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An Improved Chicken Swarm Optimization Algorithm for Handwritten Document Image Enhancement

Mugisha, Stanley, Gutu, Lynn tar, Nagabhushan, P

arXiv.org Artificial Intelligence

Chicken swarm optimization is a new meta-heuristic algorithm which mimics the foraging hierarchical behavior of chicken. In this paper, we describe the preprocessing of handwritten document by contrast enhancement while preserving detail with an improved chicken swarm optimization algorithm.The results of the algorithm are compared with other existing meta heuristic algorithms like Cuckoo Search, Firefly Algorithm and the Artificial bee colony. The proposed algorithm considerably outperforms all the above by giving good results.


Startup Farmers Learn the Art of Animal Agriculture in "Chicken Stories"

The New Yorker

At a startup farm outside of Oakland, a young man reads on his phone in a dim bedroom. He's not scrolling through social-media feeds or playing games; he's trying to learn about caring for his livestock. A Siri-like voice-over says, "I found one article on how to take care of baby chicks." On the floor, a large blue bucket sits under the warm glow of a heat lamp, with about a dozen fluffy chicks inside. "Failure to maintain a warm environment will quickly prove to be fatal," the digital voice explains.


What artificial intelligence still can't do

#artificialintelligence

Artificial intelligence in the modern day is capable of miracles. Yet today's AI still has fundamental limitations. To make better use of resources and research efforts in the future, it's helpful to take a step back and honestly examine the strengths and flaws of today's AI. Promising work is already ongoing at the frontiers of the field in each of the areas listed below to make the next generation of artificial intelligence more high-performing and robust. A man went to a restaurant.


Rugby-Bot: Utilizing Multi-Task Learning & Fine-Grained Features for Rugby League Analysis

Holbrook, Matthew, Hobbs, Jennifer, Lucey, Patrick

arXiv.org Machine Learning

Sporting events are extremely complex and require a multitude of metrics to accurate describe the event. When making multiple predictions, one should make them from a single source to keep consistency across the predictions. We present a multi-task learning method of generating multiple predictions for analysis via a single prediction source. To enable this approach, we utilize a fine-grain representation using fine-grain spatial data using a wide-and-deep learning approach. Additionally, our approach can predict distributions rather than single point values. We highlighted the utility of our approach on the sport of Rugby League and call our prediction engine "Rugby-Bot".


7 amazing robots based on animals

#artificialintelligence

When it comes to robots, science fiction has conditioned us to think of androids – bipedal machines approximating the human form. But the next generation of robots may be based on very different types of animals: snakes, flies, locusts and even the multi-tentacle octopus. Israeli scientists are hard at work on just such contraptions. Here's a look at seven of the most fascinating designs that can help with everything from exploring our insides to cleaning up the mess we make on the planet. Medrobotics' signature product, the Flex Robotic System, allows physicians to reach deep into the body with minimal risk.


Janice: Excited for eclipse

FOX News

I was 8-years-old and remember being both terrified and intrigued about something that was being talked about everywhere. This wasn't a storyline out of a science fiction movie or novel, this was real, and happening here on Earth. Millions of people were going to witness something that maybe happens a couple of times in our lifetime: A total solar eclipse. Our teachers were planning lessons about this incredible celestial event. Chalkboard diagrams, planetary mobiles and handmade viewing devices were being created out of shoe boxes.


It's gonna be huge: China factory hatches giant Trump chickens

The Japan Times

ZHEJIANG – A Chinese factory is hatching giant inflatable chickens resembling Donald Trump to usher in the Year of the Rooster. The five-meter (16-foot) fowls sport the distinctive golden mane of the U.S. president-elect and mimic his signature hand gestures with their tiny wings. Cartoon figures of animals from the Chinese zodiac are ubiquitous around Chinese New Year at the end of this month. The balloon factory is selling its presidential birds for as much as 14,400 yuan ($2,080) on Chinese shopping site Taobao for a 10-meter version. "I saw his image on the news and he has a lot of personality, and since Year of the Rooster is coming up I mixed these two elements together to make a Chinese chicken," factory owner Wei Qing said.


Google launches Animal Sounds audio files in web and app search results

Daily Mail - Science & tech

If you like to spend time perfecting your animal impressions or if you simply forget what some animals sound like, then Google's newest feature is for you. The latest addition from the search engine giant is a library of live-recorded animals sounds that will play when you search for them. It is likely to be used mostly for parents teaching their children the sounds animals make. Google has added a library of live-recorded animals sounds that will play when you search for them. The feature can be accessed by searching'animal sounds' in Google, on a computer or the Google app on a phone.


Solving Puzzles Described in English by Automated Translation to Answer Set Programming and Learning How To Do That Translation

Baral, Chitta (Arizona State University) | Dzifcak, Juraj (Arizona State University)

AAAI Conferences

We present a system capable of automatically solving combinatorial logic puzzles given in (simplified) English. It involves translating the English descriptions of the puzzles into answer set programming(ASP) and using ASP solvers to provide solutions of the puzzles. To translate the descriptions, we use a lambda-calculus based approach using Probabilistic Combinatorial Categorial Grammars (PCCG) where the meanings of words are associated with parameters to be able to distinguish between multiple meanings of the same word. Meaning of many words and the parameters are learned. The puzzles are represented in ASP using an ontology which is applicable to a large set of logic puzzles.


Solving puzzles described in English by automated translation to answer set programming and learning how to do that translation

Baral, Chitta, Dzifcak, Juraj

arXiv.org Artificial Intelligence

We present a system capable of automatically solving combinatorial logic puzzles given in (simplified) English. It involves translating the English descriptions of the puzzles into answer set programming(ASP) and using ASP solvers to provide solutions of the puzzles. To translate the descriptions, we use a lambda-calculus based approach using Probabilistic Combinatorial Categorial Grammars (PCCG) where the meanings of words are associated with parameters to be able to distinguish between multiple meanings of the same word. Meaning of many words and the parameters are learned. The puzzles are represented in ASP using an ontology which is applicable to a large set of logic puzzles.