Uncertainty In Behavior Research Articles

Audiences, automation, and AI: From structured news to language models

AbstractThe appearance of large language models (LLMs) and other forms of generative AI portend a new era of disruption and innovation for the news industry, this time focused on the production and consumption of news rather than on its distribution. Large news organizations, however, may be surprisingly well‐prepared for at least some of this disruption because of earlier innovation work on automating workflows for personalized content and formats using structured techniques. This article reviews this work and uses examples from the British Broadcasting Corporation (BBC) and other large news providers to show how LLMs have recently been successfully applied to addressing significant barriers to the deployment of structured approaches in production, and how innovation using structured techniques has more generally framed significant editorial and product challenges that might now be more readily addressed using generative AI. Using the BBC's next‐generation authoring and publishing stack as an example, the article also discusses how earlier innovation work has influenced the design of flexible infrastructure that can accommodate uncertainty in audience behavior and editorial workflows – capabilities that are likely to be well suited to the fast‐approaching AI‐mediated news ecosystem.

SocialCVAE: Predicting Pedestrian Trajectory via Interaction Conditioned Latents

Pedestrian trajectory prediction is the key technology in many applications for providing insights into human behavior and anticipating human future motions. Most existing empirical models are explicitly formulated by observed human behaviors using explicable mathematical terms with deterministic nature, while recent work has focused on developing hybrid models combined with learning-based techniques for powerful expressiveness while maintaining explainability. However, the deterministic nature of the learned steering behaviors from the empirical models limits the models' practical performance. To address this issue, this work proposes the social conditional variational autoencoder (SocialCVAE) for predicting pedestrian trajectories, which employs a CVAE to explore behavioral uncertainty in human motion decisions. SocialCVAE learns socially reasonable motion randomness by utilizing a socially explainable interaction energy map as the CVAE's condition, which illustrates the future occupancy of each pedestrian's local neighborhood area. The energy map is generated using an energy-based interaction model, which anticipates the energy cost (i.e., repulsion intensity) of pedestrians' interactions with neighbors. Experimental results on two public benchmarks including 25 scenes demonstrate that SocialCVAE significantly improves prediction accuracy compared with the state-of-the-art methods, with up to 16.85% improvement in Average Displacement Error (ADE) and 69.18% improvement in Final Displacement Error (FDE). Code is available at: https://github.com/ViviXiang/SocialCVAE.

Membangun Resiliensi UMKM Melalui Peran Digitalisasi dan Praktek Sumber Daya Manusia

<p><em>Changes in the current digital era occur very quickly and lead to conditions that continue to change dynamically, so overcoming the increasing uncertainty in the organization becomes important and urgent for an organization and the organization's ability to develop resilience is also very necessary. MSMEs face various challenges in the digital era, such as changes in consumer behavior, global competition, and economic uncertainty. These conditions have prompted several previous studies that focus on organizational resilience, which is interesting because all organizations need to survive and recover after facing difficulties and challenges. The resilience of MSMEs is the main determinant to overcome this uncertainty, by understanding how digitalization and HR can complement each other to create excellence. This study aims to determine how digitalization and human resources affect resilience (Study on MSME at Car Free Day Jalan Ibrahim Adjie Garut Regency) The research method used is quantitative with a descriptive approach. Primary data was obtained by distributing questionnaires to 80 respondents using the random sampling method. The data analysis technique used in this study used PLS-SEM. The results of the analysis show that the role of digitalization does not have a significant effect on resilience, but on the contrary, the practice of human resources proves the results that have a significant effect on resilience.</em></p>

Seismic analyses of single-layer dome structures with random geometrical imperfections under stochastic ground motions

