Concurrency and Computation

Wang, Dandan; Li, Qi

doi: 10.1002/cpe.8097pmid: N/A

A generic variation of hierarchical clustering (HC) that builds clusters from the bottom up is called agglomerative hierarchical clustering (AHC). The extension of AHC techniques using similarity criteria is the main topic of this research. Based on this, we create an AHC method that accomplishes clustering through ensemble approaches and combines the clustering of clusters with an original similarity measure. Three steps make up the proposed method's primary section. The first phase combines several individual AHC techniques to identify links between samples and create preliminary clusters. A heuristic similarity measure based on the developed clusters is used to determine how similar the samples are. The initial clusters produced using various techniques are all re‐clustered to create superclusters in the second step. The third phase involves creating the final clusters by assigning each sample to a supercluster with the greatest similarity after the clusters have been formed. Based on several benchmark datasets from the UCI machine learning repository, extensive experimental research has been done to assess the performance of the suggested approach. The outcomes unequivocally demonstrate that the suggested AHC‐based paradigm outperforms cutting‐edge techniques.

Dixon, Sam; Yao, Lina; Davidson, Robert

doi: 10.1002/cpe.8020pmid: N/A

Human activity recognition is a well‐established research problem in ubiquitous computing. The increased dependency on various smart devices in our daily lives allows us to investigate the sensor data world produced by multimodal sensors embedded in smart devices. However, the raw sensor data are often unlabeled and annotating this vast amount of data are a costly exercise that can often lead to privacy breaches. Self‐supervised learning‐based approaches are at the forefront of learning semantic representation from unlabeled sensor data, including when applied to human activity recognition tasks. As inferring human activity depends on multimodal sensors, addressing the modality difference and inter‐modality dependencies in a model is an important process. This paper proposes a novel self‐supervised learning approach, modality aware contrastive learning (MACL), for representation learning using multimodal sensor data. The approach uses different sensing modalities to create different views of an input signal. Thus, the model is able to learn the representations by maximizing the similarity among different sensing modalities of the same input signal. Extensive experiments were performed on four publicly available human activity recognition data sets to verify the effectiveness of our proposed MACL method. The experimental evaluation results show that the MACL method attains a comparable performance for human activity recognition to the compared baseline models, directly exceeding the performance of models using standard augmentation transformation strategies.

Li, Hao; Yang, Wu; Wang, Wei; Wang, Huanran

doi: 10.1002/cpe.8093pmid: N/A

This research introduces a comprehensive suite of neural network models designed to tackle the challenging task of rumor stance detection within the framework of simulating complex rumor propagation systems. Our objective centers on accurately modeling the intricate structures of rumor dialogues and propagation patterns to identify user stances—whether they are in support, denial, questioning, or commenting on rumors. Unlike conventional methods that rely on simplistic keyword targeting and fail in the nuanced context of social networks, our models delve into the complexities of dialogue and propagation structures, offering a more precise and insightful analysis of rumor dynamics. In addressing the simulation and modeling of complex systems, our approach specifically focuses on the elaborate interaction networks that underpin rumor spread and reception. While our methodology does not directly engage with brain‐like computing paradigms, it reflects a similar level of sophistication in handling layered and complex information flows, analogous to cognitive processes in understanding and interpreting human communications. Employing a hierarchical attention mechanism, our models adeptly parse through multitiered dialogue sequences, effectively distinguishing between various indicators of user stances. This allows for a nuanced and detailed representation of the rumor ecosystem, significantly enhancing the accuracy of stance detection. Through rigorous testing on diverse datasets, our approach has demonstrated superior performance over existing models, thereby establishing a new benchmark in the field.

Hong Enriquez, Rolando P.; Badia, Rosa M.; Chapman, Barbara; Bresniker, Kirk; Pakin, Scott; Mishra, Alok; Bruel, Pedro; Dhakal, Aditya; Rattihalli, Gourav; Hogade, Ninad; Frachtenberg, Eitan; Milojicic, Dejan

doi: 10.1002/cpe.8121

Lu, Chao; Hong, Feng

doi: 10.1002/cpe.8115pmid: N/A

Gathering logistics operations in an individual location offers various benefits at the macro level namely reducing environmental and community issues, replacing overflow of traffic, minimizing air pollution, and more. But the complexity in the selection of location is maximized and the logistic operation chooses the wrong location due to frequent variations in characteristics. Also, it is suitable for capturing long‐term dependencies. To overcome these difficulties, we propose ESVM‐IGS; an Ensemble support vector machine, and an Improved Genetic algorithm, with an initial search strategy to improve the efficiency. The Ensemble SVM is applied to produce a better outcome. To identify optimal configurations in the complex optimization problem an initial search‐based improved genetic algorithm is implemented. To conduct our experiments, the ESVM‐IGS is rigorously evaluated on the GIS real‐time Dataset and the efficiency of the model is validated with various performance measures. From the analysis, the proposed method results that it solved the complexity burden and improved the selection ability of long‐term dependency. The experimental results depict the better efficiency of the ESVM‐IGS method for the location selection strategy of logistics.

