Facial expressions are important cues to observe human emotions. Facial expression recognition has attracted many researchers for years, but it is still a challenging topic since expression features vary greatly with the head poses, environments, and variations in the different persons involved. In this work, three major steps are involved to improve the performance of micro-facial expression recognition. First, an Adaptive Homomorphic Filtering is used for face detection and rotation rectification processes. Secondly, Micro-facial features were used to extract the appearance variations of a testing image-spatial analysis. The features of motion information are used for expression recognition in a sequence of facial images. An effective Micro-Facial Expression Based Deep-Rooted Learning (MFEDRL) classifier is proposed in this paper to better recognize spontaneous micro-expressions by learning parameters on the optimal features. This proposed method includes two loss functions such as cross entropy loss function and centre loss function. Then the performance of the algorithm will be evaluated using recognition rate and false measures. Simulation results show that the predictive performance of the proposed method outperforms that of the existing classifiers such as Convolutional Neural Network (CNN), Deep Neural Network (DNN), Artificial Neural Network (ANN), Support Vector Machine (SVM), and k-Nearest Neighbours (KNN) in terms of accuracy and Mean Absolute Error (MAE).

Computational learning theory states that many classes of boolean formulas are learnable in polynomial time. This paper addresses the understudied subject of how, in practice, such formulas can be learned by deep neural networks. Specifically, we analyse boolean formulas associated with the decision version of combinatorial optimisation problems, model sampling benchmarks, and random 3-CNFs with varying degrees of constrainedness. Our extensive experiments indicate that: (i) regardless of the combinatorial optimisation problem, relatively small and shallow neural networks are very good approximators of the associated formulas; (ii) smaller formulas seem harder to learn, possibly due to the fewer positive (satisfying) examples available; and (iii) interestingly, underconstrained 3-CNF formulas are more challenging to learn than overconstrained ones. Source code and relevant datasets are publicly available (https://github.com/machine-reasoning-ufrgs/mlbf).

Deepfakes is a class of synthetic media generated by AI and represents another dark side of technology -- this form of Artificial Intelligence stole the headlines last year when a LinkedIn user by the name Katie Jones, who appeared on the platform & started connecting with the Who's Who of the political elite in Washington DC. It was alarming, how deep learning created a real-life image of a person & then penetrated the social media spreading misinformation. With the U.S presidential elections looming, lawmakers in the country are worried about how deepfakes can greatly jeopardize the transparency of the democratic process. Many of the leading tech companies have been asked for help and are working on developing tools that can detect this fake synthetic media. Global software giant, Microsoft, has now released two new tools that can spot if a certain media has been artificially manipulated.

In light of the Covid-19 pandemic, MIT has been charged with reimagining its campus, classes, and programs, including the Dr. Martin Luther King, Jr. (MLK) Visiting Professors and Scholars Program (VPSP). Founded in 1990, MLK VPSP honors the life and legacy of Martin Luther King, Jr. by increasing the presence of and recognizing the contributions of scholars from underrepresented groups at MIT. MLK Visiting Professors and Scholars enhance their scholarship through intellectual engagement with the MIT community and enrich the cultural, academic, and professional experience of students. But what does a virtual year mean for a visiting scholar? Even with the challenge of remote learning and limited in-person contact, MLK VPSP faculty hosts have articulated innovative ways to engage with the MIT community. Moya Bailey, for instance, will be a content contributor for the Program in Women's and Gender Studies' website and social media accounts.

The pandemic is helping to usher in a new era of food-production forecasts that rely more on satellite data and artificial intelligence and less on information gathered by people. The crop world, including major trading houses and statisticians at the U.S. Department of Agriculture, has long depended on scouts trudging through fields to count corn kernels and soybean pods. But travel restrictions and new virus safety measures have cut participation in field tours at a time of increasing scrutiny over food security. "Covid-19 is disrupting agricultural supply chains in developing countries, and observers on the ground can no longer report on crop conditions," said Lillian Kay Petersen, a student at Harvard University. She won the top prize of this year's Regeneron Science Talent Search, a 79-year-old competition for high school students held by the Society for Science and the Public, for her model that uses daily satellite images to predict crop yields in Africa.

