Group Formation Algorithm Research Articles

Experiment on extraversion distribution in groups through a group formation algorithm

Advances in technology have sparked a surge of interest in systematic group formation in educational contexts. The experimental study investigates group formation by extraversion distributions on group work outcomes, expected to influence group hierarchy. As an initial step in the experimental randomization process, an algorithmic group formation tool ensured an equal partitioning and aligned students into two experimental conditions with either consistent, homogeneous, or varied, heterogeneous, levels of extraversion. Over the course of one semester, a total of 114 students enrolled in several paralleled seminars, were surveyed on both subjective data (satisfaction with group work) and objective data (group performance) to evaluate the effect of the experimental intervention. The formation of extraversion at the group level contributed to the respective outcomes, emphasizing the value of collective social capital for both individuals and groups. Specifically, a homogeneous distribution of extraversion had a positive impact on group performance, as evident in improved grades on course group assignments and increased active participation in group meetings. Findings emphasize considering personality traits at group-level to enhance the success of groups.

Social-Aware Group Display Configuration in VR Conference

Virtual Reality (VR) has emerged due to advancements in hardware and computer graphics. During the pandemic, conferences and exhibitions leveraging VR have gained attention. However, large-scale VR conferences, face a significant problem not yet studied in the literature -- displaying too many irrelevant users on the screen which may negatively impact the user experience. To address this issue, we formulate a new research problem, Social-Aware VR Conference Group Display Configuration (SVGD). Accordingly, we design the Social Utility-Aware VR Conference Group Formation (SVC) algorithm, which is a 2-approximation algorithm to SVGD. SVC iteratively selects either the P-Configuration or S-Configuration based on their effective ratios. This ensures that in each iteration, SVC identifies and chooses the solution with the highest current effectiveness. Experiments on real metaverse datasets show that the proposed SVC outperforms 11 baselines by 75% in terms of solution quality.

Context-Aware Clustering and the Optimized Whale Optimization Algorithm: An Effective Predictive Model for the Smart Grid

For customers to participate in key peak pricing, period-of-use fees, and individualized responsiveness to demand programmes taken from multi-dimensional data flows, energy use projection and analysis must be done well. However, it is a difficult study topic to ascertain the knowledge of use of electricity as recorded in the electricity records' Multi-Dimensional Data Streams (MDDS). Context-Aware Clustering (CAC) and the Optimized Whale Optimization Algorithm were suggested by researchers as a fresh power usage knowledge finding model from the multi-dimensional data streams (MDDS) to resolve issue (OWOA). The proposed CAC-OWOA framework first performs the data cleaning to handle the noisy and null elements. The predictive features are extracted from the novel context-aware group formation algorithm using the statistical context parameters from the pre-processed MDDS electricity logs. To perform the energy consumption prediction, researchers have proposed the novel Artificial Neural Network (ANN) predictive algorithm using the bio-inspired optimization algorithm called OWOA. The OWOA is the modified algorithm of the existing WOA to overcome the problems of slow convergence speed and easily falling into the local optimal solutions. The ANN training method is used in conjunction with the suggested bio-inspired OWOA algorithm to lower error rates and boost overall prediction accuracy. The efficiency of the CAC-OWOA framework is evaluated using the publicly available smart grid electricity consumption logs. The experimental results demonstrate the effectiveness of the CAC-OWOA framework in terms of forecasting accuracy, precision, recall, and duration when compared to underlying approaches.

Three Algorithms for Grouping Students: A Bridge Between Personalized Tutoring System Data and Classroom Pedagogy

Computer-assisted instructional programs such as intelligent tutoring systems are often used to support blended learning practices in K-12 education, as they aim to meet individual student needs with personalized instruction. While these systems have been shown to be effective under certain conditions, they can be difficult to integrate into pedagogical practices. In this paper, we introduce three group formation algorithms that leverage learning data from the adaptive intelligent tutoring system ALEKS to support pedagogical and collaborative learning practices with ALEKS. Each grouping method was devised for different use cases, but they all utilize a fine-grained multidimensional view of student ability measured across several hundred skills in an academic course. As such, the grouping algorithms not only identify groups of students, but they also determine what areas of ALEKS content each group should focus on. We then evaluate each of the three methods against two alternative baseline methods, which were chosen for their plausibility of being used in practice—one that groups students randomly and one that groups students based on a unidimensional course score. To evaluate these methods, we establish a set of practical metrics based on what we anticipate teachers would care about in practice. Evaluations were performed by simulating mock groupings of students at different time periods for real ALEKS algebra classes that occurred between 2017 and 2019. We show that each devised method obtains more favorable results on the specified metrics than the alternative methods under each use-case. Moreover, we highlight examples where our methods lead to more nuanced groupings than grouping based on a unidimensional measure of ability.

