Miracle-dz/UniFAR-FTUs huggingface dataset:
Variability Evaluation of CNNs using Cross-validation on Viruses Images

: Virus description and recognition is an essential issue in medicine. It helps researchers to study virus attributes such as its morphology, chemical compositions, and modes of replication. Although it can be performed through visual inspection, it is a task highly dependent on a qualiﬁed expert. Therefore, the automation of this task has received great attention over the past few years. In this study, we applied transfer learning from pre-trained deep neural networks for virus species classiﬁcation. Given that many image datasets do not specify a ﬁxed training and test sets, and to avoid any bias, we evaluated the impact of a cross-validation scheme on the classiﬁcation accuracy. The experimental results achieved up to 89% of classiﬁcation accuracy, outperforming previous studies by 2.8% of accuracy.
CAD-based Learning for Egocentric Object Detection in Industrial Context

Industries nowadays have an increasing need of real-time and accurate vision-based algorithms. Although the performance of object detection methods improved a lot thanks to massive public datasets, instance detection in industrial context must be approached differently, since annotated images are usually unavailable or rare. In addition, when the video stream comes from a head-mounted camera, we observe a lot of movements and blurred frames altering the image content. For this purpose, we propose a framework to generate a dataset of egocentric synthetic images using only CAD models of the objects of interest. To evaluate different strategies exploiting synthetic and real images, we train a Convolutional Neural Network (CNN) for the task of object detection in egocentric images. Results show that training a CNN on synthetic images that reproduce the characteristics of egocentric vision may perform as well as training on a set of real images, reducing, if not removing, the need to manually annotate a large quantity of images to achieve an accurate performance.
Three-dimensional pituitary gland volume on MRI - A normative study

Background: Standard reference values for the pituitary gland volume of healthy Bangladeshi is necessary to facilitate assessment and diagnosis of pituitary pathology.Objectives: To obtain the dimensions of normal pituitary gland using T1-weighted magnetic resonance images (MRI) and to determine their relationship with age, sex.Materials & Methods: MRI of 100 individuals with clinically normal pituitary function (age range 14- 82 years) was taken in order to obtain volumetric measurements of the pituitary gland. The data obtained were stratified based on age and sex for analysis.Results: The mean pituitary gland volumes were 332.1 ± 146.8 mm3 and 324.2 ± 130.2 mm3 while the mean pituitary heights were 6.22 ± 1.6 mm and 6.42 ± 1.57 mm in males and females, respectively. Although there was no statistically significant difference between pituitary height and pituitary volume in both sexes, they correlated negatively with increasing age (r = -202, P = 0.04 and r = -410, P = 0.000, respectively). The mean pituitary widths (9.03 ± 2.48 mm and 9.21 ± 1.84 mm) and depths (10.5 ± 1.6 mm and 10.42 ± 1.52 mm) in males and females, respectively, did not show remarkable changes with age and sex in the individuals studied. Statistical tests applied included Student's t-test and Pearson correlation. A minimum level of statistical significance was set at P < 0.05. Conclusion: With this study, we have provided reference values in Bangladeshi population for the dimensions of normal pituitary gland.KYAMC Journal Vol. 9, No.-2, July 2018, Page 53-55
Content analysis of 150 years of British periodicals

Significance The use of large datasets has revolutionized the natural sciences and is widely believed to have the potential to do so with the social and human sciences. Many digitization efforts are underway, but the high-throughput methods of data production have not yet led to a comparable output in analysis. A notable exception has been the previous statistical analysis of the content of historical books, which started a debate about the limitations of using big data in this context. This study moves the debate forward using a large corpus of historical British newspapers and tools from artificial intelligence to extract macroscopic trends in history and culture, including gender bias, geographical focus, technology, and politics, along with accurate dates for specific events. Previous studies have shown that it is possible to detect macroscopic patterns of cultural change over periods of centuries by analyzing large textual time series, specifically digitized books. This method promises to empower scholars with a quantitative and data-driven tool to study culture and society, but its power has been limited by the use of data from books and simple analytics based essentially on word counts. This study addresses these problems by assembling a vast corpus of regional newspapers from the United Kingdom, incorporating very fine-grained geographical and temporal information that is not available for books. The corpus spans 150 years and is formed by millions of articles, representing 14% of all British regional outlets of the period. Simple content analysis of this corpus allowed us to detect specific events, like wars, epidemics, coronations, or conclaves, with high accuracy, whereas the use of more refined techniques from artificial intelligence enabled us to move beyond counting words by detecting references to named entities. These techniques allowed us to observe both a systematic underrepresentation and a steady increase of women in the news during the 20th century and the change of geographic focus for various concepts. We also estimate the dates when electricity overtook steam and trains overtook horses as a means of transportation, both around the year 1900, along with observing other cultural transitions. We believe that these data-driven approaches can complement the traditional method of close reading in detecting trends of continuity and change in historical corpora.
Resources and End-to-End Neural Network Models for Arabic Image Captioning

