搜索结果：Current opinion in insect science

Multimodal conversational generative AI has shown impressive capabilities in various vision and language understanding through learning massive text-image data. However, current conversational models still lack knowledge about visual insects since they are often trained on the general knowledge of vision-language data. Meanwhile, understanding insects is a fundamental problem in precision agriculture, helping to promote sustainable development in agriculture. Therefore, this paper proposes a novel multimodal conversational model, Insect-LLaVA, to promote visual understanding in insect-domain knowledge. In particular, we first introduce a new large-scale Multimodal Insect Dataset with Visual Insect Instruction Data that enables the capability of learning the multimodal foundation models. Our proposed dataset enables conversational models to comprehend the visual and semantic features of the insects. Second, we propose a new Insect-LLaVA model, a new general Large Language and Vision Assistant in Visual Insect Understanding. Then, to enhance the capability of learning insect features, we develop an Insect Foundation Model by introducing a new micro-feature self-supervised learning wi

Insect-pests significantly impact global agricultural productivity and quality. Effective management involves identifying the full insect community, including beneficial insects and harmful pests, to develop and implement integrated pest management strategies. Automated identification of insects under real-world conditions presents several challenges, including differentiating similar-looking species, intra-species dissimilarity and inter-species similarity, several life cycle stages, camouflage, diverse imaging conditions, and variability in insect orientation. A deep-learning model, InsectNet, is proposed to address these challenges. InsectNet is endowed with five key features: (a) utilization of a large dataset of insect images collected through citizen science; (b) label-free self-supervised learning for large models; (c) improving prediction accuracy for species with a small sample size; (d) enhancing model trustworthiness; and (e) democratizing access through streamlined MLOps. This approach allows accurate identification (>96% accuracy) of over 2500 insect species, including pollinator (e.g., butterflies, bees), parasitoid (e.g., some wasps and flies), predator species (e

Opinions are central to almost all human activities and are key influencers of our behaviors. In current times due to growth of social networking website and increase in number of e-commerce site huge amount of opinions are now available on web. Given a set of evaluative statements that contain opinions (or sentiments) about an Entity, opinion mining aims to extract attributes and components of the object that have been commented on in each statement and to determine whether the comments are positive, negative or neutral. While lot of research recently has been done in field of opinion mining and some of it dealing with ranking of entities based on review or opinion set, classifying opinions into finer granularity level and then ranking entities has never been done before. In this paper method for opinion mining from statements at a deeper level of granularity is proposed. This is done by using fuzzy logic reasoning, after which entities are ranked as per this information.

This paper revises previous work and introduces changes in spatio-temporal scales. The paper presents a model that includes layers A and B with varying degrees of forgetting and dependence over time. We also model changes in dependence and forgetting in layers A, A', B, and B' under certain conditions. In addition, to discuss the formation of opinion clusters that have reinforcing or obstructive behaviors of forgetting and dependence and are conservative or brainwashing or detoxifying and less prone to filter bubbling, new clusters C and D that recommend, obstruct, block, or incite forgetting and dependence over time are Introduction. This introduction allows us to test hypotheses regarding the expansion of opinions in two dimensions over time and space, the state of development of opinion space, and the expansion of public opinion. Challenges in consensus building will be highlighted, emphasizing the dynamic nature of opinions and the need to consider factors such as dissent, distrust, and media influence. The paper proposes an extended framework that incorporates trust, distrust, and media influence into the consensus building model. We introduce network analysis using dimerizing

In precision agriculture, the detection and recognition of insects play an essential role in the ability of crops to grow healthy and produce a high-quality yield. The current machine vision model requires a large volume of data to achieve high performance. However, there are approximately 5.5 million different insect species in the world. None of the existing insect datasets can cover even a fraction of them due to varying geographic locations and acquisition costs. In this paper, we introduce a novel "Insect-1M" dataset, a game-changing resource poised to revolutionize insect-related foundation model training. Covering a vast spectrum of insect species, our dataset, including 1 million images with dense identification labels of taxonomy hierarchy and insect descriptions, offers a panoramic view of entomology, enabling foundation models to comprehend visual and semantic information about insects like never before. Then, to efficiently establish an Insect Foundation Model, we develop a micro-feature self-supervised learning method with a Patch-wise Relevant Attention mechanism capable of discerning the subtle differences among insect images. In addition, we introduce Description Co