In this paper, seismic analyses of single-layer dome structures are carried out, with a particular emphasis on the incorporation of randomness in both geometrical imperfections and ground motions. First, the paper introduces models for random geometrical imperfections and stochastic ground motions, employing the Stochastic Imperfection Mode Superposition Method (SIMSM) for imperfections and utilizing the Spectral Representation Method (SRM) for ground motions. These models are designed to comprehensively account for the inherent uncertainties in real-world structural behavior. Subsequently, the Probability Density Evolution Method (PDEM) is applied to investigate the probabilistic response and seismic reliability of dome structures, which allows for a detailed examination of key performance indicators, such as displacement, plastic ratio, and strength damage, under the influence of random factors. Both deterministic and stochastic analyses are conducted to gain insights into the impact of randomness in geometrical imperfections and ground motions. The computational results unveil a crucial finding: an unfavorable distribution of imperfections can significantly compromise the dynamic performance and safety of single-layer dome structures. Furthermore, the paper delves into parametric analyses, with a specific focus on imperfection amplitude, the rise-span ratio and roof load as pivotal parameters. These analyses provide valuable insights into the sensitivity of the structural response to variations in these critical design factors.

VpROM: a novel variational autoencoder-boosted reduced order model for the treatment of parametric dependencies in nonlinear systems

Reduced Order Models (ROMs) are of considerable importance in many areas of engineering in which computational time presents difficulties. Established approaches employ projection-based reduction, such as Proper Orthogonal Decomposition. The limitation of the linear nature of such operators is typically tackled via a library of local reduction subspaces, which requires the assembly of numerous local ROMs to address parametric dependencies. Our work attempts to define a more generalisable mapping between parametric inputs and reduced bases for the purpose of generative modeling. We propose the use of Variational Autoencoders (VAEs) in place of the typically utilised clustering or interpolation operations, for inferring the fundamental vectors, termed as modes, which approximate the manifold of the model response for any and each parametric input state. The derived ROM still relies on projection bases, built on the basis of full-order model simulations, thus retaining the imprinted physical connotation. However, it additionally exploits a matrix of coefficients that relates each local sample response and dynamics to the global phenomena across the parametric input domain. The VAE scheme is utilised for approximating these coefficients for any input state. This coupling leads to a high-precision low-order representation, which is particularly suited for problems where model dependencies or excitation traits cause the dynamic behavior to span multiple response regimes. Moreover, the probabilistic treatment of the VAE representation allows for uncertainty quantification on the reduction bases, which may then be propagated to the ROM response. The performance of the proposed approach is validated on an open-source simulation benchmark featuring hysteresis and multi-parametric dependencies, and on a large-scale wind turbine tower characterised by nonlinear material behavior and model uncertainty.

Approach of establishing a high-resolution shading occupant behavior model in the office building

Occupant behavior in buildings is becoming a critical factor in determining indoor comfort and energy consumption performance. The diversity and uncertainty of occupant behavior affect the implementation of occupant-centric building operations. However, existing shading behavior models do not account for various shading states (fully open, fully closed, or partially open, significantly deviating from real-world scenarios), accessibility of shading behavior (ASB), room configurations (single-person rooms and multi-person rooms), occupancy, and other factors, resulting in lower model accuracy. This study aims to address these limitations by proposing a high-resolution shading occupant behavior (HRSOB) model that integrates occupancy considerations, accessibility of shading behavior, and data mining techniques. This HRSOB model can classify various shading occupant behaviors and be integrated into the optimization controls of occupant-centric buildings. The utilization of Markov chain occupancy models significantly improves the accuracy of predicting stochastic shading behavior. The ASB classification method analyzes the features of the occupant shading adjustment behavior (hereafter referred to as shading behavior) and objectively reflects shading behavior patterns using spatial features, thereby enhancing the resolution of the shading occupant behavior model. Data-driven techniques, such as feature selection and soft voting, enable the analysis, training, and application of shading behavior data, and rigorous evaluation and validation of the HRSOB model are conducted. The results demonstrated a 10.92% improvement in the predictive performance of the HRSOB model compared with the typical shading occupant behavior (SOB) model.

