Sociotechnical systems are complex systems, where nonlinear interaction among different players can obscure causal relationships. The absence of mechanisms to help us understand how to create a change in the system makes it hard to manage these systems. Influencing and shaping are social operators acting on sociotechnical systems to design a change. However, the two operators are usually discussed in an ad-hoc manner, without proper guiding models and metrics which assist in adopting these models successfully. Moreover, both social operators rely on accurate understanding of the concept of trust. Without such understanding, neither of these operators can create the required level to create a change in a desirable direction. In this paper, we define these concepts in a concise manner suitable for modelling the concepts and understanding their dynamics. We then introduce a model for influencing and shaping and use Computational Red Teaming principles to design and demonstrate how this model operates. We validate the results computationally through a simulation environment to show social influencing and shaping in an artificial society.

Despite efforts to tackle corruption around the world, progress is still frustratingly slow, according to the latest report from Transparency International. Its annual Corruption Perception index reveals some alarming trends. It shows public service corruption is still a huge problem for two-thirds of the world's economies. The report uses a scale of zero to 100 to rank countries: zero is highly corrupt and 100 is very clean. New Zealand comes out on top but with a score of 89.

Android devices are growing exponentially and are connected through the internet accessing billion of online websites. The popularity of these devices encourages malware developer to penetrate the market with malicious apps to annoy and disrupt the victim. Although, for the detection of malicious apps different approaches are discussed. However, proposed approaches are not suffice to detect the advanced malware to limit/prevent the damages. In this, very few approaches are based on opcode occurrence to classify the malicious apps. Therefore, this paper investigates the five classifiers using opcodes occurrence as the prominent features for the detection of malicious apps. For the analysis, we use WEKA tool and found that FT detection accuracy ( 79.27%) is best among the investigated classifiers. However, true positives rate i.e. malware detection rate is highest ( 99.91%) by RF and fluctuate least with the different number of prominent features compared to other studied classifiers. The analysis shows that overall accuracy is majorly affected by the false positives of the classifier.

We introduce a novel incremental decision tree learning algorithm, Hoeffding Anytime Tree, that is statistically more efficient than the current state-of-the-art, Hoeffding Tree. We demonstrate that an implementation of Hoeffding Anytime Tree---"Extremely Fast Decision Tree", a minor modification to the MOA implementation of Hoeffding Tree---obtains significantly superior prequential accuracy on most of the largest classification datasets from the UCI repository. Hoeffding Anytime Tree produces the asymptotic batch tree in the limit, is naturally resilient to concept drift, and can be used as a higher accuracy replacement for Hoeffding Tree in most scenarios, at a small additional computational cost.

For fast and energy-efficient deployment of trained deep neural networks on resource-constrained embedded hardware, each learned weight parameter should ideally be represented and stored using a single bit. Error-rates usually increase when this requirement is imposed. Here, we report large improvements in error rates on multiple datasets, for deep convolutional neural networks deployed with 1-bit-per-weight. Using wide residual networks as our main baseline, our approach simplifies existing methods that binarize weights by applying the sign function in training; we apply scaling factors for each layer with constant unlearned values equal to the layer-specific standard deviations used for initialization. For CIFAR-10, CIFAR-100 and ImageNet, and models with 1-bit-per-weight requiring less than 10 MB of parameter memory, we achieve error rates of 3.9%, 18.5% and 26.0% / 8.5% (Top-1 / Top-5) respectively. We also considered MNIST, SVHN and ImageNet32, achieving 1-bit-per-weight test results of 0.27%, 1.9%, and 41.3% / 19.1% respectively. For CIFAR, our error rates halve previously reported values, and are within about 1% of our error-rates for the same network with full-precision weights. For networks that overfit, we also show significant improvements in error rate by not learning batch normalization scale and offset parameters. This applies to both full precision and 1-bit-per-weight networks. Using a warm-restart learning-rate schedule, we found that training for 1-bit-per-weight is just as fast as full-precision networks, with better accuracy than standard schedules, and achieved about 98%-99% of peak performance in just 62 training epochs for CIFAR-10/100. For full training code and trained models in MATLAB, Keras and PyTorch see https://github.com/McDonnell-Lab/1-bit-per-weight/ .