Global biodiversity is declining at an unprecedented rate, yet little information is known about most species and how their populations are changing. Indeed, some 90% of Earth's species are estimated to be completely unknown. Machine learning has recently emerged as a promising tool to facilitate long-term, large-scale biodiversity monitoring, including algorithms for fine-grained classification of species from images. However, such algorithms typically are not designed to detect examples from categories unseen during training -- the problem of open-set recognition (OSR) -- limiting their applicability for highly diverse, poorly studied taxa such as insects. To address this gap, we introduce Open-Insect, a large-scale, fine-grained dataset to evaluate unknown species detection across different geographic regions with varying difficulty. We benchmark 38 OSR algorithms across three categories: post-hoc, training-time regularization, and training with auxiliary data, finding that simple post-hoc approaches remain a strong baseline. We also demonstrate how to leverage auxiliary data to improve species discovery in regions with limited data. Our results provide insights to guide the dev

Recent research has developed the Ising model from physics, especially statistical mechanics, and it plays an important role in quantum computing, especially quantum annealing and quantum Monte Carlo methods. The model has also been used in opinion dynamics as a powerful tool for simulating social interactions and opinion formation processes. Individual opinions and preferences correspond to spin states, and social pressure and communication dynamics are modeled through interactions between spins. Quantum computing makes it possible to efficiently simulate these interactions and analyze more complex social networks.Recent research has incorporated concepts from quantum information theory such as Graph State, Stabilizer State, and Surface Code (or Toric Code) into models of opinion dynamics. The incorporation of these concepts allows for a more detailed analysis of the process of opinion formation and the dynamics of social networks. The concepts lie at the intersection of graph theory and quantum theory, and the use of Graph State in opinion dynamics can represent the interdependence of opinions and networks of influence among individuals. It helps to represent the local stability

This study introduces a new numerical model to simulate how information is comprehended and processed on social networks, using continuous "Phase Field Modeling" variables (phiA, phiB, phiC) to represent individual users' opinions. It captures the immediate and two-way nature of social media interactions, reproducing the spread and feedback of information. The model incorporates psychological and social factors like confirmation bias and opinion rigidity to analyze information processing and opinion development among users. It also explores the dynamics of opinion segregation and interaction in and out of filter bubbles, offering a quantitative view of opinion dynamics on platforms like social networking services (SNS). This approach combines theoretical models with real-world social network data to study the effects of information concentration on opinion formation and the phenome Phase Field Modeling of opinion polarization and echo chamber effects on SNS. This paper is partially an attempt to utilize "Generative AI" and was written with educational intent. There are currently no plans for it to become a peer-reviewed paper.

Insects represent half of all global biodiversity, yet many of the world's insects are disappearing, with severe implications for ecosystems and agriculture. Despite this crisis, data on insect diversity and abundance remain woefully inadequate, due to the scarcity of human experts and the lack of scalable tools for monitoring. Ecologists have started to adopt camera traps to record and study insects, and have proposed computer vision algorithms as an answer for scalable data processing. However, insect monitoring in the wild poses unique challenges that have not yet been addressed within computer vision, including the combination of long-tailed data, extremely similar classes, and significant distribution shifts. We provide the first large-scale machine learning benchmarks for fine-grained insect recognition, designed to match real-world tasks faced by ecologists. Our contributions include a curated dataset of images from citizen science platforms and museums, and an expert-annotated dataset drawn from automated camera traps across multiple continents, designed to test out-of-distribution generalization under field conditions. We train and evaluate a variety of baseline algorithms

