Aesthetic quality prediction is a challenging task in the computer vision community because of the complex interplay with semantic contents and photographic technologies. Recent studies on the powerful deep learning based aesthetic quality assessment usually use a binary high-low label or a numerical score to represent the aesthetic quality. However the scalar representation cannot describe well the underlying varieties of the human perception of aesthetics. In this work, we propose to predict the aesthetic score distribution (i.e., a score distribution vector of the ordinal basic human ratings) using Deep Convolutional Neural Network (DCNN). Conventional DCNNs which aim to minimize the difference between the predicted scalar numbers or vectors and the ground truth cannot be directly used for the ordinal basic rating distribution. Thus, a novel CNN based on the Cumulative distribution with Jensen-Shannon divergence (CJS-CNN) is presented to predict the aesthetic score distribution of human ratings, with a new reliability-sensitive learning method based on the kurtosis of the score distribution, which eliminates the requirement of the original full data of human ratings (without normalization). Experimental results on large scale aesthetic dataset demonstrate the effectiveness of our introduced CJS-CNN in this task.

We investigate the ride-sharing assignment problem from an algorithmic resource allocation point of view. Given a number of requests with source and destination locations, and a number of available car locations, the task is to assign cars to requests with two requests sharing one car. We formulate this as a combinatorial optimization problem, and show that it is NP-hard. We then design an approximation algorithm which guarantees to output a solution with at most 2.5 times the optimal cost. Experiments are conducted showing that our algorithm actually has a much better approximation ratio (around 1.2) on synthetically generated data.

Subjective classification of galaxies can mislead us in the quest of the origin regarding formation and evolution of galaxies. Multivariate analyses are the best tools used for such kind of purpose to better understand the differences between various objects, in an objective manner. In the present study an objective classification of 362~923 galaxies of the Value Added Galaxy Catalogue (VAGC) is carried out with the help of three methods of multivariate analysis. First, independent component analysis (ICA) is used to determine a set of derived independent variables that are linear combinations of various observed parameters (viz. ionized lines, Lick indices, photometric and morphological parameters, star formation rates etc.) of the galaxies. Subsequently, K-means cluster analysis (CA) is applied on the independent components to find the optimum number of homogeneous groups. Finally, a stepwise multiple regression is carried out on each group to predict and study the star formation rate as a function of other independent observables. The properties of the ten groups thus uncovered, are used to explain their formation and evolution mechanisms. It is suggested that three groups are young and metal poor, belonging to the blue sequence, three others are old and metal rich (red sequence). The remaining four groups of intermediate ages cannot be classified in this bimodal sequence: two belong to a pronounced mixture of early and late type galaxies whereas the other two mostly contain old early type galaxies. The above result is indicative of a continuous evolutionary scenario of galaxies instead of two discrete modes, blue and red, so far suggested by previous authors. Some of our groups occupy the transition region with different quenching mechanisms. This establishes the elegance of a multivariate analysis giving rise to a sophisticated refinement over subjective inference.

A new study released by The Economist Intelligence Unit ran three econometric scenarios to 2030 on five countries -- the United States, the United Kingdom, Australia, Japan--and developing Asia as a whole. In'Risks and rewards: Scenarios around the economic impact of machine learning', commissioned by Google, two scenarios assumed greater human productivity through upskilling and greater investment in technology and access to open source data, while the third assumed insufficient policy support for structural changes in the economy. The results showed that, although the fears of those pessimistic about the impact of machine learning, and artificial intelligence in general, may be overblown, the optimists' claims are not entirely supported, either. The other area of the study, a look at the impact of machine learning on four industries, reaches a similar conclusion. For firms both developing machine learning and those using it, the reports finds that communication between themselves, and with the public and policymakers, needs to improve.

Police in China have begun using sunglasses equipped with facial recognition technology to identify suspected criminals. The glasses are connected to an internal database of suspects, meaning officers can quickly scan crowds while looking for fugitives. But critics fear the technology will give even more power to the government. The sunglasses have already helped police capture seven suspects, according to Chinese state media. Police used the new equipment at a busy train station in the central city of Zhengzhou to identify the suspects. The seven people who were apprehended are accused of crimes ranging from hit-and-runs to human trafficking.

To most individuals, commercial buildings are viewed as brick and mortar, static structures. There is, however, a complex technological side to commercial buildings --from the software platforms that control elevators to smart lighting -- that is often overlooked. It is these features that underscore how commercial buildings can benefit from disruptive technologies like Artificial Intelligence (AI). Falling costs, increased accessibility, and greater sophistication of IoT devices have made it easier to generate data on the performance of buildings, and the systems within them, on a more granular level. At its core, IoT enables different components to communicate with each other, without any intelligence.

Police in China are now sporting glasses equipped with facial recognition devices and they're using them to scan train riders and plane passengers for individuals who may be trying to avoid law enforcement or are using fake IDs. So far, police have caught seven people connected to major criminal cases and 26 who were using false IDs while traveling, according to People's Daily. The Wall Street Journal reports that Beijing-based LLVision Technology Co. developed the devices. The company produces wearable video cameras as well and while it sells those to anyone, it's vetting buyers for its facial recognition devices. LLVision says that in tests, the system was able to pick out individuals from a database of 10,000 people and it could do so in 100 milliseconds.

For the past two months, cyborg police officers have screened travellers passing through Zhengzhou railway station in China. The officers, wearing smart glasses with built-in face recognition, have caught seven fugitives and 26 fake ID holders already. According to local media, some of the fugitives were wanted for alleged involvement in human trafficking cases. Liu Tianyi, at LLVision, the firm that developed the GLXSS Pro smart glasses, says the glasses are very light so the police officers can wear them all day. Feedback so far been positive, she says.

According to IDC Research, Japan has the highest projected growth of artificial intelligence (AI) at 74% (5-year CAGR). DataRobot, pioneers of automated machine learning and a visionary principle of the 4th Industrial Revolution, saw this firsthand at last week's AI Experience conference in Tokyo. Attended by some 800 business executives and data scientists, the event delivered a deep dive on automated machine learning both from the technology's authors and its users. It is evident that this technology has come of age and offers a practical business solution to organizations looking to innovate through big data. Pop culture may have a big role to play in the high level of interest in AI in Japan.

There can be no doubt, artificial intelligence (AI) helps defend government and business systems from cyberattacks, but conversely, AI systems can be used to augment attacks against government and corporate, even SMB systems. For TechRepublic and ZDNet, I'm Dan Patterson and it's a pleasure today to speak with Mark Gazit, the CEO of ThetaRay. One of the biggest targets for cybercriminals, and cybercriminals deploying AI solutions, is the financial service industries. I wonder if you could help us understand how financial crime is being transformed by technology and artificial intelligence. So Dan, thank you very much for inviting me and I have to say it's an exciting topic, but also a bit dangerous for us as human beings and you're absolutely right, the world of financial crime has changed.