The Bank of England's warning that the pace of artificial intelligence development now threatens 15m UK jobs has prompted calls for political intervention. According to scientists and legal experts, responding to the bank's warning this November, there is now an urgent need for the development of intelligent algorithms to be put on the political agenda. This is happening now and across the board and that's the difference. That's why a lot of us need to start talking about this now. The Government needs to pick up on this and put it on the political agenda and look at regulatory issues, said Chrissie Lightfoot, a patent lawyer and author, who debated fears over unemployment caused by AI at London's Science Museum last October.

Australia's Tasmanian tiger has been extinct for more than 80 years but scientists have taken a step closer to bringing the animal back to life. Stunning 3D scans were used to reconstruct the development of the animal through its early life, using persevered specimens of the species. Unlike other marsupials, like kangaroos and wallabies, Tasmanian tigers have dog-like features - despite last sharing a common ancestor around 160 million years ago. The new scans have revealed that these canine attributes appear once their young, called joeys, have left their mother's womb. The find could help to unlock the genetic mysteries of the iconic creatures, which will aid scientists who hope to clone them and bring them back from extinction.

We study sampling as optimization in the space of measures. We focus on gradient flow-based optimization with the Langevin dynamics as a case study. We investigate the source of the bias of the unadjusted Langevin algorithm (ULA) in discrete time, and consider how to remove or reduce the bias. We point out the difficulty is that the heat flow is exactly solvable, but neither its forward nor backward method is implementable in general, except for Gaussian data. We propose the symmetrized Langevin algorithm (SLA), which should have a smaller bias than ULA, at the price of implementing a proximal gradient step in space. We show SLA is in fact consistent for Gaussian target measure, whereas ULA is not. We also illustrate various algorithms explicitly for Gaussian target measure, including gradient descent, proximal gradient, and Forward-Backward, and show they are all consistent.

We are on the verge of a technological revolution that will fundamentally alter the way we live, work, and relate to one another unlike anything humankind has experienced before. The main driver for this technological revolution is Artificial Intelligence (AI). Technological change driven by AI will change not only what we do but also who we are. It will affect our identity and all the issues associated with it: our sense of privacy, our notions of ownership, our consumption patterns, the time we devote to work and leisure, and how we develop our careers, cultivate our skills, and nurture relationships. But the development and applications of artificial intelligence can also present a dystopian threat to our collective and individual well being. From SIRI to self-driving cars, artificial intelligence (AI) is progressing rapidly. While science fiction often portrays AI as robots with human-like characteristics, AI can encompass anything from Google's search algorithms to IBM's Watson to autonomous robots and weapons systems. Artificial intelligence today is often referred to as narrow AI (or weak AI), which is designed to perform a narrow task (eg:facial recognition or only internet searches or driving a car). The other kind of Artificial Intelligence is termed general AI (AGI or strong AI) which is designed to "think," and solve problems much like humans.

It's hard to imagine truckers embracing the wider use of artificial intelligence or "AI" within the freight transportation industry. I'm still quite leery of it myself, having watched The Terminator one too many times. Yet, if a new global survey of nearly 3,000 employees across eight nations conducted by The Workforce Institute at Kronos Incorporated is correct, it seems that more and more of them believe there is "a significant opportunity" for AI to help improve "the workplace experience" and, yes, believe it or not, the logistics/transportation sector is one of the leading industries in this regard. The survey – dubbed Engaging Opportunity: Working Smarter with AI and conducted by Coleman Parkes Research – polled hourly and salaried workers across a variety of industries in Australia, Canada, France, Germany, Mexico, New Zealand, the United Kingdom, and the U.S. Not only did the survey discern there may be more "ground level support" regarding the use of AI in the workplace, it also found that company management is being largely close-mouthed regarding AI developments – and that, not AI itself, is what's sparking the most unease about the technology among workers.