The Remote Access Trojan (RAT) can almost be considered the "legacy" tool of hackers. The RAT is a malware program that uses a back door for administrative control over the targeted computer. As such, RATs are used for "low and slow", prolonged, stealthy operations such as APTs. Using this malicious technique, the attackers take their time to explore the victim's networks and assets, and then move around as quietly as possible to achieve their objectives without detection. Some APTs have been in operation for years and RATs play a crucial part in enabling attackers to access targets while avoiding detection.

Zinier, a company that uses artificial intelligence (AI) to automate field work, has raised $90 million in a Series C funding round, bringing its total amount raised to $120 million. The startup plays heavily in the telecom sector -- 80% of its existing customers are in the space, including network operators, equipment vendors and suppliers, contractors, and engineers, according to Zinier's co-founder and CEO Arka Dhar. That's also reflected by the firms that returned to invest in this latest round, including Nokia-backed NGP Capital and Qualcomm Ventures. New investor Iconiq Capital led the round with participation from Tiger Global Management, Accel, Founders Fund, and Newfund Capital. "Zinier is going to play a very, very important role there," Dhar said in a phone interview.

Humans learn language through several perceptive cues and an endless continuum of multimodal interactions. To learn the names of the objects around us, we need some kind of a supervision or a context. Either our parents explicitly point us the tangible, non-abstract objects in our first years, or we grab the meaning of the words from the peripheral context. Likewise, the movements of the objects are described by "verbs". We learn the meaning of the verbs by watching the objects in motion, or we grab a verb through a linguistic context without visually perceiving it. Then we use the learned objects and verbs in different unseen combinations, constitute novel sentences and generalize the verbs and nouns to new unseen instances or cases. There is an ongoing process of connecting, updating and renewing the inputs from different modalities [12]. In this work, we explore the possibilities of learning the verbs from multimodal cues in a similar way to humans and propose a neural network model that aims to jointly capture the visual and textual representation. The problem is to build a cross-modal joint space which will help retrieving a textual modal given a visual modal, or vice versa.

Next point-of-interest (POI) recommendation aims to offer suggestions on which POI to visit next, given a user's POI visit history. This problem has a wide application in the tourism industry, and it is gaining an increasing interest as more POI check-in data become available. The problem is often modeled as a sequential recommendation problem to take advantage of the sequential patterns of user check-ins, e.g., people tend to visit Central Park after The Metropolitan Museum of Art in New York City. Recently, self-attentive networks have been shown to be both effective and efficient in general sequential recommendation problems, e.g., to recommend products, video games, or movies. Directly adopting self-attentive networks for next POI recommendation, however, may produce sub-optimal recommendations. This is because vanilla self-attentive networks do not consider the spatial and temporal patterns of user check-ins, which are two critical features in next POI recommendation. To address this limitation, in this paper, we propose a model named SANST that incorporates spatio-temporal patterns of user check-ins into self-attentive networks. To incorporate the spatial patterns, we encode the relative positions of POIs into their embeddings before feeding the embeddings into the self-attentive network. To incorporate the temporal patterns, we discretize the time of POI check-ins and model the temporal relationship between POI check-ins by a relation-aware self-attention module. We evaluate the performance of our SANST model with three real-world datasets. The results show that SANST consistently outperforms the state-of-theart models, and the advantage in nDCG@10 is up to 13.65%.

Uncertainty quantification for deep learning is a challenging open problem. Bayesian statistics offer a mathematically grounded framework to reason about uncertainties; however, approximate posteriors for modern neural networks still require prohibitive computational costs. We propose a family of algorithms which split the classification task into two stages: representation learning and uncertainty estimation. We compare four specific instances, where uncertainty estimation is performed via either an ensemble of Stochastic Gradient Descent or Stochastic Gradient Langevin Dynamics snapshots, an ensemble of bootstrapped logistic regressions, or via a number of Monte Carlo Dropout passes. We evaluate their performance in terms of \emph{selective} classification (risk-coverage), and their ability to detect out-of-distribution samples. Our experiments suggest there is limited value in adding multiple uncertainty layers to deep classifiers, and we observe that these simple methods strongly outperform a vanilla point-estimate SGD in some complex benchmarks like ImageNet.

In the last decade, social networks became most popular medium for communication and interaction. As an example, micro-blogging service Twitter has more than 200 million registered users who exchange more than 65 million posts per day. Users express their thoughts, ideas, and even their intentions through these tweets. Most of the tweets are written informally and often in slang language, that contains misspelt and abbreviated words. This paper investigates the problem of selecting features that affect extracting user's intention from Twitter feeds based on text mining techniques. It starts by presenting the method we used to construct our own dataset from extracted Twitter feeds. Following that, we present two techniques of feature selection followed by classification. In the first technique, we use Information Gain as a one-phase feature selection, followed by supervised classification algorithms. In the second technique, we use a hybrid approach based on forward feature selection algorithm in which two feature selection techniques employed followed by classification algorithms. We examine these two techniques with four classification algorithms. We evaluate them using our own dataset, and we critically review the results.

