搜索结果：convenience

Survey researchers face two key challenges: the rising costs of probability samples and missing data (e.g., non-response or attrition), which can undermine inference and increase the use of convenience samples. Recent work explores using large language models (LLMs) to simulate respondents via persona-based prompts, often without labeled data. We study a more practical setting where partial survey responses exist: we fine-tune LLMs on available data to impute self-reported vote choice under both random and systematic nonresponse, using the German Longitudinal Election Study. We compare zero-shot prompting and supervised fine-tuning against tabular classifiers (e.g., CatBoost) and test how different convenience samples (e.g., students) used for fine-tuning affect generalization. Our results show that when data are missing completely at random, fine-tuned LLMs match tabular classifiers but outperform zero-shot approaches. When only biased convenience samples are available, fine-tuning small (3B to 8B) open-source LLMs can recover both individual-level predictions and population-level distributions more accurately than zero-shot and often better than tabular methods. This suggests fin

Host-acting agents promise a convenient interaction model in which users specify goals and the system determines how to realize them. We argue that this convenience introduces a distinct security problem: semantic under-specification in goal specification. User instructions are typically goal-oriented, yet they often leave process constraints, safety boundaries, persistence, and exposure insufficiently specified. As a result, the agent must complete missing execution semantics before acting, and this completion can produce risky host-side plans even when the user-stated goal is benign. In this paper, we develop a semantic threat model, present a taxonomy of semantic-induced risky completion patterns, and study the phenomenon through an OpenClaw-centered case study and execution-trace analysis. We further derive defense design principles for making execution boundaries explicit and constraining risky completion. These findings suggest that securing host-acting agents requires governing not only which actions are allowed at execution time, but also how goal-only instructions are translated into executable plans.

Smart voice assistants (SVAs) are embedded in the daily lives of youth, yet their privacy controls often remain opaque and difficult to manage. Through five semi-structured focus groups (N=26) with young Canadians (ages 16-24), we investigate how perceived privacy risks (PPR) and benefits (PPBf) intersect with algorithmic transparency and trust (ATT) and privacy self-efficacy (PSE) to shape privacy-protective behaviors (PPB). Our analysis reveals that policy overload, fragmented settings, and unclear data retention undermine self-efficacy and discourage protective actions. Conversely, simple transparency cues were associated with greater confidence without diminishing the utility of hands-free tasks and entertainment. We synthesize these findings into a qualitative model in which transparency friction erodes PSE, which in turn weakens PPB. From this model, we derive actionable design guidance for SVAs, including a unified privacy hub, plain-language "data nutrition" labels, clear retention defaults, and device-conditional micro-tutorials. This work foregrounds youth perspectives and offers a path for SVA governance and design that empowers young digital citizens while preserving co

While conducting probabilistic surveys is the gold standard for assessing vaccine coverage, implementing these surveys poses challenges for global health. There is a need for more convenient option that is more affordable and practical. Motivated by childhood vaccine monitoring programs in rural areas of Chad and Niger, we conducted a simulation study to evaluate calibration-weighted design-based and logistic regression-based imputation estimators of the finite-population proportion of MCV1 coverage. These estimators use a hybrid approach that anchors non-probabilistic follow-up survey to probabilistic baseline census to account for selection bias. We explored varying degrees of non-ignorable selection bias (odds ratios from 1.0-1.5), percentage of villages sampled (25-75%), and village-level survey response rate to the follow-up survey (50-80%). Our performance metrics included bias, coverage, and proportion of simulated 95% confidence intervals falling within equivalence margins of 5% and 7.5% (equivalence tolerance). For both adjustment methods, the performance worsened with higher selection bias and lower response rate and generally improved as a larger proportion of villages w

A common, yet regular, decision made by people, whether healthy or with any health condition, is to decide what to have in meals like breakfast, lunch, and dinner, consisting of a combination of foods for appetizer, main course, side dishes, desserts, and beverages. However, often this decision is seen as a trade-off between nutritious choices (e.g., low salt and sugar) or convenience (e.g., inexpensive, fast to prepare/obtain, taste better). In this preliminary work, we present a data-driven approach for the novel meal recommendation problem that can explore and balance choices for both considerations while also reasoning about a food's constituents and cooking process. Beyond the problem formulation, our contributions also include a goodness measure, a recipe conversion method from text to the recently introduced multimodal rich recipe representation (R3) format, and learning methods using contextual bandits that show promising results.

This paper investigates whether the hexagonal structure of grid cells provides any performance benefits or if it merely represents a biologically convenient configuration. Utilizing the Vector-HaSH content addressable memory model as a model of the grid cell -- place cell network of the mammalian brain, we compare the performance of square and hexagonal grid cells in tasks of storing and retrieving spatial memories. Our experiments across different path types, path lengths and grid configurations, reveal that hexagonal grid cells perform similarly to square grid cells with respect to spatial representation and memory recall. Our results show comparable accuracy and robustness across different datasets and noise levels on images to recall. These findings suggest that the brain's use of hexagonal grids may be more a matter of biological convenience and ease of implementation rather than because they provide superior performance over square grid cells (which are easier to implement in silico).