Electric Vehicle Aggregation in a PV-Battery Charging Station: A Contract-Based Approach

As the proportion of high-power fast charging piles increases, how to more stably aggregate electric vehicles to avoid market risks brought about by user charging behavior is an urgent problem for charging station operators. This is especially so for fast charging station operators. To address this issue, a retail package design method for PV-battery charging stations based on contract theory and Lyapunov optimization is proposed in this paper. The operator can formulate optimal charging and discharging packages under asymmetric information in the day-ahead and real-time stages. Packages with fixed power and prices will be offered to users to reduce the impact of user behavior uncertainty. In addition, the queuing problems of pile allocation and PV-battery energy management are resolved by the Lyapunov drift optimization method. The performance indicators, such as the average power and demand processing capability of in-station equipment, will be optimized while ensuring the stability of the queuing network. The simulation verifies that the proposed method can improve the operator's net income and make the social welfare approach the theoretical value under complete information. Moreover, it shows stronger stability in terms of user aggregation.

Diarec: Dynamic Intention-Aware Recommendation with Attention-Based Context-Aware Item Attributes Modeling

Abstract Recommender systems (RSs) often focus on learning users’ long-term preferences, while the sequential pattern of behavior is ignored. On the other hand, sequential RSs try to predict the next action by exploring relations between items in a user’s last interactions but do not consider the general preference. Recently, the performance of RSs has increased by unifying these two types of paradigms. However, existing methods still have two limitations. First, the user’s behavior uncertainty impedes precise learning of preferences. Second, being unable to understand the semantics of items makes the effect of the same item considered in the same way. These limitations jointly prevent RS from learning multifaceted preferences to capture the actual intentions of users. Existing methods have not properly addressed these problems since they ignore context-aware interactions between the user and item in terms of the links between the user and item attributes and sequential user actions over time. To address these challenges, this paper proposes a novel model, called the Dynamic Intention-Aware Recommendation with attention-based context-aware item attributes modeling (DIARec), which is capable of determining users’ preferences based on their goal intention, taking into account the influence of various item features on user decision-making in their current context. Specifically, to model users’ dynamic intentions, we introduce a dynamic intent-aware module to represent the hierarchical relations between items and their attributes in a given session. Experiments on benchmark datasets indicate that the proposed model DIARec outperforms other state-of-the-art methods.

Exploring the impacts of heterogeneity and stochasticity in air-conditioning behavior on urban building energy models

Heterogeneity and stochasticity, two main aspects of uncertainty in occupant behavior (OB), at the urban scale are not featured in most current urban building energy modeling (UBEM) platforms, and their respective impacts on urban-scale building energy consumption remain unclear. We aimed to introduce OB uncertainty into the UBEM workflow and assess the differences in the impacts of heterogeneity and stochasticity on cooling demand. Different OB models were integrated into the cooling demand simulation of residential building stocks, considering the heterogeneity and stochasticity in both occupancy and energy-use behavior. The impacts of heterogeneity and stochasticity on urban-scale cooling demand and its applications for different purposes in UBEM are discussed. We found that stochasticity in occupancy decreased the peak cooling demand by 54%, whereas the uncertainty in air-conditioning (AC) behavior had little effect. Heterogeneity is the main reason for the diversity in cooling demand, whereas stochasticity better reflects the dynamics of cooling demand., which Occupancy and AC behavior models with higher fidelity are required to obtain results with higher spatial or temporal resolution should be selected according to the UBEM applications. The proposed approach will contribute to the development of appropriate urban-scale occupant behavior models and bridge urban mobility with occupant-centric UBEMs.

Motivating factors for blockchain technology adoption: a theoretical analysis from the perspective of supply chain collaboration

Purpose This study aims to identify the driving factors that influence blockchain technology adoption in the context of a supply chain (SC), considering three dimensions: technology, transactions and collaboration. Design/methodology/approach An integrative systematic literature review of previous studies was conducted. Using three main dimensions: technology, transactions and SC collaboration, supported by the unified theory of acceptance and use of technology, transaction cost economics (TCE) and concepts of SC collaboration, the authors categorized factors that contributed to blockchain technology in SC in the extant literature and proposed a theoretical model that covers these three dimensions. Findings The findings reveal that the information sharing category – related to the SC collaboration dimension – is the category with the greatest number of motivating factors for blockchain adoption in the SC context, followed by performance expectancy and behavioral uncertainty. Research limitations/implications The review considers papers published until 2021 obtained from a specific database. Originality/value This study focuses on filling the research gap concerning technology adoption as it considers the interconnection formed by two organizations, interorganizational transactions and SC collaboration, using complementary theories to explain the phenomenon.

