We present 2DToonShade: a semi-automatic method for creating shades and self-shadows in cel animation. Besides producing attractive images, shades and shadows provide important visual cues about depth, shapes, movement and lighting of the scene. In conventional cel animation, shades and shadows are drawn by hand. As opposed to previous approaches, this method does not rely on a complex 3D reconstruction of the scene: its key advantages are simplicity and ease of use. The tool was designed to stay as close as possible to the natural 2D creative environment and therefore provides an intuitive and user-friendly interface. Our system creates shading based on hand-drawn objects or characters, given very limited guidance from the user. The method employs simple yet very efficient algorithms to create shading directly out of drawn strokes. We evaluate our system through a subjective user study and provide qualitative comparison of our method versus existing professional tools and recent state of the art.
Keywords: Toon shading | Cel shading | Hand-drawn animation | Image-based rendering | Non-photorealistic-rendering

The scanning electron microscopy (SEM) images are commonly used to understand the microstructure of the concrete. With the advancements in the field of computer vision, many researchers have adopted the image processing technique for the microstructure analysis. Most of the previous methods are not adaptable, nonreproducible, semi-automated, and most importantly all these methods are highly influenced by image magnification. Therefore, to overcome these challenges, this paper presents a machine learning based image segmentation method for microstructure analysis and degree of hydration measurement using SEM images. In addition, the authors looked into the impact of magnification of SEM images on the model accuracy and classifier training for the degree of hydration measurement considering two scenarios. First, the image segmentation was performed using a classifier of specific magnification, and then a common classifier is trained using the image of different magnification. The results show that the Random Forest classifier algorithm is suitable for microstructure analysis using SEM images. Through the statistical analysis, it has been proved that there is no significant effect of magnification on model training and accuracy for the degree of hydration measurement. So, a single classifier can be used to process the images of different magnification of a specimen which reduces the effort of training and computational time. The proposed method can generate highly accurate and reliable results in a shorter time and lower cost. Moreover, the findings in this research can be useful for researchers to determine the optimum magnification required for the microstructure analysis.
Keywords: Concrete microstructure analysis | Degree of hydration | Machine learning | Image segmentation

For over three decades, therapeutic jurisprudence (TJ) has produced rich scholarship highlighting the inseparable connection between law and personal wellbeing. Only recently, however, have TJ scholars begun to explore the influence that the lawhas on those practicing it. The current research aimsto contribute to this developing area of study. It explores the “emotional map” of public prosecutors in relation to defendants and crime victims, their awareness to these emotions and the impact that these emotions have on their professional decisions. The research involves in-depth interviews with 14 public prosecutors handling criminal cases in Israeli courts. The qualitative, phenomenological analysis of the documented interviews revealed three exposure levels in which interviewees discussed the emotional aspects of their work. The tension between resisting emotions and accepting them was lurking upon each one of the subjects. Their descriptions of specific raw emotions emerged at the deepest level of exposure, and at that level, anger was the most prominent emotion. Our findings raise some skepticism regarding the prosecutor image as a completely rational and provide the insight that prosecutors emotional world is boiling underneath the surface. Moreover, the exposure of the continuous tension between acceptance and rejection of emotions provides an explanation for the prosecutors difficulty in acknowledging their emotions in full. This tension negatively impacts the prosecutors personal and professional lives inways that resemble psychological symptoms of secondary trauma. The findings may contribute to the development of a “knowledge base” of emotional experiences of prosecutors that could enable the creation of models for regulating and managing emotions of legal agents, for the benefit of litigants, legal agents, and the legal process more broadly
Keywords: Therapeutic jurisprudence | Emotions | Prosecutors | Qualitative research | Anger | Secondary trauma

In response to the volume and sophistication of malicious software or malware, security investigators rely on dynamic analysis for malware detection to thwart obfuscation and packing issues. Dynamic analysis is the process of executing binary samples to produce reports that summarise their runtime behaviors. The investigator uses these reports to detect malware and attribute threat types leveraging manually chosen features. However, the diversity of malware and the execution environments make manual approaches not scalable because the investigator needs to manually engineer fingerprinting features for new environments. In this paper, we propose, MalDy (mal die), a portable (plug and play) malware detection and family threat attribution framework using supervised machine learning techniques. The key idea of MalDy portability is the modeling of the behavioral reports into a sequence of words, along with advanced natural language processing (NLP) and machine learning (ML) techniques for automatic engineering of relevant security features to detect and attribute malware without the investigator intervention. More precisely, we propose to use bag-of-words (BoW) NLP model to formulate the behavioral reports. Afterward, we build ML ensembles on top of BoW features. We extensively evaluate MalDy on various datasets from different platforms (Android and Win32) and execution environments. The evaluation shows the effectiveness and the portability of MalDy across the spectrum of the analyses and settings.
Keywords: Malware | Android | Win32 | Behavioral analysis | Machine learning | NLP

Traditional trial-and-error methods seriously restrict and hinder the searching of high-performance functional materials, especially when the search space is large. Rapid searching for advanced functional materials has always been a hot research topic, and attracted a lot of experimental and theoretical research attention. Here, by combining machine learning method with density functional theory (DFT) calculations, a target-driven method is proposed here to speed up the discovery of hidden hybrid organic-inorganic perovskites (HOIPs) for photovoltaic applications from 230808 HOIPs candidates which is almost two orders larger than previous studied. After imposing two criterions, i.e., charge neutrality condition and stability condition, on potential HOIPs candidates, followed by a machine learning (ML) screening, 686 orthorhombic-like HOIPs with proper bandgap are selected. In machine learning screening, ensemble learning using three ML models, including gradient boosting regression (GBR), supporting vector regression (SVR) and kernel ridge regression (KRR), are applied to predict the bandgap of 38086 HOIPs candidates. 132 stable and non-toxic (Cd-, Pb- and Hg-free) orthorhombiclike HOIPs are finally verified by DFT calculations with appropriate band gap for solar cells. In the present study, not only a series of unexplored stable and non-toxic HOIPs are discovered for further experimental synthesis, a new HOIPs database is constructed as well, thus beneficial to future functional material design.
Keywords: Machine learning | Hybrid organic-inorganic perovskites | First principle calculations | Photovoltaics