: Image Captioning (IC) is the process of automatically augmenting an image with semantically-laden descriptive text. While English IC has made remarkable strides forward in the past decade, very little work exists on IC for other languages. One possible solution to this problem is to boostrap off of existing English IC systems for image understanding, and then translate the outcome to the required language. Unfortunately, as this paper will show, translated IC is lacking due to the error accumulation of the two tasks; IC and translation. In this paper, we address the problem of image captioning in Arabic. We propose an end-to-end model that directly transcribes images into Arabic text. Due to the lack of Arabic resources, we develop an annotated dataset for Arabic image captioning (AIC). We also develop a base model for AIC that relies on text translation from English image captions. The two models are evaluated with the new dataset, and the results show the superiority of our end-to-end model.
Quote Surfing in Music and Movies with an Emotional Flavor

We all go through the situation where we listen to a movie or a song lyrics quote and without giving it much thought, immediately know where it comes from, like an instant and often emotional memory. It is also very common to be in the opposite scenario where we try hard to remember where we know these words from, want to discover, and also find it interesting to see them quoted in different contexts. These situations remind us of the importance of quotes in movies and music, that sometimes get more popular than the movie or song they belong to. In fact, quotes, music and movies are among the contents that we most treasure, for their emotional impact and the power to entertain and inspire us. In this paper, we present the motivation and the interactive support for quotes in As Music Goes By, giving users a chance of searching and surfing quotes in music and movies. Users can find, explore and compare related contents, and access quotes in a contextualized way in the movies or song lyrics where they appear. The preliminary user evaluation results, focusing on perceived usefulness, usability and user experience, were very encouraging, proving the concept, informing refinements and new developments, already being addressed. Users valued most the search, navigation, contextualization and emotional flavor: to be able to access and compare quotes in movies and in lyrics, to navigate across movies and songs, the emotional dimension and representation also for quotes. Future work will lead us further with the focus on rich, flexible and contextualized interactive access to quotes, music and movies, aiming for increased understanding of their meaning and relations, chances for serendipitous discoveries and to get inspired and moved by these media that we treasure.
Synthetic Ground Truth for Presegmentation of Known Objects for Effortless Pose Estimation

: We present a method for generating synthetic ground truth for training segmentation networks for presegmenting point clouds in pose estimation problems. Our method replaces global pose estimation algorithms such as RANSAC which requires manual ﬁne-tuning with a robust CNN, without having to hand-label segmentation masks for the given object. The data is generated by blending cropped images of the objects with arbitrary backgrounds. We test the method in two scenarios, and show that networks trained on the generated data segments the objects with high accuracy, allowing them to be used in a pose estimation pipeline.
A DSL-Driven Development Framework for Components to Provide Environmental Data in Simulation based Testing

: Developing components that produce data representing simulated environments for spatial-aware simulations could be difficult and error-prone. Knowledges of the required outputs of these components and computational models of the environmental phenomena are often held by different roles in the development. Miscommunications may appear among involved roles due to their different perspectives to view environmental phenomena. Consequently, requirements of simulated environments in simulation scenarios may not be correctly preserved in the developed components. This paper presents a domain-specific development framework to overcome this problem. It focuses on bridging the gap between human-view requirement descriptions of simulated environments and system-view component design models to produce digital representations of these environments. It specifies a CIM (Computation-Independent Model) -layer language which supports system of interest modelers to document required context of simulated environments in their simulation scenarios in a half-formal manner. Transformation rules from these CIMs are established to derive necessary data structures and computation flows as PIM (Platform-Independent Model) -layer models of simulated environment components. These transformations are further combined with general Model-Driven Development (MDD) solutions to create platform-specific component skeletons.
FSSSD: Fixed Scale SSD for Vehicle Detection

Since surveillance cameras are commonly installed in high places, the objects in the taken images are relatively small. Detecting small objects is a hard issue for the one-stage detector, and its performance in the surveillance system is not good. Two-stage detectors work better, but their speed is too slow to use in the real-time system. To remedy the drawbacks, we propose an efficient method, named as Fixed Scale SSD(FSSSD), which is an extension of SSD. The proposed method has three key points: high-resolution inputs to detect small objects, a lightweight Backbone to speed up, and prediction blocks to enrich features. FSSSD achieve 63.7% AP at 16.7 FPS in the UA-DETRAC test dataset. The performance is similar to two-stage detectors and faster than any other one-stage method.
Deep Learning for Astronomical Object Classification: A Case Study