The field of opinion dynamics has evolved steadily since the earliest studies applying magnetic physics methods to better understand social opinion formation. However, in the real world, complete agreement of opinions is rare, and biaxial consensus, especially on social issues, is rare. To address this challenge, Ishii and Kawabata (2018) proposed an extended version of the Bounded Confidence Model that introduces new parameters indicating dissent and distrust, as well as the influence of mass media. Their model aimed to capture more realistic social opinion dynamics by introducing coefficients representing the degree of trust and distrust, rather than assuming convergence of opinions. In this paper, we propose a new approach to opinion dynamics based on this Trust-Distrust Model (TDM), applying the dimer allocation and Ising model. Our goal is to explore how the interaction between trust and distrust affects social opinion formation. In particular, we analyze through mathematical models how various external stimuli, such as mass media, third-party opinions, and economic and political factors, affect people's opinions. Our approach is to mathematically represent the dynamics of tru

The study of Opinion Dynamics, which explores how individual opinions and beliefs evolve and how societal consensus is formed, has been examined across social science, physics, and mathematics. Historically based on statistical physics models like the Ising model, recent research integrates quantum information theory concepts, such as Graph States, Stabilizer States, and Toric Codes. These quantum approaches offer fresh perspectives for analyzing complex relationships and interactions in opinion formation, such as modeling local interactions, using topological features for error resistance, and applying quantum mechanics for deeper insights into opinion polarization and entanglement. However, these applications face challenges in complexity, interpretation, and empirical validation. Quantum concepts are abstract and not easily translated into social science contexts, and direct observation of social opinion processes differs significantly from quantum experiments, leading to a gap between theoretical models and real-world applicability. Despite its potential, the practical use of the Toric Code Hamiltonian in Opinion Dynamics requires further exploration and research.

The field of opinion dynamics has its roots in early research that applied methods from magnetic physics to gain insights into the formation of social opinions. A central challenge in this field lies in modeling how diverse opinions coexist and exert influence on each other. In the realm of social issues, it's In this study, we leverage the dimer construct and the dimer model to establish a theoretical framework. Through numerical simulations, we demonstrate how this proposed model can be applied to real-world scenarios of social opinion formation. The model involves the computation of the Castellain matrix (K), the distribution function (Z), and the probability of dimer configuration (P(D)) for convex regions with varying positions and distances. It explores how alterations in convex regions impact the probability of dimer configuration. Furthermore, our model takes into account two critical factors: "dependence" and "forgetting" in the process of opinion formation. It also delves into the concepts of "distance" and "location" of opinions. The results of numerical simulations shed light on how our model effectively captures the processes involved in real-world social opinion forma

The proliferation of public networks has enabled instantaneous and interactive communication that transcends temporal and spatial constraints. The vast amount of textual data on the Web has facilitated the study of quantitative analysis of public opinion, which could not be visualized before. In this paper, we propose a new theory of opinion dynamics. This theory is designed to explain consensus building and opinion splitting in opinion exchanges on social media such as Twitter. With the spread of public networks, immediate and interactive communication that transcends temporal and spatial constraints has become possible, and research is underway to quantitatively analyze the distribution of public opinion, which has not been visualized until now, using vast amounts of text data. In this paper, we propose a model based on the Like Bounded Confidence Model, which represents opinions as continuous quantities. However, the Bounded Confidence mModel assumes that people with different opinions move without regard to their opinions, rather than ignoring them. Furthermore, our theory modeled the phenomenon in such a way that it can incorporate and represent the effects of external externa

Opinion formation in the population has attracted extensive research interest. Various models have been introduced and studied, including the ones with individuals' free will allowing them to change their opinions. Such models, however, have not taken into account the fact that individuals with different opinions may have different levels of loyalty, and consequently, different probabilities of changing their opinions. In this work, we study on how the non-uniform distribution of the opinion changing probability may affect the final state of opinion distribution. By simulating a few different cases with different symmetric and asymmetric non-uniform patterns of opinion changing probabilities, we demonstrate the significant effects that the different loyalty levels of different opinions have on the final state of the opinion distribution.