Privacy-Preserving Multi-Party Cross-Chain Transaction Protocols

Cross-chain transaction technologies have greatly promoted the scalability of cryptocurrencies, which then facilitates the development of Metaverse applications. However, existing solutions rely heavily on centralized middleware (notary) or smart contracts. These schemes lack privacy considerations, and users’ cross-chain transactions are easy to master by other parties. Some signature-based payment schemes have good privacy but do not support multi-party cross-chain protocols or rely heavily on some time assumptions. The uncertainty of user behavior makes it difficult to design a secure multi-party cross-chain protocol. To solve these problems, we investigate how to design a secure multi-party cross-chain transaction protocol with offline tolerance. We propose a new signature algorithm called the pre-adaptor signature scheme, an extension of the adaptor signature scheme. The pre-adaptor signature scheme combines the multi-signature and adaptor signature schemes, which can realize the secret transmission channel between multiple parties. To provide offline tolerance, we encode our protocol into the P2SH script. Our protocol provides better privacy due to no dependence on smart contracts. The performance evaluation was conducted with ten participants. For each participant of our cross-chain protocol, the initialization and execution process can be performed in 3 milliseconds and with 6 k bytes of communication overhead at most. The cost increases linearly with the increase in the number of participants.

STOCK MARKET PRICE PREDICTION USING MACHINE LEARNING TECHNIQUES

Predicting stock market prices is a challenging task in the financial sector, where the Efficient Market Hypothesis (EMH) posits the impossibility of accurate prediction due to the inherent uncertainty and complexity of stock price behaviour. However, introducing Machine Learning algorithms has shown the feasibility of stock market price forecasting. This study employs advanced Machine Learning models that can predict stock price movements with the right level of accuracy if the correct parameter tuning and appropriate predictor models are developed. In this research work, the LSTM model, which is a type of Recurrent Neural Network (RNN), time series forecasting Facebook Prophet algorithm and Random Forest Regressor model have been implemented on 10 Dhaka Stock Market (DSEbd) listed companies and six international giants for predicting the stock and forecasting the future price. The dataset of domestic companies is extracted from the graphical representation of the DSEbd website, and the international companies' dataset is imported from Yahoo Finance. In this experiment, Facebook Prophet demonstrates a long period of forecasting with reasonable accuracy, capturing daily, weekly, and yearly seasonality, including holiday effects for market trend analysis. Remarkably, the LSTM model exhibits significant accuracy, yielding the best results with evaluation metrics, including RMSE (0.35), MAPE (0.50%), and MAE (0.30). The experimental results underscore the efficiency of LSTM for future stock forecasting, observed over 15 days of upcoming market prices. A comparison of the results shows that the LSTM model efficiently forecasts the next day's closing price.

Galactic cosmic-ray scattering due to intermittent structures

ABSTRACT Cosmic rays (CRs) with energies ≪ TeV comprise a significant component of the interstellar medium (ISM). Major uncertainties in CR behaviour on observable scales (much larger than CR gyroradii) stem from how magnetic fluctuations scatter CRs in pitch angle. Traditional first-principles models, which assume these magnetic fluctuations are weak and uniformly scatter CRs in a homogeneous ISM, struggle to reproduce basic observables such as the dependence of CR residence times and scattering rates on rigidity. We therefore explore a new category of ‘patchy’ CR scattering models, wherein CRs are pre-dominantly scattered by intermittent strong scattering structures with small volume-filling factors. These models produce the observed rigidity dependence with a simple size distribution constraint, such that larger scattering structures are rarer but can scatter a wider range of CR energies. To reproduce the empirically inferred CR scattering rates, the mean free path between scattering structures must be $\ell _{\rm mfp}\sim 10\, {\rm pc}$ at GeV energies. We derive constraints on the sizes, internal properties, mass/volume-filling factors, and the number density any such structures would need to be both physically and observationally consistent. We consider a range of candidate structures, both large scale (e.g. H ii regions) and small scale (e.g. intermittent turbulent structures, perhaps even associated with radio plasma scattering) and show that while many macroscopic candidates can be immediately ruled out as the primary CR scattering sites, many smaller structures remain viable and merit further theoretical study. We discuss future observational constraints that could test these models.

