Conventional surveillance systems for monitoring infectious diseases, such as influenza, face challenges due to shortage of skilled healthcare professionals, remoteness of communities and absence of communication infrastructures. Internet-based approaches for surveillance are appealing logistically as well as economically. Search engine queries and Twitter have been the primarily used data sources in such approaches. The aim of this study is to assess the predictive power of an alternative data source, Instagram. By using 317 weeks of publicly available data from Instagram, we trained several machine learning algorithms to both nowcast and forecast the number of official influenza-like illness incidents in Finland where population-wide official statistics about the weekly incidents are available. In addition to date and hashtag count features of online posts, we were able to utilize also the visual content of the posted images with the help of deep convolutional neural networks. Our best nowcasting model reached a mean absolute error of 11.33 incidents per week and a correlation coefficient of 0.963 on the test data. Forecasting models for predicting 1 week and 2 weeks ahead showed statistical significance as well by reaching correlation coefficients of 0.903 and 0.862, respectively. This study demonstrates how social media and in particular, digital photographs shared in them, can be a valuable source of information for the field of infodemiology.

Convolutional Neural Networks (CNN) has become more popular choice for various tasks such as computer vision, speech recognition and natural language processing. Thanks to their large computational capability and throughput, GPUs ,which are not power efficient and therefore does not suit low power systems such as mobile devices, are the most common platform for both training and inferencing tasks. Recent studies has shown that FPGAs can provide a good alternative to GPUs as a CNN accelerator, due to their re-configurable nature, low power and small latency. In order for FPGA-based accelerators outperform GPUs in inference task, both the parameters of the network and the activations must be quantized. While most works use uniform quantizers for both parameters and activations, it is not always the optimal one, and a non-uniform quantizer need to be considered. In this work we introduce a custom hardware-friendly approach to implement non-uniform quantizers. In addition, we use a single scale integer representation of both parameters and activations, for both training and inference. The combined method yields a hardware efficient non-uniform quantizer, fit for real-time applications. We have tested our method on CIFAR-10 and CIFAR-100 image classification datasets with ResNet-18 and VGG-like architectures, and saw little degradation in accuracy.

Dimensionality reduction is a common method for analyzing and visualizing high-dimensional data. However, reasoning dynamically about the results of a dimensionality reduction is difficult. Dimensionality-reduction algorithms use complex optimizations to reduce the number of dimensions of a dataset, but these new dimensions often lack a clear relation to the initial data dimensions, thus making them difficult to interpret. Here we propose a visual interaction framework to improve dimensionality-reduction based exploratory data analysis. We introduce two interaction techniques, forward projection and backward projection, for dynamically reasoning about dimensionally reduced data. We also contribute two visualization techniques, prolines and feasibility maps, to facilitate the effective use of the proposed interactions. We apply our framework to PCA and autoencoder-based dimensionality reductions. Through data-exploration examples, we demonstrate how our visual interactions can improve the use of dimensionality reduction in exploratory data analysis.

Is it Safe to Drive? Abstract--With recent advances in learning algorithms and hardware development, autonomous cars have shown promise when operating in structured environments under good driving conditions. However, for complex, cluttered and unseen environments withhigh uncertainty, autonomous driving systems still frequently demonstrate erroneous or unexpected behaviors, that could lead to catastrophic outcomes. Autonomous vehicles should ideally adapt to driving conditions; while this can be achieved through multiple routes, it would be beneficial as a first step to be able to characterize Driveability in some quantified form. To this end, this paper aims to create a framework for investigating different factors that can impact driveability. Also, one of the main mechanisms to adapt autonomous driving systems to any driving condition is to be able to learn and generalize from representative scenarios. The machine learning algorithms that currently do so learn predominantly in a supervised manner and consequently need sufficient data for robust and efficient learning. Specifically,we categorize the datasets according to use cases, and highlight the datasets that capture complicated and hazardous driving conditions which can be better used for training robust driving models. Furthermore, by discussions of what driving scenarios are not covered by existing public datasets and what driveability factors need more investigation and data acquisition, this paper aims to encourage both targeted dataset collection and the proposal of novel driveability metrics that enhance the robustness of autonomous cars in adverse environments. I. INTRODUCTION Despite testing autonomous cars in highly controlled settings, thesecars still occasionally fail in making correct decisions, often with catastrophic results According to the accident records, the failures are most likely to happen in complex or unseen driving environments. The fact remains that while autonomous cars can operate well in controlled or structured environments such as highways, they are still far from reliable when operating in cluttered, unstructured or unseen environments [2]. These apply to autonomous vehicles in general. Thesetwo different application fields also suggest that driveability could be quantified in different forms, either as a single metric or a composition of metrics. For example, with ADAS and current Level 2 or 3 autonomy, a scene can be simply defined as driveable if the car can operate safely in autonomous mode. When a non-driveable scene is detected, the autonomous car can hand over control to the human driver in a timely manner [4].