Autonomous Unbiased Study Group Formation Algorithm for Rapid Knowledge Propagation

Knowledge propagation is a necessity, both in academics and in the industry. The focus of this work is on how to achieve rapid knowledge propagation using collaborative study groups. The practice of knowledge sharing in study groups finds relevance in conferences, workshops, and class rooms. Unfortunately, there appears to be only few researches on empirical best practices and techniques on study groups formation, especially for achieving rapid knowledge propagation. This work bridges this gap by presenting a workflow driven computational algorithm for autonomous and unbiased formation of study groups. The system workflow consists of a chronology of stages, each made of distinct steps. Two of the most important steps, subsumed within the algorithmic stage, are the algorithms that resolve the decisional problem of number of study groups to be formed, as well as the most effective permutation of the study group participants to form collaborative pairs. This work contributes a number of new algorithmic concepts, such as autonomous and unbiased matching, exhaustive multiplication technique, twisted round-robin transversal, equilibrium summation, among others. The concept of autonomous and unbiased matching is centered on the constitution of study groups and pairs purely based on the participants’ performances in an examination, rather than through any external process. As part of practical demonstration of this work, study group formation as well as unbiased pairing were fully demonstrated for a collaborative learning size of forty (40) participants, and partially for study groups of 50, 60 and 80 participants. The quantitative proof of this work was done through the technique called equilibrium summation, as well as the calculation of inter-study group Pearson Correlation Coefficients, which resulted in values higher than 0.9 in all cases. Real life experimentation was carried out while teaching Object-Oriented Programming to forty (40) undergraduates between February and May 2021. Empirical result showed that the performance of the learners was improved appreciably. This work will therefore be of immense benefit to the industry, academics and research community involved in collaborative learning.

Modified direct clustering algorithm for group formation in cellular manufacturing

In cellular manufacturing system formation of machine-part families is an important task. Cellular manufacturing systems deals with various clustering algorithms which are particularly relevant to the problem of machine component group formation. In this paper, case study from well-known gearbox manufacturing company is considered where there is scope for using cellular manufacturing system. Company is manufacturing the gearbox varying from single stage reduction to four stage reduction. A modified direct clustering algorithm is proposed and is applied after the initial incidence matrix derived from direct clustering algorithm. This algorithm overcomes the problem arises during application of direct clustering algorithm and gives the optimum solution. The algorithm is specifically designed to deal with large amount of data during realistic situations. The results of proposed algorithm are compared with well-known existing algorithms and it is found that the proposed algorithm gives the better solution than the existing algorithms under consideration.

A Double Auction Framework for Multi-Channel Multi-Winner Heterogeneous Spectrum Allocation in Cognitive Radio Networks

Opportunistic availability of licensed frequency bands enables the secondary users (SUs) to avail the radio spectrum dynamically. Cognitive radio (CR) paradigm extends the dynamic spectrum access techniques to sense for free channels (called spectrum holes) which can be efficiently redistributed amongst SUs. Motivated by the adaptive technology in CR, this paper introduces a sealed-bid double auction mechanism which aims to obtain an effective allocation of the unused radio spectrum. The proposed auction model adopts multi-channel allocation where one SU can access more than one available channel, while imposing the constraints for dynamics in spectrum opportunities and varying channel availability time amongst SUs. Previously designed double auctions miss out the CR constraints which can further degrade the network performance. Also, multi-winner allocation is induced in the model which encourages spectrum reuse by allowing a common channel to be assigned to multiple non-interfering SUs. A preference list of channels is maintained at each SU using which SUs offer their bid values for the heterogeneous channels which the primary owners are competing to lease. To organize channel specific groups of non-interfering SUs, a bidder group formation algorithm is developed such that members of a winner group get access to a common channel. The auctioneer formulates a winner determination strategy and a pricing strategy which achieves truthfulness while assigning the idle spectrum. Effectiveness of the proposed model is studied by comparing it with an existing work which shows that channel allocation gets significantly improved on deploying the proposed model.

