Artificial Intelligence & Machine Learning in Computer Vision Applications

Deep learning and machine learning innovations are at the core of the ongoing revolution in Artificial Intelligence for the interpretation and analysis of multimedia data. The convergence of large-scale datasets and more affordable Graphics Processing Unit (GPU) hardware has enabled the development of neural networks for data analysis problems that were previously handled by traditional handcrafted features. Several deep learning architectures such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short Term Memory (LSTM)/Gated Recurrent Unit (GRU), Deep Believe Networks (DBN), and Deep Stacking Networks (DSNs) have been used with new open source software and libraries options to shape an entirely new scenario in computer vision processing

POSTCOLONIAL ARABIC FICTION REVISITED: NATURALISM AND EXISTENTIALISM IN GHASSAN KANAFANI’S MEN IN THE SUN

This article looks into the postcolonial Arabic narrative of Ghassan Kanafani to examine its underplayed existential and naturalistic aspects. Postcolonial texts (and their exegeses) deal with the effects of colonization/imperialism. They are expected to be political and are judged accordingly. Drawing on Kanafani’s Men in the Sun (1963), I argue that the intersection among existentialism and naturalism, on the one hand, and postcolonialism, on the other, intensifies the political relevance of the latter theory and better establishes the politically committed nature of Kanafani’s fiction of resistance. In the novella, the sun and the desert are a pivotal existential symbol juxtaposed against the despicable life led by three Palestinian refugees. The gruesome death we encounter testifies to the absurdity of life after attempts at self-definition through making choices. The gritty existence characteristic of Kanafani's work makes his representation of the lives of alienated characters more accurate and more visceral. Kanafani uses philosophical and sociological theories to augment the political nature of his protest fiction, one acting within postcolonial parameters of dispossession to object to different forms of imperialism and diaspora. Therefore, this article explores how global critical frameworks (naturalism and existentialism) enrich the localized contexts essential to any study of postcolonial literature and equally move the traditional national allegory of Kanafani to a more realist/unidealistic level of political indictment against oppression.

Adapting SMT Query Translation Reranker to New Languages in Cross-Lingual Information Retrieval

We investigate adaptation of a supervised machine learning model for reranking of query translations to new languages in the context of cross-lingual information retrieval. The model is trained to rerank multiple translations produced by a statistical machine translation system and optimize retrieval quality. The model features do not depend on the source language and thus allow the model to be trained on query translations coming from multiple languages. In this paper, we explore how this affects the final retrieval quality. The experiments are conducted on medical-domain test collection in English and multilingual queries (in Czech, German, French) from the CLEF eHealth Lab series 2013--2015. We adapt our method to allow reranking of query translations for four new languages (Spanish, Hungarian, Polish, Swedish). The baseline approach, where a single model is trained for each source language on query translations from that language, is compared with a model co-trained on translations from the three original languages

AI-driven Solutions for Sustainable Environment Monitoring

In recent years, the integration of artificial intelligence (AI) into environmental monitoring has opened up unprecedented possibilities for safeguarding our planet's ecological health. As climate change, deforestation, and biodiversity loss present pressing challenges, innovative technologies are essential for efficiently monitoring and preserving our natural ecosystems. In pursuit of this transformative potential, our esteemed journal invites researchers, scientists, and AI experts to contribute to a special issue focusing on "AI-driven Solutions for Sustainable Environment Monitoring." The urgency of environmental issues demands a collective effort to harness the power of AI in monitoring and sustaining our ecosystems. This special issue aims to showcase cutting-edge research and breakthroughs in AI-driven solutions, addressing various facets of environment monitoring and sustainability. By exploring the intersection of AI and environmental sciences, we seek to foster a deeper understanding of the role AI plays in shaping a greener, more resilient future

Applied AI Solutions on Edge Devices

Call for submission. This editorial introduces the first issue of 2023 for Embedded Selforganising Systems (ESS) journal. The focus of this issue is the deployment of AI solutions in robotics and embedded systems. Our journal uses electronic publication, which provides a flexible way to submit and review contributions of authors from all countries. The advantages of such an e-journal are multifarious. In comparison to traditional paper journals, we replace the classic review and creation process with a new sliding issue model. Each issue starts with an initial editorial and an official call for papers. The submitted articles will be reviewed and, if accepted, published as soon as the final version is received by the committee. Based on this process, each sliding issue can be filled successively until the maximum number of articles is reached. During this period, all accepted papers can, already be read by other researchers while other papers are still in the reviewing process. Accordingly, the time to publish shrinks to a minimum. In addition, multiple issues with different focus can co-exist at the same time, which provides completely new possibilities to react on latest research topics. The journal also allows the integration of discussions and other reactions on published articles in the same journal issue. We are welcoming fresh ideas, on-going research technical reports and novel scientific works. We also intend to create a promising platform for creative and constructive discussions

