Computer-based human activity recognition of daily living has recently attracted much interest due to its applicability to ambient assisted living. Such applications require the automatic recognition of high-level activities composed of multiple actions performed by human beings in a given environment. We propose a deep neural architecture for kitchen activity recognition, which uses an ensemble of machine learning models and hand-crafted features to extract more information of the data. Experiments show that our approach achieves the state-of-the-art for identifying cooking actions in a well-known kitchen dataset.

The interactive computation paradigm is reviewed and a particular example is extended to form the stochastic analog of a computational process via a transcription of a minimal Turing Machine into an equivalent asynchronous Cellular Automaton with an exponential waiting times distribution of effective transitions. Furthermore, a special toolbox for analytic derivation of recursive relations of important statistical and other quantities is introduced in the form of an Inductive Combinatorial Hierarchy.

Which authors of this paper are endorsers? Disable MathJax (What is MathJax?)

Dataset from the U.S. Department of Education that includes various metrics on outcomes from degree-granting undergraduate institutions from 1996-2015, including student debt, college completion rates, job placement, and more

It's a decent way to do modeling, especially with something that is spatial. You can apply machine learning into the ruleset each node has in the model to do some pretty cool things though.

What is Machine Learning Architecture? I'm looking for a new job in Data Science, and have found job positions titled as "Machine Learning Architect". Don't really sure about what it is, but maybe I am still a newbie. What does a Machine Learning Architect do and what are the required skills? Can you give examples, please?

Having spent the past 31 years memorizing an astonishing collection of general knowledge, the artificial-intelligence engine created by Doug Lenat is finally ready to go to work. Lenat's creation is Cyc, a knowledge base of semantic information designed to give computers some understanding of how things work in the real world. Cyc has been given many thousands of facts, including lots of information that you wouldn't find in an encyclopedia because it seems self-evident. It knows, for example, that that Sir Isaac Newton is a famous historical figure who is no longer alive. But more important, Cyc also understands that if you let go of an apple it will fall to the ground; that an apple is not bigger than a person; and that a person cannot throw an apple into space.

Systems biology, the study of the intricate, ramified, com-plex and interacting mechanisms underlying life, often proves too complex for unaided human understanding, even by groups of people working together. This difficulty is ex-acerbated by the high volume of publications in molecular biology. The Big C (‘C’ for Cyc) is a system designed to (semi-)automatically acquire, integrate, and use complex mechanism models, specifically related to cancer biology, via automated reading and a hyper-detailed refinement pro-cess resting on Cyc’s logical representations and powerful inference mechanisms. We aim to assist cancer research and treatment by achieving elements of biologist-level reason-ing, but with the scale and attention to detail that only com-puter implementations can provide.

Artificial Immune System (AIS-MACA) a novel computational intelligence technique is can be used for strengthening the automated protein prediction system with more adaptability and incorporating more parallelism to the system. Most of the existing approaches are sequential which will classify the input into four major classes and these are designed for similar sequences. AIS-MACA is designed to identify ten classes from the sequences that share twilight zone similarity and identity with the training sequences with mixed and hybrid variations. This method also predicts three states (helix, strand, and coil) for the secondary structure. Our comprehensive design considers 10 feature selection methods and 4 classifiers to develop MACA (Multiple Attractor Cellular Automata) based classifiers that are build for each of the ten classes. We have tested the proposed classifier with twilight-zone and 1-high-similarity benchmark datasets with over three dozens of modern competing predictors shows that AIS-MACA provides the best overall accuracy that ranges between 80% and 89.8% depending on the dataset.

When multiple agents want to maintain temporal information, they can employ a Multiagent Simple Temporal Network (MaSTN). Recent work has shown that the constraints in a MaSTN can be efficiently propagated by enforcing partial path consistency (PPC) with a distributed algorithm. However, new temporal constraints may arise continually due to ongoing plan construction or execution, the decisions of other agents, and other exogenous events. For these new constraints, propagation is again required to re-establish PPC. Because the affected part of the network may be small, one typically wants to exploit the similarities between the new and previous version of the MaSTN. To this end, we propose two new distributed algorithms for incrementally maintaining PPC. The first is inspired by TriSTP, the seminal PPC algorithm for STNs; the second is a distributed version of IPPC, which represents the current state of the art for incrementally enforcing PPC in a centralized setting. The worst-case time performance of these algorithms is similar to their centralized counterparts. We empirically compare our distributed algorithms, analyzing their performance under various assumptions, and demonstrate significant speedup over their centralized counterparts.