A blockchain-based group formation strategy for optimizing the social reputation capital of an IoT scenario

The “Internet of Things” (IoT) provide humans and smart objects with attractive services, based on the advanced features of the IoT devices, like high sensing, real-time acting and reasoning.In our previous research we have highlighted that these features can be improved by promoting cooperation between smart objects, and we introduced the association between Multi-Agent Systems and IoT devices. In that context, we focused on the issue of accurately choosing the best partners for cooperation, in a scenario composed by several federations. We proposed a reputation model and we have shown that the model leads to detect agents having unreliable or misleading behaviors and that the model itself can be profitably used to form groups of agents that mutually cooperate for improving the effectiveness of their tasks. In this further contribution, we focus on the important issue of the group formation, by arguing that in practical IoT situations it is necessary to improve the group formation strategy to provide it with greater adaptability. To this end we introduce – in a particular IoT context described in this work – a two-phase group formation algorithm to support the reputation model. Experimental results prove that the adoption of the group formation algorithm, along with the proposed reputation model provides a few benefits to the whole IoT ecosystem.

The Use of Personality Traits to Enhance Theory-driven Group Formation

Group formation is an important and challenging element for designing successful CSCL scenarios. Despite efforts from the scientific community in developing more effective algorithms to support group formation processes, we still face problems related to learners’ resistance and demotivation towards group work. In this sense, diverse studies highlight the importance of considering learners’ personality traits to form groups, since this factor can influence students’ performance and induce diverse actions and behaviors in group work. Therefore, this paper presents G-FusionPT (Group Formation USIng Ontology and Personality Trait), a group formation algorithm that support new learning roles, denominated Affective Collaborative Learning roles, based on relation between collaborative learning theories and students’ personality traits. The algorithm is based on a collaborative ontology to understand the learning theories (e.g., context, learning activities, group structure), and learners profile to understand learners’ needs (e.g., target/current knowledge/skill). To evaluate the algorithm, we used a 300 student simulated sample wit varying group size (three, five, and seven members), and compared G-FusionPT results to other group formation algorithms: G-Fusion (based specifically on collaborative learning theories) and Random (no strategy or criterion). The results demonstrated the effectiveness of G-FusionPT against G-Fusion and Random algorithms, as it generated the highest average percentage of learners in well-formed groups and lowest averagepercentage of learners in unfit groups.

An Enhanced Genetic Algorithm for Heterogeneous Group Formation Based on Multi-Characteristics in Social-Networking-Based Learning

Social networking-based learning (SN-learning) is one of the most promising innovations to promote learning via a social network, and thus, providing a more interactive, student-centered, cooperative, and on-demand environment. In such an environment, group formation plays an important role to the effectiveness of learning process. Adequate groups foster student interactions and increase learning outcomes. However, group formation is a complex task and requires automatic approaches to produce the optimal results in short time. To this direction, this paper presents a novel genetic algorithm for student grouping in an SN-learning system. Its innovations pertain to the attributes used for the composition of groups and genetic operators applied. In particular, student attributes refer to the three main dimensions of learning in an SN-learning environment: academic, cognitive, and social. Regarding genetic operators, the algorithm performs two crossover operators: a modification of two-point crossover and a new approach, called one-point per group crossover. Evaluating the proposed algorithm performance, the results show that it is more efficient than simple genetic algorithm approach, and considers a larger number of parameters than usual. Moreover, from the pedagogical perspective, a positive students’ attitude and high acceptance toward our group formation method is indicated.

A meritocratic trust-based group formation in an IoT environment for smart cities

Smart cities are built on top of heterogeneous IoT infrastructures, that can be viewed as communities of software agents (the intelligent objects) that interact with each other to realize complex activities. These agents operate on behalf of users that need services; for these reason agents are often in competition with each other. On the other hand, an agent can often benefit from collaborating with other agents in some circumstances, exchanging information and services. Under this viewpoint, the task of finding the best partners to collaborate is a key task for an agent. A general consensus exists about the benefits deriving by forming friendships and groups for mutual cooperation inside competitive Multi-Agent Systems (MASs). In this respect, the existing proposals are usually addressed to maximize the profit at the level of individual agent or group. Unfortunately, the most part of these approaches could advantage the most aggressive agents, also in presence of bad social behaviors. This is not a desired scenario in a smart city environment. A possible solution to this problem is that of promoting correct behaviors and meritocracy inside agent communities. To this aim, we propose to model the competitive MAS scenario in the framework of non cooperative games by assuming to represent (i) the trustworthiness of agents relationships by means of a trust model and (ii) the capability of a community to provide its members with a good environment by means of its social capital. As a result, a group formation algorithm capable to asymptotically maximize the social capital is proposed. This algorithm highlights two main features: (i) the computed solution is a Nash equilibrium in the considered game and (ii) the only rewarded agents are those having the most correct behaviors.

