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2022 ◽  
Vol 24 (53) ◽  
pp. 119-146
Rafael Kalinoski ◽  
Mario Procopiuck

Resumo Na última década, proliferaram modelos de negócios imobiliários globais substitutivos da tradicional relação proprietário-locador intermediada por empresas locais. O objetivo deste artigo é analisar como a produção imobiliária é transformada pelo capital financeiro sob a perspectiva dos produtos imobiliários direcionados a investidores com locação comercializada em plataformas digitais. Metodologicamente, a investigação qualitativa segue a “trilha do dinheiro” que financia as startups do setor imobiliário, conhecidas como proptechs, de modo a identificar os agentes financeiros envolvidos. Os resultados apontam que a financeirização imobiliária, além de continuar a ocorrer na construção e na incorporação, avança para a ocupação via empresas permeadas pelo mercado de capitais. Conclui-se que os negócios imobiliários se complexificam em quatro ondas: financeirização, financeiro-desburocratização, financeiro-virtualização e, por fim, financeiro-virtualização compartilhada da ocupação imobiliária.

2022 ◽  
Vol 22 (1) ◽  
pp. 1-21
Cosmin Avasalcai ◽  
Christos Tsigkanos ◽  
Schahram Dustdar

Edge computing offers the possibility of deploying applications at the edge of the network. To take advantage of available devices’ distributed resources, applications often are structured as microservices, often having stringent requirements of low latency and high availability. However, a decentralized edge system that the application may be intended for is characterized by high volatility, due to devices making up the system being unreliable or leaving the network unexpectedly. This makes application deployment and assurance that it will continue to operate under volatility challenging. We propose an adaptive framework capable of deploying and efficiently maintaining a microservice-based application at runtime, by tackling two intertwined problems: (i) finding a microservice placement across device hosts and (ii) deriving invocation paths that serve it. Our objective is to maintain correct functionality by satisfying given requirements in terms of end-to-end latency and availability, in a volatile edge environment. We evaluate our solution quantitatively by considering performance and failure recovery.

2022 ◽  
Vol 27 (2) ◽  
pp. 1-33
Zahra Ebrahimi ◽  
Dennis Klar ◽  
Mohammad Aasim Ekhtiyar ◽  
Akash Kumar

The rapid evolution of error-resilient programs intertwined with their quest for high throughput has motivated the use of Single Instruction, Multiple Data (SIMD) components in Field-Programmable Gate Arrays (FPGAs). Particularly, to exploit the error-resiliency of such applications, Cross-layer approximation paradigm has recently gained traction, the ultimate goal of which is to efficiently exploit approximation potentials across layers of abstraction. From circuit- to application-level, valuable studies have proposed various approximation techniques, albeit linked to four drawbacks: First, most of approximate multipliers and dividers operate only in SISD mode. Second, imprecise units are often substituted, merely in a single kernel of a multi-kernel application, with an end-to-end analysis in Quality of Results (QoR) and not in the gained performance. Third, state-of-the-art (SoA) strategies neglect the fact that each kernel contributes differently to the end-to-end QoR and performance metrics. Therefore, they lack in adopting a generic methodology for adjusting the approximation knobs to maximize performance gains for a user-defined quality constraint. Finally, multi-level techniques lack in being efficiently supported, from application-, to architecture-, to circuit-level, in a cohesive cross-layer hierarchy. In this article, we propose Plasticine , a cross-layer methodology for multi-kernel applications, which addresses the aforementioned challenges by efficiently utilizing the synergistic effects of a chain of techniques across layers of abstraction. To this end, we propose an application sensitivity analysis and a heuristic that tailor the precision at constituent kernels of the application by finding the most tolerable degree of approximations for each of consecutive kernels, while also satisfying the ultimate user-defined QoR. The chain of approximations is also effectively enabled in a cross-layer hierarchy, from application- to architecture- to circuit-level, through the plasticity of SIMD multiplier-dividers, each supporting dynamic precision variability along with hybrid functionality. The end-to-end evaluations of Plasticine  on three multi-kernel applications employed in bio-signal processing, image processing, and moving object tracking for Unmanned Air Vehicles (UAV) demonstrate 41%–64%, 39%–62%, and 70%–86% improvements in area, latency, and Area-Delay-Product (ADP), respectively, over 32-bit fixed precision, with negligible loss in QoR. To springboard future research in reconfigurable and approximate computing communities, our implementations will be available and open-sourced at https://cfaed.tu-dresden.de/pd-downloads.

