Naimah Mohd Hussin

An Investigation of a Tabu-Search-Based Hyper-Heuristic for Examination Timetabling

This paper investigates a tabu-search-based hyper-heuristic for solving examination timetabling problems. The hyper-heuristic framework uses a tabu list to monitor the performance of a collection of low-level heuristics and then make tabu heuristics that have been applied too many times, thus allowing other heuristics to be applied. Experiments carried out on examination timetabling datasets from the literature show that this approach is able to produce good quality solutions.

A tabu search hyper-heuristic approach to the examination timetabling problem at the MARA university of technology

In this paper we introduce an examination timetabling problem from the MARA University of Technology (UiTM). UiTM is the largest university in Malaysia. It has 13 branch campuses and offers 144 programmes, delivered by 18 faculties. This dataset differs from the others reported in the literature due to weekend constraints that have to be observed. We present their examination timetabling problem with respect to its size, complexity and constraints. We analyse their real-world data, and produce solutions utilising a tabu-search-based hyper-heuristic. Since this is a new dataset, and no solutions have been published in the literature, we can only compare our results with an existing manual solution. We find that our solution is at least 80% better with respect to proximity cost. We also compare our approach against a benchmark dataset and show that our method is able to produce good quality results.

Analyzing personality types to predict team performance

This paper presents an approach in analyzing personality types, temperament and team diversity to determine software engineering (SE) teams performance. The benefits of understanding personality types and its relationships amongst team members are crucial for project success. Rough set analysis was used to analyze Myers-Briggs Type Indicator (MBTI) personality types, Keirsey temperament, team diversity, and team performance. The result shows positive relationships between these attributes.

Developing a Team Performance Prediction Model: A Rough Sets Approach

This paper presents a rough sets approach in developing a team performance prediction model. To establish the prediction model, a dataset from an academic setting was obtained, consisting of four predictor variables: prior academic achievement, personality types, personality diversity, and software development methodology. In this study, four main steps in rough set, including discretisation, reduct generation rules, generation of decision rules, and evaluation were used to develop the prediction model. Two reduction algorithms; a genetic algorithm (GA) and a Johnson algorithm were used to obtain optimal classification accuracy. Results show that the Johnson algorithm outperformed the GA with 78.33% model prediction accuracy when using 10-fold cross validation. The result clearly shows that the rough sets is able to uncover complex factors in team dynamism, which revealed that the combination of the four predictor variables are important in developing the team performance prediction model. The model provides a practical contribution in predicting team performance.

Bipartite graph edge coloring approach to course timetabling

Course timetabling problem is common in schools and higher learning institutions. Courses must be allocated to teachers/lecturers, students, timeslots and venues without violating a set of predefined constraints determined by the respective institution. The problem is complex due to the different requirements set by the institutions and the process of finding a solution can be lengthy and time-consuming. This paper presents our research findings on implementing a bipartite graph edge coloring approach in solving a course timetabling problem. The data set used in this study is gathered from the Department of Information Technology in a Private College. Three data sets (from three different semesters) were tested in our experiment. The results are analyzed by comparing the total penalties of violation on a set of predefined soft constraints between the current timetable (produced manually) and the timetable from our prototype developed. The results from experimental research showed that the bipartite graph edge coloring approach on the course timetabling problem in this case study was able to reduce the penalties, thus producing better quality timetable compared to the manual timetable. In future research, more experiments can be conducted using the bipartite graph edge coloring approach with larger scope of data sets.

An improved event selection technique in a modified PSO algorithm to solve class scheduling problems

In solving class scheduling problems, among the initial steps taken by optimization algorithms is to generate initial solutions. These initial solutions which are random or feasible will be improved iteratively to obtain better solutions. In this iterative process, the re-assignment of events to a new time slot and the selection of event to be reassigned plays an important role in assuring the reassignment process is able to improve the quality of the timetable. This paper proposes an event selection technique namely Event Selection based on Selection Limit (ESSL) applied in a modified PSO algorithm to solve class scheduling problems. This technique was based on Tabu List used in Tabu Search Algorithm. Using this technique, the selection of event in each iteration step will be limited based on percentage selection limit. Five percentage selection limits plus adaptive limit were tested in order to see their effect towards the final penalty of the solution. The performance of the proposed technique was measured based on percentage penalty reduction (%PR) and execution time. Five sets of data from International Timetabling Competition were used in the experiment. The experimental results shows that ESSL with adaptive limit managed to produce the highest percentage of penalty reduction. Furthermore, this technique also managed to reduce the effect of early convergence which is known to be one of the weaknesses of the original PSO.