Secure Group based Routing and Flawless Trust Formulation in MANET using Unsupervised Machine Learning Approach for IoT Applications

INTRODUCTION: Mobile Adhoc Network (MANET) is integrated with Internet of Things (IoT) in many application cases due to its flexibility and scalability. The dynamic nature of MANET introduces some security threats in IoT environment. In those threats, Blackhole attack and Grayhole attack are severe routing attacks that disrupts routing algorithm to crack transmission in the entire network. OBJECTIVES: Many security mechanisms are introduced in MANET based on trust computation schemes. However, computation of inaccurate trust value degrades the performance of mitigation schemes. Thus the major objective of this work is to design a novel security mechanism to protect the MANET-IoT from different adversaries. METHODS: in this paper we propose a novel group based routing algorithm with recommendation filtering supported by security monitors (SMs). Unsupervised machine learning algorithm is adapted for the purpose of recommendation filtering in the network. Initially the entire network is grouped by Secure Certificate based Group Formation (SCGF) algorithm. In each group, Recommendation Filtering by K-means algorithm (RF-K means) algorithm is employed to perform trust computation. For secure and optimal route selection, hybrid optimization algorithm that combines Genetic Algorithm and Fire Fly Algorithm (GA-FFA) is proposed. Data transmission is protected by novel Hash Message Authentication Code with Advanced Encryption Standard (HMAC-AES) algorithm in which hash function is integrated with cryptography function. RESULTS: The proposed work is validated in network simulator-3 environment and the obtained results show better performance in terms of packet delivery ratio (96.3%), throughput (135kbps), delay (3.26ms), detection rate (99%), and energy consumption (8.5%). CONCLUSION: The MANET-IoT network is secured by group formation and trust filtering approaches. Further, involvement of cryptography function ensures data security whereas hash function ensures data integrity.

A Greedy Heuristic Algorithm for Location-based Group Formation

Presently, location-based Group formation has become more important in many areas. Fundamentally, it is a way to assign all members to suitable groups in order to gain the maximum benefits and accomplish their objectives. However, the process of group formation based on member’s location may cause a problem as it can be time-consuming if the number of members is large and more factors are considered in forming the groups. In order to cope with this problem, we proposed two variants of heuristic algorithm based on greedy algorithm in order to generate the optimized groups of nearby individual members, who are located in different locations. In this paper, we present our experimental results and illustrate that it produces optimal results for small problem sizes. Additionally, the experimental results demonstrated that the greedy algorithms produce the solutions with the intent of finding a global optimum.

Robust Feature Selection With LSTM Recurrent Neural Networks for Artificial Immune Recognition System

Stability and robustness of feature selection techniques have great importance in the high dimensional and small sample data. The neglected subject in the feature selection is solving the instability problem. Therefore, an ensemble gene selection framework is used in order to provide stable and accurate results of feature selection algorithms. Sequence modeling from high-dimensional data is an important research area for the discovery of biomarkers. Identifying biomarkers requires robust gene selection methods, which makes it possible to find important tumor-related genes with high accuracy. The main issue of this paper is creating a model in order to learn long sequences with the artificial immune recognition system (AIRS) for robust feature selection. Long short-term memory (LSTM) recurrent neural networks are trained with the AIRS in order to obtain the long-lived unit cells for use in the feature selection process. LSTM was used to be better understanding the mechanisms involving the “remember” feature of the immunological behavior of the immune response. We tried to apply a theory suggested by immunologists in order to develop stable associative memory, which capable of solving robustness and optimization tasks. We examined the initial gene selection step based on the different types of group formation algorithm for analysis of the most informative selected features. Microarray datasets are showing remarkable increases in their robustness and classification accuracy. The suggested framework is evaluated on six commonly used microarray datasets.

A genetic algorithm for group formation in elderly communities

- This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT - Fundacao para a Ciencia e Tecnologia within the Project Scope: UID/CEC/00319/2013. A. Costa thanks the FCT - Fundacao para a Ciencia e Tecnologia the Post-Doc scholarship with the Ref. SFRH/BPD/102696/2014. This work is also partially supported by the MINECO/FEDER TIN2017-89156-R and the Post-Doc scholarship with the Ref. SP20170057.

