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Drone attack targets Iraqi base that houses US troops

FOX News

'The Ingraham Angle' host examines the president's approach to diplomacy and foreign policy A drone reportedly dropped explosives at a U.S.-led base near the Erbil airport in Iraq on Wednesday night. There were no reports of injuries, Reuters reported, citing Kurdish officials. It was the first known drone attack believed to be targeting U.S. service members but rocket attacks have hit U.S. bases in the country. A Turkish soldier was reportedly killed in a separate rocket attack Wednesday, Turkish officials said, according to Reuters. A group thought to be aligned with Iran praised the drone attack but no one has explicitly claimed responsibility for it. The U.S. has blamed the attacks on Iran-backed militias, which have called for the withdrawal of all foreign troops, according to Reuters.


Standard digital camera and AI to monitor soil moisture for affordable smart irrigation

#artificialintelligence

Researchers at UniSA have developed a cost-effective new technique to monitor soil moisture using a standard digital camera and machine learning technology. The United Nations predicts that by 2050 many areas of the planet may not have enough fresh water to meet the demands of agriculture if we continue our current patterns of use. One solution to this global dilemma is the development of more efficient irrigation, central to which is precision monitoring of soil moisture, allowing sensors to guide'smart' irrigation systems to ensure water is applied at the optimum time and rate. Current methods for sensing soil moisture are problematic – buried sensors are susceptible to salts in the substrate and require specialised hardware for connections, while thermal imaging cameras are expensive and can be compromised by climatic conditions such as sunlight intensity, fog, and clouds. Researchers from The University of South Australia and Baghdad's Middle Technical University have developed a cost-effective alternative that may make precision soil monitoring simple and affordable in almost any circumstance.


Blockchain & AI - Convergence - IntelligentHQ

#artificialintelligence

Blockchain & AI are the major architecture techs of our time. Its convergence is a key factor for the present & future of tech. These emerging & foundation technologies deal with data, value storage creation and lead the digital transformation of the 4IR. The history of Artificial Intelligence AI began in antiquity, with the power of imagination – myths, stories, rumours making artificial beings endowed with intelligence or consciousness by master craftsmen, magic. The History of Blockchain & Ledgers start when the first recorded ledgers systems were found in Mesopotamia, today's Iraq, 7000 years ago.


Building a Research University in the Arab Region

Communications of the ACM

The establishment of King Abdullah University of Science and Technology (KAUST) in 2009 was the fulfillment of a lifelong dream of its founder, the late King Abdullah of Saudi Arabia. His vision for the university was deeply rooted in the historical and cultural contexts of the Middle East. He intended the university to be seen as a revival of the old "house of wisdom," which was a premier institution of learning in Baghdad from the 9th century until the 13th century. Starting as a private library of the fabled Caliph Harun Al-Rasheed, it developed quickly into the 9th century equivalent of a research laboratory and a university. The house of wisdom was the birthplace of algebra and was a milieu where many developments took place in various fields of science and humanities.


An affordable 'smart irrigation' proof of concept

#artificialintelligence

Researchers at UniSA have developed a cost-effective new technique to monitor soil moisture using a standard digital camera and machine learning technology. The United Nations predicts that by 2050 many areas of the planet may not have enough fresh water to meet the demands of agriculture if we continue our current patterns of use. One solution to this global dilemma is the development of more efficient irrigation, central to which is precision monitoring of soil moisture, allowing sensors to guide'smart' irrigation systems to ensure water is applied at the optimum time and rate. Current methods for sensing soil moisture are problematic – buried sensors are susceptible to salts in the substrate and require specialised hardware for connections, while thermal imaging cameras are expensive and can be compromised by climatic conditions such as sunlight intensity, fog, and clouds. Researchers from The University of South Australia and Baghdad's Middle Technical University have developed a cost-effective alternative that may make precision soil monitoring simple and affordable in almost any circumstance.


Standard Digital Camera, AI To Monitor Soil Moisture For Affordable Smart Irrigation

#artificialintelligence

Adelaide (Australia): Researchers at the University of South Australia have developed a cost-effective new technique to monitor soil moisture using a standard digital camera and machine learning technology. The United Nations predicts that by 2050 many areas of the planet may not have enough fresh water to meet the demands of agriculture if we continue our current patterns of use. One solution to this global dilemma is the development of more efficient irrigation, central to which is precision monitoring of soil moisture, allowing sensors to guide'smart' irrigation systems to ensure water is applied at the optimum time and rate. Current methods for sensing soil moisture are problematic -- buried sensors are susceptible to salts in the substrate and require specialised hardware for connections, while thermal imaging cameras are expensive and can be compromised by climatic conditions such as sunlight intensity, fog, and clouds. Researchers from The University of South Australia and Baghdad's Middle Technical University have developed a cost-effective alternative that may make precision soil monitoring simple and affordable in almost any circumstance.


