Student-Project Allocation Research Articles

An efficient two-heuristic algorithm for the student-project allocation with preferences over projects

The Student-Project Allocation with preferences over Projects problem is a many-to-one stable matching problem that aims to assign students to projects in project-based courses so that students and lecturers meet their preference and capacity constraints. In this paper, we propose an efficient two-heuristic algorithm to solve this problem. Our algorithm starts from an empty matching and iteratively constructs a maximum stable matching of students to projects. At each iteration, our algorithm finds an unassigned student and assigns her/his most preferred project to her/him to form a student-project pair in the matching. If the project or the lecturer who offered the project is over-subscribed, our algorithm uses two heuristic functions, one for the over-subscribed project and the other for the over-subscribed lecturer, to remove a student-project pair in the matching. To reach a stable matching of a maximum size, our two heuristics are designed such that the removed student has the most opportunities to be assigned to some project in the next iterations. Experimental results show that our algorithm is efficient in execution time and solution quality for solving the problem.

An Algorithmic Approach in the Development of Student-Project Allocation Software

The traditional way of allocating project/seminar to students in the higher institution needs to be reconsidered since project/seminar research writing is a sensitive aspect of student education in the higher institution. At the beginning of graduating session or semester, the students are required to write project/seminar on their field of study as part of requirement to complete the academic studies. Each student is allocated to a supervisor who will supervise the academic project/seminar to enhance correctness and academic acceptability of the project/seminar work. Normally in some institution, the allocation of students to the supervisor is done by the head of academic department/Unit or the project coordinator. This approach is characterized with two or more problems. Biasedness is one in the sense that the department head or the project coordinator may have interest in some students either due to academic performance of such a students or personal reasons and thereby allocate such student to him/herself. The time wastage in the allocations in order to avoid double allocation is another serious problem of the manual system. Hence, this paper presents an algorithmic approach to ameliorate the identified problems in project allocation. Using the algorithm, a system was implemented which allocated students to project supervisor, effortlessly and seamlessly.

FedSSC: Joint client selection and resource management for communication-efficient federated vehicular networks

As a promising distributed technology, federated learning (FL) has been widely used in vehicular networks involving large amounts of IoT-enabled sensor data, which derives federated vehicular networks (FVNs). However, the efficiency of FVN is generally limited by vehicle selection policy and communication conditions, which leads to high communication costs and latency. The original FVN transmits model parameters from a random subset of vehicles to the roadside unit (RSU) and ignores the diversity of learning quality among vehicles. In addition, a few vehicles with poor wireless channel conditions may prolong communication latency. To address these two issues, we propose a communication-efficient federated learning approach, composed of Vehicle Selection, Student–Project Allocation (SPA) matching model, and Convex Optimization, called FedSSC, to improve the communication efficiency in FVN. The parameter variations for the same vehicle in two consecutive rounds are used to quantify the quality of the learning results by cosine distance and Affinity Propagation (AP) clustering. Moreover, a subchannel allocation algorithm based on the SPA matching model, as well as a convex optimal power allocation solution are integrated to minimize the communication latency of each training round. According to extensive experiments, the proposed FedSSC reduces the communication overhead by 26.32% on average compared with the benchmarks, whereas the communication latency decreases by 21.84%.

Evaluating Effectiveness of Student Final Year Project Allocation: a Case Study at Universiti Brunei Darussalam

The allocation of final year projects is a critical task for universities to ensure that students receive the best possible academic experience. The allocation problem is often complex, and universities face several challenges, including balancing staff workload, taking into consideration students’ preferences as well as allocating resources optimally. This paper presents a Linear Programming approach for optimising student final year project allocation to address the mentioned challenges. By considering students’ preferences, staff workload, and project quality, the proposed model provides an optimal and fair project allocation scheme. The model’s effectiveness was demonstrated through a case study in Mathematical Sciences, Faculty of Science, UBD, using real data. The results obtained from the LP model are both feasible and optimal, indicating that the approach provides an unbiased allocation of supervisors to project students satisfying all constraints. The model allows for flexibility in fine-tuning the allocation scheme, as it can accommodate different levels of quality preferences and student priorities. It is believed that the proposed LP model can provide necessary support for the project coordinator to make informed decisions regarding final year project allocation. The model can also save time and resources by providing solution quickly and efficiently.