A common decision made by people, whether healthy or with health conditions, is choosing meals like breakfast, lunch, and dinner, comprising combinations of foods for appetizer, main course, side dishes, desserts, and beverages. Often, this decision involves tradeoffs between nutritious choices (e.g., salt and sugar levels, nutrition content) and convenience (e.g., cost and accessibility, cuisine type, food source type). We present a data-driven solution for meal recommendations that considers customizable meal configurations and time horizons. This solution balances user preferences while accounting for food constituents and cooking processes. Our contributions include introducing goodness measures, a recipe conversion method from text to the recently introduced multimodal rich recipe representation (R3) format, learning methods using contextual bandits that show promising preliminary results, and the prototype, usage-inspired, BEACON system.

In this position paper we present Municipan, an artefact resulting from a post-growth design experiment, applied in a student design project. In contrast to mainstream human-centered design directed at efficiency and convenience, which we argue leads to deskilling, dependency, and the progression of the climate crisis, we challenged students to envision an opposite user that is willing to invest time and effort and learn new skills. While Municipan is not a direct step towards a postgrowth society, integrating the way it was created in design education can act as a nucleus, bringing forth design professionals inclined to create technologies with potential to gradually transform society towards postgrowth living. Bringing in examples from our own research, we illustrate that designs created in this mindset, such as heating systems that train cold resistance, or navigation systems that train orientation have potential to reskill users, reduce technological dependency and steer consumption within planetary limits.

Municipal solid waste management is a paramount activity in modern cities due to the environmental, social and economic problems that can arise when mishandled. In this work, the sequencing of micro-routes in the Argentine city of Bahía Blanca is addressed, which is modeled as a vehicle routing problem with travel time limit and the vehicle's capacity. Particularly, we propose two mathematical formulations based on mixed-integer programming and we solve a set of instances of the city of Bahía Blanca based on real data. Moreover, with this model we estimate the total distance and travel time of the waste collection and use this data to analyze the possibility of installing a transfer station. The results demonstrate the competitiveness of the approach to resolve realistic instances of the target problem and suggest the convenience of installing a transfer station in the city considering the reduction of the traveled distance.

This paper studies charging scheduling problem of electric vehicles (EVs) in the scale of a microgrid (e.g., a university or town) where a set of charging stations are controlled by a central aggregator. A bi-objective optimization problem is formulated to jointly optimize total charging cost and user convenience. Then, a close-to-optimal online scheduling algorithm is proposed as solution. The algorithm achieves optimal charging cost and is near optimal in terms of user convenience. Moreover, the proposed method applies an efficient load forecasting technique to obtain future load information. The algorithm is assessed through simulation and compared to the previous studies. The results reveal that our method not only improves previous alternative methods in terms of Pareto-optimal solution of the bi-objective optimization problem, but also provides a close approximation for the load forecasting.

This article proposes a novel system concept named universal wireless power transfer, in which power can be wirelessly transferred between different entities (e.g. vehicles, robots, homes, grid facilities, consumer electronic devices, etc.) equipped with proper energy transmitters and receivers, whether stationary or in motion. This concept generalizes individually existing wireless power transfer systems, where a specific wireless power transfer technology is used, and where the wireless energy transmitter or receiver is fixed. As a result, energy mobility, flexibility, and convenience are significantly improved by the proposed universal wireless power transfer concept in this study. Moreover, factors relevant to system energy efficiency are analyzed according to each utilized wireless power transfer technology. Necessary market mechanisms for such a concept to be successfully deployed are also introduced, along with an analysis of the benefits engendered in terms of improving energy systems, the environment, human comfort, and convenience. Finally, a discussion of the proposed concept, policy implications and recommendations for future research directions which will underpin univ

Flow control with the goal of reducing the skin friction drag on the fluid-solid interface is an active fundamental research area, motivated by its potential for significant energy savings and reduced emissions in the transport sector. Customarily, the performance of drag reduction techniques in internal flows is evaluated under two alternative flow conditions, i.e. at constant mass flow rate or constant pressure gradient. Successful control leads to reduction of drag and pumping power within the former approach, whereas the latter leads to an increase of the mass flow rate and pumping power. In practical applications, however, money and time define the flow control challenge: a compromise between the energy expenditure (money) and the corresponding convenience (flow rate) achieved with that amount of energy has to be reached so as to accomplish a goal which in general depends on the specific application. Based on this idea, we derive two dimensionless parameters which quantify the total energy consumption and the required time (convenience) for transporting a given volume of fluid through a given duct. Performances of existing drag reduction strategies as well as the influence of

We study the statistical laws of the expenditure of a person in convenience stores by analysing around 100 million receipts. The density function of expenditure exhibits a fat tail that follows a power law. We observe the Pareto principle where both the top 25% and 2% of the customers account for 80% and 25% of the store's sales, respectively. Using the Lorenz curve, the Gini coefficient is estimated to be 0.70; this implies a strong economic inequality.

The density function of product life cycles in convenience stores is found to follow the Weibull distribution. To clarify the parameters that determine these life cycles, we introduce the conditional market share-defined as the probability that a product is selected by customers only if it had been previously purchased-and the market share without any conditions. The product life cycle is more strongly correlated with the conditional market share of the product than with the latter type of market share.

In a convenience store chain, a tail of the cumulative density function of the expenditure of a person during a single shopping trip follows a power law with an exponent of -2.5. The exponent is independent of the location of the store, the shopper's age, the day of week, and the time of day.