With the world moving towards being increasingly dependent on computers and automation, one of the main challenges in the current decade has been to build secure applications, systems and networks. Alongside these challenges, the number of threats is rising exponentially due to the attack surface increasing through numerous interfaces offered for each service. To alleviate the impact of these threats, researchers have proposed numerous solutions; however, current tools often fail to adapt to ever-changing architectures, associated threats and 0-days. This manuscript aims to provide researchers with a taxonomy and survey of current dataset composition and current Intrusion Detection Systems (IDS) capabilities and assets. These taxonomies and surveys aim to improve both the efficiency of IDS and the creation of datasets to build the next generation IDS as well as to reflect networks threats more accurately in future datasets. To this end, this manuscript also provides a taxonomy and survey or network threats and associated tools. The manuscript highlights that current IDS only cover 25% of our threat taxonomy, while current datasets demonstrate clear lack of real-network threats and attack representation, but rather include a large number of deprecated threats, hence limiting the accuracy of current machine learning IDS. Moreover, the taxonomies are open-sourced to allow public contributions through a Github repository.

This document gives a knowledge-oriented analysis of about 20 interesting Recognizing Textual Entailment (RTE) examples, drawn from the 2005 RTE5 competition test set. The analysis ignores shallow statistical matching techniques between T and H, and rather asks: What would it take to reasonably infer that T implies H? What world knowledge would be needed for this task? Although such knowledge-intensive techniques have not had much success in RTE evaluations, ultimately an intelligent system should be expected to know and deploy this kind of world knowledge required to perform this kind of reasoning. The selected examples are typically ones which our RTE system (called BLUE) got wrong and ones which require world knowledge to answer. In particular, the analysis covers cases where there was near-perfect lexical overlap between T and H, yet the entailment was NO, i.e., examples that most likely all current RTE systems will have got wrong. A nice example is #341 (page 26), that requires inferring from "a river floods" that "a river overflows its banks". Seems it should be easy, right? Enjoy!

WASHINGTON – One U.S. special operations soldier was killed and four U.S. service members wounded in an "enemy attack" Friday in Somalia, the U.S. military said -- casualties that are likely to put renewed scrutiny on America's counterterror operations in Africa. It's the first public announcement of a U.S. military combat death on the continent since four U.S. service members were killed in a militant ambush in the west African nation of Niger in October. U.S. Africa Command said in a statement that U.S. troops with Somali and Kenyan forces came under mortar and small-arms fire in Jubaland, Somalia, at around 2.45 p.m. local time. One member of the "partner forces" was wounded. One of the wounded U.S. service members received sufficient medical care in the field, and the other three were medically evacuated for additional treatment.

A funny thing has happened to Google and Amazon on their path toward high-tech success: They have become crucial cogs in the U.S. national security establishment. Both companies are expanding teams of employees with security clearances to work on projects that include deploying artificial intelligence and building digital "clouds" to offering law enforcement facial recognition tools that can even read the mood of people caught on camera. The security establishment's embrace of Big Tech has ruffled the feathers of traditional defense contractors and roiled employee ranks, in Google's case, over whether the company is being drawn into what disguntled employees called "the business of war." Defense industry analysts say the Pentagon views Big Tech, and particularly Google with its deep bench of artificial intelligence researchers, as vital to the nation's future safety. "They are becoming a critical part of national security, without question," said Alexander Rossino, a senior principal research analyst at Deltek, a Herndon, Virginia, firm that offers software and services to defense?

International trade has dominated the global headlines recently. Much of the discussions have been focused on the threat of a trade war, the tit-for-tat tariffs, and the health of the global trade order. While extremely important, these conversations are missing a brighter side of international trade – how innovative technologies in the Fourth Industrial Revolution are transforming trade by making the processes more inclusive and efficient. The steam power revolution connected the world like never before. The invention of shipping containers laid the foundation for globalization.

I'm a managing partner of SOMS Consulting, a firm that designs business processes and software to make companies a pleasure to manage. A graduate of Stellenbosch University, one of Africa's most prestigious academic institutions, I studied how technology and modern business ...

A new paper in the Proceedings of the National Academy of Sciences (PNAS) reports how a cutting-edge artificial intelligence technique called deep learning can automatically identify, count and describe animals in their natural habitats. Photographs that are automatically collected by motion-sensor cameras can then be automatically described by deep neural networks. The result is a system that can automate animal identification for up to 99.3 percent of images while still performing at the same 96.6 percent accuracy rate of crowdsourced teams of human volunteers. "This technology lets us accurately, unobtrusively and inexpensively collect wildlife data, which could help catalyze the transformation of many fields of ecology, wildlife biology, zoology, conservation biology and animal behavior into'big data' sciences. This will dramatically improve our ability to both study and conserve wildlife and precious ecosystems," says Jeff Clune, the senior author of the paper.

Petri Nets is very interesting tool for studying and simulating different behaviors of information systems. It can be used in different applications based on the appropriate class of Petri Nets whereas it is classical, colored or timed Petri Nets. In this paper we introduce a new approach of Petri Nets called orbital Petri Nets (OPN) for studying the orbital rotating systems within a specific domain. The study investigated and analyzed OPN with highlighting the problem of space debris collision problem as a case study. The mathematical investigation results of two OPN models proved that space debris collision problem can be prevented based on the new method of firing sequence in OPN. By this study, new smart algorithms can be implemented and simulated by orbital Petri Nets for mitigating the space debris collision problem as a next work.

Increased interest of scientists, producers and consumers in sheep identification has been stimulated by the dramatic increase in population and the urge to increase productivity. The world population is expected to exceed 9.6 million in 2050. For this reason, awareness is raised towards the necessity of effective livestock production. Sheep is considered as one of the main of food resources. Most of the research now is directed towards developing real time applications that facilitate sheep identification for breed management and gathering related information like weight and age. Weight and age are key matrices in assessing the effectiveness of production. For this reason, visual analysis proved recently its significant success over other approaches. Visual analysis techniques need enough images for testing and study completion. For this reason, collecting sheep images database is a vital step to fulfill such objective. We provide here datasets for testing and comparing such algorithms which are under development. Our collected dataset consists of 416 color images for different features of sheep in different postures. Images were collected fifty two sheep at a range of year from three months to six years. For each sheep, two images were captured for both sides of the body, two images for both sides of the face, one image from the top view, one image for the hip and one image for the teeth. The collected images cover different illumination, quality levels and angle of rotation. The allocated data set can be used to test sheep identification, weigh estimation, and age detection algorithms. Such algorithms are crucial for disease management, animal assessment and ownership.

The latest techniques from Neural Networks and Support Vector Machines (SVM) are used to investigate geometric properties of Complete Intersection Calabi-Yau (CICY) threefolds, a class of manifolds that facilitate string model building. An advanced neural network classifier and SVM are employed to (1) learn Hodge numbers and report a remarkable improvement over previous efforts, (2) query for favourability, and (3) predict discrete symmetries, a highly imbalanced problem to which the Synthetic Minority Oversampling Technique (SMOTE) is applied to boost performance. In each case study, we employ a genetic algorithm to optimise the hyperparameters of the neural network. We demonstrate that our approach provides quick diagnostic tools capable of shortlisting quasi-realistic string models based on compactification over smooth CICYs and further supports the paradigm that classes of problems in algebraic geometry can be machine learned.