On the application of uplink/downlink decoupled access in heterogeneous mobile edge computing

Mobile edge computing (MEC) is a key player in low latency 5G networks with the task to resolve the conflict between computationally-intensive mobile applications and resource-limited mobile devices (MDs). As such, there has been intense interest in this topic, especially in multi-user single-server and homogeneous multi-server scenarios. However, the research in the heterogeneous multi-server scenario is limited, where the servers are located at small base-stations (SBSs), macro base-stations (MBSs), or the cloud with different computing and communication capabilities. On the other hand, computational-tasks offloading is limited by the type of MD-BS association with almost all previous works focusing on offloading the MD’s computational tasks to the MEC servers/cloudlets at its serving BS. However, in multi-BS association, or downlink/uplink decoupled (DUDe) scenarios, an MD can be served by multiple BSs and hence has multiple offloading choices. Motivated by this, we proposed a joint BS association and subchannel allocation algorithm based on a student-project allocation (SPA) matching approach to minimize the network sum-latency, which breaks the constraint that one MD must connect to the same BS in the UL and DL, and jointly consider the communication and computational disparity of SBS and MBS cloudlets in heterogeneous MEC networks. Moreover, an optimal power allocation scheme is proposed to optimize the system performance subject to the predefined quality of service constraints. Our results show that the proposed scheme is superior to benchmark techniques in enabling the effective use of computational and communication resources in heterogeneous MEC networks.

Development of An Android-Based Visual Implementation of Student Project Allocation System

Abstract Technology is everyplace we go in moment's life. So, scholars of seminaries or sodalities, or universities bear an operation that supports smartphones to get all types of information related to examination, lecture notes, placement, systems regarding announcement, events, transportation, etc. Rather of calling systems because nearly all mobile druggies have smartphones currently. This being system takes a pupil list and allocates administrators to scholars. Using a manual system in the management and allocation of projects to students is characterized by many problems, including the Inability of the project Guide to know that a title has been approved already for a student. Difficulty and inappropriate documentation of allocated project topics. Ineffective in entering, updating, and retrieving records of allocated projects. Difficulty in accessing the project Guide for approval of the topic. Duplication in project topics approved for students. We designed an operation to attain the demand of scholars. The main ideal of the pupil design allocation system is to make a system that will give information for each pupil. Projects can efficiently be allocated to students without delay, and topic conflict between students in the same department will not arise here. Pupil updates can be fluently penetrated if the database system is enhanced. This design will give a fruitful way to manage data at a low cost. The Student Project Allocation contains colorful options similar as login/logout, viewing and streamlining data, etc. It'll be secure. Data can be used by only those with an id and word while maintaining the data.

A decision support tool for the student–supervisor allocation problem of postgraduate programs

The stable supervisor-student allocation (SSA) is crucial in the success of the research at the postgraduate education. The difference of SSA from the well-known student-project allocation (SPA) problem is that it moves the determination of the research topic to after the allocation, thus allowing the student and the lecturer to discuss the research topic. In the education systems of developing countries where decision support systems are not prevalent, the student–lecturer allocation is performed manually with a greedy approach, which can result in unstable matches. In this study, we propose a multi-objective binary model that allows decision-makers to shift between three main allocation strategies with a trade-off parameter. We test the model with a real dataset of a postgraduate program in Turkey. Experiments show that the model can successfully implement different strategies, and there can also be more than one breaking point between strategies. Also, we transform the model into a decision support tool that provides easy use to the decision-maker and reports the solutions of the trade-off parameter sensitivity analysis. The tool reports the level of satisfaction for each strategy at different breakpoints, allowing the decision-maker to see its options.

Energy-delay tradeoff for virtual machine placement in virtualized multi-access edge computing: a two-sided matching approach

By decoupling network functions from the underlying physical machines (PMs) at the edge of the networks, the virtualized multi-access edge computing (MEC) enables deployment of new network services and elastic network scaling to reduce the maintenance costs in a more flexible, scalable and cost-effective manner. Although there are appealing performance gains to be achieved, the placement of virtual machines (VMs) on top of the sharing PMs to support computation-intensive applications for smart mobile devices becomes a major challenge, especially for an increasing network scale. In this paper, we attempt to deal with the VM placement problem in virtualized MEC system, which is targeted for finding a performance balance between energy consumption and computing/offloading delay. To capture such a tradeoff for VM placement, we formulate a weighted sum based cost minimization problem as a pure 0–1 integer linear programming problem, which is NP-complete and very difficult to solve with lower complexity. Based on the one-to-one mapping relation constraint, the VM placement problem is then converted into a many-to-many two-sided matching problem between the VM instances and the PMs. Motivated by the student project allocation problem, we develop an extended two-sided matching algorithm with lower computational complexity for solving the matching problem. Simulation results are presented to show the effectiveness of our proposed algorithm, and the normalization factor is of great significance to obtain the lower total cost.

