Fine-Grained Object Recognition and Zero-Shot Learning in Remote Sensing Imagery

Fine-grained object recognition that aims to identify the type of an object among a large number of subcategories is an emerging application with the increasing resolution that exposes new details in image data. Traditional fully supervised algorithms fail to handle this problem where there is low between-class variance and high within-class variance for the classes of interest with small sample sizes. We study an even more extreme scenario named zero-shot learning (ZSL) in which no training example exists for some of the classes. ZSL aims to build a recognition model for new unseen categories by relating them to seen classes that were previously learned. We establish this relation by learning a compatibility function between image features extracted via a convolutional neural network and auxiliary information that describes the semantics of the classes of interest by using training samples from the seen classes. Then, we show how knowledge transfer can be performed for the unseen classes by maximizing this function during inference. We introduce a new data set that contains 40 different types of street trees in 1-ft spatial resolution aerial data, and evaluate the performance of this model with manually annotated attributes, a natural language model, and a scientific taxonomy as auxiliary information. The experiments show that the proposed model achieves 14.3% recognition accuracy for the classes with no training examples, which is significantly better than a random guess accuracy of 6.3% for 16 test classes, and three other ZSL algorithms.Comment: G. Sumbul, R. G. Cinbis, S. Aksoy, "Fine-Grained Object Recognition and Zero-Shot Learning in Remote Sensing Imagery", IEEE Transactions on Geoscience and Remote Sensing (TGRS), in press, 201

Evaluation of Joint Multi-Instance Multi-Label Learning For Breast Cancer Diagnosis

Multi-instance multi-label (MIML) learning is a challenging problem in many aspects. Such learning approaches might be useful for many medical diagnosis applications including breast cancer detection and classification. In this study subset of digiPATH dataset (whole slide digital breast cancer histopathology images) are used for training and evaluation of six state-of-the-art MIML methods. At the end, performance comparison of these approaches are given by means of effective evaluation metrics. It is shown that MIML-kNN achieve the best performance that is %65.3 average precision, where most of other methods attain acceptable results as well

An Anti-Cheating System for Online Interviews and Exams

Remote examination and job interviews have gained popularity and become indispensable because of both pandemics and the advantage of remote working circumstances. Most businesses and educational organizations use these platforms for recruitment as well as online exams. However, one of the critical problems of the remote examination systems is conducting the exams in a reliable environment. In this work, we present a cheating analysis pipeline for online interviews and exams. The system only requires a video of the candidate, which is recorded during the exam by using a webcam without a need for any extra tool. Then cheating detection pipeline is employed to detect the presence of another person, electronic device usage, and candidate absence status. The pipeline consists of face detection, face recognition, object detection, and face tracking algorithms. To evaluate the performance of the pipeline we collected a private video dataset. The video dataset includes both cheating activities and clean videos. Ultimately, our pipeline presents an efficient and fast guideline for detecting and analyzing cheating actions in an online interview and exam video

First detection of heat shock protein 60 and 70 in the serum of early pregnant bitches

Heat shock proteins (HSPs) belong to a group of cellular stress proteins. Heat shock protein 10 immunoregulates and promotes growth during early gestation in humans, while HSP70 is considered to regulate autophagy and apoptosis during pregnancy and parturition. Both HSPs are detectable in the serum and placentas of early pregnant women and considered to contribute to the establishment of pregnancy. Within this pilot study we aimed (1) to assess whether HSPs 10, 60 and 70 are measurable in the serum of healthy early pregnant and non-pregnant bitches, and (2) to explore whether measurable differences between groups indicate pregnancy. Blood was collected from 31 bitches on days 7, 14 and 21 after mating. At 21 days post mating, all bitches were examined for pregnancy by ultrasonography; 23 were pregnant, and the eight non-pregnant bitches served as controls. Pregnant bitches had normal parturitions and gave birth to healthy puppies. The serum concentrations of HSPs 10, 60 and 70 were measured by electrophoresis and western blot. Serum HSP10 was not detectable. Average serum HSP70 concentration was significantly (d7, P = 0.030; d14, P = 0.023; d21, P = 0.030) lower in pregnant animals at all days investigated, while serum HSP60 was significantly lower at day 21 of gestation (P = 0.024) when compared to the controls. HSP 60 and HSP70 concentrations correlated positively (d7, r = +0.386, P = 0.021; d14, r = 0.450, P = 0.008; d21, r = +0.472, P = 0.006). We conclude that in pregnant bitches, serum concentrations of HSP60 and HSP70 are significantly decreased between days 7 and 21 of gestation, in comparison to non-pregnant bitches in early dioestrus, raising the question about intrauterine functions during the peri-implantation period

Does the Efferent Auditory System Have a Role in Children with Specific Learning Disabilities?

Objective: This study aimed to compare the baseline transient otoacoustic emission (t-OAE) amplitudes and medial olivo-cochlear (MOC) efferent activity in children with specific learning disability (SLD) and children with normal development.Methods: The study was conducted in two groups. The patient group included 30 children aged 6 to 10 years and diagnosed with SLD, and the control group included 30 children in the same age range without SLD. The patient group included eight males and 22 females, and the control group included 14 females and 16 males. t-OAE and contralateral suppression test were performed in both groups.Results: In the first t-OAE measurements, a statistically significant difference was observed between the patient and the control group at frequencies of 1400, 2000, 2800, and 4000 Hz, but no such difference was observed at 1000 Hz frequency. In the control group, significantly better emission amplitudes were observed. No differences were found at any frequency between the patient and the control groups after suppression. When the subjects in the two groups were compared among themselves, there was a statistically significant difference between the before and after suppression scores in the patient group except at 4000 Hz. Likewise, an important difference was also observed in all frequencies in the control group.Conclusion: This study shows that suppression effects of t-OAE on children diagnosed with SLD and children with no SDL are not significantly different