Time-Aware Cross-Platform IoT Service Recommendation with Privacy Preservation

IoT service recommendation techniques can help a user select appropriate IoT services efficiently. Aiming at improving the recommendation efficiency and preserving the data privacy, the locality-sensitive hashing (LSH) technique is adopted in service recommendation. However, existing LSH-based service recommendation methods ignore the intrinsic temporal feature of IoT services. In light of this challenge, we integrate the temporal feature into the conventional LSH-based method and present a time-aware approach with the capability of privacy preservation for IoT service recommendation across multiple platforms. Experiments on a real-world dataset are conducted to validate the advantage of our proposed approach in terms of accuracy and efficiency in recommendation.

Time-Aware and Feature Similarity Self-Attention in Vessel Fuel Consumption Prediction

An accurate vessel fuel consumption prediction is essential for constructing a ship route network and vessel management, leading to efficient sailings. Besides, ship data from monitoring and sensing systems accelerate fuel consumption prediction research. However, the ship data consist of three properties: sequential, irregular time interval, and feature importance, making the predicting problem challenging. In this paper, we propose Time-aware Attention (TA) and Feature-similarity Attention (FA) applied to bi-directional Long Short-Term Memory (LSTM). TA acquires time importance by nonlinear function from irregular time intervals in each sequence and emphasizes data depending on the importance. FA emphasizes data based on similarities of features in the sequence by estimating feature importance with learnable parameters. Finally, we propose the ensemble model of TA and FA-based BiLSTM. The ensemble model, which consists of fully connected layers, is capable of simultaneously capturing different properties of ship data. The experimental results on ship data showed that the proposed model improves the performance in predicting fuel consumption. In addition to model performance, visualization results of attention maps and feature importance help to understand data properties and model characteristics.

Tourism and environment in cyberworld of cyber society

The nature is the origin of being. This is one of the reason why mostly the imagine of nature are present in any web-page. Searching and navigating on network we often are like tourist or better virtual tourist which explore unreachable real beauty of the moment. On it’s own human being desire to upgrade the state of his evolution. In nowadays we apprehend the motion of our everyday life through the mass use of Artificial Intelligence device which are influence by the rule created on the parallel dimension the cyber-world. The cyber-world is a dimension where each of us becomes part of the cyber-society that indicate much faster and foster the opinion which afterward will be spread through the words or news in the real life time. Aware for the multidimensional evolution of the science, we can benefit from facilitated opportunities and at the same time to have much more possibilities for reflecting our actions in positive light.

Hyperplane-based time-aware knowledge graph embedding for temporal knowledge graph completion

Knowledge Graph (KG) embedding approaches have been proved effective to infer new facts for a KG based on the existing ones–a problem known as KG completion. However, most of them have focused on static KGs, in fact, relational facts in KGs often show temporal dynamics, e.g., the fact (US, has president, Barack Obama, [2009–2017]) is only valid from 2009 to 2017. Therefore, utilizing available time information to develop temporal KG embedding models is an increasingly important problem. In this paper, we propose a new hyperplane-based time-aware KG embedding model for temporal KG completion. By employing the method of time-specific hyperplanes, our model could explicitly incorporate time information in the entity-relation space to predict missing elements in the KG more effectively, especially temporal scopes for facts with missing time information. Moreover, in order to model and infer four important relation patterns including symmetry, antisymmetry, inversion and composition, we map facts happened at the same time into a polar coordinate system. During training procedure, a time-enhanced negative sampling strategy is proposed to get more effective negative samples. Experimental results on datasets extracted from real-world temporal KGs show that our model significantly outperforms existing state-of-the-art approaches for the KG completion task.