Artificial Intelligence is one of the most rapidly growing fields in science and is one of the most sought skills of the past few years, commonly labeled as Data Science. The area has far-reaching applications, being usually divided by input type: text, audio, image, video, or graph; or by problem formulation: supervised, unsupervised, and reinforcement learning. Keeping up with everything is a massive endeavor and usually ends up being a frustrating attempt. In this spirit, I present some reading suggestions to keep you updated on the latest and classic breakthroughs in AI and Data Science. Although most papers I listed deal with image and text, many of their concepts are fairly input agnostic and provide insight far beyond vision and language tasks. Alongside each suggestion, I listed some of the reasons I believe you should read (or re-read) the paper and added some further readings, in case you want to dive a bit deeper into a given subject. Before we begin, I would like to apologize to the Audio and Reinforcement Learning communities for not adding these subjects to the list, as I have only limited experience with both.

Music preferences are strongly shaped by the cultural and socio-economic background of the listener, which is reflected, to a considerable extent, in country-specific music listening profiles. Previous work has already identified several country-specific differences in the popularity distribution of music artists listened to. In particular, what constitutes the "music mainstream" strongly varies between countries. To complement and extend these results, the article at hand delivers the following major contributions: First, using state-of-the-art unsupervised learning techniques, we identify and thoroughly investigate (1) country profiles of music preferences on the fine-grained level of music tracks (in contrast to earlier work that relied on music preferences on the artist level) and (2) country archetypes that subsume countries sharing similar patterns of listening preferences. Second, we formulate four user models that leverage the user's country information on music preferences. Among others, we propose a user modeling approach to describe a music listener as a vector of similarities over the identified country clusters or archetypes. Third, we propose a context-aware music recommendation system that leverages implicit user feedback, where context is defined via the four user models. More precisely, it is a multi-layer generative model based on a variational autoencoder, in which contextual features can influence recommendations through a gating mechanism. Fourth, we thoroughly evaluate the proposed recommendation system and user models on a real-world corpus of more than one billion listening records of users around the world (out of which we use 369 million in our experiments) and show its merits vis-a-vis state-of-the-art algorithms that do not exploit this type of context information.

Stratega, a general strategy games framework, has been designed to foster research on computational intelligence for strategy games. In contrast to other strategy game frameworks, Stratega allows to create a wide variety of turn-based and real-time strategy games using a common API for agent development. While the current version supports the development of turn-based strategy games and agents, we will add support for real-time strategy games in future updates. Flexibility is achieved by utilising YAML-files to configure tiles, units, actions, and levels. Therefore, the user can design and run a variety of games to test developed agents without specifically adjusting it to the game being generated. The framework has been built with a focus of statistical forward planning (SFP) agents. For this purpose, agents can access and modify game-states and use the forward model to simulate the outcome of their actions. While SFP agents have shown great flexibility in general game-playing, their performance is limited in case of complex state and action-spaces. Finally, we hope that the development of this framework and its respective agents helps to better understand the complex decision-making process in strategy games. Stratega can be downloaded at: https://github.research.its.qmul.ac.uk/eecsgameai/Stratega

Testing algorithms across a wide range of problem instances is crucial to ensure the validity of any claim about one algorithm's superiority over another. However, when it comes to inference algorithms for probabilistic logic programs, experimental evaluations are limited to only a few programs. Existing methods to generate random logic programs are limited to propositional programs and often impose stringent syntactic restrictions. We present a novel approach to generating random logic programs and random probabilistic logic programs using constraint programming, introducing a new constraint to control the independence structure of the underlying probability distribution. We also provide a combinatorial argument for the correctness of the model, show how the model scales with parameter values, and use the model to compare probabilistic inference algorithms across a range of synthetic problems. Our model allows inference algorithm developers to evaluate and compare the algorithms across a wide range of instances, providing a detailed picture of their (comparative) strengths and weaknesses.

Interpretable Multi-Task Learning can be expressed as learning a sparse graph of the task relationship based on the prediction performance of the learned models. Since many natural phenomenon exhibit sparse structures, enforcing sparsity on learned models reveals the underlying task relationship. Moreover, different sparsification degrees from a fully connected graph uncover various types of structures, like cliques, trees, lines, clusters or fully disconnected graphs. In this paper, we propose a bilevel formulation of multi-task learning that induces sparse graphs, thus, revealing the underlying task relationships, and an efficient method for its computation. We show empirically how the induced sparse graph improves the interpretability of the learned models and their relationship on synthetic and real data, without sacrificing generalization performance.