Swamidoss, Mathumohan; Samiayya, Duraimurugan; Gunasekar, Manikandan

doi: 10.1002/cpe.8070pmid: N/A

In green optical networking, designing an adaptive energy‐saving scheme plays a vital role, in optimizing energy consumption by dynamically adjusting resources based on network traffic and environmental conditions, to a more sustainable and efficient optical communication infrastructure. Traditional methods in optical networking face challenges such as static resource allocation, limited adaptability, inefficient power usage, environmental insensitivity, and scalability issues. Therefore this article proposed a novel method named Dynamic Quality of Service based Random update Genghis Khan (DQ‐RGK) algorithm, the proposed model can tackle the abovementioned complexities. In this study, cluster head dynamic placement is utilized to optimize the network's performance by adapting the placement of cluster heads to the current topology, load distribution, and energy levels in the network nodes. Additionally, Dynamic Quality of Service (QoS) is employed to respond dynamically to changes in network conditions, adapting to varying traffic patterns and resource availability. In this work, the Genghis Khan Shark optimization with a random update strategy is implemented for hyperparameter optimization to enhance the performance of the DQ‐RGK method. The DQ‐RGK adjusts the parameters of QoS in real‐time, and this ensures that network resources based on the requirements changed and priorities of applications, which ultimately optimizes performance and enhances user experience. By dynamically assigning and reallocating resources based on the current demand the algorithm enhances overall network efficiency and reduces energy consumption. Then, this work analyzes the experimental results, where some evaluation measures estimate the DQ‐RGK method's performance. Routing efficiency, latency, scalability, spectral efficiency, Packet Delivery Ratio, throughput, network lifetime, energy consumption, jitter, and energy consumption are the measures employed by the DQ‐RGK model. In The results, other routing models that do not provide efficiency are utilized, a comparison of these other routing models is represented in results. The overall DQ‐RGK model's effectiveness is represented in the experimental results and its effectiveness is greater among other methods.

Kumari, Ankita; Singh, Purushottam; Pranav, Prashant; Dutta, Sandip; Chakraborty, Soubhik

doi: 10.1002/cpe.8118pmid: N/A

In the rapidly evolving domain of Mobile Ad‐hoc Networks (MANETs), where their deployment spans critical military operations to essential organizational communication infrastructures, the pervasive threat of security breaches casts a long shadow on the networks' operational integrity and reliability. Central among these threats are sleep deprivation attacks, a particularly insidious form of cyber aggression that exploits the inherent decentralized and self‐organizing characteristics of MANETs to exhaust the energy reserves of nodes, compromising the network's stability and performance. This paper embarks on a journey to confront this challenge head‐on, introducing a pioneering and holistic defense mechanism that integrates a cumulative priority‐based model for the selection of cluster heads, ingeniously augmented by the principles of Chebyshev's Inequality for optimal load balancing. This novel strategy is designed not only to counteract the direct impacts of sleep deprivation attacks but also to address the underlying vulnerabilities of MANETs that these attacks exploit. Through a rigorous series of simulations, conducted across a spectrum of network scenarios to test the resilience and adaptability of our proposed model, we have observed a commendable success rate of 98% in neutralizing sleep deprivation attacks. By leveraging the dynamic nature of MANETs and integrating advanced statistical methods for load distribution and cluster management, our model offers a robust framework that significantly improves network performance and energy efficiency. This, in turn, fosters a more sustainable and reliable network environment, crucial for the high‐stakes applications MANETs support. By championing a comprehensive and adaptable approach to security, this study promises to reinstate user trust and ensure the continued reliability of these indispensable networks, securing their place as a cornerstone of modern communication infrastructure in the face of evolving cyber threats.

doi: 10.1002/cpe.7793pmid: N/A

Venkatesh Naik, Nanavath; Madhavi, Kasa

doi: 10.1002/cpe.8114pmid: N/A

With the increase in internet connectivity worldwide, people have resorted to various video streaming applications for entertainment, education, healthcare, and so on. However, this has resulted in huge internet traffic, which is predominantly occurring due to the increased transmission of videos over wireless networks to mobile devices, Transmission of ultra‐high‐definition videos can be effortlessly carried out, although the video quality perceived by the users is normally lesser than the anticipated quality. In video streaming applications, it is a pre‐requisite to provide users with high quality of experience (QoE) but this is generally affected by the dynamic variations in the network conditions. This work presents a novel QoE‐based video delivery system in a collaborative e‐learning platform. Here, a novel deep learning structure recurrent neural network‐long short term memory (RNN‐LSTM) is developed for adaptive bit rate (ABR) selection, thereby providing users with videos of high QoE. Various network, time, and buffer‐based parameters are considered while selecting the bit rate. Additionally, the proposed RNN‐LSTM is assessed for its superiority in providing high QoE videos based on measures, like QoE, buffer size, and bit rate, and is observed to attain values of 0.975, 45.654, and 90.766 b/s, respectively.

Schottenhamml, Helen; Anciaux Sedrakian, Ani; Blondel, Frédéric; Köstler, Harald; Rüde, Ulrich

doi: 10.1002/cpe.8117pmid: N/A

This article presents the development of a new wind turbine simulation software to study wake flow physics. To this end, the design and development of waLBerla‐wind, a new simulator based on the lattice‐Boltzmann method that is known for its excellent performance and scaling properties, will be presented. Here it will be used for large eddy simulations (LES) coupled with actuator wind turbine models. Due to its modular software design, waLBerla‐wind is flexible and extensible with regard to turbine configurations. Additionally it is performance portable across different hardware architectures, another critical design goal. The new solver is validated by presenting force distributions and velocity profiles and comparing them with experimental data and a vortex solver. Furthermore, waLBerla‐wind's performance is compared to a theoretical peak performance, and analyzed with weak and strong scaling benchmarks on CPU and GPU systems. This analysis demonstrates the suitability for large‐scale applications and future cost‐effective full wind farm simulations.