Utilizing Geospatial Information in Cellular Data Usage for Key Location Prediction

Previous research on the identification of key locations (e.g., home and workplace) for a user largely relies on call detail records (CDRs). Recently, cellular data usage (i.e., mobile internet) is growing rapidly and offers fine-grained insights into various human behavior patterns. In this study, we introduce a novel dataset containing both voice and mobile data usage records of mobile users. We then construct a new feature based on the geospatial distribution of cell towers connected by mobile users and employ bivariate kernel density estimation to help predict users’ key locations. The evaluation results suggest that augmented features based on both voice and mobile data usage improve the prediction precision and recall

Boosting the Cycle Counting Power of Graph Neural Networks with I2^2-GNNs

Message Passing Neural Networks (MPNNs) are a widely used class of Graph Neural Networks (GNNs). The limited representational power of MPNNs inspires the study of provably powerful GNN architectures. However, knowing one model is more powerful than another gives little insight about what functions they can or cannot express. It is still unclear whether these models are able to approximate specific functions such as counting certain graph substructures, which is essential for applications in biology, chemistry and social network analysis. Motivated by this, we propose to study the counting power of Subgraph MPNNs, a recent and popular class of powerful GNN models that extract rooted subgraphs for each node, assign the root node a unique identifier and encode the root node's representation within its rooted subgraph. Specifically, we prove that Subgraph MPNNs fail to count more-than-4-cycles at node level, implying that node representations cannot correctly encode the surrounding substructures like ring systems with more than four atoms. To overcome this limitation, we propose I$^2$-GNNs to extend Subgraph MPNNs by assigning different identifiers for the root node and its neighbors in each subgraph. I$^2$-GNNs' discriminative power is shown to be strictly stronger than Subgraph MPNNs and partially stronger than the 3-WL test. More importantly, I$^2$-GNNs are proven capable of counting all 3, 4, 5 and 6-cycles, covering common substructures like benzene rings in organic chemistry, while still keeping linear complexity. To the best of our knowledge, it is the first linear-time GNN model that can count 6-cycles with theoretical guarantees. We validate its counting power in cycle counting tasks and demonstrate its competitive performance in molecular prediction benchmarks

Study of the adsorption of Co(II) on the chitosan-hydroxyapatite

The adsorption of cobalt ions (Co2+) from aqueous solution onto chitosan-hydroxyapatite composite is investigated in this study. The effects of adsorption time, initial concentration, temperature, and pH are studied in details. Kinetics and thermodynamics of the adsorption of Co2+ onto the chitosan-hydroxyapatite are also investigated and the adsorption kinetics is found to follow the pseudo-second-order model with an activation energy (Ea) of 10.73 kJ/mol. Thermodynamic studies indicates that the adsorption follows the Langmuir adsorption equation. The value of entropy change (∆Sө) and enthalpy change (∆Hө) are found to be 83.50 and 18.09 kJ/mol, respectively. The Gibbs free energy change (∆Gө) is found to be negative at all fives temperatures, demonstrating that the adsorption process is spontaneous and endothermic.

Collaborative Edge Caching: a Meta Reinforcement Learning Approach with Edge Sampling

Current learning-based edge caching schemes usually suffer from dynamic content popularity, e.g., in the emerging short video platforms, users' request patterns shift significantly over time and across different edges. An intuitive solution for a specific local edge cache is to collect more request histories from other edge caches. However, uniformly merging these request histories may not perform satisfactorily due to heterogeneous content distributions on different edges. To solve this problem, we propose a collaborative edge caching framework. First, we design a meta-learning-based collaborative strategy to guarantee that the local model can timely meet the continually changing content popularity. Then, we design an edge sampling method to select more "valuable" neighbor edges to participate in the local training. To evaluate the proposed framework, we conduct trace-driven experiments to demonstrate the effectiveness of our design: it improves the average cache hit rate by up to $10.12\%$ (normalized) compared with other baselines.Comment: Published on IEEE International Conference on Multimedia and Expo 2023 (ICME2023

Construction of Ideological and Political Mixed Teaching in Higher Education under the Digital Transformation

This article focuses on the current demands for reform in ideological and political education in higher education, within the context of the digital information and communication era. Specifically, it proposes a plan for integrating ideological and political education into higher education courses using the widely-used blended learning mode in a digitally transformed environment. This plan aims to leverage digital teaching methods, such as the development of multimedia courseware, to enrich classroom teaching content, increase student engagement and learning outcomes, deepen students\u27 understanding and awareness, and enable them to effectively absorb a wealth of information in a limited time. By subtly linking the process of learning professional knowledge with their personal, social, and national development, this plan seeks to foster a professional education philosophy that cultivates "socialist successors.

A Real-time Non-contact Localization Method for Faulty Electric Energy Storage Components using Highly Sensitive Magnetometers

With the wide application of electric energy storage component arrays, such as battery arrays, capacitor arrays, inductor arrays, their potential safety risks have gradually drawn the public attention. However, existing technologies cannot meet the needs of non-contact and real-time diagnosis for faulty components inside these massive arrays. To solve this problem, this paper proposes a new method based on the beamforming spatial filtering algorithm to precisely locate the faulty components within the arrays in real-time. The method uses highly sensitive magnetometers to collect the magnetic signals from energy storage component arrays, without damaging or even contacting any component. The experimental results demonstrate the potential of the proposed method in securing energy storage component arrays. Within an imaging area of 80 mm $\times$ 80 mm, the one faulty component out of nine total components can be localized with an accuracy of 0.72 mm for capacitor arrays and 1.60 mm for battery arrays