The COVID-19 pandemic will accelerate new trends in Romanian business environment, including robotics. Whether we are talking about robotic process automation (RPA) or the automation of certain production processes, the post-COVID-19 reality in Romania will be based on new business models. As an example, the pandemic has generated a lot of demand for UiPath's software robots to assist hospitals with processing medical tests. Health care is predicted to have a 36 percent automation potential. This means more than a third of health care tasks--especially managerial and back-office functions--could be automated, allowing providers to offer more direct, value-based patient care at lower costs and higher efficiency rates.

The increasing rise in machine learning and deep learning applications is requiring ever more computational resources to successfully meet the growing demands of an always-connected, automated world. Neuromorphic technologies based on Spiking Neural Network algorithms hold the promise to implement advanced artificial intelligence using a fraction of the computations and power requirements by modeling the functioning, and spiking, of the human brain. With the proliferation of tools and platforms aiding data scientists and machine learning engineers to develop the latest innovations in artificial and deep neural networks, a transition to a new paradigm will require building from the current well-established foundations. This paper explains the theoretical workings of neuromorphic technologies based on spikes, and overviews the state-of-art in hardware processors, software platforms and neuromorphic sensing devices. A progression path is paved for current machine learning specialists to update their skillset, as well as classification or predictive models from the current generation of deep neural networks to SNNs. This can be achieved by leveraging existing, specialized hardware in the form of SpiNNaker and the Nengo migration toolkit. First-hand, experimental results of converting a VGG-16 neural network to an SNN are shared. A forward gaze into industrial, medical and commercial applications that can readily benefit from SNNs wraps up this investigation into the neuromorphic computing future.

Emotion recognition has a pivotal role in affective computing and in human-computer interaction. The current technological developments lead to increased possibilities of collecting data about the emotional state of a person. In general, human perception regarding the emotion transmitted by a subject is based on vocal and visual information collected in the first seconds of interaction with the subject. As a consequence, the integration of verbal (i.e., speech) and non-verbal (i.e., image) information seems to be the preferred choice in most of the current approaches towards emotion recognition. In this paper, we propose a multimodal fusion technique for emotion recognition based on combining audio-visual modalities from a temporal window with different temporal offsets for each modality. We show that our proposed method outperforms other methods from the literature and human accuracy rating. The experiments are conducted over the open-access multimodal dataset CREMA-D.

An AI developed in Vienna is now debuting in the art business, and will curate the Bucharest Biennale. Practitioners in the arts labour under the misapprehension that the human factor of creativity would shield them from the depredations of artificial intelligence. It is assumed that like machines freed us from physical labour, machine intelligence would rid us of intellectual chores. They would put production line workers, bookkeepers, bank tellers and inventory managers out of work, but novelists and artists, and the marketing networks which have developed around their products, would be unharmed. A computer at Stanford which has digested the complete works of Shakespeare does almost passable knockoffs.

Practitioners in the arts labour under the misapprehension that the human factor of creativity would shield them from the depredations of artificial intelligence. It is assumed that like machines freed us from physical labour, machine intelligence would rid us of intellectual chores. They would put production line workers, bookkeepers, bank tellers and inventory managers out of work, but novelists and artists, and the marketing networks which have developed around their products, would be unharmed. A computer at Stanford which has digested the complete works of Shakespeare does almost passable knockoffs. In 2018, a neural network went on a journey across America and wrote a digital equivalent of Jack Kerouac's Beat classic On the Road.

While previous approaches explore the effect of self-supervision on natural images, we show that patch-based pre-training performs poorly on document images because of their different structural properties and poor intra-sample semantic information. We propose two context-aware alternatives to improve performance on the Tobacco-3482 image classification task. We also propose a novel method for self-supervision, which makes use of the inherent multi-modality of documents (image and text), which performs better than other popular self-supervised methods, including supervised ImageNet pre-training, on document image classification scenarios with a limited amount of data.