News website comment sections are spaces where potentially conflicting opinions and beliefs are voiced. Addressing questions of how to study such cultural and societal conflicts through technological means, the present article critically examines possibilities and limitations of machine-guided exploration and potential facilitation of on-line opinion dynamics. These investigations are guided by a discussion of an experimental observatory for mining and analyzing opinions from climate change-related user comments on news articles from the TheGuardian.com. This observatory combines causal mapping methods with computational text analysis in order to mine beliefs and visualize opinion landscapes based on expressions of causation. By (1) introducing digital methods and open infrastructures for data exploration and analysis and (2) engaging in debates about the implications of such methods and infrastructures, notably in terms of the leap from opinion observation to debate facilitation, the article aims to make a practical and theoretical contribution to the study of opinion dynamics and conflict in new media environments.

In a social network, individuals express their opinions on several interdependent topics, and therefore the evolution of their opinions on these topics is also mutually dependent. In this work, we propose a differential game model for the multi-dimensional opinion formation of a social network whose population of agents interacts according to a communication graph. Each individual's opinion evolves according to an aggregation of disagreements between the agent's opinions and its graph neighbors on multiple interdependent topics exposed to an unknown extraneous disturbance. For a social network with strategist agents the opinions evolve over time with respect to the minimization of a quadratic cost function that solely represents each individual's motives against the disturbance. We find the unique Nash/worst-case equilibrium solution for the proposed differential game model of coupled multi-dimensional opinions under an open-loop information structure. Moreover, we propose a distributed implementation of the Nash/worst-case equilibrium solution. We examine the non-distributed and proposed distributed open-loop Nash/worst-case strategies on a small social network with strategist age

Ensuring fairness is essential for every education system. Machine learning is increasingly supporting the education system and educational data science (EDS) domain, from decision support to educational activities and learning analytics. However, the machine learning-based decisions can be biased because the algorithms may generate the results based on students' protected attributes such as race or gender. Clustering is an important machine learning technique to explore student data in order to support the decision-maker, as well as support educational activities, such as group assignments. Therefore, ensuring high-quality clustering models along with satisfying fairness constraints are important requirements. This chapter comprehensively surveys clustering models and their fairness in EDS. We especially focus on investigating the fair clustering models applied in educational activities. These models are believed to be practical tools for analyzing students' data and ensuring fairness in EDS.

The empirical validation of models remains one of the most important challenges in opinion dynamics. In this contribution, we report on recent developments on combining data from survey experiments with computational models of opinion formation. We extend previous work on the empirical assessment of an argument-based model for opinion dynamics in which biased processing is the principle mechanism. While previous work (Banisch & Shamon, in press) has focused on calibrating the micro mechanism with experimental data on argument-induced opinion change, this paper concentrates on the macro level using the empirical data gathered in the survey experiment. For this purpose, the argument model is extended by an external source of balanced information which allows to control for the impact of peer influence processes relative to other noisy processes. We show that surveyed opinion distributions are matched with a high level of accuracy in a specific region in the parameter space, indicating an equal impact of social influence and external noise. More importantly, the estimated strength of biased processing given the macro data is compatible with those values that achieve high likelihoo

This study adopts a nonlinear dynamics approach, specifically using bifurcation theory, to analyze social interactions and behavior in online communities. Referencing key works by Steven Strogatz and others, the paper explores the application of pitchfork, saddle node, and transcritical bifurcations to model collective opinion shifts and trend diffusion in social media. By integrating Strogatz's insights on complex networks and synchronization with the foundational theories of Guckenheimer, Holmes, Kuznetsov, and Wiggins, the study examines the role of small-world network effects and synchronization in collective behavior. Seidel's practical take on bifurcation theory helps apply these mathematical concepts to social science research, aiming to shed light on the dynamics of digital communication and the rapid spread of information. The research also touches on the use of Melnikov's method for analyzing the stability of homoclinic and heteroclinic orbits, and the onset of chaos, reflecting on the broader implications for understanding complex systems in nature and society. The goal is to provide a model that captures the swift and intricate cognitive processes of individuals in digi