For some reason, data exploration and cleaning are often seen as the lesser-arts of the data science world. This could not be more wrong. EDA is the only way for data scientists to really get a grasp on the problem. Exploring the data is crucial for understanding what the data really represents; rather than what we might think it represents. Indeed data often includes biases (e.g. are the label's representative of the class they are supposed to define?

Software Quality Assurance (SQA) planning aims to define proactive plans, such as defining maximum file size, to prevent the occurrence of software defects in future releases. To aid this, defect prediction models have been proposed to generate insights as the most important factors that are associated with software quality. Such insights that are derived from traditional defect models are far from actionable-i.e., practitioners still do not know what they should do or avoid to decrease the risk of having defects, and what is the risk threshold for each metric. A lack of actionable guidance and risk threshold can lead to inefficient and ineffective SQA planning processes. In this paper, we investigate the practitioners' perceptions of current SQA planning activities, current challenges of such SQA planning activities, and propose four types of guidance to support SQA planning. We then propose and evaluate our AI-Driven SQAPlanner approach, a novel approach for generating four types of guidance and their associated risk thresholds in the form of rule-based explanations for the predictions of defect prediction models. Finally, we develop and evaluate an information visualization for our SQAPlanner approach. Through the use of qualitative survey and empirical evaluation, our results lead us to conclude that SQAPlanner is needed, effective, stable, and practically applicable. We also find that 80% of our survey respondents perceived that our visualization is more actionable. Thus, our SQAPlanner paves a way for novel research in actionable software analytics-i.e., generating actionable guidance on what should practitioners do and not do to decrease the risk of having defects to support SQA planning.

Amazon today announced the general availability of Lookout for Metrics, a fully managed service that uses machine learning to monitor key factors impacting the health of enterprises. Launched at re:Invent 2020 last December in preview, Lookout for Metrics can now be accessed by most Amazon Web Services (AWS) customers via the AWS console and through supporting partners. Organizations analyze metrics or key performance indicators to help their businesses run effectively and efficiently. Traditionally, business intelligence tools are used to manage this data across disparate sources, but identifying these anomalies is challenging. Traditional rule-based methods look for data that falls outside of numerical ranges.

As organizations increasingly embark on their digital transformation journey, IT is turning into a profit center, rather than a cost center. CIOs (chief information officers) are more than often referred to as chief innovation officers. New roles like chief data officer and chief analytics officer are rising to prominence. Organizations on their digital transformation journey are facing increasing pressures due to the pandemic, remote workspaces and increasingly distributed applications. IT's ability to rapidly adapt to changing market needs is paramount to a successful digital transformation journey.

A music mashup combines audio elements from two or more songs to create a new work. To reduce the time and effort required to make them, researchers have developed algorithms that predict the compatibility of audio elements. Prior work has focused on mixing unaltered excerpts, but advances in source separation enable the creation of mashups from isolated stems (e.g., vocals, drums, bass, etc.). In this work, we take advantage of separated stems not just for creating mashups, but for training a model that predicts the mutual compatibility of groups of excerpts, using self-supervised and semi-supervised methods. Specifically, we first produce a random mashup creation pipeline that combines stem tracks obtained via source separation, with key and tempo automatically adjusted to match, since these are prerequisites for high-quality mashups. To train a model to predict compatibility, we use stem tracks obtained from the same song as positive examples, and random combinations of stems with key and/or tempo unadjusted as negative examples. To improve the model and use more data, we also train on "average" examples: random combinations with matching key and tempo, where we treat them as unlabeled data as their true compatibility is unknown. To determine whether the combined signal or the set of stem signals is more indicative of the quality of the result, we experiment on two model architectures and train them using semi-supervised learning technique. Finally, we conduct objective and subjective evaluations of the system, comparing them to a standard rule-based system.