Student-project allocation with preferences over projects: Algorithmic and experimental results

We study the Student-Project Allocation problem with lecturer preferences over Projects (spa-p). In this context it is known that stable matchings can have different sizes and the problem of finding a maximum size stable matching is NP-hard. There are two known approximation algorithms for max-spa-p, with performance guarantees 2 and 32. We show that max-spa-p is polynomial-time solvable if there is only one lecturer involved, and NP-hard to approximate within some constant c>1 if there are two lecturers involved. We also show that this problem remains NP-hard if each preference list is of length at most 3, with an arbitrary number of lecturers. We then describe an Integer Programming (IP) model to enable max-spa-p to be solved optimally in the general case. Following this, we present results arising from an empirical evaluation that investigates how the solutions produced by the approximation algorithms compare to optimal solutions obtained from the IP model, with respect to the size of the stable matchings constructed, on instances that are both randomly-generated and derived from real datasets.

Super-stability in the student-project allocation problem with ties

The Student-Project Allocation problem with lecturer preferences over Students (spa-s) involves assigning students to projects based on student preferences over projects, lecturer preferences over students, and the maximum number of students that each project and lecturer can accommodate. This classical model assumes that each project is offered by one lecturer and that preference lists are strictly ordered. Here, we study a generalisation of spa-s where ties are allowed in the preference lists of students and lecturers, which we refer to as the Student-Project Allocation problem with lecturer preferences over Students with Ties (spa-st). We investigate stable matchings under the most robust definition of stability in this context, namely super-stability. We describe the first polynomial-time algorithm to find a super-stable matching or to report that no such matching exists, given an instance of spa-st. Our algorithm runs in O(L) time, where L is the total length of all the preference lists. Finally, we present results obtained from an empirical evaluation of the linear-time algorithm based on randomly-generated spa-st instances. Our main finding is that, whilst super-stable matchings can be elusive when ties are present in the students’ and lecturers’ preference lists, the probability of such a matching existing is significantly higher if ties are restricted to the lecturers’ preference lists.

Öğrenci-Proje Takımı Oluşturma Problemi için Bir Karar Destek Sistemi Uygulaması

Student-project allocation is a multi-criteria complex problem that requires the consideration of the preferences of students and academic advisers and grouping team members with different qualifications according to some project specifications. It is thus necessary to use information technologies in the process of forming project teams. In this study, a decision support system application running on a personal computer is developed for forming student-project teams. An integrated office software environment is thus presented where the users can organize problem data, perform allocations and report results. It is a two-phase allocation process in which the allocations of students and academic advisers are performed, respectively, using an integer programming-based approach. There are two integer programming models to allocate students and advisers to project teams using the corresponding criteria, respectively. The proposed application is illustrated using a real-life student-project allocation problem in an academic institution. The results are compared with the ones presented in the literature with respect to some performance parameters.

Population Based Techniques for Solving the Student Project Allocation Problem

The student project allocation problem is a well-known constraint satisfaction problem that involves assigning students to projects or supervisors based on a number of criteria. This study investigates the use of population-based strategies inspired from physical phenomena (gravitational search algorithm), evolutionary strategies (genetic algorithm), and swarm intelligence (ant colony optimization) to solve the Student Project Allocation problem for a case study from a real university. A population of solutions to the Student Project Allocation problem is represented as lists of integers, and the individuals in the population share information through population-based heuristics to find more optimal solutions. All three techniques produced satisfactory results and the adapted gravitational search algorithm for discrete variables will be useful for other constraint satisfaction problems. However, the ant colony optimization algorithm outperformed the genetic and gravitational search algorithms for finding optimal solutions to the student project allocation problem in this study.

Semantic SPA framework for situational student-project allocation in education

Semantic SPA framework for situational student-project allocation in education

Joint D2D Group Association and Channel Assignment in Uplink Multi-Cell NOMA Networks: A Matching-Theoretic Approach

This paper studies joint device-to-device (D2D) group association and channel assignment in uplink multi-cell non-orthogonal multiple-access (NOMA) networks. Particularly, the goal is to assign D2D groups to cellular user channels at each base-station, while accounting for negative network externality due to the interference caused by pairing a user with a D2D group. To that end, a multi-objective signal-to-interference-plus-noise ratio (SINR)-maximizing power allocation solution procedure is proposed to determine the optimal power allocation for each (D2D group, user) pair, while meeting quality-of-service (QoS) requirements. After that, the joint D2D group association and channel assignment problem is modeled as a student-project allocation with preferences over (student, project) pairs matching problem. More specifically, two polynomial-time complexity stable matching algorithms are proposed to pair D2D groups with users, and associate them with base-stations. Simulation results are presented to evaluate the proposed matching algorithms when combined with the devised solution procedure, and compare them to a joint D2D group association, channel assignment and power allocation (J-GA-CA-PA) scheme. More importantly, the proposed algorithms are shown to efficiently yield comparable SINR-per user and D2D receiver-to the J-GA-CA-PA scheme, while maintaining QoS requirements.

