In recent years, indoor positioning has emerged as a critical function in many end-user applications; including military, civilian, disaster relief and peacekeeping missions. In comparison with outdoor environments, sensing location information in indoor environments requires a higher precision and is a more challenging task in part because various objects reflect and disperse signals. Ultra WideBand (UWB) is an emerging technology in the field of indoor positioning that has shown better performance compared to others. In order to set the stage for this work, we provide a survey of the state-of-the-art technologies in indoor positioning, followed by a detailed comparative analysis of UWB positioning technologies. We also provide an analysis of strengths, weaknesses, opportunities, and threats (SWOT) to analyze the present state of UWB positioning technologies. While SWOT is not a quantitative approach, it helps in assessing the real status and in revealing the potential of UWB positioning to effectively address the indoor positioning problem. Unlike previous studies, this paper presents new taxonomies, reviews some major recent advances, and argues for further exploration by the research community of this challenging problem space.

Late detection and manual resolutions of performance anomalies in Cloud Computing and Big Data systems may lead to performance violations and financial penalties. Motivated by this issue, we propose an artificial neural network based methodology for anomaly detection tailored to the Apache Spark in-memory processing platform. Apache Spark is widely adopted by industry because of its speed and generality, however there is still a shortage of comprehensive performance anomaly detection methods applicable to this platform. We propose an artificial neural networks driven methodology to quickly sift through Spark logs data and operating system monitoring metrics to accurately detect and classify anomalous behaviors based on the Spark resilient distributed dataset characteristics. The proposed method is evaluated against three popular machine learning algorithms, decision trees, nearest neighbor, and support vector machine, as well as against four variants that consider different monitoring datasets. The results prove that our proposed method outperforms other methods, typically achieving 98–99% F-scores, and offering much greater accuracy than alternative techniques to detect both the period in which anomalies occurred and their type.

The number of individuals who suffer from visual impairment is increasing rapidly. The most significant barrier to improving the lives of the blind and visually impaired people is their inability to navigate independently and safely. Indoor navigation systems for blind and visually impaired people aim to guide them in navigating independently in familiar and unfamiliar environments. Our system aims to provide an assistive technology for blind and visually impaired individuals by exploiting popular existing technologies that are often used by blind individuals, such as the smartphone. The system provides users with guidance statements that help them navigate from their current positions to desired destinations. The system consists of four main components: mapping, positioning, navigation, and interface. In order to implement these components, three main applications need to be developed: application in localization server, application in communication server, and application in smartphone, each of which is located in a different place but connected to the others. Functionalities test and blind test were conducted to evaluate the system. The system proved its ability to aid blind individuals effectively.

This paper comprehensively reviews medical VQA models, structures, and datasets, focusing on combining vision and language. Over 75 models and their statistical and SWOT (Strengths, Weaknesses, Opportunities, Threats) analyses were compared and analyzed. The study highlights whether the researchers in the general field influence those in the medical field. According to an analysis of text encoding techniques, LSTM is the approach that is utilized the most (42%), followed by non-text methods (14%) and BiLSTM (12%), whereas VGGNet (40%) and ResNet (22%) are the most often used vision methods, followed by Ensemble approaches (16%). Regarding fusion techniques, 14% of the models employed non-specific methods, while SAN (13%) and concatenation (10%) were frequently used. The study identifies LSTM-VGGNet and LSTM-ResNet combinations as the primary approaches in medical VQA, with 18% and 15% usage rates, respectively. The statistical analysis of medical VQA from 2018 to 2023 and individual yearly analyses reveals consistent preferences for LSTM and VGGNet, except in 2018 when ResNet was more commonly used. The SWOT analysis provides insights into the strengths and weaknesses of medical VQA research, highlighting areas for future exploration. These areas include addressing limited dataset sizes, enhancing question diversity, mitigating unimodal bias, exploring multi-modal datasets, leveraging external knowledge, incorporating multiple images, ensuring practical medical application integrity, improving model interpretation, and refining evaluation methods. This paper’s findings contribute to understanding medical VQA and offer valuable guidance for future researchers aiming to make advancements in this field.

Visual question answering (VQA) is a task that generates or predicts an answer to a question in human language about visual images. VQA is an active field combining two AI branches: NLP and computer vision. VQA in the medical field is still at an early stage, and it needs vast efforts and exploration to reach practical usage. This paper proposes two models that are utilized in the latest vision and NLP transformers that outperform the SOTA and have not yet been utilized in medical VQA. The ELECTRA-base transformer is used for textual feature extraction, whereas SWIN is used for visual feature extraction. In the SOTA medical VQA, selecting the model is based on the model that achieves the highest validation accuracy or the last model in training. The first proposed model, the best-value-based model, is selected based on the highest validation accuracy. The second model, the greedy-soup-based model, uses a greedy soup technique based on the fusion of multiple fine-tuned models to set the model parameters. The greedy soup selects the model parameters by fusing the model parameters that have significant performance on the validation accuracy in training. The greedy-soup-based model outperforms the best-value-based model, and both proposed models outperform the SOTA, which has an accuracy of 83.49%. The greedy-soup-based model is optimized with batch size and learning rate. During the optimization, seven extra models exceed the SOTA accuracy. The best model trained with a learning rate of $1.0\times {10}^{-4}$ and batch size 16 achieves an accuracy of 87.41%.

Software applications can feature intrinsic variability in their execution time due to interference from other applications or software contention from other users, which may lead to unexpectedly long running times and anomalous performance. There is thus a need for effective automated performance anomaly detection methods that can be used within production environments to avoid any late detection of unexpected degradations of service level. To address this challenge, we introduce TRACK-Plus a black-box training methodology for performance anomaly detection. The method uses an artificial neural networks-driven methodology and Bayesian Optimization to identify anomalous performance and are validated on Apache Spark Streaming. TRACK-Plus has been extensively validated using a real Apache Spark Streaming system and achieve a high F-score while simultaneously reducing training time by 80% compared to efficiently detect anomalies.