We introduce the problem of robust subgroup discovery, i.e., finding a set of interpretable descriptions of subsets that 1) stand out with respect to one or more target attributes, 2) are statistically robust, and 3) non-redundant. Many attempts have been made to mine either locally robust subgroups or to tackle the pattern explosion, but we are the first to address both challenges at the same time from a global perspective. First, we formulate a broad model class of subgroup lists, i.e., ordered sets of subgroups, for univariate and multivariate targets that can consist of nominal or numeric variables. This novel model class allows us to formalize the problem of optimal robust subgroup discovery using the Minimum Description Length (MDL) principle, where we resort to optimal Normalized Maximum Likelihood and Bayesian encodings for nominal and numeric targets, respectively. Notably, we show that our problem definition is equal to mining the top-1 subgroup with an information-theoretic quality measure plus a penalty for complexity. Second, as finding optimal subgroup lists is NP-hard, we propose RSD, a greedy heuristic that finds good subgroup lists and guarantees that the most significant subgroup found according to the MDL criterion is added in each iteration, which is shown to be equivalent to a Bayesian one-sample proportions, multinomial, or t-test between the subgroup and dataset marginal target distributions plus a multiple hypothesis testing penalty. We empirically show on 54 datasets that RSD outperforms previous subgroup set discovery methods in terms of quality and subgroup list size.

Forty years ago, the word "hacker" was little known. Its march from obscurity to newspaper headlines owes a great deal to tech journalist Steven Levy, who in 1984 defied the advice of his publisher to call his first book Hackers: Heroes of the Computer Revolution.11 Hackers were a subculture of computer enthusiasts for whom programming was a vocation and playing around with computers constituted a lifestyle. Hackers was published only three years after Tracy Kidder's The Soul of a New Machine, explored in my last column (January 2021, p. 32–37), but a lot had changed during the interval. Kidder's assumed readers had never seen a minicomputer, still less designed one. By 1984, in contrast, the computer geek was a prominent part of popular culture. Unlike Kidder, Levy had to make people reconsider what they thought they already knew. Computers were suddenly everywhere, but they remained unfamiliar enough to inspire a host of popular books to ponder the personal and social transformations triggered by the microchip. The short-lived home computer boom had brought computer programming into the living rooms and basements of millions of middle-class Americans, sparking warnings about the perils of computer addiction. A satirical guide, published the same year, warned of "micromania."15 The year before, the film Wargames suggested computer-obsessed youth might accidentally trigger nuclear war.

Fraud mitigation is one of the most sought-after artificial intelligence (AI) services because it can provide an immediate return on investment. Already, many companies are experiencing lucrative profits thanks to AI and machine learning (ML) systems that detect and prevent fraud in real-time. According to a new report, Highmark Inc.'s Financial Investigations and Provider Review (FIPR) department generated $260 million in savings that would have otherwise been lost to fraud, waste, and abuse in 2019. In the last five years, the company saved $850 million. "We know the overwhelming majority of providers do the right thing. But we also know year after year millions of health care dollars are lost to fraud, waste and abuse," said Melissa Anderson, executive vice president and chief audit and compliance officer, Highmark Health.

This paper addresses the interesting problem of processing and analyzing data in geographic information systems (GIS) to achieve a clear perspective on urban sprawl. The term urban sprawl refers to overgrowth and expansion of low-density areas with issues such as car dependency and segregation between residential versus commercial use. Sprawl has impacts on the environment and public health. In our work, spatiotemporal features related to real GIS data on urban sprawl such as population growth and demographics are mined to discover knowledge for decision support. We adapt data mining algorithms, Apriori for association rule mining and J4.8 for decision tree classification to geospatial analysis, deploying the ArcGIS tool for mapping. Knowledge discovered by mining this spatiotemporal data is used to implement a prototype spatial decision support system (SDSS). This SDSS predicts whether urban sprawl is likely to occur. Further, it estimates the values of pertinent variables to understand how the variables impact each other. The SDSS can help decision-makers identify problems and create solutions for avoiding future sprawl occurrence and conducting urban planning where sprawl already occurs, thus aiding sustainable development. This work falls in the broad realm of geospatial intelligence and sets the stage for designing a large scale SDSS to process big data in complex environments, which constitutes part of our future work.

Current Technologies put the organization's cybersecurity at risk. Even with the new advancements in the defence strategies, security professional fails at some point. Combining the strength of AI with the skills of security professionals from vulnerability checks to defence becomes very effective. Organizations get instant insights, in turn, get reduced response time. Artificial Intelligence for Cyber Security is the new wave in Security.