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AI fairy tale companions play an important role in early childhood education as an augmentation for parents' efforts to close the participation gap and boost kids' mental and language development. Existing systems are generally designed to provide vivid materials as unidirectional entertaining reading environments, e.g, visualizing inputting texts. However, due to the limited vocabulary of kids, these systems failed to afford effective interaction to motivate kids to write their own fairy tales. In this work, we propose AI.R Taletorium, an illustrative, immersive, and inclusive multimodal AI companion, for interactive fairy tale co-creation that actively involves kids to create fairy tales with both the AI agent and their normal peers. AI.R Taletorium consists a neural story generator and a doodler-based fairy tale visualizer. We design a character-centric bidirectional connection mechanism between the story generator and visualizer equipped with Contrastive Language Image Pretraining (CLIP), thus enabling kids to participant in the story generation process by simple sketching. Extensive experiments and user studies show that our system was able to generate and visualize meaningful and vivid fairy tales with limited training data and complete the full interaction cycle under various inputs (text, doodler) through the bidirectional connection.

Drug discovery is the most expensive, time demanding and challenging project in biopharmaceutical companies which aims at the identification and optimization of lead compounds from large-sized chemical libraries. The lead compounds should have high affinity binding and specificity for a target associated with a disease and in addition they should have favorable pharmacodynamic and pharmacokinetic properties (grouped as ADMET properties). Overall, drug discovery is a multivariable optimization and can be carried out in supercomputers using a reliable scoring function which is a measure of binding affinity or inhibition potential of the drug-like compound. The major problem is that the number of compounds in the chemical spaces is huge making the computational drug discovery very demanding. However, it is cheaper and less time consuming when compared to experimental high throughput screening. As the problem is to find the most stable (global) minima for numerous protein-ligand complexes (at the order of 10$^6$ to 10$^{12}$), the parallel implementation of in-silico virtual screening can be exploited to make the drug discovery in affordable time. In this review, we discuss such implementations of parallelization algorithms in virtual screening programs. The nature of different scoring functions and search algorithms are discussed, together with a performance analysis of several docking softwares ported on high-performance computing architectures.

Personal data is becoming one of the most essential resources in today's information-based society. Accordingly, there is a growing interest in data markets, which operate data trading services between data providers and data consumers. One issue the data markets have to address is that of the potential threats to privacy. Usually some kind of protection must be provided, which generally comes to the detriment of utility. A correct pricing mechanism for private data should therefore depend on the level of privacy. In this paper, we propose a model of data federation in which data providers, who are, generally, less influential on the market than data consumers, form a coalition for trading their data, simultaneously shielding against privacy threats by means of differential privacy. Additionally, we propose a technique to price private data, and an revenue-distribution mechanism to distribute the revenue fairly in such federation data trading environments. Our model also motivates the data providers to cooperate with their respective federations, facilitating a fair and swift private data trading process. We validate our result through various experiments, showing that the proposed methods provide benefits to both data providers and consumers.

Due to the diffusion of IoT, modern software systems are often thought to control and coordinate smart devices in order to manage assets and resources, and to guarantee efficient behaviours. For this class of systems, which interact extensively with humans and with their environment, it is thus crucial to guarantee their correct behaviour in order to avoid unexpected and possibly dangerous situations. In this paper we will present a framework that allows us to measure the robustness of systems. This is the ability of a program to tolerate changes in the environmental conditions and preserving the original behaviour. In the proposed framework, the interaction of a program with its environment is represented as a sequence of random variables describing how both evolve in time. For this reason, the considered measures will be defined among probability distributions of observed data. The proposed framework will be then used to define the notions of adaptability and reliability. The former indicates the ability of a program to absorb perturbation on environmental conditions after a given amount of time. The latter expresses the ability of a program to maintain its intended behaviour (up-to some reasonable tolerance) despite the presence of perturbations in the environment. Moreover, an algorithm, based on statistical inference, it proposed to evaluate the proposed metric and the aforementioned properties. Throughout the paper, two case studies are used to the describe and evaluate the proposed approach.

This paper describes the design and control of a support and recovery system for use with planar legged robots. The system operates in three modes. First, it can be operated in a fully transparent mode where no forces are applied to the robot. In this mode, the system follows the robot closely to be able to quickly catch the robot if needed. Second, it can provide a vertical supportive force to assist a robot during operation. Third, it can catch the robot and pull it away from the ground after a failure to avoid falls and the associated damages. In this mode, the system automatically resets the robot after a trial allowing for multiple consecutive trials to be run without manual intervention. The supportive forces are applied to the robot through an actuated cable and pulley system that uses series elastic actuation with a unidirectional spring to enable truly transparent operation. The nonlinear nature of this system necessitates careful design of controllers to ensure predictable, safe behaviors. In this paper we introduce the mechatronic design of the recovery system, develop suitable controllers, and evaluate the system's performance on the bipedal robot RAMone.

