Abstract Multi-key membership testing plays a crucial role in computing systems and networking applications, encompassing web search, mail systems, distributed databases, firewalls, and network routing. Traditional approaches, such as the Bloom filter, encounter limitations within this specific context. Addressing these challenges, we propose the Multi-key Learned Bloom Filter (MLBF), a hybrid method that combines machine learning techniques with the Bloom filter. The MLBF introduces a value-interaction-based multi-key classifier and a multi-key Bloom filter. Furthermore, we introduce an Interval-based MLBF approach, which categorizes keys into specific intervals based on data distribution to minimize the False Positive Rate (FPR). Additionally, MLBF incorporates an out-of-distribution (OOD) detection component to identify data shifts. Through extensive experimental evaluations on three authentic datasets, we demonstrate the superiority of the proposed MLBF in terms of FPR and query efficiency.

With the diffusion of online mobile devices with geo-location capabilities, the infrastructure necessary for real-world deployment of Spatial Crowdsourcing (SC), where so-called mobile workers are assigned location-sensitive tasks, is in place. Some SC tasks cannot be completed by a single worker due to their complexity, but rather must be assigned to and completed by a group of users. Achieving such group assignments that satisfy all group members evenly is an open challenge. To address this challenge, we propose a novel preference-aware group task assignment framework encompassing two components: Mutual Information-based Preference Modeling (MIPM) and Preference-aware Group Task Assignment (PGTA). The MIPM component learns the preferences of groups contrastively by maximizing the mutual information between workers and worker groups based on worker-task and group-task interaction data and by using an attention mechanism to weight group members adaptively. In addition, curriculum negative sampling is adopted to generate a small number of negative workers for each worker group, following the principles of curriculum learning. Next, the PGTA component offers an optimal task assignment algorithm that employs tree decomposition to assign tasks to appropriate worker groups, with the aim of maximizing the number of task assignments while prioritizing more interested groups when assigning tasks. The task assignment framework also features preference-constrained pruning of unpromising worker groups to speed up the assignment process. Finally, we report extensive experiments that offer evidence of the effectiveness and practicality of the paper's proposal.

In Spatial crowdsourcing, mobile users perform spatio-temporal tasks that involve travel to specified locations. Spatial crowdsourcing (SC) is enabled by SC platforms that support mobile worker recruitment and retention, as well as task assignment, which is essential to maximize profits that are accrued from serving task requests. Specifically, how to best achieve task assignment in a cost-effective manner while contending with spatio-temporal constraints is a key challenge in SC. To address this challenge, we formalize and study a novel Profit-driven Task Assignment problem. We first establish a task reward pricing model that takes into account the temporal constraints (i.e., expected completion time and deadline) of tasks. Then we adopt an optimal algorithm based on tree decomposition to achieve an optimal task assignment and propose greedy algorithms based on Random Tuning Optimization to improve the computational efficiency. To balance effectiveness and efficiency, we also provide a heuristic task assignment algorithm based on Ant Colony Optimization that assigns tasks by simulating behavior of ant colonies foraging for food. Finally, we conduct extensive experiments using real and synthetic data, offering detailed insight into effectiveness and efficiency of the proposed methods.

With the speedy increase of display resolution and the demand for interactive frame rate, rendering acceleration is becoming more critical for a wide range of virtual reality applications. Foveated rendering addresses this challenge by rendering with a non-uniform resolution for the display. Motivated by the non-linear optical lens equation, we present rectangular mapping-based foveated rendering (RMFR), a simple yet effective implementation of foveated rendering framework. RMFR supports varying level of foveation according to the eccentricity and the scene complexity. Compared with traditional foveated rendering methods, rectangular mapping-based foveated rendering provides a superior level of perceived visual quality while consuming minimal rendering cost.

With the popularity of GPS-enable smart devices and the development of wireless network, Spatial Crowdsourcing (SC), as a framework for assigning location-sensitive tasks to moving workers, has received wide attention in recent years. In real-world scenarios, some complex tasks exist that may not be completed by a single worker. In this case, the tasks are often assigned to multiple workers, which is called group task assignment. However, the assignment of tasks that satisfy all group members in an even way remains a challenge. To this end, we propose a novel preference-aware group task assignment framework that includes two components: Mutual Information-based Preference Modeling (MIPM) and Preference-aware Group Task Assignment (PGTA). Specifically, MIPM learns the preferences of worker groups by maximizing the mutual information among workers based on the worker-task interaction data and the group-task interaction data, where an attention mechanism is used. PGTA adopts an optimal task assignment algorithm based on tree decomposition to assign tasks to appropriate worker groups, which aims to maximize the overall number of assigned tasks while giving priority to the groups of workers that are more interested in the tasks. Finally, extensive experiments are conducted, verifying the effectiveness and practicality of the proposed solutions.

In this paper, model identification and adaptive control design are performed on Devanit-Hartenberg model of a humanoid robot. We focus on the modeling of the 6 degree-of-freedom upper limb of the robot using recursive Newton-Euler (RNE) formula for the coordinate frame of each joint. To obtain sufficient excitation for modeling of the robot, the particle swarm optimization method has been employed to optimize the trajectory of each joint, such that satisfied parameter estimation can be obtained. In addition, the estimated inertia parameters are taken as the initial values for the RNE-based adaptive control design to achieve improved tracking performance. Simulation studies have been carried out to verify the result of the identification algorithm and to illustrate the effectiveness of the control design.