Visual Examination and Photography in Failure Analysis

Failure analysis is an investigative process in which the visual observations of features present on a failed component and the surrounding environment are essential in determining the root cause of a failure. This article reviews the basic photographic principles and techniques that are applied to failure analysis, both in the field and in the laboratory. It discusses the processes involved in visual examination, field photographic documentation, and laboratory photographic documentation of failed components. The article describes the operating principles of each part of a professional digital camera. It covers basic photographic principles and manipulation of settings that assist in producing high-quality images. The need for accurate photographic documentation in failure analysis is also presented.

Exploring the Need of Social Change Leadership Concerning Health Communication and Health Disparities During the COVID-19 Pandemic

In March of 2020, the United States activated nationwide pandemic response protocols due to the swift spread of Novel Coronavirus Disease 2019, also known as COVID-19. Amidst the domestic response, urgency surrounded the need to build collective awareness of the signs, symptoms, and preventive measures of the virus. As the virus spread and historically marginalized communities were disproportionately impacted with rates of infection, the need to explore the presence of disparities in health communication, health education, and personal health literacy surfaced. The research contained within this study examines the root cause of the gap in health literacy for communities of color and presents actionable next steps to increase positive healthcare outcomes for all.

The Systematic Design of Responsibility Analysis by Abstract Interpretation

Given a behavior of interest, automatically determining the corresponding responsible entity (i.e., the root cause) is a task of critical importance in program static analysis. In this article, a novel definition of responsibility based on the abstraction of trace semantics is proposed, which takes into account the cognizance of observer, which, to the best of our knowledge, is a new innovative idea in program analysis. Compared to current dependency and causality analysis methods, the responsibility analysis is demonstrated to be more precise on various examples. However, the concrete trace semantics used in defining responsibility is uncomputable in general, which makes the corresponding concrete responsibility analysis undecidable. To solve this problem, the article proposes a sound framework of abstract responsibility analysis, which allows a balance between cost and precision. Essentially, the abstract analysis builds a trace partitioning automaton by an iteration of over-approximating forward reachability analysis with trace partitioning and under/over-approximating backward impossible failure accessibility analysis, and determines the bounds of potentially responsible entities along paths in the automaton. Unlike the concrete responsibility analysis that identifies exactly a single action as the responsible entity along every concrete trace, the abstract analysis may lose some precision and find multiple actions potentially responsible along each automaton path. However, the soundness is preserved, and every responsible entity in the concrete is guaranteed to be also found responsible in the abstract.

Root cause analysis of COVID-19 cases by enhanced text mining process

<p>The main focus of this research is to find the reasons behind the fresh cases of COVID-19 from the public’s perception for data specific to India. The analysis is done using machine learning approaches and validating the inferences with medical professionals. The data processing and analysis is accomplished in three steps. First, the dimensionality of the vector space model (VSM) is reduced with improvised feature engineering (FE) process by using a weighted term frequency-inverse document frequency (TF-IDF) and forward scan trigrams (FST) followed by removal of weak features using feature hashing technique. In the second step, an enhanced K-means clustering algorithm is used for grouping, based on the public posts from Twitter®. In the last step, latent dirichlet allocation (LDA) is applied for discovering the trigram topics relevant to the reasons behind the increase of fresh COVID-19 cases. The enhanced K-means clustering improved Dunn index value by 18.11% when compared with the traditional K-means method. By incorporating improvised two-step FE process, LDA model improved by 14% in terms of coherence score and by 19% and 15% when compared with latent semantic analysis (LSA) and hierarchical dirichlet process (HDP) respectively thereby resulting in 14 root causes for spike in the disease.</p>

Sexual harassment and sense of entitlement

The resignation of an Indian Administrative Services Officer named Rani Nagar and its non-acceptance by the state government of Haryana had revived the debate around sexual harassment in India. The reason given by the officer was sexual harassment by a senior and non-action of authorities leading to her feeling threatened for her safety. It highlights the fact that position of women in India is not corresponding to their professional achievements. The mindset of society remains attached to the notion of inferior status of women in general, regardless of their professional status. This paper seeks to delve more into the issue of sexual harassment per se and find the actual root cause that serves as a driving force behind such acts of perpetrator. It is done by using cases, theories and examples of contemporary times.

P1OVD: Patch-Based 1-Day Out-of-Bounds Vulnerabilities Detection Tool for Downstream Binaries

In the past decades, due to the popularity of cloning open-source software, 1-day vulnerabilities are prevalent among cyber-physical devices. Detection tools for 1-day vulnerabilities effectively protect users who fail to adopt 1-day vulnerability patches in time. However, manufacturers can non-standardly build the binaries from customized source codes to multiple architectures. The code variants in the downstream binaries decrease the accuracy of 1-day vulnerability detections, especially when signatures of out-of-bounds vulnerabilities contain incomplete information of vulnerabilities and patches. Motivated by the above observations, in this paper, we propose P1OVD, an effective patch-based 1-day out-of-bounds vulnerability detection tool for downstream binaries. P1OVD first generates signatures containing patch information and vulnerability root cause information. Then, P1OVD uses an accurate and robust matching algorithm to scan target binaries. We have evaluated P1OVD on 104 different versions of 30 out-of-bounds vulnerable functions and 620 target binaries in six different compilation environments. The results show that P1OVD achieved an accuracy of 83.06%. Compared to the widely used patch-level vulnerability detection tool ReDeBug, P1OVD ignores 4.07 unnecessary lines on average. The experiments on the x86_64 platform and the O0 optimization show that P1OVD increases the accuracy of the state-of-the-art tool, BinXray, by 8.74%. Besides, it can analyze a single binary in 4 s after a 20-s offline signature extraction on average.