Field canals improvement projects (FCIPs) are one of the ambitious projects constructed to save fresh water. To finance this project, Conceptual cost models are important to accurately predict preliminary costs at the early stages of the project. The first step is to develop a conceptual cost model to identify key cost drivers affecting the project. Therefore, input variables selection remains an important part of model development, as the poor variables selection can decrease model precision. The study discovered the most important drivers of FCIPs based on a qualitative approach and a quantitative approach. Subsequently, the study has developed a parametric cost model based on machine learning methods such as regression methods, artificial neural networks, fuzzy model and case-based reasoning.

SANAA - The Saudi-led military coalition in Yemen carried out several airstrikes on the Houthi-held capital Sanaa on Thursday after the Iranian-aligned movement claimed responsibility for drone attacks on Saudi oil installations. The Sanaa strikes targeted nine military sites in and around the city, residents said, with humanitarian agencies reporting a number of casualties. Rubble filled a populated street lined by mud-brick houses, a Reuters journalist on the scene said. A crowd of men lifted the body of a women, wrapped in a white shroud, into an ambulance. Houthi-run Masirah television quoted the Houthi health ministry as saying six civilians, including four children, had been killed and 60 wounded, including two Russian women working in the health sector.

A couple of years ago, there was a joke doing the rounds at technology conferences that AI in business is like teenagers and sex: everyone talks about it, but few actually get it. Is the ribald witticism outdated in 2019? Or has the increased hype enveloping AI that it will magically solve most business problems only further confused executives? So much so they are not engaging with AI's myriad technologies or are left clumsily fumbling with algorithms that fail to perform, while cannier rivals score big. Moreover, has the crucial point that AI in business is best utilised as a means of achieving very specific, narrow-focused objectives, and is not an end point in itself, been obscured by the sheer volume of misleading buzz?

In some approaches, the input information is simply the whole Belief revision (Alchourrón, Gärdenfors, and Makinson epistemic as in (Benferhat et al. 2000). In this paper, the 1985; Williams 1995; Williams and Rott 2001), is an important new information will be represented by a consistent set of field of research in artificial intelligence and knowledge weighted propositional logic formulas.

In this paper, we aim to predict students’ learning perfor-mance by combining two-modality sensing variables, namely eye tracking that monitors learners’ eye movements and elec-troencephalography (EEG) that measures learners’ cerebral activity. Our long-term goal is to use both data to provide ap-propriate adaptive assistance for students to enhance their learning experience and optimize their performance. An ex-perimental study was conducted in order to collet gaze data and brainwave signals of fifteen students during an interac-tion with a virtual learning environment. Different classifica-tion algorithms were used to discriminate between two groups of learners: students who successfully resolve the problem-solving tasks and students who do not. Experimental results demonstrated that the K-Nearest Neighbor classifier achieved good accuracy when combining both eye movement and EEG features compared to using solely eye movement or EEG.

Forgetting as a knowledge management operation has received much less attention than operations like inference, or revision. It was mainly in the area of logic programming that techniques and axiomatic properties have been studied systematically. However, at least from a cognitive view, forgetting plays an important role in restructuring and reorganizing a human's mind, and it is closely related to notions like relevance and independence which are crucial to knowledge representation and reasoning. In this paper, we propose axiomatic properties of (intentional) forgetting for general epistemic frameworks which are inspired by those for logic programming, and we evaluate various forgetting operations which have been proposed recently by Beierle et al. according to them. The general aim of this paper is to advance formal studies of (intentional) forgetting operators while capturing the many facets of forgetting in a unifying framework in which different forgetting operators can be contrasted and distinguished by means of formal properties.

Auditing algorithms has emerged as a methodology for holding algorithms accountable by testing whether they are fair. This process often relies on the repeated use of a platform to record inputs and their corresponding outputs. For example, to audit Google search, one repeatedly inputs queries and captures the received search pages. The goal is then to discover, in the collected data, patterns that will reveal the ``secrets'' of algorithmic decision making. This knowledge discovery process makes some algorithm auditing tasks great applications for data mining techniques. In this paper, we introduce one particular algorithm audit, that of Google's Top stories. We describe the process of data collection, exploration, and analysis for this application and share some of the gleaned insights. Concretely, our analysis suggests that Google might be trying to burst the famous ``filter bubble'' by choosing less known publishers for the 3rd position in the Top stories.

The ability to estimate task difficulty is critical for many real-world decisions such as setting appropriate goals for ourselves or appreciating others' accomplishments. Here we give a computational account of how humans judge the difficulty of a range of physical construction tasks (e.g., moving 10 loose blocks from their initial configuration to their target configuration, such as a vertical tower) by quantifying two key factors that influence construction difficulty: physical effort and physical risk. Physical effort captures the minimal work needed to transport all objects to their final positions, and is computed using a hybrid task-and-motion planner. Physical risk corresponds to stability of the structure, and is computed using noisy physics simulations to capture the costs for precision (e.g., attention, coordination, fine motor movements) required for success. We show that the full effort-risk model captures human estimates of difficulty and construction time better than either component alone.

Events in the world may be caused by other, unobserved events. We consider sequences of events in continuous time. Given a probability model of complete sequences, we propose particle smoothing---a form of sequential importance sampling---to impute the missing events in an incomplete sequence. We develop a trainable family of proposal distributions based on a type of bidirectional continuous-time LSTM: Bidirectionality lets the proposals condition on future observations, not just on the past as in particle filtering. Our method can sample an ensemble of possible complete sequences (particles), from which we form a single consensus prediction that has low Bayes risk under our chosen loss metric. We experiment in multiple synthetic and real domains, using different missingness mechanisms, and modeling the complete sequences in each domain with a neural Hawkes process (Mei & Eisner 2017). On held-out incomplete sequences, our method is effective at inferring the ground-truth unobserved events, with particle smoothing consistently improving upon particle filtering.

Imagine you're hiking through the woods near a border. Suddenly, you hear a mechanical buzzing, like a gigantic bee. Two quadcopters have spotted you and swoop in for a closer look. They send the signals to a central server, which triangulates your exact location and feeds it back to the drones. Cameras and other sensors on the machines recognize you as human and try to ascertain your intentions.