User association and channel assignment in downlink multi-cell NOMA networks: A matching-theoretic approach

This paper studies the problem of stable user association and channel assignment in downlink multi-cell non-orthogonal multiple-access (NOMA) networks. To be specific, the goal is to assign network users to the channels at each base station, while accounting for inter-user interference and maintaining quality of service (QoS) per user. To that end, a low-complexity iterative solution procedure is devised to obtain the optimal power allocation for proportional fairness signal-to-interference-plus-noise ratio (SINR)-based maximization, which is then utilized to determine the preferences of network users over the channels available at each base station and the preferences of base stations over the network users. In turn, a many-to-one matching-theoretic model based on the student-project allocation problem is applied. Particularly, two polynomial-time complexity stable matching algorithms are proposed to associate users with base stations and perform channel assignment, such that no user or base station would deviate and change its association or channel assignment unilaterally. To validate the efficacy of the proposed solution procedure and stable matching algorithms, extensive simulation results are presented to compare them to a centralized joint user association, channel assignment, and power allocation (C-J-UA-CA-PA) scheme. It is demonstrated that the proposed algorithms efficiently associate users with base stations and assign them to channels as well as efficiently yielding comparable SINR per user to the C-J-UA-CA-PA scheme, while maximizing proportional fairness and satisfying QoS constraints.

Semantic SPA Framework for situational Student-Project Allocation in Education

Web 3.0 technologies have redefined the way classical problems in education are solved. The classical problems are constrained by interoperability challenges that can be addressed using semantic technologies. Student-project allocation (SPA) is one such classical problem in education, whose solution lies in finding a stable matching between student-project pairs. Although many methods exist to solve the SPA problem, recent developments in higher education, such as globalisation, has triggered interoperability challenge because of diversity in student and expert availability across disciplines, campuses, and universities. The author proposes the semantic SPA framework that addresses the interoperability challenge, by leveraging the existing semantic investments made by the educational institutions. The framework standardises the SPA data elements formally using semantic Web concepts, proposes reference ontology for the situational SPA problem, and adopts a semantic Web service approach, using OWL-S, for development. The OWL-S specification enables the automatic discovery, composition, and invocation of the Web services by educational Institutions or semantic agents on the Web, for situational SPA scenarios.

Fuzzy Approach to Student-Project Allocation (SPA) Problem

This paper presents a novel algorithm for solving a Student-Project Allocation problem, a variation of a two-sided matching algorithm, where a large number of students are allocated to numerous projects with limited available allocation places. Several new or improved algorithms are proposed, inspired by recommender systems and combined into a genetic algorithm. The main two algorithms are (i) variating the master list of student average grade ranking inspired by a fuzzy approach and (ii) extending the incomplete project preference lists by exploring the similarities of student choices, thus minimizing the number of randomly allocated students. The algorithm was implemented for allocating more than 500 students to more than 200 projects, in a course at the University of Zagreb, Faculty of Electrical Engineering and Computing. We compared our algorithm with the Deferred Acceptance (DA) Algorithm to check its validity. Using the fuzzy approach, the number of unallocated students decreased by 10%, with almost no effect to the top-ranked students. The combined usage of all proposed algorithms increased the number of successfully allocated students by more than 25%.

Development of a Web-based Student Project Allocation and Management System

Development of a Web-based Student Project Allocation and Management System

Joint radio resource allocation in fog radio access network for healthcare

With the rapid development of healthcare, mobile cloud computing can improve medical efficiency by capturing and analyzing patient data. Fog computing, as an emerging paradigm to complement cloud computing, has significant advantages in local wireless signal processing, resource management and distributed storage capabilities to potentially meet future healthcare demands. However, performance is limited by the capacity of fronthaul links. In this paper, we propose a novel fog radio access network (F-RAN) model, where cooperation caching strategy and content transmission are jointly optimized. We formulate a mixed integer nonlinear programming problem in order to achieve an ultra-low delay for the proposed F-RAN. We also propose a novel matching algorithm based on the student project allocation (SPA) algorithm instead of the traditional optimization algorithm to solve the formulated problem. Numerical results reveal that the proposed joint optimization design can significantly improve the performance of the considered F-RAN.

Implementation of Stable Marriage Algorithm in Student Project Allocation


 
 
 
 Project allocation is an annual challenge for lecturers and students. The process of allocating project involves matching preferences of students over project and with of staff over the student, and is thus an instance of stable marriage problem from theoretical computer science aspect. The aim is to find a stable allocation of project to students, such that it is impossible to find a project swap that would make all involved parties (both students, both staff) happier. This paper investigated efficacy of stable marriage algorithm and deployed basic Gale Sharply Algorithm into the process of allocating student project. A system was developed using ruby and MySQL to handle the task. The result showed that the algorithm was able to improve the process by enhancing the stability involved.