Over the past years, literature has shown that attacks exploiting the microarchitecture of modern processors pose a serious threat to the privacy of mobile phone users. This is because applications leave distinct footprints in the processor, which can be used by malware to infer user activities. In this work, we show that these inference attacks are considerably more practical when combined with advanced AI techniques. In particular, we focus on profiling the activity in the last-level cache (LLC) of ARM processors. We employ a simple Prime+Probe based monitoring technique to obtain cache traces, which we classify with Deep Learning methods including Convolutional Neural Networks. We demonstrate our approach on an off-the-shelf Android phone by launching a successful attack from an unprivileged, zeropermission App in well under a minute. The App thereby detects running applications with an accuracy of 98% and reveals opened websites and streaming videos by monitoring the LLC for at most 6 seconds. This is possible, since Deep Learning compensates measurement disturbances stemming from the inherently noisy LLC monitoring and unfavorable cache characteristics such as random line replacement policies. In summary, our results show that thanks to advanced AI techniques, inference attacks are becoming alarmingly easy to implement and execute in practice. This once more calls for countermeasures that confine microarchitectural leakage and protect mobile phone applications, especially those valuing the privacy of their users.

A cafe featuring robot waiters remotely controlled from home by people with severe physical disabilities has been launched in Minato Ward, Tokyo. Five robots measuring 1.2 meters tall, controlled by people with conditions such as amyotrophic lateral sclerosis (ALS), a form of motor neuron disease, took orders and served food as the cafe opened Monday on a trial basis. The cafe will be open until Dec. 7. The OriHime-D robots transmit video images and audio via the internet, allowing their controllers to direct them from home via computer. "(The robots) enable physical work and social participation," said Kentaro Yoshifuji, CEO of Ory Lab Inc., the developer of the robot and one of the three entities organizing the cafe.

U.K. citizens are a proud people with a proud history, but they are not exactly leading the way when it comes to technological innovation. In the matter of self-driving vehicles, the U.K. refuses to be left behind. Engadget reports the "U.K. to get self-driving buses and taxis by 2021." "The U.K. won't sit idly by while the U.S., Japan, and China put self-driving vehicles on their roads. The country's government has announced an ambitious driverless public transport plan for 2021, including autonomous buses in Scotland and self-driving taxis in several of London's boroughs, with state funding to the tune of £25 million ($33 million.)"

Chinese company, Didi Chuxing may be known by most as the world's largest ride-sharing company with a goal "to build a better journey," but its vision reveals its future ambitions: "to become a global leader in the revolution in transportation and automotive technology." With significant investment in artificial technology, Didi which means "beep beep" in Mandarin (like a car's horn), is focused on staying ahead of the competition. Harvard-educated Jean Liu, president of Didi Chuxing, is focused on growing the global footprint of the $56 billion-company that she leads. In China, the company has 550 million registered customers in more than 400 cities and delivers 30 million rides per day but its reach extends to Australia, Brazil, Japan and Mexico as well as Southeast Asia, India, Europe and Africa through various partnerships. Didi employs 7,000 people, nearly half who are engineers and data scientists, and they continue to recruit other tech professionals to support its artificial intelligence labs, autonomous vehicles and other tech operations.

Some of the most critical work in advancing China's technology goals takes place in a former cement factory in the middle of the country's heartland, far from the aspiring Silicon Valleys of Beijing and Shenzhen. An idled concrete mixer still stands in the middle of the courtyard. Boxes of melamine dinnerware are stacked in a warehouse next door. Inside, Hou Xiameng runs a company that helps artificial intelligence make sense of the world. Two dozen young people go through photos and videos, labeling just about everything they see.

Cyber crime, according to the National Crime Agency (NCA) Cyber Crime Assessment 2016 report, accounted for 53 per cent of all crimes in 2015. Cameron Brown, an independent cyber defence adviser, who has conducted research into emerging trends in cyber-crime offending, warns that opportunities to earn a living through cyber crime "will propel the disenfranchised and those in lower income bands to pursue a life of crime given the low risk and potential high yields". Mr Brown insists that cyber crime will continue to grow into a highly lucrative and well organised enterprise, seeking competitive advantage with the aid of sophisticated cyber operations. Operations that include research and development, with cyber criminals becoming increasingly innovative as far as the threats they can leverage are concerned. Jamie Saunders, director of the NCA National Cyber Crime Unit, argues that "senior members of UK business must think seriously about ways they can improve their defences and help law enforcement in the fight against cyber crime".