Applying Q-learning to high-dimensional or continuous action spaces can be difficult due to the required maximization over the set of possible actions. Motivated by techniques from amortized inference, we replace the expensive maximization over all actions with a maximization over a small subset of possible actions sampled from a learned proposal distribution. The resulting approach, which we dub Amortized Q-learning (AQL), is able to handle discrete, continuous, or hybrid action spaces while maintaining the benefits of Q-learning. Our experiments on continuous control tasks with up to 21 dimensional actions show that AQL outperforms D3PG (Barth-Maron et al, 2018) and QT-Opt (Kalashnikov et al, 2018). Experiments on structured discrete action spaces demonstrate that AQL can efficiently learn good policies in spaces with thousands of discrete actions.

FirstWord MedTech's Digital Ten is a fortnightly round-up of the 10 most read and noteworthy headlines related to digital health, including industry deals, alliances, collaborations, innovations and R&D news. The biggest M&A deal inked this year so far comes from the telemedicine field with Teladoc Health agreeing to fork out $600 million to acquire InTouch Health, a provider of cloud-based telemedicine software and physician services for hospitals and health systems. Teladoc's existing telehealth service platform targets consumers and with this deal, it will gain a complementary business that is expected to generate revenue of $80 million in 2019, representing 35% year-over year growth, and a new facility-based virtual care platform. Mojo Vision's smart contact lens receives FDA breakthrough device designation In yet another first for 2020, Mojo Vision's Mojo smart contact lens is the first ophthalmic product to get FDA breakthrough device designation this year. The lens incorporates what the company describes as the "smallest and densest dynamic display ever made," along with a power-efficient image sensor optimised for computer vision, a custom wireless radio, and motion sensors for eye-tracking and image stabilisation.

Diabetes is a global public health disease projected to affect 642 million adults by 2040, with about 75% residing in low- and middle-income countries. Diabetic retinopathy (DR) affects 1 in 3 people with diabetes and remains the leading cause of blindness in working-aged adults. There are 3 broad strategic imperatives to prevent blindness caused by DR. Primary prevention requires preventing or delaying the onset of DR in those with diabetes by systems-level lifestyle modifications such as increasing physical activity or dietary modifications, pharmacological interventions for glycaemic and blood pressure control, and systematic screening for the onset of DR. Secondary prevention requires preventing the progression of DR in patients with DR by continuing systemic risk factor control, regular screening to monitor for the progression of mild DR to vision-threatening stages, and the development and implementation of evidence-based guidelines for managing DR. In this aspect, telemedicine-based DR screening incorporating artificial intelligence technology has the potential to facilitate more widespread and cost-effective screening, particularly in low- and middle-income countries. Tertiary prevention of DR blindness has been the main focus of the clinical ophthalmology community, classically based on laser photocoagulation treatment and ocular surgery but with an increasing use of anti-vascular endothelial growth factor (anti-VEGF) for vision-threatening DR. Evidence from serial epidemiological studies shows blindness due to DR has declined in high-income countries (e.g., the USA and UK) due to coordinated public health education efforts, increased awareness, early detection by DR screening, sustained systemic risk factor control, and the availability of effective tertiary level treatment. However, the progress made in reducing DR blindness in high-income countries may be overwhelmed by the increasing numbers of patients with diabetes and DR in low- and middle-income countries (e.g., China, India, Indonesia, etc.).

Cows chat to each other about food and the weather, according to an astonishing new study that revealed they all have their own unique'moo', writes Laura Sharman. The study revealed how dairy cows respond to positive and negative emotional situations each with their own individual voice and linked their moods to their moos. Experts reckon they can decipher the language of the cows, made up from different-pitched moos, using computer software they dubbed'Google translate for cows.' Biologists made the discovery by listening to Holstein-Fresian heifer cattle, a European breed, mooing into a microphone and analysing the pitch. They found each cow retains its own distinct moo and can give cues in different situations which helps them to maintain contact with the herd and express excitement, arousal, engagement or distress. Lead study author Alexandra Green, from the University of Sydney in Australia, said: "We found that cattle vocal individuality is relatively stable across different emotionally loaded farming contexts. "Recognising individual cattle could assist farmers in the non-invasive detection of welfare." The findings could help farmers keep their cattle healthy and happy by understanding each cow's mood by translating their individual moos. Scientists hope it will also help improve animal welfare thanks to an area of study which historically had not received much attention. Cattle mothers and offspring are known to communicate by maintaining individuality in their lowing, according to existing research. But the new study confirms that cows maintain this individual mooing throughout their lives, even when they are nattering among themselves. Dairy cows communicate with each other all the time, but when they are talking about happier things, like food, their moos are more sonorous. And when they are moaning about the weather, their moos, while still retaining their fingerprint-like individuality, are pitched lower. PhD candidate Alexandra said: "Cows are gregarious, social animals.