The ability of a system to meet its requirements is a strong determinant of success. Thus effective requirements specification is crucial. Explicit Requirements are well-defined needs for a system to execute. IMplicit Requirements (IMRs) are assumed needs that a system is expected to fulfill though not elicited during requirements gathering. Studies have shown that a major factor in the failure of software systems is the presence of unhandled IMRs. Since relevance of IMRs is important for efficient system functionality, there are methods developed to aid the identification and management of IMRs. In this paper, we emphasize that Common Sense Knowledge, in the field of Knowledge Representation in AI, would be useful to automatically identify and manage IMRs. This paper is aimed at identifying the sources of IMRs and also proposing an automated support tool for managing IMRs within an organizational context. Since this is found to be a present gap in practice, our work makes a contribution here. We propose a novel approach for identifying and managing IMRs based on combining three core technologies: common sense knowledge, text mining and ontology. We claim that discovery and handling of unknown and non-elicited requirements would reduce risks and costs in software development.

Biometric surveillance is coming for you, even if you have'nothing to hide' Clare Garvie is a Senior Associate at Georgetown University's Center on Privacy and Technology, where she has dedicated her work to studying law enforcement's use of face recognition technology on the American public. She is considered the foremost expert on face recognition technology; last year she testified in front of Congress. She writes extensively on its use in law enforcement investigations. As well, she brings to light the worrying ways the technology disrupts privacy, circumvents judicial norms and legal precedents, and promotes chilling effects on free speech and civil liberties. All of this happens under a veil of secrecy, without public consent and largely outside of the purview of American lawmakers. Garvie's research spotlights the ways these technologies are disproportionately used on Black and Brown communities and the failures of face recognition algorithms when deployed on people of color and women. The technology's efficacy, already cause for concern, is further problematized by law enforcement's cavalier practices.

With food at the core of the business, Glovo delivers any product within your city at any time of day. Our vision and ambition are not only to give everyone easy access to everything in their city, but it is also to offer our employees the job of their lives. A job where you'll be challenged and have the most fun working in through tech-enabled experiences. Your work-life opportunity: Glovo is looking for a Business Analyst for the Global Partner Operations team. You will join a team of project managers and fellow analysts aiming to deconstruct operational performance of our Partners and deploy creative solutions to ensure a great experience for Glovo customers.

Pest and disease control plays a key role in agriculture since the damage caused by these agents are responsible for a huge economic loss every year. Based on this assumption, we create an algorithm capable of detecting rust (Hemileia vastatrix) and leaf miner (Leucoptera coffeella) in coffee leaves (Coffea arabica) and quantify disease severity using a mobile application as a high-level interface for the model inferences. We used different convolutional neural network architectures to create the object detector, besides the OpenCV library, k-means, and three treatments: the RGB and value to quantification, and the AFSoft software, in addition to the analysis of variance, where we compare the three methods. The results show an average precision of 81,5% in the detection and that there was no significant statistical difference between treatments to quantify the severity of coffee leaves, proposing a computationally less costly method. The application, together with the trained model, can detect the pest and disease over different image conditions and infection stages and also estimate the disease infection stage.

This paper presents a supervised learning method to generate continuous cost-to-go functions of non-holonomic systems directly from the workspace description. Supervision from informative examples reduces training time and improves network performance. The manifold representing the optimal trajectories of a non-holonomic system has high-curvature regions which can not be efficiently captured with uniform sampling. To address this challenge, we present an adaptive sampling method which makes use of sampling-based planners along with local, closed-form solutions to generate training samples. The cost-to-go function over a specific workspace is represented as a neural network whose weights are generated by a second, higher order network. The networks are trained in an end-to-end fashion. In our previous work, this architecture was shown to successfully learn to generate the cost-to-go functions of holonomic systems using uniform sampling. In this work, we show that uniform sampling fails for non-holonomic systems. However, with the proposed adaptive sampling methodology, our network can generate near-optimal trajectories for non-holonomic systems while avoiding obstacles. Experiments show that our method is two orders of magnitude faster compared to traditional approaches in cluttered environments.

