--Entity alignment (EA) identifies entities that refer to the same real-world object but locate in different knowledge graphs (KGs), and has been harnessed for KG construction and integration. When generating EA results, current embedding-based solutions treat entities independently and fail to take into account the interdependence between entities. In addition, most of embedding-based EA methods either fuse different features on representation-level and generate unified entity embedding for alignment, which potentially causes information loss, or aggregate features on outcome-level with hand-tuned weights, which is not practical with increasing numbers of features. T o tackle these deficiencies, we propose a collective embedding-based EA framework with adaptive feature fusion mechanism. We first employ three representative features, i.e., structural, semantic and string signals, for capturing different aspects of the similarity between entities in heterogeneous KGs. These features are then integrated at outcome-level, with dynamically assigned weights generated by our carefully devised adaptive feature fusion strategy. Eventually, in order to make collective EA decisions, we formulate EA as the classical stable matching problem between entities to be aligned, with preference lists constructed using fused feature matrix. It is further effectively solved by deferred acceptance algorithm. Our proposal is evaluated on both cross-lingual and monolingual EA benchmarks against state-of- the-art solutions, and the empirical results verify its effectiveness and superiority. We also perform ablation study to gain insights into framework modules. I NTRODUCTION Knowledge graph (KG) is playing an increasingly more important role in intelligent information services, e.g., information retrieval [27], automatic question answering [14] and recommendation systems [3]. Despite that a large number of KGs have been constructed over recent years, none of them can reach full coverage . These KGs, however, usually contain complementary contents, making it compelling to study the integration of heterogeneous KGs. To incorporate the knowledge from target KGs into the source KG, an indispensable step would be entity alignment (EA). EA aims to discover entities that have the same meaning but locate in different KGs.

"If you want to command a multiyear, seven-figure salary, you used to have only four career options: chief executive officer, banker, celebrity entertainer, or pro athlete. Imagine a glass with balls. This glass is a field of Computer science knowledge, and balls are various fields: back end, front end development, embedded. One of these balls is artificial intelligence, and it is special because there are other balls inside: machine learning, natural language processing, and a whole slew of other things. Each of these units individually -- a powerful force and new opportunities to change any sphere.

As one of the world's busiest airports, (ranked No. 3 in 2018 according to Airports Council International's world traffic report), Dubai International Airport is also a leader in using artificial intelligence (AI). In fact, the United Arab Emirates (UAE) leads the Arab world with its adoption of artificial intelligence in other sectors and areas of life and has a government that prioritizes artificial intelligence including an AI strategy and Ministry of Artificial Intelligence with a mandate to invest in technologies and AI tools. The Emirates Ministry of the Interior said that by 2020, immigration officers would no longer be needed in the UAE. They will be replaced by artificial intelligence. The plan is to have people just walk through an AI-powered security system to be scanned without taking off shoes or belts or emptying pockets.

If recent television shows are anything to go by, we're a little concerned about the consequences of technological development. Black Mirror projects the negative consequences of social media, while artificial intelligence turns rogue in The 100 and Better Than Us. The potential extinction of the human race is up for grabs in Travellers, and Altered Carbon frets over the separation of human consciousness from the body. And Humans and Westworld see trouble ahead for human-android relations. Narratives like these have a long lineage.

This paper formulates the problem of building a context-aware predictive model based on user diverse behavioral activities with smartphones. In the area of machine learning and data science, a tree-like model as that of decision tree is considered as one of the most popular classification techniques, which can be used to build a data-driven predictive model. The traditional decision tree model typically creates a number of leaf nodes as decision nodes that represent context-specific rigid decisions, and consequently may cause overfitting problem in behavior modeling. However, in many practical scenarios within the context-aware environment, the generalized outcomes could play an important role to effectively capture user behavior. In this paper, we propose a behavioral decision tree, "BehavDT" context-aware model that takes into account user behavior-oriented generalization according to individual preference level. The BehavDT model outputs not only the generalized decisions but also the context-specific decisions in relevant exceptional cases. The effectiveness of our BehavDT model is studied by conducting experiments on individual user real smartphone datasets. Our experimental results show that the proposed BehavDT context-aware model is more effective when compared with the traditional machine learning approaches, in predicting user diverse behaviors considering multi-dimensional contexts.

