In the literature of mitigating unfairness in machine learning, many fairness measures are designed to evaluate predictions of learning models and also utilised to guide the training of fair models. It has been theoretically and empirically shown that there exist conflicts and inconsistencies among accuracy and multiple fairness measures. Optimising one or several fairness measures may sacrifice or deteriorate other measures. Two key questions should be considered, how to simultaneously optimise accuracy and multiple fairness measures, and how to optimise all the considered fairness measures more effectively. In this paper, we view the mitigating unfairness problem as a multi-objective learning problem considering the conflicts among fairness measures. A multi-objective evolutionary learning framework is used to simultaneously optimise several metrics (including accuracy and multiple fairness measures) of machine learning models. Then, ensembles are constructed based on the learning models in order to automatically balance different metrics. Empirical results on eight well-known datasets demonstrate that compared with the state-of-the-art approaches for mitigating unfairness, our proposed algorithm can provide decision-makers with better tradeoffs among accuracy and multiple fairness metrics. Furthermore, the high-quality models generated by the framework can be used to construct an ensemble to automatically achieve a better tradeoff among all the considered fairness metrics than other ensemble methods. Our code is publicly available at https://github.com/qingquan63/FairEMOL

While generative models have recently become ubiquitous in many scientific areas, less attention has been paid to their evaluation. For molecular generative models, the state-of-the-art examines their output in isolation or in relation to its input. However, their biological and functional properties, such as ligand-target interaction is not being addressed. In this study, a novel biologically-inspired benchmark for the evaluation of molecular generative models is proposed. Specifically, three diverse reference datasets are designed and a set of metrics are introduced which are directly relevant to the drug discovery process. In particular we propose a recreation metric, apply drug-target affinity prediction and molecular docking as complementary techniques for the evaluation of generative outputs. While all three metrics show consistent results across the tested generative models, a more detailed comparison of drug-target affinity binding and molecular docking scores revealed that unimodal predictiors can lead to erroneous conclusions about target binding on a molecular level and a multi-modal approach is thus preferrable. The key advantage of this framework is that it incorporates prior physico-chemical domain knowledge into the benchmarking process by focusing explicitly on ligand-target interactions and thus creating a highly efficient tool not only for evaluating molecular generative outputs in particular, but also for enriching the drug discovery process in general.

Python is a dynamic language, so usually, you'll only see errors in your code when you attempt to run it. This library is a static checker, so it finds bugs in your programs without even running them. It will also add type checkers that will help to ensure that you're using variables and functions in your code correctly. It includes lots of features such as type inference, generics, callable types, tuple types, union types, structural subtyping, and more. It has more than 14k stars on GitHub. If you are using Jupyter Notebooks then this is a good resource for you.

Educational robots allow experimenting with a variety of principles from mechanics, electronics, and informatics. Here we propose ClipBot, a low-cost, do-it-yourself, robot whose skeleton is made of two paper clips. An Arduino nano microcontroller actuates two servo motors that move the paper clips. However, such mechanical configuration confers physical impairments to movement. This creates the need for and allows experimenting with artificial intelligence methods to overcome hardware limitations. We report our experience in the usage of this robot during the study week 'fascinating informatics', organized by the Swiss Foundation Schweizer Jugend Forscht (www.sjf.ch). Students at the high school level were asked to implement a genetic algorithm to optimize the movements of the robot until it learned to walk. Such a methodology allowed the robot to learn the motor actuation scheme yielding straight movement in the forward direction using less than 20 iterations.

Artificial intelligence and semantic technologies are evolving and have been applied in various research areas, including the education domain. Higher Education institutions strive to improve students' academic performance. Early intervention to at-risk students and a reasonable curriculum is vital for students' success. Prior research opted for deploying traditional machine learning models to predict students' performance. In terms of curriculum semantic analysis, after conducting a comprehensive systematic review regarding the use of semantic technologies in the Computer Science curriculum, a major finding of the study is that technologies used to measure similarity have limitations in terms of accuracy and ambiguity in the representation of concepts, courses, etc. To fill these gaps, in this study, three implementations were developed, that is, to predict students' performance using marks from the previous semester, to model a course representation in a semantic way and compute the similarity, and to identify the prerequisite between two similar courses. Regarding performance prediction, we used the combination of Genetic Algorithm and Long-Short Term Memory (LSTM) on a dataset from a Brazilian university containing 248730 records. As for similarity measurement, we deployed BERT to encode the sentences and used cosine similarity to obtain the distance between courses. With respect to prerequisite identification, TextRazor was applied to extract concepts from course description, followed by employing SemRefD to measure the degree of prerequisite between two concepts. The outcomes of this study can be summarized as: (i) a breakthrough result improves Manrique's work by 2.5% in terms of accuracy in dropout prediction; (ii) uncover the similarity between courses based on course description; (iii) identify the prerequisite over three compulsory courses of School of Computing at ANU.

