ReuseTracker : Fast Yet Accurate Multicore Reuse Distance Analyzer

One widely used metric that measures data locality is reuse distance —the number of unique memory locations that are accessed between two consecutive accesses to a particular memory location. State-of-the-art techniques that measure reuse distance in parallel applications rely on simulators or binary instrumentation tools that incur large performance and memory overheads. Moreover, the existing sampling-based tools are limited to measuring reuse distances of a single thread and discard interactions among threads in multi-threaded programs. In this work, we propose ReuseTracker —a fast and accurate reuse distance analyzer that leverages existing hardware features in commodity CPUs. ReuseTracker is designed for multi-threaded programs and takes cache-coherence effects into account. By utilizing hardware features like performance monitoring units and debug registers, ReuseTracker can accurately profile reuse distance in parallel applications with much lower overheads than existing tools. It introduces only 2.9× runtime and 2.8× memory overheads. Our tool achieves 92% accuracy when verified against a newly developed configurable benchmark that can generate a variety of different reuse distance patterns. We demonstrate the tool’s functionality with two use-case scenarios using PARSEC, Rodinia, and Synchrobench benchmark suites where ReuseTracker guides code refactoring in these benchmarks by detecting spatial reuses in shared caches that are also false sharing and successfully predicts whether some benchmarks in these suites can benefit from adjacent cache line prefetch optimization.

How instructors can use case based learning (CBL): CBL approach can inspire more self-regulated learning in students

Purpose The purpose of this paper is to demonstrate the linkage of case-based instruction with the enhancement of self-regulated learning of employees. Design/methodology/approach The authors carried out a literature review of SRL and CBL, including reviewing the theories of situated learning and constructivism. They then provided a detailed design presentation for using CBL with trainees. Findings The findings of the analysis enable a full, detailed approach to the application of CBL for practitioner use Originality/value Case-based instruction has not previously been directly linked to the self-regulation of learning.

Blockchain-Based Digital Twins Collaboration for Smart Pandemic Alerting: Decentralized COVID-19 Pandemic Alerting Use Case

Emerging technologies such as digital twins, blockchain, Internet of Things (IoT), and Artificial Intelligence (AI) play a vital role in driving the industrial revolution in all domains, including the healthcare sector. As a result of COVID-19 pandemic outbreak, there is a significant need for medical cyber-physical systems to adopt these emerging technologies to combat COVID-19 paramedic crisis. Also, acquiring secure real-time data exchange and analysis across multiple participants is essential to support the efforts against COVID-19. Therefore, we have introduced a blockchain-based collaborative digital twins framework for decentralized epidemic alerting to combat COVID-19 and any future pandemics. The framework has been proposed to bring together the existing advanced technologies (i.e., blockchain, digital twins, and AI) and then provide a solution to decentralize epidemic alerting to combat COVID-19 outbreaks. Also, we have described how the conceptual framework can be applied in the decentralized COVID-19 pandemic alerting use case.

Designing Disease-Specific mHealth Apps for Clinical Value

mHealth apps for patient use are promising but continue to face a plateau in usage. Current apps work for a limited segment of the patient population, i.e., those who enjoy tracking for intrinsic rewards. There are many opportunities to support patient care in between health care provider visits that are not currently being met for many diseases and patient types (personas). This is an area of great potential growth for mHealth apps and could contribute greatly to patient health and wellness. In this chapter, we propose a framework for how to think about the between-visit needs of patients that would motivate continued use of mhealth apps. We view the app design process from the following perspectives: 1) disease-specific needs, 2) non-disease specific needs, 3) behavioral theoretical aspects of app usage and 4) app-intrinsic usage motivators. Myasthenia gravis serves as the use case for illustrating these perspectives and how to use them in designing a disease-specific mHealth app.

SSM: A Semantic Metasearch Platform for Scientific Data retrieval

Scientific research in all fields has advanced in complexity and in the amount of data generated. The heterogeneity of data repositories, data meaning and their metadata standards makes this problem even more significant. In spite of several proposals to find and retrieve research data from public repositories, there is still need for more comprehensive retrieval solutions. In this article, we specify and develop a mechanism to search for scientific data that takes advantage of metadata records and semantic methods. We present the conception of our architecture and how we have implemented it in a use case in the agriculture domain.

Stablecoins and Central Bank Digital Currencies: Policy and Regulatory Challenges

Stablecoins and central bank digital currencies are on the horizon in Asia, and in some cases have already arrived. This paper provides new analysis and a critique of the use case for both forms of digital currency. It provides time-varying estimates of devaluation risk for the leading stablecoin, Tether, using data from the futures market. It describes the formidable obstacles to widespread use of central bank digital currencies in cross-border transactions, the context in which their utility is arguably greatest. The bottom line is that significant uncertainties continue to dog the region's digital currency initiatives.