Resource analysis aims to derive symbolic resource bounds of programs. Although numerous resource-analysis techniques have been developed—ranging from static to dynamic and manual to automated techniques—they each come with their own distinct strengths and weaknesses. To overcome the limitations of individual resource-analysis techniques, a promising approach is to combine them in such a way that retains their complementary strengths while mitigating their respective weaknesses. This article proposes a novel program translation method called resource decomposition that facilitates the combination of different resource-analysis techniques. The key idea of resource decomposition is to first identify and annotate the program with resource components , which are user-specified variables that serve as an interface between different analysis techniques. Using these resource components, our method generates a resource-guarded program , where one analysis technique is used to infer an overall cost bound parametric in the resource components, and other analysis techniques are used to infer symbolic bounds to be substituted for the resource components. We establish the soundness of resource decomposition using a denotational cost semantics and a binary logical relation. It states that composing sound bounds results in a sound bound for the original program. Furthermore, we present three instantiations of the resource-decomposition framework, each representing distinct combinations of static, data-driven, and manual resource analyses. The data-driven part of these instantiations is a novel Bayesian approach to inferring linear and logarithmic bounds of recursion depths. An implementation and empirical evaluation of resource decomposition demonstrates that it can effectively infer sound and asymptotically tight cost bounds for a number of challenging benchmarks that are beyond the reach of previous analysis methods.

As Artificial Intelligence (AI) continues to permeate various aspects of societies, understanding the disparities in AI knowledge and skills across different living areas becomes imperative. Small living areas have emerged as significant contributors to Europe's economy, offering an alternative to the bustling environment of larger cities for those seeking an improved quality of life. Nonetheless, they often encounter challenges related to digital infrastructure, access to financial resources, and digital skills gaps, limiting their economic and social growth prospects. This study investigates the digital and AI skills gaps in the context of small and large European living areas, shedding light on the potential hindrances to unleashing the full economic and social potentials of these regions in an AI-enabled economy. Drawing from a comprehensive dataset encompassing 4,006 respondents across eight EU countries, this research examines the current perceptions and understandings of AI and digital skills within two distinct population groups: residents of smaller living areas and their counterparts in larger communities. Through bivariate analysis, notable insights are revealed concerning trust in AI solutions and entities, self-assessed digital skills, AI Awareness, AI Attitudes and demography variables in both population groups. These insights may refer to the significance of addressing digital and AI skills gaps in fostering growth and preparedness for the AI-driven future. As AI becomes increasingly integral to various aspects of society, targeted interventions and policies are essential to bridge these gaps and enable individuals and communities to harness the transformative potential of AI-enabled economies.

Facebook commerce has become increasingly popular, especially for consumer-to-consumer (C2C) transactions. This trading channel is highly utilized for local transactions and for connecting people with similar interests, thus making it an especially useful channel for buying and selling pre-owned luxury products. This study aims at examining the determinants of customers' social image satisfaction with the purchase of pre-owned luxury brand watches via the Facebook-commerce channel. Data were collected from two hundred Thai consumers who are the members of the pre-owned luxury brand watch Facebook group. The results revealed factors which significantly influence customers' social image satisfaction toward a pre-owned luxury brand watch. The findings also showed that the perceived symbolic value plays an important mediating role in the relationship between perceived experiential value and social image satisfaction.

Worst-case input generation aims to automatically generate inputs that exhibit the worst-case performance of programs. It has several applications, and can, for example, detect vulnerabilities to denial-of-service (DoS) attacks. However, it is non-trivial to generate worst-case inputs for concurrent programs, particularly for resources like memory where the peak cost depends on how processes are scheduled. This article presents the first sound worst-case input generation algorithm for concurrent programs under non-monotone resource metrics like memory. The key insight is to leverage resource-annotated session types and symbolic execution. Session types describe communication protocols on channels in process calculi. Equipped with resource annotations, resource-annotated session types not only encode cost bounds but also indicate how many resources can be reused and transferred between processes. This information is critical for identifying a worst-case execution path during symbolic execution. The algorithm is sound: if it returns any input, it is guaranteed to be a valid worst-case input. The algorithm is also relatively complete: as long as resource-annotated session types are sufficiently expressive and the background theory for SMT solving is decidable, a worst-case input is guaranteed to be returned. A simple case study of a web server's memory usage demonstrates the utility of the worst-case input generation algorithm.

Probabilistic programming languages (PPLs) provide language support for expressing flexible probabilistic models and solving Bayesian inference problems. PPLs with programmable inference make it possible for users to obtain improved results by customizing inference engines using guide programs that are tailored to a corresponding model program. However, errors in guide programs can compromise the statistical soundness of the inference. This article introduces a novel coroutine-based framework for verifying the correctness of user-written guide programs for a broad class of Markov chain Monte Carlo (MCMC) inference algorithms. Our approach rests on a novel type system for describing communication protocols between a model program and a sequence of guides that each update only a subset of random variables. We prove that, by translating guide types to context-free processes with finite norms, it is possible to check structural type equality between models and guides in polynomial time. This connection gives rise to an efficient type-inference algorithm for probabilistic programs with flexible constructs such as general recursion and branching. We also contribute a coverage-checking algorithm that verifies the support of sequentially composed guide programs agrees with that of the model program, which is a key soundness condition for MCMC inference with multiple guides. Evaluations on diverse benchmarks show that our type-inference and coverage-checking algorithms efficiently infer types and detect sound and unsound guides for programs that existing static analyses cannot handle.

