Modern computing: Vision and challenges

Over the past six decades, the computing systems field has experienced significant transformations, profoundly impacting society with transformational developments, such as the Internet and the commodification of computing. Underpinned by technological advancements, computer systems, far from being static, have been continuously evolving and adapting to cover multifaceted societal niches. This has led to new paradigms such as cloud, fog, edge computing, and the Internet of Things (IoT), which offer fresh economic and creative opportunities. Nevertheless, this rapid change poses complex research challenges, especially in maximizing potential and enhancing functionality. As such, to maintain an economical level of performance that meets ever-tighter requirements, one must understand the drivers of new model emergence and expansion, and how contemporary challenges differ from past ones. To that end, this article investigates and assesses the factors influencing the evolution of computing systems, covering established systems and architectures as well as newer developments, such as serverless computing, quantum computing, and on-device AI on edge devices. Trends emerge when one traces technological trajectory, which includes the rapid obsolescence of frameworks due to business and technical constraints, a move towards specialized systems and models, and varying approaches to centralized and decentralized control. This comprehensive review of modern computing systems looks ahead to the future of research in the field, highlighting key challenges and emerging trends, and underscoring their importance in cost-effectively driving technological progress

Het nemen van beslissingen door volwassenen met ADHD:Een systematisch literatuuronderzoek

Personen met aandachtstekortstoornis met hyperactiviteit (ADHD) hebben een grotere kans om minder goede (levens)beslissingen te nemen en om risicovolle activiteiten te ondernemen dan personen zonder ADHD. Mogelijk komt dit doordat de kenmerken van ADHD van invloed zijn op het besluitvormingsproces. Hoewel beslissingsproblematiek reeds uitgebreid is onderzocht bij kinderen en adolescenten met ADHD, is er nog relatief weinig bekend over de besluitvorming van volwassenen met ADHD. Om die reden was het doel van dit literatuuronderzoek de aard en omvang van eventuele tekorten in het besluitvormingsproces van volwassenen met ADHD vast te stellen. Hiertoe is de bestaande literatuur, waarin de prestatie van volwassenen met ADHD op beslissingstaken werd vergeleken met de prestatie van een gezonde controlegroep, systematisch doorzocht, waartoe de databases PsycINFO, MEDLINE en PubMed zijn geraadpleegd. In totaal werden er 31 studies geïncludeerd. In de meerderheid van de studies (i.e. 55 %) weken de prestaties van volwassenen met ADHD af op een of meer van de gebruikte beslissingstaken in vergelijking met de controlegroep(en). Dit literatuuronderzoek levert daarmee voorzichtig bewijs voor het bestaan van verschillen in het besluitvormingsproces tussen gezonde individuen en volwassenen met ADHD. De grote inconsistentie in de bevindingen wordt deels verklaard door de verscheidenheid aan domeinen van besluitvorming die werden onderzocht, de comorbide stoornissen van de participanten en het medicatiegebruik in de ADHD-groepen. Het literatuuronderzoek besluit met een bespreking van de implicaties die de bevindingen hebben voor theorieën over de onderliggende mechanismen van ADHD

Contemporary Methods In Delayed Discounting: Applications for Suicidology With Simulation

Objective: To present an approach for integrating recently developed methods in behavioral economics into suicidology research. At present, existing applications of delay discounting in suicidology have focused predominantly on hypothetical choices related to monetary value as a proxy to “risky” choices linked to unsafe or suicidal behavior. In this report, we outline a more targeted approach that directly indexes choices related to treatment in suicide prevention initiatives and incorporates the strengths afforded by multi-level modeling. This more targeted approach precludes the need for multi-step comparisons (improving power), avoids compressing choice variability across delays into individual values (improving precision), and better accommodates decision-making at the upper and lower extremes (improving reliability). Method: We present this analytical approach within the context of a Hypothetical Firearm Decision-making Task with simulated participants. A simulated study is provided to illustrate how this approach can be used to evaluate how individuals make temporally delayed decisions related to treatment for suicidal behavior (i.e., temporarily limiting their access to firearms while undergoing treatment). Results and Conclusions: The results of this simulated study are provided to illustrate how more advanced behavioral decision-making models can be used to supplement existing research methods in suicidology

AI for next generation computing: Emerging trends and future directions

Autonomic computing investigates how systems can achieve (user) specified “control” outcomes on their own, without the intervention of a human operator. Autonomic computing fundamentals have been substantially influenced by those of control theory for closed and open-loop systems. In practice, complex systems may exhibit a number of concurrent and inter-dependent control loops. Despite research into autonomic models for managing computer resources, ranging from individual resources (e.g., web servers) to a resource ensemble (e.g., multiple resources within a data center), research into integrating Artificial Intelligence (AI) and Machine Learning (ML) to improve resource autonomy and performance at scale continues to be a fundamental challenge. The integration of AI/ML to achieve such autonomic and self-management of systems can be achieved at different levels of granularity, from full to human-in-the-loop automation. In this article, leading academics, researchers, practitioners, engineers, and scientists in the fields of cloud computing, AI/ML, and quantum computing join to discuss current research and potential future directions for these fields. Further, we discuss challenges and opportunities for leveraging AI and ML in next generation computing for emerging computing paradigms, including cloud, fog, edge, serverless and quantum computing environments

The impact of intrinsic and extrinsic features on delay discounting

International audienceDelay discounting refers to the tendency of people to evaluate immediate rewards as being more valuable than those that are distant in time. Several models explain this phenomenon by a set of intrinsic and extrinsic features. Intrinsic features are related to the inherent traits and neurological conditions of the individual, whereas extrinsic features are related to the characteristics of the reward. In this study, we refer to extraversion and attention-deficit/hyperactivity disorder symptoms (attention and hyperactivity-impulsivity) as intrinsic features, and to fungibility, perishability, and magnitude of the reward as extrinsic features. Whereas there is a known main effect to these intrinsic and extrinsic features, the current research examines their additive and interactive contributions to delay discounting. A total of 222 participants filled out an online questionnaire measuring intrinsic features and presenting decision tasks with different types of rewards. The scores of the intrinsic variables and the delay discounting rate for each reward were calculated and analyzed. The results replicated previous findings showing main effects of hyperactivity, fungibility, perishability, and magnitude. They also provided new findings on an interaction between fungibility-perishability and hyperactivity—the effect of hyperactivity on delay discounting was larger when the rewards were fungible and nonperishable than when the rewards were perishable and nonfungible. This interaction has practical implications that can help in moderating delay discounting in clinical treatments of impulsivity as well as in constructing efficient economic models for consumers