The development of graph neural networks (GCN) makes it possible to learn structural features from evolving complex networks. Even though a wide range of realistic networks are directed ones, few existing works investigated the properties of directed and temporal networks. In this paper, we address the problem of temporal link prediction in directed networks and propose a deep learning model based on GCN and self-attention mechanism, namely TSAM. The proposed model adopts an autoencoder architecture, which utilizes graph attentional layers to capture the structural feature of neighborhood nodes, as well as a set of graph convolutional layers to capture motif features. A graph recurrent unit layer with self-attention is utilized to learn temporal variations in the snapshot sequence. We run comparative experiments on four realistic networks to validate the effectiveness of TSAM. Experimental results show that TSAM outperforms most benchmarks under two evaluation metrics.

Multi-view clustering methods have been a focus in recent years because of their superiority in clustering performance. However, typical traditional multi-view clustering algorithms still have shortcomings in some aspects, such as removal of redundant information, utilization of various views and fusion of multi-view features. In view of these problems, this paper proposes a new multi-view clustering method, low-rank subspace multi-view clustering based on adaptive graph regularization. We construct two new data matrix decomposition models into a unified optimization model. In this framework, we address the significance of the common knowledge shared by the cross view and the unique knowledge of each view by presenting new low-rank and sparse constraints on the sparse subspace matrix. To ensure that we achieve effective sparse representation and clustering performance on the original data matrix, adaptive graph regularization and unsupervised clustering constraints are also incorporated in the proposed model to preserve the internal structural features of the data. Finally, the proposed method is compared with several state-of-the-art algorithms. Experimental results for five widely used multi-view benchmarks show that our proposed algorithm surpasses other state-of-the-art methods by a clear margin.

If you happen to fall outside that specified range, navigating the internet, your community, even your own home, can become exponentially more difficult. But it doesn't have to be this way, argues artist, writer and design researcher Sara Hendren. In her new book, What Can a Body Do, Hendren examines the challenges that people with disabilities face on a daily basis in a world that often doesn't take their needs into account and shows that more inclusive design -- from cybernetic prosthetic arms and more accessible city streets to tactile doorbells for the deaf -- isn't just possible, it's already practical. In the excerpt below, Hendren looks at the Jaipur Foot, an unpowered, low-cost prosthetic that has helped nearly two million lower leg amputees in India and other countries regain their ability to walk. From WHAT CAN A BODY DO: How We Meet the Built World by Sara Hendren published on August 18, 2020 by Riverhead, an imprint of Penguin Publishing Group, a division of Penguin Random House LLC.

Global Artificial Intelligence-based Security Market reports provide in-depth analysis of Top Players, Geography, End users, Applications, Competitor analysis, Revenue, Price, Gross Margin, Market Share, Import-Export data, Trends and Forecast. Firstly, the Artificial Intelligence-based Security Market Report provides a basic overview of the industry including definitions, classifications, applications and chain structure. The Artificial Intelligence-based Security market analysis is provided for the international markets including development trends, competitive landscape analysis, and key regions development status. Key Players covered in this report are Nvidia Corporation, Intel Corporation, Xilinx Inc, Samsung Electronics Co., Ltd, Micron Technology, IBM Corporation, Cylance Inc, Threatmetrix, Securonix, Inc, Amazon, Sift Science, Acalvio Technologies, Skycure Inc,. Our industry professionals are working reluctantly to understand, assemble and timely deliver assessment on impact of COVID-19 disaster on many corporations and their clients to help them in taking excellent business decisions.

The latest study report on the Artificial Intelligence (AI) in Construction Market published by Stratagem Market Insights offers a profound awareness of the various market dynamics like trends, drivers, the challenges, and opportunities. The report further elaborates on the micro and macro-economic elements that are predicted to shape the increase of the Artificial Intelligence (AI) in Construction market throughout the forecast period (2020-2027). This study highlights the vital indicators of Market growth which comes with a comprehensive analysis of this value chain, CAGR development, and Porter's Five Forces Analysis. This data may enable readers to understand the quantitative growth parameters of this international industry that is Artificial Intelligence (AI) in Construction. Get FREE Sample Copy of this Report: https://www.stratagemmarketinsights.com/sample/3230

Emilia Díaz-Struck is research editor and Latin American coordinator for the International Consortium of Investigative Journalists (ICIJ). She oversees data projects and has been involved in some major cross-border investigations including the Panama Papers, the Paradise Papers and the Offshore Leaks. The ICIJ receives vast amounts of files from whistleblowers and uses AI-powered technologies to sift through that data more efficiently. For our interview series with women working on the intersection of AI and journalism, Emilia spoke to us about how exactly AI is deployed and what impact it will have on investigative journalism. JournalismAI: You have a very diverse background in journalism, having worked with major organisations such as The Washington Post, the Press and Society Institute of Venezuela and co-founding your own news site Armando.info. How did you initially move into a data-driven role?

Episodic control provides a highly sample-efficient method for reinforcement learning while enforcing high memory and computational requirements. This work proposes a simple heuristic for reducing these requirements, and an application to Model-Free Episodic Control (MFEC) is presented. Experiments on Atari games show that this heuristic successfully reduces MFEC computational demands while producing no significant loss of performance when conservative choices of hyperparameters are used. Consequently, episodic control becomes a more feasible option when dealing with reinforcement learning tasks.

Manually labeling video datasets for segmentation tasks is extremely time consuming. In this paper, we introduce ScribbleBox, a novel interactive framework for annotating object instances with masks in videos. In particular, we split annotation into two steps: annotating objects with tracked boxes, and labeling masks inside these tracks. We introduce automation and interaction in both steps. Box tracks are annotated efficiently by approximating the trajectory using a parametric curve with a small number of control points which the annotator can interactively correct. Our approach tolerates a modest amount of noise in the box placements, thus typically only a few clicks are needed to annotate tracked boxes to a sufficient accuracy. Segmentation masks are corrected via scribbles which are efficiently propagated through time. We show significant performance gains in annotation efficiency over past work. We show that our ScribbleBox approach reaches 88.92% J&F on DAVIS2017 with 9.14 clicks per box track, and 4 frames of scribble annotation.

A key challenge of oversampling in imbalanced classification is that the generation of new minority samples often neglects the usage of majority classes, resulting in most new minority sampling spreading the whole minority space. In view of this, we present a new oversampling framework based on the counterfactual theory. Our framework introduces a counterfactual objective by leveraging the rich inherent information of majority classes and explicitly perturbing majority samples to generate new samples in the territory of minority space. It can be analytically shown that the new minority samples satisfy the minimum inversion, and therefore most of them locate near the decision boundary. Empirical evaluations on benchmark datasets suggest that our approach significantly outperforms the state-of-the-art methods.

Interpretability and effectiveness are two essential and indispensable requirements for adopting machine learning methods in reality. In this paper, we propose a knowledge distillation based decision trees extension, dubbed rectified decision trees (ReDT), to explore the possibility of fulfilling those requirements simultaneously. Specifically, we extend the splitting criteria and the ending condition of the standard decision trees, which allows training with soft labels while preserving the deterministic splitting paths. We then train the ReDT based on the soft label distilled from a well-trained teacher model through a novel jackknife-based method. Accordingly, ReDT preserves the excellent interpretable nature of the decision trees while having a relatively good performance. The effectiveness of adopting soft labels instead of hard ones is also analyzed empirically and theoretically. Surprisingly, experiments indicate that the introduction of soft labels also reduces the model size compared with the standard decision trees from the aspect of the total nodes and rules, which is an unexpected gift from the `dark knowledge' distilled from the teacher model.