Implementation of Group Formation Algorithms in the ELARS Recommender System

Collaborative learning is recognized as an effective way of gaining knowledge in an online environment. Therefore, e-courses frequently include collaborative e-learning activities (e-tivities) that are performed in pairs or small groups of students. One of the challenges for teachers who organize e-tivities is the effective group forming. This paper presents algorithms that can be used to divide a set of students participating in an e-tivity to homogeneous or heterogeneous groups. The criterion for automatic group formation includes the following characteristics: the program of study, gender, learning styles preferences, Web 2.0 tools preferences, knowledge level and activity level. Designed algorithms were implemented in the educational recommender system ELARS and tested in the context of e tivities.

A two way authentication using bilinear mapping function for wireless sensor networks

The deployment of sensor networks for security and safety related environments requires securing communication primitives such as broadcast, multicast, and point to point communication. Security services are vital for ensuring the integrity, authenticity, and confidentiality of the critical information. Therefore, the authentication mechanisms are required to support these security services and to be resilient to distinct attacks. Recent research shows that two-way authentication provides better security and high energy saving for wireless sensor networks (WSN). In this work, we investigate a shared authentication protocol for WSNs using bilinear mapping function. Many one-way broadcast authentication protocol proposed such as Timed Efficient Stream Loss-Tolerant Authentication (TESLA), Bin and Balls Authentication (BiBA), etc., which cannot provide energy efficient authentication. Here, we propose a two-way authentication based on bilinear map function and used in secure group formation algorithm. The performance metrics such as message cost, computation and communication overhead are calculated.

Energy-Efficient Multicast Transmission for Underlay Device-to-Device Communications: A Social-Aware Perspective

In this paper, by utilizing the social relationships among mobile users, we present a framework of energy-efficient cluster formation and resource allocation for multicast D2D transmission. In particular, we first deal with D2D multicast cluster/group formation strategy from both physical distance and social trust level. Then we aim to maximize the overall energy-efficiency of D2D multicast groups through resource allocation and power control scheme, which considers the quality-of-service (QoS) requirements of both cellular user equipment and D2D groups. A heuristic algorithm is proposed to solve above energy-efficiency problem with less complexity. After that, considering the limited battery capacity of mobile users, we propose an energy and social aware cluster head update algorithm, which incorporates both the energy constraint and social centrality measurement. Numerical results indicate that the proposed social-tie based D2D multicast group formation and update algorithm form a multicast group in an energy efficient way. Moreover, the proposed resource and power allocation scheme achieves better energy efficiency in terms of throughput per energy consumption. These results show that, by exploiting social domain information, underlay D2D multicast transmission has high practical potential in saving the source on wireless links and in the backhaul.

A Reinforcement Learning-Based Sleep Scheduling Algorithm for Desired Area Coverage in Solar-Powered Wireless Sensor Networks

In this paper, a reinforcement learning-based sleep scheduling for coverage (RLSSC) algorithm is proposed for sustainable time-slotted operation in rechargeable sensor networks. RLSSC is a two-stage sleep scheduling algorithm. It includes the precedence operator-based group formation algorithm and the Q learning-based active node selection algorithm. First, a precedence operator is designed in the group formation algorithm to ensure the desired area coverage. All the nodes are formed into groups. Then, Q learning algorithm is expanded into a multi-sensor cooperation Q learning group model. The learning and action selection strategy are designed in a group to direct nodes in collaborative learning of working modes selection while adapting to the dynamic environment. Through the role changes of active nodes, the algorithm accomplishes the entire team learning of sleep scheduling while scheduling others into sleep modes. Experiments of RLSSC on a solar-powered wireless sensor network for area surveillance tasks are presented. Compared with LEACH and a random algorithm, the results show that RLSSC can effectively adjust the working modes of nodes in a group by perceiving the environment. In addition, it achieves the energy consumption balance between nodes so as to prolong the network lifetime while maintaining the desired coverage.

HCI Model with Learning Mechanism for Cooperative Design in Pervasive Computing Environment

This paper presents a human-computer interaction model with a three layers learning mechanism in a pervasive environment. We begin with a discussion around a number of important issues related to human-computer interaction followed by a description of the architecture for a multi-agent cooperative design system for pervasive computing environment. We present our proposed three-layer HCI model and introduce the group formation algorithm, which is predicated on a dynamic sharing niche technology. Finally, we explore the cooperative reinforcement learning and fusion algorithms; the paper closes with concluding observations and a summary of the principal work and contributions of this paper.