IPv6 attack scenarios testbed

Deploying IPv6 in the enterprise network will increase the security issues since some of IPv6 features bring vulnerabilities. Thus, mitigating them with appropriate security policy is vital. By having attack scenario testing, it will expose network administrators to the IPv6 potential attack. For example, Bad ACK-Reset attack is used by an attacker to reset a new connection after exploiting network. Also, Packet Fragmentation attack is capable to control over the packet fragmentation services yet can cause problems in security measurement. Hence, this paper tested Bad ACK-Reset and Packet Fragmentation attack scenarios for analysis. This paper used Scapy (2.0.1) to generate packets for testing. A testbed simulation has been designed by using Graphical Network Simulator 3 (GNS3). Several ip6tables rules and access control lists (ACLs) were implemented at the host and the router respectively to counter Bad ACK-Reset and Packet Fragmentation attack scenarios. Information gained from the testing will provide a clear understanding on IPv6 security issues and help to design a proper network security policy. The current results from the testing can be used for future research in generating the security policies. Thus, our further research will focus on modelling the created security policy for IPv6 deployment.

Constructing population of initial solutions for curriculum-based course timetabling problem

This paper presents an investigation of a combination of graph coloring heuristics in construction approach in University course timetabling problem (UCTP) to produce a population of initial solutions. The graph coloring heuristics were set as individual, combination of two heuristics and combination of three heuristics. In addition, several steps of courses assignment were applied to all the settings. All settings of heuristics are then tested on the same curriculum-based problem instances and are compared with each other in terms of number of population produced. The results can be used for improvement phase which are the second phase of UCTP. This approach allows generalization over a set of problems instead of producing feasible timetables for some of the problems only. Future work will use the best settings of heuristic to the improvement phase in population-based improvement algorithm.

Assignments acceptance strategy in a Modified PSO Algorithm to elevate local optima in solving class scheduling problems

Local optima in optimization problems describes a state where no small modification of the current best solution will produce a solution that is better. This situation will make the optimization algorithm unable to find a way to global optimum and finally the quality of the generated solution is not as expected. This paper proposes an assignment acceptance strategy in a Modified PSO Algorithm to elevate local optima in solving class scheduling problems. The assignments which reduce the value of objective function will be totally accepted and the assignment which increases or maintains the value of objective function will be accepted based on acceptance probability. Five combinations of acceptance probabilities for both types of assignments were tested in order to see their effect in helping particles moving out from local optima and also their effect towards the final penalty of the solution. The performance of the proposed technique was measured based on percentage penalty reduction (%PR). Five sets of data from International Timetabling Competition were used in the experiment. The experimental results shows that the acceptance probability of 1 for neutral assignment and 0 for negative assignments managed to produce the highest percentage of penalty reduction. This combination of acceptance probability was able to elevate the particle stuck at the local optima which is one of the unwanted situations in solving optimization problems.

Distributed security policy for IPv6 deployment

Internet Protocol version 6 (IPv6) is a next generation protocol that is designed to solve the problem of the current Internet Protocol version 4 (IPv4) depletion. With IPv6, almost anything in the world can be assigned an IPv6 address which makes communication between every single person to another possible. Besides, monitoring and sensing every single node or instrument can be done due to each item has its own IPv6 address. Realizing the features of IPv6, enterprise networks have begun deploying IPv6. Although they have not decided to deploy IPv6, IPv6 packet is possibly already in the network due to the most present operating systems supporting IPv6 and IPv6 enable is set as default. Deploying IPv6 in the existing IPv4 network results in coexistence of both protocols in the network. Thus, the coexistence condition exposed enterprises network to higher probability of vulnerabilities and attacks. Hence, several security policies should be created to maintain security for both IPv4 and IPv6. A proper mechanism to manage the policies to ensure a secure IPv6 deployment is a necessity. Since enterprises have many branches and counterparts, it is essential to have a mechanism to distribute the policies among their branches or subnets. Therefore, the proper mechanism to distribute the security policy which will also support the green computing environment should be formed.