2022 ◽  
Vol 20 (3) ◽  
pp. 430-442
Gladston Juliano Prates Moreira ◽  
Eduardo Jose da Silva Luz

2022 ◽  
Vol 137 ◽  
pp. 103591
Ioannis T. Christou ◽  
Nikos Kefalakis ◽  
John K. Soldatos ◽  
Angela-Maria Despotopoulou

2022 ◽  
Vol 40 (3) ◽  
pp. 1-28
Surong Yan ◽  
Kwei-Jay Lin ◽  
Xiaolin Zheng ◽  
Haosen Wang

Explicit and implicit knowledge about users and items have been used to describe complex and heterogeneous side information for recommender systems (RSs). Many existing methods use knowledge graph embedding (KGE) to learn the representation of a user-item knowledge graph (KG) in low-dimensional space. In this article, we propose a lightweight end-to-end joint learning framework for fusing the tasks of KGE and RSs at the model level. Our method proposes a lightweight KG embedding method by using bidirectional bijection relation-type modeling to enable scalability for large graphs while using self-adaptive negative sampling to optimize negative sample generating. Our method further generates the integrated views for users and items based on relation-types to explicitly model users’ preferences and items’ features, respectively. Finally, we add virtual “recommendation” relations between the integrated views of users and items to model the preferences of users on items, seamlessly integrating RS with user-item KG over a unified graph. Experimental results on multiple datasets and benchmarks show that our method can achieve a better accuracy of recommendation compared with existing state-of-the-art methods. Complexity and runtime analysis suggests that our method can gain a lower time and space complexity than most of existing methods and improve scalability.

2022 ◽  
Vol 18 (1) ◽  
pp. 1-41
Pamela Bezerra ◽  
Po-Yu Chen ◽  
Julie A. McCann ◽  
Weiren Yu

As sensor-based networks become more prevalent, scaling to unmanageable numbers or deployed in difficult to reach areas, real-time failure localisation is becoming essential for continued operation. Network tomography, a system and application-independent approach, has been successful in localising complex failures (i.e., observable by end-to-end global analysis) in traditional networks. Applying network tomography to wireless sensor networks (WSNs), however, is challenging. First, WSN topology changes due to environmental interactions (e.g., interference). Additionally, the selection of devices for running network monitoring processes (monitors) is an NP-hard problem. Monitors observe end-to-end in-network properties to identify failures, with their placement impacting the number of identifiable failures. Since monitoring consumes more in-node resources, it is essential to minimise their number while maintaining network tomography’s effectiveness. Unfortunately, state-of-the-art solutions solve this optimisation problem using time-consuming greedy heuristics. In this article, we propose two solutions for efficiently applying Network Tomography in WSNs: a graph compression scheme, enabling faster monitor placement by reducing the number of edges in the network, and an adaptive monitor placement algorithm for recovering the monitor placement given topology changes. The experiments show that our solution is at least 1,000× faster than the state-of-the-art approaches and efficiently copes with topology variations in large-scale WSNs.

2022 ◽  
Vol 54 (8) ◽  
pp. 1-36
Shubhra Kanti Karmaker (“Santu”) ◽  
Md. Mahadi Hassan ◽  
Micah J. Smith ◽  
Lei Xu ◽  
Chengxiang Zhai ◽  

As big data becomes ubiquitous across domains, and more and more stakeholders aspire to make the most of their data, demand for machine learning tools has spurred researchers to explore the possibilities of automated machine learning (AutoML). AutoML tools aim to make machine learning accessible for non-machine learning experts (domain experts), to improve the efficiency of machine learning, and to accelerate machine learning research. But although automation and efficiency are among AutoML’s main selling points, the process still requires human involvement at a number of vital steps, including understanding the attributes of domain-specific data, defining prediction problems, creating a suitable training dataset, and selecting a promising machine learning technique. These steps often require a prolonged back-and-forth that makes this process inefficient for domain experts and data scientists alike and keeps so-called AutoML systems from being truly automatic. In this review article, we introduce a new classification system for AutoML systems, using a seven-tiered schematic to distinguish these systems based on their level of autonomy. We begin by describing what an end-to-end machine learning pipeline actually looks like, and which subtasks of the machine learning pipeline have been automated so far. We highlight those subtasks that are still done manually—generally by a data scientist—and explain how this limits domain experts’ access to machine learning. Next, we introduce our novel level-based taxonomy for AutoML systems and define each level according to the scope of automation support provided. Finally, we lay out a roadmap for the future, pinpointing the research required to further automate the end-to-end machine learning pipeline and discussing important challenges that stand in the way of this ambitious goal.

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