With the emergence of photometric surveys in astronomy, came the challenge of processing and understanding an enormous amount of image data. In this paper, we systematically compare the performance of five popular ConvNet architectures when applied to three different image classification problems in astronomy to determine which architecture works best for each problem. We show that a VGG-style architecture pre-trained on ImageNet yields the best results on all studied problems, even when compared to architectures which perform much better on the ImageNet competition.
User-centered Approach to Developing Solutions for Electronic Medical Records: Extending EMR Data Entry

: The rapid advancement of technology presents the opportunity to digitize practice management. With a doctor to patient ratio of 1:33,000, digitizing health records in the Philippines is seen as one solution in providing more efﬁcient health care services. With the deployment of EMRs in the Philippines at its infancy, there is a need to initiate studies on feasibility, usability and user perception. This paper reports ﬁndings on usability of EMRs in a developing economy. Speciﬁcally, a system usability scale (SUS) was used to assess the usability of an EMR and interviews were conducted to acquire user feedback. Results of the survey indicated an overall mean SUS score of 70.76 with age and conﬁdence in technology being key deciding factors. Further observations and future research to streamline the heavy task of encoding on an EMR during patient-physician consultation are explained.
Using Local Refinements on 360 Stitching from Dual-fisheye Cameras

: Full panoramic images have several applications, ranging from virtual reality to 360 ◦ broadcasting. Such visualization method is growing, especially after the popularization of dual-ﬁsheye cameras, which are compact and easy-to-use 360 ◦ imaging devices, and low-cost platforms that allow immersive experiences. However, low-quality registration and compositing in which artifacts are noticeable in the stitching area can harm the user experience. Although it is challenging to compose such images due to their narrow overlap area, recent works can provide good results when performing a global alignment. Nevertheless, they often cause artifacts since global alignment is not able to address every aspect of an image. In this work, we present a stitching method that performs local reﬁnements to improve the registration and compositing quality of 360 ◦ images and videos. It builds on a feature clustering approach for global alignment. The proposed technique applies seam estimation and rigid moving least squares to remove undesired artifacts locally. Finally, we evaluate both to select the best result between them using a seam evaluation metric. Experiments showed that our method reduced the stitching error in at least 42.56% for images and 49.45% for videos when compared with existing techniques. Moreover, it provided the best results in all tested images and in 94.52% of the video frames.
Vehicle Detection and Classification in Aerial Images using Convolutional Neural Networks

: Due to the popularity of unmanned aerial vehicles, the acquisition of aerial images has become widely available. The aerial images have been used in many applications such as the investigation of roads, buildings, agriculture distribution, and land utilization, etc. In this paper, we propose a technique for vehicle detection and classiﬁcation from aerial images based on the modiﬁcation of Faster R-CNN framework. A new dataset for vehicle detection, VAID (Vehicle Aerial Imaging from Drone), is also introduced for public use. The images in the dataset are annotated with 7 common vehicle categories, including sedan, minibus, truck, pickup truck, bus, cement truck and trailer, for network training and testing. We compare the results of vehicle detection in aerial images with widely used network architectures and training datasets. The experiments demonstrate that the proposed method and dataset can achieve high vehicle detection and classiﬁcation rates under various road and trafﬁc conditions.
A Deep Transfer Learning Framework for Pneumonia Detection from Chest X-ray Images

: Pneumonia occurs when the lungs are infected by a bacterial, viral, or fungal infection. Globally, it is the largest solo infectious disease causing child mortality. Early diagnosis and treatment of this disease are critical to avoid death, especially in infants. Traditionally, pneumonia diagnosis was performed by expert radiologists and/or doctors by analysing X-ray images of the chest. Automated diagnostic methods have been developed in recent years as an alternative to expert diagnosis. Deep learning-based image processing has been shown to be effective in automated diagnosis of pneumonia. However, deep leaning typically requires a large number of labelled samples for training, which is time consuming and expensive to obtain in medical applications as it requires the input of human experts. Transfer learning, where a model pretrained for a task on an existing labelled database is adapted to be reused for a different but related task, is a common workaround to this issue. Here, we explore the use of deep transfer learning to diagnose pneumonia using X-ray images of the chest. We demonstrate that using two individual pretrained models as feature extractors and training an artiﬁcial neural network on these features is an effective way to diagnose pneumonia. We also show through experiments that the proposed method outperforms similar existing methods with respect to accuracy and time.
Vessel-speed Enforcement System by Multi-camera Detection and Re-identification

