We introduce MeSys, a meaning-based approach, for solving English math word problems (MWPs) via understanding and reasoning in this paper. It first analyzes the text, transforms both body and question parts into their corresponding logic forms, and then performs inference on them. The associated context of each quantity is represented with proposed role-tags (e.g., nsubj, verb, etc.), which provides the flexibility for annotating an extracted math quantity with its associated context information (i.e., the physical meaning of this quantity). Statistical models are proposed to select the operator and operands. A noisy dataset is designed to assess if a solver solves MWPs mainly via understanding or mechanical pattern matching. Experimental results show that our approach outperforms existing systems on both benchmark datasets and the noisy dataset, which demonstrates that the proposed approach understands the meaning of each quantity in the text more.

GENEVA – Major U.S. trading partners including the European Union, China and Japan voiced deep concern at the World Trade Organization (WTO) on Tuesday about possible U.S. measures imposing additional duties on imported autos and parts. Japan, which along with Russia had initiated the discussion at the WTO Council on Trade in Goods, warned that such measures could trigger a spiral of countermeasures and result in the collapse of the rules-based multilateral trading system, an official who attended the meeting said. Over 40 WTO members, including the 28 countries of the European Union -- warned that the U.S. action could seriously disrupt the world market and threaten the WTO system, given the importance of cars to world trade. The United States has imposed tariffs on European steel and aluminum imports and is conducting another national security study that could lead to tariffs on imports of cars and car parts. Both sets of tariffs would be based on concerns about U.S. national security. U.S. President Donald Trump said on June 29 that the probe would be completed in three to four weeks.

Monitoring transactions for suspicious ones can be more efficient. All it takes is doing it intelligently. Up to $2 trillion dollars representing 5% of global GDP – that's the estimated amount of money laundered worldwide each year according to the United Nations Office on Drugs and Crime. The fight against money laundering is one of top priorities of financial institutions – but it also poses a significant challenge for them. To combat the phenomenon, one needs to have a large number of human and technology resources at hand.

Under dynamic conditions on bridges, we need a real-time management. To this end, this paper presents a rule-based decision support system in which the necessary rules are extracted from simulation results made by Aimsun traffic micro-simulation software. Then, these rules are generalized by the aid of fuzzy rule generation algorithms. Then, they are trained by a set of supervised and the unsupervised learning algorithms to get an ability to make decision in real cases. As a pilot case study, Nasr Bridge in Tehran is simulated in Aimsun and WEKA data mining software is used to execute the learning algorithms. Based on this experiment, the accuracy of the supervised algorithms to generalize the rules is greater than 80%. In addition, CART decision tree and sequential minimal optimization (SMO) provides 100% accuracy for normal data and these algorithms are so reliable for crisis management on bridge. This means that, it is possible to use such machine learning methods to manage bridges in the real-time conditions.

The Herald-Times reports that the City Council voted Wednesday to solidify policies developed and reviewed by Bloomington Police Chief Mike Diekhoff and the city's Board of Public Safety. Bloomington Municipal Code now also prohibits the armored truck from carrying affixed firearms, water cannons and devices that can fire a projectile.

The South Bend Tribune reports the lawsuit was filed Tuesday in U.S. District Court for Northern Indiana. In addition to Notre Dame's abortion policies, it challenges the Trump administration's interim rules allowing universities to disregard a requirement of the Affordable Care Act that health plans cover birth control for women without out-of-pocket costs.

Marketing automation has driven a lot of hype in the marketing space in recent years, and for good reason. Innovations like Marketo and Eloqua have given companies the ability to scale up their marketing efforts like never before and run multi-channel campaigns that are truly measurable. That said, this is a fast-paced industry – and now, there's a whole new crop of entrants to the martech space that are transforming the way that marketers think about marketing automation. This year, we're seeing a true shift in how marketers think about campaign automation. Instead of designing campaigns aimed to guide prospects through the sales funnel, now companies are looking to design holistic marketing campaigns that aren't just aimed at converting prospects, but also at walking leads through the entire customer lifecycle.

Cognitive applications are being applied to a wide variety of uses and across various industries. Based on statistical and rule-based methods, they are excellent to process a large volume of information. But many companies are battling with the imprecise results this technology delivers. Complex algorithms to simulate how the human brain works lead data scientists to a bottleneck for taking cognitive computing to the next level.

Recent advances in interpretable Machine Learning (iML) and eXplainable AI (XAI) construct explanations based on the importance of features in classification tasks. However, in a high-dimensional feature space this approach may become unfeasible without restraining the set of important features. We propose to utilize the human tendency to ask questions like "Why this output (the fact) instead of that output (the foil)?" to reduce the number of features to those that play a main role in the asked contrast. Our proposed method utilizes locally trained one-versus-all decision trees to identify the disjoint set of rules that causes the tree to classify data points as the foil and not as the fact. In this study we illustrate this approach on three benchmark classification tasks.

A plant is operated on the basis of its manual usually; however, it is not realistic that a manual contains instructions for all cases, especially regarding abnormal ones. For obtaining appropriate operation procedures for a wide variety of cases, multilevel flow modeling (MFM) has been studied ([1]-[3]). MFM is a functional modeling framework, in which a plant structure is expressed as a directed graph. The framework also has a set of influence propagation rules, which consists of if-then rules regarding the states of related components. If the state of a component has changed, the resulting state of the other components can be obtained by applying the rules in the forward direction. Conversely, given a desired state of a component, we can obtain the states of other components to be satisfied for achieving the desired state by tracing back the propagation rules. This leads an action to a desired state. Our contributions are as follows: 1) We propose a method to translate MFM into an FOL. This enables the application of techniques used in the FOL to MFM, such as inference engines and abductive reasoners [6].