The main goal of this thesis is to point out that the bias-variance tradeoff is not always true (e.g. in neural networks). We advocate for this lack of universality to be acknowledged in textbooks and taught in introductory courses that cover the tradeoff. We first review the history of the bias-variance tradeoff, its prevalence in textbooks, and some of the main claims made about the bias-variance tradeoff. Through extensive experiments and analysis, we show a lack of a bias-variance tradeoff in neural networks when increasing network width. Our findings seem to contradict the claims of the landmark work by Geman et al. (1992). Motivated by this contradiction, we revisit the experimental measurements in Geman et al. (1992). We discuss that there was never strong evidence for a tradeoff in neural networks when varying the number of parameters. We observe a similar phenomenon beyond supervised learning, with a set of deep reinforcement learning experiments. We argue that textbook and lecture revisions are in order to convey this nuanced modern understanding of the bias-variance tradeoff.

Recent work has exhibited the surprising cross-lingual abi lities of multilingual BERT ( M-BERT) - surprising since it is trained without any cross-lingual objective and with no aligned data. In this work, we provide a compr ehensive study of the contribution of different components in M-BERT to its cross-lingual ability. The experimental study is done in the context of three typologically different languages - Spani sh, Hindi, and Russian - and using two conceptually different NLP tasks, textual en tailment and named entity recognition. Among our key conclusions is the fact th at the lexical overlap between languages plays a negligible role in the cross-ling ual success, while the depth of the network is an integral part of it. Embeddings of natural language text via unsupervised learn ing, coupled with sufficient supervised training data, have been ubiquitous in NLP in recent years an d have shown success in a wide range of monolingual NLP tasks, mostly in English. Training models f or other languages have been shown more difficult, and recent approaches relied on bilingual em beddings that allowed the transfer of supervision in high resource languages like English to mode ls in lower resource languages; however, inducing these bilingual embeddings required some level of supervision (Upadhyay et al., 2016). Not only the model is contextual, but its training also requires no supervisio n - no alignment between the languages is done. Nevertheless, and despite being trained with no exp licit cross-lingual objective, M-BERT produces a representation that seems to generalize well acr oss languages for a variety of downstream tasks (Wu & Dredze, 2019). In this work, we attempt to develop an understanding of the su ccess of M-BERT.

Planning is a critical component of any artificial intelligence system that concerns the realization of strategies or action sequences typically for intelligent agents and autonomous robots. Given predefined parameterized actions, a planning service should accept a query with the goal and initial state to give a solution with a sequence of actions applied to environmental objects. This paper addresses the problem by providing a repository of actions generically applicable to various environmental objects based on Semantic Web technologies. Ontologies are used for asserting constraints in common sense as well as for resolving compatibilities between actions and states. Constraints are defined using Web standards such as SPARQL and SHACL to allow conditional predicates. We demonstrate the usefulness of the proposed planning domain description language with our robotics applications.

Several examples for the explanation of Betti numbers, demonstrating their ability to capture structural information even in the presence of local deformations. Betti numbers are a way to describe the connectivity within a topological space. In simplest terms, the k -th Betti number β k counts the the number of k -dimensional holes in a topological space, for example, - β 0 is the number of connected components; - β 1 is the number of planar holes (1-dimensional holes); - β 2 is the number of two-dimensional voids (2-dimensional holes); - ... Betti numbers are topological invariants. If two Betti numbers are the same for two different spaces then the spaces are homotopy equivalent [1]. To demonstrate Betti numbers more 6 vividly, some examples are shown in Figure 1.

People need to be held accountable for the mistakes AI and algorithms make on their behalf, such as that seen in the government's robodebt scandal, according to Australian human rights commissioner Ed Santow. The proposal comes in a new discussion paper on the impact of new technologies on human rights in Australia, released by the commission on Tuesday. After the Australian government backed down on the use of automatic debt notices based on income averaging, and had legislation for its facial recognition system rejected by a government-dominated parliamentary committee, Santow said it was time to set some rules to govern how these new technologies are used. "Robodebt is just a prominent example of data science and government AI being used in decision-making," he said. All government use of AI should be enshrined in legislation, he said.