Intelligent Tutoring Systems (ITS), such as Massive Open Online Courses, offer new opportunities for human learning. At the core of such systems, knowledge tracing (KT) predicts students' future performance by analyzing their historical learning activities, enabling an accurate evaluation of students' knowledge states over time. We show that existing KT methods often encounter correlation conflicts when analyzing the relationships between historical learning sequences and future performance. To address such conflicts, we propose to extract so-called Follow-up Performance Trends (FPTs) from historical ITS data and to incorporate them into KT. We propose a method called Forward-Looking Knowledge Tracing (FINER) that combines historical learning sequences with FPTs to enhance student performance prediction accuracy. FINER constructs learning patterns that facilitate the retrieval of FPTs from historical ITS data in linear time; FINER includes a novel similarity-aware attention mechanism that aggregates FPTs based on both frequency and contextual similarity; and FINER offers means of combining FPTs and historical learning sequences to enable more accurate prediction of student future performance. Experiments on six real-world datasets show that FINER can outperform ten state-of-the-art KT methods, increasing accuracy by 8.74% to 84.85%.

One key communication block in 5G and 6G radios is the active phased array (APA). To ensure reliable operation, efficient and timely fault diagnosis of APAs on-site is crucial. To date, fault diagnosis has relied on measurement of frequency domain radiation patterns using costly equipment and multiple strictly controlled measurement probes, which are time-consuming, complex, and therefore infeasible for on-site deployment. This paper proposes a novel method exploiting a Deep Neural Network (DNN) tailored to extract the features hidden in the baseband in-phase and quadrature signals for classifying the different faults. It requires only a single probe in one measurement point for fast and accurate diagnosis of the faulty elements and components in APAs. Validation of the proposed method is done using a commercial 28 GHz APA. Accuracies of 99% and 80% have been demonstrated for single- and multi-element failure detection, respectively. Three different test scenarios are investigated: on-off antenna elements, phase variations, and magnitude attenuation variations. In a low signal to noise ratio of 4 dB, stable fault detection accuracy above 90% is maintained. This is all achieved with a detection time of milliseconds (e.g 6~ms), showing a high potential for on-site deployment.

Earlier this month, Odense Robotics, in collaboration with Aalborg University, opened a robotics hub in Aalborg, Denmark. The fifth and final hub in the country completes Odense Robotics' national setup. Odense Robotics has already established hubs in Aarhus, Copenhagen, Odense and Sonderborg. The hub will provide robotics, automation and drone startups, scaleups and SMEs with opportunities for growth and innovation. It will work with robotics companies in the northern region of Jutland.

The Department of Computer Science at Aalborg University's Technical Faculty of IT and Design is looking to appoint a number of Assistant Professors (tenure-track) for its new group at the university's Copenhagen Campus, commencing September 1, 2020 or soon thereafter. In 2020, the Department of Computer Science will begin building a new research group at Aalborg University's Copenhagen Campus. The group will be responsible for the newly approved bachelor's and master's educations in "Software" and will over time build its own research profile and capacity. The Copenhagen group will build upon, and contribute to the Department's broad range of synergistic activities within research and education in the general area of computer science, including curiosity-driven research and targeted research in collaboration with industrial partners, as well as traditional university education, with a unique problem- and project-based focus, and continued education and knowledge dissemination. As Assistant Professor in Computer Science at the Copenhagen Campus you are expected to deliver teaching of an international standard on the new Software education commencing in September 2020.

We present a joint message passing approach that combines belief propagation and the mean field approximation. Our analysis is based on the region-based free energy approximation method proposed by Yedidia et al. We show that the message passing fixed-point equations obtained with this combination correspond to stationary points of a constrained region-based free energy approximation. Moreover, we present a convergent implementation of these message passing fixedpoint equations provided that the underlying factor graph fulfills certain technical conditions. In addition, we show how to include hard constraints in the part of the factor graph corresponding to belief propagation. Finally, we demonstrate an application of our method to iterative channel estimation and decoding in an orthogonal frequency division multiplexing (OFDM) system.