Topic modeling (TM) is an unsupervised technique used to recognize hidden or abstract topics in large corpora, extracting meaningful patterns of words (semantics). This paper explores TM within data mining (DM), focusing on challenges and advancements in extracting insights from datasets, especially from social media platforms (SMPs). Traditional techniques like latent Dirichlet allocation (LDA), alongside newer methodologies such as bidirectional encoder representations from transformers (BERT), generative pre-trained transformers (GPT), and extra long-term memory networks (XLNet) are examined. This paper highlights the limitations of LDA, prompting the adoption of embedding-based models like BERT and GPT, rooted in transformer architecture, offering enhanced context-awareness and semantic understanding. The paper emphasizes leveraging pre-trained transformer-based language models to generate document embedding, refining TM and improving accuracy. Notably, integrating BERT with XLNet summaries emerges as a promising approach. By synthesizing insights, the paper aims to inform researchers on optimizing TM techniques, potentially shifting how insights are extracted from textual data.

In graphics processing unit (GPU) computing community, bitonic mergesort (BM) is recognized as one of the most investigated sorting algorithms. It is specially designed for parallel architectures, requires minor inter-process communication, can be implemented in-place, and is logically appropriate for single instructions multiple data platforms. In addition, GPUs have shown tremendous improvements in power and performance efficiency and thus have become essential ingredients in pursuit of the prospective exascale systems whose major obstacle is the excessive power consumption. In a recent research work, we found that fundamental software building blocks can offer a reasonable amount of power and energy saving that can offer new ways to tackle the power obstacle of the prospective exascale systems. We evaluated average peak power, average energy, and average kernel runtime of BM under various workloads and compared it with advanced quicksort (AQ). The results showed that BM outperformed AQ based on all the three metrics in most cases. In this paper, we further investigate BM to identify the factors that result in its underlying power and energy efficiency advantage over AQ. We analyze the power and energy efficiency of BM and AQ based on their performance evaluation on NVIDIA K40 GPU. The performance of both the algorithms is investigated using various experiments offered by NVIDIA Nsight Visual Studio.

Excessive power consumption is expected to be the major obstacle to achieve exascale performance within a reasonable power budget in the upcoming years. In addition, graphics processing units (GPUs) are expected to become a significant ingredient in the pursuit of exascale computing due to their fine-grained, highly parallel architecture and advancements in performance and power efficiency. To address the power obstacle of exascale systems, we suggest evaluating power and energy consumption of the fundamental software building blocks. We experimentally investigate power consumption, energy consumption, and kernel runtime of Bitonic Mergesort (a promising sort for parallel architectures) under various workloads on NVIDIA K40 GPU. The results show some insights in terms of power and energy consumption advantage of Bitonic Mergesort compared with NVIDIA’s Advanced Quicksort (a highly optimized parallel quicksort).

Power consumption is likely to remain a significant concern for exascale performance in the foreseeable future. In addition, graphics processing units (GPUs) have become an accepted architectural feature for exascale computing due to their scalable performance and power efficiency. In a recent study, we found that we can achieve a reasonable amount of power and energy savings based on the selection of algorithms. In this research, we suggest that we can save more power and energy by varying the block size in the kernel configuration. We show that we may attain more savings by selecting the optimum block size while executing the workload. We investigated two kernels on NVIDIA Tesla K40 GPU, a Bitonic Mergesort and Vector Addition kernels, to study the effect of varying block sizes on GPU power and energy consumption. The study should offer insights for upcoming exascale systems in terms of power and energy efficiency.

Style analysis, which is relatively a less explored topic, enables several interesting applications. For instance, it allows authors to adjust their writing style to produce a more coherent document in collaboration. Similarly, style analysis can also be used for document provenance and authentication as a primary step. In this paper, we propose an ensemble-based text-processing framework for the classification of single and multi-authored documents, which is one of the key tasks in style analysis. The proposed framework incorporates several state-of-the-art text classification algorithms including classical Machine Learning (ML) algorithms, transformers, and deep learning algorithms both individually and in merit-based late fusion. For the merit-based late fusion, we employed several weight optimization and selection methods to assign merit-based weights to the individual text classification algorithms. We also analyze the impact of the characters on the task that are usually excluded in NLP applications during pre-processing by conducting experiments on both clean and un-clean data. The proposed framework is evaluated on a large-scale benchmark dataset, significantly improving performance over the existing solutions.

Proliferating the internet of things (IoT) across all industry fields offers numerous possibilities for invention. It also multiplied the issues of ensuring uniform interoperability among a wide range of devices and platforms. The focus of this paper is to propose an approach for enhancing the security of IoT networks. This study investigated the possible efficacy of employing a federal public key infrastructure (PKI) structure system to serve IoT-based ecosystems. To achieve this goal, we have developed an elaborate experimental framework incorporating different trust models, security protocols, privacy enhancements, and performance metrics that demonstrate the practical benefits of this kind of federated system. One of the contributions of this study is an experimental model that mimics a real IoT ecosystem. It entails many IoT devices, installing vital PKI elements, and the development of safe information transmission channels. Measurements such as latency pointed out the feasibility of various IoT concepts, including such short response time as 2.8 ms for vehicular IoT (V2X). Measures for interoperability ranged with V2X having a 96.4% success, indicating the strength of the standards within that segment. This study reveals the benefits of a federated PKI management system for solving issues of IoT interconnectedness.