Distributed fuzzy neural networks (DFNNs) have attracted increasing attention recently due to their learning abilities in handling data uncertainties in distributed scenarios. However, it is challenging for DFNNs to handle cases in which the local data are non-independent and identically distributed (non-IID). In this paper, we propose a federated fuzzy neural network (FedFNN) with evolutionary rule learning (ERL) to cope with non-IID issues as well as data uncertainties. The FedFNN maintains a global set of rules in a server and a personalized subset of these rules for each local client. ERL is inspired by the theory of biological evolution; it encourages rule variations while activating superior rules and deactivating inferior rules for local clients with non-IID data. Specifically, ERL consists of two stages in an iterative procedure: a rule cooperation stage that updates global rules by aggregating local rules based on their activation statuses and a rule evolution stage that evolves the global rules and updates the activation statuses of the local rules. This procedure improves both the generalization and personalization of the FedFNN for dealing with non-IID issues and data uncertainties. Extensive experiments conducted on a range of datasets demonstrate the superiority of the FedFNN over state-of-the-art methods.

Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. "The environment selects those few mutations that enhance survival, resulting in a series of slow transformations of one lifeform into another, the origin of a new species."-

For example, in [16], the authors discovered become automated and more "end-to-end," the data augmentation that SpecAugment [11] did not compose well with multi-style policy (what augmentation functions to use, and how training augmentation [4, 17], and found that they needed to to apply them) remains hand-crafted. We present G(raph)- ensemble the augmentations to benefit from both. Augment, a technique to define the augmentation space as In this work, we address this problem by a scheme we refer directed acyclic graphs (DAGs) and search over this space to as G(raph)-Augment, where a stochastic augmentation to optimize the augmentation policy itself. We show that policy is parameterized by a directed acyclic graph (DAG) given the same computational budget, policies produced by whose edges are labeled by sampling probabilities and augmentation G-Augment are able to perform better than SpecAugment parameters. By simultaneously searching for the policies obtained by random search on fine-tuning tasks on graph structure and the parameters that label the graph, we CHiME-6 and AMI. G-Augment is also able to establish are able to optimize not only the augmentation parameters of a new state-of-the-art ASR performance on the CHiME-6 the individual augmentations, but how those augmentations evaluation set (30.7% WER). We further demonstrate that are being applied. We utilize 17 ASR augmentations in our G-Augment policies show better transfer properties across search space, details of which can be found in section 3.3.

Filter pruning is a common method to achieve model compression and acceleration in deep neural networks (DNNs).Some research regarded filter pruning as a combinatorial optimization problem and thus used evolutionary algorithms (EA) to prune filters of DNNs. However, it is difficult to find a satisfactory compromise solution in a reasonable time due to the complexity of solution space searching. To solve this problem, we first formulate a multi-objective optimization problem based on a sub-network of the full model and propose a Sub-network Multiobjective Evolutionary Algorithm (SMOEA) for filter pruning. By progressively pruning the convolutional layers in groups, SMOEA can obtain a lightweight pruned result with better performance.Experiments on VGG-14 model for CIFAR-10 verify the effectiveness of the proposed SMOEA. Specifically, the accuracy of the pruned model with 16.56% parameters decreases by 0.28% only, which is better than the widely used popular filter pruning criteria.

A high-dimensional and incomplete (HDI) matrix frequently appears in various big-data-related applications, which demonstrates the inherently non-negative interactions among numerous nodes. A non-negative latent factor (NLF) model performs efficient representation learning to an HDI matrix, whose learning process mostly relies on a single latent factor-dependent, non-negative and multiplicative update (SLF-NMU) algorithm. However, an SLF-NMU algorithm updates a latent factor based on the current update increment only without appropriate considerations of past learning information, resulting in slow convergence. Inspired by the prominent success of a proportional-integral (PI) controller in various applications, this paper proposes a Proportional-Integral-incorporated Non-negative Latent Factor (PI-NLF) model with two-fold ideas: a) establishing an Increment Refinement (IR) mechanism via considering the past update increments following the principle of a PI controller; and b) designing an IR-based SLF-NMU (ISN) algorithm to accelerate the convergence rate of a resultant model. Empirical studies on four HDI datasets demonstrate that a PI-NLF model outperforms the state-of-the-art models in both computational efficiency and estimation accuracy for missing data of an HDI matrix. Hence, this study unveils the feasibility of boosting the performance of a non-negative learning algorithm through an error feedback controller.