Cipher Skin raises $5 million for mesh sensors that detect motion in real time

#artificialintelligence

Cipher Skin, a startup developing a network of wraparound sensors that can deliver big data diagnostics, today announced it has raised $5 million in a series A round led by Boyett Group. The company says the funds, which bring Cipher's total raised to date to $7.8 million, will bolster development of the company's existing product line and new products in markets like oil, gas, and winemaking. After his career in the U.S. special operations forces, Cipher CEO Phillip Bogdanovich started training in the gym with Craig Weller, a physical coach he met when serving in Baghdad. Bogdanovich says that as soon as he was separated from Weller, he noticed his recovery began slowing. While back in the U.S., Bogdanovich and Weller began brainstorming how the training process could be scaled to allow people at home to experience the equivalent of a coach watching and providing feedback.


A Marine who worked on 'Six Days in Fallujah' in 2009 helped us see why it exists

Mashable

There's no easy way to talk about Six Days in Fallujah. That was clear in 2009 when Konami revealed the Atomic Games project set during the Iraq War's second battle of Fallujah, in 2004. Intense backlash led the Japanese publisher to cancel the game just weeks later. And it's been just as clear in 2021, with Atomic once again taking flak in the midst of a fresh attempt to make the game happen. There are plenty of reasons to be suspicious of an effort to recreate one of the deadliest engagements in a controversial war as an entertainment product. But even allowing for that, it's always bugged me that Six Days in Fallujah got canceled way back when. Very few people ever got a chance to even see the game.


Design a Technology Based on the Fusion of Genetic Algorithm, Neural network and Fuzzy logic

arXiv.org Artificial Intelligence

This paper describes the design and development of a prototype technique for artificial intelligence based on the fusion of genetic algorithm, neural network and fuzzy logic. It starts by establishing a relationship between the neural network and fuzzy logic. Then, it combines the genetic algorithm with them. Information fusions are at the confidence level, where matching scores can be reported and discussed. The technique is called the Genetic Neuro-Fuzzy (GNF). It can be used for high accuracy real-time environments.


Jira: a Kurdish Speech Recognition System Designing and Building Speech Corpus and Pronunciation Lexicon

arXiv.org Artificial Intelligence

In this paper, we introduce the first large vocabulary speech recognition system (LVSR) for the Central Kurdish language, named Jira. The Kurdish language is an Indo-European language spoken by more than 30 million people in several countries, but due to the lack of speech and text resources, there is no speech recognition system for this language. To fill this gap, we introduce the first speech corpus and pronunciation lexicon for the Kurdish language. Regarding speech corpus, we designed a sentence collection in which the ratio of di-phones in the collection resembles the real data of the Central Kurdish language. The designed sentences are uttered by 576 speakers in a controlled environment with noise-free microphones (called AsoSoft Speech-Office) and in Telegram social network environment using mobile phones (denoted as AsoSoft Speech-Crowdsourcing), resulted in 43.68 hours of speech. Besides, a test set including 11 different document topics is designed and recorded in two corresponding speech conditions (i.e., Office and Crowdsourcing). Furthermore, a 60K pronunciation lexicon is prepared in this research in which we faced several challenges and proposed solutions for them. The Kurdish language has several dialects and sub-dialects that results in many lexical variations. Our methods for script standardization of lexical variations and automatic pronunciation of the lexicon tokens are presented in detail. To setup the recognition engine, we used the Kaldi toolkit. A statistical tri-gram language model that is extracted from the AsoSoft text corpus is used in the system. Several standard recipes including HMM-based models (i.e., mono, tri1, tr2, tri2, tri3), SGMM, and DNN methods are used to generate the acoustic model. These methods are trained with AsoSoft Speech-Office and AsoSoft Speech-Crowdsourcing and a combination of them. The best performance achieved by the SGMM acoustic model which results in 13.9% of the average word error rate (on different document topics) and 4.9% for the general topic.