A biclique of a graph $G$ is a maximal induced complete bipartite subgraph of $G$. The edge-biclique graph of $G$, $KB_e(G)$, is the edge-intersection graph of the bicliques of $G$. A graph $G$ diverges (resp. converges or is periodic) under an operator $H$ whenever $\lim_{k \rightarrow \infty}|V(H^k(G))|=\infty$ (resp. $\lim_{k \rightarrow \infty}H^k(G)=H^m(G)$ for some $m$ or $H^k(G)=H^{k+s}(G)$ for some $k$ and $s \geq 2$). The iterated edge-biclique graph of $G$, $KB_e^k(G)$, is the graph obtained by applying the edge-biclique operator $k$ successive times to $G$. In this paper, we first study the connectivity relation between $G$ and $KB_e(G)$. Next, we study the iterated edge-biclique operator $KB_e$. In particular, we give sufficient conditions for a graph to be convergent or divergent under the operator $KB_e$, we characterize the behavior of \textit{burgeon graphs} and we propose some general conjectures on the subject.

We present a comprehensive set of conditions and rules to control the correctness of aggregation queries within an interactive data analysis session. The goal is to extend self-service data preparation and BI tools to automatically detect semantically incorrect aggregate queries on analytic tables and views built by using the common analytic operations including filter, project, join, aggregate, union, difference, and pivot. We introduce aggregable properties to describe for any attribute of an analytic table which aggregation functions correctly aggregates the attribute along which sets of dimension attributes. These properties can also be used to formally identify attributes which are summarizable with respect to some aggregation function along a given set of dimension attributes. This is particularly helpful to detect incorrect aggregations of measures obtained through the use of non-distributive aggregation functions like average and count. We extend the notion of summarizability by introducing a new generalized summarizability condition to control the aggregation of attributes after any analytic operation. Finally, we define propagation rules which transform aggregable properties of the query input tables into new aggregable properties for the result tables, preserving summarizability and generalized summarizability.

This paper provides a review of the job recommender system (JRS) literature published in the past decade (2011-2021). Compared to previous literature reviews, we put more emphasis on contributions that incorporate the temporal and reciprocal nature of job recommendations. Previous studies on JRS suggest that taking such views into account in the design of the JRS can lead to improved model performance. Also, it may lead to a more uniform distribution of candidates over a set of similar jobs. We also consider the literature from the perspective of algorithm fairness. Here we find that this is rarely discussed in the literature, and if it is discussed, many authors wrongly assume that removing the discriminatory feature would be sufficient. With respect to the type of models used in JRS, authors frequently label their method as `hybrid'. Unfortunately, they thereby obscure what these methods entail. Using existing recommender taxonomies, we split this large class of hybrids into subcategories that are easier to analyse. We further find that data availability, and in particular the availability of click data, has a large impact on the choice of method and validation. Last, although the generalizability of JRS across different datasets is infrequently considered, results suggest that error scores may vary across these datasets.

Aggregating data in a database could also be called "integrating along fibers": given functions $\pi\colon E\to D$ and $s\colon E\to R$, where $(R,\circledast)$ is a commutative monoid, we want a new function $(\circledast s)_\pi$ that sends each $d\in D$ to the "sum" of all $s(e)$ for which $\pi(e)=d$. The operation lives alongside querying -- or more generally data migration -- in typical database usage: one wants to know how much Canadians spent on cell phones in 2021, for example, and such requests typically require both aggregation and querying. But whereas querying has an elegant category-theoretic treatment in terms of parametric right adjoints between copresheaf categories, a categorical formulation of aggregation -- especially one that lives alongside that for querying -- appears to be completely absent from the literature. In this paper we show how both querying and aggregation fit into the "polynomial ecosystem". Starting with the category $\mathbf{Poly}$ of polynomial functors in one variable, we review the relatively recent results of Ahman-Uustalu and Garner, which showed that the framed bicategory $\mathbb{C}\mathbf{at}^\sharp$ of comonads in $\mathbf{Poly}$ is precisely the right setting for data migration: its objects are categories and its bicomodules are parametric right adjoints between their copresheaf categories. We then develop a great deal of theory, compressed for space reasons, including local monoidal closed structures, a coclosure to bicomodule composition, and an understanding of adjoints in $\mathbb{C}\mathbf{at}^\sharp$. Doing so allows us to derive interesting mathematical results, e.g.\ that the ordinary operation of transposing a span can be decomposed into the composite of two more primitive operations, and then finally to explain how aggregation arises, alongside querying, in $\mathbb{C}\mathbf{at}^\sharp$.

Perturbations targeting the graph structure have proven to be extremely effective in reducing the performance of Graph Neural Networks (GNNs), and traditional defenses such as adversarial training do not seem to be able to improve robustness. This work is motivated by the observation that adversarially injected edges effectively can be viewed as additional samples to a node's neighborhood aggregation function, which results in distorted aggregations accumulating over the layers. Conventional GNN aggregation functions, such as a sum or mean, can be distorted arbitrarily by a single outlier. We propose a robust aggregation function motivated by the field of robust statistics. Our approach exhibits the largest possible breakdown point of 0.5, which means that the bias of the aggregation is bounded as long as the fraction of adversarial edges of a node is less than 50\%. Our novel aggregation function, Soft Medoid, is a fully differentiable generalization of the Medoid and therefore lends itself well for end-to-end deep learning. Equipping a GNN with our aggregation improves the robustness with respect to structure perturbations on Cora ML by a factor of 3 (and 5.5 on Citeseer) and by a factor of 8 for low-degree nodes.