We present graph-based translation models which translate source graphs into target strings. Source graphs are constructed from dependency trees with extra links so that non-syntactic phrases are connected. Inspired by phrase-based models, we first introduce a translation model which segments a graph into a sequence of disjoint subgraphs and generates a translation by combining subgraph translations left-to-right using beam search. However, similar to phrase-based models, this model is weak at phrase reordering. Therefore, we further introduce a model based on a synchronous node replacement grammar which learns recursive translation rules. We provide two implementations of the model with different restrictions so that source graphs can be parsed efficiently. Experiments on Chinese--English and German--English show that our graph-based models are significantly better than corresponding sequence- and tree-based baselines.

Most American retail banks are designed the same way: Customers must pass several desks set aside for loan and mortgage officers before they can talk to a customer representative. I only step inside a bank a few times each year, but even pre-pandemic, I can't remember the last time I saw someone sitting at one of those desks. Everyone I know who's obtained a home or business loan in the recent past started with an online application process. For this morning's column, Alex Wilhelm interviewed Dave Girouard, CEO of Upstart, an AI-powered fintech lender that expects to see growth increase 114% this year. A forecast like that suggests that retail banks have gotten comfortable with using automated tools to calculate risk, which may help explain all the empty desks at my local branch.

The illegal animal trade is a global scourge but a lucrative one, worth $8 to 10 billion annually, according to the United Nations Office on Drugs and Crime (UNODC) -- trailing only human, drug and weapons trafficking in value. With so much money to be made, conservationists and wildlife rangers face overwhelming odds against well-organized poaching operations fueled by incessant demand for illicit animal products. The results of this protracted conflict have been nothing short of devastating for the species caught in the middle. At the start of the 20th century, more than 100,000 tigers are estimated to have roamed throughout Southeast Asia. Today, due to a combination of habitat loss and aggressive poaching, fewer than 4,000 currently remain in the wild.

Britain's military will unveil a shift towards more lethal, hi-tech and drone-enabled warfare on Monday as ministers and chiefs attempt to stave off criticism of impending cuts in the size of the armed forces. The plan will be highlighted in a defence command paper setting out the military's ambitions for the next five years and confirming a cut in the size of the army to an anticipated 72,500 troops, and a string of other savings as day-to-day defence budgets are squeezed. Ben Wallace, the defence secretary, said on Friday it was time to end "the Top Trumps game of numbers" because previous reviews that had emphasised size had left the military with "lots of ships that are tied up and not available, or lots of regiments". Instead, ministers and service chiefs will highlight how forces such as the Royal Marines could use a mobile phone app to locate friends and enemies on a battlefield while using Ghost drones, 6ft-long single-blade helicopter-like devices that can highlight and even fire at targets. Gen Sir Nick Carter, the head of the armed forces, said that "rather than focus on size and shape, I would focus on lethality, the relevance, the resilience and the readiness of our army and our armed forces."

We develop an algorithm to train individually fair learning-to-rank (LTR) models. The proposed approach ensures items from minority groups appear alongside similar items from majority groups. This notion of fair ranking is based on the definition of individual fairness from supervised learning and is more nuanced than prior fair LTR approaches that simply ensure the ranking model provides underrepresented items with a basic level of exposure. The crux of our method is an optimal transport-based regularizer that enforces individual fairness and an efficient algorithm for optimizing the regularizer. We show that our approach leads to certifiably individually fair LTR models and demonstrate the efficacy of our method on ranking tasks subject to demographic biases. Information retrieval (IR) systems are everywhere in today's digital world, and ranking models are integral parts of many IR systems. In light of their ubiquity, issues of algorithmic bias and unfairness in ranking models have come to the fore of the public's attention. In many applications, the items to be ranked are individuals, so algorithmic biases in the output of ranking models directly affect people's lives. For example, gender bias in job search engines directly affect the career success of job applicants (Dastin, 2018).