Verified compilation of C programs with a nominal memory model

Memory models play an important role in verified compilation of imperative programming languages. A representative one is the block-based memory model of CompCert---the state-of-the-art verified C compiler. Despite its success, the abstraction over memory space provided by CompCert's memory model is still primitive and inflexible. In essence, it uses a fixed representation for identifying memory blocks in a global memory space and uses a globally shared state for distinguishing between used and unused blocks. Therefore, any reasoning about memory must work uniformly for the global memory; it is impossible to individually reason about different sub-regions of memory (i.e., the stack and global definitions). This not only incurs unnecessary complexity in compiler verification, but also poses significant difficulty for supporting verified compilation of open or concurrent programs which need to work with contextual memory, as manifested in many previous extensions of CompCert. To remove the above limitations, we propose an enhancement to the block-based memory model based on nominal techniques; we call it the nominal memory model. By adopting the key concepts of nominal techniques such as atomic names and supports to model the memory space, we are able to 1) generalize the representation of memory blocks to any types satisfying the properties of atomic names and 2) remove the global constraints for managing memory blocks, enabling flexible memory structures for open and concurrent programs. To demonstrate the effectiveness of the nominal memory model, we develop a series of extensions of CompCert based on it. These extensions show that the nominal memory model 1) supports a general framework for verified compilation of C programs, 2) enables intuitive reasoning of compiler transformations on partial memory; and 3) enables modular reasoning about programs working with contextual memory. We also demonstrate that these extensions require limited changes to the original CompCert, making the verification techniques based on the nominal memory model easy to adopt.

Development of a Stock Price Prediction Framework for Intelligent Media and Technical Analysis

Equity traders are always looking for tools that will help them maximise returns and minimise risk, be it fundamental or technical analysis techniques. This research integrates tools used by equity traders and uses them together with machine learning and deep learning techniques. The presented work introduces a South African-based sentiment classifier to extract sentiment from new headlines and tweets. The experimental work uses four machine learning models for fundamental analysis and six long short-term memory model architectures, including a developed encoder-decoder long short-term memory model for technical analysis. Data used in the experiments is mined and collected from news sites, tweets from Twitter and Yahoo Finance. The results from 2 experiments show an accuracy of 96% in predicting one of the major telecommunication companies listed on the JSE closing price movement while using the linear discriminant analysis model and an RMSE of 0.023 in predicting a significant telecommunication company closing price using encoder-decoder long short-term memory. These findings reveal that the sentiment feature contains an essential fundamental value, and technical indicators also help move closer to predicting the closing price.

Task Scheduling in Fog Node within the Tactical Cloud

Fog computing architecture competent to support the mission-oriented network-centric warfare provides the framework for a tactical cloud in this work. The tactical cloud becomes situation-aware of the war from the information relayed by fog nodes (FNs) on the battlefield. This work aims to sustain the network of FNs by maintaining the operational efficiency of the FNs on the battlefield at the tactical edge. The proposed solution monitors and predicts the likely overloading of an FN using the long short-term memory model through a buddy FN at the fog server (FS). This paper also proposes randomised task scheduling (RTS) algorithm to avert the likely overloading of an FN by pre-empting tasks from the FN and scheduling them to another FN. The experimental results demonstrate that RTS with linear complexity has a schedulability measure 8% - 26% higher than that of other base scheduling algorithms. The results show that the LSTM model has low mean absolute error compared to other time-series forecasting models.

Groundwater Radon Precursor Anomalies Identification by EMD-LSTM Model

Groundwater radon concentrations can reflect the changes of crustal stress and strain. Scholars and scientific institutions have also recorded groundwater radon precursor anomalies before earthquakes. Therefore, groundwater radon monitoring is an effective means of predicting seismic activities. However, the variation of radon concentrations within groundwater is not only affected by structural factors, but also by environmental factors, such as air pressure, temperature, and rainfall. This causes difficulty in identifying the possible precursor anomalies. Therefore, the EMD-LSTM model is proposed to identify the radon anomalies. This study investigated the time series data of groundwater radon from well #32 located in Sichuan province. Three models (including the LSTM (Long Short-Term Memory) model with auxiliary data, the EMD-LSTM (Empirical Mode Decomposition Long Short-Term Memory) model with auxiliary data, and the EMD-LSTM model without auxiliary data) were developed in order to predict groundwater radon variations. The results indicated that the prediction accuracy of the EMD-LSTM model was much higher than that of the LSTM model, and the EMD-LSTM model without auxiliary data also can obtain an ideal prediction result. Furthermore, the different durations of seismic activities T (T = ±10, ±30, ±50, and ±100) were also investigated by comparing the identification results. The identification rate of the precursor anomalies was the highest when T = ±30. The EMD-LSTM model identified five possible radon anomalies among the seven selected earthquakes. Taking well #32 as an example, we provided a promising method, that was the EMD-LSTM model, to detect the groundwater radon anomalies. It also suggested that the EMD-LSTM model can be used to identify the possible precursor anomalies within future studies.

A Novel Flickering Multi-Target Joint Detection Method Based on a Biological Memory Model

The robust target detection ability of marine navigation radars is essential for safe shipping. However, time-varying river and sea surfaces will induce target scattering changes, known as fluctuating characteristics. Moreover, the targets exhibiting stronger fluctuation disappear in some frames of the radar images, which is known as flickering characteristics. This phenomenon causes a severe decline in the detection performance of traditional detection methods. A biological memory model-based dynamic programming multi-target joint detection method was proposed to address this issue in this paper. Firstly, a global detection operator is used to discretize the multi-target state into multiple single-target states, achieving the discretization of numerous targets. Meanwhile, updating the formula of the memory weight merit function can strengthen the joint frame correlation of the flickering characteristics target. The progressive loop integral is utilized to update the target states to optimize the candidate target set. Finally, a two-stage threshold criterion is utilized to detect the target at different amplitude levels accurately. Simulation and experimental results are given to validate the assertion that the detection performance of the proposed method is greatly improved under a low SCR of 3-8 dB for multiple flickering target detection.