2-D Coherence Factor for Sidelobe and Ghost Suppressions in Radar Imaging

The coherence factor (CF) is defined as the ratio of coherent power to incoherent power received by the radar aperture. The incoherent power is computed by the multi-antenna receiver based on only the spatial variable. In this respect, it is a one-dimensional (1-D) CF, and thereby the image sidelobes in down-range cannot be effectively suppressed. We propose a two-dimensional (2-D) CF by supplementing the 1-D CF by an incoherent sum dealing with the frequency dimension. In essence, we employ both spatial diversity and frequency diversity which, respectively, enhance imaging quality in cross range and range. Simulations and experimental results are provided to demonstrate the performance advantages of the proposed approach.Comment: 7 pages, 21 figure

Learning to Determine the Quality of News Headlines

Today, most newsreaders read the online version of news articles rather than traditional paper-based newspapers. Also, news media publishers rely heavily on the income generated from subscriptions and website visits made by newsreaders. Thus, online user engagement is a very important issue for online newspapers. Much effort has been spent on writing interesting headlines to catch the attention of online users. On the other hand, headlines should not be misleading (e.g., clickbaits); otherwise, readers would be disappointed when reading the content. In this paper, we propose four indicators to determine the quality of published news headlines based on their click count and dwell time, which are obtained by website log analysis. Then, we use soft target distribution of the calculated quality indicators to train our proposed deep learning model which can predict the quality of unpublished news headlines. The proposed model not only processes the latent features of both headline and body of the article to predict its headline quality but also considers the semantic relation between headline and body as well. To evaluate our model, we use a real dataset from a major Canadian newspaper. Results show our proposed model outperforms other state-of-the-art NLP models.Comment: 10 Pages, Accepted at the 12th International Conference on Agents and Artificial Intelligence (ICAART) 202

Stabilization of a highly expansive soil using waste-tire-derived aggregates and lime treatment

This study investigates the combined efficacy of waste-tire-derived aggregate (TDA) materials and hydrated lime on the compactability, compressive strength and swelling potential of a highly expansive soil from South Australia. A total of 21 mix-designs, covering a comprehensive range of soil–TDA–lime combinations, were examined through standard Proctor compaction, unconfined compressive strength (UCS) and oedometer swell tests. The mobilized UCS exhibited a ‘rise–fall’ behavior, peaking at 5% TDA content and subsequently decreasing (monotonically) for higher inclusions of TDA. Increasing the TDA mean particle size (from 1.67 to 3.34 mm) also contributed positively to the UCS development. Addition of TDA to the soil/soil–lime-blends produced notable reductions in the swelling potential; the reduction was primarily governed by higher TDA contents, and, to a lesser degree, for larger TDA mean particle sizes. However, the role of TDA particle size in reducing swelling was found to be more significant than that of enhancing the UCS. As expected, lime treatment of the soil–TDA blends provided major further improvements to the UCS and swelling potential reduction; the achieved UCS improvements being positively proportional to the lime content and curing time. In view of the experimental results, soil–lime blends containing TDA to soil–lime mass ratios of up to 10% (preferably employing coarse-sand-sized equivalent TDA) can be deemed as suitable choices (capable of adequately mitigating the swelling potential, while simultaneously enhancing the UCS). © 2022 The Author

Robust Prediction-Based Analysis for Genome-Wide Association and Expression Studies

Here we describe a prediction-based framework to analyze omic data and generate models for both disease diagnosis and identification of cellular pathways which are significant in complex diseases. Our framework differs from previous analysis in its use of underlying biology (cellular pathways/gene-sets) to produce predictive feature-disease models. In our study of alcoholism, lung cancer, and schizophrenia, we demonstrate the framework’s ability to robustly analyze omic data of multiple types and sources, identify significant features sets, and produce accurate predictive models

Improved shear strength performance of compacted rubberized clays treated with sodium alginate biopolymer

This study examines the potential use of sodium alginate (SA) biopolymer as an environmentally sustainable agent for the stabilization of rubberized soil blends prepared using a high plasticity clay soil and tire-derived ground rubber (GR). The experimental program consisted of uniaxial compression and scanning electron microscopy (SEM) tests; the former was performed on three soil–GR blends (with GR-to-soil mass ratios of 0%, 5% and 10%) compacted (and cured for 1, 4, 7 and 14 d) employing distilled water and three SA solutions—prepared at SA-to-water (mass-tovolume) dosage ratios of 5, 10 and 15 g/L—as the compaction liquid. For any given GR content, the greater the SA dosage and/or the longer the curing duration, the higher the uniaxial compressive strength (UCS), with only minor added benefits beyond seven days of curing. This behaviour was attributed to the formation and propagation of so-called “cationic bridges” (developed as a result of a “Ca2+/Mg2

How to Certify Machine Learning Based Safety-critical Systems? A Systematic Literature Review

Context: Machine Learning (ML) has been at the heart of many innovations over the past years. However, including it in so-called 'safety-critical' systems such as automotive or aeronautic has proven to be very challenging, since the shift in paradigm that ML brings completely changes traditional certification approaches. Objective: This paper aims to elucidate challenges related to the certification of ML-based safety-critical systems, as well as the solutions that are proposed in the literature to tackle them, answering the question 'How to Certify Machine Learning Based Safety-critical Systems?'. Method: We conduct a Systematic Literature Review (SLR) of research papers published between 2015 to 2020, covering topics related to the certification of ML systems. In total, we identified 217 papers covering topics considered to be the main pillars of ML certification: Robustness, Uncertainty, Explainability, Verification, Safe Reinforcement Learning, and Direct Certification. We analyzed the main trends and problems of each sub-field and provided summaries of the papers extracted. Results: The SLR results highlighted the enthusiasm of the community for this subject, as well as the lack of diversity in terms of datasets and type of models. It also emphasized the need to further develop connections between academia and industries to deepen the domain study. Finally, it also illustrated the necessity to build connections between the above mention main pillars that are for now mainly studied separately. Conclusion: We highlighted current efforts deployed to enable the certification of ML based software systems, and discuss some future research directions.Comment: 60 pages (92 pages with references and complements), submitted to a journal (Automated Software Engineering). Changes: Emphasizing difference traditional software engineering / ML approach. Adding Related Works, Threats to Validity and Complementary Materials. Adding a table listing papers reference for each section/subsection

Cutting tool tracking and recognition based on infrared and visual imaging systems using principal component analysis (PCA) and discrete wavelet transform (DWT) combined with neural networks

The implementation of computerised condition monitoring systems for the detection cutting tools’ correct installation and fault diagnosis is of a high importance in modern manufacturing industries. The primary function of a condition monitoring system is to check the existence of the tool before starting any machining process and ensure its health during operation. The aim of this study is to assess the detection of the existence of the tool in the spindle and its health (i.e. normal or broken) using infrared and vision systems as a non-contact methodology. The application of Principal Component Analysis (PCA) and Discrete Wavelet Transform (DWT) combined with neural networks are investigated using both types of data in order to establish an effective and reliable novel software program for tool tracking and health recognition. Infrared and visual cameras are used to locate and track the cutting tool during the machining process using a suitable analysis and image processing algorithms. The capabilities of PCA and Discrete Wavelet Transform (DWT) combined with neural networks are investigated in recognising the tool’s condition by comparing the characteristics of the tool to those of known conditions in the training set. The experimental results have shown high performance when using the infrared data in comparison to visual images for the selected image and signal processing algorithms