There are two approaches to automatically deriving symbolic worst-case resource bounds for programs: static analysis of the source code and data-driven analysis of cost measurements obtained by running the program. Static resource analysis is usually sound but incomplete. Data-driven analysis can always return a result, but its lack of robustness often leads to unsound results. This paper presents the design, implementation, and empirical evaluation of hybrid resource bound analyses that tightly integrate static analysis and data-driven analysis. The static analysis part builds on automatic amortized resource analysis (AARA), a state-of-the-art type-based resource analysis method that performs cost bound inference using linear optimization. The data-driven part is rooted in novel Bayesian modeling and inference techniques that improve upon previous data-driven analysis methods by reporting an entire probability distribution over likely resource cost bounds. A key innovation is a new type inference system called Hybrid AARA that coherently integrates Bayesian inference into conventional AARA, combining the strengths of both approaches. Hybrid AARA is proven to be statistically sound under standard assumptions on the runtime cost data. An experimental evaluation on a challenging set of benchmarks shows that Hybrid AARA (i) effectively mitigates the incompleteness of purely static resource analysis; and (ii) is more accurate and robust than purely data-driven resource analysis.

Previous studies on the intention to use mobile payment were mainly conducted using traditional technology acceptance models, which focus on positive factors and ignore negative factors influencing the intention to use mobile payment in both developed and developing countries. This study is conducted in a newly emerging country, Vietnam – a trusted destination for multinational companies to do business and reposition their global supply chains. With the integration of positive and negative factors into an extended research model to examine their influence on the intention to use mobile payment, the results show that perceived privacy and perceived security contribute to overall perceived risk. Moreover, perceived risk and perceived compatibility are two determinants of intention to use mobile payment. Theoretical and managerial implications are drawn and directions for future research are outlined.

Online consumer reviews have been shown to play an important role that can influence consumers' attitudes, behaviors, and purchasing decisions. This study seeks to understand factors that affect user intention to use online consumer reviews. To examine this area, this paper theorizes multiple constructs that may influence intention to use. The subjects in this study were 466 online Vietnamese consumers. The data were analyzed using structural equation modeling, and five of the seven hypotheses were found to be significant. This study found several results not found in prior studies. The construct perceived online review importance is found to be important in influencing intention to use online reviews. Both perceived usefulness and perceived credibility influenced consumers' perceptions on the importance of online reviews. The study findings contribute to the research field of online consumer reviews and provide new insights into intention to use online consumer reviews for a developing country, different from prior studies that have focused on developed countries.

Being a fully automated technique for resource analysis, automatic amortized resource analysis (AARA) can fail in returning worst-case cost bounds of programs, fundamentally due to the undecidability of resource analysis. For programmers who are unfamiliar with the technical details of AARA, it is difficult to predict whether a program can be successfully analyzed in AARA. Motivated by this problem, this article identifies classes of programs that can be analyzed in type-based polynomial AARA. Firstly, it is shown that the set of functions that are typable in univariate polynomial AARA coincides with the complexity class PTIME. Secondly, the article presents a sufficient condition for typability that axiomatically requires every sub-expression of a given program to be polynomial-time. It is proved that this condition implies typability in multivariate polynomial AARA under some syntactic restrictions.

In systems constrained by battery power or scavenged energy limits, ADC energy efficiency as expressed by the fJ/step figure-of-merit is a critical design driver. This paper describes a time-domain approach to determine the fundamental limit on ADC performance for techniques such as VCO-based ADCs which perform the ADC function in the time domain.

This paper describes approaches and methodologies facilitating the analysis of large amounts of distributed scientific data. The existence of full-featured analysis tools, such as the Algorithm Development and Mining (ADaM) toolkit and online data repositories now provide easy access and analysis capabilities to large amounts of data. However, there are obstacles to getting the analysis tools and the data together in a workable environment. Does one bring the data to the tools or deploy the tools close to the data? The large size of many current Earth science datasets incurs significant overhead in network transfer for analysis workflows, even with the current advanced networking capabilities. We are developing two solutions for this problem that address different analysis scenarios. The first is a Data Center Deployment of the analysis services for large data selections, orchestrated by a remotely defined analysis workflow. The second is a Data Mining Center approach of providing a cohesive analysis solution for smaller subsets of data. The two approaches can be complementary and thus provide flexibility for researchers to exploit the best solution for their data requirements.

The ability to use data stored in the current Earth Observing System (EOS) archives for studying regional or global phenomena is highly dependent on having a detailed understanding of the data's internal structure and physical implementation. Gaining this understanding and applying it to data reduction is a time-consuming task that must be undertaken before the core investigation can begin. This is an especially difficult challenge when science objectives require users to deal with large multi-sensor data sets that are usually of different formats, structures, and resolutions. The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) has taken a major step towards meeting this challenge by developing an infrastructure that supports a Web interface that allows users to perform interactive analysis online without downloading any data. The underlying infrastructure that integrates all the components is called GES-DISC Interactive Online Visualization and Analysis Infrastructure or "Giovanni". Giovanni provides interactive, online, analysis tools for data users to facilitate their research