was established. Secondly, according to the fault record dataset, the K2 algorithm is used to learn the structure, and the learned Bayesian network structure is appropriately parsized. The maximum likelihood estimation algorithm is used to learn the parameters to obtain the optimal Bayesian network for fault diagnosis. Finally, through diagnostic reasoning, the diagnostic accuracy rate of the fault diagnosis model is 89.6%, which verifies the feasibility and effectiveness of the Bayesian network model.