Designing trust-enabling blockchain systems for the inter-organizational exchange of capacity

In times of rapid and unpredictable developments, companies experience significant volatility in capacity utilization. Virtual capacity exchange platforms help to mitigate this challenge by exchanging capacities with anonymous participants in market-like peer-to-peer networks. However, its efficiency is hindered by behavioral uncertainties, including a lack of inter-organizational trust in other participants. To leverage the potential of such exchange platforms, this paper reports on a Design Science Research project aiming to derive and validate design principles for establishing trust in inter-organizational capacity exchange in two design iterations. Using blockchain technology, we instantiate six design principles into an artifact and perform experimental evaluations to investigate their effect on perceived trust. Our paper contributes to research and practice by identifying and applying prescriptive design knowledge and advancing our understanding of how to design trust-enabling inter-organizational systems. The originality lies within the empirical investigation of how and why different design principle combinations can be established through blockchain technology as one of the promising approaches for establishing trust. In doing this, we also disclose future pathways for IS and blockchain researchers and practitioners.

Komunikasi Interpersonal Customer Service Lotte Grosir dalam Melayani Pelanggan di Kota Samarinda

Interpersonal communication plays an important role in a company, one of which is retail, the quality of good interpersonal communication needs to be mastered by customer service because it deals directly with customers, the quality of interpersonal communication is needed to create good service for customers. The purpose of this study was to determine the interpersonal communication process of Lotte Grosir's customer service in serving customers in Samarinda City. The research method used is a qualitative method. Data collection techniques include interviews, observation and documentation. The informants in this study consisted of 3 customer service people, 1 guard manager and 2 customers. The results of this study indicate that there are 6 elements found in the field, namely self-introduction and other individuals from customer service openness to customers and customer service digging into customer backgrounds to reduce uncertainty to customers. Knowing the situation can be seen from the way customer service listens to complaints experienced by customers before providing solutions. Creating and maintaining meaningful relationships is evident in customer service. Providing service to customers in a friendly and courteous manner, not hurting customer feelings, giving trust to customers. Persuasion is seen when customer service provides options for exchanging goods before a refund is made if there is a discrepancy with the customer. Relaxed interactions can be seen from the way customer service makes jokes to customers, but one customer service that lacks initiative tends to wait for customers who ask questions. Helping seen from customer service helps reduce cognitive and behavioral uncertainty in customers in making decisions if customers feel unsure about their choices.

Impact of LWR assembly structural features on cladding burst behavior under LOCA conditions

This provides an initial scoping study on clad balloon and burst behavior for burnup extension of reactor fuel. The associated issues with burnup extension are fuel fragmentation, relocation, and dispersal in the event of cladding failure. The general finding of this work is that the structural features, spacer grids and mixing vanes, locally suppress cladding deformation but have little impact on the overall clad performance during loss-of-coolant accidents. The work detailed in a previous report by Capps et al. focused on core optimization via neutronics, thermal hydraulics and thermomechanical analysis for burnups beyond 62 GWD/tU and enrichments above 5%. Uncertainty of fuel fragmentation relocation and dispersal in high-burnup rods during accident conditions was also investigated. The dispersal aspect of fuel fragmentation depends on cladding rupture. Thus, assessing uncertainties in the rupture behavior is helpful in estimating the dispersal of high-burnup fuel. This study builds on the previous work by assessing the impact of assembly structural features on cladding balloon and burst behavior in a full-length fuel rod.In this work, the BISON fuel performance code was used to generate 2D radial and height meshes containing structural features commonly used in nuclear fuel assemblies. First, meshes were generated with spacer grids. Results were then compared to the cladding burst temperature and balloon strain results from the previous work. A mesh sensitivity study was performed to ensure that mixing vanes and spacer grid effects were appropriately considered, resulting in a more refined mesh than the previous study. The balloon deformation and burst times of the cladding were compared to the original case. Consideration was also given to the effect of rod initial pressure. 3D quarter rod simulations were also performed and found good agreement with the 2D simulations in clad deformation and reasonable agreement in burst times.

