SaBRe: load-time selective binary rewriting

Binary rewriting consists in disassembling a program to modify its instructions. However, existing solutions suffer from shortcomings in terms of soundness and performance. We present SaBRe, a load-time system for selective binary rewriting. SaBRe rewrites specific constructs—particularly system calls and functions—when the program is loaded into memory, and intercepts them using plugins through a simple API. We also discuss the theoretical underpinnings of disassembling and rewriting. We developed two backends—for x86_64 and RISC-V—which were used to implement three plugins: a fast system call tracer, a multi-version executor, and a fault injector. Our evaluation shows that SaBRe imposes little overhead, typically below 3%.

     

Unraveling the Lagged Effect of Hydro-meteorological Conditions On the Trophic State of a Reservoir By Applying Dynamic Regression

In this study we develop a novel approach to quantify the relative importance of hydro-meteorological (HM) conditions on the trophic state index (TSI) of a water reservoir (San Roque, Córdoba, Argentina). Seven HM variables measured at four reservoir sites and different depths over a time period of near 2 decades are used. We propose a dynamic regression model to predict the TSI from these variables aggregated over a range of time lags, which has not been applied in such a complex setting so far. By performing coefficient analysis, we quantify the relative importance of these variables on the TSI, as well as the time duration over which they have significant impact (lagged effect). Additionally, the analysis of the autoregressive and moving average (ARIMA) terms reveals the impact of the residual effects of previous trophic states on the current trophic state. We find that surface temperature and precipitation have the largest direct relationship to the TSI in the short-term, while the reservoir water level is inversely related to the TSI in the short- to mid-term. Also, the residual effects of the trophic state impact from 1 month (generally) up to 2 years (exceptionally). This approach can be applied to other water bodies affected by similar eutrophication phenomena.

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Bringing runtime verification home: a case study on the hierarchical monitoring of smart homes using decentralized specifications

International Journal on Software Tools for Technology Transfer - We use runtime verification (RV) to check various specifications in a smart apartment. The specifications can be broken down into...     

Experimental determination of 3D crack propagation scenario in resistance spot welds of a martensitic stainless steel

International Journal of Fracture - The development of high-performance automotive steels involves increasingly complex chemistries and heat treating sequences, to produce stronger and lighter...     

QoS metrics-in-the-loop for endowing runtime self-adaptation to robotic software architectures

Multimedia Tools and Applications - The design of robots capable of operating autonomously in changing and unstructured environments, requires using complex software architectures in which,...     

Design of a Control System Using an Artificial Neural Network to Optimize the Energy Efficiency of Water Distribution Systems

Sustainable management of water supply systems is a major challenge within the framework of the water-energy nexus. The main strategies to improve the operation of these systems are related to increasing the hydraulic and energy efficiency of pumping systems. In this context, this work presents a new artificial neural network (ANN) controller to improve the operation of water distribution systems (WDSs) that includes in its algorithm the specific energy consumption (SEC) as a decision parameter. Therefore, pressure control at the measuring points is also based on the energy efficiency of the pumps. The technique was applied to control the pressures in an experimental setup that emulates a WDS with two consumption zones with different topographies. For this purpose, the controller acted on a conventional pump, a booster pump and a control valve. To analyze the performance under the controller action, tests were performed emulating water-demand scenarios, introducing perturbations and changing the pressure setpoints. The real-time control performance was proven based on the dynamic performance, steady-state performance and SEC. The experimental results showed that the proposed controller kept the pressures close to the setpoints and provided a reduction in the SEC between 15.1% and 17.8%, compared with the uncontrolled system, and an economy that varied from 2.5% to 8.1% compared with the performance of the ANN based only on pressure control.

     

Validation of scientific topic models using graph analysis and corpus metadata

Probabilistic topic modeling algorithms like Latent Dirichlet Allocation (LDA) have become powerful tools for the analysis of large collections of documents (such as papers, projects, or funding applications) in science, technology an innovation (STI) policy design and monitoring. However, selecting an appropriate and stable topic model for a specific application (by adjusting the hyperparameters of the algorithm) is not a trivial problem. Common validation metrics like coherence or perplexity, which are focused on the quality of topics, are not a good fit in applications where the quality of the document similarity relations inferred from the topic model is especially relevant. Relying on graph analysis techniques, the aim of our work is to state a new methodology for the selection of hyperparameters which is specifically oriented to optimize the similarity metrics emanating from the topic model. In order to do this, we propose two graph metrics: the first measures the variability of the similarity graphs that result from different runs of the algorithm for a fixed value of the hyperparameters, while the second metric measures the alignment between the graph derived from the LDA model and another obtained using metadata available for the corresponding corpus. Through experiments on various corpora related to STI, it is shown that the proposed metrics provide relevant indicators to select the number of topics and build persistent topic models that are consistent with the metadata. Their use, which can be extended to other topic models beyond LDA, could facilitate the systematic adoption of this kind of techniques in STI policy analysis and design.

     

Two-step sinter-crystallization of K2O–CaO–P2O5–SiO2 (45S5-K) bioactive glass

Bioactive glasses and glass-ceramics (GCs) effectively regenerate bone tissue, however most GCs show improved mechanical properties. In this work, we developed and tested a rarely studied bioactive glass composition (24.4K₂O-26.9CaO-46.1SiO₂-2.6P₂O₅ mol%, identified as 45S5-K) with different particle sizes and heating rates to obtain a sintered GC that combines good fracture strength, low elastic modulus, and bioactivity. We analyzed the influence of the sintering processing conditions in the elastic modulus, Vickers microhardness, density, and crystal phase formation in the GC. The best GC shows improved properties compared with its parent glass. This glass achieves a good densification degree with a two-step viscous flow sintering approach and the resulting GC shows as high bioactivity as that of the standard 45S5 Bioglass®. Furthermore, the GC elastic modulus (56 GPa) is relatively low, minimizing stress shielding. Therefore, we unveiled the glass sintering behavior with concurrent crystallization of this complex bioactive glass composition and developed a potential GC for bone regeneration.