Mobile Crowdsensing (MCS) is gaining attention for large-scale sensing that involves three types of entities: task requesters, workers equipped with sensing devices, and the platform that assigns tasks to workers considering their objectives and constraints. However, finding an allocation solution that satisfies the conditions above is NP-hard. A few studies suggested approximate solutions to this problem, focusing on one of the task’s objectives: coverage maximization. Yet, they implement it in a single-task environment or with weak objective consideration, i.e., they consider other objectives, reducing the utility the task will receive. This study proposes a task allocation that focuses only on maximizing the task coverage, where we improved the solution to consider future task coverage possibilities. We consider an opportunistic MCS environment in which sensing has no impact on user trajectories. We assume a one-to-many matching where a task can be assigned to several workers, while a worker can be matched to at most one task. We first formulate the problem mathematically and prove it to be NP-hard. Then, we design three heuristic-based solutions that are more efficient and perform extensive performance evaluations based on a real-world dataset. Each solution improves the data quality and has a maximum execution time of milliseconds.

Abstract Modern Recommender Systems (RSs) compete to maintain rich user profiles that can accurately reflect user behavior, interests, and service contexts. While benefiting from an online service supported by an RS, user preferences and interests may rapidly change over time. To keep up with the changes from the user perspective, an RS should maintain the making of effective personalization as supported by robust profile construction methods. Building an effective user profile database requires exhaustive data and behavior analysis over extended periods. In this paper, we delve into traditional RS architectures to identify limitations, gaps, and opportunities for improvements in existing user profile mechanisms. To that end, a Global User Profile Framework (GUPF) is proposed towards achieving increased effectiveness. Furthermore, the adoption of the developed framework is exemplified by presenting different potential scenarios. The presented work concludes with the identification of important venues and research directions that are enabled by the proposed GUPF.

New, original, and innovative techniques have emerged unexpectedly from online teaching during the pandemic. The Watch and Learn strategy emerged as an effective way for the students to learn the fundamental concepts of programming by debugging and fixing the code in the programming course. The students started sending screenshots of their code when facing problems. This collection of code snippets has been used to provide a good learning experience. It created a competitive environment for the students and increased their interaction and engagement.

In this paper we propose a reputation management scheme for partially decentralized peer-to-peer systems. The reputation scheme helps building trust between peers based on their past experiences and feedbacks from other peers. Our system is novel in that it is able to detect not only malicious peers sending inauthentic files but also malicious peers that are lying in their feedbacks. To detect those peers, we introduce the new concept of suspicious transactions. The simulation results show that the proposed scheme is able to effectively detect malicious peers and isolate them from the system, hence reducing the amount of inauthentic uploads and increasing peers' satisfaction.

Trust is required in a file sharing peer-to-peer system to achieve better cooperation among peers. In reputation-based peer-to-peer systems, reputation is used to build trust among peers. In these systems, highly reputable peers will usually be selected to upload requested files, decreasing significantly malicious uploads in the system. However, these peers need to be motivated to upload files by increasing the benefits that they receive from the system. In addition, it is necessary to motivate free riders to contribute to the system by sharing files. Malicious peers must be forced to contribute positively by uploading authentic files instead of malicious ones. In this paper, the contribution behavior of the peer is used as a guideline for service differentiation. The new concept of availability is introduced for partially-decentralized peer-to-peer systems. Both availability and involvement of the peer are used to assess its contribution behavior. Simulation results confirm the ability of the proposed scheme to effectively identify both free riders and malicious peers and reduce the level of service provided to them. Simulation results also confirm that based on rational behavior, peers are motivated to increase their contribution to receive services. Moreover, using our scheme, peers must continuously participate, reducing significantly the so-called milking phenomenon