Stackelberg-Nash asymmetric bargaining-based scheduling optimization and revenue-allocation for multi-operator regional integrated energy system considering competition-cooperation relationship and source-load uncertainties

The integration of source-grid-load-storage greatly improves the energy managing flexibility of regional integrated energy systems (RIES). Considering the conflicting interests of multi-operator, this paper divides RIES into 1) energy supply center (ESC); 2) load aggregator (LA); 3) energy storage aggregator (ESA), and establishes a multi-operator two-layer optimal scheduling model by combining 1) the Stackelbelberg-Nash asymmetric bargaining game based on the genetic algorithm; and 2) the alternating direction method of multipliers algorithm (GA-ADMM). Firstly, considering the absence of historical data, trapezoidal fuzzy parameters are introduced to construct fuzzy chance constraints based on credibility theory to simulate the source-load uncertainties. Secondly, the uncertainty of demand response behavior, real-time carbon credits trading within RIES, and cooperation-competition behavior of followers (LA and ESA) are considered to simulate the asymmetric relationship between followers and leaders (ESC) based on GA. Then, based on the ADMM algorithm, a Nash asymmetric bargaining game considering energy dependency and energy supply-reception status is proposed to realize the revenue allocation between followers. Finally, simulations are implemented to verify the validity and reliability of the proposed model. Results show that: 1) Cooperation improves the energy-managing flexibility of followers, reduces their dependence on ESC, and thus enhances their game ability. 2) The real-time carbon credits trading enables zero-carbon energy interactions and natural gas replenishment within RIES. 3) Increases in the price of various energy and carbon credits can enhance cooperation between followers.

Digital twin-driven framework for fatigue life prediction of welded structures considering residual stress

The welding of steel generates substantial welding residual stress (WRS), which exerts a significant impact on the fatigue life of steel bridges. In this study, a physical model for calculating the fatigue crack growth (FCG) life of welded specimens in the WRS field is established based on the weight function method. Experimental data validates the reliability and precision of the proposed physical model. The impact of WRS on the fatigue life of structural components is scrutinized and analyzed. On this basis, a digital twin (DT) framework driven by a physical-data model is proposed to consider the inherent parameter uncertainty in FCG behavior within the WRS domain. A dynamic Bayesian network (DBN) is used to characterize the evolution characteristics of fatigue crack states over time in the digital space. Particle filter algorithm is used as DBN inference method. The results show that the calculation of the physical model is in good agreement with the experimental values. Neglecting the influence of WRS distribution may lead to an overestimation of fatigue life in welded structures. The DT framework can update uncertain parameters online and realize the accurate prediction of the FCG life in the WRS field.

The Role of Interpersonal Uncertainty in Prosocial Behavior

The Role of Interpersonal Uncertainty in Prosocial Behavior

Linking dynamics in consumer behavior and product life cycles in environmental assessments of shared mobility systems: A literature review

Sharing economy businesses (SEB) have attracted attention for their potential to reduce environmental impacts compared to conventional business models. While it is often assumed that SEB enable more sustainable modes of consumption, it is still a challenge to clarify the desirable linkage between consumers and business providers by means of services, products, and digital platforms, due to complexity and uncertainty in consumer behavior. To address this, a systematic review of literature that evaluates the environmental impacts of SEB, using shared mobility systems (SMS) as a case study, was conducted. An analysis is presented on how studies have addressed the above challenges, extracting key factors identified in literature to determine the net environmental effects of SMS, which can be useful in re-designing them for environmental sustainability.