In crowded waterways, maritime traffic is bound to speed regulations for safety reasons. Although several speed measurement techniques exist for road traffic, no known systems are available for maritime traffic. In this paper, we introduce a novel vessel speed enforcement system, based on visual detection and reidentification between two cameras along a waterway. We introduce a newly captured Vessel-reID dataset, containing 2,474 unique vessels. Our vessel detector is based on the Single Shot Multibox Detector and localizes vessels in each camera individually. Our re-identification algorithm, based on the TriNet model, matches vessels between the cameras. In general, vessels are detected over a large range of their in-view trajectory (over 92% and 95%, for Camera 1 and 2, respectively), which makes the re-identification experiments reliable. For re-identification, application specific techniques, i.e. trajectory matching and time filtering, improve our baseline re-identification model (49:5% mAP) with over 20% mAP. In the final evaluation, we show that 77% (Rank-1 score) of the vessels are correctly re-identified in the other camera. This final result presents a feasible score for our novel vessel re-identification application. Moreover, our result could be further improved, as we have tested on new unseen data during other weather conditions.
A UML Profile for Automatic Code Generation of Optimistic Graceful Degradation Features at the Application Level

: Safety standards such as ISO26262 or IEC61508 recommend a variety of safety mechanisms for the development of safety-critical systems. One of these mechanisms is graceful degradation, which aims to provide a degraded service of an application after an error has occurred. While several safety standards recommend graceful degradation, they do not provide any concrete development or implementation assistance. This paper employs model-driven development to realize such an automated approach for optimistic graceful degradation, which is a speciﬁc variant of the graceful degradation safety mechanism. We introduce a UML proﬁle that may be used to model optimistic graceful degradation at the application level within a UML class diagram. We leverage this model representation to automatically generate productive source code that is capable of optimistic graceful degradation. This source code is generated without requiring any additional developer actions.
Researching Mobile-Assisted English Language Learning Among Adult Distance Learners in China: Emerging Practices and Learner Perception of Teacher Role

Guided by the conceptual framework for next generation designs for mobile-assisted language learning (MALL) in informal setting, our study investigates how mobile devices impact the learning practices and habits amongst adult distant learners of English at a higher education institution in China. Data sources include quantitative data of 148 completed responses to an online survey and qualitative data from follow-up email interviews. The study demonstrates that students' main motivation for engaging in mobile assisted English learning is to fill in the gaps in their daily schedule in order to maximize available time, and the majority use mobile devices to support formal course learning. The skills and knowledge areas practiced with mobile devices are listening, followed by speaking and pronunciation, vocabulary, reading, and translation. Our study also reveals that teachers play a very limited role as perceived by students, while students expect more support from teachers for their out-of-class hour mobile assisted language learning. The research findings bear significant pedagogical implications in terms of integrating MALL into language learning curriculum, learner support and the need for continuous teacher training.
MobText: A Compact Method for Scene Text Localization

: Multiple research initiatives have been reported to yield highly effective results for the text detection problem. However, most of those solutions are very costly, which hamper their use in several applications that rely on the use of devices with restrictive processing power, like smartwatches and mobile phones. In this paper, we address this issue by investigating the use of efﬁcient object detection networks for this problem. We propose the combination of two light architectures, MobileNetV2 and Single Shot Detector (SSD), for the text detection problem. Experimental results in the ICDAR’11 and ICDAR’13 datasets demonstrate that our solution yields the best trade-off between effectiveness and efﬁciency and also achieved the state-of-the-art results in the ICDAR’11 dataset with an f-measure of 96 . 09%.
Automatic Skin Lesion Segmentation based on Saliency and Color

: Segmenting skin lesions in dermoscopic images is a key step for the automatic diagnosis of melanoma. In this framework, this paper presents a new algorithm that after a pre-processing phase aimed at reducing the computation burden, removing artifacts and improving contrast, selects the skin lesion pixels in terms of their saliency and color. The method is tested on a publicly available dataset and is evaluated both qualitatively and quantitatively.
The pharmacokinetics of itraconazole in animals and man: an overview

Itraconazole is a triazole antifungal agent with pronounced lipophilic properties. This physicochemical property determines to a large extent the pharmacokinetics of itraconazole and differentiates it from the more hydrophilic agent, ketoconazole. The pharmacokinetics of itraconazole in animals and man are characterized by a good oral absorption, an extensive tissue distribution with tissue concentrations many times higher than in plasma, a relatively long elimination half-life of about one day in man and a biotransformation into a large number of (antifungal) inactive metabolites. Unlike most other antifungal drugs, itraconazole does not interfere with mammalian drug metabolizing enzymes, minimizing the risk of interaction with concomitantly administered drugs and suggesting a lower risk for hepatic reactions. These pharmacokinetic properties of itraconazole may contribute to the higher efficacy and improved safety of this drug in animal models and in patients with various mycotic infections.