BENGALURU: Mission Admission 2019, a two-day higher education seminar and expo conducted by The Times of India to equip students with skills to make informed career choices, will kick off on Saturday at White Orchid Convention Centre, near Manyata Tech Park, Nagawara. From counsellors who will guide students on how to crack competitive examinations such as CET and ComedK UGET, to speakers who will guide those looking to pursue higher studies in art, design, visual effects, acting and photography, Mission Admission will be a one-stop shop for students looking for guidance. Entry for the event is free. Kannada filmmaker Indrajit Lankesh, who is making his Bollywood debut soon, said: "I am looking forward to interacting with students and speak about filmmaking as a career, especially in the context of the digital content boom." Tapping technologies With Bengaluru being the startup capital and entrepreneurship being the flavour of the season, the expo will help students understand careers in areas such as Artificial Intelligence, Machine Learning and Big Data.

In India, we celebrated the festival of color "Holi" last week. We celebrate the end of the winter with a splash of color because that's what the spring will bring us in a few days. When I was young, the celebrations were sparse. It was the decade of frugal parenting. We waited for festivals so eagerly because it meant parent approved outing and fun.

Artificial intelligence developed by a Samsung lab in Russia can fabricate video from a single image, including a painting. Imagine someone creating a deepfake video of you simply by stealing your Facebook profile pic. The bad guys don't have their hands on that tech yet, but Samsung has figured out how to make it happen. Software for creating deepfakes -- fabricated clips that make people appear to do or say things they never did -- usually requires big data sets of images in order to create a realistic forgery. Now Samsung has developed a new artificial intelligence system that can generate a fake clip by feeding it as little as one photo.

NASA is plowing ahead with its plans to send humans back to the moon, selecting its first commercial partner for developing the lunar'Gateway.' In a statement NASA announced that Maxar Technologies -- formerly SSL -- has been awarded $375 million to build power and propulsion systems for the lunar'Gateway,' a small spaceship that will orbit the moon and act as a layover for astronauts on lunar missions. From the'Gateway' astronauts will board landers and make the descent to the moon's service. A power and propulsion element of the Gateway is a 50-kilowatt solar spacecraft. An artist's impression is shown As reported by Ars Technica, Maxar will be joined by Jeff Bezo's aerospace company, Blue Origin, and Draper, who will help build, design, and operate the craft.

As we live longer and technology continues its rapid arc of development, we can imagine a future where machines will augment our human abilities and help us make better life choices, from health to wealth. Instead of conducting a question and answer with a device on the countertop, we will be able to converse naturally with our virtual assistant that is fully embedded in our physical environment. Through our dialogue and digital breadcrumbs, it will understand our life goals and aspirations, our obligations and limitations. It will seamlessly and automatically help us budget and save for different life events, so we can spend more time enjoying life's moments. While we can imagine this future, the technology itself is not without challenges -- at least for now.

Jordan French is a multi-media journalist on the editorial staff at TheStreet.com He is also the Founder and Executive Editor at Grit Daily News. Formerly an engineer and attorney he represented the "People of the United States" in energy market manipulation cases as an enforcement attorney at the Federal Energy Regulatory Commission. As an engineer he worked on the Mars Gravity Biosatellite Program and later co-founded BeeHex, Inc., the personalized nutrition and robotics company that popularized 3D-printed pizza. The author of forthcoming book, The Gritty Entrepreneur, he is a frequent public speaker, technology evangelist and media moderator.

Long Short-Term Memory (LSTM) infers the long term dependency through a cell state maintained by the input and the forget gate structures, which models a gate output as a value in [0,1] through a sigmoid function. However, due to the graduality of the sigmoid function, the sigmoid gate is not flexible in representing multi-modality or skewness. Besides, the previous models lack correlation modeling between the gates, which would be a new method to adopt domain knowledge. This paper proposes a new gate structure with the bivariate Beta distribution. The proposed gate structure enables hierarchical probabilistic modeling on the gates within the LSTM cell, so the modelers can customize the cell state flow. Also, we observed that our structured flexible gate modeling is enabled by the probability density estimation. Moreover, we theoretically show and empirically experiment that the bivariate Beta distribution gate structure alleviates the gradient vanishing problem. We demonstrate the effectiveness of bivariate Beta gate structure on the sentence classification, image classification, polyphonic music modeling, and image caption generation.

We present SemEval-2019 Task 8 on Fact Checking in Community Question Answering Forums, which features two subtasks. Subtask A is about deciding whether a question asks for factual information vs. an opinion/advice vs. just socializing. Subtask B asks to predict whether an answer to a factual question is true, false or not a proper answer. We received 17 official submissions for subtask A and 11 official submissions for Subtask B. For subtask A, all systems improved over the majority class baseline. For Subtask B, all systems were below a majority class baseline, but several systems were very close to it. The leaderboard and the data from the competition can be found at http://competitions.codalab.org/competitions/20022

Deep Neural Networks (DNNs) are vulnerable to adversarial attacks, especially white-box targeted attacks. One scheme of learning attacks is to design a proper adversarial objective function that leads to the imperceptible perturbation for any test image (e.g., the Carlini-Wagner (C&W) method). Most methods address targeted attacks in the Top-1 manner. In this paper, we propose to learn ordered Top-k attacks (k>= 1) for image classification tasks, that is to enforce the Top-k predicted labels of an adversarial example to be the k (randomly) selected and ordered labels (the ground-truth label is exclusive). To this end, we present an adversarial distillation framework: First, we compute an adversarial probability distribution for any given ordered Top-k targeted labels with respect to the ground-truth of a test image. Then, we learn adversarial examples by minimizing the Kullback-Leibler (KL) divergence together with the perturbation energy penalty, similar in spirit to the network distillation method. We explore how to leverage label semantic similarities in computing the targeted distributions, leading to knowledge-oriented attacks. In experiments, we thoroughly test Top-1 and Top-5 attacks in the ImageNet-1000 validation dataset using two popular DNNs trained with clean ImageNet-1000 train dataset, ResNet-50 and DenseNet-121. For both models, our proposed adversarial distillation approach outperforms the C&W method in the Top-1 setting, as well as other baseline methods. Our approach shows significant improvement in the Top-5 setting against a strong modified C&W method.

Dynamic Epistemic Logic (DEL) is a family of multimodal logics that has proved to be very successful for epistemic reasoning in planning tasks. In this logic, the agent's knowledge is captured by modal epistemic operators whereas the system evolution is described in terms of (some subset of) dynamic logic modalities in which actions are usually represented as semantic objects called event models. In this paper, we study a variant of DEL, that wecall DEL[ASP], where actions are syntactically described by using an Answer Set Programming (ASP) representation instead of event models. This representation directly inherits high level expressive features like indirect effects, qualifications, state constraints, defaults, or recursive fluents that are common in ASP descriptions of action domains. Besides, we illustrate how this approach can be applied for obtaining conditional plans in single-agent, partially observable domains where knowledge acquisition may be represented as indirect effects of actions.