On the outskirts of Hangzhou, eastern China, in Alibaba's Cloud Town, a Silicon Valley-style working and leisure hub, cutting-edge research in artificial intelligence (AI) and smart city developments at one of China's largest tech companies are showcased. It is a half-empty dystopia of almost comic proportions where the future of automation and infrastructure in southern China is played out. While Alibaba exhibitions and conferences occur sporadically, the full scope of what goes on there remains hidden. Having long claimed to be apolitical, Jack Ma, the billionaire co-founder and executive chairman of the tech giant Alibaba, was recently revealed to be a member of the ruling Communist party of China (CCP). Nevertheless, a glimpse into the projects the company is working on in Cloud Town, considered in light of these revelations, should set the alarm bells ringing with fear of a dystopian future of state and corporate control.

The first time many of us heard about China's use of facial recognition on jaywalkers was just this week when a prominent Chinese businesswoman was publicly "named and shamed" for improper street crossing. Turns out, she wasn't even there: China's terrifyingly over-the-top use of tech for citizen surveillance made a mistake. The AI system identified Dong Mingzhu's face from a bus advertisement for her company's products. "[The] president of China's biggest air conditioning maker," wrote The Telegraph, "had her image flashed up on a public display screen in the city of Ningbo, near Shanghai, with a caption saying she had illegally crossed the street on a red light." Shortly after, Ningbo traffic police admitted the mistake and claimed to have "completely upgraded the system to reduce the false recognition rate."

Scott Chen, president of the gay dating app Grindr, said he believes "marriage is a holy matrimony between a man and a woman" in a Facebook post. Mr Chen's remarks have sparked criticism within the company, forcing him to clarify his position on gay marriage and LGBT rights. The tech executive's comments related to Taiwan's recent referendum rejecting gay marriage and were first translated and reported by Into – a website owned by Grindr. Although Mr Chen deleted his original post, written in Chinese, he responded to the article with another post written in English explaining his views. Delta passenger receives Grindr message from pilot mid-flight Should Grindr users worry about what China will do with their data?

Cars in China are watching their drivers and reporting where they are to the government. Authorities claim that the data is only used to ensure that the roads, cars and drivers are safe. But privacy experts fear the data could be used for more invasive forms of surveillance. Hundreds of car manufacturers of electric vehicles – including Tesla, as well as more traditional companies like Volkswagen, BMW and Ford – transmit dozens of different kinds of information to the government. The information is sent without the driver or owner of the car even knowing it.

A booking database run by the Marriott hotel chain has been hit by a vast hack that could affect half a billion people. The vast collection of people's personal information, used to book rooms at its Starwood properties, has been accessed by unauthorised people since 2014, it said. The cyberattack included information about those people's credit cards that could be used to steal money, Marriott warned. Uber has halted testing of driverless vehicles after a woman was killed by one of their cars in Tempe, Arizona. The I.F.O. is fuelled by eight electric engines, which is able to push the flying object to an estimated top speed of about 120mph The giant human-like robot bears a striking resemblance to the military robots starring in the movie'Avatar' and is claimed as a world first by its creators from a South Korean robotic company Waseda University's saxophonist robot WAS-5, developed by professor Atsuo Takanishi and Kaptain Rock playing one string light saber guitar perform jam session A man looks at an exhibit entitled'Mimus' a giant industrial robot which has been reprogrammed to interact with humans during a photocall at the new Design Museum in South Kensington, London Electrification Guru Dr. Wolfgang Ziebart talks about the electric Jaguar I-PACE concept SUV before it was unveiled before the Los Angeles Auto Show in Los Angeles, California, U.S The Jaguar I-PACE Concept car is the start of a new era for Jaguar.

fMRI semantic category understanding using linguistic encoding models attempt to learn a forward mapping that relates stimuli to the corresponding brain activation. Classical encoding models use linear multi-variate methods to predict the brain activation (all voxels) given the stimulus. However, these methods essentially assume multiple regions as one large uniform region or several independent regions, ignoring connections among them. In this paper, we present a mixture of experts-based model where a group of experts captures brain activity patterns related to particular regions of interest (ROI) and also show the discrimination across different experts. The model is trained word stimuli encoded as 25-dimensional feature vectors as input and the corresponding brain responses as output. Given a new word (25-dimensional feature vector), it predicts the entire brain activation as the linear combination of multiple experts brain activations. We argue that each expert learns a certain region of brain activations corresponding to its category of words, which solves the problem of identifying the regions with a simple encoding model. We showcase that proposed mixture of experts-based model indeed learns region-based experts to predict the brain activations with high spatial accuracy.

Understanding and evaluating the robustness of neural networks against adversarial attacks is a subject of growing interest. Attacks proposed in the literature usually work with models that are trained to minimize cross-entropy loss and have softmax activations. In this work, we present interesting experimental results that suggest the importance of considering other loss functions and target representations. Specifically, (1) training on mean-squared error and (2) representing targets as codewords generated from a random codebook show a marked increase in robustness against targeted and untargeted attacks under white-box and black-box settings. Our results show an increase in accuracy against untargeted attacks of up to 98.7\% and a decrease of targeted attack success rates of up to 99.8\%. For our experiments, we use the DenseNet architecture trained on three datasets (CIFAR-10, MNIST, and Fashion-MNIST).

The economic and social impact of poor air quality in towns and cities is increasingly being recognised, together with the need for effective ways of creating awareness of real-time air quality levels and their impact on human health. With local authority maintained monitoring stations being geographically sparse and the resultant datasets also featuring missing labels, computational data-driven mechanisms are needed to address the data sparsity challenge. In this paper, we propose a machine learning-based method to accurately predict the Air Quality Index (AQI), using environmental monitoring data together with meteorological measurements. To do so, we develop an air quality estimation framework that implements a neural network that is enhanced with a novel Non-linear Autoregressive neural network with exogenous input (NARX), especially designed for time series prediction. The framework is applied to a case study featuring different monitoring sites in London, with comparisons against other standard machine-learning based predictive algorithms showing the feasibility and robust performance of the proposed method for different kinds of areas within an urban region.

A fuzzy expert system (FES) for the prediction of prostate cancer (PC) is prescribed in this article. Age, prostate-specific antigen (PSA), prostate volume (PV) and $\%$ Free PSA ($\%$FPSA) are fed as inputs into the FES and prostate cancer risk (PCR) is obtained as the output. Using knowledge based rules in Mamdani type inference method the output is calculated. If PCR $\ge 50\%$, then the patient shall be advised to go for a biopsy test for confirmation. The efficacy of the designed FES is tested against a clinical data set. The true prediction for all the patients turns out to be $68.91\%$ whereas only for positive biopsy cases it rises to $73.77\%$. This simple yet effective FES can be used as supportive tool for decision making in medical diagnosis.

Discourse structures are beneficial for various NLP tasks such as dialogue understanding, question answering, sentiment analysis, and so on. This paper presents a deep sequential model for parsing discourse dependency structures of multi-party dialogues. The proposed model aims to construct a discourse dependency tree by predicting dependency relations and constructing the discourse structure jointly and alternately. It makes a sequential scan of the Elementary Discourse Units (EDUs) in a dialogue. For each EDU, the model decides to which previous EDU the current one should link and what the corresponding relation type is. The predicted link and relation type are then used to build the discourse structure incrementally with a structured encoder. During link prediction and relation classification, the model utilizes not only local information that represents the concerned EDUs, but also global information that encodes the EDU sequence and the discourse structure that is already built at the current step. Experiments show that the proposed model outperforms all the state-of-the-art baselines.