The abundance of information in web applications make recommendation essential for users as well as applications. Despite the effectiveness of existing recommender systems, we find two major limitations that reduce their overall performance: (1) inability to provide timely recommendations for both new and existing users by considering the dynamic nature of user preferences, and (2) not fully optimized for the ranking task when using implicit feedback. Therefore, we propose a novel deep learning based unified cross-network solution to mitigate cold-start and data sparsity issues and provide timely recommendations for new and existing users.Furthermore, we consider the ranking problem under implicit feedback as a classification task, and propose a generic personalized listwise optimization criterion for implicit data to effectively rank a list of items. We illustrate our cross-network model using Twitter auxiliary information for recommendations on YouTube target network. Extensive comparisons against multiple time aware and cross-network base-lines show that the proposed solution is superior in terms of accuracy, novelty and diversity. Furthermore, experiments conducted on the popular MovieLens dataset suggest that the proposed listwise ranking method outperforms existing state-of-the-art ranking techniques.

To prolong the lifetime of the unmanned aerial vehicles (UAVs), the UAVs need to fulfill their missions in the shortest possible time. In addition to this requirement, in many applications, the UAVs require a reliable internet connection during their flights. In this paper, we minimize the travel time of the UAVs, ensuring that a probabilistic connectivity constraint is satisfied. To solve this problem, we need a global model of the outage probability in the environment. Since the UAVs have different missions and fly over different areas, their collected data carry local information on the network's connectivity. As a result, the UAVs can not rely on their own experiences to build the global model. This issue affects the path planning of the UAVs. To address this concern, we utilize a two-step approach. In the first step, by using Federated Learning (FL), the UAVs collaboratively build a global model of the outage probability in the environment. In the second step, by using the global model obtained in the first step and rapidly-exploring random trees (RRTs), we propose an algorithm to optimize UAVs' paths. Simulation results show the effectiveness of this two-step approach for UAV networks.

Note: The methodology behind the approach discussed in this post stems from a collaborative publication between myself and Irene Anthi. Spam SMS text messages often show up unexpectedly on our phone screens. That's aggravating enough, but it gets worse. Whoever is sending you a spam text message is usually trying to defraud you. Most spam text messages don't come from another phone.

Intelligent signal processing for wireless communications is a vital task in modern wireless systems, but it faces new challenges because of network heterogeneity, diverse service requirements, a massive number of connections, and various radio characteristics. Owing to recent advancements in big data and computing technologies, artificial intelligence (AI) has become a useful tool for radio signal processing and has enabled the realization of intelligent radio signal processing. This survey covers four intelligent signal processing topics for the wireless physical layer, including modulation classification, signal detection, beamforming, and channel estimation. In particular, each theme is presented in a dedicated section, starting with the most fundamental principles, followed by a review of up-to-date studies and a summary. To provide the necessary background, we first present a brief overview of AI techniques such as machine learning, deep learning, and federated learning. Finally, we highlight a number of research challenges and future directions in the area of intelligent radio signal processing. We expect this survey to be a good source of information for anyone interested in intelligent radio signal processing, and the perspectives we provide therein will stimulate many more novel ideas and contributions in the future.

Read full article In The KnowArtificial intelligence camera can identify different bird species for youAlex LaskerAugust 14, 2020, 8:03 AMBird watching just got a whole lot more high tech.Birdsy, a wildlife-spotting camera, has developed a unique artificial intelligence (AI) that records and identifies birds and other animals all by itself, meaning you never have to miss out on priceless nature moments again.The Wi-Fi-connected camera can monitor your bird feeders and yard 24/7, identify each species it spots, record and log each animal visitor, and send the data directly to a smartphone app for you to enjoy.The app even makes it easy to share your favorite bird spots on social media. Credit: BirdsySince Birdsy was launched on Kickstarter, it has raced past its original goal of $60,000, with 506 backers pledging a total of $114,790 to help bring the project to life.Birdsy's AI is constantly learning and changing. Currently, it is able to identify species in North America and Europe and, with the power of ultra-fast Verizon 5G, the number of birds and other animals it can recognize will continue to grow.Watch the futuristic gadget in action in the video above.If you enjoyed this article, check out the Hollywood sci-fi technology …

Qualcomm, a leader in wireless technology, has recently released the world's first 5G and AI-enabled robotics platform. Qualcomm says the Robotics RB5 platform includes novel technology that will accelerate the development of power-efficient, high-computing robots and drones for applications ranging from industrial and enterprise applications to defense and military sectors. Specifically, some of the applications this platform can enhance include inventory robots, retail robots, pick-sort-place robots, cleaning robots, delivery drones, healthcare robots, and defense robots. Qualcomm's Robotics RB5 platform, punctuated by their QRB5165 processor, supports a number of connectivity protocols through Qualcomm's FastConnect subsystems: Wi-Fi 6, Bluetooth 5.1, 4G, and 5G. This platform has been designed specifically for applications in robotics, and it comes with hardware, software, and other development tools to help designers with such applications.

Google today announced the launch of an OpenCL-based mobile GPU inference engine for its TensorFlow framework on Android. It's available now in the latest version of the TensorFlow Lite library, and the company claims it offers a two times speedup over the existing OpenGL backend with "reasonably-sized" AI models. OpenGL, which is nearly three decades old, is a platform-agnostic API for rendering 2D and 3D vector graphics. Compute shaders were added with OpenGL ES 3.1, but the TensorFlow team says backward-compatible design decisions limited them from reaching device GPUs' full potential. On the other hand, OpenCL was designed for computation with various accelerators from the beginning, and was thus more relevant to the domain of mobile GPU inference.

Bird watching just got a whole lot more high tech. Birdsy, a wildlife-spotting camera, has developed a unique artificial intelligence (AI) that records and identifies birds and other animals all by itself, meaning you never have to miss out on priceless nature moments again. The Wi-Fi-connected camera can monitor your bird feeders and yard 24/7, identify each species it spots, record and log each animal visitor, and send the data directly to a smartphone app for you to enjoy. The app even makes it easy to share your favorite bird spots on social media. Since Birdsy was launched on Kickstarter, it has raced past its original goal of $60,000, with 506 backers pledging a total of $114,790 to help bring the project to life.

Communications technology reached a historic milestone at the beginning of April this year as Hong Kong's fifth-generation mobile (5G) network was officially fully connected, linking all major communication equipment. A 5G network provides much more than just increased bandwidth and speed – 5G provides the opportunity to deploy artificial intelligence (AI) technology to bring the much-discussed Internet of Things (IoT) out of the laboratory and into the real world. To give people from all walks of life a better understanding of 5G networks, the Hong Kong Trade Development Council (HKTDC) held the Tech Trends Symposium 2020 – The Future of Intelligent Connectivity on 28 July. Forming part of the HKTDC's Summer Sourcing Weeks Go ONLINE virtual trade fair (27 July-7 August), the symposium was streamed online. At a session titled "Empowering a Connected Future with 5G", industry leaders introduced the latest developments in 5G networks and expressed the hope that Hong Kong will keep pace with the world in moving towards 5G and building a smart city.

Networks can describe the structure of a wide variety of complex systems by specifying how pairs of nodes interact. This choice of representation is flexible, but not necessarily appropriate when joint interactions between groups of nodes are needed to explain empirical phenomena. Networks remain the de facto standard, however, as relational datasets often fail to include higher-order interactions. Here, we introduce a Bayesian approach to reconstruct these missing higher-order interactions, from pairwise network data. Our method is based on the principle of parsimony and only includes higher-order structures when there is sufficient statistical evidence for them.