Bayesian method is capable of capturing real world uncertainties/incompleteness and properly addressing the over-fitting issue faced by deep neural networks. In recent years, Bayesian Neural Networks (BNNs) have drawn tremendous attentions of AI researchers and proved to be successful in many applications. However, the required high computation complexity makes BNNs difficult to be deployed in computing systems with limited power budget. In this paper, an efficient BNN inference flow is proposed to reduce the computation cost then is evaluated by means of both software and hardware implementations. A feature decomposition and memorization (\texttt{DM}) strategy is utilized to reform the BNN inference flow in a reduced manner. About half of the computations could be eliminated compared to the traditional approach that has been proved by theoretical analysis and software validations. Subsequently, in order to resolve the hardware resource limitations, a memory-friendly computing framework is further deployed to reduce the memory overhead introduced by \texttt{DM} strategy. Finally, we implement our approach in Verilog and synthesise it with 45 $nm$ FreePDK technology. Hardware simulation results on multi-layer BNNs demonstrate that, when compared with the traditional BNN inference method, it provides an energy consumption reduction of 73\% and a 4$\times$ speedup at the expense of 14\% area overhead.

Alice envisions the future is a unique program run by Microsoft in partnership with Avanade and Accenture which allows high school girls around the world to develop their understanding of Artificial Intelligence (AI) – the world's leading technology in terms of its potential for building a better future. Named after Alice's inspirational and curiosity-driven adventures in Wonderland, participants are treated to workshops led by industry experts, in addition to receiving help from Microsoft, Avanade and Accenture mentors to develop their own projects, before pitching them to a panel of judges. "When we look at the gender gap among AI workers, it makes me think that we really need to do something," says Ana Maria Stanciuc, EMEA Education Marketing Lead at Microsoft. "Events like this help teach girls to trust their imagination, to believe in their ideas, and to show that there are no limits. "Having different mentors helping them with design-lead thinking, technical and business skills is an incredible resource.

We propose our solution to a particular practical problem in the domain of vehicle routing and scheduling. The generic task is finding the best allocation of the minimum number of \emph{mobile resources} that can provide periodical services in remote locations. These \emph{mobile resources} are based at a single central location. Specifications have been defined initially for a real-life application that is the starting point of an ongoing project. Particularly, the goal is to mitigate health problems in rural areas around a city in Romania. Medically equipped vans are programmed to start daily routes from county capital, provide a given number of examinations in townships within the county and return to the capital city in the same day. From the health care perspective, each van is equipped with an ultrasound scanner, and they are scheduled to investigate pregnant woman each trimester aiming to diagnose potential problems. The project is motivated by reports currently ranking Romania as the country with the highest infant mortality rate in the European Union. We developed our solution in two phases: modeling of the most relevant parameters and data available for our goal and then design and implement an algorithm that provides an optimized solution. The most important metric of an output scheduling is the number of vans that are necessary to provide a given amount of examination time per township, followed by total travel time or fuel consumption, number of different routes, and others. Our solution implements two probabilistic algorithms out of which we chose the one that performs the best.

Stochastic optimization lies at the core of most statistical learning models. The recent great development of stochastic algorithmic tools focused significantly onto proximal gradient iterations, in order to find an efficient approach for nonsmooth (composite) population risk functions. The complexity of finding optimal predictors by minimizing regularized risk is largely understood for simple regularizations such as $\ell_1/\ell_2$ norms. However, more complex properties desired for the predictor necessitates highly difficult regularizers as used in grouped lasso or graph trend filtering. In this chapter we develop and analyze minibatch variants of stochastic proximal gradient algorithm for general composite objective functions with stochastic nonsmooth components. We provide iteration complexity for constant and variable stepsize policies obtaining that, for minibatch size $N$, after $\mathcal{O}(\frac{1}{N\epsilon})$ iterations $\epsilon-$suboptimality is attained in expected quadratic distance to optimal solution. The numerical tests on $\ell_2-$regularized SVMs and parametric sparse representation problems confirm the theoretical behaviour and surpasses minibatch SGD performance.