Automated Identification of Wood Surface Defects Based on Deep Learning

Wood plates are widely used in the interior design of houses primarily for their aesthetic value. However, considering its esthetical values, surface defect detection is necessary. The development of computer vision and CNN-based object detection methods has opened the way for wood surface defect detection process automation. This paper investigates deep-learning applications for automatic wood surface defect detection. It includes the evaluation of deep learning algorithms, including data generation and labeling, preprocessing, model training, and evaluation. Many adjustments regarding the dataset size, the model, and the modification of the neural network were made to evaluate the model's performance in the specified challenge. The results indicate that modifications can increase the YOLOv5s performance in detection. The model with GCNet added and trained in 4800 images has achieved 88.1% of mAP. The paper also evaluates the time performance of models based on different GPU units. The results show that in A100 40GB GPU, the maximum time to process a wood plate is 2.2 seconds. Finally, an Active learning approach for the continual increase in performance while detecting with the smaller size of manual labeling has been implemented. After detecting 500 images in 5 cycles, the model achieved 98.8% of mAP. This scientific paper concludes that YOLOv5s modified model is suitable for wood surface defect detection. It can perform with high accuracy in real time. Moreover, applying the active learning approach can facilitate the labeling process by increasing the performance during detection

Real-time 3D Perception of Scene with Monocular Camera

Depth is a vital prerequisite for the fulfillment of various tasks such as perception, navigation, and planning. Estimating depth using only a single image is a challenging task since the analytic mapping is not available between the intensity image and its depth where the features cue of the context is usually absent in the single image. Furthermore, most current researchers rely on the supervised Learning approach to handle depth estimation. Therefore, the demand for recorded ground truth depth is important at the training time, which is actually tricky and costly. This study presents two approaches (unsupervised learning and semi-supervised learning) to learn the depth information using only a single RGB-image. The main objective of depth estimation is to extract a representation of the spatial structure of the environment and to restore the 3D shape and visual appearance of objects in imagery

Exploring SSD Detector for Power Line Insulator Detection on Edge Platform

Power line insulator detection is pivotal for the consistent performance of the entire power system. It forms the basis of Unmanned Aerial Vehicle (UAV) inspection, an emerging trend in power line surveillance. This paper addresses the challenge of insulator detection in cluttered aerial images, given the constraints of a limited dataset and lower computational resources, specifically on the NVIDIA Jetson Nano platform. We have developed two approaches based on active and passive deep learning algorithms, underpinned by the Single Shot Multibox Detector (SSD) meta-architecture with MobileNetV2 as its backbone - SSD300 and SSD640. The proposal models managed a frame rate of 9 fps in 10W power mode and 5.6 fps in 5W power mode. Our experiments demonstrated that the proposed active learning model could conduct robust insulator detection, achieving a mAP of 94.5% while using only 43% of the total dataset, comparable to the traditional deep learning approach's 94.6% mAP using the entire dataset. Significantly, the active learning model seeks feedback during the training process, enabling it to learn from its mistakes and enhance accuracy over time. This also contributes to improved generalizability and interpretability of the model by seeking diverse and representative samples during training, all while reducing the computational and annotation overhead

Vision-based Propeller Damage Inspection Using Machine Learning

Unmanned Aerial Vehicles (UAVs) play an increasingly pivotal role in day-to-day rescue operations, offering crucial aerial support in challenging terrain and emergencies, such as drowning. Drone hangars are strategically deployed to ensure swift response in remote locations, overcoming range-limiting constraints posed by battery capacity. However, the UAV's airworthiness, typically ensured through conventional inspections by a technical individual, is paramount to guarantee mission safety. Over time, UAVs are prone to degradation through contact with the external environment, with propellers often being the cause of flight instability and potential crashes. This paper presents an innovative approach to automate UAV propeller inspection to avert incidents preemptively. Leveraging visual recordings and deep learning methodologies, we train a Convolutional Neural Network (CNN) model using both passive and active learning strategies. Our approach successfully detects physical damage on propellers with an impressive accuracy of 85.8%, promising a significant improvement in maintaining UAV flight safety and effectiveness in rescue operations

TRUSTING THE PHARMACIST IN DELIVERING MEDICATION INFORMATION: A COMMUNITY-BASED PERSPECTIVE

Objective: Optimal disease management is influenced by a solid patient-health provider relationship; which includes trust in the provider. The study compares respondents’ trust in pharmacists and physicians for the delivery of drug information. Methods: Residents of 3 rural communities in Lebanon, aged 40 and above, were invited to participate in the study, 760 accepted. Participants were asked who they trust the most with information about their medication: their physician or their pharmacist. Results: Of the total sample, 154 chose the pharmacist as their most trusted source of medication information (20%). Characteristics associated with choosing the pharmacist were: being a male (29.3% vs 16.2% p<.001), of younger age (31.5% among<50 y, 18.8% among 50-64 y, and 14.6% among 65+years p<.001), single (31.6% vs 21.9% married and 9.3 others, p=0.023), working (39.2% vs15.7% p<.001), and insured (2.3% vs 16.4% p=0.048). The multivariate logistic regression model revealed that having a family member with hypertension (OR=1.86 95% 1.23-2.82), or cardiovascular (OR=3.39 95%CI 1.55-7.45) increased the likelihood of trusting pharmacists over medical doctor. On the other hand, a self-report of cardiovascular disease (OR=0.34 95% CI 0.12-0.95) and taking medication (OR=0.41 95% CI 0.25-0.67) were associated with a decrease in the trust in the pharmacist in favor of the physician. Conclusion: Although pharmacists are the drug specialists, the majority of the Lebanese rural community residents reported higher trust in their physicians with information about their medication(s)